Concurrent bilateral negotiation for open e-markets: the <Emphasis Type="SmallCaps">Conan</Emphasis> strategy

In the last decade, outlier detection for temporal data has received much attention from data mining and machine learning communities. While other works have addressed this problem by two-way approaches (similarity and clustering), we propose in this paper an embedded technique dealing with both methods simultaneously. We reformulate the task of outlier detection as a weighted clustering problem based on entropy and dynamic time warping for time series. The outliers are then detected by an optimization problem of a new proposed cost function adapted to this kind of data. Finally, we provide some experimental results for validating our proposal and comparing it with other methods of detection.     

New algorithms for budgeted learning

We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples’ class labels but has to pay for each attribute that is specified. This learning model is appropriate in many areas, including medical applications. We present new algorithms for choosing which attributes to purchase of which examples, based on algorithms for the multi-armed bandit problem. In addition, we also evaluate a group of algorithms based on the idea of incorporating second-order statistics into decision making. Most of our algorithms are competitive with the current state of art and performed better when the budget was highly limited (in particular, our new algorithm AbsoluteBR2). Finally, we present new heuristics for selecting an instance to purchase after the attribute is selected, instead of selecting an instance uniformly at random, which is typically done. While experimental results showed some performance improvements when using the new instance selectors, there was no consistent winner among these methods.     

An economic dispatch algorithm of combined cycle units

This paper presents a method to solve the economic dispatch (ED) problem for thermal unit systems involving combined cycle (CC) units. The ED problem finds the optimal generation of each unit in order to minimize the total generation cost while satisfying the total demand and generating-capacity constraints. A CC unit presents multiple configurations or states, each state having its own unique cost curve. Therefore, in performing ED, we need to be able to shift between these cost curves. Moreover, the cost curve is not convex for some of these states. Hence, ED becomes a non-convex optimization problem, which is difficult to solve by conventional methods. In this paper we present a new technique, developed to find the global solution, that is based on the calculation of the infimal convolution. The paper includes the results for a case test and we compare our solution with other techniques.     

An artificial bee colony algorithm for learning Bayesian networks

One basic approach to learn Bayesian networks (BNs) from data is to apply a search procedure to explore the set of candidate networks for the database in light of a scoring metric, where the most popular stochastic methods are based on some meta-heuristic mechanisms, such as Genetic Algorithm, Evolutionary Programming and Ant Colony Optimization. In this paper, we have developed a new algorithm for learning BNs which employs a recently introduced meta-heuristic: artificial bee colony (ABC). All the phases necessary to tackle our learning problem using this meta-heuristic are described, and some experimental results to compare the performance of our ABC-based algorithm with other algorithms are given in the paper.     

基于锁集合的动态数据竞争检测方法

数据竞争使得共享存储程序难于调试．以前大部分针对共享存储程序的动态数据竞争检测工作都是通过维护发生序来实现．这种方法有一个重要缺点，即针对程序的一种输入，对程序的一次执行进行检测，不能检测出所有的可行数据竞争．文中利用存储一致性模型的框架模型，针对域一致性模型提出了增强发生序概念，并依此得出一种基于锁集合的动态数据竞争检测算法，克服了这个问题．在软件DSM系统JIAJIA上的实现获得了很好的性能，应用平均减速比为3．14．利用该方法，在TSP程序中找到了大量的读写数据竞争的情况．     

Parallel multigrid solution of the Navier-Stokes equations on general 2D domains

Multigrid methods are distinguished by their optimal (sequential) efficiency and by the fact that all their algorithmical components are fully parallelizable. For this reason, this class of numerical methods is especially attractive for use on parallel (MIMD, local memory) computers. In this paper, we describe a parallel multigrid solver for steady-state incompressible Navier-Stokes equations on general domains which is currently being developed at the GMD. Due to the geometrical generality of the problem, our approach is based on a non-staggered (nodal-point) finite volume scheme on multi-block boundary fitted grids. The typical instability of non-staggered schemes is overcome by suitably modifying the discrete continuity equation without affecting the overall order of consistency.

Starting from the most simple Cartesian case, we discuss several possible multigrid approaches to the general 2D-problem. This motivates the basic design decisions of our multigrid solver in regard to both the discretization and the choice of multigrid components (smoothing schemes). Furthermore, the principal technique of parallelization (grid partitioning) is described as well as some fundamental aspects of the implementation (communication library).     

Exact and heuristic methods for solving Boolean games

Boolean games are a framework for reasoning about the rational behavior of agents whose goals are formalized using propositional formulas. Compared to normal form games, a well-studied and related game framework, Boolean games allow for an intuitive and more compact representation of the agents’ goals. So far, Boolean games have been mainly studied in the literature from the Knowledge Representation perspective, and less attention has been paid on the algorithmic issues underlying the computation of solution concepts. Although some suggestions for solving specific classes of Boolean games have been made in the literature, there is currently no work available on the practical performance. In this paper, we propose the first technique to solve general Boolean games that does not require an exponential translation to normal-form games. Our method is based on disjunctive answer set programming and computes solutions (equilibria) of arbitrary Boolean games. It can be applied to a wide variety of solution concepts, and can naturally deal with extensions of Boolean games such as constraints and costs. We present detailed experimental results in which we compare the proposed method against a number of existing methods for solving specific classes of Boolean games, as well as adaptations of methods that were initially designed for normal-form games. We found that the heuristic methods that do not require all payoff matrix entries performed well for smaller Boolean games, while our ASP based technique is faster when the problem instances have a higher number of agents or action variables.     

On Efficiently Implementing Global Time for Performance Evaluation on Multiprocessor Systems

Because of its flexibility, event tracing is a commonly used technique for performance evaluation of parallel applications. To generate the time-stamps of events, a precise global time reference is needed so that the total event order reflected by a trace is coherent with the partial causal event order inherent to parallel executions. Different statistical methods to estimate global time, which are based on a linear model, have been proposed in the literature. Those methods are appropriate for performance evaluation because they do not perturb the analyzed applications. However, the estimation algorithms must collect some sample data from which global time is computed. The longer the sample collection process, the more precise the global time and the greater the delay before global time is available. This paper familiarizes the reader with statistical global time estimation methods by presenting two methods, which have been introduced in the literature. Then, we show how a good balance between length of sample period and global time precision can be achieved through a detailed experimental analysis of the estimation error observed on samples.     

SOLVING THE SAILING PROBLEM WITH A NEW PRIORITIZED VALUE ITERATION

In this paper we tackle the sailing strategies problem, a stochastic shortest-path Markov decision process. The problem of solving large Markov decision processes accurately and quickly is challenging. Because the computational effort incurred is considerable, current research focuses on finding superior acceleration techniques. For instance, the convergence properties of current solution methods depend, to a great extent, on the order of backup operations. On one hand, algorithms such as topological sorting are able to find good orderings, but their overhead is usually high. On the other hand, shortest path methods, such as Dijkstra's algorithm, which is based on priority queues, have been applied successfully to the solution of deterministic shortest-path Markov decision processes. Here, we propose improved value iteration algorithms based on Dijkstra's algorithm for solving shortest path Markov decision processes. The experimental results on a stochastic shortest-path problem show the feasibility of our approach.     

一种基于高阶统计量的图象平滑去噪法

为了消除或衰减存在于图象上的噪声，同时尽可能地保留图象细节，提出了一种基于高阶统计量的图象平滑去噪法，该方法是根据高阶统计量对高斯噪声不敏感的特性，对一个像素点采用其周围梯度和最小的几个点的灰度平均值来代替其灰度值，以便可以在对噪声进行平滑滤波的同时，最大限度地保留图象的细节信息，仿真结果表明，该方法较好地实现了这一要求，与常用的中值滤波法相比，该方法处理后的图象，其PSNR可提高约1dB以上。     

Quantum-inspired firefly algorithm with particle swarm optimization for discrete optimization problems

The firefly algorithm is a recent meta-heuristic inspired from nature. It is based on swarm intelligence of fireflies and generally used for solving continuous optimization problems. This paper proposes a new algorithm called “Quantum-inspired Firefly Algorithm with Particle Swarm Optimization (QIFAPSO)” that among other things, adapts the firefly approach to solve discrete optimization problems. The proposed algorithm uses the basic concepts of quantum computing such as superposition states of Q-bit and quantum measure to ensure a better control of the solutions diversity. Moreover, we use a discrete representation for fireflies and we propose a variant of the well-known Hamming distance to compute the attractiveness between them. Finally, we combine two strategies that cooperate in exploring the search space: the first one is the move of less bright fireflies towards the brighter ones and the second strategy is the PSO movement in which a firefly moves by taking into account its best position as well as the best position of its neighborhood. Of course, these two strategies of fireflies’ movement are adapted to the quantum representation used in the algorithm for potential solutions. In order to validate our idea and show the efficiency of the proposed algorithm, we have used the multidimensional knapsack problem which is known as an NP-Complete problem and we have conducted various tests of our algorithm on different instances of this problem. The experimental results of our algorithm are competitive and in most cases are better than that of existing methods.     

A probabilistic dynamic technique for the distributed generation of very large state spaces

Conventional methods for state space exploration are limited to the analysis of small systems because they suffer from excessive memory and computational requirements. We have developed a new dynamic probabilistic state exploration algorithm which addresses this problem for general, structurally unrestricted state spaces.

Our method has a low state omission probability and low memory usage that is independent of the length of the state vector. In addition, the algorithm can be easily parallelised. This combination of probability and parallelism enables us to rapidly explore state spaces that are an order of magnitude larger than those obtainable using conventional exhaustive techniques.

We derive a performance model of this new algorithm in order to quantify its benefits in terms of distributed run-time, speedup and efficiency. We implement our technique on a distributed-memory parallel computer and demonstrate results which compare favourably with the performance model. Finally, we discuss suitable choices for the three hash functions upon which our algorithm is based.     

Providing adaptation and guidance for design learning by problem solving: The design planning approach in DomoSim-TPC environment

Experimental learning environments based on simulation usually require monitoring and adaptation to the actions the users carry out. Some systems provide this functionality, but they do so in a way which is static or cannot be applied to problem solving tasks. In response to this problem, we propose a method based on the use of intermediate languages to provide adaptation in design learning scenarios. Although we use some approaches which are familiar from other domains (e.g., programming tutors) they are novel as regards their application to a very different domain and as a result we have incorporated new strategies. The purpose of our proposal is to provide monitoring, guidance and adaptive features for PlanEdit, a tool for the learning of integral automation methods in buildings and housing by design. This tool is part of a collaborative environment, called DomoSim-TPC, which supports distance learning of domotical design. We have carried out an experiment to obtain some data which confirm that our position can be effective for group learning of domotical design, studying the relationship between the quantity of model work carried out and the errors made.     

Wavelet kernel learning

This paper addresses the problem of optimal feature extraction from a wavelet representation. Our work aims at building features by selecting wavelet coefficients resulting from signal or image decomposition on an adapted wavelet basis. For this purpose, we jointly learn in a kernelized large-margin context the wavelet shape as well as the appropriate scale and translation of the wavelets, hence the name “wavelet kernel learning”. This problem is posed as a multiple kernel learning problem, where the number of kernels can be very large. For solving such a problem, we introduce a novel multiple kernel learning algorithm based on active constraints methods. We furthermore propose some variants of this algorithm that can produce approximate solutions more efficiently. Empirical analysis show that our active constraint MKL algorithm achieves state-of-the art efficiency. When used for wavelet kernel learning, our experimental results show that the approaches we propose are competitive with respect to the state-of-the-art on brain–computer interface and Brodatz texture datasets.     

Evolutionary techniques for optimization problems in integrated manufacturing system: State-of-the-art-survey

Integrated manufacturing system (IMS) is a novel manufacturing environment which has been developed for the next generation of manufacturing and processing technologies. It consists of engineering design, process planning, manufacturing, quality management, and storage and retrieval functions. Improving the decision quality in those fields give rise to complex combinatorial optimization problems, unfortunately, most of them fall into the class of NP-hard problems. Find a satisfactory solution in an acceptable time play important roles. Evolutionary techniques (ET) have turned out to be potent methods to solve such kind of optimization problems. How to adapt evolutionary technique to the IMS is very challenging but frustrating. Many efforts have been made in order to give an efficient implementation of ET to optimize the specific problems in IMS.In this paper, we address four crucial issues in IMS, including design, planning, manufacturing, and distribution. Furthermore, some hot topics in these issues are selected to demonstrate the efficiency of ET’s application, such as layout design (LD) problem, flexible job-shop scheduling problem (fJSP), multistage process planning (MPP) problem, and advanced planning and scheduling (APS) problem. First, we formulate a generalized mathematic models for all those problems; several evolutionary algorithms which adapt to the problems have been proposed; some test instances based on the practical problems demonstrate the effectiveness and efficiency of our proposed approach.     

Analyses of unsatisfiability for equational logic programming

The problem of unifying pairs of terms with respect to an equational theory (as well as detecting the unsatisfiability of a system of equations) is, in general, undecidable. In this work, we define a framework based on abstract interpretation for the (static) analysis of the unsatisfiability of equation sets. The main idea behind the method is to abstract the process of semantic unification of equation sets based on narrowing. The method consists of building an abstract narrower for equational theories, and executing the sets of equations to be detected for unsatisfiability in the approximated narrower. As an instance of our framework, we define a new analysis whose accuracy is enhanced by some simple loop-checking technique. This analysis can also be actively used for pruning the search tree of an incremental equational constraint solver, and can be integrated with other methods in the literature. Standard methods are shown to be an instance of our framework. To the best of our knowledge, this is the first framework proposed for approximating equational unification.     

A comprehensive study on the effects of using data mining techniques to predict tie strength

The use of social networks has grown noticeably in recent years and this fact has led to the production of numerous volumes of data. Data that are widely used by users on the social media sites are very large, noisy, unstructured and dynamic. Providing a flexible framework and method to apply in all of these networks can be the perfect solution. The uncertainties arising from the complexity of decisions in recognition of the Tie Strength among people have led researchers to seek effective variables of intimacy among people. Since there are several effective variables which their effectiveness rate are not precisely determined and their relations are nonlinear and complex, using data mining techniques can be considered as one of the practical solutions for this problem. Some types of unsupervised mining methods have been conducted in the field of detecting the type of tie. Data mining could be considered as one of the applicable tools for researchers in exploring the relationships among users.In this paper, the problem of tie strength prediction is modeled as a data mining problem on which different supervised and unsupervised mining methods are applicable. We propose a comprehensive study on the effects of using different classification techniques such as decision trees, Naive Bayes and so on; in addition to some ensemble classification methods such as Bagging and Boosting methods for predicting tie strength of users of a social network. LinkedIn social network is used as a real case study and our experimental results are proposed on its extracted data. Several models, based on basic techniques and ensemble methods are created and their efficiencies are compared based on F-Measure, accuracy, and average executing time. Our experimental results show that, our profile-behavioral based model has much better accuracy in comparison with profile-data based models techniques.     

Portfolio selection using neural networks

In this paper we apply a heuristic method based on artificial neural networks (NN) in order to trace out the efficient frontier associated to the portfolio selection problem. We consider a generalization of the standard Markowitz mean-variance model which includes cardinality and bounding constraints. These constraints ensure the investment in a given number of different assets and limit the amount of capital to be invested in each asset. We present some experimental results obtained with the NN heuristic and we compare them to those obtained with three previous heuristic methods. The portfolio selection problem is an instance from the family of quadratic programming problems when the standard Markowitz mean-variance model is considered. But if this model is generalized to include cardinality and bounding constraints, then the portfolio selection problem becomes a mixed quadratic and integer programming problem. When considering the latter model, there is not any exact algorithm able to solve the portfolio selection problem in an efficient way. The use of heuristic algorithms in this case is imperative. In the past some heuristic methods based mainly on evolutionary algorithms, tabu search and simulated annealing have been developed. The purpose of this paper is to consider a particular neural network (NN) model, the Hopfield network, which has been used to solve some other optimisation problems and apply it here to the portfolio selection problem, comparing the new results to those obtained with previous heuristic algorithms.     

A learning automata based scheduling solution to the dynamic point coverage problem in wireless sensor networks

The dynamic point coverage problem in wireless sensor networks is to detect some moving target points in the area of the network using as few sensor nodes as possible. One way to deal with this problem is to schedule sensor nodes in such a way that a node is activated only at the times a target point is in its sensing region. In this paper we propose SALA, a scheduling algorithm based on learning automata, to deal with the problem of dynamic point coverage. In SALA each node in the network is equipped with a set of learning automata. The learning automata residing in each node try to learn the maximum sleep duration for the node in such a way that the detection rate of target points by the node does not degrade dramatically. This is done using the information obtained about the movement patterns of target points while passing throughout the sensing region of the nodes. We consider two types of target points; events and moving objects. Events are assumed to occur periodically or based on a Poisson distribution and moving objects are assumed to have a static movement path which is repeated periodically with a randomly selected velocity. In order to show the performance of SALA, some experiments have been conducted. The experimental results show that SALA outperforms the existing methods such as LEACH, GAF, PEAS and PW in terms of energy consumption.     

Personalized Concept-Based Clustering of Search Engine Queries

The exponential growth of information on the Web has introduced new challenges for building effective search engines. A major problem of web search is that search queries are usually short and ambiguous, and thus are insufficient for specifying the precise user needs. To alleviate this problem, some search engines suggest terms that are semantically related to the submitted queries so that users can choose from the suggestions the ones that reflect their information needs. In this paper, we introduce an effective approach that captures the user's conceptual preferences in order to provide personalized query suggestions. We achieve this goal with two new strategies. First, we develop online techniques that extract concepts from the web-snippets of the search result returned from a query and use the concepts to identify related queries for that query. Second, we propose a new two-phase personalized agglomerative clustering algorithm that is able to generate personalized query clusters. To the best of the authors' knowledge, no previous work has addressed personalization for query suggestions. To evaluate the effectiveness of our technique, a Google middleware was developed for collecting clickthrough data to conduct experimental evaluation. Experimental results show that our approach has better precision and recall than the existing query clustering methods.