Reviewerly/exHarmony huggingface dataset:
On the issue of teaching psychological and pedagogical disciplines at universities using immersive technologies

The use of immersive technology enables high-quality educational simulations. The immersive technology in the classroom initiative provides access to virtual, augmented, and mixed-reality simulations for students and faculty across the pedagogical spectrum. The Virtual Immersive Teaching and Learning Initiative contributes to a growing body of research in this area. Through research that compares traditional educational simulation methods with immersive methods, the findings from an experiment conducted among BSMU medical psychology students are presented. Until recently the teaching of any discipline was considered to be the mastering of the necessary theoretical and practical knowledge and skills by the students, but recently, with the development of science and the increase in the necessary amount of knowledge, it has become more and more important to provide students with an excellent theoretical basis for independent study of the subject, and this in distance conditions becomes possible thanks to technology and immersive learning. To confirm the working hypothesis, a qualitative survey of 34 students of the Bukovinian State Medical University, specialty “Medical Psychology” was conducted. A closed questionnaire survey of students helped to diagnose their deep learning needs and determine the effectiveness and feasibility of using immersive learning. In addition, the results of the survey made it possible to reveal students' opinions about the profile of the ideal teacher in the era of digital and immersive technologies. It turned out that the use of immersive learning for psychologists yields high results is perceived with enthusiasm by students and is a feasible element for the methodology of teaching a foreign language.
What is software quality for AI engineers?

It is often overseen that AI-enabled systems are also software systems and therefore rely on software quality assurance (SQA). Thus, the goal of this study is to investigate the software quality assurance strategies adopted during the development, integration, and maintenance of AI/ML components and code. We conducted semi-structured interviews with representatives of ten Austrian SMEs that develop AI-enabled systems. A qualitative analysis of the interview data identified 12 issues in the development of AI/ML components. Furthermore, we identified when quality issues arise in AI/ML components and how they are detected. The results of this study should guide future work on software quality assurance processes and techniques for AI/ML components.
Students' Perception of Using the Internet to Develop Reading Habits

The internet ubiquity in education has flooded the teaching and learning process with digital tools and drastically changed many aspects of academic life, including students' reading habits. This study aimed at exploring students' perceptions of using the internet to develop reading habits. The study employed the qualitative descriptive method with survey design and involved 43 students of the English department of Universitas Kristen Indonesia Jakarta. Data was collected by means of a questionnaire consisting of 20 items. The results showed that 43% of the respondents had a positive perception that the internet developed their reading habits. However, the majority of them regarded reading as merely an activity for getting information to finish school assignments. They viewed the internet as the main source of information they needed to finish their learning assignments. Since about one-fifth of them preferred paper-based reading, it was concluded that not the whole of Generation Z members totally ignored printed reading materials.
Comparison of Naïve Bayes Algorithm and XGBoost on Local Product Review Text Classification

Online reviews are critical in supporting purchasing decisions because, with the development of e-commerce, there are more and more fake reviews, so more and more consumers are worried about being deceived in online shopping. Sentiment analysis can be applied to Marketplace product reviews. This study aims to compare the two categories of Naïve Bayes and XGBoost by using the two vector spaces wod2vec and TFIDF. The methods used in this research are data collection, data cleaning, data labelling, data pre-processing, classification and evaluation. The data scraping process produced 25,581 data which was divided into 80% training data and 20% test data. The data is divided into two classes, namely good sentiment and bad sentiment. Based on the research that has been done, the combination of Word2vec + XGBoost F1 scores higher by 0.941, followed by TF-IDF + XGBoost by 0.940. Meanwhile, Naïve Bayes has an F1-Score of 0.915 with TF-IDF and 0.900 with word2vec. Classification using XGBoost proved to be able to classify unbalanced data better than Naïve Bayes.
Towards a middleware design for efficient blockchain oracles selection

Blockchain smart contracts can only operate on data available on-chain, and face a major challenge of not being able to communicate with the world outside their own network. Blockchain oracles solve this problem by bridging the gap between on-chain and off-chain data. Chainlink is one of the most widely used decentralized oracle network with competing independent data providers with distinct characteristics in terms of price, performance, security, trust, etc. As a Chainlink service purchaser with unique a service level agreement (SLA) proposal for each job, a random selection of oracles to execute a certain job can result in sub-optimal solution, that could be alleviated by another oracle data provider. Therefore, in this work, we propose a middleware design to select the best data providers available in Chainlink oracle network based on individual job's service requirements. We model the oracle selection problem as a decision optimization problem and prove it to be NP-hard. We also propose two heuristic algorithms to approximate a solution to the modelled problem. We experimentally evaluate and compare the proposed design, and conclude that the proposed middleware architecture efficiently selects the oracles in terms of performance parameters.
Hierarchical Semi-supervised Contrastive Learning for Contamination-Resistant Anomaly Detection

Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables effective discrimination on anomalies. However, when contaminated with unlabeled abnormal samples in training set under semi-supervised settings, current contrastive-based methods generally 1) ignore the comprehensive relation between training data, leading to suboptimal performance, and 2) require fine-tuning, resulting in low efficiency. To address the above two issues, in this paper, we propose a novel hierarchical semi-supervised contrastive learning (HSCL) framework, for contamination-resistant anomaly detection. Specifically, HSCL hierarchically regulates three complementary relations: sample-to-sample, sample-to-prototype, and normal-to-abnormal relations, enlarging the discrimination between normal and abnormal samples with a comprehensive exploration of the contaminated data. Besides, HSCL is an end-to-end learning approach that can efficiently learn discriminative representations without fine-tuning. HSCL achieves state-of-the-art performance in multiple scenarios, such as one-class classification and cross-dataset detection. Extensive ablation studies further verify the effectiveness of each considered relation. The code is available at https://github.com/GaoangW/HSCL .
Cloud and HPC Headway for Next-Generation Management of Projects and Technologies

In the last decade, cloud computing has changed dramatically. More providers and administration contributions have entered the market, and cloud infrastructure, once limited to single-provider data centers, is expanding. This article discusses the shifting cloud foundation and the benefits of decentralizing computing from data centers. These patterns necessitate novel cloud computing architectures. These models may affect linking people and devices, data-intensive computing, the service space, and self-learning frameworks. Finally, we compiled a list of issues to consider while assessing modern cloud frameworks. Architectural and urban design projects breach scale and predictability constraints and seek enhanced competency, maintainability, energy performance, and cost-efficiency. Simulation and large-scale information processing drive this cycle. Advances in calculations and computer power help address the complex elements of a coordinated whole-structure framework. Adaptability is a barrier to the configuration, control, and development of whole-system frameworks. This position paper proposes several solutions for semi-or fully automated projects, such as short-plan boundary space exploration, large-scope high-accuracy simulation, and integrated multidisciplinary development. These computer-intensive operations were previously only accessible to the exam network. Once empowered by cloud computing and high-performance computing, these methods can stimulate intelligent plan measures, leading to enhanced results and shorter development times.
Digital competencies of the teacher of the future

Digital literacy has now established itself as a mandatory professional competency for teachers. Considering the perspectives of digital skills, we note the contradictions between the basic skills of pedagogical excellence, potentially actualized already in the short-term dimension. The research exploration aims to analyze the status of digital competencies in the general matrix of knowledge, skills, and abilities of teacher of the future. The article aims to position digital skills in three interpretations: as a part of flexible skills, as an element of fundamental skills, and as an independent component of pedagogical professional training. The optimal methodological arsenal to investigate this scientific and pedagogical problem consists of general scientific, pedagogical, and scientific-philosophical methods. The results of the article describe an acceptable balance of competencies of the teacher, which provides a high level of qualification, determining the role and place of digital skills in the system of professional excellence. Prospects for further research are predicting the correlation of theoretical-methodological and practical elements of digital competencies of a pedagogical specialist with segments of heavy and flexible skills of his professional training.
The Synchronization of Hyperchaotic Systems Using a Novel Interval Type-2 Fuzzy Neural Network Controller

This paper proposed a novel interval type-2 fuzzy neural network controller (NT2FC) to synchronize 5-D hyperchaotic systems with noise disturbance and system uncertainties. In the proposed controller, the type 2 fuzzy set is designed with the 3-dimensional Gaussian membership functions (3DGMFs) to increase the system’s ability to respond to uncertainty. The parameters of the NT2FC controller are updated online via adaptation laws, which are built based on the gradient descent approach. The system stability is ensured through the Lyapunov stability analysis. In addition, the modified Jaya algorithm (MJA) is applied to optimize the learning rates in adaptation laws. Finally, the efficiency of the proposed NT2FC is examined by the numerical simulation of the hyperchaotic system’s synchronization.
Whole Slide Image Quality in Digital Pathology: Review and Perspectives

With the advent of whole slide image (WSI) scanners, pathology is undergoing a digital revolution. Simultaneously, with the development of image analysis algorithms based on artificial intelligence tools, the application of computerized WSI analysis can now be expected. However, transferring such tools into clinical practice is very challenging as they must deal with many artifacts that can occur during sample preparation and digitization. Therefore, the quality of WSIs is of prime importance, and we propose a review of the state-of-the-art of computational approaches for quality control. In particular, we focus on WSI quality issues related to the presence of sample preparation artifacts, compression artifacts, color variations, and out-of-focus areas. An analysis of the monthly WSI clinical routine in a cytological laboratory confirms the importance of implementing quality control measures. Given this observation, we draw perspectives on how a computational quality process can be included in a computational pathology diagnosis pipeline.
Cooperative Computational Offloading in Mobile Edge Computing for Vehicles: A Model-Based DNN Approach

Many advancements are being made in vehicular networks, such as self-driving, dynamic route scheduling, real-time traffic condition monitoring, and on-board infotainment services. However, these services require high computation power and precision and can be met using mobile edge computing (MEC) mechanisms for vehicular networks. MEC operates through the edge servers available at the roadside, also known as roadside units (RSU). MEC is very useful for vehicular networks because it has extremely low latency and supports operations that require near-real-time access to rapidly changing data. This paper proposes an efficient computational offloading, smart division of tasks, and cooperation among RSUs to increase service performance and decrease the delay in a vehicular network via MEC. The computational delay is further reduced by parallel processing. In the division of tasks, each task is divided into two sub-components which are fed to a deep neural network (DNN) for training. Consequently, this reduces the overall time delay and overhead. We also adopt an efficient routing policy to deliver the results through the shortest path to improve service reliability. The offloading, computing, division, and routing policies are formulated, and a model-based DNN approach is used to obtain an optimal solution. Simulation results prove that our proposed approach is suitable in a dynamic environment. We also compare our results with the existing state-of-the-art, showing that our proposed approach outperforms the existing schemes.
A Tale of Two Subreddits: Measuring the Impacts of Quarantines on Political Engagement on Reddit

Concerns over the spread of abusive content in social media have led online platforms to explore large scale community-level forms of moderation. As an alternative to full bans, Reddit has implemented quarantines of subreddits, limiting access to controversial communities without explicit removal. Previous evaluations of the effectiveness of quarantines have focused on their impact on toxicity in affected communities. In this work, however, we focus on examining the impacts of quarantines of political subreddits on issues of political engagement in the broader Reddit community. We present a case study of two ideologically distinct political subreddits, r/The_Donald and r/ChapoTrapHouse, and examine the impact of quarantines on activity, visibility, and political discussion. We find that while quarantines had a homogenizing effect on participation in political subreddits, they had limited impact on the visibility of community-internal issues and political language, due to the entrenchment of subreddit norms.
The specificity of activating the personal potential of students in the context of digitalization of university education

Problem and objective. In the conditions of intensive implementation of information technologies in higher education, there is a problem of adequate application of these technologies for the development of students’ personal capacities and potential. The aim of the study is to determine the structural characteristics of personal potential, as well as the conditions and features of the manifestation this potential among students in different forms of digitalization university training. Research methods. The study involved 98 students and 10 teachers of the Belgorod National Research University and the Belgorod State Technological University named after V.G. Shukhov. The authors used methods of stimulating students’ personal activity in the information space of education. Methods of progress monitoring and psychological testing were used, such as: Method of study self-realization in higher education, Academic Motivation Scale, Hardiness Test, Self-organization Questionnaire, Self-actualization test (SAT) (version of POI Shostrom). For statistical processing of data the authors used the methods of percentage distribution and analysis of the significance of differences (Student's t-test). Results of the study. The authors presented a structural reconstruction of the phenomenon personal potential in the form of level and content structures of its psychological organization. A mechanism for personalization of educational communications was developed and applied in teaching a group of students of a blended (classroom-remote) form of training. In particular, the authors implemented the method of organizing an learning microsocial network, including a teacher and students for joint work. The application of this mechanism in practice has shown its sufficient effectiveness. Compared with the control group of students (remote learning), students of the experimental group showed higher academic performance, as well as signs of activation of their personal potential. Particularly significant differences were found in such parameters of this potential as: motivation of self-development (temp=10.9), cognitive motivation (temp=8.5), motivation of self-regard (temp=7.5), self-organization (temp=9.9), risk acceptance (temp=8.6), purposefulness (temp=6.5), inner-directed (temp=7.0), contact (temp=7.5), social integration into the university environment (temp=7.2). Conclusion. The mechanism of personalization of educational communications presented in the study has no exact analogues in contemporary innovations and can be used in providing personal development learning for students in the context of digitalization of education.
Plain, Edge, and Texture Detection Based on Orthogonal Moment

Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOMs). Therefore, finding a fast PET classification method that accurately classify image pattern is crucial. To this end, this paper proposes a new scheme for accurate and fast image pattern classification using an efficient DOM. To reduce the computational complexity of feature extraction, an election mechanism is proposed to reduce the number of processed block patterns. In addition, support vector machine is used to classify the extracted features for different block patterns. The proposed scheme is evaluated by comparing the accuracy of the proposed method with the accuracy achieved by state-of-the-art methods. In addition, we compare the performance of the proposed method based on different DOMs to get the robust one. The results show that the proposed method achieves the highest classification accuracy compared with the existing methods in all the scenarios considered.
Utilizing motion and spatial features for sign language gesture recognition using cascaded CNN and LSTM models

Sign language is a language produced by body parts gestures and facial expressions.The aim of an automatic sign language recognition system is to assign meaning to each sign gesture.Recently, several computer vision systems have been proposed for sign language recognition using a variety of recognition techniques, sign languages, and gesture modalities.However, one of the challenging problems involves image preprocessing, segmentation, extraction and tracking of relevant static and dynamic features related to manual and nonmanual gestures from different images in sequence.In this paper, we studied the efficiency, scalability, and computation time of three cascaded architectures of convolutional neural network (CNN) and long short-term memory (LSTM) for the recognition of dynamic sign language gestures.The spatial features of dynamic signs are captured using CNN and fed into a multilayer stacked LSTM for temporal information learning.To track the motion in video frames, the absolute temporal differences between consecutive frames are computed and fed into the recognition system.Several experiments have been conducted on three benchmarking datasets of two sign languages to evaluate the proposed models.We also compared the proposed models with other techniques.The attained results show that our models capture better spatio-temporal features pertaining to the recognition of various sign language gestures and consistently outperform other techniques with over 99% accuracy.
IoT: A Decentralized Trust Management System Using Blockchain-Empowered Federated Learning

We propose a decentralized medical trust management system using blockchain-based federated learning for large-scale Internet of Things (IoT) systems. The proposed system enables health institutions to share data without revealing the privacy of data owners. Health institutions form coalitions and the leader of each coalition is elected based on the proposed proof-of-trust collaboration (PoTC) consensus protocol. The PoTC consensus protocol is based on a weight difference game where trust scores, trust consistency value, and trust deviation are factors used for evaluating nodes in the blockchain. The trust of a node is obtained either through direct trust or recommended trust evaluations. Each leader elects an aggregator who has the most credibility to manage the proposed federated learning system. The leaders become the federated clients as well as validators while the aggregator is the federated server. To ensure the decentralization of nodes, a consortium blockchain is employed. Extensive simulations are performed, which show that the proposed system not only demonstrates scalability and credibility without compromising the accuracy, convergence, and resilience properties against malicious attackers but also outperforms existing trust management systems. A security analysis is also conducted, which shows that the proposed system is robust against trust-related attacks.
Structural Optimization of Small Diameter Deep Well Rescue Robot Based on Hyperworks-Optistruct

This paper aims at the problems of poor motion continuity and abrupt acceleration of small diameter deep well rescue robot in the process of motion characteristics analysis. According to the movement characteristics and structural forms of the grasping mechanism and bracket mechanism of the deep well rescue robot, the finite element analysis of the key mechanism is carried out based on Hyperworks-Optistruct solver, according to the analysis results, the specific parameters to be optimized are obtained. And the topology optimization of the key mechanism is carried out, the optimal design scheme of clamping mechanism and bracket mechanism of deep well rescue robot are obtained. The optimization results show that on the premise of meeting the strength requirements, the grid density distribution law is obtained. According to the variation law of lightweight curve, the overall weight of grasping mechanism and bracket mechanism decreases obviously. The whole optimization process is completed and the final optimization result is obtained.