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Contour trees describe the topology of level sets in scalar fields and are widely used in topological data analysis and visualization. A main challenge of utilizing contour trees for large-scale scientific data is their computation at scale using highperformance computing. To address this challenge, recent work has introduced distributed hierarchical contour trees for distributed computation and storage of contour trees. However, effective use of these distributed structures in analysis and visualization requires subsequent computation of geometric properties and branch decomposition to support contour extraction and exploration. In this work, we introduce distributed algorithms for augmentation, hypersweeps, and branch decomposition that enable parallel computation of geometric properties, and support the use of distributed contour trees as query structures for scientific exploration. We evaluate the parallel performance of these algorithms and apply them to identify and extract important contours for scientific visualization.

Charged particle reconstruction in the presence of many simultaneous proton–proton (pp) collisions in the LHC is a challenging task for the ATLAS experiment’s reconstruction software due to the combinatorial complexity. This paper describes the major changes made to adapt the software to reconstruct high-activity collisions with an average of 50 or more simultaneous pp interactions per bunch crossing (pile-up) promptly using the available computing resources. The performance of the key components of the track reconstruction chain and its dependence on pile-up are evaluated, and the improvement achieved compared to the previous software version is quantified. For events with an average of 60pp collisions per bunch crossing, the updated track reconstruction is twice as fast as the previous version, without significant reduction in reconstruction efficiency and while reducing the rate of combinatorial fake tracks by more than a factor two.

We propose a rich foundational theory of typed data streams and stream transformers, motivated by two high-level goals. First, the type of a stream should be able to express complex sequential patterns of events over time. And second, it should describe the internal parallel structure of the stream, to support deterministic stream processing on parallel and distributed systems. To these ends, we introduce stream types, with operators capturing sequential composition, parallel composition, and iteration, plus a core calculus λST of transformers over typed streams that naturally supports a number of common streaming idioms, including punctuation, windowing, and parallel partitioning, as first-class constructions. λST exploits a Curry-Howard-like correspondence with an ordered variant of the Logic of Bunched Implication to program with streams compositionally and uses Brzozowski-style derivatives to enable an incremental, prefix-based operational semantics. To illustrate the programming style supported by the rich types of λST, we present a number of examples written in Delta, a prototype high-level language design based on λST.

I/O performance monitoring tools such as Darshan and Recorder collect I/O-related metrics on production systems and help understand the applications' behavior. However, some gaps prevent end-users from seeing the whole picture when it comes to detecting and drilling down to the root causes of I/O performance slowdowns and where those problems originate. These gaps arise from limitations in the available metrics, their collection strategy, and the lack of translation to actionable items that could advise on optimizations. This paper highlights such gaps and proposes solutions to drill down to the source code level to pinpoint the root causes of I/O bottlenecks scientific applications face by relying on cross-layer analysis combining multiple performance metrics related to I/O software layers. We demonstrate with two real applications how metrics collected in high-level libraries (which are closer to the data models used by an application), enhanced by source-code insights and natural language translations, can help streamline the understanding of I/O behavior and provide guidance to end-users, developers, and supercomputing facilities on how to improve I/O performance. Using this cross-layer analysis and the heuristic recommendations, we attained up to 6.9× speedup from run-as-is executions.

Over the academic year 2022-23, we discussed the teaching of software performance engineering with more than a dozen faculty across North America and beyond. Our outreach was centered on research-focused faculty with an existing interest in this course material. These discussions revealed an enthusiasm for making software performance engineering a more prominent part of a curriculum for computer scientists and engineers. Here, we discuss how MIT's longstanding efforts in this area may serve as a launching point for community development of a software performance engineering curriculum, challenges in and solutions for providing the necessary infrastructure to universities, and future directions.

Abstract: This Roadmap article provides a succinct, comprehensive overview of the state of electronic structure methods and software for molecular and materials simulations. Seventeen distinct sections collect insights by 51 leading scientists in the field. Each contribution addresses the status of a particular area, as well as current challenges and anticipated future advances, with a particular eye towards software related aspects and providing key references for further reading. Foundational sections cover density functional theory and its implementation in real-world simulation frameworks, Green's function based many-body perturbation theory, wave-function based and stochastic electronic structure approaches, relativistic effects and semiempirical electronic structure theory approaches. Subsequent sections cover nuclear quantum effects, real-time propagation of the electronic structure, challenges for computational spectroscopy simulations, and exploration of complex potential energy surfaces. The final sections summarize practical aspects, including computational workflows for complex simulation tasks, the impact of current and future high-performance computing architectures, software engineering practices, education and training to maintain and broaden the community, as well as the status of and needs for electronic structure based modeling from the vantage point of industry environments. Overall, the field of electronic structure software and method development continues to unlock immense opportunities for future scientific discovery, based on the growing ability of computations to reveal complex phenomena, processes and properties that are determined by the make-up of matter at the atomic scale, with high precision.

Hardware execution attacks exploit subtle microarchitectural interactions to leak secret data. While checking programs for the existence of such attacks is essential, verification of software against the full hardware implementation does not scale. Verification using abstract formal models of the hardware can help provide strong security guarantees while leveraging abstraction to achieve scalability. However, handwriting accurate abstract models is tedious and error-prone. Hence, we need techniques to generate models that enable sound yet scalable security analysis automatically.In this work, we propose micro-update models as a modelling framework that enables sound and abstract modelling of microarchitectural features. We also develop algorithms to generate micro-update models from RTL semi-automatically. We implement our modelling and generation framework in a prototype tool called PAUL. We evaluate our approach by synthesizing micro-update models for the Sodor5Stage processor and components from the cva6 (Ariane) processor. We demonstrate how these models can be generated hierarchically, thus increasing scalability to larger designs. We observe up to 8× improvement in run time when performing analysis with the generated models as compared to the source RTL.

The high-performance computing I/O stack has been complex due to multiple software layers, the inter-dependencies among these layers, and the different performance tuning options for each layer. In this complex stack, the definition of an "I/O access pattern"has been reappropriated to describe what an application is doing to write or read data from the perspective of different layers of the stack, often comprising a different set of features. It has become common to have to redefine what is meant when discussing a pattern in every new study, as no assumption can be made. This survey aims to propose a baseline taxonomy, harnessing the I/O community's knowledge over the past 20 years. This definition can serve as a common ground for high-performance computing I/O researchers and developers to apply known I/O tuning strategies and design new strategies for improving I/O performance. We seek to summarize and bring a consensus to the multiple ways to describe a pattern based on common features already used by the community over the years.

Exascale supercomputers are first-of-their-kind instruments with potentially paradigm-shifting capabilities. However, enabling complex applications at scale and high performance can be difficult, requiring hard work and deep technical understanding. Specific programming models are often needed to achieve the desired performance, and, early on, systems are undergoing debugging and rapid development. This makes it challenging to run on new systems quickly following their deployment. The Application Integration (AppInt) area of the Exascale Computing Project was designed to be the integration point between applications, supporting software, system environments, high-performance computing facilities, and vendors. We describe how the AppInt team addressed these challenges while also promoting the use of portable and sustainable programming models and helping harden systems prior to general availability.

The Exascale Computing Project software deployment effort developed and advanced DevOps capabilities. One goal was to enable robust continuous integration (CI) workflows that span the protected high-performance computing environments found within many of the U.S. Department of Energy's (DOE's) national laboratories. This article highlights several challenges encountered with enabling automation, such as charging models for CI jobs and meeting individualized security requirements that revolve around strongly associating running code with a human identity. It also describes how the Jacamar CI tool evolved to meet later requirements and became a key aspect of the solutions currently offered. Derived from this experience, we offer a conceptual framework for understanding current and future CI challenges at DOE facilities and offer suggestions for long-term solutions.

Computational and data-enabled science and engineering are revolutionizing advances throughout science and society, at all scales of computing. For example, teams in the U.S. Department of Energy's Exascale Computing Project have been tackling new frontiers in modeling, simulation, and analysis by exploiting unprecedented exascale computing capabilities-building an advanced software ecosystem that supports next-generation applications and addresses disruptive changes in computer architectures. However, concerns are growing about the productivity of the developers of scientific software. Members of the Interoperable Design of Extreme-scale Application Software project serve as catalysts to address these challenges through fostering software communities, incubating and curating methodologies and resources, and disseminating knowledge to advance developer productivity and software sustainability. This article discusses how these synergistic activities are advancing scientific discovery-mitigating technical risks by building a firmer foundation for reproducible, sustainable science at all scales of computing, from laptops to clusters to exascale and beyond.

Over the last decade merge trees have been proven to support a plethora of visualization and analysis tasks since they effectively abstract complex datasets. This paper describes the ExTreeM-Algorithm: A scalable algorithm for the computation of merge trees via extremum graphs. The core idea of ExTreeM is to first derive the extremum graph G of an input scalar field f defined on a cell complex K, and subsequently compute the unaugmented merge tree of f on G instead of K; which are equivalent. Any merge tree algorithm can be carried out significantly faster on G, since K in general contains substantially more cells than G. To further speed up computation, ExTreeM includes a tailored procedure to derive merge trees of extremum graphs. The computation of the fully augmented merge tree, i.e., a merge tree domain segmentation of K, can then be performed in an optional post-processing step. All steps of ExTreeM consist of procedures with high parallel efficiency, and we provide a formal proof of its correctness. Our experiments, performed on publicly available datasets, report a speedup of up to one order of magnitude over the state-of-the-art algorithms included in the TTK and VTK-m software libraries, while also requiring significantly less memory and exhibiting excellent scaling behavior.

The U.S. Exascale Computing Project (ECP) has succeeded in preparing applications to run efficiently on the first reported exascale supercomputers in the world. To achieve this, it modernized the whole leadership software stack, from libraries to simulation codes. In this article, we contrast selected leadership software before and after the ECP. We discuss how sustainable research software development for leadership computing can embrace the conversation with the hardware vendors, leadership computing facilities, software community, and domain scientists who are the application developers and integrators of software products. We elaborate on how software needs to take portability as a central design principle and to benefit from interdependent teams; we also demonstrate how moving to programming languages with high momentum, like modern C++, can help improve the sustainability, interoperability, and performance of research software. Finally, we showcase how cross-institutional efforts can enable algorithm advances that are beyond incremental performance optimization.

Information communication and technology (ICT) services and solutions can improve resource efficiency in a variety of sector, but also result in direct environmental impacts. This study assesses the direct environmental impacts of an ICT service that improves vehicle fuel efficiency using a cradle‐to‐grave life cycle assessment (LCA). This is one of the first studies to examine the entire life cycle of an ICT service from development to use and maintenance, with a focus on software—an aspect that is typically neglected in previous studies. The results suggest that software development and maintenance and the use of in‐vehicle communicators for data transmission have the largest environmental impacts across multiple categories. Deployed across a fleet of 150,000 vehicles over 5 years, we estimate that the ICT service is responsible for 174 tCO2e. However, this is negligible compared with the total emissions of the fleet and the potential savings from the service, given a single diesel vehicle in this fleet emits around 130 tCO2e over the same period. We explore several scenarios to reduce the footprint of the ICT service. The largest potential reduction of around one‐third is achieved by replacing in‐house servers with cloud computing in a data center located in a region with low‐carbon electricity. The study demonstrates how LCA can be used to assess the environmental impacts of ICT services and the importance of considering software in these assessments. [ABSTRACT FROM AUTHOR]

ABSTRACT Software development has a relevant human side, and this could, for example, imply that developers' feelings have an impact on certain aspects of software development such as quality, productivity, or performance. This paper explores the effects of toxic emotions on code quality and presents the SentiQ tool, which gathers and analyzes sentiments from commit messages (obtained from GitHub) and code quality measures (obtained from SonarQube). The SentiQ tool we proposed performs a sentiment analysis (based on natural language processing techniques) and relates the results to the code quality measures. The datasets extracted are then used as the basis on which to conduct a preliminary case study, which demonstrates that there is a relationship between toxic comments and code quality that may affect the quality of the whole software project. This has resulted in the drafting of a predictive model to validate the correlation of the impact of toxic comments on code quality. The main implication of this work is that these results could, in the future, make it possible to estimate code quality as a function of developers' toxic comments. [ABSTRACT FROM AUTHOR]

Blockchain technology has attracted significant attention in both academia and industry. Recently, the application of blockchain has been advocated in software engineering. The global software engineering paradigm exacerbates trust issues, as distributed and cross‐organizational teams need to share software artifacts. In such a context, there is a need for a decentralized yet reliable traceability knowledge base to keep track of what/how/when/by whom software artifacts were created or changed. This study presents a blockchain‐enabled framework for trustworthy and collaborative traceability management and identifies benefits, challenges, and potential improvements based on the feedback of software engineering experts. A qualitative approach was followed in this study through semistructured interviews with software engineering (SE) experts. Transcripts were analyzed by applying the content analysis technique. The results indicated the emergence of five categories, further grouped into three main categories: experts' perceptions, blockchain‐based software process improvement, and experts' recommendations. In addition, the findings suggested four archetypes of organizations that may be interested in blockchain technology: distributed organizations, organizations with contract‐based projects, organizations in regulated domains, and regulators who may push the use of this technology. Further efforts should be devoted to the integration of the proposal with tools used throughout the software development lifecycle and leveraging the potential of smart contracts in validating the implementation of requirements automatically. [ABSTRACT FROM AUTHOR]

The present study aimed to merge expertise from evidence-based practice and user-centered design to develop a rating scale for considering user input and other sources of information about end-users in studies reporting on the design of technology-based support for autism. We conducted a systematic review of the relevant literature to test the reliability and validity of the scale. The scale demonstrated acceptable reliability and validity based on a randomized sample of 211 studies extracted from the output of the systematic review. The scale can help provide a more complete assessment of the quality of the design process of technology-based supports for autism and be beneficial to autistic people, their families, and related professionals in making informed decisions regarding such supports. [ABSTRACT FROM AUTHOR]

The traditional practice of assessing academic performance through conventional letter and numerical grades has faced criticism for its limitations in promoting active engagement and curiosity among students. In response, the concept of gradeless education has gained traction, with the aim of fostering a more holistic learning experience. This work explores the implementation of the residency model, a form of gradeless education, in the context of engineering education. The model focuses on skill acquisition and competency demonstration while enhancing student wellness by minimizing assessment-related anxiety that students often face in graded assessments. This study evaluates the effectiveness of the residency model through a comprehensive survey conducted in a software engineering technology program at McMaster University. The survey investigates student perspectives on the model's impact on motivation, learning experience, and attitudes towards learning. The results reveal a complex interplay of attitudes, with students acknowledging the importance of grades while appreciating the model's rigorous assignments. The findings suggest that the residency model can encourage transformative learning experiences while warranting ongoing attention to optimize both learning outcomes and student well-being. Further research is recommended to assess the long-term impact and effectiveness of gradeless education models, emphasizing both their benefits and challenges. [ABSTRACT FROM AUTHOR]

Web‐based test automation heavily relies on accurately finding web elements. Traditional methods compare attributes but do not grasp the context and meaning of elements and words. The emergence of large language models (LLMs) like GPT‐4, which can show human‐like reasoning abilities on some tasks, offers new opportunities for software engineering and web element localization. This paper introduces and evaluates VON Similo LLM, an enhanced web element localization approach. Using an LLM, it selects the most likely web element from the top‐ranked ones identified by the existing VON Similo method, ideally aiming to get closer to human‐like selection accuracy. An experimental study was conducted using 804 web element pairs from 48 real‐world web applications. We measured the number of correctly identified elements as well as the execution times, comparing the effectiveness and efficiency of VON Similo LLM against the baseline algorithm. In addition, motivations from the LLM were recorded and analysed for 140 instances. VON Similo LLM demonstrated improved performance, reducing failed localizations from 70 to 40 (out of 804), a 43% reduction. Despite its slower execution time and additional costs of using the GPT‐4 model, the LLM's human‐like reasoning showed promise in enhancing web element localization. LLM technology can enhance web element localization in GUI test automation, reducing false positives and potentially lowering maintenance costs. However, further research is necessary to fully understand LLMs' capabilities, limitations and practical use in GUI testing. [ABSTRACT FROM AUTHOR]

Summary: As model‐driven engineering gains traction and poses as the new paradigm for software engineering, it raises a need for efficient approaches and tools to manage, discover, and retrieve relevant modeling artifacts. Hence, industry and academia are conceiving effective ways to store, search, and retrieve heterogeneous model artifacts that employ advanced discovery mechanisms. This paper presents MDEForge‐Search, a novel approach to discovering heterogeneous model artifacts over MDEForge, a distributed cloud‐based model repository. We designed advanced discovery mechanisms that retrieve heterogeneous artifacts within their context (megamodel) and reuse them across model management services. In addition, a domain‐specific approach has been proposed to formulate queries in terms of keywords, search tags, conditional operators, quality model assessment services and a transformation chain discoverer. Finally, the applicability of our approach was assessed in a recommender system modeling framework, which, thanks to the operated integration, can rely on the availability of more than 5000 model artifacts currently persisted in our cloud‐based model repository. [ABSTRACT FROM AUTHOR]

Insufficient resources and high costs are hindering industrial development, potentially impeding adaptation to market demands. Overcoming this challenge necessitates advancements in software engineering techniques to streamline processes and meet industrial requirements. Crucially, automating manual tasks and enhancing interoperability between engineering stages can yield efficiency gains. This paper presents a model‐based system engineering approach aimed at automating the transition from design to implementation, incorporating autonomous generation and validation features. Implemented as plugins and utilizing model transformation techniques, this solution targets reducing engineering time and facilitating the adoption of new technologies. Developed, implemented, and tested within the Arrowhead framework, the approach is followed by a discussion on its benefits and limitations. [ABSTRACT FROM AUTHOR]

This study evaluates the effectiveness of common engineering processes in conducting system assessments, with a focus on legacy system assessments, which are currently a barrier to legacy system evolution. These processes include reverse systems engineering (RSE), which remains in its infancy, and more established processes such as software and hardware reverse engineering. The study assesses these methods to better understand their ability to define systems of interest and meet the criteria set by the four horsemen of traditional systems engineering, as defined by Rebovich and White, and the International Council of Systems Engineering. Additionally, RE techniques are reviewed to ensure that a formal, systems‐level process—with detailed steps and diagrams—is documented and accessible to systems or reverse engineers. Lastly, this investigation elucidates the ability of each reverse engineering process to discover, recover, or reproduce information pertaining to the systems of interest that is missing, incomplete, or outdated in documentation relevant to the development or operations of that system. This information can be used to better understand the areas of RSE that still need to be developed and aid in the maturation of a repeatable process that is applicable to legacy systems. The results of this study are expected to contribute to the further development of the Enterprise Lifecycle Model proposed by Terry and Chandrasekar. [ABSTRACT FROM AUTHOR]

Game development studios deal with numerous challenges to achieve the publication of games that are acclaimed by the public. The difficulties range from a simple choice of color for a particular object within the game to mechanics and narratives. In this way, user experience (UX) techniques provide a better perception of the player's experience. UX techniques are consolidated in the context of software engineering industries and literature. However, few works in the literature (which will be presented in this work) address this knowledge and treat digital games as a specific type of software. In this sense, we aim to explore and present innovative approaches and best techniques adopted by game development studios to integrate UX into their processes. The research included the participation of 41 digital game development studios from all regions of Brazil, of which 31 were interviewed and 10 chose to participate via questionnaire. The analysis revealed that many participants work in small companies (up to five employees) and apply UX techniques intuitively without formal knowledge and justification for using them. Furthermore, we noticed that many studios are unclear about their techniques and confuse them with game design. However, most understand that UX is fundamental for designing a successful game with community support and buy‐in. In addition, most of the justifications for not using UX techniques converged on a lack of knowledge. Finally, we developed a visual scheme to assist studios and future research, capable of consolidating the information extracted from participants about the UX techniques adopted, the benefits and challenges of applying them to the development process, the justifications for not adopting UX techniques (when not adopted), the test instruments implemented, and the UX dimensions evaluated by them. [ABSTRACT FROM AUTHOR]

Agile methodologies, initially designed for the project level, face challenges when applied at enterprise levels where complex projects and diverse stakeholders are involved. To meet this challenge, several large-scale agile methodologies have been proposed. However, these approaches are not flexible enough or tailored to the needs of organizations, projects, and their teams. It is in this context that hybrid methodologies have emerged. This study aims to conduct a systematic literature review to trace the evolution of hybrid scaling of agile and characterize different approaches to implement it. This study starts by assessing 1509 studies through the use of the PRISMA 2020 framework and identifies 38 relevant studies in this field. The findings indicate that the majority of studies are from 2021 onwards and that qualitative methodologies supported by case studies predominate, making it possible to characterize tailoring processes in these organizations. Moreover, the implementation of hybrid scaling of agile is supported by the paradigm of ambidextrous strategy, a combination of agile with traditional project management methodologies, and continuous improvements. This study contributes insights into navigating the complexities of agile scaling, offering practical guidance for organizations seeking to optimize their project management practices. [ABSTRACT FROM AUTHOR]

Complex assistive systems providing human behavior support independent of the age or abilities of users are broadly used in a variety of domains including automotive, production, aviation, or medicine. Current research lacks a common understanding of which architectural components are needed to create assistive systems that use models at runtime. Existing descriptions of architectural components are focused on particular domains, consider only some parts of an assistive system, or do not consider models at runtime. We have analyzed common functional requirements for such systems to be able to propose a set of reusable components, which have to be considered when creating assistive systems that use models. Such components constitute a reference architecture that we propose within this paper. To validate the proposed architecture, we have expressed the architectures of two assistive systems from different domains, namely assistance for elderly people and assistance for operators in smart manufacturing in terms of compliance with such architecture. The proposed reference architecture will facilitate the creation of future assistive systems. [ABSTRACT FROM AUTHOR]

Currently, digital technologies for modeling buildings and infrastructure are successfully used in international and national practice for the implementation of complex construction projects and large-scale programmes. At the same time, the transition to machine-readable standards, implemented in many countries to improve the quality of design documentation and automate its verification, faces serious methodological and technical challenges. First of all, these challenges are due to the complexity of digital models, as well as the variety of requirements formulated in natural languages and imposed on these models at the state, regional, departmental, and corporate levels. Attempts to create catalogs of requirements and software tools for their management and use generally have specific purposes and do not provide necessary completeness, normalization, consistency, interconnectedness, unambiguity, traceability, and validability of requirements description. In this regard, it seems reasonable to use formal methods for specification and verification of requirements that have proven themselves in system and software engineering. This paper provides a comparative analysis of software tools for automated verification of regulatory requirements in the construction domain. There is a growing interest in tools focused on international standards, such as Industry Foundation Classes (IFC) and Information Delivery Specification (IDS), that allow one to control the completeness of the object and attribute composition of models, as well as the clarification of acceptable domains of values values. However, the IDS standard is not formalized and does not enable the specification of requirements expressed in terms of arbitrary algebraic conditions. The EXPRESS object-oriented data modeling language, in which the IFC data schema is specified, seems promising for the formal specification and verification of requirements for digital models used in the construction industry. As a substantiation, it is shown that IDS specifications can be represented by logical expressions and EXPRESS functions, as well as that arbitrary algebraic conditions can be specified as EXPRESS declarative rules. To illustrate the proposed approach, we consider some examples of the formalization of requirements from national construction standards and sets of rules concerning the safety of buildings, structures, and processes. The possibility of harmonizing the proposed formal approach with the IDS standard by defining new facets for the representation of local, uniqueness, and global EXPRESS rules is also discussed. [ABSTRACT FROM AUTHOR]

Software defect prediction is a critical challenge within software engineering aimed at enhancing software quality by proactively identifying potential defects. This approach involves selecting defect-prone modules ahead of the testing phase, thereby reducing testing time and costs. Machine learning methods provide developers with valuable models for categorising faulty software modules. However, the challenge arises from the numerous elements present in the training dataset, which frequently reduce the accuracy and precision of classification. Addressing this, selecting effective features for classification from the dataset becomes an NP-hard problem, often tackled using metaheuristic algorithms. This study introduces a novel approach, the Binary Chaos-based Olympiad Optimisation Algorithm, specifically designed to select the most impactful features from the training dataset. By selecting these influential features for classification, the precision and accuracy of software module classifiers can be notably improved. The study's primary contributions involve devising a binary variant of the chaos-based Olympiad optimisation algorithm to meticulously select effective features and construct an efficient classification model for identifying faulty software modules. Five real-world and standard datasets were utilised across both the training and testing phases of the classifier to evaluate the proposed method's effectiveness. The findings highlight that among the 21 features within the training datasets, specific metrics such as basic complexity, the sum of operators and operands, lines of code, quantity of lines containing code and comments, and the sum of operands have the most significant influence on software defect prediction. This research underscores the combined effectiveness of the proposed method and machine learning algorithms, significantly boosting accuracy (91.13%), precision (92.74%), recall (97.61%), and F1 score (94.26%) in software defect prediction. [ABSTRACT FROM AUTHOR]

The swift expansion of e-commerce and supply chain operations has significantly enhanced the efficiency of warehouse management systems, establishing them as vital components in augmenting organizations' competitiveness. This paper delves into warehouse sorting systems to enhance the sorting process, reduce error rates, and simplify internal warehouse procedures. It aims to develop a scalable, adaptable, and efficient warehouse sorting system that can maximize sorting efficiency while effectively responding to changing market demands through the use of advanced automation technologies. The study provides an in-depth review of the literature that has explored the Automated Storage and Retrieval System (ASRS) within the context of warehouse operations. It offers a comprehensive introduction to the operational systems of warehouses, detailing each type of ASRS along with the technologies that can improve the efficiency and accuracy of these systems. Moreover, the paper thoroughly investigates and classifies the ASRS design decision problem and compares multiple types of ASRS. The analysis aims to delineate the distinctions among various ASRS configurations, assessing their scalability, adaptability, and their impact on operational efficacy in warehouse environments Through this comparative review, the paper emphasizes the potential enhancements in sorting processes that modern ASRS can provide, ensuring that warehouse operations can rapidly adapt to market changes and demands. The goal is to highlight best practices and technological innovations that can lead to more responsive and efficient warehouse management systems. This exploration contributes to a better understanding of how cutting-edge automation and adaptable system designs can significantly influence the efficiency of warehouse sorting processes. [ABSTRACT FROM AUTHOR]

This paper proposes a virtual reality based Left Brain System named BrainUp World to improve left brain thinking. The left-brain system was employed with mobile virtual reality technology, hand motion tracking and haptic feedback system. The implementation of these systems is to enhance the experience and sense of embodiment. BrainUp World includes virtual reality-based brain training games to improve a person's attention level, reasoning skill, auditory cognition, arithmetic skill, sequencing skill and memory. The hand tracking system was utilized with an IR camera to capture the hand orientation and return the gesture data to the VR application. A low-cost and lightweight haptic glove was invented which provides the sense of touch using vibrations while interacting with VR contents. An experimental study was conducted to assess the efficacy of BrainUp World compared to traditional PC-based training approaches. Participants were randomly assigned to either the VRbased or PC-based training group and underwent 6 different games to test a person's attention level, reasoning skill, auditory cognition, arithmetic skill, sequencing skill, and memory. The results revealed a statistically significant improvement in left brain function in the VR-based training group compared to the PC-based group, with a T-score growth of 4.73. Analysis using ANOVA confirmed the significance of this difference (p-Value = 0.04). Notably, the study also identified age-related differences in thinking fluency, highlighting the importance of personalized cognitive training interventions. In conclusion, BrainUp World demonstrates the potential of VR technology in promoting left brain development, as evidenced by empirical findings from the conducted study. By offering immersive and interactive cognitive training experiences, VR-based systems hold promise for enhancing various aspects of cognitive function associated with left hemisphere dominance. Further research in this area is encouraged to explore the full potential of VR-based interventions for cognitive enhancement. [ABSTRACT FROM AUTHOR]

E-commerce recommender systems (RS) nowadays are essential for promoting products. These systems are expected to offer personalized recommendations for users based on the user preference. This can be achieved by employing cutting-edge technology such as artificial intelligence (AI) and machine learning (ML). Tailored recommendations for users can boost user experience in using the application and hence increase income as well as the reputation of a company. The purpose of this study is to investigate popular ML methods for e-commerce recommendation and study the potential of ensemble methods to combine the strengths of individual approaches. These recommendations are derived from a multitude of factors, including users' prior purchases, browsing history, demographic information, and others. To forecast the interests and preferences of users, several techniques are chosen to be investigated in this study, which include Singular Value Decomposition (SVD), k-Nearest Neighbor Baseline (KNN Baseline) and CoClustering. In addition, several evaluation metrics including the fraction of concordant pairs (FCP), mean absolute error (MAE), root mean square error (RMSE) and normalized discounted cumulative gain (NDCG) will be used to assess how well different techniques work. To provide a better understanding, the outcomes produced in this study will be incorporated into a graphical user interface (GUI). [ABSTRACT FROM AUTHOR]

This research work develops a new framework that combines patient feedback with evidence-based best practices across disease states to improve drug recommendations. It employs BERT as its free-text processing engine to deal with sentiment judgment and classification. The functionality of the system, named 'PharmaBERT', includes acceptance of drug review data as a comprehensive input, drug categorization when dealing with a wide range of treatments and fine-tuning the BERT-based model for gaining positive or negative sentiment towards specific medications. PharmaBERT categorizes various drugs and fine-tunes the BERT structure to perceive lots of possible sentiments for specific medications. Consequently, PharmaBERT brings all its training and optimization capabilities together and through this, the system reaches a higher accuracy of up to 91% thus showcasing the potency of the model in capturing patient sentiments. While being a BERT spinoff, PharmaBERT utilizes its own set of experienced techniques to comprehend and sense the health-related text input given by the patient, doctor, or pharmacist. It uses transfer learning, that is, it learns from language representations to adapt quickly to the intricacies of drug reviewing. Through PharmaBERT, healthcare professionals may expand their diagnoses with insights from patient feedback to constitute more neutral decisions. [ABSTRACT FROM AUTHOR]

Cybersecurity has emerged as a critical imperative in contemporary digital landscapes, necessitating heightened awareness and proficiency across all demographic segments. Accordingly, this research has been meticulously crafted to delve into the complexities of cybersecurity awareness, with a specific focus on university students. The study embarks on an exhaustive analysis encompassing the evaluation of cybersecurity awareness levels within the targeted groups, the identification of prevailing issues and practices, and an exploration of novel methodologies, notably gamification, to fortify cybersecurity knowledge and skills among diverse user cohorts. Central to this investigation is the efficacy of gamified learning environments tailored expressly for augmenting cybersecurity awareness among university students. Through a comprehensive examination of existing platforms, methodological frameworks, and user interactions, this research outlines critical trends, challenges, and latent opportunities within the cybersecurity awareness domain, with a specific emphasis on gamification's transformative potential. The study not only identifies key areas for improvement but also proposes innovative solutions rooted in gamified learning paradigms, with the overarching goal of fostering engaging, effective, and sustainable cybersecurity awareness initiatives among students. Drawing upon a synthesis of theoretical constructs, empirical insights, and pragmatic recommendations, this research significantly contributes to the evolving discourse on cybersecurity education. By underscoring the transformative efficacy of gamification as a pivotal tool in cybersecurity awareness initiatives, this study overlays the way for substantial advancements in cybersecurity education paradigms, offering a roadmap for enhancing cybersecurity awareness levels among university students and beyond. [ABSTRACT FROM AUTHOR]

MobiTest is an application that serves as a valuable tool in the fast-growing field of software testing. Efficiency is crucial in this industry, where testers, quality assurance teams, and others must meticulously test each application, avoiding the need to repeat the entire cycle to identify bugs. This application is a breeze thanks to its intuitive features and educational content. Thanks to continuous integration, testers can easily keep up with the fast-paced development cycle and start automating tasks as soon as the user interface development is completed. This saves valuable time and ensures a smoother and more efficient process. During the development of this application, a need arose for manual testing, which unfortunately resulted in the inefficient use of resources. MobiTest was designed to overcome these limitations by providing the ability to generate generic test scripts for any application as needed. It can efficiently and adaptively handle intricate tasks according to predefined parameters. This application thoroughly examines every possible detail, allowing the hacker to exploit the system. [ABSTRACT FROM AUTHOR]

Effective from Volume 3, JIWE has transitioned to a triannual publication release effort. Specifically, releases occur each February, June, and October. This particular October 2024 release contains 12 papers within the regular section that covers a broad range of application concerning Machine Learning (ML), Artificial Intelligence (AI), Data Mining (DM), Software Engineering, Recommender Systems, Cybersecurity, Healthcare, and other key areas in web engineering. Additionally, this edition presents a captivating collection of 7 papers curated by our Thematic Editor, Dr. Ji-Jian Chin, under the theme "Pervasive Computing." In his editorial, Dr. Ji-Jian Chin highlights cutting-edge research on the integration of computing into everyday environments. Moreover, these papers are aligned to some of the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) through advancements in healthcare technologies, SDG 9 (Industry, Innovation, and Infrastructure) through software and systems innovation research, and SDG 16 (Peace, Justice, and Strong Institutions) through contributions to privacy and cybersecurity research. [ABSTRACT FROM AUTHOR]

Comprehensive measures of quality are a research imperative, yet the development of software quality models is a wicked problem. Definitive solutions do not exist and quality is subjective at its most abstract. Definitional measures of quality are contingent on a domain, and even within a domain, the choice of representative characteristics to decompose quality is subjective. Thus, the operationalization of quality models brings even more challenges. A promising approach to quality modeling is the use of hierarchies to represent characteristics, where lower levels of the hierarchy represent concepts closer to real-world observations. Building upon prior hierarchical modeling approaches, we developed the Platform for Investigative software Quality Understanding and Evaluation (PIQUE). PIQUE surmounts several quality modeling challenges because it allows modelers to instantiate abstract hierarchical models in any domain by leveraging organizational tools tailored to their specific contexts. Here, we introduce PIQUE; exemplify its utility with two practical use cases; address challenges associated with parameterizing a PIQUE model; and describe algorithmic techniques that tackle normalization, aggregation, and interpolation of measurements. [ABSTRACT FROM AUTHOR]

In the 21st century, the software industry has achieved great development, and the development complexity and volume of software projects are also continuously increasing. Therefore, the design of software engineering supervision network plans is becoming increasingly important. The Pareto optimal solution set construction method, global extremum selection method, and fitness value determination method of the multi-objective particle swarm optimization algorithm have been improved in response to the poor optimization performance and poor convergence and distribution of optimal solutions in existing network planning algorithms. However, traditional methods only optimize one or two objectives of network planning, resulting in inconsistency with actual engineering. Thus, this study establishes a multiobjective model based on resources, duration, cost, and quality for comprehensive optimization. Based on the results, the Pareto optimal solution curves obtained by the proposed algorithm on three classic test functions were consistent with the actual theoretical Pareto frontier curves. The proposed method was applied to engineering project examples. 10 solutions that met the schedule requirements were obtained. Most engineering projects had a quality of over 80%, which verified the practicality of the algorithm. The algorithm has achieved good results in optimizing engineering quality. Therefore, this model can consider various indicators such as resources and costs to obtain software engineering quality improvement plans. It has certain application potential. [ABSTRACT FROM AUTHOR]

A domain specific language (DSL) ties the business and technical models, by letting technical developers write programs with the business domain properties. Yet, DSLs are not used due to the cost of developing them. Such cost stems from the needed expertise within both the domain knowledge and language development technicalities for any DSL engineer who would design such a language. This paper proposes a semantic-based DSL development approach that utilizes an ontology as a formal way for domain representation. The domain ontology is semi-automatically transformed into a DSL. Then, an ontology reasoning algorithm provides reasoning services on the DSL structure and the programs developed using such DSL by application developers. Such reasoning services can automatically detect flaws in the DSL design like possible inconsistency or the presence of unsatisfiable or redundant classes thus serving the DSL engineer. The reasoning services can also discover inconsistency or redundant classes in programs built using the designed DSL, thus serving the application developer. The proposed approach was implemented within a language workbench using projectional-editing and was evaluated on two different ontologies from varied domains. The results show correct transformation of the input ontology, valid instantiation of designed application, and efficient reasoning services. [ABSTRACT FROM AUTHOR]