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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]

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]

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]

• 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]

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]

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]

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]

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]

• 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]

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]

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]

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]

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]

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]

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]

• 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]

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]

Spectral Clustering (SC) is an important method in areas such as data mining, image processing, computer science and so on. It attracts more and more attention owing to its effectiveness in unsupervised learning. However, SC has poor performance in the high-dimensional data. Traditional SC methods conduct the spectral embedding of the affinity matrix among data at the first beginning, and then obtain clustering results by the K -means clustering. The also have drawbacks int two processing steps: the clustering results are sensitive to the affinity matrix which may be inaccurate and the post-processing K -means may also be limited by its initialization problem. In the paper, a new approach which joints spectral embedding and clustering with structured graph optimization (called JSEGO) is proposed. In the new model, the low-dimensional representation of data can first be obtained by the spectral embedding method, which can handle with the high-dimensional data better. Then, the optimization similarity matrix would be obtained with such the embedded data. Furthermore, the learning structure graph gives feedback to the similarity matrix to generate better spectral embedded data. As a result, better similarity matrix and clustering result can be obtained by the iterations simultaneously, which are often conducted in two separate steps in the spectral clustering. As a result, the drawbacks introduced by the two processing introduces can be solved. At last, we use an alternative optimization method in the new model and conduct the theoretical analysis by comparing this proposed method with K -means clustering. Experiments based on synthetic data and actual benchmark data prove the advantage of this new approach. [ABSTRACT FROM AUTHOR]

In this paper, a novel numerical algorithm for efficient modeling of three-dimensional shape transformation governed by the modified Allen-Cahn (A-C) equation is developed, which has important significance for computer science and graphics technology. The new idea of the proposed method is as follows. Firstly, the operator splitting method is used to decompose the three-dimensional problem into a series of one-dimensional subproblems that can be solved in parallel in the same direction. Secondly, a temporal p-adaptive strategy, which is based on the extrapolation technique, is proposed to improve the convergence order in time and preserve the computational efficiency simultaneously. Finally, a parallel least distance modification technique is developed to force the discrete maximum bound principle. The proposed method achieves high precision and high efficiency at the same time. Numerical examples include the effectiveness of the p-adaptive method and the bound preserving least distance modification, and a series of complex three-dimensional shape transformation modelings. • The operator splitting method is used to solve 3D shape transformation PDE. • A temporal p-adaptive strategy is developed to improve computational efficiency. • A least-distance modification is developed to force the discrete maximum bound. [ABSTRACT FROM AUTHOR]

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

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

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

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

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]

This paper concerns quantum heuristics that are able to extend the domain of quantum computing defining a promising way in the large number of well-known classical algorithms. Quantum approximate heuristics take advantage of alternation between one Hamiltonian defining the problem to solve and one mixing Hamiltonian. The adiabatic theorem initially defines in quantum physic allow to compute a solution for the Schrödinger equation, but the foundation of this methods requires strong skill in physics and mathematics. Our main objectives in this paper are at first to provide an algorithm-based presentation (as close as possible of the classical computer science community in operational research practice) of the adiabatic optimization and secondly to give a comprehensive resolution of the well-known SAT problem. This presentation gives opportunities to provide a concise but explicit analysis of the adiabatic capability to define a new efficient operational research trend. Our experiments encompass numerical evaluations on both simulator and on real quantum computer provided by IBM. Numerical evaluations using the QLM library meet the Qiskit evaluations. This contribution is at the crossroad of physic and computer science in the sense it proves the capabilities of quantum concepts to define a new and promising research trends in optimization. [ABSTRACT FROM AUTHOR]

Threshold graphs have numerous applications in computer science and psychology. The eccentricity matrix ɛ (G) of G is defined as ɛ (G) i j = d (v i , v j) if d (v i , v j) = m i n { e (v i) , e (v j) } , 0 otherwise. In this paper, we give some properties of the eccentricity eigenvalues of threshold graphs and determine threshold graphs with distinct eccentricity eigenvalues in [ − 2 , 0 ]. [ABSTRACT FROM AUTHOR]

Influence maximization is an important research area in social network analysis where researchers are concerned with detecting influential nodes. The detection of influential nodes is of great interest in several disciplines including computer science, opinion propagation, political movements, or economics, where systems are often modeled as graphs. The Influence Maximization problem is proved NP-hard. This computational complexity is justified by two main factors. The first factor is about the important size of social networks. Modern social networks like TikTok and Facebook have reached an unprecedented number of users. Dynamic social networks, whose topology or/and informational content is able to evolve, represent the second factor. Maximizing influence in such networks remains a significant task. In this light, several methods have been proposed. Being motivated by this fact, we provide in this paper a detailed survey of influence maximization approaches. Our main concern is to provide a taxonomy of existing models in both static and dynamic networks. In addition, we provide a comparison of the state-of-the-art approaches according to a clear categorization. New trends for detecting influential nodes are also discussed. We provide then some challenges as well as future directions. • Taxonomy of existing IM models in both static and dynamic networks is provided. • A comparison of existing approaches according to a clear categorization is presented. • New trends for detecting influential nodes are also discussed. [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]

The seven papers included in this special issue represent a selection of extended contributions presented at the 16th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2021, held in Bilbao, Spain, September 22nd-24th, 2021, and organized by the BISITE group and the University of Deusto. The SOCO 2021 international conference represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines that investigate, simulate, and analyse very complex issues and phenomena. This special issue is aimed at practitioners, researchers, and postgraduate students who are engaged in developing and applying advanced intelligent systems principles to solve real-world problems in the mentioned fields. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

Fuzzy Switch Graphs (FSG s) are reactive fuzzy graphs that model systems in which accessibility relations and fuzzy values are changed whenever an edge is crossed [19]. Reversal Fuzzy Switch Graphs (R F S G s) were presented in [6] and model fuzzy reactive systems which provide the activation and deactivation of resources, a functionality that FSG s do not offer [19]. Activation and deactivation of arrows in a switch graph makes its applicability wider in fields like computer science and engineering. In this sense, the definition of operations for R F S G s is an important issue, since it enables to understand the algebraic structure of these graphs and allows to generate new model of systems from the previous one. This paper introduces some aggregation-based operations: union, intersection and extension for R F S G s. For each operation, the paper verify some properties. It ends with an application for engineering. [ABSTRACT FROM AUTHOR]

• We consider a variant of the online buffer management problem. • This variant is called the 2-bounded delay buffer management problem with lookahead. • We design an optimal online algorithm for this variant. The bounded delay buffer management problem, which was proposed by Kesselman et al. (STOC 2001 and SIAM Journal on Computing 33(3), 2004), is an online problem focusing on buffer management of a switch supporting Quality of Service (QoS). The problem definition is as follows: Packets arrive to a buffer over time and each packet is specified by the release time , deadline and value. An algorithm can transmit at most one packet from the buffer at each integer time and can gain its value as the profit if transmitting the packet by its deadline after its release time. The objective of this problem is to maximize the gained profit. We say that an instance of the problem is s -bounded if for any packet, an algorithm has at most s chances to transmit it. For any s ≥ 2 , Hajek (CISS 2001) showed that the competitive ratio of any deterministic algorithm is at least (1 + 5) / 2 ≥ 1.618. Recently, Veselý et al. (SODA 2019) designed an online algorithm matching the lower bound. Böhm et al. (ISAAC 2016 and Theoretical Computer Science, 2019) introduced the lookahead ability to an online algorithm. At a time t , the algorithm obtains information about packets arriving at time t + 1 , and showed that for s = 2 , there is an algorithm which achieves the competitive ratio of (− 1 + 13) / 2 ≤ 1.303. Also, they showed that the competitive ratio of any deterministic algorithm is at least (1 + 17) / 4 ≥ 1.280. In this paper, for the 2-bounded model with lookahead, we design an algorithm with a matching competitive ratio of (1 + 17) / 4. [ABSTRACT FROM AUTHOR]

• Generalising confluence to confluence up to garbage. • Critical pair analysis for confluence up to garbage. • Back-tracking free recognition of graph languages. The transformation of graphs and graph-like structures is ubiquitous in computer science. When a system is described by graph-transformation rules, it is often desirable that the rules are both terminating and confluent so that rule applications in an arbitrary order produce unique resulting graphs. However, there are application scenarios where the rules are not globally confluent but confluent on a subclass of graphs that are of interest. In other words, non-resolvable conflicts can only occur on graphs that are considered as "garbage". In this paper, we introduce the notion of confluence up to garbage and generalise Plump's critical pair lemma for double-pushout graph transformation, providing a sufficient condition for confluence up to garbage by non-garbage critical pair analysis. We apply our results in two case studies about efficient language recognition: we present backtracking-free graph reduction systems which recognise a class of flow diagrams and a class of labelled series-parallel graphs, respectively. Both systems are non-confluent but confluent up to garbage. We also give a critical pair condition for subcommutativity up to garbage which, together with closedness, implies confluence up to garbage even in non-terminating systems. [ABSTRACT FROM AUTHOR]

• Study the minsum k -sink problems on dynamic flow path networks for the confluent/non-confluent flow model. • Develop algorithms which run in almost linear time regardless of the number of sinks k. • Improve upon the previous results for the same problem with the confluent flow model. • Provide the first polynomial time algorithm for the problem with the non-confluent flow model. • The main theoretical contribution is to construct novel data structures for solving subproblems in polylogarithmic time. We address the facility location problems on dynamic flow path networks. A dynamic flow path network consists of an undirected path with positive edge lengths, positive edge capacities, and positive vertex weights. A path can be considered as a road, an edge length as the distance along the road and a vertex weight as the number of people at the site. An edge capacity limits the number of people that can enter the edge per unit time. In the dynamic flow network, given particular points on edges or vertices, called sinks , all the people evacuate from the vertices to the sinks as quickly as possible. The problem is to find the location of sinks on a dynamic flow path network in such a way that the aggregate evacuation time (i.e., the sum of evacuation times for all the people) to sinks is minimized. We consider two models of the problem: the confluent flow model and the non-confluent flow model. In the former model, the way of evacuation is restricted so that all the people at a vertex have to evacuate to the same sink, and in the latter model, there is no such restriction. In this paper, for both the models, we develop algorithms which run in almost linear time regardless of the number of sinks. It should be stressed that for the confluent flow model, our algorithm improves upon the previous result by Benkoczi et al. [Theoretical Computer Science, 2020], and one for the non-confluent flow model is the first polynomial time algorithm. [ABSTRACT FROM AUTHOR]

Abstract This paper offers a critical examination of how the discourse function of definition is being used in academic writing. The main argument is that definitions do not only serve metadiscursive purposes but also ideational ones. As a form of elaboration in various discourse genres and disciplines, definitions have often been regarded as non-propositional because they help in explaining the meaning of utterances and ideas rather than in construing meaning or in building argumentation. In order to understand how definitional chunks fill both roles, that is, propositional and non-propositional ones, this paper investigates a corpus of research articles within two disciplines, Linguistics and Computer Science. Instances of definitions are classified in terms of their building units and their functions. Results indicate that definitions are neither homogeneous in their forms nor in the functions they serve in academic writing. The main conclusion is that the extent to which they serve propositional and non-propositional ends is governed, on the one hand, by functional reasons and on the second hand, by disciplinary practices. [ABSTRACT FROM AUTHOR]

Highlights • And-cross transitions are a useful abstraction that can make some constructs easier to specify for the modeler. • They can be transformed to and from alternative modeling solutions that require a greater number of transitions. • With fewer transitions, the time taken and memory required to certify a model to be deterministic using formal methods can be much reduced compared to other approaches. This is related to the time and memory required to compute the set of pairs of potentially conflicting transitions. Abstract In this paper, we present an approach to formally encode state machines expressed in Umple for symbolic verification. We illustrate this with a real-world modeling example that encodes and analyzes and-cross transitions. This paper discusses a formal description of our approach to represent state machine systems under analysis (SSUAs); a systematic approach to certifying that SSUAs are deterministic; and an evaluation of performance (memory usage and execution time) on the case study. Method We describe a formalization of state machines in Umple that enables their translation to model checking tools and also to code that is consistent with this. We present three alternative modeling solutions for a sample problem and a solution based on the use of and-cross transitions. State machine models corresponding to these solutions are represented in Umple, a model-oriented programming language. These are automatically transformed to SMV, the input language of the nuXmv (or NuSMV) model checker. By cleanly separating concerns, we systematically integrate components of hierarchical state systems as opposed to the traditional flattening approach, yet manage the complexity introduced by concurrency and and-crossing. We then compose and verify a set of requirements (e.g., correctness, safety, liveliness, etc.) on the resulting systems of all the modeling approaches to empirically compare the different modeling alternatives with the use of and-cross transitions. Results We can encode and formally analyse complex state machines with and-cross transition(s). We observed a large reduction in the number of required transitions for encoding the SSUA, as opposed to the alternative approaches. We asserted that solutions derived from the approaches are identical behavior-wise even though each approach models the SSUA differently. Each of the approaches yielded the same result for potentially conflicting pairs which is a false positive (i.e., the SSUAs are deterministic). We observe that each approach maintains the same global state-space irrespective of the variations in their number of transitions. Furthermore, we observe that it is untrue that a more abstract method applied to an SSUA outperforms its less abstract counterpart whenever parameters (such as execution time, memory usage and the number of Binary Decision Diagrams - BDDs) are the factors under consideration. Contributions A systematic approach to encode state machines with and-cross transitions (including unusual transitions). An enhanced but fully automated approach to discovering nondeterminism in state machines even in the presence of unbounded domains and multiple and-cross transitions within the same enclosing orthogonal state. An empirical study of the impact of abstraction on some performance parameters. We also present an extended formalization of Umple state machines. [ABSTRACT FROM AUTHOR]

Abstract Many metrics have been proposed to measure coupling—the degree of association between modules in a system. They have often been described in different ways, hindering comparison and research. Their definitions are often incomplete regarding language features in some languages, meaning that different tool developers may implement the same metric differently. This complicates comparing results from studies that use different tools. This paper therefore aims to define coupling metrics consistently and unambiguously. The paper describes a model of coupling that uses the reification of the concept of dependency as its fundamental unit. Based on this model, it defines a framework for defining coupling metrics. It shows how to define several well-known coupling metrics in the framework, and how defining different metrics based on the same model facilitates direct comparisons. It discusses how the framework resolves issues due to incomplete metric definitions, such as different language features. This formal framework is sufficiently simple that it can be implemented in such a way as to provide multiple metrics. [ABSTRACT FROM AUTHOR]

Suppose that two independent sets I b and I r of a graph such that | I b | = | I r | are given, and a token is placed on each vertex in I b. The sliding token problem is to determine whether there exists a sequence of independent sets which transforms I b into I r so that each independent set in the sequence results from the previous one by sliding exactly one token along an edge in the graph. The sliding token problem is one of the reconfiguration problems that attract the attention from the viewpoint of theoretical computer science. Recently, the problems that aim at finding a shortest reconfiguration sequence are investigated. In general, even if it is polynomial time solvable to decide whether two instances are reconfigurable into each other, it can be NP-hard to find a shortest sequence between them. In this paper, we show that the problem for finding a shortest sequence between two independent sets is polynomial time solvable for some graph classes which are subclasses of the class of interval graphs. As far as the authors know, this is the first polynomial time algorithm for the shortest sliding token problem for a graph class that requires detours. [ABSTRACT FROM AUTHOR]

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

This paper will discuss whether there are chances for a paradigm shift, i.e., a shift away from the (almost without much thought) taken-for-granted sequential, from piece-to-whole mainstream understanding of a cognitivist, now almost 70-year old information processing perspective to a perspective that takes as its starting point the whole, and hence meaning. The whole may stand for an object as embedded in its immediately salient as well as inconspicuous environment, where parts cannot be made sense of without knowing and understanding their roles in the larger configurations. It may stand for an organism or an organismic collectivity as embedded in its environment, a person or a collectivity of people in their embedded immediate and phenomenal field, which we will also not understand unless we understand their larger environmental, societal and cultural embeddedness. In today's scientific climate where the mainstream information-processing perspective is serving as an unchallenged, often hidden assumption within neuroscience and computer science, I will look for recent, promising developments which might nonetheless be paving the road to a perspective so long ago proposed by the Gestaltists yet somehow "lost in translation". In that sense, Gestalt theory, which till today has been widely distorted as a theory that talks about 'a bunch of grouping principles in (static) vision', is the very first dynamical theory within the psychological sciences that meticulously proposed this with a firm philosophical grounding, something that is ever more missing within theory building in psychology. [ABSTRACT FROM AUTHOR]

Community plays an important role in the field of sociology, biology and especially in domains of computer science, where systems are often represented as networks. And community detection is of great importance in the domains. A community is a dense subgraph of the whole graph with more links between its members than between its members to the outside nodes, and nodes in the same community probably share common properties or play similar roles in the graph. Communities overlap when nodes in a graph belong to multiple communities. A vast variety of overlapping community detection methods have been proposed in the literature, and the local expansion method is one of the most successful techniques dealing with large networks. The paper presents a density-based seeding method, in which dense disjoint local clusters are searched and selected as seeds. The proposed method selects a seed by the total degree and density of local clusters utilizing merely local structures of the network. Furthermore, this paper proposes a novel community refining phase via minimizing the conductance of each community, through which the quality of identified communities is largely improved in linear time. Experimental results in synthetic networks show that the proposed seeding method outperforms other seeding methods in the state of the art and the proposed refining method largely enhances the quality of the identified communities. Experimental results in real graphs with ground-truth communities show that the proposed approach outperforms other state of the art overlapping community detection algorithms, in particular, it is more than two orders of magnitude faster than the existing global algorithms with higher quality, and it obtains much more accurate community structure than the current local algorithms without any priori information. [ABSTRACT FROM AUTHOR]

The problem of subspace clustering which refers to segmenting a collection of data samples approximately drawn from a union of linear subspaces is considered in this paper. Among existing subspace clustering algorithms, low rank representation (LRR) based subspace clustering is a very powerful method and has demonstrated that its performance is good. Latent low rank representation (LLRR) subspace clustering algorithm is an improvement of the original LRR algorithm when the observed data samples are insufficient. The clustering accuracy of LLRR is higher than that of LRR. Recently, Frobenius norm minimization based LRR algorithm has been proposed and its clustering accuracy is higher than that of LRR demonstrating the effectiveness of Frobenius norm as another convex surrogate of the rank function. Combining LLRR and Frobenius norm, a new low rank representation subspace clustering algorithm is proposed in this paper. The nuclear norm in the LLRR algorithm is replaced by the Frobenius norm. The resulting optimization problem is solved via alternating direction method of multipliers (ADMM). Experimental results show that compared with LRR, LLRR and several other state-of-the-art subspace clustering algorithms, the proposed algorithm can get higher clustering accuracy. Compared with LLRR, the running time of the proposed algorithm is reduced significantly. [ABSTRACT FROM AUTHOR]

The lambda calculus can be seen as a language that connects mathematics with computer science. However, the models used in both disciplines have different basic properties. In computer science these models are mainly based on domain theory with its partial functions and its fixed point property. In mathematics the models use total functions and relations and allow the integration of logic. Typically, mathematical models are based on set theory with its actual infinite sets, which are incongruous with computer science. We will present a dynamic model that is based consequently on the potential infinite. It uses total functions, whereby functions and the function spaces are seen as interdependent, increasing finite entities. It moreover allows the integration of higher-order logic (to be demonstrated in a separate paper), provided the universal quantifier is interpreted in a specific, dynamic way. Such a model can serve as a common denotational semantics of the lambda calculus for both, mathematics and computer science. • The paper presents a mathematical formulation of a potential infinite set, called factor system. • Factor systems are closed under the function space construction, so they are potential infinite models of simple type theory. • Each factor system has a limit whose elements can be characterized as maximal, consistent sets of states. • Models based on factor systems allow the integration of higher-order logic. The presentation of this is a future task. [ABSTRACT FROM AUTHOR]