Reviewerly/exHarmony huggingface dataset:
Relations Between Entropy and Accuracy Trends in Complex Artificial Neural Networks

Training Artificial Neural Networks (ANNs) is a non-trivial task. In the last years, there has been a growing interest in the academic community in understanding how those structures work and what strategies can be adopted to improve the efficiency of the trained models. Thus, the novel approach proposed in this paper is the inclusion of the entropy metric to analyse the training process. Herein, indeed, an investigation on the accuracy computation process in relation to the entropy of the intra-layers’ weights of multilayer perceptron (MLP) networks is proposed. From the analysis conducted on two well-known datasets with several configurations of the ANNs, we discovered that there is a connection between those two metrics (i.e., accuracy and entropy). These promising results can be helpful in defining, in the future, new criteria to evaluate the training process goodness in real-time by optimising it and allow faster detection of its trend.
ARTIFICIAL NEURAL NETWORK-BASED POWER SYSTEM RESTORATION

Power system restoration is a common technique used in the restoration of power by power companies.This involves laid down procedures which the system operator must follow.Due to the challenges encountered during power restoration process, the use of computer aided systems which create the power system restoration plan has become necessary.This research focuses on the use of artificial neural network in power system restoration.It throws more light on artificial neural network, the various types of artificial neural network, learning methods, its structure, strengths and its application in power system restoration.The artificial neural network develops the power system restoration plan used by the system operator for power restoration.This paper discussed majorly the Island restoration scheme which is most applicable when dealing with large transmission networks and works effectively with artificial neural network.
Machine Learning Augmented Breast Tumors Classification using Magnetic Resonance Imaging Histograms

At present, breast cancer survival rate significantly varies with the stage at which it was first detected.It is crucial to achieve early detection of malignant tumors to reduce their negative effects.Magnetic resonance imaging (MRI) is currently an important imaging modality in the detection of breast tumors.A need exists to develop computer aided methods to provide early diagnosis of malignancy.In this study, I present machine learning models utilizing new image histogram features using the pixels least significant bit.The models were first trained on an MRI breast dataset that included 227 images captured using the short TI inversion recovery (STIR) sequence and diagnosed as either benign or malignant.Three data classification methods were utilized to differentiate between the tumor's classes.The examined classification methods were the Discriminant Analysis, K-Nearest Neighborhood, and the Random Forest.Algorithms' testing was performed on a completely different dataset that included another 186 MRI STIR images showing breast tumors with verified biopsy diagnostics.A significant tumor classification efficiency was found, as judged by the pathological diagnosis.Classification's accuracy was calculated as 94.1% for the DA, 94.6% for the KNN and 80.6% for the RF algorithm.Receiver operating curves also showed significant classification performances.The proposed tumor classification techniques can be used as non-invasive and fast diagnostic tools for breast tumors, with the capability of significantly reducing false errors associated with common MRI imaging-based diagnosis.
Batching CSIDH Group Actions using AVX-512

Commutative Supersingular Isogeny Diffie-Hellman (or CSIDH for short) is a recently-proposed post-quantum key establishment scheme that belongs to the family of isogeny-based cryptosystems. The CSIDH protocol is based on the action of an ideal class group on a set of supersingular elliptic curves and comes with some very attractive features, e.g. the ability to serve as a “drop-in” replacement for the standard elliptic curve Diffie-Hellman protocol. Unfortunately, the execution time of CSIDH is prohibitively high for many real-world applications, mainly due to the enormous computational cost of the underlying group action. Consequently, there is a strong demand for optimizations that increase the efficiency of the class group action evaluation, which is not only important for CSIDH, but also for related cryptosystems like the signature schemes CSI-FiSh and SeaSign. In this paper, we explore how the AVX-512 vector extensions (incl. AVX-512F and AVX-512IFMA) can be utilized to optimize constant-time evaluation of the CSIDH-512 class group action with the goal of, respectively, maximizing throughput and minimizing latency. We introduce different approaches for batching group actions and computing them in SIMD fashion on modern Intel processors. In particular, we present a hybrid batching technique that, when combined with optimized (8 × 1)-way prime-field arithmetic, increases the throughput by a factor of 3.64 compared to a state-of-the-art (non-vectorized) x64 implementation. On the other hand, vectorization in a 2-way fashion aimed to reduce latency makes our AVX-512 implementation of the group action evaluation about 1.54 times faster than the state-of-the-art. To the best of our knowledge, this paper is the first to demonstrate the high potential of using vector instructions to increase the throughput (resp. decrease the latency) of constant-time CSIDH.
Grace licenses UNIPOL PP Process Technology to PetroChina Guangdong Petrochemical Company Limited

Image segmentation is a key issue in image processing. New image segmentation algorithms have been proposed in the last years. However, there is no optimal algorithm for every image processing task. The selection of the most suitable algorithm usually occurs by testing every possible algorithm or using knowledge from previous problems. These processes can have a high computational cost. Meta-learning has been successfully used in the machine learning research community for the recommendation of the most suitable machine learning algorithm for a new dataset. We believe that meta-learning can also be useful to select the most suitable image segmentation algorithm. This hypothesis is investigated in this paper. For such, we perform experiments with eight segmentation algorithms from two approaches using a segmentation benchmark of 300 images and 2100 augmented images. The experimental results showed that meta-learning can recommend the most suitable segmentation algorithm with more than 80% of accuracy for one group of algorithms and with 69% for the other group, overcoming the baselines used regarding recommendation and segmentation performance.
READINESS FOR LEADERSHIP AS A FACTOR IN STUDENTS’ STRESS RESISTANCE

The article reflects the actual problem of the influence of readiness for leadership on students’ stress resistance. The aim of the research is to study the dependence of students’ stress resistance on their readiness for leadership. Scientific consideration of the problem was carried out on the basis of the Borys Grinchenko Kyiv University, which was attended by 234 students. In the process of implementing the research, the content, structure of stress resistance, readiness for leadership of students was determined, psychodiagnostic tools for studying the indicators of these phenomena were selected, the ascertaining stage of the experiment, correlation, regression analysis of the dependence of the level of stress resistance of students on the level of readiness for leadership was carried out. Stress resistance of students is presented as the ability to overcome the influence of external stressors with the help of potential expenditures. Among the criteria of students’ stress resistance, the following are highlighted: cognitive, motivational, emotional, activity. Students’ readiness for leadership is characterized as a state of mobilization of leadership potential, which ensures the effective implementation of certain actions, the presence of needs, motives, the level of taking responsibility for their actions. The readiness for leadership of students consists of the following criteria: executive, evaluative, target, personal. As a result of the experiment, it was found that stress resistance and readiness for leadership have insufficient levels of development, since more than half of the participants have an average level of development of the presented phenomena. It was also found that there is a direct relationship between the level of students’ stress resistance and their readiness for leadership. Students with a developed readiness for leadership have a high level of stress resistance. The results obtained can help in the implementation of the educational process of higher educational institutions in order to increase the level of stress resistance of young people.
ARFA-QR Code Based Furniture Assembly Using Augmented Reality

The current system of providing 2D instruction set and the products was a less efficient way to guide the user to assemble the product. Hence, the proposed application helps the user with an interactive augmented reality-based assembly instruction application. This application is mainly based on furniture models. The build and deployment of the model is performed on LabVIEW 2014 simulation environment. Unity GUI, Blender, Vuforia, Android Studio, and QR technology are used to create and deploy the model. The proposed model enables seamless integration of augmented reality into business applications. The outcome of this research is an Android application which shows different objects assembled using augmented reality.
Insight: futurology

Abstract From chicken farms to server farms: Matt Lucas FBCS looks at what the poultry industry can teach us about effcient computing.
Peer review declaration

Abstract All papers published in this volume of IOP Conference Series: Earth and Environmental Science have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Single-blind a. Relevance and timeliness (the importance and timeliness of the topic addressed in the paper) b. Technical content and scientific rigour (e.g.,: completeness of the analysis or simulation study, thoroughness of the treatise, accuracy of the models, etc.) c. Novelty and originality (novelty and originality of the ideas or results presented in the paper) d. Quality of presentation (the paper organization, the clearness of text and figures, the completeness, and accuracy of references) e. Similarity (no more than 25%) The reviewers decided to accept, accept with revision, and reject after the review process. If authors had poor quality or did not follow these criteria, the paper would be rejected. If the decision was accepted with revision, the authors had the opportunity to revise their papers, and the editor will evaluate the revision. The paper would be accepted after the authors revise addressing to reviewer comments. • Conference submission management system: submissions were handled by EDAS Conference and Journal Management System (https://edas.info/) • Number of submissions received: 83 • Number of submissions sent for review: 83 • Number of submissions accepted: 59 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 71,1% • Average number of reviews per paper: 1 • Total number of reviewers involved: 42 • Any additional info on the review process: All papers were reviewed by recognized scientific experts in their fields and have academic status. The similarity was checked using Plagiarism Checker Docol©c provided by EDAS and papers passed 25% similarities. • Contact person for queries: Name : Yuni Kusumastuti Affiliation : Chemical Engineering Department, Faculty of Engineering, Universitas Gadjah Mada Email : yuni_kusumastuti@ugm.ac.id; chemengevent.ft@ugm.ac.id
Research and Application of Data Fusion Technology Based on Knowledge Map

This paper briefly introduces the research and application of data fusion technology based on knowledge map in order to further promote the intelligence, convenience, and visualization of data application and uses the related technology of big data fusion to combine business development, knowledge map technology, and power enterprise business system to sort out the relationship between business and assets, business and data, and data and assets, so as to better solve the difficult problem of cross-professional data fusion application. By using the characteristics of structural consistency, natural expression and visual display of the graph data structure and the actual grid, the whole topology of the grid, including transmission, transformation, distribution, and data fusion of the topology structure inside all links and the connection relationship between them, can be used to connect the different voltage levels of the grid to form a “grid topology diagram”. At the same time, the power grid operation, monitoring, operation and maintenance, production and marketing, and other business departments in different management angles, different organizational methods, different levels of data integration.
Introduction to Cognitive Neuroscience

This chapter discusses the cognitive neuroscience. It provides an overview, charting how different disciplines and methods combined to illuminate how the brain gives rise to mind and cognition. Cognitive neuroscience is an interdisciplinary approach to understanding the neural bases of cognition and behavior. Although cognitive neuroscience, by that name, only emerged in the late 1970s, it draws from a number of fields, including cognitive psychology, neuropsychology, the multiple disciplines of neuroscience, and computational approaches. Finally, computational approaches have been central to advancing theoretical and empirical developments in cognitive neuroscience. Realistic modeling of single cells and small networks has contributed to our understanding of, for example, neural plasticity and attention. Connectionism has provided ways to model large-scale distributed representations and aspects of dynamics. Cognitive neuroscience emerged after the cognitive revolution in psychology, and in particular after cognitive science had already achieved some recognition as an interdisciplinary approach to understanding mind and cognition.
Digital Tools In The Organization Of The Regional Track Projects Contest

Modern reality is an example of the active development of digital technologies and their implementation into all spheres of social life. Such concepts as “digital platforms”, “infocommunication technologies”, “digitalization of knowledge” are a significant feature of today`s society. The system of education is not an exception; on the opposite, it was one of the first to undergo digital modernization. This paper is concerned with a promising direction, which is digital tools of organizing, monitoring and managing the research work of the students of educational institutions. It is well-known that the work with gifted youth is one of the most important and topical agendas at all the levels of authority. Every year a great number of different contests for identifying, development and support of talented young people are organized and held in the Russian Federation. The authors describe the experience, analyze the results and reveal the problems of holding the Samara regional track of the all-Russian scientific-technological projects contest “Great Challenges” for the last three years. The main achievement is the application of the specially developed information system for the support of project activity of the students of educational institutions. The practical result of the efforts of a big team of people is the undeniable success of the students from the Samara region at the all-Russian scientific-technological projects contest “Great Challenges”.
Cross curriculum interaction in teacher training as pedagogical reality

Rapid process of obsolescence of knowledge, necessity of its constant modernization, differentiation and practical orientation require from teacher professional training a set of conditions, implementation of which needs harmonious combination (integration, complementarity, connection) of different subjects for integrity, thoroughness and effectiveness of such training. The results of anonymous online survey are presented, which have demonstrated that the majority of teachers in higher educational establishments are aware of crosscurriculum approach, associated with cooperation, complement, help, interaction, as well as the importance of its practical implementation in future teacher training even in the absence of motivation. The results of the survey allowed the authors to rationalize principles of crosscurriculum approach to the design of the content of future teachers’ psychological and pedagogical training, in which principle of integrity is a leading one, with such priority principals as systematic, structural, integrative, hierarchical ones.
Improving the robustness of binarized neural network using the EFAT method

In recent years with the explosion of research in artificial intelligence, deep learning models based on convolutional neural networks (CNNs) are one of the promising architectures for practical applications thanks to their reasonably good achievable accuracy. However, CNNs characterized by convolutional layers often have a large number of parameters and computational workload, leading to large energy consumption for training and network inference. The binarized neural network (BNN) model has been recently proposed to overcome that drawback. The BNNs use binary representation for the inputs and weights, which inherently reduces memory requirements and simplifies computations while still maintaining acceptable accuracy. BNN thereby is very suited for the practical realization of Edge-AI application on resource- and energy-constrained devices such as embedded or mobile devices. As CNN and BNN both compose linear transformations layers, they can be fooled by adversarial attack patterns. This topic has been actively studied recently but most of them are for CNN. In this work, we examine the impact of the adversarial attack on BNNs and propose a solution to improve the accuracy of BNN against this type of attack. Specifically, we use an Enhanced Fast Adversarial Training (EFAT) method to train the network that helps the BNN be more robust against major adversarial attack models with a very short training time. Experimental results with Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attack models on our trained BNN network with MNIST dataset increased accuracy from 31.34% and 0.18% to 96.96% and 85.08%, respectively.
A Study on the Application of Teacher Feedback Language in Middle School English Class

Teacher feedback language has a very significant impact on students' learning. Appropriate feedback language will promote students' learning motivation, arouse students' interest in learning, and have a positive role in promoting students' language ability development. However, in middle school English classrooms, many teachers do not pay attention to the role of feedback language, ignore the positive feedback to students or lack the application skills of feedback and students do not participate in classroom communication, and lose interest in English. This article proposes corresponding improvement measures in response to the current problems in the application of teacher feedback in middle school English classrooms.
Design of Customer Satisfaction Application at BCA Kcp Rengasdengklok Using C.45 Algorithm Method

Bank BCA always improves service quality by the slogan "Always By Your Side". The assessment consists of 4 attributes (time, accuracy, focus, and satisfaction). Each attribute has a weighted value of 1 (very dissatisfied) to 5 (very satisfied). The assessment is still done manually (using paper), therefore the authors in this study used the C.45 algorithm with 3 tests carried out to produce a classification obtained that the accuracy value reached 88.75% with an AUC value of 0.744 and testing on the application that was made resulted in 0.722. It can be concluded that service assessment at BCA KCP Rengasdengklok belongs to a fairly good classification group because its AUC value is between 0.70-0.80.
The Study of Optimal Layout of Command Recognition Area in Implicit Eye-controlled Interfaces

The optimal layout of the command recognition area in implicit eye-controlled interfaces is determined by comparing the accuracy of user command recognition in different layout schemes of the command recognition area through an ergonomics experiment. It provides a theoretical basis and reference for the layout of the command recognition area in the design of the implicit eye-controlled interface.
Path-Following with Radar-based Obstacle Localization of Unmanned Ground Vehicle in Campus Environment

Unmanned ground vehicle (UGV) has a promising application in industry and society. Many kinds of research are proposed to develop UGV technology. However, the UGV for a particular application environment is very meaningful for UGV development. In this paper, a UGV which can move in the campus environment (UGV-CE) is proposed. In detail, the Dijkstra algorithm is used to calculate the shortest path from the start point of each vehicle to the destination for UGV-CE. The incremental PID controller of UGV-CE uses the combination of preview point and closest point to follow the path steadily; Besides, the transformation from latitude and longitude to Frenet-Serret system is applied for UGV-CE. Under the Frenet-Serret system, vector collision time is proposed to conduct the collision prediction between vehicle and obstacle. In addition, the experiment results suggest that obstacle can be recognized validly and the PID controller operates steadily.
The Effect of Openness to Diversity on Innovative Behavior: The Mediating Effect of Unit-members Trust

Purpose - This study deals with the Openness to Diversity(OD) of soldiers in the military organization for efficient unit operation of the military organization composed of various soldier. The purpose of this study was to examine the effects of information and value OD on innovative behavior and the mediating effect of unit-members trust. Design/methodology/approach - The 269 samples of this study were surveyed on army soldiers. The Exploratory Factor Analysis (EFA), the multiple regression, bootstraping analysis were hired in order to analyze the data. Findings - The results showed that the information & value OD were positive(+) effect on innovative behavior. Unit-members trust showed a mediation effect between OD and innovative behavior. Research implications or Originality - First, according to Defense Reform 2.0, the military is expected to be composed of more diverse classes in the future. There is a theoretical contribution that examine their perception of diversity within the military. Second, OD was confirmed as a variable predicting the innovative behavior of soldiers and unit-memeber trust mediate the relationship between OD and invative behavior.