Abstract: The increasing adoption of Point-of-Sale (POS) systems in retail and food & beverage (F&B) establishments necessitates user-friendly, secure, and platform-compatible offerings. StoreHub, a prominent POS system among Southeast Asian retail establishments, is examined in this research, and key problems are identified through real-world interaction with SateHut, an avid long-time user. Non-standard export format, non-interactive user experience, limited Android compatibility, outmoded training resources, ineffectiveness regarding manual verification, and inadequate multi-factor authentication are key problems identified. To address these, we recommend prioritized enhancements such as standardised data exports, facial recognition presence, Android optimization, configurable interfaces, in-app guides, and improved security policies. Utilising user comments and agile design practice, solutions were assessed on the basis of technical feasibility, financial viability, and broader industry applicability. The findings demonstrate that such advancements can significantly boost user experience, operational efficiency, and business scalability, making StoreHub a viable, affordable POS solution for SMEs operating in multicultural markets.

Abstract: The use of technology in mental health programs has significantly increased access and interaction for users and psychology care. There is a newer platform known as BetterMood that focuses on making user interaction easier and making the registration of users requiring mental health care less complicated. Here, the work of research describes the system architecture of BetterMood, that is, the user registration procedure, and presents a sequence diagram describing the used verification mechanisms. Through the application of a formal validation process, BetterMood guarantees data accuracy and protection, ultimately providing an uninterrupted and trusted user experience. The results demonstrate the significance of clearly defined system interactions in instilling user trust and usability in digital mental health interventions.

Abstract: Mental well-being is a significant part of overall wellness, but access to decent resources and support is a problem for most people. This project seeks to enhance an available mental wellness software system in terms of its functionality, performance, and usability. Our team of six committed members has a vision where mental healthcare is highly prioritized and accessible for everyone. By means of software improvement, addition of community-based support, and provision of large-scale mental health resources, we try to create a platform capable of empowering users to be able to deal with their well-being. This paper speaks to our approach to software improvement, the key improvements made, and the possible impact of our system on mental health care support services.

Abstract: With increasing rates of obesity and being underweight, proper classification of types of weights has emerged as a very serious matter of public health. Here, data science techniques are applied to investigate and analyze a real dataset on demographic and physical attributes such as age, gender, height, weight, and body mass index (BMI). The objective is to construct a robust classification model to accurately identify an individual's weight class—underweight, normal weight, overweight, or obese—from the k-Nearest Neighbors (k-NN) algorithm. The sample set of 110 examples with six features creates challenges with the asymmetrical distribution of the weight classes, predominantly the preponderance of underweight patients. The research aims to not only gain accurate classification but also to establish the most meaningful factors contributing to weight classification. By assessing the performance of the k-NN model, the research measures its ability to answer classification issues based on health and aims to optimize it. The results yield knowledge of the viability of data science in healthcare decision-making, i.e., formulation of targeted health interventions.

Abstract: The retail industry is undergoing a paradigm shift with the increasing adoption of self-checkout systems with the goal of improving customer experience and operational efficiency. The present study focuses on exploring the design, development, and testing of a multi-functional self-checkout system incorporating features such as barcode scanning, AI-based product identification, multiple payment options, and security features. With an Agile project management approach, the system was developed for user experience, security, and integration with current retail infrastructure. The study proposes various testing approaches, including black-box, white-box, integration, and security testing, to achieve reliability and usability of the system. A comprehensive risk management and maintenance plan is also proposed to achieve system longevity and flexibility. The findings show that well-designed self-checkout systems can potentially reduce checkout time significantly, improve customer satisfaction, and optimize retail operations without exposing the business to security and user error risks.

Abstract: High-Performance Computing (HPC) revolutionized the field of computational science by enabling it to process vast amounts of data and execute complex simulations at remarkable speed and efficiency. This paper describes various paradigms of computing, namely, client-server computing, distributed computing, cloud computing, and edge computing, and elaborates on their technology drivers. Particular focus is given to HPC, its architecture, programming models, performance metrics, scalability, and applications. The article highlights the significant impact of HPC to science in fields of medicine, biophysics, business, and engineering by facilitating paradigm-altering scientific discoveries, economic forecasting, and simulations of complex engineering. In addition, the article mentions challenges to deploying HPC, such as scalability, handling resources, and integration of multi-core processors. Through comparative analysis and benchmarking techniques, the research points to the necessity of continuous hardware and software innovations to make HPC systems efficient and sustainable. The study brings out the revolutionary potential of HPC in modern computing and its central role in solving the most complex computational problems in the modern age.

Abstract: Quantum computing promises to offer significant computational advantages over classical computing, leveraging principles such as superposition and entanglement. This necessitates effective metrics and measurement techniques for evaluating quantum systems, aiding in their development and performance optimization. However, due to fundamental differences in computing paradigms, and current immaturity of quantum software abstractions, classical software and hardware metrics may not directly apply to quantum computing, where the distinction between software and hardware can still be somewhat indiscernible compared to classical computing. This paper provides a comprehensive review of existing quantum software and hardware metrics in the scientific literature, highlighting key challenges in the field. Additionally, it investigates the application of Functional Size Measurement (FSM) based on COSMIC ISO 19761 to measure quantum software. Three FSM approaches are analyzed by applying them to Shor’s and Grover’s algorithms, with measurement results compared to assess their effectiveness. A comparative analysis highlights the strengths and limitations of each approach, emphasizing the need for further refinement. The insights from this study contribute to the advancement of quantum metrics, especially software metrics and measurement, paving the way for the development of a unified and standardized approach to quantum software measurement and assessment.

Abstract: This study looks into the expanding importance of machine learning (ML) in software engineering, to provide a thorough evaluation of its applications, categorise existing approaches, and propose prospective areas of future research. As machine learning (ML) continues transforming software development processes, understanding its potential and limitations is critical for success. A systematic literature review was carried out utilising major scientific databases, including IEEE Xplore, ACM Digital Library, Scopus, Arxiv, and Web of Science. After applying strict criteria for inclusion and exclusion to an initial pool of 105 publications, 57 were chosen for further review. The study synthesised concepts from the reviewed literature using both quantitative and qualitative approaches, including thematic coding and statistical analysis of publishing trends. The findings highlight major applications of machine learning in software engineering, including code generation, error detection, and program maintenance. Furthermore, the paper identifies a growing trend in the usage of graph neural networks (GNNs) to analyse code architectures, which are validated by experimental evidence. These achievements demonstrate ML's transformational potential for optimising the software development life cycle. While machine learning offers significant opportunities for automation and optimisation in software engineering, challenges such as low model interpretability, high computation costs, and limited integration into existing workflows remain. Addressing these challenges is important to fully realise ML's potential. Integrating machine learning into traditional programming methodologies, utilising federated learning for privacy-preserving collaboration, and developing interpretable ML models targeted to software engineering roles are all intriguing research avenues.

Abstract: Individuals with alexithymia suffer from a reduced ability to feel, express, and recognize emotions; however, the treatments and therapies that are currently in use are often expensive and inaccessible to some. Moreover, autism spectrum disorder is associated with reduced facial expression recognition ability. In this project, the aim was to develop AlexiLearn, a free application that teaches various aspects of emotions to people with these conditions. This application will include a real-time facial expression recognition (FER) system and other educational features like lessons, practices, and emotional reflections. The result was an 80.714% accuracy model performing real-time FER on a mobile device with the use of Google’s ML Kit face detection model. Gamified elements were added to several other features to increase engagement, like points and upgrades. This paper also considers possible further research, improvements, and features to address common struggles of individuals with these mental conditions, including expressing and feeling emotions.

Abstract: Understanding the world requires us to create mental models based on experience. In Extended Reality (XR), this becomes more challenging as digital and physical realities blend. Digital intelligence can transform inanimate objects, whilst entirely virtual characters and objects can seem real. This paper examines this new blended reality through the lens of human cognition and perception, arguing for a shift away from outdated user interface metaphors such as the desktop metaphor from the personal computer era, the cards and stacks of mobile devices, and the timeline streams of social media. Instead, an emerging “Objects and Actors" metaphor better aligns with advances in XR, Spatial Computing, and Artificial Intelligence (AI). We face a collective challenge to shape this paradigm shift responsibly, ensuring that design prioritizes human well-being over pure economic interests. Context-aware systems offer significant potential but require personal data. Who controls this data, and how is it used? We explore these concerns and propose guidelines inspired by Calm Technology to design tools that assist rather than overload humans. Additionally, we present an implementation (‘Reality2’) using semi-embodied sentient digital agents (‘Sentants’) on a distributed platform that prioritizes privacy, distributes computation and storage, and enhances the intuitiveness of human-technology interactions. This approach paves the way for tools that respect users’ autonomy whilst enriching their experiences in an increasingly blended world.

Abstract: Software requirements engineering is one of the most critical and time-consuming phases of the software development process. The lack of communication with stakeholders and the use of natural language for communicating leads to misunderstanding and misidentification of requirements or the creation of ambiguous requirements, which can jeopardize all subsequent steps in the software development process and can compromise the quality of the final software product. Natural Language Processing is an old area of research, however, it is currently undergoing strong and very positive impacts with recent advances in the area of ML, namely with the emergence of Deep Learning and, more recently, with the so-called transformer models such as BERT and GPT. Software requirements engineering is also being strongly affected by the entire evolution of ML and other areas of AI. In this article we make a systematic review on how AI, ML and NLP are being used in the various stages of requirements engineering, including requirements elicitation, specification, classification, prioritization, requirements management, requirements traceability, etc. Furthermore, we identify which algorithms are most used in each of these stages.

Abstract:

Recent advances in DevOps have dramatically reshaped software development and operations by emphasizing automation, continuous integration/delivery, and rapid feedback. However, organizations still struggle to achieve predictable improvements despite widespread adoption. In this study, we propose an “IDEAL-Enhanced DevOps” framework that integrates the five-phase IDEAL model—Initiate, Diagnose, Establish, Act, and Learn—into a DevOps transformation process. The proposed method lays out a structured approach to applying incremental improvements throughout the software delivery process. By a review of literature, in-depth analysis of case studies, and dis-semination of a questionnaire to practitioners in the field, this research explains how the IDEAL stages can be mapped to key processes in DevOps, address automation and scalability challenges, and facilitate a learning-centered, cooperative culture. The results show that a well-defined process-improvement approach can effectively reduce error incidence, enhance usability of tools, and significantly shorten time to get products to market. Our analysis shows that coupling IDEAL with DevOps not only clarifies responsibilities and organizational roles, but also lays a foundation for more resilient, high-quality, and adaptable software engineering methods.

Abstract: This study focuses on developing of a software verification method using SI (The International System of Units) - based Template Libraries (TL). It analyses and enhances the reliability of cyber-physical systems (CPS) and IoT software by addressing SI prefixes, units, and orientation errors in C/C++ programs without increasing memory usage. Such errors often go undetected by conventional methods, leading to critical issues. Inspired by Siano’s approach, the study extends the SI to include orientation metrics. The technique leverages C++ metaprogramming to enforce the correct use of SI units and prefixes through dimensional and orientational analysis, enabling precise control of physical object rotations. A reliable SI unit model was developed, demonstrating the advantages of the extended system. Verifying dimensions and orientations at compile-time allows early detection of potential software defects, reducing debugging time and costs. The SI-based template library enhances software safety and reliability, identifying 90% of incorrect variable usage and over 50% of erroneous operations in large-scale programs. It was validated through development and integration Euler's differential equations and formal software verification of CPS. This SITL-based approach significantly advances software development, improving compile-time and run-time verification across domains such as manufacturing, transport, space and other systems.

Abstract: Software defect prediction aims to identify defect-prone modules before testing, reducing costs and duration. Machine learning (ML) techniques are widely used to develop predictive models for classifying defective software components. However, high-dimensional training datasets often degrade classification accuracy and precision due to irrelevant or redundant features. To address this, effective feature selection is crucial, but it poses an NP-hard challenge that can be efficiently tackled using heuristic algorithms. This study introduces a Binary Multi-Objective Starfish Optimizer (BMOSFO) for optimal feature selection, enhancing classification accuracy and precision. The proposed BMOSFO balances two conflicting objectives: minimizing the number of selected features and maximizing classification performance. A Choquet Fuzzy Integral-based Ensemble Classifier is then employed to further enhance prediction reliability by aggregating multiple classifiers. The effectiveness of the proposed approach is validated using five real-world NASA benchmark datasets, demonstrating superior performance compared to traditional classifiers. Experimental results reveal that key software metrics—such as design complexity, operators and operands count, lines of code, and number of branches—significantly influence defect prediction. The findings confirm that BMOSFO not only reduces feature dimensionality but also enhances classification performance, providing a robust and interpretable solution for software defect prediction. This approach shows strong potential for generalization to other high-dimensional classification tasks.

Abstract: As with many software systems whether manually engineered or automatically generated, the need to identify and eliminate or resolve errors in the system's implementation --- so-called "bugs" is an important aspect of good and effective software construction and maintenance. In standard Software Engineering parlance, this practice is what is known as "debugging" the system --- or rather "software debugging", and for the case of software implemented using the TEA programming language, is such an important aspect of the language's ecosystem, the debugging mechanisms have been designed and implemented as part of the core language's runtime --- essentially, the TEA debugger is part of the language's Software Operating Environment (SOE), and in this paper, we highlight what features the TEA debugger offers, how it works and what remains to be done so as to help software engineers build robust and error-free software in the TEA language by leveraging the essential software debugging features of the TEA language runtime; tttt.

Abstract:

The Social Promotion Copilot (SPC) is designed to autonomously engage with users on social platforms, leveraging advanced NLP and action-oriented automation. This chapter delves into its architectural foundation, focusing on the integration of Theory of Mind to enhance SPC’s ability to interpret user emotions, predict reactions, and tailor responses dynamically. By modeling mental states, SPC can distinguish between different user intents, such as seeking support, expressing dissatisfaction, or driving discussions. This capability enables more context-aware and persuasive interactions, making SPC a more effective tool for both social engagement and marketing strategies. Beyond cognitive modeling, the chapter also examines the run-time execution framework, detailing how SPC processes textual tasks in real-time. The system selects optimal actions based on multi-modal inputs, including textual context, sentiment analysis, and platform-specific engagement patterns. Through a structured decision-making pipeline, SPC adapts its posting and response strategies to maximize visibility and interaction. By balancing automation with adaptive intelligence, SPC transforms from a simple content-promotion tool into an autonomous social agent, capable of managing long-term engagement and fostering meaningful digital interactions, which is confirmed by evaluation on social network sites.

Abstract:

The Sky Runner Project is an endless runner-style game based on Java, set in a futuristic 2077 Dubai. Players control one of the flying cars through the sky, dodging obstacles while gathering points to purchase new vehicles. The game is designed with a heavy emphasis on entertainment, with simple but engaging gameplay throughout. It will incorporate OOP principles and have a well-defined package organization for UI management, game logic, media handling, and data storage through binary-based files. The Sky Runner game will incorporate a dynamic scoring system that adjusts the difficulty level and has a structure that saves player progress, settings, and purchases. Some testing was done to check if the UI interacts with the rest of the application correctly if game states transition correctly, and if the difficulty adjustments functioned properly. The game has been inspired by the simplicity of the way that Google Chrome Dinosaur Game has put out, giving it an advanced futuristic setting while also expanding the views, immersive sound design, and progression system. The project demonstrates high software engineering principles, being modular coded, and maintainable. The future scopes entail saving progress on an account basis, providing options for changing backgrounds, and allowing for alternative gameplay modes.

Abstract: Automated software testing has the potential to enhance efficiency and reliability in software development, yet its adoption remains hindered by challenges in aligning test generation with software requirements. REquirements-Driven Automated Software Testing (REDAST) aims to bridge this gap by leveraging requirements as the foundation for automated test artifact generation. This systematic literature review (SLR) explores the landscape of REDAST by analyzing requirements input, transformation techniques, test outcomes, evaluation methods, and existing limitations. We conducted a comprehensive review of 156 papers selected from six major research databases. Our findings reveal the predominant types, formats, and notations used for requirements in REDAST, the automation techniques employed for generating test artifacts from requirements, and the abstraction levels of resulting test cases. Furthermore, we evaluate the effectiveness of various testing frameworks and identify key challenges such as scalability, automation gaps, and dependency on input quality. This study synthesizes the current state of REDAST research, highlights trends, and proposes future directions, serving as a reference for researchers and practitioners aiming to advance automated software testing.

Abstract: The augmented JavaScript programming language completed as general computing instrument concept, referred as @JavaScript, is presented. The augmentation consists in the minimalist dialectal completion of the language, keeping compatibility with the standard version, in order to transform the language into a general-purpose language and to expand its use as universal programming instrument suitable in any computing system areas and for any type of computing device. The augmentation includes integrating existing high and low level elements along with introducing new elements in order to be transformed into a bootstrap-type self-hosting language, scalable and flexible in being usable in any type of computing processing and contexts as a ubiquitous instrument, suitable for incorporating natural languages and other present and future computing paradigms.

Abstract: We developed a web application demo, dynamically monitoring and visualizing the traffic condition in New York City, with a focus on Manhattan. We took advantage of two public API providers: Tomtom and Openweather, for traffic-related streaming data including real-time intersection speed, historical density, and weather details. We proposed a novel congestion model and updated the traffic heatmap every 15 minutes. We implemented our visualization by Google Maps APIs and similar to Google Map layout, we quantified the congestion into three degrees and plotted streets with colors. Our demo achieved similar performance with Google Maps, but with more reasonable and accurate results. Our work can also accept streaming data to achieve high-level concurrency.