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

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Neuroscience data analysis framework for reproducible research built by Loren Frank Lab at UCSF

D-Lab's 9 hour introduction to performing data analysis with Stata. Learn how to program, conduct data analysis, create visualization, and conduct statistical analyses in Stata.

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

D-Lab's 6-part, 12-hour introduction to Python. Learn how to create variables, use methods and functions, work with if-statements and for-loops, and do data analysis with Pandas, using Python and Jupyter.

Picotti lab data analysis package.

LYSO-SiPM gamma scatter analysis from data collected by Ultralytics at PETSYS labs in Lisbon, Portugal, 2017.

Microbial Whole Genome Sequence data analysis labs for 2014

​ ​#​ ​Ukraine-Cyber-Operations ​Curated Intelligence is working with analysts from around the world to provide useful information to organisations in Ukraine looking for additional free threat intelligence. Slava Ukraini. Glory to Ukraine. ([​Blog​](https://www.curatedintel.org/2021/08/welcome.html) | [​Twitter​](https://twitter.com/CuratedIntel) | [​LinkedIn​](https://www.linkedin.com/company/curatedintelligence/)) ​ ​ ​###​ ​Analyst Comments: ​-​ 2022-02-25 ​  ​-​ Creation of the initial repository to help organisations in Ukraine ​  ​-​ Added [​Threat Reports​](https://github.com/curated-intel/Ukraine-Cyber-Operations#threat-reports) section ​  ​-​ Added [​Vendor Support​](https://github.com/curated-intel/Ukraine-Cyber-Operations#vendor-support) section ​-​ 2022-02-26 ​  ​-​ Additional resources, chronologically ordered (h/t Orange-CD) ​  ​-​ Added [​Vetted OSINT Sources​](https://github.com/curated-intel/Ukraine-Cyber-Operations#vetted-osint-sources) section  ​  ​-​ Added [​Miscellaneous Resources​](https://github.com/curated-intel/Ukraine-Cyber-Operations#miscellaneous-resources) section ​-​ 2022-02-27 ​  ​-​ Additional threat reports have been added ​  ​-​ Added [​Data Brokers​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/README.md#data-brokers) section ​  ​-​ Added [​Access Brokers​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/README.md#access-brokers) section ​-​ 2022-02-28 ​  ​-​ Added Russian Cyber Operations Against Ukraine Timeline by ETAC ​  ​-​ Added Vetted and Contextualized [​Indicators of Compromise (IOCs)​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/ETAC_Vetted_UkraineRussiaWar_IOCs.csv) by ETAC ​-​ 2022-03-01 ​  ​-​ Additional threat reports and resources have been added ​-​ 2022-03-02 ​  ​-​ Additional [​Indicators of Compromise (IOCs)​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/ETAC_Vetted_UkraineRussiaWar_IOCs.csv#L2011) have been added ​  ​-​ Added vetted [​YARA rule collection​](https://github.com/curated-intel/Ukraine-Cyber-Operations/tree/main/yara) from the Threat Reports by ETAC ​  ​-​ Added loosely-vetted [​IOC Threat Hunt Feeds​](https://github.com/curated-intel/Ukraine-Cyber-Operations/tree/main/KPMG-Egyde_Ukraine-Crisis_Feeds/MISP-CSV_MediumConfidence_Filtered) by KPMG-Egyde CTI (h/t [​0xDISREL​](https://twitter.com/0xDISREL)) ​    ​-​ IOCs shared by these feeds are ​`LOW-TO-MEDIUM CONFIDENCE`​ we strongly recommend NOT adding them to a blocklist ​    ​-​ These could potentially be used for ​`THREAT HUNTING`​ and could be added to a ​`WATCHLIST` ​    ​-​ IOCs are generated in ​`MISP COMPATIBLE`​ CSV format ​-​ 2022-03-03 ​  ​-​ Additional threat reports and vendor support resources have been added ​  ​-​ Updated [​Log4Shell IOC Threat Hunt Feeds​](https://github.com/curated-intel/Log4Shell-IOCs/tree/main/KPMG_Log4Shell_Feeds) by KPMG-Egyde CTI; not directly related to Ukraine, but still a widespread vulnerability. ​  ​-​ Added diagram of Russia-Ukraine Cyberwar Participants 2022 by ETAC ​  ​-​ Additional [​Indicators of Compromise (IOCs)​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/ETAC_Vetted_UkraineRussiaWar_IOCs.csv#L2042) have been added ​####​ ​`Threat Reports` ​| Date | Source | Threat(s) | URL | ​| --- | --- | --- | --- | ​| 14 JAN | SSU Ukraine | Website Defacements | [ssu.gov.ua](https://ssu.gov.ua/novyny/sbu-rozsliduie-prychetnist-rosiiskykh-spetssluzhb-do-sohodnishnoi-kiberataky-na-orhany-derzhavnoi-vlady-ukrainy)| ​| 15 JAN | Microsoft | WhisperGate wiper (DEV-0586) | [microsoft.com](https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/) | ​| 19 JAN | Elastic | WhisperGate wiper (Operation BleedingBear) | [elastic.github.io](https://elastic.github.io/security-research/malware/2022/01/01.operation-bleeding-bear/article/) | ​| 31 JAN | Symantec | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [symantec-enterprise-blogs.security.com](https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/shuckworm-gamaredon-espionage-ukraine) | ​| 2 FEB | RaidForums | Access broker "GodLevel" offering Ukrainain algricultural exchange | RaidForums [not linked] | ​| 2 FEB | CERT-UA | UAC-0056 using SaintBot and OutSteel malware | [cert.gov.ua](https://cert.gov.ua/article/18419) | ​| 3 FEB | PAN Unit42 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [unit42.paloaltonetworks.com](https://unit42.paloaltonetworks.com/gamaredon-primitive-bear-ukraine-update-2021/) | ​| 4 FEB | Microsoft | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [microsoft.com](https://www.microsoft.com/security/blog/2022/02/04/actinium-targets-ukrainian-organizations/) | ​| 8 FEB | NSFOCUS | Lorec53 (aka UAC-0056, EmberBear, BleedingBear) | [nsfocusglobal.com](https://nsfocusglobal.com/apt-retrospection-lorec53-an-active-russian-hack-group-launched-phishing-attacks-against-georgian-government) | ​| 15 FEB | CERT-UA | DDoS attacks against the name server of government websites as well as Oschadbank (State Savings Bank) & Privatbank (largest commercial bank). False SMS and e-mails to create panic | [cert.gov.ua](https://cert.gov.ua/article/37139) | ​| 23 FEB | The Daily Beast | Ukrainian troops receive threatening SMS messages | [thedailybeast.com](https://www.thedailybeast.com/cyberattacks-hit-websites-and-psy-ops-sms-messages-targeting-ukrainians-ramp-up-as-russia-moves-into-ukraine) | ​| 23 FEB | UK NCSC | Sandworm/VoodooBear (GRU) | [ncsc.gov.uk](https://www.ncsc.gov.uk/files/Joint-Sandworm-Advisory.pdf) | ​| 23 FEB | SentinelLabs | HermeticWiper | [sentinelone.com]( https://www.sentinelone.com/labs/hermetic-wiper-ukraine-under-attack/ ) | ​| 24 FEB | ESET | HermeticWiper | [welivesecurity.com](https://www.welivesecurity.com/2022/02/24/hermeticwiper-new-data-wiping-malware-hits-ukraine/) | ​| 24 FEB | Symantec | HermeticWiper, PartyTicket ransomware, CVE-2021-1636, unknown webshell | [symantec-enterprise-blogs.security.com](https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/ukraine-wiper-malware-russia) | ​| 24 FEB | Cisco Talos | HermeticWiper | [blog.talosintelligence.com](https://blog.talosintelligence.com/2022/02/threat-advisory-hermeticwiper.html) | ​| 24 FEB | Zscaler | HermeticWiper | [zscaler.com](https://www.zscaler.com/blogs/security-research/hermetic-wiper-resurgence-targeted-attacks-ukraine) | ​| 24 FEB | Cluster25 | HermeticWiper | [cluster25.io](https://cluster25.io/2022/02/24/ukraine-analysis-of-the-new-disk-wiping-malware/) | ​| 24 FEB | CronUp | Data broker "FreeCivilian" offering multiple .gov.ua | [twitter.com/1ZRR4H](https://twitter.com/1ZRR4H/status/1496931721052311557)| ​| 24 FEB | RaidForums | Data broker "Featherine" offering diia.gov.ua | RaidForums [not linked] | ​| 24 FEB | DomainTools | Unknown scammers | [twitter.com/SecuritySnacks](https://twitter.com/SecuritySnacks/status/1496956492636905473?s=20&t=KCIX_1Ughc2Fs6Du-Av0Xw) | ​| 25 FEB | @500mk500 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [twitter.com/500mk500](https://twitter.com/500mk500/status/1497339266329894920?s=20&t=opOtwpn82ztiFtwUbLkm9Q) | ​| 25 FEB | @500mk500 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [twitter.com/500mk500](https://twitter.com/500mk500/status/1497208285472215042)| ​| 25 FEB | Microsoft | HermeticWiper | [gist.github.com](https://gist.github.com/fr0gger/7882fde2b1b271f9e886a4a9b6fb6b7f) | ​| 25 FEB | 360 NetLab | DDoS (Mirai, Gafgyt, IRCbot, Ripprbot, Moobot) | [blog.netlab.360.com](https://blog.netlab.360.com/some_details_of_the_ddos_attacks_targeting_ukraine_and_russia_in_recent_days/) | ​| 25 FEB | Conti [themselves] | Conti ransomware, BazarLoader | Conti News .onion [not linked] | ​| 25 FEB | CoomingProject [themselves] | Data Hostage Group | CoomingProject Telegram [not linked] | ​| 25 FEB | CERT-UA | UNC1151/Ghostwriter (Belarus MoD) | [CERT-UA Facebook](https://facebook.com/story.php?story_fbid=312939130865352&id=100064478028712)| ​| 25 FEB | Sekoia | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com/sekoia_io](https://twitter.com/sekoia_io/status/1497239319295279106) | ​| 25 FEB | @jaimeblascob | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com/jaimeblasco](https://twitter.com/jaimeblascob/status/1497242668627370009)| ​| 25 FEB | RISKIQ | UNC1151/Ghostwriter (Belarus MoD) | [community.riskiq.com](https://community.riskiq.com/article/e3a7ceea/) | ​| 25 FEB | MalwareHunterTeam | Unknown phishing | [twitter.com/malwrhunterteam](https://twitter.com/malwrhunterteam/status/1497235270416097287) | ​| 25 FEB | ESET | Unknown scammers | [twitter.com/ESETresearch](https://twitter.com/ESETresearch/status/1497194165561659394) | ​| 25 FEB | BitDefender | Unknown scammers | [blog.bitdefender.com](https://blog.bitdefender.com/blog/hotforsecurity/cybercriminals-deploy-spam-campaign-as-tens-of-thousands-of-ukrainians-seek-refuge-in-neighboring-countries/) | ​| 25 FEB | SSSCIP Ukraine | Unkown phishing | [twitter.com/dsszzi](https://twitter.com/dsszzi/status/1497103078029291522) | ​| 25 FEB | RaidForums | Data broker "NetSec"  offering FSB (likely SMTP accounts) | RaidForums [not linked] | ​| 25 FEB | Zscaler | PartyTicket decoy ransomware | [zscaler.com](https://www.zscaler.com/blogs/security-research/technical-analysis-partyticket-ransomware) | ​| 25 FEB | INCERT GIE | Cyclops Blink, HermeticWiper | [linkedin.com](https://www.linkedin.com/posts/activity-6902989337210740736-XohK) [Login Required] | ​| 25 FEB | Proofpoint | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com/threatinsight](https://twitter.com/threatinsight/status/1497355737844133895?s=20&t=Ubi0tb_XxGCbHLnUoQVp8w) | ​| 25 FEB | @fr0gger_ | HermeticWiper capabilities Overview | [twitter.com/fr0gger_](https://twitter.com/fr0gger_/status/1497121876870832128?s=20&t=_296n0bPeUgdXleX02M9mg) ​| 26 FEB | BBC Journalist | A fake Telegram account claiming to be President Zelensky is posting dubious messages | [twitter.com/shayan86](https://twitter.com/shayan86/status/1497485340738785283?s=21) | ​| 26 FEB | CERT-UA | UNC1151/Ghostwriter (Belarus MoD) | [CERT_UA Facebook](https://facebook.com/story.php?story_fbid=313517477474184&id=100064478028712) | ​| 26 FEB | MHT and TRMLabs | Unknown scammers, linked to ransomware | [twitter.com/joes_mcgill](https://twitter.com/joes_mcgill/status/1497609555856932864?s=20&t=KCIX_1Ughc2Fs6Du-Av0Xw) | ​| 26 FEB | US CISA | WhisperGate wiper, HermeticWiper | [cisa.gov](https://www.cisa.gov/uscert/ncas/alerts/aa22-057a) | ​| 26 FEB | Bloomberg | Destructive malware (possibly HermeticWiper) deployed at Ukrainian Ministry of Internal Affairs & data stolen from Ukrainian telecommunications networks | [bloomberg.com](https://www.bloomberg.com/news/articles/2022-02-26/hackers-destroyed-data-at-key-ukraine-agency-before-invasion?sref=ylv224K8) | ​| 26 FEB | Vice Prime Minister of Ukraine | IT ARMY of Ukraine created to crowdsource offensive operations against Russian infrastructure | [twitter.com/FedorovMykhailo](https://twitter.com/FedorovMykhailo/status/1497642156076511233) | ​| 26 FEB | Yoroi | HermeticWiper | [yoroi.company](https://yoroi.company/research/diskkill-hermeticwiper-a-disruptive-cyber-weapon-targeting-ukraines-critical-infrastructures) | ​| 27 FEB | LockBit [themselves] | LockBit ransomware | LockBit .onion [not linked] |  ​| 27 FEB | ALPHV [themselves] | ALPHV ransomware | vHUMINT [closed source] | ​| 27 FEB | Mēris Botnet [themselves] | DDoS attacks | vHUMINT [closed source] | ​| 28 FEB | Horizon News [themselves] | Leak of China's Censorship Order about Ukraine | [TechARP](https://www-techarp-com.cdn.ampproject.org/c/s/www.techarp.com/internet/chinese-media-leaks-ukraine-censor/?amp=1)| ​| 28 FEB | Microsoft | FoxBlade (aka HermeticWiper) | [Microsoft](https://blogs.microsoft.com/on-the-issues/2022/02/28/ukraine-russia-digital-war-cyberattacks/?preview_id=65075) | ​| 28 FEB | @heymingwei | Potential BGP hijacks attempts against Ukrainian Internet Names Center | [https://twitter.com/heymingwei](https://twitter.com/heymingwei/status/1498362715198263300?s=20&t=Ju31gTurYc8Aq_yZMbvbxg) | ​| 28 FEB | @cyberknow20 | Stormous ransomware targets Ukraine Ministry of Foreign Affairs | [twitter.com/cyberknow20](https://twitter.com/cyberknow20/status/1498434090206314498?s=21) |  ​| 1 MAR | ESET | IsaacWiper and HermeticWizard | [welivesecurity.com](https://www.welivesecurity.com/2022/03/01/isaacwiper-hermeticwizard-wiper-worm-targeting-ukraine/) | ​| 1 MAR | Proofpoint | Ukrainian armed service member's email compromised and sent malspam containing the SunSeed malware (likely TA445/UNC1151/Ghostwriter) | [proofpoint.com](https://www.proofpoint.com/us/blog/threat-insight/asylum-ambuscade-state-actor-uses-compromised-private-ukrainian-military-emails) | ​| 1 MAR | Elastic | HermeticWiper | [elastic.github.io](https://elastic.github.io/security-research/intelligence/2022/03/01.hermeticwiper-targets-ukraine/article/) | ​| 1 MAR | CrowdStrike | PartyTicket (aka HermeticRansom), DriveSlayer (aka HermeticWiper) | [CrowdStrike](https://www.crowdstrike.com/blog/how-to-decrypt-the-partyticket-ransomware-targeting-ukraine/) | ​| 2 MAR | Zscaler | DanaBot operators launch DDoS attacks against the Ukrainian Ministry of Defense | [zscaler.com](https://www.zscaler.com/blogs/security-research/danabot-launches-ddos-attack-against-ukrainian-ministry-defense) | ​| 3 MAR | @ShadowChasing1 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [twitter.com/ShadowChasing1](https://twitter.com/ShadowChasing1/status/1499361093059153921) | ​| 3 MAR | @vxunderground | News website in Poland was reportedly compromised and the threat actor uploaded anti-Ukrainian propaganda | [twitter.com/vxunderground](https://twitter.com/vxunderground/status/1499374914758918151?s=20&t=jyy9Hnpzy-5P1gcx19bvIA) | ​| 3 MAR | @kylaintheburgh | Russian botnet on Twitter is pushing "#istandwithputin" and "#istandwithrussia" propaganda (in English) | [twitter.com/kylaintheburgh](https://twitter.com/kylaintheburgh/status/1499350578371067906?s=21) | ​| 3 MAR | @tracerspiff | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com](https://twitter.com/tracerspiff/status/1499444876810854408?s=21) | ​####​ ​`Access Brokers` ​| Date | Threat(s) | Source | ​| --- | --- | --- | ​| 23 JAN | Access broker "Mont4na" offering UkrFerry | RaidForums [not linked] | ​| 23 JAN | Access broker "Mont4na" offering PrivatBank | RaidForums [not linked] | ​| 24 JAN | Access broker "Mont4na" offering DTEK | RaidForums [not linked] | ​| 27 FEB | KelvinSecurity Sharing list of IP cameras in Ukraine | vHUMINT [closed source] | ​| 28 FEB | "w1nte4mute" looking to buy access to UA and NATO countries (likely ransomware affiliate) | vHUMINT [closed source] | ​####​ ​`Data Brokers` ​| Threat Actor    | Type            | Observation                                                                                               | Validated | Relevance                     | Source                                                     | ​| --------------- | --------------- | --------------------------------------------------------------------------------------------------------- | --------- | ----------------------------- | ---------------------------------------------------------- | ​| aguyinachair    | UA data sharing | PII DB of ukraine.com (shared as part of a generic compilation)                                           | No        | TA discussion in past 90 days | ELeaks Forum \[not linked\]                                | ​| an3key          | UA data sharing | DB of Ministry of Communities and Territories Development of Ukraine (minregion\[.\]gov\[.\]ua)           | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| an3key          | UA data sharing | DB of Ukrainian Ministry of Internal Affairs (wanted\[.\]mvs\[.\]gov\[.\]ua)                              | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (40M) of PrivatBank customers (privatbank\[.\]ua)                                                  | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | DB of "border crossing" DBs of DPR and LPR                                                                | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (7.5M) of Ukrainian passports                                                                      | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB of Ukrainian car registration, license plates, Ukrainian traffic police records                    | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (2.1M) of Ukrainian citizens                                                                       | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (28M) of Ukrainian citizens (passports, drivers licenses, photos)                                  | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (1M) of Ukrainian postal/courier service customers (novaposhta\[.\]ua)                             | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (10M) of Ukrainian telecom customers (vodafone\[.\]ua)                                             | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (3M) of Ukrainian telecom customers (lifecell\[.\]ua)                                              | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (13M) of Ukrainian telecom customers (kyivstar\[.\]ua)                                             | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| danieltx51      | UA data sharing | DB of Ministry of Foreign Affairs of Ukraine (mfa\[.\]gov\[.\]ua)                                         | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| DueDiligenceCIS | UA data sharing | PII DB (63M) of Ukrainian citizens (name, DOB, birth country, phone, TIN, passport, family, etc)          | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Featherine      | UA data sharing | DB of Ukrainian 'Diia' e-Governance Portal for Ministry of Digital Transformation of Ukraine              | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of Ministry for Internal Affairs of Ukraine public data search engine (wanted\[.\]mvs\[.\]gov\[.\]ua)  | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of Ministry for Communities and Territories Development of Ukraine (minregion\[.\]gov\[.\]ua)          | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of Motor Insurance Bureau of Ukraine (mtsbu\[.\]ua)                                                    | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | PII DB of Ukrainian digital-medicine provider (medstar\[.\]ua)                                            | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of ticket.kyivcity.gov.ua                                                                              | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of id.kyivcity.gov.ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of my.kyivcity.gov.ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of portal.kyivcity.gov.ua                                                                              | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of anti-violence-map.msp.gov.ua                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dopomoga.msp.gov.ua                                                                                 | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of e-services.msp.gov.ua                                                                               | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of edu.msp.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of education.msp.gov.ua                                                                                | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of ek-cbi.msp.gov.ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mail.msp.gov.ua                                                                                     | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of portal-gromady.msp.gov.ua                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of web-minsoc.msp.gov.ua                                                                               | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of wcs-wim.dsbt.gov.ua                                                                                 | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of bdr.mvs.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of motorsich.com                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dsns.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mon.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of minagro.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of zt.gov.ua                                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of kmu.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mvs.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dsbt.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of forest.gov.ua                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of nkrzi.gov.ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dabi.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of comin.gov.ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dp.dpss.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of esbu.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mms.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mova.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mspu.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of nads.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of reintegration.gov.ua                                                                                | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of sies.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of sport.gov.ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mepr.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mfa.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of va.gov.ua                                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mtu.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of cg.mvs.gov.ua                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of ch-tmo.mvs.gov.ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of cp.mvs.gov.ua                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of cpd.mvs.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of hutirvilnij-mrc.mvs.gov.ua                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dndekc.mvs.gov.ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of visnyk.dndekc.mvs.gov.ua                                                                            | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dpvs.hsc.gov.ua                                                                                     | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of odk.mvs.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of e-driver\[.\]hsc\[.\]gov\[.\]ua                                                                     | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of wanted\[.\]mvs\[.\]gov\[.\]ua                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of minregeion\[.\]gov\[.\]ua                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of health\[.\]mia\[.\]solutions                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mtsbu\[.\]ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of motorsich\[.\]com                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of kyivcity\[.\]com                                                                                    | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of bdr\[.\]mvs\[.\]gov\[.\]ua                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of gkh\[.\]in\[.\]ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of kmu\[.\]gov\[.\]ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mon\[.\]gov\[.\]ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of minagro\[.\]gov\[.\]ua                                                                              | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mfa\[.\]gov\[.\]ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| Intel\_Data     | UA data sharing | PII DB (56M) of Ukrainian Citizens                                                                        | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Kristina        | UA data sharing | DB of Ukrainian National Police (mvs\[.\]gov\[.\]ua)                                                      | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | 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A project to build a data analysis lab for scientists

Code for neural and behavioral data analysis from the Brain Computation and Behavior Lab, aka AyA Lab, at Cornell University.

Human locomotion affects our daily living activities. Losing limbs or having neurological disorders with motor deficits could affect the quality of life. Gait analysis is a systematic study of human locomotion, which is defined as body movements through aerial, aquatic, or terrestrial space. This analysis has been used to study people ambulation, registration, and reconstruction of physical location and orientation of individual limbs used to quantify and characterize human locomotion using different gait parameters including gait activities such as walking, stairs ascending/descending, … etc., phases, and spatiotemporal parameters of human gait. Additionally, gait analysis parameters can be used to evaluate the functionality of patients and wearable system users. The evaluation is based on patient's stability, energy consumption, gait symmetry, ability to recover from perturbations, and ability to perform activities of daily living. Many companies develop assistive, wearable, and rehabilitation devices for patients with lower limb neurological disorders. These devices are tested and evaluated inside controlled lab environments. However, they don’t have enough data on the patient's performance in real world and harsh environments. Collecting large datasets of device users and their gait performance data in real environment are notoriously difficult. Additionally, collecting data on less prevalent or on gait activities other than level walking, stair ascending/descending, sitting, standing, …etc. on hard surfaces is rarely attempted. However, the scope for collecting gait data from alternative sources other than traditional gait labs could be attained with the help of IoT data collection embedded on the wearable and assistive devices and well-established cloud platforms equipped with big-data analytics and data visualization capabilities. This project aims to develop a cloud platform capable of collect data from wearable and assistive devices such as prostheses, exoskeleton, gait analysis wearable sensors, …etc. using IoT technologies. This platform is capable of automatically use data mining and visualization tools. Additionally, it uses statistical and machine learning techniques to estimate gait events, gait symmetry, gait speed, gait activities, stability, energy consumption, …etc. Also, it is capable of predicting patient's progress over time. The project will be composed of two major components, hardware component and software component. In hardware component, the students will design and implement the IoT that collects the different readings for gait analysis and send them to the cloud. Meanwhile, in the software component, the students will design and implement a set of algorithms to visualize the collected data, then design and implement data analytics to automatically analyze the collected data, so that we can estimate gait events, gait symmetry, gait speed, classify gait activities, stability, energy consumption, …etc. and predicting patient's progress over time. By analyzing the collected data, the patient's progress can be predicted over time. Additionally, these data can be used through manufacturers of prostheses legs to improve their products, as well as through health-care centers to assess the patient's performance. The following figures describe the main modules of our graduation project.

Eye tracking data analysis tools used in the Adolphs Lab

For this project, I used publicly available Electronic Health Records (EHRs) datasets. The MIT Media Lab for Computational Physiology has developed MIMIC-IIIv1.4 dataset based on 46,520 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center of Boston between 2001 and 2012. MIMIC-IIIv1.4 dataset is freely available to researchers across the world. A formal request should be made directly to www.mimic.physionet.org, to gain access to the data. There is a required course on human research ‘Data or Specimens Only Research’ prior to data access request. I have secured one here -www.citiprogram.org/verify/?kb6607b78-5821-4de5-8cad-daf929f7fbbf-33486907. We built flexible and better performing model using the same 17 variables used in the SAPS II severity prediction model. The question ‘Can we improve the prediction performance of widely used severity scores using a more flexible model?’ is the central question of our project. I used the exact 17 variables used to develop the SAPS II severity prediction algorithm. These are 13 physiological variables, three underlying (chronic) disease variables and one admission variable. The physiological variables includes demographic (age), vital (Glasgow Comma Scale, systolic blood pressure, Oxygenation, Renal, White blood cells count, serum bicarbonate level, blood sodium level, blood potassium level, and blood bilirubin level). The three underlying disease variables includes Acquired Immunodeficiency Syndrome (AIDS), metastatic cancer, and hematologic malignancy. Finally, whether admission was scheduled surgical or unscheduled surgical was included in the model. The dataset has 26 relational tables including patient’s hospital admission, callout information when patient was ready for discharge, caregiver information, electronic charted events including vital signs and any additional information relevant to patient care, patient demographic data, list of services the patient was admitted or transferred under, ICU stay types, diagnoses types, laboratory measurments, microbiology tests and sensitivity, prescription data and billing information. Although I have full access to the MIMIC-IIIv1.4 datasets, I can not share any part of the data publicly. If you are interested to learn more about the data, there is a MIMIC III Demo dataset based on 100 patients https://mimic.physionet.org/gettingstarted/demo/. If you are interested to requesting access to the data - https://mimic.physionet.org/gettingstarted/access/. Linked repositories: Exploratory-Data-Analysis-Clinical-Deterioration, Data-Wrangling-MIMICIII-Database, Clinical-Deterioration-Prediction-Model--Inferential-Statistics, Clinical-Deterioration-Prediction-Model--Ensemble-Algorithms-, Clinical-Deterioration-Prediction-Model--Logistic-Regression, Clinical-Deterioration-Prediction-Model---KNN © 2020 GitHub, Inc.

Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.

Detailed analysis for data collected from M-Lab.

Collection of statistical scripts, image analysis workflows, data used in publications by the Misteli Lab.

Cloud Native Data Journey is a one-day program consisting of a series of workshops accompanied with hands-on labs for hands-on technical audience. The program covers the entire data lifecycle from collection, through transforming and analysis, using cutting edge managed ML, and up to actionable delivery via visualisation tools.

Use machine learning to classify malware. Malware analysis 101. Set up a cybersecurity lab environment. Learn how to tackle data class imbalance. Unsupervised anomaly detection. End-to-end deep neural networks for malware classification. Create a machine learning Intrusion Detection System (IDS). Employ machine learning for offensive security. Learn how to address False Positive constraints. Break a CAPTCHA system using machine learning.

南方科技大学数据结构与算法分析课程 lab code and tests(cheating paper) in SUSTech CS203 Data Structures and Algorithm Analysis course

MGnify documentation and Jupyter Lab notebooks to support downstream analysis of MGnify data (EMBL-EBI's metagenomics platform)

This repository contains Lab 01 of the Machine Learning course, focusing on predicting salaries using simple linear regression in R. It includes data exploration, correlation analysis, model building, and visualization of the relationship between years of experience and salary.

This is a comprehensive MATLAB-based software platform developed for real-time measurement and feedback control of a custom mask-projection photopolymerization based additive manufacturing system (referred as "ECPL", i.e., Exposure Controlled Projection Lithography) using a lab-built interferometry (referred as "ICM&M", i.e., Interferometric Curing Monitoring and Measurement). A graphical user interface using the graphical user interface development environment (GUIDE) of MATLAB was created to implement the ICM&M method for the ECPL process. The software interfaces with the hardware of the ECPL system’s ultraviolet lamp and DMD, and the ICM&M system’s camera. It was designed to streamline the operation of the ECPL process with the aid of parallel computing that implements online both the ICM&M acquisition and measurement analysis as well as the feedback control method. The application logs the acquired interferogram video data, performs numerical computations for the ICM&M measurement algorithms and control law, saves the real-time data and measurement results for all voxels in the region of interest. Meanwhile, it displays interferogram frames and visualize the photocuring process without a substantial sacrifice in temporal performance of other key functions such as data acquisition and measurement & control analysis. The software could be extended to real-time process measurement and control for other additive manufacturing systems, for example, metal based additive manufacturing aided by in-situ thermal images analysis.

An introduction to Jupyter and Jupyter Labs for data analysis, data science, and Python development

Materials for D-Lab / UC Berkeley Graduate Division's Data Science for Social Justice summer workshop. These materials provide an introduction to Python, natural language processing, text analysis, word embeddings, and network analysis. They also include discussions on critical approaches to data science to promote social justice.

Almost in every image processing or analysis work, image pre-preprocessing is crucial step. In medical image analysis, pre-processing is a very important step because the further success or performance of the algorithm mostly dependent on pre-processed image. In this lab, we are working with 3D Brain MRI data. In case of working with brain MRI removing the noise and bias field (which is due to inhomogeneity of the magnetic field) is very important part of preprocessing of brain MRI. To do so, we widely used algorithm Anisotropic diffusion, isotropic diffusion which can diffuse in any direction, and Multiplicative intrinsic component optimization (MICO) have been used for noise removal and bias field correction respectfully. Both quantitative and qualitative performance of the algorithms also have been analyzed.

Twitter & Crowdtangle Data Access and Analysis Workshop for the Social Identity and Morality Lab

Lab Report Automation is a Streamlit based automation tool for generating diagnostic summaries from medical datasets. It creates visual insights, compiles reports as PDFs, and sends them via email. Ideal for streamlining lab data analysis and reporting workflows.

Variety of Python code for using in lab environment (data acquisition, device communication, data analysis)