Authors:Alberto Belussi; Sara Migliorini Pages: 175 - 218 Abstract: Space and time are two important characteristics of data in many domains. This is particularly true in the archaeological context where information concerning the discovery location of objects allows one to derive important relations between findings of a specific survey or even of different surveys, and time aspects extend from the excavation time, to the dating of archaeological objects. In recent years, several attempts have been performed to develop a spatio-temporal information system tailored for archaeological data.The first aim of this paper is to propose a model, called \(\mathcal {S}\) tar, for representing spatio-temporal data in archaeology. In particular, since in this domain dates are often subjective, estimated and imprecise, \(\mathcal {S}\) tar has to incorporate such vague representation by using fuzzy dates and fuzzy relationships among them. Moreover, besides to the topological relations, another kind of spatial relations is particularly useful in archeology: the stratigraphic ones. Therefore, this paper defines a set of rules for deriving temporal knowledge from the topological and stratigraphic relations existing between two findings. Finally, considering the process through which objects are usually manually dated by archeologists, some existing automatic reasoning techniques may be successfully applied to guide such process. For this purpose, the last contribution regards the translation of archaeological temporal data into a Fuzzy Temporal Constraint Network for checking the overall data consistency and reducing the vagueness of some dates based on their relationships with other ones. PubDate: 2017-08-01 DOI: 10.1007/s10472-017-9535-0 Issue No:Vol. 80, No. 3-4 (2017)

Authors:Fabio Grandi Pages: 219 - 245 Abstract: In this work we introduce two lean temporal index structures to efficiently support snapshot access (i.e., timeslice queries) in a transaction-time database. The two proposed structures, the RABTree and its RAB−Tree variant, are conceptually simple, easy to implement and efficient index solutions. In particular, the RABTree index guarantees optimal performances for transaction-time data which are naturally clustered according to their insertion time without redundancy. A theoretical and experimental evaluation of the two indexes, in comparison with their previously proposed competitors, is also provided. PubDate: 2017-08-01 DOI: 10.1007/s10472-016-9509-7 Issue No:Vol. 80, No. 3-4 (2017)

Authors:Alejandro Sánchez; César Sánchez Pages: 249 - 282 Abstract: This paper studies the problem of verifying temporal properties (including liveness properties) of parametrized concurrent systems executed by an unbounded number of threads. To solve this problem we introduce parametrized verification diagrams (PVDs), that extend the so-called generalized verification diagrams (GVDs) adding support for parametrized verification. Even though GVDs are known to be a sound and complete proof system for non-parametrized systems, the application of GVDs to parametrized systems requires using quantification or finding a potentially different diagram for each instantiation of the parameter (number of threads). As a consequence, the use of GVDs in parametrized verification requires discharging and proving either quantified formulas or an unbounded collection of verification conditions. Parametrized verification diagrams enable the use of asinglediagram to represent the proof that all possible instances of the parametrized concurrent system satisfy the given temporal specification. Checking the proof represented by a PVD requires proving only a finite collection of quantifier-free verification conditions. The PVDs we present here assume that the parametrized systems are symmetric, which covers a large class of concurrent and distributed systems, including concurrent data types. Our second contribution is an implementation of PVDs and its integration into Leap, our prototype theorem prover. Finally, we illustrate empirically, using Leap, the practical applicability of PVDs by building and checking proofs of liveness properties of mutual exclusion protocols and concurrent data structures. To the best of our knowledge, these are the first machine-checkable proofs of liveness properties of these concurrent data types. PubDate: 2017-08-01 DOI: 10.1007/s10472-016-9531-9 Issue No:Vol. 80, No. 3-4 (2017)

Authors:Carlo A. Furia; Paola Spoletini Pages: 283 - 316 Abstract: Deciding validity of Metric Temporal Logic (MTL) formulas is generally very complex and even undecidable over dense time domains; bounded variability is one of the several restrictions that have been proposed to bring decidability back. A temporal model has bounded variability if no more than v events occur over any time interval of length V, for constant parameters v and V. Previous work has shown that MTL validity over models with bounded variability is less complex—and often decidable—than MTL validity over unconstrained models. This paper studies the related problem of deciding whether an MTL formula has intrinsic bounded variability, that is whether it is satisfied only by models with bounded variability. The results of the paper are mainly negative: over dense time domains, the problem is mostly undecidable (even if with an undecidability degree that is typically lower than deciding validity); over discrete time domains, it is decidable with the same complexity as deciding validity. As a partial complement to these negative results, the paper also identifies MTL fragments where deciding bounded variability is simpler than validity, which may provide for a reduction in complexity in some practical cases. PubDate: 2017-08-01 DOI: 10.1007/s10472-016-9532-8 Issue No:Vol. 80, No. 3-4 (2017)

Authors:Francisco Botana; Tomas Recio Pages: 3 - 20 Abstract: We review the behavior of some popular dynamic geometry software when computing envelopes, relating the diverse methods implemented in these programs with the various definitions of envelope. Special attention is given to the new GeoGebra 5.0 version, that incorporates a mathematically rigorous approach for envelope computations. Furthermore, a discussion on the role, in this context, of the cooperation between GeoGebra and a recent parametric polynomial solving algorithm is detailed. This approach seems to yield accurate results, allowing for the first time sound computations of envelopes of families of plane curves in interactive environments. PubDate: 2017-05-01 DOI: 10.1007/s10472-016-9500-3 Issue No:Vol. 80, No. 1 (2017)

Authors:Juana Sendra; David Gómez Sánchez-Pascuala; Valerio Morán Pages: 47 - 64 Abstract: In this paper we present two packages, implemented in the computer algebra system Maple, for dealing with offsets and conchoids to algebraic curves, respectively. Help pages and procedures are described. Also in an annex, we provide a brief atlas, created with these packages, and where the offset and the conchoid of several algebraic plane curves are obtained, their rationality is analyzed, and parametrizations are computed. Practical performance of the implemented algorithms shows that the packages execute in reasonable time; we include time cost tables of the computation of the offset and conchoid curves of two rational families of curves using the implemented packages. PubDate: 2017-05-01 DOI: 10.1007/s10472-016-9504-z Issue No:Vol. 80, No. 1 (2017)

Authors:M. Martinez; A. M. H. Abdel-Fattah; U. Krumnack; D. Gómez-Ramírez; A. Smaill; T. R. Besold; A. Pease; M. Schmidt; M. Guhe; K.-U. Kühnberger Pages: 65 - 89 Abstract: In Cognitive Science, conceptual blending has been proposed as an important cognitive mechanism that facilitates the creation of new concepts and ideas by constrained combination of available knowledge. It thereby provides a possible theoretical foundation for modeling high-level cognitive faculties such as the ability to understand, learn, and create new concepts and theories. Quite often the development of new mathematical theories and results is based on the combination of previously independent concepts, potentially even originating from distinct subareas of mathematics. Conceptual blending promises to offer a framework for modeling and re-creating this form of mathematical concept invention with computational means. This paper describes a logic-based framework which allows a formal treatment of theory blending (a subform of the general notion of conceptual blending with high relevance for applications in mathematics), discusses an interactive algorithm for blending within the framework, and provides several illustrating worked examples from mathematics. PubDate: 2017-05-01 DOI: 10.1007/s10472-016-9505-y Issue No:Vol. 80, No. 1 (2017)

Authors:Manuele Leonelli; Eva Riccomagno; Jim Q. Smith Abstract: Influence diagrams provide a compact graphical representation of decision problems. Several algorithms for the quick computation of their associated expected utilities are available in the literature. However, often they rely on a full quantification of both probabilistic uncertainties and utility values. For problems where all random variables and decision spaces are finite and discrete, here we develop a symbolic way to calculate the expected utilities of influence diagrams that does not require a full numerical representation. Within this approach expected utilities correspond to families of polynomials. After characterizing their polynomial structure, we develop an efficient symbolic algorithm for the propagation of expected utilities through the diagram and provide an implementation of this algorithm using a computer algebra system. We then characterize many of the standard manipulations of influence diagrams as transformations of polynomials. We also generalize the decision analytic framework of these diagrams by defining asymmetries as operations over the expected utility polynomials. PubDate: 2017-06-21 DOI: 10.1007/s10472-017-9553-y

Authors:S. Zhou; E. N. Smirnov; G. Schoenmakers; R. Peeters Abstract: Instance transfer for classification aims at boosting generalization performance of classification models for a target domain by exploiting data from a relevant source domain. Most of the instance-transfer approaches assume that the source data is relevant to the target data for the complete set of features used to represent the data. This assumption fails if the target data and source data are relevant only for strict subsets of the input features which we call “partially input-feature relevant”. In this case these approaches may result in sub-optimal classification models or even in a negative transfer. This paper proposes a new decision-tree approach to instance transfer when the source data are partially input-feature relevant to the target data. The approach selects input features for tree nodes using univariate transfer of source instances. The instance transfer is guided by a conformal test for source relevance estimation. Experimental results on real-world data sets demonstrate that the new decision-tree approach is capable of outperforming existing instance-transfer approaches, especially, when the source data are partially input-feature relevant to the target data. PubDate: 2017-06-17 DOI: 10.1007/s10472-017-9554-x

Authors:Paolo Toccaceli; Ilia Nouretdinov; Alexander Gammerman Abstract: The paper presents an application of Conformal Predictors to a chemoinformatics problem of predicting the biological activities of chemical compounds. The paper addresses some specific challenges in this domain: a large number of compounds (training examples), high-dimensionality of feature space, sparseness and a strong class imbalance. A variant of conformal predictors called Inductive Mondrian Conformal Predictor is applied to deal with these challenges. Results are presented for several non-conformity measures extracted from underlying algorithms and different kernels. A number of performance measures are used in order to demonstrate the flexibility of Inductive Mondrian Conformal Predictors in dealing with such a complex set of data. This approach allowed us to identify the most likely active compounds for a given biological target and present them in a ranking order. PubDate: 2017-06-16 DOI: 10.1007/s10472-017-9556-8

Authors:Shufeng Kong; Sanjiang Li; Yongming Li; Zhiguo Long Abstract: The study of tractable subclasses of constraint satisfaction problems is a central topic in constraint solving. Tree convex constraints are extensions of the well-known row convex constraints. Just like the latter, every path-consistent tree convex constraint network is globally consistent. However, it is NP-complete to decide whether a tree convex constraint network has solutions. This paper studies and compares three subclasses of tree convex constraints, which are called chain-, path-, and tree-preserving constraints respectively. The class of tree-preserving constraints strictly contains the subclasses of path-preserving and arc-consistent chain-preserving constraints. We prove that, when enforcing strong path-consistency on a tree-preserving constraint network, in each step, the network remains tree-preserving. This ensures the global consistency of consistent tree-preserving networks after enforcing strong path-consistency, and also guarantees the applicability of the partial path-consistency algorithms to tree-preserving constraint networks, which is usually much more efficient than the path-consistency algorithms for large sparse constraint networks. As an application, we show that the class of tree-preserving constraints is useful in solving the scene labelling problem. PubDate: 2017-05-29 DOI: 10.1007/s10472-017-9552-z

Authors:Alexander Kuleshov; Alexander Bernstein Abstract: Consider unknown smooth function which maps high-dimensional inputs to multidimensional outputs and whose domain of definition is unknown low-dimensional input manifold embedded in an ambient high-dimensional input space. Given training dataset consisting of ‘input-output’ pairs, regression on input manifold problem is to estimate the unknown function and its Jacobian matrix, as well to estimate the input manifold. By transforming high-dimensional inputs in their low-dimensional features, initial regression problem is reduced to certain regression on feature space problem. The paper presents a new geometrically motivated method for solving both interrelated regression problems. PubDate: 2017-05-16 DOI: 10.1007/s10472-017-9551-0

Authors:Lior Aronshtam; Havazelet Cohen; Tammar Shrot Abstract: This article focuses on the question of whether a certain candidate’s (player’s) chance to advance further in a tennis tournament can be increased when the ordering of the tournament can be controlled (manipulated by the organizers) according to his own preferences. Is it possible to increase the number of ranking points a player will receive? And most importantly, can it be done in reasonable computational time? The answers to these questions differ for different settings. e.g., the information available on the outcome of each game, the significance of the number of points gained or of the number of games won. We analyzed five different variations of these tournament questions. First the complexity hardness of trying to control the tournaments is shown. Then, the tools of parametrized complexity are used to investigate the source of the problems’ hardness. Specifically, we check whether this hardness holds when the size of the problem is bounded. The findings of this analysis show that it is possible under certain circumstances to control the tournament in favor of a specific candidate in order to help him advance further in the tournament. PubDate: 2017-04-24 DOI: 10.1007/s10472-017-9549-7

Authors:John McCabe-Dansted; Mark Reynolds Abstract: Attempts to manage the reasoning about systems with fairness properties are long running. The popular but restricted Computational Tree Logic (CTL) is amenable to automated reasoning but has difficulty expressing some fairness properties. More expressive languages such as CTL* and CTL+ are computationally complex. The main contribution of this paper is to show the usefulness and practicality of employing the bundled variants of these languages to handle fairness. In particular we present a tableau for a bundled variant of CTL that still has the similar computational complexity to the CTL tableau and a simpler implementation. We further show that the decision problem remains in EXPTIME even if a bounded number of CTL* fairness constraints are allowed in the input formulas. By abandoning limit closure the bundled logics can simultaneously be easier to automate and express many typical fairness constraints. PubDate: 2017-04-19 DOI: 10.1007/s10472-017-9546-x

Authors:Weifu Ding; Jiangshe Zhang Abstract: This paper presents a novel online object tracking algorithm with sparse representation for learning effective appearance models under a particle filtering framework. Compared with the state-of-the-art ℓ 1 sparse tracker, which simply assumes that the image pixels are corrupted by independent Gaussian noise, our proposed method is based on information theoretical Learning and is much less sensitive to corruptions; it achieves this by assigning small weights to occluded pixels and outliers. The most appealing aspect of this approach is that it can yield robust estimations without using the trivial templates adopted by the previous sparse tracker. By using a weighted linear least squares with non-negativity constraints at each iteration, a sparse representation of the target candidate is learned; to further improve the tracking performance, target templates are dynamically updated to capture appearance changes. In our template update mechanism, the similarity between the templates and the target candidates is measured by the earth movers’ distance(EMD). Using the largest open benchmark for visual tracking, we empirically compare two ensemble methods constructed from six state-of-the-art trackers, against the individual trackers. The proposed tracking algorithm runs in real-time, and using challenging sequences performs favorably in terms of efficiency, accuracy and robustness against state-of-the-art algorithms. PubDate: 2017-03-23 DOI: 10.1007/s10472-017-9543-0

Authors:Federico Bergenti; Stefania Monica Abstract: This paper describes an algorithm to enforce hyper-arc consistency of polynomial constraints defined over finite domains. First, the paper describes the language of so called polynomial constraints over finite domains, and it introduces a canonical form for such constraints. Then, the canonical form is used to transform the problem of testing the satisfiability of a constraint in a box into the problem of studying the sign of a related polynomial function in the same box, a problem which is effectively solved by using the modified Bernstein form of polynomials. The modified Bernstein form of polynomials is briefly discussed, and the proposed hyper-arc consistency algorithm is finally detailed. The proposed algorithm is a subdivision procedure which, starting from an initial approximation of the domains of variables, removes values from domains to enforce hyper-arc consistency. PubDate: 2017-03-20 DOI: 10.1007/s10472-017-9544-z