For classification with multiple labels, a common approach is to learn a classifier for each label. With a kernel-based classifier, there are two options to set up kernels: select a specific kernel for each label or the same kernel for all labels.  In this work, we present a unified framework for multi-label multiple kernel learning, in which the above two approaches can be considered as two extreme cases. Moreover, our framework allows the kernels shared partially among multiple labels, enabling flexible degrees of label commonality. We systematically study how the sharing of kernels among multiple labels affects the performance based on extensive experiments on various benchmark data including images and microarray data. Interesting findings concerning efficacy and efficiency are reported.

Standard belief contraction assumes an underlying logic containing full classical propositional logic, but there are good reasons for considering contraction in less expressive logics. In this paper we focus on Horn logic. In addition to being of interest in its own right, our choice is motivated by the use of Horn logic in several areas, including ontology reasoning in description logics. We consider three versions of contraction: entailment-based and inconsistency-basedcontraction (e-contraction and i-contraction, resp.), introduced by Delgrande for Horn logic, and package contraction (p-contraction), studied by Fuhrmann and Hansson for the classical case. We show that the standard basic form of contraction, partial meet, is too strong in the Horn case. We define more appropriate notions of basic contraction for all three types above, and provide associated representation results in terms of postulates. Our results stand in contrast to Delgrande's conjectures that orderly maxichoice is the appropriate contraction for both e- and i-contraction. Our interest in p-contraction stems from its relationship with an important reasoning task in ontological reasoning:repairing the subsumption hierarchy in EL. This is closely related to p-contraction with sets of basic Horn clauses (Horn clauses of the form p -> q). We show that this restricted version of p-contraction can also be represented as i-contraction.

Many local search algorithms are based on searching in the k-exchange neighborhood. This is the set of solutions that can be obtained from the current solution by exchanging at most k elements. As a rule of thumb, the larger k is, the better are the chances of  finding an improved  solution. However,  for inputs of size n,  a naive brute-force search of the k-exchange neighborhood requires n (O(k)) time, which is not practical even for very small values of k. We show that for several classes of sparse graphs, like planar graphs, graphs of bounded vertex degree and graphs excluding some fixed graph as a minor, an improved solution in the k-exchange neighborhood for many problems can be found much more efficiently.  Our algorithms run in time O(T(k)*n c ), where T is a function depending on k only and c is a constant independent of k.  We demonstrate the applicability of this  approach on different  problems like r-Center, Vertex Cover,  Odd Cycle Transversal, Max-Cut, and  Min-Bisection.  In particular, on planar graphs, all our algorithms searching for a k-local improvement run in time O(2 (O(k) ) * n (2) ), which is polynomial for k=O(log n). We also complement the algorithms with complexity results indicating that brute force search is unavoidable in more general classes of sparse graphs.

Possibilistic networks are useful tools for reasoning under uncertainty. Uncertain pieces of information can be described by different measures: possibility measures, necessity measures and more recently, guaranteed possibility measures, denoted by Delta. This paper first proposes the use of guaranteed possibility measures to define a so-called Delta-based possibilistic network. This graphical representation tries to express and to deal with the minimal (lower-bound) possibility degree guaranteed for each variable. We then establish relationships between graphical and logical-based representations of uncertain information encoded by guaranteed possibility measures. We show that possibilistic networks based on guaranteed possibility measures can be easily transformed, in a polynomial time, in Delta-based knowledge bases. Then we analyze propagation algorithms in Delta-based possibilistic networks. In fact, standard possibilistic propagation algorithms can be re-used since we show that a simple rewriting of the chain rule allows the transformation of the initial Delta-based possibilistic networks into standard min-based possibilistic networks.

This paper is a contribution to formal ontology study. We propose a new model of knowledge representation by combining ontologies and topology. In order to represent atypical entities in the ontologies, we introduce topological operators of interior, exterior, border and closure. These operators allow us to describe whether an entity, belonging to a class, is typical or not. We define a system of relations of inclusion and membership by adapting the topological operators. We propose to formalize the topological relations of inclusion and membership by using the mathematical properties of topological operators. However, there are properties of combining operators of interior, exterior, border and closure allowing the definition of an algebra (Kuratowski, 1958). We propose to use these mathematical properties as a set of axioms. This set of axioms allows us to establish the properties of topological relations of inclusion and membership.

The paper concentrates on obtaining hidden relationships among individual clauses of complex sentences from the Prague Dependency Treebank. The treebank contains only an information about mutual relationships among individual tokens (words, punctuation marks), not about more complex units (clauses). For the experiments with clauses and their parts (segments) it was therefore necessary to develop an automatic method transforming the original annotation into a scheme describing the syntactic relationships between clauses. The task was complicated by a certain degree of inconsistency in original annotation with regard to clauses and their structure. The paper describes the algorithm of deriving clause-related information from the existing annotation and its evaluation.

In this paper, we propose a universal solution to web search and web browsing on handheld devices for visually impaired people. For this purpose, we propose (1) to automatically cluster web page results and (2) to summarize all the information in web pages so that speech-to-speech interaction is used efficiently to access information.

We propose a new approach for Temporal Diagnosis Problems. This approach is an extension of  Bouzid and Ligeza's  method for temporal diagnosis problems. In this latter work, the authors define a Temporal Causal Graph (TCG) where time delays are expressed as temporal instants. We extend the TCG by including two quantitative relations in order to handle temporal intervals. We call ExTCG this new model. Solving a temporal diagnosis problem represented by the ExTCG consists of finding all possible explanations. It is performed using a backtrack search algorithm. In many diagnosis applications, the generation of all possible explanations is not necessary. For this reason, we augment the ExTCG in order to consider the degree of causality between symptoms. We call weighted ExTCG this extended model. Solving it consists of finding the explanation  having the highest probability to occur. Through a real world diagnosis application in medicine, we illustrate the weighted ExTCG and its corresponding solving algorithm.

Conference and special track committees responsible for the 2009 FLAIRS conference.

The need for efficient monitoring of spatio-temporal dynamics in large environmental applications, such as the water quality monitoring in rivers and lakes, motivates the use of robotic sensors in order to achieve sufficient spatial coverage. Typically, these robots have bounded resources, such as limited battery or limited amounts of time to obtain measurements. Thus, careful coordination of their paths is required in order to maximize the amount of information collected, while respecting the resource constraints. In this paper, we present an efficient approach for near-optimally solving the NP-hard optimization problem of planning such informative paths. In particular, we first develop eSIP (efficient Single-robot Informative Path planning), an approximation algorithm for optimizing the path of a single robot. Hereby, we use a Gaussian Process to model the underlying phenomenon, and use the mutual information between the visited locations and remainder of the space to quantify the amount of information collected. We prove that the mutual information collected using paths obtained by using eSIP is close to the information obtained by an optimal solution. We then provide a general technique, sequential allocation, which can be used to extend any single robot planning algorithm, such as eSIP, for the multi-robot problem. This procedure approximately generalizes any guarantees for the single-robot problem to the multi-robot case. We extensively evaluate the effectiveness of our approach on several experiments performed in-field for two important environmental sensing applications, lake and river monitoring, and simulation experiments performed using several real world sensor network data sets.