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Bitcoin White Paper: A Peer-to-Peer Electronic Cash System [Full Text] By Satoshi Nakamoto

The two most widely-used open-source machine learning frameworks for training and building deep learning models are TensorFlow and PyTorch. These frameworks have unique differences in their approach to building and training models. While TensorFlow uses a static graph and declarative programming, which provides better optimization opportunities and allows for distributed training, PyTorch makes use of a dynamic computational graph and imperative programming, which allows for a more flexible and easy debugging process. The choice of frameworks depends on the user's specific needs and preferences. This article looks at the available today. Best PyTorch Datasets for Building Deep Learning Models Ultimate List of Open PyTorch Datasets 1. Penn Treebank The Penn Treebank is a collection of annotated English text, which is extensively studied in the natural language processing (NLP) research community. It comprises over 4.5 million words of text from various genres such as magazines, news articles and fictional stories. The dataset also consists of manually annotated information about name entitles, part of speech tags and syntactic structure, used to train and evaluate a wide range of NLP models including language models, parsers and machine translation systems. To download this dataset, click . here 2. Stanford Question Answering Dataset (SQuAD) The SQuAD (Stanford Question Answering Dataset) is a popular benchmark dataset in natural language processing (NLP) that comprises more than 100,000 question-answer pairs, extracted from a set of Wikipedia articles. It is used to evaluate the performance of various NLP models built using PyTorch or other deep learning frameworks. The answer spans have an average length of 3.6 words, and there are 11.0 words in the corresponding passages on average. Here are some extra details about the SQuAD: - It contains 100,000+ question-answer pairs on 536 articles from English Wikipedia. SQuAD 1.0 - It contains 150,000+ question-answer pairs on 100,000+ Wikipedia articles, with additional unanswerable questions to test the model's ability to abstain from answering. SQuAD 2.0 The goal of the dataset is to provide a challenging task for machine learning models to answer questions about a given text passage. Click here to download the . latest version (SQuAD 2.0) 3. Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) This PyTorch dataset is a public dataset of computed tomography images of the chest that has been annotated for lung nodules by multiple radiologists. It comprises 1,018 CT scans collected from various institutions and contains over 23,000 annotated nodules. Each scan in the dataset is accompanied by annotations from four experienced radiologists which provide information on shape, size, nodule location and texture. The dataset was created to support research into the development of computer-aided diagnosis (CAD) systems for lung cancer screening and diagnosis. Note: The dataset is publicly available but subject to certain restrictions and requirements for usage. Click to download. here 4. Fashion-MNIST The PyTorch Fashion MNIST dataset was created by Zalando Research as a replacement for the original MNIST dataset and is available in a PyTorch-compatible format.  The PyTorch Fashion MNIST dataset comprises 70,000 grayscale images of clothing items, including 60,000 training images and 10,000 test images. The images are 28x28 pixels in size and represent 10 different classes of clothing items, including ­T-shirts/tops, trousers, pullovers, dresses, coats, sandals, shirts, sneakers, bags, and ankle boots. It is similar to the original MNIST dataset, but with more challenging classification tasks due to the greater complexity and variety of the clothing items. The dataset can be downloaded . here 5. Yelp Reviews The Yelp Reviews dataset is an extensive collection of over 5 million reviews of local businesses from 11 metropolitan areas in the United States. Each review in the dataset contains information such as the star rating, business category, review text, date, and location. It is a valuable resource interested in building deep learning models with PyTorch. Please sign up and click here to download the . dataset 6. Stanford Dogs This PyTorch Dataset comprises a collection of 20,580 high-quality images of 120 different breeds of dogs, each labelled with information about the breed of the dog in the image. It can be used for image classification and object recognition. With the dataset provided in JPEG format and annotations in a text file, the images are of size 224x224 pixels. The breeds in the dataset range from common breeds such as Golden Retrievers and Labradors to more obscure breeds such as the Otterhound and the Sussex Spaniel. You can download the here. dataset 7. Caltech 101 While the limited number of images per category can be a challenge, the detailed annotations make the PyTorch Caltech 101 dataset a valuable resource for evaluating deep learning models. The dataset is a labelled computer vision dataset with 9,144 high-quality images of objects across 101 categories.  It also covers a wide range of object categories and the images were obtained from various sources. Each image is labelled with the category of object and image, which makes it simpler to use with a variety of deep learning frameworks. Click here to . download 8. STS-B (Semantic Textual Similarity Benchmark) The STS-B (Semantic Textual Similarity Benchmark) dataset is an English dataset used in the STS tasks organized in the context of SemEval between 2012 and 2017. It comprises 8628 sentence pairs with human-assigned similarity scores on a scale of 1 to 5. Drawn from various sources, like news articles, forum posts, images with captions and covering a wide range of topics, it is a popular dataset used with PyTorch for evaluating models’ performance in determining the semantic similarity between two sentences. The STS-B dataset is available in multiple formats, including PyTorch-compatible formats, as PyTorch is a deep-learning framework for training and evaluating models on this dataset. \To download this dataset, click . here 9. WMT'14 English-German This PyTorch dataset is a benchmark dataset for machine translation between English and German created by Stanford in 2015. It comprises parallel corpora of sentence-aligned texts in both English and German, which are used to build and evaluate deep learning models. While the test sets contain 3,000 sentence pairs each, the training set consists of approximately 4.5 million sentence pairs and the average sentence length is 26 words in English and 30 words in German, with a vocabulary size of about 160,000 words for English and 220,000 words for German. You can scroll down and download it . here 10. CelebA This dataset is a popular large-scale face attributes dataset which comprises over 200,000 celebrity images. It was first released by researchers at the Chinese University of Hong Kong in 2015. An image in the CelebA consists of 40 facial attributes such as age, hair colour, facial expression and gender. Also, these images were retrieved from the internet and cover a wide range of facial appearances, including different races, ages and genders. Bounding box annotations for the location of the face in each image, as well as 5 landmark points for the eyes, nose and mouth. Note: The CelebA dataset is under the license of the Creative Commons Attribution-Noncommercial-Share, which permits it to be used for non-commercial research purposes as long as proper credit is given. To use the CelebA dataset in PyTorch, you can use the class, which is part of the module. You can download the dataset from the or . torchvision.datasets.CelebA torchvision website here 11. UCF101 The UFC101 dataset is widely used for video classification in the field of computer vision. It comprises 13,230 videos of human actions from 101 action categories, each containing around 100 to 300 videos. The PyTorch UCF101 dataset is a preprocessed version of the original UCF101 that is ready to use in PyTorch. The preprocessed dataset comprises video frames that have been normalized and resized, including corresponding labels for each video. It’s also split into three sets: training, validation and testing, with approximately 9,500, 3,500 and 3,000 videos, respectively. To download the dataset, click . here 12. HMDB51 The HMDB51 dataset is a collection of videos retrieved from collected from various sources, including movies, TV shows and online videos, comprising 51 action classes, each with at least 101 video clips. It was created by researchers at the University of Central Florida in 2011 for research in human action recognition. The videos are in AVI format and have a resolution of 320x240 pixels, with ground-truth annotations for each video, including the action class label and the start and end frames of the action within the video. Each video in the dataset represents a person performing an action in front of a static camera. The actions include a wide range of activities every day, such as jumping, waving, drinking, and brushing teeth, as well as complex actions like playing guitar and horseback riding. Note: It is used in concomitance with the dataset, which contains a larger number of action classes but with fewer videos per class. UCF101 You can download the dataset . here 13. ActivityNet The ActivityNet is a large-scale video understanding dataset that comprises over 20,000 videos from a diverse set of categories like cooking, sports, dancing etc. The videos have an average length of 3 minutes and are annotated with an average of 1.41 activity segments.  It is available in PyTorch, which is easy to use in deep learning frameworks. The PyTorch version offers preprocessed features extracted from the RGB frames and optical flow fields of each video, as well as ground truth annotations for the temporal segments and labels of activity. You can download the dataset . here 14. VOC dataset (Visual Object Classes) The VOC dataset (Visual Object Classes) was first introduced in 2005 as part of the PASCAL VOC Challenge, which aimed at advancing the state of the art in visual recognition. It consists of images of 20 different object categories, including animals, vehicles, and common household objects. Each of these images is annotated with the locations and classifications of objects within the image. The annotations include both bounding boxes and pixel-level segmentation masks. The dataset is split into two main sets: the training and validation sets. The training set contains approximately 5,000 images with annotations, while the validation set contains around 5,000 images without annotations. In addition, the dataset also includes a test set with approximately 10,000 images, but the annotations for this set are not publicly available. To download the recent dataset, you can download it from the , load it in Python via our open source with just one line of code, or download it from . website Deep Lake Kaggle 15. YCB-Video This dataset is a collection of 3D object models and video sequences designed for object recognition and pose estimation tasks. It contains 21 everyday household items, with each object captured in various lighting conditions and camera viewpoints. The dataset provides pixel-level ground truth annotations and is commonly used for evaluating computer vision algorithms and robotic systems. Click here to download the . dataset 16. KITTI The KITTI dataset is a collection of computer vision data for autonomous driving research. It includes over 4000 high-resolution images, LIDAR point clouds, and sensor data from a car equipped with various sensors. The dataset provides annotations for object detection, tracking, and segmentation, as well as depth maps and calibration parameters. The KITTI dataset is widely used for training and evaluating deep learning models for autonomous driving and robotics. To download the recent dataset, you can download it from the , or download it from . website GitHub Kaggle 17. BraTS The BRATS PyTorch dataset is a collection of magnetic resonance imaging (MRI) scans for brain tumour segmentation. It consists of over 200 high-resolution 3D brain images, each with four modalities (T1, T1c, T2, and FLAIR) and corresponding binary segmentation masks. The dataset is commonly used for training and evaluating deep learning models for automated brain tumour detection and segmentation. You can download this dataset on Kaggle by clicking . here 18. Multi-Human Parsing The Multi-Human Parsing PyTorch dataset is a large-scale human image dataset with pixel-level annotations for human part parsing. It contains over 26,000 images of humans, each segmented into 18 human part labels. The dataset is used for training and evaluating deep learning models for human pose estimation, segmentation and action recognition. To download the dataset, click . here 19. Charades This dataset is a large-scale video dataset for action recognition and localization. It comprises over 9,800 videos of daily activities, such as cooking, cleaning, and socializing, with an average length of 30 seconds per video. The dataset provides detailed annotations for each video, including temporal boundaries for actions and atomic visual concepts, making it suitable for training and evaluating deep learning models for action recognition, detection and segmentation. The Charades PyTorch Dataset is widely used in the computer vision research community and is freely available for and use. download 20. TU Berlin This dataset is a rich collection of high-resolution images and 3D object poses for object detection and pose estimation. It contains over 11,000 images of 60 object categories, with annotations for 2D and 3D poses. With its large size and diverse object categories, the TU Berlin PyTorch dataset provides an excellent testbed for developing robust and accurate object detection and pose estimation models. You can get the dataset directly from the website by clicking . here Common Use Cases for PyTorch Datasets Natural Language Processing Penn Treebank Stanford Question Answering Dataset (SQuAD) STS-B (Semantic Textual Similarity Benchmark) WMT'14 English-German Yelp Reviews Computer Vision CelebA Caltech 101 Stanford Dogs YCB-Video VOC dataset (Visual Object Classes) KITTI Multi-Human Parsing Charades TU Berlin Fashion-MNIST Medical Image Analysis Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) BraTS Human Activity Recognition UCF101 HMDB51 ActivityNet Final Thoughts PyTorch is useful for research and experimentation, where the focus is often on developing deep learning models and exploring new approaches. Additionally, PyTorch has gained a reputation as a research-focused framework with a growing community of developers and researchers contributing to the ecosystem. These datasets have applications in multiple fields and are also available for anyone to download and use freely. The lead image of this article was generated via HackerNoon's AI Stable Diffusion model using the prompt 'PyTorch superimposed images'.

20 Best PyTorch Datasets for Building Deep Learning Models

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There is no instruction to the decision-making process. However, important decisions are usually made by analyzing tons of data to find the optimal way to solve a problem. That’s where we truly rely on logic and deduction. That’s why surgeons dig into anamnesis, or businesses gather key persons to see a bigger picture before making a turn. Relying on AI decision-making can significantly reduce the time spent on research and data gathering. But, as the final decision is up to the human, we still need to understand how our support system came up with these insights. So in this article, we’ll discuss AI explainability and why it’s important for such areas as healthcare, jurisprudence, or finance to justify any piece of information. What is explainability in AI? AI decision-support systems are used in a range of industries that base their decisions on the information. This is what’s called a data-driven approach – when we try to analyze available records to extract insights, and support decision-makers. The key value here is data processing, something a regular person can’t do fast. On the flipside, neural networks can grasp enormous amounts of data to find correlations and patterns, or simply search for the required item within an array of information. This can be applied to domain data like stock market reports, insurance claims, medical analysis, or radiology imagery. The problem is that, while AI algorithms can derive logical statements from data, it remains unclear what these determinations were based on. For instance, some computer vision systems for lung cancer detection show up to in their predictions. But can a pulmonologist rely just on model prediction without knowing if it is not mistaken tumor for fluid? 94% accuracy This concept can be represented as a black box, as the decision process inside the algorithm is hidden and often can’t be understood even by its designer. refers to techniques by which a model can interpret its findings in a way humans can understand. In other words, it’s a part of AI functionality that is responsible for explaining how the AI came up with a specific output. To interpret the logic of AI decision processes, we need to compare three factors: AI explainability Data input Patterns found by the model Model prediction These components basically are the essence of AI explainability implemented into decision-making systems. The explanation in this case, can be defined as how exactly the system provides insights to the user. Depending on the implementation method, this can be presented as details on how the model derived its opinion, a decision tree path or data visualization. AI decision-support system types Decision-support systems can be categorized by purposes and audiences. But we can split them into two large groups: consumer and production-oriented. The first group of systems unites AI decision making systems that are generally used for everyday tasks. Tools like recommendation engines don’t require explainability as it is. We don’t question the logic behind music recommendations made by neural networks. Unless it suggests complete garbage, it’s unnecessary to know how the system picked that song and brought it to you. You’ll probably just listen to it. consumer-oriented , in contrast, usually deal with critical decisions that require justification. This is like how a person in charge of a final decision is responsible for any possible financial loss or harm. In this case, the AI model acts as an assistant for professional activity, providing explicit information for a decision-maker. Production-oriented systems So now, let’s look at the examples of explainability in production-oriented AI systems and how it can support decision-makers in , finance, social monitoring, and other industries. healthcare Explainable AI for medical image processing and cancer screening Computer vision techniques are actively used in the processing of medical images, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Whole-Slide images (WSI). Typically, these are 2D images used for diagnostics, and surgical planning, or research tasks. However, CT and WSI can often depict the 3D structure of the human organism reaching enormous size. For example, WSI of human tissues 21,500 × 21,500 pixel size and even more. Efficient, fast and accurate processing of such images serves as the basis for early detection of various , such as: can reach cancer types Renal Cell Carcinoma Non-small cell lung cancer Breast cancer lymph node metastasis The doctor’s task in this case, is to strip the information down to visually analyze the image to find suspicious patterns in the cellular structure. Based on the analysis, the doctor will provide diagnosis, or make a decision for surgical intervention. However, since WSI is a really large image, it takes heaps of time, attention and qualification to analyze. Traditional computer vision methods will also require too much computational resources for end-to-end processing. So how can explainable AI help here? In terms of WSI analysis, explainable AI will act as a support system that scans image sectors and highlights regions of interest with suspicious cellular structures. The machine won’t make any decision, but will speed up the process and make the work of a doctor easier. This is possible because WSI is more accurate and quicker in terms of image scanning, giving less chance to omit specific regions. Here is an of renal clear cell carcinoma on the low resolution WSI. example As you can see on the image on the right, the AI support system highlights the regions of high risk where cancer cells are more likely to be. This eliminates the need to physically analyze the whole image of a kidney, providing hints for medical expertise and attention. Similar heatmaps are used for even closer and more high resolution analysis of kidney tissue. The advantage of such systems is not only in operational speed and greater accuracy of analysis. The image can also be manipulated in different ways to provide a fuller picture for diagnostics and to make more accurate surgical decisions if needed. You can check the of kidney tissue WSI analysis to see how the interface looks and how exactly it works. live demo Explainable AI for text processing Text processing is a standalone task, as well as a part of bigger workflows in different industries such jurisprudence, healthcare, finance, media, and marketing. There are plenty of techniques for deep learning that are capable of analyzing text from different angles. Here we can list , natural language understanding (NLU), and generation (NLG). These algorithms can classify text by its tone and emotional shades. It can also detect spam, produce a short summary, or generate a large contextual text based on a small initial piece. natural language processing (NLP) Besides use in document processing, machine translation, and communication technologies, there are other applications based on explainable AI. SELF-HARM OR SUICIDE PREDICTION One of the applications of text classification was found in . Facebook employees have developed an algorithm that allows you to determine users’ suicidal moods based on an analysis of the text of their posts, as well as responses to these posts of relatives and friends. Meta If suddenly the system gives a signal that there is a high probability that a person can harm himself, then this information is submitted to the community operations team for manual processing and verification. Explainable AI supports teams with constant scanning of media fields to find risky commentaries. After that, the human agent can use data explanations presented by the model that will point to how the machine understood if that unique person is showing the risk for suicide. FINANCIAL NEWS MINING Another interesting case is, for example, the use of financial news texts in conjunction with fundamental analysis to forecast stock values. A prime example of this would be “trading on Elon Musk’s tweets,” whose tweets had a dramatic effect on the value of several cryptocurrencies (Dogecoin and Bitcoin), as well as the value of his own company. The point is that if someone can impact stock prices so much through social media, it is worth knowing about these posts in advance. AI algorithms allow you to process a huge number of news headlines and texts in the shortest possible time by: analyzing the tonality of the text identifying the runoff or company referred to in the article identifying the name and position of the specialist quoted by the publication All this allows you to quickly make decisions on a purchase or sale, based on the summary of social media information. In other words, such AI applications can support individuals and businesses in their decision by supplying information about recent events that could impact stock prices and different fluctuations in the market. The explainability part is responsible for justifying the data by recognizing company, event, persona, or resource entities with named entity recognition technology. This allows the decision-support system user to understand the logic behind the model’s prediction. What do we need to make the work of AI algorithms with text understandable? We need to uncover the patterns AI “saw” in the text. We want to see what words and connections between the words that the algorithm relied on, proving its conclusion about the tonality of the text. How AI explainability works in text processing Consider explainable AI using the example of financial news headlines of articles. Below will be several headings and the reader is invited to assess the tonality of these headings (positive, neutral, negative), as well as try to track and reflect on which words in the title you, as a reader, relied on when you assigned the title to a particular group: Brazil’s Real and Stocks Are World’s Best as Foreigners Pile In Analysis: U.S. Treasury market pain amplifies worry about liquidity Policymaker Warns of Prolonged Inflation due to Political Crisis A 10-Point Plan to Reduce the European Union’s Reliance on Russian Natural Gas Company Continues Diversification with Renewables Market Software Firm Partnership Now let’s compare your expectations with how these headers classify AI and see what words were important for the algorithm to make a decision. In the pictures below, the words on which the AI algorithm relies in making a decision are highlighted. The redder the shade of the highlight, the more important the word was in making the decision. The more the shade was blue, the more the word tilted the algorithm toward making the opposite decision. Brazil’s Real and Stocks Are World’s Best as Foreigners Pile In
Class: Positive 2. Analysis: U.S. Treasury market pain amplifies worry about liquidity
Class: Negative 3. Policymaker Warns of Prolonged Inflation due to Political Crisis
Class: Negative 4. A 10-Point Plan to Reduce the European Union’s Reliance on Russian Natural Gas
Class: Neutral 5. Company Continues Diversification with Renewables Market Software Firm Partnership
Class: Positive How long did it take you to read all these headlines, analyze and make a decision? Now imagine that AI algorithms can do the same, but hundreds of times faster! How to integrate Explainable AI features into your project? One might think that the integration of explainable AI into a project is quite a sophisticated and challenging step. Let us demystify it. In general, from the perspective of explainability, AI systems may be classified into two classes: Those which support explainability out-of-the-box Those which need some third-party libraries to be applied to make them explainable The first class of AI includes instance and semantic segmentation algorithms. AI algorithms based on attention are good examples of self-explainable algorithms we’ve shown above based on examples of WSI kidney tissue cancer risk images. The second class of AI algorithms, in general, doesn’t support out-of-the-box self-explainability. But this doesn’t make them completely unexplainable. As an example of such an algorithm, we’ve shown text classification above. We can use 3rd-party libraries on top of AI algorithms that make them explainable. Usually, it is easy to use LIME or SHAP libraries to make your AI explainable. However, the explainability requirements may differ on the project or user objectives, so we suggest you enlist the support of an AI expert’s team to receive proper consulting and development. AI-explainability enhances trust One of the key factors that prevents mass adoption of AI is the lack of trust. Decision-support tools in data driven organizations already perform routine tasks of reporting and analysis. But at the end of the day, these systems are still not capable of providing a justified opinion on business-critical or even life-critical decisions. Explainable AI is a step further in data-driven decision making, as it breaks the black box concept and makes the AI process more transparent, more verifiable and trusted. Depending on the implementation, a support system can provide more than just a hint, because as we track down the decision process of an AI algorithm, we can uncover new ways to solve our task, or be provided with optional types of decisions. . Featured image credits: Tara Winstead Written by , AI/ML Team Leader at . Evgeniy Krasnokutsky MobiDev The full article was originally published and is based on MobiDev technology research. here

Using Explainable AI in Decision-Making Applications

Sonic resonance is a manifestation of energy that shatters targeted microscopic organisms, and potentially infected Coronavirus cells - without harming surrounding cells. I should be particularly careful when it comes to discussing cures for Coronavirus, or any medical condition. So I'll start by saying I'm not a doctor, virologist, or biologist. However, I've been studying this field for most of my life. I believe it has potential to treat a very wide range of diseases. This includes the Coronavirus, otherwise known as COVID-19. I am in no way saying the technology I explain here irrefutably cures any medical condition. But I believe it can, and eventually will, once the technology is properly developed. But use your own personal judgement to determine whether or not what I present is credible. I believe wholeheartedly this and related technology has awe-inspiring potential. In fact, I believe a cure for COVID-19 is barely the tip of the iceberg. It's a big thing to say, right? But my main limitation to develop it is time. I'm writing this in hopes others, who have the time and resources, follow-up on the work. Before I continue, I'd like to get a few things off my chest about COVID-19. How Deadly is Coronavirus? This is up for debate. But you don't need to be a genius in statistics to understand many cases will likely be unreported. Already the has stated that many cases are mild, with minor symptoms. So it's entirely possible you may have the Coronavirus, and not even be aware. You may feel like you have only a mild cold. If this is the case, assume you have Coronavirus, and isolate yourself from others. Many local authorities suggest only get tested if you have recently been overseas, or been in contact with someone else that has tested positive (due to shortage of test kits). World Health Organization So far it appears the majority of deaths or serious cases occur with elderly people, or others who are similarly compromised in some way. This includes . If you're currently a smoker, now is a great time to quit. In fact any time is a great time to quit. smokers, who inevitably have less-healthy lungs Some nations have a higher mortality rate. You may assume this comes from statistical variance. Basically "statistical variance" is the term used to describe events that occur outside the expected odds or frequency. A simple example is the , where red and black have an equal chance of winning. But after perhaps 100 spins, you probably won't have an equal amount of red and black spins. You can have 40 blacks, and 60 reds (this is excluding the existence of green zero). It doesn't mean red is more likely to win. This is an example of statistical variance. odds of a roulette wheel While we'd expect at least some statistical variance, the difference in mortality rates between countries is large and significant. And perhaps too large to put down to only variance. Potential Causes of Variance in Mortality Rates Consider mortality rates of two groups of people jumping out of an aeroplane. Assume each group has 100 people. Group 1 has a mortality rate of 80%. Group 2 has a mortality rate of 10%. You could say the average mortality rate is 45%. But this would be a clumsy assumption, because it does not include critical variables. The missing variables may not be initially obvious, because of assumptions you may make. Such missing variables include which subjects: Wore parachutes. Had any training in deploying their parachute. Were hit by another plane on the way down. Jumped from an airborne airplane, or one on the ground. I'm renowned for arguably stupid examples, but you get my point. There are a lot of things we are missing when addressing the mortality rates of COVID-19. We already know about the age and overall health of the patient. But if we dig deeper, we can consider things like: Specific countries have a higher population of smokers. And these specific countries have significantly more infected citizens. Some cultures may eat specific foods that improve immune systems, and specifically aid the lungs. How many of these deaths would have been prevented, if the virus didn't spread so quickly, and put undue strain on the healthcare system? The quality of health care, and staff training. The accuracy rate of COVID-19 tests. How coroners determine the cause of death. The list goes on. So ultimately we don't know what the true mortality rate is. There are just too many variables. Simply the more data we have, the more reliable the assessment becomes. However, at this stage, it appears safe to say the mortality rate (for whatever reason) is significantly higher than the typical flu. But it does not appear so high that we should expect imminent death. The majority of social distancing rules appear not to completely stop the virus, but more to reduce the strain on the healthcare system (at least that's what we're told). Stopping the virus completely may not realistically be possible, without causing huge global financial problems - although we may already be past that point. Questionable Death & Infection Rates Now let's look deeper into the statistics. At such a critical time, you'd expect the authorities to be "on-the-ball" with everything, including accurately reporting statistics. It's no time to be sloppy. Two obvious problems are: : . That's a bit of a problem because it's clumsy guesswork, and will inevitably inflate figures. 1. Death rates are exaggerated The CDC instructed hospitals to list COVID-19 as the "cause of death", even if they assume it contributed to death . Previous reports indicate . If this is the case, then from 1,000,000 positive results, 800,000 people aren't actually infected. That just might explain why so many people don't experience any symptoms, and didn't even know they were "infected". 2. The COVID-19 tests have seriously questionable accuracy the false-positive rate was 80% Put these two points together, and you've got a really scary pandemic with so many people infected, and people dying everywhere. At least that's what it leads us to believe. Before this pandemic: About 8,000 people in the USA died every day, from various causes - completely unrelated to COVID-19. The scary numbers need to be put into perspective. And we need swift action, but not panic. It's All About Saving Lives, Right? . Some believe it the debt is so bad that it will pass even to the next generation. The Coronavirus has cost the World TRILLIONS of dollars The #1 killer in the world is heart disease. I'm not so sure the governments have spent TRILLIONS on researching heart disease. Or Cancer. It's kind of like how governments will spend trillions fighting terrorism, in other countries, when terrorism kills relatively few people. . That's around 350 deaths per year. Between 1994 and 2003, there were 3064 deaths from terrorism Don't get me wrong here. Terrorism is disgusting and must be fought. But can anyone honestly look at these statistics, and say it's all about saying lives? Is Coronavirus a New World Order Conspiracy? If the Coronavirus ends with a strong push towards a global cashless digital currency, greater digital surveillance, and greater centralized control of the World's population, it would certainly be questionable. Refer to my article about the ( ), and distinguishing between conspiracy fact vs conspiracy nonsense. New World Order which Google has censored Wild and incorrect conspiracies are harmful. But dismissing real conspiracies, just because some people believe in "Flat Earth", is even more harmful. Yes, real conspiracies exist. But I'm not aware of any solid evidence to suggest Coronavirus was released on-purpose, as part of a conspiracy. Maybe it was, maybe it wasn't. There have been claims from qualified people who say the virus was engineered. And there have been counter-arguments from other qualified people. I believe likely us ordinary people will probably never know the truth. But at the very least, some groups will benefit from Coronavirus. It certainly cleared up the protests in Hong Kong, didn't it? The , and weren't achieving much anyway. Hong Kong was arguably in a civil war. Not anymore. At least for now. protests weren't going anywhere Furthermore, I believe clearly specific groups and organizations are attempting to "profiteer" from the outbreak - just as some groups inevitably attempt to do during wars. And I'm not talking about people selling over-priced toilet paper. Often their actions are not in our best interests. So we need to be aware of any changes that we may hastily make in panic, and the long-term ramifications. One thing I'm particularly watching very closely is how the global financial problems are resolved. I suspect that it will lead to greater centralization of banking and finance. If that's the case, then I suggest we all back right off from that. Centralization is convenient, IF the central power can be trusted. But I don't believe any central authority can or should be trusted, especially with something that affects everyone. There's just too much potential for abuse. Put another way: Keep in mind the USA government alone has spent . They claim they don't even know where it went. Imagine if you were being audited by the IRS and gave them that excuse. Again, we're talking about TRILLIONS. And again, we're talking about the organizations that made the laws they expect us to follow, and are expected to follow best. Why isn't at least someone in jail for a very, very long time? TRILLIONS of tax-payer's money they can't account for So do you trust people like this with absolute power to do the right thing by you, especially when they can so easily avoid an audit or just say "we don't know where your money went, and there's nothing you can do about it"? I didn't think so. In 2001, the USA government declared it "misplaced" $2.3 TRILLION of taxpayer money. Whether it's conspiracy, or incomprehensible incompetence, obviously they can't be trusted with our money. Put yet another way: Do you think a computer database is more robust and resistant to hacking if it's centralized, or decentralized? So should we be pushing towards centralization, or decentralization. It's pretty obvious: Centralization = weaker and more vulnerable to collapse and corruption. Decentralization = stronger and less vulnerable to collapse and corruption. Now that I've gotten these things off my chest, I'll discuss what may be a breakthrough cure for many diseases - not just Coronavirus. Using Sound Resonance To Destroy Disease Sound can easily shatter glass if the resonant frequency is used. You may have seen opera singers shatter a wine glass with only their voice. This is possible not just because they have a loud voice, although that's part of it. It is achieved with . the right sound pitch (frequency) that shakes apart the glass Similarly, every object has a resonant frequency. But most ordinary objects aren't as fragile as a wine glass. There is evidence that ancient civilizations used sound and vibration for medical treatments. No, it's not just hippie-talk. Yes, there are many so-called new-age healers that claim to heal with all kinds of apparently bizarre technologies, including sound. And many of these people may be snake-oil salesman. However, some people may just not be doing it right. So should we be so quick to dismiss an approach as quackery? TED Talk about using sound resonance to destroy cancer and diseased cells Have a close look at this: I'm a big believer that if I can't physically see something work, or have some kind of verifiable proof, I won't believe it. You could say I have a scientific mind, and an open mind. I believe by definition, a scientific mind be an open mind. We don't know everything. Just consider how much we as a species have learned in the last 100 years, and how quickly we're evolving. Even the rate at which we're learning is increasing. https://www.youtube.com/watch?v=1w0_kazbb_U should “If you want to find the secrets of the universe, think in terms of energy, frequency and vibration.” - Nikola Tesla It's not just hippies that talk about frequency and vibration. So did Tesla - the man responsible for technology we use every day. The genius of Tesla was (until recently) mostly forgotten. His achievements are gradually becoming better understood in modern times. There are many more notable inventors who speak of frequency and vibration being the key. Another is Raymond Rife, who claimed he could cure cancer using vibrations. If you rely on Wikipedia, you'd believe his claims were fraudulent. Were they? It's not something we can easily determine, because we'd realistically need his complete technology to know. I believe it's more important to know whether or not his approach has merit. This is because often development of critical technology begins with PROOF OF CONCEPT. And there's no shortage of proof of concept. On the note of Wikipedia: I'm no fan of Wikipedia because I know from my own experience, with matters I know to be fact, that Wikipedia is prone to inaccuracy. And I've personally found its editors to be incredibly biased, for whatever reason. Why I Believe Sound Can Heal Almost All Disease At first it may be hard to get your head around how something as common as sound can have such far-reaching implications. You'll need to do through research to better understand it, but here's a start: . 1. Everything is energy and vibration in constant motion This constant motion is only possible with vibration. Nothing is static. This means all form is vibrating and pulsing energy. Even with our current model of atomic particles, there is an orbit, which is frequency and vibration. You may be also familiar with , which is just another interpretation of "everything is energy and vibration". string theory 2. Sound is just one manifestation of frequency and vibration. You need to stop perceiving sound as something we merely hear. . It can therefore be manipulated and used for various purposes. Even light and radiation is energetic vibration. All energy comes down to some form of vibration. Also consider that daylight may be "just light". But it grows our food. It runs our planet. Normal daylight may not burn paper. But focus it with a magnifying glass, and it will. Or consider what a beam of laser light can do. It is energy and vibration 3. Ancient civilizations appear to have used sound technology for healing too. Ancient doesn't mean inferior. We're not the most advanced we've ever been. You can do your own research on this. There's ample evidence to indicate ancient civilizations used sound for many purposes. For example, whoever constructed the pyramids in Egypt clearly used tools to cut and shape building blocks. Dig deeper and you'll conclude it appears to have been done with resonant sound frequencies. Watch these two videos: https://www.youtube.com/watch?v=Ez2TA6VYyuQ https://www.youtube.com/watch?v=8xVYrzbBfKE 4. Sound resonance can shatter anything All you need is the right pitch and intensity. All "form" comes from frequency and vibration, including living cells. Anything that is living or biological can be "disassembled" by similar forces. The "proof of concept" is clear. Remember, we are not talking hocus-pocus here. It's simple resonance, but on a different scale. . 5. Every cell in your body can be manipulated by frequency and vibration Harmonic vibrations strengthen "form". Dis-harmonic frequencies can destroy form, like radiation from mobile phones. Also see the above videos and see how sound frequencies create form in sand. The destruction of "form" of living cells is cancer, otherwise considered to be "damaged DNA". I believe clearly all disease comes from some form of disharmony, whether it be from toxins, genes or whatever. There are many things that can cause disharmony and cancer. So think of sound healing technologies to be more like an advanced version of using music for better health. It's just on a bigger scale. Keep in mind my example of ordinary light vs laser light. It doesn't mean you can listen to Mozart and be cured of cancer. At the very least, there is sufficient evidence harmonic music can improve your health, and even improve the structure of water (which we owe our existence to). Where To From Here? While it's everywhere on the news now, Coronavirus will eventually become something that happened some time ago. There will be a heavy toll to pay. There already is. will survive. Mostly common people and smaller businesses will be affected. Big businesses like banks and casinos the middle-class will probably be most affected; expanding the difference between rich and poor. Still I have little doubt we as a species will overcome it. We've overcome much worse. Clearly we all need to do our part in reducing its spread. The same goes for any disease. I believe the major and longer-lasting impact will be global finances, not the virus itself. That's a whole other topic. It may appear we're headed for financial catastrophe. In some ways, we're already there. But there's much more to the story. Some of the questions are: 1. What changes will we be pushed into, to apparently avoid disaster? 2. Was there already a financial catastrophe, and Coronavirus is a convenient excuse? 3. Who will be the greatest winners and loses from the changes? While I believe sound technology has the ability to heal a broad range of diseases, I also believe it's a technology that has even more uses, which we'll "rediscover" in time. If you in any capacity have the resources or capability to conduct related research, remember to think of the universe in terms of frequency and vibration. And remember, decentralization of power is perhaps the best way to avoid tyranny. Power corrupts. Absolute power corrupts absolutely. This is something we must keep in mind, in any crisis.

Is This a Breakthrough Cure For COVID-19 Coronavirus?

Why We Need to Think About the Full Stack of AI Artificial Intelligence is an emerging technology, with significantly more end users than the usual techno-shift. As discussed in both and , this is one of the fastest growing technologies without a mature bedrock of security and reliability support. Security Threats to High Open Source Impact Large Language Models A Tale of Two LLMs: Open Source vs the US Military's LLM Trials In this, we’ll talk about the larger scope of understanding and protecting artificial intelligence pipelines, whether it’s a large language or a large model. anything With models like ChatGPT representing its cognitive capabilities in the way the end user engages with them, a lot of people talk about AI think of it like a magical thinking head, this bot that can talk, but not quite walk. It Gets More Interesting, The More You Know To ensure the overall health and well-being of AI, we must recognize that it requires more than just a brain. Like the human body, AI relies on multiple interconnected components. Just as our lungs provide vital oxygen to sustain life, the carbon consumption of AI represents its energy source. Equally crucial is AI's immune system—cybersecurity—protecting against threats and vulnerabilities. In this series, we will explore the comprehensive nature of AI, focusing on the critical role of cybersecurity and the importance of holistic treatment for addressing security concerns. The AI Ecosystem: Beyond the Cognitive "Talking Head" AI, with models like ChatGPT at its forefront, possesses remarkable cognitive abilities. These models excel in generating human-like responses and assisting with various tasks. However, AI is not solely reliant on its cognitive prowess. To function effectively, AI requires a stable and secure ecosystem that sustains its operations. This ecosystem includes data sources, computational infrastructure, energy consumption, and cybersecurity measures. Carbon Consumption: AI's Lungs Similar to how our lungs enable the exchange of oxygen and carbon dioxide, the carbon consumption of AI represents its energy source. As AI continues to evolve and become more prevalent, addressing its energy demands and environmental impact becomes crucial. Striving for energy efficiency and sustainable practices is essential for the long-term viability of AI technology. “A main problem to tackle in reducing AI’s climate impact is to quantify its energy consumption and carbon emission, and to make this information transparent”, says Payal Dhar in a recent . Nature Machine Intelligence journal article To be clear, it’s not always about making the technology more efficient. Sometimes, it’s about making humans less stupid about wasting compute. Shifting developer mindsets to massive state-space feature engineering means getting people to understand why they need to use (guesstimate your feature space before you compute it, and how many different types of hay are in it). It’s why you have to statistically consider (how you want to investigate that haystack with best-of-n sampling, helping to decide if executing with the you have/want to spend is worth the byte flips). HyperLogLog Goodheart’s Law compute resources Cybersecurity: AI's Immune System Just as our immune system protects our body from infections, AI relies on robust cybersecurity measures to defend against threats. The interconnectedness of AI systems with various networks and data sources exposes them to vulnerabilities and potential attacks. Ensuring the security of AI requires comprehensive protection mechanisms, including data privacy, secure infrastructure, and resilient defenses against malicious actors. AI Security Groups and Resources OWASP Foundation AI/ML Security work being done: The OWASP foundation more broadly aims to improve the security of software through its community-led open source software projects. They have an and broadly light on technical implementation. provides developer centered information about the top known cybersecurity risks for open source machine learning, with a description, example attack scenario, and a suggestion of how to prevent it. AI Security Guide OWASP Project Machine Learning Security Top 10 The LFAI Security Committee LFAI supports AI and Data open source projects through incubation, education, and best practices. AI Security Regulatory Organizations focus on common sense regulation and standards around AI/ML that are compliant to exclusively EU regulation. Similarly, the is an independent working group to advance AI Risk aimed at security governance. The was similarly established solely for regulation, and only scoped to cloud services. efforts are also largely regulatory advisements. ETSI’s Securing Artificial Intelligence (SAI) AIRS group CSA Artificial Intelligence group IEE’s AI/ML Central Nervous System Infections: Addressing Security Concerns with Large Language (and Large Models) Anything In the context of AI, security concerns around the data and model processing around large langauge (and large anything models) can be likened to a central nervous system infection, affecting the core functionality of the entire system. While traditional cybersecurity threats often focus on breaches, data leaks, and unauthorized access, the challenges posed by Large Language Models (LLMs) and other generative technologies extend beyond conventional threats. Understanding these distinctive aspects is crucial for effectively addressing security concerns in this domain. Hallucinations One of the notable challenges associated with LLMs is the problem of hallucinations or the generation of false information. Due to their ability to generate human-like responses based on training data, LLMs may inadvertently produce content that lacks factual accuracy or even fabricate entirely false narratives. This presents a unique security risk as it can lead to the dissemination of misinformation, manipulation of public opinion, and potential damage to the credibility of AI systems. Mitigating this risk requires a combination of careful training data curation, robust fact-checking mechanisms, and ongoing validation processes to ensure the veracity of the generated content. . This is a security problem when downloading open source package registries at the moment Internal Drift Another significant concern is internal drift within LLMs and generative models. Over time, these models can exhibit a shift in behavior, generating responses that deviate from the intended purpose or ethical guidelines. This phenomenon poses a challenge to maintaining control and predictability in AI systems, as it introduces an element of uncertainty that can be exploited by adversaries. Addressing internal drift requires continuous monitoring and adaptation, establishing feedback loops between developers and the models, and implementing techniques such as reinforcement learning to reinforce desired behavior and align the outputs with ethical standards. Proactive threat detection involves actively monitoring the behavior of LLMs and generative models to identify potential vulnerabilities or anomalies in their output. Secure development practices entail incorporating robust security measures throughout the entire development lifecycle, including secure coding practices, vulnerability testing, and adherence to established industry standards. A Holistic Treatment for AI Security To ensure the longevity and trustworthiness of AI, we’ll have to integrate security considerations throughout the AI development lifecycle, establishing strong partnerships with cybersecurity experts, and fostering a culture of security awareness and education. Implementing ethical guidelines, robust encryption practices, and continuous monitoring. . It’s All About Attention I’ve spent a lot of my life thinking about how humans interact with computers and computers that interact with humans. I used to study how we could design better Boeing Airline cockpits by getting real pilots into a 4D flight simulator of potentially life-threatening conditions to see where they looked, how they moved, and what they said to prevent a crash - . Later, I worked at the National Institutes of Health on , where we timed simple visual feedback to real-time brain networks to improve clinical psychiatric outcomes without any pill-based intervention. back or not real-time fMRI neurofeedback Just attention. Attention is powerful. It’s probably the most powerful thing to learn about cybersecurity. When threat modeling any technical stack, you have to be able to think about where are people paying attention. That’s your most exploitable surface area. aren’t Emerging technologies are always a really interesting place to observe people’s attention. There hasn’t really been an emerging technology that so many people have had an opinion on before, because this time it’s just a face-to-deep technology that’s genuinely easy to start using. That’s beautiful. Attention and awareness are different things though. We need to understand how the whole thing works. You Should Be Paying Attention to Chip Security AI Chips: The Heart of the AI Body AI-specific chips, often referred to as neural processing units (NPUs) or graphics processing units (GPUs), serve as the vital heart of the AI ecosystem. Much like the human heart, these specialized hardware components play a crucial role in enabling the efficient and optimal functioning of AI systems. In this series, we delve into the significance of chip technology and its pivotal role in ensuring the security and robustness of the AI body. The Powerhouse Organs: AI-specific chips are specifically designed to accelerate AI computations, enabling exceptional performance, efficiency, and speed. These chips are tailored to meet the unique requirements of AI workloads, such as training deep neural networks and running complex AI algorithms. Just like the heart pumps blood to supply oxygen and nutrients to the body, AI-specific chips empower AI systems to process and analyze vast amounts of data with remarkable speed and efficiency. The Importance of Chip Technology: Advancements in chip architectures and designs have led to significant breakthroughs in AI capabilities. Parallel processing, specialized matrix calculations, and optimized memory access are just a few examples of the technological advancements that AI-specific chips have brought to the field. These advancements push the boundaries of AI performance, enabling AI systems to tackle increasingly complex tasks and deliver accurate results. Additionally, chip technology plays a vital role in addressing energy consumption concerns. As AI deployment expands, the energy efficiency of AI systems becomes a crucial factor. Advancements in chip technology contribute to reducing energy consumption, making AI more sustainable and environmentally friendly. The Role of Secure Chip Technology: As AI becomes more prevalent and critical in various sectors, ensuring the security of AI-specific chips becomes paramount. The article " " has some great key points to understand, but I’ll boil them down to two that you need to understand today: Security Becomes Much Bigger Issue For AI/ML Chips, Tools The rapid development of AI and machine learning chips has led to increased potential for security threats, including intellectual property theft and attacks that can corrupt data or lead to ransomware (this can be supply chain and supplier threats) The need for standards that include both hardware and software. It suggests using multiple AI algorithms and diverse sets of training data to reduce the risk of attacks. If you are optimizing - yes, I said optimising, not just running, an AI system, you will be building a system that maximizes the compute over cost. (GPUs) and Intel (CPUs and VPUs), and with (TPU) and that’s just the ones who have been on the market the longest - emerging tech means emerging market. You can do this with hyperscalers (kind, of) but what if you are building one from scratch? You’ve got NVidia Google Tensor Processing Unit But, oof, do we have some these days, as seen in the “Blessings of Scale” from . computational power Compute Trends Across Three Eras of Machine Learning Securing the Evolving AI Body: Just as the heart is vital for maintaining overall health, ensuring the security of AI-specific chips is crucial for the resilience and trustworthiness of the AI body. Secure chip technology must be integrated into every stage of the AI development process, from chip design and manufacturing to deployment and maintenance. Collaborative efforts among chip manufacturers, AI developers, and cybersecurity experts are essential to identify and address potential vulnerabilities in chip architectures and mitigate emerging threats. By prioritizing chip security, we can ensure that the AI body remains robust, efficient, and secure. Where do you want to see this go next? Town Hall mode is on this Hackernoon article, so feel free to comment anywhere on this article where you have a question, comment or better idea than myself. The author will absolutely read it.

Securing AI: Concerns & Immune Systems for Emerging Technologies

AI in Biotech: Challenges, Use Cases and Prospects

This paper is under CC 4.0 license. available on arxiv Authors: (1) Kota Chin, University of Tsukuba, National Institute of Information and Communications Technology Japan; (2) Keita Emura, Kanazawa University, Japan National Institute of Information and Communications Technology Japan; (3) Kazumasa Omote, University of Tsukuba National Institute of Information and Communications Technology Japan. Table of Links Abstract & Introduction Preliminaries Proposed Anonymous Yet Accountable Contract Wallet System Implementation Conclusion & References V. CONCLUSION In this paper, we proposed an anonymous yet accountable contract wallet system based on account abstraction and accountable ring signatures. The proposed system is implemented using Solidity for zkSync. Moreover, we discussed potential of the proposed system, e.g., medical information sharing and asset management. Since the current implementation results using Solidity show the required costs are expensive, our result here might be regarded as somewhat conceptual. However, to the best of our knowledge, no previous implementation result is known that confirms the cost to run an accountable ring signature scheme in Solidity to date, and we believe that our result can be seen as an important stepping stone to provide anonymity and accountability simultaneously in blockchain systems. Investigating other applications of the proposed system will be left to future work. The underlying account ring signature scheme does not provide post-quantum security due to the discrete logarithm-based construction. Thus, it is difficult to accept the current construction as a platform to manage large amounts of assets due to the progress of quantum computing. Because a post-quantum accountable ring signature scheme has been proposed in [7], it would be interesting to employ the scheme, precisely, how to implement it using Solidity is left to future work. The authors would like to thank Dr. Miyako Ohkubo (NICT) for her invaluable comments and suggestions. This work was supported by JSPS KAKENHI Grant Numbers JP21K11897 and JP22H03588. Acknowledgment: REFERENCES [1] Arrest of suspected developer of Tornado Cash. https://www.fiod.nl/arrest-of-suspected-developer-of-tornado-cash/. August 12, 2022. [2] StarkNet. https://starkware.co/starknet/. [3] zkSync. https://zksync.io/. [4] Jean-Philippe Aumasson, Daniel J. Bernstein, Ward Beullens, Christoph Dobraunig, Maria Eichlseder, Scott Fluhrer, Stefan-Lukas Gazdag, Andreas H ¨ulsing, Panos Kampanakis, Stefan K ¨olbl, Tanja Lange, Martin M. Lauridsen, Florian Mendel, Ruben Niederhagen, Christian Rechberger, Joost Rijneveld, Peter Schwabe, and Bas Westerbaan. SPHINCS+: Submission to the NIST post-quantum project, v.3.1. https://sphincs.org/data/sphincs+-r3.1-specification.pdf. [5] Asaph Azaria, Ariel Ekblaw, Thiago Vieira, and Andrew Lippman. MedRec: Using blockchain for medical data access and permission management. In 2016 2nd International Conference on Open and Big Data (OBD), pages 25–30, 2016. [6] Eli Ben-Sasson, Iddo Bentov, Yinon Horesh, and Michael Riabzev. Scalable, transparent, and post-quantum secure computational integrity. IACR Cryptol. ePrint Arch., page 46, 2018. [7] Ward Beullens, Samuel Dobson, Shuichi Katsumata, Yi-Fu Lai, and Federico Pintore. Group signatures and more from isogenies and lattices: Generic, simple, and efficient. In EUROCRYPT, pages 95–126, 2022. [8] Ward Beullens, Shuichi Katsumata, and Federico Pintore. Calamari and Falafl: Logarithmic (linkable) ring signatures from isogenies and lattices. In ASIACRYPT, pages 464–492, 2020. [9] Dmytro Bogatov, Angelo De Caro, Kaoutar Elkhiyaoui, and Bj ¨orn Tackmann. Anonymous transactions with revocation and auditing in hyperledger fabric. In CANS, pages 435–459, 2021. [10] Jonathan Bootle, Andrea Cerulli, Pyrros Chaidos, Essam Ghadafi, Jens Groth, and Christophe Petit. Short accountable ring signatures based on DDH. In ESORICS, pages 243–265, 2015. [11] Vitalik Buterin, Ansgar Dietrichs, Matt Garnett, Will Villanueva, and Sam Wilson. EIP-2938: Account Abstraction. https://eips.ethereum.org/EIPS/eip-2938, 2020. [12] Jan Camenisch, Manu Drijvers, and Maria Dubovitskaya. Practical ucsecure delegatable credentials with attributes and their application to blockchain. In ACM CCS, pages 683–699, 2017. [13] David Chaum and Eug`ene van Heyst. Group signatures. In EUROCRYPT, pages 257–265, 1991. [14] Aisling Connolly, J´er ˆome Deschamps, Pascal Lafourcade, and Octavio Perez-Kempner. Protego: Efficient, revocable and auditable anonymous credentials with applications to hyperledger fabric. In INDOCRYPT, pages 249–271, 2022. [15] L´eo Ducas, Eike Kiltz, Tancr`ede Lepoint, Vadim Lyubashevsky, Peter Schwabe, Gregor Seiler, and Damien Stehl´e. CRYSTALS-Dilithium: A lattice-based digital signature scheme. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2018(1):238–268, 2018. [16] Kai Fan, Shangyang Wang, Yanhui Ren, Hui Li, and Yintang Yang. MedBlock: Efficient and secure medical data sharing via blockchain. Journal of Medical Systems, 42(8):136:1–136:11, 2018. [17] Tomilayo Fatokun, Avishek Nag, and Sachin Sharma. Towards a blockchain assisted patient owned system for electronic health records. Electronics, 10(5), 2021. [18] Pierre-Alain Fouque, Jeffrey Hoffstein, Paul Kirchner, Vadim Lyubashevsky, Thomas Pornin, Thomas Prest, Thomas Ricosset, Gregor Seiler, William Whyte, and Zhenfei Zhang. FALCON: Fast-fourier lattice-based compact signatures over NTRU. https://falcon-sign.info/falcon.pdf. [19] Tomoki Fujitani, Keita Emura, and Kazumasa Omote. A privacypreserving enforced bill collection system using smart contracts. In AsiaJCIS, pages 51–60. IEEE, 2021. [20] Merlin George and Anu Mary Chacko. MediTrans - patient-centric interoperability through blockchain. International Journal of Network Management, 32(3), 2022. [21] Jens Groth. Short non-interactive zero-knowledge proofs. In ASIACRYPT, pages 341–358, 2010. [22] Yiheng Liang. Identity verification and management of electronic health records with blockchain technology. In ICHI, pages 1–3. IEEE, 2019. [23] Joseph K. Liu, Victor K. Wei, and Duncan S. Wong. Linkable spontaneous anonymous group signature for ad hoc groups (extended abstract). In ACISP, pages 325–335, 2004. [24] Xingye Lu, Man Ho Au, and Zhenfei Zhang. Raptor: A practical latticebased (linkable) ring signature. In ACNS, pages 110–130, 2019. [25] Mohammad Moussa Madine, Ammar Ayman Battah, Ibrar Yaqoob, Khaled Salah, Raja Jayaraman, Yousof Al-Hammadi, Sasa Pesic, and Samer Ellahham. 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