This study introduces the Fuzzy-Rule-Enhanced Autoencoder (FREA), a novel hybrid approach for interpretable text clustering that addresses the limitations of traditional methods in comprehending nuanced linkages and incorporating domain-specific knowledge. FREA integrates fuzzy physical rules generated by Large Language Models (LLMs) that derive weighted related terms for provisional news labels within an autoencoder framework. This approach enhances interpretability and flexibility by including these principles into the feature learning process, aligning them with Word2Vec and RoBERTa embeddings to ensure contextually relevant and semantically robust representations. The fuzzy rule layer adaptively adjusts feature learning based on the similarity of inputs to predefined linguistic rules, allowing the model to incorporate domain-specific constraints in a completely trainable manner. FREA demonstrates its effectiveness and adaptability through evaluations on benchmark datasets utilizing both hard and soft clustering methods, along with different quantitative evaluation metrics, primarily focusing on news articles as the primary test case. This study amalgamates the benefits of fuzzy rules and deep learning, offering a flexible, interpretable, and human-aligned methodology for unsupervised clustering problems, therefore effectively linking rule-based reasoning with neural networks.

Software Engineering (SE) education aims to seamlessly blend theoretical knowledge with practical exposure, ensuring students the grasp of SE's foundational principles, design methodologies, implementation, testing, and maintenance capabilities with communication paradigms and problem-solving strategies. Under rapid evolvement and advancement, new technologies such as blockchain and artificial intelligence are poised to redefine the SE landscape. However, traditional SE curricula often lag behind in incorporating cutting-edge technologies, particularly in student class projects. This paper underscores the significance of blockchain within the SE pedagogical framework. Specifically, we introduce a project-based learning approach with blockchain technology to create an open-source software repository for students to engage in requirement analysis, design, implementation, and maintenance of blockchain-oriented software development. These activities will allow students to understand the nature and architecture of blockchain applications that have long been ignored in both computer science and SE education. Practicing these projects will enable learners to become experts in blockchain software development and rethink the need for improving requirements elicitation, design, development, and testing strategies. The repository consists of ten modules, each of which contains real-world topics. We have developed one of ten modules so far, which focuses on a blockchain-based student record database. A preliminary survey, both pre and post-exposure, was conducted with 24 university students of both undergraduate and graduate levels pursuing degrees in either computer science or software engineering programs. The feedback was overwhelmingly positive, highlighting the value of our approach in enhancing the learning experience of novel technologies. The participants also emphasized the need for adopting blockchain-based ideas in course projects. Our future endeavors will focus on expanding the repository to encompass a diverse range of topics, further embedding the role of blockchain in SE education.

Cardiovascular health is of utmost importance, and the rapid evolution of monitoring devices and applications has revolutionized the way we manage it. From smartwatches to electrocardiogram (ECG) devices and implantable solutions, these technological advancements aim to improve patient outcomes. However, the integration of patients' health information with technology necessitates stringent measures to protect personal health information (PHI) and ensure compliance with the Health Insurance Portability and Accountability Act (HIPAA). This study investigates the compliance of various cardiovascular health management (CHM) software, applications, and systems with HIPAA regulations. Through an extensive literature review and systematic analysis, we assessed over 25 scholarly articles that discuss the intersection of cardiovascular health monitoring devices and HIPAA compliance. Our evaluation of the current state of compliance across different applications revealed both strengths and weaknesses in the industry’s privacy and security measures. Our findings indicate that although many CHM solutions demonstrate compliance with HIPAA regulations, there remain areas of concern, particularly regarding data encryption, storage, and access control. To address these gaps, we propose specific recommendations for enhancing privacy and security, including the adoption of robust encryption methods, strict access control policies, and regular security audits. Future research should focus on the development of standardized compliance frameworks for cardiovascular health management systems, as well as the assessment of emerging technologies and their potential impact on HIPAA compliance. By advancing our understanding of the challenges and opportunities in this field, we aim to contribute to the ongoing efforts to protect patients' PHI and promote secure, reliable, and effective health management solutions.

The dark web is often discussed in taboo by many who are unfamiliar with the subject. However, this paper takes a dive into the skeleton of what constructs the dark web by compiling the research of published essays. The Onion Router (TOR) and other discussed browsers are specialized web browsers that provide anonymity by going through multiple servers and encrypted networks between the host and client, hiding the IP address of both ends. This provides difficulty in terms of controlling or monitoring the dark web, leading to its popularity in criminal underworlds. In this work, we provide an overview of data mining and penetration testing tools that are being widely used to crawl and collect data. We compare the tools to provide strengths and weaknesses of the tools while providing challenges of harnessing massive data from dark web using crawlers and penetration testing tools including machine learning (ML) techniques. Despite the effort to crawl dark web has progressed, there are still rooms to advance existing approaches to combat the ever-changing landscape of the dark web.

Software Engineering is the process of systematic, disciplined, quantifiable approach that has significant impact on large-scale and complex software development. Scores of well-established software process models have long been adopted in the software development life cycle that pour stakeholders towards the completion of final software products development. Within the boundary of advanced technology, various emerging and futuristic technology is evolving that really need the attention of software engineering community whether the conventional software process techniques are capable to inherit the core fundamental into the futuristic software development. In this paper, we study the impact of existing software engineering processes and models including Agile, and DevOps in Blockchain-Oriented Software Engineering. We also examine the essentiality of adopting state-of-art concepts and evolving the current soft-ware engineering process for blockchain-oriented systems. We discuss the insight of software project management practices in BOS development. The findings of this study indicate that utilizing state-of-art techniques in software processes for futur-istic technology would be challenging and promising research is needed extensively towards addressing and improving state-of - the-art software engineering processes and methodology for novel technologies.