Reviewerly/exHarmony huggingface dataset:
STACK for Computational Science, Mathematics and Engineering e-Learners

E-Learning platforms such as Moodle, Blackboard and Canvas have got reasonable attention in teaching and learning processes. However, when it comes to assessment and interactive learning activities they offer little service to science, engineering, and mathematics e-learners. In this work, we present the application of the System for Teaching and Assessment using Computer Algebra Kernel (STACK) as a plugin in a Learning Management System (LMS) to address the issue. Different features of STACK are demonstrated and discussed. As an LMS plugin, STACK can be used for interactive delivery of content as well as an assessment tool. Here, examples from mathematics, physical sciences and engineering are demonstrated. The use of STACK extends the applicability of LMS for a wider range of subjects to address existing inability to handle higher level mathematical and computational skills. Additionally, the use of STACK in an LMS is useful in handling tutorials for large classes especially when a blended delivery mode is preferred.
Item Enhanced Diversification in the Recommendation System using Graph Neural Network

A recommendation system is a set of programs that utilize different methodologies for relevant item selection for the user. Graph neural networks have been extensively used in recent years to improve the quality of recommendations across all domains. A general recommendation system’s main goal is to recommend items to the user accurately, and it frequently prioritizes items that are well-liked or mainstream. If the model concentrates only on one specific item category from the users’ past preferences, then recommendations for the target user will become too obvious. To address this problem, diversity in the recommendation system is introduced. The model IG-DivRS (Item-Enhanced Graph Neural Network for a Diversified Recommendation System) is proposed. Our proposed model uses a Graph Neural Network (GNN) with the user’s interacted and non-interacted item history for diversified recommendation generation. The novelty of our proposed model is to explore the effect of non-interacted items on the target user for diversified recommendation generation. Instead of selecting random non-interacted items for the target user, we apply the DPP(Determinantal Point Process) algorithm to select the non-interacted item appropriately. The detailed experimental analysis shows that our model ID-DivRS outperforms the state-of-the-art model in accuracy and diversity.
Financial Original Voucher Classification and Verification System Based on Deep Learning

Aiming at the problems of voucher preparation errors, numerous verification rules, lengthy audit cycle and low efficiency in the process of voucher preparation and audit of financial reimbursement in Colleges and universities, based on the implementation process of financial reimbursement process, this paper designs a framework of financial original voucher classification and verification system based on deep learning, and proposes a deep learning algorithm to identify the key contents of financial original vouchers, so as to support the voucher preparation process of financial reimbursement personnel Review and verification process of financial personnel. By testing the existing financial reimbursement process, the original voucher can be accurately classified according to the requirements of the national tax system for the classification of goods, for the identification of the type of daily reimbursement; based on the preprocessing of bills, FRCNN algorithms in deep learning can be applied to identify the key contents of bills.
Research on the Correlation Between College Students’ Financial Status and Feedback Awareness

Student funding is an important project that ensures people’s livelihoods and warms the hearts of the people, which is related to poverty alleviation and social equity. In the continuous progress of society, China’s student aid policy is also gradually improving, and its focus has shifted from basic guarantee based aid to development based aid. But it is worth studying how college students’ feedback awareness after financial status. This article takes 51 subsidized college students from vocational colleges in Wuhan as the research sample, and uses Eviews13 software to empirically study the correlation between college students’ financial status and feedback awareness. The research conclusion shows that there is a significant correlation between financial status and feedback awareness, indicating that funded college students are grateful and know how to give back to society. Receiving drips of water when in need, and returning the kindness with a spring.
The Establishing of Practice Teaching System for Intelligent Manufacturing Engineering Major on Application-Oriented Universities

Driven by China’s strategy of strength manufacturing country, the production mode of Chinese enterprises will change from traditional mechanical and electrical mode to digitizing, networking and intelligent production mode. Therefore, the intelligent manufacturing engineering major grows as a new major under that background. It has become the key to the construction of this major how to build a practical teaching system suitable for the intelligent manufacturing engineering major. Through cooperation with companies such as Huazhong CNC and Gaode Information, the application-oriented universities such as Wenhua college and Ezhou Vocational University have established a practical teaching system: Centered around intelligent manufacturing production lines, taking equipment of production line as the main line, focus on semi physical simulation platforms and digital twin virtual simulation platforms, taking vocational technical level certificates as the guide, taking digital design and programming as the foundation, and strengthened network data collection and communication control. Under the guidance of the practical teaching system, an intelligent manufacturing practical teaching platforms have been built. Which will be laid a solid practical teaching foundation for the construction of intelligent manufacturing engineering majors.
Neural Networks for Environmental Monitoring: A Web Application for Image Recognition

Environmental monitoring is increasingly reliant on image recognition through neural networks, automating the identification and classification of environmental threats. This automation optimizes resource allocation, enhances monitoring accuracy, and empowers researchers to address climate-related challenges. In response to this, we present a web application built using React.js and TensorFlow.js, which enables real-time image recognition. This application facilitates tasks ranging from climate analysis to disaster response and wildlife conservation, offering valuable insights into environmental trends. Moreover, it engages the public, promotes awareness, and contributes to conservation efforts, making it a versatile and cost-effective tool for environmental monitoring. The application leverages advancements in image recognition technology, availability of datasets, open-source frameworks, and cloud computing infrastructure. It supports a wide range of applications, promotes global collaboration, and ensures data security. While challenges exist, the potential of neural networks in environmental monitoring is promising. The web application combines the power of React.js and artificial neural networks, enhancing environmental monitoring efficacy and fostering sustainable practices on a global scale. Ongoing research and standardization efforts are essential for refining and expanding this approach.
The Preliminary Construction of an Evaluation Index System for Digital Competence of University Leaders in the Context of Digital Transformation

Digital transformation puts forward new requirements for colleges and universities. This paper takes university leaders as an example and combines the competence model of “knowledge-ability-attitude” to construct the evaluation index system of digital competence of university leaders under the background of digital transformation. Through the expert scoring method, this paper has measured the weights of the evaluation index system with G1 method, principal component analysis and combined weighting model. The results of the study show that in the weighting system, the digital competence characteristics of leaders show a trend of “ability (0.4218) &gt; knowledge (0.2586) &gt; attitude (0.1965)”. Finally, based on the results of the weight calculation, the conclusions and shortcomings of this paper are summarized, and insights for future studies are presented.
Optimal Global Positioning System/European Geostationary Navigation Overlay Service Positioning Model Using Smartphone

The potential for the use of smartphones in GNSSs (Global Navigation Satellite Systems) positioning has increased in recent years due to the emergence of the ability of Android-based devices used to process raw satellite data. This paper presents the results of a study on the use of SBAS data transmitted by the EGNOS (European Geostationary Navigation Overlay Service) system in GNSS positioning using a Xiaomi Mi8 smartphone. Raw data recorded at a fixed point were used in post-processing calculations in GPS/EGNOS positioning by determining the coordinates for every second of a session of about 5 h and comparing the results to those obtained with a Septentrio AsteRx2 GNSS receiver operating at the same time at a distance of about 3 m. The calculations were performed using the assumptions of the GNSS/SBAS positioning algorithms, which were modified with carrier-phase smoothed code observations and the content of the corrections transmitted by EGNOS.
Analyzing and Discovering Spatial Algorithm Complexity Vulnerabilities in Recursion

The algorithmic complexity vulnerability (ACV) that may lead to denial of service attacks greatly disrupts the security and availability of applications, and due to the widespread use of third-party libraries, its impact may be amplified through the software supply chain. The existing work in the field is dedicated to abstract loop or iterative patterns and fuzzing the entire application to discover algorithm complexity vulnerabilities, but they still face efficiency and effectiveness issues. Our research focuses on: (1) proposing a representation and extraction method for code features related to algorithmic complexity vulnerabilities, helping analysts quickly understand program logic; (2) providing a new ACV detecting model, focusing on the spatial complexity anomalies caused by deep recursion structures, and proposing a new filtering method; and (3) aiming at the difficulty of efficiently generating complex-data-type-related payloads using existing symbol execution techniques, a call-chain-guided payload construction method is proposed. We tested third-party components in the open-source Java Maven Repository, identified many unexposed vulnerabilities, and eight of them received Common Vulnerabilities and Exposures (CVE) identifiers, and demonstrated that our method can discover more algorithmic complexity vulnerabilities compared to existing tools with better performance.
Enhancement of Prediction Model for Students’ Performance in Intelligent Tutoring System

Adapting Artificial Intelligence to Intelligent Tutoring System (ITS) has made teaching and learning more effective. Prediction of students’ performance has gained more interest among researchers to know whether the students master their learning before moving to another topic. For the research scope, we have analyzed numerous Bayesian Knowledge Tracing (BKT) variations in methodology and found that the most precise way to forecast students’ success is through Individualized Bayesian Knowledge Tracing (iBKT). Although iBKT makes a good prediction, iBKT does not consider other knowledge-related elements, such as learning and guess rate, and only uses students’ prior knowledge as the parameters. Due to issues concerning uncertainties in students’ interactions, this study proposes to enhance the prediction function of the iBKT using a feature relating to students’ confidence levels. Thus, this new confidence parameter is defined as P(C), assumed to improve prediction accuracy when forecasting student achievement. The prediction accuracy is tested using the attributes of the ASSISTment and Knowledge Discovery and Data Mining (KDD) datasets as input. In addition, Root Mean Square Error (RMSE) is applied to calculate the performance accuracy of iBKT and enhanced iBKT with the confidence parameter. As a result, the RMSE performance accuracy of iBKT with the confidence parameter shows a low RMSE score for both datasets. The ASSISTment dataset provides a higher prediction when applying the confidence parameter, 0.21190. Therefore, it is concluded that enhancing the confidence parameter is an effective method with accuracy improvement for predicting students’ success in ITS.
Development of Ice Jellyger Beverage Products as a Business to Train the Spirit of Entrepreneurship Among Students Through Social

In addition to marketing products through social media and having an entrepreneurial spirit, sellers or students or entrepreneurs must choose a business that is contemporary and can compete in the social media market such as the Es Jellygar (Fresh Jelly) beverage product business. This research aims to train the spirit of entrepreneurship among students in developing and marketing Ice Jellygar beverage products. This research is an R&amp;D research, the development model used is the 4D model (Define, Design, Develop, and Disseminate). The data analysis method used in this study was carried out through product acceptance tests on consumers. The result of this research is the development of Es Jellygar beverage products, which go through four stages of development: Define, Design, Develop, and Disseminate. The Define stage focuses on finding reference recipes, the Design Stage creates prototypes or product designs, the Develop Stage involves evaluation by experts and prototype testing, and the Disseminate Stage is about introducing products to the public through exhibitions and consumer testing. Marketing products through social media and having an entrepreneurial spirit, sellers or students or entrepreneurs must choose businesses that are contemporary and can compete in the social media market such as the Es Jellygar (Fresh Jelly) beverage product business. So it can be concluded that this business can be an opportunity for entrepreneurs or students as an easy side business, and can compete in the sales field.
STUDY OF THE INTERNET OF THINGS NETWORK CONSTRUCTION TASKS

The emergence and development of the Internet of Things stimulates the development of telecommunications and computing technologies. The current state of this process and its prospects allow the inclusion of a large number of devices connected to communication networks. This leads to the need to select an adequate model and methodological apparatus that allows working with such a quantity. The article discusses the concept of the IoT, which determines that any devices or things can now interact with each other at any time at any point in space.
LEVERAGING CLOUD TECHNOLOGIES AND SERVERLESS ARCHITECTURE FOR EFFICIENT WEB DEVELOPMENT: A CASE STUDY FROM REAL-WORLD APPLICATION

This article provides a review of modern cloud solutions and serverless architecture using Backend as a Service (BaaS) and Function as a Service (FaaS) architecture as an example. In the scope of the article, a parallel is drawn between the consistently growing computing power and cloud technologies' growing popularity and availability for business. The results of an analytical review include a list of the most popular cloud providers from leading corporations and a comparison of low-level and high-level cloud technologies. The advantages and disadvantages of Google Cloud Platform (GCP) and Google Firebase are presented, where GCP is a low-level cloud provider and Firebase is a high-level cloud provider. The importance of understanding the context and specifics of the project when choosing cloud project solutions is emphasized which helps to maintain a balance between flexibility and development efficiency. The study introduces the practical utilization of the Supabase cloud platform for the development of a modern web application. The article convincingly proves the actuality of using Supabase for the development of an information system to optimize the modern personnel recruitment process, indicating specific advantages. An example of Supabase Edge Functions usage to generate feedback using the OpenAI Completions API and the Deno software platform is presented. The article convincingly proves that the use of cloud technologies is a modern strategy for building flexible, efficient, and scalable information systems. The advantages of the usage of the provision of infrastructure provided by world industry leaders are summarized.
PERSONALIZATION OF VISUAL CONTENT OF INTERACTIVE ART IN AUGMENTED REALITY BASED ON INDIVIDUAL USER PREFERENCES

Topicality. In connection with the development of AR technologies and their use in interactive art, there is a growing need to develop methods of personalizing visual content, focused on the individual preferences of users. Research methods. Neural collaborative filtering method, generalized matrix factorization method, mood analysis on video. The purpose of the article: Researching the possibilities of improving the personalization of visual content in interactive art by evaluating the emotional reactions of users and their implicit feedback. The results obtained. The application of neural collaborative filtering and generalized matrix factorization to create adapted visual content in interactive art in AR was considered, which will significantly increase the relevance and immersion of users in interactive works. Conclusion. The considered approach can be used to improve immersiveness and personalization during user interaction with interactive art in AR.
The Approach to AI Regulation for the Global South — The difference in balancing two regulatory approaches to AI

Article The Approach to AI Regulation for the Global South — The difference in balancing two regulatory approaches to AI was published on February 1, 2024 in the journal Computer Law Review International (volume 25, issue 1).
Virtual Reality: Characteristics and application in anxiety disorders and other clinical settings

Virtual Reality (VR) technology places users in a computer-generated three-dimensional environment where they experience a variety of visual and auditory cues related to their clinical needs. Despite the considerable growth of scientific knowledge in this field, the application of this knowledge to clinical practice has been slow. The purpose of this paper is to synthesize the evidence related to the application of VRET interventions in the treatment of various disorders such as eating disorders, or even psychotic disorders and addictions, dwelling on anxiety disorders, in adults and in developmental age. VR technologies make it possible to create and replicate dangerous and impossible situations in the real world by allowing the precise acquisition of data, facilitating a more careful evaluation of rehabilitation progress; moreover, by exploiting the features of immersiveness and presence, it is possible to virtually recreate the perfect clinical setting for each intervention.
- Evaluation of an Open Access Version of the Trier Social Stress Test in Virtual Reality

The Trier Social Stress Test (TSST) is one of the most frequently used psychosocial stressors under laboratory conditions. However, the implementation causes significant costs and is sensitive towards protocol variations, for instance the conduct of the judges. Therefore, in recent years, there has been growing interest in adapting the TSST in virtual reality (VR) to increase the standardization and economy of the procedure. This study aimed to validate a freely accessible version of the TSST-VR. Heart rate, salivary cortisol, and subjective stress ratings were measured in fifty male participants randomly assigned to the TSST-VR or a control procedure in VR. The analysis revealed a significant rise in subjective stress as well as in salivary cortisol in the stress condition compared to the control group indicating a significant induction of stress. Based on these findings, we conclude that the Open TSST-VR provides a suitable tool for eliciting psychosocial stress. In addition, this open access version offers various options to customize the VR environment to individual needs and enables collaborations between research groups.
Banking and Debunking: Applying Freirean Theory to the Educational Challenges of Conspiracy Culture

Abstract The rise of conspiracy culture and the growing influence of conspiracy theories have attracted the attention of scholars from a range of fields. In recent years, Daniel Jolley, Asbjørn Dyrendal and others have noted the prevalence of conspiracy theories amongst adolescent schoolchildren in Scandinavia and the UK. This article draws on Paulo Freire’s concept of the ‘banking model’ of education to make the case against a ‘debunking’ approach to anti-conspiracist education. It argues that conspiracism should be understood as a feature of our culture and as such it should be interrogated rather than dismissed by educators who are engaged in the process of educating for democracy.
UCGAN: a contrastive learning-based unaligned image translation model with importance reweighting

Image-to-image translation with unaligned domains is a seminal work that is capable of automatically selecting suitable image of unaligned dataset for translation. Unaligned datasets often contain irrelevant images that disrupt translation. However, the accuracy of selecting appropriate images is an issue that needs to be urgently improved. To improve the grasp of image features and enhance the performance of unaligned image-to-image translation without changing much semantic information, we propose a novel framework unaligned image-to-image translation model based on contrastive learning and generative adversarial networks (UCGAN), which can focus on both local and global features of the image. Inspired by contrastive learning, here we propose the patch-by-patch feature contrastive (PPFC). PPFC reuses the encoder of the generator to extract multi-layer features of different image patches. We then normalize the various features using L1-norm instead of L2-norm. To further improve translation performance, we also set up paired feature comparisons. We compare our proposed method UCGAN with state-of-the-art algorithms and the experimental results have demonstrated the superiority and practicality of our method.