Reviewerly/exHarmony huggingface dataset:
Detection of Hostile Intent by Spatial Movements

Objective The ability of people to infer intentions from movement of other vessels was investigated. Across three levels of variability in movements in the path of computer-controlled ships, participants attempted to determine which entity was hostile. Background Detection of hostile intentions through spatial movements of vessels is important in an array of real-world scenarios. This experiment sought to determine baseline abilities of humans to do so. Methods Participants selected a discrete movement direction of their ship. Six other ships’ locations then updated. A single entity displayed one of two hostile behaviors: shadowing, which involved mirroring the participant’s vessel’s movements; and hunting, which involved closing in on the participant’s vessel. Trials allowed up to 35 moves before identifying the hostile ship and its behavior. Uncertainty was introduced through adding variability to ships’ movements such that their path was 0%, 25%, or 50% random. Results Even with no variability in the ships’ movements, accurate detection was low, identifying the hostile entity about 60% of the time. Variability in the paths decreased detection. Detection of hunting was strongly degraded by distance between ownship and the hostile ship, but shadowing was not. Strategies employing different directions of movement across the trial, but also featuring some runs of consecutive movements, facilitated detection. Conclusions Early identification of threats based on movement characteristics alone is likely to be difficult, but particularly so when adversaries employ some level of uncertainty to mask their intentions. These findings highlight the need to develop decision aids to support human performance.
Hangman–Hangaroo Game Design Using Automata Theory

Automata theory plays an important role in various areas especially in game design. This paper describes the concept of automata theory in designing one of the most popular classical game which is Hangman. In this study, we focused on a game called Hangaroo, which implemented the same concept as in Hangman game. We studied and discussed the combination of automata and game theory that can be considered in order to design the game. As a result, we found that, automata theory is the fundamental access in designing and developing Games.
Benchmark of Encoders of Nominal Features for Regression

Mixed-type data is common in the real world. However, supervised learning algorithms such as support vector machines or neural networks can only process numerical features. One may choose to drop qualitative features, at the expense of possible loss of information. A better alternative is to encode them as new numerical features. Under the constraints of time, budget, and computational resources, we were motivated to search for a general-purpose encoder but found the existing benchmarks to be limited. We review these limitations and present an alternative. Our benchmark tests 16 encoding methods, on 15 regression datasets, using 7 distinct predictive models. The top general-purpose encoders were found to be Catboost, LeaveOneOut, and Target.
Specific features of value orientations among the student youth in the context of digital transformation of the education system

The importance of the paper is determined by the fact that the development of digital technologies is currently one of the main priorities of any country, both at the highest political level and at the federal and regional ones. The introduction of digital initiatives into the education system leads to their reforming, which has a direct influence on all subjects of the educational process. The goal of the study was to investigate the characteristics of the value orientations of student youth in the context of the digital transformation of the higher education institution. The research methods comprised theoretical and methodological analyses of the problem, empirical research methods (testing, interviewing and a survey), an educational experiment and methods of mathematical statistics. The results of the study include a description of the value framework structure of the personality of student youth (values and meanings) in the era of digital transformation. In addition, as a result of the study, the authors of the paper designed and tested a programme of additional education for students, aimed at developing the value orientations of the ‘digital personality’. The materials of the paper can be useful for the introduction into the educational process in the system of higher and secondary vocational education.&#x0D; &#x0D; Keywords: Value orientations, student youth, global risks and challenges, information and educational environment of a higher education institution, digital transformation of society.
Analysis of Heterogeneous Data Storage and Access Control Management for Cloud Computing Under M/M/c Queueing Model

This study focuses on challenges faced during optimization of the performance of multiple users under encrypted data storage. Traditional single storage instance schemes are much concerned about the application scenarios rather than data sensitivity. This paper provides an optimization function for analyzing the performance parameters of developed queueing system. On the other hand, simulation results are further examined in order to optimize the average waiting time, average queue length, and total number of data packets. In addition, deduplication check provides more storage by eliminating duplicate files, and the remaining files further get encrypted by two cryptographic algorithms, namely advanced encryption standard (AES) and Rivest–Shamir–Adleman (RSA), respectively. Moreover, this paper emphasises performance of data in terms of storage and security; on the same basis, its comparison and implementation has been done. At last, conclusion is given.
A Deep Neural Network Model with Multihop Self-attention Mechanism for Topic Segmentation of Texts

Topic segmentation is an important task in the field of natural language processing (NLP), which finds its importance in applications such as information retrieval, text summarization, e-learning. Current neural methods for topic segmentation represent a sentence by a single feature vector that generates single semantic information. However, the dependencies between different parts in a sentence relies on more complex semantic information, which cannot be learned by a single-vector representation. In this paper, we present a deep neural model to capture the multi-aspect semantic information for topic segmentation of texts by multi-hop attention mechanism to address this issue, which named MHOPSA-SEG. At each attention step, the model assigns different weights to words depending on the previous memory weights. Thus, it can capture multiple sentence semantic vector representation. We conduct experiments on four datasets, including written texts and lectures transcripts. And the experimental results show that MHOPSA-SEG outperforms the state-of-the-art models.
A Video Production Method of Microclass Combined with MOOC

In recent years, video production has become one of the key challenges for researchers in the area. In the face of the poor video effects of earlier video production methods and approaches, this study has considered the MOOC-based microclass combined with the MOOC video production method for improving the video production effect. According to the idea of MOOC, based on the early design stage and shooting preparation stage of video development, the video development team prepares the text, manuscript, and course type according to the initial design stage of video development and formulates the recording process. In the video recording stage, all activities of classroom teaching are recorded through the multislot mode for enhancing the sense of scene of the video picture. In the later stage of video production, video editing and feature processing are implemented by means of film and television to improve the effect of video production. The video production method based on the Hidden Markov model is used to ensure the continuity of front and back shots and complete online open video production and release to the cloud. The experimental results of the study show that the application effect of this method is effective, and the average satisfaction is as high and measured as 91.36%.
Automation of formation of the content of the educational programs of university on the basis of professional standards in accordance with the requirements of the Federal State Educational Standards of Higher Education 3++ as a factor of the digital transformation of educational systems

The article discusses the actual task of digital transformation of a university that is the automation of the processes of developing Basic Professional Educational Programs of Higher Education. It is these processes that largely determine and characterize the degree and depth of such a transformation, since they reorganize the activities of the main staff of the university. The experience of automation of the development processes of Basic Professional Educational Programs of Higher Education in the Belgorod State Agrarian University is analyzed. The necessity of using automation tools for the development and updating of the documentation support of the Basic Professional Educational Programs of Higher Education is shown.An approach is proposed for the automated formation of the content of the Basic Professional Educational Programs of Higher Education based on the Professional Standards according to the requirements of the Federal State Educational Standard 3++. The possibility of using for these purposes the new software product “Intellect Info: Educational Programs” (an extension of 1C:University PROF) is shown. The principles for the automated inclusion of the content of Professional Standards in the content of the Basic Professional Educational Programs of Higher Education are proposed, including the principles of taking into account the requirements of employers (labor market), formulating indicators of achieving professional competencies, automated preparation of work programs for disciplines, practice programs, programs of state final certification.The proposed approach to the formation of the content of Basic Professional Educational Programs of Higher Education based on the Professional Standards according to the requirements of the Federal State Educational Standard 3++ can be used by any university operating the 1C:University PROF system.
Sentiment Analysis of the Academic Services of ESSU Salcedo Campus using Plutchik Model And Latent Dirichlet Allocation Algorithm

The continuous pursuit of quality education has always been a concern of higher institutions. This can be seen in the way university teachers deliver academic services to the students in terms of professionalism, commitment, knowledge of the subject matter, teaching for independent learning, and management of learning. Students as recipients of these services are significant sources of information about their course interaction that takes place in an educational system. Utilizing Latent Dirichlet Allocation (LDA) algorithm and sentiment analysis through NRC emotion lexicons based on Plutchik Model, this study aimed to decipher students’ sentiments of the academic services and reveal commonalities contained in their qualitative responses. Results revealed five latent themes in the students’ responses as: The Disparity of Teaching Assignment to Professors Field of Expertise, Professors’ Expression of Willingness to Help Students in School-Related Matters, Desirable Traits Portrayed by a Professional Teacher, Professor’s Commitment and Dedication to Classroom Instruction, and Enhancement of Teaching Practices to Improve Quality of Academic Services. The results also suggest that majority of the students have a positive sentiments (64.42%), some of were negative (34.62%), and very few were neutral (0.95%). This study aimed to give inputs to any academic interventions undertaken by institution.
UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks

Hypergraph, an expressive structure with flexibility to model the higher-order correlations among entities, has recently attracted increasing attention from various research domains. Despite the success of Graph Neural Networks (GNNs) for graph representation learning, how to adapt the powerful GNN-variants directly into hypergraphs remains a challenging problem. In this paper, we propose UniGNN, a unified framework for interpreting the message passing process in graph and hypergraph neural networks, which can generalize general GNN models into hypergraphs. In this framework, meticulously-designed architectures aiming to deepen GNNs can also be incorporated into hypergraphs with the least effort. Extensive experiments have been conducted to demonstrate the effectiveness of UniGNN on multiple real-world datasets, which outperform the state-of-the-art approaches with a large margin. Especially for the DBLP dataset, we increase the accuracy from 77.4\% to 88.8\% in the semi-supervised hypernode classification task. We further prove that the proposed message-passing based UniGNN models are at most as powerful as the 1-dimensional Generalized Weisfeiler-Leman (1-GWL) algorithm in terms of distinguishing non-isomorphic hypergraphs. Our code is available at \url{https://github.com/OneForward/UniGNN}.
Dynamic Data Visualization for Soccer Performance Metrics

Analysis of large dataset of soccer can be extremely difficult if it is not represented graphically. This paper proposes SoccerMetrics, an interactive, open source data visualization system for predicting the individual and team performance. The effectiveness of SoccerMetrics is demonstrated by the available dataset and its visualization.
Text Recognition Using Convolutional Neural Network for Visually Impaired People

In image processing and document analysis, accurate identification and recognition of the text are very important. It is difficult to recognize some alphanumeric symbols which are similar in appearance. Hence, the best deep learning neural network and convolutional neural network are used. In continuation of this, optical character recognition (OCR) is coupled with voice-over utility that reads out the text recognized, and conversion to Braille is done to help the blind or visually impaired people. The experimental results show high-level precision for the detection of machine-printed documents. The model can handle different font types and sizes and provides value accuracy of 90.34%.
Graph-Classes of Argumentation Frameworks with Collective Attacks

Argumentation frameworks with collective attacks (SETAFs) have gained increasing attention in recent years as they provide a natural extension of the well-known abstract argumentation frameworks (AFs) due to Dung. Concerning complexity, it is known that for the standard reasoning tasks in abstract argumentation, SETAFs show the same behavior as AFs, i.e. they are mainly located on the first or second level of the polynomial hierarchy. However, while for AFs there is a rich literature on easier fragments, complexity analyses in this direction are still missing for SETAFs. In particular, the well-known graph-classes of acyclic AFs, even-cycle-free AFs, symmetric AFs, and bipartite AFs have been shown tractable. In this paper, we aim to extend these results to the more general notion of SETAFs. In particular, we provide various syntactic notions on SETAFs that naturally generalize the graph properties for directed hypergraphs, and perform a complexity analysis of the prominent credulous and skeptical acceptance problems for several different widely used semantics.
Using latent features for building an interpretable recommendation system

Purpose Aspects extracted from the user’s historical records are widely used to define user’s fine-grained preferences for building interpretable recommendation systems. As the aspects were extracted from the historical records, the aspects that represent user’s negative preferences cannot be identified because of their absence from the records. However, these latent aspects are also as important as those aspects representing user’s positive preferences for building a recommendation system. This paper aims to identify the user’s positive preferences and negative preferences for building an interpretable recommendation. Design/methodology/approach First, high-frequency tags are selected as aspects to describe user preferences in aspect-level. Second, user positive and negative preferences are calculated according to the positive and negative preference model, and the interaction between similar aspects is adopted to address the aspect sparsity problem. Finally, an experiment is designed to evaluate the effectiveness of the model. The code and the experiment data link is: https://github.com/shiyu108/Recommendation-system Findings Experimental results show the proposed approach outperformed the state-of-the-art methods in widely used public data sets. These latent aspects are also as important as those aspects representing the user’s positive preferences for building a recommendation system. Originality/value This paper provides a new approach that identifies and uses not only users’ positive preferences but also negative preferences, which can capture user preference precisely. Besides, the proposed model provides good interpretability.
DIGITAL SOCIETY LIBRARY IN PROVIDING SYSTEM SUPPORT TO SCIENCE RESEARCH

The purpose of the article is to review and analyze the participation of libraries in the maintenance of modern research information management systems that provide scientists with the necessary digital services and effectively support digital scientific communication. The methodology of the study is a set of system analysis of changes in the providing of libraries to support research in the digital environment. The scientific novelty of the article is the generalization of key trends in the development of integrated library systems for supporting scientific activities and the development of the concept of the library portal of the National Academy of Sciences of Ukraine. Conclusions. Modern research information systems are an effective tool for managing research data. A critical aspect of supporting such systems is the quality of the data that librarians can provide at the appropriate level. The library portal of the National Academy of Sciences of Ukraine, created and maintained by the Vernadsky National Library of Ukraine, built on the principles of integrated scientific information systems, will preserve and provide access to documentary heritage sources and modern sources of scientific information, provide a single access point to consolidated achievements academic science, will ensure the integration of portal metadata into international scientific information systems.&#x0D; Key words: research information systems, research support systems, research libraries, academic science, research information management, research metadata, library knowledge portals.
Social‐trust‐aware variational recommendation

Most existing studies that employ social-trust information to solve the data sparsity issue in recommender systems assume that socially connected users have equal influence on each other. However, this assumption does not hold in practice since users and their friends may not have similar interests because social connections are multifaceted and exhibit heterogeneous strengths in different scenarios. Therefore, estimating the diverse levels of influence among entities (users/items/social connections) is very important in advancing social recommender systems. Towards this goal, we propose a new model named Social-Trust-Aware Variational Recommendation (SOAP-VAE). Particularly, SOAP-VAE leverages graph attention network techniques to capture the varying levels of influence and the complex interaction patterns among all the entities collectively and holistically. In doing so, heterogeneity among entities is obtained seamlessly. Consequently, we generate social-trust-aware item embedding representations in which the right level of influence has been integrated. Next, based on these rich social-trust-aware item representations, we formulate the first-ever social-trust-aware prior in literature. Unlike priors utilized in earlier VAE-based recommendation models, this novel prior aids in dealing with the issue of posterior-collapse and can effectively capture the uncertainty of latent space. In effect, the model produces better latent representations, which significantly alleviates the data sparsity issue. Finally, we empirically show that SOAP-VAE outperforms several state-of-the-art baselines on three real-world data sets.
A Study of Agile Iterative Development Methodology on Web Application Quality

With the increase in use of web based applications in different fields, the competition for providing secure efficient and quality software increases with a demand for decreased cost of the product. To improve the product development process in environment of the ever dynamic customer requirement and to meet the quality parameters of the software products many in the industry, have adopted agile software development. Although the nuances of agile methods like iterative development existed decades back the term Agile software development was coined in 2001 in a small group industry meet. In this paper we have tried to scan the literature for any correlation between the agile development methods and software quality. We have conducted a survey to understand how one of the major factors of agile development i.e. iterative development influences the quality of the software.
New Format of the Final State Examination at Bachelor Graduates in the Field of Pedagogical Education

The purpose of this study is to build a model of fund tasks aimed at assessing general professional and universal competencies of bachelor's graduates studying in the field of training «Pedagogical education» at the stage of preparation and conduct of the final state exam. Systemic, action-oriented, competency-based and contextual approaches were used as methodological guidelines in the development of fundraising tasks. The authors defined the notions of "fund task", "key actions" and highlighted the types of fund tasks, justified the need to change the form and content of evaluation tools used in the preparation and conduct of the final state exam.
Minimal scheme for certifying three-outcome qubit measurements in the prepare-and-measure scenario

The number of outcomes is a defining property of a quantum measurement, in particular, if the measurement cannot be decomposed into simpler measurements with fewer outcomes. Importantly, the number of outcomes of a quantum measurement can be irreducibly higher than the dimension of the system. The certification of this property is possible in a semi-device-independent way either based on a Bell-like scenario or by utilizing the simpler prepare-and-measure scenario. Here we show that in the latter scenario the minimal scheme for a certifying an irreducible three-outcome qubit measurement requires three state preparations and only two measurements and we provide experimentally feasible examples for this minimal certification scheme. We also discuss the dimension assumption characteristic to the semi-device-independent approach and to which extend it can be mitigated.