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

Sentiment analysis is one of the fastest growing research areas in computer science, making it challenging to keep track of all the activities in the area. We present a computer-assisted literature review, where we utilize both text mining and qualitative coding, and analyze 6996 papers from Scopus. We find that the roots of sentiment analysis are in the studies on public opinion analysis at the beginning of 20th century and in the text subjectivity analysis performed by the computational linguistics community in 1990’s. However, the outbreak of computer-based sentiment analysis only occurred with the availability of subjective texts on the Web. Consequently, 99% of the papers have been published after 2004. Sentiment analysis papers are scattered to multiple publication venues, and the combined number of papers in the top-15 venues only represent ca. 30% of the papers in total. We present the top-20 cited papers from Google Scholar and Scopus and a taxonomy of research topics. In recent years, sentiment analysis has shifted from analyzing online product reviews to social media texts from Twitter and Facebook. Many topics beyond product reviews like stock markets, elections, disasters, medicine, software engineering and cyberbullying extend the utilization of sentiment analysis. [ABSTRACT FROM AUTHOR]

This paper presents results of topic modeling and network models of topics using the ICCS corpus, which contains domain-specific (computational science) papers over sixteen years (a total of 5695 papers). We discuss topical structures of ICCS, how these topics evolve over time in response to the topicality of various problems, technologies and methods, and how all these topics relate to one another. This analysis illustrates multidisciplinary research and collaborations among scientific communities, by constructing static and dynamic networks from the topic modeling results and the authors’ keywords. The results of this study give insights about the past and future trends of core discussion topics in computational science. We used the Non-negative Matrix Factorization(NMF) topic modeling algorithm to discover topics and labeled and grouped results hierarchically. We used Gephi to study static networks of topics, and an R library called DyA to analyze the dynamic networks of topics. [ABSTRACT FROM AUTHOR]

Asking questions and making comments in conference Q&A sessions can be challenging for novice academics. Discussants not only try to seek answers or exchange ideas with the presenter but also endeavour to project a positive image of themselves by asking the 'right' questions in the 'right' way. However, many novice academics fear looking foolish because of unfocused and convoluted questions. Surprisingly, little guidance is available for how to formulate Q&A questions. This study attempts to address the gap by investigating the forms, functions and generic moves of 268 turns taken by discussants in 80 computer science conference Q&A sessions. Certain forms (i.e., question and comment) were found to be associated with certain pragmatic functions. This study reveals that different types of question/comment have major moves in common. The study also shows that the more evaluative and face-threatening the question is, the more important it is for the discussant to make the moves of building rapport with the presenter, and contextualising and justifying the main question/statement. The importance of justifying the main question/statement also resonates with the persuasive nature of scientific argumentative discourse. The findings can offer novice academics detailed explanations of the communicative purposes and strategies for this important conference interaction. • The two forms (question & comment) are associated with different functions. • All types of questions/comments were found to share the major generic moves. • Evaluative/critical questions entail building rapport as a move. • Evaluative/critical questions entail justifying the question as a move. • The importance of justification resonates with scientific argumentative discourse. [ABSTRACT FROM AUTHOR]

• This study explores the impact of different collaboration modes on the cited frequency of publications. • Compared with the existing works, our PSM-based method is more innovative since we investigate the impact of author's collaboration mode from a casual view. • Our method reduces the selection bias of samples and makes the variables more balanced. • Research collaboration, especially international collaboration, plays a significant role in promoting the impact of research results in three subfields of computer science. This study explores the impact of different collaboration modes on the cited frequency of publications. Though several studies have obtained some research results, most of them exploit association or regression-based methods, which may not lead to causal conclusions. To overcome the above challenges, we use the Propensity Score Matching (PSM) method to analyze and compare the citation frequencies resulting from four groups of collaboration models: international versus domestic, international multilateral versus international bilateral, domestic inter-organizational versus domestic intra-organizational, and domestic multi-author versus domestic single-author. More specifically, we conduct this analysis by exploring the publications with three computer science subfields from the Web of Science (WoS) database. The experimental results show that international collaboration, especially international multilateral collaboration, has a significant role in increasing the frequency of citations to scientific publications, showing that internationalization and collaboration are critical factors in the growth of the impact of the papers. Among national co-publications, collaborative publications within national organizations receive a higher citation impact. Multi-author collaborations significantly increase citation frequency compared to single-author publications. Our heterogeneity analysis across the different subfields of the computer science domain finds that the treatment effects for the three subfields differ modestly and mostly significant from the whole sample. Moreover, besides the implications for developing research policy and scientist collaboration, our study can capture the causal effect between author collaboration patterns and citation frequency to reveal their causal effects. [ABSTRACT FROM AUTHOR]

Abstract: Image acquisition has great influence on the performance of any computer vision application. Different methods can be utilized to acquire the digital image of a paper, whilst scanning scheme is among the most attractive methods. This attractiveness is because of the fewer types of potential deformations and the low cost of the scanning devices, e.g. flatbed scanners. However, paper is commonly placed imperfectly on the scanner. This slight rotation is not usually based on a pivot around the paper''s geometrical center (the well known regular rotation) but instead it is based on a pivot placed at the corner of the paper. Thus, the result is a digital image that is deformed with an “irregular rotation”. The characteristic of this deformation phenomenon is currently unknown to computer vision scientists. In this paper we provide an extensive investigation of this deformation. In addition, a new set of equations that sway and measure the transformation is proposed. Our investigation leads to the conclusion that the “irregular rotation” phenomenon produces a shear transformation. Furthermore, the experimental results confirm the theoretical findings. [Copyright &y& Elsevier]

Abstract: The need for applications that are developed especially for Computational Science and Engineering (CSE) has been growing rapidly in the recent years. These applications are often a prerequisite for research and have to be evolved and maintained for considerable periods of time. However, CSE researchers have traditionally put focus on achieving better computational performance and results rather than the software’s comprehensibility, maintainability and extensibility. This paper first presents two case studies on two different CSE research projects, where common and specific problems are identified. Second we propose solutions that intend to apply software engineering methodologies and tools to improve the CSE research software development. [ABSTRACT FROM AUTHOR]

Highlights: [•] Granular computing aspects of natural computing. [•] Review of different granular soft computing research. [•] Biological motivation, design principles, application areas, open research problems and challenging issues of these models. [Copyright &y& Elsevier]

Abstract: We present Open Review, a web-based platform aimed at stimulating executable papers by means of post-publication peer-review. Its goal is to bring computer science researchers to collaboratively build their work upon previous research results, in such a way that transparency, reproducibility and sustainability of research results are greatly improved. The main design goals of the platform are clarity, conciseness, and reproducibility. Its main features are to: (i) provide incentives for making research communities to participate, (ii) make papers executable by means of boards’ annotations, without necessarily involving the authors of an article, and (iii) give snapshots of the current research state on any given article. [Copyright &y& Elsevier]

Nowadays, there has been a rapidly increasing number of scientific submissions in multiple research domains. A large number of journals have various acceptance rates, impact factors, and rankings in different publishers. It becomes time-consuming for many researchers to select the most suitable journal to submit their work with the highest acceptance rate. A paper submission recommendation system is more critical for the research community and publishers as it gives scientists another support to complete their submission conveniently. This paper investigates the submission recommendation system for two main research topics: computer science and applied mathematics. Unlike the previous works (Wang et al., 2018; Son et al., 2020) that extract TF–IDF and statistical features as well as utilize numerous machine learning algorithms (logistics regression and multiple perceptrons) for building the recommendation engine, we present an efficient paper submission recommendation algorithm by using different bidirectional transformer encoders and the Mixture of Transformer Encoders technique. We compare the performance between our methodology and other approaches by one dataset from Wang et al. (2018) with 14012 papers in computer science and another dataset collected by us with 223,782 articles in 178 Springer applied mathematics journals in terms of top K accuracy (K = 1 , 3 , 5 , 10). The experimental results show that our proposed method extensively outperforms other state-of-the-art techniques with a significant margin in all top K accuracy for both two datasets. We publish all datasets collected and our implementation codes for further references. 1 1 https://github.com/BinhMisfit/PSRMTE. • Bidirectional transformer encoders can improve the performance of the paper submission recommendation system. • The Mixture of Transformer Encoders framework shows the efficiency in the paper submission recommendation problem. • Proposed techniques can surpass other recent techniques on two datasets related. [ABSTRACT FROM AUTHOR]

Abstract: The Workshop for the Executable Paper Grand Challenge, taking place at the 2011 International Conference on Computational Science, is a platform for Grand Challenge contestants and finalists to present their concept of an Executable Paper, a method by which data intensive research is better represented in a scholarly journal article. The Executable Paper Grand Challenge is designed to enhance how scientific information is used and communicated in computer- and computational sciences. [Copyright &y& Elsevier]

Diagnosability is a fundamental consideration when designing an interconnected network. The PMC and MM ∗ fault diagnosis models are the two most commonly used models. Both the g -good-neighbour diagnosability and g -extra diagnosability of an interconnection network have been two of the hot topics in the intersectional research areas of Graph theory and Computer Science, which become increasingly attractive for new solutions to real-world problems. However, there are still some problems in the transformation from the concepts of Computer Science to that of mathematics. In this paper, we systematically study such problems and give a strict proof from concepts to mathematical conclusions. In the terms of results, we not only give the relationship between g -good-neighbour diagnosabilities of the network under PMC model and MM ∗ model, but also between g -extra diagnosabilities of the network under PMC and MM ∗ models. To apply our results, we give an application on the enhanced hypercube in the end and derive a lemma explaining whether these are 3-cycles in enhanced hypercubes and how many common neighbours for two vertices of enhanced hypercubes under different values of k in the meantime. [ABSTRACT FROM AUTHOR]

• First survey of relevant research activities on Digital Calibration Certificate-DCC. • A thorough analysis of both the calibration process and the calibration certificate. • Modeling the calibration process, the calibration certificate life-cycle. • Identifying Calibration use-cases and stakeholders. • Advantages and disadvantages of various potential DCC formats. Calibration Certificates (CC) are essential for maintaining the accuracy of measurement instruments, which in turn are necessary to maintain the quality of products and services. Such certificates are mainly used in legal metrology for quality audits, accreditation and examination processes; and also in the operational metrology to adjust measurement results according to device uncertainty. Paper-based CC (PCC) or a digital copy of the paper format has been successfully used for this purpose for many decades. However, the PCC starts to fail short with the emergence of new technologies and applications that require automated creation, processing and updates of the calibration certificates. The last years have witnessed an intense effort on digital transformation in metrology and especially developing Digital Calibration Certificates (DCC). Until recently, we noticed the lack of agreement on the nature and the usage of such important new metrology artifacts as well as some repetition in the work by different researchers. To help reaching a minimum consensus on the subject, we present in this paper, a thorough analysis of the calibration certificate and the calibration process. Besides that, we aim by this work to provide an overview of the carried research on that point to help peer researchers to view the big picture and to avoid wasting their efforts in repeating same work on same ideas. Consequently, they can recognize the peer researchers and organizations working on the same topics, find available resources, identify research gaps and identify the best potential directions of their efforts as well. Finally, our paper aims also to establish a bridge between the metrological concepts and information technology concepts such that more computer science researchers can be involved in solving the problem. [ABSTRACT FROM AUTHOR]

With the development of Internet and AI technology, university education is facing more and more challenges and opportunities. In this paper, an intelligent learning solution based on deep neural network (DNN) and WeChat applet is proposed to solve the problems of insufficient information acquisition and difficult to meet the personalized learning needs in the learning process of college professional basic courses. To start, we construct a comprehensive knowledge map for foundational university courses across various majors utilizing deep learning techniques. This map encompasses key concepts and interconnections spanning disciplines such as mathematics, physics, and computer science. Subsequently, we devise and deploy an intelligent recommendation algorithm leveraging DNN architecture. Drawing insights from user historical data and the intricate subject interrelations within the knowledge map, our algorithm tailors personalized recommendations for learning resources. Integrating this intelligent recommendation system into the WeChat applet platform, we realize a dynamic resource recommendation service tailored to university students' foundational coursework. Empirical findings demonstrate the superior performance of our algorithm over traditional methods, showcasing enhanced accuracy, recall rates, and F1 values. Notably, users benefit from an improved recommendation efficacy and overall learning experience. This research offers a novel framework for advancing the personalized and intelligent facets of university education, bearing both theoretical significance and practical implications. [ABSTRACT FROM AUTHOR]

• Evidence-based early childhood computer science curriculum utilizing the free ScratchJr programming, Coding as Another Language (CAL). • Results from cluster randomized controlled trial show CAL's feasibility to implement, and effectiveness for improving coding skills. • Despite the time taken away from the math and literacy blocks to teach CAL, standardized test for literacy improved and math remained the same. • During the cluster randomized controlled trial, both the control and the intervention groups improved equally on computational thinking. This paper describes the iterative research and evaluation of the Coding as Another Language (CAL) curriculum that utilizes the free ScratchJr programming language in kindergarten to second grade. CAL was designed using principles of three theoretical frameworks: Curriculum Research Framework (CRF), which proposes different phases in the creation of research-based curriculum; Constructionism, which presents a computationally-rich project-based methodology based on identifying powerful ideas from a learning domain; and Positive Technological Development, which intentionally integrates socio-emotional and ethical dimensions into curricular experiences. The pedagogical foundation of CAL involves the understanding of coding as a literacy, that is, putting developmentally-appropriate powerful ideas of computer science in conversation with those taught in language arts. The paper first describes CAL and then presents results from both a pilot study and a cluster randomized controlled trial that set to evaluate CAL's feasibility and impact on students' learning outcomes. Our findings showed that the CAL curriculum was not only feasible to implement, but also effective for improving coding skills. However, CAL's impact on computational thinking is less clear given that in the cluster randomized controlled trial, both the control and the intervention groups improved equally on a measure of computational thinking. [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 development of the computer and what is now known as artificial intelligence (AI) has evolved over more than two centuries in a long series of steps. The date of the invention of the first computer is estimated at 1822, when Charles Babbage (1791-1871) developed his first design of a working computer on paper, based mainly on a Jacquard loom. He worked on his project together with Augusta Ada King, Countess Lovelace (née Byron) (Ada Lovelace) (1815-1852), whom he called the "Sorceress of Numbers." This work will present the profile and achievements of Charles Babbage, Augusta Ada King, Countess Lovelace, and Alan Mathison Turing (1912 - 1954), who is considered the father of computer science and artificial intelligence, and then provide an outline of the tumultuous events affecting AI up to the present. [ABSTRACT FROM AUTHOR]

Scholarly interest in Green IT has been rapidly growing over the past 15 years in a wide variety of disciplines including computer science, information systems, sustainability, production management and engineering. This is not surprising given the growing environmental impacts of information and communication technologies. This article presents a bibliometric analysis of academic literature across 1862 sources to improve our understanding of the evolution and current state of this multidisciplinary field. The corpus includes 4067 published journal and conference papers identified using the Scopus database. A descriptive performance analysis of the corpus using Bibliometrix software first allows us to identify the most influential works, authors, sources, institutions, and countries in the field. Examination of its conceptual structure reveals that most work has focused on the design and use phases of ICTs and neglected the manufacturing and end-of-life phases. Further insights are provided and opportunities for future research are identified. [ABSTRACT FROM AUTHOR]

Graph mining is one of the significant tasks in the field of computer science. Most of the applications generate a vast amount of data which is represented with the help of a graph. Due to this graph representation, these applications have become complex and increased in size. Finding relevant information from that graph becomes a complicated task. For this community detection algorithms play a vital role in graph partitioning to retrieve relevant information. Finding communities in a graph reduces the complexity of the graph due to related data comes closer to forming a community. Many algorithms have been introduced in the last decade; the Clique percolation method (CPM) is the benchmark algorithm for finding an overlapping community. But in this method, some nodes remain unclassified, nodes that are not part of the clique. Paper proposed the clique-based Louvain algorithm(CBLA), which can classify the non-classified node (NCN) obtained after finding cliques in one of the communities by applying the Louvain algorithm. Louvain algorithm is used to classify the non-overlapped community, but with the help of cliques, it will also detect the overlapped nodes. This paper compared the proposed algorithm with four other benchmark algorithms. The proposed algorithm gives equal or enhanced performance among all compared algorithms. [ABSTRACT FROM AUTHOR]

Graph theory is a powerful and essential tool for applied scientists and engineers in analyzing and designing algorithms for several problems. Graph theory has a vital role in complex systems, especially in computer sciences. Applications of graph theory can be found in many scientific disciplines such as operational research, engineering, life sciences, management sciences, coding, and computer science. In the literature, there are two algorithms for constructing decompositions for the circulant graphs C 2 r , r with 2 r vertices and r degree. For r , m ⩾ 2 , a circulant-balanced complete multipartite graph C mr , (m - 1) r is a generalization to C 2 r , r. Here, the major contributions are the generalization of the algorithms used for constructing the decompositions for the circulant graphs C 2 r , r to generalized algorithms that can be used for the decomposition of C mr , (m - 1) r that have mr vertices and (m - 1) r degree. These generalized algorithms manage us to decompose the circulant graphs C mr , (m - 1) r by different graph classes. The paper's novelty is demonstrated by the fact that it is the first to suggest general approaches for decomposing circulant-balanced complete multipartite graphs. The constructed results in the present paper are suitable for generating different codes and also have great importance in the design of experiments. [ABSTRACT FROM AUTHOR]

Boolean networks have been used in a variety of settings, as models for general complex systems as well as models of specific systems in diverse fields, such as biology, engineering, and computer science. Traditionally, their properties as dynamical systems have been studied through simulation studies, due to a lack of mathematical structure. This paper uses a common mathematical technique to identify a class of Boolean networks with a "simple" structure and describes an algorithm to construct arbitrary extensions of a collection of simple Boolean networks. In this way, all Boolean networks can be obtained from a collection of simple Boolean networks as building blocks. The paper furthermore provides a formula for the number of extensions of given simple networks and, in some cases, provides a parametrization of those extensions. This has potential applications to the construction of networks with particular properties, for instance in synthetic biology, and can also be applied to develop efficient control algorithms for Boolean network models. • Network restrictions/extensions are defined for building Boolean networks modularly. • Parameterized all extensions of two Boolean networks represented by labeled graphs. • A combinatorial formula enumerates all extensions of two Boolean networks. • Extensions of Boolean networks with nested canalizing functions are enumerated. [ABSTRACT FROM AUTHOR]

This paper presents the novel task of bluespotted ribbontail (BR) ray (Taeniura lymma) recognition using deep learning based computer vision methods to enable the identification of specific individuals of this species. Mapping the specific individuals in relation to location and time will allow marine researchers to understand their movement patterns, habitat choice, life span, size of the population and more – data which could allow monitoring and establishing a tailor-made conservation plan for this species. Our work is pioneer on this recognition problem. We give a detailed description of the three basic steps of detection, feature extraction and recognition in this vision problem and perform experiments to explore the system configuration and what improves the performance. A feature extraction enhancement as well as a crucial effect of a split into different main poses are demonstrated. Though the precision results achieved in this paper are still moderate and should be further improved, they are nevertheless promising and reasonable for practical use if the six best matches are chosen. For this scenario, almost 85% precision for upper-pose model, and almost 80% precision for left- and right-pose models, are achieved demonstrating the feasibility of the pipeline suggested as well as opportunities for improvement. • We present a pioneer novel application of deep learning based computer vision methods for bluespotted ribbontail (BR) ray recognition, to facilitate the ecological study of this species. • We explored what improves the performance. • An effective feature extraction enhancement technique is applied in our framework. • The crucial effect of a split into different main poses in order to achieve BR ray recognition is demonstrated. [ABSTRACT FROM AUTHOR]

Fair division of indivisible items is a well-studied topic in Economics and Computer Science. The objective is to allocate items to agents in a fair manner, where each agent has a valuation for each subset of items. Envy-freeness is one of the most widely studied notions of fairness. Since complete envy-free allocations do not always exist when items are indivisible, several relaxations have been considered. Among them, possibly the most compelling one is envy-freeness up to any item (EFX), where no agent envies another agent after the removal of any single item from the other agent's bundle. However, despite significant efforts by many researchers for several years, it is known that a complete EFX allocation always exists only in limited cases. In this paper, we show that a complete EFX allocation always exists when each agent is of one of two given types, where agents of the same type have identical additive valuations. This is the first such existence result for non-identical valuations when there are any number of agents and items and no limit on the number of distinct values an agent can have for individual items. We give a constructive proof, in which we iteratively obtain a Pareto dominating (partial) EFX allocation from an existing partial EFX allocation. [ABSTRACT FROM AUTHOR]

• Eulerian trails are classical and important objects in theoretical computer science field. • Enumeration of Eulerian trails is known to be #P-complete. • Although the existence of an algorithm with linear time per output is known, the existence of a faster algorithm is open. • This paper shows optimal algorithms for Eulerian trails which run in constant time per solution on average. In this paper, we consider enumeration problems for edge-distinct and vertex-distinct Eulerian trails. Two Eulerian trails are said to be edge-distinct if the edge sequences are not identical, and they are said to be vertex-distinct if the vertex sequences are not identical. To solve these problems, we propose optimal enumeration algorithms that run in O (N + m) total time, where N is the number of solutions and m is the number of edges in an input connected graph. The proposed algorithms are based on the reverse search technique introduced by [Avis and Fukuda, DAM 1996], and the push-out amortization technique introduced by [Uno, WADS 2015]. [ABSTRACT FROM AUTHOR]

Memory is the core of cognition. The exploration of the memory encoding mechanism or the representation mechanism of information in the Spiking Neural Network (SNN) is the basis for the in-depth study of memory. In this paper, we study the memory encoding mechanism of multilayer SNN models from a biomimetic perspective and explore a method using the high biological likelihood of SNN to enable the network to effectively simulate memory effects. We proposed a series of heuristic neuron-growing connection algorithms and supervised network weight learning algorithms, which were applied to the unsupervised and supervised training process of the presentation layer. These methods optimized the structure of the representation layer, achieved functional differentiation of neurons, and enabled the network to generate differentiated representations for different data modes. Under our algorithm, the proposed model achieves stable convergence with identical pattern inputs, demonstrating distinct representations and sensitivities to different visual modalities. To achieve stable information expression within the network, we conducted various comparative experiments to determine diverse parameters of the complex network. This paper contributes to the development of Brain-inspired Intelligence by bridging the gap between computer science and neuroscience by using simulations to validate biological hypotheses and guide machine learning. [Display omitted] • This study proposes and constructs a multi-layer spiking neural network model with input, representation, supervision, and observation layers, in which the structure enables our model to exhibit high information capacity and the ability to simulate biological nervous systems, well connecting computer science and biological neuroscience. • This study uses a series of unsupervised connection generating algorithms and bipolar supervised learning algorithm to optimize the structure of the representation layer, achieve functional differentiation of neurons, and enable the network to generate differentiated representations for different data modes. • This study analyzes the abnormal states that are prone to occur in complex spiking neural networks and proposes solutions for situations where the network enters a paralyzed state. The relevant methods provide a new technical path for improving the robustness and stability of spiking neural networks. [ABSTRACT FROM AUTHOR]

• Examined titles, abstracts, conclusions of research papers by highly ranked authors. • Analyzed readability, lexical density, lexical diversity, and coherence. • Highly-cited articles tend to be more complex to read. • Minimal difference in lexical frequency and coherence between high, low citation papers. • Abstract readability, Total references exhibit a positive association with citation values. The Scholar's success is indicated by the number of citations it received for its publication. Examining the correlation between the linguistic attributes of scholarly publications and their scientific influence holds significant importance. This study analyzed 1000 research papers by highly ranked authors from computer science and electronics backgrounds. The title, abstract, and conclusion sections of the paper were analyzed. This study utilizes readability, lexical diversity, lexical density, syntactic features, and coherence measures to establish the correlation between citations and the textual content of an article. The characteristics of the publication were evaluated in relation to its research impact, which was classified into two categories, high citations and low citations. Additionally, the influence of various aspects on citations was assessed through the utilisation of the negative binomial regression model, ordinary least square model, and spearman correlation. This analysis took into account the characteristics of length and structure. The results highlight a clear positive link between abstract readability, and number of references with increased citations. Additionally, each additional page contributes to a 0.2 % increase in citation count. However, the number of diagrams and conclusion readability show no significant connection with citations. Factors like title length, abstract length, and conclusion length also exhibit associations, though with slightly lower percentages. The results indicate that linguistic characteristics exert a limited impact on the acquisition of citations. [ABSTRACT FROM AUTHOR]

Since 2000, the field of machine scheduling—an integral part of computer science and operations research—has seen significant advancements. This paper explores the dynamic progression of machine scheduling, offering a detailed overview of its past advancements, current practices, and future directions. Anchoring the research in robust data analysis and statistical methodologies, the paper reveals the subtle yet impactful changes that have characterized the field in the last two decades. It examines the prominence of various scheduling problems, identifies leading research journals, and highlights international contributions and collaborations, thereby offering a thorough guide to the machine scheduling ecosystem. The study delves into specific problem characteristics and assesses performance criteria and solution methods to provide an in-depth view of the field's multifaceted nature. Ultimately, this paper captures the essence of machine scheduling's evolution and suggests new paths for exploration. The insights gained contribute significantly to academic discussions and equip practitioners with a comprehensive understanding of the dynamic landscape of machine scheduling. [ABSTRACT FROM AUTHOR]

Automatic classification of the plants is growing area of association with computer science and Botany, it has attracted many researchers to subsidize plant classification using image processing and machine learning techniques. Plants can be classified using number of traits such as leaf color, flowers, leaves, roots, leaf shape, leaf size etc. highly depends upon feature selection methods. However extraction of features from selected trait is most significant state in classification. State-of-the-art classification can be achieved by using leaf characteristics such as leaf venation patterns, leaf spectral signatures, leaf color, leaf shape, etc. This paper describes multimodal plant classification system using leaf venation patterns and its spectral signatures as a significant features. This paper shows that the feature fusion can be used to achieve efficient plant identification. The accuracy of identification for leaf spectral data, leaf venation features and HOG features is validated, it signifies that feature fusion technique performs better than that of non-imaging spectral signatures features only, with recognition result of 98.03% GAR and 93.51% GAR, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning approach for solving non-smooth convex optimization problems (NCOPs), which have broad applications in computer science, engineering, and physics. Our approach combines neurodynamic optimization with physics-informed neural networks (PINNs) to provide an efficient and accurate solution. We first use neurodynamic optimization to formulate an initial value problem (IVP) that involves a system of ordinary differential equations for the NCOP. We then introduce a modified PINN as an approximate state solution to the IVP. Finally, we develop a dedicated algorithm to train the model to solve the IVP and minimize the NCOP objective simultaneously. Unlike existing numerical integration methods, a key advantage of our approach is that it does not require the computation of a series of intermediate states to produce a prediction of the NCOP. Our experimental results show that this computational feature results in fewer iterations being required to produce more accurate prediction solutions. Furthermore, our approach is effective in finding feasible solutions that satisfy the NCOP constraint. [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]

The research domains and solutions of information technology have been rapidly changed as information technology continuously and deeply deployed into our society. On the other hand, it can be considered that there are some common knowledge creation processes in information technology researches. In this paper, we propose a common research process on creating new researches which were extracted by retrospectively reengineering knowledge creation model of our Information Technology research papers that were conducted with my co-authors. The research creation model is applied and evaluated by our publications. [ABSTRACT FROM AUTHOR]

The large number of scientific publications around the world is increasing at a rate of approximately 4%–5% per year. This fact has resulted in the need for tools that deal with relevant and high-quality publications. To address this necessity, search and reference management tools that include some recommendation algorithms have been developed. However, many of these solutions are proprietary tools and the full potential of recommender systems is rarely exploited. There are some solutions which provide recommendations for specific domains, by using ad-hoc resources. Furthermore, some other systems do not consider any personalization strategy to generate the recommendations. This paper presents ArZiGo , a web-based full prototype system for the search, management, and recommendation of scientific articles, which feeds on the Semantic Scholar Open Research Corpus, a corpus that is growing continually with more than 190M papers from all fields of science so far. ArZiGo combines different recommendation approaches within a hybrid system, in a configurable way, to recommend those papers that best suit the preferences of the users. A group of 30 human experts has participated in the evaluation of 500 recommendations in 10 research areas, 7 of which belong to the area of Computer Science and 3 to the area of Medicine, obtaining quite satisfactory results. Besides the appropriateness of the articles recommended, the execution time of the implemented algorithms has also been analyzed. • A web system for the search, management, and recommendation of scientific articles. • A hybrid and multidisciplinary scientific article recommendation system. • A modular and scalable recommendation system. • Use of the Semantic Scholar Open Research Corpus as a corpus of articles. [ABSTRACT FROM AUTHOR]

The multiple-attribute decision-making (MADM) problem is resolved through the q -rung complex diophantine neutrosophic normal set (q -rung CDNNS). An important way to express uncertain information is using q -rung orthopair fuzzy sets (q -ROFs). Yager introduced q -ROFs as a generalization of intuitionistic fuzzy sets in which the sum of membership and non-membership degrees is one. In addition, they have superiority over intuitionistic fuzzy sets and Pythagorean fuzzy sets. Complex diophantine fuzzy sets are generalizations of neutrosophic and diophantine fuzzy sets, respectively. Several aggregating operations (AOs) are discussed here, as well as their respective interpretations. The paper discusses q -rung CDNN weighted averaging (q -rung CDNNWA), q -rung CDNN weighted geometric (q -rung CDNNWG), q -rung generalized CDNN weighted averaging (q -rung GCDNNWA) and q -rung generalized CDNN weighted geometric (q -rung GCDNNWG). We will review several of these sets with important properties in greater detail using algebraic operations. Additionally, we develop an algorithm for solving MADM problems using these operators. Several real-world examples illustrate how enhanced score values can be applied. Sensor robots are said to rely heavily on computer science and machine tool technology. Four factors are to evaluate when determining a sensor robotics system's quality: resolution, sensitivity, error, and environment. It is possible to compare expert opinions with the criteria and determine the best alternative. Therefore, the value of q significantly impacts the model's results. To prove that the models considered are valid and useful, we will compare the current and proposed models. Thus, q has a significant impact on the results of the model. [ABSTRACT FROM AUTHOR]

Today's production methods are challenged by changing paradigms from a static constraint mass production and mass customization to continuing personalization and regionalization of product, production and markets. It is yet unclear what future production systems will be the most appropriate, how they will look like and how the current, uncertain trends in manufacturing can be tackled most effectively. In this paper, first results from a comprehensive questionnaire on »The Intelligent Production of the Future«, performed by a consortium of researchers from the Institute for Computer Science and Control (SZTAKI) and the Fraunhofer-Institute for Manufacturing Engineering and Automation IPA are outlined. During this survey, stakeholders and peers from more than 70 companies and research institutions in Germany, Austria and Hungary have been asked to share their impressions, describe their opinions and rate central statements on future production systems, methodologies and trends. The paper outlines the most important topics of future production systems and lessons learned during the evaluation of the survey. [ABSTRACT FROM AUTHOR]

The technological progress in computer science (particularly, in machine learning) has contributed to the improvement of medical services, both in detecting and treating diseases. The large volumes of data, that are overwhelming for human experts (doctors, nurses), can easily be managed by automated systems, as long as we have the computational resources. Obviously, human experts are still essential in the process - we think of the use of computer science in medicine as a collaboration between medical staff and artificial intelligence. The usual types of data that can be processed by automated systems are text, sound, and image types. In this paper, we approach the diagnosis subject and focus on data consisting of sound. We created a heart murmur detection system - it analyzes recordings and tells the user whether the sound samples indicate a heart murmur or not, based on a trained machine learning model. One of the main advantages of our system is the fact that we ran a large number of experiments, with different configurations of denoising techniques and features taken into consideration. We were able to draw some interesting conclusions, for example, we found out which features are the most important for the classification and which features are not worth computing. Also, our work denotes a thorough understanding of sound processing. [ABSTRACT FROM AUTHOR]

In a world whose attention to environmental and health problems is very high, the issue of properly managing nuclear waste is of a primary importance. Information and Communication Technologies have the due to support the definition of the next-generation plants for temporary storage of such wasting materials. This paper investigates on the adoption of one of the most cutting-edge techniques in computer science and engineering, i.e. Digital Twins, with the combination of other modern methods and technologies as Internet of Things, model-based and data-driven approaches. The result is the definition of a methodology able to support the construction of risk-aware facilities for storing nuclear waste. [ABSTRACT FROM AUTHOR]

This article discusses the correlation between first-year students' prior knowledge of programming and their academic achievements. Many students who enroll in the Faculty of Computer Science and Information Technology at Riga Technical University drop out during their first year. Therefore, it is crucial to support first-year students in acquiring programming knowledge successfully. The Faculty of Computer Science and Information Technology provides an additional course called "Algorithmization Practice" for students without prior programming experience. The authors of this paper conducted research to investigate whether this course helps bridge the gap between students with and without prior programming knowledge and whether it improves the academic performance of students without prior knowledge in their first year. The collected data was summarized using descriptive statistics, and the final scores of students from different groups were compared to identify any differences between those who attended the "Algorithmization Practice" course and those who did not. Based on a sample of 1744 introductory programming course students, our analysis indicates that students who attended the additional course demonstrated better academic performance. [ABSTRACT FROM AUTHOR]

The NHS England Radiotherapy Service Specification calls for routine use of patient-reported outcome measures (PROMs). However, barriers exist at patient, healthcare professional and service levels. The aim of the present study was to determine the current use of PROMs within radiotherapy services in England. The current attitudes, barriers and enablers to the implementation of PROMs in radiotherapy practice were evaluated and practical recommendations to inform future implementation were developed. A mixed-methods approach was adopted to obtain quantitative and qualitative data. An online questionnaire was developed and disseminated to all radiotherapy operational delivery network managers across England. The questionnaire consisted of 12 open and closed questions relating to PROMs use, with the option to provide free-text responses. Inductive thematic analysis was conducted on free-text comments, whereas descriptive statistics were used to analyse quantitative data. In total, 182 responses were received from 40 of the 50 radiotherapy providers, resulting in a response rate of 84%. The current use of PROMs was analysed, including rationale for use, tools used, format of PROMs collection and timing within the radiotherapy pathway. Most respondents indicated that PROMs were used in the context of clinical trials only. Through thematic analysis, four identical key themes were identified relating to both barriers and enablers to PROMs use; these included IT infrastructure, time, human/financial resources and training/education. A fifth theme, standardisation, was identified as a key enabler to PROMs use. Our findings show that outside of clinical trials, PROMs are not routinely used in radiotherapy services due to barriers identified at professional and service levels. Here we provide recommendations to mitigate the barriers identified and implement PROMs in radiotherapy, including training for healthcare professionals and standardisation of PROMs tools and storage. This study provides a key first step in driving PROMs implementation within radiotherapy services across England. • Outside of clinical trials, PROMs were not widely used within radiotherapy services. • Common PROMs used included QOL, anxiety/depression and urinary/bowel function tools. • PROMs are completed using either paper or electronic forms, by staff or patients. • Barriers include IT infrastructure, time, resources (staff/finances) and training. • Enablers included IT, time, resources, training and standardisation. [ABSTRACT FROM AUTHOR]

Internet has been source of knowledge for decades. The pool of information cannot be sustained in absence of the network of networks. Internet has many useful applications in commercial, social and educational areas. In today's scenario, e-learning is also one of the useful applications in the world of Internet. The medium of e-learning has achieved advancement in various fields such as adaptive e-learning systems. The branch of computer science with psycholinguistics has done tremendous job in providing technical solutions to learners. However, learning disorders on the platform of e-learning still require lots of research. Therefore, this paper provides a personalized assessment model for alphabet learning with learning objects for children's who face dyslexia. The cognitive inclination of dyslexic learner has been determined using assessment model. This paper studies the cognitive potential of dyslexic learner and has built a personalized e-learning platform to alleviate their alphabetical problems. [ABSTRACT FROM AUTHOR]

This paper aims at identifying the key role of "Smart Energy and Power Systems Modelling", within the context of Energy Informatics. The main objective is to describe how the specific subject of "Smart Energy and Power Systems Modelling" can give a key contribution within the novel domain of Energy Informatics, by successfully linking and integrating the different disciplines involved. First the paper will present how and where the specific subject of "Smart Energy and Power Systems Modelling" can position itself within the broad Energy Informatics domain. Afterwards the paper will explain how Cyber-Physical Systems (CPS) and Information and Communication Technologies (IoT), coupled with "Smart Energy and Power Systems Modelling", can enhance the Energy Informatics domain. In addition, the main challenges and opportunities for Energy Informatics specialists will be outlined, with regard to the interdisciplinary approach that characterises this field. [ABSTRACT FROM AUTHOR]

Automatic grading is not a new approach but the need to adapt the latest technology to automatic grading has become very important. As the technology has rapidly became more powerful on scoring exams and essays, especially from the 1990s onwards, partially or wholly automated grading systems using computational methods have evolved and have become a major area of research. In particular, the demand of scoring of natural language responses has created a need for tools that can be applied to automatically grade these responses. In this paper, we focus on the concept of automatic grading of short answer questions such as are typical in the UK GCSE system, and providing useful feedback on their answers to students. We present experimental results on a dataset provided from the introductory computer science class in the University of North Texas. We first apply standard data mining techniques to the corpus of student answers for the purpose of measuring similarity between the student answers and the model answer. This is based on the number of common words. We then evaluate the relation between these similarities and marks awarded by scorers. We consider an approach that groups student answers into clusters. Each cluster would be awarded the same mark, and the same feedback given to each answer in a cluster. In this manner, we demonstrate that clusters indicate the groups of students who are awarded the same or the similar scores. Words in each cluster are compared to show that clusters are constructed based on how many and which words of the model answer have been used. The main novelty in this paper is that we design a model to predict marks based on the similarities between the student answers and the model answer. We argue that computational methods be used to enhance the reliability of human scoring, and not replace it. Humans are required to calibrate the system, and to deal with situations that are challenging. Computational methods can provide insight into which student answers will be found challenging and thus be a place human judgement is required. [ABSTRACT FROM AUTHOR]

The problem of reducing ϵ -NN to (c , r) -NN is very important in theoretical computer science area and has attracted many research efforts. In this paper a new algorithm for such problem is proposed, which achieves O (log ⁡ n) query time. Comparing with the former algorithms for the same problem, this query time is the lowest, which is the main contribution of this paper. The low query time complexity is achieved by raising the preprocessing time and space complexity. How to reduce the cost added into the two complexities is also discussed in this paper. [ABSTRACT FROM AUTHOR]

Turing (1937) introduces a model of code that is followed by other pioneers of computing machines (such as Flowers 1983, Eckert, Mauchly, Brainerd 1945 and others). One of them is John von Neumann, who defines the concept of optimal code in the context of the conception of EDVAC. He later uses it to build on in his theoretical considerations of the universal constructor (von Neumann 1966). Von Neumann (1963) further presents one of the first neural network models, in relation to the work of McCulloch and Pitts (1943), for both theoretical purposes (von Neumann probe) and practical applications (computer architecture of EDVAC). The aim of this paper is (1) to describe the differences between Turingʼs and von Neumannʼs conceptualizations of code and the mechanical computing model. Between von Neumann's abstract technical conception (von Neumann 1963 and 1966) and Turingʼs more concrete biochemical conception (Turing 1952). Furthermore, (2) we want to answer the question why these influential models of mechanisms (predominantly in computer science) have so far been ignored by philosophers of the new mechanism (Machamer, Darden, Craver 2000, Glennan 2017). We will show that these classical models of machines are not only compatible with the new mechanism, but moreover complement it, since they represent a completely separate type of model of mechanism, alongside producing, maintaining and underlying (Zámečník 2021). The final (3) and main goal of our paper will be an attempt to relate von Neumannʼs and Turingʼs notion of mechanism to Barbieriʼs notion of extended mechanism (Barbieri 2015). [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]

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]