Reviewerly/exHarmony huggingface dataset:
Evaluating the Contribution of Relationship Information in Detecting Fraud Using Graph Neural Networks

Fraud detection plays a crucial role in various domains, including e-commerce. Frauds can be reviews about one product or service, or transaction to buy an item. Traditionally, a fraud detector looks at individual features to detect fraudsters. In recent years, graph neural networks research attracts a lot of attention from academia. Based on that, more and more research works that incorporating the relationship and interactions of nodes into consideration. We study the contribution of the information to the model. Our empirical results show that the relationship information does not contribute to the model as much as we expected, and the most important feature set is still the individual behavior-based features.
Student offline classroom concentration identification research based on deep learning

During the reform of the deep teaching model, students’ deep learning quality was affected and restricted by various factors. During the offline class learning process of students, the concentration of deep learning directly affects the quality of learning. This article analyzes the study focus of students in deep learning models, conducts research on the quality of class offline learning of different students, quantifies the factors that affect students’ deep learning, and builds an analysis model for quantitative comparison. Important influence factor affecting students’ offline classroom concentration, through targeted measures, improve teaching methods and quality, optimize classroom teaching models, use various methods and measures to effectively improve learning focus, and further promote the reform of teaching models. The level of concentration of students’ learning has been steadily improved, and the model of deep learning is proposed to help the teaching model reform.
Priority approaches to universal design in the modern education system

The presented article defines and summarises the universal approaches to teaching in education as a system of purposeful principles and methods aimed at taking into account the needs of recipients of educational services and satisfying them in the educational process. The priority approaches of universal design in teaching in the modern education system have been analysed, which include the approach in teaching based on the pedagogy of partnership, the approach in teaching based on the pedagogy of tolerance, the approach in teaching based on non-violent communication. Each of the identified approaches is universal. The purpose of the article is to highlight the outlined problem, to define and generalize universal approaches to teaching in educational activities, to use priority approaches of universal design to create an effective inclusive educational environment in modern educational institutions. The relevance of each of the identified approaches is high today. The approach to teaching based on partnership pedagogy, which implements one of the components of the NUS formula, requires new methodological developments, studies on the successful interaction of participants in the educational process. In this context, we offer a 3-D formula for partnership interaction, which involves the systematic application and implementation of its components in action: dialogue-trust-agreements. The teaching approach based on the pedagogy of tolerance consists in observing and using the principles of humanism, mutual trust, mutual understanding, reliance on the positive moments, which will always be a priority in the interaction of all subjects of the educational process. Another approach of universality in teaching was identified by us as an approach based on non-violent communication (NVC), which involves the creation of an effective inclusive educational environment, which is urgently needed today, during the war in the country. The approach of non-violent communication is aimed at building relationships between people, based on honesty, empathy, kindness, openness and tolerance; it allows you to make positive decisions even in difficult conflict situations, taking into account the feelings and needs of each person.
Using mini-projects in work with philology students

The article is devoted to the theoretical substantiation and practical use of the project method in working with future philologists. Under the influence of the comprehensive technologization of education, project technologies have evolved from the project method and function in modern pedagogical research under the term “project method”. It is proved that the project method is becoming increasingly popular in the world, there are many experimental schools where the main method of teaching is the project method and its varieties. Teachers all over the world note the enormous possibilities of the project method, in particular its impact on the development of activity, independence, creativity, collectivism of students. The purpose of the article is to reveal the originality of using the project method, in particular mini-projects, in the process of teaching students of higher education institutions. The main stages of work on the project (organizational and preparatory, planning, technological, final) are revealed. Since the project method directly develops the creative, research and analytical activities of students, the main task is to teach them to think independently, find and solve various issues, applying knowledge from different fields, the ability to predict the results and possible consequences of different solutions, the ability to establish cause and effect relationships. The student's activity should be subordinated to the result that he will get by solving a practical or theoretical problem. The authors analyzed the theoretical and practical experience of both domestic and foreign scientists, identified and described the classification of A. Klymenko and O. Podkolzina who distribute projects according to the dominant method or type of activity. It is substantiated that the use of project tasks changes practically all components of the educational process: the type of interaction between the teacher and the student, the evaluation system, the selection of educational material, etc.
Project Activities of University Students as A Means of Achieving Learning Outcomes

Modern socio-economic conditions for the development of the Russian Federation have determined the requirements for the training of highly professional competitive specialists who must have not only professional knowledge and skills, but also a whole set of universal competencies that a person needs throughout his life for self-education, self-realization, change of occupation, productive professional and other activities. The article substantiates the relevance and need to organize project activities of students of a technical university as one of the innovative pedagogical technologies aimed at developing social and personal competencies, activating cognitive interests, and developing creative abilities. The authors analyze the essence of the method of project activity in education, emphasizing its problematic practice-oriented nature. Presented examples of projects completed by students of the Bauman Moscow State Technical University based as on the integration of knowledge in general subjects, so that disciplines of professional training; proposed a system of project evaluation criteria. Highlighted professional and universal competencies of students of a technical university which formed in the process of implementing practice-oriented projects of a problematic nature, and which facilitate the mobility of a university graduates in the labor market, their socialization in society.
Watermarking System for Medical Images Using Optimization Algorithm

One of the main methods used to provide security for medical records when exchanging these records through open networks is digital watermarking. In order to preserve the privacy of patients, this system also requires a means to secure images. In this paper, a watermarking based on discrete wavelet transform (DWT), and discrete and discrete cosine transform (DCT) in cascade provides more robustness and security. DCT divides the image into low and high-frequency regions, watermarking message can be embedded into low-frequency regions to prevent distortion of the original image. DWT splits the image into four frequency coefficients; horizontal, vertical, approximation, and detailed frequency component. The judgment factors for the strength of the watermark system are robustness, invisibility, and embedded message capacity. Invisibility means transparency of the watermark logo or data in the original or host image without any distortion. Capacity data payload means the size of the embedded image which is related to the amount of data or logo size that will be embedded in the host image. Robustness refers to the capability of the watermark to stand with the host image operations. In this paper, we propose an optimizer to trade-off between robustness, invisibility, and message capacity. Three metrics were employed to assess the results achieved by the proposed approach, namely, Peak Signal-to-Noise Ratio (PSNR), Normalized Cross Correlation (NCC), and Image Fidelity (IF). The achieved results confirmed the effectiveness and superiority of the proposed approach for real-world digital watermarking applications.
Deep Learning Techniques for Sentiment Analysis

Sentiment analysis covers a wide range of computational research, including research on the opinions, feelings, emotions, evaluations of people, and attitudes toward products, services, organizations, individuals, issues, events, topics, and their attributes. It plays an increasingly important role in the era of big data. In fact, it has spread from computer science to management and social sciences such as marketing, finance, political science, communications, medical science and even history, generating common interest throughout society due to its commercial importance. TEA is a basic task with the typical used of NLP methods which is full of interest, particularly for fine-grained classification of textual emotional content. It is the process of mastering, inductive analysis and reasoning about emotional content. Simply put, it is the process of analysing, processing, summarising and reasoning about emotive and subjective texts. The Internet generates a large numberof user reviews to gain valuable information about people, events and products. These reviews express a wide range of emotions and emotional tendencies, including joy, anger, sadness, delight, criticism and praise. Potential users can therefore view these subjective reviews to understand how public opinion views an event or product.
Evidential Supervised Classifier System: A New Learning Classifier System Dealing with Imperfect Information

Learning Classifier Systems (LCSs) are a kind of evolutionary machine learning algorithms that provide highly adaptive components to deal with real world problems. They have been widely used in resolving complex problems such as decision making and classification. LCSs are flexible algorithms that are able to construct, incrementally, a set of rules and evolve them through the Evolutionary Algorithm (EA). Despite their efficiency, LCSs are not capable of handling imperfect information, which may lead to reduced performance in terms of classification accuracy. We propose a new accuracy-based Michigan-style LCS that integrates the belief function theory in the supervised classifier system. The belief function or evidence theory represents an efficient framework for treating imperfect information. The new approach shows promising results in real world classification problems.
Predicting Risk Matrix in Software Development Projects using BERT and K-Means

This study aimed to create a risk matrix model for a software development project with 60.000 datasets from the Stack Overflow Question on Kaggle.com. BERT has been used for sentence embedding techniques to represent entire sentences and their semantic information as vectors, K-Means has been used to group sentences and calculate the frequency of occurrence so that the probability is expressed in the vertical direction, and TextBlob estimates the severity of the impact with sentiment analysis represented in the horizontal direction. Validation with BERT on the cluster formed by K-Means by creating a classification text with 86% accuracy results with 30 epochs. The proposed risk matrix formation model is a novelty that can be integrated into the risk management framework. The model determines risk priorities, especially in software development projects.
Unitary Recurrent Networks: Algebraic and Linear Structures for Syntax

The emergence of powerful deep learning systems has largely displaced classical symbolic algebraic models of linguistic representation in computational linguistics. While deep neural networks have achieved impressive results across a wide variety of AI and NLP tasks, they have become increasingly opaque and inaccessible to a clear understanding of how they acquire the generalisations that they extract from the data to which they apply. This is particularly true of BERT, and similar non-directional transformers. We study an alternative deep learning system, Unitary-Evolution Recurrent Neural Networks (URNs) (Arjovsky et al., 2016), which are strictly compositional in their combination of state matrices. As a result they are fully transparent. They can be understood entirely in terms of the linear algebraic operations that they apply to each input state matrix to obtain its output successor. We review these operations in some detail, clarifying the compositional nature of URNs. We then present experimental evidence from three NLP tasks to show that these models achieve an encouraging level of precision in handling long distance dependencies. The learning required to solve these tasks involves acquiring and representing complex hierarchical syntactic structures.
Full-stack S-DOVS: Autonomous Navigation in Complete Real-World Dynamic Scenarios

Autonomous navigation in dynamic environments is a nowadays unsolved challenge. Several approaches have been proposed to solve it, but they either have a low success rate, do not consider robot kinodynamic constraints or are not able to navigate through big scenarios where the known map information is needed. In this work, a previously existing planner, the Strategy-based Dynamic Object Velocity Space, S-DOVS, is modified and adapted to be included in a full navigation stack, with a localization system, an obstacle tracker and a global planner. The result is a system that is able to navigate successfully in real-world scenarios, where it may face complex challenges as dynamic obstacles or replanning. The final work is exhaustively tested in simulation and in a ground robot.
Adaptive mobile VR content delivery for industrial 5.0

Mobile virtual reality (VR) is expected to be a key component of the next-generation industrial internet-of-things, which uses immersive technologies to boost virtualization and facilitate human-machine collaboration in Industry 5.0. In this paper, we design a VR content delivery scheme to enhance VR content playback quality in mobile edge computing. The proposed scheme schedules computing resources on network edge to satisfy VR content requests from multiple user devices while reducing the likelihood of rebuffering and improving content freshness during VR video playback. With limited computing resources at the edge server, we develop a deep reinforcement learning (DRL) approach to determine which requests should be satisfied first, given the network and the service dynamics. By analyzing the network dynamics using the Whittle index method, the proposed DRL-based scheme can improve VR service quality with minimal communication overhead in computing scheduling. Simulation results demonstrate that the proposed scheme significantly improves the quality of service for VR content delivery.
Visualizing Machine Learning in 3D

Understanding machine learning models can be difficult when the models at hand have many parts to them. Having a visual model can help aid in understanding how the model functions. A way to visualize these models is to use a 3D (three-dimensional) game development application. An application that will have an interactive element allowing the users to interact with the model (rotating and scaling it) and see changes at run-time. An interactive element will keep the users engaged, understanding, and seeing how a machine learning model looks and behaves. This paper describes the process of visualizing a machine learning model in a 3D application.
Understanding Perspectives for Single- and Multi-Limb Movement Guidance in Virtual 3D Environments

Movement guidance in virtual reality has many applications ranging from physical therapy, assistive systems to sport learning. These movements range from simple single-limb to complex multi-limb movements. While VR supports many perspectives – e.g., first person and third person – it remains unclear how accurate these perspectives communicate different movements. In a user study (N=18), we investigated the influence of perspective, feedback, and movement properties on the accuracy of movement guidance. Participants had on average an angle error of 6.2° for single arm movements, 7.4° for synchronous two arm movements, and 10.3° for synchronous two arm and leg movements. Furthermore, the results show that the two variants of third-person perspectives outperform a first-person perspective for movement guidance (19.9% and 24.3% reduction in angle errors). Qualitative feedback confirms the quantitative data and shows users have a clear preference for third-person perspectives. Through our findings we provide guidance for designers and developers of future VR movement guidance systems.
Augmented Reality Patient-Specific Registration for Medical Visualization

In recent years, medical research has made extensive use of Augmented Reality (AR) for visualization. These visualizations provide improved 3D understanding and depth perception for surgeons and medical staff during surgical planning, medical training, and procedures. Often, AR in medicine involves impractical and extensive instrumentation in order to provide the precision needed for clinical use. We propose a mobile AR 3D model registration system for use in a practical, non-instrumented hospital setting. Our registration system takes as input a patient-specific model and overlays it on the patient using an accurate pose registration technique that requires a single marker as a point of reference to initialize a point cloud-based pose refinement technique. Our method is automatic, easy to use, and runs in real-time on a mobile phone. We conduct quantitative and qualitative analysis of the registration. The results confirm that our AR pose registration system produces an accurate and visually correct overlay of the medical data in real-time.
Immersive Analytics for Spatio-Temporal Data on a Virtual Globe: Prototype and Emerging Research Challenges

We present our approach for the immersive analysis of spatio-temporal data, using a three-dimensional virtual globe. We display quantitative data as country-shaped elevated polygons and animate elevation levels over time to represent the temporal dimension. This approach allows us to investigate global patterns of behaviour, like pandemic infection data. By using a virtual reality setting, we intend to increase our understanding of spatial data and potential global relationships. Based on the development of our prototype, we outline research challenges we see emerging in this context.
Adoption of Cyber Insurance in Malaysian Organisations

As businesses have embraced more and more technology, cyber risk has become one of the essential components of a risk management initiative that seeks to mitigate and analyze the multitude of new risks. One risk mitigation process is investing in cyber insurance to safeguard the organisations’ assets from cyber threats by transferring them to the insurer. For businesses to decide on cyber insurance, choosing the right insurance plan and policy is crucial. To do so, they need to consider the factors of its implementation. Therefore, this paper aims to investigate the adoption of cyber insurance in Malaysian organisations, thus further exploring the organisations' perceptions of the factors of adopting cyber insurance. A qualitative study is conducted to understand and describe Malaysian organisations’ needs, obstacles, and processes related to cyber insurance adoption by interviewing cybersecurity professionals and cyber insurance personnel to explore valuable insight into the existing knowledge. 30% of the respondents have adopted cyber insurance. While 40% of the respondents were unsure, the other 30% did not adopt cyber insurance in their organisation respectively. These insights include the factors in adopting cyber insurance among Malaysian organisations, which might be crucial for informing future research and practice in the cyber insurance industry. The novelty of this research is the overview of knowledge gaps and perceptions related to the adoption of cyber insurance in Malaysian organisations. Also, this research aims to consider the advantages of the relevant obstacles that influence the decision makers to adopt cyber insurance.
Towards Algorithms for Argumentation Frameworks with Higher-order Attacks

Computation and decision problems related to argumentation frameworks with higher-order attacks have not received a lot of attention so far. This paper is a step towards these issues. First, it provides a labelling counterpart for the structure semantics of Recursive Argumentation Frameworks (RAF). Second, it investigates the complexity of decision problems associated with RAF. This investigation shows that, for the higher expressiveness offered by these enriched systems, the complexity is the same as for classical argumentation frameworks. As a side contribution, a new semantics for RAF, the semi-stable semantics, and a new process for translating RAF into Argumentation Frameworks without higher-order attacks (AF), are introduced. Finally, new notions which are the counterparts of equivalent notions already existing for AF (among them, the Strongly Connected Components — SCC) are defined and investigated in order to involve them in the future development of algorithms for computing RAF labelling semantics.
Leveraging Blockchain-Based Electronic Health Record Systems in Healthcare 4.0

Digitalization has become a crucial part of healthcare 4.0 by transforming systems such as electronic health records (EHR), electronic medical records (EMR), and electronic personal medical records (ePHR). Healthcare 4.0 is derived from industry 4.0 and aims to enhance collaboration, virtualization, coherence, and convergence, which helps transform modern healthcare into more personalized and predictive. Healthcare 4.0 also aims to develop digital enablers which will support coordination among various stakeholders and seamless information flow in the patient journey towards wellbeing. These systems enhance patient care through the timely sharing of patient data across different providers globally. Timely sharing helps, but it also makes the electronic system vulnerable to alteration and breaches. In healthcare, blockchain application is widely used in various areas, such as health information exchange, pharmaceutical counterfeit, clinical trials, health supply chain management, patient data management, insurance claims, and product recall in case of adverse events. This research paper aims to identify how blockchain technology can help enhance the privacy and security of electronic health record systems. This paper discusses various blockchain-based systems, which provide a more efficient and secure option than client-server architecture-based traditional EHR systems.