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Costlocation aware heuristic algorithm for hybrid SDN deployment Ann. Math. Artif. Intel. (IF 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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. The ability of predicting how well an auction will perform prior to the start comes in handy for auctioneers. If an item is expected to be a lowperforming item, the auctioneer can take certain actions to influence

Schema mapping coverage Ann. Math. Artif. Intel. (IF 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) 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 0.778) Pub Date : 20200919
Laurens Bliek, Sicco Verwer, Mathijs de WeerdtWhen a blackbox optimization objective can only be evaluated with costly or noisy measurements, most standard optimization algorithms are unsuited to find the optimal solution. Specialized algorithms that deal with exactly this situation make use of surrogate models. These models are usually continuous and smooth, which is beneficial for continuous optimization problems, but not necessarily for combinatorial

On integer closure in a system of unit two variable per inequality constraints Ann. Math. Artif. Intel. (IF 0.778) 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 0.778) 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 0.778) 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 0.778) 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: Log A G : An algebraic nonmonotonic logic for reasoning with graded propositions Ann. Math. Artif. Intel. (IF 0.778) 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 0.778) 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 0.778) 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

Mutual conditional independence and its applications to model selection in Markov networks Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200721
Niharika Gauraha, Swapan K. ParuiThe fundamental concepts underlying Markov networks are the conditional independence and the set of rules called Markov properties that translate conditional independence constraints into graphs. We introduce the concept of mutual conditional independence in an independent set of a Markov network, and we prove its equivalence to the Markov properties under certain regularity conditions. This extends

On a hypergraph probabilistic graphical model Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200710
Mohammad Ali Javidian, Zhiyu Wang, Linyuan Lu, Marco ValtortaWe propose a directed acyclic hypergraph framework for a probabilistic graphical model that we call Bayesian hypergraphs. The space of directed acyclic hypergraphs is much larger than the space of chain graphs. Hence Bayesian hypergraphs can model much finer factorizations than Bayesian networks or LWF chain graphs and provide simpler and more computationally efficient procedures for factorizations

L o g A G : An algebraic NonMonotonic logic for reasoning with graded propositions Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200620
Nourhan Ehab, Haythem O. IsmailWe present LogAG, a weighted algebraic nonmonotonic logic for reasoning with graded beliefs. LogAG is algebraic in that it is a language of only terms, some of which denote propositions and may be associated with ordered grades. The grades could be taken to represent a wide variety of phenomena including preference degrees, priority levels, trust ranks, and uncertainty measures. Reasoning in LogAG

Generalized feature similarity measure Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200520
Firuz KamalovQuantifying the degree of relation between a feature and target class is one of the key aspects of machine learning. In this regard, information gain (IG) and χ2 are two of the most widely used measures in feature evaluation. In this paper, we discuss a novel approach to unifying these and other existing feature evaluation measures under a common framework. In particular, we introduce a new generalized

On biased random walks, corrupted intervals, and learning under adversarial design Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200518
Daniel Berend, Aryeh Kontorovich, Lev Reyzin, Thomas RobinsonWe tackle some fundamental problems in probability theory on corrupted random processes on the integer line. We analyze when a biased random walk is expected to reach its bottommost point and when intervals of integer points can be detected under a natural model of noise. We apply these results to problems in learning thresholds and intervals under a new model for learning under adversarial design

Leveraging cluster backbones for improving MAP inference in statistical relational models Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200507
MohamedHamza Ibrahim, Christopher Pal, Gilles PesantA wide range of important problems in machine learning, expert system, social network analysis, bioinformatics and information theory can be formulated as a maximum aposteriori (MAP) inference problem on statistical relational models. While offtheshelf inference algorithms that are based on local search and messagepassing may provide adequate solutions in some situations, they frequently give poor

Instance space analysis for a personnel scheduling problem Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200424
Lucas Kletzander, Nysret Musliu, Kate SmithMilesThis paper considers the Rotating Workforce Scheduling Problem, and shows how the strengths and weaknesses of various solution methods can be understood by the indepth evaluation offered by a recently developed methodology known as Instance Space Analysis. We first present a set of features aiming to describe hardness of test instances. We create a new, more diverse set of instances based on an initial

What do you really want to do? Towards a Theory of Intentions for HumanRobot Collaboration Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200324
Rocio Gomez, Mohan Sridharan, Heather RileyThe architecture described in this paper encodes a theory of intentions based on the key principles of nonprocrastination, persistence, and automatically limiting reasoning to relevant knowledge and observations. The architecture reasons with transition diagrams of any given domain at two different resolutions, with the fineresolution description defined as a refinement of, and hence tightlycoupled

Learning nonconvex abstract concepts with regulated activation networks Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200321
Rahul Sharma, Bernardete Ribeiro, Alexandre Miguel Pinto, F. Amílcar CardosoPerceivable objects are customarily termed as concepts and their representations (localistdistributed, modalityspecific, or experiencedependent) are ingrained in our lives. Despite a considerable amount of computational modeling research focuses on concrete concepts, no comprehensible method for abstract concepts has hitherto been considered. Abstract concepts can be viewed as a blend of concrete

Humanintheloop active learning via brain computer interface Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200316
Eitan Netzer, Amir B. GevaThis paper develops and examines an innovative methodology for training an artificial neural network to identify and tag target visual objects in a given database. While the field of Artificial Intelligence in general, and computer vision in particular, has greatly advanced in recent years, fast and efficient methods for tagging (i.e., labeling) visual targets are still lacking. Tagging data is important

Multiple k −opt evaluation multiple k −opt moves with GPU high performance local search to largescale traveling salesman problems Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200416
WenBao Qiao, JeanCharles CréputThe 2opt, 3opt or k–opt heuristics are classical local search algorithms for traveling salesman problems (TSP) in combinatorial optimization area, while sequential k–opt complete neighborhood examination takes polynomial time complexity which is timeconsuming to approach large scale TSP instances. This paper introduces a reasonable methodology called “multiple k–opt evaluation, multiple k–opt moves”

Approximate kernel partial least squares Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200327
Xiling Liu, Shuisheng ZhouAs an extension of partial least squares (PLS), kernel partial least squares (KPLS) is an very important methods to find nonlinear patterns from data. However, the application of KPLS to largescale problems remains a big challenge, due to storage and computation issues in the number of examples. To address this limitation, we utilize randomness to design scalable new variants of the kernel matrix

Characterization Of sampling patterns for lowttrank tensor retrieval Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200316
Morteza Ashraphijuo, Xiaodong WangIn this paper, we analyze the fundamental conditions for lowrank tensor completion given the separation or tensortrain (TT) rank, i.e., ranks of TT unfoldings. We exploit the algebraic structure of the TT decomposition to obtain the deterministic necessary and sufficient conditions on the locations of the samples to ensure finite completability. Specifically, we propose an algebraic geometric analysis

Energy allocation and payment: a gametheoretic approach Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200218
Paolo Giuliodori, Stefano Bistarelli, Dimitri MugnaiNowadays, energy represents the most important resource; however, we need to face several energyrelated rising issues, one main concern is how energy is consumed. In particular, how we can stimulate consumers on a specific behaviour. In this work, we present a model facing energy allocation and payment. Thus, we start with the explanation of the first step of our work concerning a mechanism design

DiscrepancyBased Theory and Algorithms for Forecasting NonStationary Time Series Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200204
Vitaly Kuznetsov, Mehryar MohriWe present datadependent learning bounds for the general scenario of nonstationary nonmixing stochastic processes. Our learning guarantees are expressed in terms of a datadependent measure of sequential complexity and a discrepancy measure that can be estimated from data under some mild assumptions. Our learning bounds guide the design of new algorithms for nonstationary time series forecasting

Semantic string operation for specializing AHC algorithm for text clustering Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200113
Taeho JoThis article proposes the modified AHC (Agglomerative Hierarchical Clustering) algorithm which clusters string vectors, instead of numerical vectors, as the approach to the text clustering. The results from applying the string vector based algorithms to the text clustering were successful in previous works and synergy effect between the text clustering and the word clustering is expected by combining

Sufficient conditions for the existence of a sample mean of time series under dynamic time warping Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200110
Brijnesh Jain, David SchultzTime series averaging is an important subroutine for several time series data mining tasks. The most successful approaches formulate the problem of time series averaging as an optimization problem based on the dynamic time warping (DTW) distance. The existence of an optimal solution, called sample mean, is an open problem for more than four decades. Its existence is a necessary prerequisite to formulate

A branch & bound algorithm to determine optimal crosssplits for decision tree induction Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200103
Ferdinand Bollwein, Martin Dahmen, Stephan WestphalStateoftheart decision tree algorithms are topdown induction heuristics which greedily partition the attribute space by iteratively choosing the best split on an individual attribute. Despite their attractive performance in terms of runtime, simple examples, such as the XORProblem, point out that these heuristics often fail to find the best classification rules if there are strong interactions