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SDIs offer access to a wealth of distributed data sources through standardised service interfaces. Recently, also geoprocessing capabilities are offered as services in SDIs. Combining data sources and processing services in service chains enable the generation of information that is tailored to the users' needs. In this paper, we present a rule-based description framework and an associated discovery and composition method that helps service developers to create such service chains from existing services. The goal of the description framework is to describe services at a conceptual level rather than closely mirroring specific implementation details. It consists of a simple top-level ontology as well as a domain ontology, which provide the basic vocabulary for creating descriptions of both services and the information the service chain is to produce as a result. The composition method uses these descriptions to discover appropriate services and compose them into a service chain that can produce the required information. The method is illustrated using an example from the domain of risk management. [ABSTRACT FROM AUTHOR]

The objective of this paper was to aggregate research done during several different Small Business Innovative Research (SBIR) grants as they apply to the design of complex environments and Augmented Cognition. This paper provides a high level exploration of the definition of situational awareness (SA), the action loop, an advanced mitigation framework, and a repeatable design methodology that was created to overcome several key mistakes made by UI designers. The discussion is illustrated using a recent user interface metaphor design project that maximizes information flow in a novel F-35 Joint Strike Fighter Cockpit. While testing is not complete on the resulting UI metaphor, initial observations indicate that the results of using these models and processes offer a significant improvement in performance and user acceptance appears to be high. [ABSTRACT FROM AUTHOR]

This paper discusses the use of cognitive models as augmented metacognition on task allocation for tasks requiring visual attention. In the domain of naval warfare, the complex and dynamic nature of the environment makes that one has to deal with a large number of tasks in parallel. Therefore, humans are often supported by software agents that take over part of these tasks. However, a problem is how to determine an appropriate allocation of tasks. Due to the rapidly changing environment, such a work division cannot be fixed beforehand: dynamic task allocation at runtime is needed. Unfortunately, in alarming situations the human does not have the time for this coordination. Therefore system-triggered dynamic task allocation is desirable. The paper discusses the possibilities of such a system for tasks requiring visual attention. [ABSTRACT FROM AUTHOR]

This paper takes an interdisciplinary look at how the electronic record is likely to evolve in the future. From what new sources will data be drawn? Will such data be directly recorded from sensory devices and from one's personal "memex"? Will these data enable a new set of outputs to more fully interconnect patients with their health care system. The paper considers the combined impact of a host of emerging technologies: 1the impact of the networking phenomenon1the impact of adding robust patient access to the EMR1the impact of the growing emergence of PHRs1the impact of emerging technologies on the usability of the EMR1the impact of direct sensory input devices1the impact of bioinformatics and genomics1the impact of the personal memex [ABSTRACT FROM AUTHOR]

Association, or LD (linkage disequilibrium), mapping is an intensely-studied approach to gene mapping (genome-wide or in candidate regions) that is widely hoped to be able to efficiently locate genes influencing both complex and Mendelian traits. The logic underlying association mapping implies that the best possible mapping results would be obtained if the genealogical history of the sampled individuals were explicitly known. Such a history would be in the form of an "ancestral recombination graph (ARG)". But despite the conceptual importance of genealogical histories to association mapping, few practical association mapping methods have explicitly used derived genealogical aspects of ARGs. Two notable exceptions are [35] and [23]. In this paper we develop an association mapping method that explicitly constructs and samples minARGs (ARGs that minimize the number of recombinations). We develop an ARG sampling method that provably samples minARGs uniformly at random, and that is practical for moderate sized datasets. We also develop a different, faster, ARG sampling method that still samples from a well-defined subspace of ARGs, and that is practical for larger sized datasets. We present novel efficient algorithms on extensions of the "phenotype likelihood" problem, a key step in the method in [35]. We also prove that computing the phenotype likelihood for a different natural extension of the penetrance model in [35] is NP-hard, answering a question unresolved in that paper. Finally, we put all of these results into practice, and examine how well the implemented methods perform, compared to the results in [35]. The empirical results show great speed ups, and definite but sometimes small, improvements in mapping accuracy. Speed is particularly important in doing genome-wide scans for causative mutations. [ABSTRACT FROM AUTHOR]

Pawlak introduced approximation spaces in his seminal work on rough sets more than two decades ago. In this paper, we show that approximation spaces are basic structures for knowledge discovery from multi-relational data. The utility of approximation spaces as fundamental objects constructed for concept approximation is emphasized. Examples of basic concepts are given throughout this paper to illustrate how approximation spaces can be beneficially used in many settings. The contribution of this paper is the presentation of an approximation space-based framework for doing research in various forms of knowledge discovery in multi relational data. [ABSTRACT FROM AUTHOR]

This paper extends the basic rough set formalism introduced by Pawlak [1] to a rule-based knowledge representation language, called Rough Datalog, where rough sets are represented by predicates and described by finite sets of rules. The rules allow us to express background knowledge involving rough concepts and to reason in such a knowledge base. The semantics of the new language is based on a four-valued logic, where in addition to the usual values True and False, we also have the values Boundary, representing uncertainty, and Unknown corresponding to the lack of information. The semantics of our language is based on a truth ordering different from the one used in the well-known Belnap logic [2, 3] and we show why Belnap logic does not properly reflect natural intuitions related to our approach. The declarative semantics and operational semantics of the language are described. Finally, the paper outlines a query language for reasoning about rough concepts. [ABSTRACT FROM AUTHOR]

Many monitoring and control computer systems contain a rule-based system as a part of them. Such a rule based system is used to determine which actions should be taken depending on the data collected from sensors. For both embedded and rule-based systems many different approaches have been proposed, but it is hardly possible to find a formalism that can cope with both of them. The paper deals with a problem of including decision tables into colour Petri net models. A few kinds of decision tables are considered and methods of transformation them into coloured Petri nets form called D-nets are presented. Both non-hierarchical and hierarchical D-nets are considered in the paper. [ABSTRACT FROM AUTHOR]

In this paper, the design, simulation, implementation and experiment of rough set processor are described. The experiment result shows that the proposed processor is ten times faster than PC, though the clock frequency is about 70 times lower. [ABSTRACT FROM AUTHOR]

Detection of natural selection at the molecular level is one of the crucial problems in contemporary population genetics. There exists a number of statistical tests designed for it, however, the interpretation of the outcomes is often obscure, because of the existence of factors like population growth, migration and recombination. In his earlier work, the author has proposed the multi-null methodology, and he applied it for four genes implicated in human familial cancer: ATM, RECQL, WRN and BLM. Because of high computational effort required for estimating critical values under nonclassical nulls, mentioned methodology is not appropriate for selection screening. In the current paper, the author presents novel, rough set based methodology, helpful in the interpretation of tests outcomes applied versus only classical nulls. This method does not require long-lasting simulations and, as it is shown in the paper, it gives reliable results. [ABSTRACT FROM AUTHOR]

In this paper a method of creating layers of feed-forward neural network that does not need to be learned is presented. Described approach is based on algorithms used in synthesis of logic circuits. Experimental results presented in the paper prove that this method may significantly decrease the time of learning process, increase generalization ability and decrease a probability of sticking in a local minimum. Further work and goals to achieve are also discussed. [ABSTRACT FROM AUTHOR]

Synonymy has been of high importance in information retrieval and automatic indexing. Recently, in the view of special needs for domain ontology building and maintenance, the problem returns with a higher demand. In the presented paper, we present a novel text mining approach to discovering synonyms or close meaning terms. The offered measures of closeness of terms (or their contexts) are expressed by means of data mining notions; namely, frequent termsets and association rules. The measures can be calculated by using data mining techniques, such as the well known Apriori algorithm. The approach is domain-independent and large-scale. It is, however, restricted to the recognition of parts of speech. In that sense the approach is language dependent, up to the language dependency of the parts of speech tagging process. The experimental results obtained with the approach are presented. [ABSTRACT FROM AUTHOR]

The identification of appropriate text tokens (words or sequences of words representing concepts) is one of the most important tasks of text preprocessing and may have great influence on the final results of text analysis. In our paper, we introduce a new approach to discovering compound nouns, including proper compound nouns. Our approach combines the data mining methods with shallow lexical analysis. We propose a simple pattern language for specifying grammatical patterns to be satisfied by extracted compound nouns. Our method requires annotating the words with part of speech tags, thus to this extent, it is language-dependent. Based on the data mining GSP algorithm, we propose T-GSP as its modification for extracting frequent text patterns, and in particular, frequent word sequences that satisfy given grammatical rules. The obtained sequences are regarded as candidates for compound nouns. The experiments have proven very high quality of the method. [ABSTRACT FROM AUTHOR]

As a result of advances in technology, large amounts of data can be collected and stored automatically. Significant development of the Internet and easier access to it have contributed to collecting large amounts of information about users' characteristics. Along with these changes, concerns about privacy of data have emerged. Several methods of preserving privacy for association rules mining have been proposed in literature: MASK scheme and its optimizations. This paper provides new solutions concerning efficiency for this scheme and considers different methods of distorting data using randomization techniques. Effectiveness of these solutions has been tested and presented in this paper. [ABSTRACT FROM AUTHOR]

Reduction in rough set theory is useful to compact given attributes of large-scale decision tables in data mining. In this paper a new method called grey-rough reduction is proposed for decision tables containing non-interval data and interval data complexly called grey-decision tables. First of all, a grey-rough approximation is introduced after summarized grey numbers, their operations and functions. Two sorts of reduction based on grey-rough sets, a basic approach and advanced approach are proposed with several illustrative examples. Three experiments, compatibility with the classical model, an application of the basic approach to decision-making and influence of the parameter in the advanced approach are shown. The advantages of the proposal are (1) it is compatible with the classical reduction model for non-interval data, (2) it is useful for complex decision tables and (3) it provides a possible reduction of attributes with a parameter by the advanced approach. [ABSTRACT FROM AUTHOR]

Non-standard logics departs from traditional logic mostly in extended views, on one hand syntactically related to logical operators, and on the other hand semantically related to truth values. Typical for these approaches is the remaining traditional view on 'sets and relations' and on terms based on signatures. Thus the cornerstones of the languages remain standard, and so does mostly the view on knowledge representation and reasoning using traditional substitution theories and unification styles. In previous papers we have dealt with particular problems such as generalizing terms and substitution, extending our views on sets and relations, and demonstrated the use of these non-standard language elements in various applications such as for fuzzy logic, generalized convergence spaces, rough sets and Kleene algebras. In this paper we provide an overview and summarized picture of what indeed happens when we drop the requirement for using traditional sets with relations and terms with equational settings. [ABSTRACT FROM AUTHOR]

The present paper is concerned with Rough Set Theory (RST) and Similarity Coverage Model (SCM) of category-based induction. It redefines basic concepts of RST in the light of SCM, and explains how RST may be seen as an elegant formal model of inductive reasoning. Furthermore, following SCM, we enrich RST by the concept of an ontology defined as a subset of the family of all definable sets. The paper also presents a model of inductive reasoning which is driven by recent works on RST and nearness-type structures. We show how approximation spaces can be characterised in terms of non-Archimedean nearness spaces. [ABSTRACT FROM AUTHOR]

The paper discusses mathematical concept of granular model for data and knowledge manipulation. In order to overcome the difficulties caused by extensive data representation a new model based on granular sets and granular relations is put forward. The key idea is that the notion of set may consist of basic elements grouped into bigger granules. A granular set is formed from a universe set and a semi-partition defining granules of its elements. Formal definition of granular sets and some basic algebraic operations on granular sets are introduced in the paper. Further, the concept of granular relation is also defined and some possibilities of application of granular sets and relations to knowledge representation are put forward. [ABSTRACT FROM AUTHOR]

In machine learning and knowledge discovery, rough set theory is a useful tool to be employed as a preprocessing step for dimension reduction. However, for a given system, there may be more than one reduct to be selected. Different reducts will lead to discovered knowledge, which may be concise, precise, general, understandable and practically useful in different levels. It is a crucial issue to select the most suitable features or properties of the objects in a dataset in the machine learning process. In this paper, some external information is added to information system and may be simply regarded as user preference on attributes. Consequently, it will guide the procedure of retrieving reducts, which will give birth to the reduct subject to preference order of attributes. [ABSTRACT FROM AUTHOR]

This paper concerns the development of a new direction in machine learning, called natural induction, which requires from computer-generated knowledge not only to have high predictive accuracy, but also to be in human-oriented forms, such as natural language descriptions and/or graphical representations. Such forms facilitate understanding and acceptance of the learned knowledge, and making mental models that are useful for decision making. An initial version of the AQ21-NI program for natural induction and its several novel features are briefly described. The performance of the program is illustrated by an example of deriving medical diagnostic rules from micro-array data. [ABSTRACT FROM AUTHOR]

The edited k-nearest neighbor consists of the application of the k-nearest neighbor classifier with an edited training set, in order to reduce the classification error rate. This edited training set is a subset of the complete training set in which some of the training patterns are excluded. In recent works, genetic algorithms have been successfully applied to generate edited sets. In this paper we propose three improvements of the edited k-nearest neighbor design using genetic algorithms: the use of a mean square error based objective function, the implementation of a clustered crossover, and a fast smart mutation scheme. Results achieved using the breast cancer database and the diabetes database from the UCI machine learning benchmark repository demonstrate the improvement achieved by the joint use of these three proposals. [ABSTRACT FROM AUTHOR]

Recommender System provides certain products adapted to a target user, from a large number of products. One of the most successful recommendation algorithms is Collaborative Filtering, and it is used in many websites. However, the recommendation result is influenced by community characteristics such as the number of users and bias of users' preference, because the system uses ratings of products by the users at the recommendation. In this paper, we evaluate an effect of community characteristics on recommender system, using multi-agent based simulation. The results show that a certain number of ratings are necessary to effective recommendation based on collaborative filtering. Moreover, the results also indicate that the number of necessary ratings for recommendation depends on the number of users and bias of the users' preference. [ABSTRACT FROM AUTHOR]

The paper deals with power system topology verification, being an important problem in real-time power system modeling. In the paper, the approach with use of multi-agent system is proposed. At the beginning, benefits from utilization of the agent technology are presented. Then, a theoretical background for the power system topology verification is described. Next, multi-agent system for topology verification and functions of particular agents are characterized. At the end, features of the presented approach to power system topology verification are summed up. [ABSTRACT FROM AUTHOR]

Classification of micro-array data has been studied extensively but only a small amount of research work has been done on classification of micro-array data involving more than two classes. This paper proposes a learning strategy that deals with building a multi-target classifier and takes advantage from well known data mining techniques. To address the intrinsic difficulty of selecting features in order to promote the classification accuracy, the paper considers the use of a set of binary classifiers each of ones is devoted to predict a single class of the multi-classification problem. These classifiers are similar to local experts whose knowledge (about the features that are most correlated to each class value) is taken into account by the learning strategy for selecting an optimal set of features. Results of the experiments performed on a publicly available dataset demonstrate the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

The successful application of machine learning techniques to industrial problems places various demands on the collaborators. The system designers must possess appropriate analytical skills and technical expertise, and the management of the industrial or commercial partner must be sufficiently convinced of the potential benefits that they are prepared to invest in money and equipment. Vitally, the collaboration also requires a significant investment in time from the end-users in order to provide training data from which the system can (hopefully) learn. This poses a problem if the developed Machine Learning system is not sufficiently accurate, as the users and management may view their input as wasted effort, and lose faith with the process. In this paper we investigate techniques for making early predictions of the error rate achievable after further interactions. In particular we show how decomposing the error in different components can lead to useful predictors of achievable accuracy, but that this is dependent on the choice of an appropriate sampling methodology. [ABSTRACT FROM AUTHOR]

With vibrant and rapidly growing web, website complexity is constantly increasing, making it more difficult for users to quickly locate the information they are looking for. This, on the other hand, becomes more and more important due to the widespread reliance on the many services available on the Internet nowadays. Web mining techniques have been successfully used for quite some time, for example in search engines like Google, to facilitate retrieval of relevant information. This paper takes a different approach, as we believe that not only search engines can facilitate the task of finding the information one is looking for, but also an optimization of a website's internal structure, which is based on previously recorded user behavior. In this paper, we will present a novel approach to identifying problematic structures in websites. This method compares user behavior, derived via web log mining techniques, to an analysis of the website's link structure obtained by applying the Weighted PageRank algorithm (see [19]). We will then show how to use these intermediate results in order to point out problematic website structures to the website owner. [ABSTRACT FROM AUTHOR]

This paper presents a method for combining classifiers that uses k-nearest localized templates. The localized templates are estimated from a training set using C-means clustering algorithm, and matched to the decision profile of a new incoming sample by a similarity measure. The sample is assigned to the class which is most frequently represented among the k most similar templates. The appropriate value of k is determined according to the characteristics of the given data set. Experimental results on real and artificial data sets show that the proposed method performs better than the conventional fusion methods. [ABSTRACT FROM AUTHOR]

In combination with efficient kernel-base learning machines such as Support Vector Machine (SVM), string kernels have proven to be significantly effective in a wide range of research areas (e.g. bioinformatics, text analysis, voice analysis). Many of the string kernels proposed so far take advantage of simpler kernels such as trivial comparison of characters and/or substrings, and are classified into two classes: the position-aware string kernel which takes advantage of positional information of characters/substrings in their parent strings, and the position-unaware string kernel which does not. Although the positive semidefiniteness of kernels is a critical prerequisite for learning machines to work properly, a little has been known about the positive semidefiniteness of the position-aware string kernel. The present paper is the first paper that presents easily checkable sufficient conditions for the positive semidefiniteness of a certain useful subclass of the position-aware string kernel: the similarity/matching of pairs of characters/substrings is evaluated with weights determined according to shifts (the differences in the positions of characters/substrings). Such string kernels have been studied in the literature but insufficiently. In addition, by presenting a general framework for converting positive semidefinite string kernels into those for richer data structures such as trees and graphs, we generalize our results. [ABSTRACT FROM AUTHOR]

The paper aims to provide for the improvement of software estimation research through a new regression model. The study design of the paper is organized as follows. Evaluation of estimation methods based on historical data sets requires that these data sets be representative for current or future projects. For that reason the data set for software cost estimation model the International Software Benchmarking Standards Group (ISBSG) data set Release 9 is used. The data set records true project values in the real world, and can be used to extract information to predict new projects cost in terms of effort. As estimation method regression models are used. The main contribution of this study is the new cost production function that is used to obtain software cost estimation. The new proposed cost estimation function performance is compared with related work in the literature. In the study same calibration on the production function is made in order to obtain maximum performance. There is some important discussion on how the results can be improved and how they can be applied to other estimation models and datasets. [ABSTRACT FROM AUTHOR]

In this paper we study the application of Bayesian network models to classify multispectral and hyperspectral remote sensing images. Different models of Bayesian networks as: Naive Bayes (NB), Tree Augmented Naive Bayes (TAN) and General Bayesian Networks (GBN), are applied to the classification of hyperspectral data. In addition, several Bayesian multi-net models: TAN multi-net, GBN multi-net and the model developed by Gurwicz and Lerner, TAN-Based Bayesian Class-Matched multi-net (tBCM2) (see [1]) are applied to the classification of multispectral data. A comparison of the results obtained with the different classifiers is done. [ABSTRACT FROM AUTHOR]

When searching information, any user has to face huge cognitive efforts to obtain accurate and relevant results. The search task includes a set of complementary sub-tasks in which the user needs to be necessarily involved. But, the real place of the users is not obvious without an effective knowledge of their context, environment, and so on. So we assume that a better knowledge of the user and of available information should make it possible to implement techniques aimed at adapting the retrieved information contents, as well as the search process itself. This personalization mainly relies on the definition of profiles. Since applications principally manage specific user/information profiles (structure and content), we propose in this paper a generic and a flexible profile model. This latter facilitates the construction and the interoperability of various profiles coming from different applications and/or having different structure/content. This paper presents the way the different resources (user, information...) can be modeled within the information search process and its related tasks. Then, we discuss the usefulness of profiles in such processes/tasks. Finally we present the generic and the flexible profile model we propose. [ABSTRACT FROM AUTHOR]

Many activities on the web are driven by high-level goals of users, such as "plan a trip" or "buy some product". In this paper, we are interested in exploring the role and structure of users' goals in web search. We want to gain insights into how users express goals, and how their goals can be represented in a semi-formal way. This paper presents results from an exploratory study that focused on analyzing selected search sessions from a search engine log. In a detailed example, we demonstrate how goal-oriented search can be represented and understood as a traversal of goal graphs. Finally, we provide some ideas on how to construct large-scale goal graphs in a semi-algorithmic, collaborative way. We conclude with a description of a series of challenges that we consider to be important for future research. [ABSTRACT FROM AUTHOR]

This paper addresses a systemic problem in science: although datasets collected through scientific protocols may be properly stored, the protocol itself is often only recorded on paper or stored electronically as the script developed to implement the protocol. Once the scientist who has implemented the protocol leaves the laboratory, this record may be lost. Collected datasets without a description of the process used to produce them become meaningless; furthermore, the experiment designed to produce the data is not reproducible. In this paper we present the ProtocolDB system that aims at assisting scientists in the process of (1) designing and implementing scientific protocols, (2) storing, querying, and transforming scientific protocols, and (3) reasoning about collected experimental data (data provenance). [ABSTRACT FROM AUTHOR]

A new fuzzy neural network is introduced in this paper which employs self-organization competition neural network to optimize the structure of the fuzzy neural network, and applies a genetic algorithm to adjust the connection weights of the fuzzy neural network so as to get the best structure and weights of the fuzzy neural network. Simulations are made when the pole becomes 2 meters and the random white noise is added in the cart-pendulum system, and control effects of the Adaptive Neural Fuzzy Illation System (ANFIS) and Genetic Algorithm Fuzzy Neural Network (GAFNN) are analyzed. Simulation results indicate that GAFNN controller has greater control performance, high convergence speed, strong robustness and better dynamic characteristics. The effectiveness of the method introduced in this paper is demonstrated by its encouraging study results. [ABSTRACT FROM AUTHOR]

To circumvent the drawbacks in nonlinear controller designing of chemical processes, an adaptive single neuron control scheme is proposed in this paper. A class of nonlinear processes is approximated by a fuzzy neural network-based model. The key of this work is, an adaptive single neuron controller, which mimics PID controller, is considered in the proposed control scheme. Applying this result and Lyapunov stability theory, a novel-updating algorithm to adjust the parameters of the single neuron controller is presented. Simulation results illustrate the effectiveness of the proposed adaptive single neuron control scheme. [ABSTRACT FROM AUTHOR]

In Multi-Agent System, the generation of agent coalition is a key topic. It mainly researches how to generate the task-oriented optimal agent coalition. This paper proposes a novel coalition generation method based on fuzzy set theory. Defines a membership function, by which the membership degree of an agent to a coalition is calculated as a value between 0 and 1, and then the task-oriented coalition can be generated by λ matrix theory. An example is presented to illustrate the validity of the method. [ABSTRACT FROM AUTHOR]

To solve the cost control problem of outsourcing procurement inherent in the complexness and difficulty for mathematical analysis, this paper established a simulation-based cost Optimization framework. Firstly, we adopted Poisson process to simulate multi-supplier outsourcing market environment, the procurement cost expectations objective function and constraints of the model was introduced. Secondly, taking decision strategy as feedback variables, procurement cost optimization model was proposed. Thirdly, several key technologies for algorithm implement were described. Finally, based on the history demand of an aircraft model factory, numerical simulation is carried out to show that this optimization model can effectively reduce the outsourcing procurement total cost and outsourcing parts inventory fluctuation. [ABSTRACT FROM AUTHOR]

In this paper, we propose the integration between Strength Pareto Evolutionary Algorithm 2 (SPEA2) with two types of coevolution concept, Competitive Coevolution (CE) and Cooperative Coevolution (CC), to solve 3 dimensional multiobjective optimization problems. The resulting algorithms are referred to as Strength Pareto Evolutionary Algorithm 2 with Competitive Coevolution (SPEA2-CE) and Strength Pareto Evolutionary Algorithm 2 with Cooperative Coevolution (SPEA2-CC). The main objective of this paper is to compare competitive against cooperative coevolution to ascertain which coevolutionary approach is preferable for multiobjective optimization. The competitive coevolution will be implemented with K-Random Opponents strategy. The performances of SPEA2-CE and SPEA2-CC for solving tri-objective problems using the DTLZ suite of test problems are presented. The results show that the cooperative approach far outperforms the competitive approach when used to augment SPEA2 for tri-objective optimization in terms of all the metrics (generational distance, spacing and coverage). [ABSTRACT FROM AUTHOR]

The paper presents a method for the identification of bilinear system parameters by using an improved Genetic Algorithm. Good results could still be obtained when the system output was influenced by Gaussian noise in the simulation. By comparing with RLS and COR through a simulation experiment to a SISO bilinear system, it is found that the method can get better result than the other two methods. Through a simulation experiment to a MIMO bilinear system, the method can get reasonably good results too. These simulations show that the method is simpler and can get better results than RLS and COR. Through a simulation study to an MIMO bilinear system, good results can still be got. In the last section, the paper describes that a hybrid GA, the combination of Genetic Algorithm and nonlinear Least Square, was developed to identify bilinear system structure and parameters simultaneously. [ABSTRACT FROM AUTHOR]

This paper describes an algorithm for generating a figure in a two-dimensional plane from a qualitative spatial representation of PLCA. In general, it is difficult to generate a figure from qualitative spatial representations, since they contain positional relationships but do not hold quantitative information such as position and size. Therefore, an algorithm is required to determine the coordinates of the objects while preserving the positional relationships. Moreover, it is more desirable that the resulting figure meets a user's requirement. PLCA is a simple symbolic representation consisting of points, lines, circuits and areas. We have already proposed one algorithm for drawing, but the resulting figures are far from a "good" one. In that algorithm, we generate the graph corresponding to a given PLCA expression, decompose it into connected subgraphs, determine the coordinates in a unit circle for each subgraph independently, and finally determine the position and size of each subgraph by locating the circles in appropriate positions. This paper aims at generating a "good" figure for a PLCA expression. We use a genetic algorithm to determine the locations and the sizes of circles in the last step of the algorithm. We have succeeded in producing a figure in which objects are drawn as large as possible, with complex parts larger than others. This problem is considered to be a type of "circle packing," and the method proposed here is applicable to the other problems in which locating objects in a non-convex polygon. [ABSTRACT FROM AUTHOR]

This paper shows how to use a top-level ontology to create robust and logically coherent domain ontology in a way that facilitates computational implementation and interoperability. It uses a domain ontology of ecosystem classification and delineation outlined informally Bailey's paper on ‘Delineation of Ecoregions' as a running example. Baily's (from an ontological perspective) rather imprecise and ambiguous definitions are made more logically rigorous and precise by (a) restating the informal definitions formally using the top-level terms whose semantics was specified rigorously in a logic-based top-level ontology and (b) by enforcing the clear distinction of types of relations as specified at the top-level and specific relations of a given type as they occur in the ecosystem domain. In this way it becomes possible to formally distinguish a number of relations which logical interrelations are important but which have been confused and been taken to be a single relation before. [ABSTRACT FROM AUTHOR]

This paper presents a method of target tracking for a robotic vision system employing reinforcement learning with feedback based on average rough coverage performance values. The application is for a line-crawling inspection robot (ALiCE II, the second revision of Automated Line Crawling Equipment) designed to automate the inspection of hydro electric transmission lines and related equipment. The problem considered in this paper is how to train the vision system to track targets of interest and acquire useful images for further analysis. To train the system, two versions of Watkins' Q-learning were implemented, the classical single-step version and a modified strain using an approximation space-based form of what we term rough feedback. The robot is briefly described along with experimental results for the two forms of the Q-learning control algorithm. The contribution of this article is an introduction to a modified version of Q-learning control with rough feedback to monitor and adjust the learning rate during target tracking. [ABSTRACT FROM AUTHOR]

This paper first presents a brief survey of the existing works on comparing and ranking any two interval numbers and then, on the basis of this, gives the inclusion measure approach to compare any two interval numbers. The monotonic inclusion measure is defined over the strict partial order set proposed by Moore and illustrate that the possibility degrees in the literature are monotonic inclusion measures defined in this paper; Then a series of monotonic inclusion measures are constructed based on t-norms. Finally, we give illustrations by using the monotonic inclusion measures and gain good results. [ABSTRACT FROM AUTHOR]

The schemata theorem, on which the working of Genetic Algorithm (GA) is based in its current form, has a fallacious selection procedure and incomplete crossover operation. In this paper, generalization of the schemata theorem has been provided by correcting and removing these limitations. The analysis shows that similarity growth within GA population is inherent due to its stochastic nature. While the stochastic property helps in GA's convergence. The similarity growth is responsible for stalling and becomes more prevalent for hard optimization problem like protein structure prediction (PSP). While it is very essential that GA should explore the vast and complicated search landscape, in reality, it is often stuck in local minima. This paper shows that, removal of members of population having certain percentage of similarity would keep GA perform better, balancing and maintaining convergence property intact as well as avoids stalling. [ABSTRACT FROM AUTHOR]

The paradigm of pattern discovery based on constraints has been recognized as a core technique in inductive querying: constraints provide to the user a tool to drive the discovery process towards potentially interesting patterns, with the positive side effect of achieving a more efficient computation. So far the research on this paradigm has mainly focussed on the latter aspect: the development of efficient algorithms for the evaluation of constraint-based mining queries. Due to the lack of research on methodological issues, the constraint-based pattern mining framework still suffers from many problems which limit its practical relevance. In our previous work [5], we analyzed such limitations and showed how they flow out from the same source: the fact that in the classical constraint-based mining, a constraint is a rigid boolean function which returns either true or false. To overcome such limitations we introduced the new paradigm of pattern discovery based on Soft Constraints, and instantiated our idea to the fuzzy soft constraints. In this paper we extend the framework to deal with probabilistic and weighted soft constraints: we provide theoretical basis and detailed experimental analysis. We also discuss a straightforward solution to deal with top-k queries. Finally we show how the ideas presented in this paper have been implemented in a real Inductive Database system. [ABSTRACT FROM AUTHOR]

This paper deals with novel approaches for discovering phrases expressing technical capabilities in technical literature (such as patents), intended to support strategic consultants introducing new technologies and their capabilities to their clients. An extracted capability phrase is scored based on its expected business impact, which can also be considered as unexpectedness of the capability in a specified technology field. The proposed capability extraction method and unexpectedness estimation method are implemented in a "Future Technology Projection tool." The tool will be utilized by the consultants to provide lists of capability phrases related to a technology field of interest to the consultants. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new concept, thinning-out, for reducing the number of trials in skill discovery. Thinning-out means to skip over such trials that are unlikely to improve discovering results, in the same way as "pruning" in a search tree. We show that our thinning-out technique significantly reduces the number of trials. In addition, we apply thinning-out to the discovery of good physical motions by legged robots in a simulation environment. By using thinning-out, our virtual robots can discover sophisticated motions that is much different from the initial motion in a reasonable amount of trials. [ABSTRACT FROM AUTHOR]

The two dominant costs in continuous spatial query systems are the wireless communication cost for location update, and the evaluation cost for query processing. Existing works address both of these scalability factors by employing the distributed computation strategy, in which some part of query processing is carried out by mobile objects. In this paper, we make one important assumption about mobile objects' energy; that is for many applications, mobile objects' energy is not limited, as opposed to the battery-powered objects assumed in existing works. Under this new assumption, we re-examine the scalability issues for continuous spatial query systems. Our examination points out that the major bottleneck of these systems is now the wide-area wireless uplink bandwidth, which has not been addressed adequately in the past. We attack the problem by leveraging the local-area wireless communication between mobile objects, leading to our proposal of a hybrid communication architecture to be used in these continuous spatial query systems. The hybrid communication architecture unifies the two communication paradigms, wide-area and local-area wireless networks. We then propose a proof of concept system, called P2MRQ (Peer-to-peer technique for Moving Range Queries), to answer continuous moving range queries over moving objects. While MobiEyes [1], an existing continuous range query system, only utilizes distributed computation, our P2MRQ is able to leverage both distributed computation and local-area wireless communication. Our performance study shows that the required wide-area wireless uplink bandwidth from P2MRQ is consistently less than that of MobiEyes; for all considered cases, P2MRQ requires at most 50% of the wide-area wireless uplink bandwidth as MobiEyes does. [ABSTRACT FROM AUTHOR]

Because of increased stream data, managing stream data has become quite important. This paper describes our data stream management system, which employs an architecture combining a stream processing engine and DBMS. Based on the architecture, the system processes both continuous queries and traditional one-shot queries. Our proposed query language supports not only filtering, join, and projection over data streams, but also continuous persistence requirements for stream data. Users can also specify continuous queries that integrate streaming data and historical data stored in DBMS. Another contribution of this paper is feasibility validation of queries. Processing queries on streams with frequent inputs may cause the system to overflow its capacity. Specifically, the maximum writing rate to DBMS is a significant bottleneck when we try to store stream data into DBMS. Our system detects infeasible queries in advance. [ABSTRACT FROM AUTHOR]

Mining weighted association rules considers the profits of items in a transaction database, such that the association rules about important items can be discovered. However, high profit items may not always be high revenue products, since purchased quantities of items would also influence the revenue for the items. This paper considers both profits and purchased quantities of items to calculate utility for the items. Mininghigh utility quantitative association rules is to discover that when some items are purchased on some quantities, the other items on some quantities are purchased too, which have high utility. In this paper, we propose a data mining algorithm to find high utility itemsets with purchased quantities, from which high utility quantitative association rules also can be generated. Our algorithm needs not generate candidate itemsets and just need to scan the original database twice. The experimental results show that our algorithm is more efficient than the other algorithms which only discovered high utility association rules. [ABSTRACT FROM AUTHOR]