Search

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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.

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]

This editorial discusses the importance of reproducibility in the field of evolutionary computation (EC) and highlights the need for improvement in this area. The editorial acknowledges that reproducibility issues in EC have been a topic of discussion, but there are few studies and reproducibility studies themselves are rare. The editorial also introduces three papers featured in a special issue of Evolutionary Computation that address different aspects of reproducibility in EC research. The editorial concludes by noting that there is a cultural shift within computer science, including EC, towards expecting and rewarding reproducibility efforts. [Extracted from the article]

The seven papers included in this special issue represent a selection of extended contributions presented at the 16th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2021, held in Bilbao, Spain, September 22nd-24th, 2021, and organized by the BISITE group and the University of Deusto. The SOCO 2021 international conference represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines that 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]

Combinatorial optimization problem (COP) is an NP-hard problem for which finding an optimal solution is difficult, especially as the problem size increases. The Traveling Salesman Problem (TSP), one of the COPs that can be formulated over a graph, is a well-researched area in operations research and computer science. Deep Reinforcement Learning (DRL) is now regarded as a promising approach for solving TSP and other NP hard problems. In this paper, we propose a novel Gated Deep Graph Attention Network (G-DGANet) which builds upon the existing Graph Neural Network (GNN) to solve TSP. The proposed G-DGANet uses gating mechanism between subsequent layers of the network to extract representations of nodes deeper in the network without loss in performance. G-DGANet also designs a novel aggregator to construct global graph embeddings from different embedding preferences. In addition, to effectively learn underlying structure of a graph, G-DGANet integrates node and edge information of the graph while updating node representations in the message passing mechanism. We used proximal policy optimization (PPO) to train G-DGANet on randomly generated instances. We conducted an experiment on randomly generated instances and on real-world road network data generated from digital maps to verify the performance of G-DGANet. The findings from experiments demonstrate that G-DGANet outperforms most traditional heuristics and existing DRL approaches, with high generalization abilities from random instance training to random instance testing and real-world road network instance testing. [ABSTRACT FROM AUTHOR]

Fair division of indivisible resources is a fundamental problem in many disciplines, including computer science, economy, operations research, etc. The existence of PMMS allocations of goods is a major open problem in fair division, even for additive valuations. At the state of the art, there is no identified setting where PMMS allocations are deemed impossible, and the known existing results regarding their existence are limited to highly constrained settings. In this paper, we provide an algorithm for computing a PMMS allocation for identical variant. We also use the algorithm to find the PMMS allocations for 3 agents in line graphs. Moreover, we show that a 4 5 -PMMS allocation can be computed in polynomial time when agents agree on the ordinal ranking of the goods. A quantitative gauge for assessing the impact on social welfare is known as the price of fairness , which quantifies the maximum possible reduction in social welfare due to the imposition of fairness constraints. Originally explored in the context of divisible goods, the concept of the price of fairness continues to be a pivotal metric for evaluating the effect of fairness considerations on overall social welfare. Consequently, we study the efficiency decrease under PMMS allocations and prove a tight upper bound on the price of PMMS allocations. • We present an algorithm to compute a PMMS allocation if agents' valuation functions are identical. • We analyze an existing algorithm terminates with a 4 5 -PMMS allocation in polynomial time when agents have identical ordinal ranking values of the goods. • We also examine the efficiency loss associated with PMMS allocations and establish their precise price bound. [ABSTRACT FROM AUTHOR]

Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning. [ABSTRACT FROM AUTHOR]

In natural language processing (NLP), transformer based models have surpassed recurrent neural networks (RNN) as state of the art, being introduced specifically to address the limitations of RNNs originating from its sequential nature. As a similar sequence modeling problem, transformer methods can be readily adapted for deep learning time series prediction. This paper proposes a sparse transformer based approach for electricity load prediction. The layers of a transformer addresses the shortcomings of RNNs and CNNs by applying the attention mechanism on the entire time series, allowing any data point in the input to influence any location in the output of the layer. This allows transformers to incorporate information from the entire sequence in a single layer. Attention computations can also be parallelized. Thus, transformers can achieve faster speeds, or trade this speed for more layers and increased complexity. In experiments on public datasets, the sparse transformer attained comparable accuracy to an RNN-based SOTA method (Liu et al., 2022) while being up to 5× faster during inference. Moreover, the proposed model is general enough to forecast the load from individual households to city levels as shown in the extensive experiments conducted. [ABSTRACT FROM AUTHOR]

Event sequence data is increasingly available. Many business operations are supported by information systems that record transactions, events, state changes, message exchanges, and similar elements. This observation also applies to various industries, including production, logistics, healthcare, financial services, and education. The variety of application areas explains that techniques for event sequence data analysis have been developed rather independently in different fields of computer science. Most prominent are contributions from information visualization and from process mining. So far, the contributions from these two fields have neither been compared nor have they been mapped to an integrated framework. Such intransparency is problematic since it bears the risk of opportunities of integration are missed and concepts established in one field are independently reinvented in the other one. In this paper, we develop the Event Sequence Visualization framework (ESeVis) that gives due credit to the traditions of both fields. Our mapping study provides an integrated perspective on both fields and identifies potential for synergies for future research. • We propose the ESeVis Framework to categorize visualizations of event sequence data. • We review the literature of event sequence data visualizations. • Instance representations of event sequences are mostly from information visualization. • Process mining focuses on generating formal models. • Potential for synergy of process mining and information visualization. [ABSTRACT FROM AUTHOR]

An eigenvalue λ of a graph G of order n is a main eigenvalue if its eigenspace is not orthogonal to the all-ones vector j n. In 1978, Cvetković proved that G has exactly one main eigenvalue if and only if G is regular, and posed the following long-standing problem: characterize the graphs with exactly k (2 ≤ k ≤ n) main eigenvalues. Graphs of order n with n , n − 1 main eigenvalues are called controllable, almost controllable, respectively. Cographs, threshold graphs are frequently studied in structural graph theory and computer science. In this paper, all almost controllable cographs, all almost controllable threshold graphs and all almost controllable graphs with second largest eigenvalue less than or equal to 5 − 1 2 are characterized. Furthermore, we give some results about cographs with exactly n − 2 main eigenvalues, and propose some additional problems for further study. [ABSTRACT FROM AUTHOR]