Reviewerly/exHarmony huggingface dataset:
Modernisation of foreign-language education in the epoch of social challenges and technological transformation: a case study of Master degree programme transformation

Problem and aim. The radical changes in the labour market, digitalisation, internationalisation and internal processes involving transformation of university educational activities with regard for the new conditions require a qualitative review of both the substantive and technological components of the educational process organisation. The aim of the research is to present a methodology for transforming the educational process organisation model on the basis of modern technological and didactic solutions, the one implementing the principles of contextual/ interdisciplinary and intercultural approaches. Materials and methods. The research was carried out on the basis of the diagnostics data considering satisfaction with educational practices, career and educational plans of Master degree students of the Faculty of Foreign Languages at Tomsk State University (Russian Federation). The remote survey was conducted in April 2022 and covered 19 Master degree graduate students, 20 first-year Master degree students and 9 head executives of educational organisations – employers of young professionals. The questioning was administered anonymously by the Accreditation Centre of Tomsk State University. The questionnaire used both closed and open-ended questions related to the graduates’ satisfaction with the educational programme, and the employers’ satisfaction with the quality of professional training of the young specialists. The method of SWOT analysis of the educational process was applied. The analysis of questionnaire findings was carried out using the methods of content analysis, frequency analysis and descriptive statistics. Survey results. It was revealed that the leading criterion of attractiveness of the Master degree programme is the prospect of career and status growth, which was mentioned by 63% of the respondents. Special importance is given by 63% of the respondents to the need for applying information technologies. The desirability of industrial partnerbased training is noted by 53% of the students. A total of 89% of the employers attach importance to professional competence of the graduates; 56% appreciate the ability to resolve tasks independently, to work in a team, and professionals’ initiative. The formulation of graduates’ professional competences and indicators of their results have been clarified. The basic mechanisms for modernisation of educational programmes in the context of social challenges and technological transformation have been specified. Conclusion. The need to secure due quality of foreign language education necessitates modernisation of educational programmes in the epoch of social challenges and technological transformation. The obtained results can be used to modernise the basic educational Master degree programme not only in the mentioned profile, but also for other programmes aimed at interdisciplinary research. Keywords: modernisation, education programme, competence-based approach, educational mode
A Generate Adversarial Network with Structural Branch Assistance for Image Inpainting

In digital image inpainting tasks, existing deep-learning-based image inpainting methods have achieved remarkable staged results by introducing structural prior information into the network. However, the corresponding relationship between texture and structure is not fully considered, and the inconsistency between texture and structure appears in the results of the current method. In this paper, we propose a dual-branch network with structural branch assistance, which decouples the inpainting of low-frequency and high-frequency information utilizing parallel branches. The feature fusion (FF) module is introduced to integrate the feature information from the two branches, which effectively ensures the consistency of structure and texture in the image. The feature attention (FA) module is introduced to extract long-distance feature information, which enhances the consistency between the local features of the image and the overall image and gives the image a more detailed texture. Experiments on the Paris StreetView and CelebA-HQ datasets prove the effectiveness and superiority of our method.
User Compliance With the Health Emergency and Disaster Management System: Systematic Literature Review

Health-related hazards have a detrimental impact on society. The health emergency and disaster management system (Health EDMS), such as a contact-tracing application, is used to respond to and cope with health-related hazards. User compliance with Health EDMS warnings is key to its success. However, it was reported that user compliance with such a system remains low.Through a systematic literature review, this study aims to identify the theories and corresponding factors that explain user compliance with the warning message provided by Health EDMS.The systematic literature review was conducted using Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 guidelines. The search was performed using the online databases Scopus, ScienceDirect, ProQuest, IEEE, and PubMed, for English journal papers published between January 2000 and February 2022.A total of 14 papers were selected for the review based on our inclusion and exclusion criteria. Previous research adopted 6 theories when examining user compliance, and central to the research was Health EDMS. To better understand Health EDMS, based on the literature reviewed, we mapped the activities and features of Health EDMS with the key stakeholders involved. We identified features that require involvement from individual users, which are surveillance and monitoring features and medical care and logistic assistance features. We then proposed a framework showing the individual, technological, and social influencing factors of the use of these features, which in turn affects compliance with the warning message from Health EDMS.Research on the Health EDMS topic increased rapidly in 2021 due to the COVID-19 pandemic. An in-depth understanding of Health EDMS and user compliance before designing the system is essential for governments and developers to increase the effectiveness of Health EDMS. Through a systematic literature review, this study proposed a research framework and identified research gaps for future research on this topic.
A Contrastive Learning Framework for Detecting Anomalous Behavior in Commodity Trading Platforms

For bulk commodity, stock, and e-commerce platforms, it is necessary to detect anomalous behavior for the security of users and platforms. Anomaly-detection methods currently used on these platforms train a model for each user since different users have different habits. However, the model cannot be trained adequately due to insufficient individual user behavior data. In this study, to utilize information between users and avoid underfitting, we propose a contrastive learning framework to train a complete global model (GM) for anomaly detection in a trading platform. By confusing the data between different users to generate negative samples, the model can learn the differences between users by contrastive learning. To reduce the need for individual user behavior data, this framework uses a GM instead of a model for each user to learn similarities between users. Experiments on four datasets show that models trained using our framework achieve better area-under-the-curve (AUC) scores than do the original models, proving that contrastive learning and GM are useful for anomaly detection in trading platforms.
Enhancing Organizational Data Security on Employee-Connected Devices Using BYOD Policy

To address a business need, most organizations allow employees to use their own devices to enhance productivity and job satisfaction. For this purpose, the Bring Your Own Device (BYOD) policy provides controllable access for employees to organize data through their personal devices. Although the BYOD practice implies plenty of advantages, this also opens the door to a variety of security risks. This study investigates these security risks and proposes a complementary encryption approach with a digital signature that uses symmetric and asymmetric algorithms, depending on the organization’s digital certificate, to secure sensitive information stored in employees’ devices within the framework of BYOD policies. The method uses Advanced Encryption System (AES), Blowfish, RSA and ElGamal with a digital signature to achieve strong encryption and address critical security considerations such as user authentication, confidentiality and data integrity. The proposed encryption approach offers a robust and effective cryptographic solution for securing sensitive information in organizational settings that involve BYOD policies. The study includes experimental results demonstrating the proposed approach’s efficiency and performance, with reasonable encryption and decryption times for different key and file sizes. The results of the study revealed that AES and Blowfish have the best execution time. AES has a good balance of security and performance. RSA performs better than ElGamal in encryption and signature verification, while RSA is slower than ElGamal in decryption. The study also provides a comparative analysis with previous studies of the four encryption algorithms, highlighting the strengths and weaknesses of each approach.
Traffic Sign Identifier

Traffic sign recognition (TSR) is one of the most important background research topics for enabling autonomous vehicle driving systems. Drivers are exposed to a variety of risks while driving as a result of the increase in the number of vehicles on the road, which may result in accidents. Every year, a large number of accidents occur all around the world. The driver's failure to interpret all of the visual information available while driving is the primary cause of these incidents. This challenge get more difficult to meeting a city like environment where multiple traffic signs, ads, parking vehicles, pedestrians, and other moving or background objects make the recognition much more difficult. While numerous solutions have been pub- lished, solutions are tested on autoways, country-side, or at a very low speed. In this paper, we give a short overview on main problems and known strategies to solve these problems, and we give a general solution to tackle real-time issues in urban traffic sign recognition
Research on flight path planning method based on genetic ant colony algorithm and Maklink diagram

Abstract To reduce the cost of a flight, a calculation method is proposed in this paper, which takes the shortest distance as the goal and avoids the obstacle zone, and controls the turning angle as a constraint. Firstly, the Maklink diagram is used to build the obstacle zone model and establish the relationship between the obstacle zones. Then Dijkstra algorithm is used to construct the initial flight path to form a suboptimal path. Then the genetic ant colony algorithm is used to optimize the obtained suboptimal path. When there is a conflict risk between the two planned paths, the flight altitude of the aircraft on one path is adjusted, and the constraints are checked to form the optimal path. The simulation results show that the method can realize flight path optimization and verify the feasibility of the method.
A search and rescue robot device realization based on ROS operating system combined with visual algorithm

Abstract Aiming at the problem of low accuracy of intelligent devices to find missing people, a search and rescue robot device based on ROS intelligent navigation system was proposed. SolidWorks was used to improve the new mechanical structure that can switch autonomously to different terrain and combined with Yolov5 deep learning algorithm to complete face recognition. Using the stm32 single-chip microcomputer, we adjusted the cascade PID parameters of the motor and simulation to realize the omnidirectional movement control of the robot. Through the analysis and verification of the experimental data, the adaptive ability of the improved robot platform to the terrain was significantly improved. The method can accurately complete the task of face recognition and autonomous navigation, which is practical and improves the accuracy of finding the missing people.
Cross-Camera Multi-Object Tracking based on Person Re-Identification and Spatial-Temporal Constraints

Abstract In order to reduce the influence of occlusion on the overall feature representation of tracks and improve the accuracy of track correlation between cameras, this paper proposes a cross camera multi-target tracking method based on person appearance and spatial-temporal constraints: a new cross-camera multi-object tracking framework is constructed. Then the person spatial-temporal probability model is established. Finally, the spatial-temporal probability model and the person appearance similarity are jointly measured and the person trajectory correlation under cross-camera is completed by using data correlation; Comparative experiments on the dataset proved that the method is effective.
Learning Vector Quantization, Hebbian Learning, and Self-Organizing Map for Classification

Deep Learning has been rapidly developed. Almost all proposed methods already have very high accuracy. Most of these methods still use techniques from the past with some modifications to adapt to existing modules. Sometimes it is necessary to understand past methods to produce new methods. Therefore, this research examines past models that have the potential to improve the performance of existing deep learning models. The methods to be examined include Learning Vector Quantization (LVQ), Hebbian learning, and Self-Organizing Map (SOM). The iris dataset available on Scikit-learn (SKlearn) is used here for testing in cases of supervised learning and unsupervised learning (especially SOM). The results show that LVQ has a good accuracy of 93%, while Hebbian learning has an accuracy of 56%. SOM fluctuates between 88% and 93%. Although the accuracy of SOM does not exceed LVQ, this model does not require labels in its training process.
Cross-Viewpoint Template Matching Based on Heterogeneous Feature Alignment and Pixel-Wise Consensus for Air- and Space-Based Platforms

Template matching is the fundamental task in remote sensing image processing of air- and space-based platforms. Due to the heterogeneous image sources, different scales and different viewpoints, the realization of a general end-to-end matching model is still a challenging task. Considering the abovementioned problems, we propose a cross-view remote sensing image matching method. Firstly, a spatial attention map was proposed to solve the problem of the domain gap. It is produced by two-dimensional Gaussian distribution and eliminates the distance between the distributed heterogeneous features. Secondly, in order to perform matching at different flight altitudes, a multi-scale matching method was proposed to perform matching on three down-sampling scales in turn and confirm the optimal result. Thirdly, to improve the adaptability of the viewpoint changes, a pixel-wise consensus method based on a correlation layer was applied. Finally, we trained the proposed model based on weakly supervised learning, which does not require extensive annotation but only labels one pair of feature points of the template image and search image. The robustness and effectiveness of the proposed methods were demonstrated by evaluation on various datasets. Our method accommodates three types of template matching with different viewpoints, including SAR to RGB, infrared to RGB, and RGB to RGB.
Guidelines on the Prevention of Offenses under Thailand’s Computer-Related Crime Act for Industrial Business

The objective of this research was to develop guidelines on the prevention of offenses under Thailand’s computer-related crime act for industrial business. This research was conducted both qualitatively and quantitatively. Quantitative data were obtained from a questionnaire survey with 500 IT executives in the industrial sector. The data analysis employed descriptive, inferential, and descriptive statistics. The results found that guidelines on the prevention of offenses under Thailand's computer-related crime act for industrial business can be prioritized in all four components as follows: 1) morality (X̅ = 4.21), 2) workforce (X̅ = 4.18), 3) internal control (X̅ = 4.16), and 4) punishment (X̅ = 4.14). The hypothesis testing revealed that large, small, and medium-sized industrial businesses gave a significant difference to guidelines for the prevention of offenses under Thailand's computer-related crime act at a statistically significant level of 0.05. The results of the developed structural equation modeling showed that all values were above the evaluation criteria and consistent with the empirical data. The chi-square probability level value was 0.092. The relative chi-square was 1.134. The conformity index was 0.960. The root index of the squared mean of the error estimation was 0.016.
Educational Opportunities

Journal AWWAVolume 115, Issue 4 p. 88-88 Educational Opportunities Educational Opportunities First published: 05 May 2023 https://doi.org/10.1002/awwa.2097Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume115, Issue4May 2023Pages 88-88 RelatedInformation
Multi-agent multi-criteria decision framework with interactive explanation tools

In this paper, we present the formalization and implementation of a decision framework in which a group of agents can express their preferences among a set of alternatives by using several comparison criteria. We define special kind of argument to support agents’ preferences, and the set of generated arguments is used both to obtain the set of selected alternatives and to explain why they were selected. We introduce a software application that implements our proposed approach and provides interactive tools for obtaining different kinds of explanations for the computed results. Also, we formally study the proposed argumentation process and we prove that the set of warranted conclusions, which support the obtained results, is consistent. We also show that our framework works properly on choice scenarios with incomplete evidence, and we propose how to optimize the computation process for dynamic environments.
Can Knowledge and Culture Eradicate Poverty and Reduce Income Inequality? The Evidence from Indonesia

This research aims to investigate the potential role of knowledge and cultural in reducing income inequality and alleviating poverty in Indonesia. The method used includes panel data regression estimation with the least squares dummy variable approach, compounded with an interactive session. The results showed that knowledge and cultural have a significant effect on income inequality, poverty, and development. This research has developed a knowledge-based and cultural-based development framework, by incorporating democracy-political institution as a new proxy variable for knowledge and tourism-cultural, as well as active participation as determinants of the cultural economy. The significance of cultural heritage possesses a remarkable capacity to attract tourists, thereby generating revenue, and bears indigenous wisdom and knowledge, inherent to local communities. This research stands out from the previous due to the unique combination of democracy-political institution determinants and cultural economy, which creates a blend of knowledge and cultural. By filling the gap left by previous research that solely focused on these areas independently, this research presents a holistic understanding of the subject matter.
Content recommendation with two-level TransE predictors and interaction-aware embedding enhancement: An information seeking behavior perspective

In community-based social media, users consume content from multiple communities and provide feedback. The community-related data reflect user interests, but they are poorly used as additional information to enrich user-content interaction for content recommendation in existing studies. This paper employs an information seeking behavior perspective to describe user content consumption behavior in community-based social media, therefore revealing the relations between user, community and content. Based on that, the paper proposes a Community-aware Information Seeking based Content Recommender (abbreviated as CISCRec) to use the relations for better modeling user preferences on content and increase the reasoning on the recommendation results. CISCRec includes two key components: a two-level TransE prediction framework and interaction-aware embedding enhancement. The two-level TransE prediction framework hierarchically models users’ preferences for content by considering community entities based on the TransE method. Interaction-aware embedding enhancement is designed based on the analysis of users’ continued engagement in online communities, aiming to add expressiveness to embeddings in the prediction framework. To verify the effectiveness of the model, the real-world Reddit dataset (4,868 users, 115,491 contents, 850 communities, and 602,025 interactions) is chosen for evaluation. The results show that CISCRec outperforms 8 common baselines by 9.33%, 4.71%, 42.13%, and 14.36% on average under the Precision, Recall, MRR, and NDCG respectively.
An efficient encoding mechanism against eavesdropper with side channel information

In this paper, we investigate the privacy-preserving problem in remote state estimation by extending the lightweight encoding mechanism (Yang et al., 2020) to a more competitive scenario, where the adversary can intercept the system data more effectively, i.e., it is considered to be able to gather data from both the channel and the side channel. A transferable framework based on side channel information is established for analyzing the validity of encoding mechanisms. By resorting to padding and random injecting, a lightweight encoding mechanism is proposed to resist eavesdropping attacks. Furthermore, a sufficient condition to ensure the privacy of information is derived by transforming the communication data. Finally, numerical examples are given to verify the results.