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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]

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]

Metaphors that compare the computer to a human brain are common in computer science and can be traced back to a fertile period of research that unfolded after the Second World War. To conceptualize the emerging "intelligent" properties of computing machines, researchers of the era created a series of virtual objects that served as interpretive devices for representing the immaterial functions of the computer. This paper analyses the use of the terms "artificial" and "virtual" in scientific papers, textbooks, and popular articles of the time, and examines how, together, they shaped models in computer science used to conceptualize computer processes. [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]

ChatGPT, a recently developed product by openAI, is successfully leaving its mark as a multi-purpose natural language based chatbot. In this paper, we are more interested in analyzing its potential in the field of computational biology. A major share of work done by computational biologists these days involve coding up bioinformatics algorithms, analyzing data, creating pipelining scripts and even machine learning modeling and feature extraction. This paper focuses on the potential influence (both positive and negative) of ChatGPT in the mentioned aspects with illustrative examples from different perspectives. Compared to other fields of computer science, computational biology has (1) less coding resources, (2) more sensitivity and bias issues (deals with medical data), and (3) more necessity of coding assistance (people from diverse background come to this field). Keeping such issues in mind, we cover use cases such as code writing, reviewing, debugging, converting, refactoring, and pipelining using ChatGPT from the perspective of computational biologists in this paper. [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]

The approximate uniform sampling of graph realizations with a given degree sequence is an everyday task in several social science, computer science, engineering etc. projects. One approach is using Markov chains. The best available current result about the well-studied switch Markov chain is that it is rapidly mixing on P-stable degree sequences (see DOI:10.1016/j.ejc.2021.103421). The switch Markov chain does not change any degree sequence. However, there are cases where degree intervals are specified rather than a single degree sequence. (A natural scenario where this problem arises is in hypothesis testing on social networks that are only partially observed.) Rechner, Strowick, and Müller–Hannemann introduced in 2018 the notion of degree interval Markov chain which uses three (separately well studied) local operations (switch, hinge-flip and toggle), and employing on degree sequence realizations where any two sequences under scrutiny have very small coordinate-wise distance. Recently, Amanatidis and Kleer published a beautiful paper (DOI:10.4230/LIPIcs.STACS.2023.7), showing that the degree interval Markov chain is rapidly mixing if the sequences are coming from a system of very thin intervals which are centered not far from a regular degree sequence. In this paper, we substantially extend their result, showing that the degree interval Markov chain is rapidly mixing if the intervals are centered at P-stable degree sequences. [ABSTRACT FROM AUTHOR]

Deep neural networks are extensively used in text mining and Natural Language Processing is to enable computers to understand, analyze, and generate natural language data, such as text or speech, but semantic resources, such as taxonomies and ontologies, are not fully included in deep learning. In this paper, we use Deep Convolutional Neural Network (Deep CNN) to classify research papers using the Computer Science Ontology, an ontology of research areas in the field of computer science. It takes as input the abstract and keywords of a particular research paper and returns the relevant research topic. To evaluate our ontology, we used a gold standard dataset that includes research articles. To further improve text classification results, we propose to design a Deep CNN model. We then used ontology matching to reduce the classes and get better results. Experimental results show that the proposed approach outperforms the one with the highest precision, recall, and F1-score. [ABSTRACT FROM AUTHOR]

One of the most widely referenced Swarm Intelligence (SI) algorithms is the Grey Wolf Optimizer (GWO), which is based on the pack hunting and natural leadership organization of grey wolves. The GWO algorithm offers several significant benefits, including simple implementation, rapid convergence, and superior convergence outcomes, leading to its effective application in diverse fields for solving optimization issues. Consequently, the GWO has rapidly garnered substantial research interest and a broad audience across numerous areas. To better understand the literature on this algorithm, this review paper aims to consolidate and summarize research publications that utilized the GWO. The paper begins with a concise introduction to the GWO, providing insight into its natural establishment and conceptual framework for optimization. It then lays out the theoretical foundation and key procedures involved in the GWO, following which it comprehensively examines the most recent iterations of the algorithm and categorizes them into parallel, modified, and hybridized variations. Subsequently, the primary applications of the GWO are thoroughly explored, spanning various fields such as computer science, engineering, energy, physics and astronomy, materials science, environmental science, and chemical engineering, among others. This review paper concludes by summarizing the key arguments in favour of GWO and outlining potential lines of inquiry in the future research. [ABSTRACT FROM AUTHOR]

This document is an editorial summarizing the Applied Geometric Algebras in Computer Science and Engineering (AGACSE) conference held in Brno, Czech Republic in September 2021. The conference aimed to promote the use of geometric algebra in fields such as image processing, robotics, and quantum computing. The conference proceedings were published in the journal Mathematical Methods in the Applied Sciences. The editorial provides a list of accepted papers, covering topics such as applied geometry, technological applications, algebra, and quantum phenomena. One specific paper explores the use of geometric algebra in teaching rotations through neural networks. The document is a compilation of research papers showcasing the applications of geometric algebra in various fields, including robotics, control systems, image processing, cryptography, and physics. Each paper presents a specific problem or application and proposes a unique approach or solution using geometric algebra. The authors compare their methods with existing techniques and provide mathematical analysis to support their claims. Overall, the papers demonstrate the versatility and effectiveness of geometric algebra in different domains. [Extracted from the article]

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]

With the tremendous increase in human population the demand of milk and meat is also increasing. This necessitates the centralization of animal husbandry sector both in developed and developing countries. More production has to be taken from fewer animals which seem to be possible only with the usage of advanced technologies. Earlier this seems to be impractical due to non-availability of such technologies to the farmers. Nowadays, computational power is very cheap and easily available. Artificial intelligence is one such field of computer science devoted to creating computing machines and systems that perform operations analogous to human learning and decision-making. Artificial intelligence includes the machine learning, deep learning and predictive analytics. With the implementation of various software and algorithms, the production has been increased from the farm animals along with better management and welfare. This paper illustrates the application domains and maneuvering of artificial intelligence in the animal husbandry sector. [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]

In the present paper, we propose a new approach on 'distributed systems': the processes are represented through total orders and the communications are characterized by means of biorders. The resulting distributed systems capture situations met in various fields (such as computer science, economics and decision theory). We investigate questions associated to the numerical representability of order structures, relating concepts of economics and computing to each other. The concept of 'quasi-finite partial orders' is introduced as a finite family of chains with a communication between them. The representability of this kind of structure is studied, achieving a construction method for a finite (continuous) Richter–Peleg multi-utility representation. [ABSTRACT FROM AUTHOR]

Boolean functions used in symmetric-key encryption should have high higher-order nonlinearity to resist several known cryptographic attacks, such as algebraic attacks and low-degree approximation attacks. The higher-order nonlinearity also plays an important role in coding theory and theoretical computer science, since it relates to the covering radius of Reed–Muller codes and the Gowers norm, respectively. It is well-known that bent functions have the highest nonlinearity in an even number of variables and thus they possess the best ability to withstand fast correlation attacks and best affine approximation attacks. However, there is currently limited knowledge regarding the higher-order nonlinearity of bent functions because computing the higher-order nonlinearity, or even providing tight lower bounds, is an extremely hard task. In 1974, Dillon proposed two well-known classes of bent functions based on partial spread (in brief, PS), called PS − and PS + , respectively. He also exhibited a subclass of bent functions in PS − , known as partial spread affine plane(PS a p for short). In this paper, we provide a lower bound on the third-order nonlinearity of the simplest PS a p bent functions in n variables, where n ≥ 6 is even, by calculating the nonlinearities of all second-order derivatives of this kind of bent functions. Compared to the two known lower bounds on the third-order nonlinearity given by Carlet and Tang et al. respectively, our lower bound is much better than these two ones. [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]

Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear recommendations for conducting and reporting ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist (recommendations for machine-learning-based science). It consists of 32 questions and a paired set of guidelines. REFORMS was developed on the basis of a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility. [ABSTRACT FROM AUTHOR]

The development of the computer and what is now known as artificial intelligence (AI) has evolved over more than two centuries in a long series of steps. The date of the invention of the first computer is estimated at 1822, when Charles Babbage (1791-1871) developed his first design of a working computer on paper, based mainly on a Jacquard loom. He worked on his project together with Augusta Ada King, Countess Lovelace (née Byron) (Ada Lovelace) (1815-1852), whom he called the "Sorceress of Numbers." This work will present the profile and achievements of Charles Babbage, Augusta Ada King, Countess Lovelace, and Alan Mathison Turing (1912 - 1954), who is considered the father of computer science and artificial intelligence, and then provide an outline of the tumultuous events affecting AI up to the present. [ABSTRACT FROM AUTHOR]

This paper describes the implementation of a STEM scholarship program which utilized a holistic approach to providing a multi-dimensional student support system. The program has been successful in encouraging and supporting women in Applied Mathematics and Computer Science by offering a diverse suite of extracurricular opportunities, actively engaging them in organized events, research projects, and participation in STEM communities, and helping them achieve higher GPAs and shorter times to graduation. The supported women also benefitted from close mentoring relationships with the faculty mentors. The program emphasized the development of empowering settings for women's engagement and achievement, which act to sustain and expand interest in mathematics and computing, and thereby help them to see themselves as future professionals in the field. [ABSTRACT FROM AUTHOR]

The concept of book embedding, originating in computer science, has found extensive applications in various problem domains. A book embedding of a graph G involves arranging the vertices of G in an order along a line and assigning the edges to one or more half-planes. The page number of a graph is the smallest possible number of half-planes for any book embedding of the graph. Determining the page number is a key aspect of book embedding and carries significant importance. This paper aims to investigate the non-trivial lower bound of the page number for both a graph G and a random graph G ∈ G (n , p) by incorporating two seemingly unrelated concepts: edge-arboricity and Euler's Formula. Our analysis reveals that for a graph G, which is not a path, p n (G) ≥ ⌈ 1 3 a 1 (G) ⌉ , where a 1 (G) denotes the edge-arboricity of G, and for an outerplanar graph, the lower bound is optimal. For G ∈ G (n , p) , p n (G) ≥ ⌈ 1 6 n p (1 - o (1)) ⌉ with high probability, as long as c n ≤ p ≤ 3 (n - 1) 2 n log n . [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]

With the spreading of Industry 4.0, cyber-physical systems, and tools for augmented and virtual reality, Digital Twin (DT) is gaining momentum in several areas of Computer Science and Engineering. This paper presents a systematic literature review that investigates the way DTs are described in Human-Computer Interaction (HCI) scientific literature. The study includes 23 papers selected through a systematic search on the 21 most ranked journals and conferences in the HCI area. As a result of this work, it appears clear that the way humans interact with DTs is a topic still far from being widely studied. A set of hints for future research about the relationship between HCI and DT constitute the main outcome of this paper. [ABSTRACT FROM AUTHOR]

Lecture Notes in Computer Science (LNCS) is a globally recognised publication outlet for the field of Computer Science, including in South Africa. In this study, spanning from 1973 to 2022, we investigated the research participation of South African based authors in LNCS. The publication output and citation impact of these authors were compared to the global Computer Science and LNCS output. The authorship patterns and collaborative behaviour of South African LNCS papers were explored, and a keyword or topic analysis also conducted. Of the total of 518 662 LNCS papers published globally between 1973 and 2022, South African based researchers contributed 1150 papers (0.22%). The LNCS papers from South Africa exhibit a strong collaborative publication culture, with 1043 (91%) co-authored and 107 (9%) singleauthored works. Local LNCS researchers prefer institutional collaboration (43%), followed by international (37%) and national collaboration (11%). Europe emerged as the most significant collaboration partner for LNCS researchers in South Africa. Of the 1150 papers, 836 (73%) had received citations, while 314 (27%) had not. On average, papers published by South African based authors received 6.05 citations, compared to the global LNCS average of 9.49 citations per paper. A keyword analysis revealed that the majority of papers by South African authors focus on artificial intelligence. The results indicate that, although LNCS serves as a reputable dissemination platform for Computer Science research output both globally and locally, South African authors should consider publishing more journal articles to build and improve their researcher profiles. Significance: * The study shows that LNCS is the most frequent publication outlet for Computer Science researchers, globally and in South Africa. * The study offers insight into the publication output, authorship patterns, collaborative behaviour and citation impact of South African based Computer Science researchers. [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]

As part of ongoing research bridging ethnomethodology and computer science, in this article we offer an alternate reading of Alan Turing's 1936 paper, "On Computable Numbers". Following through Turing's machinic respecification of computation, we hope to contribute to a deflationary position on AI by showing that the activities attributed to AIs are achieved in the course of methodic hands-on work with computational systems and not in isolation by them. Turing's major innovation was a demonstration that mathematical and logical operations could be broken down into elementary, mechanically executable operations, devoid of intellectual content. Drawing out lessons from a re-enactment of Turing's methods as a means of reflecting on contemporary artificial intelligence (AI), including the way those methods disappear into the technology, we will suggest the interesting question raised in "On Computable Numbers" is less about the possibilities of designing machines that "can think" (cf. Turing, 1950), but the practical work we do, and which is made possible, when we ourselves set out to think like machines. [ABSTRACT FROM AUTHOR]

The XOR operator is a simple yet crucial computation in computer science, especially in cryptography. In symmetric cryptographic schemes, particularly in block ciphers, the AddRoundKey transformation is commonly used to XOR an internal state with a round key. One method to enhance the security of block ciphers is to diversify this transformation. In this paper, we propose some straightforward yet highly effective techniques for generating t-bit random XOR tables. One approach is based on the Hadamard matrix, while another draws inspiration from the popular intellectual game Sudoku. Additionally, we introduce algorithms to animate the XOR transformation for generalized block ciphers. Specifically, we apply our findings to the AES encryption standard to present the key-dependent AES algorithm. Furthermore, we conduct a security analysis and assess the randomness of the proposed key-dependent AES algorithm using NIST SP 800-22, Shannon entropy based on the ENT tool, and min-entropy based on NIST SP 800-90B. Thanks to the key-dependent random XOR tables, the key-dependent AES algorithm have become much more secure than AES, and they also achieve better results in some statistical standards than AES. [ABSTRACT FROM AUTHOR]

Numerous technologies and systems, including autonomous vehicles, surveillance systems, and robotic applications, rely on the capability to accurately detect pedestrians to ensure their safety. As the demand for realtime object detection continues to rise, many researchers have dedicated their efforts to developing effective and trustworthy algorithms for pedestrian recognition. By integrating learning complexity-aware cascades with an enhanced you only look once (YOLO) algorithm, the paper presents a real-time system for identifying both items and pedestrians. The performance of the proposed approach is evaluated using the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) pedestrian dataset across both the v4 and v8 versions of the YOLO framework. Prioritizing both speed and accuracy, the enhanced YOLO algorithm outperforms its baseline counterpart. The demonstrated superiority of the suggested technique on the KITTI pedestrian dataset underscores its effectiveness in real-world contexts. Furthermore, the complexity-aware learning cascades contribute to a streamlined detection model without compromising performance. When applied to scenarios requiring real-time identification of objects and individuals, the proposed method consistently delivers promising outcomes. [ABSTRACT FROM AUTHOR]

Trust can be considered as a multidisciplinary concept, which is strongly related to the context and it falls in different fields such as Philosophy, Psychology or Computer Science. Trust is fundamental in every relationship, because without it, an entity will not interact with other entities. This aspect is very important especially in the Internet of Things (IoT), where many entities produced by different vendors and created for different purposes have to interact among them through the internet often under uncertainty. Trust can overcome this uncertainty, creating a strong basis to ease the process of interaction among these entities. We believe that considering trust in the IoT is fundamental, and in order to implement it in any IoT entity, it is fundamental to consider it through the whole System Development Life Cycle. In this paper, we propose an analysis of different works that consider trust for the IoT. We will focus especially on the analysis of frameworks that have been developed in order to include trust in the IoT. We will make a classification of them providing a set of parameters that we believe are fundamental in order to properly consider trust in the IoT. Thus, we will identify important aspects to be taken into consideration when developing frameworks that implement trust in the IoT, finding gaps and proposing possible solutions. [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]

Large-scale benchmark datasets are crucial in advancing research within the computer science communities. They enable the development of more sophisticated AI models and serve as "golden" benchmarks for evaluating their performance. Thus, ensuring the quality of these datasets is of utmost importance for academic research and the progress of AI systems. For the emerging vision-language tasks, some datasets have been created and frequently used, such as Flickr30k, COCO, and NoCaps, which typically contain a large number of images paired with their ground-truth textual descriptions. In this paper, an automatic method is proposed to assess the quality of large-scale benchmark datasets designed for vision-language tasks. In particular, a new cross-modal matching model is developed, which is capable of automatically scoring the textual descriptions of visual images. Subsequently, this model is employed to evaluate the quality of vision-language datasets by automatically assigning a score to each 'ground-truth' description for every image picture. With a good agreement between manual and automated scoring results on the datasets, our findings reveal significant disparities in the quality of the ground-truth descriptions included in the benchmark datasets. Even more surprising, it is evident that a small portion of the descriptions are unsuitable for serving as reliable ground-truth references. These discoveries emphasize the need for careful utilization of these publicly accessible benchmark databases. [ABSTRACT FROM AUTHOR]

Our world is turbulent: ecological, social, political, technological, economic, and competitive business environments change constantly. Consumers have changing preferences, learn, build trust in brands, adopt new products, and are persuaded by advertising. Firms innovate and engage in and respond to competition. Exogenous events, such as changes in economic conditions and regulations, as well as human crises, also cause major shifts in markets. This special issue focuses on novel Marketing data and modern methodologies from different fields (e.g., Operations Research (OR), Statistics, Econometrics, and Computer Science), which help firms understand, utilize, and respond to market dynamics more efficiently. Here we propose a framework comprising analytical methods and data for dynamic markets that is useful for structuring research in this domain. Next, we summarize the history of the Marketing/OR interface. We highlight studies at the Marketing/OR interface from the last decade focusing specifically on dynamic markets and use our proposed framework to identify trends and gaps in the extant literature. After that, we present and summarize the papers of the current special issue and their contributions to the field against the backdrop of our framework and the trends in the literature. Finally, we conclude and discuss which future Marketing/OR research could tackle important issues in dynamic markets. [ABSTRACT FROM AUTHOR]

Computational models are foundational concepts in computer science; many of these models such as P systems are based on natural biological processes. P systems represent a wide framework for a variety of concepts of data mining, as models of data clustering approaches. Data clustering is a technique for analyzing data based on its structure that is widely utilized for many applications. In this paper, the proposed model (PSO-MFM) has combined the Particle Swarm Optimization algorithm (PSO) with Mitochondrial Fusion Model to overcome some constraints of clustering techniques. The solving of clustering problem based on particle swarm is investigated in the proposed model when mutual dynamic rules are used. It can find the best cluster centers for a data set and improve clustering performance by utilizing the distributed parallel computing concept of mutual dynamic rules of mitochondrial fusion model. The comparative results demonstrate that the proposed strategy outperforms competition models when it comes to clustering accuracy, stability and the most efficient in time complexity. [ABSTRACT FROM AUTHOR]

With the increase interest and number of the users in Social media, the file handling has also increased. To manage the load, cloud servers are being used by the service providers. To identify and cluster file is a difficult task that is important in the domain of computer science. Various traditional approaches for identification exists that uses design features. The problem with these methods is get that they can be easily spoofed. To resolve the issue, in this paper, a hybrid algorithm combining the features of Random Forest with AdaBoost is proposed. The algorithm Internet of Thing (IoT) data file formatting (IDFF) classifies data as (text, image, audio and video) and gives better accuracy. Our proposed research obtains better Accuracy (93%), Precision (95%), Recall (95%), F-Measure (95%), and G-Mean (96%). [ABSTRACT FROM AUTHOR]

With the advent of technological advancement in learning, such as context-awareness, ubiquity and personalisation, various innovations in teaching and learning have led to improved learning. This research paper aims to develop a system that supports personalised learning through adaptive content, adaptive learning path and context awareness to meet individual learner's requirements and promote the effectiveness and performance of the learning process. Furthermore, the study used an experimental control group to ascertain the significance of learners' learning styles and preferences in improving learning performance. The model considers different modules such as personalisation, location support, learning plan and context awareness while relying on the proper classification of learners' learning styles. The system was experimented using 127 learners of a Computer Science Course. The result obtained using 10-fold cross-validation on various algorithms shows better performance in ROC, Kappa and Accuracy, which were all used to evaluate the quality of classification outcome and compared with learners' performances in assessments. During classification, Naïve Bayes Model has the highest average ROC value of 0.979, TP Rate of 0.951 and FP Rate of 0.045. The results obtained show an improvement in learning. [ABSTRACT FROM AUTHOR]

Let d-claw (or d-star) stand for K 1 , d , the complete bipartite graph with 1 and d ≥ 1 vertices on each part. The d-claw vertex deletion problem, d -claw-vd, asks for a given graph G and an integer k if one can delete at most k vertices from G such that the resulting graph has no d-claw as an induced subgraph. Thus, 1 -claw-vd and 2 -claw-vd are just the famous vertex cover problem and the cluster vertex deletion problem, respectively. In this paper, we strengthen a hardness result recently proved in Jena and Subramani (in: Du, Du, Wu, and Zhu (eds) Theory and applications of models of computation - 17th annual conference, TAMC 2022, Tianjin, China, September 16–18, 2022, Proceedings, 2022), by showing that cluster vertex deletion remains NP -complete even when restricted to planar bipartite graphs of maximum degree 3 and arbitrary large girth. Moreover, for every d ≥ 3 , we show that d -claw-vd is NP -complete even when restricted to planar bipartite graphs of maximum degree d. These hardness results are optimal with respect to degree constraint. By extending the hardness result in Bonomo-Braberman et al (in: Computing and combinatorics - 26th international conference, COCOON 2020, Proceedings, Lecture Notes in Computer Science, vol 12273, Springer, 2020, pp 14–26, 2020), we show that, for every d ≥ 3 , d -claw-vd is NP -complete even when restricted to split graphs without (d + 1) -claws, and split graphs of diameter 2. On the positive side, we prove that d -claw-vd is polynomially solvable on what we call d-block graphs, a class properly contains all block graphs. This result extends the polynomial-time algorithm in Cao et al (Theor Comput Sci, 2018) for 2 -claw-vd on block graphs to d -claw-vd for all d ≥ 2 and improves the polynomial-time algorithm proposed by Bonomo-Brabeman et al. for (unweighted) 3 -claw-vd on block graphs to 3-block graphs. [ABSTRACT FROM AUTHOR]

The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%. [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]

Since the Ronan Point collapse in the UK in 1968, the progressive collapse analysis of residential buildings has gradually drawn the attention of civil engineers and the scientific community. Recent advances in computer science and the development of new numerical methodologies allow us to perform high-fidelity collapse simulations. This paper assesses different scenarios that could have hypothetically caused the collapse of the Champlain Tower South Condo in Surfside, Florida, in 2021, one of the most catastrophic progressive collapse events that has ever occurred. The collapse analysis was performed using the latest developments in the Applied Element Method (AEM). A high-fidelity numerical model of the building was developed according to the actual structural drawings. Several different collapse hypotheses were examined, considering both column failures and degradation scenarios. The analyses showed that the failure of deep beams at the pool deck level, directly connected to the perimeter columns of the building, could have led to the columns' failure and subsequent collapse of the eastern wing of the building. The simulated scenario highlights the different stages of the collapse sequence and appears to be consistent with what can be observed in the footage of the actual collapse. To improve the performance of the structure against progressive collapse, two modifications to the original design of the building were introduced. From the analyses, it was found that disconnecting the pool deck beam from the perimeter columns could have been effective in preventing the local collapse of the pool deck slab from propagating to the rest of the building. Moreover, these analyses indicate that enhancing the torsional strength and stiffness of the core could have prevented the collapse of the eastern part of the building, given the assumptions and initiation scenarios considered. Building catastrophic collapses can cause significant lives and economic losses. Poor design and maintenance, in combination with aging, will more likely increase, in the next years, the number of buildings potentially vulnerable to the risk of collapse, due to either seismic, accidental, or degradation actions. This research focuses on the analysis of the Champlain Tower South condo collapse, which occurred in Surfside, Florida, in 2021. Different hypothetical collapse scenarios were simulated, comparing the analysis results with the actual evidence of the collapse. The analyses have shown that the degradation of the pool deck slab, due to corrosion, may have contributed to the collapse of the building. Finally, two different minor revisions of the original design of the building were analyzed to reduce the risk of failure and understand how the collapse of similar residential buildings could be prevented. [ABSTRACT FROM AUTHOR]

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. [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.

Since 2000, the field of machine scheduling—an integral part of computer science and operations research—has seen significant advancements. This paper explores the dynamic progression of machine scheduling, offering a detailed overview of its past advancements, current practices, and future directions. Anchoring the research in robust data analysis and statistical methodologies, the paper reveals the subtle yet impactful changes that have characterized the field in the last two decades. It examines the prominence of various scheduling problems, identifies leading research journals, and highlights international contributions and collaborations, thereby offering a thorough guide to the machine scheduling ecosystem. The study delves into specific problem characteristics and assesses performance criteria and solution methods to provide an in-depth view of the field's multifaceted nature. Ultimately, this paper captures the essence of machine scheduling's evolution and suggests new paths for exploration. The insights gained contribute significantly to academic discussions and equip practitioners with a comprehensive understanding of the dynamic landscape of machine scheduling. [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 large number of scientific publications around the world is increasing at a rate of approximately 4%–5% per year. This fact has resulted in the need for tools that deal with relevant and high-quality publications. To address this necessity, search and reference management tools that include some recommendation algorithms have been developed. However, many of these solutions are proprietary tools and the full potential of recommender systems is rarely exploited. There are some solutions which provide recommendations for specific domains, by using ad-hoc resources. Furthermore, some other systems do not consider any personalization strategy to generate the recommendations. This paper presents ArZiGo , a web-based full prototype system for the search, management, and recommendation of scientific articles, which feeds on the Semantic Scholar Open Research Corpus, a corpus that is growing continually with more than 190M papers from all fields of science so far. ArZiGo combines different recommendation approaches within a hybrid system, in a configurable way, to recommend those papers that best suit the preferences of the users. A group of 30 human experts has participated in the evaluation of 500 recommendations in 10 research areas, 7 of which belong to the area of Computer Science and 3 to the area of Medicine, obtaining quite satisfactory results. Besides the appropriateness of the articles recommended, the execution time of the implemented algorithms has also been analyzed. • A web system for the search, management, and recommendation of scientific articles. • A hybrid and multidisciplinary scientific article recommendation system. • A modular and scalable recommendation system. • Use of the Semantic Scholar Open Research Corpus as a corpus of articles. [ABSTRACT FROM AUTHOR]

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]

Graph energies and spectral radii have application in molecular computing and computer sciences. In this paper, we concentrate on new findings about the Symmetric Division Deg, (SDD) energies, and (SDD) spectral radii of p splitting and p shadow graphs built on the foundation of any regular graph. In fact it becomes natural to ask how the SDD energies and spectral radii of newly formed graphs depend on the SDD energies and spectral radii of the given regular graph. [ABSTRACT FROM AUTHOR]

In this paper, we discuss methods to assess the interestingness of a query in an environment of data cubes. We assume a hierarchical multidimensional database, storing data cubes and level hierarchies. We start with a comprehensive review of related work in the fields of human behavior studies and computer science. We define the interestingness of a query as a vector of scores along different aspects, like novelty, relevance, surprise and peculiarity and complement this definition with a taxonomy of the information that can be used to assess each of these aspects of interestingness. We provide both syntactic (result-independent) and extensional (result-dependent) checks, measures and algorithms for assessing the different aspects of interestingness in a quantitative fashion. We also report our findings from a user study that we conducted, analyzing the significance of each aspect, its evolution over time and the behavior of the study's participants. [ABSTRACT FROM AUTHOR]

The frequent detection of persistent per- and polyfluoroalkyl substances (PFAS) in organisms and environment coupled with surging evidence for potential detrimental impacts, have attracted widespread attention throughout the world. In order to reveal research hotspots and trends of technologies for PFAS removal, herein, we performed a data-driven analysis of 3975 papers and 436 patents from Web of Science Core Collection and Derwent Innovation Index databases up to 2023. The results showed that China and the USA led the way in the research of PFAS removal with outstanding contributions to publications. The progression generally transitioned from accidental discovery of decomposition, to experimentation with removal effects and mechanisms of existing methods, and finally to enhanced defluorination and mechanism-driven design approaches. The keywords co-occurrence network and technology classification together revealed the main knowledge framework, which was constructed and correlated through contaminants, substrates, materials, processes and properties. Moreover, adsorption was demonstrated to be the dominant removal process among the current studies. Subsequently, we concluded the principles, advances and drawbacks of enrichment and separation, biological methods, advanced oxidation and reduction processes. Further exploration indicated the hotspots such as alternatives and precursors for PFAS ("genx": 1.258, "f-53b": 0.337), degradable mineralization technologies ("photocatalytic degrad": 0.529, "hydrated electron": 0.374), environment-friendly remediation technologies ("phytoremedi": 0.939, "constructed wetland": 0.462) and combination with novel materials ("metal-organic framework": 1.115, "layered double hydroxid": 0.559) as well as computer science ("molecular dynamics simul": 0.559, "machine learn"). Furthermore, the future direction of technological innovation might lie in high-performance processes that minimize secondary pollution, the development of recyclable and renewable treatment agents, and collaborative control strategies for multiple pollutants. Overall, this study offers comprehensive and objective review for researchers and industry professionals in this field, enabling rapid access to knowledge guidance and insights into research frontiers. [Display omitted] [ABSTRACT FROM AUTHOR]

The eight papers included in this special issue represent a selection of extended contributions presented at the 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022 held in Salamanca, Spain, September 6th-8th, 2022, and organized by the BISITE group at University of Salamanca. SOCO 2022 international conference represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines which investigate, simulate, and analyse very complex issues and phenomena. This special issue is aimed at practitioners, researchers, and postgraduate students who are engaged in developing and applying advanced intelligent systems principles to solve real-world problems in the mentioned fields. [ABSTRACT FROM AUTHOR]

Achieving a widely accepted definition of human intelligence has been challenging, a situation mirrored by the diverse definitions of artificial intelligence in computer science. By critically examining published definitions, highlighting both consistencies and inconsistencies, this paper proposes a refined nomenclature that harmonizes conceptualizations across the two disciplines. Abstract and operational definitions for human and artificial intelligence are proposed that emphasize maximal capacity for completing novel goals successfully through respective perceptual-cognitive and computational processes. Additionally, support for considering intelligence, both human and artificial, as consistent with a multidimensional model of capabilities is provided. The implications of current practices in artificial intelligence training and testing are also described, as they can be expected to lead to artificial achievement or expertise rather than artificial intelligence. Paralleling psychometrics, 'AI metrics' is suggested as a needed computer science discipline that acknowledges the importance of test reliability and validity, as well as standardized measurement procedures in artificial system evaluations. Drawing parallels with human general intelligence, artificial general intelligence (AGI) is described as a reflection of the shared variance in artificial system performances. We conclude that current evidence more greatly supports the observation of artificial achievement and expertise over artificial intelligence. However, interdisciplinary collaborations, based on common understandings of the nature of intelligence, as well as sound measurement practices, could facilitate scientific innovations that help bridge the gap between artificial and human-like intelligence. • Proposes unified definitions for human and artificial intelligence. • Distinguishes between artificial achievement/expertise and artificial intelligence. • Advocates for AI metrics to ensure good quality AI system evaluations. • Describes artificial general intelligence (AGI) mirroring human general intelligence. • Evidence currently favours presence of artificial achievement over intelligence. [ABSTRACT FROM AUTHOR]