Reviewerly/exHarmony huggingface dataset:
Augmented reality technology in the foreign language classroom in a non-linguistic university

The article determines the necessity of using augmented reality technologies in the formation of professional foreign language competence of chemistry students of non-linguistic universities with the use of the methods of modeling of educational material, complementing it with visual and audio sequences, and developing students’ skills of multifaceted projecting of situational reality. The goal of the study was to prove that, within the innovative educational augmented reality platform, the efficiency of the process of forming of the students’ professional foreign language competence significantly increases. As part of the experimental work based on complex methods of research, such as: general theoretical methods (analysis, comparison, matching, generalization of the pedagogical, methodological, psychological, special literature, the implementation of the introduction of new augmented reality technologies); logical and comparative analytical methods (study of the accumulated the Russian and foreign experience in implementing of augmented reality technologies); empirical methods (survey, pedagogical experiment, observation, study and generalization of the advanced pedagogical experience, diagnostics, surveys, interviews, testing), – the effectiveness of using augmented reality technology during the formation of chemistry students’ professional foreign language competence is proved: students’ progress and understanding of the material improves, professional foreign language competence forms faster, the level of motivation increases, the degree of involvement in the learning process and interest in the subject increases, the level of communication between students grows.
A Critical Evaluation of the FBST ev for Bayesian Hypothesis Testing

Abstract The “Full Bayesian Significance Test e -value”, henceforth FBST e v , has received increasing attention across a range of disciplines including psychology. We show that the FBST e v leads to four problems: (1) the FBST e v cannot quantify evidence in favor of a null hypothesis and therefore also cannot discriminate “evidence of absence” from “absence of evidence”; (2) the FBST e v is susceptible to sampling to a foregone conclusion; (3) the FBST e v violates the principle of predictive irrelevance, such that it is affected by data that are equally likely to occur under the null hypothesis and the alternative hypothesis; (4) the FBST e v suffers from the Jeffreys-Lindley paradox in that it does not include a correction for selection. These problems also plague the frequentist p -value. We conclude that although the FBST e v may be an improvement over the p -value, it does not provide a reasonable measure of evidence against the null hypothesis.
Introduction: The Data Problem

Machine learning has been growing in scale, breadth of applications, and the amounts of required data. This presents an important problem, as the requirements of state-of-the-art machine learning models, especially data-hungry deep neural networks, are pushing the boundaries of what is economically feasible and physically possible. In this introductory chapter, we show and illustrate this phenomenon, discuss several approaches to solving the data problem, introduce the main topic of this book, synthetic data, and outline a plan for the rest of the book.
Explaining Path Plan Optimality: Fast Explanation Methods for Navigation Meshes Using Full and Incremental Inverse Optimization

Path planners are important components of various products from video games to robotics, but their output can be counter-intuitive due to problem complexity. As a step towards improving the understanding of path plans by various users, here we propose methods that generate explanations for the optimality of paths. Given the question "why is path A optimal, rather than B which I expected?", our methods generate an explanation based on the changes to the graph that make B the optimal path. We focus on the case of path planning on navigation meshes, which are heavily used in the computer game industry and robotics. We propose two methods - one based on a single inverse-shortest-paths optimization problem, the other incrementally solving complex optimization problems. We show that these methods offer computation time improvements of up to 3 orders of magnitude relative to domain-independent search-based methods, as well as scaling better with the length of explanations. Finally, we show through a user study that, when compared to baseline cost-based explanations, our explanations are more satisfactory and effective at increasing users' understanding of problems.
Literature Review of Location-Based Mobile Games in Education: Challenges, Impacts and Opportunities

With the universal use of mobile computing devices, there has been a notable increase in the number of mobile applications developed for educational purposes. Gamification strategies offer a new set of tools to educators and, combined with the location services provided by those devices, allow the creation of innovative location-based mobile learning experiences. In this literature review, we conduct an analysis of educational mobile location-based games. The review includes articles published from January of 2010 to October of 2020, and from 127 records screened, 26 articles were analysed in full-text form. This analysis allowed us to answer the following six predefined research questions: Who are the target audiences for location-based games? In which subjects are location-based games most used? Which strategies are implemented with mobile devices to improve the student’s learning process? What are the main impacts of location-based games on students’ learning? What are the main challenges to the development of location-based games for education? What future tendencies and research opportunities can be identified from the analysis of the current state of the art?
Online Judge Support for Programming Teaching

Online programming judges are considered useful and sometimes indispensable tools to support competitive programming, professionals' recruiting, and programming education. In this last field, the scientific literature on these tools focuses on the learners' needs, but neglects the requirements of the professors, even though they are who mainly decide whether or not an educational tool is adopted in the courses they teach. This article collected 132 functional requirements for educative online judges, from the scientific literature and programming teachers with experience in the use of this type of tool. To know the degree of support, the requirements were grouped into 27 categories, and a requirements verification was performed with four available educative online judges reported in a recent systematic literature review. A low degree of satisfaction of requirements was found. This result encourages future research to create tools that better support teaching-learning processes and the requirements collected are a useful contribution as a starting point for such research.
Centralization of Big Data Using Distributed Computing Approach in IoT

Centralized big data is an emerging field that manages information in one common repository.Currently, Internet of Things (IoT) devices are increasing massively for sensing and transmitting data to the big data environment.This information is sensitive and easily traced by attackers due to the centralized data management.To address the security and privacy issues and avoid the system in a single point of failure, in this paper the researchers proposed a new model, namely, BOBS CRABID i.e.Blockchain Based Secure Centralized Big Data model using distributed computing approach.In big data, Hadoop MapReduce is one of the distributed computing approaches which handles all information in a distributed manner.The BOBS CRABID model is designed by three ideas as Multi-Factor authentication, IoT Devices Data Collection and Processing, and Optics based MapReduce for Data Clustering.In multi-factor authentication, a lightweight camellia key generation algorithm (LCKGA) is used for their authenticating all devices based on ID, IP address, MAC and PUF.These credentials are saved in Blockchain Security Entity for authentication verification.All transactions are hashed and stored in the blockchain using Keccak hash algorithm which performs better than the traditional hashing (SHA) 2. Then the researchers conducted IoT devices data collection and pre-processing using Cross Correlation and Min-Max Normalization for redundant data pruning and normalization, respectively.Finally, clustering is performed in a distributed way for optimizing the storage and scalability performance.For that MapReduce is used in which OPTICS algorithm is applied for clustering based on the data size, type of context and nature of data (sensitive or non-sensitive).In this way, data is stored in the big data environment.Experiments were conducted for the proposed BOBS CRABID model and compared with the well-known methods using Hadoop 2.7.2.The proposed BOBS-CRABID model achieves better performance in terms of throughput (increase 1500mbps), response time (reduce 150ms), energy consumption (reduce 35%), attack detection rate (increase 30%), accuracy (increase 20%), computation overhead (300 kb), and time reduce 4s).
On additive MDS codes over small fields

Let $ C $ be a $ (n,q^{2k},n-k+1)_{q^2} $ additive MDS code which is linear over $ {\mathbb F}_q $. We prove that if $ n \geq q+k $ and $ k+1 $ of the projections of $ C $ are linear over $ {\mathbb F}_{q^2} $ then $ C $ is linear over $ {\mathbb F}_{q^2} $. We use this geometrical theorem, other geometric arguments and some computations to classify all additive MDS codes over $ {\mathbb F}_q $ for $ q \in \{4,8,9\} $. We also classify the longest additive MDS codes over $ {\mathbb F}_{16} $ which are linear over $ {\mathbb F}_4 $. In these cases, the classifications not only verify the MDS conjecture for additive codes, but also confirm there are no additive non-linear MDS codes which perform as well as their linear counterparts. These results imply that the quantum MDS conjecture holds for $ q \in \{ 2,3\} $.
The Future of Deep Learning Is Photonic: Reducing the energy needs of neural networks might require computing with light

Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.
Detection of Phishing Websites Using Deep Learning Techniques

Phishing is a deceitful trick of cyber-attack designed and implemented by scammers and hackers with purpose of stealing personal data by impersonating the original websites. Phishing is like fishing in a lake wherein the users are very conveniently be fooled by scammers (phishers) by impersonating original websites and contents to leak their valuable personal and professional data. Currently a lot of anti phishing tools and techniques are being applied to detect and nullify the phishing cyber threat viz, heuristic feature, blacklist or white list and visual similarity-based approaches. In this research paper, we have anticipated robust and novel anti-phishing models via (I) Long Short-Term Memory (LSTM), (II) Deep-Neural Network (DNN) and (III) Convolution-Neural Network (CNN) using 10 features. The anticipated model achieves an accuracy of 98.67% for LSTM, 96.33% for DNN and 97.23% for CNN. The proposed techniques are highly efficient and robust which increases the phishing detection manifold.
Graph Convolutional Embeddings for Recommender Systems

Modern recommender systems (RS) work by processing a number of signals that can be inferred from large sets of user-item interaction data.The main signal to analyze stems from the raw matrix that represents interactions.However, we can increase the performance of RS by considering other kinds of signals like the context of interactions, which could be, for example, the time or date of the interaction, the user location, or sequential data corresponding to the historical interactions of the user with the system.These complex, context-based interaction signals are characterized by a rich relational structure that can be represented by a multi-partite graph.Graph Convolutional Networks (GCNs) have been used successfully in collaborative filtering with simple user-item interaction data.In this work, we generalize the use of GCNs for N-partite graphs by considering N multiple context dimensions and propose a simple way for their seamless integration in modern deep learning RS architectures.More specifically, we define a graph convolutional embedding layer for N-partite graphs that processes user-item-context interactions and constructs node embeddings by leveraging their relational structure.Experiments on several datasets show the benefits of the introduced GCN embedding layer by measuring the performance of different context-enriched tasks.
Accuracy improvements for cold-start recommendation problem using indirect relations in social networks

Abstract Recent advances in Internet applications have facilitated information spreading and, thanks to a wide variety of mobile devices and the burgeoning 5G networks, users easily and quickly gain access to information. Great amounts of digital information moreover have contributed to the emergence of recommender systems that help to filter information. When the rise of mobile networks has pushed forward the growth of social media networks and users get used to posting whatever they do and wherever they visit on the Web, such quick social media updates already make it difficult for users to find historical data. For this reason, this paper presents a social network-based recommender system. Our purpose is to build a user-centered recommender system to exclude the products that users are disinterested in according to user preferences and their friends' shopping experiences so as to make recommendations effective. Since there might be no corresponding reference value for new products or services, we use indirect relations between friends and “friends’ friends” as well as sentinel friends to improve the recommendation accuracy. The simulation result has proven that our proposed mechanism is efficient in enhancing recommendation accuracy.
Students’ Experiences of 2D and 360° Videos With or Without a Low-Cost VR Headset: An Experimental Study in Higher Education

Aim/Purpose: This case study examines students’ affective responses to and relationships with two-dimensional (2D) and 360° videos that were experienced with or without low-cost virtual reality (VR) headsets. Background: The prior research on low-cost VR technology is scant. Schools and universities are not financially able to purchase tens or hundreds of expensive inbuilt VR headsets. Therefore, we recommend an alternative, low-cost solution. Methodology: We conducted the experiment with students’ (N=100) responses to videos and VR technologies used in a higher education setting. We also applied a quantitative research approach examined in light of media richness and affective experience theories. Contribution: This study provides evidence of the integral role that VR technologies and 360° video content play, because using low-cost VR headsets potentially decreases the initial affective experiences of 360° videos among students. Although VR headsets improve media richness, they might simultaneously weaken students’ overall affective experiences if they experience usability challenges. Findings: The results showed that using low-cost VR headsets decreased positive user experiences when they were watching 360° videos. The 360° video experience was noted to be better without low-cost VR headsets. Low-cost VR headsets with a smartphone and 360° videos were found to be complicated to set up and use among first-time users. However, 360° videos created a more positive affect than did 2D videos. We also found that the positive affect of videos enhanced the social sharing intention. Recommendations for Practitioners: Educational institutes and teachers with limited financial budgets need to plan and manage courses that increases their need to adopt low-cost VR headsets. However, a poor initial user experience of low-cost VR technology usability might create negative student attitudes, which might hinder VR’s adoption rate in higher education. Recommendation for Researchers: This study provides a new understanding about students’ affective experiences of 2D videos and 360° videos with and without low-cost VR headsets. The results show that positive user experiences of 2D and 360° videos enhance students’ interest in sharing and collaboration in digital learning environments. Impact on Society: The results help educators to predict possible usability challenges in selecting the proper rich media for different learning situations. Additionally, the results assist educators to design VR assisted courses that motivate students. Future Research: The experimental comparison of different VR solutions and traditional learning technologies merits further examination. Additionally, more research is needed to determine the relationship of VR technologies, video content and learning methods, because technological features and content are tightly integrated in VR.
Application of the LDA Model to Semantic Annotation of Web-based English Educational Resources

Based on the LDA model, this paper builds a three-layer semantic model of Web English educational resources “document-topic-keyword”, models the semantic topics of resource documents, and obtains the semantic topics and keywords of document resources as the semantic labels of resources. The experimental results show that document LDA topic modeling is beneficial to the macroscopic classification of Web English educational resources. The experimental results show that LDA topic modeling of documents is useful for macroscopic cataloging of Web English educational resources, highlighting teaching priorities, difficulties, and interrelationships, while LDA modeling of teaching topics with the same teaching content expands the metadata generation method of resource description based on the basic education metadata standard and provides more information about the inherent characteristics of resources. The semantic information can be used to mine the semantic thematic features and detailed differences inherent in the resources, and the final performance analysis verifies the parallel computing advantages of the LDA model in a big data environment.
Research on the Problems and Countermeasures of the Cultivation of Adult College Students’ Innovation and Entrepreneurship Ability in the Internet Era

In the Internet era, the cultivation of innovation and entrepreneurship ability of adult college students has always been the focus of adult education in China.The rapid development of China's economy and the rapid popularization of Internet technology provide an unprecedented opportunity and stage for the cultivation of innovation and entrepreneurship ability of adult college students and open a new engine for the cultivation of innovation and entrepreneurship ability of adult education in China.Through consulting the relevant literature and practical exploration, this paper finds that there are many problems in the cultivation of adult college students' innovation and entrepreneurship ability in the Internet era, such as lack of talents, low entrepreneurship rate and success rate, imperfect school-enterprise cooperation system and mechanism, and unbalanced willingness of both sides, which are related to the low effectiveness of adult college entrepreneurship education, poor financing channels for college students' entrepreneurship, unperfect school-enterprise cooperation system and mechanism, weak entrepreneurial environment and other factors are closely related.In view of the above problems, this paper puts forward countermeasures from four aspects, in order to provide useful reference for the cultivation of adult college students' innovation and entrepreneurship ability in the Internet era.
From Verification to Causality-Based Explications (Invited Talk)

In view of the growing complexity of modern software architectures, formal models are increasingly used to understand why a system works the way it does, opposed to simply verifying that it behaves as intended. This paper surveys approaches to formally explicate the observable behavior of reactive systems. We describe how Halpern and Pearl’s notion of actual causation inspired verification-oriented studies of cause-effect relationships in the evolution of a system. A second focus lies on applications of the Shapley value to responsibility ascriptions, aimed to measure the influence of an event on an observable effect. Finally, formal approaches to probabilistic causation are collected and connected, and their relevance to the understanding of probabilistic systems is discussed.
Learning Chinese political formulaic phraseology from a self-built bilingual United Nations Security Council corpus

Abstract This pilot study investigates the formulaic phraseology most frequently used in highly formulaic political documents by examining a self-built bilingual parallel corpus of 43 speeches delivered in United Nations Security Council (UNSC) meetings by Chinese representatives. The study also probes corpus-based approaches to explore formulaic phraseology and demonstrates a method to retrieve Chinese formulaic phraseology from the UNSC corpus. Formulaic phraseology is often seen in political discourse. It can be defined as a sequence, continuous or discontinuous, of words or other meaning elements that are, or appear to be, prefabricated, stored and retrieved whole from memory at the time of use rather than being subject to generation or analysis by the language grammar. This study begins with a literature review of formulaic phraseology, including its features and significance in simultaneous interpreting. It then exhibits a four-step retrieval process with the Sketch Engine software program to acquire Chinese formulaic phraseology from the corpus to fill previous studies’ gap. Key functional units of the Sketch Engine, including Wordlist, N-grams, and Concordance, are used to extract formulaic phraseology from the UNSC corpus. Methodological issues involved in identifying formulaic phraseology, such as length of phraseology and quantitative criteria (frequency and dispersion thresholds), are also discussed in the study. Three types of formulaic phraseology are identified: (1) greeting representatives and other members and expressing appreciation; (2) expressing concerns about the topic of the meeting; (3) expressing China’s viewpoints about the topic of the meeting. The training of interpreters would be more effective if this categorization of formulaic phraseology is incorporated into the curriculum.