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Many practical problems, such as computer science, communications network, product design, system control, statistics and finance, etc.,can be formulated as a probabilistic constrained optimization problem (PCOP) which is challenging to solve since it is usually nonconvex and nonsmooth. Effective methods for the probabilistic constrained optimization problem mostly focus on approximation techniques, such as convex approximation, D.C. (difference of two convex functions) approximation, and so on. This paper aims at studying a smooth approximation approach. A smooth approximation to the probabilistic constraint function based on a sigmoid function is analyzed. Equivalence of PCOP and the corresponding approximation problem are shown under some appropriate assumptions. Sequential convex approximation (SCA) algorithm is implemented to solve the smooth approximation problem. Numerical results suggest that the smooth approximation approach proposed is effective for optimization problems with probabilistic constraints. [ABSTRACT FROM AUTHOR]

This document is a guest editorial for a special issue of the journal "Programming & Computer Software" titled "Recent Trends on Advanced Computing: The Converging Technologies." The issue features research and practical implementation results from researchers and industry experts in computer science, engineering, and technology. The papers cover a range of topics including microservices, software quality, user engagement on social media, non-functional requirements, medical software architecture, fallacies in political speeches, intelligent learning environments, and more. The guest editors for this special issue are Prof. Dr. Andrei Tchernykh, Prof. Reyes Juárez Ramírez, Dr. Esteban Mocskos, and Prof. Sergio Nesmachnow. [Extracted from the article]

Representing a static set of integers S, | S | = n from a finite universe U = [ 1.. u ] is a fundamental task in computer science. Our concern is to represent S in small space while supporting the operations of rank and select on S; if S is viewed as its characteristic vector, the problem becomes that of representing a bit-vector, which is arguably the most fundamental building block of succinct data structures. Although there is an information-theoretic lower bound of B (n , u) = lg u n bits on the space needed to represent S, this applies to worst-case (random) sets S, and sets found in practical applications are compressible. We focus on the case where elements of S contain runs of| ℓ > 1 consecutive elements, one that occurs in many practical situations. Let C (n) denote the class of u n distinct sets of n elements over the universe [ 1.. u ] . Let also C g (n) ⊂ C (n) contain the sets whose n elements are arranged in g ≤ n runs of ℓ i ≥ 1 consecutive elements from U for i = 1 , ... , g , and let C g , r (n) ⊂ C g (n) contain all sets that consist of g runs, such that r ≤ g of them have at least 2 elements. This paper yields the following insights and contributions related to rank / select succinct data structures: We introduce new compressibility measures for sets, including: B 1 (g , n , u) = lg | C g (n) | = lg u - n + 1 g + lg n - 1 g - 1 , and B 2 (r , g , n , u) = lg | C g , r (n) | = lg u - n + 1 g + lg n - g - 1 r - 1 + lg g r , such that B 2 (r , g , n , u) ≤ B 1 (g , n , u) ≤ B (n , u) . We give data structures that use space close to bounds B 1 (g , n , u) and B 2 (r , g , n , u) and support rank and select in O (1) time. We provide additional measures involving entropy-coding run lengths and gaps between items, and data structures to support rank and select using space close to these measures. [ABSTRACT FROM AUTHOR]

Although true in some aspects, the suggested characterization of today's science as a dichotomy between traditional science and data-driven science misses some of the nuance, complexity, and possibility that exists between the two positions. Part of the problem is the claim that Data Science works without theories. There are many theories behind the data that are used in science. However, for data science, the only theories that matter are those in mathematics, statistics, and computer science. In this conceptual paper, we add two other philosophy of science tenets, experiments and data, to the discussion to create a more nuanced view of how data science uses theories. Following Ihde's concept of technoscience and the incessant quest for more precision, magnification, and resolution, we argue that technology-driven science created a need for more technology-driven science, culminating in data science. Further, we adapt Hacking and Galison's views on physics to argue that data science is also an experimental science, which uses data objects in experiments. Drawing from Heelan (The Journal of Philosophy 85:515–524, 1988), we called these objects "data-objects-for-knowing". Finally, we conclude that data science is a science to study artificially created phenomena—a science to study the data manipulated by the equations and operations of AI. It disregards the connections between data and the real world that were carefully built by the theories from other sciences. In the experiments of data science, data are the world itself. The knowledge created by data science is purposely disconnected from any theory from other sciences; it is a knowledge for the sake of itself. [ABSTRACT FROM AUTHOR]

Indoor objects detection and recognition plays an important role in computer science and artificial intelligence fields. This task plays also a crucial role especially for blind and visually impaired persons (VIP) assistance navigation. Aiming to address this problem, we propose in this paper to develop a new indoor object detection system based on deep learning algorithms. Unfortunately, this type of algorithms requires heavy computational resources, and energy consumption. To address this problem, we propose a CUDA multi-GPU framework implementation of the proposed system. Generally deep learning based algorithms require huge amount of data to train and test networks. We propose to develop a new indoor dataset which consists of 11,000 indoor images containing 25 indoor landmark objects highly recommended for blind and VIP navigation. Based on the obtained results, the developed system shows big efficiency in terms of detection accuracy as well as processing time. [ABSTRACT FROM AUTHOR]

Models are a universal instrument in science, technology, and daily life. They function as instruments in almost every scenario. Any human activity can be (and is) supported by models, e.g. reason, explain, design, act, predict, explore, communicate, collaborate, interact, orient, direct, guide, socialises, perceive, reflect, develop, making sense, teach, learn, imagine, etc. This universal suitability is also the basis for a wide use of models and modelling in Computer Science and Engineering. We claim that models form the fourth dimension in Computer Science. This paper sketches and systematises the main ingredients of the study model and modelling. [ABSTRACT FROM AUTHOR]

Shortest distance computation is one of the widely researched areas in theoretical computer science and graph databases. Distance labeling are well-known for improving the performance of shortest distance queries. One of the best distance labeling approaches is Pruned Landmark Labeling (PLL). PLL is a 2-hop distance labeling which prunes a lot of unnecessary labels while doing breadth-first-search. Another well-known 2-hop labeling is Pruned Highway Labeling (PHL) which is designed for undirected road networks. Both PLL and PHL suffer from the problem of large index size. In this paper, we propose two approaches to address the problem, one is to compress the PLL index as well as the graph for directed graphs; the other is to compress undirected road networks using linear sets, which are essentially maximal-length non-branching paths. Our aim is to reduce the index size and index construction time without significantly compromising query performance. Extensive experiments with real world datasets confirm the effectiveness of our approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new effective algorithm for the categorical syllogisms by using a calculus system Syllogistic Logic with Carroll Diagrams, which determines a formal approach to logical reasoning with diagrams, for representations of the fundamental Aristotelian categorical syllogisms. We show that this logical reasoning is closed under the syllogistic criterion of inference. Therefore, the calculus system is implemented to let the formalism which comprises synchronically bilateral and trilateral diagrammatical appearance and naive algorithmic nature. And also, there is no need specific knowledge or exclusive ability to understand this decision procedure as well as to use it in an algorithmic system. Consequently, the empirical contributions of this paper are to design a polynomial-time algorithm at the first time in the literature to conduce to researchers getting into the act in different areas of science used categorical syllogisms such as artificial intelligence, engineering, computer science and etc. [ABSTRACT FROM AUTHOR]

Rules in artificial intelligence are able to provide inference facilities for making effective decisions in information retrieval. In such a scenario, a keyword-based information retrieval systems use the keywords as indexes generated by Web crawlers without considering semantics. Moreover, many Web search engines perform re-ranking based on relevance feedback. However, the relevant documents in e-learning applications must be stored in local repositories and must be updated dynamically based on the level of the users. Therefore, it is necessary to have an information retrieval system which performs relevant information extraction and stores such relevant information dynamically in local e-learning repositories. In order to address these issues, a new information retrieval and local storage system is proposed in this paper by applying rules for making effective decisions in the storage and retrieval algorithms which are newly proposed in this paper. For this purpose, two new algorithms called intelligent rule-based relevant information retrieval algorithm with semantics and a secured information storage using semantic knowledge representation are proposed in this paper for effectively retrieving the e-learning contents from the Web on computer science subject and to store them in local repositories with semantic indexing. The major advantages of the proposed information retrieval system include increase in accuracy, reduction in retrieval time and effective storage in local repositories for further use. [ABSTRACT FROM AUTHOR]

This paper examines the ethical pitfalls and challenges that non-ethicists, such as researchers and programmers in the fields of computer science, artificial intelligence and robotics, face when building moral machines. Whether ethics is "computable" depends on how programmers understand ethics in the first place and on the adequacy of their understanding of the ethical problems and methodological challenges in these fields. Researchers and programmers face at least two types of problems due to their general lack of ethical knowledge or expertise. The first type is so-called rookie mistakes, which could be addressed by providing these people with the necessary ethical knowledge. The second, more difficult methodological issue concerns areas of peer disagreement in ethics, where no easy solutions are currently available. This paper examines several existing approaches to highlight the ethical pitfalls and challenges involved. Familiarity with these and similar problems can help programmers to avoid pitfalls and build better moral machines. The paper concludes that ethical decisions regarding moral robots should be based on avoiding what is immoral (i.e. prohibiting certain immoral actions) in combination with a pluralistic ethical method of solving moral problems, rather than relying on a particular ethical approach, so as to avoid a normative bias. [ABSTRACT FROM AUTHOR]

We study the Maximum Cardinality Matching (MCM) and the Maximum Weight Matching (MWM) problems, on trees and on some special classes of graphs, in the online preemptive and the incremental graph models. In the Online Preemptive model, the edges of a graph are revealed one by one and the algorithm is required to always maintain a valid matching. On seeing an edge, the algorithm has to either accept or reject the edge. If accepted, then the adjacent edges are discarded, and all rejections are permanent. In this model, the complexity of the problems is settled for deterministic algorithms (McGregor, in: Proceedings of the 8th international workshop on approximation, randomization and combinatorial optimization problems, and proceedings of the 9th international conference on randomization and computation: algorithms and techniques, APPROX'05/RANDOM'05, Springer, Berlin, pp. 170–181, 2005; Varadaraja, in: Automata, languages and programming: 38th international colloquium, ICALP 2011, Zurich, Switzerland, proceedings, part I, pp. 379–390, 2011. https://doi.org/10.1007/978-3-642-22006-7%5f32). Epstein et al. (in: 30th international symposium on theoretical aspects of computer science, STACS 2013, Kiel, Germany, pp. 389–399, 2013. https://doi.org/10.4230/LIPIcs.STACS.2013.389) gave a 5.356-competitive randomized algorithm for MWM, and also proved a lower bound on the competitive ratio of (1 + ln 2) ≈ 1.693 for MCM. The same lower bound applies for MWM. In the Incremental Graph model, at each step an edge is added to the graph, and the algorithm is supposed to quickly update its current matching. Gupta (in: 34th international conference on foundation of software technology and theoretical computer science, FSTTCS 2014, 15–17 Dec 2014, New Delhi, India, pp. 227–239, 2014. https://doi.org/10.4230/LIPIcs.FSTTCS.2014.227) proved that for any ϵ ≤ 1 / 2 , there exists an algorithm that maintains a (1 + ϵ) -approximate MCM for an incremental bipartite graph in an amortized update time of O log 2 n ϵ 4 . No (2 - ϵ) -approximation algorithm with a worst case update time of O(1) is known in this model, even for special classes of graphs. In this paper we show that some of the results can be improved for trees, and for some special classes of graphs. In the online preemptive model, we present a 64 / 33-competitive randomized algorithm (which uses only two bits of randomness) for MCM on trees. Inspired by the above mentioned algorithm for MCM, we present the main result of the paper, a randomized algorithm for MCM with a worst case update time of O(1), in the incremental graph model, which is 3 / 2-approximate (in expectation) on trees, and 1.8-approximate (in expectation) on general graphs with maximum degree 3. Note that this algorithm works only against an oblivious adversary. We derandomize this algorithm, and give a (3 / 2 + ϵ) -approximate deterministic algorithm for MCM on trees, with an amortized update time of O (1 / ϵ) . We also present a minor result for MWM in the online preemptive model, a 3-competitive randomized algorithm (that uses only O(1) bits of randomness) on growing trees (where the input revealed upto any stage is always a tree, i.e. a new edge never connects two disconnected trees). [ABSTRACT FROM AUTHOR]

We study a security game over a network played between a defender and kattackers. Every attacker chooses, probabilistically, a node of the network to damage. The defender chooses, probabilistically as well, a connected induced subgraph of the network of λ nodes to scan and clean. Each attacker wishes to maximize the probability of escaping her cleaning by the defender. On the other hand, the goal of the defender is to maximize the expected number of attackers that she catches. This game is a generalization of the model from the seminal paper of Mavronicolas et al. Mavronicolas et al. (in: International symposium on mathematical foundations of computer science, MFCS, pp 717–728, 2006). We are interested in Nash equilibria of this game, as well as in characterizing defense-optimal networks which allow for the best equilibrium defense ratio; this is the ratio of k over the expected number of attackers that the defender catches in equilibrium. We provide a characterization of the Nash equilibria of this game and defense-optimal networks. The equilibrium characterizations allow us to show that even if the attackers are centrally controlled the equilibria of the game remain the same. In addition, we give an algorithm for computing Nash equilibria. Our algorithm requires exponential time in the worst case, but it is polynomial-time for λ constantly close to 1 or n. For the special case of tree-networks, we further refine our characterization which allows us to derive a polynomial-time algorithm for deciding whether a tree is defense-optimal and if this is the case it computes a defense-optimal Nash equilibrium. On the other hand, we prove that it is NP -hard to find a best-defense strategy if the tree is not defense-optimal. We complement this negative result with a polynomial-time constant-approximation algorithm that computes solutions that are close to optimal ones for general graphs. Finally, we provide asymptotically (almost) tight bounds for the Price of Defense for any λ ; this is the worst equilibrium defense ratio over all graphs. [ABSTRACT FROM AUTHOR]

Monotone modal logics have emerged in several application areas such as computer science and social choice theory. Since many of the most studied selfextensional logics have a conjunction, in this paper we study some distributive extensions obtained from a semilattice based deductive system with monotonic modal operators, and we give them neighborhood and algebraic semantics. For each logic defined our main objective is to prove completeness with respect to its characteristic class of monotonic frames. [ABSTRACT FROM AUTHOR]

In this paper we go further with the study initiated by Behle, Krebs and Reifferscheid (in: Proceedings CAI 2011, Lecture Notes in Computer Science, vol 6742, pp 97–114, 2011), who gave an Eilenberg-type theorem for non-regular languages via typed monoids. We provide a new extension of that result, inspired by the one carried out by Pin in the regular case in 1995, who considered classes of languages not necessarily closed under complement. We introduce the so-called positively typed monoids, and give a correspondence between varieties of such algebraic structures and positive varieties of possibly non-regular languages. We also prove a similar result for classes of languages with weaker closure properties. [ABSTRACT FROM AUTHOR]

The area of computer science and electronics have merged into one of the most notable technologies called the internet of things (IoT). A wide range of applications offered by IoT, including smart agricultural, smart transportation, smart home, and healthcare. In recent times, the market for IoT in healthcare service has increased exponentially. At the same time, security issues in the system also increase this situation threatening the health and safety of patients. Medical device usage in the hospital like Implantable Medical Devices, Radio Frequency Identification tags, and wearable devices are prone to a severe security vulnerability. This scenario is emphasizing the importance of providing privacy and confidentiality of a patient's medical information. Therefore, this paper attempts to review security issues present on the Internet of Medical Things (IoMT). Also, this work reviews the solution of various security issues presented in the exiting works. Accordingly, the review revealed that the solution of device-level security issues is desirable. At the same time, moderate level focus only happened in communication level security. In this work, various security issues presented in IoMTs analyzed and different risk factors of security attacks on IoMTs identified. The empirical result shows DDoS in IoMTs dangerous compare with other security issues with 95.00% risk factor and authentication issue in wireless insulin pump 55.00% risk factor. The proposed risk assessment helps to identify the highest impact, causing security attacks in various IoMTs. [ABSTRACT FROM AUTHOR]

This paper provides an overview of some of the most relevant deep learning approaches to pattern extraction and recognition in visual arts, particularly painting and drawing. Recent advances in deep learning and computer vision, coupled with the growing availability of large digitized visual art collections, have opened new opportunities for computer science researchers to assist the art community with automatic tools to analyse and further understand visual arts. Among other benefits, a deeper understanding of visual arts has the potential to make them more accessible to a wider population, ultimately supporting the spread of culture. [ABSTRACT FROM AUTHOR]

This paper presents a game-design workshop built around a digital art installation featuring video games displayed over a real-world skyscraper to stimulate students' interest in computer science and a study testing its short-term effects on improving middle school students' computational thinking (CT) skills and attitudes towards computing. Following a STEAM approach, the workshop aimed to engage participants in age-appropriate activities that focus on CT skills through the lens of creating their own game. A web-based game design interface that allows students to code and play games as simulated on a skyscraper was developed to support the workshop's core activities. The web environment also featured step-by-step tutorials and fully functional games to promote the accessibility of the learning materials for a diverse body of students and educators around the globe. The results of the study indicated that the workshop helped students improve their CT skills and differentially influence their attitudes towards computing. In particular, the workshop experience led students from underserved community districts to lower their attitude ratings, whereas the reverse pattern was observed for students from more affluent districts. The workshop reportedly informed students' perception of computing as a profession and their appreciation of the analytical effort required for developing functional games. Qualitative analysis of artifact-based interviews indicated that students could begin to make abstractions and devise algorithms by associating variables through conditional statements while solving problems related to game development. Interview analysis also revealed that students took pride in the effort that they made during the workshop. [ABSTRACT FROM AUTHOR]

As computers have become commonplace in everyday life, educators have begun to shift focus from working with computers (computer literacy) to thinking with computers (computational thinking). This article describes the progression of computational thinking (CT) from a historical perspective. This paper will first review the early stages of CT in the mid-1900s, along with its evolution over the succeeding decades. Finally, the article concludes with a discussion of proposed educational benefits, along with implications for future learning. [ABSTRACT FROM AUTHOR]

This work furnishes mathematical apparatus for theoretical foundations of multisets as used in Algebraic, Modal and Temporal Logics. It establishes a sufficiently generalized concept of mappings on multisets, thus resolving a long-standing obstacle in the structural study of multiset processing. It has been shown that the mapping defined herein is applicable to a vast array of already defined rewriting problems within the domain of Theoretical Computer Science. Specifically, this paper unifies and generalizes the works of Parikh in Dassow (2000), Hickman (1980), Khomenko (2003) and Nazmul et al. (2013). [ABSTRACT FROM AUTHOR]

Reports that Massachusetts Institute of Technology computer science graduate Jeremy Stribling's paper "Rooter: A Methodology for the Typical Unification of Access Points and Redundancy," co-authored with Daniel Aguayo and Maxwell Krohn, was accepted for the 9th World Multi-Conference on Systemics, Cybernetics and Informatics to be held in Florida in July. Development of an automatic computer-science paper generator that cobbles together articles adorned with randomly generated graphs; Other information provided by the paper.

Apart from the principles and methodologies inherited from Economics and Game Theory, the studies in Algorithmic Mechanism Design typically employ the worst-case analysis and design of approximation schemes of Theoretical Computer Science. For instance, the approximation ratio, which is the canonical measure of evaluating how well an incentive-compatible mechanism approximately optimizes the objective, is defined in the worst-case sense. It compares the performance of the optimal mechanism against the performance of a truthful mechanism, for all possible inputs. In this paper, we take the average-case analysis approach, and tackle one of the primary motivating problems in Algorithmic Mechanism Design—the scheduling problem (Nisan and Ronen, in: Proceedings of the 31st annual ACM symposium on theory of computing (STOC), 1999). One version of this problem, which includes a verification component, is studied by Koutsoupias (Theory Comput Syst 54(3):375–387, 2014). It was shown that the problem has a tight approximation ratio bound of (n + 1) / 2 for the single-task setting, where n is the number of machines. We show, however, when the costs of the machines to executing the task follow any independent and identical distribution, the average-case approximation ratio of the mechanism given by Koutsoupias (Theory Comput Syst 54(3):375–387, 2014) is upper bounded by a constant. This positive result asymptotically separates the average-case ratio from the worst-case ratio. It indicates that the optimal mechanism devised for a worst-case guarantee works well on average. [ABSTRACT FROM AUTHOR]

Sparse representation (SR) has been widely studied and successfully applied to many areas of computer science in recent years. However, whether sparsity is essential to improve the classification performance is still an open question. Some studies reveal that it is the collaborative representation (CR) rather than SR that truly improves the classification performance. In this paper, the advantage of CR is further investigated and exploited, and a CR-based coding method is proposed. This method improves the classification performance by applying CR to the traditional nearest subspace (NS) method. Compared to the other NS method which codes the test sample on each class separately, the proposed method employs all samples to code the test sample collaboratively and preserves the subspace structure at the same time. The test sample is then classified to the class with the smallest representation error. Besides, a corresponding dictionary learning algorithm is also proposed so that the coding can be conducted on a dictionary learned from the training dataset. Since analytical solutions for coding and dictionary learning have been derived, our algorithm can be implemented efficiently. Experiments are conducted on seven face databases and the USPS handwritten digit database, and the results show that the proposed algorithm outperforms many state-of-the-art coding methods and dictionary learning methods, which demonstrates the power brought by CR. [ABSTRACT FROM AUTHOR]

A natural way to interpret a cellular automaton (CA) is as a mechanism that computes, in a distributed way, some function f. In other words, from a computer science point of view, CAs can be seen as distributed systems where the cells of the CAs are nodes of a network linked by communication channels. A classic measure of efficiency in such distributed systems is the number of bits exchanged during the computation process. A typical approach is to look for bottlenecks: channels through which the nature of the function f forces the exchange of a significant number of bits. In practice, a widely used way to understand such congestion phenomena is to partition the system into two subsystems and try to find bounds for the number of bits that must pass through the channels that join them. Finding these bounds is the focus of communication complexity theory. Measuring the communication complexity of some problems induced by a CA ϕ turned out to be tremendously useful to give clues regarding the intrinsic universality of ϕ (a CA is said to be intrinsically universal if it is capable of emulating any other). In fact, there exist particular problems P 's for which the following key property holds: if ϕ is intrinsically universal, then the communication complexity of P (ϕ) must be maximal. In this tutorial, we intend to explain the connections that were found, through a series of papers, between intrinsic universality and communication complexity in CAs. [ABSTRACT FROM AUTHOR]

In the eternal domination game played on graphs, an attacker attacks a vertex at each turn and a team of guards must move a guard to the attacked vertex to defend it. The guards may only move to adjacent vertices on their turn. The goal is to determine the eternal domination number γ all ∞ of a graph, which is the minimum number of guards required to defend against an infinite sequence of attacks. This paper first continues the study of the eternal domination game on strong grids P n ⊠ P m . Cartesian grids P n □ P m have been vastly studied with tight bounds existing for small grids such as k × n grids for k ∈ { 2 , 3 , 4 , 5 } . It was recently proven that γ all ∞ (P n □ P m) = γ (P n □ P m) + O (n + m) where γ (P n □ P m) is the domination number of P n □ P m which lower bounds the eternal domination number [Lamprou et al. Eternally dominating large grids. Theoretical Computer Science, 794:27–46, 2019]. We prove that, for all n , m ∈ N ∗ such that m ≥ n , ⌊ n 3 ⌋ ⌊ m 3 ⌋ + Ω (n + m) = γ all ∞ (P n ⊠ P m) = ⌈ n 3 ⌉ ⌈ m 3 ⌉ + O (m n) (note that ⌈ n 3 ⌉ ⌈ m 3 ⌉ is the domination number of P n ⊠ P m ). We then generalise our technique to prove that γ all ∞ (G) = γ (G) + o (γ (G)) for all graphs G ∈ F , where F is a large family of D-dimensional grids which are supergraphs of the D-dimensional Cartesian grid and subgraphs of the D-dimensional strong grid. In particular, F includes both the D-dimensional Cartesian grid and the D-dimensional strong grid. [ABSTRACT FROM AUTHOR]

Algorithmic statistics looks for models of observed data that are good in the following sense: a model is simple (i.e., has small Kolmogorov complexity) and captures all the algorithmically discoverable regularities in the data. However, this idea can not be used in practice as is because Kolmogorov complexity is not computable. In this paper we develop an algorithmic version of algorithmic statistics that uses space-bounded Kolmogorov complexity. We prove a space-bounded version of a basic result from "classical" algorithmic statistics, the connection between optimality and randomness deficiences. The main tool is the Nisan–Wigderson pseudo-random generator. An extended abstract of this paper was presented at the 12th International Computer Science Symposium in Russia (Milovanov 10). [ABSTRACT FROM AUTHOR]

This paper presents possibilities offered by a diagnostic system called FeD. The system is completely original; it has been developed by the authors on the basis of Arduino platform. The system has been designed to perform and record measurements and to carry out different numerical operations. The real-time function for several operations is incorporated in this system. The necessary input data for the system consist of the electrical voltage waveforms only. Rescaled voltage quantities can be displayed, measured, recorded or computed in any chosen way. The system has been developed particularly for measurements and computations in the ferroresonant circuits. The strongest part of the system is its versatility. It works with a standard PC and supports a universal connection (USB standard). This is undeniably a cost-wise solution. Driving and control of the system functions are carried out using the authors' original software implemented in SciLab environment. This is free software, similar to and compatible with other existing CAD programs such as Octave and MATLAB. The obtained data, scripts and results can be freely transferred between them. The program is equipped with a transparent GUI. The need of constructing a special system to diagnose the ferroresonant circuit has emerged during earlier ferroresonance analyses and computations. Every ferroresonant circuit requires specific kind of diagnostics to estimate and display its base features in order to determine the best scientific approach to the problem. The ferroresonance phenomenon belongs to the domain of nonlinear problems. Its analysis requires excellent skills in mathematics and physics as well as computer science. Moreover, this subject also requires specialized engineering knowledge, particularly in the field of power engineering and power system equipment. Modern mathematical models and analyses used in ferroresonant computations are quite accurate; however, in case of a common user, they are often difficult to understand or implement. This paper provides full description of construction, features and test results of the developed hardware/software system designed for diagnostics of ferroresonant circuits. The test circuit case study has been performed in the entire power supply range. Results of measurements and computations as well as screenshots captured from authors' original software are shown in different figures. The developed software and recorded data have been finally used in modeling and further simulations. During this, the application of the fractional derivative iron core coil model to ferroresonance analysis has been shown. The waveforms obtained from computer simulations have been compared with those obtained from measurements performed in the test circuit. [ABSTRACT FROM AUTHOR]

The computer is one of the most complex artifacts ever built. Given its complexity, it can be described from many different points of view. The aim of this paper is to investigate the representational structure and multifunctionality of a particular subset of computers, namely personal devices (PCs, laptops, smartphones, tablets) from a user-centred perspective. The paper also discusses the concept of “cognitive task”, as recently employed in some definitions of cognitive artifacts, and investigates the metaphysical properties of such artifacts. From a representational point of view, the article introduces the concepts of artifactual meta-representation and of semi-transparency, two features that personal devices share with some cognitive and non-cognitive artifacts. Recognising the meta-representational nature of personal devices and of other cognitive artifacts, thus overcoming semi-transparency, is important for the understanding of why different artifacts offer us different cognitive affordances as well as different cognitive advantages. In this sense, it is not simply a theoretical achievement, but has some important practical consequences. In our highly technological world we can use different kinds of computers and artifacts for solving the same tasks, and we need to understand why some artifacts are better suited for some tasks than others. The ultimate characterisation of personal devices that emerges from this work is that of a sort of super-artifact. This special status is given to personal devices because of their distinctive features. They are in fact intrinsically multifunctional and meta-representational artifacts, with extremely variable structures. As super-artifacts, personal devices are characterised by macro-functionality and can be easily used as both cognitive artifacts and tools for other functions, depending on the kind of representations they instantiate. [ABSTRACT FROM AUTHOR]

Developing an intelligent chatbot has evolved in the last few years to become a trending topic in the area of computer science. However, a chatbot often fails to understand the user's intent, which can lead to the generation of inappropriate responses that cause dialogue breakdown and user dissatisfaction. Detecting the dialogue breakdown is essential to improve the performance of the chatbot and increase user satisfaction. Recent approaches have focused on modeling conversation breakdown using serveral approaches, including supervised and unsupervised approaches. Unsupervised approach relay heavy datasets, which make it challenging to apply it to the breakdown task. Another challenge facing predicting breakdown in conversation is the bias of human annotation for the dataset and the handling process for the breakdown. To tackle this challenge, we have developed a supervised ensemble automated approach that measures Chatbot Quality of Service (CQoS) based on dialogue breakdown. The proposed approach is able to label the datasets based on sentiment considering the context of the conversion to predict the breakdown. In this paper we aim to detect the affect of sentiment change of each speaker in a conversation. Furthermore, we use the supervised ensemble model to measure the CQoS based on breakdown. Then we handle this problem by using a hand-over mechanism that transfers the user to a live agent. Based on this idea, we perform several experiments across several datasets and state-of-the-art models, and we find that using sentiment as a trigger for breakdown outperforms human annotation. Overall, we infer that knowledge acquired from the supervised ensemble model can indeed help to measure CQoS based on detecting the breakdown in conversation. [ABSTRACT FROM AUTHOR]

We understand this paper as a contribution to the "anatomy" of conceptual models. We propose a signature of conceptual models for their characterization, which allows a clear distinction from other types of models. The motivation for this work arose from the observation that conceptual models are widely discussed in science and practice, especially in computer science, but that their potential is far from being exploited. We combine our proposal of a more transparent explanation of the nature of conceptual models with an approach that classifies conceptual models as a link between the dimension of linguistic terms and the encyclopedic dimension of notions. As a paradigm we use the triptych, whose central tableau represents the model dimension. The effectiveness of this explanatory approach is illustrated by a number of examples. We derive a number of open research questions that should be answered to complete the anatomy of conceptual models. [ABSTRACT FROM AUTHOR]

Private Set Intersection Cardinality (PSI-CA) and Private Set Union Cardinality (PSU-CA) are two cryptographic primitives whereby two or more parties are able to obtain the cardinalities of the intersection and the union of their respective private sets, and the privacy of their sets is preserved. In this paper, we propose a three-party protocol to finish these tasks by using quantum resources, where every two, as well as three, parties can obtain the cardinalities of the intersection and the union of their private sets with the help of a semi-honest third party (TP). In our protocol, GHZ states play a role in encoding private information that will be used by TP to compute the cardinalities. We show that the presented protocol is secure against well-known quantum attacks. In addition, we analyze the influence of six typical kinds of Markovian noise on our protocol. [ABSTRACT FROM AUTHOR]

In order to improve the robustness of text detector on scene text of various scales, a single shot text detector that combines local and non-local features is proposed in this paper. A dilated inception module for local feature extraction and a text self-attention module for non-local feature extraction are presented, and these two kinds of modules are integrated into single shot detector (SSD) of generic object detection so as to perform multi-oriented text detection in natural scene. The proposed modules make a contribution to richer and wider receptive field and enhance feature representation. Furthermore, the performance of our text detector is improved. In addition, compared with previous text detectors based on SSD which classify positive and negative samples depending on default boxes, we exploit pixels as reference for more accurate matching with ground truth which avoids complex anchor design. Furthermore, to evaluate the effectiveness of the proposed method, we carry out several comparative experiments on public standard benchmarks and analyze the experimental results in detail. The experimental results illustrate that the proposed text detector can compete with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Minimum-cost flow problems widely exist in graph theory, computer science, information science, and transportation science. The network simplex algorithm is a fast and frequently used method for solving minimum-cost flow problems. However, the conventional sequential algorithms cannot satisfy the requirement of high-computational efficiency for large-scale networks. Parallel computing has resulted in numerous significant advances in science and technology over the past decades and is potential to develop an effective means to solve the computational bottleneck problem of large-scale networks. This paper first analyzes the parallelizability of network simplex algorithm and then presents a multi-granularity parallel network simplex algorithm (MPNSA) with fine- and coarse-granularity parallel strategies, which are suitable for shared- and distributed-memory parallel applications, respectively. MPNSA is achieved by message-passing interface, open multiprocessing, and compute unified device architecture, so that it can be compatible with different high-performance computing platforms. Experimental results demonstrated that MPNSA has very great accelerating effects and the maximum speedup reaches 18.7. [ABSTRACT FROM AUTHOR]

The Internet of Things is a new round of information technology revolution after computers, the Internet and mobile communications. Internet of Things technology is an important means to improve the level of social information, which will have a profound impact on economic development and social life. IoT can stimulate the economy, increase employment, improve efficiency and make people's lives and work more convenient. Since fuzzy control can make good use of expert fuzzy information and effectively deal with the complex process of modeling, fuzzy control has received extensive attention once it has been proposed. Fuzzy logic system has become a research hotspot in academic and application fields due to its wide application. Fuzzy system identification includes structure identification and parameter identification. Fuzzy cognitive graph is a kind of soft computing method. It has stronger semantics than neural network because of its intuitive expression ability and powerful reasoning ability. Due to the widespread popularity of visual data acquisition devices, people can use the device to capture a large number of videos and images and spread them over the network in daily learning, production, life, work and entertainment. Computer science and technology, information computing technology, automated detection technology and Internet of Things technology contribute to the research of visual information data. In this paper, we conduct research on the novel QoS optimization paradigm for the IoT systems based on fuzzy logic and visual information mining integration. The experimental results show that the proposed optimization scheme has higher robustness. [ABSTRACT FROM AUTHOR]

Searching for all occurrences of a pattern in a text is a fundamental problem in computer science with applications in many other fields, like natural language processing, information retrieval and computational biology. Sampled string matching is an efficient approach recently introduced in order to overcome the prohibitive space requirements of an index construction, on the one hand, and drastically reduce searching time for the online solutions, on the other hand. In this paper we present a new algorithm for the sampled string matching problem, based on a characters distance sampling approach. The main idea is to sample the distances between consecutive occurrences of a given pivot character and then to search online the sampled data for any occurrence of the sampled pattern, before verifying the original text. From a theoretical point of view we prove that, under suitable conditions, our solution can achieve both linear worst-case time complexity and optimal average-time complexity. From a practical point of view it turns out that our solution shows a sub-linear behaviour in practice and speeds up online searching by a factor of up to 9, using limited additional space whose amount goes from 11 to 2.8% of the text size, with a gain up to 50% if compared with previous solutions. [ABSTRACT FROM AUTHOR]

The authors construct a polynomial-time algorithm for the problem on domain size HT ht where the signatures are realizable by "matchgates" over a basis of size 1. The paper "Polynomial-time axioms of choice and polynomial-time cardinality" by Joshua Grochow studies the Axiom of Choice in complexity theory. They also show that the HT ht non-random numbers are the points where the HT ht ergodic theorems do not hold. [Extracted from the article]

Statistical Machine Learning (SML) refers to a body of algorithms and methods by which computers are allowed to discover important features of input data sets which are often very large in size. The very task of feature discovery from data is essentially the meaning of the keyword 'learning' in SML. Theoretical justifications for the effectiveness of the SML algorithms are underpinned by sound principles from different disciplines, such as Computer Science and Statistics. The theoretical underpinnings particularly justified by statistical inference methods are together termed as statistical learning theory. This paper provides a review of SML from a Bayesian decision theoretic point of view—where we argue that many SML techniques are closely connected to making inference by using the so called Bayesian paradigm. We discuss many important SML techniques such as supervised and unsupervised learning, deep learning, online learning and Gaussian processes especially in the context of very large data sets where these are often employed. We present a dictionary which maps the key concepts of SML from Computer Science and Statistics. We illustrate the SML techniques with three moderately large data sets where we also discuss many practical implementation issues. Thus the review is especially targeted at statisticians and computer scientists who are aspiring to understand and apply SML for moderately large to big data sets. [ABSTRACT FROM AUTHOR]

Cooperative relaying is an effective technology to improve the secrecy capacity of physical-layer (PHY) security. Multiple relays can help further exploit the spatial diversity of cooperative networks. In power-limited networks, relay selection scheme is crucial important for it determines whether the optimal relay combination can be selected. This paper studies the problem of multi-relay selection in amplify-and-forward compressed sensing (AF-CS) networks, in which relays help all sources amplify and forward the signal, and the transmission matrix is used as the measurement matrix to encrypt the information. A self-organizing algorithm based on stochastic learning automata (SLA) is proposed for the AF-CS network to look for the best relay combination in a self-learning and self-optimizing way, and named "learning-based multi-relay selection algorithm" (L-MRS). In L-MRS, the destination node is self-optimizing to select the best state autonomously, and relays are self-organizing to decide whether to join the cooperation or not according to the environment feedback. Simulation studies verify the L-MRS's is able to select the optimal relay-combination in a very stable way, and can get higher secrecy capacity compared with the coalition formation game method. [ABSTRACT FROM AUTHOR]

The automatic classification of abstract sentences into its main elements (background, objectives, methods, results, conclusions) is a key tool to support scientific database querying, to summarize relevant literature works and to assist in the writing of new abstracts. In this paper, we propose a novel deep learning approach based on a convolutional layer and a bidirectional gated recurrent unit to classify sentences of abstracts. First, the proposed neural network was tested on a publicly available repository containing 20 thousand abstracts from the biomedical domain. Competitive results were achieved, with weight-averaged Precision, Recall and F1-score values around 91%, and an area under the ROC curve (AUC) of 99%, which are higher when compared to a state-of-the-art neural network. Then, a crowdsourcing approach using gamification was adopted to create a new comprehensive set of 4111 classified sentences from the computer science domain, focused on social media abstracts. The results of applying the same deep learning modeling technique trained with 3287 (80%) of the available sentences were below the ones obtained for the larger biomedical dataset, with weight-averaged Precision, Recall and F1-score values between 73 and 76%, and an AUC of 91%. Considering the dataset dimension as a likely important factor for such performance decrease, a data augmentation approach was further applied. This involved the use of text mining to translate sentences of the computer science abstract corpus while retaining the same meaning. Such approach resulted in slight improvements (around 2 percentage points) for the weight-averaged Recall and F1-score values. [ABSTRACT FROM AUTHOR]

In this paper, we prove that, given a clique-width k-expression of an n-vertex graph, Hamiltonian Cycle can be solved in time n O (k) . This improves the naive algorithm that runs in time n O (k 2) by Espelage et al. (Graph-theoretic concepts in computer science, vol 2204. Springer, Berlin, 2001), and it also matches with the lower bound result by Fomin et al. that, unless the Exponential Time Hypothesis fails, there is no algorithm running in time n o (k) (Fomin et al. in SIAM J Comput 43:1541–1563, 2014). We present a technique of representative sets using two-edge colored multigraphs on k vertices. The essential idea is that, for a two-edge colored multigraph, the existence of an Eulerian trail that uses edges with different colors alternately can be determined by two information: the number of colored edges incident with each vertex, and the connectedness of the multigraph. With this idea, we avoid the bottleneck of the naive algorithm, which stores all the possible multigraphs on k vertices with at most n edges. [ABSTRACT FROM AUTHOR]

In many discussions of the ways in which abstraction is applied in computer science (CS), researchers and advocates of CS education argue that CS students should be taught to consciously and explicitly move among levels of abstraction (Armoni Journal of Computers in Mathematics and Science Teaching, 32(3), 265–284, 2013; Kramer Communications of the ACM, 50(4), 37–42, 2007; Wing Communications of the ACM, 49(3), 33–35, 2006). In this paper, we describe one way that attention to levels of abstraction could also support learning in mathematics. Specifically, we propose a framework for using abstraction in elementary mathematics based on Armoni's (2013) framework for teaching computational abstraction. We propose that such a framework could address an enduring challenge in mathematics for helping elementary students solve word problems with attention to context. In a discussion of implications, we propose that future research using the framework for instruction and teacher education could also explore ways that attention to levels of abstraction in elementary school mathematics may support later learning of mathematics and computer science. [ABSTRACT FROM AUTHOR]

We consider the problems of maintaining an approximate maximum matching and an approximate minimum vertex cover in a dynamic graph undergoing a sequence of edge insertions/deletions. Starting with the seminal work of Onak and Rubinfeld (in: Proceedings of the ACM symposium on theory of computing (STOC), 2010), this problem has received significant attention in recent years. Very recently, extending the framework of Baswana et al. (in: Proceedings of the IEEE symposium on foundations of computer science (FOCS), 2011) , Solomon (in: Proceedings of the IEEE symposium on foundations of computer science (FOCS), 2016) gave a randomized dynamic algorithm for this problem that has an approximation ratio of 2 and an amortized update time of O(1) with high probability. This algorithm requires the assumption of an oblivious adversary, meaning that the future sequence of edge insertions/deletions in the graph cannot depend in any way on the algorithm's past output. A natural way to remove the assumption on oblivious adversary is to give a deterministic dynamic algorithm for the same problem in O(1) update time. In this paper, we resolve this question. We present a new deterministic fully dynamic algorithm that maintains a O(1)-approximate minimum vertex cover and maximum fractional matching, with an amortized update time of O(1). Previously, the best deterministic algorithm for this problem was due to Bhattacharya et al. (in: Proceedings of the ACM-SIAM symposium on discrete algorithms (SODA), 2015); it had an approximation ratio of (2 + ε) and an amortized update time of O (log n / ε 2) . Our result can be generalized to give a fully dynamic O (f 3) -approximate algorithm with O (f 2) amortized update time for the hypergraph vertex cover and fractional hypergraph matching problem, where every hyperedge has at most f vertices. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligence and bio-inspired computing methodologies have risen rapidly and have been successfully applied to many fields. Bio-inspired network systems are a field of biology and computer science, it has the high relation to the bio-inspired computing and bio-inspired system. It has the self-organizing and self-healing characteristics that help them in achieving complex tasks with much ease in the network environment. Software-defined networking provides a breakthrough in network transformation. However, increasing network requirement and focus on the controller for determining the network functionality and resources allocations aims at self-management capabilities. More recently, the artificial bee colony (ABC) algorithm has been used to solve the issues of parameter optimization. In this paper, a discretized food source for an artificial bee colony (DfABC) optimization algorithm is proposed and applied to optimize the kernel parameters of a support vector machine (SVM) model, creating a new hybrid. In order to further improve prediction accuracy, the proposed DfABC algorithm is applied to six popular UCI datasets. We also compare the DfABC algorithm to particle swarm optimization (PSO), the genetic algorithm (GA), and the original ABC algorithm. The experimental results show that the proposed DfABC-SVM model achieves better classification accuracy with a shorter convergence time, outperforming the other hybrid artificial intelligence models. [ABSTRACT FROM AUTHOR]

In the presented paper, the functioning and the results of the work of two metaheuristic algorithms, namely cuckoo search algorithm (CS) and firefly algorithm (FA), are described using the apparatus of generalized nets (GNs), which is an appropriate and efficient tool for describing the essence of various optimization methods. The two developed GN-models mimic the optimization processes based on the nature of cuckoos and fireflies, respectively. The proposed GN-models execute the two considered metaheuristic algorithms conducting basic steps and performing optimal search. Building upon these two GN-models, a universal GN-model is constructed that can be used for describing and simulating both the CS and the FA by setting different characteristic functions of the GN-tokens. Moreover, the universal GN-model itself can be transformed to each of the herewith presented GN-models by applying appropriate hierarchical operators. In order to validate the proposed universal GN-model, numerical experiments are performed for the operating of the universal GN-model (CS and FA) on benchmark mathematical functions. The obtained results are compared with the results of the GN-model of CS, GN-model of FA, as well as the results of the standard CS and FA. [ABSTRACT FROM AUTHOR]

Great technological advances in such areas as computer science, artificial intelligence, and robotics have brought the advent of artificially intelligent robots within our reach within the next century. Against this background, the interdisciplinary field of machine ethics is concerned with the vital issue of making robots "ethical" and examining the moral status of autonomous robots that are capable of moral reasoning and decision-making. The existence of such robots will deeply reshape our socio-political life. This paper focuses on whether such highly advanced yet artificially intelligent beings will deserve moral protection (in the form of being granted moral rights) once they become capable of moral reasoning and decision-making. I argue that we are obligated to grant them moral rights once they have become full ethical agents, i.e., subjects of morality. I present four related arguments in support of this claim and thereafter examine four main objections to the idea of ascribing moral rights to artificial intelligent robots. [ABSTRACT FROM AUTHOR]

Nowadays, deep learning is one of the popular research areas of the computer sciences, and many deep networks have been proposed to solve artificial intelligence and machine learning problems. Residual networks (ResNet) for instance ResNet18, ResNet50 and ResNet101 are widely used deep network in the literature. In this paper, a novel ResNet-based signal recognition method is presented. In this study, ResNet18, ResNet50 and ResNet101 are utilized as feature extractor and each network extracts 1000 features. The extracted features are concatenated, and 3000 features are obtained. In the feature selection phase, 1000 most discriminative features are selected using ReliefF, and these selected features are used as input for the third-degree polynomial (cubic) activation-based support vector machine. The proposed method achieved 99.96% and 99.61% classification accuracy rates for gender and activity recognitions, respectively. These results clearly demonstrate that the proposed pre-trained ensemble ResNet-based method achieved high success rate for sensors signals. [ABSTRACT FROM AUTHOR]

Ultra-precision machining is the recent realm subsequent to conventional precision machining processes. Recently, achieving nanoscale features on products has become important in manufacturing of critical components. One of the main objectives in advanced manufacturing of optics is to reach ultimately high precision in accuracy of optical surface generation. Through further development of computer numerical controlled machinery technology, single-point diamond turning (SPDT) has evolved rapidly and became a key step in the process chain of nano-machining. In SPDT, advanced and competitive technology for optical surface generation combined with ultra-precision fixtures and accurate metrological systems, high-precision surface machining with scales down to 1 nanometer, even less than 1 nanometer, are successfully achieved. Different engineering applications including medical, dental, defense, aerospace, computer science, and electronic components demand extreme smoothness and optical quality of the machined surfaces. However, there are limitations and drawbacks in SPDT process and surface generation using this technology. Different factors may significantly influence turning conditions, affect surface generation, and limit the outcome of the process. This paper attempts to provide a review of ultra-precision SPDT: technology and characteristics, manufacturing process, applications, machinable materials, and surface generation. Subsequently, influencing factors on surface generation are introduced and comprehensively discussed. Studying influencing factors on surface generation could enable setting optimized sets of machining factors and providing best possible machining conditions for generating high quality optical surfaces. Furthermore, limitations and drawbacks of standard structure SPDT process is discussed. Although a number of published studies have attempted to provide a good perspective of the SPDT process by looking into the effect of influencing factors on surface generation and existing limitations, more investigation needs to be undertaken to discover all destructive effects, origins, and influences in order to further extend the machinability of materials, reduce side effects, and improve the outcome of SPDT. [ABSTRACT FROM AUTHOR]

Nature-inspired algorithms take inspiration from living things and imitate their behaviours to accomplish robust systems in engineering and computer science discipline. Symbiotic organisms search (SOS) algorithm is a recent metaheuristic algorithm inspired by symbiotic interaction between organisms in an ecosystem. Organisms develop symbiotic relationships such as mutualism, commensalism, and parasitism for their survival in ecosystem. SOS was introduced to solve continuous benchmark and engineering problems. The SOS has been shown to be robust and has faster convergence speed when compared with genetic algorithm, particle swarm optimization, differential evolution, and artificial bee colony which are the traditional metaheuristic algorithms. The interests of researchers in using SOS for handling optimization problems are increasing day by day, due to its successful application in solving optimization problems in science and engineering fields. Therefore, this paper presents a comprehensive survey of SOS advances and its applications, and this will be of benefit to the researchers engaged in the study of SOS algorithm. [ABSTRACT FROM AUTHOR]

Neuroevolution is the name given to a field of computer science that applies evolutionary computation for evolving some aspects of neural networks. After the AI Winter came to an end, neural networks reemerged to solve a great variety of problems. However, their usage requires designing their topology, a decision with a potentially high impact on performance. Whereas many works have tried to suggest rules-of-thumb for designing topologies, the truth is that there are not analytic procedures for determining the optimal one for a given problem, and trial-and-error is often used instead. Neuroevolution arose almost 3 decades ago, with some works focusing on the evolutionary design of the topology and most works describing techniques for learning connection weights. Since then, evolutionary computation has been proved to be a convenient approach for determining the topology and weights of neural networks, and neuroevolution has been applied to a great variety of fields. However, for more than 2 decades neuroevolution has mainly focused on simple artificial neural networks models, far from today's deep learning standards. This is insufficient for determining good architectures for modern networks extensively used nowadays, which involve multiple hidden layers, recurrent cells, etc. More importantly, deep and convolutional neural networks have become a de facto standard in representation learning for solving many different problems, and neuroevolution has only focused in this kind of networks in very recent years, with many works being presented in 2017 onward. In this paper, we review the field of neuroevolution during the last 3 decades. We will put the focus on very recent works on the evolution of deep and convolutional neural networks, which is a new but growing field of study. To the best of our knowledge, this is the best survey reviewing the literature in this field, and we have described the features of each work as well as their performance on well-known databases when available. This work aims to provide a complete reference of all works related to neuroevolution of convolutional neural networks up to the date. Finally, we will provide some future directions for the advancement of this research area. [ABSTRACT FROM AUTHOR]

Let B = (X , Y , E) be a bipartite graph. A half-square of B has one color class of B as vertex set, say X; two vertices are adjacent whenever they have a common neighbor in Y. Every planar graph is a half-square of a planar bipartite graph, namely of its subdivision. Until recently, only half-squares of planar bipartite graphs, also known as map graphs (Chen et al., in: Proceedings of the thirtieth annual ACM symposium on the theory of computing, STOC '98, pp 514–523. 10.1145/276698.276865, 1998; J ACM 49(2):127–138. 10.1145/506147.506148, 2002), have been investigated, and the most discussed problem is whether it is possible to recognize these graphs faster and simpler than Thorup's O (n 120) -time algorithm (Thorup, in: Proceedings of the 39th IEEE symposium on foundations of computer science (FOCS), pp 396–405. 10.1109/SFCS.1998.743490, 1998). In this paper, we identify the first hardness case, namely that deciding if a graph is a half-square of a balanced bisplit graph is NP-complete. (Balanced bisplit graphs form a proper subclass of star convex bipartite graphs). For classical subclasses of tree convex bipartite graphs such as biconvex, convex, and chordal bipartite graphs, we give good structural characterizations of their half-squares that imply efficient recognition algorithms. As a by-product, we obtain new characterizations of unit interval graphs, interval graphs, and of strongly chordal graphs in terms of half-squares of biconvex bipartite, convex bipartite, and of chordal bipartite graphs, respectively. Good characterizations of half-squares of star convex and star biconvex bipartite graphs are also given, giving linear-time recognition algorithms for these half-squares. [ABSTRACT FROM AUTHOR]