pinecone/core-2020-05-10-deduplication huggingface dataset:
From Collaboration Models to BPEL Processes Through Service Models

This paper proposes a model-based lifecycle for the development of web services, which is based on two kinds of models, collaboration models and service ones. After agreeing upon a collaboration model, which is a public specification, each party can work out a service model and then can turn it into a process written in an orchestration language such as BPEL. As the conceptual gap between a service model and its BPEL implementation is relevant, this paper is concerned with the automatic mapping of service models to BPEL processes, in line with model-based development. Moreover it discusses how to validate services with respect to collaboration models both at-design time and at run-time, and presents the bProgress software environment, which is made up of a number tools developed during this research
Enriching a MAS environment with institutional services

Most environments for multi-agent systems limit themselves to providing message transport and white/yellow page services. While these are generic facilities, in some domains other services are necessary, which may map real-world services provided by institutions. The Electronic Institution concept represents the virtual counterpart of real-world institutions, and one of its benefits is to provide a regulated and trustful environment by enforcing norms and providing specific institutional services. This paper presents some of such institutional services. Ontology-based services are provided to assist agent interaction, making the establishment of business agreements more efficient. After the establishment of an agreement through an appropriate negotiation process, it is necessary to verify the execution of the resulting contract, For this, we introduce an institutional normative environment based on the concept of institutional reality and norms
A formal approach for reasoning about a class of Diffle-Hellman protocols

We present a framework for reasoning about secrecy in a class of Diffie-Hellman protocols. The technique, which shares a conceptual origin with the idea of a rank function, uses the notion of a message-template to determine whether a given value is generable by an intruder in a protocol model. Traditionally, the rich algebraic structure of Diffie-Hellman messages has made it difficult to reason about such protocols using formal, rather than complexity-theoretic, techniques. We describe the approach in the context of the MTI A(0) protocol, and derive the conditions under which this protocol can be considered secure
A Robust Method to Count and Locate Audio Sources in a Stereophonic Linear Instantaneous Mixture

International audienceWe propose a robust method to estimate the number of audio sources and the mixing matrix in a linear instantaneous mixture, even with more sources than sensors. Our method is based on a multiscale Short Time Fourier Trans- form (STFT), and relies on the assumption that in the neighborhood of some (unknown) scales and time-frequency points, only one source contributes to the mixture. Such time-frequency regions provide local estimates of the correspond- ing columns of the mixing matrix. Our main contribution is a new clustering al- gorithm called DEMIX to estimate the number of sources and the mixing matrix based on such local estimates. In contrast to DUET or other similar sparsity-based algorithms, which rely on a global scatter plot, our algorithm exploits a local confidence measure to weight the influence of each time-frequency point in the estimated matrix. Inspired by the work of Deville, the confidence measure relies on the time-frequency local persistence of the activity/inactivity of each source. Experiments are provided with stereophonic mixtures and show the improved performance of DEMIX compared to K-means or ELBG clustering algorithms
Under-determined source separation: comparison of two approaches based on sparse decompositions

International audienceThis paper focuses on under-determined source separation when the mixing parameters are known. The approach is based on a sparse decomposition of the mixture. In the proposed method, the mixture is decomposed with Matching Pursuit by introducing a new class of multi-channel dictionaries, where the atoms are given by a spatial direction and a waveform. The knowledge of the mixing matrix is directly integrated in the decomposition. Compared to the separation by multi-channel Matching Pursuit followed by a clustering, the new algorithm introduces less artifacts whereas the level of residual interferences is about the same. These two methods are compared to Bofill & Zibulevsky's separation algorithm and DUET method. We also study the effect of smoothing the decompositions and the importance of the quality of the estimation of the mixing matrix
A comparing method of two team behaviours in the simulation coach competition

Proceeding of: Third International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2006, Tarragona, Spain, April 3-5, 2006.The main goal of agent modelling is to extract and represent the knowledge about the behaviour of other agents. Nowadays, modelling an agent in multi-agent systems is increasingly becoming more complex and significant. Also, robotic soccer domain is an interesting environment where agent modelling can be used. In this paper, we present an approach to classify and compare the behaviour of a multi-agent system using a coach in the soccer simulation domain of the RoboCup.Publicad
Robust Model-Checking of Linear-Time Properties in Timed Automata

International audienceFormal verification of timed systems is well understood, but their \emphimplementation is still challenging. Recent works by Raskin \emphet al. have brought out a model of parameterized timed automata that can be used to prove \emphimplementability of timed systems for safety properties. We define here a more general notion of robust model-checking for linear-time properties, which consists in verifying whether a property still holds even if the transitions are slightly delayed or expedited. We provide PSPACE algorithms for the robust model-checking of Büchi-like and LTL properties. We also verify bounded-response-time properties
The computational complexity of the parallel knock-out problem.

We consider computational complexity questions related to parallel knock-out schemes for graphs. In such schemes, in each round, each remaining vertex of a given graph eliminates exactly one of its neighbours. We show that the problem of whether, for a given graph, such a scheme can be found that eliminates every vertex is NP-complete. Moreover, we show that, for all fixed positive integers <i>k</i> > 1, the problem of whether a given graph admits a scheme in which all vertices are eliminated in at most <i>k</i> rounds is NP-complete. For graphs with bounded tree-width, however, both of these problems are shown to be solvable in polynomial time
Integration of decentralized economic models for resource self-management in application layer networks

Resource allocation is one of the challenges for self-management of large scale distributed applications running in a dynamic and heterogeneous environment. Considering Application Layer Networks (ALN) as a general term for such applications including computational Grids, Content Distribution Networks and P2P applications, the characteristics of the ALNs and the environment preclude an efficient resource allocation by a central instance. The approach we propose integrates ideas from decentralized economic models into the architecture of a resource allocation middleware, which allows the scalability towards the participant number and the robustness in very dynamic environments. At the same time, the pursuit of the participants for their individual goals should benefit the global optimization of the application. In this work, we describe the components of this middleware architecture and introduce an ongoing prototype.Peer Reviewe
Multi-agent relational reinforcement learning Explorations in multi-state coordination tasks

In this paper we report on using a relational state space in multi-agent reinforcement learning. There is growing evidence in the Reinforcement Learning research community that a relational representation of the state space has many benefits over a propositional one. Complex tasks as planning or information retrieval on the web can be represented more naturally in relational form. Yet, this relational structure has not been exploited for multi-agent reinforcement learning tasks and has only been studied in a single agent context so far. In this paper we explore the powerful possibilities of using Relational Reinforcement Learning (RRL) in complex multi-agent coordination tasks. More precisely, we consider an abstract multi-state coordination problem, which can be considered as a variation and extension of repeated stateless Dispersion Games. Our approach shows that RRL allows to represent a complex state space in a multi-agent environment more compactly and allows for fast convergence of learning agents, Moreover, with this technique, agents are able to make complex interactive models (in the sense of learning from an expert), to predict what other agents will do and generalize over this model. This enables to solve complex multi-agent planning tasks, in which agents need to be adaptive and learn, with more powerful tools.status: publishe
GPSL: A Programming Language for Service Implementation

At present, there is a dichotomy of approaches to supporting web service implementation: extending mainstream programming languages with libraries and metadata notations vs. designing new languages. While the former approach has proven suitable for interconnecting services on a simple point-to-point fashion, it turns to be unsuitable for coding concurrent, multi-party, and interrelated interactions requiring extensive XML manipulation. As a result, various web service programming languages have been proposed, most notably (WS-)BPEL. However, these languages still do not meet the needs of highly concurrent and dynamic interactions due to their bias towards statically-bounded concurrency. In this paper we introduce a new web service programming language with a set of features designed to address this gap. We describe the implementations in this language of non-trivial scenarios of service interaction and contrast them to the corresponding BPEL implementations. We also define a formal semantics for the language by translation to the join calculus. A compiler for the language has been implemented based on this semantics
Negative slope coefficient. a measure to characterize genetic programming fitness landscapes

International audienceNegative slope coefficient has been recently introduced and empirically proven a suitable hardness indicator for some well known genetic programming benchmarks, such as the even parity problem, the binomial-3 and the artificial ant on the Santa Fe trail. Nevertheless, the original definition of this measure contains several limitations. This paper points out some of those limitations, presents a new and more relevant definition of the negative slope coefficient and empirically shows the suitability of this new definition as a hardness measure for some genetic programming benchmarks, including the multiplexer, the intertwined spirals problem and the royal tree
Using weighted nearest neighbor to benefit from unlabeled data

The development of, data-mining applications such as text-classification and molecular profiling has shown the need for machine learning algorithms that can benefit from both labeled and unlabeled data, where often the unlabeled examples greatly outnumber the labeled examples. In this paper we present a two-stage classifier that improves its predictive accuracy by making use of the available unlabeled data. It uses a weighted nearest neighbor classification algorithm using the combined example-sets as a knowledge base. The examples from the unlabeled set are "pre-labeled" by an initial classifier that is build using the limited available training data. By choosing appropriate weights for this prelabeled data, the nearest neighbor classifier consistently improves on the original classifier.status: publishe
An intelligent system based on kernel methods for crop yield prediction

This paper presents work on developing a software system for predicting crop yield from climate and plantation data. At the core of this system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data. For this purpose, a robust weighted kernel k-means algorithm incorporating spatial constraints is presented. The algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis, and thus can be used for predicting oil-palm yield by analyzing various factors affecting the yield
Functional classification of G-Protein coupled receptors, based on their specific ligand coupling patterns

Functional identification of G-Protein Coupled Receptors (GPCRs) is one of the current focus areas of pharmaceutical research. Although thousands of GPCR sequences are known, many of them re- main as orphan sequences (the activating ligand is unknown). Therefore, classification methods for automated characterization of orphan GPCRs are imperative. In this study, for predicting Level 2 subfamilies of Amine GPCRs, a novel method for obtaining fixed-length feature vectors, based on the existence of activating ligand specific patterns, has been developed and utilized for a Support Vector Machine (SVM)-based classification. Exploiting the fact that there is a non-promiscuous relationship between the specific binding of GPCRs into their ligands and their functional classification, our method classifies Level 2 subfamilies of Amine GPCRs with a high predictive accuracy of 97.02% in a ten-fold cross validation test. The presented machine learning approach, bridges the gulf between the excess amount of GPCR sequence data and their poor functional characterization
Multiple sequence alignment based on set covers

We introduce a new heuristic for the multiple alignment of a set of
sequences. The heuristic is based on a set cover of the residue alphabet of the
sequences, and also on the determination of a significant set of blocks
comprising subsequences of the sequences to be aligned. These blocks are
obtained with the aid of a new data structure, called a suffix-set tree, which
is constructed from the input sequences with the guidance of the
residue-alphabet set cover and generalizes the well-known suffix tree of the
sequence set. We provide performance results on selected BAliBASE amino-acid
sequences and compare them with those yielded by some prominent approaches
A methodology for determining amino-acid substitution matrices from set
  covers

We introduce a new methodology for the determination of amino-acid
substitution matrices for use in the alignment of proteins. The new methodology
is based on a pre-existing set cover on the set of residues and on the
undirected graph that describes residue exchangeability given the set cover.
For fixed functional forms indicating how to obtain edge weights from the set
cover and, after that, substitution-matrix elements from weighted distances on
the graph, the resulting substitution matrix can be checked for performance
against some known set of reference alignments and for given gap costs. Finding
the appropriate functional forms and gap costs can then be formulated as an
optimization problem that seeks to maximize the performance of the substitution
matrix on the reference alignment set. We give computational results on the
BAliBASE suite using a genetic algorithm for optimization. Our results indicate
that it is possible to obtain substitution matrices whose performance is either
comparable to or surpasses that of several others, depending on the particular
scenario under consideration
Improving the Segmentation Stage of a Pedestrian Tracking Video-based System by means of Evolution Strategies

12 pages, 7 figures.-- Contributed to: Eighth European Workshop on Evolutionary Computation in Image Analysis and Signal Processing (EvoIASP 2006, Budapest, Hungary, Apr 10-12, 2006).Pedestrian tracking video-based systems present particular problems such as the multi fragmentation or low level of compactness of the resultant blobs due to the human shape or movements. This paper shows how to improve the segmentation stage of a video surveillance system by adding morphological post-processing operations so that the subsequent blocks increase their performance. The adjustment of the parameters that regulate the new morphological processes is tuned by means of Evolution Strategies. Finally, the paper proposes a group of metrics to assess the global performance of the surveillance system. After the evaluation over a high number of video sequences, the results show that the shape of the tracks match up more accurately with the parts of interests. Thus, the improvement of segmentation stage facilitates the subsequent stages so that global performance of the surveillance system increases.Funded by CICYT (TIC2002-04491-C02-02)Publicad
An experimental comparative study for interactive evolutionary computation problems

Proceeding of: EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC, Budapest, Hungary, April 10-12, 2006This paper presents an objective experimental comparative study between four algorithms: the Genetic Algorithm, the Fitness Prediction Genetic Algorithm, the Population Based Incremental Learning algorithm and the purposed method based on the Chromosome Appearance Probability Matrix. The comparative is done with a non subjective evaluation function. The main objective is to validate the efficiency of several methods in Interactive Evolutionary Computation environments. The most important constraint of working within those environments is the user interaction, which affects the results adding time restrictions for the experimentation stage and subjectivity to the validation. The experiments done in this paper replace user interaction with several approaches avoiding user limitations. So far, the results show the efficiency of the purposed algorithm in terms of quality of solutions and convergence speed, two known keys to decrease the user fatigue.This article has been financed by the Spanish founded research MCyT project OPLINK, Ref: TIN2006-08818-C04-02
A probabilistic model for gene content evolution with duplication, loss,
  and horizontal transfer

We introduce a Markov model for the evolution of a gene family along a
phylogeny. The model includes parameters for the rates of horizontal gene
transfer, gene duplication, and gene loss, in addition to branch lengths in the
phylogeny. The likelihood for the changes in the size of a gene family across
different organisms can be calculated in O(N+hM^2) time and O(N+M^2) space,
where N is the number of organisms, $h$ is the height of the phylogeny, and M
is the sum of family sizes. We apply the model to the evolution of gene content
in Preoteobacteria using the gene families in the COG (Clusters of Orthologous
Groups) database
Constraint based induction of multi-objective regression trees

Constrained based inductive systems are a key component of inductive databases and responsible for building the models that satisfy the constraints in the inductive queries. In this paper, we propose a constraint based system for building multi-objective regression trees. A multi-objective regression tree is a decision tree capable of predicting several numeric variables at once. We focus on size and accuracy constraints. By either specifying maximum size or minimum accuracy, the user can trade-off size (and thus interpretability) for accuracy. Our approach is to first build a large tree based on the training data and to prune it in a second step to satisfy the user constraints. This has the advantage that the tree can be stored in the inductive database and used for answering inductive queries with different constraints. Besides size and accuracy constraints, we also briefly discuss syntactic constraints. We evaluate our system on a number of real world data sets and measure the size versus accuracy trade-off.status: publishe
Learning predictive clustering rules

The two most commonly addressed data mining tasks are predictive modelling and clustering. Here we address the task of predictive clustering, which contains elements of both and generalizes them to some extent. Predictive clustering has been mainly evaluated in the context of trees. In this paper, we extend predictive clustering toward rules. Each cluster is described by a rule and different clusters are allowed to overlap since the sets of examples covered by different rules do not need to be disjoint. We propose a system for learning these predictive clustering rules, which is based on a heuristic sequential covering algorithm. The heuristic takes into account both the precision of the rules (compactness w.r.t. the target space) and the compactness w.r.t. the input space, and the two can be traded-off by means of a parameter. We evaluate our system in the context of several multi-objective classification problems.status: publishe
Judging the spatial relevance of documents for GIR

Geographic Information Retrieval (GIR) is concerned with the retrieval of documents based on both thematic and geographic content. An important issue in GIR, as for all IR, is relevance. In this paper we argue that spatial relevance should be considered independently from thematic relevance, and propose an initial scheme. A pilot study to assess this relevance scheme is presented, with initial results suggesting that users can distinguish between these two relevance dimensions, and that furthermore they have different properties. We suggest that spatial relevance requires greater assessor effort and more localised geographic knowledge than judging thematic relevance
Probabilistic score normalization for rank aggregation

The final publication is available at Springer via http://dx.doi.org/10.1007/11735106_63Proceedings of 28th European Conference on IR Research, ECIR 2006, London, UK, April 10-12, 2006.Rank aggregation is a pervading operation in IR technology. We hypothesize that the performance of score-based aggregation may be affected by artificial, usually meaningless deviations consistently occurring in the input score distributions, which distort the combined result when the individual biases differ from each other. We propose a score-based rank aggregation model where the source scores are normalized to a common distribution before being combined. Early experiments on available data from several TREC collections are shown to support our proposal.This research was supported by the EC (FP6-001765 – aceMedia), and the Spanish
Ministry of Science and Education (TIN2005-06885). The content expressed is the
view of the authors but not necessarily the view of the aceMedia project as a whole