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The document "Featured Papers in Computer Methods in Biomedicine" from the journal Bioengineering (Basel) highlights seven research papers showcasing the intersection of computer science and biomedicine. The papers cover topics such as predicting low bone mineral density in older women, improving ML models for disease prediction, creating patient-specific anatomical reconstructions, detecting atrial fibrillation, classifying Parkinson's disease patients, analyzing EEG data for brain connectivity, and exploring EEG-based brain-machine interfaces for older adults. The document emphasizes the potential of computational methods to revolutionize healthcare through personalized treatments, improved diagnostics, and enhanced patient outcomes. [Extracted from the article]

Misinformation is a complex and urgent sociotechnical problem that requires meaningful governance, in addition to technical efforts aimed at detection or classification and intervention or literacy efforts aimed at promoting awareness and identification. This review draws on interdisciplinary literature—spanning information science, computer science, management, law, political science, public policy, journalism, communications, psychology, and sociology—to deliver an adaptable, descriptive governance model synthesized from past scholarship on the governance of misinformation. Crossing disciplines and contexts of study and cases, we characterize: the complexity and impact of misinformation as a governance challenge, what has been managed and governed relative to misinformation, the institutional structure of different governance parameters, and empirically identified sources of success and failure in different governance models. Our approach to support this review is based on systematic, structured literature review methods to synthesize and compare insights drawn from conceptual, qualitative, and quantitative empirical works published in or translated into English from 1991 to the present. This review contributes a model for misinformation governance research, an agenda for future research, and recommendations for contextually‐responsive and holistic governance. [ABSTRACT FROM AUTHOR]

Reflective practice is an essential catalyst through which the benefits of teaching and learning can be reaped. Through it, weaknesses and strengths can be identified in a way that helps raise the level of addressing challenges that may arise as well as overcome them. This paper presents the critical reflective practices among computer science secondary school teachers from their point of view in Riyadh, Saudi Arabia. To this extent, the study aims to determine the degree of critical reflective practices among computer science secondary school teachers in Riyadh from their perspective. The paper also seeks to investigate the effects of variables such as gender, qualifications and experience on the perceptions of the aforementioned teachers, towards the critical reflective practices among computer science secondary school teachers. The study tool is a questionnaire which consisted of two dimensions and was distributed to a population of 739 participants. From this, the study sample comprised (223) computer science teachers working in secondary school in Riyadh. The findings revealed that there is no significant difference in the estimation degree concerning the critical reflective practices due to the gender. From the results, it was also established that there is no significant difference in the degree of estimation in relation to the critical reflective practices due to educational qualification variables. On the contrary however, there is a significant difference in the degree of estimation in regard to the critical reflective practices due to the years of experience variable. These differences were evident in a group of those with more than 10 years of experience. The other findings produced by the study highlight that the participants are in agreement about the importance of critical reflective practices. The degree of reflective practice, which is from the participants' point of view, is considered to be of a high value. The majority of the subjects opted to agree with the practice of reflection after a training session. It was determined from the results that some of the most common strategies favored by practitioners involved the communal practice of mind reflection with individuals from outside the school.

Pre-evaluating scientific paper quality aids in alleviating peer review pressure and fostering scientific advancement. Although prior studies have identified numerous quality-related features, their effectiveness and representativeness of paper content remain to be comprehensively investigated. Addressing this issue, we propose a content-based interpretable method for pre-evaluating the quality of scientific papers. Firstly, we define quality attributes of computer science (CS) papers as integrity , clarity , novelty , and significance , based on peer review criteria from 11 top-tier CS conferences. We formulate the problem as two classification tasks: Accepted/Disputed/Rejected (ADR) and Accepted/Rejected (AR). Subsequently, we construct fine-grained features from metadata and knowledge entity networks, including text structure, readability, references, citations, semantic novelty, and network structure. We empirically evaluate our method using the ICLR paper dataset, achieving optimal performance with the Random Forest model, yielding F1 scores of 0.715 and 0.762 for the two tasks, respectively. Through feature analysis and case studies employing SHAP interpretable methods, we demonstrate that the proposed features enhance the performance of machine learning models in scientific paper quality evaluation, offering interpretable evidence for model decisions. • Define four criteria for quality evaluation of scientific papers: integrity, clarity, novelty, and significance. • Propose a framework for quality evaluation of scientific papers based on coarse features and knowledge entity network. • An effective algorithm for measuring the novelty and significance of scientific papers based on knowledge entity networks. • Create and release a rigorous dataset, which could serve as the gold standard for quality evaluation of scientific papers. • Conduct extensive experiments to validate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

In this paper, we continue describing the project and the experimentation of "Ricerca Operativa Applicazioni Reali" (ROAR; in English, Real Applications of Operations Research), a three-year project for higher secondary schools, introduced. ROAR is composed of three teaching units, addressed to Grades 10, 11, and 12, respectively, having the main aim to improve students' interest, motivation, and skills related to Science, Technology, Engineering, and Mathematics disciplines by integrating mathematics and computer science through operations research. In a previous paper, we reported on the design and implementation of the first unit, started in Spring 2021 at the scientific high school IIS Antonietti in Iseo (Brescia, Italy), in a Grade-10 class. Here, we focus on the second unit, carried out in Winter/Spring 2022 with the same students, now in a Grade-11 class. In particular, we describe objectives, prerequisites, topics and methods, the organization of the lectures, digital technologies used, and a challenging final project. Moreover, we analyze the feedback from students and teachers involved in the experimentation.

In our post-pandemic world, where the majority of higher education institutions have transitioned back to in-person classes, this paper argues that we must not return to pre-COVID teaching practices. Instead, we have the obligation and opportunity to create an educational experience and environment that better facilitates learning and instruction. This paper presents post-COVID best practices for employing technology in higher education based on an original survey and follow-up interviews of seventeen computing instructors at our institution. After a literature review, we describe four general categories of practices that enhance the post-COVID classroom: online student activities, digital instructor notes, remote classroom participation and collaboration, and a paperless classroom. For each of these categories, we provide vignettes to illustrate scope and intent. We also offer recommendations for addressing digital dishonesty, required infrastructure, institutional support, and being prepared to seamlessly return to a blended or fully remote environment in the event of another crisis. Finally, we identify additional emerging technological challenges and opportunities that require further effort. Overall, this paper emphasizes the need for a shift towards improved practices in the classroom rather than just a return to pre-pandemic norms. We believe implementing these recommendations will result in a more flexible, accessible, and robust post-COVID educational experience.

The use of digital media among student community has increased their level of distraction during classroom teaching. Capturing their attention in classrooms, has become a cumbersome task for teaching faculty. Therefore, most of the academic institutions have resorted to the use of activity-based learning for science and engineering students that were originally used for teaching school students. This paper presents a comparative study on the effectiveness of different teaching learning methods applied to three groups of engineering students in Computer Science and Bio-Technology, evaluated by conducting tests and feedbacks. Also, a comparative study was performed to assess the effectiveness of conventional and modern evaluation tools. The results show that the subject understanding level of the students increased by 67-76% when any of the activity-based learning method was used. The study confirms the potential use of activity-based method for teaching learning and ICT tools for performance evaluation, for engineering students.

The 33rd International Workshop On Combinatorial Algorithms (IWOCA 2022) was held at the University of Trier in Germany. This workshop covers a wide range of topics related to combinatorial algorithms. The special issue of the journal Algorithmica contains extended versions of selected papers from IWOCA 2022, which were nominated by the Program Committee and underwent a rigorous reviewing process. The special issue includes nine papers on various topics such as perfect matchings, algorithmic questions, and winner determination algorithms. One paper was chosen as the Best Paper of IWOCA 2022 and another as the Best Student Paper. The special issue is recommended for readers interested in exploring more papers from IWOCA 2022. [Extracted from the article]

Cultural heritage is no longer something that can only be experienced in a museum exhibition. Digital tools have facilitated the distribution of material relating to artefacts, both in its representation and in presenting its context. This paper describes how digital modelling techniques can be synthesised with 3D scanning to digitally restore artefacts and create authentic replicas of their original states. The digital artefacts can then be used to assist the process of interpreting these artefacts in diverse forms, both in the museum and outside the museum. The study looks at Pictish sculpture as a case-study, restoring 3D models of two stones, and creating varying opportunities for their interpretation. As part of this study, new interactive tools, a virtual reality environment, and a virtual tour are built to assist immersive interpretation of the Pictish sculpture. The application of these digitised objects serves as an opportunity for informal learning. These applications were evaluated during a drop-in session. Findings show that all participants enjoyed the immersive mode of learning with 89% also showing a willingness to learn more about the topic.

Science as a whole is organized into broad fields, and as a consequence, research, resources, students, etc., are also classified, assigned, or invited following a similar structure. Some fields have been established for centuries, and some others are just flourishing. Funding, staff, etc., to support fields are offered if there is some activity on it, commonly measured in terms of the number of published scientific papers. How to find them? There exist well-respected listings where scientific journals are ascribed to one or more knowledge fields. Such lists are human-made, but the complexity begins when a field covers more than one area of knowledge. How to discern if a particular paper is devoted to a field not considered in such lists? In this work, we propose a methodology able to classify the universe of papers into two classes; those belonging to the field of interest, and those that do not. This proposed procedure learns from the title and abstract of papers published in monothematic or "pure" journals. Provided that such journals exist, the procedure could be applied to any field of knowledge. We tested the process with Geographic Information Science. The field has contacts with Computer Science, Mathematics, Cartography, and others, a fact which makes the task very difficult. We also tested our procedure and analyzed its results with three different criteria, illustrating its power and capabilities. Interesting findings were found, where our proposed solution reached similar results as human taggers also similar results compared with state-of-the-art related work. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has emerged as an innovative technology with the potential to revolutionize various industries including libraries and information science. Academic libraries are increasingly adopting artificial intelligence (AI) to enhance services, improve efficiency, and enhance user experience. This study utilizes a bibliometric approach to comprehensively analyze current research on AI in academic libraries (AI in ALs). This study employed bibliometric indicators to identify key trends, patterns, and research gaps in the existing literature. A comprehensive dataset of 373 research papers on AI in ALs published between 2002 and 2022 was collected and analyzed using the Scopus database. Various bibliometric tools, such as Biblioshiny, VOSviewer, and BibExcel, have enhanced this analysis. The findings of this study provide important insights. By 2022, there were 64 publications, constituting 17.16% of the total corpus, accompanied by 65 citations. In contrast, 2019 witnessed only 33 publications yet accumulated a substantial number of citations, amounting to 294, representing 8.85% of the overall citations. Conference papers exhibited the highest frequency among different publication types, with 165 publications, whereas journal articles had the highest citation count, accumulating 217 citations. Geographically, China emerged as the leading contributor with 119 publications, and Wuhan University stood out as the most prominent affiliation. Notably, the "Lecture Notes in Computer Science" series emerged as the most prolific source title, publishing 15 articles, of which eight were cited. The authors Wang J., Wang C., and Wang X. from China demonstrated significant contributions, consistently publishing four papers annually from 2010 to 2022. [ABSTRACT FROM AUTHOR]

Purpose: Supporting community college transfer students represents a critical strategy for broadening participation in STEM. In addition to being a racially diverse group, students who pursue STEM degrees by way of community college report frequent interests in graduate study and academic careers. Thus, supporting and expanding transfer students' PhD interests can help to diversify the STEM professoriate. This study aims to identify the experiences that predict PhD interests among students who transferred into the computer science major from a community college. Design/methodology/approach: Relying on longitudinal survey data from over 150 community college transfer students throughout their first year at their receiving four-year university, we used regression analysis to identify the post-transfer college experiences that predict early interest in PhDs. Findings: We found that receiving information about PhDs from a professor strongly predicted PhD interest among transfer students. Relationships with other variables indicate that the provision of information about graduate school was more likely to occur for students who participated in undergraduate research experiences than for those participating in internships. Descriptive data document inequities in who has access to these types of experiences. Originality/value: This paper provides new insight into how STEM departments can develop targeted efforts to ensure that information about PhD training is equitably available to all transfer students. Working to ensure that faculty equitably communicate with students about PhD opportunities may go a long way in countering potential deterrents among transfer students who may be interested in such pathways.

In this paper we discuss the types of knowledge used by six middle school students as they engaged with a debugging task designed to integrate ideas from computer science, mathematics and science. Our findings show that the computational thinking practice of debugging is a rich source of opportunities to integrate these different disciplines. The analysis illustrates how the types of knowledge the students did and did not use at each step of the debugging process were related to their ability to succeed at each step. Our work contributes to theory and practice by uncovering implications for studying debugging through two refined frameworks and for designing debugging tasks to support transdisciplinary learning.

Recently, researchers have proposed to leverage technology-supported data (log files) to investigate temporal and sequential patterns of interaction behaviors in learning processes. There are two major challenges to be addressed: clarifying the positioning of interaction levels and identifying the evolution of the interaction action patterns in learning processes, particularly for students with differing achievements. This paper explores the use of sequential pattern mining to address the evolution of student action patterns in Massive Private Online Courses (MPOCs) and compare these patterns between different achievement groups. The study was conducted with first-year undergraduate computer science students enrolled in a computer application course at a traditional open university in one of the Chinese provinces (N = 1375). The results showed the development of various action patterns in each phase of the course and the distinct action patterns for high-achieving and low-achieving students. The findings of study provide a new perspective for instructors and students to understand interaction patterns at the fine-grained level, and can help instructional designers develop learner-cantered courses and platforms to improve online learning.

Learning styles are a topic of interest in educational research about how individuals acquire and process information in offline or online learning. Identification of learning styles in the online learning environment is challenging. The existing approaches for the identification of learning styles are limited. This study aims to review the many learning styles characterized by various classification approaches toward the automatic prediction of learning styles from learning management system (LMS) datasets. A systematic literature review (SLR) was conducted to select and analyze the most pertinent and significant papers for automatically predicting learning styles. Fifty-two research papers were published between 2015-2023. This research divides analysis into five categories: the classification of learning style models, the collection of the collected dataset, learning styles based on the curriculum, research objectives related to learning styles, and the comprehensive analysis of learning styles. This study found that learning style research encompasses diverse theories, models, and algorithms to understand individual learning preferences. Statistical analysis, explicit data collection, and the Felder-Silverman model are prevalent in research, highlighting the significance of algorithm improvement for optimizing learning processes, particularly in computer science. The categorization and understanding of various methods offer valuable insights for enhancing learning experiences in the future. [ABSTRACT FROM AUTHOR]

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]

The superiority of BWM over other weighting methods for obtaining the weight values of the attributes is that it achieves high-confidence results with a reasonable number of pairwise comparisons. Although the best-worst method (BWM) is a well-known multi-criteria decision-making (MCDM) method that has been successfully utilized in almost all scientific areas to solve challenging real-life problems, no research has comprehensively examined the state-of-the-art in this regard. The present study depicts a detailed overview of publications concerned with BWM during the period 2015–2022. Based on the information obtained from the Scopus database, this work presents a big picture of current research on BWM. In other words, this paper analyzes the existing literature about BWM and identifies thematic contexts, application areas, emerging trends, and remaining research gaps to shed light on future research agendas aligning with those gaps. Further, the most recent BWM research is analyzed in the top ten scientific areas, from engineering to materials science. “Engineering”, “computer science”, and “business, management, and accounting” are the hottest fields of BWM research. China is the most active country regarding “engineering” and “computer science”, whereas India is the leader in “business, management, and accounting”. The study also reveals that there are still many research gaps in BWM research. The big picture taken in this study will not only showcase the current situation of BWM research but will also positively impact the direction and quality of new research. [ABSTRACT FROM AUTHOR]

This paper argues that ethical AI cannot be fostered in a vacuum, challenging the perspective that AI ethics research should be isolated from technological advancements and industry collaborations. It refutes the argument presented by Gerdes (Discov Artif Intell. 2022;2(25)), which suggests that industry involvement inherently undermines the integrity of AI ethics research. Through an exploration of historical and contemporary examples of successful academia-industry collaborations, the paper advocates for a synergistic approach that harnesses industry resources and insights to advance ethical AI development. Emphasising the importance of diverse funding, the value of industry insights, and the impracticality of separating AI ethics from computer science, the paper contends that a collaborative, transparent, and inclusive model of AI ethics research is essential for developing practical, relevant, and ethically sound AI technologies aligned with societal values and norms. [ABSTRACT FROM AUTHOR]

System Software is a fundamental core course for undergraduate students of Computer Science and Engineering. The traditional approach to teaching the System Software course within the School of Computer Science and Engineering lacked a meaningful connection to real-world machine architectures, leading to disinterest and reduced engagement among undergraduate students. This paper introduces an innovative teaching method designed to empower students to grasp the system programs of real systems effectively. Our approach involves effortlessly integrating the delivery of system software content with the Atmel AVR ATmega32 real-time machine, which students have previously encountered in a prior semester. Moreover, this paper provides a detailed examination of the use of a hypothetical machine in traditional teaching methodologies. While this method allowed for a more in-depth exploration of system software concepts, it struggled to establish a practical link to real machine. The novel teaching approach employed in this study adopts a unique method that links all the system software concepts with the practical system program of a real-time machine. This paper also explains how the advances in Technology has played a crucial role in considering real-time machines as examples. And it also discusses the limitations of teaching concepts using only hypothetical machine and concise overview of the chosen real-time machine is provided, followed by the observation of enhanced knowledge of system software concepts through its integration. To measure the effectiveness of the proposed methodology, we also gathered valuable feedback from the students. The course result analysis shows substantial improvement in understanding the concepts, performance and lifelong learning of the students. [ABSTRACT FROM AUTHOR]

The main scientific results of the 16th International Conference on Pattern Recognition and Information Processing (PRIP-2023), Minsk, Republic of Belarus, October 2023, are reviewed and analyzed. The history of this series of conferences is outlined, and its significant role in the development of the theory and practice of image analysis, pattern recognition, and artificial intelligence is indicated. A list of articles in the special issue is provided, prepared from reports selected by the PRIP-2023 Program Committee. [ABSTRACT FROM AUTHOR]

By utilising the National Graduate Survey (NGS) – class of 2005 and 2009/10 – this paper examines the effects of fields of study on the time it takes to find full-time employment that lasts at least six months among graduates of Canadian Universities. Within cohorts, the results suggest considerable differences in the duration to first job after graduation for various fields of study – with 'Agriculture, natural resources and conservation', 'Health and related fields', and STEM fields like Math, Computer Science, and Engineering landing jobs the quickest, respectively. In contrast, the graduates of 'Humanities' and 'Education' had the longest duration of finding employment. The results also show large differences between cohorts, with the 2009/10 cohort taking much longer to find employment. Lastly, this paper did not find clear evidence that the effects of fields of study on the duration to exiting unemployment changed across the cohorts. [ABSTRACT FROM AUTHOR]

Conceptual metaphor theory has been criticized due to its emphasis on concepts instead of words and its top-down direction of analysis. In response to these criticisms, this paper employs a new strategy, utilizing established mathematical modeling methods to allow a systematic, quantitative analysis of the entire dataset produced by the Mapping Metaphor project at the University of Glasgow. This dataset consists of 9609 words performing 18916 metaphorical mappings between 414 domains. The data is represented as a network consisting of 414 nodes, the domains, connected by shared words. Words are represented by groups of directed mappings between all domains in which they occur. This is made possible by the use of a directed hypergraph representation, a tool commonly used in discrete mathematics and various areas of computer science but not previously applied to the metaphorical meanings of words. Examining the dataset as a whole, rather than focusing on individual words or metaphors, allows global patterns of behavior to emerge from the data without pre-filtering or selection by the authors. Outcomes of the analysis relating to the distributions of source and target domains within the network, the growth mechanisms at work in the spread of metaphorical meanings and how these relate to existing concepts in CMT are discussed. [ABSTRACT FROM AUTHOR]

The article focuses on the importance of clinical informatics in neonatal care, highlighting its potential to provide critical resources for clinicians. Topics include the specialized data needed for neonatal care, the challenges in transitioning from paper to electronic health records, and the impact of informatics on real-time patient management and research.

This document is a foreword for a special issue of the journal Microsystem Technologies. The special issue features selected papers from the 11th International Conference on Computing, Communication and Sensor Network (CCSN) held in September 2022 at Utkal University in Bhubaneswar, Odisha, India. The conference covered various interdisciplinary areas such as computing, communication, sensor networks, and circuit designs. The papers included in this special issue have undergone a rigorous review process and were selected for publication. The foreword expresses the hope that readers will find the papers informative. [Extracted from the article]

The article discusses the intersection of machine learning and the law, highlighting how machine learning is revolutionizing social life and impacting legal processes. It explores various research topics, such as law as data, predictive policing, and machine learning decision-aids for judicial decision-making. The article also examines the advantages and limitations of using machine learning in legal research, including the reproducibility of results and the potential for bias in algorithms. Overall, the symposium aimed to foster interdisciplinary exchange and explore the ways in which machine learning can contribute to legal scholarship and empirical legal research. [Extracted from the article]

This paper discusses the application of concepts from computer science in healthcare management systems and the strengthening of data security measures, the effectiveness of patient observation, and development of recommendations for clinical practice. The present work also illustrates that healthcare services can be delivered efficiently with the help of improved algorithms like the Harris Hawks optimization algorithm and blockchain technology. From the experiments, there is an impressive improvement on the accuracy of monitoring Diabetic patients' systems by 30/40% and data security breach which has also reduced by 40/40%. It also pointed out that the implementation of business intelligence systems which enhanced the operational efficiency by 25% because of efficient data analysts. The presented results underline the importance of the focus on the interdisciplinary strategies for solving the modern health care issues. In addition to highlighting the importance of computer science innovations in the area of healthcare, this study offers suggestions for further improvements in patient outcomes. The findings of the study indicate that it is possible for the health care industry to foster these technologies for better and more efficiency, security and responsiveness, to the advantage of the patient as well as the health care provider. [ABSTRACT FROM AUTHOR]

With the help of clustering analysis tools in the field of computer science, this paper conducts a visual study on the structure of contemporary large-scale public buildings. It summarizes the expression strategies of large-scale public building structures and provides references for research and practice in the new era. Based on the visualization analysis results of VOS viewer, this paper conducts analysis from three aspects: first, by reflecting on the development of traditional wood structure technology, it reveals the lag in the development of contemporary architectural structures; second, based on the new temporal context, it distinguishes the cognitive paradigm of contemporary architectural structures; third, it clarifies the inherent requirements of architectural structure representation and explores the possible answers to the expression of architectural structures in the new era. [ABSTRACT FROM AUTHOR]

Standards are ubiquitous in contemporary society and play a clear role in technological development, organizational functioning, and business success. Standards are very diverse and often boundary crossing in terms of stakeholders and impact, but are such diversity and range reflected by academic studies? We take stock of standardization research over the past decade, considering the full interdisciplinary breadth of this growing field. We use bibliometrics and network analysis to map emergent trends, and conduct an in-depth review of the literature. In doing so, we find thatmanagement science, along with economics, is at the core of work on standardization, bridging academic disciplines, and leading theoretical development. Technical disciplines, such as engineering and computer science, supply the largest body of literature, but rarely cross disciplinary boundaries and remain rather isolated. Building on our review, we discuss current debates and controversies and distill four interpretative perspectives on the recent and current developments of standardization research. Finally, we propose a research agenda for standardization research and practice for the years to come. [ABSTRACT FROM AUTHOR]

Computational sustainability has become a key topic bridging environmental science, computer science, and sustainability research. This literature review explores the readiness dimensions necessary for advancing computational sustainability projects and examines how computational tools are applied to address sustainability challenges across various domains. This study examines 33 case studies and 56 empirical research papers that demonstrate the use of computational tools to improve readiness in a variety of scenarios, including technology readiness, faculty readiness, teaching readiness, e-learning readiness, and green education. By extensively reviewing previous material, this synthesis identifies recurring themes and emerging trends in readiness assessment across many sustainability sectors, case studies, and empirical research. The study blends several views and approaches, resulting in a better understanding of how readiness aspects might aid in the application and efficacy of computational tools in sustainability research. The literature synthesis highlights the dimensions of readiness in this study, ranking technological knowledge (35%), content knowledge (25%), teaching strategies (20%), training (15%), and equipment/software (5%) based on their significance in determining how well societies are prepared to effectively adopt sustainable practices. This literature synthesis explores readiness factors in computational sustainability, highlighting recent advancements and trends. The review focused on English-language publications from 2018 to 2024, with additional research from 2010 to 2017. This comprehensive analysis of faculty readiness for computational sustainability aims to enhance its effectiveness, paving the way for broader studies that benefit researchers, faculties, students, policymakers, and society. [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]

The increasing demand for mobility in our society poses various challenges to traffic engineering, computer science in general, and artificial intelligence in particular. Increasing the capacity of road networks is not always possible, thus a more efficient use of the available transportation infrastructure is required. Another issue is that many problems in traffic management and control are inherently decentralized and/or require adaptation to the traffic situation. Hence, there is a close relationship to multiagent reinforcement learning. However, using reinforcement learning poses the challenge that the state space is normally large and continuous, thus it is necessary to find appropriate schemes to deal with discretization of the state space. To address these issues, a multiagent system with agents learning independently via a learning algorithm was proposed, which is based on estimating Q-values from k-nearest neighbors. In the present paper, we extend this approach and include transfer of experiences among the agents, especially when an agent does not have a good set of k experiences. We deal with traffic signal control, running experiments on a traffic network in which we vary the traffic situation along time, and compare our approach to two baselines (one involving reinforcement learning and one based on fixed times). Our results show that the extended method pays off when an agent returns to an already experienced traffic situation. [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 editorial introduces the special issue, Resources for Undergraduate Cryptology. We begin by describing possible roles for cryptology in the undergraduate mathematics curriculum together with a brief overview of the subject. We conclude with a brief preview of each paper included in this issue. [ABSTRACT FROM AUTHOR]

A drug molecule is a substance that changes an organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications (which describes the disease, condition or symptoms for which the drug is used), or vice versa. Addressing this challenge could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI. [ABSTRACT FROM AUTHOR]

In this paper we study the classical problem of throughput maximization. In this problem we have a collection J of n jobs, each having a release time r j , deadline d j , and processing time p j . They have to be scheduled non-preemptively on m identical parallel machines. The goal is to find a schedule which maximizes the number of jobs scheduled entirely in their [ r j , d j ] window. This problem has been studied extensively (even for the case of m = 1 ). Several special cases of the problem remain open. Bar-Noy et al. (Proceedings of the Thirty-First Annual ACM Symposium on Theory of Computing, May 1–4, 1999, Atlanta, Georgia, USA, pp. 622–631. ACM, 1999, https://doi.org/10.1145/301250.301420) presented an algorithm with ratio 1 - 1 / (1 + 1 / m) m for m machines, which approaches 1 - 1 / e as m increases. For m = 1 , Chuzhoy et al. (42nd Annual Symposium on Foundations of Computer Science (FOCS) 2001, 14–17 October 2001, Las Vegas, Nevada, USA, pp. 348–356. IEEE Computer Society, 2001) presented an algorithm with approximation with ratio 1 - 1 e - ε (for any ε > 0 ). Recently Im et al. (SIAM J Discrete Math 34(3):1649–1669, 2020) presented an algorithm with ratio 1 - 1 / e + ε 0 for some absolute constant ε 0 > 0 for any fixed m. They also presented an algorithm with ratio 1 - O (log m / m) - ε for general m which approaches 1 as m grows. The approximability of the problem for m = O (1) remains a major open question. Even for the case of m = 1 and c = O (1) distinct processing times the problem is open (Sgall in: Algorithms - ESA 2012 - 20th Annual European Symposium, Ljubljana, Slovenia, September 10–12, 2012. Proceedings, pp 2–11, 2012). In this paper we study the case of m = O (1) and show that if there are c distinct processing times, i.e. p j 's come from a set of size c, then there is a randomized (1 - ε) -approximation that runs in time O (n m c 7 ε - 6 log T) , where T is the largest deadline. Therefore, for constant m and constant c this yields a PTAS. Our algorithm is based on proving structural properties for a near optimum solution that allows one to use a dynamic programming with pruning. [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]

In this paper, we present and discuss a promising research avenue, that is the use of graph-based models and software for prosopographical data sets. Our case study will be constituted by Romans 1by1 (http://romans1by1.com/), a digital-born prosopography focusing on people attested in classical era inscriptions; it presently hosts approximately 18,000 open access persons files. The project aimed at employing new techniques and methodologies that come from other fields (i.e. computer science), in order to approach the study of ancient population in an innovative way, to ease the research, and to create an open-access tool, available for the academic community. In the scope of this paper, we use Romans1by1 as an example to explore the perspectives of ingesting the information from a prosopographical relational database into a graph database. Graph based modelling was employed to reveal data and details on the lives of the 'ordinary' people who lived in the Roman Empire. [ABSTRACT FROM AUTHOR]

The problem of finding a maximum size matching in a graph (known as the maximum matching problem) is one of the most classical problems in computer science. Despite a significant body of work dedicated to the study of this problem in the data stream model, the state-of-the-art single-pass semi-streaming algorithm for it is still a simple greedy algorithm that computes a maximal matching, and this way obtains 1 / 2 -approximation. Some previous works described two/three-pass algorithms that improve over this approximation ratio by using their second and third passes to improve the above mentioned maximal matching. One contribution of this paper continues this line of work by presenting new three-pass semi-streaming algorithms that work along these lines and obtain improved approximation ratios of 0.6111 and 0.5694 for triangle-free and general graphs, respectively. Unfortunately, a recent work Konrad and Naidu (Approximation, randomization, and combinatorial optimization. Algorithms and techniques, APPROX/RANDOM 2021, August 16–18, 2021. LIPIcs, vol 207, pp 19:1–19:18, 2021. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.19) shows that the strategy of constructing a maximal matching in the first pass and then improving it in further passes has limitations. Additionally, this technique is unlikely to get us closer to single-pass semi-streaming algorithms obtaining a better than 1 / 2 -approximation. Therefore, it is interesting to come up with algorithms that do something else with their first pass (we term such algorithms non-maximal-matching-first algorithms). No such algorithms were previously known, and the main contribution of this paper is describing such algorithms that obtain approximation ratios of 0.5384 and 0.5555 in two and three passes, respectively, for general graphs. The main significance of our results is not in the numerical improvements, but in demonstrating the potential of non-maximal-matching-first algorithms. [ABSTRACT FROM AUTHOR]

Motivated by the fact that the fuzzy quasi-normed space provides a suitable framework for complexity analysis and has important roles in discussing some questions in theoretical computer science, this paper aims to study the nearest point problems in fuzzy quasi-normed spaces. First, by using the theory of dual space and the separation theorem of convex sets, the properties of the fuzzy distance from a point to a set in a fuzzy quasi-normed space are studied comprehensively. Second, more properties of the nearest point are given, and the existence, uniqueness, characterizations, and qualitative properties of the nearest points are obtained. The results obtained in this paper are of great significance for expanding the application fields of optimization theory. [ABSTRACT FROM AUTHOR]

"Multi-agent systems (MAS)" have been extensively studied across various fields, including robotics, economics, biology, and computer science. A distinctive feature of these systems is the ability of multiple agents, each with different characteristics, to perform system-wide tasks through local bottom-up interactions. Furthermore, design and control methods for system networks based on graph theory are being developed. Recent applications of these methods include autonomous driving technology, smart grids, and understanding social systems. This special issue aims to deepen the understanding of MAS, focusing on their control and applications. It features 16 papers, including one review paper. The accepted papers cover a wide range of topics, including reinforcement learning, autonomous mobility systems, and machine learning, presenting the latest research findings on MAS. These studies provide valuable insights into various aspects and potential applications of MAS. We hope that this issue will be beneficial to our readers and contribute to the advancement of future research. [ABSTRACT FROM AUTHOR]

The International Journal of Artificial Intelligence in Education has released a special issue on Artificial Intelligence for Higher Education in the COVID-19 era, featuring two rigorously reviewed papers. The Guest Editors include professors from Mexico, Spain, Korea, and the United Kingdom. The papers can be accessed through the journal's homepage under the "Collections" link. Springer Nature maintains neutrality regarding jurisdictional claims and institutional affiliations. [Extracted from the article]

In the realm of connected networks, distance-based parameters, particularly the partition dimension of graphs, have extensive applications across various fields, including chemistry and computer science. A notable variant of the partition dimension is the fault-tolerant resolving partition, which is critical in computer science for networking, optimization, and navigation tasks. In networking, fault-tolerant partitioning ensures robust communication pathways even in the event of network failures or disruptions. In optimization, it aids in developing efficient algorithms capable of withstanding errors or changes in input data. In navigation systems, fault-tolerant partitioning supports reliable route planning and navigation services under uncertain or dynamic conditions. This paper focuses on the fault-tolerant partition dimension within the specific context of the cycle with chord graphs, exploring its properties and implications for enhancing the robustness and reliability of networked systems. [ABSTRACT FROM AUTHOR]

Animating the relationship between affect and ideology in histories of artificial intelligence, this paper explores how the transatlantic post-war quest to engineer common sense via computational means has profoundly shaped both the social logics of machine learning systems and the sensorial politics of everyday knowledge production. Focusing on the Cyc project, a logic-based AI endeavour to 'codify human common sense' which began at the USA-based Microelectronics and Computer Technology Corporation in 1984, and making links to MIT Media Lab's Open Mind Common Sense Project inaugurated in 1999, I trace how the imperative within late twentieth-century computer science to make intelligent systems more intuitive by translating implicit human knowledge into explicit machine knowledge involved not only mathematical and technological challenges but also affective, ideological and socio-political ones. In tracking the interactions between intuition and common sense across these genealogies of machine intelligence, I tease out some of the key atmospheres, processes, and correlations via which AI technologies have become embedded with ideology, normativity and prejudice at the levels of logic, procedure and data. Through adjudicating the meanings of reason, truth and perceptibility as matters of algorithmically calibrated fit and popularity, intelligent architectures are also radically reconstituting the intelligible and the sensible - in ways, I argue, that complicate any notion of a clean epistemological or ontological break between first and second wave AI. Dwelling within these unfinished histories, however, also points to how inhabiting counter-intuitive tendencies may open up new possibilities for (un)common sense and distributed intuition within computational cultures. [ABSTRACT FROM AUTHOR]

Iteration is one of the most important topics in computer science and attentions are paid to functional equations involving iterates, one of which is the linear combination of alternative iterates. Recently existence, uniqueness and dependence for increasing Lipschitzian solutions were obtained for linear combination of alternative iterates on $ [0, 1] $ [ 0 , 1 ] and $ \mathbb {R} $ R in the case that all given functions are strictly increasing. In this paper we work in various cases of monotonicity (increasing and decreasing) among given functions and the unknown function. [ABSTRACT FROM AUTHOR]

In this paper, I take the cultural effects of generative artificial intelligence (generative AI) as a context for examining a broader perspective of AI's impact on contemporary art notions. After the introductory overview of generative AI, I summarize the distinct but often confused aspects of art notions and review the principal lines in which AI influences them: the strategic normalization of AI through art, the representation of AI art in the artworld, academia, and AI research, and the mutual permeability of art and kitsch in the digital culture. I connect these notional factors with the conceptual and ideological substrate of the computer science and AI industry, which blends the machinic agency fetishism, the equalization of computers and humans, the sociotechnical blindness, and cyberlibertarianism. The overtones of alienation, sociopathy, and misanthropy in the disparate but somehow coalescing philosophical premises, technical ideas, and political views in this substrate remain underexposed in AI studies so, in the closing discussion, I outline their manifestations in generative AI and introduce several viewpoints for a further critique of AI's cultural zeitgeist. They add a touch of skepticism to pondering how technological trends change our understanding of art and in which directions they stir its social, economic, and political roles. [ABSTRACT FROM AUTHOR]

This paper introduces and explains cyber physics, which we define as mathematical equations, i.e., physics-like laws, that control, regulate, or otherwise govern the inner workings of the hardware architecture, operating system, application code, algorithms, and networks on a classical computing machine. Cyber physics integrates computer science with conventional physics, in particular with quantum physics, thermodynamics, and statistical mechanics. Naturally, the technical description of cyber physics in the paper draws on both of these fields of science. [ABSTRACT FROM AUTHOR]

The study of linguistics applied to computer science is a much-discussed topic today. In this area, particularly relevant is the software localization process describing the linguistic and cultural adaptation of software products to a specific market scenario. Software localization is going through a phase of strong development due to the great market demand and the current trend of making the computer more human-like in the way it interacts with the user. This paper focuses on "linguistic" localization by addressing the language translation process from the perspective of translation studies. In particular, the process of translating the language assets in a game and making the game linguistically and culturally appropriate for the target market will be explored. The study provides a systematic literature review of the main localization methods developed over the last four decades, along with the major issues and challenges mainly related to the main linguistic and cultural aspects of videogames. The review results are integrated with the results of a qualitative analysis conducted through a focus group with the participation of both academic and professional experts in software and videogame localization. The results of this study are worthwhile for many academics and industry professionals as they provide an in-depth overview of the localization process in software and videogames as well as potential directions for future research. [ABSTRACT FROM AUTHOR]