Reviewerly/exHarmony huggingface dataset:
DC operating circuit anomaly detection based on node voltage unsupervised time series

Abstract With the increase in power system load, the number of switch cabinets increases rapidly, and the accidents caused by DC operating circuits become more and more obvious. Abnormal detection has become an important part of the DC operating circuit. Abnormal data, mainly voltage measurement, often contain important information. Finding and analyzing these abnormal data can give early warning for circuit failure or failure. However, abnormal data is often difficult to obtain and lacks marks, that is, the problem of sample imbalance and unsupervised problems, resulting in abnormal detection methods that can not meet the increasing accuracy and efficiency requirements. Generative adversarial network (GAN) is an unsupervised deep learning model, which has been successfully applied to unsupervised time series anomaly detection. However, GAN has technical difficulties in the anomaly detection process, and there is still room for optimization in accuracy. In this paper, the unsupervised time series anomaly detection method based on GAN is studied, and an anomaly detection method of a DC operating circuit combined with LSTM and GAN is designed.
PFKMaster: A Knowledge-Driven Flow Control System for Large-Scale Power Grid

Various stability analyses of the power system are based on the results of power flow calculation, which is not always convergent. In practice, large manual efforts are required to be repeated many times by electrical engineers to ensure the convergence of power flow calculation, which consumes much manpower and time cost. Motivated by this, we develop a novel knowledge-driven system that can automatically improve power flow convergence based on knowledge from experiences, called PFKMaster. Based on the features of power flow calculation, a SAS triplet mechanism is designed to better represent the experience knowledge of humans. In our system, a knowledge model is proposed to guide the power flow calculation automatically and universally. To achieve the goal, we build a specific knowledge base. In this system, new knowledge is discovered based on the method of representation learning and affair logic. To ensure knowledge quality, we also propose knowledge cleaning techniques in our system. We test PFKMaster on a real large-scale power grid, and the experimental results demonstrate that 94.8% of the non-convergent samples can be improved by 1.92 s per sample.
Development of digital competencies in the context of digitalization of the education system: The experience of Mari State University

At the moment, the question of a unified model of digital competencies for teachers remains open both in our country and globally. There are many different approaches to the definition of the concept of “digital competence” and the content of such models. However, the analysis of several models allows us to identify common components and build a single model based on them. This article presents the experience of Mari State University on the creation and implementation of a teachers’ digital competencies model and the development of diagnostic tools for their assessment. The expediency of applying this model of digital competencies in the training of students of pedagogical specialties is established. The problems and challenges associated with the digital transformation of science and education are noted. The description of the magistracy program “Digital pedagogy” is given. This program is based on the model of digital competencies adopted at Mari State University. It provides continuous professional training of teachers capable of working in the conditions of digital transformation, which meets the challenges and needs of both the national program “Digital economy of the Russian Federation” and the “Strategy of digital transformation of economic sectors, social sphere, and public administration of the Mari El Republic”.
Perbandingan Algoritma K-Means dan Algoritma K-Medoids Pada Kasus Covid-19 di Indonesia

Analyzing Covid-19 data has been conducted in many types of research, but research on classifying each case from Covid-19 data in all provinces in Indonesia has yet to be available. This study uses two clustering algorithms, namely K-Means and K-Medoids, to classify positive cases recovered and died in the Covid-19 data into three clusters, namely low, medium and high. The research data is Covid-19 case data in all provinces in Indonesia from 2020 to 2021. In the clustering calculations, the three distance methods used in this study are the Chebyshev Distance, Manhattan Distance, and Euclidean Distance. Based on the Silhouette Coefficient test results for the three distance calculation methods, it was found that Manhattan Distance is the best distance calculation method for K-Means and K-Medoids. Furthermore, the results of testing the Sum Squared Error (SSE), Silhouette Coefficient (SC) and Davies Index Bouldin (DBI) methods for the resulting clusters show that the value generated by the K-Means algorithm is higher in the SC and DBI methods. This result is evidenced by the SC value of 0.838; 0.838; and 0.925 in positive cases, recovered and died. While the DBI value is 0.305 for positive cases, 0.295 for recovered cases and 1.569 for dead cases. Based on these values, it proves that K-Means is superior in grouping and placing clusters compared to K-Medoids.
Artificial intelligence: 5 questions answered that you should know

What actually is a neural network and are AIs conscious? Here are five important questions about artificial intelligence answered
Application of Classification Techniques on Breast Cancer Prognosis

The most prevalent form of cancer for females is breast cancer in Americans, and additionally, it is Asia's and the United States' second most common cause of death among females. In the United States in 2009, 40,600 persons died from breast cancer, 400 of whom were men. Clinical breast exams, radiographs, and ultrasounds are all excellent methods for testing for breast cancer today. A strategy for presenting a set of input-output sets to a network is referred to as supervised learning. The subsequent model parameters are updated iteratively to minimize the discrepancy around system prediction and real outcomes for training data. Three classification methods were tested on a breast cancer database: Probabilistic Learning, Logistic Regression, and Neural Net. Experiments demonstrated that Neuro Net categorization surpasses Tree Based categorization and Naïve Bayesian classification in terms of accuracy and precision for breast cancer early detection. Although it is established that the use of Ml techniques can enhance our knowledge of cancer progression, these methods must be confirmed before they're able to be employed in clinical practice. In this paper, we give a review of contemporary ML techniques used in cancer progression modelling. The prediction models talked about here have been trained using an assortment of supervised algorithms for machine learning, as well as varied input features and data. We have put together an inventory of the most recent articles that use such methods for modelling cancer risk or patient outcomes in context with the increasing desire to employ ML methods in cancer studies.
Neural Network-Guided Synthesis of Recursive List Functions

Abstract Kobayashi et al. have recently proposed NeuGuS , a framework of neural-network-guided synthesis of logical formulas or simple program fragments, where a neural network is first trained based on data, and then a logical formula over integers is constructed by using the weights and biases of the trained network as hints. The previous method was, however, restricted the class of formulas of quantifier-free linear integer arithmetic. In this paper, we propose a NeuGuS method for the synthesis of recursive predicates over lists definable by using the left fold function. To this end, we design and train a special-purpose recurrent neural network (RNN), and use the weights of the trained RNN to synthesize a recursive predicate. We have implemented the proposed method and conducted preliminary experiments to confirm the effectiveness of the method.
Cryptocurrency Wallets: Assessment and Security

Digital wallet as a software program or a digital device allows users to conduct various transactions. Hot and cold digital wallets are considered as two types of this wallet. Digital wallets need an online connection fall into the first group, whereas digital wallets can operate without internet connection belong to the second group. Prior to buying a digital wallet, it is important to define for what purpose it will be utilized. The ease with which a mobile phone transaction may be completed in a couple of seconds and the speed with which transactions are executed are reflection of efficiency. One of the most important elements of digital wallets is data organization. Digital wallets are significantly less expensive than classic methods of transaction, which entails various charges and fees. Constantly, demand for their usage is growing due to speed, security, and the ability to conduct transactions between two users without the need of a third party. As the popularity of digital currency wallets grows, the number of security concerns impacting them increases significantly. The current status of digital wallets on the market, as well as the options for an efficient solution for obtaining and utilizing digital wallets. Finally, the digital wallets’ security and future improvement prospects are discussed in this chapter.
Analyze the innovative exploration of war history movies from "Eight Hundred"

War movies in the new era not only bear the box office, but also bear a nation's attitude towards history and its own cognition. The innovation of technology, the liberation of ideas and the increasing distance of time and space between the audience and history make the development of war films face new problems and challenges. More and more mainstream war movies still do not break the traditional mainstream war movies' plot conception, characterization and didactic meaning are too rigid. As one of the few successful war movies in recent years, Babai overturns the flat characters in traditional movies and uses imagistic symbols interspersed with each other, which is a major breakthrough. And in the plot of a large number of "black and white contrast" to the film invisibly added a bit of tragedy. At the end of the film, the image of the white horse and the rat will be displayed, and the artistic depth of the film will be raised abruptly. These new explorations in the film will undoubtedly become an important reference for future war films.
Towards Reconstruction of 3D Geometry of Underwater Rubble Mounds via Structure from Motion

Reconstruction of underwater 3D geometry is indispensable in many marine science fields. Compared with traditionally employed devices like acoustic equipment for reconstructing 3D underwater scenes, Structure from Motion (SfM) advantageously give detailed, colored underwater 3D structures. Although reconstruction of 3D geometry via SfM is successfully performed in clear underwater conditions, is yet challenging in turbid environments, because of severe underwater image degradation which makes feature matching difficult necessary for SfM. Towards reconstruction of underwater 3D geometry via SfM, we first evaluate the degree of feature matching among enhanced underwater images. Second, we reconstruct 3D geometry of underwater rubble mounds in port construction. We show that color correction and image sharpening improve results of feature matching and reconstruction of 3D geometry.
Conversational Intelligent Chatbot for Placement Service

Nowadays it becomes very difficult to get placed in an organization which will perfectly suits to your profile because of the huge number of competition and lack of knowledge. Most of the students are not able to take a right decision at starting point of their career which always becomes first step towards their goals. The main aim of this project is to build a conversational AI Chatbot model which will interact with students who are walking through placement activities as well as students who are in a situation dealing with job related issues after completion of their respected degree's. This conversational AI will act as a human for the user and will help the user to resolve their queries and get a clear vision. For this we have used Dialog Flow, a Natural Language Processing (NLP) module to translate students’ queries during conversation in order to understand their intension. Information related to placement activities is provided to students. Key Word: Conversational AI chatbot, queries, placement activities, dialog flow, Kore.ai, Natural Language Processing.
Automatically balancing relocatable distributed collections

Summary In previous work, we introduced a distributed collections library for the APGAS for Java programming model. This library makes it possible for programmers to develop complex distributed programs thanks to the many abstractions and computation patterns supported. In particular, programmers can fully and dynamically change the distribution of data entries through high‐level abstractions. However, the problem of balancing the load between processes remains, especially in cases where multiple processes may be concurrently executing on a single host, or when the performance of the hosts used differs. To address this issue and to relieve the burden of programming a load balancing strategy for a specific application, we created a dynamic load balancer integrated into our library. This load balancer operates within a specific context in a manner which does not interfere with the program legibility. Internally, we implement a scheme inspired by the lifeline‐based global load balancer scheme first introduced in 10. We evaluate the performance of our integrated load balancer on a small‐scale Beowulf cluster.
Machine Learning Based Handwritten Text Recognition System for Postal Address Identification

Currently, dealing with the enormous variety of handwriting styles is a major issue in society, and we face more challenges. Blind people are facing a lot of problems reading the material, and a few people wrote the details in cheque books, which are not understandable. Handwritten text recognition is an important task in the field of image processing. It is very important to recognise handwritten characters that are available on a piece of paper, such as pin codes, place names, forensics, filled forms, and cheque books and old papers. In this view, this work has been framed to process the handwritten English characters in the form of images and, with the help of machine learning algorithms, handwritten text was predicted. This work will be very useful even for village people who are struggling to read the text in the document. Using the real time data set that was collected, simulation was done using machine learning algorithms, and the results were discussed.
Sign Language Recognition Enabling Communication Strategies for Persons with Hearing Impairment

Sign Language is mainly used by deaf (hard hearing) and dumb people to exchange information One of the important problems that our society faces is that people with disabilities are finding it hard to cope up with the fast-growing technology. The access to communication technologies has become essential for the handicapped people. Generally deaf and dumb people use sign language for communication but they find difficulty in communicating with others who don’t understand sign language. Sign language is an expressive and natural way for communication between normal and dumb people (information majorly conveyed through the hand gesture). So, we need a translator to understand what they speak and communicate with us. The sign language translation system translates the normal sign language to speech and hence makes the communication between normal person and dumb people easier. So, the whole idea is to build a communication system that enables communications between speech-hearing impaired and a normal person. Key Word: Foundation, Communication, ENT, hearing, hearing impairment, hearing difficulties.
105 Emotions in affective human-computer interaction

Emotions play a critical role in motivation and influence cognitive processing, both the fundamental processes (attention, perception, memory) and higher-level processes (learning, decision-making, planning). Emotions also play a crucial role in social interactions, mediating attachment, communicating behavioral intent, and facilitating the development of the empathic loop and interpersonal trust, and are also intricately linked with language. Linguistic stimuli can trigger powerful emotional states and emotions influence both the content and prosody of utterances, as well as the interpretation of linguistic stimuli. As computer systems proliferate into most aspects of our lives, it is increasingly important to consider the effects and roles of emotions in human-computer interaction (HCI). This is especially important in contexts where computers and computerized agents are transitioning from tools to collaborators, assistants and coaches. This chapter provides an overview of the cross-disciplinary area of Affective Computing (AC), and outlines how AC enables the development of affective HCI. The chapter then elaborates in greater detail the area of computational affective modeling, and argues that deeper models of emotion generation and emotion effects are necessary to support more complex human-machine interaction, both non-verbal and verbal. The chapter concludes with a discussion of the promises and challenges in affective HCI.
86 Hurtful words and hate speech

This chapter addresses speakers’ uses of emotionally hurtful words and their victims’ emotional responses to hurtful words in American English. Hurtful words are defined here as negatively valenced or bad words used to express one’s feelings and convey those feelings to others. Topics include anger and frustration, hate speech, obscene telephone calls, workplace sexual harassment, verbal abuse, internet and online offenses, and research involving children as speaker-victims. Research cited here is drawn primarily from psychological and social science empirical studies on the topic of how people produce and comprehend offensive speech. The chapter includes an examination of ethical and methodological problems associated with hurtful word research and concludes with suggestions for conducting meaningful research on hurtful words.
Optimization of a Hydrodynamic Computational Reservoir through Evolution

As demand for computational resources reaches unprecedented levels, research is expanding into the use of complex material substrates for computing. In this study, we interface with a model of a hydrodynamic system, under development by a startup, as a computational reservoir and optimize its properties using an evolution in materio approach. Input data are encoded as waves applied to our shallow water reservoir, and the readout wave height is obtained at a fixed detection point. We optimized the readout times and how inputs are mapped to the wave amplitude or frequency using an evolutionary search algorithm, with the objective of maximizing the system's ability to linearly separate observations in the training data by maximizing the readout matrix determinant. Applying evolutionary methods to this reservoir system substantially improved separability on an XNOR task, in comparison to implementations with hand-selected parameters. We also applied our approach to a regression task and show that our approach improves out-of-sample accuracy. Results from this study will inform how we interface with the physical reservoir in future work, and we will use these methods to continue to optimize other aspects of the physical implementation of this system as a computational reservoir.
How Well Does the Metropolis Algorithm Cope With Local Optima?

The Metropolis algorithm (MA) is a classic stochastic local search heuristic. It avoids getting stuck in local optima by occasionally accepting inferior solutions. To better and in a rigorous manner understand this ability, we conduct a mathematical runtime analysis of the MA on the CLIFF benchmark. Apart from one local optimum, cliff functions are monotonically increasing towards the global optimum. Consequently, to optimize a cliff function, the MA only once needs to accept an inferior solution. Despite seemingly being an ideal benchmark for the MA to profit from its main working principle, our mathematical runtime analysis shows that this hope does not come true. Even with the optimal temperature (the only parameter of the MA), the MA optimizes most cliff functions less efficiently than simple elitist evolutionary algorithms (EAs), which can only leave the local optimum by generating a superior solution possibly far away. This result suggests that our understanding of why the MA is often very successful in practice is not yet complete. Our work also suggests to equip the MA with global mutation operators, an idea supported by our preliminary experiments.
Eye Controlled Wheelchair for Physically Disabled Persons using Viola Jones Algorithm and Hough Transform

Tetraplegia or normally known as quadriplegia is a kind of immobility activated by an ailment or cervical damage bringing about the total or halfway hindrance of the appendages and torso function. Every year many people are suffering from this illness. This paper attempts to implement a way to resolve this problem by making the disabled persons move an their own. The motivation behind this paper is to exhibit ease and low-cost method for empowering differently-abled individuals to explore their wheelchair freely through the movement of eyes. In this paper, we proposed a system where the camera will be put in front of the wheelchair. The camera will detect the face using the Viola-Jones algorithm and will detect eyeball using the Hough transform using the MATLAB environment. MATLAB interacts with Arduino microcontroller utilizing serial correspondence. Arduino transfers the control signal to L298N stepper motor driver, which gives the commanded signal to the motors of the wheelchair. In our proposed method, comparison and observation were made between other eye controlling methods and our method. The framework is reasonable and henceforth can he utilized by patients spread over a vast economy run.
Using Diagrams to Present Information

In this chapter, you’ll see how to refine cluttered diagrams into something more elegant and fit for presentation – and much more about creating and layouting diagrams, including
Introduction

This thesis presents algorithms for tightly-coupled robotic perception and action. According to this paradigm, in an iterative and infinite loop, action controls the amount of information coming from sensory data, and perception guides and provides feedback to action. Ecological psychology showed that the seamless integration of sensing and control is a fundamental feature of biological agents [55]. Can also artificial and embodied agents benefit from a tightly-coupled perception and action loop? My work addresses this question in the context of high-speed agile quadrotor flight.