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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]

This paper presents a preliminary comparison of citation impact between two types of Best Paper Awards recipients: pre-and post-publication peer-selected best papers. Two hundred and ninety-nine pairs of pre-and post-publication peer-selected best papers from 15 highly prestigious computer science conferences spanning more than two decades were gathered for empirical analysis. Each pair was published in the same proceedings, and their citation information was collected within a 10-year period after their formal publication. A series of pairwise comparisons were performed in order to unbiasedly uncover their differences in citation counts (i.e., a good proxy for citation impact), as well as in citation speed, citation diversity, and citation location distributions (i.e., three characteristic properties of citation impact). The empirical results demonstrate that in computer science, the post-publication peer-selected best papers receive significantly more citations than their pre-publication counterparts, with an estimated probability of up to 0.79 (95% CI: 0.74 ~ 0.83). Meanwhile, they tend to accumulate their citations more quickly in the later (versus earlier) stages—they can scarcely be fast-aging papers, and their accumulated citations tend to cover a broader scope of academic disciplines. Overall, however, the pre-and post-publication peer-selected best papers have very similar citation location distributions. Both of them are most frequently cited in the Background sections, followed by the Methods sections, and finally the Results sections. In addition, the citation impact superiority of the post-publication peer-selected best papers remains consistent across different fields of research and over a long period of time, from 1997 to 2022. [ABSTRACT FROM AUTHOR]

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]

The 26th International Conference on Developments in Language Theory (DLT 2022) took place in Tampa, Florida, USA in May 2022. The conference, organized by the Department of Mathematics and Statistics of the University of South Florida, focused on formal languages and automata. This special issue of Theory of Computing Systems contains extended versions of selected papers presented at DLT 2022, as well as papers based on invited talks. The papers cover a range of topics in the field of formal languages and automata, and underwent a rigorous review process. The preface expresses gratitude to the authors and reviewers involved in the publication. [Extracted from the article]

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]

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]

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]

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]

Large language models are transforming scientific writing and publishing. But the productivity boost that these tools bring could have a downside. [ABSTRACT FROM AUTHOR]

Currently, generative applications are reshaping different fields, such as art, computer vision, speech processing, and natural language. The computer science personalization area is increasingly relevant since large companies such as Spotify, Netflix, TripAdvisor, Amazon, and Google use recommender systems. Then, it is rational to expect that generative learning will increasingly be used to improve current recommender systems. In this paper, a method is proposed to generate synthetic recommender system datasets that can be used to test the recommendation performance and accuracy of a company on different simulated scenarios, such as large increases in their dataset sizes, number of users, or number of items. Specifically, an improvement in the state-of-the-art method is proposed by applying the Wasserstein concept to the generative adversarial network for recommender systems (GANRS) seminal method to generate synthetic datasets. The results show that our proposed method reduces the mode collapse, increases the sizes of the synthetic datasets, improves their ratings distributions, and maintains the potential to choose the desired number of users, number of items, and starting size of the dataset. Both the baseline GANRS and the proposed Wasserstein-based WGANRS deep learning architectures generate fake profiles from dense, short, and continuous embeddings in the latent space instead of the sparse, large, and discrete raw samples that previous GAN models used as a source. To enable reproducibility, the Python and Keras codes are provided in open repositories along with the synthetic datasets generated to test the proposed architecture (https://github.com/jesusbobadilla/ganrs.git). [ABSTRACT FROM AUTHOR]

Hour of Code is a widely recognized global event that aims to introduce programming to novice users and integrate computer science into education. This paper presents an analysis of the effectiveness of the support system and user interface of Minecraft Adventurer, a serious game designed for the Hour of Code global event. Although previous studies have primarily focused on the educational benefits of Hour of Code games, there has been limited research on their support methods. Therefore, this paper aims to address this gap with an empirical study of the experience of 104 students who played the game for one hour. Student progress was tracked by an administering teacher and after the game session, a questionnaire was administered to collect data on the participant's perceptions of the support system, interface efficiency, and overall experience with Hour of Code. The results of the study reveal significant problems with the aforementioned systems, which apply not only to Minecraft Adventurer but also to several other similar serious games. Additionally, the findings showed a correlation between the utilization of the support system and student performance, indicating that student's comprehension of the support system significantly influences their learning outcomes. This paper concludes by providing potential solutions to address the identified insufficiencies, offering valuable insights for future researchers and game developers on the design and evaluation of serious games for educational purposes. [ABSTRACT FROM AUTHOR]

Homomorphic evaluation of hash functions offers a solution to the challenge of data integrity authentication in the context of homomorphic encryption. The earliest attempt to achieve homomorphic evaluation of SHA-256 hash function was proposed by Mella and Susella (in: Cryptography and coding—14th IMA international conference, IMACC 2013. Lecture notes in computer science, vol 8308. Springer, Heidelberg, pp 28–44, 2013. https://doi.org/10.1007/978-3-642-45239-0%5f3.) based on the BGV scheme. Unfortunately, their implementation faced significant limitations due to the exceedingly high multiplicative depth, rendering it impractical. Recently, a homomorphic implementation of SHA-256 based on the TFHE scheme (Homomorphic evaluation of SHA-256. https://github.com/zama-ai/tfhe-rs/tree/main/tfhe/examples/sha256%5fbool) brings it from theory to reality, however, its current efficiency remains insufficient. In this paper, we revisit the homomorphic evaluation of the SHA-256 hash function in the context of TFHE, further reducing the reliance on gate bootstrapping and enhancing evaluation latency. Specifically, we primarily utilize ternary gates to reduce the number of gate bootstrappings required for logic functions in message expansion and addition of modulo 2 32 in iterative compression. Furthermore, we demonstrate that our optimization techniques are applicable to the Chinese commercial cryptographic hash SM3. Finally, we give specific comparative implementations based on the TFHE-rs library. Experiments demonstrate that our optimization techniques lead to an improvement of approximately 35–50% compared with the state-of-the-art result under different cores. [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]

Recommender systems can strongly influence which information we see online, e.g., on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterward, through a review of more than 160 scholarly publications, we present an overview of how research in this field is currently operationalized, e.g., in terms of general research methodology, fairness measures, and algorithmic approaches. Overall, our analysis of recent works points to certain research gaps. In particular, we find that in many research works in computer science, very abstract problem operationalizations are prevalent and questions of the underlying normative claims and what represents a fair recommendation in the context of a given application are often not discussed in depth. These observations call for more interdisciplinary research to address fairness in recommendation in a more comprehensive and impactful manner. [ABSTRACT FROM AUTHOR]

The text is a preface to the Journal of Computer Science & Technology, specifically a special section on the Computer Graphics International (CGI) conference. The CGI conference is a prestigious international academic conference in the field of computer graphics and virtual reality. It aims to foster collaboration between different disciplines and showcase advancements in computer graphics technology. The CGI 2023 conference was held in Shanghai, China, and featured discussions on various topics including computer graphics, virtual reality, geometric design, animation, and healthcare. The special section in the journal includes five selected papers that cover a range of topics and provide an overview of the latest trends in computer graphics and virtual reality. The preface expresses gratitude to the authors and reviewers and encourages readers to explore the special section to stay updated on the developments in these fields. [Extracted from the article]

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]

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]

Co-authored publications can bring positive results for those who participate, such as gaining additional expertise, accessing more funding or increasing the publication impact. China, the European Union, and the United States have been collaborating between each other throughout the years in the field of Computer Science. These collaborations varied over time, as well as they impacted the regions in different ways. In this paper, we collected the publications from these territories across 31 years on the topic of Computer Science and studied them focusing on how the regions have approached co-authorship. In particular, we have analyzed the number of collaborations during that period, the impact of those papers measured as the number of citations, and the topics that have been researched. We conclude that China's focus on Computer Science fields has led it to be the most productive region in recent years; plus, it has benefited from the American and European reputation, by increasing its citation rates when collaborating with them. On the other hand, the EU and the US have benefited from Chinese interest in computer science, increasing the number of publications together. [ABSTRACT FROM AUTHOR]

The theory of Formal Concept Analysis (FCA) finds diverse applications in fields like knowledge extraction, cognitive concept learning and data mining. The construction of a concept lattice significantly influences the effectiveness of formal concept analysis; hence, the development of high-performance algorithms for concept construction is crucial. In this paper, we introduce a novel algorithm called "Bit-Close" for formal concept construction. Bit-Close leverages bit representation and operations, fundamental to computer science, to enhance the In-Close algorithm. Furthermore, we explore the parallel method of Bit-Close. Our experimental results, obtained from multiple public and random datasets, demonstrate that Bit-Close outperforms In-Close by approximately 20% and is significantly better than other competing algorithms. [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]

This document is a guest editorial for the special section on SEFM 2020 and 2021 in the journal Software & Systems Modeling. The International Conference on Software Engineering and Formal Methods (SEFM) aims to bring together practitioners and researchers to advance the state of the art in formal methods and their application in the software industry. This special section includes selected papers from SEFM 2020 and 2021, which were held virtually during the COVID pandemic. Each article in this section has undergone rigorous peer review and has been revised and extended compared to its conference version. The selected papers cover topics such as embedded software portability and verification, nontermination inference, quantitative modeling and analysis of BDI agents, lazy model checking for recursive state machines, P-stable abstractions of hybrid systems, fairness and guarantees for extended bounded response LTL+P synthesis, counterexample classification, analyzing the impact of human errors on interactive service robotic scenarios, and active model learning of stochastic reactive systems. [Extracted from the article]

Computer system resilience refers to the ability of a computer system to continue functioning even in the face of unexpected events or disruptions. These disruptions can be caused by a variety of factors, such as hardware failures, software glitches, cyber attacks, or even natural disasters. Modern computational environments need applications that can recover quickly from major disruptions while also being environmentally sustainable. Balancing system resilience with energy efficiency is challenging, as efforts to improve one can harm the other. This paper presents a method to enhance disaster survivability in microservice architectures, particularly those using Kubernetes in cloud-based environments, focusing on optimizing electrical energy use. Aiming to save energy, our work adopt the consolidation strategy that means grouping multiple microservices on a single host. Our aproach uses a widely adopted analytical model, the Generalized Stochastic Petri Net (GSPN). GSPN are a powerful modeling technique that is widely used in various fields, including engineering, computer science, and operations research. One of the primary advantages of GSPN is its ability to model complex systems with a high degree of accuracy. Additionally, GSPN allows for the modeling of both logical and stochastic behavior, making it ideal for systems that involve a combination of both. Our GSPN models compute a number of metrics such as: recovery time, system availability, reliability, Mean Time to Failure, and the configuration of cloud-based microservices. We compared our approach against others focusing on survivability or efficiency. Our approach aligns with Recovery Time Objectives during sudden disasters and offers the fastest recovery, requiring 9% less warning time to fully recover in cases of disaster with alert when compared to strategies with similar electrical consumption. It also saves about 27% energy compared to low consolidation strategies and 5% against high consolidation under static conditions. [ABSTRACT FROM AUTHOR]

In our paper, we consider the following general problems: check feasibility, count the number of feasible solutions, find an optimal solution, and count the number of optimal solutions in P ∩ Z n , assuming that P is a polyhedron, defined by systems A x ≤ b or A x = b , x ≥ 0 with a sparse matrix A. We develop algorithms for these problems that outperform state-of-the-art ILP and counting algorithms on sparse instances with bounded elements in terms of the computational complexity. Assuming that the matrix A has bounded elements, our complexity bounds have the form s O (n) , where s is the minimum between numbers of non-zeroes in columns and rows of A, respectively. For s = o (log n) , this bound outperforms the state-of-the-art ILP feasibility complexity bound (log n) O (n) , due to Reis & Rothvoss (in: 2023 IEEE 64th Annual symposium on foundations of computer science (FOCS), IEEE, pp. 974–988). For s = ϕ o (log n) , where ϕ denotes the input bit-encoding length, it outperforms the state-of-the-art ILP counting complexity bound ϕ O (n log n) , due to Barvinok et al. (in: Proceedings of 1993 IEEE 34th annual foundations of computer science, pp. 566–572, https://doi.org/10.1109/SFCS.1993.366830, 1993), Dyer, Kannan (Math Oper Res 22(3):545–549, https://doi.org/10.1287/moor.22.3.545, 1997), Barvinok, Pommersheim (Algebr Combin 38:91–147, 1999), Barvinok (in: European Mathematical Society, ETH-Zentrum, Zurich, 2008). We use known and new methods to develop new exponential algorithms for Edge/Vertex Multi-Packing/Multi-Cover Problems on graphs and hypergraphs. This framework consists of many different problems, such as the Stable Multi-set, Vertex Multi-cover, Dominating Multi-set, Set Multi-cover, Multi-set Multi-cover, and Hypergraph Multi-matching problems, which are natural generalizations of the standard Stable Set, Vertex Cover, Dominating Set, Set Cover, and Maximum Matching problems. [ABSTRACT FROM AUTHOR]

Adaptive learning is a teaching approach aiming to personalize the learning experience for each student. In Computer Science Education (CSE), Adaptive Learning Systems (ALS) can provide students with customized lessons, exercises, and assessments based on their previous knowledge, strengths, and weaknesses. Some literature reviews focus on adaptive learning techniques, but none specifically analyze the methods used in adaptive systems in CSE. Our overall objective is to identify the computing techniques that are implemented within ALS to provide adaptive computer education in higher education. We conducted a scoping review (SR) based on the Guidelines for performing Systematic Literature Reviews in Software Engineering, defining six research questions and a search string. We performed searches in four databases, retrieving 512 documents. After applying the selection criteria, we selected 29 works. The results present quantitative and qualitative data from those 29 studies. Most research has used adaptive learning in programming education (12 articles) and databases (9 papers). Nine articles used learning styles as the core of adaptation, with the Felder-Silverman model being used by seven of them. The most commonly used approach was the rule-based system approach built by experts within the proposal, which was used in 17.24% of the articles. Clustering, collaborative filtering, fuzzy logic, and K-NN were used in 10.34% of the proposals. The positive aspect of "delivering adaptive content" was pointed out in seven articles. As for negative aspects, the problem of lack of student engagement was mentioned in four articles. This study discusses the application of dynamic and static models based on learning styles (LS) and the challenges involved in their use. Additionally, another aspect pointed out is that it is crucial to carefully consider pedagogical aspects in learning systems and have mixed development teams. The study also discusses student motivation and the use of gamification elements as a strategy to engage students in electronic learning systems. Finally, the results also indicate that the level of student knowledge is typically represented by a student model (SM), and the growing trend is the use of hybrid SMs. The research identified the most studied areas in ALS in CSE, such as computer programming and databases. The most common techniques used in ALS were rule-based systems built by experts, fuzzy logic, and clustering. The most common feature of ALS identified was learning resources, and the majority of studies focused on investigating the impact of system accuracy. This research can help researchers design better adaptive learning systems and understand challenges that need to be addressed. [ABSTRACT FROM AUTHOR]

We consider the weighted k-set packing problem, where, given a collection S of sets, each of cardinality at most k, and a positive weight function w : S → Q > 0 , the task is to find a sub-collection of S consisting of pairwise disjoint sets of maximum total weight. As this problem does not permit a polynomial-time o (k log k) -approximation unless P = N P (Hazan et al. in Comput Complex 15:20–39, 2006. https://doi.org/10.1007/s00037-006-0205-6), most previous approaches rely on local search. For twenty years, Berman's algorithm SquareImp (Berman, in: Scandinavian workshop on algorithm theory, Springer, 2000. https://doi.org/10.1007/3-540-44985-X%5f19), which yields a polynomial-time k + 1 2 + ϵ -approximation for any fixed ϵ > 0 , has remained unchallenged. Only recently, it could be improved to k + 1 2 - 1 63 , 700 , 993 by Neuwohner (38th International symposium on theoretical aspects of computer science (STACS 2021), Leibniz international proceedings in informatics (LIPIcs), 2021. https://doi.org/10.4230/LIPIcs.STACS.2021.53). In her paper, she showed that instances for which the analysis of SquareImp is almost tight are "close to unweighted" in a certain sense. But for the unit weight variant, the best known approximation guarantee is k + 1 3 + ϵ (Fürer and Yu in International symposium on combinatorial optimization, Springer, 2014. https://doi.org/10.1007/978-3-319-09174-7%5f35). Using this observation as a starting point, we conduct a more in-depth analysis of close-to-tight instances of SquareImp. This finally allows us to generalize techniques used in the unweighted case to the weighted setting. In doing so, we obtain approximation guarantees of k + ϵ k 2 , where lim k → ∞ ϵ k = 0 . On the other hand, we prove that this is asymptotically best possible in that local improvements of logarithmically bounded size cannot produce an approximation ratio below k 2 . [ABSTRACT FROM AUTHOR]

Computer science is often integrated within early childhood education (ECE) through the use of educational robots. This requires adequate preparation of ECE teachers to program and debug, which can be done with scaffolding. In this paper, we use a QUANT + qual mixed method approach incorporating lag sequential analysis, ICAP framework coding, sentiment analysis, and supplementary qualitative analysis. Students tended to engage more with tasks in the revision and evaluation phases than in the exploration phase, and became less dependent on scaffolding prompts and more competent in debugging, as debugging task complexity increased. This study addresses a key gap in the scaffolding literature in that it examined learners' interactions with scaffolding for evidence of decreased reliance on scaffolding. [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]

In this paper, we introduce the concept of M-fuzzifying norm, which can be seen as a fuzzy co-norm. Fuzzy norm plays a key role in fuzzy functional analysis, which can be applied in theoretical computer science and information theory. We also present a characterization of M-fuzzifying norm using cut-sets. Furthermore, we prove that Morsi's fuzzy norm and M-fuzzifying norm are equivalent when M = [ 0 , 1 ] . In addition, we show that an M-fuzzifying norm can induce an M-fuzzifying topology. Finally, we study an M-fuzzifying Banach space. We also study the boundedness of M-fuzzifying normed space. In particular, the mapping corresponding to the degree of fuzzifying boundedness is exactly an M-fuzzifying bornology. [ABSTRACT FROM AUTHOR]

This document is a guest editorial for a special section in the journal "Software & Systems Modeling." The special section features revised and extended versions of selected papers from the 20th International Conference on Software Engineering and Formal Methods (SEFM'22), which was held in Berlin, Germany in September 2022. The SEFM conference series aims to advance the state of the art in formal methods and their integration into practical software engineering methods and tools. The special section includes papers on topics such as user-centric analysis, automated generation of finite automata, formal specifications of smart contracts, distributed verification of programs, and abstraction-based verification of neural networks. The authors express their gratitude to the authors and reviewers for their contributions and thank Springer for their support in publishing the special section. [Extracted from the article]

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]

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]

Digital data transmission and storage are indispensable components for our contemporary information-driven society. As data volumes keep increasing, the need for efficient coding schemes grows. This paper gives a novel coding scheme for generating sixteen codes from generators with quaternary codes, thereby offering a significant increase in data representation capacity, new possibilities for data encoding and decoding, and an overall improvement in the efficiency of the encoding scheme. This provides up a new perspective for numerous fields that can profit from the proposed scheme, including coding theory, computer science, modeling, and experimental design. We focus on only experimental design and modeling, due to space limits and their importance in designing and modeling real-world and scientific phenomena. The main findings indicate that the new generated sixteen codes can be used as optimal experimental designs with good space-filling behavior when designing large-scale experiments, and as efficient datasets that perform well in fitting various functions even with a small number of points and without modeling assumptions. The relationships between the codewords of the generated sixteen codes and the codewords of the generator quaternary codes are investigated. These relationships provide a benchmark by which to select the generator most efficiently, resulting in codewords that are efficient from multiple angles. Furthermore, the space-filling behavior and isomorphism examination of the sixteen codes generated as designs are deeply theoretically studied. [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]

To explain why, in scientific problem solving, a diagram can be "worth ten thousand words," Jill Larkin and Herbert Simon (1987) relied on a computer model: two representations can be "informationally" equivalent but differ "computationally," just as the same data can be encoded in a computer in multiple ways, more or less suited to different kinds of processing. The roots of this proposal lay in cognitive psychology, more precisely in the "imagery debate" of the 1970s on whether there are image-like mental representations. Simon (1972, 1978) hoped to solve this debate by thoroughly reducing the differences between forms of mental representations (e.g., between images and sentences) to differences in computational efficiency; to carry out this reduction, he borrowed from computer science the concepts of data type and of data structure. I argue that, in the end, his account amounted to nothing more than characterizing representations by the fast operations on them. This analysis then allows me to assess what Simon's approach actually achieves when transported from psychology to the study of scientific representations, as in Larkin and Simon (1987): it allows comparing, not representations in and of themselves, but rather the computational roles they play in particular problem-solving processes—that is, representations together with a particular way of using them. [ABSTRACT FROM AUTHOR]

Within the field of social reintegration and re-education, this paper presents an educational experience carried out at the Iquitos Penitentiary Center, Lima, Peru, with the aim of providing an introduction to informatics to 25 inmates who volunteered to take part in the project. Twenty students and a teacher from the Scientific University of the South also in Peru, were responsible for initiating the transmission of knowledge from the university to inmates, with the collaboration and participation of the penitentiary coordinator. The main objectives of the case study were to validate both the suitability of the CS unplugged proposal and the adaptability of the L2T2L pedagogic strategy to the transmission of knowledge to adults, specifically penitentiary inmates. This strategy had been originally designed to transmit informatics knowledge from university to primary school. The validity and effectiveness of the experience was assessed using surveys. Results confirm that inmates achieved a good level of understanding when endeavoring to resolve most of the CS unplugged assignments designed for them. It was also seen that L2T2L is adaptable and valid for different scenarios other than those for which it was initially designed. Indeed, it was proven to be valid for transmitting knowledge to the prison population. Finally, it should be pointed out that the experience is easily replicable and that it brings an opportunity to introduce informatics into education programs in prisons, something which can contribute enormously to social reintegration and re-education, facilitating the subsequent reentry of inmates into the community once their period of imprisonment has ended. [ABSTRACT FROM AUTHOR]

Artificial-intelligence algorithms could identify scientists who need support with translating their work into real-world applications and more. Leaders must step up. [ABSTRACT FROM AUTHOR]

Generative AI models are now widely accessible, enabling everyone to create their own machine-made something. But these models can collapse if their training data sets contain too much AI-generated content.Generative-AI models collapse when trained recursively. [ABSTRACT FROM AUTHOR]

This document is a special issue of Mathematical Programming, Series B (MPB) that focuses on integer programming and combinatorial optimization. It contains 23 high-quality articles that were previously presented at the 23rd Conference on Integer Programming and Combinatorial Optimization (IPCO) in 2022. The IPCO conference is held annually and provides a platform for researchers and practitioners to share developments in theory, computation, and applications in these areas. The papers in this special issue underwent a rigorous refereeing process and represent the best of the field. The document also acknowledges the authors, referees, and the Editor-in-Chief of MPB for their contributions. [Extracted from the article]

The number of errors produced by an LLM can be reduced by grouping its outputs into semantically similar clusters. Remarkably, this task can be performed by a second LLM, and the method’s efficacy can be evaluated by a third.Confabulations detected by clustering semantically similar LLM responses. [ABSTRACT FROM AUTHOR]

The categorical concepts, tools, and techniques advanced not only the theory of automata and languages but also developed the theory of fuzzy automata and languages and played a significant role in many other branches of theoretical computer science. Unlike theory of classical/fuzzy automata with output and their languages, this paper aims to incorporate the concept of fuzziness and roughness together in the theory of automata with output and their languages, to introduce the concepts of crisp deterministic automata with fuzzy rough output, and fuzzy rough language and study their categorical aspects. We have shown that the crisp deterministic automata with fuzzy rough outputs and fuzzy rough languages, along with their morphisms, respectively, form categories CDAFRO and FRL. We have also shown the existence of a functor between the categories CDAFRO and FRL. The categorical concepts of product and coproduct of a subfamily of object-class, equalizer, and coequalizer of a pair of parallel morphisms in category CDAFRO are studied. Finally, for a given fuzzy rough language, a minimal crisp deterministic automaton having fuzzy rough output recognizing it is constructed using the fuzzy generalization of foremost Myhill–Nerode’s theory of automata and languages. [ABSTRACT FROM AUTHOR]

DeFi blockchain technology, known as decentralized finance today, separates from the traditional financial ecosystem and ushers the new financial landscape onto digital platforms. In decentralized financial applications, all digital assets are safeguarded by blockchain technology. Thanks to this technology, investors can transfer their financial assets without being dependent on banking authorities. Despite the numerous advantages they bring, financial assets based on the decentralized finance ecosystem come with certain disadvantages. These assets are difficult to control, easily manipulated, and are at risk due to their vulnerability to cyberattacks. This study conducted bibliometric analyses on a total of 930 publications registered in the Web of Science (WoS) and Scopus databases using the VOSviewer program. In both databases, "all fields" were filtered and scanned with the keyword "decentralized finance." According to the results, the Scopus database has much richer content compared to the WoS database. The most cited author in the Scopus database was Chen Y, while in the WoS database, it was Nakomoto S. There has been a significant increase in the number of publications in both databases since 2020. Additionally, it was detected that the most cited countries in both databases were the USA, China and England, respectively. It has been observed that computer science comes to the fore in the publication rankings. Decentralized finance is an interdisciplinary field of study. Therefore, many more qualified hybrid studies are needed. More studies are needed, especially examining investor behavior. The analyses presented in this article will enable researchers to grasp the bigger picture from a holistic perspective. [ABSTRACT FROM AUTHOR]

Starting from Picasso's quote ("Computers are useless. They can only give you answers"), we discuss the introduction of generative artificial intelligence (AI), including generative adversarial networks (GANs) and transformer-based architectures such as large language models (LLMs) in radiology, where their potential in reporting, image synthesis, and analysis is notable. However, the need for improvements, evaluations, and regulations prior to clinical use is also clear. Integration of LLMs into clinical workflow needs cautiousness, to avoid or at least mitigate risks associated with false diagnostic suggestions. We highlight challenges in synthetic image generation, inherent biases in AI models, and privacy concerns, stressing the importance of diverse training datasets and robust data privacy measures. We examine the regulatory landscape, including the 2023 Executive Order on AI in the United States and the 2024 AI Act in the European Union, which set standards for AI applications in healthcare. This manuscript contributes to the field by emphasizing the necessity of maintaining the human element in medical procedures while leveraging generative AI, advocating for a "machines-in-the-loop" approach. [ABSTRACT FROM AUTHOR]

Artificial intelligence in the workplace is becoming increasingly common. These tools are sometimes used to aid users in performing their task, for example, when an artificial intelligence tool assists a radiologist in their search for abnormalities in radiographic images. The use of artificial intelligence brings a wealth of benefits, such as increasing the efficiency and efficacy of performance. However, little research has been conducted to determine how the use of artificial intelligence assistants might affect the user's cognitive skills. In this theoretical perspective, we discuss how artificial intelligence assistants might accelerate skill decay among experts and hinder skill acquisition among learners. Further, we discuss how AI assistants might also prevent experts and learners from recognizing these deleterious effects. We then discuss the types of questions: use-inspired basic cognitive researchers, applied researchers, and computer science researchers should seek to answer. We conclude that multidisciplinary research from use-inspired basic cognitive research, domain-specific applied research, and technical research (e.g., human factors research, computer science research) is needed to (a) understand these potential consequences, (b) design artificial intelligence systems to mitigate these impacts, and (c) develop training and use protocols to prevent negative impacts on users' cognitive skills. Only by answering these questions from multidisciplinary perspectives can we harness the benefits of artificial intelligence in the workplace while preventing negative impacts on users' cognitive skills. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) techniques are increasingly applied across various domains, favoured by the growing acquisition and public availability of large, complex datasets. Despite this trend, AI publications often suffer from lack of reproducibility and poor generalisation of findings, undermining scientific value and contributing to global research waste. To address these issues and focusing on the learning aspect of the AI field, we present RENOIR (REpeated random sampliNg fOr machIne leaRning), a modular open-source platform for robust and reproducible machine learning (ML) analysis. RENOIR adopts standardised pipelines for model training and testing, introducing elements of novelty, such as the dependence of the performance of the algorithm on the sample size. Additionally, RENOIR offers automated generation of transparent and usable reports, aiming to enhance the quality and reproducibility of AI studies. To demonstrate the versatility of our tool, we applied it to benchmark datasets from health, computer science, and STEM (Science, Technology, Engineering, and Mathematics) domains. Furthermore, we showcase RENOIR's successful application in recently published studies, where it identified classifiers for SET2D and TP53 mutation status in cancer. Finally, we present a use case where RENOIR was employed to address a significant pharmacological challenge—predicting drug efficacy. RENOIR is freely available at https://github.com/alebarberis/renoir. [ABSTRACT FROM AUTHOR]

Criticism of our decision to publish AlphaFold3 raises important questions. We welcome readers’ views. [ABSTRACT FROM AUTHOR]

Some researchers see superhuman qualities in artificial intelligence. All scientists need to be alert to the risks this creates. [ABSTRACT FROM AUTHOR]

In the modern digital sphere, graph theory is a significant field of research that has a great deal of significance. It finds widespread application in computer science, robotic directions, and chemistry. Additionally, graph theory is used in robot network localization, computer network problems and the formation of various chemical structures for networks. Moreover, it finds uses in exploring diffusion mechanisms and scheduling aircraft as well. The present research project examines and concentrates on the edge version of metric dimension of the Concealed Non-Kekuléan Benzenoid Hydrocarbon, Polythiophene, Backbone DNA network and Bakelite networks. All the mentioned networks have constant edge metric dimension except Bakelite network, as demonstrated by the results. If we talk about the applications of these networks, Polythiophene are used to treat prion disorders. It is also capable of detecting metal ions. The concept of Bakelite, which finds applications in the jewelry, electrical, cookware, sports, and clock industries, had an impact on the invention of modern polymers. The functions of DNA include information encoding, replication, mutation, and recombination gene expression. [ABSTRACT FROM AUTHOR]

We report the results of a 4800-respondent survey among students at a technical university regarding their usage of artificial intelligence tools, as well as their expectations and attitudes about these tools. We find that many students have come to differentiated and thoughtful views and decisions regarding the use of artificial intelligence. The majority of students wishes AI to be integrated into their studies, and several wish that the university would provide tools that are based on reliable, university-level materials. We find that acceptance of and attitudes about artificial intelligence vary across academic disciplines. We also find gender differences in the responses, which however are smaller the closer the student's major is to informatics (computer science). [ABSTRACT FROM AUTHOR]