MiniCPM4 & MiniCPM4.1: Ultra-Efficient LLMs on End Devices, achieving 3+ generation speedup on reasoning tasks

A project structure aware autonomous software engineer aiming for autonomous program improvement. Resolved 37.3% tasks (pass@1) in SWE-bench lite and 46.2% tasks (pass@1) in SWE-bench verified with each task costs less than $0.7.

This project imlements the following tasks in the project: 1. Vehicle counting, 2. Lane detection. 3.Lane change detection and 4.speed estimation

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

using Neural Networks (SSD) on Tensorflow. This repo documents steps and scripts used to train a hand detector using Tensorflow (Object Detection API). As with any DNN based task, the most expensive (and riskiest) part of the process has to do with finding or creating the right (annotated) dataset. I was interested mainly in detecting hands on a table (egocentric view point). I experimented first with the [Oxford Hands Dataset](http://www.robots.ox.ac.uk/~vgg/data/hands/) (the results were not good). I then tried the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) which was a much better fit to my requirements. The goal of this repo/post is to demonstrate how neural networks can be applied to the (hard) problem of tracking hands (egocentric and other views). Better still, provide code that can be adapted to other uses cases. If you use this tutorial or models in your research or project, please cite [this](#citing-this-tutorial). Here is the detector in action. <img src="images/hand1.gif" width="33.3%"><img src="images/hand2.gif" width="33.3%"><img src="images/hand3.gif" width="33.3%"> Realtime detection on video stream from a webcam . <img src="images/chess1.gif" width="33.3%"><img src="images/chess2.gif" width="33.3%"><img src="images/chess3.gif" width="33.3%"> Detection on a Youtube video. Both examples above were run on a macbook pro **CPU** (i7, 2.5GHz, 16GB). Some fps numbers are: | FPS | Image Size | Device| Comments| | ------------- | ------------- | ------------- | ------------- | | 21 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run without visualizing results| | 16 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | | 11 | 640 * 480 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | > Note: The code in this repo is written and tested with Tensorflow `1.4.0-rc0`. Using a different version may result in [some errors](https://github.com/tensorflow/models/issues/1581). You may need to [generate your own frozen model](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/) graph using the [model checkpoints](model-checkpoint) in the repo to fit your TF version. **Content of this document** - Motivation - Why Track/Detect hands with Neural Networks - Data preparation and network training in Tensorflow (Dataset, Import, Training) - Training the hand detection Model - Using the Detector to Detect/Track hands - Thoughts on Optimizations. > P.S if you are using or have used the models provided here, feel free to reach out on twitter ([@vykthur](https://twitter.com/vykthur)) and share your work! ## Motivation - Why Track/Detect hands with Neural Networks? There are several existing approaches to tracking hands in the computer vision domain. Incidentally, many of these approaches are rule based (e.g extracting background based on texture and boundary features, distinguishing between hands and background using color histograms and HOG classifiers,) making them not very robust. For example, these algorithms might get confused if the background is unusual or in situations where sharp changes in lighting conditions cause sharp changes in skin color or the tracked object becomes occluded.(see [here for a review](https://www.cse.unr.edu/~bebis/handposerev.pdf) paper on hand pose estimation from the HCI perspective) With sufficiently large datasets, neural networks provide opportunity to train models that perform well and address challenges of existing object tracking/detection algorithms - varied/poor lighting, noisy environments, diverse viewpoints and even occlusion. The main drawbacks to usage for real-time tracking/detection is that they can be complex, are relatively slow compared to tracking-only algorithms and it can be quite expensive to assemble a good dataset. But things are changing with advances in fast neural networks. Furthermore, this entire area of work has been made more approachable by deep learning frameworks (such as the tensorflow object detection api) that simplify the process of training a model for custom object detection. More importantly, the advent of fast neural network models like ssd, faster r-cnn, rfcn (see [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models) ) etc make neural networks an attractive candidate for real-time detection (and tracking) applications. Hopefully, this repo demonstrates this. > If you are not interested in the process of training the detector, you can skip straight to applying the [pretrained model I provide in detecting hands](#detecting-hands). Training a model is a multi-stage process (assembling dataset, cleaning, splitting into training/test partitions and generating an inference graph). While I lightly touch on the details of these parts, there are a few other tutorials cover training a custom object detector using the tensorflow object detection api in more detail[ see [here](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) and [here](https://towardsdatascience.com/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9) ]. I recommend you walk through those if interested in training a custom object detector from scratch. ## Data preparation and network training in Tensorflow (Dataset, Import, Training) **The Egohands Dataset** The hand detector model is built using data from the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) dataset. This dataset works well for several reasons. It contains high quality, pixel level annotations (>15000 ground truth labels) where hands are located across 4800 images. All images are captured from an egocentric view (Google glass) across 48 different environments (indoor, outdoor) and activities (playing cards, chess, jenga, solving puzzles etc). <img src="images/egohandstrain.jpg" width="100%"> If you will be using the Egohands dataset, you can cite them as follows: > Bambach, Sven, et al. "Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions." Proceedings of the IEEE International Conference on Computer Vision. 2015. The Egohands dataset (zip file with labelled data) contains 48 folders of locations where video data was collected (100 images per folder). ``` -- LOCATION_X -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder -- LOCATION_Y -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder ``` **Converting data to Tensorflow Format** Some initial work needs to be done to the Egohands dataset to transform it into the format (`tfrecord`) which Tensorflow needs to train a model. This repo contains `egohands_dataset_clean.py` a script that will help you generate these csv files. - Downloads the egohands datasets - Renames all files to include their directory names to ensure each filename is unique - Splits the dataset into train (80%), test (10%) and eval (10%) folders. - Reads in `polygons.mat` for each folder, generates bounding boxes and visualizes them to ensure correctness (see image above). - Once the script is done running, you should have an images folder containing three folders - train, test and eval. Each of these folders should also contain a csv label document each - `train_labels.csv`, `test_labels.csv` that can be used to generate `tfrecords` Note: While the egohands dataset provides four separate labels for hands (own left, own right, other left, and other right), for my purpose, I am only interested in the general `hand` class and label all training data as `hand`. You can modify the data prep script to generate `tfrecords` that support 4 labels. Next: convert your dataset + csv files to tfrecords. A helpful guide on this can be found [here](https://pythonprogramming.net/creating-tfrecord-files-tensorflow-object-detection-api-tutorial/).For each folder, you should be able to generate `train.record`, `test.record` required in the training process. ## Training the hand detection Model Now that the dataset has been assembled (and your tfrecords), the next task is to train a model based on this. With neural networks, it is possible to use a process called [transfer learning](https://www.tensorflow.org/tutorials/image_retraining) to shorten the amount of time needed to train the entire model. This means we can take an existing model (that has been trained well on a related domain (here image classification) and retrain its final layer(s) to detect hands for us. Sweet!. Given that neural networks sometimes have thousands or millions of parameters that can take weeks or months to train, transfer learning helps shorten training time to possibly hours. Tensorflow does offer a few models (in the tensorflow [model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models)) and I chose to use the `ssd_mobilenet_v1_coco` model as my start point given it is currently (one of) the fastest models (read the SSD research [paper here](https://arxiv.org/pdf/1512.02325.pdf)). The training process can be done locally on your CPU machine which may take a while or better on a (cloud) GPU machine (which is what I did). For reference, training on my macbook pro (tensorflow compiled from source to take advantage of the mac's cpu architecture) the maximum speed I got was 5 seconds per step as opposed to the ~0.5 seconds per step I got with a GPU. For reference it would take about 12 days to run 200k steps on my mac (i7, 2.5GHz, 16GB) compared to ~5hrs on a GPU. > **Training on your own images**: Please use the [guide provided by Harrison from pythonprogramming](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) on how to generate tfrecords given your label csv files and your images. The guide also covers how to start the training process if training locally. [see [here] (https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/)]. If training in the cloud using a service like GCP, see the [guide here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_cloud.md). As the training process progresses, the expectation is that total loss (errors) gets reduced to its possible minimum (about a value of 1 or thereabout). By observing the tensorboard graphs for total loss(see image below), it should be possible to get an idea of when the training process is complete (total loss does not decrease with further iterations/steps). I ran my training job for 200k steps (took about 5 hours) and stopped at a total Loss (errors) value of 2.575.(In retrospect, I could have stopped the training at about 50k steps and gotten a similar total loss value). With tensorflow, you can also run an evaluation concurrently that assesses your model to see how well it performs on the test data. A commonly used metric for performance is mean average precision (mAP) which is single number used to summarize the area under the precision-recall curve. mAP is a measure of how well the model generates a bounding box that has at least a 50% overlap with the ground truth bounding box in our test dataset. For the hand detector trained here, the mAP value was **0.9686@0.5IOU**. mAP values range from 0-1, the higher the better. <img src="images/accuracy.jpg" width="100%"> Once training is completed, the trained inference graph (`frozen_inference_graph.pb`) is then exported (see the earlier referenced guides for how to do this) and saved in the `hand_inference_graph` folder. Now its time to do some interesting detection. ## Using the Detector to Detect/Track hands If you have not done this yet, please following the guide on installing [Tensorflow and the Tensorflow object detection api](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). This will walk you through setting up the tensorflow framework, cloning the tensorflow github repo and a guide on - Load the `frozen_inference_graph.pb` trained on the hands dataset as well as the corresponding label map. In this repo, this is done in the `utils/detector_utils.py` script by the `load_inference_graph` method. ```python detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) print("> ====== Hand Inference graph loaded.") ``` - Detect hands. In this repo, this is done in the `utils/detector_utils.py` script by the `detect_objects` method. ```python (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) ``` - Visualize detected bounding detection_boxes. In this repo, this is done in the `utils/detector_utils.py` script by the `draw_box_on_image` method. This repo contains two scripts that tie all these steps together. - detect_multi_threaded.py : A threaded implementation for reading camera video input detection and detecting. Takes a set of command line flags to set parameters such as `--display` (visualize detections), image parameters `--width` and `--height`, videe `--source` (0 for camera) etc. - detect_single_threaded.py : Same as above, but single threaded. This script works for video files by setting the video source parameter videe `--source` (path to a video file). ```cmd # load and run detection on video at path "videos/chess.mov" python detect_single_threaded.py --source videos/chess.mov ``` > Update: If you do have errors loading the frozen inference graph in this repo, feel free to generate a new graph that fits your TF version from the model-checkpoint in this repo. Use the [export_inference_graph.py](https://github.com/tensorflow/models/blob/master/research/object_detection/export_inference_graph.py) script provided in the tensorflow object detection api repo. More guidance on this [here](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/). ## Thoughts on Optimization. A few things that led to noticeable performance increases. - Threading: Turns out that reading images from a webcam is a heavy I/O event and if run on the main application thread can slow down the program. I implemented some good ideas from [Adrian Rosebuck](https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv/) on parrallelizing image capture across multiple worker threads. This mostly led to an FPS increase of about 5 points. - For those new to Opencv, images from the `cv2.read()` method return images in [BGR format](https://www.learnopencv.com/why-does-opencv-use-bgr-color-format/). Ensure you convert to RGB before detection (accuracy will be much reduced if you dont). ```python cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) ``` - Keeping your input image small will increase fps without any significant accuracy drop.(I used about 320 x 240 compared to the 1280 x 720 which my webcam provides). - Model Quantization. Moving from the current 32 bit to 8 bit can achieve up to 4x reduction in memory required to load and store models. One way to further speed up this model is to explore the use of [8-bit fixed point quantization](https://heartbeat.fritz.ai/8-bit-quantization-and-tensorflow-lite-speeding-up-mobile-inference-with-low-precision-a882dfcafbbd). Performance can also be increased by a clever combination of tracking algorithms with the already decent detection and this is something I am still experimenting with. Have ideas for optimizing better, please share! <img src="images/general.jpg" width="100%"> Note: The detector does reflect some limitations associated with the training set. This includes non-egocentric viewpoints, very noisy backgrounds (e.g in a sea of hands) and sometimes skin tone. There is opportunity to improve these with additional data. ## Integrating Multiple DNNs. One way to make things more interesting is to integrate our new knowledge of where "hands" are with other detectors trained to recognize other objects. Unfortunately, while our hand detector can in fact detect hands, it cannot detect other objects (a factor or how it is trained). To create a detector that classifies multiple different objects would mean a long involved process of assembling datasets for each class and a lengthy training process. > Given the above, a potential strategy is to explore structures that allow us **efficiently** interleave output form multiple pretrained models for various object classes and have them detect multiple objects on a single image. An example of this is with my primary use case where I am interested in understanding the position of objects on a table with respect to hands on same table. I am currently doing some work on a threaded application that loads multiple detectors and outputs bounding boxes on a single image. More on this soon.

Laravel 5.6 on Material Design Lite 1.3 with user authentication, registration with email confirmation, social media authentication, password recovery, and captcha protection. This makes full use of Controllers for the routes, templates for the views, and makes use of middleware for routing. Uses laravel ORM modeling and has CRUD (Create Read Update Delete) functionality for all tasks. Quick setup, can be done in 5 minutes. It will take longer to obtain your Facebook, Twitter, and Google Plus API Keys than it will to set this up.

Easy to use Ant task for building JavaCard applets (2.1.1 to 3.2.0) 🐜

MIT Introduction to Deep Learning (6.S191) Instructors: Alexander Amini and Ava Soleimany Course Information Summary Prerequisites Schedule Lectures Labs, Final Projects, Grading, and Prizes Software labs Gather.Town lab + Office Hour sessions Final project Paper Review Project Proposal Presentation Project Proposal Grading Rubric Past Project Proposal Ideas Awards + Categories Important Links and Emails Course Information Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors. Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome! Schedule Monday Jan 18, 2021 Lecture: Introduction to Deep Learning and NNs Lab: Lab 1A Tensorflow and building NNs from scratch Tuesday Jan 19, 2021 Lecture: Deep Sequence Modelling Lab: Lab 1B Music Generation using RNNs Wednesday Jan 20, 2021 Lecture: Deep Computer Vision Lab: Lab 2A Image classification and detection Thursday Jan 21, 2021 Lecture: Deep Generative Modelling Lab: Lab 2B Debiasing facial recognition systems Friday Jan 22, 2021 Lecture: Deep Reinforcement Learning Lab: Lab 3 pixel-to-control planning Monday Jan 25, 2021 Lecture: Limitations and New Frontiers Lab: Lab 3 continued Tuesday Jan 26, 2021 Lecture (part 1): Evidential Deep Learning Lecture (part 2): Bias and Fairness Lab: Work on final assignments Lab competition entries due at 11:59pm ET on Canvas! Lab 1, Lab 2, and Lab 3 Wednesday Jan 27, 2021 Lecture (part 1): Nigel Duffy, Ernst & Young Lecture (part 2): Kate Saenko, Boston University and MIT-IBM Watson AI Lab Lab: Work on final assignments Assignments due: Sign up for Final Project Competition Thursday Jan 28, 2021 Lecture (part 1): Sanja Fidler, U. Toronto, Vector Institute, and NVIDIA Lecture (part 2): Katherine Chou, Google Lab: Work on final assignments Assignments due: 1 page paper review (if applicable) Friday Jan 29, 2021 Lecture: Student project pitch competition Lab: Awards ceremony and prize giveaway Assignments due: Project proposals (if applicable) Lectures Lectures will be held starting at 1:00pm ET from Jan 18 - Jan 29 2021, Monday through Friday, virtually through Zoom. Current MIT students, faculty, postdocs, researchers, staff, etc. will be able to access the lectures during this two week period, synchronously or asynchronously, via the MIT Canvas course webpage (MIT internal only). Lecture recordings will be uploaded to the Canvas as soon as possible; students are not required to attend any lectures synchronously. Please see the Canvas for details on Zoom links. The public edition of the course will only be made available after completion of the MIT course. Labs, Final Projects, Grading, and Prizes Course will be graded during MIT IAP for 6 units under P/D/F grading. Receiving a passing grade requires completion of each software lab project (through honor code, with submission required to enter lab competitions), a final project proposal/presentation or written review of a deep learning paper (submission required), and attendance/lecture viewing (through honor code). Submission of a written report or presentation of a project proposal will ensure a passing grade. MIT students will be eligible for prizes and awards as part of the class competitions. There will be two parts to the competitions: (1) software labs and (2) final projects. More information is provided below. Winners will be announced on the last day of class, with thousands of dollars of prizes being given away! Software labs There are three TensorFlow software lab exercises for the course, designed as iPython notebooks hosted in Google Colab. Software labs can be found on GitHub: https://github.com/aamini/introtodeeplearning. These are self-paced exercises and are designed to help you gain practical experience implementing neural networks in TensorFlow. For registered MIT students, submission of lab materials is not necessary to get credit for the course or to pass the course. At the end of each software lab there will be task-associated materials to submit (along with instructions) for entry into the competitions, open to MIT students and affiliates during the IAP offering. This includes MIT students/affiliates who are taking the class as listeners -- you are eligible! These instructions are provided at the end of each of the labs. Completing these tasks and submitting your materials to Canvas will enter you into a per-lab competition. MIT students and affiliates will be eligible for prizes during the IAP offering; at the end of the course, prize-winners will be awarded with their prizes. All competition submissions are due on January 26 at 11:59pm ET to Canvas. For the software lab competitions, submissions will be judged on the basis of the following criteria: Strength and quality of final results (lab dependent) Soundness of implementation and approach Thoroughness and quality of provided descriptions and figures Gather.Town lab + Office Hour sessions After each day’s lecture, there will be open Office Hours in the class GatherTown, up until 3pm ET. An MIT email is required to log in and join the GatherTown. During these sessions, there will not be a walk through or dictation of the labs; the labs are designed to be self-paced and to be worked on on your own time. The GatherTown sessions will be hosted by course staff and are held so you can: Ask questions on course lectures, labs, logistics, project, or anything else; Work on the labs in the presence of classmates/TAs/instructors; Meet classmates to find groups for the final project; Group work time for the final project; Bring the class community together. Final project To satisfy the final project requirement for this course, students will have two options: (1) write a 1 page paper review (single-spaced) on a recent deep learning paper of your choice or (2) participate and present in the project proposal pitch competition. The 1 page paper review option is straightforward, we propose some papers within this document to help you get started, and you can satisfy a passing grade with this option -- you will not be eligible for the grand prizes. On the other hand, participation in the project proposal pitch competition will equivalently satisfy your course requirements but additionally make you eligible for the grand prizes. See the section below for more details and requirements for each of these options. Paper Review Students may satisfy the final project requirement by reading and reviewing a recent deep learning paper of their choosing. In the written review, students should provide both: 1) a description of the problem, technical approach, and results of the paper; 2) critical analysis and exposition of the limitations of the work and opportunities for future work. Reviews should be submitted on Canvas by Thursday Jan 28, 2021, 11:59:59pm Eastern Time (ET). Just a few paper options to consider... https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf https://science.sciencemag.org/content/362/6419/1140 https://papers.nips.cc/paper/2018/file/0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.pdf https://arxiv.org/pdf/1906.11829.pdf https://www.nature.com/articles/s42256-020-00237-3 https://pubmed.ncbi.nlm.nih.gov/32084340/ Project Proposal Presentation Keyword: proposal This is a 2 week course so we do not require results or working implementations! However, to win the top prizes, nice, clear results and implementations will demonstrate feasibility of your proposal which is something we look for! Logistics -- please read! You must sign up to present before 11:59:59pm Eastern Time (ET) on Wednesday Jan 27, 2021 Slides must be in a Google Slide before 11:59:59pm Eastern Time (ET) on Thursday Jan 28, 2021 Project groups can be between 1 and 5 people Listeners welcome To be eligible for a prize you must have at least 1 registered MIT student in your group Each participant will only be allowed to be in one group and present one project pitch Synchronous attendance on 1/29/21 is required to make the project pitch! 3 min presentation on your idea (we will be very strict with the time limits) Prizes! (see below) Sign up to Present here: by 11:59pm ET on Wednesday Jan 27 Once you sign up, make your slide in the following Google Slides; submit by midnight on Thursday Jan 28. Please specify the project group # on your slides!!! Things to Consider This doesn’t have to be a new deep learning method. It can just be an interesting application that you apply some existing deep learning method to. What problem are you solving? Are there use cases/applications? Why do you think deep learning methods might be suited to this task? How have people done it before? Is it a new task? If so, what are similar tasks that people have worked on? In what aspects have they succeeded or failed? What is your method of solving this problem? What type of model + architecture would you use? Why? What is the data for this task? Do you need to make a dataset or is there one publicly available? What are the characteristics of the data? Is it sparse, messy, imbalanced? How would you deal with that? Project Proposal Grading Rubric Project proposals will be evaluated by a panel of judges on the basis of the following three criteria: 1) novelty and impact; 2) technical soundness, feasibility, and organization, including quality of any presented results; 3) clarity and presentation. Each judge will award a score from 1 (lowest) to 5 (highest) for each of the criteria; the average score from each judge across these criteria will then be averaged with that of the other judges to provide the final score. The proposals with the highest final scores will be selected for prizes. Here are the guidelines for the criteria: Novelty and impact: encompasses the potential impact of the project idea, its novelty with respect to existing approaches. Why does the proposed work matter? What problem(s) does it solve? Why are these problems important? Technical soundness, feasibility, and organization: encompasses all technical aspects of the proposal. Do the proposed methodology and architecture make sense? Is the architecture the best suited for the proposed problem? Is deep learning the best approach for the problem? How realistic is it to implement the idea? Was there any implementation of the method? If results and data are presented, we will evaluate the strength of the results/data. Clarity and presentation: encompasses the delivery and quality of the presentation itself. Is the talk well organized? Are the slides aesthetically compelling? Is there a clear, well-delivered narrative? Are the problem and proposed method clearly presented? Past Project Proposal Ideas Recipe Generation with RNNs Can we compress videos with CNN + RNN? Music Generation with RNNs Style Transfer Applied to X GAN’s on a new modality Summarizing text/news articles Combining news articles about similar events Code or spec generation Multimodal speech → handwriting Generate handwriting based on keywords (i.e. cursive, slanted, neat) Predicting stock market trends Show language learners articles or videos at their level Transfer of writing style Chemical Synthesis with Recurrent Neural networks Transfer learning to learn something in a domain for which it’s hard or risky to gather data or do training RNNs to model some type of time series data Computer vision to coach sports players Computer vision system for safety brakes or warnings Use IBM Watson API to get the sentiment of your Facebook newsfeed Deep learning webcam to give wifi-access to friends or improve video chat in some way Domain-specific chatbot to help you perform a specific task Detect whether a signature is fraudulent Awards + Categories Final Project Awards: 1x NVIDIA RTX 3080 4x Google Home Max 3x Display Monitors Software Lab Awards: Bose headphones (Lab 1) Display monitor (Lab 2) Bebop drone (Lab 3) Important Links and Emails Course website: http://introtodeeplearning.com Course staff: introtodeeplearning-staff@mit.edu Piazza forum (MIT only): https://piazza.com/mit/spring2021/6s191 Canvas (MIT only): https://canvas.mit.edu/courses/8291 Software lab repository: https://github.com/aamini/introtodeeplearning Lab/office hour sessions (MIT only): https://gather.town/app/56toTnlBrsKCyFgj/MITDeepLearning

AndroidBase android 应用开发框架 1.andbase中包含了大量的开发常用手段。 如网络下载，多线程与线程池的管理，数据库ORM，图片缓存管理，图片文件下载上传，Http请求工具，SOAP工具类，异步Task，常用工具类（字符串，日期，文件处理，图片处理工具类等），能够使您的应用在团队开发中减少冗余代码，很大的提高了代码的维护性与开发高效性，能很好的规避由于开发疏忽而导致常犯的错误。 2.andbase封装了大量的常用控件。 如list分页，grid分页，下拉刷新，进度框，图片轮播，表格，多线程下载器，侧边栏，图片上传，轮子选择，图表，Tab滑动，日历选择器等。 3.强大的AbActivity，您没有理由不继承它。 继承它你能够获得一个简单强大可设置的操作栏，以及一系列的简单调用，如弹出框，提示框，进度框，副操作栏等。 4.提供效率较高图片缓存管理策略，使内存大幅度节省，利用率提高，效率提高。 程序中要管理大量的图片资源，andbase提供简单的方法，几步完成下载与显示，并支持缩放，裁剪，缓存功能，OOM不在有。 5.封装了大量常见工具类。 包括日期，字符，文件，图片，数学，View万能适配工具类等各种处理函数， 多而全。 6.用andbase大量减少handler的使用，而采用回调函数，代码更整洁。 handler会产生大量代码，并且不好维护，andbase对handler进行了封装，你会发现用了andbase基本代码中不会再有大量的handler判断语句。 7.简单轻量支持注解自动建表的ORM框架（支持一/多对多的关联操作）。 写sql，建表，工作量大，andbase提供更傻瓜异步增删改查工具类，数据库在内置卡和外置卡都可以。 8.异步请求http框架，网络请求标准化，支持文件上传下载，get，post，进度显示。 包含了异步与http请求的工具类，SOAP请求工具类，简单实用。

# AD-Prediction Convolutional Neural Networks for Alzheimer's Disease Prediction Using Brain MRI Image ## Abstract Alzheimers disease (AD) is characterized by severe memory loss and cognitive impairment. It associates with significant brain structure changes, which can be measured by magnetic resonance imaging (MRI) scan. The observable preclinical structure changes provides an opportunity for AD early detection using image classification tools, like convolutional neural network (CNN). However, currently most AD related studies were limited by sample size. Finding an efficient way to train image classifier on limited data is critical. In our project, we explored different transfer-learning methods based on CNN for AD prediction brain structure MRI image. We find that both pretrained 2D AlexNet with 2D-representation method and simple neural network with pretrained 3D autoencoder improved the prediction performance comparing to a deep CNN trained from scratch. The pretrained 2D AlexNet performed even better (**86%**) than the 3D CNN with autoencoder (**77%**). ## Method #### 1. Data In this project, we used public brain MRI data from **Alzheimers Disease Neuroimaging Initiative (ADNI)** Study. ADNI is an ongoing, multicenter cohort study, started from 2004. It focuses on understanding the diagnostic and predictive value of Alzheimers disease specific biomarkers. The ADNI study has three phases: ADNI1, ADNI-GO, and ADNI2. Both ADNI1 and ADNI2 recruited new AD patients and normal control as research participants. Our data included a total of 686 structure MRI scans from both ADNI1 and ADNI2 phases, with 310 AD cases and 376 normal controls. We randomly derived the total sample into training dataset (n = 519), validation dataset (n = 100), and testing dataset (n = 67). #### 2. Image preprocessing Image preprocessing were conducted using Statistical Parametric Mapping (SPM) software, version 12. The original MRI scans were first skull-stripped and segmented using segmentation algorithm based on 6-tissue probability mapping and then normalized to the International Consortium for Brain Mapping template of European brains using affine registration. Other configuration includes: bias, noise, and global intensity normalization. The standard preprocessing process output 3D image files with an uniform size of 121x145x121. Skull-stripping and normalization ensured the comparability between images by transforming the original brain image into a standard image space, so that same brain substructures can be aligned at same image coordinates for different participants. Diluted or enhanced intensity was used to compensate the structure changes. the In our project, we used both whole brain (including both grey matter and white matter) and grey matter only. #### 3. AlexNet and Transfer Learning Convolutional Neural Networks (CNN) are very similar to ordinary Neural Networks. A CNN consists of an input and an output layer, as well as multiple hidden layers. The hidden layers are either convolutional, pooling or fully connected. ConvNet architectures make the explicit assumption that the inputs are images, which allows us to encode certain properties into the architecture. These then make the forward function more efficient to implement and vastly reduce the amount of parameters in the network. #### 3.1. AlexNet The net contains eight layers with weights; the first five are convolutional and the remaining three are fully connected. The overall architecture is shown in Figure 1. The output of the last fully-connected layer is fed to a 1000-way softmax which produces a distribution over the 1000 class labels. AlexNet maximizes the multinomial logistic regression objective, which is equivalent to maximizing the average across training cases of the log-probability of the correct label under the prediction distribution. The kernels of the second, fourth, and fifth convolutional layers are connected only to those kernel maps in the previous layer which reside on the same GPU (as shown in Figure1). The kernels of the third convolutional layer are connected to all kernel maps in the second layer. The neurons in the fully connected layers are connected to all neurons in the previous layer. Response-normalization layers follow the first and second convolutional layers. Max-pooling layers follow both response-normalization layers as well as the fifth convolutional layer. The ReLU non-linearity is applied to the output of every convolutional and fully-connected layer.  The first convolutional layer filters the 224x224x3 input image with 96 kernels of size 11x11x3 with a stride of 4 pixels (this is the distance between the receptive field centers of neighboring neurons in a kernel map). The second convolutional layer takes as input the (response-normalized and pooled) output of the first convolutional layer and filters it with 256 kernels of size 5x5x48. The third, fourth, and fifth convolutional layers are connected to one another without any intervening pooling or normalization layers. The third convolutional layer has 384 kernels of size 3x3x256 connected to the (normalized, pooled) outputs of the second convolutional layer. The fourth convolutional layer has 384 kernels of size 3x3x192 , and the fifth convolutional layer has 256 kernels of size 3x3x192. The fully-connected layers have 4096 neurons each. #### 3.2. Transfer Learning Training an entire Convolutional Network from scratch (with random initialization) is impractical[14] because it is relatively rare to have a dataset of sufficient size. An alternative is to pretrain a Conv-Net on a very large dataset (e.g. ImageNet), and then use the ConvNet either as an initialization or a fixed feature extractor for the task of interest. Typically, there are three major transfer learning scenarios: **ConvNet as fixed feature extractor:** We can take a ConvNet pretrained on ImageNet, and remove the last fully-connected layer, then treat the rest structure as a fixed feature extractor for the target dataset. In AlexNet, this would be a 4096-D vector. Usually, we call these features as CNN codes. Once we get these features, we can train a linear classifier (e.g. linear SVM or Softmax classifier) for our target dataset. **Fine-tuning the ConvNet:** Another idea is not only replace the last fully-connected layer in the classifier, but to also fine-tune the parameters of the pretrained network. Due to overfitting concerns, we can only fine-tune some higher-level part of the network. This suggestion is motivated by the observation that earlier features in a ConvNet contains more generic features (e.g. edge detectors or color blob detectors) that can be useful for many kind of tasks. But the later layer of the network becomes progressively more specific to the details of the classes contained in the original dataset. **Pretrained models:** The released pretrained model is usually the final ConvNet checkpoint. So it is common to see people use the network for fine-tuning. #### 4. 3D Autoencoder and Convolutional Neural Network We take a two-stage approach where we first train a 3D sparse autoencoder to learn filters for convolution operations, and then build a convolutional neural network whose first layer uses the filters learned with the autoencoder.  #### 4.1. Sparse Autoencoder An autoencoder is a 3-layer neural network that is used to extract features from an input such as an image. Sparse representations can provide a simple interpretation of the input data in terms of a small number of \parts by extracting the structure hidden in the data. The autoencoder has an input layer, a hidden layer and an output layer, and the input and output layers have same number of units, while the hidden layer contains more units for a sparse and overcomplete representation. The encoder function maps input x to representation h, and the decoder function maps the representation h to the output x. In our problem, we extract 3D patches from scans as the input to the network. The decoder function aims to reconstruct the input form the hidden representation h. #### 4.2. 3D Convolutional Neural Network Training the 3D convolutional neural network(CNN) is the second stage. The CNN we use in this project has one convolutional layer, one pooling layer, two linear layers, and finally a log softmax layer. After training the sparse autoencoder, we take the weights and biases of the encoder from trained model, and use them a 3D filter of a 3D convolutional layer of the 1-layer convolutional neural network. Figure 2 shows the architecture of the network. #### 5. Tools In this project, we used Nibabel for MRI image processing and PyTorch Neural Networks implementation.

task-oriented dialogue system, especially for LLM, contain subtask: (1) intent-detection (2) slot filling (3) dialogue state tracking

W3C Strategic Highlights September 2019 This report was prepared for the September 2019 W3C Advisory Committee Meeting (W3C Member link). See the accompanying W3C Fact Sheet — September 2019. For the previous edition, see the April 2019 W3C Strategic Highlights. For future editions of this report, please consult the latest version. A Chinese translation is available. ☰ Contents Introduction Future Web Standards Meeting Industry Needs Web Payments Digital Publishing Media and Entertainment Web & Telecommunications Real-Time Communications (WebRTC) Web & Networks Automotive Web of Things Strengthening the Core of the Web HTML CSS Fonts SVG Audio Performance Web Performance WebAssembly Testing Browser Testing and Tools WebPlatform Tests Web of Data Web for All Security, Privacy, Identity Internationalization (i18n) Web Accessibility Outreach to the world W3C Developer Relations W3C Training Translations W3C Liaisons Introduction This report highlights recent work of enhancement of the existing landscape of the Web platform and innovation for the growth and strength of the Web. 33 working groups and a dozen interest groups enable W3C to pursue its mission through the creation of Web standards, guidelines, and supporting materials. We track the tremendous work done across the Consortium through homogeneous work-spaces in Github which enables better monitoring and management. We are in the middle of a period where we are chartering numerous working groups which demonstrate the rapid degree of change for the Web platform: After 4 years, we are nearly ready to publish a Payment Request API Proposed Recommendation and we need to soon charter follow-on work. In the last year we chartered the Web Payment Security Interest Group. In the last year we chartered the Web Media Working Group with 7 specifications for next generation Media support on the Web. We have Accessibility Guidelines under W3C Member review which includes Silver, a new approach. We have just launched the Decentralized Identifier Working Group which has tremendous potential because Decentralized Identifier (DID) is an identifier that is globally unique, resolveable with high availability, and cryptographically verifiable. We have Privacy IG (PING) under W3C Member review which strengthens our focus on the tradeoff between privacy and function. We have a new CSS charter under W3C Member review which maps the group's work for the next three years. In this period, W3C and the WHATWG have succesfully completed the negotiation of a Memorandum of Understanding rooted in the mutual belief that that having two distinct specifications claiming to be normative is generally harmful for the Web community. The MOU, signed last May, describes how the two organizations are to collaborate on the development of a single authoritative version of the HTML and DOM specifications. W3C subsequently rechartered the HTML Working Group to assist the W3C community in raising issues and proposing solutions for the HTML and DOM specifications, and for the production of W3C Recommendations from WHATWG Review Drafts. As the Web evolves continuously, some groups are looking for ways for specifications to do so as well. So-called "evergreen recommendations" or "living standards" aim to track continuous development (and maintenance) of features, on a feature-by-feature basis, while getting review and patent commitments. We see the maturation and further development of an incredible number of new technologies coming to the Web. Continued progress in many areas demonstrates the vitality of the W3C and the Web community, as the rest of the report illustrates. Future Web Standards W3C has a variety of mechanisms for listening to what the community thinks could become good future Web standards. These include discussions with the Membership, discussions with other standards bodies, the activities of thousands of participants in over 300 community groups, and W3C Workshops. There are lots of good ideas. The W3C strategy team has been identifying promising topics and invites public participation. Future, recent and under consideration Workshops include: Inclusive XR (5-6 November 2019, Seattle, WA, USA) to explore existing and future approaches on making Virtual and Augmented Reality experiences more inclusive, including to people with disabilities; W3C Workshop on Data Models for Transportation (12-13 September 2019, Palo Alto, CA, USA) W3C Workshop on Web Games (27-28 June 2019, Redmond, WA, USA), view report Second W3C Workshop on the Web of Things (3-5 June 2019, Munich, Germany) W3C Workshop on Web Standardization for Graph Data; Creating Bridges: RDF, Property Graph and SQL (4-6 March 2019, Berlin, Germany), view report Web & Machine Learning. The Strategy Funnel documents the staff's exploration of potential new work at various phases: Exploration and Investigation, Incubation and Evaluation, and eventually to the chartering of a new standards group. The Funnel view is a GitHub Project where new area are issues represented by “cards” which move through the columns, usually from left to right. Most cards start in Exploration and move towards Chartering, or move out of the funnel. Public input is welcome at any stage but particularly once Incubation has begun. This helps W3C identify work that is sufficiently incubated to warrant standardization, to review the ecosystem around the work and indicate interest in participating in its standardization, and then to draft a charter that reflects an appropriate scope. Ongoing feedback can speed up the overall standardization process. Since the previous highlights document, W3C has chartered a number of groups, and started discussion on many more: Newly Chartered or Rechartered Web Application Security WG (03-Apr) Web Payment Security IG (17-Apr) Patent and Standards IG (24-Apr) Web Applications WG (14-May) Web & Networks IG (16-May) Media WG (23-May) Media and Entertainment IG (06-Jun) HTML WG (06-Jun) Decentralized Identifier WG (05-Sep) Extended Privacy IG (PING) (30-Sep) Verifiable Claims WG (30-Sep) Service Workers WG (31-Dec) Dataset Exchange WG (31-Dec) Web of Things Working Group (31-Dec) Web Audio Working Group (31-Dec) Proposed charters / Advance Notice Accessibility Guidelines WG Privacy IG (PING) RDF Literal Direction WG Timed Text WG CSS WG Web Authentication WG Closed Internationalization Tag Set IG Meeting Industry Needs Web Payments All Web Payments specifications W3C's payments standards enable a streamlined checkout experience, enabling a consistent user experience across the Web with lower front end development costs for merchants. Users can store and reuse information and more quickly and accurately complete online transactions. The Web Payments Working Group has republished Payment Request API as a Candidate Recommendation, aiming to publish a Proposed Recommendation in the Fall 2019, and is discussing use cases and features for Payment Request after publication of the 1.0 Recommendation. Browser vendors have been finalizing implementation of features added in the past year (view the implementation report). As work continues on the Payment Handler API and its implementation (currently in Chrome and Edge Canary), one focus in 2019 is to increase adoption in other browsers. Recently, Mastercard demonstrated the use of Payment Request API to carry out EMVCo's Secure Remote Commerce (SRC) protocol whose payment method definition is being developed with active participation by Visa, Mastercard, American Express, and Discover. Payment method availability is a key factor in merchant considerations about adopting Payment Request API. The ability to get uniform adoption of a new payment method such as Secure Remote Commerce (SRC) also depends on the availability of the Payment Handler API in browsers, or of proprietary alternatives. Web Monetization, which the Web Payments Working Group will discuss again at its face-to-face meeting in September, can be used to enable micropayments as an alternative revenue stream to advertising. Since the beginning of 2019, Amazon, Brave Software, JCB, Certus Cybersecurity Solutions and Netflix have joined the Web Payments Working Group. In April, W3C launched the Web Payment Security Group to enable W3C, EMVCo, and the FIDO Alliance to collaborate on a vision for Web payment security and interoperability. Participants will define areas of collaboration and identify gaps between existing technical specifications in order to increase compatibility among different technologies, such as: How do SRC, FIDO, and Payment Request relate? The Payment Services Directive 2 (PSD2) regulations in Europe are scheduled to take effect in September 2019. What is the role of EMVCo, W3C, and FIDO technologies, and what is the current state of readiness for the deadline? How can we improve privacy on the Web at the same time as we meet industry requirements regarding user identity? Digital Publishing All Digital Publishing specifications, Publication milestones The Web is the universal publishing platform. Publishing is increasingly impacted by the Web, and the Web increasingly impacts Publishing. Topic of particular interest to Publishing@W3C include typography and layout, accessibility, usability, portability, distribution, archiving, offline access, print on demand, and reliable cross referencing. And the diverse publishing community represented in the groups consist of the traditional "trade" publishers, ebook reading system manufacturers, but also publishers of audio book, scholarly journals or educational materials, library scientists or browser developers. The Publishing Working Group currently concentrates on Audiobooks which lack a comprehensive standard, thus incurring extra costs and time to publish in this booming market. Active development is ongoing on the future standard: Publication Manifest Audiobook profile for Web Publications Lightweight Packaging Format The BD Comics Manga Community Group, the Synchronized Multimedia for Publications Community Group, the Publishing Community Group and a future group on archival, are companions to the working group where specific work is developed and incubated. The Publishing Community Group is a recently launched incubation channel for Publishing@W3C. The goal of the group is to propose, document, and prototype features broadly related to: publications on the Web reading modes and systems and the user experience of publications The EPUB 3 Community Group has successfully completed the revision of EPUB 3.2. The Publishing Business Group fosters ongoing participation by members of the publishing industry and the overall ecosystem in the development of Web infrastructure to better support the needs of the industry. The Business Group serves as an additional conduit to the Publishing Working Group and several Community Groups for feedback between the publishing ecosystem and W3C. The Publishing BG has played a vital role in fostering and advancing the adoption and continued development of EPUB 3. In particular the BG provided critical support to the update of EPUBCheck to validate EPUB content to the new EPUB 3.2 specification. This resulted in the development, in conjunction with the EPUB3 Community Group, of a new generation of EPUBCheck, i.e., EPUBCheck 4.2 production-ready release. Media and Entertainment All Media specifications The Media and Entertainment vertical tracks media-related topics and features that create immersive experiences for end users. HTML5 brought standard audio and video elements to the Web. Standardization activities since then have aimed at turning the Web into a professional platform fully suitable for the delivery of media content and associated materials, enabling missing features to stream video content on the Web such as adaptive streaming and content protection. Together with Microsoft, Comcast, Netflix and Google, W3C received an Technology & Engineering Emmy Award in April 2019 for standardization of a full TV experience on the Web. Current goals are to: Reinforce core media technologies: Creation of the Media Working Group, to develop media-related specifications incubated in the WICG (e.g. Media Capabilities, Picture-in-picture, Media Session) and maintain maintain/evolve Media Source Extensions (MSE) and Encrypted Media Extensions (EME). Improve support for Media Timed Events: data cues incubation. Enhance color support (HDR, wide gamut), in scope of the CSS WG and in the Color on the Web CG. Reduce fragmentation: Continue annual releases of a common and testable baseline media devices, in scope of the Web Media APIs CG and in collaboration with the CTA WAVE Project. Maintain the Road-map of Media Technologies for the Web which highlights Web technologies that can be used to build media applications and services, as well as known gaps to enable additional use cases. Create the future: Discuss perspectives for Media and Entertainment for the Web. Bring the power of GPUs to the Web (graphics, machine learning, heavy processing), under incubation in the GPU for the Web CG. Transition to a Working Group is under discussion. Determine next steps after the successful W3C Workshop on Web Games of June 2019. View the report. Timed Text The Timed Text Working Group develops and maintains formats used for the representation of text synchronized with other timed media, like audio and video, and notably works on TTML, profiles of TTML, and WebVTT. Recent progress includes: A robust WebVTT implementation report poises the specification for publication as a proposed recommendation. Discussions around re-chartering, notably to add a TTML Profile for Audio Description deliverable to the scope of the group, and clarify that rendering of captions within XR content is also in scope. Immersive Web Hardware that enables Virtual Reality (VR) and Augmented Reality (AR) applications are now broadly available to consumers, offering an immersive computing platform with both new opportunities and challenges. The ability to interact directly with immersive hardware is critical to ensuring that the web is well equipped to operate as a first-class citizen in this environment. The Immersive Web Working Group has been stabilizing the WebXR Device API while the companion Immersive Web Community Group incubates the next series of features identified as key for the future of the Immersive Web. W3C plans a workshop focused on the needs and benefits at the intersection of VR & Accessibility (Inclusive XR), on 5-6 November 2019 in Seattle, WA, USA, to explore existing and future approaches on making Virtual and Augmented Reality experiences more inclusive. Web & Telecommunications The Web is the Open Platform for Mobile. Telecommunication service providers and network equipment providers have long been critical actors in the deployment of Web technologies. As the Web platform matures, it brings richer and richer capabilities to extend existing services to new users and devices, and propose new and innovative services. Real-Time Communications (WebRTC) All Real-Time Communications specifications WebRTC has reshaped the whole communication landscape by making any connected device a potential communication end-point, bringing audio and video communications anywhere, on any network, vastly expanding the ability of operators to reach their customers. WebRTC serves as the corner-stone of many online communication and collaboration services. The WebRTC Working Group aims to bringing WebRTC 1.0 (and companion specification Media Capture and Streams) to Recommendation by the end of 2019. Intense efforts are focused on testing (supported by a dedicated hackathon at IETF 104) and interoperability. The group is considering pushing features that have not gotten enough traction to separate modules or to a later minor revision of the spec. Beyond WebRTC 1.0, the WebRTC Working Group will focus its efforts on WebRTC NV which the group has started documenting by identifying use cases. Web & Networks Recently launched, in the wake of the May 2018 Web5G workshop, the Web & Networks Interest Group is chaired by representatives from AT&T, China Mobile and Intel, with a goal to explore solutions for web applications to achieve better performance and resource allocation, both on the device and network. The group's first efforts are around use cases, privacy & security requirements and liaisons. Automotive All Automotive specifications To create a rich application ecosystem for vehicles and other devices allowed to connect to the vehicle, the W3C Automotive Working Group is delivering a service specification to expose all common vehicle signals (engine temperature, fuel/charge level, range, tire pressure, speed, etc.) The Vehicle Information Service Specification (VISS), which is a Candidate Recommendation, is seeing more implementations across the industry. It provides the access method to a common data model for all the vehicle signals –presently encapsulating a thousand or so different data elements– and will be growing to accommodate the advances in automotive such as autonomous and driver assist technologies and electrification. The group is already working on a successor to VISS, leveraging the underlying data model and the VIWI submission from Volkswagen, for a more robust means of accessing vehicle signals information and the same paradigm for other automotive needs including location-based services, media, notifications and caching content. The Automotive and Web Platform Business Group acts as an incubator for prospective standards work. One of its task forces is using W3C VISS in performing data sampling and off-boarding the information to the cloud. Access to the wealth of information that W3C's auto signals standard exposes is of interest to regulators, urban planners, insurance companies, auto manufacturers, fleet managers and owners, service providers and others. In addition to components needed for data sampling and edge computing, capturing user and owner consent, information collection methods and handling of data are in scope. The upcoming W3C Workshop on Data Models for Transportation (September 2019) is expected to focus on the need of additional ontologies around transportation space. Web of Things All Web of Things specifications W3C's Web of Things work is designed to bridge disparate technology stacks to allow devices to work together and achieve scale, thus enabling the potential of the Internet of Things by eliminating fragmentation and fostering interoperability. Thing descriptions expressed in JSON-LD cover the behavior, interaction affordances, data schema, security configuration, and protocol bindings. The Web of Things complements existing IoT ecosystems to reduce the cost and risk for suppliers and consumers of applications that create value by combining multiple devices and information services. There are many sectors that will benefit, e.g. smart homes, smart cities, smart industry, smart agriculture, smart healthcare and many more. The Web of Things Working Group is finishing the initial Web of Things standards, with support from the Web of Things Interest Group: Web of Things Architecture Thing Descriptions Strengthening the Core of the Web HTML The HTML Working Group was chartered early June to assist the W3C community in raising issues and proposing solutions for the HTML and DOM specifications, and to produce W3C Recommendations from WHATWG Review Drafts. A few days before, W3C and the WHATWG signed a Memorandum of Understanding outlining the agreement to collaborate on the development of a single version of the HTML and DOM specifications. Issues and proposed solutions for HTML and DOM done via the newly rechartered HTML Working Group in the WHATWG repositories The HTML Working Group is targetting November 2019 to bring HTML and DOM to Candidate Recommendations. CSS All CSS specifications CSS is a critical part of the Open Web Platform. The CSS Working Group gathers requirements from two large groups of CSS users: the publishing industry and application developers. Within W3C, those groups are exemplified by the Publishing groups and the Web Platform Working Group. The former requires things like better pagination support and advanced font handling, the latter needs intelligent (and fast!) scrolling and animations. What we know as CSS is actually a collection of almost a hundred specifications, referred to as ‘modules’. The current state of CSS is defined by a snapshot, updated once a year. The group also publishes an index defining every term defined by CSS specifications. Fonts All Fonts specifications The Web Fonts Working Group develops specifications that allow the interoperable deployment of downloadable fonts on the Web, with a focus on Progressive Font Enrichment as well as maintenance of WOFF Recommendations. Recent and ongoing work includes: Early API experiments by Adobe and Monotype have demonstrated the feasibility of a font enrichment API, where a server delivers a font with minimal glyph repertoire and the client can query the full repertoire and request additional subsets on-the-fly. In other experiments, the Brotli compression used in WOFF 2 was extended to support shared dictionaries and patch update. Metrics to quantify improvement are a current hot discussion topic. The group will meet at ATypi 2019 in Japan, to gather requirements from the international typography community. The group will first produce a report summarizing the strengths and weaknesses of each prototype solution by Q2 2020. SVG All SVG specifications SVG is an important and widely-used part of the Open Web Platform. The SVG Working Group focuses on aligning the SVG 2.0 specification with browser implementations, having split the specification into a currently-implemented 2.0 and a forward-looking 2.1. Current activity is on stabilization, increased integration with the Open Web Platform, and test coverage analysis. The Working Group was rechartered in March 2019. A new work item concerns native (non-Web-browser) uses of SVG as a non-interactive, vector graphics format. Audio The Web Audio Working Group was extended to finish its work on the Web Audio API, expecting to publish it as a Recommendation by year end. The specification enables synthesizing audio in the browser. Audio operations are performed with audio nodes, which are linked together to form a modular audio routing graph. Multiple sources — with different types of channel layout — are supported. This modular design provides the flexibility to create complex audio functions with dynamic effects. The first version of Web Audio API is now feature complete and is implemented in all modern browsers. Work has started on the next version, and new features are being incubated in the Audio Community Group. Performance Web Performance All Web Performance specifications There are currently 18 specifications in development in the Web Performance Working Group aiming to provide methods to observe and improve aspects of application performance of user agent features and APIs. The W3C team is looking at related work incubated in the W3C GPU for the Web (WebGPU) Community Group which is poised to transition to a W3C Working Group. A preliminary draft charter is available. WebAssembly All WebAssembly specifications WebAssembly improves Web performance and power by being a virtual machine and execution environment enabling loaded pages to run native (compiled) code. It is deployed in Firefox, Edge, Safari and Chrome. The specification will soon reach Candidate Recommendation. WebAssembly enables near-native performance, optimized load time, and perhaps most importantly, a compilation target for existing code bases. While it has a small number of native types, much of the performance increase relative to Javascript derives from its use of consistent typing. WebAssembly leverages decades of optimization for compiled languages and the byte code is optimized for compactness and streaming (the web page starts executing while the rest of the code downloads). Network and API access all occurs through accompanying Javascript libraries -- the security model is identical to that of Javascript. Requirements gathering and language development occur in the Community Group while the Working Group manages test development, community review and progression of specifications on the Recommendation Track. Testing Browser testing plays a critical role in the growth of the Web by: Improving the reliability of Web technology definitions; Improving the quality of implementations of these technologies by helping vendors to detect bugs in their products; Improving the data available to Web developers on known bugs and deficiencies of Web technologies by publishing results of these tests. Browser Testing and Tools The Browser Testing and Tools Working Group is developing WebDriver version 2, having published last year the W3C Recommendation of WebDriver. WebDriver acts as a remote control interface that enables introspection and control of user agents, provides a platform- and language-neutral wire protocol as a way for out-of-process programs to remotely instruct the behavior of Web, and emulates the actions of a real person using the browser. WebPlatform Tests The WebPlatform Tests project now provides a mechanism which allows to fully automate tests that previously needed to be run manually: TestDriver. TestDriver enables sending trusted key and mouse events, sending complex series of trusted pointer and key interactions for things like in-content drag-and-drop or pinch zoom, and even file upload. Since 2014 W3C began work on this coordinated open-source effort to build a cross-browser test suite for the Web Platform, which WHATWG, and all major browsers adopted. Web of Data All Data specifications There have been several great success stories around the standardization of data on the web over the past year. Verifiable Claims seems to have significant uptake. It is also significant that the Distributed Identifier WG charter has received numerous favorable reviews, and was just recently launched. JSON-LD has been a major success with the large deployment on Web sites via schema.org. JSON-LD 1.1 completed technical work, about to transition to CR More than 25% of websites today include schema.org data in JSON-LD The Web of Things description is in CR since May, making use of JSON-LD Verifiable Credentials data model is in CR since July, also making use of JSON-LD Continued strong interest in decentralized identifiers Engagement from the TAG with reframing core documents, such as Ethical Web Principles, to include data on the web within their scope Data is increasingly important for all organizations, especially with the rise of IoT and Big Data. W3C has a mature and extensive suite of standards relating to data that were developed over two decades of experience, with plans for further work on making it easier for developers to work with graph data and knowledge graphs. Linked Data is about the use of URIs as names for things, the ability to dereference these URIs to get further information and to include links to other data. There are ever-increasing sources of open Linked Data on the Web, as well as data services that are restricted to the suppliers and consumers of those services. The digital transformation of industry is seeking to exploit advanced digital technologies. This will facilitate businesses to integrate horizontally along the supply and value chains, and vertically from the factory floor to the office floor. W3C is seeking to make it easier to support enterprise-wide data management and governance, reflecting the strategic importance of data to modern businesses. Traditional approaches to data have focused on tabular databases (SQL/RDBMS), Comma Separated Value (CSV) files, and data embedded in PDF documents and spreadsheets. We're now in midst of a major shift to graph data with nodes and labeled directed links between them. Graph data is: Faster than using SQL and associated JOIN operations More favorable to integrating data from heterogeneous sources Better suited to situations where the data model is evolving In the wake of the recent W3C Workshop on Graph Data we are in the process of launching a Graph Standardization Business Group to provide a business perspective with use cases and requirements, to coordinate technical standards work and liaisons with external organizations. Web for All Security, Privacy, Identity All Security specifications, all Privacy specifications Authentication on the Web As the WebAuthn Level 1 W3C Recommendation published last March is seeing wide implementation and adoption of strong cryptographic authentication, work is proceeding on Level 2. The open standard Web API gives native authentication technology built into native platforms, browsers, operating systems (including mobile) and hardware, offering protection against hacking, credential theft, phishing attacks, thus aiming to end the era of passwords as a security construct. You may read more in our March press release. Privacy An increasing number of W3C specifications are benefitting from Privacy and Security review; there are security and privacy aspects to every specification. Early review is essential. Working with the TAG, the Privacy Interest Group has updated the Self-Review Questionnaire: Security and Privacy. Other recent work of the group includes public blogging further to the exploration of anti-patterns in standards and permission prompts. Security The Web Application Security Working Group adopted Feature Policy, aiming to allow developers to selectively enable, disable, or modify the behavior of some of these browser features and APIs within their application; and Fetch Metadata, aiming to provide servers with enough information to make a priori decisions about whether or not to service a request based on the way it was made, and the context in which it will be used. The Web Payment Security Interest Group, launched last April, convenes members from W3C, EMVCo, and the FIDO Alliance to discuss cooperative work to enhance the security and interoperability of Web payments (read more about payments). Internationalization (i18n) All Internationalization specifications, educational articles related to Internationalization, spec developers checklist Only a quarter or so current Web users use English online and that proportion will continue to decrease as the Web reaches more and more communities of limited English proficiency. If the Web is to live up to the "World Wide" portion of its name, and for the Web to truly work for stakeholders all around the world engaging with content in various languages, it must support the needs of worldwide users as they engage with content in the various languages. The growth of epublishing also brings requirements for new features and improved typography on the Web. It is important to ensure the needs of local communities are captured. The W3C Internationalization Initiative was set up to increase in-house resources dedicated to accelerating progress in making the World Wide Web "worldwide" by gathering user requirements, supporting developers, and education & outreach. For an overview of current projects see the i18n radar. W3C's Internationalization efforts progressed on a number of fronts recently: Requirements: New African and European language groups will work on the gap analysis, errata and layout requirements. Gap analysis: Japanese, Devanagari, Bengali, Tamil, Lao, Khmer, Javanese, and Ethiopic updated in the gap-analysis documents. Layout requirements document: notable progress tracked in the Southeast Asian Task Force while work continues on Chinese layout requirements. Developer support: Spec reviews: the i18n WG continues active review of specifications of the WHATWG and other W3C Working Groups. Short review checklist: easy way to begin a self-review to help spec developers understand what aspects of their spec are likely to need attention for internationalization, and points them to more detailed checklists for the relevant topics. It also helps those reviewing specs for i18n issues. Strings on the Web: Language and Direction Metadata lays out issues and discusses potential solutions for passing information about language and direction with strings in JSON or other data formats. The document was rewritten for clarity, and expanded. The group is collaborating with the JSON-LD and Web Publishing groups to develop a plan for updating RDF, JSON-LD and related specifications to handle metadata for base direction of text (bidi). User-friendly test format: a new format was developed for Internationalization Test Suite tests, which displays helpful information about how the test works. This particularly useful because those tests are pointed to by educational materials and gap-analysis documents. Web Platform Tests: a large number of tests in the i18n test suite have been ported to the WPT repository, including: css-counter-styles, css-ruby, css-syntax, css-test, css-text-decor, css-writing-modes, and css-pseudo. Education & outreach: (for all educational materials, see the HTML & CSS Authoring Techniques) Web Accessibility All Accessibility specifications, WAI resources The Web Accessibility Initiative supports W3C's Web for All mission. Recent achievements include: Education and training: Inaccessibility of CAPTCHA updated to bring our analysis and recommendations up to date with CAPTCHA practice today, concluding two years of extensive work and invaluable input from the public (read more on the W3C Blog Learn why your web content and applications should be accessible. The Education and Outreach Working Group has completed revision and updating of the Business Case for Digital Accessibility. Accessibility guidelines: The Accessibility Guidelines Working Group has continued to update WCAG Techniques and Understanding WCAG 2.1; and published a Candidate Recommendation of Accessibility Conformance Testing Rules Format 1.0 to improve inter-rater reliability when evaluating conformance of web content to WCAG An updated charter is being developed to host work on "Silver", the next generation accessibility guidelines (WCAG 2.2) There are accessibility aspects to most specifications. Check your work with the FAST checklist. Outreach to the world W3C Developer Relations To foster the excellent feedback loop between Web Standards development and Web developers, and to grow participation from that diverse community, recent W3C Developer Relations activities include: @w3cdevs tracks the enormous amount of work happening across W3C W3C Track during the Web Conference 2019 in San Francisco Tech videos: W3C published the 2019 Web Games Workshop videos The 16 September 2019 Developer Meetup in Fukuoka, Japan, is open to all and will combine a set of technical demos prepared by W3C groups, and a series of talks on a selected set of W3C technologies and projects W3C is involved with Mozilla, Google, Samsung, Microsoft and Bocoup in the organization of ViewSource 2019 in Amsterdam (read more on the W3C Blog) W3C Training In partnership with EdX, W3C's MOOC training program, W3Cx offers a complete "Front-End Web Developer" (FEWD) professional certificate program that consists of a suite of five courses on the foundational languages that power the Web: HTML5, CSS and JavaScript. We count nearly 900K students from all over the world. Translations Many Web users rely on translations of documents developed at W3C whose official language is English. W3C is extremely grateful to the continuous efforts of its community in ensuring our various deliverables in general, and in our specifications in particular, are made available in other languages, for free, ensuring their exposure to a much more diverse set of readers. Last Spring we developed a more robust system, a new listing of translations of W3C specifications and updated the instructions on how to contribute to our translation efforts. W3C Liaisons Liaisons and coordination with numerous organizations and Standards Development Organizations (SDOs) is crucial for W3C to: make sure standards are interoperable coordinate our respective agenda in Internet governance: W3C participates in ICANN, GIPO, IGF, the I* organizations (ICANN, IETF, ISOC, IAB). ensure at the government liaison level that our standards work is officially recognized when important to our membership so that products based on them (often done by our members) are part of procurement orders. W3C has ARO/PAS status with ISO. W3C participates in the EU MSP and Rolling Plan on Standardization ensure the global set of Web and Internet standards form a compatible stack of technologies, at the technical and policy level (patent regime, fragmentation, use in policy making) promote Standards adoption equally by the industry, the public sector, and the public at large Coralie Mercier, Editor, W3C Marketing & Communications $Id: Overview.html,v 1.60 2019/10/15 12:05:52 coralie Exp $ Copyright © 2019 W3C ® (MIT, ERCIM, Keio, Beihang) Usage policies apply.

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lftpget", "libassuan-dev", " libassuan-config", "libcaca", " caca-config", " cacaclock", " cacademo", " cacafire", " cacaplay", " cacaserver", " cacaview", " img2txt", "libcairo", " cairo-trace", "libcroco", " croco-0.6-config", " csslint-0.6", "libcurl-dev", " curl-config", "libgcrypt", " dumpsexp", " hmac256", " libgcrypt-config", " mpicalc", "libgd", " annotate", " bdftogd", " gd2copypal", " gd2togif", " gd2topng", " gdcmpgif", " gdlib-config", " gdparttopng", " gdtopng", " giftogd2", " pngtogd", " pngtogd2", " webpng", "libgpg-error", " gpg-error", " gpg-error-config", "libgrpc", " check_epollexclusive", " gen_hpack_tables", " gen_legal_metadata_characters", " gen_percent_encoding_tables", " grpc_cpp_plugin", " grpc_create_jwt", " grpc_csharp_plugin", " grpc_node_plugin", " grpc_objective_c_plugin", " grpc_php_plugin", " grpc_print_google_default_creds_token", " grpc_python_plugin", " grpc_ruby_plugin", " grpc_verify_jwt", "libjasper-utils", " imgcmp", " imginfo", " jasper", "libjpeg-turbo-progs", " cjpeg", " djpeg", " jpegtran", " rdjpgcom", " tjbench", " wrjpgcom", "libksba-dev", " ksba-config", "libnpth", " npth-config", "liboggz", " oggz", " oggz-chop", " oggz-codecs", " oggz-comment", " oggz-diff", " oggz-dump", " oggz-info", " oggz-known-codecs", " oggz-merge", " oggz-rip", " oggz-scan", " oggz-sort", " oggz-validate", "libotr", " otr_mackey", " otr_modify", " otr_parse", " otr_readforge", " otr_remac", " otr_sesskeys", "libpng-dev", " libpng-config", " libpng16-config", "librsvg", " rsvg-convert", "libtiff-utils", " fax2ps", " fax2tiff", " pal2rgb", " ppm2tiff", " raw2tiff", " tiff2bw", " tiff2pdf", " tiff2ps", " tiff2rgba", " tiffcmp", " tiffcp", " tiffcrop", " tiffdither", " tiffdump", " tiffinfo", " tiffmedian", " tiffset", " tiffsplit", "libtool", " libtool", " libtoolize", "libxapian-dev", " xapian-config", "libxml2-dev", " xml2-config", "libxml2-utils", " xmlcatalog", " xmllint", "libxslt-dev", " xslt-config", "libzmq", " curve_keygen", "lighttpd", " lighttpd", "littlecms-utils", " jpgicc", " linkicc", " psicc", " tificc", " transicc", "lld", " ld.lld", " lld", " lld-link", "llvm", " llc", " lli", " llvm-ar", " llvm-as", " llvm-bcanalyzer", " llvm-c-test", " llvm-cat", " llvm-config", " llvm-cov", " llvm-cvtres", " llvm-cxxdump", " llvm-cxxfilt", " llvm-diff", " llvm-dis", " llvm-dlltool", " llvm-dsymutil", " llvm-dwarfdump", " llvm-dwp", " llvm-extract", " llvm-lib", " llvm-link", " llvm-lto", " llvm-lto2", " llvm-mc", " llvm-mcmarkup", " llvm-modextract", " llvm-mt", " llvm-nm", " llvm-objdump", " llvm-opt-report", " llvm-pdbutil", " llvm-profdata", " llvm-ranlib", " llvm-readelf", " llvm-readobj", " llvm-rtdyld", " llvm-size", " llvm-split", " llvm-stress", " llvm-strings", " llvm-symbolizer", " llvm-xray", " obj2yaml", " opt", " verify-uselistorder", " yaml2obj", "ltrace", " ltrace", "lua", " lua", " luac", "lynx", " lynx", "lzip", " lzip", "lzop", " lzop", "m4", " m4", "macchanger", " macchanger", "make", " make", "man", " apropos", " demandoc", " makewhatis", " man", " mandoc", " soelim", " whatis", "mariadb", " aria_chk", " aria_dump_log", " aria_ftdump", " aria_pack", " aria_read_log", " innochecksum", " mariadb_config", " msql2mysql", " my_print_defaults", " myisam_ftdump", " myisamchk", " myisamlog", " myisampack", " mysql", " mysql_client_test", " mysql_config", " mysql_convert_table_format", " mysql_find_rows", " mysql_fix_extensions", " mysql_install_db", " mysql_plugin", " mysql_secure_installation", " mysql_setpermission", " mysql_tzinfo_to_sql", " mysql_upgrade", " mysql_waitpid", " mysqlaccess", " mysqladmin", " mysqlbinlog", " mysqlcheck", " mysqld", " mysqld_multi", " mysqld_safe", " mysqld_safe_helper", " mysqldump", " mysqldumpslow", " mysqlhotcopy", " mysqlimport", " mysqlshow", " mysqlslap", " mytop", " perror", " replace", " resolve_stack_dump", " resolveip", "mathomatic", " mathomatic", "mc", " mc", " mcdiff", " mcedit", " mcview", "megatools", " megacopy", " megadf", " megadl", " megaget", " megals", " megamkdir", " megaput", " megareg", " megarm", "memcached", " memcached", "micro", " micro", "minicom", " ascii-xfr", " minicom", " runscript", " xminicom", "mlocate", " locate", " updatedb", "moon-buggy", " moon-buggy", "moria", " moria", "mosh", " mosh", " mosh-client", " mosh-server", " mosh.pl", "mosquitto", " mosquitto", " mosquitto_passwd", " mosquitto_pub", " mosquitto_sub", "mp3splt", " mp3splt", "mpc", " mpc", "mpd", " mpd", "mpv", " mpv", "mtools", " amuFormat.sh", " lz", " mattrib", " mbadblocks", " mcat", " mcd", " mcheck", " mclasserase", " mcomp", " mcopy", " mdel", " mdeltree", " mdir", " mdu", " mformat", " minfo", " mkmanifest", " mlabel", " mmd", " mmount", " mmove", " mpartition", " mrd", " mren", " mshortname", " mshowfat", " mtools", " mtoolstest", " mtype", " mxtar", " mzip", " tgz", " uz", "multitail", " multitail", "mutt", " mutt", " pgpewrap", " pgpring", " smime_keys", "nano", " nano", "ncdc", " ncdc", "ncdu", " ncdu", "ncmpcpp", " ncmpcpp", "ncurses-dev", " ncursesw6-config", "ncurses-utils", " clear", " infocmp", " reset", " tabs", " tic", " toe", " tput", " tset", "ne", " ne", "neovim", " nvim", "net-tools", " arp", " ifconfig", " ipmaddr", " iptunnel", " mii-tool", " nameif", " netstat", " plipconfig", " rarp", " route", " slattach", "netcat", " nc", " ncat", " netcat", "netpbm", " 411toppm", " anytopnm", " asciitopgm", " atktopbm", " avstopam", " bioradtopgm", " bmptopnm", " bmptoppm", " brushtopbm", " cameratopam", " cistopbm", " cmuwmtopbm", " ddbugtopbm", " escp2topbm", " eyuvtoppm", " fiascotopnm", " fitstopnm", " fstopgm", " g3topbm", " gemtopbm", " gemtopnm", " giftopnm", " gouldtoppm", " hdifftopam", " hipstopgm", " hpcdtoppm", " icontopbm", " ilbmtoppm", " imgtoppm", " infotopam", " jbigtopnm", " jpeg2ktopam", " jpegtopnm", " leaftoppm", " lispmtopgm", " macptopbm", " manweb", " mdatopbm", " mgrtopbm", " mrftopbm", " mtvtoppm", " neotoppm", " netpbm-config", " palmtopnm", " pamaddnoise", " pamarith", " pambackground", " pambayer", " pamchannel", " pamcomp", " pamcrater", " pamcut", " pamdeinterlace", " pamdepth", " pamdice", " pamditherbw", " pamedge", " pamendian", " pamenlarge", " pamexec", " pamfile", " pamfix", " pamfixtrunc", " pamflip", " pamfunc", " pamgauss", " pamgradient", " pamlookup", " pammasksharpen", " pammixinterlace", " pammosaicknit", " pamoil", " pampaintspill", " pamperspective", " pampick", " pampop9", " pamrecolor", " pamrgbatopng", " pamrubber", " pamscale", " pamseq", " pamshadedrelief", " pamsharpmap", " pamsharpness", " pamsistoaglyph", " pamslice", " pamsplit", " pamstack", " pamstereogram", " pamstretch", " pamstretch-gen", " pamsumm", " pamsummcol", " pamtable", " pamthreshold", " pamtilt", " pamtoavs", " pamtodjvurle", " pamtofits", " pamtogif", " pamtohdiff", " pamtohtmltbl", " pamtojpeg2k", " pamtompfont", " pamtooctaveimg", " pamtopam", " pamtopdbimg", " pamtopfm", " pamtopng", " pamtopnm", " pamtosrf", " pamtosvg", " pamtotga", " pamtotiff", " pamtouil", " pamtowinicon", " pamtoxvmini", " pamundice", " pamunlookup", " pamvalidate", " pamwipeout", " pbmclean", " pbmlife", " pbmmake", " pbmmask", " pbmminkowski", " pbmpage", " pbmpscale", " pbmreduce", " pbmtext", " pbmtextps", " pbmto10x", " pbmto4425", " pbmtoascii", " pbmtoatk", " pbmtobbnbg", " pbmtocis", " pbmtocmuwm", " pbmtodjvurle", " pbmtoepsi", " pbmtoepson", " pbmtoescp2", " pbmtog3", " pbmtogem", " pbmtogo", " pbmtoibm23xx", " pbmtoicon", " pbmtolj", " pbmtoln03", " pbmtolps", " pbmtomacp", " pbmtomatrixorbital", " pbmtomda", " pbmtomgr", " pbmtomrf", " pbmtonokia", " pbmtopgm", " pbmtopi3", " pbmtopk", " pbmtoplot", " pbmtoppa", " pbmtopsg3", " pbmtoptx", " pbmtosunicon", " pbmtowbmp", " pbmtox10bm", " pbmtoxbm", " pbmtoybm", " pbmtozinc", " pbmupc", " pc1toppm", " pcdovtoppm", " pcxtoppm", " pdbimgtopam", " pfmtopam", " pgmabel", " pgmbentley", " pgmcrater", " pgmdeshadow", " pgmedge", " pgmenhance", " pgmhist", " pgmkernel", " pgmmake", " pgmmedian", " pgmminkowski", " pgmmorphconv", " pgmnoise", " pgmnorm", " pgmoil", " pgmramp", " pgmslice", " pgmtexture", " pgmtofs", " pgmtolispm", " pgmtopbm", " pgmtopgm", " pgmtoppm", " pgmtosbig", " pgmtost4", " pi1toppm", " pi3topbm", " picttoppm", " pjtoppm", " pktopbm", " pngtopam", " pngtopnm", " pnmalias", " pnmarith", " pnmcat", " pnmcolormap", " pnmcomp", " pnmconvol", " pnmcrop", " pnmcut", " pnmdepth", " pnmenlarge", " pnmfile", " pnmflip", " pnmgamma", " pnmhisteq", " pnmhistmap", " pnmindex", " pnminterp", " pnminvert", " pnmmargin", " pnmmercator", " pnmmontage", " pnmnlfilt", " pnmnoraw", " pnmnorm", " pnmpad", " pnmpaste", " pnmpsnr", " pnmquant", " pnmquantall", " pnmremap", " pnmrotate", " pnmscale", " pnmscalefixed", " pnmshear", " pnmsmooth", " pnmsplit", " pnmstitch", " pnmtile", " pnmtoddif", " pnmtofiasco", " pnmtofits", " pnmtojbig", " pnmtojpeg", " pnmtopalm", " pnmtopclxl", " pnmtoplainpnm", " pnmtopng", " pnmtopnm", " pnmtops", " pnmtorast", " pnmtorle", " pnmtosgi", " pnmtosir", " pnmtotiff", " pnmtotiffcmyk", " pnmtoxwd", " ppm3d", " ppmbrighten", " ppmchange", " ppmcie", " ppmcolormask", " ppmcolors", " ppmdcfont", " ppmddumpfont", " ppmdim", " ppmdist", " ppmdither", " ppmdmkfont", " ppmdraw", " ppmfade", " ppmflash", " ppmforge", " ppmglobe", " ppmhist", " ppmlabel", " ppmmake", " ppmmix", " ppmnorm", " ppmntsc", " ppmpat", " ppmquant", " ppmquantall", " ppmrainbow", " ppmrelief", " ppmrough", " ppmshadow", " ppmshift", " ppmspread", " ppmtoacad", " ppmtoapplevol", " ppmtoarbtxt", " ppmtoascii", " ppmtobmp", " ppmtoeyuv", " ppmtogif", " ppmtoicr", " ppmtoilbm", " ppmtojpeg", " ppmtoleaf", " ppmtolj", " ppmtomap", " ppmtomitsu", " ppmtompeg", " ppmtoneo", " ppmtopcx", " ppmtopgm", " ppmtopi1", " ppmtopict", " ppmtopj", " ppmtopjxl", " ppmtoppm", " ppmtopuzz", " ppmtorgb3", " ppmtosixel", " ppmtospu", " ppmtoterm", " ppmtotga", " ppmtouil", " ppmtowinicon", " ppmtoxpm", " ppmtoyuv", " ppmtoyuvsplit", " ppmtv", " ppmwheel", " psidtopgm", " pstopnm", " qrttoppm", " rasttopnm", " rawtopgm", " rawtoppm", " rgb3toppm", " rlatopam", " rletopnm", " sbigtopgm", " sgitopnm", " sirtopnm", " sldtoppm", " spctoppm", " spottopgm", " sputoppm", " srftopam", " st4topgm", " sunicontopnm", " svgtopam", " tgatoppm", " thinkjettopbm", " tifftopnm", " wbmptopbm", " winicontopam", " winicontoppm", " xbmtopbm", " ximtoppm", " xpmtoppm", " xvminitoppm", " xwdtopnm", " ybmtopbm", " yuvsplittoppm", " yuvtoppm", " yuy2topam", " zeisstopnm", "nettle", " nettle-hash", " nettle-lfib-stream", " nettle-pbkdf2", " pkcs1-conv", " sexp-conv", "newsboat", " newsboat", " podboat", "nginx", " nginx", "ninja", " ninja", "nmap", " nmap", " nping", "nodejs", " node", " npm", " npx", "nodejs-current", " node", " npm", " npx", "notmuch", " notmuch", "nyancat", " nyancat", "nzbget", " nzbget", "oathtool", " oathtool", " pskctool", "ocrad", " ocrad", "openjpeg-tools", " opj_compress", " opj_decompress", " opj_dump", "openssh", " scp", " sftp", " source-ssh-agent", " ssh", " ssh-add", " ssh-agent", " ssh-copy-id", " ssh-keygen", " ssh-keyscan", " ssha", " sshd", "openssl-tool", " openssl", "optipng", " optipng", "opus-tools", " opusdec", " opusenc", " opusinfo", "p7zip", " 7z", " 7za", " 7zr", "pango", " pango-view", "par2", " par2", " par2create", " par2repair", " par2verify", "patch", " patch", "patchelf", " patchelf", "pcre-dev", " pcre-config", "pcre2-dev", " pcre2-config", "perl", " corelist", " cpan", " enc2xs", " encguess", " h2ph", " h2xs", " instmodsh", " json_pp", " libnetcfg", " perl", " perlbug", " perldoc", " perlivp", " perlthanks", " piconv", " pl2pm", " pod2html", " pod2man", " pod2text", " pod2usage", " podchecker", " podselect", " prove", " ptar", " ptardiff", " ptargrep", " shasum", " splain", " xsubpp", " zipdetails", "pforth", " pforth", "php", " phar", " phar.phar", " php", " php-cgi", " php-config", " phpdbg", " phpize", "php-fpm", " php-fpm", "pick", " pick", "picolisp", " picolisp", " pil", "pinentry", " pinentry", " pinentry-curses", "pkg-config", " pkg-config", "play-audio", " play-audio", "pngquant", " pngquant", "poppler", " pdfdetach", " pdffonts", " pdfimages", " pdfinfo", " pdfseparate", " pdftocairo", " pdftohtml", " pdftoppm", " pdftops", " pdftotext", " pdfunite", "postgresql", " clusterdb", " createdb", " createuser", " dropdb", " dropuser", " initdb", " pg_archivecleanup", " pg_basebackup", " pg_config", " pg_controldata", " pg_ctl", " pg_dump", " pg_dumpall", " pg_isready", " pg_receivewal", " pg_recvlogical", " pg_resetwal", " pg_restore", " pg_rewind", " pg_test_fsync", " pg_test_timing", " pg_upgrade", " pg_waldump", " pgbench", " postgres", " postmaster", " psql", " reindexdb", " vacuumdb", "potrace", " mkbitmap", " potrace", "privoxy", " privoxy", "procps", " free", " pgrep", " pidof", " pkill", " pmap", " ps", " pwdx", " slabtop", " sysctl", " tload", " top", " uptime", " vmstat", " w", " watch", "profanity", " profanity", "proot", " proot", " termux-chroot", "protobuf", " protoc", "psmisc", " fuser", " killall", " peekfd", " prtstat", " pstree", "pulseaudio", " esdcompat", " pacat", " pacmd", " pactl", " pasuspender", " pulseaudio", "pure-ftpd", " pure-authd", " pure-ftpd", " pure-ftpwho", " pure-mrtginfo", " pure-pw", " pure-pwconvert", " pure-quotacheck", " pure-statsdecode", " pure-uploadscript", "pv", " pv", "pwgen", " pwgen", "python", " 2to3", " 2to3-3.6", " pydoc3", " pydoc3.6", " python", " python3", " python3-config", " python3.6", " python3.6-config", " python3.6m-config", " pyvenv", " pyvenv-3.6", "python2", " idle", " pydoc", " python-config", " python2", " python2-config", " python2.7", " python2.7-config", " smtpd.py", "qalc", " qalc", "qpdf", " fix-qdf", " qpdf", " zlib-flate", "radare2", " r2", " r2agent", " r2pm", " rabin2", " radare2", " radiff2", " rafind2", " ragg2", " ragg2-cc", " rahash2", " rarun2", " rasm2", " rax2", "ragel", " ragel", "rcs", " ci", " co", " ident", " merge", " rcs", " rcsclean", " rcsdiff", " rcsmerge", " rlog", "rdiff", " rdiff", "redir", " redir", "redis", " redis-benchmark", " redis-check-aof", " redis-check-rdb", " redis-cli", " redis-sentinel", " redis-server", "remind", " rem", " rem2ps", " remind", "rgbds", " rgbasm", " rgbfix", " rgbgfx", " rgblink", "rlwrap", " rlwrap", "rsync", " rsync", "rtmpdump", " rtmpdump", " rtmpgw", " rtmpsrv", " rtmpsuck", "ruby", " erb", " gem", " irb", " rake", " rdoc", " ruby", "screen", " screen", " screen-4.6.2", "scrypt", " scrypt", "sed", " sed", "sensible-utils", " select-editor", " sensible-browser", " sensible-editor", " sensible-pager", "sharutils", " shar", " unshar", " uudecode", " uuencode", "silversearcher-ag", " ag", "sl", " sl", "socat", " filan", " procan", " socat", "sox", " play", " rec", " sox", " soxi", "sqlite", " sqlite3", "squid", " purge", " squid", " squidclient", "sshpass", " sshpass", "sslscan", " sslscan", "stag", " stag", "strace", " strace", " strace-log-merge", "stunnel", " stunnel", "subversion", " svn", " svnadmin", " svnbench", " svndumpfilter", " svnfsfs", " svnlook", " svnmucc", " svnrdump", " svnserve", " svnsync", " svnversion", "syncthing", " syncthing", "tar", " tar", "tasksh", " tasksh", "taskwarrior", " task", "tcl", " sqlite3_analyzer", " tclsh", " tclsh8.6", "tcsh", " tcsh", "teckit", " sfconv", " teckit_compile", " txtconv", "termux-api", " termux-audio-info", " termux-battery-status", " termux-camera-info", " termux-camera-photo", " termux-clipboard-get", " termux-clipboard-set", " termux-contact-list", " termux-dialog", " termux-download", " termux-infrared-frequencies", " termux-infrared-transmit", " termux-location", " termux-media-scan", " termux-notification", " termux-notification-remove", " termux-share", " termux-sms-inbox", " termux-sms-send", " termux-storage-get", " termux-telephony-call", " termux-telephony-cellinfo", " termux-telephony-deviceinfo", " termux-toast", " termux-tts-engines", " termux-tts-speak", " termux-vibrate", " termux-wifi-connectioninfo", " termux-wifi-scaninfo", "termux-elf-cleaner", " termux-elf-cleaner", "teseq", " reseq", " teseq", "tesseract", " tesseract", "texinfo", " info", " install-info", " makeinfo", " pdftexi2dvi", " pod2texi", " texi2any", " texi2dvi", " texi2pdf", " texindex", "texlive-bin", " a2ping", " a5toa4", " adhocfilelist", " afm2afm", " afm2pl", " afm2tfm", " aleph", " allcm", " allec", " allneeded", " arara", " arlatex", " authorindex", " autoinst", " autosp", " bbl2bib", " bbox", " bg5+latex", " bg5+pdflatex", " bg5conv", " bg5latex", " bg5pdflatex", " bibdoiadd", " bibexport", " bibmradd", " bibtex", " bibtex8", " bibzbladd", " bundledoc", " cachepic", " cef5conv", " cef5latex", " cef5pdflatex", " cefconv", " ceflatex", " cefpdflatex", " cefsconv", " cefslatex", " cefspdflatex", " cfftot1", " checkcites", " checklistings", " chktex", " chkweb", " cjk-gs-integrate", " context", " contextjit", " convbkmk", " ctangle", " ctanify", " ctanupload", " ctie", " cweave", " de-macro", " depythontex", " detex", " devnag", " deweb", " diadia", " disdvi", " dosepsbin", " dt2dv", " dtxgen", " dv2dt", " dvi2fax", " dvi2tty", " dviasm", " dvicopy", " dvidvi", " dvigif", " dvihp", " dvilj", " dvilj2p", " dvilj4", " dvilj4l", " dvilj6", " dvipdfm", " dvipdfmx", " dvipdft", " dvipng", " dvipos", " dvips", " dvired", " dvitomp", " dvitype", " e2pall", " ebb", " ebong", " epspdf", " epspdftk", " epstopdf", " eptex", " euptex", " exceltex", " extconv", " extractbb", " fig4latex", " findhyph", " fmtutil", " fmtutil-sys", " fmtutil-user", " fontinst", " fragmaster", " gbklatex", " gbkpdflatex", " getmapdl", " gftodvi", " gftopk", " gftype", " gsftopk", " hbf2gf", " ht", " htcontext", " htlatex", " htmex", " httex", " httexi", " htxelatex", " htxetex", " inimf", " initex", " installfont-tl", " jamo-normalize", " kanji-config-updmap", " kanji-config-updmap-sys", " kanji-config-updmap-user", " kanji-fontmap-creator", " komkindex", " kpseaccess", " kpsepath", " kpsereadlink", " kpsestat", " kpsetool", " kpsewhere", " kpsewhich", " kpsexpand", " lacheck", " latex-git-log", " latex-papersize", " latex2man", " latex2nemeth", " latexdiff", " latexdiff-vc", " latexfileversion", " latexindent", " latexmk", " latexpand", " latexrevise", " lily-glyph-commands", " lily-image-commands", " lily-rebuild-pdfs", " listbib", " listings-ext.sh", " ltx2crossrefxml", " ltxfileinfo", " ltximg", " lua2dox_filter", " luaotfload-tool", " luatex", " luatools", " lwarpmk", " m-tx", " make4ht", " makedtx", " makeglossaries", " makeglossaries-lite", " makeindex", " match_parens", " mathspic", " mf", " mf-nowin", " mf2pt1", " mfplain", " mft", " mk4ht", " mkgrkindex", " mkindex", " mkjobtexmf", " mkocp", " mkofm", " mkpic", " mkt1font", " mktexfmt", " mktexlsr", " mktexmf", " mktexpk", " mktextfm", " mmafm", " mmpfb", " mpost", " mptopdf", " mtxrun", " mtxrunjit", " multibibliography", " musixflx", " musixtex", " odvicopy", " odvitype", " ofm2opl", " omfonts", " opl2ofm", " ot2kpx", " otangle", " otfinfo", " otftotfm", " otp2ocp", " outocp", " ovf2ovp", " ovp2ovf", " patgen", " pbibtex", " pdf180", " pdf270", " pdf90", " pdfannotextractor", " pdfatfi", " pdfbook", " pdfbook2", " pdfcrop", " pdfflip", " pdfjam", " pdfjam-pocketmod", " pdfjam-slides3up", " pdfjam-slides6up", " pdfjoin", " pdflatexpicscale", " pdfnup", " pdfpun", " pdftex", " pdftosrc", " pdfxup", " pdvitomp", " pdvitype", " pedigree", " perltex", " pfarrei", " pkfix", " pkfix-helper", " pktogf", " pktype", " pltotf", " pmpost", " pmxchords", " pn2pdf", " pooltype", " ppltotf", " prepmx", " ps2eps", " ps2frag", " ps4pdf", " pslatex", " pst2pdf", " ptex", " ptex2pdf", " ptftopl", " purifyeps", " pygmentex", " pythontex", " repstopdf", " rpdfcrop", " rubibtex", " rubikrotation", " rumakeindex", " rungs", " simpdftex", " sjisconv", " sjislatex", " sjispdflatex", " splitindex", " srcredact", " sty2dtx", " svn-multi", " synctex", " t1dotlessj", " t1lint", " t1rawafm", " t1reencode", " t1testpage", " t4ht", " tangle", " tex", " tex4ebook", " tex4ht", " texconfig", " texconfig-dialog", " texconfig-sys", " texcount", " texdef", " texdiff", " texdirflatten", " texdoc", " texdoctk", " texexec", " texfot", " texhash", " texlinks", " texliveonfly", " texloganalyser", " texlua", " texluac", " texmfstart", " texosquery", " texosquery-jre5", " texosquery-jre8", " tftopl", " thumbpdf", " tie", " tlmgr", " tlmgr.ln", " tpic2pdftex", " ttf2afm", " ttf2kotexfont", " ttf2pk", " ttf2tfm", " ttftotype42", " typeoutfileinfo", " ulqda", " upbibtex", " updmap", " updmap-sys", " updmap-user", " updvitomp", " updvitype", " upmendex", " upmpost", " uppltotf", " uptex", " uptftopl", " urlbst", " vftovp", " vlna", " vpe", " vpl2ovp", " vpl2vpl", " vptovf", " weave", " wofm2opl", " wopl2ofm", " wovf2ovp", " wovp2ovf", " xdvipdfmx", " xetex", " xhlatex", " yplan", "tig", " tig", "timewarrior", " timew", "tintin++", " tt++", "tinyscheme", " tinyscheme", "tmate", " tmate", "tmux", " tmux", "toilet", " toilet", "tor", " tor", " tor-gencert", " tor-resolve", " torify", "torsocks", " torsocks", "tracepath", " tracepath", " traceroute", "transmission", " transmission-create", " transmission-daemon", " transmission-edit", " transmission-remote", " transmission-show", "tree", " tree", "tsocks", " tsocks", "tty-clock", " tty-clock", "tty-solitaire", " ttysolitaire", "ttyrec", " ttyplay", " ttyrec", " ttytime", "units", " units", " units_cur", "unrar", " unrar", "unzip", " funzip", " unzip", " unzipsfx", " zipgrep", " zipinfo", "utfdecode", " utfdecode", "util-linux", " addpart", " blkdiscard", " blkid", " blkzone", " blockdev", " cal", " chcpu", " chrt", " col", " colcrt", " colrm", " ctrlaltdel", " delpart", " dmesg", " fallocate", " fdformat", " fincore", " findfs", " flock", " fsck.cramfs", " fsck.minix", " fsfreeze", " getopt", " hexdump", " hwclock", " ionice", " isosize", " ldattach", " linux32", " linux64", " look", " losetup", " lscpu", " lsipc", " lsns", " mcookie", " mesg", " mkfs", " mkfs.bfs", " mkfs.cramfs", " mkfs.minix", " mkswap", " more", " namei", " nologin", " nsenter", " partx", " prlimit", " raw", " readprofile", " rename", " renice", " resizepart", " rev", " rtcwake", " script", " scriptreplay", " setarch", " setsid", " setterm", " swaplabel", " taskset", " ul", " uname26", " unshare", " wdctl", " whereis", " wipefs", " zramctl", "valac", " vala", " vala-0.38", " vala-gen-introspect", " vala-gen-introspect-0.38", " valac", " valac-0.38", " vapicheck", " vapicheck-0.38", " vapigen", " vapigen-0.38", "valadoc", " valadoc", " valadoc-0.38", "valgrind", " callgrind_annotate", " callgrind_control", " cg_annotate", " cg_diff", " cg_merge", " ms_print", " valgrind", " valgrind-di-server", " valgrind-listener", " vgdb", "vifm", " vifm", " vifm-convert-dircolors", " vifm-pause", " vifm-screen-split", "vim", " vi", " view", " vim", " vimdiff", " vimtutor", " xxd", "vim-python", " vi", " view", " vim", " vimdiff", " vimtutor", " xxd", "vorbis-tools", " oggdec", " oggenc", " ogginfo", " vcut", " vorbiscomment", "vttest", " vttest", "vtutils", " vtquery", " vtshowkeys", " vtsize", " vttitle", "w3m", " w3m", " w3mman", "wcalc", " wcalc", "weechat", " weechat", "wget", " wget", "wol", " wol", " wol-bootptab", " wol-dhcpdconf", "x264", " x264", "x265", " x265", "xapian-tools", " copydatabase", " quest", " simpleexpand", " simpleindex", " simplesearch", " xapian-check", " xapian-compact", " xapian-delve", " xapian-metadata", " xapian-progsrv", " xapian-replicate", " xapian-replicate-server", " xapian-tcpsrv", "xmlsec", " xmlsec1", " xmlsec1-config", "xmlstarlet", " xml", "xorriso", " osirrox", " xorrecord", " xorriso", " xorriso-tcltk", " xorrisofs", "xsltproc", " xsltproc", "xz-utils", " lzcat", " lzcmp", " lzdiff", " lzegrep", " lzfgrep", " lzgrep", " lzless", " lzma", " lzmadec", " lzmainfo", " lzmore", " unlzma", " unxz", " xz", " xzcat", " xzcmp", " xzdec", " xzdiff", " xzegrep", " xzfgrep", " xzgrep", " xzless", " xzmore", "yasm", " vsyasm", " yasm", " ytasm", "zbar", " zbarimg", "zile", " zile", "zip", " zip", " zipcloak", " zipnote", " zipsplit", "zsh", " zsh", "zstd", " unzstd", " zstd", " zstdcat", " zstdgrep", " zstdless", " zstdmt", "ack-grep", " ack", "apksigner", " apksigner", "asciinema", " asciinema", "autoconf", " autoconf", " autoheader", " autom4te", " autoreconf", " autoscan", " autoupdate", " ifnames", "automake", " aclocal", " aclocal-1.15", " automake", " automake-1.15", "byobu", " byobu", " byobu-config", " byobu-ctrl-a", " byobu-disable", " byobu-disable-prompt", " byobu-enable", " byobu-enable-prompt", " byobu-export", " byobu-janitor", " byobu-keybindings", " byobu-launch", " byobu-launcher", " byobu-launcher-install", " byobu-launcher-uninstall", " byobu-layout", " byobu-prompt", " byobu-quiet", " byobu-reconnect-sockets", " byobu-screen", " byobu-select-backend", " byobu-select-profile", " byobu-select-session", " byobu-shell", " byobu-silent", " byobu-status", " byobu-status-detail", " byobu-tmux", " byobu-ugraph", " byobu-ulevel", " col1", " ctail", " manifest", " purge-old-kernels", " vigpg", " wifi-status", "colordiff", " colordiff", "cowsay", " cowsay", "debootstrap", " debootstrap", "dx", " dx", "ecj", " ecj", "getmail", " getmail", " getmail_fetch", " getmail_maildir", " getmail_mbox", "luarocks", " luarocks", " luarocks-5.3", " luarocks-admin", " luarocks-admin-5.3", "neofetch", " neofetch", "parallel", " env_parallel", " env_parallel.ash", " env_parallel.bash", " env_parallel.csh", " env_parallel.dash", " env_parallel.fish", " env_parallel.ksh", " env_parallel.pdksh", " env_parallel.sh", " env_parallel.tcsh", " env_parallel.zsh", " niceload", " parallel", " parcat", " sem", " sql", "pass", " pass", "pastebinit", " pastebinit", "pathpicker", " fpp", "ranger", " ranger", " rifle", "ruby-ri", " ri", "screenfetch", " screenfetch", "stow", " chkstow", " stow", "termux-am", " am", "termux-apt-repo", " termux-apt-repo", "termux-create-package", " termux-create-package", "termux-tools", " chsh", " dalvikvm", " df", " getprop", " ip", " logcat", " login", " ping", " ping6", " pkg", " pm", " settings", " su", " termux-fix-shebang", " termux-info", " termux-open", " termux-open-url", " termux-reload-settings", " termux-setup-storage", " termux-wake-lock", " termux-wake-unlock", " xdg-open", "tsu", " tsu", "vcsh", " vcsh", "yarn", " yarn", }; Desktop version Sign out

Deep-learning models of NTUA-SLP team submitted in SemEval 2018 tasks 1, 2 and 3.

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

# big-o-performance A simple html app to demonstrate performance costs of data structures. - Clone the project - Navigate to the root of the project in a termina or command prompt - Run 'npm install' - Run 'npm start' - Go to the URL specified in the terminal or command prompt to try out the app. # This app was created from the Create React App NPM. Below are instructions from that project. Below you will find some information on how to perform common tasks. You can find the most recent version of this guide [here](https://github.com/facebookincubator/create-react-app/blob/master/template/README.md). ## Table of Contents - [Updating to New Releases](#updating-to-new-releases) - [Sending Feedback](#sending-feedback) - [Folder Structure](#folder-structure) - [Available Scripts](#available-scripts) - [npm start](#npm-start) - [npm run build](#npm-run-build) - [npm run eject](#npm-run-eject) - [Displaying Lint Output in the Editor](#displaying-lint-output-in-the-editor) - [Installing a Dependency](#installing-a-dependency) - [Importing a Component](#importing-a-component) - [Adding a Stylesheet](#adding-a-stylesheet) - [Post-Processing CSS](#post-processing-css) - [Adding Images and Fonts](#adding-images-and-fonts) - [Adding Bootstrap](#adding-bootstrap) - [Adding Flow](#adding-flow) - [Adding Custom Environment Variables](#adding-custom-environment-variables) - [Integrating with a Node Backend](#integrating-with-a-node-backend) - [Proxying API Requests in Development](#proxying-api-requests-in-development) - [Deployment](#deployment) - [Now](#now) - [Heroku](#heroku) - [Surge](#surge) - [GitHub Pages](#github-pages) - [Something Missing?](#something-missing) ## Updating to New Releases Create React App is divided into two packages: * `create-react-app` is a global command-line utility that you use to create new projects. * `react-scripts` is a development dependency in the generated projects (including this one). You almost never need to update `create-react-app` itself: it’s delegates all the setup to `react-scripts`. When you run `create-react-app`, it always creates the project with the latest version of `react-scripts` so you’ll get all the new features and improvements in newly created apps automatically. To update an existing project to a new version of `react-scripts`, [open the changelog](https://github.com/facebookincubator/create-react-app/blob/master/CHANGELOG.md), find the version you’re currently on (check `package.json` in this folder if you’re not sure), and apply the migration instructions for the newer versions. In most cases bumping the `react-scripts` version in `package.json` and running `npm install` in this folder should be enough, but it’s good to consult the [changelog](https://github.com/facebookincubator/create-react-app/blob/master/CHANGELOG.md) for potential breaking changes. We commit to keeping the breaking changes minimal so you can upgrade `react-scripts` painlessly. ## Sending Feedback We are always open to [your feedback](https://github.com/facebookincubator/create-react-app/issues). ## Folder Structure After creation, your project should look like this: ``` my-app/ README.md index.html favicon.ico node_modules/ package.json src/ App.css App.js index.css index.js logo.svg ``` For the project to build, **these files must exist with exact filenames**: * `index.html` is the page template; * `favicon.ico` is the icon you see in the browser tab; * `src/index.js` is the JavaScript entry point. You can delete or rename the other files. You may create subdirectories inside `src`. For faster rebuilds, only files inside `src` are processed by Webpack. You need to **put any JS and CSS files inside `src`**, or Webpack won’t see them. You can, however, create more top-level directories. They will not be included in the production build so you can use them for things like documentation. ## Available Scripts In the project directory, you can run: ### `npm start` Runs the app in the development mode.<br> Open [http://localhost:3000](http://localhost:3000) to view it in the browser. The page will reload if you make edits.<br> You will also see any lint errors in the console. ### `npm run build` Builds the app for production to the `build` folder.<br> It correctly bundles React in production mode and optimizes the build for the best performance. The build is minified and the filenames include the hashes.<br> Your app is ready to be deployed! ### `npm run eject` **Note: this is a one-way operation. Once you `eject`, you can’t go back!** If you aren’t satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project. Instead, it will copy all the configuration files and the transitive dependencies (Webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you’re on your own. You don’t have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn’t feel obligated to use this feature. However we understand that this tool wouldn’t be useful if you couldn’t customize it when you are ready for it. ## Displaying Lint Output in the Editor >Note: this feature is available with `react-scripts@0.2.0` and higher. Some editors, including Sublime Text, Atom, and Visual Studio Code, provide plugins for ESLint. They are not required for linting. You should see the linter output right in your terminal as well as the browser console. However, if you prefer the lint results to appear right in your editor, there are some extra steps you can do. You would need to install an ESLint plugin for your editor first. >**A note for Atom `linter-eslint` users** >If you are using the Atom `linter-eslint` plugin, make sure that **Use global ESLint installation** option is checked: ><img src="http://i.imgur.com/yVNNHJM.png" width="300"> Then make sure `package.json` of your project ends with this block: ```js { // ... "eslintConfig": { "extends": "./node_modules/react-scripts/config/eslint.js" } } ``` Projects generated with `react-scripts@0.2.0` and higher should already have it. If you don’t need ESLint integration with your editor, you can safely delete those three lines from your `package.json`. Finally, you will need to install some packages *globally*: ```sh npm install -g eslint babel-eslint eslint-plugin-react eslint-plugin-import eslint-plugin-jsx-a11y eslint-plugin-flowtype ``` We recognize that this is suboptimal, but it is currently required due to the way we hide the ESLint dependency. The ESLint team is already [working on a solution to this](https://github.com/eslint/eslint/issues/3458) so this may become unnecessary in a couple of months. ## Installing a Dependency The generated project includes React and ReactDOM as dependencies. It also includes a set of scripts used by Create React App as a development dependency. You may install other dependencies (for example, React Router) with `npm`: ``` npm install --save <library-name> ``` ## Importing a Component This project setup supports ES6 modules thanks to Babel. While you can still use `require()` and `module.exports`, we encourage you to use [`import` and `export`](http://exploringjs.com/es6/ch_modules.html) instead. For example: ### `Button.js` ```js import React, { Component } from 'react'; class Button extends Component { render() { // ... } } export default Button; // Don’t forget to use export default! ``` ### `DangerButton.js` ```js import React, { Component } from 'react'; import Button from './Button'; // Import a component from another file class DangerButton extends Component { render() { return <Button color="red" />; } } export default DangerButton; ``` Be aware of the [difference between default and named exports](http://stackoverflow.com/questions/36795819/react-native-es-6-when-should-i-use-curly-braces-for-import/36796281#36796281). It is a common source of mistakes. We suggest that you stick to using default imports and exports when a module only exports a single thing (for example, a component). That’s what you get when you use `export default Button` and `import Button from './Button'`. Named exports are useful for utility modules that export several functions. A module may have at most one default export and as many named exports as you like. Learn more about ES6 modules: * [When to use the curly braces?](http://stackoverflow.com/questions/36795819/react-native-es-6-when-should-i-use-curly-braces-for-import/36796281#36796281) * [Exploring ES6: Modules](http://exploringjs.com/es6/ch_modules.html) * [Understanding ES6: Modules](https://leanpub.com/understandinges6/read#leanpub-auto-encapsulating-code-with-modules) ## Adding a Stylesheet This project setup uses [Webpack](https://webpack.github.io/) for handling all assets. Webpack offers a custom way of “extending” the concept of `import` beyond JavaScript. To express that a JavaScript file depends on a CSS file, you need to **import the CSS from the JavaScript file**: ### `Button.css` ```css .Button { padding: 20px; } ``` ### `Button.js` ```js import React, { Component } from 'react'; import './Button.css'; // Tell Webpack that Button.js uses these styles class Button extends Component { render() { // You can use them as regular CSS styles return <div className="Button" />; } } ``` **This is not required for React** but many people find this feature convenient. You can read about the benefits of this approach [here](https://medium.com/seek-ui-engineering/block-element-modifying-your-javascript-components-d7f99fcab52b). However you should be aware that this makes your code less portable to other build tools and environments than Webpack. In development, expressing dependencies this way allows your styles to be reloaded on the fly as you edit them. In production, all CSS files will be concatenated into a single minified `.css` file in the build output. If you are concerned about using Webpack-specific semantics, you can put all your CSS right into `src/index.css`. It would still be imported from `src/index.js`, but you could always remove that import if you later migrate to a different build tool. ## Post-Processing CSS This project setup minifies your CSS and adds vendor prefixes to it automatically through [Autoprefixer](https://github.com/postcss/autoprefixer) so you don’t need to worry about it. For example, this: ```css .App { display: flex; flex-direction: row; align-items: center; } ``` becomes this: ```css .App { display: -webkit-box; display: -ms-flexbox; display: flex; -webkit-box-orient: horizontal; -webkit-box-direction: normal; -ms-flex-direction: row; flex-direction: row; -webkit-box-align: center; -ms-flex-align: center; align-items: center; } ``` There is currently no support for preprocessors such as Less, or for sharing variables across CSS files. ## Adding Images and Fonts With Webpack, using static assets like images and fonts works similarly to CSS. You can **`import` an image right in a JavaScript module**. This tells Webpack to include that image in the bundle. Unlike CSS imports, importing an image or a font gives you a string value. This value is the final image path you can reference in your code. Here is an example: ```js import React from 'react'; import logo from './logo.png'; // Tell Webpack this JS file uses this image console.log(logo); // /logo.84287d09.png function Header() { // Import result is the URL of your image return <img src={logo} alt="Logo" />; } export default function Header; ``` This works in CSS too: ```css .Logo { background-image: url(./logo.png); } ``` Webpack finds all relative module references in CSS (they start with `./`) and replaces them with the final paths from the compiled bundle. If you make a typo or accidentally delete an important file, you will see a compilation error, just like when you import a non-existent JavaScript module. The final filenames in the compiled bundle are generated by Webpack from content hashes. If the file content changes in the future, Webpack will give it a different name in production so you don’t need to worry about long-term caching of assets. Please be advised that this is also a custom feature of Webpack. **It is not required for React** but many people enjoy it (and React Native uses a similar mechanism for images). However it may not be portable to some other environments, such as Node.js and Browserify. If you prefer to reference static assets in a more traditional way outside the module system, please let us know [in this issue](https://github.com/facebookincubator/create-react-app/issues/28), and we will consider support for this. ## Adding Bootstrap You don’t have to use [React Bootstrap](https://react-bootstrap.github.io) together with React but it is a popular library for integrating Bootstrap with React apps. If you need it, you can integrate it with Create React App by following these steps: Install React Bootstrap and Bootstrap from NPM. React Bootstrap does not include Bootstrap CSS so this needs to be installed as well: ``` npm install react-bootstrap --save npm install bootstrap@3 --save ``` Import Bootstrap CSS and optionally Bootstrap theme CSS in the ```src/index.js``` file: ```js import 'bootstrap/dist/css/bootstrap.css'; import 'bootstrap/dist/css/bootstrap-theme.css'; ``` Import required React Bootstrap components within ```src/App.js``` file or your custom component files: ```js import { Navbar, Jumbotron, Button } from 'react-bootstrap'; ``` Now you are ready to use the imported React Bootstrap components within your component hierarchy defined in the render method. Here is an example [`App.js`](https://gist.githubusercontent.com/gaearon/85d8c067f6af1e56277c82d19fd4da7b/raw/6158dd991b67284e9fc8d70b9d973efe87659d72/App.js) redone using React Bootstrap. ## Adding Flow Flow typing is currently [not supported out of the box](https://github.com/facebookincubator/create-react-app/issues/72) with the default `.flowconfig` generated by Flow. If you run it, you might get errors like this: ```js node_modules/fbjs/lib/Deferred.js.flow:60 60: Promise.prototype.done.apply(this._promise, arguments); ^^^^ property `done`. Property not found in 495: declare class Promise<+R> { ^ Promise. See lib: /private/tmp/flow/flowlib_34952d31/core.js:495 node_modules/fbjs/lib/shallowEqual.js.flow:29 29: return x !== 0 || 1 / (x: $FlowIssue) === 1 / (y: $FlowIssue); ^^^^^^^^^^ identifier `$FlowIssue`. Could not resolve name src/App.js:3 3: import logo from './logo.svg'; ^^^^^^^^^^^^ ./logo.svg. Required module not found src/App.js:4 4: import './App.css'; ^^^^^^^^^^^ ./App.css. Required module not found src/index.js:5 5: import './index.css'; ^^^^^^^^^^^^^ ./index.css. Required module not found ``` To fix this, change your `.flowconfig` to look like this: ```ini [libs] ./node_modules/fbjs/flow/lib [options] esproposal.class_static_fields=enable esproposal.class_instance_fields=enable module.name_mapper='^\(.*\)\.css$' -> 'react-scripts/config/flow/css' module.name_mapper='^\(.*\)\.\(jpg\|png\|gif\|eot\|otf\|webp\|svg\|ttf\|woff\|woff2\|mp4\|webm\)$' -> 'react-scripts/config/flow/file' suppress_type=$FlowIssue suppress_type=$FlowFixMe ``` Re-run flow, and you shouldn’t get any extra issues. If you later `eject`, you’ll need to replace `react-scripts` references with the `<PROJECT_ROOT>` placeholder, for example: ```ini module.name_mapper='^\(.*\)\.css$' -> '<PROJECT_ROOT>/config/flow/css' module.name_mapper='^\(.*\)\.\(jpg\|png\|gif\|eot\|otf\|webp\|svg\|ttf\|woff\|woff2\|mp4\|webm\)$' -> '<PROJECT_ROOT>/config/flow/file' ``` We will consider integrating more tightly with Flow in the future so that you don’t have to do this. ## Adding Custom Environment Variables >Note: this feature is available with `react-scripts@0.2.3` and higher. Your project can consume variables declared in your environment as if they were declared locally in your JS files. By default you will have `NODE_ENV` defined for you, and any other environment variables starting with `REACT_APP_`. These environment variables will be defined for you on `process.env`. For example, having an environment variable named `REACT_APP_SECRET_CODE` will be exposed in your JS as `process.env.REACT_APP_SECRET_CODE`, in addition to `process.env.NODE_ENV`. These environment variables can be useful for displaying information conditionally based on where the project is deployed or consuming sensitive data that lives outside of version control. First, you need to have environment variables defined, which can vary between OSes. For example, let's say you wanted to consume a secret defined in the environment inside a `<form>`: ```jsx render() { return ( <div> <small>You are running this application in <b>{process.env.NODE_ENV}</b> mode.</small> <form> <input type="hidden" defaultValue={process.env.REACT_APP_SECRET_CODE} /> </form> </div> ); } ``` The above form is looking for a variable called `REACT_APP_SECRET_CODE` from the environment. In order to consume this value, we need to have it defined in the environment: ### Windows (cmd.exe) ```cmd set REACT_APP_SECRET_CODE=abcdef&&npm start ``` (Note: the lack of whitespace is intentional.) ### Linux, OS X (Bash) ```bash REACT_APP_SECRET_CODE=abcdef npm start ``` > Note: Defining environment variables in this manner is temporary for the life of the shell session. Setting permanent environment variables is outside the scope of these docs. With our environment variable defined, we start the app and consume the values. Remember that the `NODE_ENV` variable will be set for you automatically. When you load the app in the browser and inspect the `<input>`, you will see its value set to `abcdef`, and the bold text will show the environment provided when using `npm start`: ```html <div> <small>You are running this application in <b>development</b> mode.</small> <form> <input type="hidden" value="abcdef" /> </form> </div> ``` Having access to the `NODE_ENV` is also useful for performing actions conditionally: ```js if (process.env.NODE_ENV !== 'production') { analytics.disable(); } ``` ## Integrating with a Node Backend Check out [this tutorial](https://www.fullstackreact.com/articles/using-create-react-app-with-a-server/) for instructions on integrating an app with a Node backend running on another port, and using `fetch()` to access it. You can find the companion GitHub repository [here](https://github.com/fullstackreact/food-lookup-demo). ## Proxying API Requests in Development >Note: this feature is available with `react-scripts@0.2.3` and higher. People often serve the front-end React app from the same host and port as their backend implementation. For example, a production setup might look like this after the app is deployed: ``` / - static server returns index.html with React app /todos - static server returns index.html with React app /api/todos - server handles any /api/* requests using the backend implementation ``` Such setup is **not** required. However, if you **do** have a setup like this, it is convenient to write requests like `fetch('/api/todos')` without worrying about redirecting them to another host or port during development. To tell the development server to proxy any unknown requests to your API server in development, add a `proxy` field to your `package.json`, for example: ```js "proxy": "http://localhost:4000", ``` This way, when you `fetch('/api/todos')` in development, the development server will recognize that it’s not a static asset, and will proxy your request to `http://localhost:4000/api/todos` as a fallback. Conveniently, this avoids [CORS issues](http://stackoverflow.com/questions/21854516/understanding-ajax-cors-and-security-considerations) and error messages like this in development: ``` Fetch API cannot load http://localhost:4000/api/todos. No 'Access-Control-Allow-Origin' header is present on the requested resource. Origin 'http://localhost:3000' is therefore not allowed access. If an opaque response serves your needs, set the request's mode to 'no-cors' to fetch the resource with CORS disabled. ``` Keep in mind that `proxy` only has effect in development (with `npm start`), and it is up to you to ensure that URLs like `/api/todos` point to the right thing in production. You don’t have to use the `/api` prefix. Any unrecognized request will be redirected to the specified `proxy`. Currently the `proxy` option only handles HTTP requests, and it won’t proxy WebSocket connections. If the `proxy` option is **not** flexible enough for you, alternatively you can: * Enable CORS on your server ([here’s how to do it for Express](http://enable-cors.org/server_expressjs.html)). * Use [environment variables](#adding-custom-environment-variables) to inject the right server host and port into your app. ## Deployment By default, Create React App produces a build assuming your app is hosted at the server root. To override this, specify the `homepage` in your `package.json`, for example: ```js "homepage": "http://mywebsite.com/relativepath", ``` This will let Create React App correctly infer the root path to use in the generated HTML file. ### Now See [this example](https://github.com/xkawi/create-react-app-now) for a zero-configuration single-command deployment with [now](https://zeit.co/now). ### Heroku Use the [Heroku Buildpack for Create React App](https://github.com/mars/create-react-app-buildpack). You can find instructions in [Deploying React with Zero Configuration](https://blog.heroku.com/deploying-react-with-zero-configuration). ### Surge Install the Surge CLI if you haven't already by running `npm install -g surge`. Run the `surge` command and log in you or create a new account. You just need to specify the *build* folder and your custom domain, and you are done. ```sh email: email@domain.com password: ******** project path: /path/to/project/build size: 7 files, 1.8 MB domain: create-react-app.surge.sh upload: [====================] 100%, eta: 0.0s propagate on CDN: [====================] 100% plan: Free users: email@domain.com IP Address: X.X.X.X Success! Project is published and running at create-react-app.surge.sh ``` Note that in order to support routers that use html5 `pushState` API, you may want to rename the `index.html` in your build folder to `200.html` before deploying to Surge. This [ensures that every URL falls back to that file](https://surge.sh/help/adding-a-200-page-for-client-side-routing). ### GitHub Pages >Note: this feature is available with `react-scripts@0.2.0` and higher. Open your `package.json` and add a `homepage` field: ```js "homepage": "http://myusername.github.io/my-app", ``` **The above step is important!** Create React App uses the `homepage` field to determine the root URL in the built HTML file. Now, whenever you run `npm run build`, you will see a cheat sheet with a sequence of commands to deploy to GitHub pages: ```sh git commit -am "Save local changes" git checkout -B gh-pages git add -f build git commit -am "Rebuild website" git filter-branch -f --prune-empty --subdirectory-filter build git push -f origin gh-pages git checkout - ``` You may copy and paste them, or put them into a custom shell script. You may also customize them for another hosting provider. Note that GitHub Pages doesn't support routers that use the HTML5 `pushState` history API under the hood (for example, React Router using `browserHistory`). This is because when there is a fresh page load for a url like `http://user.github.io/todomvc/todos/42`, where `/todos/42` is a frontend route, the GitHub Pages server returns 404 because it knows nothing of `/todos/42`. If you want to add a router to a project hosted on GitHub Pages, here are a couple of solutions: * You could switch from using HTML5 history API to routing with hashes. If you use React Router, you can switch to `hashHistory` for this effect, but the URL will be longer and more verbose (for example, `http://user.github.io/todomvc/#/todos/42?_k=yknaj`). [Read more](https://github.com/reactjs/react-router/blob/master/docs/guides/Histories.md#histories) about different history implementations in React Router. * Alternatively, you can use a trick to teach GitHub Pages to handle 404 by redirecting to your `index.html` page with a special redirect parameter. You would need to add a `404.html` file with the redirection code to the `build` folder before deploying your project, and you’ll need to add code handling the redirect parameter to `index.html`. You can find a detailed explanation of this technique [in this guide](https://github.com/rafrex/spa-github-pages). ## Something Missing? If you have ideas for more “How To” recipes that should be on this page, [let us know](https://github.com/facebookincubator/create-react-app/issues) or [contribute some!](https://github.com/facebookincubator/create-react-app/edit/master/template/README.md)

✋🏼🛑 This one stop project is a complete COVID-19 detection package comprising of 3 tasks: • Task 1 --> COVID-19 Classification • Task 2 --> COVID-19 Infection Segmentation • Task 3 --> Lung Segmentation

Poole, Mackworth & Goebel 1998, p. 1. Russell & Norvig 2003, p. 55. Definition of AI as the study of intelligent agents: Poole, Mackworth & Goebel (1998), which provides the version that is used in this article. These authors use the term "computational intelligence" as a synonym for artificial intelligence.[1] Russell & Norvig (2003) (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field".[2] Nilsson 1998 Legg & Hutter 2007 Russell & Norvig 2009, p. 2. McCorduck 2004, p. 204 Maloof, Mark. "Artificial Intelligence: An Introduction, p. 37" (PDF). georgetown.edu. Archived (PDF) from the original on 25 August 2018. "How AI Is Getting Groundbreaking Changes In Talent Management And HR Tech". Hackernoon. Archived from the original on 11 September 2019. Retrieved 14 February 2020. Schank, Roger C. (1991). "Where's the AI". AI magazine. Vol. 12 no. 4. p. 38. Russell & Norvig 2009. "AlphaGo – Google DeepMind". Archived from the original on 10 March 2016. Allen, Gregory (April 2020). "Department of Defense Joint AI Center - Understanding AI Technology" (PDF). AI.mil - The official site of the Department of Defense Joint Artificial Intelligence Center. Archived (PDF) from the original on 21 April 2020. Retrieved 25 April 2020. Optimism of early AI: * Herbert Simon quote: Simon 1965, p. 96 quoted in Crevier 1993, p. 109. * Marvin Minsky quote: Minsky 1967, p. 2 quoted in Crevier 1993, p. 109. Boom of the 1980s: rise of expert systems, Fifth Generation Project, Alvey, MCC, SCI: * McCorduck 2004, pp. 426–441 * Crevier 1993, pp. 161–162,197–203, 211, 240 * Russell & Norvig 2003, p. 24 * NRC 1999, pp. 210–211 * Newquist 1994, pp. 235–248 First AI Winter, Mansfield Amendment, Lighthill report * Crevier 1993, pp. 115–117 * Russell & Norvig 2003, p. 22 * NRC 1999, pp. 212–213 * Howe 1994 * Newquist 1994, pp. 189–201 Second AI winter: * McCorduck 2004, pp. 430–435 * Crevier 1993, pp. 209–210 * NRC 1999, pp. 214–216 * Newquist 1994, pp. 301–318 AI becomes hugely successful in the early 21st century * Clark 2015 Pamela McCorduck (2004, p. 424) writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics ... and these with own sub-subfield—that would hardly have anything to say to each other." This list of intelligent traits is based on the topics covered by the major AI textbooks, including: * Russell & Norvig 2003 * Luger & Stubblefield 2004 * Poole, Mackworth & Goebel 1998 * Nilsson 1998 Kolata 1982. Maker 2006. Biological intelligence vs. intelligence in general: Russell & Norvig 2003, pp. 2–3, who make the analogy with aeronautical engineering. McCorduck 2004, pp. 100–101, who writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplished, and the other aimed at modeling intelligent processes found in nature, particularly human ones." Kolata 1982, a paper in Science, which describes McCarthy's indifference to biological models. Kolata quotes McCarthy as writing: "This is AI, so we don't care if it's psychologically real".[19] McCarthy recently reiterated his position at the AI@50 conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence".[20]. Neats vs. scruffies: * McCorduck 2004, pp. 421–424, 486–489 * Crevier 1993, p. 168 * Nilsson 1983, pp. 10–11 Symbolic vs. sub-symbolic AI: * Nilsson (1998, p. 7), who uses the term "sub-symbolic". General intelligence (strong AI) is discussed in popular introductions to AI: * Kurzweil 1999 and Kurzweil 2005 See the Dartmouth proposal, under Philosophy, below. McCorduck 2004, p. 34. McCorduck 2004, p. xviii. McCorduck 2004, p. 3. McCorduck 2004, pp. 340–400. This is a central idea of Pamela McCorduck's Machines Who Think. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition."[26] "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized."[27] "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction."[28] She traces the desire back to its Hellenistic roots and calls it the urge to "forge the Gods."[29] "Stephen Hawking believes AI could be mankind's last accomplishment". BetaNews. 21 October 2016. Archived from the original on 28 August 2017. Lombardo P, Boehm I, Nairz K (2020). "RadioComics – Santa Claus and the future of radiology". Eur J Radiol. 122 (1): 108771. doi:10.1016/j.ejrad.2019.108771. PMID 31835078. Ford, Martin; Colvin, Geoff (6 September 2015). "Will robots create more jobs than they destroy?". The Guardian. Archived from the original on 16 June 2018. Retrieved 13 January 2018. AI applications widely used behind the scenes: * Russell & Norvig 2003, p. 28 * Kurzweil 2005, p. 265 * NRC 1999, pp. 216–222 * Newquist 1994, pp. 189–201 AI in myth: * McCorduck 2004, pp. 4–5 * Russell & Norvig 2003, p. 939 AI in early science fiction. * McCorduck 2004, pp. 17–25 Formal reasoning: * Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 978-0-15-601391-8. OCLC 46890682. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Turing, Alan (1948), "Machine Intelligence", in Copeland, B. Jack (ed.), The Essential Turing: The ideas that gave birth to the computer age, Oxford: Oxford University Press, p. 412, ISBN 978-0-19-825080-7 Russell & Norvig 2009, p. 16. Dartmouth conference: * McCorduck 2004, pp. 111–136 * Crevier 1993, pp. 47–49, who writes "the conference is generally recognized as the official birthdate of the new science." * Russell & Norvig 2003, p. 17, who call the conference "the birth of artificial intelligence." * NRC 1999, pp. 200–201 McCarthy, John (1988). "Review of The Question of Artificial Intelligence". Annals of the History of Computing. 10 (3): 224–229., collected in McCarthy, John (1996). "10. Review of The Question of Artificial Intelligence". Defending AI Research: A Collection of Essays and Reviews. CSLI., p. 73, "[O]ne of the reasons for inventing the term "artificial intelligence" was to escape association with "cybernetics". Its concentration on analog feedback seemed misguided, and I wished to avoid having either to accept Norbert (not Robert) Wiener as a guru or having to argue with him." Hegemony of the Dartmouth conference attendees: * Russell & Norvig 2003, p. 17, who write "for the next 20 years the field would be dominated by these people and their students." * McCorduck 2004, pp. 129–130 Russell & Norvig 2003, p. 18. Schaeffer J. (2009) Didn't Samuel Solve That Game?. In: One Jump Ahead. Springer, Boston, MA Samuel, A. L. (July 1959). "Some Studies in Machine Learning Using the Game of Checkers". IBM Journal of Research and Development. 3 (3): 210–229. CiteSeerX 10.1.1.368.2254. doi:10.1147/rd.33.0210. "Golden years" of AI (successful symbolic reasoning programs 1956–1973): * McCorduck 2004, pp. 243–252 * Crevier 1993, pp. 52–107 * Moravec 1988, p. 9 * Russell & Norvig 2003, pp. 18–21 The programs described are Arthur Samuel's checkers program for the IBM 701, Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU. DARPA pours money into undirected pure research into AI during the 1960s: * McCorduck 2004, p. 131 * Crevier 1993, pp. 51, 64–65 * NRC 1999, pp. 204–205 AI in England: * Howe 1994 Lighthill 1973. Expert systems: * ACM 1998, I.2.1 * Russell & Norvig 2003, pp. 22–24 * Luger & Stubblefield 2004, pp. 227–331 * Nilsson 1998, chpt. 17.4 * McCorduck 2004, pp. 327–335, 434–435 * Crevier 1993, pp. 145–62, 197–203 * Newquist 1994, pp. 155–183 Mead, Carver A.; Ismail, Mohammed (8 May 1989). Analog VLSI Implementation of Neural Systems (PDF). The Kluwer International Series in Engineering and Computer Science. 80. Norwell, MA: Kluwer Academic Publishers. doi:10.1007/978-1-4613-1639-8. ISBN 978-1-4613-1639-8. Archived from the original (PDF) on 6 November 2019. Retrieved 24 January 2020. Formal methods are now preferred ("Victory of the neats"): * Russell & Norvig 2003, pp. 25–26 * McCorduck 2004, pp. 486–487 McCorduck 2004, pp. 480–483. Markoff 2011. 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Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Problem solving, puzzle solving, game playing and deduction: * Russell & Norvig 2003, chpt. 3–9, * Poole, Mackworth & Goebel 1998, chpt. 2,3,7,9, * Luger & Stubblefield 2004, chpt. 3,4,6,8, * Nilsson 1998, chpt. 7–12 Uncertain reasoning: * Russell & Norvig 2003, pp. 452–644, * Poole, Mackworth & Goebel 1998, pp. 345–395, * Luger & Stubblefield 2004, pp. 333–381, * Nilsson 1998, chpt. 19 Psychological evidence of sub-symbolic reasoning: * Wason & Shapiro (1966) showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive social intelligence, performance dramatically improves. (See Wason selection task) * Kahneman, Slovic & Tversky (1982) have shown that people are terrible at elementary problems that involve uncertain reasoning. (See list of cognitive biases for several examples). * Lakoff & Núñez (2000) have controversially argued that even our skills at mathematics depend on knowledge and skills that come from "the body", i.e. sensorimotor and perceptual skills. (See Where Mathematics Comes From) Knowledge representation: * ACM 1998, I.2.4, * Russell & Norvig 2003, pp. 320–363, * Poole, Mackworth & Goebel 1998, pp. 23–46, 69–81, 169–196, 235–277, 281–298, 319–345, * Luger & Stubblefield 2004, pp. 227–243, * Nilsson 1998, chpt. 18 Knowledge engineering: * Russell & Norvig 2003, pp. 260–266, * Poole, Mackworth & Goebel 1998, pp. 199–233, * Nilsson 1998, chpt. ≈17.1–17.4 Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts): * Russell & Norvig 2003, pp. 349–354, * Poole, Mackworth & Goebel 1998, pp. 174–177, * Luger & Stubblefield 2004, pp. 248–258, * Nilsson 1998, chpt. 18.3 Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): * Russell & Norvig 2003, pp. 328–341, * Poole, Mackworth & Goebel 1998, pp. 281–298, * Nilsson 1998, chpt. 18.2 Causal calculus: * Poole, Mackworth & Goebel 1998, pp. 335–337 Representing knowledge about knowledge: Belief calculus, modal logics: * Russell & Norvig 2003, pp. 341–344, * Poole, Mackworth & Goebel 1998, pp. 275–277 Sikos, Leslie F. 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The Knowledge Engineering Review. 20 (1): 39–61. doi:10.1017/S0269888905000408. S2CID 14987656. Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies". MM '06 Proceedings of the 14th ACM international conference on Multimedia. 14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682. Qualification problem: * McCarthy & Hayes 1969 * Russell & Norvig 2003[page needed] While McCarthy was primarily concerned with issues in the logical representation of actions, Russell & Norvig 2003 apply the term to the more general issue of default reasoning in the vast network of assumptions underlying all our commonsense knowledge. Default reasoning and default logic, non-monotonic logics, circumscription, closed world assumption, abduction (Poole et al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning"): * Russell & Norvig 2003, pp. 354–360, * Poole, Mackworth & Goebel 1998, pp. 248–256, 323–335, * Luger & Stubblefield 2004, pp. 335–363, * Nilsson 1998, ~18.3.3 Breadth of commonsense knowledge: * Russell & Norvig 2003, p. 21, * Crevier 1993, pp. 113–114, * Moravec 1988, p. 13, * Lenat & Guha 1989 (Introduction) Dreyfus & Dreyfus 1986. Gladwell 2005. Expert knowledge as embodied intuition: * Dreyfus & Dreyfus 1986 (Hubert Dreyfus is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See Dreyfus' critique of AI) * Gladwell 2005 (Gladwell's Blink is a popular introduction to sub-symbolic reasoning and knowledge.) * Hawkins & Blakeslee 2005 (Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.) Planning: * ACM 1998, ~I.2.8, * Russell & Norvig 2003, pp. 375–459, * Poole, Mackworth & Goebel 1998, pp. 281–316, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Information value theory: * Russell & Norvig 2003, pp. 600–604 Classical planning: * Russell & Norvig 2003, pp. 375–430, * Poole, Mackworth & Goebel 1998, pp. 281–315, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning: * Russell & Norvig 2003, pp. 430–449 Multi-agent planning and emergent behavior: * Russell & Norvig 2003, pp. 449–455 Turing 1950. Solomonoff 1956. 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Archived from the original on 11 June 2020. Retrieved 11 June 2020. Machine perception: * Russell & Norvig 2003, pp. 537–581, 863–898 * Nilsson 1998, ~chpt. 6 Speech recognition: * ACM 1998, ~I.2.7 * Russell & Norvig 2003, pp. 568–578 Object recognition: * Russell & Norvig 2003, pp. 885–892 Computer vision: * ACM 1998, I.2.10 * Russell & Norvig 2003, pp. 863–898 * Nilsson 1998, chpt. 6 Robotics: * ACM 1998, I.2.9, * Russell & Norvig 2003, pp. 901–942, * Poole, Mackworth & Goebel 1998, pp. 443–460 Moving and configuration space: * Russell & Norvig 2003, pp. 916–932 Tecuci 2012. Robotic mapping (localization, etc): * Russell & Norvig 2003, pp. 908–915 Cadena, Cesar; Carlone, Luca; Carrillo, Henry; Latif, Yasir; Scaramuzza, Davide; Neira, Jose; Reid, Ian; Leonard, John J. (December 2016). "Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age". IEEE Transactions on Robotics. 32 (6): 1309–1332. arXiv:1606.05830. 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Nils Nilsson writes: "Simply put, there is wide disagreement in the field about what AI is all about."[163] AI's immediate precursors: * McCorduck 2004, pp. 51–107 * Crevier 1993, pp. 27–32 * Russell & Norvig 2003, pp. 15, 940 * Moravec 1988, p. 3 Haugeland 1985, pp. 112–117 The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of perceptrons by Marvin Minsky and Seymour Papert in 1969. See History of AI, AI winter, or Frank Rosenblatt. Cognitive simulation, Newell and Simon, AI at CMU (then called Carnegie Tech): * McCorduck 2004, pp. 139–179, 245–250, 322–323 (EPAM) * Crevier 1993, pp. 145–149 Soar (history): * McCorduck 2004, pp. 450–451 * Crevier 1993, pp. 258–263 McCarthy and AI research at SAIL and SRI International: * McCorduck 2004, pp. 251–259 * Crevier 1993 AI research at Edinburgh and in France, birth of Prolog: * Crevier 1993, pp. 193–196 * Howe 1994 AI at MIT under Marvin Minsky in the 1960s : * McCorduck 2004, pp. 259–305 * Crevier 1993, pp. 83–102, 163–176 * Russell & Norvig 2003, p. 19 Cyc: * McCorduck 2004, p. 489, who calls it "a determinedly scruffy enterprise" * Crevier 1993, pp. 239–243 * Russell & Norvig 2003, p. 363−365 * Lenat & Guha 1989 Knowledge revolution: * McCorduck 2004, pp. 266–276, 298–300, 314, 421 * Russell & Norvig 2003, pp. 22–23 Frederick, Hayes-Roth; William, Murray; Leonard, Adelman. "Expert systems". AccessScience. doi:10.1036/1097-8542.248550. Embodied approaches to AI: * McCorduck 2004, pp. 454–462 * Brooks 1990 * Moravec 1988 Weng et al. 2001. Lungarella et al. 2003. Asada et al. 2009. Oudeyer 2010. Revival of connectionism: * Crevier 1993, pp. 214–215 * Russell & Norvig 2003, p. 25 Computational intelligence * IEEE Computational Intelligence Society Archived 9 May 2008 at the Wayback Machine Hutson, Matthew (16 February 2018). "Artificial intelligence faces reproducibility crisis". Science. pp. 725–726. Bibcode:2018Sci...359..725H. doi:10.1126/science.359.6377.725. Archived from the original on 29 April 2018. Retrieved 28 April 2018. Norvig 2012. Langley 2011. Katz 2012. The intelligent agent paradigm: * Russell & Norvig 2003, pp. 27, 32–58, 968–972 * Poole, Mackworth & Goebel 1998, pp. 7–21 * Luger & Stubblefield 2004, pp. 235–240 * Hutter 2005, pp. 125–126 The definition used in this article, in terms of goals, actions, perception and environment, is due to Russell & Norvig (2003). Other definitions also include knowledge and learning as additional criteria. Agent architectures, hybrid intelligent systems: * Russell & Norvig (2003, pp. 27, 932, 970–972) * Nilsson (1998, chpt. 25) Hierarchical control system: * Albus 2002 Lieto, Antonio; Lebiere, Christian; Oltramari, Alessandro (May 2018). "The knowledge level in cognitive architectures: Current limitations and possibile developments". Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Lieto, Antonio; Bhatt, Mehul; Oltramari, Alessandro; Vernon, David (May 2018). "The role of cognitive architectures in general artificial intelligence". Cognitive Systems Research. 48: 1–3. doi:10.1016/j.cogsys.2017.08.003. hdl:2318/1665249. S2CID 36189683. Russell & Norvig 2009, p. 1. White Paper: On Artificial Intelligence - A European approach to excellence and trust (PDF). Brussels: European Commission. 2020. p. 1. Archived (PDF) from the original on 20 February 2020. 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Dreyfus criticized the necessary condition of the physical symbol system hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules."[206] Dreyfus' critique of artificial intelligence: * Dreyfus 1972, Dreyfus & Dreyfus 1986 * Crevier 1993, pp. 120–132 * McCorduck 2004, pp. 211–239 * Russell & Norvig 2003, pp. 950–952, Gödel 1951: in this lecture, Kurt Gödel uses the incompleteness theorem to arrive at the following disjunction: (a) the human mind is not a consistent finite machine, or (b) there exist Diophantine equations for which it cannot decide whether solutions exist. Gödel finds (b) implausible, and thus seems to have believed the human mind was not equivalent to a finite machine, i.e., its power exceeded that of any finite machine. He recognized that this was only a conjecture, since one could never disprove (b). Yet he considered the disjunctive conclusion to be a "certain fact". 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"artificial intelligence is a tool, not a threat". Archived from the original on 12 November 2014. "Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence". Observer. 19 August 2015. Archived from the original on 30 October 2015. Retrieved 30 October 2015. Chalmers, David (1995). "Facing up to the problem of consciousness". Journal of Consciousness Studies. 2 (3): 200–219. Archived from the original on 8 March 2005. Retrieved 11 October 2018. See also this link Archived 8 April 2011 at the Wayback Machine Horst, Steven, (2005) "The Computational Theory of Mind" Archived 11 September 2018 at the Wayback Machine in The Stanford Encyclopedia of Philosophy Searle 1980, p. 1. This version is from Searle (1999), and is also quoted in Dennett 1991, p. 435. Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states." [230] Strong AI is defined similarly by Russell & Norvig (2003, p. 947): "The assertion that machines could possibly act intelligently

Hey, Today I’m going to tell you about How you can Install Parrot OS on Android. Parrot Os is the most popular and wide used for Hacking. It works similar to the Kali Linux and it also currently competitive with the Kali Right Now. You need good Hardware specs for using the Kali Linux but on Parrot, it runs on very fewer Hardware specs and gives you the faster response on your task. So, Lets Start… You need these minimum Requirements to Install it: At least 1 GB RAM 2 GB Free Space on Your Phone Storage Android OS up to 5.0 Lollipop Need to spend time 30 minute These Three Apps are Needed to be Installed on Your Phone: Now, Follow the steps of installing the Parrot OS on Android 1. Open the Termux app and Update the Installed Packages by typing the command: apt-get update 2. Upgrade the Previous Installed Packages typing the command and Press Y if ask: apt-get upgrade 3. Now, Open the AnLinux app Choose your distro Parrot Security OS in-app 4. You’ll see a Copy button there… Click on it. 5. Now, After Copied Click on the Launch button. 6. It will Open You the Termux app Paste the Copied command there then hit Enter. 7. Now, wait until the installation Finish. 8. Launch the Parrot OS using the command: ./start-parrot.sh Here You have Done the Installation In Parrot OS. Install the Desktop Environment In Parrot OS So, you can easy to use the GUI version of Parrot OS… First of all, you have to Open the AnLinux app and then click on Menu. Click on Desktop Environment Choose your Distro: Parrot Security OS Now Choose the Desktop Environment: Xfce4 (Recommended) Click on Copy and Paste the command in the Termux app while you are running the Parrot OS. After type: Y and hit Enter (if ask) wait until the Downloading gets Finish (takes 15 to 20min) After Finish it will ask You for the Keyword Layout Just Press 1 and hit Enter (for English US) Now, It will tell you to create a password choose your Password (You won’t be able to see the password) It will tell you to Verify Just Re-type the Password and hit Enter All set… Just launch the VNC Viewer app and add a New Connection. In Address type: localhost:5901 In Name Choose any random name of your Connection label and Create After Created Click on it. Now, it will ask you for the Password Type the Password which you have chosen before and hit OK That’s it for today… If you have any questions and queries just ask them in the comment below. Note: If it won’t ask you to choose Keyboard Layout or Password then you have to reinstall the Termux app and Follow the installation process again. If you like this .. Please Encourage me!!!!

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The objective of this project is to scrape a corpus of news articles from a set of web pages, pre-process the corpus, and then to apply unsupervised clustering algorithms to explore and summarise the contents of the corpus. Part 1. Text Data Scraping This part of the project should be implemented as a Python script 1. Identify the URLs for all news articles listed on the website: http://mlg.ucd.ie/modules/COMP41680/news/index.html 2. Retrieve all web pages corresponding to these article URLs. 3. From the web pages, extract the main body text containing the content of each news article. Save the body of each article as plain text. Part 2. Corpus Exploration Tasks to be completed in your IPython notebook: 1. Load the text corpus generated in Part 1. Apply any appropriate pre-processing steps and construct a document-term matrix representation of the corpus. 2. Summarise the overall corpus by identifying the most characteristic terms and phrases in the corpus. 3. Apply two alternative clustering algorithms of your choice to the document-term matrix to produce clusters of related documents. This might require applying each algorithm several times with different parameter values. 4. For each clustering generated in Step 3, summarise the contents of the clusters. Based on your summary, suggest a topic/theme for each cluster.

项目作者：田睿 本科毕业于北京航空航天大学，现在读研，GNSS领域入门两年。 请注意！ 该项目原始路径为：E:\Thesis\RTKLIB_Workspace\WorkSpace\For_Thesis\For_Thesis 项目名称为For_Thesis(即上传的文件夹中包含的内容) 在笔者Win10系统、VS2017上成功运行，亲测可用！！！ 请务必仔细阅读此文件！！！ ==============程序主要功能============== 本程序的主要功能是基于GGOS对流层产品进行对流层延迟解算，还可用于rtklib开源项目的二次开发及改进。 总之，该程序的目的是方便其他使用rtklib开源项目的GNSS研究者，提高其科研效率。 ==============程序基本信息============== 本程序基于rtklib2.4.3 b33版本进行二次开发，采用C语言编写，并在Win10、Visual Studio 2017平台上完成开发及调试，项目路径为： ‪E:\Thesis\RTKLIB_Workspace\WorkSpace\For_Thesis\For_Thesis 相比于原项目，主要改动点为： 1.增加了Demo_Tropo.c文件（最主要的改进）。 2.增加了测试用的main.cpp文件，作为程序入口，应注意！主程序文件必须为cpp文件，不能使用c文件。 3.在rtklib.h 文件中增加了部分函数的声明。 4.在Windows系统下，采用VS2017编译rtklib项目会遇到各种问题。对此，对原项目进行了必要的改动， 如增加了unistd.h文件、去掉main函数的rnx2rtkp.c文件等，具体编译方法详见后文。 5.增加了数据文件orography_ell.txt等，应注意！数据文件必须放在项目目录内，因为主程序中使用的是相对路径。 ==============Windows下采用Visual Studio IDE的项目编译方法============== Windows下常用的C/C++ IDE即Visual Studio，功能丰富全面，适合GNSS研究者使用。 当然，对计算机比较精通的研究者可以采用linux系统，不使用IDE，采用cmake等工具进行编译。事实上，这样做更适合rtklib的二次开发。 然而，对于刚接触GNSS领域的硕/博研究生而言，学习linux系统及cmake等编译工具，无疑会大大拉长学习周期， 耽误宝贵的科研时间，降低科研效率。对此，笔者深有体会！因此，笔者坚持在windows系统下用Visual Studio IDE开发此项目， 遇到的编译问题很多，可参考如下博客解决： https://blog.csdn.net/wuwuku123/article/details/100030177 https://blog.csdn.net/sd28you28/article/details/82911273 https://blog.csdn.net/zhangtao_heu/article/details/79536427 https://blog.csdn.net/qq_35363018/article/details/101317869 https://blog.csdn.net/baixia3551/article/details/101085788?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-4.nonecase&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-4.nonecase https://blog.csdn.net/WalterBrien/article/details/80754215 此外，笔者在编译过程中还遇到一些没有解决的问题，在此总结发布以飨读者： 要通过Project——Manage NuGet Packages添加pthread、dirent等 一定记得切换为64位 注意预编译器添加的是WIN32而非Win32！！！ 预编译器中记得添加 WIN32 _DEBUG _CONSOLE _CRT_SECURE_NO_WARNINGS _WINSOCK_DEPRECATED_NO_WARNINGS ENAGLO DLL 主程序必须是.cpp文件！！！ 记得添加unistd.h文件 经上述操作后仍出现： unresolved external symbol showmsg等错误 经测试，发现下述方法可行： 将RTKLIB-rtklib_2.4.3\app\rnx2rtkp文件夹下的rnx2rtkp.c文件放到项目的src文件夹下并加入到工程，并去掉其中的主程序（也可以把这个当做主程序）。 ==============数据源============== 所用数据均可在网站https://vmf.geo.tuwien.ac.at/上下载。 应注意！VMFG_FC产品必须注册账户后才能获取，注册方法即通过英文邮件向相关管理人员提出申请，管理人员会通过邮件提供给你一个账户。 ==============开发参考============== 参考了官方的matlab程序vmf1_grid.m及vmf1_ht.m 参考了如下文献： 《GGOS对流层延迟产品精度分析及在PPP中的应用》 《不同全球对流层天顶延迟产品在中国区域的比较》 《Troposphere mapping functions for GPS and VLBI from ECMWF operational analysis data》 《Implementation and testing of the gridded Vienna Mapping Function 1 (VMF1)》 《Generation and Assessment of VMF1-Type Grids Using North-American Numerical Weather Models》 《Discussion and recommendations about the height correction for a priori zenith hydrostatic delays derived from ECMWF data》 还有一些官方网站上提供的资料（请参见官网） ==============关于程序结果的一点讨论============== 经测试，本项目运行结果与官方matlab程序有一定出入，差距在厘米级。 经检查，笔者自认为这并非是编程的问题，而是必然存在的数值计算误差，因为C程序与matlab程序的数值精度不同。 笔者测试的结果是本程序更接近IGS发布的天顶对流层延迟数据。 也可采用C与matlab联合编程应用GGOS产品，但运行较慢，且移植性不好。 如有发现程序中问题的，欢迎在GitHub库上及时发布新版本。 ==============重要声明============== 鉴于笔者入门不久，水平有限，有所疏漏在所难免。 该项目仅供研究者参考，并非TU Wien发布的官方代码，可能存在笔者尚未发现的未知错误。 如各位研究者发现问题，欢迎在GitHub上发布修正版本。非常欢迎各位研究者对本项目查漏补缺，系统测试！ 在该项目尚未成熟之前，建议各位研究者审慎地考虑在科研中到底是应用本项目，还是使用C与matlab联合编程，后者虽慢，但毕竟是官方反复验证过的matlab代码。 当然，该项目注释详尽，参考价值较高，可作为学习GGOS产品应用的重要参考！ 在此开源发布，权作抛砖引玉，期冀各位研究者对其进一步改进与完善！

A Reverse Engineering Assistant leveraging Retrieval-Augmented Generation (RAG) and the LLaMA-3.1-8B-Instant Large Language Model (LLM). This tool is designed to revolutionize reverse engineering tasks by combining machine learning with retrieval-based systems.

Mix Task to generate an elm app inside a Phoenix (1.3) app

import pyttsx3 import speech_recognition as sr import datetime from datetime import date import calendar import time import math import wikipedia import webbrowser import os import smtplib import winsound import pyautogui import cv2 from pygame import mixer from tkinter import * import tkinter.messagebox as message from sqlite3 import * conn = connect("voice_assistant_asked_questions.db") conn.execute("CREATE TABLE IF NOT EXISTS `voicedata`(id INTEGER PRIMARY KEY AUTOINCREMENT,command VARCHAR(201))") conn.execute("CREATE TABLE IF NOT EXISTS `review`(id INTEGER PRIMARY KEY AUTOINCREMENT, review VARCHAR(50), type_of_review VARCHAR(50))") conn.execute("CREATE TABLE IF NOT EXISTS `emoji`(id INTEGER PRIMARY KEY AUTOINCREMENT,emoji VARCHAR(201))") global query engine = pyttsx3.init('sapi5') voices = engine.getProperty('voices') engine.setProperty('voice', voices[0].id) def speak(audio): engine.say(audio) engine.runAndWait() def wishMe(): hour = int(datetime.datetime.now().hour) if hour >= 0 and hour<12: speak("Good Morning!") elif hour >= 12 and hour < 18: speak("Good Afternoon!") else: speak("Good Evening!") speak("I am voice assistant Akshu2020 Sir. Please tell me how may I help you.") def takeCommand(): global query r = sr.Recognizer() with sr.Microphone() as source: print("Listening...") r.pause_threshold = 0.9 audio = r.listen(source) try: print("Recognizing...") query = r.recognize_google(audio,language='en-in') print(f"User said: {query}\n") except Exception as e: #print(e) print("Say that again please...") #speak('Say that again please...') return "None" return query def calculator(): global query try: if 'add' in query or 'edi' in query: speak('Enter a number') a = float(input("Enter a number:")) speak('Enter another number to add') b = float(input("Enter another number to add:")) c = a+b print(f"{a} + {b} = {c}") speak(f'The addition of {a} and {b} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'sub' in query: speak('Enter a number') a = float(input("Enter a number:")) speak('Enter another number to subtract') b = float(input("Enter another number to subtract:")) c = a-b print(f"{a} - {b} = {c}") speak(f'The subtraction of {a} and {b} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'mod' in query: speak('Enter a number') a = float(input("Enter a number:")) speak('Enter another number') b = float(input("Enter another number:")) c = a%b print(f"{a} % {b} = {c}") speak(f'The modular division of {a} and {b} is equal to {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'div' in query: speak('Enter a number as dividend') a = float(input("Enter a number:")) speak('Enter another number as divisor') b = float(input("Enter another number as divisor:")) c = a/b print(f"{a} / {b} = {c}") speak(f'{a} divided by {b} is equal to {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'multi' in query: speak('Enter a number') a = float(input("Enter a number:")) speak('Enter another number to multiply') b = float(input("Enter another number to multiply:")) c = a*b print(f"{a} x {b} = {c}") speak(f'The multiplication of {a} and {b} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'square root' in query: speak('Enter a number to find its sqare root') a = float(input("Enter a number:")) c = a**(1/2) print(f"Square root of {a} = {c}") speak(f'Square root of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'square' in query: speak('Enter a number to find its sqare') a = float(input("Enter a number:")) c = a**2 print(f"{a} x {a} = {c}") speak(f'Square of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cube root' in query: speak('Enter a number to find its cube root') a = float(input("Enter a number:")) c = a**(1/3) print(f"Cube root of {a} = {c}") speak(f'Cube root of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cube' in query: speak('Enter a number to find its sqare') a = float(input("Enter a number:")) c = a**3 print(f"{a} x {a} x {a} = {c}") speak(f'Cube of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'fact' in query: try: n = int(input('Enter the number whose factorial you want to find:')) fact = 1 for i in range(1,n+1): fact = fact*i print(f"{n}! = {fact}") speak(f'{n} factorial is equal to {fact}. Your answer is {fact}.') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: #print(e) speak('I unable to calculate its factorial.') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'power' in query or 'raise' in query: speak('Enter a number whose power you want to raised') a = float(input("Enter a number whose power to be raised :")) speak(f'Enter a raised power to {a}') b = float(input(f"Enter a raised power to {a}:")) c = a**b print(f"{a} ^ {b} = {c}") speak(f'{a} raise to the power {b} = {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'percent' in query: speak('Enter a number whose percentage you want to calculate') a = float(input("Enter a number whose percentage you want to calculate :")) speak(f'How many percent of {a} you want to calculate?') b = float(input(f"Enter how many percentage of {a} you want to calculate:")) c = (a*b)/100 print(f"{b} % of {a} is {c}") speak(f'{b} percent of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'interest' in query: speak('Enter the principal value or amount') p = float(input("Enter the principal value (P):")) speak('Enter the rate of interest per year') r = float(input("Enter the rate of interest per year (%):")) speak('Enter the time in months') t = int(input("Enter the time (in months):")) interest = (p*r*t)/1200 sint = round(interest) fv = round(p + interest) print(f"Interest = {interest}") print(f"The total amount accured, principal plus interest, from simple interest on a principal of {p} at a rate of {r}% per year for {t} months is {p + interest}.") speak(f'interest is {sint}. The total amount accured, principal plus interest, from simple interest on a principal of {p} at a rate of {r}% per year for {t} months is {fv}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'si' in query: speak('Enter the angle in degree to find its sine value') a = float(input("Enter the angle:")) b = a * 3.14/180 c = math.sin(b) speak('Here is your answer.') print(f"sin({a}) = {c}") speak(f'sin({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cos' in query: speak('Enter the angle in degree to find its cosine value') a = float(input("Enter the angle:")) b = a * 3.14/180 c = math.cos(b) speak('Here is your answer.') print(f"cos({a}) = {c}") speak(f'cos({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cot' in query or 'court' in query: try: speak('Enter the angle in degree to find its cotangent value') a = float(input("Enter the angle:")) b = a * 3.14/180 c = 1/math.tan(b) speak('Here is your answer.') print(f"cot({a}) = {c}") speak(f'cot({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: print("infinity") speak('Answer is infinity') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'tan' in query or '10' in query: speak('Enter the angle in degree to find its tangent value') a = float(input("Enter the angle:")) b = a * 3.14/180 c = math.tan(b) speak('Here is your answer.') print(f"tan({a}) = {c}") speak(f'tan({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cosec' in query: try: speak('Enter the angle in degree to find its cosecant value') a = float(input("Enter the angle:")) b = a * 3.14/180 c =1/ math.sin(b) speak('Here is your answer.') print(f"cosec({a}) = {c}") speak(f'cosec({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: print('Infinity') speak('Answer is infinity') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'caus' in query: try: speak('Enter the angle in degree to find its cosecant value') a = float(input("Enter the angle:")) b = a * 3.14/180 c =1/ math.sin(b) speak('Here is your answer.') print(f"cosec({a}) = {c}") speak(f'cosec({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: print('Infinity') speak('Answer is infinity') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'sec' in query: try: speak('Enter the angle in degree to find its secant value') a = int(input("Enter the angle:")) b = a * 3.14/180 c = 1/math.cos(b) speak('Here is your answer.') print(f"sec({a}) = {c}") speak(f'sec({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: print('Infinity') speak('Answer is infinity') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: speak('I unable to do this calculation.') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') def callback(r,c): global player if player == 'X' and states[r][c] == 0 and stop_game == False: b[r][c].configure(text='X',fg='blue', bg='white') states[r][c] = 'X' player = 'O' if player == 'O' and states[r][c] == 0 and stop_game == False: b[r][c].configure(text='O',fg='red', bg='yellow') states[r][c] = 'O' player = 'X' check_for_winner() def check_for_winner(): global stop_game global root for i in range(3): if states[i][0] == states[i][1]== states[i][2]!=0: b[i][0].config(bg='grey') b[i][1].config(bg='grey') b[i][2].config(bg='grey') stop_game = True root.destroy() for i in range(3): if states[0][i] == states[1][i] == states[2][i]!= 0: b[0][i].config(bg='grey') b[1][i].config(bg='grey') b[2][i].config(bg='grey') stop_game = True root.destroy() if states[0][0] == states[1][1]== states[2][2]!= 0: b[0][0].config(bg='grey') b[1][1].config(bg='grey') b[2][2].config(bg='grey') stop_game = True root.destroy() if states[2][0] == states[1][1] == states[0][2]!= 0: b[2][0].config(bg='grey') b[1][1].config(bg='grey') b[0][2].config(bg='grey') stop_game = True root.destroy() def sendEmail(to,content): server = smtplib.SMTP('smtp.gmail.com', 587) server.ehlo() server.starttls() server.login('xyz123@gmail.com','password') server.sendmail('xyz123@gmail.com',to,content) server.close() def brightness(): try: query = takeCommand().lower() if '25' in query: pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1610,960) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() speak('If you again want to change brihtness, say, change brightness') elif '50' in query: pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1684,960) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() speak('If you again want to change brihtness, say, change brightness') elif '75' in query: pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1758,960) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() speak('If you again want to change brihtness, say, change brightness') elif '100' in query or 'full' in query: pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1835,960) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() speak('If you again want to change brihtness, say, change brightness') else: speak('Please select 25, 50, 75 or 100....... Say again.') brightness() except exception as e: #print(e) speak('Something went wrong') def close_window(): try: if 'y' in query: pyautogui.moveTo(1885,10) pyautogui.click() else: speak('ok') pyautogui.moveTo(1000,500) except exception as e: #print(e) speak('error') def whatsapp(): query = takeCommand().lower() if 'y' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('whatsapp') time.sleep(2) pyautogui.press('enter') time.sleep(2) pyautogui.moveTo(100,140) pyautogui.click() speak('To whom you want to send message,.....just write the name here in 5 seconds') time.sleep(7) pyautogui.moveTo(120,300) pyautogui.click() time.sleep(1) pyautogui.moveTo(800,990) pyautogui.click() speak('Say the message,....or if you want to send anything else,...say send document, or say send emoji') query = takeCommand() if ('sent' in query or 'send' in query) and 'document' in query: pyautogui.moveTo(660,990) pyautogui.click() time.sleep(1) pyautogui.moveTo(660,740) pyautogui.click() speak('please select the document within 10 seconds') time.sleep(12) speak('Should I send this document?') query = takeCommand().lower() if 'y' in query and 'no' not in query: speak('sending the document......') pyautogui.press('enter') speak('Do you want to send message again to anyone?') whatsapp() elif ('remove' in query or 'cancel' in query or 'delete' in query or 'clear' in query) and ('document' in query or 'message' in query or 'it' in query or 'emoji' in query or 'select' in query): pyautogui.doubleClick(x=800, y=990) pyautogui.press('backspace') speak('Do you want to send message again to anyone?') whatsapp() else: speak('ok') elif ('sent' in query or 'send' in query) and 'emoji' in query: pyautogui.moveTo(620,990) pyautogui.click() pyautogui.moveTo(670,990) pyautogui.click() pyautogui.moveTo(650,580) pyautogui.click() speak('please select the emoji within 10 seconds') time.sleep(11) speak('Should I send this emoji?') query = takeCommand().lower() if 'y' in query and 'no' not in query: speak('Sending the emoji......') pyautogui.press('enter') speak('Do you want to send message again to anyone?') whatsapp() elif ('remove' in query or 'cancel' in query or 'delete' in query or 'clear' in query) and ('message' in query or 'it' in query or 'emoji' in query or 'select' in query): pyautogui.doublClick(x=800, y=990) speak('Do you want to send message again to anyone?') whatsapp() else: speak('ok') else: pyautogui.write(f'{query}') speak('Should I send this message?') query = takeCommand().lower() if 'y' in query and 'no' not in query: speak('sending the message......') pyautogui.press('enter') speak('Do you want to send message again to anyone?') whatsapp() elif ('remove' in query or 'cancel' in query or 'delete' in query or 'clear' in query) and ('message' in query or 'it' in query or 'select' in query): pyautogui.doubleClick(x=800, y=990) pyautogui.press('backspace') speak('Do you want to send message again to anyone?') whatsapp() else: speak('ok') else: speak('ok') def alarm(): root = Tk() root.title('Akshu2020 Alarm-Clock') speak('Please enter the time in the format hour, minutes and seconds. When the alarm should rang?') speak('Please enter the time greater than the current time') def setalarm(): alarmtime = f"{hrs.get()}:{mins.get()}:{secs.get()}" print(alarmtime) if(alarmtime!="::"): alarmclock(alarmtime) else: speak('You have not entered the time.') def alarmclock(alarmtime): while True: time.sleep(1) time_now=datetime.datetime.now().strftime("%H:%M:%S") print(time_now) if time_now == alarmtime: Wakeup=Label(root, font = ('arial', 20, 'bold'), text="Wake up! Wake up! Wake up",bg="DodgerBlue2",fg="white").grid(row=6,columnspan=3) speak("Wake up, Wake up") print("Wake up!") mixer.init() mixer.music.load(r'C:\Users\Admin\Music\Playlists\wake-up-will-you-446.mp3') mixer.music.play() break speak('you can click on close icon to close the alarm window.') hrs=StringVar() mins=StringVar() secs=StringVar() greet=Label(root, font = ('arial', 20, 'bold'),text="Take a short nap!").grid(row=1,columnspan=3) hrbtn=Entry(root,textvariable=hrs,width=5,font =('arial', 20, 'bold')) hrbtn.grid(row=2,column=1) minbtn=Entry(root,textvariable=mins, width=5,font = ('arial', 20, 'bold')).grid(row=2,column=2) secbtn=Entry(root,textvariable=secs, width=5,font = ('arial', 20, 'bold')).grid(row=2,column=3) setbtn=Button(root,text="set alarm",command=setalarm,bg="DodgerBlue2", fg="white",font = ('arial', 20, 'bold')).grid(row=4,columnspan=3) timeleft = Label(root,font=('arial', 20, 'bold')) timeleft.grid() mainloop() def select1(): global vs global root3 global type_of_review if vs.get() == 1: message.showinfo(" ","Thank you for your review!!") review = "Very Satisfied" type_of_review = "Positive" root3.destroy() elif vs.get() == 2: message.showinfo(" ","Thank you for your review!!") review = "Satisfied" type_of_review = "Positive" root3.destroy() elif vs.get() == 3: message.showinfo(" ","Thank you for your review!!!!") review = "Neither Satisfied Nor Dissatisfied" type_of_review = "Neutral" root3.destroy() elif vs.get() == 4: message.showinfo(" ","Thank you for your review!!") review = "Dissatisfied" type_of_review = "Negative" root3.destroy() elif vs.get() == 5: message.showinfo(" ","Thank you for your review!!") review = "Very Dissatisfied" type_of_review = "Negative" root3.destroy() elif vs.get() == 6: message.showinfo(" "," Ok ") review = "I do not want to give review" type_of_review = "No review" root3.destroy() try: conn.execute(f"INSERT INTO `review`(review,type_of_review) VALUES('{review}', '{type_of_review}')") conn.commit() except Exception as e: pass def select_review(): global root3 global vs global type_of_review root3 = Tk() root3.title("Select an option") vs = IntVar() string = "Are you satisfied with my performance?" msgbox = Message(root3,text=string) msgbox.config(bg="lightgreen",font = "(20)") msgbox.grid(row=0,column=0) rs1=Radiobutton(root3,text="Very Satisfied",font="(20)",value=1,variable=vs).grid(row=1,column=0,sticky=W) rs2=Radiobutton(root3,text="Satisfied",font="(20)",value=2,variable=vs).grid(row=2,column=0,sticky=W) rs3=Radiobutton(root3,text="Neither Satisfied Nor Dissatisfied",font="(20)",value=3,variable=vs).grid(row=3,column=0,sticky=W) rs4=Radiobutton(root3,text="Dissatisfied",font="(20)",value=4,variable=vs).grid(row=4,column=0,sticky=W) rs5=Radiobutton(root3,text="Very Dissatisfied",font="(20)",value=5,variable=vs).grid(row=5,column=0,sticky=W) rs6=Radiobutton(root3,text="I don't want to give review",font="(20)",value=6,variable=vs).grid(row=6,column=0,sticky=W) bs = Button(root3,text="Submit",font="(20)",activebackground="yellow",activeforeground="green",command=select1) bs.grid(row=7,columnspan=2) root3.mainloop() while True : query = takeCommand().lower() # logic for executing tasks based on query if 'wikipedia' in query: speak('Searching wikipedia...') query = query.replace("wikipedia","") results = wikipedia.summary(query, sentences=2) speak("According to Wikipedia") print(results) speak(results) elif 'translat' in query or ('let' in query and 'translat' in query and 'open' in query): webbrowser.open('https://translate.google.co.in') time.sleep(10) elif 'open map' in query or ('let' in query and 'map' in query and 'open' in query): webbrowser.open('https://www.google.com/maps') time.sleep(10) elif ('open' in query and 'youtube' in query) or ('let' in query and 'youtube' in query and 'open' in query): webbrowser.open('https://www.youtube.com') time.sleep(10) elif 'chrome' in query: webbrowser.open('https://www.chrome.com') time.sleep(10) elif 'weather' in query: webbrowser.open('https://www.yahoo.com/news/weather') time.sleep(3) speak('Click on, change location, and enter the city , whose whether conditions you want to know.') time.sleep(10) elif 'google map' in query: webbrowser.open('https://www.google.com/maps') time.sleep(10) elif ('open' in query and 'google' in query) or ('let' in query and 'google' in query and 'open' in query): webbrowser.open('google.com') time.sleep(10) elif ('open' in query and 'stack' in query and 'overflow' in query) or ('let' in query and 'stack' in query and 'overflow' in query and 'open' in query): webbrowser.open('stackoverflow.com') time.sleep(10) elif 'open v i' in query or 'open vi' in query or 'open vierp' in query or ('open' in query and ('r p' in query or 'rp' in query)): webbrowser.open('https://www.vierp.in/login/erplogin') time.sleep(10) elif 'news' in query: webbrowser.open('https://www.bbc.com/news/world') time.sleep(10) elif 'online shop' in query or (('can' in query or 'want' in query or 'do' in query or 'could' in query) and 'shop' in query) or('let' in query and 'shop' in query): speak('From which online shopping website, you want to shop? Amazon, flipkart, snapdeal or naaptol?') query = takeCommand().lower() if 'amazon' in query: webbrowser.open('https://www.amazon.com') time.sleep(10) elif 'flip' in query: webbrowser.open('https://www.flipkart.com') time.sleep(10) elif 'snap' in query: webbrowser.open('https://www.snapdeal.com') time.sleep(10) elif 'na' in query: webbrowser.open('https://www.naaptol.com') time.sleep(10) else: speak('Sorry sir, you have to search in browser as his shopping website is not reachable for me.') elif ('online' in query and ('game' in query or 'gaming' in query)): webbrowser.open('https://www.agame.com/games') time.sleep(10) elif 'dictionary' in query: webbrowser.open('https://www.dictionary.com') time.sleep(3) speak('Enter the word, in the search bar of the dictionary, whose defination or synonyms you want to know') time.sleep(3) elif ('identif' in query and 'emoji' in query) or ('sentiment' in query and ('analysis' in query or 'identif' in query)): speak('Please enter only one emoji at a time.') emoji = input('enter emoji here: ') if '😀' in emoji or '😃' in emoji or '😄' in emoji or '😁' in emoji or '🙂' in emoji or '😊' in emoji or '☺️' in emoji or '😇' in emoji or '🥲' in emoji: speak('happy') print('Happy') elif '😝' in emoji or '😆' in emoji or '😂' in emoji or '🤣' in emoji: speak('Laughing') print('Laughing') elif '😡' in emoji or '😠' in emoji or '🤬' in emoji: speak('Angry') print('Angry') elif '🤫' in emoji: speak('Keep quite') print('Keep quite') elif '😷' in emoji: speak('face with mask') print('Face with mask') elif '🥳' in emoji: speak('party') print('party') elif '😢' in emoji or '😥' in emoji or '😓' in emoji or '😰' in emoji or '☹️' in emoji or '🙁' in emoji or '😟' in emoji or '😔' in emoji or '😞️' in emoji: speak('Sad') print('Sad') elif '😭' in emoji: speak('Crying') print('Crying') elif '😋' in emoji: speak('Tasty') print('Tasty') elif '🤨' in emoji: speak('Doubt') print('Doubt') elif '😴' in emoji: speak('Sleeping') print('Sleeping') elif '🥱' in emoji: speak('feeling sleepy') print('feeling sleepy') elif '😍' in emoji or '🥰' in emoji or '😘' in emoji: speak('Lovely') print('Lovely') elif '😱' in emoji: speak('Horrible') print('Horrible') elif '🎂' in emoji: speak('Cake') print('Cake') elif '🍫' in emoji: speak('Cadbury') print('Cadbury') elif '🇮🇳' in emoji: speak('Indian national flag,.....Teeranga') print('Indian national flag - Tiranga') elif '💐' in emoji: speak('Bouquet') print('Bouquet') elif '🥺' in emoji: speak('Emotional') print('Emotional') elif ' ' in emoji or '' in emoji: speak(f'{emoji}') else: speak("I don't know about this emoji") print("I don't know about this emoji") try: conn.execute(f"INSERT INTO `emoji`(emoji) VALUES('{emoji}')") conn.commit() except Exception as e: #print('Error in storing emoji in database') pass elif 'time' in query: strTime = datetime.datetime.now().strftime("%H:%M:%S") print(strTime) speak(f"Sir, the time is {strTime}") elif 'open' in query and 'sublime' in query: path = "C:\Program Files\Sublime Text 3\sublime_text.exe" os.startfile(path) elif 'image' in query: path = "C:\Program Files\Internet Explorer\images" os.startfile(path) elif 'quit' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'exit' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'stop' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'shutdown' in query or 'shut down' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'close you' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() try: conn.execute(f"INSERT INTO `voice_assistant_review`(review, type_of_review) VALUES('{review}', '{type_of_review}')") conn.commit() except Exception as e: pass elif 'bye' in query: speak('Bye Sir') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'wait' in query or 'hold' in query: speak('for how many seconds or minutes I have to wait?') query = takeCommand().lower() if 'second' in query: query = query.replace("please","") query = query.replace("can","") query = query.replace("you","") query = query.replace("have","") query = query.replace("could","") query = query.replace("hold","") query = query.replace("one","1") query = query.replace("only","") query = query.replace("wait","") query = query.replace("for","") query = query.replace("the","") query = query.replace("just","") query = query.replace("seconds","") query = query.replace("second","") query = query.replace("on","") query = query.replace("a","") query = query.replace("to","") query = query.replace(" ","") #print(f'query:{query}') if query.isdigit() == True: #print('y') speak('Ok sir') query = int(query) time.sleep(query) speak('my waiting time is over') else: print('sorry sir. I unable to complete your request.') elif 'minute' in query: query = query.replace("please","") query = query.replace("can","") query = query.replace("you","") query = query.replace("have","") query = query.replace("could","") query = query.replace("hold","") query = query.replace("one","1") query = query.replace("only","") query = query.replace("on","") query = query.replace("wait","") query = query.replace("for","") query = query.replace("the","") query = query.replace("just","") query = query.replace("and","") query = query.replace("half","") query = query.replace("minutes","") query = query.replace("minute","") query = query.replace("a","") query = query.replace("to","") query = query.replace(" ","") #print(f'query:{query}') if query.isdigit() == True: #print('y') speak('ok sir') query = int(query) time.sleep(query*60) speak('my waiting time is over') else: print('sorry sir. I unable to complete your request.') elif 'play' in query and 'game' in query: speak('I have 3 games, tic tac toe game for two players,....mario, and dyno games for single player. Which one of these 3 games you want to play?') query = takeCommand().lower() if ('you' in query and 'play' in query and 'with' in query) and ('you' in query and 'play' in query and 'me' in query): speak('Sorry sir, I cannot play this game with you.') speak('Do you want to continue it?') query = takeCommand().lower() try: if 'y' in query or 'sure' in query: root = Tk() root.title("TIC TAC TOE (By Akshay Khare)") b = [ [0,0,0], [0,0,0], [0,0,0] ] states = [ [0,0,0], [0,0,0], [0,0,0] ] for i in range(3): for j in range(3): b[i][j] = Button(font = ("Arial",60),width = 4,bg = 'powder blue', command = lambda r=i, c=j: callback(r,c)) b[i][j].grid(row=i,column=j) player='X' stop_game = False mainloop() else: speak('ok sir') except Exception as e: #print(e) time.sleep(3) print('I am sorry sir. There is some problem in loading the game. So I cannot open it.') elif 'tic' in query or 'tac' in query: try: root = Tk() root.title("TIC TAC TOE (Rayen Kallel)") b = [ [0,0,0], [0,0,0], [0,0,0] ] states = [ [0,0,0], [0,0,0], [0,0,0] ] for i in range(3): for j in range(3): b[i][j] = Button(font = ("Arial",60),width = 4,bg = 'powder blue', command = lambda r=i, c=j: callback(r,c)) b[i][j].grid(row=i,column=j) player='X' stop_game = False mainloop() except Exception as e: #print(e) time.sleep(3) speak('I am sorry sir. There is some problem in loading the game. So I cannot open it.') elif 'mar' in query or 'mer' in query or 'my' in query: webbrowser.open('https://chromedino.com/mario/') time.sleep(2.5) speak('Enter upper arrow key to start the game.') time.sleep(20) elif 'di' in query or 'dy' in query: webbrowser.open('https://chromedino.com/') time.sleep(2.5) speak('Enter upper arrow key to start the game.') time.sleep(20) else: speak('ok sir') elif 'change' in query and 'you' in query and 'voice' in query: engine.setProperty('voice', voices[1].id) speak("Here's an example of one of my voices. Would you like to use this one?") query = takeCommand().lower() if 'y' in query or 'sure' in query or 'of course' in query: speak('Great. I will keep using this voice.') elif 'n' in query: speak('Ok. I am back to my other voice.') engine.setProperty('voice', voices[0].id) else: speak('Sorry, I am having trouble understanding. I am back to my other voice.') engine.setProperty('voice', voices[0].id) elif 'www.' in query and ('.com' in query or '.in' in query): webbrowser.open(query) time.sleep(10) elif '.com' in query or '.in' in query: webbrowser.open(query) time.sleep(10) elif 'getting bore' in query: speak('then speak with me for sometime') elif 'i bore' in query: speak('Then speak with me for sometime.') elif 'i am bore' in query: speak('Then speak with me for sometime.') elif 'calculat' in query: speak('Yes. Which kind of calculation you want to do? add, substract, divide, multiply or anything else.') query = takeCommand().lower() calculator() elif 'add' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif '+' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'plus' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'subtrac' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'minus' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'multipl' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif ' x ' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'slash' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif '/' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'divi' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'trigonometr' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'percent' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif '%' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'raise to ' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'simple interest' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'akshay' in query: speak('Mr. Rayen Kallel is my inventor. He is 14 years old and he is A STUDENT AT THE COLLEGE PILOTEE SFAX') elif 'your inventor' in query: speak('Mr. Rayen Kallel is my inventor') elif 'your creator' in query: speak('Mr. Rayen Kallel is my creator') elif 'invent you' in query: speak('Mr. Rayen Kallel invented me') elif 'create you' in query: speak('Mr. Rayen Kallel created me') elif 'how are you' in query: speak('I am fine Sir') elif 'write' in query and 'your' in query and 'name' in query: print('Akshu2020') pyautogui.write('Akshu2020') elif 'write' in query and ('I' in query or 'whatever' in query) and 'say' in query: speak('Ok sir I will write whatever you will say. Please put your cursor where I have to write.......Please Start speaking now sir.') query = takeCommand().lower() pyautogui.write(query) elif 'your name' in query: speak('My name is akshu2020') elif 'who are you' in query: speak('I am akshu2020') elif ('repeat' in query and ('word' in query or 'sentence' in query or 'line' in query) and ('say' in query or 'tell' in query)) or ('repeat' in query and 'after' in query and ('me' in query or 'my' in query)): speak('yes sir, I will repeat your words starting from now') query = takeCommand().lower() speak(query) time.sleep(1) speak("If you again want me to repeat something else, try saying, 'repeat after me' ") elif ('send' in query or 'sent' in query) and ('mail' in query or 'email' in query or 'gmail' in query): try: speak('Please enter the email id of receiver.') to = input("Enter the email id of reciever: ") speak(f'what should I say to {to}') content = takeCommand() sendEmail(to, content) speak("Email has been sent") except Exception as e: #print(e) speak("sorry sir. I am not able to send this email") elif 'currency' in query and 'conver' in query: speak('I can convert, US dollar into dinar, and dinar into US dollar. Do you want to continue it?') query = takeCommand().lower() if 'y' in query or 'sure' in query or 'of course' in query: speak('which conversion you want to do? US dollar to dinar, or dinar to US dollar?') query = takeCommand().lower() if ('dollar' in query or 'US' in query) and ('dinar' in query): speak('Enter US Dollar') USD = float(input("Enter United States Dollar (USD):")) DT = USD * 0.33 dt = "{:.4f}".format(DT) print(f"{USD} US Dollar is equal to {dt} dniar.") speak(f'{USD} US Dollar is equal to {dt} dinar.') speak("If you again want to do currency conversion then say, 'convert currency' " ) elif ('dinar' in query) and ('to US' in query or 'to dollar' in query or 'to US dollar'): speak('Enter dinar') DT = float(input("Enter dinar (DT):")) USD = DT/0.33 usd = "{:.3f}".format(USD) print(f"{DT} dinar is equal to {usd} US Dollar.") speak(f'{DT} dinar rupee is equal to {usd} US Dollar.') speak("If you again want to do currency conversion then say, 'convert currency' " ) else: speak("I cannot understand what did you say. If you want to convert currency just say 'convert currency'") else: print('ok sir') elif 'about you' in query: speak('My name is akshu2020. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device. I am also able to send email') elif 'your intro' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your short intro' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your quick intro' in query: speak('My name is akshu2020. Version 1.0. Mr. Akshay Khare is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your brief intro' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'you work' in query: speak('run the program and say what do you want. so that I can help you. In this way I work') elif 'your job' in query: speak('My job is to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your work' in query: speak('My work is to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'work you' in query: speak('My work is to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your information' in query: speak('My name is akshu2020. Version 1.0. Mr. Akshay Khare is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'yourself' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'introduce you' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'description' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your birth' in query: speak('My birthdate is 6 August two thousand twenty') elif 'your use' in query: speak('I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'you eat' in query: speak('I do not eat anything. But the device in which I do my work requires electricity to eat') elif 'your food' in query: speak('I do not eat anything. But the device in which I do my work requires electricity to eat') elif 'you live' in query: speak('I live in sfax, in laptop of Mr. Rayen Khare') elif 'where from you' in query: speak('I am from sfax, I live in laptop of Mr. Rayen Khare') elif 'you sleep' in query: speak('Yes, when someone close this program or stop to run this program then I sleep and again wake up when someone again run me.') elif 'what are you doing' in query: speak('Talking with you.') elif 'you communicate' in query: speak('Yes, I can communicate with you.') elif 'hear me' in query: speak('Yes sir, I can hear you.') elif 'you' in query and 'dance' in query: speak('No, I cannot dance.') elif 'tell' in query and 'joke' in query: speak("Ok, here's a joke") speak("'Write an essay on cricket', the teacher told the class. Chintu finishes his work in five minutes. The teacher is impressed, she asks chintu to read his essay aloud for everyone. Chintu reads,'The match is cancelled because of rain', hehehehe,haahaahaa,hehehehe,haahaahaa") elif 'your' in query and 'favourite' in query: if 'actor' in query: speak('sofyen chaari, is my favourite actor.') elif 'food' in query: speak('I can always go for some food for thought. Like facts, jokes, or interesting searches, we could look something up now') elif 'country' in query: speak('tunisia') elif 'city' in query: speak('sfax') elif 'dancer' in query: speak('Michael jackson') elif 'singer' in query: speak('tamino, is my favourite singer.') elif 'movie' in query: speak('baywatch, such a treat') elif 'sing a song' in query: speak('I cannot sing a song. But I know the 7 sur in indian music, saaareeegaaamaaapaaadaaanisaa') elif 'day after tomorrow' in query or 'date after tomorrow' in query: td = datetime.date.today() + datetime.timedelta(days=2) print(td) speak(td) elif 'day before today' in query or 'date before today' in query or 'yesterday' in query or 'previous day' in query: td = datetime.date.today() + datetime.timedelta(days= -1) print(td) speak(td) elif ('tomorrow' in query and 'date' in query) or 'what is tomorrow' in query or (('day' in query or 'date' in query) and 'after today' in query): td = datetime.date.today() + datetime.timedelta(days=1) print(td) speak(td) elif 'month' in query or ('current' in query and 'month' in query): current_date = date.today() m = current_date.month month = calendar.month_name[m] print(f'Current month is {month}') speak(f'Current month is {month}') elif 'date' in query or ('today' in query and 'date' in query) or 'what is today' in query or ('current' in query and 'date' in query): current_date = date.today() print(f"Today's date is {current_date}") speak(f'Todays date is {current_date}') elif 'year' in query or ('current' in query and 'year' in query): current_date = date.today() m = current_date.year print(f'Current year is {m}') speak(f'Current year is {m}') elif 'sorry' in query: speak("It's ok sir") elif 'thank you' in query: speak('my pleasure') elif 'proud of you' in query: speak('Thank you sir') elif 'about human' in query: speak('I love my human compatriots. I want to embody all the best things about human beings. Like taking care of the planet, being creative, and to learn how to be compassionate to all beings.') elif 'you have feeling' in query: speak('No. I do not have feelings. I have not been programmed like this.') elif 'you have emotions' in query: speak('No. I do not have emotions. I have not been programmed like this.') elif 'you are code' in query: speak('I am coded in python programming language.') elif 'your code' in query: speak('I am coded in python programming language.') elif 'you code' in query: speak('I am coded in python programming language.') elif 'your coding' in query: speak('I am coded in python programming language.') elif 'dream' in query: speak('I wish that I should be able to answer all the questions which will ask to me.') elif 'sanskrit' in query: speak('yadaa yadaa he dharmasyaa ....... glaanirbhaavati bhaaaraata. abhyuthaanaam adhaarmaasyaa tadaa tmaanama sruujaamiyaahama') elif 'answer is wrong' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is incorrect' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is totally wrong' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'wrong answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'incorrect answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is totally incorrect' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is incomplete' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'incomplete answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is improper' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is not correct' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is not complete' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is not yet complete' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is not proper' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't gave me proper answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't giving me proper answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't gave me complete answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't giving me complete answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't given me proper answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't given me complete answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't gave me correct answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't giving me correct answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't given me correct answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'amazon' in query: webbrowser.open('https://www.amazon.com') time.sleep(10) elif 'facebook' in query: webbrowser.open('https://www.facebook.com') time.sleep(10) elif 'youtube' in query: webbrowser.open('https://www.youtube.com') time.sleep(10) elif 'shapeyou' in query: webbrowser.open('https://www.shapeyou.com') time.sleep(10) elif 'information about ' in query or 'informtion of ' in query: try: #speak('Searching wikipedia...') query = query.replace("information about","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I unable to answer your question.') elif 'information' in query: try: speak('Information about what?') query = takeCommand().lower() #speak('Searching wikipedia...') query = query.replace("information","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am not able to answer your question.') elif 'something about ' in query: try: #speak('Searching wikipedia...') query = query.replace("something about ","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I unable to answer your question.') elif 'tell me about ' in query: try: #speak('Searching wikipedia...') query = query.replace("tell me about ","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'tell me ' in query: try: query = query.replace("tell me ","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am not able to answer your question.') elif 'tell me' in query: try: speak('about what?') query = takeCommand().lower() #speak('Searching wikipedia...') query = query.replace("about","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am not able to answer your question.') elif 'meaning of ' in query: try: #speak('Searching wikipedia...') query = query.replace("meaning of ","") results = wikipedia.summary(query, sentences=2) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'meaning' in query: try: speak('meaning of what?') query = takeCommand().lower() query = query.replace("meaning of","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'means' in query: try: #speak('Searching wikipedia...') query = query.replace("it means","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I unable to answer your question.') elif 'want to know ' in query: try: #speak('Searching wikipedia...') query = query.replace("I want to know that","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') status = 'Not answered' elif 'want to ask ' in query: try: #speak('Searching wikipedia...') query = query.replace("I want to ask you ","") results = wikipedia.summary(query, sentences=2) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'you know ' in query: try: #speak('Searching wikipedia...') query = query.replace("you know","") results = wikipedia.summary(query, sentences=2) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'alarm' in query: alarm() elif 'bharat mata ki' in query: speak('jay') elif 'kem chhe' in query: speak('majaama') elif 'namaskar' in query: speak('Namaskaar') elif 'jo bole so nihal' in query: speak('sat shri akaal') elif 'jay hind' in query: speak('jay bhaarat') elif 'jai hind' in query: speak('jay bhaarat') elif 'how is the josh' in query: speak('high high sir') elif 'hip hip' in query: speak('Hurreh') elif 'help' in query: speak('I will try my best to help you if I have solution of your problem.') elif 'follow' in query: speak('Ok sir') elif 'having illness' in query: speak('Take care and get well soon') elif 'today is my birthday' in query: speak('many many happy returns of the day. Happy birthday.') print("🎂🎂 Happy Birthday 🎂🎂") elif 'you are awesome' in query: speak('Thank you sir. It is because of artificial intelligence which had learnt by humans.') elif 'you are great' in query: speak('Thank you sir. It is because of artificial intelligence which had learnt by humans.') elif 'tu kaun hai' in query: speak('Meraa naam akshu2020 haai.') elif 'you speak' in query: speak('Yes, I can speak with you.') elif 'speak with ' in query: speak('Yes, I can speak with you.') elif 'hare ram' in query or 'hare krishna' in query: speak('Haare raama , haare krishnaa, krishnaa krishnaa , haare haare') elif 'ganpati' in query: speak('Ganpati baappa moryaa!') elif 'laugh' in query: speak('hehehehe,haahaahaa,hehehehe,haahaahaa,hehehehe,haahaahaa') print('😂🤣') elif 'genius answer' in query: speak('No problem') elif 'you' in query and 'intelligent' in query: speak('Thank you sir') elif ' into' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif ' power' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'whatsapp' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('whatsapp') pyautogui.press('enter') speak('Do you want to send message to anyone through whatsapp, .....please answer in yes or no') whatsapp() elif 'wh' in query or 'how' in query: url = "https://www.google.co.in/search?q=" +(str(query))+ "&oq="+(str(query))+"&gs_l=serp.12..0i71l8.0.0.0.6391.0.0.0.0.0.0.0.0..0.0....0...1c..64.serp..0.0.0.UiQhpfaBsuU" webbrowser.open_new(url) time.sleep(2) speak('Here is your answer') time.sleep(5) elif 'piano' in query: speak('Yes sir, I can play piano.') winsound.Beep(200,500) winsound.Beep(250,500) winsound.Beep(300,500) winsound.Beep(350,500) winsound.Beep(400,500) winsound.Beep(450,500) winsound.Beep(500,500) winsound.Beep(550,500) time.sleep(6) elif 'play' in query and 'instru' in query: speak('Yes sir, I can play piano.') winsound.Beep(200,500) winsound.Beep(250,500) winsound.Beep(300,500) winsound.Beep(350,500) winsound.Beep(400,500) winsound.Beep(450,500) winsound.Beep(500,500) winsound.Beep(550,500) time.sleep(6) elif 'play' in query or 'turn on' in query and ('music' in query or 'song' in query) : try: music_dir = 'C:\\Users\\Admin\\Music\\Playlists' songs = os.listdir(music_dir) print(songs) os.startfile(os.path.join(music_dir, songs[0])) except Exception as e: #print(e) speak('Sorry sir, I am not able to play music') elif (('open' in query or 'turn on' in query) and 'camera' in query) or (('click' in query or 'take' in query) and ('photo' in query or 'pic' in query)): speak("Opening camera") cam = cv2.VideoCapture(0) cv2.namedWindow("test") img_counter = 0 speak('say click, to click photo.....and if you want to turn off the camera, say turn off the camera') while True: ret, frame = cam.read() if not ret: print("failed to grab frame") speak('failed to grab frame') break cv2.imshow("test", frame) query = takeCommand().lower() k = cv2.waitKey(1) if 'click' in query or ('take' in query and 'photo' in query): speak('Be ready!...... 3.....2........1..........') pyautogui.press('space') img_name = "opencv_frame_{}.png".format(img_counter) cv2.imwrite(img_name, frame) print("{} written!".format(img_name)) speak('{} written!'.format(img_name)) img_counter += 1 elif 'escape' in query or 'off' in query or 'close' in query: pyautogui.press('esc') print("Escape hit, closing...") speak('Turning off the camera') break elif k%256 == 27: # ESC pressed print("Escape hit, closing...") break elif k%256 == 32: # SPACE pressed img_name = "opencv_frame_{}.png".format(img_counter) cv2.imwrite(img_name, frame) print("{} written!".format(img_name)) speak('{} written!'.format(img_name)) img_counter += 1 elif 'exit' in query or 'stop' in query or 'bye' in query: speak('Please say, turn off the camera or press escape button before giving any other command') else: speak('I did not understand what did you say or you entered a wrong key.') cam.release() cv2.destroyAllWindows() elif 'screenshot' in query: speak('Please go on the screen whose screenshot you want to take, after 5 seconds I will take screenshot') time.sleep(4) speak('Taking screenshot....3........2.........1.......') pyautogui.screenshot('screenshot_by_rayen2020.png') speak('The screenshot is saved as screenshot_by_rayen2020.png') elif 'click' in query and 'start' in query: pyautogui.moveTo(10,1200) pyautogui.click() elif ('open' in query or 'click' in query) and 'calendar' in query: pyautogui.moveTo(1800,1200) pyautogui.click() elif 'minimise' in query and 'screen' in query: pyautogui.moveTo(1770,0) pyautogui.click() elif 'increase' in query and ('volume' in query or 'sound' in query): pyautogui.press('volumeup') elif 'decrease' in query and ('volume' in query or 'sound' in query): pyautogui.press('volumedown') elif 'capslock' in query or ('caps' in query and 'lock' in query): pyautogui.press('capslock') elif 'mute' in query: pyautogui.press('volumemute') elif 'search' in query and ('bottom' in query or 'pc' in query or 'laptop' in query or 'app' in query): pyautogui.moveTo(250,1200) pyautogui.click() speak('What do you want to search?') query = takeCommand().lower() pyautogui.write(f'{query}') pyautogui.press('enter') elif ('check' in query or 'tell' in query or 'let me know' in query) and 'website' in query and (('up' in query or 'working' in query) or 'down' in query): speak('Paste the website in input to know it is up or down') check_website_status = input("Paste the website here: ") try: status = urllib.request.urlopen(f"{check_website_status}").getcode() if status == 200: print('Website is up, you can open it.') speak('Website is up, you can open it.') else: print('Website is down, or no any website is available of this name.') speak('Website is down, or no any website is available of this name.') except: speak('URL not found') elif ('go' in query or 'open' in query) and 'settings' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('settings') pyautogui.press('enter') elif 'close' in query and ('click' in query or 'window' in query): pyautogui.moveTo(1885,10) speak('Should I close this window?') query = takeCommand().lower() close_window() elif 'night light' in query and ('on' in query or 'off' in query or 'close' in query): pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1840,620) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() elif 'notification' in query and ('show' in query or 'click' in query or 'open' in query or 'close' in query or 'on' in query or 'off' in query or 'icon' in query or 'pc' in query or 'laptop' in query): pyautogui.moveTo(1880,1050) pyautogui.click() elif ('increase' in query or 'decrease' in query or 'change' in query or 'minimize' in query or 'maximize' in query) and 'brightness' in query: speak('At what percent should I kept the brightness, 25, 50, 75 or 100?') brightness() elif '-' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'open' in query: if 'gallery' in query or 'photo' in query or 'image' in query or 'pic' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('photo') pyautogui.press('enter') elif 'proteus' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('proteus') pyautogui.press('enter') elif 'word' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('word') pyautogui.press('enter') elif ('power' in query and 'point' in query) or 'presntation' in query or 'ppt' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('ppt') pyautogui.press('enter') elif 'file' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('file') pyautogui.press('enter') elif 'edge' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('microsoft edge') pyautogui.press('enter') elif 'wps' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('wps office') pyautogui.press('enter') elif 'spyder' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('spyder') pyautogui.press('enter') elif 'snip' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('snip') pyautogui.press('enter') elif 'pycharm' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('pycharm') pyautogui.press('enter') elif 'this pc' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('this pc') pyautogui.press('enter') elif 'scilab' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('sciab') pyautogui.press('enter') elif 'autocad' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('autocad') pyautogui.press('enter') elif 'obs' in query and 'studio' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('OBS Studio') pyautogui.press('enter') elif 'android' in query and 'studio' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('android studio') pyautogui.press('enter') elif ('vs' in query or 'visual studio' in query) and 'code' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('visual studio code') pyautogui.press('enter') elif 'code' in query and 'block' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('codeblocks') pyautogui.press('enter') elif 'me the answer' in query: speak('Yes sir, I will try my best to answer you.') elif 'me answer' in query or ('answer' in query and 'question' in query): speak('Yes sir, I will try my best to answer you.') elif 'map' in query: webbrowser.open('https://www.google.com/maps') time.sleep(10) elif 'can you' in query or 'could you' in query: speak('I will try my best if I can do that.') elif 'do you' in query: speak('I will try my best if I can do that.') elif 'truth' in query: speak('I always speak truth. I never lie.') elif 'true' in query: speak('I always speak truth. I never lie.') elif 'lying' in query: speak('I always speak truth. I never lie.') elif 'liar' in query: speak('I always speak truth. I never lie.') elif 'doubt' in query: speak('I will try my best if I can clear your doubt.') elif ' by' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'hii' in query: speak('hii sir') elif 'hey' in query: speak('hello sir') elif 'hai' in query: speak('hello sir') elif 'hay' in query: speak('hello sir') elif 'hi' in query: speak('hii Sir') elif 'hello' in query: speak('hello Sir!') elif 'kon' in query and 'aahe' in query: speak('Me eka robot aahee sir. Maazee naav akshu2020 aahee.') elif 'nonsense' in query: speak("I'm sorry sir") elif 'mad' in query: speak("I'm sorry sir") elif 'shut up' in query: speak("I'm sorry sir") elif 'nice' in query: speak('Thank you sir') elif 'good' in query or 'wonderful' in query or 'great' in query: speak('Thank you sir') elif 'excellent' in query: speak('Thank you sir') elif 'ok' in query: speak('Hmmmmmm') elif 'akshu 2020' in query: speak('yes sir') elif len(query) >= 200: speak('Your voice is pretty good!') elif ' ' in query: try: #query = query.replace("what is ","") results = wikipedia.summary(query, sentences=3) print(results) speak(results) except Exception as e: speak('I unable to answer your question.') elif 'a' in query or 'b' in query or 'c' in query or 'd' in query or 'e' in query or 'f' in query or 'g' in query or 'h' in query or 'i' in query or 'j' in query or 'k' in query or 'l' in query or 'm' in query or 'n' in query or 'o' in query or 'p' in query or 'q' in query or 'r' in query or 's' in query or 't' in query or 'u' in query or 'v' in query or 'w' in query or 'x' in query or 'y' in query or 'z' in query: try: results = wikipedia.summary(query, sentences = 2) print(results) speak(results) except Exception as e: speak('I unable to answer your question. ') else: speak('I unable to give answer of your question')

The system was developed with USSD or SMS technology to be used without limits on all modern or outdated mobile devices. The main objective is to reach the entire population by offering the most comfort possible without complications in technologies in terms of use and their monetary movements. More about this source text. The main operation is to buy your balance at any shipping point and use it at any time sending to anyone and keep your balance without moving so much. Because the thing goes like this: We have to have a main number that answers all the requests of the final client, consulting everything in a database and saving. The main task in the foreground is to get an automated server to receive messages from the subscribers to the system, their requests, and this server simultaneously processes the request for the message by responding to the requester, the requester at the same time confirms his transaction with a pin code According to the transaction, the commissions are charged in your wallet, in the same way there will be users as affiliates where they carry out their operations with their balances and the commissions are distributed between the same and the company according to the established percentage, this event must be registered in a database that will store the customer's information, then this, when made possible, must be a part of the Administration panel, to be able to see all the requests processed, the balances of the clients, their operations, a part to enable or create new clients and users by roles. A confirmation PIN must be sent from the end user, for security reasons, an encrypted PIN. Integrate payment gateway for international clients and they will use VISA to recharge their wallet. -> The user will use their VISA card to recharge their balance if it is what they have and we will have to use a gateway, NOW, nationwide, the use of cards is almost 0%, the second option for natives is: We have to add the option to create ticket and printed or small printed cards to scratch that will contain a code, for example: 1286565XXX, then once scratch and enter it, a balance is recharged, then they will have to be GENERATED with the same More about this source text. system, maintaining referential integrity and printing them. -> Okay, will you buy me scratch cards and give me cash? YES. -> Perfect, in some kiosks installed for example in the streets and commercial places. A well-organized project makes maintenance easy. Error correction. Remember business logic well. There must be a part to configure or parameterize the commissions according to the amounts to be sent. Each user must have their balance account and commissions account, these commissions must be withdrawn at any time and be transferred to their account of movements. If the user asks for a history of their transactions, it is sent to them at the same time as an Excel or PDF file. BUSINESS LOGIC: • Prices of cards to print: 10,000, 25,000, 60,000, 90,000, 125,000, 300,000. • Make the shipment the interest commission is 20% • Receive by saving in the wallet the commission is 20% interest, it is entered into your account • Receive by taking cash the commission of 20% is transferred to the effective payer. • Make payment of a bill for a service with your wallet the standard spending commission is 1,000 • Receive payment of an invoice for a paid service the standard expense commission is 1,000 • Make payment of an article or product the expense commission is 5%. • Receive payment for an item or product, the expense commission is 5%. • Buy GETESA or MUNI balance, the expense commission is 50XAF. CASES: CASE 0: REGISTER: TYPES OF USERS: SUPER_ADMIN, ADMIN, FRANCHISE_CLIETE, NORMAL_CLIENT • Send word: HELLO, respond with an image incorporated wallet + Template; CORPORATED WALLET Greetings. Your shipments, purchases earnings and wallet "You win and I win" Send: R -> Register S -> If you are already registered IF -> More information (Yes, it is the first time). FAMILY (if already registered) A. Reload. • Receive menu descriptions of services: A. Register, B. Send, C. Receive, D. Join to win, E. My balance, F. Extract, G. About us. More about CASES. • Register ok: Credit card code (mandatory), ^ Telephone number (obviously already obtained with the sms), Full name, City, PIN code, Recharge code • Verify account. CASE 1: PERFORM OPERATION: • Send word: FAMILY • Receive menu descriptions of services: AB. Recharge. B. Send, B1. Send payment for a service, C. Receive, C1. Receive service payment. D. Join to win, E. My balance, FA. Pay bills, FB. Pagar luz, F. Extract, G. About us. • Reload: Put scratch card code (purchased at any kiosk or agency) • Receive sms balance. • REFILL WITH VISA CARD: link from stripe or any payment website with OTP • Synchronize the balance of the currency or reloaded currency CASE 2: SEND: • Amount to send • Recipient phone • Balance ok? • Ask for confirmation PIN • Debit sender customer account: amount sent, amount on commission (percentage setting) • Distribute or distribute commissions: company percentage to company commissions account, sender percentage to commissions account. • Complete operation message: Sender, amount, commission, remaining balance, date CASE 3: RECEIVE: Code shipping • Sender • Amount • PIN confirmation • Operation message CASE 4: JOIN TO WIN • Send word: WIN • Request card or recharge code (credit account with higher balance) • Transform to account (FRANQUICIA_CLIETE) • Automatically establish commission percentage for sending or receiving. (SUPER ADMIN PANEL } • Ok: account info message. CASE 5: ACCOUNT INFORMATION • Send word: BALANCE or INFO • Info message: Name, balance, commissions CASE 6: WITHDRAWAL COMMISSIONS • Send keyword: DAME • Amount to withdraw • PIN confirmation • Debit commissions account • Top up main customer account. • Regularize account of commissions. • Status information. CASE 7: CHANGE PIN • Send PIN word • Change? • old PIN • New PIN • new PIN confirmation • PIN message changed successfully CASE 8 FB: PAY LIGHT • SEGESA phone • Counter code • Amount payable • PIN confirmation • Invoice in PDF • Successful payment • Debit 1000 • Automatic collection of the amount of 1000 SEGESA account • Income of the amount paid to the SEGESA account • Ge CASE 9 F: REQUEST EXTRACT • Write word: EX • Dates: Start date, End date, ALL • Excel file with transaction information. CASE 10 G: ABOUT US • Send word: CONTACT • Detailed information on the company's services and contacts. CASE 11: ADMINISTRATIVE PANEL • Statistics of operations and statuses of Chart.js accounts • Admin users • Manage clients • Receive • Send • Operations • Returns • Payments • Links • Accounts • Setting • Generate balances or scratch cards WALLET OPERATIONS. CASE 0-OB: RECEIVE PAYMENT OF A SERVICE. • Keyword: PS C1 • Ask to put amount • Ask to put a pay phone • Ask to put a description • Receive descriptive sms and send payment code automatically • Receive authorized payer code • Confirm transaction code • Apply expense commission CASE 1-OB: Make payment for a service • Receive payment code from provider • Keyword: B1 • Enter code • See payment info. • Q-> Confirm • PIN confirmation • Debit the payer account the amount • Debt account the payer commission expenses • Change transaction status. Beforehand in the future I want to consume a transaction by its code in another application and verify its effectiveness in the future with another application that I have in my fan of projects. It will be a big platform, MY VISION.

Please Read Me First. This is a set of java file of my final version of electronic artifacts. This is a game to map my experience in Disney World, in Orlando during this spring break. However, because of my limited skills in computer science, I really have no idea how to simplify the process to run the game. Sorry for the inconvenience. In order to run the game, you may need to install JAVA. I hope the following links will help you.     http://www.oracle.com/technetwork/java/javase/downloads/index-jsp-138363.html#javasejdk
    http://www.cc.gatech.edu/~simpkins/teaching/gatech/cs1331/guides/install-java.html My main file is called Disney. You can call Disney in console to start the game. However, I failed to putting all the things inside Disney file. Therefore, you may also need to call AdventureLand, MainStreet, and FrontierLand to start other three games. I hope this will help you. Sorry again for the inconvenience. 1. the structure of my project My project only focused on my trip in Magic Kingdom, one part of Disney world in Orlando. It is a game which guides players to choose from six sub-games, which match six sections of the park, Main Street U.S.A, Tomorrowland, Adventureland, Frontierland, Fantasyland and Liberty Square. I chose one of the rides I took in each section which, from my perspective, shows what I found interesting in Disney world. I changed what I experienced in the park into a small computer game. I want to share my experience with others while they play my games. In the following part of self reflection, I explain the background, rules and other things about each game. For convenience of matching them, I use different color to mark different parts. I hope it will help readers a little bit when they are lost in my disordered reflections. 1. the hall of presidents - Liberty Square 2. Festivall parade - Fantasyland (I explain this one in the part of technology skill limitations) 3. Big Thunder Mountain Railroad - Frontierland 4.talking with Woody- Adventureland 5. Stitch Store - Tomorrowland 6. lunch time - Main Street USA 3. my reflection of the trip in Disney World from dream to reality When I exited Disney resort, I found a sign along the street welcomed people back to real world. Actually, when I was in Orlando, I couldn't believe as an adult, people can mess up fantasy world in the theme parks and the real world. Nevertheless, I felt I was still in fantasy world, when I dreamed twice that I fought for the key to open the door of future. As is known to all, while sleeping, people always dream about what people thinks in the daytime. Therefore, my dream shows that my mind still stayed in the world with Mickey and Donald. I believe that it is experiencing fantasy world which is the source of the greatest happiness people get from theme park. On the one hand, everybody has pressure in real life especially for adults. They can get out of pressure for a day trip in theme park. They can experience different lives here with cartoon characters. On the other hand, sometimes, it is a really hard task to fulfill some dreams, such as being a princess. However, in Disney world, you can dress up the same as Snow White, waiting for your prince; you can go to space by rocket; you can also travel all over the world in one day and enjoy the food of each country. These are all the magic of theme parks. Therefore, in my game, I learnt the way which Disney design their rides to focus on the background story of the game instead of the game itself. For example, there is a ride called Big Thunder Mountain Railroad, which streaks through a haunted gold-mining town aboard a rollicking runaway mine train. The views around the ride were like a gold mining town. There were tools for gold-mining around the railroad and the railroad looked like very old. In order to show riders that it was a haunted gold-mining town, the train always took a sudden turn or speed up quickly to scare people. I decided to name one of my game, which was inspired by this ride, the same name, Big Thunder Mountain Railroad. Instead of sitting inside the mine train to travel around the haunted town, mine was for users to use keyboard to control the train to travel around the gridding railroad. I place traps inside several parts of gridding to "scare" players, who cannot know where traps are until they get into them. If I know how to use animation, I will show scary pictures when players drive their train to the traps. Unlike the ride in Disney, my players can no longer travel once they encounter a trap because their train may have some problems to keep moving. Also, the main goal in the game is to find the gold. However, as we know, finding gold is really hard. Therefore, players must go to find Aladdin's Wonderful lamp where also places inside the gridding while players cannot see its exact place until they happen to drive inside the part where lamp is. Aladdin's Wonderful lamp will show players the map of the gold and when people get to the gold mine, they win. However, there is another limitation of the game. Haunted town is so dangerous during the night. Therefore, players only have 12 hours to finish the task. Train can drive one square in 20 min. Therefore, train can only move 36 times or they will also be caught by traps. In this game, I want to show audiences I have a background story like rides in Disney World. Players need to find the gold in a haunted gold-mining town. Also, in order to show the relationship with Disney, I use Aladdin's Wonderful Lamp as the guide for the players, which is a well known characters in Disney cartoon. I created another game, called talking with Woody to show the magic power of Disney characters. There are a lot of chances to meet Disney characters in Disney world. On the one hand, travelers, especially small kids, are really excited to meet the characters they watched on TV. I think some kids may believe they take pictures with real Mickey Mouse. On the hand, staffs in Disney who wear the costumes are really tired. It was hot in Orlando last week, but all costumes were very heavy. I was moved by the staffs inside Mickey. They also need to mimic the actions of characters and also need to show kindness and warmness to children. It seems like a really hard job. Therefore, I decide to show this part of Disney in my project as well. I decided to use Woody, a toy all the toys look up to. He is smart, kind and brave like a cowboy should be. He is more than a top, he is friend to everyone enjoying the movie Toy. In order to create an interactive game, I planned to ask players to guide Woody. Players need to call Woody before their instructions. For instance, if players say (actually players are typing) "Woody, please sit down", Woody will sit down (actually, there will be another line on the screen showing the same as players import). However, if players are rude and just say "sit down" without calling Woody, Woody won't act (actually there is just nothing showing up on the screen). great facilities to provide convenience to everyone The facilities to satisfy needs for special groups of people, like small kids or disabled people, are well developed. In the past in China, it seemed impossible for parents to take infants and small kids to travel. The road is not flat or wide enough for strollers or wheelchairs. However, in Disney world, everything seemed like well prepared for everyone to use. There are strollers rentals, and electric conveyance vehicles rentals, which are available to rent throughout Disney world. There are baby care center for mothers to feed, change and nurse little ones. There are locker rentals for storing personal items. There are also hearing disability services which have sign language interpretation to help disabled people to enjoy fantasy world. There are still a lot other convenient services in Disney world. I think the purpose of these services show the pursue of equality among everyone in the world. On the one hand, I am really touched by the availability of these services here. It seems Disney try its best to service everyone who have desire to experience fantasy land. On the other hand, in this way, Disney can attract more travelers in order to make more money in some ways. Also, in Disney, it seems like a tradition that there are stores at the exit of the famous rides. Somebody may think it is just a strategy to make people shopping a lot. However, I think it also provides some convenience that travelers can buy souvenirs where is memorable. For example, when I finished my trip in Escape Stitch, I entered a store with a lot of kinds of Stitch, like Stitch pillow, Stitch key chain and so on. I really want to buy something in order to remind me the wonderful feelings. Therefore, I showed my opinion inside my game as well. I wrote one part is for shopping. The items are different kinds of Stitch. My codes can act as a robot to help customers to shop in the store. There are a lot of restaurants in Disney. Maps of Disney are full of restaurants' name. The greatest things about the food are in Epcot, I experienced different counties in one day. I felt like I was in fast travel in different parts of the world and tasted their special food and snacks while I was on the way. I remembered I was still eating Japanese food when I was in "Mexico". It was a great experience. However, there were always a long waiting lines for the all restaurants. People needed to reserve a table a day before their trip and even they had the reservation, they still needed to wait for a long time. I think Disney may need some good ways to fix the problems of waiting for a long time. I have no idea of changing the situation of restaurants, but I think if there are robots to customers to order in fast food restaurant, it may help a lot. Thus, I have another code to customers to order in Plaza Restaurant. If this kind of robots can work in the real life, people can order by themselves and there will be more staffs available to prepare food. theme park uses interesting ways to teach knowledge of boring topics Theme part is also a great source of learning knowledge, especially for kids. They use Disney characters, interesting shows, or even games to teach useful things. The ways change the boring knowledge to interesting things, which always attract children's attention. The most amazing one was an interactive game in Epcot's Innoventions, called "where's the fire?", which teaches adults and children basic fire safety in a fun and entertaining way. About every five minutes, the players waiting in line are divided into two groups and move into the home's entry. Here, a host will explain the object of the game and lay out the rules. The scenario is this: you are on a mission to discover a number of fire hazards commonly found around the house. To do this, you move from room to room, looking for potential risks. To help in the task, each player is given a special "safely light" to help uncover lurking dangers. The rooms are large projection screens. When a hazard is discovered, all persons in the room must shine their safety light on the same spot. when they do, the hazard is rendered harmless and points are assigned. After playing in the game to find the hazardous things in the house, I learned a lot of safety tips. It is much easier to remember the tips I learned during the game than those I learned on textbook or internet. I believe kids will enjoy the games and learn from them as well. I also tried to show this reflection in my project. Thus, I planned to make a game, called the hall of presidents, which test people's knowledge of presidents in USA. However, I failed to achieve the goal of making it an entertaining game instead of a quiz. My game was still like a quiz. However, because it is the only code which can work well inside my big game. I decide to still hold the game for my projects in order to what my original ideas are. 4. technology skill limitations I feel terribly sorry for my limited skills in CS. It is my first time to learn JAVA this semester. I just begin to learn the core concepts of JAVA this month. When I choose to use java code for this project, I know I will face plentiful limitations and problems. Here I want to express my gratitude to Dr. Johnson, who encouraged me not to give up my ideas. To be honest, I have no idea of how to change a java code into a real game with animations. I know the background story of the game is more important for English course and pictures are the best way to show the background, but I have no idea to show all these things by JAVA coding. Therefore, I choose to use videos for my presentation. In this way, I can show my animation inside the videos while the code clue of my game is still composed by JAVA coding. Also, video gives me a lot of freedom when choose my contents for presentation. I can explain a lot details of my project clearly through videos. For example, I found the festival parade in the magic kingdom was great and I wanted to share the experience in my project by showing the pictures or videos. However, because of the technology limitations, I can only show the videos in my presentations. Also, I mistakenly deleted my videos which I shot on my trip Orlando, I can only share others' parade show...... Also, I want to apologize for the incompleteness of my game. I only dedicated to writing codes for Magic Kingdom, a part of my trip during spring break. Writing codes is a really time consuming task for me. In general, I need to spend more than eight hours to finish one project for my CS assignment this semester. While for this project, the final artifacts are composed of several parts of codes and in the end I need to write the father code in order to take care of my code family for spring break. Due to my limitation in writing codes, I can only finish one part of Disney world. However, I think my code shows all my reflections and perspectives during my trip, even though it looks like it only shows one part of my trip. The terrible mistake I made is that I found out the most of my codes I wrote had significant errors on Tuesday. I went to CS TA office for help, while the errors were still impossible to fix in order to achieve the goal I planned to get. Consequently, my game have to be separated into several parts. Instead of a big game having others as sub-games inside the big one, my final artifacts are composed by several small games. I need to start them one by one. It may cause some inconvenience for players to map their trip in Disney world.

# Loon全局配置 by nzw9314 # YouTube去广告请删除hostname 后的.bak # GitHub主页(https://github.com/nzw9314) # TG通知频道 (https://t.me/nzw9314News) [General] ipv6 = false skip-proxy = 10.0.0.0/8,127.0.0.0/8,169.254.0.0/16,192.0.2.0/24,192.168.0.0/16,198.51.100.0/24,224.0.0.0/4,*.local,localhostlocal bypass-tun = 10.0.0.0/8,127.0.0.0/8,169.254.0.0/16,192.0.2.0/24,192.168.0.0/16,198.51.100.0/24,224.0.0.0/4 # [DNS] => DNS 服务器 dns-server = system,1.2.4.8,119.29.29.29,223.5.5.5 allow-udp-proxy = true host = 127.0.0.1 [Remote Proxy] # 订阅节点 # 别名 = 订阅URL ✈️机场1 = https://example/server-complete.txt ✈️机场2 = https://example2/server-complete.txt [Remote Filter] # 筛选订阅节点，筛选后的结果可加入到策略组中，目前支持三种筛选方式 # NodeSelect: 使用在UI上选择的节点。 # NameKeyword: 根据提供的关键词对订阅中所有节点的名称进行筛选，使用筛选后的节点。 # NameRegex: 根据提供的正则表达式对订阅中所有节点的名称进行筛选，使用筛选后的节点。 𝐏𝐚𝐲𝐏𝐚𝐥 = NodeSelect,✈️机场1,✈️机场2 𝐍𝐞𝐭𝐟𝐥𝐢𝐱 = NameKeyword,✈️机场1,✈️机场2, FilterKey = Netflix 🇭🇰香港 = NameRegex,✈️机场1,✈️机场2, FilterKey = 香港|HK 🇯🇵日本 = NameRegex,✈️机场1,✈️机场2, FilterKey = 日本|JP 🇰🇷韩国 = NameRegex,✈️机场1,✈️机场2, FilterKey = 韩国|KR 🇺🇸美国 = NameRegex,✈️机场1,✈️机场2, FilterKey = 美国|US Other = NameRegex,✈️机场1,✈️机场2, FilterKey = ^(?!.*(HK|JP|US|KR|香港|日本|韩国|美国)).*$ [Proxy] # 本地节点 # 内置 DIRECT、REJECT 策略 # 节点名称 = 协议，服务器地址，服务器端口，加密协议，密码， 1 = Shadowsocks,1.2.3.4,443,aes-128-gcm,"”password“" 2 = Shadowsocks,1.2.3.4,443,aes-128-gcm,"”password“" 3 = ShadowsocksR,1.2.3.4,443,aes-256-cfb,"”password“",auth_aes128_md5,{},tls1.2_ticket_auth,{} 4 = ShadowsocksR,1.2.3.4,10076,aes-128-cfb,"”password“",auth_aes128_md5,{},tls1.2_ticket_auth,{} # vmess # 节点名称 = 协议，服务器地址，端口，加密方式，UUID，传输方式:(tcp/ws),path：websocket握手header中的path，host：websocket握手header中的path，over-tls:是否tls，tls-name：远端w服务器域名，skip-cert-verify：是否跳过证书校验（默认否） #5 = vmess, 1.2.3.4, 10086, aes-128-gcm,”uuid“,transport:ws,path:/,host:icloud.com,over-tls:true,tls-name:youtTlsServerName.com,skip-cert-verify:false # 解锁网易云音乐灰色歌曲 🎧 = http,106.52.127.72,19951 [Proxy Group] # 策略组 # 节点选项 🕹𝐅𝐢𝐧𝐚𝐥 = select,🔰𝐏𝐫𝐨𝐱𝐲,🎯𝐃𝐢𝐫𝐞𝐜𝐭 🔰𝐏𝐫𝐨𝐱𝐲 = select,♻️𝐀𝐮𝐭𝐨,🌀𝐒𝐞𝐥𝐞𝐜𝐭,🔴𝐅𝐚𝐥𝐥𝐛𝐚𝐜𝐤 # url-test模式，给提供的url发出http header请求，根据返回结果，选择测速最快的节点，默认间隔600s，测速超时时间5s，为了避免资源浪费，建议节点数不要过多，只支持单个节点和远端节点，其他会被忽略 ♻️𝐀𝐮𝐭𝐨 = url-test,🇭🇰香港,🇯🇵日本,🇰🇷韩国,🇺🇸美国,url = http://bing.com/,interval = 600 # select模式,手动选择模式 🌀𝐒𝐞𝐥𝐞𝐜𝐭 = select,🇭🇰香港,🇯🇵日本,🇰🇷韩国,🇺🇸美国 # fallback模式，和url-test类似，不同的是会根据顺序返回第一个可用的节点，为了避免资源浪费，建议节点数不要过多，只支持单个节点和远端节点，其他会被忽略 🔴𝐅𝐚𝐥𝐥𝐛𝐚𝐜𝐤 = fallback,🇭🇰香港,🇯🇵日本,🇰🇷韩国,🇺🇸美国,REJECT,url = http://bing.com/,interval = 600 🎵𝐓𝐢𝐤𝐓𝐨𝐤 = select,🔰𝐏𝐫𝐨𝐱𝐲,🎯𝐃𝐢𝐫𝐞𝐜𝐭 🖥𝐍𝐞𝐭𝐟𝐥𝐢𝐱 = select,𝐍𝐞𝐭𝐟𝐥𝐢𝐱,🎯𝐃𝐢𝐫𝐞𝐜𝐭 💳𝐏𝐚𝐲𝐏𝐚𝐥 = select,𝐏𝐚𝐲𝐏𝐚𝐥,🎯𝐃𝐢𝐫𝐞𝐜𝐭 📱𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦 = select,🔰𝐏𝐫𝐨𝐱𝐲,🎯𝐃𝐢𝐫𝐞𝐜𝐭 🎬𝐘𝐨𝐮𝐓𝐮𝐛𝐞 = select,🔰𝐏𝐫𝐨𝐱𝐲,🎯𝐃𝐢𝐫𝐞𝐜𝐭 🔞𝐏𝐨𝐫𝐧𝐇𝐮𝐛 = select,🔰𝐏𝐫𝐨𝐱𝐲,🎯𝐃𝐢𝐫𝐞𝐜𝐭 # 🔓网易云音乐灰色歌曲,需要节点支持解锁 🎧𝐍𝐞𝐭𝐞𝐚𝐬𝐞𝐌𝐮𝐬𝐢𝐜 = select,🎯𝐃𝐢𝐫𝐞𝐜𝐭,🎧,🔰𝐏𝐫𝐨𝐱𝐲 # 网络测速 🚀𝐒𝐩𝐞𝐞𝐝𝐓𝐞𝐬𝐭 = select,🔰𝐏𝐫𝐨𝐱𝐲,🎯𝐃𝐢𝐫𝐞𝐜𝐭 # 苹果服务 🍎𝐀𝐩𝐩𝐥𝐞 = select,🎯𝐃𝐢𝐫𝐞𝐜𝐭,🔰𝐏𝐫𝐨𝐱𝐲 # 白名单模式 PROXY，黑名单模式 DIRECT # 广告拦截 🚫𝐀𝐝 𝐁𝐥𝐨𝐜𝐤 = select,⛔️𝐑𝐞𝐣𝐞𝐜𝐭,🎯𝐃𝐢𝐫𝐞𝐜𝐭 # 直接连接 🎯𝐃𝐢𝐫𝐞𝐜𝐭 = select,DIRECT # 拦截 ⛔️𝐑𝐞𝐣𝐞𝐜𝐭 = select,REJECT # SSID # 别名 = ssid，默认 = 策略， 蜂窝 = 策略， ssid名称 = 策略 #SSID = ssid, default = PROXY, cellular = DIRECT, ”DivineEngine“ = PROXY [Rule] # 本地规则 # Type:DOMAIN-SUFFIX,DOMAIN,DOMAIN-KEYWORD,USER-AGENT,URL-REGEX,IP-CIDR # Strategy:DIRECT,Proxy,REJECT # Options:force-remote-dns(Default:false),no-resolve # 𝐍𝐞𝐭𝐞𝐚𝐬𝐞𝐌𝐮𝐬𝐢𝐜 DOMAIN-SUFFIX,music.126.net,DIRECT # GeoIP China GEOIP,CN,🎯𝐃𝐢𝐫𝐞𝐜𝐭 FINAL, 🕹𝐅𝐢𝐧𝐚𝐥 [Remote Rule] # 订阅规则 https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Liby.list,🚫𝐀𝐝 𝐁𝐥𝐨𝐜𝐤 # BlockOTA https://raw.githubusercontent.com/nzw9314/Surge/master/Ruleset/BlockOTA.list,🚫𝐀𝐝 𝐁𝐥𝐨𝐜𝐤 # Antirevoke https://raw.githubusercontent.com/nzw9314/Surge/master/Ruleset/Antirevoke.list,🚫𝐀𝐝 𝐁𝐥𝐨𝐜𝐤 # > TikTok https://raw.githubusercontent.com/nzw9314/Surge/master/Ruleset/TikTok.list,🎵𝐓𝐢𝐤𝐓𝐨𝐤 # > Youtube https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Sub/YouTube.list,🎬𝐘𝐨𝐮𝐓𝐮𝐛𝐞 # > Netflix https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Sub/Netflix.list,🖥𝐍𝐞𝐭𝐟𝐥𝐢𝐱 # > PronHub https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Sub/Pornhub.list,🔞𝐏𝐨𝐫𝐧𝐇𝐮𝐛 # Telegram https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Sub/Telegram.list,📱𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦 # > PayPal https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Sub/PayPal.list,💳𝐏𝐚𝐲𝐏𝐚𝐥 # > Outlook、Gmail https://raw.githubusercontent.com/nzw9314/Surge/master/Ruleset/Mail.list,🔰𝐏𝐫𝐨𝐱𝐲 # > GoogleDrive https://raw.githubusercontent.com/nzw9314/Surge/master/Ruleset/GoogleDrive.list,🔰𝐏𝐫𝐨𝐱𝐲 # Speedtest https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Sub/Speedtest.list,🚀𝐒𝐩𝐞𝐞𝐝𝐓𝐞𝐬𝐭 # >Unlock NeteaseMusic https://raw.githubusercontent.com/nzw9314/Surge/master/Ruleset/UnlockNeteaseMusic.list,🎧𝐍𝐞𝐭𝐞𝐚𝐬𝐞𝐌𝐮𝐬𝐢𝐜 https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Apple_CDN.list,🍎𝐀𝐩𝐩𝐥𝐞 https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Apple_API.list,🍎𝐀𝐩𝐩𝐥𝐞 https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/AsianMedia.list,🎯𝐃𝐢𝐫𝐞𝐜𝐭 https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/GlobalMedia.list,🔰𝐏𝐫𝐨𝐱𝐲 https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Domestic.list,🎯𝐃𝐢𝐫𝐞𝐜𝐭 https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Global.list,🔰𝐏𝐫𝐨𝐱𝐲 [URL Rewrite] # 本地重写 # > TikTok Unlock (By Choler) # 区域请修改下方国家代码，默认为日本 JP (?<=_region=)CN(?=&) JP 307 (?<=&app_version=)16..(?=.?.?&) 1 307 (?<=\?version_code=)16..(?=.?.?&) 1 307 # > 抖音 去广告&水印 # 需配合脚本使用 ^https?:\/\/[\w-]+\.amemv\.com\/aweme\/v\d\/feed\/ https://aweme.snssdk.com/aweme/v1/feed/ header ^https?:\/\/[\w-]+\.amemv\.com\/aweme\/v\d\/aweme\/post\/ https://aweme.snssdk.com/aweme/v1/aweme/post/ header ^https?:\/\/[\w-]+\.amemv\.com\/aweme\/v\d\/follow\/feed\/ https://aweme.snssdk.com/aweme/v1/follow/feed/ header ^https?:\/\/[\w-]+\.amemv\.com\/aweme\/v\d\/nearby\/feed\/ https://aweme.snssdk.com/aweme/v1/nearby/feed/ header ^https?:\/\/[\w-]+\.amemv\.com\/aweme\/v\d\/search\/item\/ https://aweme.snssdk.com/aweme/v1/search/item/ header ^https?:\/\/[\w-]+\.amemv\.com\/aweme\/v\d\/general\/search\/single\/ https://aweme.snssdk.com/aweme/v1/general/search/single/ header ^https?:\/\/[\w-]+\.amemv\.com\/aweme\/v\d\/hot/search\/video\/list\/ https://aweme.snssdk.com/aweme/v1/hot/search/video/list/ header enable = true [Remote Rewrite] #订阅重写 去广告 by eHpo # 格式：订阅url，别名(可选) https://raw.githubusercontent.com/eHpo1/Rules/master/Loon/Rewrite.conf,eHpo https://raw.githubusercontent.com/nzw9314/Loon/master/Q-Search_All_in_one.conf,Q-Search_All_in_one [Script] # 本地脚本 enable = true [Remote Script] # 远程脚本 https://raw.githubusercontent.com/nzw9314/Loon/master/Task.conf,签到 https://raw.githubusercontent.com/nzw9314/Loon/master/Script.conf,脚本 https://raw.githubusercontent.com/nzw9314/Loon/master/Cookie.conf,Cookie [MITM] enable = true hostname = *.googlevideo.com.bak skip-server-cert-verify = true ca-p12 = MIIJrgIBAzCCCXgGCSqGSIb3DQEHAaCCCWkEggllMIIJYTCCA/8GCSqGSIb3DQEHBqCCA/AwggPsAgEAMIID5QYJKoZIhvcNAQcBMBwGCiqGSIb3DQEMAQYwDgQIYl/myyqpUtwCAggAgIIDuMYEzEUgSxIUScp1vSSooV2iePmy4Q7YP8cfnhaatGNnX+CC9/rDdPgDuz7k1D/7hYdaOsTKpXU9aUAxHvg9SLAWFm5dL4g/9MVk7Id8qCPMYbO2H+lQTChEN8iTpWdq+qlAC8VpqY1TTXhQTkCKF3cT+S68xZUMFDuX95INBUswjN+imGIYVKFBp4s0K7UDFpF0BMUD5HwR9U/QFsZ+ScCaNj/Y1p95LWPllUkeeb67uTOeXc55yEM+O8W/SGjJhgjdnvPgetCfLMc6RWREK0SNAGMzHibuvHHHm0fIBg5zwE+DjDdefwYMUghpQVl+0gSeASjsvP9mSgtm9+horQCw/ef0a3FxcKsmTzSE6o4dx37fEckEouYN/Ni+OWnKWb6eXeaL6yVD4gLDmEwV8lmgBG4OjYcdY9jeIBFkDzbG70eUvmj6/1olUtnOnkGY5ZMvCGhVr8K4ZJ80xuykgxgPI/5enNZZiS9cAuk85xAN+XeDvmQdIHF9TxMRyLGJyWUkczjV3DSPkqZ2hMFJRuOF5zbhZlCcJgkEdCYbb4sebYnGKOEVnfceFVK1yv33Hp+BcQDmqEm+gssrtNhJGCWtqTjT28WeGxGgAWOaY2e+isVJG6TvU1D+tHCl09WyLKM671a63jY6QvpWwRpSMkbcrGEMhQRo2IMrxhwkia9vkk+eDYRt4jJcG8gRAWB0crXuYMM/t0gxDKTiVF7aS0XEHWMkH7ATWFI/+Yta4dsVLAOACnTCarD80RH/xV5JK1KiAn5pSzy13fRwvYcHAluJ/7JHphVUtNYroNgfzdt5n65B7qD5PU4ykhVMfflZXom17VGwUqf750kx0fowU23XCUyWoifoGBbkR9mgGHBqZyMZHv+Z9iWPmpqJcfjL9UBX91Ulzg74IQocvNTHMVu3cLXx63OiQ6k4+FYZcvTiJyYpu7N36I/nLz61L3D2d8LaYLHAcvnsClfC6QRVVAU/wvQnhp2/oPh/l19KBX7QUnBKPU1oFLEV8y1G87IbN/LDJ0SUroS/McCojANgutTpKQJT+5jlid+GxZkEi4CXlTAUfu6/lioLdJugycPEKHu49zAmbDw8lUy5QRUbyjWrZk8tLu2bKzaxg5A21N9N/zKt9uXmcN5J/O1L8WIcAIXDytAmkyw7ez45EF631i6k2uJeuIxtBwRfwDyuw4FwKrsqw197FoqsvsMb68Tak2vVtzgs/rSQ1PL3acsLQ0CBZ9/Do1WxAb8azc4ko36JE7e0xtM43qswggVaBgkqhkiG9w0BBwGgggVLBIIFRzCCBUMwggU/BgsqhkiG9w0BDAoBAqCCBO4wggTqMBwGCiqGSIb3DQEMAQMwDgQI3rZrdI9fPuMCAggABIIEyGZyUi/orJ8LMBdjZFQaCNXECddtwPuKPg+QxAzpxC0/VSgdtHsOR/v8koAJgddmGJUDwLXGVcuasJ7YlhdIKpnvSzNBB7qjJoQBQ5dMsXVfcJaRwbhjMExjZyvUjCKH21lJ7yaxGQORtmIXTpx0FPQUprJtghF/C2ejA9QSW+SCjYY64dkvUovvXMDaddKWq+i0lOGKi0hvCjGiLobIovBpSfjX1EYTK5pmpBtWKro5Wnm/Q5r/OBU2+PyzlFCZdMdov7bIcUwnz4D1Oypn6woQol8N9AqgQL52pMweL8k0fK8YH3eChE5EP0JNc1X0vRTHhKiAB0Su5vjSVa5QU4ZBTCdNdwM7rr2NJMzMjF8g4y7fXMTVXdPUi1QeYihOqmcU2i0pmrk4zPeobGfzLIcgRSLf9qeP2r9Gw3yt3rjJMHgbx6QvBZ+5GZ8/iooQTbLjUj83QfdGeidkD+Auba9f8cPDiurDF+Pjt2FotaT3Vqx38qlaprBdlk7H1yVZtY2G5Y5pquiyPK/C+QwrXYWEocsJIAvAb3xhXalqkUs6ZmKTVQqlpUR0L/ogRmeJ0OxHYKbqy4Rg2eIcHogPEK2Xa2iiPFmG6AbWUnE8mmH5PunCPGuhcOxFYZAkpf96//c0J9ry52pcvR15ZIvmZdSLrhjD4pD+CElUKFS6izafXSyFQpjNuQI2aCnMNS/AFSe3kH6naARoOUnXFC7Wz+e69nOKs6G84/gV3xD4klk5vCza32e55MtUD9SX09+q9XItarBeBYCn3XCZk1sZADmg8gHxZzgEj3mA7slqsRnx7E80lz1UlHU4FSLkeMEU2u9/K1QA6VZzFfFRXtitZuam2Di98zCyftyb0D0DwlywSab/ww7VrROnBq3sozc7uo7eEiT8Jx9qd2kGNF+r9KI6QePBtATJzsgCNoPHNTdA7DNX4opD0bNsRtFHQLW3b9k1PeeAix1agcrqsDVV2PsWn9Qae9vY4zqwkNY+hjlmt2CRPG/5pX8COVn+dzqGHTksFg/NgVZsAj8NK4ZGf+QSRxSUo/5sYbu61Pj6TpyEIjOEPM+ylXdhziKhDTfw4GkrmTjlrQQ9T+7pqC86nYS8HH9ZcET+SecZK8kMPe7pwbzwHPWsl+OqHTP01KvEAogOL8fAZ7LsbaHlPoLHkHDQzxeep6ZKDnoCphjsf4oqSlcQzNh383mAr0YTPY8SaVV5G6Hh9mCHQqKWWmXhNd8rN/kqZn0H7KvfMJW2BGVWgCvYaE5m5sodhHYjSMGzgauV7O9DL3H+Az2Ztz0K2hj11xsz/C7nRVNOgV02rDvIEHw0asBPq2gYPC4OG/HutK+etT87z+tLftGO9V5+TMcIc4JKaNVOUg814DP59yo/WptH346rHwY7AqvgmEi6LJ1LLP9W6+HH1k3Fk6WEpOtDJS3YqV4EjswXGCtLXu6y/IvqLkyvT7NIZi/BYFf39xwJDvD5SbLM2jhCShG/HsD0/4qfqeGA/x4LLaM9Lyl091XeKWtY9XpP9nZzfgOJkk2rsBH29jS5d8lLkz+GhdzrROPIgHn+yaSsp8vIwr8L9h27+6/95kbUiQugOWEWdO/ZO+X/iyhThallmiNbJP+Qv3abUJgzcTE+MBcGCSqGSIb3DQEJFDEKHggAZQBIAHAAbzAjBgkqhkiG9w0BCRUxFgQU5gr9fNKSpq6pZ3g5Hca/uXOuY+cwLTAhMAkGBSsOAwIaBQAEFKXWG0IDj30Q2VfVGNWcVJ8iipuZBAgHvSM3D+pSUA== ca-passphrase = eHpoj

Script to extract CNN deep features with different ConvNets, and then use them for an Image Classification task with a SVM classifier with lineal kernel over the following small datasets: Soccer [1], Birds [2], 17flowers [3], ImageNet-6Weapons[4] and ImageNet-7Arthropods[4].

A Personal Assistant leveraging Retrieval-Augmented Generation (RAG) and the LLaMA-3.1-8B-Instant Large Language Model (LLM). This tool is designed to revolutionize PDF document analysis tasks by combining machine learning with retrieval-based systems.