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Buildings are complex structures composed of heterogeneous elements; these require building management systems (BMSs) to dynamically adapt them to occupants' needs and leverage building resources. The fast growth of information and communication technologies (ICTs) has transformed the BMS field into a multidisciplinary one. Consequently, this has caused several research papers on data-driven solutions to require examination and classification. This paper provides a broad overview of BMS by conducting a systematic literature review (SLR) summarizing current trends in this field. Unlike similar reviews, this SLR provides a rigorous methodology to review current research from a computer science perspective. Therefore, our goal is four-fold: (i) Identify the main topics in the field of building; (ii) Identify the recent data-driven methods; (iii) Understand the BMS's underlying computing architecture (iv) Understand the features of BMS that contribute to the smartization of buildings. The result synthesizes our findings and provides research directions for further research. [ABSTRACT FROM AUTHOR]

Researchers have proposed different obfuscation transformations supported by numerous smartphone protection tools (obfuscators and deobfuscators). However, there is a need for a comprehensive study to empirically characterize these tools that belong to different categories of transformations. We propose a property-based framework to systematically classify twenty cutting-edge tools according to their features, analysis type, programming language support, licensing, applied obfuscation transformations, and general technical drawbacks. Our analysis predominantly reveals that very few tools work at the dynamic level, and most tools (which are static-based) work for Java or Java-based ecosystems (e.g., Android). The findings also show that the widespread adoption of renaming transformations is followed by formatting and code injection. In addition, this paper pinpoints the technical shortcomings of each tool; some of these drawbacks are common in static-based analyzers (e.g., resource consumption), and other drawbacks have negative effects on the experiment conducted by students (e.g., a third-party library involved). According to these critical limitations, we provide some timely recommendations for further research. This study can assist not only Android developers and researchers to improve the overall health of their apps but also the managers of computer science and cybersecurity academic programs to embed suitable obfuscation tools in their curricula. [ABSTRACT FROM AUTHOR]

Continuous improvement in the clinical performance of neonatal intensive care units (NICU) depends on the use of locally relevant, reliable data. However, neonatal databases with these characteristics are typically unavailable in NICUs using paper-based records, while in those using electronic records, the inaccuracy of data and the inability to customize commercial data systems limit their usability for quality improvement or research purposes. We describe the characteristics and uses of a simple, neonatologist-centered data system that has been successfully maintained for 30 years, with minimal resources and serving multiple purposes, including quality improvement, administrative, research support and educational functions. Structurally, our system comprises customized paper and electronic components, while key functional aspects include the attending-based recording of diagnoses, integration into clinical workflows, multilevel data accuracy and validation checks, and periodic reporting on both data quality and NICU performance results. We provide examples of data validation methods and trends observed over three decades, and discuss essential elements for the successful implementation of this system. This database is reliable and easily maintained; it can be developed from simple paper-based forms or used to supplement the functionality and end-user customizability of existing electronic medical records. This system should be readily adaptable to NICUs in either high- or limited-resource environments. [ABSTRACT FROM AUTHOR]

This paper investigates the evolving landscape of blockchain technology in renewable energy. The study, based on a Scopus database search on 21 February 2024, reveals a growing trend in scholarly output, predominantly in engineering, energy, and computer science. The diverse range of source types and global contributions, led by China, reflects the interdisciplinary nature of this field. This comprehensive review delves into 33 research papers, examining the integration of blockchain in renewable energy systems, encompassing decentralized power dispatching, certificate trading, alternative energy selection, and management in applications like intelligent transportation systems and microgrids. The papers employ theoretical concepts such as decentralized power dispatching models and permissioned blockchains, utilizing methodologies involving advanced algorithms, consensus mechanisms, and smart contracts to enhance efficiency, security, and transparency. The findings suggest that blockchain integration can reduce costs, increase renewable source utilization, and optimize energy management. Despite these advantages, challenges including uncertainties, privacy concerns, scalability issues, and energy consumption are identified, alongside legal and regulatory compliance and market acceptance hurdles. Overcoming resistance to change and building trust in blockchain-based systems are crucial for successful adoption, emphasizing the need for collaborative efforts among industry stakeholders, regulators, and technology developers to unlock the full potential of blockchains in renewable energy integration. [ABSTRACT FROM AUTHOR]

As the world grapples with the pressing challenge of environmental sustainability, the need for innovative solutions to combat air pollution has become paramount. Air pollution is a complex issue that necessitates real-time monitoring of pollution sources for effective mitigation. This paper explores the potential of swarm algorithms applied as a novel and efficient approach to address this critical environmental concern. Swarm algorithms offer a promising framework for coordinating fleets of drones to collaboratively monitor and analyze air pollution sources. The unique capabilities of drones, including their agility, accessibility, and versatility, make them ideal candidates for aerial data collection. When harnessed in a swarm, these drones can create a dynamic and adaptable network that provides a more comprehensive and fine-grained understanding of air pollution dynamics. This paper delves into the conceptual foundations of using swarm algorithms in drone-based air pollution monitoring. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive survey of knowledge graphs in education. It covers the patterns and prospects of research in this area. A total of 48 relevant publications between 2011 and 2023 were collected from the Web of Science, Scopus, and ProQuest for review. The findings reveal a sharp increase in recent years in the body of research into educational knowledge graphs which was mainly conducted from institutions in China. Most of the relevant research work adopted a quantitative method, such as performance evaluation, user surveys, and controlled experiments, to assess the effectiveness of knowledge graph approaches. The findings also suggest that knowledge graph approaches were primarily researched and implemented in higher education institutions, with a focus on computer science, mathematics, and engineering. The most frequently addressed objectives included enhancing knowledge representation and providing personal learning recommendations, and the most common applications were concept instruction and educational recommendations. Diverse data resources, such as course materials, student learning behaviours, and online encyclopaedia, were processed to implement knowledge graph approaches in different scenarios. Relevant technical means employed for the implementation of knowledge graphs dealt with the purposes of building knowledge ontology, achieving recommendations, and creating knowledge graphs. Various pedagogies such as personalised learning and collaborative learning are supported by the knowledge graph approaches. The findings also identified key limitations in the relevant work, including insufficient information for knowledge graph construction, difficulty in extending applications across subject areas, the restricted scale and scope of data resources, and the lack of comprehensive user feedback and evaluation processes. [ABSTRACT FROM AUTHOR]

This paper explores the computational challenges of clique transversal problems in d-degenerate graphs, which are commonly encountered across theoretical computer science and various network applications. We examine d-degenerate graphs to highlight their utility in representing sparse structures and assess several variations of clique transversal problems, including the b-fold and { b } -clique transversal problems, focusing on their computational complexities for different graph categories. Our analysis identifies that certain instances of these problems are polynomial-time solvable in specific graph classes, such as 1-degenerate or 2-degenerate graphs. However, for d-degenerate graphs where d ≥ 2 , these problems generally escalate to NP-completeness. We also explore the parameterized complexity, pinpointing specific conditions that render these problems fixed-parameter tractable. [ABSTRACT FROM AUTHOR]

Electricity theft comes with various disadvantages for power utilities, governments, businesses, and the general public. This continues despite the various solutions employed to detect and prevent it. Some of the disadvantages of electricity theft include revenue loss and load shedding, leading to a disruption in business operations. This study aimed to conduct a systematic literature review to identify what technology solutions have been offered to solve electricity theft and the effectiveness of those solutions by considering peer-reviewed empirical studies. The systematic literature review was undertaken following the guidelines for conducting a literature review in computer science to assess potential bias. A total of 11 journal articles published from 2012 to 2022 in SCOPUS, Science Direct, and Web of Science were analysed to reveal solutions, the type of theft addressed, and the success and limitations of the solutions. The findings show that the focus in research is channelled towards solving electricity theft in Smart Grids (SGs) and Advanced Metering Infrastructure (AMI); moreover, there is a neglect in the recent literature on finding solutions that can prevent electricity theft in countries that do not have SG and AMI installed. Although the results reported in this study are confined to the analysed research papers, the leading limitation in the selected studies, lack of real-life data for dishonest users. This study's contribution is to show what technology solutions are prevalent in solving electricity theft in recent years and the effectiveness of such solutions. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to examine the implementation of active methodologies in the teaching–learning process in computer science. To achieve this objective, a systematic review using the PRISMA method was performed; the search for articles was conducted through the Scopus and Web of Science databases and the scientific search engine Google Scholar. By establishing inclusion and exclusion criteria, 15 research papers were selected addressing the use of various active methodologies which have had a positive impact on students' learning processes. Among the principal active methodologies highlighted are problem-based learning, flipped classrooms, and gamification. The results of the review show how active methodologies promote significant learning, in addition to fostering more outstanding commitment, participation, and motivation on the students' part. It was observed that active methodologies contribute to the development of fundamental cognitive and socio-emotional skills for their professional growth. [ABSTRACT FROM AUTHOR]

Searching over a sorted list is a classical problem in computer science. Binary Search takes at most log 2 ⁡ n + 1 tries to find an item in a sorted list of size n. Interpolation Search achieves an average time complexity of O (log ⁡ log ⁡ n ) for uniformly distributed data. Hybrids of Binary Search and Interpolation Search are also available to handle data with unknown distributions. This paper analyzes the computation cost of these methods and shows that interpolation can significantly affect their performance—accordingly, a new method, Interpolation Once Binary Search (IOBS), is proposed. The experimental results show that IOBS outperforms the hybrids of Binary Search and Interpolation Search for nonuniformly distributed data. [ABSTRACT FROM AUTHOR]

Social media platforms have completely revolutionized human communication and social interactions. Their positive impacts are simply undeniable. What has also become undeniable is the prevalence of harmful antisocial behaviors on these platforms. Cyberbullying, misinformation, hate speech, radicalization, and extremist propaganda have caused significant harms to society and its most vulnerable populations. Thus, the social media forensics field was born to enable investigators and law enforcement agents to better investigate and prosecute these cybercrimes. This paper surveys the latest research works in the field to explore how artificial intelligence (AI) techniques are being utilized in social media forensics investigations. We examine how natural language processing can be used to identify extremist ideologies, detect online bullying, and analyze deceptive profiles. Additionally, we explore the literature on GNNs and how they are applied in social network modeling for forensic purposes. We conclude by discussing the key challenges in the field and suggest future research directions. [ABSTRACT FROM AUTHOR]

There is a nationwide effort to increase the representation and engagement of minoritized students in computer science education. Discourse about the barriers to diversity among computer science majors is often characterized by student pathologies and does not consider the impacts of classroom culture and instructor pedagogies. Amid the push for strategies to recruit and retain minoritized students in computer science, little has been done to transform curriculum and analyze faculty perspectives on curriculum and pedagogy as methods to increase students' access to the computer science major. This paper presents an example of curriculum redesign for an undergraduate introductory computer science course (ICS) that sought to address issues of inequitable representation by centering student identities and redistributing power in favor of students. The authors draw upon critical sociocultural and the 4Es heuristic for disciplinary literacy to reimagine the ICS course as a space that centers on the important roles of identity and power in solving for diversity in computer science education. We highlight for researchers and practitioners how our work may be used to disrupt meritocratic practices that alienate minoritized and economically disadvantaged students and to expand definitions of mastery and expertise in computer science education. [ABSTRACT FROM AUTHOR]

This research paper delves into the effectiveness and impact of behavior change techniques fostered by information technologies, particularly wearables and Internet of Things (IoT) devices, within the realms of engineering and computer science. By conducting a comprehensive review of the relevant literature sourced from the Scopus database, this study aims to elucidate the mechanisms and strategies employed by these technologies to facilitate behavior change and their potential benefits to individuals and society. Through statistical measurements and related works, our work explores the trends over a span of two decades, from 2000 to 2023, to understand the evolving landscape of behavior change techniques in wearable and IoT technologies. A specific focus is placed on a case study examining the application of behavior change techniques (BCTs) for monitoring vital signs using wearables, underscoring the relevance and urgency of further investigation in this critical intersection of technology and human behavior. The findings shed light on the promising role of wearables and IoT devices for promoting positive behavior modifications and improving individuals' overall well-being and highlighting the need for continued research and development in this area to harness the full potential of technology for societal benefit. [ABSTRACT FROM AUTHOR]

The ordered weighted averaging (OWA) operator was proposed by Yager back in 1988 and constitutes a parameterized family of aggregation functions between the minimum and the maximum. The purpose of this paper is to perform a bibliometric review of this aggregation operator during the last 35 years through the Web of Science (WoS) Core Collection database and the Visualization of Similarities (VOS) viewer software. The results show that the OWA operator is an increasingly popular aggregation operator, especially in Computer Science. The results also allow the assertion that Yager, as expected, is still the most influential and productive author. Moreover, the study reveals that institutions from over 80 countries have contributed to OWA research, highlighting the high presence of Chinese universities and the emergence of Pakistani ones. Other interesting findings are presented to provide a comprehensive and up-to-date analysis of the OWA operator literature. [ABSTRACT FROM AUTHOR]

Researchers in cognitive science have long been interested in modeling human perception using statistical methods. This requires maneuvers because these multiple dimensional data are always intertwined with complex inner structures. The previous studies in cognitive sciences commonly applied principal component analysis (PCA) to truncate data dimensions when dealing with data with multiple dimensions. This is not necessarily because of its merit in terms of mathematical algorithm, but partly because it is easy to conduct with commonly accessible statistical software. On the other hand, dimension reduction might not be the best analysis when modeling data with no more than 20 dimensions. Using state-of-the-art techniques, researchers in various research disciplines (e.g., computer vision) classified data with more than hundreds of dimensions with neural networks and revealed the inner structure of the data. Therefore, it might be more sophisticated to process human perception data directly with neural networks. In this paper, we introduce the multi-dimensional data analysis platform (MuDAP), a powerful toolbox for data analysis in cognitive science. It utilizes artificial intelligence as well as network analysis, an analysis method that takes advantage of data symmetry. With the graphic user interface, a researcher, with or without previous experience, could analyze multiple dimensional data with great ease. [ABSTRACT FROM AUTHOR]

Affective computing is a multidisciplinary field encompassing artificial intelligence, natural language processing, linguistics, computer science, and social sciences. This field aims to deepen our comprehension and capabilities by deploying inventive algorithms. This article presents a groundbreaking approach, the Cumulative Attribute-Weighted Graph Neural Network, which is innovatively designed to integrate trimodal textual, audio, and visual data from the two multimodal datasets. This method exemplifies its effectiveness in performing comprehensive multimodal sentiment analysis. Our methodology employs vocal inputs to generate speaker embeddings trimodal analysis. Using a weighted graph structure, our model facilitates the efficient integration of these diverse modalities. This approach underscores the interrelated aspects of various emotional indicators. The paper's significant contribution is underscored by its experimental results. Our novel algorithm achieved impressive performance metrics on the CMU-MOSI dataset, with an accuracy of 94% and precision, recall, and F1-scores above 92% for Negative, Neutral, and Positive emotion categories. Similarly, on the IEMOCAP dataset, the algorithm demonstrated its robustness with an overall accuracy of 93%, where exceptionally high precision and recall were noted in the Neutral and Positive categories. These results mark a notable advancement over existing state-of-the-art models, illustrating the potential of our approach in enhancing Sentiment Recognition through the synergistic use of trimodal data. This study's comprehensive analysis and significant results demonstrate the proposed algorithm's effectiveness in nuanced emotional state recognition and pave the way for future advancements in affective computing, emphasizing the value of integrating multimodal data for improved accuracy and robustness. [ABSTRACT FROM AUTHOR]

Community detection has been a subject of extensive research due to its broad applications across social media, computer science, biology, and complex systems. Modularity stands out as a predominant metric guiding community detection, with numerous algorithms aimed at maximizing modularity. However, modularity encounters a resolution limit problem when identifying small community structures. To tackle this challenge, this paper presents a novel approach by defining community structure information from the perspective of encoding edge information. This pioneering definition lays the foundation for the proposed fast community detection algorithm CSIM, boasting an average time complexity of only O (n log n) . Experimental results showcase that communities identified via the CSIM algorithm across various graph data types closely resemble ground truth community structures compared to those revealed via modularity-based algorithms. Furthermore, CSIM not only boasts lower time complexity than greedy algorithms optimizing community structure information but also achieves superior optimization results. Notably, in cyclic network graphs, CSIM surpasses modularity-based algorithms in effectively addressing the resolution limit problem. [ABSTRACT FROM AUTHOR]

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN. [ABSTRACT FROM AUTHOR]

Background: it is widely accepted that living in the digital transformation era, the need to develop and update new professional skills and tools in health sectors is crucially important. Therefore, this study aimed to explore the reliability and validity of the Digital Competence Indicators tool in assessing the digital skills of Greek health professionals. Methods: in this cross-sectional study, 494 health professionals, including doctors (175) and registered nurses (319) working in four Greek hospitals were recruited and willingly participated using a convenience-sampling method. The original framework of Digital Competence Indicators was translated from English to Greek based on guidelines for cross-cultural adaptation of questionnaires. The validity of the tool was explored using confirmatory factor analysis (CFA) to verify the fit of the model using inductive techniques. The instrument reliability was confirmed using Cronbach's alpha (α) and McDonald's Omega coefficients. Results: the reliability was estimated at 0.826 (Cronbach's-α) and 0.850 (McDonald's Omega-ω). The indicators of CFA were all calculated within an ideal range of acceptance. Specifically, the CFA comparative fit index produced the following adjustment indices: x2/df = 1.152 (p = 0.037), CFI = 0.997, Lewis index (TLI) = 0.966, and root mean square error of approximation (RMSEA) = 0.018. Conclusions: The present study demonstrated that the Digital Competence Indicator instrument has high reliability, internal consistency, and construct validity and, therefore, it is suitable for measuring digital skills of health professionals. [ABSTRACT FROM AUTHOR]

Cyber–physical systems (CPSs), which combine computer science, control systems, and physical elements, have become essential in modern industrial and societal contexts. However, their extensive integration presents increasing security challenges, particularly due to recurring cyber attacks. Therefore, it is crucial to explore CPS security control. In this review, we systematically examine the prevalent cyber attacks affecting CPSs, such as denial of service, false data injection, and replay attacks, explaining their impacts on CPSs' operation and integrity, as well as summarizing classic attack detection methods. Regarding CPSs' security control approaches, we comprehensively outline protective strategies and technologies, including event-triggered control, switching control, predictive control, and optimal control. These approaches aim to effectively counter various cyber threats and strengthen CPSs' security and resilience. Lastly, we anticipate future advancements in CPS security control, envisioning strategies to address emerging cyber risks and innovations in intelligent security control techniques. [ABSTRACT FROM AUTHOR]

3D printing has been adopted into routine use for certain medical applications, but more widespread usage has been hindered by, among other things, unclear legislation. We performed an analysis, using legal doctrinal study and legal informatics, of relevant EU legislation and case law in four issues relevant to medical 3D printing (excluding bioprinting or pharmacoprinting): pre-market approval, post-market liability, intellectual property rights, and data protection. Several gaps and uncertainties in the current legislation and interpretations were identified. In particular, we regard the current EU regulatory framework to be quite limiting and inflexible, exemplifying a cautionary approach common in EU law. Though the need to establish high safety standards in order to protect patients as a disadvantaged population is understood, both legal uncertainties and overregulation are seen as harmful to innovation. Hence, more adaptive legislation is called for to ensure continuous innovation efforts and enhanced patient outcomes. [ABSTRACT FROM AUTHOR]

Since the introduction of the Kolmogorov complexity of binary sequences in the 1960s, there have been significant advancements on the topic of complexity measures for randomness assessment, which are of fundamental importance in theoretical computer science and of practical interest in cryptography. This survey reviews notable research from the past four decades on the linear, quadratic and maximum-order complexities of pseudo-random sequences, and their relations with Lempel–Ziv complexity, expansion complexity, 2-adic complexity and correlation measures. [ABSTRACT FROM AUTHOR]

Gait and balance have emerged as a critical area of research in health technology. Gait and balance studies have been affected by the researchers' slow follow-up of research advances due to the absence of visual inspection of the study literature across decades. This study uses advanced search methods to analyse the literature on gait and balance in older adults from 1993 to 2022 in the Web of Science (WoS) database to gain a better understanding of the current status and trends in the field for the first time. The study analysed 4484 academic publications including journal articles and conference proceedings on gait and balance in older adults. Bibliometric analysis methods were applied to examine the publication year, number of publications, discipline distribution, journal distribution, research institutions, application fields, test methods, analysis theories, and influencing factors in the field of gait and balance. The results indicate that the publication of relevant research documents has been steadily increasing from 1993 to 2022. The United States (US) exhibits the highest number of publications with 1742 articles. The keyword "elderly person" exhibits a strong citation burst strength of 18.04, indicating a significant focus on research related to the health of older adults. With a burst factor of 20.46, Harvard University has made impressive strides in the subject. The University of Pittsburgh displayed high research skills in the area of gait and balance with a burst factor of 7.7 and a publication count of 103. The research on gait and balance mainly focuses on physical performance evaluation approaches, and the primary study methods include experimental investigations, computational modelling, and observational studies. The field of gait and balance research is increasingly intertwined with computer science and artificial intelligence (AI), paving the way for intelligent monitoring of gait and balance in the elderly. Moving forward, the future of gait and balance research is anticipated to highlight the importance of multidisciplinary collaboration, intelligence-driven approaches, and advanced visualization techniques. [ABSTRACT FROM AUTHOR]

This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive the evolution of automated sensing systems, overcoming traditional limitations in manual data collection. Environmental sensor networks (ESNs) address challenges in weather constraints and cost considerations, providing high-quality time-series data, although issues in interoperability, calibration, communication, and longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important role in environmental monitoring due to their versatility and cost-effectiveness. Despite challenges in regulatory compliance and technical limitations, UAVs offer detailed spatial and temporal information. Pollution monitoring faces challenges related to high costs and maintenance requirements, prompting the exploration of cost-efficient alternatives. Smart agriculture encounters hurdle in data integration, interoperability, device durability in adverse weather conditions, and cybersecurity threats, necessitating privacy-preserving techniques and federated learning approaches. Financial barriers, including hardware costs and ongoing maintenance, impede the widespread adoption of smart technology in agriculture. Integrating robotics, notably underwater vehicles, proves indispensable in various environmental monitoring applications, providing accurate data in challenging conditions. This review details the significant role of transfer learning and edge computing, which are integral components of robotics and wireless monitoring frameworks. These advancements aid in overcoming challenges in environmental sensing, underscoring the ongoing necessity for research and innovation to enhance monitoring solutions. Some state-of-the-art frameworks and datasets are analyzed to provide a comprehensive review on the basic steps involved in the automation of environmental sensing applications. [ABSTRACT FROM AUTHOR]

In academia, it is rare for an event or issue to foster the extensive participation of multiple disciplines. Research related to COVID-19 has undeniably yielded a wealth of valuable insights and impetus for the progress of interdisciplinary research, encompassing concepts, methodologies, intellectual approaches, theories, frameworks, data integration and analysis, and pertinent considerations. In the academic community, there is a widespread expectation that as science and technology continue to progress, the convergence of medicine with various other fields will gain momentum. Fields like computer science are anticipated to see expanded applications in domains such as medicine, vaccine research, disease diagnosis, and more. This study aims to examine interdisciplinary approaches in health-related research, particularly in the context of COVID-19. The goal is to analyze and comprehend the involvement and collaboration patterns of various disciplines in pandemic research, with a specific emphasis on the role and integration level of computer science. This study analyzed 240,509 COVID-19 related articles published from December 2019 to September 2022 using methods such as chord diagrams, modularity analysis, and eigenvector centrality analysis in Social Networking Analysis (SNA). The findings revealed an emerging trend of integration trend between Humanities & Social Sciences and Natural Sciences. Expectations that computer science would prominently feature in pandemic research during this technology-driven era haven't materialized. While it maintains links with engineering, it hasn't formed strong connections with medicine. This indicates a gap between computer science and core medical research in large-scale health crises, where COVID-19 research remains centered on medicine with varying interdisciplinary collaboration, and high-tech disciplines like computer science have not achieved their expected influence in these studies. [ABSTRACT FROM AUTHOR]

A critical worldwide problem is that ransomware cyberattacks can be costly to organizations. Moreover, accidental employee cybercrime risk can be challenging to prevent, even by leveraging advanced computer science techniques. This exploratory project used a novel cognitive computing design with detailed explanations of the action-research case-study methodology and customized machine learning (ML) techniques, supplemented by a workflow diagram. The ML techniques included language preprocessing, normalization, tokenization, keyword association analytics, learning tree analysis, credibility/reliability/validity checks, heatmaps, and scatter plots. The author analyzed over 8 GB of employee behavior big data from a multinational Fintech company global intranet. The five-factor personality theory (FFPT) from the psychology discipline was integrated into semi-supervised ML to classify retrospective employee behavior and then identify cybercrime risk. Higher levels of employee neuroticism were associated with a greater organizational cybercrime risk, corroborating the findings in empirical publications. In stark contrast to the literature, an openness to new experiences was inversely related to cybercrime risk. The other FFPT factors, conscientiousness, agreeableness, and extroversion, had no informative association with cybercrime risk. This study introduced an interdisciplinary paradigm shift for big data cognitive computing by illustrating how to integrate a proven scientific construct into ML—personality theory from the psychology discipline—to analyze human behavior using a retrospective big data collection approach that was asserted to be more efficient, reliable, and valid as compared to traditional methods like surveys or interviews. [ABSTRACT FROM AUTHOR]

The relationship between philosophy and science has always been complementary. Today, while science moves increasingly fast and philosophy shows some problems in catching up with it, it is not always possible to ignore such relationships, especially in some disciplines such as philosophy of mind, cognitive science, and neuroscience. However, the methodological procedures used to analyze these data are based on principles and assumptions that require a profound dialogue between philosophy and science. Following these ideas, this work aims to raise the problems that a classical connectionist theory can cause and problematize them in a cognitive framework, considering both philosophy and cognitive sciences but also the disciplines that are near to them, such as AI, computer sciences, and linguistics. For this reason, we embarked on an analysis of both the computational and theoretical problems that connectionism currently has. The second aim of this work is to advocate for collaboration between neuroscience and philosophy of mind because the promotion of deeper multidisciplinarity seems necessary in order to solve connectionism's problems. In fact, we believe that the problems that we detected can be solved by a thorough investigation at both a theoretical and an empirical level, and they do not represent an impasse but rather a starting point from which connectionism should learn and be updated while keeping its original and profoundly convincing core. [ABSTRACT FROM AUTHOR]

A new algorithm is presented for detecting the local weak reflection symmetries in raster images. It uses contours extracted from the segmented image. A convex hull is constructed on the contours, and so-called anchor points are placed on it. The bundles of symmetry line candidates are placed in these points. Each line splits the plane into two open half-planes and arranges the contours into three sets: the first contains the contours pierced by the considered line, while the second and the third include the contours located in one or the other half-plane. The contours are then checked for the reflection symmetry. This means looking for self-symmetries in the first set, and symmetric pairs with one contour in the second set and one contour in the third set. The line which is evaluated as the best symmetry line is selected. After that, the symmetric contours are removed from sets two and three. The remaining contours are then checked again for symmetry. A multi-branch tree representing the hierarchy of the detected local symmetries is the result of the algorithm. [ABSTRACT FROM AUTHOR]

Engineering education has continually evolved to embrace Project–Based Learning (PBL), a dynamic classroom approach emphasizing learning through engagement in real–world projects. The study conducts a comparative analysis of multidisciplinary Capstone Senior Design Projects across Electrical Engineering, Mechanical Engineering, and Computer Science at Texas A&M University at Texarkana. The research emphasizes understanding the dynamics of student collaboration within these disciplines and scrutinizes the impact of industry and faculty sponsorship on these projects. The methodology involves a comprehensive comparative analysis, employing diverse performance metrics to evaluate the effectiveness of different sponsorship models. This approach aims to uncover the influence of these models on project outcomes and students' educational experiences. The results reveal notable differences between industry–sponsored and faculty–sponsored projects. Industry sponsorship leads to higher performance in overall project execution and professional skills development. In contrast, faculty–sponsored projects are more effective in nurturing teamwork and communication abilities among students. The findings suggest that each sponsorship type presents unique benefits and challenges. Industry–sponsored projects provide valuable hands–on problem–solving experience, though they may suffer from inconsistencies in mentorship and varying expectations. Faculty–sponsored projects offer a more stable and consistent educational environment but might lag slightly in performance metrics. Integrating elements from both sponsorship models could provide students with a more balanced and enriching learning experience. [ABSTRACT FROM AUTHOR]

In computer network security, the escalating use of computer networks and the corresponding increase in cyberattacks have propelled Intrusion Detection Systems (IDSs) to the forefront of research in computer science. IDSs are a crucial security technology that diligently monitor network traffic and host activities to identify unauthorized or malicious behavior. This study develops highly accurate models for detecting a diverse range of cyberattacks using the fewest possible features, achieved via a meticulous selection of features. We chose 5, 9, and 10 features, respectively, using the Artificial Bee Colony (ABC), Flower Pollination Algorithm (FPA), and Ant Colony Optimization (ACO) feature-selection techniques. We successfully constructed different models with a remarkable detection accuracy of over 98.8% (approximately 99.0%) with Ant Colony Optimization (ACO), an accuracy of 98.7% with the Flower Pollination Algorithm (FPA), and an accuracy of 98.6% with the Artificial Bee Colony (ABC). Another achievement of this study is the minimum model building time achieved in intrusion detection, which was equal to 1 s using the Flower Pollination Algorithm (FPA), 2 s using the Artificial Bee Colony (ABC), and 3 s using Ant Colony Optimization (ACO). Our research leverages the comprehensive and up-to-date CSE-CIC-IDS2018 dataset and uses the preprocessing Discretize technique to discretize data. Furthermore, our research provides valuable recommendations to network administrators, aiding them in selecting appropriate machine learning algorithms tailored to specific requirements. [ABSTRACT FROM AUTHOR]

Designing automatic optimizing compilers is an advanced engineering process requiring a great deal of expertise, programming, testing, and experimentation. Maintaining the approach and adapting it to evolving libraries and environments is a time-consuming effort. In recent years, OpenAI has presented the GPT model, which is designed for many fields like computer science, image processing, linguistics, and medicine. It also supports automatic programming and translation between programming languages, as well as human languages. This article will verify the usability of the commonly known LLM model, GPT, for the non-trivial NPDP Nussinov's parallel algorithm code within the OpenMP standard to create a parallel equivalent of CUDA for NVIDIA graphics cards. The goal of this approach is to avoid creating any post-processing scripts and writing any lines of target code. To validate the output code, we compare the resulting arrays with the ones calculated by the optimized code for the CPU generated employing the polyhedral compilers. Finally, the code will be checked for scalability and performance. We will concentrate on assessing the capabilities of GPT, highlighting common challenges that can be refined during future learning processes. This will enhance code generation for various platforms by leveraging the outcomes from polyhedral optimizers. [ABSTRACT FROM AUTHOR]

Digital transformation in the public sector provides digital services to the citizens aiming at increasing their quality of life, as well as the transparency and accountability of a public administration. Since adaptation to the citizens changing needs is central for its success, Agile methodologies seem best suited for the software development of digital services in that area. However, as well documented by an attempt to use Scrum for an important Public Administration in Italy, substantial modifications to standard Agile were needed, giving rise to a new proposal called improved Agile (in short, iAgile). Another notable example is the Scrum@IMI method developed by the City of Barcelona for the deployment of its digital services. However, given the importance of digital transformation in the public sector and the scarcity of efforts (documented in the scholarly literature) to effectively bring Agile within it, a strategically important contribution that Computer Science can offer is a general paradigm describing how to tailor Agile methodologies and, in particular, Scrum, for such a specific context. Our proposal, called Scrum@PA, addresses this strategic need. Based on it, a public administration has a technically sound avenue to follow to adopt Scrum rather than a generic set of guidelines as in the current state of the art. We show the validity of our proposal by describing how the quite successful Scrum@IMI approach can be derived from Scrum@PA. Although iAgile can also be derived from our paradigm, we have chosen Scrum@IMI as a pilot example since it is publicly available on GitHub. [ABSTRACT FROM AUTHOR]

Starting from the contribution of such thinkers as the famous Giordano Bruno (1583) and the great mathematician and physicist Henri Poincaré (1889) and the surprising discovery of the meteorologist Edward Lorenz (1963), we consider the expansion of the mathematics of chaos in this article, paying attention to topology, qualitative geometry, and Catastrophe Theory, on the one hand, and addressing the possibilities derived from the new Computer Science as Quantum Algorithms and the advances in Artificial Intelligence, on the other. We especially highlight the section on computing chaos, which we consider to be new calculation and analysis instruments, such as machine learning and its algorithm called reservoir computing, through which we can know the dynamics of a chaotic system. With past data, with equations like Karamoto–Sivashinsky, one can improve predictions of the system eight times further ahead than in previous methods. Integrating the machine learning approach and traditional model-based prediction, one could obtain accurate predictions twelve Lyapunov times. As we know, in the framework of chaos theory, it is habitually accepted that the idea of long-term prediction seems impossible because we live under a veil of uncertainty. But with technological advances, the landscape begins to change, both in chaos theory and in its applications, especially in the field of economics, to which we devote particular attention, carrying out as an example the analysis of the evolution of the Madrid Stock Exchange in the 2006–2013 crisis. Above all this, a reflection of a general nature is necessary to enlighten us on the possibility of opening a new horizon. [ABSTRACT FROM AUTHOR]

This review underscores the escalating global research trend in this field since 2000. The primary scientific disciplines contributing to extensive research on smart water meters are engineering, computer science, and energy. In terms of countries, the analysis reveals that the United States, India, and China exhibit the highest scientific production. Concerning affiliations, prominent contributors include Griffith University, Politecnico di Milano, and the Università degli Studi di Salerno. Regarding worldwide research trends, an examination of distinct clusters defined by their principal keywords was conducted. The following clusters were identified in order of significance based on the number of publications: Urban Water Meters, IoT Connection, Communication and Security, Grid Management, Water Networks, Hot Water, Groundwater Monitoring, and Smart Irrigation. Of particular note is the growing use of machine learning applications, especially in the management of distribution networks. This trend opens up a promising avenue for addressing complex problems in real time. [ABSTRACT FROM AUTHOR]

Automated Machine Learning (AutoML) tools are revolutionizing the field of machine learning by significantly reducing the need for deep computer science expertise. Designed to make ML more accessible, they enable users to build high-performing models without extensive technical knowledge. This study delves into these tools in the context of time series analysis, which is essential for forecasting future trends from historical data. We evaluate three prominent AutoML tools—AutoGluon, Auto-Sklearn, and PyCaret—across various metrics, employing diverse datasets that include Bitcoin and COVID-19 data. The results reveal that the performance of each tool is highly dependent on the specific dataset and its ability to manage the complexities of time series data. This thorough investigation not only demonstrates the strengths and limitations of each AutoML tool but also highlights the criticality of dataset-specific considerations in time series analysis. Offering valuable insights for both practitioners and researchers, this study emphasizes the ongoing need for research and development in this specialized area. It aims to serve as a reference for organizations dealing with time series datasets and a guiding framework for future academic research in enhancing the application of AutoML tools for time series forecasting and analysis. [ABSTRACT FROM AUTHOR]

This article delves into the issues of equity, diversity, and inclusiveness (EDI) in the engineering disciplines in Canada and Spain and presents the challenges faced by underrepresented individuals and ways to promote an inclusive and diverse environment. Two strategic lines are identified: (a) facilitating university education access to underrepresented and minority groups and (b) guiding such students during university training to set them up for successful future careers. Accordingly, this article shows how the strategies mentioned above are implemented in some selected Canadian and Spanish universities, clearly distinguishing the approach taken in the two countries. In Canada, there is a more decentralized approach to addressing EDI issues, wherein the universities devise their agendas independently. In Spain, on the other hand, there is a stronger and more direct involvement of the government to ensure a comprehensive, system-wide approach to tackling EDI issues in academia. This article helps education policymakers to devise and implement pragmatic strategies for achieving EDI and the relevant UN-defined sustainable development goals. [ABSTRACT FROM AUTHOR]

This document discusses the integration of advanced technologies and algorithms to enhance the performance, efficiency, and reliability of network systems in various application areas such as smart cities, smart homes, industrial automation, and transportation systems. The special issue focuses on future complex cyber-physical networks (CCPNs) and smart sensors, with a particular emphasis on control and optimization strategies. The document provides a summary of 10 original research articles and 1 review article that were accepted for publication in the special issue, covering topics such as remote sensing image recognition, fusion filtering, transmission line planning, optimal filtering for sensor networks, traversal exploration and path coverage, map-merging for UAVs, area coverage control for robot networks, adaptive deskewing algorithm for document images, intelligent positioning for climbing robots, and pneumatic control of valve position. The review article discusses recent developments and methodologies for the secure control of complex cyber-physical networks. The authors express their gratitude to the authors and reviewers for their contributions and acknowledge the support of the Editor-in-Chief and the Editorial Office of Sensors. [Extracted from the article]