Reviewerly/exHarmony huggingface dataset:
The Use of Video Materials for the Development of Inner Motivation in Learning Foreign Languages of University Students

Our research aims to distinguish the types of motivation that are especially significant for undergraduate students and how authentic video materials foster the development of these types of motivation.Sample for the research was 67 undergraduate students from two Moscow universities -from MRSU (31 students) and from RUDN (36 students).The experiment was carried out in the autumn term of the 2019-2020 academic year.In the course of the research, a survey of students was conducted to identify the most significant factors contributing to an increase in the motivation of learning activities.According to the results of the survey, for senior undergraduate students, the opportunity to plan their learning activities independently acquires the greatest value.The creation of a favourable emotional environment in the classroom and adequate assessment of students are important but not determining factors in the development of motivation.The implementation of innovative pedagogical technologies and psychological and pedagogical support of the learning process are equally important for the formation of motivation.As part of the study, authentic professional video materials were selected and techniques for working with them were developed.The developed materials can be used for teaching language for specific purposes in the areas of Pedagogy, Economics, and Management.Students note that the use of the foreign language being studied in real situations of professional communication is the leading motivational factor.The participation of students in the film making process encourages the development of social and communicative competencies, prepares them for future professional activity.
Preventing Route Leaks using a Decentralized Approach: An Experimental Evaluation

In the inter-domain routing infrastructure, a route leak is defined as a violation of the routing policy agreed between two Autonomous Systems (AS). Route leaks have resulted in large-scale outages on the Internet, taking down several services. Although route leaks seem a simple problem, the solution is complex because: (i) ASes consider -partially- routing policy private, (ii) lack of a formal and standard language to express routing policy and (iii) BGP lacks adequate cryptographic-based security. In this paper, we present an experimental analysis of a distributed ledger-based architecture that provides a solution to route leaks. Specifically, the routing policy is unambiguously expressed using a formal language, that is then stored in a blockchain. This decentralized architecture allows private policies and interfaces seamlessly with the current BGP infrastructure, requiring no changes to routers. We build a prototype to evaluate our proposed architecture using Hyperledger, we analyze its performance using a real-world BGP dataset. Our results show that our architecture scales linearly with relevant metrics. Additionally, we validate the architecture preventing an artificially introduced route leak in a realistic 10 AS topology.
From fitness landscapes evolution to automatic local search algorithm generation

Abstract Solving an optimization problem with local search algorithms consists of evolving a solution by means of an evaluation function, which is usually directly derived from the objective function of the problem. The resolution difficulties appear when the fitness landscape naturally induced by the problem instance is not perfectly exploitable, has a certain level of ruggedness, and therefore has many local optima. We propose here to shift the problem of searching a solution, from searching an evaluation function that maximizes the efficiency of the corresponding local search algorithm. In particular, we propose an evolution strategy scheme designed to evolve fitness functions and their corresponding fitness landscapes. The purpose is to generate a local search algorithm guided by an evolved fitness function specifically dedicated to tackling the problem instance to solve. Here, we focus on hill‐climbing algorithms and NK landscapes and show that such a strategy can be efficient to generate relevant search algorithms whose components are not problem‐specific.
NLP in Banking, Financial Services, and Insurance (BFSI)

The banking and financial industries have been making data-driven decisions for more than a century. Since a wrong decision could have heavy cost for a financial institution, they have been one of the early adopters of big data. A lot of machine learning use cases in the banking, financial services, and insurance industries (BFSI) have been using structured data like transactional history or CRM history. However, over the last few years there has been an increasing tendency to use text data to mainstream underwriting and risk or fraud detecting.
Traveling Salesman Problem Solving Using Evolutionary Algorithms Guided by Complex Networks

The evolutionary computation is an optimization strategy of the artificial intelligence, it is widely used for solving problems, which traditional algorithms cannot solve. Moreover, complex networks model systems, in which, the relationship structures are representedas graphs. The Traveling Salesman Problem (TSP) is a well known computational problem solved using many strategies, included evolutionary computation. The literature review shows that the population of evolutionary computation strategies can be represented as complex networks, as follows: a graph represents the group of solutions, where the vertices (nodes) represent the individuals within the population of solutions, and the edges (links) represent the crosses among individuals (solutions). Thus, to improve solutions, links among existing nodes of the population are created. This work aims at the development of an algorithm to guide the population dynamics of an evolutionary algorithm that solves TSP. It is based on the hypothesis that one strategy guided by a complex dynamic network yields better results than a traditional algorithm. Furthermore, the results substantiate that the model proposed not only improves convergence compared to the traditional solution, but also enables shorter execution times. Strategies based on small-world networks show enhanced overall performance in the experiments conducted compared tothose using other network topologies, and subsequently the traditional solution.
Switching Transistor Circuit [Enigmas, etc.]

Various puzzles, quizzes, games, humorous definitions, or mathematical that should engage the interest of readers.
Indonesian student's perception of online learning activities in the time of pandemic

#Dirumahaja or stay at home movement is thought the most effective policy to break the chain of Covid-19 transmission People cannot do nearly allactivities, including formal education, together anymore However, in fact, the ongoing onlinelearning has itw own advantages and disadvantages This research aims to describe student's experience with online learning This study used online survey as data collection technique Three hundred and fifteen students participated in filling the questionnaire The result showed that 43 33% of students learnthrough LMS, 19 44% through video conferencing, and 37,22% through WhatsAppgroups and emails As many as 39 21% of them felt uncomfortable, 37 25% normal, and 23 52% comfortable with it Their discomfort is generally caused by tiring learning activities,i e doing lots of tasks in spite of incomprehensive materials © 2020 Ubiquity Press All rights reserved
Enhanced Educational Mobile Augmented Reality Application for Musculoskeletal System and Warm-Up Exercises

The purpose of this study is to examine the use of augmented reality technology in the field of sports science and to develop a sample educational software including musculoskeletal system and warm-up exercises. Each stage of the software development process is explained in detail. The basis of augmented reality technology is the simultaneous visualization of real and virtual objects through an interface system. The instant information obtained by using various methods from the real world is presented in interaction with virtual objects. Virtual objects are developed with three-dimensional modelling software. Code libraries are used for applications that will be created for products such as mobile phones, tablets, and computers. The compiler software makes applications available for use. In this study, Daz3d and Blender software were used for three-dimensional modelling. The Vuforia system has been preferred as an augmented reality code library. The Unity software was used in the development and compilation of the application. It will be beneficial to use the developed mobile augmented reality application in sports and health education. Augmented reality technology can be used in many different areas in sports science.
An Automated Essay Grading Framework Based on Neural Networks (Dept.E)

This paper presents a proposed framework depending on Artificial Neural Networks (ANN) that enhances the process of assessing student's essays in order to save the line and cost of manual scoring of these essays. This framework consists of five phases and the UML model of that framework is defined. Also the activity diagrams of that system are explained. During the learning algorithm phase, the network trained on a set of pre-graded student responses by teachers and then a set of essays not used in the training are introduced to a 10 tie network to judge the essay. The proposed framework was tested on case study and presents good results compared 10 a previously published work using the same case study with another technique.
Virtualizing a University Campus Tour: A Pilot Study on its Usability and User Experience, and Perception

In-person campus tours given to prospective students is an essential undertaking by universities. Such tours offer new faculty and students an overview of expectations and norms in the university community as a whole. The International Christian University (ICU) has been utilizing in-person campus tours in open campus activities as part of its normal daily operations but due to limited human and temporal resources, along with the unforeseen unexpected outbreak of the COVID-19 pandemic in 2019, there has developed a need to provide prospective students, family members, and other visitors alternative ways to learn about the campus. In this regard, the proliferation of 360° or virtual reality (VR) photo cameras and the ever-increasing supply of low-cost technologies for experiencing VR applications provide a practical and virtual alternative. This paper describes a pilot project utilizing a VR photo-based tour as an alternative to ICU’s traditional physical campus tours. It presents a usability analysis and a preliminary draft measure of user experience arrived at after conducting correlation analysis, t-test, and text analysis, and obtaining preliminary results such as the effect of spatial presence on usability, desire to visit, and variables related to interest and intrinsic motivation. The key findings show how a VR campus tour can, if used correctly, complement and augment in-person campus tours, while opening up opportunities for further investigating spatial presence and motivation in educational contexts.&#x0D; Keywords: virtual reality, VR photo-based tour, virtual tour, usability, 360° technology
Content-Based Image Retrieval (CBIR) in Big Histological Image Databases

Background: Automatic analysis of Histopathological Images (HIs) demands image processing and Computational Intelligence (CI) techniques. Both Computer-Aided Diagnosis (CAD) and Content-Based Image-Retrieval (CBIR) systems assist diagnosis, disease discovery, and biological decision-making. Classical tests comprise screening examinations and biopsy. Histopathology slides offer more ample diagnosis data. However, manual examination of microscopic images is labor-intensive and time-consuming and may depend on a subjective assessment by the pathologist, which can be a challenge.&#x0D; Methods: This work discusses a CBIR framework to extract and handle histological data, histological metadata, integrated patient records, specimen metadata, attributes, and similar stored files. This work presents a scalable image-retrieval framework for intelligent HI analysis with real-time retrieval. The potential applications of this framework include image-guided diagnosis, decision support, healthcare education, and efﬁcient biological data management.&#x0D; Results: The considerable amount of biological-related data prompted the development and deployment of large-scale databases and data-driven techniques to bridge the semantic gap between images and diagnostic information. The new cloud computing technologies and the concept of cyber-physical systems have improved the CBIR architectures considerably. The proposed scalable architecture relies on CI and validates performance on several HIs acquired from microscopic tissues. Extensive assessments show improvements in terms of disease classiﬁcation and retrieval tests.&#x0D; Conclusion: This research effort significant contributions are twofold. 1) Defining a comprehensive and large-scale CBIR framework to analyze HIs with high-dimensional features and CI methods successfully. 2) high-performance updating and optimization strategies improve the querying while better handling new training samples than traditional methods.
Rewriter-Evaluator Framework for Neural Machine Translation

Encoder-decoder architecture has been widely used in neural machine translation (NMT). A few methods have been proposed to improve it with multiple passes of decoding. However, their full potential is limited by a lack of appropriate termination policy. To address this issue, we present a novel framework, Rewriter-Evaluator. It consists of a rewriter and an evaluator. Translating a source sentence involves multiple passes. At every pass, the rewriter produces a new translation to improve the past translation and the evaluator estimates the translation quality to decide whether to terminate the rewriting process. We also propose a prioritized gradient descent (PGD) method that facilitates training the rewriter and the evaluator jointly. Though incurring multiple passes of decoding, Rewriter-Evaluator with the proposed PGD method can be trained with similar time to that of training encoder-decoder models. We apply the proposed framework to improve the general NMT models (e.g., Transformer). We conduct extensive experiments on two translation tasks, Chinese-English and English-German, and show that the proposed framework notably improves the performances of NMT models and significantly outperforms previous baselines.
Data‐assisted differential diagnosis of dementia by deep neural networks using MRI: A study from the European DLB consortium

Abstract Background Clinicopathological overlap between neurodegenerative diseases contributes to challenging differentiation between dementia types. Improving differential diagnosis is important to provide optimal patient care and better predict future needs. Deep learning techniques such as convolutional neural networks have in recent years gained much attention for their high performance in image classification. Magnetic resonance imaging (MRI) computer‐assisted diagnosis (CAD) systems based on deep learning could potentially contribute to increased reliability of clinical diagnosis. Method An ensemble model of 3D‐convolutional neural networks was trained and tested on a class‐balanced dataset consisting of T1‐weighted MRI brain scans for the differentiation of subjects diagnosed with Alzheimer’s disease (AD), dementia with Lewy bodies (DLB) and normal controls (NC). Total number of brain scans used for training and testing were 690 (nAD=nDLB=nNC=230) and 171 (nAD=nDLB=nNC=57) respectively. Diagnoses were clinical, not including MRI, and in a subgroup of the DLB patients the diagnosis was supported by DAT scan. All scans were skull‐stripped and spatially normalized. All classes were matched for age and sex. Data augmentation was applied using the left and right hemisphere as separate instances and a Bayesian approach was used for hyperparameter optimization. Experiments were conducted using 6‐fold cross‐validation, with the final model constructed as an ensemble of all models. Result A final accuracy of 71.9% was reported for the three‐class differential classification of 171 stand‐out test subjects. The model classified the NC, DLB and AD subjects with 71.9%, 86.0% and 57.9% accuracy respectively. Further inspection showed that most of the miss‐classified AD patients were wrongly classified as DLB by the CAD system. Conclusion The results of this work show promising potential for deep learning based CAD systems trained on MR brain images for differential diagnosis of dementia. Most data‐driven dementia classification models, including the one presented in this work, are limited by the amount of available data, comorbidity, and the inaccuracy of clinical diagnosis. Increasing the amount of data might significantly improve such models. The prospective E‐DLB study will provide more brain MRI data for future improvement of the suggested deep learning based CAD system.
Using WebGL to Visualize Training Materials in the Educational Process

The digitalization of modern education relies on the digitization of educational materials. Digitizing text or drawings is not difficult, but creating and rendering a three-dimensional digital copy of a physical object is not a trivial task. This article is about WebGL, a browser-based technology for working with graphics that can be used to visualize digitized educational materials.
Music production utilising Internet of Things technologies

The Internet of Things (IoT) paradigm consists of ubiquitous, interconnected devices that freely communicate and exchange data worldwide utilising the internet and widespread computing networks. The development and normalisation of IoT-enabled music processes can therefore enable novel advances in music production and impact future standards in the production process. An iterative, design-build methodology was followed that required researching available IoT technologies, and following incremental stages of prototyping, building, and testing until a full IoT music system was implemented, following a similar approach to that described by Paterson et al. The practical design of the IoT music system focused on the development of a proof-of-concept IoT-enabled application that can control a minimum of two physical audio processors in real time. Two processing techniques were chosen for implementation of the IoT music system due to their noticeable effects: equalisation and reverb.
Proof of Virtue: Nonce-Free Hash Generation in Blockchain

Blockchain is a decentralized peer-to-peer system with no centralized authority. By definition, this will be a system devoid of any type of corruption from a single point; but it results in some complex problems like reaching a collective decision and scheming mechanisms to get things done in such an environment. Consensus mechanisms solve these problems in blockchain by allowing the nodes to collectively reach an agreement on the transactions without relying on a third party. One such consensus mechanism which is proved to be successful by the famous Bitcoin implementation is proof of work. Though this mechanism is working successfully today, it does not come without major limitations, like heavy computational and energy requirements, which makes it unsuccessful in other real-world applications. The proposed proof of virtue model will be working based on the credit scores and incentives of nodes, rather than the intensive puzzle-solving process. Nodes pledge their entire share of incentive and credit score during block generation, which is redeemed on successful validation. Mining power does not play any role in block generation, which prevents 51% of attacks. A new user has to wait for the initial credit score before participating in the block generation, making the network Sybil resistant. There is no restriction on the minimum time taken for block generation, ensuring performance and scalability.
Workload Based Geo-Distributed Data Center Planning in Fast Developing Economies

Data centers (DCs), as the core infrastructure of cloud computing, are experiencing explosive growth worldwide. The planning of geo-distributed DCs has to take into account geographically differentiated costs, so that the overall expenditure could be minimized. In fast developing economies, the planning of DCs is further complicated by the fact that demands are quickly evolving, both in the type of services, and in geographical distributions. This paper is dedicated to the planning of DCs in fast developing economies. Instead of modeling demands by aggregated number of servers, we introduce a workload-based model to better capture the quickly changing nature of demand composition. The variability and dynamics of computational demand are modeled by dynamic compositions of workflows of different types. We provide approaches to solve the planning problem. Finally, we apply the model to China by using real-life data, and show how factors like economic growth, population migration and latency constraint may affect DC planning. Our research suggests that factors endemic in fast developing economies can significantly influence the overall cost and performance. For instance, by considering these factors, one can save 5.8% in cost in a five-year planning period, and 5% population migration may lead to 30ms increase in latency.
LIFESTYLE OF STUDENTS IN TERMS OF SELF-ISOLATION

The Abstract the Article is devoted to the study of the lifestyle of students in self-isolation. New cases of COVID-19, which continue to be registered around the world, dictate to society new norms of behaviour in the social space. The self-isolation regime as a quarantine measure has become a new reality for Russians. Compliance with self-isolation and other preventive measures will help to contain the spread of coronavirus infection. Self-isolation is characterised by serious changes in the body and lifestyle of various socio-demographic groups. These changes are especially relevant for students in the transition from the usual active lifestyle to new unusual forms of social behaviour. The way of life covers all essential spheres of people's life: work, forms of its social organization, everyday life, forms of people's use of their free time, their participation in political and social life, forms of satisfaction of their material needs. To Sum up, we believe that the analysis of the components of the students' lifestyle in the conditions of self-isolation is only a part of the study and requires further research. The current epidemiological situation associated with the threat of COVID-19 has largely changed the worldview, behaviour and social attitudes, and the student's life meanings are being re-evaluated and values are being transformed. It is necessary to develop a set of measures for socio-psychological support of students who find themselves in a difficult situation in self-isolation.