Reviewerly/exHarmony huggingface dataset:
The language of discussion forums with student-student and student-content relations

Discussion forums, as a form of asynchronous communication, represent an auxiliary tool in teaching that helps students to be involved in a discussion on a given topic, regardless their obligations, level of knowledge of a foreign language, or inhibitions when expressing themselves in a foreign language. The aim of the paper is to analyze two relationships in asynchronous communication: student-student (interpersonal register) and student-content (subject register), as well as the linguistic characteristics of discussion forums (media register). The paper will analyze the frequency of personal forms (first and second person singular and plural), shorter forms of auxiliary verbs and negation, passive, nominalization, as well as the sentence structure complexity in discussion forums created during the course in English for Professional Purposes, in the field of computing, at the Faculty of Technical Sciences in Novi Sad. Additionally, the analysis will be supplemented by the analysis of emoticons and abbreviations, since they form an indispensable part of the Internet language. All analyzed elements were present in two analyzed corpora, although with significant differences in frequency. In the corpus related to the student-student relationship, a greater use of personal forms, a greater degree of interactivity in the text, a large representation of shorter forms of auxiliary verbs and negation, a simpler sentence structure, as well as a greater use of abbreviations and emoticons were observed. It can be concluded that the analyzed corpus in the student-student relationship is more similar to oral discourse - it has the characteristics of everyday speech. In the student-content corpus, on the other hand, the sentences are longer and more complex with a large number of nominalizations and a smaller number of personal forms, while the use of the passive voice is very frequent, which indicates a focus on the text, but also distancing from the text and the object of communication. These features make the analyzed corpus in the student-content relationship more similar to a written discourse, i.e., a more scientific text. Finally, the paper concludes that the language of discussion forums depends on the very context in which communication takes place.
INTERACTIVE LEARNING METHODS IN HIGHER EDUCATION INSTITUTIONS

The article is devoted to the actual problem of using interactive learning methods in the educational process in institutions of higher education. Attention is focused on the fact that student-centred learning requires teachers to use innovative methods that ensure the maximum connection of theoretical information with its practical application, and the development of professional skills by directly solving cases and problem situations. With this in mind, the authors analysed some modern methods of interactive learning and found that a characteristic feature of modern higher education is the development of students' critical thinking using such methods as ‘Fishbone’, Bloom's daisy and cube, and Brainstorming. It was determined that an integral component of the modern educational process is the use of various digital applications that help the teacher to develop interesting lessons. Usually, they are aimed at solving several tasks: gamification of learning, consolidation of learned material and evaluation of learning results. The tutorial was analysed as an interactive form of independent work. It is aimed at in-depth familiarization of students with the studied subject, discussion of creative projects during classes and development of creative potential. This form of independent student work allows you to teach them to think, for example, to synthesize various sources, formulate their own theses and prove them, anticipate criticism of their arguments and answer questions. At the same time, the authors emphasize that active methods should be organically combined with passive ones. In view of this, the teacher should carefully consider the procedure for using traditional and interactive teaching methods, not to abuse a significant number of the latter, as this can lead to a decrease in motivation.
Understanding the Performance Impact of Queue-Based Resource Allocation in Scalable Disaggregated Memory Systems

Disaggregated memory systems are expected to replace traditional servers in next-generation data centers. Disaggregation overcomes the problem of memory under-utilization and scalability by allowing on-demand memory allocation to compute nodes from in-network memory pools. However, accessing on-network memory resources incurs additional access costs and significantly impacts the system’s performance, which is further impacted by multiple compute nodes sharing the same memory pools. However, the performance can be improved by ensuring fairness in queue allocation for network and remote memory resources to compute nodes running workloads with different access patterns. This paper explores the performance evaluation of multi-node disaggregated systems with different queue allocation methods for network and memory bandwidth partition. We propose using an in-network global memory controller to control the flow of memory requests to the remote memory pools to enforce Quality-of-Service. Finally, we utilize the memory request rate of each compute node to set their weights/priorities for different queue allocation methods. We evaluate all the scheduling policies using a custom trace-based disaggregated simulator over various benchmarks with different access patterns. Our results show that scheduling policies significantly impact the average memory latency and the system performance in different configurations.
Patient Similarity using Electronic Health Records and Self-supervised Learning

In medical big data mining, patient similarity matching seeks to quantify the resemblance between two patients by analyzing their Electronic Health Records (EHR). A difficulty in establishing patient similarity is extracting good features from healthcare text notes. Current methods of patient similarity can be classified into two groups, supervised and unsupervised. In this paper, we propose a self-supervised method for better feature extraction. Specifically, we try to take advantage of various tags (e.g. outcomes, diagnosis codes, categories) available in the data to help a deep learning model adjust to the specific characteristics of the given dataset. The experimental results show that when the number of tags is sufficiently provided, the performance of similarity matching is significantly increased.
SwinGaze: Egocentric Gaze Estimation with Video Swin Transformer

Egocentric gaze estimation represents a challenging and immensely significant task which has promising future applications in areas such as human-computer interaction and AR/VR. In this work, we propose a novel model based on the Video Swin Transformer architecture. Through the introduction of localized inductive bias, our model extracts essential local features from first person videos during the windowed self-attention computation process. Additionally, we approximate the modeling of the global context within the gaze region using a shift window approach. We evaluate our approach on the EGTEA Gaze+ dataset, a publicly available dataset for egocentric activity videos. Experimental results unequivocally demonstrate that our model achieves state-of-the-art performance.
Interlinked Chain Method for Blockchain-Based Collaborative Learning in Vehicular Networks

Collaborative edge learning has emerged as a fundamental paradigm shift in vehicular technology, improving the efficiency and privacy of machine learning in edge environments. Nevertheless, the prevailing blockchain-based methods for collaborative learning have faced challenges such as communication delays and network congestion, mainly due to the continuous exchange of local models. In response to these challenges, this paper proposed an innovative interlinked chain method (ICM). The ICM uses interconnected sub-networks in which selected vehicles serve as inner aggregators. This setup ensures that only global models are transmitted on the blockchain, optimizing communication infrastructures and minimizing overhead. Our experimental results show that the proposed ICM outperforms conventional approaches by exhibiting higher efficiency and requiring significantly less communication exchange compared to traditional clustered and unified architectures, and reducing the communication overhead by more than 70%. Remarkably, the interlinked architecture avoids the performance degradation of collaborative learning and maintains consistency even when different federated schemes are implemented.
Light Weight Semantic Segmentation: A Modified DDRNET Approach Trained on Cityscapes and COCO-Stuff Datasets for Efficient Image Analysis

This paper presents research findings on a lightweight semantic segmentation model. The objective of this research was to train and test a semantic segmentation model on the Cityscapes and COCO-Stuff datasets, with the aim of achieving a minimum performance speed of 20 and 10 frames per second (FPS) on target CPU and mobile devices, respectively. Various models were implemented and evaluated for semantic segmentation, and among them, DDRNET-32-slim exhibited the best performance. Therefore, solutions were proposed and implemented to improve DDRNET-32-slim. The corresponding experiments demonstrated that the enhanced model achieved improved speeds of 28 and 15 FPS on CPU and mobile devices, respectively.
Tailored Learning Rates for Reinforcement Learning: A Visual Exploration and Guideline Formulation

Reinforcement learning (RL) continues to advance, but the selection of an appropriate learning rate (LR) remains a fundamental challenge. In this study, we conduct a comprehensive exploration and guideline formulation for LR selection in RL. We leverage a diverse set of RL simulations, each featuring a 128-dimensional input vector and a complex 18-dimensional action space, to examine the efficacy of LR adaptation. These simulations encompass a range of RL tasks, emphasizing the relevance of LR selection in various contexts. To gain insights into LR strategies, we utilize two distinct artificial neural networks (ANNs), one with 27,000 hidden layer connections and another with 44,000 connections. We employ a range of tailored LR values, ranging from an initial high of 0.25 to a minuscule 0.000000025. Our experiments focus on the cumulative agent scores across multiple training episodes and the stability of training, where episodes consistently achieve scores close to the average. Our results shed light on LR strategies that optimize both performance and stability across different RL scenarios, offering valuable guidance to researchers and practitioners.
Role-Based Bayesian Decision Framework for Autonomous Unmanned Systems

In the process of performing a task, autonomous unmanned systems face the problem of scene changing, which requires the ability of real-time decision-making under dynamically changing scenes. Therefore, taking the unmanned system coordinative region control operation as an example, this paper combines knowledge representation with probabilistic decision-making and proposes a role-based Bayesian decision model for autonomous unmanned systems that integrates scene cognition and individual preferences. Firstly, according to utility value decision theory, the role-based utility value decision model is proposed to realize task coordination according to the preference of the role that individual is assigned. Then, multi-entity Bayesian network is introduced for situation assessment, by which scenes and their uncertainty related to the operation are semantically described, so that the unmanned systems can conduct situation awareness in a set of scenes with uncertainty. Finally, the effectiveness of the proposed method is verified in a virtual task scenario. This research has important reference value for realizing scene cognition, improving cooperative decision-making ability under dynamic scenes, and achieving swarm level autonomy of unmanned systems.
Value-sense approach to the professional development of a modern teacher

In the conditions of modern challenges, it is important to understand what values form the pedagogical worldview of the future teacher, determine the value orientations and ideological attitudes of the teacher. Based on the content analysis of psychological and pedagogical literature, there was created a theoretical model of professional development of a teacher, the key levels of this process are described, and its components are highlighted. The analysis of the data obtained as a result of pedagogical specialties students’ survey, and further systematization of the received responses, made it possible to identify the priority values of future teachers and give key characteristics of the main four axiotypes of modern teachers. An attempt was also made to form a portrait of a modern teacher taking into account the dominant axiotype.
Quality of education: monitoring and evaluation of educational results

The quality of education is one of the most important and urgent problems in the modern educational world. With the advent of new technologies, global challenges and changes in the socio-cultural environment, education is becoming increasingly important as a factor of successful integration into society and achievement of personal and professional goals. The quality of education determines not only the level of knowledge and skills, but also forms competencies, develops critical thinking and promotes personal growth of students. In this context, the issues of the quality of education become the subject of universal interest and discussion, both among educational institutions and in society as a whole. How to ensure high quality training? How to evaluate and measure this quality? How to adapt educational standards to the changing needs of students and the labor market? This article discusses research on the quality of education, the search for optimal ways to modernize educational practices and achieve high standards of learning quality. The author examines various aspects of the quality of education, its impact on the personal and professional development of students, as well as ways to improve the quality of educational process in accordance with modern requirements and challenges, ways of students’ competencies in the modern educational system. The article also focuses on standardization and personalization of evaluation procedures, monitoring of educational results. Recommendations for improving the intra-university system of monitoring and evaluation of students’ educational results are offered.
Blockchain Revolution in Education

Blockchain technology has the potential to revolutionize higher education by enabling the secure and efficient sharing of academic records, digital credentials, and other important information. This chapter explores the potential of blockchain technology to transform higher education by examining its key features, benefits, and challenges. It also discusses several use cases of blockchain in higher education, such as student records management, digital credentialing, enabling micro-credentials and digital badges, and learning analytics. The study concludes by highlighting the opportunities, limitations, and future directions of blockchain technology in higher education. The chapter will propose future trends and the way forward for the revolution to advent. The major stakeholders will be explored: Learners, teachers, government, top education management, UNDP, technocrats, and major corporates involvement and consensus. Expert opinion is consolidated to suggest the blockchain education framework.
Blockchain Revolution in Education and Lifelong Learning

The current chapter presents an overview on the importance of blockchain technology in the field of education and lifelong learning. It discusses the existing blockchain models applicable to the education sector such as decentralized student records and credentials verification systems, secure and transparent transaction platforms for micro-credentials, and blockchain-based student identity management systems. Sustainable blockchain-led strategies and framework for responsible blockchain for empowering the stakeholders involved, enhancing educational innovation and infrastructure, and reducing the inequalities are discussed. Moreover, the chapter throws light on the innovative practices to revolutionize traditional education systems, credentialing, and payment methods, offering several significant benefits to the world of higher education.
Mechanism for the Systematic Generation of Functional Tests of Smart Contracts in Digital Publication Management Systems

The application of state-of-the-art technologies in functional fields is complex and offers a significant challenge to user and expert teams as well as to technical teams. This chapter presents a mechanism that has been used in a project in the context of digital publications. Ensuring the traceability of digital publications (e-books and e-journals) is a critical aspect of the utmost importance for authors, publishers, and buyers. The SmartISBN project has used blockchain technology to define a protocol for the identification, tracking, and traceability of digital publications. As this was an innovative project that required communication between functional experts (authors, publishers, booksellers, etc.) and technical experts, it was necessary to identify protocols to facilitate communication. This chapter presents the protocol by which the functional tests have been defined and how this has favoured the validation of the project.
Blockchain Technology and Data Mining Tools for Combating Fraud

For the last few years, rapid digitalization has been observed in the world banking industry, which helped boost global economic growth. At the same time, fraud cases in the banking sector have been increasing immensely. Regulating authorities of banks nationwide have been issuing numerous circulars and guidelines for preventing fraud incidents. However, fraudsters are taking advantage of the digitalization of and shortfalls in the industry, by which the fraudsters easily defraud customers and banks. Hence, it resulted in a worsening of the asset quality of banks and a loss of public trust and confidence in the banking industry. In this regard, banks must be equipped with sophisticated technological tools for fraud identification and preventive measures apart from the conventional systems and procedures since core banking solutions in the banking industry have evolved drastically over the last few years. In this context, this chapter is intended to study the role of blockchain technology and data mining tools in identifying and preventing frauds in specific bank products.
Blockchain Implications and Utility for Higher Education

Blockchain has emerged as an important concept at the interface of ICT and higher education. Blockchain is perceived as a revolutionary technology offering a considerable impact of vast magnitude on various sectors since it enables the creation of decentralized applications programmed to run on networks and records sets of data that can be shared securely without third-party mediation. The Blockchain's emphasis on variety in terms of applications may be due to its capacity to build a trusted and decentralized contract environment. The higher education sector is, therefore, a potential user of blockchain technology due to its capacity in allowing stakeholders to validate learning records and identity management. On the other hand, higher education may be understood as a system that includes, among others, two major stakeholders, higher education institutions, (HEIs) and students. Adopting a systematic literature review and thematic content analysis, this chapter aims to understand the blockchain implications and utility in higher education.
Geospatial Blockchain Applications for Land Administration in Pakistan

In an era characterized by rapid technological progress, the persistent challenges of land administration are on the verge of a groundbreaking transformation through the adoption of blockchain technology. Originating with the advent of Bitcoin in 2008, blockchain has emerged as a beacon of hope, promising transparency, trust, immutability, and security in recording transactions. This innovative technology holds immense potential to revolutionize the landscape of land administration in Pakistan, a nation burdened by antiquated paper-based systems, corruption, land disputes, and inefficiencies. Embracing blockchain can offer Pakistan the prospect of an era marked by improved governance, diminished fraud, streamlined record-keeping, and equitable access to vital land information. Nonetheless, the path forward is strewn with challenges, necessitating the establishment of robust legal frameworks, capacity-building initiatives, and the resolution of privacy concerns.
Adoption of Blockchain in Supply Chain Financing

This study explores the factors that impact the adoption of blockchain in supply chain financing (SCF). Blockchain's unique features make it a good solution to the current problems in SCF. However, given that both blockchain and SCF are relatively new, there are almost no commercially viable large-scale implementations yet in this area. Research in the factors that drive the adoption of blockchain in SCF, is also scarce. Of the six identified determinants of adoption of blockchain in SCF, the study found four to be significant. Relative advantage, compatibility, organization readiness, and environment readiness influence the adoption of blockchain in SCF. Complexity and technology readiness are insignificant determinants, indicating a technically mature industry capable of handling current blockchain implementations in SCF and associated changes. The authors also found that trust has a mediating effect between compatibility and adoption and between environment readiness and adoption.
Accountability Revolution in the Digital Asset Business

Cryptocurrencies have experienced an unprecedented surge in popularity over the past few years. This surge can be attributed to a multitude of factors, including the unique characteristics and advantages that cryptocurrencies offer. The manuscript highlights the trans-formative nature of accountability within the digital asset business and the ongoing revolution surrounding it. It explores emerging trends and practices that shape the accountability mechanisms in the cryptocurrency market. By critically assessing cryptocurrencies, the manuscript provides readers with a comprehensive understanding of the opportunities and risks in this evolving landscape. Through robust accountability mechanisms, the cryptocurrency market can address challenges such as volatility, investor protection, illicit activities, etc. By adopting an economist's perspective, it explores how cryptocurrencies function within the broader economic structure and the potential impact they have on traditional financial systems, monetary policy, and the overall economy.
Cybersecurity Crisis Management in Higher Education Institutions

In the wake of the global pandemic, higher education institutions have faced numerous challenges, not only in terms of maintaining academic continuity but also in safeguarding their digital infrastructure against ever-evolving cyber threats. The shift towards remote learning and increased reliance on technology has exposed educational institutions to new vulnerabilities, making cybersecurity a critical concern. Through a comprehensive exploration of cybersecurity in the context of higher education, crisis management, problem-solving strategies, and leadership competencies, this chapter aims to equip educational stakeholders with the knowledge and tools necessary to fortify their institutions against cyber threats, using the University of Sunderland in London as a case study. By understanding the interconnectedness of these areas, educational leaders can proactively protect their digital assets, and foster a resilient educational environment in the face of future challenges.