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

Introduction Parkinson’s Disease is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting more than 10 million people worldwide. Parkinson’s is characterized primarily by the deterioration of motor and cognitive ability. There is no single test which can be administered for diagnosis. Instead, doctors must perform a careful clinical analysis of the patient’s medical history. Unfortunately, this method of diagnosis is highly inaccurate. A study from the National Institute of Neurological Disorders finds that early diagnosis (having symptoms for 5 years or less) is only 53% accurate. This is not much better than random guessing, but an early diagnosis is critical to effective treatment. Because of these difficulties, I investigate a machine learning approach to accurately diagnose Parkinson’s, using a dataset of various speech features (a non-invasive yet characteristic tool) from the University of Oxford. Why speech features? Speech is very predictive and characteristic of Parkinson’s disease; almost every Parkinson’s patient experiences severe vocal degradation (inability to produce sustained phonations, tremor, hoarseness), so it makes sense to use voice to diagnose the disease. Voice analysis gives the added benefit of being non-invasive, inexpensive, and very easy to extract clinically. Background Parkinson's Disease Parkinson’s is a progressive neurodegenerative condition resulting from the death of the dopamine containing cells of the substantia nigra (which plays an important role in movement). Symptoms include: “frozen” facial features, bradykinesia (slowness of movement), akinesia (impairment of voluntary movement), tremor, and voice impairment. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. Performance Metrics TP = true positive, FP = false positive, TN = true negative, FN = false negative Accuracy: (TP+TN)/(P+N) Matthews Correlation Coefficient: 1=perfect, 0=random, -1=completely inaccurate Algorithms Employed Logistic Regression (LR): Uses the sigmoid logistic equation with weights (coefficient values) and biases (constants) to model the probability of a certain class for binary classification. An output of 1 represents one class, and an output of 0 represents the other. Training the model will learn the optimal weights and biases. Linear Discriminant Analysis (LDA): Assumes that the data is Gaussian and each feature has the same variance. LDA estimates the mean and variance for each class from the training data, and then uses properties of statistics (Bayes theorem , Gaussian distribution, etc) to compute the probability of a particular instance belonging to a given class. The class with the largest probability is the prediction. k Nearest Neighbors (KNN): Makes predictions about the validation set using the entire training set. KNN makes a prediction about a new instance by searching through the entire set to find the k “closest” instances. “Closeness” is determined using a proximity measurement (Euclidean) across all features. The class that the majority of the k closest instances belong to is the class that the model predicts the new instance to be. Decision Tree (DT): Represented by a binary tree, where each root node represents an input variable and a split point, and each leaf node contains an output used to make a prediction. Neural Network (NN): Models the way the human brain makes decisions. Each neuron takes in 1+ inputs, and then uses an activation function to process the input with weights and biases to produce an output. Neurons can be arranged into layers, and multiple layers can form a network to model complex decisions. Training the network involves using the training instances to optimize the weights and biases. Naive Bayes (NB): Simplifies the calculation of probabilities by assuming that all features are independent of one another (a strong but effective assumption). Employs Bayes Theorem to calculate the probabilities that the instance to be predicted is in each class, then finds the class with the highest probability. Gradient Boost (GB): Generally used when seeking a model with very high predictive performance. Used to reduce bias and variance (“error”) by combining multiple “weak learners” (not very good models) to create a “strong learner” (high performance model). Involves 3 elements: a loss function (error function) to be optimized, a weak learner (decision tree) to make predictions, and an additive model to add trees to minimize the loss function. Gradient descent is used to minimize error after adding each tree (one by one). Engineering Goal Produce a machine learning model to diagnose Parkinson’s disease given various features of a patient’s speech with at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9. Compare various algorithms and parameters to determine the best model for predicting Parkinson’s. Dataset Description Source: the University of Oxford 195 instances (147 subjects with Parkinson’s, 48 without Parkinson’s) 22 features (elements that are possibly characteristic of Parkinson’s, such as frequency, pitch, amplitude / period of the sound wave) 1 label (1 for Parkinson’s, 0 for no Parkinson’s) Project Pipeline pipeline Summary of Procedure Split the Oxford Parkinson’s Dataset into two parts: one for training, one for validation (evaluate how well the model performs) Train each of the following algorithms with the training set: Logistic Regression, Linear Discriminant Analysis, k Nearest Neighbors, Decision Tree, Neural Network, Naive Bayes, Gradient Boost Evaluate results using the validation set Repeat for the following training set to validation set splits: 80% training / 20% validation, 75% / 25%, and 70% / 30% Repeat for a rescaled version of the dataset (scale all the numbers in the dataset to a range from 0 to 1: this helps to reduce the effect of outliers) Conduct 5 trials and average the results Data a_o a_r m_o m_r Data Analysis In general, the models tended to perform the best (both in terms of accuracy and Matthews Correlation Coefficient) on the rescaled dataset with a 75-25 train-test split. The two highest performing algorithms, k Nearest Neighbors and the Neural Network, both achieved an accuracy of 98%. The NN achieved a MCC of 0.96, while KNN achieved a MCC of 0.94. These figures outperform most existing literature and significantly outperform current methods of diagnosis. Conclusion and Significance These robust results suggest that a machine learning approach can indeed be implemented to significantly improve diagnosis methods of Parkinson’s disease. Given the necessity of early diagnosis for effective treatment, my machine learning models provide a very promising alternative to the current, rather ineffective method of diagnosis. Current methods of early diagnosis are only 53% accurate, while my machine learning model produces 98% accuracy. This 45% increase is critical because an accurate, early diagnosis is needed to effectively treat the disease. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. With an earlier diagnosis, much of this degradation could have been slowed or treated. My results are very significant because Parkinson’s affects over 10 million people worldwide who could benefit greatly from an early, accurate diagnosis. Not only is my machine learning approach more accurate in terms of diagnostic accuracy, it is also more scalable, less expensive, and therefore more accessible to people who might not have access to established medical facilities and professionals. The diagnosis is also much simpler, requiring only a 10-15 second voice recording and producing an immediate diagnosis. Future Research Given more time and resources, I would investigate the following: Create a mobile application which would allow the user to record his/her voice, extract the necessary vocal features, and feed it into my machine learning model to diagnose Parkinson’s. Use larger datasets in conjunction with the University of Oxford dataset. Tune and improve my models even further to achieve even better results. Investigate different structures and types of neural networks. Construct a novel algorithm specifically suited for the prediction of Parkinson’s. Generalize my findings and algorithms for all types of dementia disorders, such as Alzheimer’s. References Bind, Shubham. "A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction." International Journal of Computer Science and Information Technologies 6 (2015): n. pag. International Journal of Computer Science and Information Technologies. 2015. Web. 8 Mar. 2017. Brooks, Megan. "Diagnosing Parkinson's Disease Still Challenging." Medscape Medical News. National Institute of Neurological Disorders, 31 July 2014. Web. 20 Mar. 2017. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) Hashmi, Sumaiya F. "A Machine Learning Approach to Diagnosis of Parkinson’s Disease."Claremont Colleges Scholarship. Claremont College, 2013. Web. 10 Mar. 2017. Karplus, Abraham. "Machine Learning Algorithms for Cancer Diagnosis." Machine Learning Algorithms for Cancer Diagnosis (n.d.): n. pag. Mar. 2012. Web. 20 Mar. 2017. Little, Max. "Parkinsons Data Set." UCI Machine Learning Repository. University of Oxford, 26 June 2008. Web. 20 Feb. 2017. Ozcift, Akin, and Arif Gulten. "Classifier Ensemble Construction with Rotation Forest to Improve Medical Diagnosis Performance of Machine Learning Algorithms." Computer Methods and Programs in Biomedicine 104.3 (2011): 443-51. Semantic Scholar. 2011. Web. 15 Mar. 2017. "Parkinson’s Disease Dementia." UCI MIND. N.p., 19 Oct. 2015. Web. 17 Feb. 2017. Salvatore, C., A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri, M. Lopez, G. Arabia, M. Morelli, M.c. Gilardi, and A. Quattrone. "Machine Learning on Brain MRI Data for Differential Diagnosis of Parkinson's Disease and Progressive Supranuclear Palsy."Journal of Neuroscience Methods 222 (2014): 230-37. 2014. Web. 18 Mar. 2017. Shahbakhi, Mohammad, Danial Taheri Far, and Ehsan Tahami. "Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine."Journal of Biomedical Science and Engineering 07.04 (2014): 147-56. Scientific Research. July 2014. Web. 2 Mar. 2017. "Speech and Communication." Speech and Communication. Parkinson's Disease Foundation, n.d. Web. 22 Mar. 2017. Sriram, Tarigoppula V. S., M. Venkateswara Rao, G. V. Satya Narayana, and D. S. V. G. K. Kaladhar. "Diagnosis of Parkinson Disease Using Machine Learning and Data Mining Systems from Voice Dataset." SpringerLink. Springer, Cham, 01 Jan. 1970. Web. 17 Mar. 2017.

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http://www.seedpeer.me/details/6571637/TutsPlus---Modern-Testing-in-PHP-with-Codeception.html http://www.seedpeer.me/details/6095651/Tutsplus---Parallax-Scrolling-for-Web-Design.html http://www.seedpeer.me/details/6574591/TutsPlus---Say-Yo-to-Yeoman.html http://www.seedpeer.me/details/4811335/Tutsplus---Test-Driven-Development-in-Ruby.html http://www.seedpeer.me/details/6268980/TutsPlus-Test-Driven-Development-With-CoffeeScript-and-Jasmine.html http://www.seedpeer.me/details/6185755/TutsPlus---The-MVC-Mindser-Jeffery-Way---ICARUS.html http://www.seedpeer.me/details/5024493/TutsPlus---Venture-Into-Vim.html http://www.seedpeer.me/details/6286416/Tutsplus---Vim-for-Advanced-Users.html http://www.seedpeer.me/details/6585031/Tutsplus---WordPress-Hackers-Guide-to-the-Galaxy.html http://www.seedpeer.me/details/4848477/TutsPlus---Writing-Modular-JavaScript.html @giansalex Owner Author giansalex commented on 26 Feb 2018 • SOLID 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I am looking for learning phython with Joe Marini. It would be great if you post it. @FirstBoy1 FirstBoy1 commented on 25 May 2019 Can anyone provide this book "Getting started with Spring Framework: covers Spring 5" by " J Sharma (Author), Ashish Sarin ". Thanks in advance @okreka okreka commented on 31 May 2019 Can anyone provide "Windows Presentation Foundation Masterclass" course from Udemy. Thanks in advance @singhaltanvi singhaltanvi commented on 8 Aug 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. @kumarsreenivas051 kumarsreenivas051 commented on 9 Sep 2019 Can anyone provide "Programming languages A,B and C" course from Coursera. Thanks in advance. @BrunoMoreno BrunoMoreno commented on 11 Sep 2019 The link for the torrents in piratebay, now is .org to the correct url. @sany2k8 sany2k8 commented on 24 Sep 2019 Can anyone add this The Complete Hands-On Course to Master Apache Airflow @pharaoh1 pharaoh1 commented on 30 Sep 2019 can you pls add this course to your list https://www.udemy.com/course/advanced-python3/ @SushantDhote936 SushantDhote936 commented on 1 Oct 2019 Can you add Plural Sight CISSP @allayGerald allayGerald commented on 1 Oct 2019 open directive for lynda courses: https://drive.google.com/drive/folders/1zQan1cq1ZnqXmueRF5IqKoOtpFxl6Y4G @ezekielskottarathil ezekielskottarathil commented on 3 Oct 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. "wrong place boy" @pulkitd2699 pulkitd2699 commented on 8 Oct 2019 Does anyone has a link for 'Cyber security: Python and web applications' course? 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Thanks https://codewithmosh.com/p/mastering-react @evilprince2009 evilprince2009 commented on 27 Oct 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 @nunusandio nunusandio commented on 30 Oct 2019 Can anyone post torrent file for ASP.NET Authentication: The Big Picture https://app.pluralsight.com/library/courses/aspdotnet-authentication-big-picture/table-of-contents @EslamElmadny EslamElmadny commented on 9 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 hay brother! do you have html5 game development with javascript course ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 This link is not working brother! http://www.seedpeer.me/details/4657790/Lynda.com-Building-Facebook-Applications-with-HTML-and-JavaScript.html @smithtuka smithtuka commented on 20 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? 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Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms @edward-teixeira edward-teixeira commented on 1 Jan 2020 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms Yea i'm looking for it too @kaneyxx kaneyxx commented on 1 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? 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Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? @ravisharmaa ravisharmaa commented on 7 Jan Please add this . https://www.letsbuildthatapp.com/course/AppStore-JSON-APIs @WaleedAlrashed WaleedAlrashed commented on 13 Jan This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ @Sopheakmorm Sopheakmorm commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 @EslamElmadny EslamElmadny commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 +1 @EslamElmadny EslamElmadny commented on 20 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses @mohanrajrc mohanrajrc commented on 22 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. @shihab122 shihab122 commented on 22 Jan Please give me the torrnet file or link to download The Ultimate Design Patterns @EslamElmadny EslamElmadny commented on 22 Jan • Please give me the torrnet file or link to download The Ultimate Design Patterns Waiting for it also :D @K-wachira K-wachira commented on 23 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. You are a saviour .. Altho i feel bad i cant really buy the course... its really good @msdyn95 msdyn95 commented 25 days ago • Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ @K-wachira K-wachira commented 23 days ago This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ Hey did you find this one? @edward-teixeira edward-teixeira commented 22 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? @msdyn95 msdyn95 commented 21 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. @edward-teixeira edward-teixeira commented 20 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. Found it ! https://vminhsang.name.vn/category/it-courses/codewithmosh/ @ZainA14 ZainA14 commented 16 days ago • Can someone please link me to this mosh course for torrent or direct download link https://codewithmosh.com/p/the-ultimate-full-stack-net-developer-bundle @khushiigupta khushiigupta commented 9 days ago Can any one please provide me link for jenkins so that I can learn as al as possible to join this conversation on GitHub. Already have an account? Sign in to comment © 2020 GitHub, Inc. Terms Privacy Security Status Help Contact GitHub Pricing API Training Blog About

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. "Ask the AI experts: What's driving today's progress in AI?". McKinsey & Company. Archived from the original on 13 April 2018. Retrieved 13 April 2018. Administrator. "Kinect's AI breakthrough explained". i-programmer.info. Archived from the original on 1 February 2016. Rowinski, Dan (15 January 2013). "Virtual Personal Assistants & The Future Of Your Smartphone [Infographic]". ReadWrite. Archived from the original on 22 December 2015. "Artificial intelligence: Google's AlphaGo beats Go master Lee Se-dol". BBC News. 12 March 2016. Archived from the original on 26 August 2016. Retrieved 1 October 2016. Metz, Cade (27 May 2017). "After Win in China, AlphaGo's Designers Explore New AI". Wired. Archived from the original on 2 June 2017. "World's Go Player Ratings". May 2017. Archived from the original on 1 April 2017. "柯洁迎19岁生日 雄踞人类世界排名第一已两年" (in Chinese). May 2017. Archived from the original on 11 August 2017. Clark, Jack (8 December 2015). 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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. (June 2017). Description Logics in Multimedia Reasoning. Cham: Springer. doi:10.1007/978-3-319-54066-5. ISBN 978-3-319-54066-5. S2CID 3180114. Archived from the original on 29 August 2017. Ontology: * Russell & Norvig 2003, pp. 320–328 Smoliar, Stephen W.; Zhang, HongJiang (1994). "Content based video indexing and retrieval". IEEE Multimedia. 1 (2): 62–72. doi:10.1109/93.311653. S2CID 32710913. Neumann, Bernd; Möller, Ralf (January 2008). "On scene interpretation with description logics". Image and Vision Computing. 26 (1): 82–101. doi:10.1016/j.imavis.2007.08.013. Kuperman, G. J.; Reichley, R. M.; Bailey, T. C. (1 July 2006). "Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations". Journal of the American Medical Informatics Association. 13 (4): 369–371. doi:10.1197/jamia.M2055. PMC 1513681. PMID 16622160. MCGARRY, KEN (1 December 2005). "A survey of interestingness measures for knowledge discovery". 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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Retrieved 26 April 2018. Domingos 2015. Artificial brain arguments: AI requires a simulation of the operation of the human brain * Russell & Norvig 2003, p. 957 * Crevier 1993, pp. 271 and 279 A few of the people who make some form of the argument: * Moravec 1988 * Kurzweil 2005, p. 262 * Hawkins & Blakeslee 2005 The most extreme form of this argument (the brain replacement scenario) was put forward by Clark Glymour in the mid-1970s and was touched on by Zenon Pylyshyn and John Searle in 1980. Goertzel, Ben; Lian, Ruiting; Arel, Itamar; de Garis, Hugo; Chen, Shuo (December 2010). "A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures". Neurocomputing. 74 (1–3): 30–49. doi:10.1016/j.neucom.2010.08.012. Nilsson 1983, p. 10. 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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In the early 1970s, Kenneth Colby presented a version of Weizenbaum's ELIZA known as DOCTOR which he promoted as a serious therapeutic tool.[216] Joseph Weizenbaum's critique of AI: * Weizenbaum 1976 * Crevier 1993, pp. 132–144 * McCorduck 2004, pp. 356–373 * Russell & Norvig 2003, p. 961 Weizenbaum (the AI researcher who developed the first chatterbot program, ELIZA) argued in 1976 that the misuse of artificial intelligence has the potential to devalue human life. Wendell Wallach (2010). Moral Machines, Oxford University Press. Wallach, pp 37–54. Wallach, pp 55–73. Wallach, Introduction chapter. Michael Anderson and Susan Leigh Anderson (2011), Machine Ethics, Cambridge University Press. "Machine Ethics". aaai.org. Archived from the original on 29 November 2014. Rubin, Charles (Spring 2003). "Artificial Intelligence and Human Nature". The New Atlantis. 1: 88–100. Archived from the original on 11 June 2012. Brooks, Rodney (10 November 2014). 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[230] Strong AI is defined similarly by Russell & Norvig (2003, p. 947): "The assertion that machines could possibly act intelligently

Sichuan University C++ Course End-of-term Project (Convolutional Neural Network Handwriting Digit Recognize)

This is a Machine Learning project using Convolutional Neural Network to predict the stock price. It is done after a project article by Ashwin Siripurapu from Stanford University but in a different way.

Network Simulation for Urban Mobility: Interface between ns-3 and SUMO. The project allow user to control ns-3 LTE module and the UE mobility from SUMO. (developed by Wireless and Mobile Networking Laboratory in National Tsing Hua University)

Network dataset extraction library – part of the KONECT project by Jérôme Kunegis, University of Namur

Octave/Matlab toolbox for network analysis – part of the KONECT project by Jérôme Kunegis, University of Namur

This repository is created as a part of the P2p project for Computer Networks (CNT5106C) at the University of Florida for the Master's in Computer Science program. A simplified P2p network where any number of peers can share any type of file among themselves. Implemented in Java.

This project is developed for smart aquaculture. We use a camera to capture pictures of fishes in the tank. A deep neural network (MobileNet) is applied to recognize the images to identify the situation of fishes in the tank. EdgeTPU is used for edge computing. We also monitor the water quality by some environmental sensors. All the information is integrated into a website constructed by Laravel, and a Linebot is used to deliver information on mobile phones.Four undergraduate students develop this project from the Department of Management Information Systems (MIS), National Pingtung University of Science and Technology (NPUST), Taiwan.

CryptoNets using Python and ctypes. This repository is part of the final project of Neural Networks at Sapienza University of Rome.

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http://livecdnh3.tvanywhere.ae/hls/mbcmax/05.m3u8 #EXTINF:-1 group-title="MOVIE",MKC MOVIE 8 http://js.hls.huya.com/huyalive/30765679-2478268764-10644083292078342144-2847699106-10057-A-0-1_1200.m3u8 #EXTINF:-1 group-title="MOVIE",Film 29 http://aldirect.hls.huya.com/huyalive/30765679-2504742278-10757786168918540288-3049003128-10057-A-0-1_1200.m3u8 #EXTINF:-1 group-title="MOVIE",Sky-MoviesDisney http://95.170.215.118:80/hls/m3u8/Sky-MoviesDisney.m3u8 #EXTINF:-1 group-title="MOVIE",Sky-MoviesDrama. http://95.170.215.118:80/hls/m3u8/Sky-MoviesDrama.m3u8 #EXTINF:-1 group-title="MOVIE",Sky-MoviesFamily http://95.170.215.118:80/hls/m3u8/Sky-MoviesFamily.m3u8 #EXTINF:-1 group-title="MOVIE",Sky-MoviesPremie http://95.170.215.118:80/hls/m3u8/Sky-MoviesPremie.m3u8 #EXTINF:-1 group-title="MOVIE",film VH1 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http://www.deadlyblogger.com/NewRelease/300rise.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",47 RONIN http://www.deadlyblogger.com/NewRelease/47ronin.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",A Haunted HOUSE 2 (COMEDY) http://www.deadlyblogger.com/NewRelease/haunted2.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",MALAM DI ROXBURY http://www.deadlyblogger.com/NewRelease/roxbury.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",A.Dog's.Way.Home.2019 http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/547.mkv #EXTINF:-1 group-title="FILM CAMPURAN",A.Dog's.Way.Home.2019.720p.BluRay.x264-[YTS.AM] http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/581.mkv #EXTINF:-1 group-title="FILM CAMPURAN",DENGAR http://www.deadlyblogger.com/NewRelease/afflicted2013.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",AFTER EARTH http://www.deadlyblogger.com/NewRelease/after.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Air_Strike.2018.720p.WEBRip.x264-[YTS.AM] http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/553.mkv #EXTINF:-1 group-title="FILM CAMPURAN",Aladdin (2019) https://www.googleapis.com/drive/v3/files/1WgX85ihHMCU5lJ-2rO9XqKbCnlvUczY-?alt=media&key=AIzaSyDrQ86QJRiJlyPcLFYdxT22_oC2H1verR4 #EXTINF:-1 group-title="FILM CAMPURAN",Alexander-DVDRip.AC3-5.1[Eng]2004_arc http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/554.mkv #EXTINF:-1 group-title="FILM CAMPURAN",ALI http://www.deadlyblogger.com/NewRelease/ali1.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Alladin 2019 http://ftp.alphamediazone.com/Movies/Hollywood/2019/Aladdin%20%282019%29/Adventures.Of.Aladdin.2019.1080p.BluRay.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Alpha.2018.WEB-DL.x264-FGT http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/556.mkv #EXTINF:-1 group-title="FILM CAMPURAN",Selalu.Jadilah.Saya.Mungkin.2019.720p.WEBRip.x264-[YTS.AM] http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/557.mkv #EXTINF:-1 group-title="FILM CAMPURAN",AMERICAN GANGSTER http://www.deadlyblogger.com/NewRelease/agangster.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",AMERICAN HUSTLE http://www.deadlyblogger.com/NewRelease/hustle2013.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",An.Interview.with.God.2018 http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/558.mkv #EXTINF:-1 group-title="FILM CAMPURAN",ANCHORMAN 2: LEGENDA BERLANJUT http://www.deadlyblogger.com/NewRelease/anchorman2.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",ANCHORMAN THE LEGENDS OF RON BURGUNDY http://www.deadlyblogger.com/NewRelease/anchorman.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Angel Has Fallen (2019) https://www.googleapis.com/drive/v3/files/1e8zI98_BM0K4sVMo9byeInovIqfwLk7F?alt=media&key=AIzaSyDrQ86QJRiJlyPcLFYdxT22_oC2H1verR4 #EXTINF:-1 group-title="FILM CAMPURAN",Angel Of Mine (2019) https://www.googleapis.com/drive/v3/files/19JVSSekPOaBJhx6uATOHKuadjHVIarV7?alt=media&key=AIzaSyDrQ86QJRiJlyPcLFYdxT22_oC2H1verR4 #EXTINF:-1 group-title="FILM 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BOYS 2 http://www.deadlyblogger.com/NewRelease/badboys2.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",BAD BOYS http://www.deadlyblogger.com/NewRelease/badboys1.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",KECUK BURUK http://www.deadlyblogger.com/NewRelease/badgrandpa2013.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",KATA BURUK http://www.deadlyblogger.com/NewRelease/badwords.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Balkanska_medja_(2019) http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/567.mkv #EXTINF:-1 group-title="FILM CAMPURAN",BALLER BLOCKIN http://www.deadlyblogger.com/NewRelease/blockin.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",BATTLE FOR THE PLANET OF THE APES http://www.deadlyblogger.com/NewRelease/apes5.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",BATTLE OF THE YEAR http://www.deadlyblogger.com/NewRelease/battleyear2013.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",BEANEATH PLANET OF THE APES http://www.deadlyblogger.com/NewRelease/apes2.mp4 #EXTINF:-1 group-title="FILM 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http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/573.mkv #EXTINF:-1 group-title="FILM CAMPURAN",Centurion.2010.BRRip.XviD.AC3.VDON_arc http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/576.mkv #EXTINF:-1 group-title="FILM CAMPURAN",Chimera.Strain.2018.720p.WEBRip.x264-[YTS.AM] http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/577.mkv #EXTINF:-1 group-title="FILM CAMPURAN",CINDERELLA (1950) http://www.deadlyblogger.com/NewRelease/cinder1.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",TINDAKAN KELAS http://www.deadlyblogger.com/NewRelease/classact.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Cold Pursuit (2019) https://www.googleapis.com/drive/v3/files/1ywvuRRvTjqLDRVuQZ4gXYB6JKoFDSvFs?alt=media&key=AIzaSyDrQ86QJRiJlyPcLFYdxT22_oC2H1verR4 #EXTINF:-1 group-title="FILM CAMPURAN",Cold.Blood.2019.HDRip http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/578.mkv #EXTINF:-1 group-title="FILM CAMPURAN",Cold.Pursuit.2019.KORSUB.HDRip.XviD.MP3-STUTTERSHIT_arc http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/579.mkv #EXTINF:-1 group-title="FILM CAMPURAN",COLORS http://www.deadlyblogger.com/NewRelease/colors.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",CONGO 1995 http://www.deadlyblogger.com/NewRelease/congo1995.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",CONQUEST OF THE PLANET OF THE APES http://www.deadlyblogger.com/NewRelease/apes4.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Crawl (2019) https://www.googleapis.com/drive/v3/files/1kdgOD9pM9ZuVV3JoJv2RcsIwbOsdF1sM?alt=media&key=AIzaSyDrQ86QJRiJlyPcLFYdxT22_oC2H1verR4 #EXTINF:-1 group-title="FILM CAMPURAN",Creed.2.2019.HDRip http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/584.mkv #EXTINF:-1 group-title="FILM CAMPURAN",CRIMSON http://www.deadlyblogger.com/NewRelease/crimson.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Daddys.Home.2.2017.BRRip.XviD.MP3-XVID_arc http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/585.mkv #EXTINF:-1 group-title="FILM CAMPURAN",PIKIR 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DANCING (1987) http://www.deadlyblogger.com/NewRelease/dirtydancing1987.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",DIRTY DANCING HAVANA http://www.deadlyblogger.com/NewRelease/dirtydancing2004.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",DIRTY http://www.deadlyblogger.com/NewRelease/dirty1.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Distorted.2018.WEB-DL.x264-FGT http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/589.mkv #EXTINF:-1 group-title="FILM CAMPURAN",DOGMA http://www.deadlyblogger.com/NewRelease/dogma1999.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",JANGAN MENACE http://www.deadlyblogger.com/NewRelease/menace.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",JANGAN KATAKAN IBU THE BABYSITTERS MATI http://www.deadlyblogger.com/NewRelease/babysitter1991.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Dora dan Kota Emas yang Hilang (2019) https://www.googleapis.com/drive/v3/files/1EUDzPiS24mzTflfh0BFtBmkglxZBEYE4?alt=media&key=AIzaSyDrQ86QJRiJlyPcLFYdxT22_oC2H1verR4 #EXTINF:-1 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http://www.deadlyblogger.com/NewRelease/repentance.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",KEMBALI KE KABIN TEPI DANAU http://www.deadlyblogger.com/NewRelease/cabin2.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",RIDE ALONG http://www.deadlyblogger.com/NewRelease/ridealong2014.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Ringe.ringe.raja.2018.HRSink.BRRip http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/644.mkv #EXTINF:-1 group-title="FILM CAMPURAN",RIO 2 http://www.deadlyblogger.com/NewRelease/rio2.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",BANGKIT PLANET KERAS http://www.deadlyblogger.com/NewRelease/apes7.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",ROBIN HOOD http://www.deadlyblogger.com/NewRelease/robinhood1.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",Robin.Hood.2018.720p.WEBRip.x264-[YTS.AM] http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/645.mkv #EXTINF:-1 group-title="FILM CAMPURAN",Rocky1_arc http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/646.mkv 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http://www.deadlyblogger.com/NewRelease/twotowers.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",TUHAN CINCIN:PERSAHABATAN CINCIN http://www.deadlyblogger.com/NewRelease/fellowship1.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",TUHAN CINCIN:KEMBALINYA RAJA http://www.deadlyblogger.com/NewRelease/returnking1.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",THE MATRIX RELOADED http://www.deadlyblogger.com/NewRelease/matrix2.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",REVOLUSI MATRIKS http://www.deadlyblogger.com/NewRelease/matrix3.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",THE MATRIX http://www.deadlyblogger.com/NewRelease/matrix1.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",THE MIGHTY DUCKS 1 http://www.deadlyblogger.com/NewRelease/mighty1.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",THE MIGHTY DUCKS 2 http://www.deadlyblogger.com/NewRelease/mighty2.mp4 #EXTINF:-1 group-title="FILM CAMPURAN",The Secret Life Of Pets 2 (2019) 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http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/676.mkv #EXTINF:-1 group-title="FILM CAMPURAN",The.Professor.and.the.Madman.2019.WEB-DL.XviD.MP3-FGT_arc http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/677.mkv #EXTINF:-1 group-title="FILM CAMPURAN",The.Secret.Life.Of.Pets.2.2019 http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/678.mkv #EXTINF:-1 group-title="FILM CAMPURAN",The.Vice.2018.DVDScr.Xvid.AC3.HQ.Hive-CM8[EtMovies]_arc http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/679.mkv #EXTINF:-1 group-title="FILM CAMPURAN",The_Bridges_Of_Madison_County_arc http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/680.mkv #EXTINF:-1 group-title="FILM CAMPURAN",THE_GODFATHER_arc http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/660.mkv #EXTINF:-1 group-title="FILM CAMPURAN",The_Godfather2_arc http://tv.fast-iptv.info:12974/movie/vQCsltW5Ie/PnSVtgXTKV/681.mkv #EXTINF:-1 group-title="FILM CAMPURAN",The_Old.Man.and.the.Gun.2018.HDRip 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NB-IoT network simulator: This project allow user to explore the performance of NB-IoT random access and NB-IoT scheduler with ns-3. (developed by Wireless and Mobile Networking Laboratory in National Tsing Hua University)

Computer Networks Project - Khulna University

Final project for Social Network Mining(DATA130007) in Fudan university

MTracker is a tool for automatic splining tongue shapes in ultrasound images by harnessing the power of deep convolutional neural networks. It is developed at the Department of Linguistics, University of Michigan to address the need of splining a large-scale ultrasound project.

The aim of this project is to design the topology of the university network using the software Cisco Packet Tracer.

Clash is a 3D online networked multiplayer turn-based rpg game developed using Mirror and Unity, a university project created by team 9 in the team project module.

This is our repo for the final project of the Advanced Network Management course at Tsinghua University

Steady-State AC Network Visualization in the Browser - Developed at DATA61/Faculty of IT, Monash University - as a part of the summer research project.

This project has been conducted under the supervision of Dr. Jinoh Kim and Dr. Donghwoon Kwon at Texas A&M University-Commerce. The research outcome are published in the proceeding of IEEE ICNC 2018 (http://www.conf-icnc.org/2018/), with the title of “An Empirical Evaluation of Deep Learning for Network Anomaly Detection”.

Virtual Reality is an experimental and innovative framework containing advanced tools and options for creating virtual world applications. Virtual Reality is not a virtual world, nor a stand-alone application, it is a scalable and customizable platform containing some basic modules and based on some fundamental innovative pillars (peer-to-peer architecture, secure communication infrastructure, leg al framework, powerful scripting language); additional modules can be built on top of it on demand. The core of Virtual Reality is the innovative Virtual Reality Engine based on a hybrid peer-to-peer infrastructure that allows the sharing of computational load in experiencing the virtual environment obtaining infrastructural resource optimization and bandwidth reduction. It enhances the platform in terms of robustness, availability, scalability, load balancing. From an higher level point of view, Virtual Reality is provided with a Legal Framework that allows to conclude deals and to carry out transaction directly in-world. To obtain this aim, Virtual Reality is integrated with a secure communication infrastructure based on a strong identity system that bounds the avatar to the real identity of the person behind, that is responsible of his actions as in real life. In case of infringement of the contract terms, an in-world dispute resolution is implemented within the platform The design of such system carefully mimics the Model Agreement provided by The International Mediation Institute, www.IMImediation.org . The main Science & Technology objectives of the project were: to create a scalable, reliable p2p architecture for a 3d environment: two innovative custom libraries have been developed, namely vrnet and vrengine that allow the creation of a coherent world on top of a overlay network leveraging on a newly introduced authority mechanism; to create a secure and trusted infrastructure and a certified authentication system: it allows end-to-end security in communications between either users or participating virtual nodes; this layer is built upon PKI and stresses the role of mutually entrusted communication streams as well as digital signature of documents and proof of identity; to implement a virtual law system: leveraging the platform built-in strong identity, users are allowed to carry out transactions directly in-world (contracts and online dispute resolution system) and to participate to its ruling via customizable Constitution and Law System; to create a powerful scripting engine, allowing the users to interact with the virtual world. Scripting has been targeted toward programmers, privileging power over ease-of-use; This innovative approach has received some encouraging feedbacks from private universities and training companies, so that a specific applicative scenario has been customized for educational purposes (Virtual Campus). The Virtual Campus scenario takes also advantage from the interactivity enabled by the Virtual Reality scripting engine. From the legal side, some Contracts template specifically targeted to the relationships existing among private universities, teachers and students, cover the main use cases. Moreover, after a careful examination of potential business applications, thanks to the powerful scripting engine has been possible to obtain a specific applicative scenario (industrial scenario) aimed, for example, to provide a testing platform for streetcars/railway designers/manufacturers. The project has devoted great attention also on dissemination and exploitation activities; more than thirty publications have been written and more than one hundred fifty potential stakeholders have been contacted in order to build a business relationship. Platform Validation has demonstrated that Virtual Reality offers the architectural support for adoption of the platform, whether in autonomous or assisted usage, in educational and industrial application domains (i.e. Virtual Campus and Street-cars). The validation highlighted some improvements area, in particular with respect to the usability and 3D interaction mode. Notwithstanding this finding, the users in both the domains were able to perform the planned scenarios and the validation exercises. Virtual Reality demonstrates to be fully compliant with the requirements provided by the pool of involved users. As for Virtual Campus the platform proved to enhance teachers and students to carry out their duties. In particular the possibility to visualize complex objects, as well as their exploration, and the persistence of the virtual world, were assessed as valuable features to support the educational scenarios. The experimental results demonstrated that P2P framework effectively balances richness, responsiveness, robustness to provide an enhanced user experience to what is possible on traditional client/server architectures on regular Internet connections. In addition, our P2P framework reduces the requirements of bandwidth and CPU power on the service provider of a Virtual Reality-based virtual world service, and hence reduces the total cost of ownership

The purpose of this application is to analyze and create statistics of repetitive lock patterns that everyday users create and use. This project was created for the "security of wireless and mobile network communications" class of 2019. ICSD Department of University of Aegean.

An Inexpensive Vector Network Analyzer Project for the Walla Walla University Electronics II Class

This is a Proof of Concept model of Blockchain Technology based E-Voting System. It was our Computer Networks Project at "School of Engineering and Applied Science", Ahmedabad University.

Simple video chat project for the university network course

Data from my web crawling project that ranked universities using the PageRank algorithm on a network comprised of faculty relationships.

Quantum Variational Monte Carlo with Neural Networks - Project repository for my master's thesis in computational physics at the University of Oslo

Full-stack Go+Vue.js REST API for a University project - Photo social network