Reviewerly/exHarmony huggingface dataset:
Regularization

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
Generative Networks

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
Least-Squares Problems

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
Deep Belief Networks

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
Path Planning for Collaborative Robot under Complex Biomedical Lab Environment

Collaborative robots are widely utilized in biomedical labs to assist or even replace humans to conduct operations to promote efficiency and accuracy. Robot assisted operations also provide improved safety when dealing with biochemical or biomedical research. To achieve advanced robot assisted collaborative operations, a fast, efficient, and intelligent path planning algorithm is necessary. Current methods lack specific considerations for the complex biomedical lab environment, resulting in relatively slow, long, and potentially dangerous paths. To address these issues, this paper proposes a new path planing algorithm based on classical rapidly exploring random tree method. Adaptive searching step size, target orientated searching direction, and further path optimization to avoid obstacle outline following are added to obtain faster and safer robot motion paths in biomedical labs. Finally, Matlab and ROS/Gazebo simulations are conducted to demonstrate the efficacy and efficiency of the proposed method.
Decision Trees

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
Generalization Theory

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
Principal Component Analysis

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
Kernel Methods

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
Explainable Learning

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
Recurrent Networks

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
Logistic Regression

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
Subject Index

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
Review: Video Analytics Technologies Available for Surveillance Systems

Smart Surveillance Systems are becoming an important aspect of our lives, reducing man labour and additionally increasing the accuracy of detection by reducing false positives. Specifically for an ATM, Surveillance system is very crucial because of the transactions happening being sensitive along with that drop-box containing confidential documents like cheques and bank forms. Hence, there is a need to develop a fool-proof system which can handle a lot of load and perform various surveillance tasks. Moreover, the systems also need to have network security to protect the data from being illegally traced and changed. In this paper, we will be reviewing and comparing various smart surveillance system methods which involve various technologies.
Network Security Issues in Blockchain Architectures

The aim of blockchain technology is providing transparency. Therefore, although blockchain does not ensure security of data, there exists no reason why any action should be regarded as fraudulent. With its wide application in various fields, including finance, banking, e-commerce and even public administration, it has become necessary for many organizations in different industries to adopt it as the best approach to solving problems and making decisions. Many businesses and individuals are realizing that blockchain makes it possible for them make business transactions faster and easier to carry out. This helps them save money compared to traditional financial institutions.
Breast Cancer Classification by Implementation of Deep-Learning with Dataset Analysis

Cancer is a fatal disease recognized and researched about, around the globe. Researchers and scientists have been investing their time and imparting their expertise, and knowledge for the advancements of traditional methods and treatments to tackle it. Recent surveys reveal that the mortality rate among the female populous, over the world, is also one of the results of breast cancer. The definition of breast cancer can be described as an uncontrolled aggressive growth of old cells which thereby aid the formation of a pernicious mass in the tissue of a breast. Gradually, this may result in the formation of a tumor of malignant nature. Deep learning, considered a sub-field of Machine Learning, enables experts to analyze, model, and study complicated or rather complex scientific data over a comprehensive list of medical applications. This study aims to create a user-friendly, adept system to perform the classification of breast tumors of malignant or benign nature. The proposed system is divided into two halves or stages. The initial stage is the pre-processing and analysis of the acquired dataset which also involves training of the neural network. The next and final stage is the classification of breast tumors by utilizing the created model and loading it onto an API through which users can upload tissue images and check what type of breast cancer the tissue contains. This would eliminate the time spent on studying every particular data using traditional clinical methods. This project would help support the radiologists in training, research, and diagnostic aspects and overall support the entire process of cancer diagnosis and treatment.
COVID-19 Risk Prediction and Contact Tracing

The emergence of the coronavirus COVID- 19 switched the limelight onto digital health technologies. To help the infection rates from surging, numerous governments are looking into applications that could help disrupt infection chains beforehand. We created a Self-Assessment Test using COVID Symptoms, that's capable of assessing the threat of COVID- 19 in the user using ML. The data also tracks the user and gives safety tips and recommendations. Using the Track Module, the user is notified of the nearby containment zones. The contact tracing module helps the user to maintain a specified distance from others.
Sentiment Analysis on Text with Emoticons Using Supervised Algorithm

Use of Social media is on a high level now a days. Everything going to be socially uploaded with a large interest Usually people uses text as well as emotional icons called emoticons to express their emotions. Sentiment Analysis is to show person's emotions, opinion by their reviews or posts. There are already many techniques to show calculations of sentiment analysis by posts or reviews like naive bayes or support vector machine. These gives better result in sentiment analysis field. Existing system uses unsupervised method of polarity calculation to analyze sentiment from text and emoticons. Without use of predefined dictionary it calculates polarity on the basis of presence of emoticons. However it restrict presence of emoticons to classify text and analyze sentiment. Proposed system works on Supervised learning method. It reduces restriction of presence of emoticons in existing system. It can work on only text, only emoticons as well as combination of both. Preprocessing steps contains feature extraction and removal of stop words. Here c5 classifier has been used for better accuracy and classify posts by rule-set.
English to Marathi Machine Translation Linguistic Divergence Using ANN

The field of machine translation is included in Natural Language Procession. The development of MT system Marathi to English language is much more challenging task because of linguistic divergences. The most significant aspect of MT is that it maximizes meaning, such that understanding the output requires the least amount of effort and time. In machine translation different Types of Technique Available but as per Literature review Neural Machine Translation gives the Best results. In This paper focus on three types of divergences and also Artificial Neural Network techniqueMachine translation's major problem of divergence has led to the development of neural machine translation (NMT), a different strategy to address the same problemIn this study, used a neural machine translation technique to construct a separate module. Here create thematic divergence module, Categorical divergence module and structural divergence module with 66.67 %, 71.43 %, and 51.25 % accuracy and Calculated BLEU score.
Layer 2 Solutions to Improve the Scalability of Blockchain

Blockchain based decentralized currencies are getting much attention these days and being used widely and deployed commercially, many popular platforms are accepting them as a mode of payment. The most popular among all is Bitcoin (BTC) has achieved great success, but these increased transaction volumes introduced problems like low throughput and high transaction processing time. It's not just with the Bitcoin but also other currencies based on Bitcoin which basically use proof of work principle to register a transaction on distributed ledger also facing similar issues leading to question of scalability of blockchain. This paper focuses on the solutions which can be implemented to make the blockchain scalable.
Video Captcha Proposition based on VQA, NLP, Deep Learning and Computer Vision

Visual Question Answering or VQA is a technique used in diverse domains ranging from simple visual questions and answers on short videos to security. Here in this paper, we talk about the video captcha that will be deployed for user authentication. Randomly any short video of length 10 to 20 seconds will be displayed and automated questions and answers will be generated by the system using AI and ML. Automated Programs have maliciously affected gateways such as login, registering etc. Therefore, in today's environment it is necessary to deploy such security programs that can recognize the objects in a video and generate automated MCQs real time that can be of context like the object movements, color, background etc. The features in the video highlighted will be recorded for generating MCQs based on the short videos. These videos can be random in nature. They can be taken from any official websites or even from your own local computer with prior permission from the user. The format of the video must be kept as constant every time and must be cross checked before flashing it to the user. Once our system identifies the captcha and determines the authenticity of a user, the other website in which the user wants to login, can skip the step of captcha verification as it will be done by our system. A session will be maintained for the user, eliminating the hassle of authenticating themselves again and again for no reason. Once the video will be flashed for an IP address and if the answers marked by the user for the current video captcha are correct, we will add the information like the IP address, the video and the questions in our database to avoid repeating the same captcha for the same IP address. In this paper, we proposed the methodology of execution of the aforementioned and will discuss the benefits and limitations of video captcha along with the visual questions and answering.