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Exploration and exploitation analysis for the sonar inspired optimization algorithm Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210722
Alexandros Tzanetos, Georgios DouniasIn the recent years, extensive discussion takes place in literature, on the effectiveness of metaheuristics, and especially Nature Inspired Algorithms. Usually, authors state that such an approach should embody a wellbalanced exploration and exploitation strategy. Sonar Inspired Optimization (SIO) is a recently presented algorithm, which counts already a number of successful realworld applications

Chunking and cooperation in particle swarm optimization for feature selection Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210719
Malek Sarhani, Stefan VoßBioinspired optimization aims at adapting observed natural behavioral patterns and social phenomena towards efficiently solving complex optimization problems, and is nowadays gaining much attention. However, researchers recently highlighted an inconsistency between the need in the field and the actual trend. Indeed, while nowadays it is important to design innovative contributions, an actual trend

On matrices and Krelations Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210715
Robert Brijder, Marc Gyssens, Jan Van den BusscheWe show that the matrix query language MATLANG corresponds to a natural fragment of the positive relational algebra on Krelations. The fragment is defined by introducing a composition operator and restricting Krelation arities to 2. We then proceed to show that MATLANG can express all matrix queries expressible in the positive relational algebra on Krelations, when intermediate arities are restricted

On the correspondence between abstract dialectical frameworks and nonmonotonic conditional logics Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210706
Jesse Heyninck, Gabriele KernIsberner, Matthias Thimm, Kenneth SkibaThe exact relationship between formal argumentation and nonmonotonic logics is a research topic that keeps on eluding researchers despite recent intensified efforts. We contribute to a deeper understanding of this relation by investigating characterizations of abstract dialectical frameworks in conditional logics for nonmonotonic reasoning. We first show that in general, there is a gap between argumentation

Topological measurement of deep neural networks using persistent homology Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210703
Satoru Watanabe, Hayato YamanaThe inner representation of deep neural networks (DNNs) is indecipherable, which makes it difficult to tune DNN models, control their training process, and interpret their outputs. In this paper, we propose a novel approach to investigate the inner representation of DNNs through topological data analysis (TDA). Persistent homology (PH), one of the outstanding methods in TDA, was employed for investigating

The undecidability of proof search when equality is a logical connective Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210703
Dale Miller, Alexandre VielOne prooftheoretic approach to equality in quantificational logic treats equality as a logical connective: in particular, term equality can be given both left and right introduction rules in a sequent calculus proof system. We present a particular example of this approach to equality in a firstorder logic setting in which there are no predicate symbols (apart from equality). After we illustrate some

Parameterized and exact algorithms for finding a readonce resolution refutation in 2CNF formulas Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210629
K. Subramani, Piotr WojciechowskiIn this paper, we discuss algorithms for the problem of finding readonce resolution refutations of unsatisfiable 2CNF formulas within the resolution refutation system. Broadly, a readonce resolution refutation is one in which each constraint (input or derived) is used at most once. Readonce resolution refutations have been widely studied in the literature for a number of constraint systemrefutation

Constraintbased learning for nonparametric continuous bayesian networks Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210626
Marvin Lasserre, Régis Lebrun, PierreHenri WuilleminModeling highdimensional multivariate distributions is a computationally challenging task. In the discrete case, Bayesian networks have been successfully used to reduce the complexity and to simplify the problem. However, they lack of a general model for continuous variables. In order to overcome this problem, Elidan (2010) proposed the model of copula Bayesian networks that parametrizes Bayesian

RAMP algorithms for the capacitated facility location problem Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210625
Telmo Matos, Óscar Oliveira, Dorabela GamboaIn this paper, we address the Capacitated Facility Location Problem (CFLP) in which the assignment of facilities to customers must ensure enough facility capacity and all the customers must be served. We propose both sequential and parallel Relaxation Adaptive Memory Programming approaches for the CFLP, combining a Lagrangean subgradient search with an improvement method to explore primaldual relationships

Irreducible bin packing and normality in routing open shop Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210622
Ilya Chernykh, Artem PyatkinThe open shop is a classical scheduling problem known since 1976, which can be described as follows. A number of jobs have to be processed by a given set of machines, each machine should perform an operation on every job, and the processing times of all the operations are given. One has to construct a schedule to perform all the operations to minimize finish time also known as the makespan. The open

A dual RAMP algorithm for single source capacitated facility location problems Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210621
Óscar Oliveira, Telmo Matos, Dorabela GamboaIn this paper, we address the Single Source Capacitated Facility Location Problem (SSCFLP) which considers a set of possible locations for opening facilities and a set of clients whose demand must be satisfied. The objective is to minimize the cost of assigning the clients to the facilities, ensuring that all clients are served by only one facility without exceeding the capacity of the facilities.

Least squares approach to KSVCR multiclass classification with its applications Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210621
Hossein Moosaei, Milan HladíkThe support vector classificationregression machine for Kclass classification (KSVCR) is a novel multiclass classification method based on the “1versus1versusrest” structure. In this paper, we propose a least squares version of KSVCR named LSKSVCR. Similarly to the KSVCR algorithm, this method assesses all the training data into a “1versus1versusrest” structure, so that the algorithm

Twophase Matheuristic for the vehicle routing problem with reverse crossdocking Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210612
Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. YuCrossdockingis a useful concept used by many companies to control the product flow. It enables the transshipment process of products from suppliers to customers. This research thus extends the benefit of crossdocking with reverse logistics, since return process management has become an important field in various businesses. The vehicle routing problem in a distribution network is considered to be

Ranking kinematics for revising by contextual information Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210528
Meliha Sezgin, Gabriele KernIsberner, Christoph BeierleProbability kinematics is a leading paradigm in probabilistic belief change. It is based on the idea that conditional beliefs should be independent from changes of their antecedents’ probabilities. In this paper, we propose a reinterpretation of this paradigm for Spohn’s ranking functions which we call Generalized Ranking Kinematics as a new principle for iterated belief revision of ranking functions

Neighborhood density information in clustering Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210527
Mujahid N. SyedDensity Based Clustering (DBC) methods are capable of identifying arbitrary shaped data clusters in the presence of noise. DBC methods are based on the notion of local neighborhood density estimation. A major drawback of DBC methods is their poor performance in highdimensions. In this work, a novel DBC method that performs well in highdimensions is presented. The novelty of the proposed method can

Evolution of Gaussian Process kernels for machine translation postediting effort estimation Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210525
Ibai Roman, Roberto Santana, Alexander Mendiburu, Jose A. LozanoIn many Natural Language Processing problems the combination of machine learning and optimization techniques is essential. One of these problems is the estimation of the human effort needed to improve a text that has been translated using a machine translation method. Recent advances in this area have shown that Gaussian Processes can be effective in postediting effort prediction. However, Gaussian

Learning tractable NATmodeled Bayesian networks Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210520
Yang Xiang, Qian WangBayesian networks (BNs) encode conditional independence to avoid combinatorial explosion on the number of variables, but are subject to exponential growth of space and inference time on the number of causes per effect variable. Among spaceefficient local models, we focus on the NonImpeding NoisyAND Tree (NINAND Tree or NAT) models, due to their multiple merits, and on NATmodeled BNs, where each

Evolutionary game analysis on government subsidy policy and bank loan strategy in China’s distributed photovoltaic market Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210512
Xing Zhu, Baoyu Liao, Shanlin Yang, Panos M. PardalosAiming to meet increasing energy demand and reduce carbon emissions caused by fossil fuel consumption, China is vigorously supporting the diffusion of photovoltaic (PV) generation equipment. The government and banks are recognized as playing irreplaceable and important roles in promoting PV investment. Therefore, this study applies a tripartite evolutionary game model to theoretically analyze the evolutionary

Costlocation aware heuristic algorithm for hybrid SDN deployment Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210506
Siew Hong Wei, Tan Saw Chin, Lee Ching KwangSoftwareDefined Networking (SDN) has gained tremendous attention in the past few years for its advantages over network controllability. Nonetheless, the deployment of SDN in legacy network is likely to span multiperiods over months or years for budget consideration. Network operators, especially for a network consists of thousand or more nodes, are eager to understand how legacy networks can be deploying

Breaking the curse of dimensionality: hierarchical Bayesian network model for multiview clustering Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210506
Hasna Njah, Salma Jamoussi, Walid MahdiClustering highdimensional data under the curse of dimensionality is an arduous task in many applications domains. The wide dimension yields the complexityrelated challenges and the limited number of records leads to the overfitting trap. We propose to tackle this problematic using the graphical and probabilistic power of the Bayesian network. Our contribution is a new loose hierarchical Bayesian

Boosting evolutionary algorithm configuration Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210504
Carlos Ansótegui, Josep Pon, Meinolf SellmannAlgorithm configuration has emerged as an essential technology for the improvement of highperformance solvers. We present new algorithmic ideas to improve stateoftheart solver configurators automatically by tuning. Particularly, we introduce 1. a forwardsimulation method to improve parallel performance, 2. an improvement to the configuration process itself, and 3. a new technique for instancespecific

A robust multiobjective model for the integrated berth and quay crane scheduling problem at seaside container terminals Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210501
Abtin Nourmohammadzadeh, Stefan VoßThe ever increasing demand for container transportation has led to the congestion of maritime container terminals in the world. In this work, the two interrelated problems of berth and quay crane scheduling are considered in an integrated multiobjective mathematical model. A special character of this model is that the arrival times of vessels and the failure (working) times of quay cranes are not deterministic

General information spaces: measuring inconsistency, rationality postulates, and complexity Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210427
John Grant, Francesco ParisiAI systems often need to deal with inconsistent information. For this reason, since the early 2000s, some AI researchers have developed ways to measure the amount of inconsistency in a knowledge base. By now there is a substantial amount of research about various aspects of inconsistency measuring. The problem is that most of this work applies only to knowledge bases formulated as sets of formulas

Coherent lower and upper conditional previsions defined by Hausdorff inner and outer measures to represent the role of conscious and unconscious thought in human decision making Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210426
Serena DoriaThe model of coherent lower and upper conditional previsions, based on Hausdorff inner and outer measures, is proposed to represent the preference orderings and the equivalences, respectively assigned by the conscious and unconscious thought in human decision making under uncertainty. Complexity of partial information is represented by the Hausdorff dimension of the conditioning event. When the events

An adaptive model for human syllogistic reasoning Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210424
Jonas Bischofberger, Marco RagniHow humans reason in general about syllogisms is, despite a century of research and many proposed cognitive theories, still an unanswered question. It is even more difficult, however, to answer how an individual human reasons. The goal of this article is twofold: First, it analyses the predictive quality of existing cognitive theories by providing a standardized (re) implementation of existing theories

Integrating domain and constraint privacy reasoning in the distributed stochastic algorithm with breakouts Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210420
Julien Vion, René Mandiau, Sylvain Piechowiak, Marius SilaghiPrivacy has traditionally been a major motivation of distributed problem solving. One popular approach to enable privacy in distributed environments is to implement complex cryptographic protocols. In this paper, we propose a different, orthogonal approach, which is to control the quality and the quantity of publicized data. We consider the Open Constraint Programming model and focus on algorithms

Joint desirability foundations of social choice and opinion pooling Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210419
Arianna Casanova, Enrique Miranda, Marco ZaffalonWe develop joint foundations for the fields of social choice and opinion pooling using coherent sets of desirable gambles, a general uncertainty model that allows to encompass both complete and incomplete preferences. This leads on the one hand to a new perspective of traditional results of social choice (in particular Arrow’s theorem as well as sufficient conditions for the existence of an oligarchy

Classifierbased constraint acquisition Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210417
S. D. Prestwich, E. C. Freuder, B. O’Sullivan, D. BrowneModeling a combinatorial problem is a hard and errorprone task requiring significant expertise. Constraint acquisition methods attempt to automate this process by learning constraints from examples of solutions and (usually) nonsolutions. Active methods query an oracle while passive methods do not. We propose a known but not widelyused application of machine learning to constraint acquisition: training

Classes of linear programs solvable by coordinatewise minimization Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210414
Tomáš Dlask, Tomáš WernerCoordinatewise minimization is a simple popular method for largescale optimization. Unfortunately, for general (nondifferentiable and/or constrained) convex problems, its fixed points may not be global minima. We present two classes of linear programs (LPs) that coordinatewise minimization solves exactly. We show that these classes subsume the dual LP relaxations of several wellknown combinatorial

Integrated inventory and production policy for manufacturing with perishable raw materials Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210407
Chaoming Hu, Min Kong, Jun Pei, Xinbao Liu, Panos M. PardalosThis research investigates an integrated inventory and production scheduling problem (IIPSP) in a manufacturer that deals with the perishable goods. The objective is to find an optimal schedule to minimize the sum of inventory cost and production cost. Both singleplant problem and multiplant problem are investigated in this paper. For the singleplant problem, we prove that it is optimal to arrange

Automated nonmonotonic reasoning in System P Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210407
Tatjana Stojanović, Nebojša Ikodinović, Tatjana Davidović, Zoran OgnjanovićThis paper presents a novel approach to automated reasoning in System P. System P axiomatizes a set of core properties that describe reasoning with defeasible assertions (defaults) of the form: if α then normally (usually or typically) β. A logic with approximate conditional probabilities is used for modeling default rules. That representation enables reducing the satisfiability problem for default

Critical Properties and Complexity Measures of ReadOnce Boolean Functions Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210322
Vadim Lozin, Mikhail MoshkovIn this paper, we define a quasiorder on the set of readonce Boolean functions and show that this is a wellquasiorder. This implies that every parameter measuring complexity of the functions can be characterized by a finite set of minimal subclasses of readonce functions, where this parameter is unbounded. We focus on two parameters related to certificate complexity and characterize each of them

Alternating DCA for reducedrank multitask linear regression with covariance matrix estimation Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210320
Hoai An Le Thi, Vinh Thanh HoWe study a challenging problem in machine learning that is the reducedrank multitask linear regression with covariance matrix estimation. The objective is to build a linear relationship between multiple output variables and input variables of a multitask learning process, taking into account the general covariance structure for the errors of the regression model in one hand, and reducedrank regression

Parameterised complexity of model checking and satisfiability in propositional dependence logic Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210227
Yasir Mahmood, Arne MeierDependence Logic was introduced by Jouko Väänänen in 2007. We study a propositional variant of this logic (PDL) and investigate a variety of parameterisations with respect to central decision problems. The model checking problem (MC) of PDL is NPcomplete (Ebbing and Lohmann, SOFSEM 2012). The subject of this research is to identify a list of parameterisations (formulasize, formuladepth, treewidth

A survey on the applications of variable neighborhood search algorithm in healthcare management Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210211
Shaowen Lan, Wenjuan Fan, Shanlin Yang, Panos M. Pardalos, Nenad MladenovicThis paper reviews the papers on the applications of VNS in the health care area by analyzing the characteristics of VNS in different problems. In the health care field, many complex optimization problems need to be tackled in a short time considering multiple influencing factors, such as personnel preferences, resources limitations, etc. As a metaheuristic, Variable neighborhood search (VNS) algorithm

Interpreting ratedistortion of variational autoencoder and using model uncertainty for anomaly detection Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210202
Seonho Park, George Adosoglou, Panos M. PardalosBuilding a scalable machine learning system for unsupervised anomaly detection via representation learning is highly desirable. One of the prevalent methods is using a reconstruction error of variational autoencoder (VAE) by maximizing the evidence lower bound. We revisit VAE from the perspective of information theory to provide some theoretical foundations on using the reconstruction error and finally

Improving parity games in practice Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210123
Antonio Di Stasio, Aniello Murano, Vincenzo Prignano, Loredana SorrentinoParity games are infiniteround twoplayer games played on directed graphs whose nodes are labeled with priorities. The winner of a play is determined by the smallest priority (even or odd) that is encountered infinitely often along the play. In the last two decades, several algorithms for solving parity games have been proposed and implemented in PGSolver, a platform written in OCaml. PGSolver includes

The Universal Approximation Property Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210122
Anastasis KratsiosThe universal approximation property of various machine learning models is currently only understood on a casebycase basis, limiting the rapid development of new theoretically justified neural network architectures and blurring our understanding of our current models' potential. This paper works towards overcoming these challenges by presenting a characterization, a representation, a construction

Column generation for the equilibrium routeflow traffic assignment problem Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210108
Alexander KrylatovToday efficient traffic management seems to be impossible without the support of the artificial intelligence systems based on mathematical models of traffic flow assignment since a modern road network is a largescale system with huge amounts of elements. The present paper is devoted to the routeflow traffic assignment problem, which solution is the most valuable from decisionmaking perspectives

Optimal training for adversarial games Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210107
Ovidiu CalinWe consider adversarial games solved by a continuous version of the simultaneous gradient descent method, whose associated differential system is induced by a Hamiltonian function. In this case the solution obtained by training does never converge to the Nash equilibrium, but it might be closest to it at some special time instance. We analyse this optimal training time in two distinct situations: the

Data driven design for online industrial auctions Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210105
Qing Chuan Ye, Jason Rhuggenaath, Yingqian Zhang, Sicco Verwer, Michiel Jurgen HilgemanDesigning auction parameters for online industrial auctions is a complex problem due to highly heterogeneous items. Currently, online auctioneers rely heavily on their experts in auction design. In this paper, we propose a data driven auction design framework that seamlessly combines prediction models and knowledge from experts into an optimization model. We show the proposed data driven approach improves

Schema mapping coverage Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20210102
Ning Deng, Jan ChomickiIn this paper, we introduce and study schema mapping coverage for relational databases. Given a relational schema mapping in the presence of both source dependencies and target dependencies, the coverage problem is to decide and describe which source instances have solutions under the mapping. Our main motivation is to describe limitations of schema mappings and hence to effectively determine if the

An integrated biobjective Ushaped assembly line balancing and parts feeding problem: optimization model and exact solution method Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20201120
Ömer Faruk YılmazIn this study, an integrated biobjective objective Ushaped assembly line balancing and parts feeding problem is explored by considering the heterogeneity inherent of workers. An optimization model is developed to formulate the addressed problem. Since the problem includes two different objectives, namely the minimizing the operational cost and maximum workload imbalance, the Paretooptimal solutions

On generalization in momentbased domain adaptation Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20201119
Werner Zellinger, Bernhard A. Moser, Susanne SamingerPlatzDomain adaptation algorithms are designed to minimize the misclassification risk of a discriminative model for a target domain with little training data by adapting a model from a source domain with a large amount of training data. Standard approaches measure the adaptation discrepancy based on distance measures between the empirical probability distributions in the source and target domain. In this

Valid attacks in argumentation frameworks with recursive attacks Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20201119
C. Cayrol, J. Fandinno, L. Fariñas del Cerro, M.C. LagasquieSchiexThe purpose of this work is to study a generalisation of Dung’s abstract argumentation frameworks that allows representing recursive attacks , that is, a class of attacks whose targets are other attacks. We do this by developing a theory of argumentation where the classic role of attacks in defeating arguments is replaced by a subset of them, which is “extensiondependent” and which, intuitively, represents

Efficient implicit Lagrangian twin parametric insensitive support vector regression via unconstrained minimization problems Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20201119
Deepak Gupta, Bharat RichhariyaIn this paper, an efficient implicit Lagrangian twin parametric insensitive support vector regression is proposed which leads to a pair of unconstrained minimization problems, motivated by the works on twin parametric insensitive support vector regression (Peng: Neurocomputing. 79, 26–38, 2012), and Lagrangian twin support vector regression (Balasundaram and Tanveer: Neural Comput. Applic. 22(1), 257–267

Datadriven algorithm selection and tuning in optimization and signal processing Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20201112
Jesús A. De Loera, Jamie Haddock, Anna Ma, Deanna NeedellMachine learning algorithms typically rely on optimization subroutines and are well known to provide very effective outcomes for many types of problems. Here, we flip the reliance and ask the reverse question: can machine learning algorithms lead to more effective outcomes for optimization problems? Our goal is to train machine learning methods to automatically improve the performance of optimization

Correction to: Directed Lovász Local Lemma and Shearer’s Lemma Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20201109
Lefteris Kirousis, John Livieratos, Kostas I. PsaromiligkosThe proof of Theorem 1a of our article that appears in Annals of Mathematics and Artificial Intelligence 88(1–3):133155 (2020) has a mistake. We give here the corrected proof, together with a new version of Definition 3 in that article that the correction necessitated.

Multiplesource adaptation theory and algorithms Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20201105
Ningshan Zhang, Mehryar Mohri, Judy HoffmanWe present a general theoretical and algorithmic analysis of the problem of multiplesource adaptation, a key learning problem in applications. We derive new normalized solutions with strong theoretical guarantees for the crossentropy loss and other similar losses. We also provide new guarantees that hold in the case where the conditional probabilities for the source domains are distinct. We further

Query answering DLlite knowledge bases from hidden datasets Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20201105
Ghassen Hamdi, Mohamed Nazih Omri, Salem Benferhat, Zied Bouraoui, Odile PapiniUnifying access to data using structured knowledge is the main problem studied in ontologybased data access (OBDA). Data are often provided by several information sources, and this has led to a number of methods that merge them in order to get a unified point of view. Existing merging approaches assume that the content of datasets is known and available. However, in several applications, it might

NSLPCD: Topic based tweets clustering using Node significance based label propagation community detection algorithm Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20200924
Jagrati Singh, Anil Kumar SinghSocial networks like Twitter, Facebook have recently become the most widely used communication platforms for people to propagate information rapidly. Fast diffusion of information creates accuracy and scalability issues towards topic detection. Most of the existing approaches can detect the most popular topics on a large scale. However, these approaches are not effective for faster detection. This

Categorical study for algebras of Fitting’s latticevalued logic and latticevalued modal logic Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20200921
Kumar Sankar Ray, Litan Kumar DasThe paper explores categorical interconnections between latticevalued relational systems and algebras of Fitting’s latticevalued modal logic. We define latticevalued Boolean systems, and then we study adjointness and coadjointness of functors defined on them. As a result, we get a duality for algebras of latticevalued logic. Following this duality result, we establish a duality for algebras of

Blackbox combinatorial optimization using models with integervalued minima Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20200919
Laurens Bliek, Sicco Verwer, Mathijs de WeerdtA challenging problem in both engineering and computer science is that of minimising a function for which we have no mathematical formulation available, that is expensive to evaluate, and that contains continuous and integer variables, for example in automatic algorithm configuration. Surrogatebased algorithms are very suitable for this type of problem, but most existing techniques are designed with

On integer closure in a system of unit two variable per inequality constraints Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20200915
K. Subramani, P. WojciechowskiIn this paper, we study the problem of computing the lattice point closure of a conjunction of Unit Two Variable Per Inequality (UTVPI) constraints. We accomplish this by adapting Johnson’s all pairs shortest path algorithm to UTVPI constraint systems (UCSs). Thus, we obtain a closure algorithm that is efficient for sparse constraint systems. This problem has been extremely wellstudied in the literature

Multivariate time series analysis from a Bayesian machine learning perspective Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20200904
Jinwen Qiu, S. Rao Jammalamadaka, Ning NingIn this paper, we perform multivariate time series analysis from a Bayesian machine learning perspective through the proposed multivariate Bayesian time series (MBTS) model. The multivariate structure and the Bayesian framework allow the model to take advantage of the association structure among target series, select important features, and train the datadriven model at the same time. Extensive analyses

Realtime solving of computationally hard problems using optimal algorithm portfolios Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20200828
Yair Nof, Ofer StrichmanVarious hard realtime systems have a desired requirement which is impossible to fulfill: to solve a computationally hard optimization problem within a short and fixed amount of time T, e.g., T = 0.5 seconds. For such a task, the exact, exponential algorithms, as well as various PolynomialTime Approximation Schemes, are irrelevant because they can exceed T. What is left in practice is to combine various

Classifying the valence of autobiographical memories from fMRI data Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20200804
Alex Frid, Larry M. Manevitz, Norberto Eiji NawaWe show that fMRI analysis using machine learning tools are sufficient to distinguish valence (i.e., positive or negative) of freely retrieved autobiographical memories in a crossparticipant setting. Our methodology uses feature selection (ReliefF) in combination with boosting methods, both applied directly to data represented in voxel space. In previous work using the same data set, Nawa and Ando

Correction to: LogAG: An algebraic nonmonotonic logic for reasoning with graded propositions Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20200802
Nourhan Ehab, Haythem O. IsmailDue to an oversight by the Publisher during the typesetting stage, an uncorrected version of the paper was published.

Derivation and analysis of parallelintime neural ordinary differential equations Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20200725
E. LorinThe introduction in 2015 of Residual Neural Networks (RNN) and ResNET allowed for outstanding improvements of the performance of learning algorithms for evolution problems containing a “large” number of layers. Continuousdepth RNNlike models called Neural Ordinary Differential Equations (NODE) were then introduced in 2019. The latter have a constant memory cost, and avoid the a priori specification

Digitized rotations of 12 neighbors on the triangular grid Ann. Math. Artif. Intel. (IF 0.789) Pub Date : 20200722
Aydın Avkan, Benedek Nagy, Müge SaadetoğluThere are various geometric transformations, e.g., translations, rotations, which are always bijections in the Euclidean space. Their digital counterpart, i.e., their digitized variants are defined on discrete grids, since most of our pictures are digital nowadays. Usually, these digital versions of the transformations have different properties than the original continuous variants have. Rotations