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In the non-uniform k-center problem, the objective is to cover points in a metric space with specified number of balls of different radii. Chakrabarty, Goyal, and Krishnaswamy [ICALP 2016, Trans. on Algs. 2020] (CGK, henceforth) give a constant factor approximation when there are two types of radii. In this paper, we give a constant factor approximation for the two radii case in the presence of outliers. To achieve this, we need to bypass the technical barrier of bad integrality gaps in the CGK approach. We do so using "the ellipsoid method inside the ellipsoid method": use an outer layer of the ellipsoid method to reduce to stylized instances and use an inner layer of the ellipsoid method to solve these specialized instances. This idea is of independent interest and could be applicable to other problems. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) have been widely adopted as general computational tools both in computer science as well as many other engineering fields. Stochastic gradient descent (SGD) and adaptive methods such as Adam are popular as robust optimization algorithms used to train the ANNs. However, the effectiveness of these algorithms is limited because they calculate a search direction based on a first-order gradient. Although higher-order gradient methods such as Newton's method have been proposed, they require the Hessian matrix to be semi-definite, and its inversion incurs a high computational cost. Therefore, in this paper, we propose a variable three-term conjugate gradient (VTTCG) method that approximates the Hessian matrix to enhance search direction and uses a variable step size to achieve improved convergence stability. To evaluate the performance of the VTTCG method, we train different ANNs on benchmark image classification and generation datasets. We also conduct a similar experiment in which a grasp generation and selection convolutional neural network (GGS-CNN) is trained to perform intelligent robotic grasping. After considering a simulated environment, we also test the GGS-CNN with a physical grasping robot. The experimental results show that the performance of the VTTCG method is superior to that of four conventional methods, including SGD, Adam, AMSGrad, and AdaBelief. [ABSTRACT FROM AUTHOR]

This paper traces the empiricist program from early debates between nativism and behaviorism within philosophy, through debates about early connectionist approaches within the cognitive sciences, and up to their recent iterations within the domain of deep learning. We demonstrate how current debates on the nature of cognition via deep network architecture echo some of the core issues from the Chomsky/Quine debate and investigate the strength of support offered by these various lines of research to the empiricist standpoint. Referencing literature from both computer science and philosophy, we conclude that the current state of deep learning does not offer strong encouragement to the empiricist side despite some arguments to the contrary. [ABSTRACT FROM AUTHOR]

Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. However, perception and sensing for autonomous driving under adverse weather conditions have been the problem that keeps autonomous vehicles (AVs) from going to higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in a systematic way, and surveys the solutions against inclement weather conditions. State-of-the-art algorithms and deep learning methods on perception enhancement with regard to each kind of weather, weather status classification, and remote sensing are thoroughly reported. Sensor fusion solutions, weather conditions coverage in currently available datasets, simulators, and experimental facilities are categorized. Additionally, potential ADS sensor candidates and developing research directions such as V2X (Vehicle to Everything) technologies are discussed. By looking into all kinds of major weather problems, and reviewing both sensor and computer science solutions in recent years, this survey points out the main moving trends of adverse weather problems in perception and sensing, i.e., advanced sensor fusion and more sophisticated machine learning techniques; and also the limitations brought by emerging 1550 nm LiDARs. In general, this work contributes a holistic overview of the obstacles and directions of perception and sensing research development in terms of adverse weather conditions. [ABSTRACT FROM AUTHOR]

The metric dimension has various applications in several fields, such as computer science, image processing, pattern recognition, integer programming problems, drug discovery, and the production of various chemical compounds. The lowest number of vertices in a set with the condition that any vertex can be uniquely identified by the list of distances from other vertices in the set is the metric dimension of a graph. A resolving function of the graph G is a map ϑ : V (G) → [ 0 , 1 ] such that ∑ u ∈ R { v , w } ϑ (u) ≥ 1 , for every pair of adjacent distinct vertices v , w ∈ V (G) . The local fractional metric dimension of the graph G is defined as ldim f (G) = min { ∑ v ∈ V (G) ϑ (v) , where ϑ is a local resolving function of G } . This paper presents a new family of planar networks namely, rotationally heptagonal symmetrical graphs by means of up to four cords in the heptagonal structure, and then find their upper-bound sequences for the local fractional metric dimension. Moreover, the comparison of the upper-bound sequence for the local fractional metric dimension is elaborated both numerically and graphically. Furthermore, the asymptotic behavior of the investigated sequences for the local fractional metric dimension is addressed. [ABSTRACT FROM AUTHOR]

This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020–2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application. [ABSTRACT FROM AUTHOR]

Boolean logic is widely used in almost every discipline including linguistics, philosophy, mathematics, computer science and engineering. Boolean logic is characterized by the two possible truth values, and various logical connectives/operations allow us to make compound statements, conditions. Most of the programming languages, if not all, have some of the logic operations: conjunction, disjunction and negation. Actually, since the set of these three operations form a basis, any logical statement can be formed by them. However, on the one hand, there are smaller bases as well, i.e., one of the conjunction or disjunction is already superfluous. Moreover, there are bases with only one operation, e.g., by NAND. On the other hand, one may allow other operations helping the programmer/user to define the conditions of conditional statements and loops in a simpler manner. In this paper we discuss these issues, including some practical points, implementation issues and short cut evaluations for various operations. [ABSTRACT FROM AUTHOR]

Laser-plasma physics has developed rapidly over the past few decades as lasers have become both more powerful and more widely available. Early experimental and numerical research in this field was dominated by single-shot experiments with limited parameter exploration. However, recent technological improvements make it possible to gather data for hundreds or thousands of different settings in both experiments and simulations. This has sparked interest in using advanced techniques from mathematics, statistics and computer science to deal with, and benefit from, big data. At the same time, sophisticated modeling techniques also provide new ways for researchers to deal effectively with situation where still only sparse data are available. This paper aims to present an overview of relevant machine learning methods with focus on applicability to laser-plasma physics and its important sub-fields of laser-plasma acceleration and inertial confinement fusion. [ABSTRACT FROM AUTHOR]

The global COVID-19 pandemic has had impact not just on physical health, but also on mental health, particularly among undergraduate students. Previous studies have demonstrated that Computer Science students in particular already exhibit more symptoms of anxiety and depression than the general population and students in other fields. The study aimed to measure the impact of COVID-19 on Computer Science students' mental health, by assessing the prevalence and associated factors of anxiety and depression symptoms, and the factors that most affected their well-being. An online cross-sectional survey focused on concerns and impacts of COVID-19 was designed. The Beck Anxiety and Depression Inventories were used to measure the anxiety and depression symptoms. The data were anonymously collected, and analyzed using the TIBCO Statistica™ software, by non-parametric tests. 174 students (135 males; age = 21.07±2.47 years) participated in the study. The prevalence of symptoms was 36.7% for anxiety and 60.3% for depression. 81% of students reported that their well-being had been negatively affected by the pandemic, with the most reported concerns including social isolation and delay in academic progress. Correlations between anxiety and depression symptoms, strong social relationships, regular practice of physical exercise, and students' level of satisfaction with their academic program were found. This paper sheds light on the impact of the COVID-19 pandemic on CS students' mental health, suggests ways in which Computer Science students may be able to reduce symptoms of anxiety and depression, and improve their mental health during and after the pandemic. [ABSTRACT FROM AUTHOR]

Electrical utilities and system operators (SOs) are constantly looking for solutions to problems in the management and control of the power network. For this purpose, SOs are exploring new research fields, which might bring contributions to the power system environment. A clear example is the field of computer science, within which artificial intelligence (AI) has been developed and is being applied to many fields. In power systems, AI could support the fault prediction of cable joints. Despite the availability of many legacy methods described in the literature, fault prediction is still critical, and it needs new solutions. For this purpose, in this paper, the authors made a further step in the evaluation of machine learning methods (ML) for cable joint health assessment. Six ML algorithms have been compared and assessed on a consolidated test scenario. It simulates a distributed measurement system which collects measurements from medium-voltage (MV) cable joints. Typical metrics have been applied to compare the performance of the algorithms. The analysis is then completed considering the actual in-field conditions and the SOs' requirements. The results demonstrate: (i) the pros and cons of each algorithm; (ii) the best-performing algorithm; (iii) the possible benefits from the implementation of ML algorithms. [ABSTRACT FROM AUTHOR]

The problem of counting independent sets of a graph G is not only mathematically relevant and interesting, but also has many applications in physics, mathematics, and theoretical computer science. In this paper, a novel method for counting independent sets on grid-like structures is presented. It starts by explaining the recurrences used by the method to count independent sets on basic topologies of graphs. The method is extended to process grid-like structures of quadratic faces. The proposal has a lower time complexity than the required on the leading and current method based on the transfer matrix for counting independent sets on grids. [ABSTRACT FROM AUTHOR]

Cystic fibrosis (CF) is an inherited, life-limiting chronic health condition that impacts many organ systems including the lungs, pancreas, liver, intestines, and reproductive organs. Given the various organ systems impacted, treatments can be complex, burdensome, and time consuming. In addition, the stress and emotional impact of CF on caregivers as well as patients is significant. This paper describes delivery of psychosocial care across developmental stages in the largest pediatric CF center in the country. The focus will be on goals of care as well as assessment and intervention strategies. Furthermore, clinical informatics and next steps also will be discussed. [ABSTRACT FROM AUTHOR]

In this paper we first study k × n Youden rectangles of small orders. We have enumerated all Youden rectangles for a range of small parameter values, excluding the almost square cases where k = n -- 1, in a large scale computer search. In particular, we verify the previous counts for (n, k) = (7, 3), (7, 4), and extend this to the cases (11, 5), (11, 6), (13, 4) and (21, 5). For small parameter values where no Youden rectangles exist, we also enumerate rectangles where the number of symbols common to two columns is always one of two possible values, differing by 1, which we call near Youden rectangles. For all the designs we generate, we calculate the order of the autotopism group and investigate to which degree a certain transformation can yield other row-column designs, namely double arrays, triple arrays and sesqui arrays. Finally, we also investigate certain Latin rectangles with three possible pairwise intersection sizes for the columns and demonstrate that these can give rise to triple and sesqui arrays which cannot be obtained from Youden rectangles, using the transformation mentioned above. [ABSTRACT FROM AUTHOR]

The matroidal connectivity and conditional matroidal connectivity are novel indicators to measure the real faulty tolerability. In this paper, for the $n$ n -dimensional alternating group graph $AG_{n}$ A G n , the structure properties and (conditional) matroidal connectivity are studied based on the dimensional partition of $E(AG_{n})$ E (A G n) . We prove that for $S\subseteq E(AG_{n})$ S ⊆ E (A G n) under some limitation on the number of faulty edges in each dimensional edge set, if $|S|\leq (n-1)!-1$ | S | ≤ (n - 1) ! - 1 , then $AG_{n}-S$ A G n - S is connected. We study the value of matroidal connectivity and conditional matroidal connectivity of $AG_{n}$ A G n . Furthermore, simulations have been carried out to compare the matroidal connectivity with other types of conditional connectivity in $AG_{n}$ A G n . The simulation result shows that the matroidal connectivity significantly improves these known fault-tolerant capability of alternating group graphs. [ABSTRACT FROM AUTHOR]

In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. This emerging field has witnessed an extensive growth of promising techniques that have been applied with success to computer science, mathematics, biology, physics and chemistry. But for any successful field to become mainstream and reliable, benchmarks must be developed to quantify progress. This led us in March 2020 to release a benchmark framework that i) comprises of a diverse collection of mathematical and real-world graphs, ii) enables fair model comparison with the same parameter budget to identify key architectures, iii) has an open-source, easy-to-use and reproducible code infrastructure, and iv) is flexible for researchers to experiment with new theoretical ideas. As of December 2022, the GitHub repository1 has reached 2,000 stars and 380 forks, which demonstrates the utility of the proposed open-source framework through the wide usage by the GNN community. In this paper, we present an updated version of our benchmark with a concise presentation of the aforementioned framework characteristics, an additional medium-sized molecular dataset AQSOL, similar to the popular ZINC, but with a real-world measured chemical target, and discuss how this framework can be leveraged to explore new GNN designs and insights. As a proof of value of our benchmark, we study the case of graph positional encoding (PE) in GNNs, which was introduced with this benchmark and has since spurred interest of exploring more powerful PE for Transformers and GNNs in a robust experimental setting. [ABSTRACT FROM AUTHOR]

Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs), as a key building block for combinatorial tasks, either directly as solvers or by enhancing exact solvers. The inductive bias of GNNs effectively encodes combinatorial and relational input due to their invariance to permutations and awareness of input sparsity. This paper presents a conceptual review of recent key advancements in this emerging field, aiming at optimization and machine learning researchers. [ABSTRACT FROM AUTHOR]

Recommendation systems play a key role in everyday life; they are used to suggest items that are selected among many candidates that usually belong to huge datasets. The recommendations require a good performance both in terms of speed and the effectiveness of the provided suggestions. At the same time, one of the most challenging approaches in computer science is quantum computing. This computational paradigm can provide significant acceleration for resource-demanding and time-consuming algorithms. It has become very popular in recent years, thanks to the different tools available to the scientific and technical communities. Since performance has great relevance in recommendation systems, many researchers in the scientific community have recently proposed different improvements that exploit quantum approaches to provide better performance in recommendation systems. This paper gives an overview of the current state of the art in the literature, outlining the different proposed methodologies and techniques and highlighting the challenges that arise from this new approach to the recommendation systems domain. [ABSTRACT FROM AUTHOR]

Solid codes provide outstanding fault-tolerance when used for information transmission through a noisy channel involving not only symbol substitutions, but also synchronisation errors and black-outs. In this paper we provide an automaton theoretic characterisation of solid codes which takes this fault-tolerance into account. The fault-tolerance afforded by a solid code L can be summarised as follows: Consider messages, encoded using L , being sent through a noisy channel. Any code words in L , which are present in the received message, will be decoded correctly, unless they themselves happen to be the results of errors. Thus, errors in the received message will not lead to incorrect decodings of those parts which are error-free. In this paper we consider acceptors which are fault-tolerant in this sense when analysing such received messages. These acceptors characterise the class of solid codes. For finite solid codes an automaton characterisation was published in the sixties by Levenshtein and Romanov. The characterisation uses state-invariant finite-state transducers which act as decoders in such a way that an output is generated exactly when a code word has been read completely. State-invariance means that acceptance does not depend on the initial state — every state can be used as the initial state. The results of Levenshtein and Romanov depend strongly on the fact that the code is finite. In this paper we provide a general automaton theoretic characterisation of arbitrary solid codes without any such restriction. Moreover, the solid code is regular as a language if and only if the automaton used in the characterisation can be reduced to an equivalent finite automaton with equivalent properties. The main results of this paper are as follows: Every acceptor defines a solid code. For every solid code there is a fault-tolerant acceptor defining the code. Such acceptors expose the decomposition of potentially faulty received messages according to the code. For solid codes which are regular as languages these acceptors can be chosen to be finite while preserving all important combinatorial properties. Part of this work was presented at the 14th Journées Montoises of Theoretical Computer Science [32]. [ABSTRACT FROM AUTHOR]

Every year, around 28,100 journals publish 2.5 million research publications. Search engines, digital libraries, and citation indexes are used extensively to search these publications. When a user submits a query, it generates a large number of documents among which just a few are relevant. Due to inadequate indexing, the resultant documents are largely unstructured. Publicly known systems mostly index the research papers using keywords rather than using subject hierarchy. Numerous methods reported for performing single-label classification (SLC) or multi-label classification (MLC) are based on content and metadata features. Content-based techniques offer higher outcomes due to the extreme richness of features. But the drawback of content-based techniques is the unavailability of full text in most cases. The use of metadata-based parameters, such as title, keywords, and general terms, acts as an alternative to content. However, existing metadata-based techniques indicate low accuracy due to the use of traditional statistical measures to express textual properties in quantitative form, such as BOW, TF, and TFIDF. These measures may not establish the semantic context of the words. The existing MLC techniques require a specified threshold value to map articles into predetermined categories for which domain knowledge is necessary. The objective of this paper is to get over the limitations of SLC and MLC techniques. To capture the semantic and contextual information of words, the suggested approach leverages the Word2Vec paradigm for textual representation. The suggested model determines threshold values using rigorous data analysis, obviating the necessity for domain expertise. Experimentation is carried out on two datasets from the field of computer science (JUCS and ACM). In comparison to current state-of-the-art methodologies, the proposed model performed well. Experiments yielded average accuracy of 0.86 and 0.84 for JUCS and ACM for SLC, and 0.81 and 0.80 for JUCS and ACM for MLC. On both datasets, the proposed SLC model improved the accuracy up to 4%, while the proposed MLC model increased the accuracy up to 3%. [ABSTRACT FROM AUTHOR]

Recent advancements in computer science include some optimization models that have been developed and used in real applications. Some metaheuristic search/optimization algorithms have been tested to obtain optimal solutions to speed controller applications in self-driving cars. Some metaheuristic algorithms are based on social behaviour, resulting in several search models, functions, and parameters, and thus algorithm-specific strengths and weaknesses. The present paper proposes a fitness function on the basis of the mathematical description of proportional integrative derivate controllers showing that mean square error is not always the best measure when looking for a solution to the problem. The fitness developed in this paper contains features and equations from the mathematical background of proportional integrative derivative controllers to calculate the best performance of the system. Such results are applied to quantitatively evaluate the performance of twenty-one optimization algorithms. Furthermore, improved versions of the fitness function are considered, in order to investigate which aspects are enhanced by applying the optimization algorithms. Results show that the right fitness function is a key point to get a good performance, regardless of the chosen algorithm. The aim of this paper is to present a novel objective function to carry out optimizations of the gains of a PID controller, using several computational intelligence techniques to perform the optimizations. The result of these optimizations will demonstrate the improved efficiency of the selected control schema. [ABSTRACT FROM AUTHOR]

The paper presents a bibliometric analysis from 2014 to 2020 of the emerging and engaging field of quantum computing called Quantum Machine Learning (QML). The study discusses the analysis results from the comprehensive high indexed databases worldwide such as Institute of Electrical and Electronics Engineers (IEEE), Scopus, Web of Science (WOS), Google Scholar and the Association for Computing Machinery (ACM). Tools like iMapbuilder, IBM and SPSS Statistics are used to provide meaningful insights and flawless representations of the extracted data. There has been little research to provide a macroscopic overview of renowned authors, subject areas, funding agencies and patent applications related to Quantum Clustering (QC). The result and analysis of this study show an interesting fact, most researchers are now aware of quantum technology from the past few years. The purpose of bibliometric and patentometric analysis papers is to figure out the importance and utility of the QC research area. Most of the countries are taking an initiative to seek attention towards QC but the analysis shows that China and the US are leading. The survey revealed that the maximum numbers of publications of QC are from Physics and Astronomy followed by Computer Science. [ABSTRACT FROM AUTHOR]

A coreset of an input set is its small summarization, such that solving a problem on the coreset as its input, provably yields the same result as solving the same problem on the original (full) set, for a given family of problems (models/classifiers/loss functions). Coresets have been suggested for many fundamental problems, for example, in machine/deep learning, computer vision, databases, and theoretical computer science. This introductory paper was written following requests regarding the many inconsistent coreset definitions, lack of source code, the required deep theoretical background from different fields, and the dense papers that make it hard for beginners to apply and develop coresets. The article provides folklore, classic, and simple results including step‐by‐step proofs and figures, for the simplest (accurate) coresets. Nevertheless, we did not find most of their constructions in the literature. Moreover, we expect that putting them together in a retrospective context would help the reader to grasp current results that usually generalize these fundamental observations. Experts might appreciate the unified notation and comparison table for existing results. Open source code is provided for all presented algorithms, to demonstrate their usage, and to support the readers who are more familiar with programming than mathematics. This article is categorized under:Algorithmic Development > Structure DiscoveryFundamental Concepts of Data and Knowledge > Big Data MiningTechnologies > Machine Learning [ABSTRACT FROM AUTHOR]

This article defines and explores the direct citations between citing publications (DCCPs) of a publication. We construct an ego-centred citation network for each paper that contains all of its citing papers and itself, as well as the citation relationships among them. By utilising a large-scale scholarly dataset from the computer science field in the Microsoft Academic Graph (MAG-CS) dataset, we find that DCCPs exist universally in medium and highly cited papers. For those papers that have DCCPs, DCCPs do occur frequently; highly cited papers tend to contain more DCCPs than others. Meanwhile, the number of DCCPs of papers published in different years does not vary dramatically. This paper also discusses the relationship between DCCPs and some indirect citation relationships (e.g. co-citation and bibliographic coupling). [ABSTRACT FROM AUTHOR]

Recent advances in network representation learning have enabled significant improvement in the link prediction task, which is at the core of many downstream applications. As an increasing amount of mobility data become available because of the development of location-based technologies, we argue that this resourceful mobility data can be used to improve link prediction tasks. In this paper, we propose a novel link prediction framework that utilizes user offline check-in behavior combined with user online social relations. We model user offline location preference via a probabilistic factor model and represent user social relations using neural network representation learning. To capture the interrelationship of these two sources, we develop an anchor link method to align these two different user latent representations. Furthermore, we employ locality-sensitive hashing to project the aggregated user representation into a binary matrix, which not only preserves the data structure but also improves the efficiency of convolutional network learning. By comparing with several baseline methods that solely rely on social networks or mobility data, we show that our unified approach significantly improves the link prediction performance. Summary of Contribution: This paper proposes a novel framework that utilizes both user offline and online behavior for social link prediction by developing several machine learning algorithms, such as probabilistic factor model, neural network embedding, anchor link model, and locality-sensitive hashing. The scope and mission has the following aspects: (1) We develop a data and knowledge modeling approach that demonstrates significant performance improvement. (2) Our method can efficiently manage large-scale data. (3) We conduct rigorous experiments on real-world data sets and empirically show the effectiveness and the efficiency of our proposed method. Overall, our paper can contribute to the advancement of social link prediction, which can spur many downstream applications in information systems and computer science. [ABSTRACT FROM AUTHOR]

In this paper, the parallel machine scheduling problem with DeJong's learning effect, is addressed. The objective function to be minimized is the makespan. This problem is proofed to be NP-Hard in the strong sense. This is the challenging theoretical side of the studied problem. Furthermore, several real-life situations in manufacturing and computer science are modeled using the current problem. Several algorithms intended to solve the studied problem within a reasonable computing time are proposed in literature. Among these algorithms the exact methods, which failed to solve the studied problem to optimality even for small size instances. In this paper several new heuristics and meta-heuristics are proposed. These heuristics are classified into three types. The first type is based on Longest Processing Time (LPT) rule. The innovation is the modification of the LPT rule in a way to cope efficiently with the learning effect concept, by randomizing the selection of the next scheduled job. The second type of heuristics, is taking advantage of an exact Branch and Bound algorithm, developed originally for the classical parallel machine scheduling problem. The contribution for this kind of heuristics is lying in the modification of the processing time values, according to a prefixed selected functions. The third type of methods is based in an adaptation of the Genetic Algorithm to the learning effect concept. This adaptation consists in enlarging the area of selection of the parameters values. To assess the performance and the efficiency of the proposed heuristics, a newly developed lower bound is proposed. This lower bound is based on a relaxation of the studied problem, which allows to obtain a minimum cost flow problem. Finally, an extensive experimental study is conducted over a benchmark test problems. The obtained results provide strong evidence that the proposed procedures outperform the earlier existing ones. • Modeling the learning effect in parallel machine scheduling problem. • Development of dispatching rules based heuristics. • Development of heuristics based on the resolution of NP-Hard problems. • Development of new tight lower bounds based on the maximum allowed position. • Caring out an extensive experimental study that confirms the efficiency of the proposed procedures. [ABSTRACT FROM AUTHOR]

This paper has several purposes. We present through a critical review the results from already published papers on the constructive semigroup theory, and contribute to its further development by giving solutions to open problems. We also draw attention to its possible applications in other (constructive) mathematics disciplines, in computer science, social sciences, economics, etc. Another important goal of this paper is to provide a clear, understandable picture of constructive semigroups with apartness in Bishop's style both to (classical) algebraists and the ones who apply algebraic knowledge. [ABSTRACT FROM AUTHOR]

Fractional-order derivative is attracting more and more attention of researchers in image processing because of its better property in restoring more texture than the total variation. To improve the performance of fractional-order variation model in image restoration, a truncated fractional-order variation model was proposed in Chan and Liang [Truncated fractional-order variation model for image restoration, J. Oper. Res. Soc. China]. In this paper, we propose a dual approach to solve this truncated fractional-order variation model on noise removal. The proposed algorithm is based on the dual approach proposed by Chambolle [An algorithm for total variation minimisation and applications, J. Math Imaging Vis. 20 (2004), pp. 89–97]. Conversely, the Chambolle's dual approach can be treated as a special case of the proposed algorithm with fractional order α = 1. The work of this paper modifies the result in Zhang et al. [Adaptive fractional-order multi-scale method for image denoising, J. Math. Imaging Vis. 43(1) (2012), pp. 39–49. Springer Netherlands 0924–9907, Computer Science, pp. 1–11, 2011], where the convergence is not analysed. Based on the truncation, the convergence of the proposed dual method can be analysed and the convergence criteria can be provided. In addition, the accuracy of the reconstruction is improved after the truncation is taken. [ABSTRACT FROM AUTHOR]

We recently renamed our "education" track's papers from Education Pearl to Education Matters. This would be a good time to explain our vision for the track as a whole, as well as this specific renaming. [ABSTRACT FROM AUTHOR]

Distributed computing, computer networking, and the Internet of Things (IoT) are all around us, yet only computer science and engineering majors learn the technologies that enable our modern lives. This paper introduces PhoneIoT, a mobile app that makes it possible to teach some of the basic concepts of distributed computation and networked sensing to novices. PhoneIoT turns mobile phones and tablets into IoT devices and makes it possible to create highly engaging projects through NetsBlox, an open-source block-based programming environment focused on teaching distributed computing at the high school level. PhoneIoT lets NetsBlox programs—running in the browser on the student's computer—access available sensors. Since phones have touchscreens, PhoneIoT also allows building a Graphical User Interface (GUI) remotely from NetsBlox, which can be set to trigger custom code written by the student via NetsBlox's message system. This approach enables students to create quite advanced distributed projects, such as turning their phone into a game controller or tracking their exercise on top of an interactive Google Maps background with just a few blocks of code. [ABSTRACT FROM AUTHOR]

Finding the saddle point of a matrix game is a classical problem that arises in various fields, e.g., economics, computer science, and engineering. The standard problem-solving methods consist of formulating the problem as a linear program (LP). However, this approach seems less efficient when many instances need to be solved. In this paper, we propose a Convolutional Neural Network based approach, which is able to predict both the strategy profile (x , y) and the optimal value v of the game. We call this approach Matrix Game-Conventional Neural Network or MG-CNN for short. Thanks to a global pooling technique, MG-CNN can solve matrix games with different shapes. We propose a specialized algorithm to train MG-CNN, which includes both data generation and model training. Our numerical experiments show that MG-CNN outperforms standard LP solvers in terms of computational CPU time and provides a high-quality prediction. [ABSTRACT FROM AUTHOR]

Highly efficient algorithms for solving the time constrained shortest path problem have been highlighted over the past decades to reduce the cost of vehicle travel in the road network. The paper presents a novel graph algorithm comprising three stages to acquire a time constrained shortest path between any two nodes on the map. In the first stage, the undirected graph is transformed into a directed graph (DT graph) by deleting edges that must not be included in any of the time constrained shortest paths. A variant DT tree with the destination as the root and the source as the leaf is then constructed from the DT graph in the second stage. By finding the minimal difference value between leaves and the root of the variant DT tree, we can eventually obtain a time constrained shortest path from the variant DT tree in the third stage. Experimental results show that our algorithm not only requires less running time than some classical graph algorithms but also can work immediately in the first stage without knowing the information of both the destination and a specific time constraint. [ABSTRACT FROM AUTHOR]

Background: Plagiarism is a very serious offence in academic institutions. Yet there is some reluctance to address plagiarism by educators as its enforcement can require a significant time commitment if not handled wisely. Handling plagiarism at scale has the potential to exacerbate this problem. Objectives: This article explores the challenges educators face when it comes to enforcing plagiarism from within the course environment, presents a solution that addresses these challenges, and provides guidelines for preventing and detecting plagiarism in our at‐scale courses. Methods: A workflow is shared which was created from our experience of handling plagiarism within our large online computer science graduate level program. We present an empirical study of this workflow that shows the overall prevalence of misconduct. Results and Conclusions: Results demonstrate this workflow being effectively and efficiently applied to address plagiarism across four courses and three semesters by a single individual working half time. We have observed that warnings on low‐stakes assignments can be very effective at deterring future misconduct. We have also observed that cases come to a quicker resolution when students readily acknowledge their own misconduct after being promptly notified. Takeaways from the study: Plagiarism should not only be managed through institution wide policies, but also through effective strategies implemented in the course environment. This is particularly important when handling plagiarism at scale. Addressing plagiarism starts in the course and therefore effective strategies for handling it there are needed. Lay Description: What is currently known about the subject matter?The area of plagiarism in at scale academic programs as a whole is relatively young.Many questions have been raised about the prevalence of cheating in remote classes.The past several years has seen the growth of at‐scale online programs.Little evidence exists that shows cheating is more common in online courses than face‐to‐face. What this paper adds to this?We present some of the challenges we encounter in handling plagiarism in our at scale courses.We present a workflow for handling plagiarism derived from our experience in our courses.We address both code and essay plagiarism as our program is in computer science.We present results over the last three semesters of addressing plagiarism through this workflow. The implications of study findingsWe show that prompt warnings on low‐stakes assignments lead to a reduced rate of plagiarism.We show that cases can be handled efficiently through appropriate intervention strategies.We show that cheating can be significantly reduced through careful course design.We provide generalizable guidelines to address plagiarism in at‐scale programs. [ABSTRACT FROM AUTHOR]

Given the evolving and diverse nature of post-16 computing curricula within England, this papers' primary objective was to identify the factors which influence the decision-making processes for education providers when choosing computing curricula. As a main provider of the vast array of post-16 qualifications, and due to their neglect both politically and in research, further education colleges were chosen as the subject of inquiry. Due to the focus on understanding employee perspectives, a qualitative research method was employed where semi-structured interviews were conducted with thirty-two employees from across thirteen colleges within England. These employees included computing lecturers, heads of departments, and members of senior leadership. Findings indicate the extent of the range of post-16 computing qualifications offered by colleges, in addition to identifying who the key players are for computing curricula decisions. Additionally, ten factors were identified as pivotal to influencing curricula choice, and from these factors, a model has been created classifying four central areas that should be considered regarding curriculum choice: labour market information, qualification relevance to industry needs, qualification attractiveness, and current college resources. This model should help inform education providers in making more informed computing curricula decisions. [ABSTRACT FROM AUTHOR]

Course-based Undergraduate Research Experiences (CUREs) have been well developed in the hard sciences, but math CUREs are all but absent from the literature. Like biology and chemistry, math programs suffer from a lack of research experiences and many students are not able to participate in programs like REUs (Research Experiences for Undergraduates). CUREs are a great alternative, but the current definition of CURE (see [1]) has potential barriers when applied to mathematics (e.g. time, nov- elty of project). Our solution to these barriers was to develop a math CURE pathway in which students complete "Math CUREs" in targeted courses. After finishing the pathway (or part of the pathway), students complete a research project in at least one of the following areas: Lie theory, representation theory, or combinatorics. The focus of this paper is the math CURE implemented in a discrete mathe- matics course for math and computer science majors. We share our experiences with the development and implementation of this CURE over several iterations as well as the impact of the CURE on students experiences through participant survey data obtained from this CURE. [ABSTRACT FROM AUTHOR]

The stream ciphers RCR-64 and RCR-32 designed by Sekar et al. are the most recent additions to the Py-family of stream ciphers, originally designed by Biham et al. The ciphers are among the fastest stream ciphers on software. To the best of our knowledge, the only reported attacks on the ciphers are due to Ding et al. , published in the Journal of Universal Computer Science. In this paper, we review these alleged attacks on the RCR ciphers and show that they are based on non-existent keystream biases stemming from flawed probability calculations. [ABSTRACT FROM AUTHOR]

The rapid deployment of emergency remote teaching in the pandemic presents sweeping societal‐level information systems phenomena worthy of scholarly inquiry. This paper reports findings from teacher interviews conducted with K‐12 public school teachers, exploring how digital access and use gaps in communities reflect wider digital and social inequalities as schools fulfilled emergency remote teaching mandates, becoming swept up into e‐learning technology expansion trends propelled by mandates, and unfettered corporate edtech. Results show persistence of home and school level digital affordance gaps as hindrances to pandemic pedagogy. We build upon theory of the digital divide, and crisis and critical informatics literature considering how critical approaches to the study of socio‐technical systems research can inform these understandings, providing insights into how localized digital inequities contribute to broader digital inequality and social inequality, in the educative processes expected of public education in democratic societies. Our work gives voice to one highly pressured and conflicted stakeholder in these dynamics—K‐12 public school teachers—and demonstrates some of the ways in which digital inequity gaps may play a further magnifying role of societal division through expanding edtech deployment in K‐12 grades, if current edtech trends hold. [ABSTRACT FROM AUTHOR]

Boolean functions have important applications in information technology and computer science. Certificate complexity is an important combinatorial measure of Boolean function complexity. In this paper, we first introduce many concise and efficient notations then we study the elementary symmetric Boolean functions and obtain their certificate complexities when their degrees are odd or powers of 2. We show that both upper and lower bounds of certificate complexities can be attained. [ABSTRACT FROM AUTHOR]

Divide-and-conquer dividing by a half recurrences, of the form x n = a · x ⌈ n / 2 ⌉ + a · x ⌊ n / 2 ⌋ + p (n) , n ⩾ 2 , appear in many areas of applied mathematics, from the analysis of algorithms to the optimization of phylogenetic balance indices. These equations are usually "solved" by means of a Master Theorem that provides a bound for the growing order of xn, but not the solution's explicit expression. In this paper we give a finite explicit expression for this solution, in terms of the binary decomposition of n, when the independent term p(n) is a polynomial in ⌈n/2⌉ and ⌊n/2⌋. As an application, we obtain explicit formulas for several sequences of interest in phylogenetics, combinatorics, and computer science, for which no such formulas were known so far: for instance, for the Total Cophenetic index and the rooted Quartet index of the maximally balanced bifurcating phylogenetic trees with n leaves, and the sum of the bitwise AND operator applied to pairs of complementary numbers up to n. [ABSTRACT FROM AUTHOR]

In this paper, we explore the use of spatial clustering algorithms as a new computational approach for modelling the cosmic web. We demonstrate that such algorithms are efficient in terms of computing time needed. We explore three distinct spatial methods which we suitably adjust for (i) detecting the topology of the cosmic web and (ii) categorizing various cosmic structures as voids, walls, clusters, and superclusters based on a variety of topological and physical criteria such as the physical distance between objects, their masses, and local densities. The methods explored are (1) a new spatial method called Gravity Lattice ; (2) a modified version of another spatial clustering algorithm, the abacus ; and (3) the well known spatial clustering algorithm hdbscan. We utilize hdbscan in order to detect cosmic structures and categorize them using their overdensity. We demonstrate that the abacus method can be combined with the classic dtfe method to obtain similar results in terms of the achieved accuracy with about an order of magnitude less computation time. To further solidify our claims, we draw insights from the computer science domain and compare the quality of the results with and without the application of our method. Finally, we further extend our experiments and verify their effectiveness by showing their ability to scale well with different cosmic web structures that formed at different redshifts. [ABSTRACT FROM AUTHOR]

Network diffusion processes or how information spreads through networks have been widely examined in numerous disciplines such as epidemiology, physics, sociology, politics, or computer science. In this paper, we extend previous developments by considering a generalization of the diffusion by considering the possibility of differences in the speed of diffusion and reduction depending on the forces' directions. In this situation, the differential speed of diffusion produces deviations from the standard solution around the average of the initial conditions in the network. In fact, this asymmetry gives rise to non-linear dynamics in which, contrary to the symmetric case, the final solution depends on the topology of the graph as well as on the distribution of the initial values. Counter-intuitively, less central nodes in the network are able to exert a higher influence on the final solution. This behavior applies also for different simulated networks such as random, small-world, and scale-free. We show an example of this kind of asymmetric diffusion process in a real case. To do so, we use a network of US Boards of Directors, where boards are the nodes and the directors working for more than one board, are the links. Changes in the proportion of women serving on each board are influenced by the gradient between adjacent boards. We also show that there is an asymmetry: the gradient is reduced at a slower (faster) rhythm if the board has less (more) women than neighboring boards. We are able to quantify the accumulated effect of this asymmetry from 2000 to 2015 in the overall proportion of women on boards, in a 4.7 percentage points (the proportion should have been an 14.61% instead of the observed 9.93% in 2015). [ABSTRACT FROM AUTHOR]

Several studies have shown that the main challenge in teaching complex systems involving many theoretical backgrounds to senior students is their lack of confidence. In this paper, we present an approach to the teaching methodology of an undergraduate course in a telecommunication study program. The first goal of this methodology is to help students understand the theoretical concepts behind wireless digital communication systems through direct practice to give them more exposure to a real system through affordable practice activities in an experiential context, utilizing an HF radio and a single-board computer (SBC). The second goal is to challenge senior students to understand the data link layer by using white box testing of the syntax of the programming language embedded in the SBC. This approach is ideally carried out based on offline and hands-on class activities, however, the pandemic condition made it unavoidable to do it online using a demonstration model. Feedback from students was collected through written comments, post-testing, including a facility index analysis, and a questionnaire that was distributed using the available e-learning system. The post-test results showed that the average score was 72 out of a maximum of 100. [ABSTRACT FROM AUTHOR]

The explosive growth of the social media community has increased many kinds of misinformation and is attracting tremendous attention from the research community. One of the most prevalent ways of misleading news is cheapfakes. Cheapfakes utilize non-AI techniques such as unaltered images with false context news to create false news, which makes it easy and "cheap" to create and leads to an abundant amount in the social media community. Moreover, the development of deep learning also opens and invents many domains relevant to news such as fake news detection, rumour detection, fact-checking, and verification of claimed images. Nevertheless, despite the impact on and harmfulness of cheapfakes for the social community and the real world, there is little research on detecting cheapfakes in the computer science domain. It is challenging to detect misused/false/out-of-context pairs of images and captions, even with human effort, because of the complex correlation between the attached image and the veracity of the caption content. Existing research focuses mostly on training and evaluating on given dataset, which makes the proposal limited in terms of categories, semantics and situations based on the characteristics of the dataset. In this paper, to address these issues, we aimed to leverage textual semantics understanding from the large corpus and integrated with different combinations of text-image matching and image captioning methods via ANN/Transformer boosting schema to classify a triple of (image, caption1, caption2) into OOC (out-of-context) and NOOC (no out-of-context) labels. We customized these combinations according to various exceptional cases that we observed during data analysis. We evaluate our approach using the dataset and evaluation metrics provided by the COSMOS baseline. Compared to other methods, including the baseline, our method achieves the highest Accuracy, Recall, and F1 scores. [ABSTRACT FROM AUTHOR]

Bringing evidence into policy and practice discussions is political; more so when evidence from health studies or programme data are deemed controversial or unexpected, or when results are manipulated and misrepresented. Furthermore, opinion and misinformation in recent years has challenged our notions about how to achieve evidence-informed decision-making (EIDM). Health policy and systems (HPS) researchers and practitioners are battling misrepresentation that only serves to detract from important health issues or, worse, benefit powerful interests. This paper describes cases of politically and socially controversial evidence presented by researchers, practitioners and journalists during the Health Systems Research Symposium 2020. These cases cut across global contexts and range from public debates on vaccination, comprehensive sexual education, and tobacco to more inward debates around performance-based financing and EIDM in refugee policy. The consequences of engaging in controversial research include threats to commercial profit, perceived assaults on moral beliefs, censorship, fear of reprisal, and infodemics. Consequences for public health include research(er) hesitancy, contribution to corruption and leakage, researcher reflexivity, and ethical concerns within the HPS research and EIDM fields. Recommendations for supporting researchers, practitioners and advocates include better training and support structures for responding to controversy, safe spaces for sharing experiences, and modifying incentive structures. [ABSTRACT FROM AUTHOR]

This paper addresses a problem of global optimization in a non-Archimedean fuzzy metric space context without fuzzy P-property. Specifically, it concerns the determination of the fuzzy distance between two subsets of a non-Archimedean fuzzy metric space. Our approach to solving this problem is to find an optimal approximate solution to a fixed point equation. This approach has been well studied within a category of problems called proximity point problems. We explore some new types of (ψ − ϕ) -weak proximal contractions and investigate the existence of the unique best proximity point for such kinds of mappings. Subsequently, some fixed point results for corresponding contractions are proved, and some illustrative examples are presented to support the validity of the main results. Moreover, an interesting application in computer science, particularly in the domain of words has been provided. Our work is a fuzzy generalization of the proximity point problem by means of fuzzy fixed point method. [ABSTRACT FROM AUTHOR]

A graph structure is a useful tool in solving the combinatorial problems in different areas of computer science and computational intelligence systems. In this paper, we introduce the concept of cubic soft graph, complete cubic soft graph, (internal, external) cubic soft graphs and investigate some of their properties. Then we deal with fundamental operations, union, intersection, Cartesian product, composition of cubic soft graphs and illustrate these notions by several examples. We prove that cubic soft graphs under these operations are also a cubic soft graph. Finally, we describe an application of cubic soft graphs in decision making. [ABSTRACT FROM AUTHOR]

String matching is one of the most extensively studied problems in computer science, mainly due to its direct applications to such diverse areas as text, image and signal processing, speech analysis and recognition, information retrieval, data compression, computational biology and chemistry. In the last few decades a myriad of alternative solutions have been proposed, based on very different techniques. However, automata have always played a very important role in the design of efficient string matching algorithms. In this paper we present the Range Automaton , a weak yet efficient variant of the non-deterministic suffix automaton of a string whose configuration can be encoded in a very simple form and which is particularly suitable to be used for solving a multitude of text-searching problems. We will firstly develop our approach in the case of exact string matching and present an efficient algorithm, named Backward Range Automaton Matcher, which turns out to be very fast in many practical cases. Later, we will show how the Range Automaton can be adapted in an effective way also to non-standard string matching problems such as swap matching and multiple string matching. Experimental results suggest that our approach is flexible and effective for all three search problems addressed, especially in the case of long patterns. • We present a simple and efficient technique for simulating the suffix automaton of a string. • The new technique leads to the definition of an automaton, called Range Automaton, for the weak recognition of the suffixes of a string. • We apply the Range Automaton to the exact string matching problem, obtaining an efficient and flexible solution in practice, especially in the case of long patterns. • We extend the application of the Range Automaton to the swap matching problem, a special case of approximate string matching. • We extend the application of the Range Automaton to the problem of multiple pattern matching. [ABSTRACT FROM AUTHOR]

In this paper, we study some classes of terms, called trees, which play a key role in both universal algebra and theoretical computer science. We introduce the concept of terms defined by singular transformations and provide some concrete examples. We also prove that the set of such terms together with an operation of type (n + 1) for a fixed natural number n defined on that set forms an algebra of type (n + 1) satisfying the axiom of superassociativity. [ABSTRACT FROM AUTHOR]

Fractional metric dimension of connected graph G was introduced by Arumugam et al. in [Discrete Math. 312, (2012), 1584-1590] as a natural extension of metric dimension which have many applications in different areas of computer sciences for example optimization, intelligent systems, networking and robot navigation. In this paper fractional metric dimension of generalized prism graph Pm × Cn is computed using combinatorial criterion devised by Liu et al. in [Mathematics, 7(1), (2019), 100]. [ABSTRACT FROM AUTHOR]