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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

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

Digitized rotations of 12 neighbors on the triangular grid Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20200514
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

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

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

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

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

Effective IG heuristics for a singlemachine scheduling problem with family setups and resource constraints Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190627
Júlio C. S. N. Pinheiro, José Elias C. ArroyoIn this paper we investigate the problem of scheduling a set of jobs on a singlemachine. The jobs are classified in families and setup times are required between the processing of two jobs of different families. Each job requires a certain amount of a common resource that is supplied through upstream processes. The total resource consumed must not exceed the resource supply up. Therefore, jobs may

Constructing orthogonal designs in powers of two via symbolic computation and rewriting techniques Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20181129
Ilias Kotsireas, Temur Kutsia, Dimitris E. SimosIn the past few decades, design theory has grown to encompass a wide variety of research directions. It comes as no surprise that applications in coding theory and communications continue to arise, and also that designs have found applications in new areas. Computer science has provided a new source of applications of designs, and simultaneously a field of new and challenging problems in design theory

Targeting solutions in Bayesian multiobjective optimization: sequential and batch versions Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190820
David Gaudrie, Rodolphe Le Riche, Victor Picheny, Benoît Enaux, Vincent HerbertMultiobjective optimization aims at finding tradeoff solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensivetoevaluate functions, it is impossible and often noninformative to look for the entire set. As an enduser would typically prefer a certain part of the objective space, we modify the Bayesian multiobjective optimization algorithm which uses

Combining intelligent heuristics with simulators in hotel revenue management Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190703
Mauro Brunato, Roberto BattitiRevenue Management uses datadriven modelling and optimization methods to decide what to sell, when to sell, to whom to sell, and for which price, in order to increase revenue and profit. Hotel Revenue Management is a very complex context characterized by nonlinearities, many parameters and constraints, and stochasticity, in particular in the demand by customers. It suffers from the curse of dimensionality

Exact algorithms for two integervalued problems of searching for the largest subset and longest subsequence Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190321
Alexander Kel’manov, Sergey Khamidullin, Vladimir Khandeev, Artem PyatkinThe following two strongly NPhard problems are considered. In the first problem, we need to find in the given finite set of points in Euclidean space the subset of largest size. The sum of squared distances between the elements of this subset and its unknown centroid (geometrical center) must not exceed a given value. This value is defined as percentage of the sum of squared distances between the

Kernel classification using a linear programming approach Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190614
Alexander M. Malyscheff, Theodore B. TrafalisA support vector machine (SVM) classifier corresponds in its most basic form to a quadratic programming problem. Various linear variations of support vector classification have been investigated such as minimizing the L1norm of the weightvector instead of the L2norm. In this paper we introduce a classifier where we minimize the boundary (lower envelope) of the epigraph that is generated over a set

Soft computing methods for multiobjective location of garbage accumulation points in smart cities Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190620
Jamal Toutouh, Diego Rossit, Sergio NesmachnowThis article describes the application of soft computing methods for solving the problem of locating garbage accumulation points in urban scenarios. This is a relevant problem in modern smart cities, in order to reduce negative environmental and social impacts in the waste management process, and also to optimize the available budget from the city administration to install waste bins. A specific problem

Complexity and approximability of the Euclidean generalized traveling salesman problem in grid clusters Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190328
Michael Khachay, Katherine NeznakhinaWe consider the geometric version of the wellknown Generalized Traveling Salesman Problem introduced in 2015 by Bhattacharya et al. that is called the Euclidean Generalized Traveling Salesman Problem in Grid Clusters (EGTSPGC). They proved the intractability of the problem and proposed first polynomial time algorithms with fixed approximation factors. The extension of these results in the field of

Dynamic search trajectory methods for global optimization Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190827
StamatiosAggelos N. Alexandropoulos, Panos M. Pardalos, Michael N. VrahatisA detailed review of the dynamic search trajectory methods for global optimization is given. In addition, a family of dynamic search trajectories methods that are created using numerical methods for solving autonomous ordinary differential equations is presented. Furthermore, a strategy for developing globally convergent methods that is applicable to the proposed family of methods is given and the

A BRKGADE algorithm for parallelbatching scheduling with deterioration and learning effects on parallel machines under preventive maintenance consideration Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20180927
Min Kong, Xinbao Liu, Jun Pei, Hao Cheng, Panos M. PardalosThis paper introduces a parallelbatching scheduling problem with deterioration and learning effects on parallel machines, where the actual processing time of a job is subject to the phenomena of deterioration and learning. All jobs are first divided into different parallel batches, and the processing time of the batches is equal to the largest processing time of their belonged jobs. Then, the generated

Feature uncertainty bounds for explicit feature maps and large robust nonlinear SVM classifiers Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20191115
Nicolas Couellan, Sophie JanWe consider the binary classification problem when data are large and subject to unknown but bounded uncertainties. We address the problem by formulating the nonlinear support vector machine training problem with robust optimization. To do so, we analyze and propose two bounding schemes for uncertainties associated to random approximate features in low dimensional spaces. The proposed bound calculations

Model simplification for supervised classification of metabolic networks Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190717
Ilaria Granata, Mario R. Guarracino, Valery A. Kalyagin, Lucia Maddalena, Ichcha Manipur, Panos M. PardalosMany real applications require the representation of complex entities and their relations. Frequently, networks are the chosen data structures, due to their ability to highlight topological and qualitative characteristics. In this work, we are interested in supervised classification models for data in the form of networks. Given two or more classes whose members are networks, we build mathematical

Directed Lovász local lemma and Shearer’s lemma Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20191202
Lefteris Kirousis, John Livieratos, Kostas I. PsaromiligkosMoser and Tardos (J. ACM (JACM) 57(2), 11 2010) gave an algorithmic proof of the lopsided Lovász local lemma (LLL) in the variable framework, where each of the undesirable events is assumed to depend on a subset of a collection of independent random variables. For the proof, they define a notion of a lopsided dependency between the events suitable for this framework. In this work, we strengthen this

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

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

Learning under p tampering poisoning attacks Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20191203
Saeed Mahloujifar, Dimitrios I. Diochnos, Mohammad MahmoodyRecently, Mahloujifar and Mahmoody (Theory of Cryptography Conference’17) studied attacks against learning algorithms using a special case of Valiant’s malicious noise, called ptampering, in which the adversary gets to change any training example with independent probability p but is limited to only choose ‘adversarial’ examples with correct labels. They obtained ptampering attacks that increase

Nonterminating processes in the situation calculus Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20191118
Giuseppe De Giacomo, Eugenia Ternovska, Ray ReiterBy their very design, many robot control programs are nonterminating. This paper describes a situation calculus approach to expressing and proving properties of nonterminating programs expressed in Golog, a high level logic programming language for modeling and implementing dynamical systems. Because in this approach actions and programs are represented in classical (secondorder) logic, it is natural

Mixed deterministic and probabilistic networks. Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20081101
Robert Mateescu,Rina DechterThe paper introduces mixed networks, a new graphical model framework for expressing and reasoning with probabilistic and deterministic information. The motivation to develop mixed networks stems from the desire to fully exploit the deterministic information (constraints) that is often present in graphical models. Several concepts and algorithms specific to belief networks and constraint networks are

Statistical learning based on Markovian data maximal deviation inequalities and learning rates Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190829
Stephan Clémençon, Patrice Bertail, Gabriela CiołekIn statistical learning theory, numerous works established nonasymptotic bounds assessing the generalization capacity of empirical risk minimizers under a large variety of complexity assumptions for the class of decision rules over which optimization is performed, by means of sharp control of uniform deviation of i.i.d. averages from their expectation, while fully ignoring the possible dependence

The price to pay for forgoing normalization in fair division of indivisible goods Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190816
Pascal Lange, NhanTam Nguyen, Jörg RotheWe study the complexity of fair division of indivisible goods and consider settings where agents can have nonzero utility for the empty bundle. This is a deviation from a common normalization assumption in the literature, and we show that this inconspicuous change can lead to an increase in complexity: In particular, while an allocation maximizing social welfare by the Nash product is known to be easy

Blending under deconstruction Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190725
Roberto Confalonieri, Oliver KutzThe cognitivelinguistic theory of conceptual blending was introduced by Fauconnier and Turner in the late 90s to provide a descriptive model and foundational approach for the (almost uniquely) human ability to invent new concepts. Whilst blending is often described as ‘fluid’ and ‘effortless’ when ascribed to humans, it becomes a highly complex, multiparadigm problem in Artificial Intelligence. This

Dual embeddings and metrics for word and relational similarity Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190701
Dandan Li, Douglas SummersStayWord embedding models excel in measuring word similarity and completing analogies. Word embeddings based on different notions of context trade off strengths in one area for weaknesses in another. Linear bagofwords contexts, such as in word2vec, can capture topical similarity better, while dependencybased word embeddings better encode functional similarity. By combining these two word embeddings

Spatial reasoning about qualitative shape compositions Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190624
Zoe Falomir, Albert Pich, Vicent CostaShape composition is a challenge in spatial reasoning. Qualitative Shape Descriptors (QSD) have proven to be rotation and location invariant, which make them useful in spatial reasoning tests. QSD uses qualitative representations for angles and lengths, but their composition operations have not been defined before. In this paper, the Qualitative Model for Angles (QMAngles) and the Qualitative Model

Optimal probability aggregation based on generalized brier scoring Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190620
Christian J. FeldbacherEscamilla, Gerhard SchurzIn this paper we combine the theory of probability aggregation with results of machine learning theory concerning the optimality of predictions under expert advice. In probability aggregation theory several characterization results for linear aggregation exist. However, in linear aggregation weights are not fixed, but free parameters. We show how fixing such weights by successbased scores, a generalization

Privacy stochastic games in distributed constraint reasoning Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190411
Julien Savaux, Julien Vion, Sylvain Piechowiak, René Mandiau, Toshihiro Matsui, Katsutoshi Hirayama, Makoto Yokoo, Shakre Elmane, Marius SilaghiIn this work, we approach the issue of privacy in distributed constraint reasoning by studying how agents compromise solution quality for preserving privacy, using utility and game theory. We propose a utilitarian definition of privacy in the context of distributed constraint reasoning, detail its different implications, and present a model and solvers, as well as their properties. We then show how

Discovering state constraints for planning with conditional effects in Discoplan (part I) Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190408
Alfonso Emilio Gerevini, Lenhart SchubertDiscoplan is a durable and efficient system for inferring state constraints (invariants) in planning domains, specified in the PDDL language. It is exceptional in the range of constraint types it can discover and verify, and it directly allows for conditional effects in action operators. However, although various aspects of Discoplan have been previously described and its utility in planning demonstrated

Towards a model of creative understanding: deconstructing and recreating conceptual blends using image schemas and qualitative spatial descriptors Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190214
Zoe Falomir, Enric PlazaComputational models of novel concept understanding and creativity are addressed in this paper from the viewpoint of conceptual blending theory (CBT). In our approach, a novel, unknown concept is addressed in a communication setting, where this novel concept, created as a blend by an emitter agent, sends a communicative object (words, or in this paper, a visual representation of that concept) to another

A computer agent that develops visual compositions based on the ERmodel Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20190129
Rafael Pérez y Pérez, Iván Guerrero RománThis paper describes a computer agent for the automatic generation of visual compositions based on the EngagementReflection Model of creative writing (Pérez y Pérez Cogn. Syst. Res. 8, 89–109, 2007; Pérez y Pérez and Sharples J. Exp. Theor. Artif. Intell. 13, 119–139, 2001). During engagement the system progresses the composition; during reflection the agent evaluates, and if necessary modifies, the

Changing channels: divergent approaches to the creative streaming of texts Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20181218
Tony VealeText is an especially malleable medium for human and machine creativity. When guided by the appropriate symbolic and/or statistical models, even a small and seemingly superficial change at the formal level can result in a predictable yet profound change at the semantic and pragmatic level. Text is also a virtually unlimited resource on the web, which offers abundant, freeflowing channels of topical

“All the world’s a stage”: incongruity humour revisited Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20181212
Anton NijholtEighteenth and nineteenth century philosophers took interest in humour and, in particular, humorous incongruities. Humour was not necessarily their main interest; however, observations on humour could support their more general philosophical theories. Spontaneous and unintentional humour such as anecdotes, witty remarks and absurd events were the styles of humour that they analysed and made part of

Acronyms: identification, expansion and disambiguation Ann. Math. Artif. Intel. (IF 0.778) Pub Date : 20181206
Kayla Jacobs, Alon Itai, Shuly WintnerAcronyms—words formed from the initial letters of a phrase—are important for various natural language processing applications, including information retrieval and machine translation. While handcrafted acronym dictionaries exist, they are limited and require frequent updates. We present a new machinelearningbased approach to automatically build an acronym dictionary from unannotated texts. This