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Learning classifier systems have previously been shown to have some application in single-step tasks. This paper extends work in the area by applying the classifier system to progressively more complex multi-modal test environments, each with typical search space characteristics, convex/non-convex regions of high performance and complex interplay between variables. In particular, two test environments are used to investigate the effects of different degrees of feature sampling, parameter sensitivity, training set size and rule subsumption. Results show that XCSR is able to deduce the characteristics of such problem spaces to a suitable level of accuracy. This paper provides a foundation for the possible use of XCS as an exploratory tool that can provide information from conceptual design spaces enabling a designer to identify the best direction for further investigation as well as a better representation of their design problem through redefinition and reformulation of the design space. [ABSTRACT FROM AUTHOR]

This paper demonstrates that, with minimal modifications, a classifier system can be made to operate just as an ant colony optimizer does for solving the TSP. The paper contains a formal proof of this result, and suggests that the modifications made could be useful in other ways. In effect, the paper suggests that there may be a new role for classifier systems in optimization, inspired by the way that ant colony optimizers have achieved their successes. The paper also suggests that there may be ways suggested by classifier systems to modify ant colony optimization practice. [ABSTRACT FROM AUTHOR]

After two papers comparing ATNoSFERES with XCSM, a Learning Classifier System with internal states, this paper is devoted to a comparison between ATNoSFERES and ACS (an Anticipatory Learning Classifier System). As previously, we focus on the way perceptual aliazing problems encountered in non-Markov environments are solved with both kinds of systems. We shortly present ATNoSFERES, a framework based on an indirect encoding Genetic Algorithm which builds finite-state automata controllers, and we compare it with ACS through two benchmark experiments. The comparison shows that the difference in performance between both system depends on the environment. This raises a discussion of the adequacy of both adaptive mechanisms to particular subclasses of non-Markov problems. Furthermore, since ACS converges much faster than ATNoSFERES, we discuss the need to introduce learning capabilities in our model. As a conclusion, we advocate for the need of more experimental comparisons between different systems in the Learning Classifier System community. [ABSTRACT FROM AUTHOR]

It is a big challenge to guarantee the quality of association rules in some application areas (e.g., in Web information gathering) since duplications and ambiguities of data values (e.g., terms). Rough set based decision tables could be efficient tools for solving this challenge. This paper first illustrates the relationship between decision tables and association mining. It proves that a decision rule is a kind of closed pattern. It also presents an alternative concept of rough association rules to improve the quality of discovered knowledge in this area. The premise of a rough association rule consists of a set of terms (items) and a weight distribution of terms (items). The distinct advantage of rough association rules is that they contain more specific information than normal association rules. This paper also conducts some experiments to compare the proposed method with association rule mining and decision tables; and the experimental results verify that the proposed approach is promising. [ABSTRACT FROM AUTHOR]

Fuzzy integrals have attracted the attention of many researchers as a solution for expressing the interactions between classifiers in multiple-classifier fusion. In a classifier fusion system based on fuzzy integrals, the fuzzy measures will have a major impact on a system's performance. Much work has been carried out by numerous authors on how to determine the fuzzy measures to improve results. Our paper presents some new characteristics of multiple-classifier fusion based on fuzzy integrals. This paper discusses the conditions under which the fusion system must give the incorrect classification and that the fusion system can give the correct classification even if all classifiers have given an incorrect classification. It will be helpful for improving classifier fusion systems and designing classifiers in application. [ABSTRACT FROM AUTHOR]

An artificial neural network was used for sample preprocessing in this paper. Firstly, the data points were classified into three types as follows: the high load type, the medium load type and the low load type. Then, the artificial neural network was adopted to forecast the load type of the predict point. Finally, a support vector machine forecasting model was created on the basis of data points whose load type is the same as the predict point. Comparisons were made between different methods. The results show that the model established in this paper is better than other methods in forecasting accuracy and computing speed. [ABSTRACT FROM AUTHOR]

This paper contributes to the analysis and prediction of deviate intentional behaviour of human operators in Human-Machine Systems using Artificial Neural Networks that take uncertainty into account. Such deviate intentional behaviour is a particular violation, called Barrier Removal. The objective of the paper is to propose a predictive Benefit-Cost-Deficit model that allows a multi-reference, multi-factor and multi-criterion evaluation. Human operator evaluations can be uncertain. The uncertainty of their subjective judgements is therefore integrated into the prediction of the Barrier Removal. The proposed approach is validated on a railway application, and the prediction convergence of the uncertainty-integrating model is demonstrated. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology to generate the personalized answer based on a question situation. To construct the question situation, personal learning characteristics should be obtained by principal component analysis and question type vector is used in the process of semantic analysis. After these analyses, the question situation is constructed based on a harmony network. The answer parameters, including answer depth and answer presentation pattern, are calculated by harmony function. According to these parameters, the personalized answer is matched by the adaptive neuro-fuzzy inference (ANFI). The system architecture of personalized answer generation is proposed in this paper and takes a learner's question to demonstrate. [ABSTRACT FROM AUTHOR]

Many methods based on rough sets to deal with incomplete information system have been proposed in recent years. However, they are only suitable for the nominal datasets. So far only a few methods based on rough sets to deal with incomplete information system with continuous-valued attributes have been proposed. In this paper we propose one generalized model of rough sets to reduce continuous-valued attributes and learn some rules in an incomplete information system. The definition of a relative discernible measure is firstly proposed, which is the underlying concept to redefine the concepts of knowledge reduction such as the reduct and core. We extend a number of underlying concepts of knowledge reduction (such as the reduct and core), and finally propose a heuristic algorithm to generate fuzzy reduct from initial data. The main contribution of this paper is that the underlying relationship between the reduct and core of rough sets is proved to be still correct after our extension. The advantage of the proposed method is that instead of preprocessing continuous data by discretization or fuzzification, we can reduce an incomplete information system with continuous-valued attributes directly based on the generalized model of rough sets, Finally a numerical example is given to show the feasibility of our proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a new definition of string stability with bounded input from the input-to-state view. By viewing the interconnection as a kind of input to a subsystem, it specifies sufficient conditions of string stability for a class of directed circular interconnected nonlinear systems, which is based on the input-to-state stability analysis and singular perturbation theory. The proof is first conducted on a system with two subsystems, and then expanded to finite N subsystems. Furthermore, directed graph is used as an illustrative tool in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes the core theoretical foundations on design of the Compound Zeno Behavior in hierarchical hybrid behaviors, including the definitions of different types of behaviors and some theorems. New hierarchical fuzzy behavior-based control architecture of an autonomous mobile robot using the information extracted from its sensors in unknown environments is presented. After that, the paper focuses on the coordination and fusion of the elementary behaviors, which are achieved by means of fuzzy reasoning scheme. Simulation results illustrate the effective performance of the control architecture. [ABSTRACT FROM AUTHOR]

Internet auction is not only an integral part of Electronic Commerce but is also becoming a promising field for applying autonomous agents and multi-agent system (MAS) technologies. And auction, as an efficient resource allocation method, has an important role in MAS problems and as such is receiving increasing attention from scholars. This paper suggests a protocol (VAMA) and strategies for multi-attribute auction. Some useful properties such as strategy-proof are also proven. This paper also includes an analysis of the strategy of the buyer, and gives a decentralized mechanism for VAMA implementation as well as discussing some questions about VAMA. Finally, this protocol is compared with existing ones, improving on the work of Esther David etc. [ABSTRACT FROM AUTHOR]

As reputation mechanism has been widely accepted and adopted to enhance trust in electronic communities, how to cope with the attack and disturbance problems on reputation mechanism, such as collusion, malicious or unfair rating, becomes a key challenge. This paper extends the normal mechanism, which mainly focuses on the trustworthiness of transactions, to a double-layer reputation mechanism, by distinguishing two type reputations: capability reputation and rating reputation. Based on the double-layer reputations, we present the Rating Reputation Feedback (RRF) mechanism to confront above problems. Basic concepts, key issues, instantiated sample and the effectiveness of RRF mechanism are discussed in the paper. [ABSTRACT FROM AUTHOR]

The Immune System (IS) represents the defence mechanism of higher level organisms to micro organismic threats. It is a genuinely distributed system provided with mechanisms of adaptation to unknown threats by means of the cooperation of heterogenous autonomous entities. Features of the overall systems, such as learning capabilities, possibility to tackle unknown threats in any part of the body, are a consequence of these interactions. This paper describes how a Multi-Agent approach, and more precisely the Situated Cellular Agents (SCA) model, can be suitably applied to represent specific elements and mechanisms of the IS. After a brief description of the composing parts and the internal mechanisms of the IS, the SCA model will be introduced and exploited to represent themThe work presented in this paper has been partially funded by the Italian Ministry of University and Research within the project ‘Cofinanziamento Programmi di Ricerca di Interesse Nazionale'.. [ABSTRACT FROM AUTHOR]

Nowadays, the processing of KANSEI information is very important in intelligent computing field. Particularly, it is very interesting in image retrieval to deal with human's KANSEI. In this paper, we use natural language for the representation of KANSEI, including the image structure of Human's idea, which we can not observe. And then, a KANSEI-Adjective is used as a natural language querying method: In other words, this paper presents the image retrieval based on KANSEI. We propose the background image retrieval based on KAC (KANSEI-Adjective of Color) to represent the sensibility of color. Our method for processing of KANSEI information is the measure of similarity by using the adaptive Lesk algorithm in WordNet. In our experimental results, we are able to retrieve background images with the most appropriate color in term of the query's feeling. Furthermore, the method achieves an average rate of 63% user's satisfaction. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is a heuristic optimization technique showing relationship with Evolutionary Algorithms and strongly based on the concept of swarm. It is used in this paper to face the problem of classification of instances in multiclass databases. Only a few papers exist in literature in which PSO is tested on this problem and there are no papers showing a thorough comparison for it against a wide set of techniques typically used in the field. Therefore in this paper PSO performance is compared on nine typical test databases against those of nine classification techniques widely used for classification purposes. PSO is used to find the optimal positions of class centroids in the database attribute space, via the examples contained in the training set. Performance of a run, instead, is computed as the percentage of instances of testing set which are incorrectly classified by the best individual achieved in the run. Results show the effectiveness of PSO, which turns out to be the best on three out of the nine challenged problems. Keywords: Particle Swarm Optimization, Classification. [ABSTRACT FROM AUTHOR]

The method of speaker normalization has been known as the successful method for improving the speech recognition at speaker independent speech recognition system. This paper propose a new power spectrum warping approach to making improvement of speaker normalization better than a frequency warping. The power spectrum warping uses Mel-frequency cepstral of Mel filter bank in MFCC. Also, this paper proposes the hybrid VTN combined the power spectrum warping and a frequency warping. Experiment of this paper did a comparative analysis about the recognition performance of the SKKU PBW DB applied each the power spectrum is 3.06%, and hybrid VTN is 4.07% word error rate reduction as word recognition performance of baseline system. [ABSTRACT FROM AUTHOR]

This paper focuses on the integration of the (also integrated) declarative paradigms of functional logic and fuzzy logic programming, in order to obtain a richer and much more expressive framework where mathematical functions cohabit with fuzzy logic features. In this sense, this paper must be seen as a first stage in the development of this new research line. Starting with two representative languages from both settings, namely Curry and Likelog, we propose an hybrid dialect where a set of rewriting rules associated to the functional logic dimension of the language, are accompanied with a set of similarity equations between symbols of the same nature and arity, which represents the fuzzy counterpart of the new environment. We directly act inside the kernel of the operational mechanism of the language, thus obtaining a fuzzy variant of needed narrowing which fully exploits the similarities collected in a given program. A key point in the design of this last operational method is that, apart from computing at least the same elements of the crisp case, all similar terms of a given goal are granted to be completely treated too while avoiding the risk of infinite loops associated to the intrinsic (reflexive, symmetric and transitive) properties of similarity relations. Keywords: Fuzzy Logic, Similarity, Functional Logic Programming. [ABSTRACT FROM AUTHOR]

Fuzzy techniques can be applied in several domains of image processing. In this paper we will show how fuzzy set theory is used in establishing measures for image quality evaluation. Objective quality measures or measures of comparison are of great importance in the field of image processing. These measures serve as a tool to evaluate and to compare different algorithms designed to solve problems, such as noise reduction, deblurring, compression ... Consequently these measures serve as a basis on which one algorithm is preferred to another. It is well-known that classical quality measures, such as the MSE (mean square error) or the PSNR (peak signal to noise ratio), do not always correspond to visual observations. Therefore, several researchers are - and have been - looking for new quality measures, better adapted to human perception. In [1] we illustrated how similarity measures, originally introduced to express the degree of comparison between two fuzzy sets, can be used in the construction of neighbourhood-based similarity measures which outperform the MSE in the sense of image quality evaluation because the results of the neighbourhood-based similarity measures coincide better with human perception. In this paper we show how the neighbourhood-based similarity measures can be combined with similarity measures for histogram comparison in order to improve the perceptive behaviour of these similarity measures. [ABSTRACT FROM AUTHOR]

Pyramid computation is a natural paradigm of computation in planning strategies and multi-resolution image analysis. This paper introduces a new paradigm that is based on the concept of soft-hierarchical operators implemented in a pyramid architecture to retrieve global versus local symmetries. The concept of symmetry is mathematically well defined in geometry whenever patterns are crisp images (two levels). Necessity for a soft approach occurs whenever images are multi-levels and the separation between object and background is subjective or not well defined. The paper describes a new pyramid operator to detect symmetries and shows some experiments supporting the approach. This work has been partially supported by the French Ministry of Research and the University Paris XI and the Agenzia Spaziale Italiana. [ABSTRACT FROM AUTHOR]

In this paper, we address the issue of designing optimal fuzzy interfaces, which are fundamental components of fuzzy models. In particular, we prove that for a class of so-called bi-monotonic fuzzy sets, optimal interfaces can be guaranteed if mild conditions hold. The class of bi-monotonic fuzzy sets covers a broad range of fuzzy sets shapes, including convex fuzzy sets, so that the provided theoretical results can be applied to a wide range of fuzzy models. As a practical result, the paper provides a set of guidelines that can be followed for the design of optimal fuzzy interfaces. [ABSTRACT FROM AUTHOR]

U-shaped learning behaviour in cognitive development involves learning, unlearning and relearning. It occurs, for example, in learning irregular verbs. The prior cognitive science literature is occupied with how humans do it, for example, general rules versus tables of exceptions. This paper is mostly concerned with whether U-shaped learning behaviour may be necessary in the abstract mathematical setting of inductive inference, that is, in the computational learning theory following the framework of Gold. All notions considered are learning from text, that is, from positive data. Previous work showed that U-shaped learning behaviour is necessary for behaviourally correct learning but not for syntactically convergent, learning in the limit (= explanatory learning). The present paper establishes the necessity for the whole hierarchy of classes of vacillatory learning where a behaviourally correct learner has to satisfy the additional constraint that it vacillates in the limit between at most k grammars, where k ≥ 1. Non U-shaped vacillatory learning is shown to be restrictive: Every non U-shaped vacillatorily learnable class is already learnable in the limit. Furthermore, if vacillatory learning with the parameter k=2 is possible then non U-shaped behaviourally correct learning is also possible. But for k=3, surprisingly, there is a class witnessing that this implication fails. [ABSTRACT FROM AUTHOR]

This paper deals with an unusual phenomenon where most machine learning algorithms yield good performance on the training set but systematically worse than random performance on the test set. This has been observed so far for some natural data sets and demonstrated for some synthetic data sets when the classification rule is learned from a small set of training samples drawn from some high dimensional space. The initial analysis presented in this paper shows that anti-learning is a property of data sets and is quite distinct from over-fitting of a training data. Moreover, the analysis leads to a specification of some machine learning procedures which can overcome anti-learning and generate machines able to classify training and test data consistently. [ABSTRACT FROM AUTHOR]

This paper presents a conceptual framework for the development of Knowledge Management (KM) systems to support experts in complex design activities. Designing a complex object is not simple, since it is concerned not only with problem solving issues, but also with the needs for capturing and managing the core knowledge involved in it. A complex object is typically made of a huge number of parts that are put together according to a first set of constraints (i.e. the dynamic knowledge or functional knowledge), dependable on the functional properties it must satisfy, and a second set of rules, dependable on what the expert thinks abut the problem and how he/she would represent it (i.e. the static knowledge or ontological knowledge). The paper introduces how to unify both types of knowledge, exploiting the SA-Nets formalism to capture the dynamic knowledge and an ontological approach to represent static knowledge. [ABSTRACT FROM AUTHOR]

When dealing with real systems, it is unrealistic to suppose that observations can be totally ordered according to their emission dates. The partially ordered observations and the system are thus both represented as finite-state machines (or automata) and the diagnosis formally defined as the synchronized composition of the model with the observations. The problem we deal with in this paper is that, taking into account partially ordered observations rather than sequential ones, it becomes difficult to consider the observations one after the other and to incrementally compute the global diagnosis. In this paper, we rely on a slicing of the observation automata and propose to compute diagnosis slices (for each observation slice) before combining them to get the global diagnosis. In order to reach this objective, we introduce the concept of automata chain and define the computation of the diagnosis using this chain, first in a modular way and then, more efficiently, in an incremental way. These results are then extended to the case where observations are sliced according to temporal windows. This study is done in an off-line context. It is a first and necessary step before considering the on-line context which is discussed in the conclusion. [ABSTRACT FROM AUTHOR]

Building profiles for processes and for interactive users is a important task in intrusion detection. This paper presents the results obtained with a Hierarchical Hidden Markov Model. The algorithm discovers typical "motives" of a process behavior, and correlates them into a hierarchical model. Motives can be interleaved with possibly long gaps where no regular behavior is detectable. We assume that motives could be affected by noise,modeled as insertion, deletion and substitution errors. In this paper the learning algorithm is briefly recalled and then it is experimentally evaluated on three profiling case studies. The first case is built on a suite of artificial traces automatically generated by a set of given HHMMs. The challenge for the algorithm is to reconstruct the original model from the traces. It will be shown that the algorithm is able to learn HHMMs very similar to the original ones, in presence of noise and distractors. The second and third case studies refer to the problem of constructing a discriminant model for a user typing on a keyboard. [ABSTRACT FROM AUTHOR]

In previous work, we have defined a two-step procedure called Solve-and-Robustify for generating flexible, partial order schedules. This partitioned problem solving approach — first find a viable solution and then generalize it to enhance robustness properties — has been shown to provide an effective basis for generating flexible, robust schedules while simultaneously achieving good quality with respect to optimization objectives. This paper extends prior analysis of this paradigm, by investigating the effects of using different start solutions as a baseline to generate partial order schedules. Two approaches are compared: the first constructs partial order schedules from a single fixed-time schedule, obtained by first performing an extended makespan optimization search phase; the second considers the search for fixed-time schedules and flexible schedules in a more integrated fashion, and constructs partial order schedules from a number of different fixed-time starting solutions. The paper experimentally shows how the characteristics of the fixed-time solutions may lower the robustness of the final partial order schedules and discusses the reasons for such behavior. [ABSTRACT FROM AUTHOR]

In machine vision, template matching is key component and used usefully in various tasks such as pick and place, mark identification, and alignment. In this paper, we propose fast template matching algorithm using edge projection. Proposed algorithm reduces the search problem from 2D into 1D using edge projection within the 2D template area. By this, it could effectively reduce the computational burden. Also, it gives comparable discriminating power compared to template matching using intensity. In this paper, rotation and translation search is implemented to cope with typical machine vision application where the height between camera and target object is fixed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for the recognition of passports based on a FCM-based RBF network. First, for the extraction of individual codes for recognizing, this paper targets code sequence blocks including individual codes by applying Sobel masking, horizontal smearing and a contour tracking algorithm on the passport image. As the last step, individual codes are recovered and extracted from the binarized areas by applying CDM masking and vertical smearing. This paper also proposes a FCM-based RBF network that adapts the FCM algorithm for the middle layer. This network is applied to the recognition of individual codes. The results of the experiments for performance evaluation on the real passport images showed that the proposed method has the better performance compared with other approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a partitional approach for estimating null value (1) Firstly, we utilize stepwise regression to select the important attributes from the database. (2) Secondly, we use a partitional approach to build the data category. The data partitioned by the first two important attributes. (3) Thirdly, we apply the clustering method to cluster output data. (4) Fourthly, Calculate the degree of influential between the attributes. There are two ways to calculate the degree of influential. One is correlation coefficient and the other is regression coefficients. (5) To verify our method, this paper utilizes a practical human resource database in Taiwan, and Mean of Absolute Error Rate (MAER) as evaluation criterion to compare with other methods; it is shown that our proposed method proves better than other methods for estimating null values in relational database systems. [ABSTRACT FROM AUTHOR]

In this paper we improve on the supervised classification method developed in Kornienko et al. (2002) by the introduction of Principal Components Analysis to the inference process. We also extend the classifier from dealing with binomial (two-class) problems only to multinomial (multi-class) problems. The application to which the MML criterion has been applied in this paper is the classification of objects via a linear hyperplane, where the objects are able to come from any multi-class distribution. The inclusion of Principal Component Analysis to the original inference scheme reduces the bias present in the classifier's search technique. Such improvements lead to a method which, when compared against three commercial Support Vector Machine (SVM) classifiers on Binary data, was found to be as good as the most successful SVM tested. Furthermore, the new scheme is able to classify objects of a multiclass distribution with just one hyperplane, whereas SVMs require several hyperplanes. Keywords: Machine Learning, Knowledge discovery and data mining. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the conditioning issue in D-S evidence theory in multi-dimensional space. Based on Dempster conditioning, Bayes' rule and product rule, which are similar to that in probability theory, are presented in this paper. Two kinds of conditional independence called weak conditional independence and strong conditional independence are introduced, which can significantly simplify the inference process when evidence theory is applied to practical application. [ABSTRACT FROM AUTHOR]

This paper provides a framework of using news articles and economic data to model the exchange rate changes between Euro and US dollars. Many studies have conducted on the approach of regressing exchange rate movement using numerical data such as macroeconomic indicators. However, this approach is effective in studying the long term trend of the movement but not so accurate in short to middle term behaviour. Recent research suggests that the market daily movement is the result of the market reaction to the daily news. In this paper, it is proposed to use text mining methods to incorporate the daily economic news as well as economic and political events into the prediction model. While this type of news is not included in most of existing models due to its non-quantitative nature, it has important influence in short to middle terms of market behaviour. It is expected that this approach will lead to an exchange rate model with improved accuracy. [ABSTRACT FROM AUTHOR]

This paper introduces a classification system that exploits the content information as well as citation structure for scientific paper classification. The system first applies a content-based statistical classification method which is similar to general text classification. We investigate several classification methods including K-nearest neighbours, nearest centroid, naive Bayes and decision trees. Among those methods, the K-nearest neighbours is found to outperform others while the rest perform comparably. Using phrases in addition to words and a good feature selection strategy such as information gain can improve system accuracy and reduce training time in comparison with using words only. To combine citation links for classification, the system proposes an iterative method to update the labellings of classified instances using citation links. Our results show that, combining contents and citations significantly improves the system performance. [ABSTRACT FROM AUTHOR]

In content-based image retrieval (CBIR), the user usually poses several labelled images and then the system attempts to retrieve all the images relevant to the target concept defined by these labelled images. It may be helpful if the system can return relevant images where the regions of interest (ROI) are explicitly located. In this paper, this task is accomplished with the help of multi-instance learning techniques. In detail, this paper proposes the CkNN-ROI algorithm, which regards each image as a bag comprising many instances and picks from positive bag the instance that has great chance to meet the target concept to help locate ROI. Experiments show that the proposed algorithm can efficiently locate ROI in CBIR process. [ABSTRACT FROM AUTHOR]

Minimal change is a fundamental principle for modelling system dynamics. In this paper, we study the issue of minimal change for Computational Tree Logic (CTL) model update. We first consider five primitive updates which capture the basic update operations in the CTL model. Based on these primitive updates, we then define the minimal change criteria for CTL model update and develop formal algorithms that embed the underlying minimal change principle. We also present the well known microwave oven scenario to demonstrate our update algorithms. Our work presented in this paper can be viewed as the first formalization towards an integration of model checking and model updating for system modification. [ABSTRACT FROM AUTHOR]

In the paper, there will be presented the new way of application of structural methods of pattern recognition, in the form of picture languages, for creation of knowledge representation for medical pattern analysis and understanding. Specialist medical databases storing data in visual form constitute a large group among multimedia databases; those patterns originate from numerous diagnostic examinations of practically all organs of the human body. One of the main problems in the fast accessing and interpretation of such visual information is the way of transformation of these visual data into a form enabling intelligent analysis and understanding of medical meaning of these patterns. The basic problems leading to achieve this goal is the method of representation and indexing the important objects constituting the data contents. In the paper we describe some examples presenting ways of applying picture languages techniques in the creation of intelligent cognitive knowledge representation for selected class of medical images. [ABSTRACT FROM AUTHOR]

Hard/fuzzy c-means and agglomerative hierarchical method are famous and useful clustering algorithms. The former are algorithms using "global information of data", the latter is one using "local information of data". One of main results in this paper is proposal for new clustering algorithm (Dynamic Clustering; DC), which has the advantage of them. In DC, clusters are updated by some model introduced in advance. That is, the clusters are moved according to the model, and merged. Here, merging two clusters means that two clusters contact each other. The model is called option of DC. In this paper, two options of DC are proposed, i.e., interaction power model(IP) and unit weight model(UW). Moreover, the way to determine sampling time, which is one of the parameters in DC, is discussed for precise merging of data and shortening of calculation time. Furthermore, through numerical examples, it is shown that DC gives good classification for the data which is difficult to be classified by the former clustering algorithms. [ABSTRACT FROM AUTHOR]

Cryptography is the ancient science of encrypting messages so that only the sender and receiver can recover them. To achieve this goal, an agreed key between the sender and receiver is required. In asymmetric cryptosytems, so far, only a few ID-based key agreement protocols are one-way and most of them can only provide authentication for only one entity to the other. In this paper, two ID-based one-way key agreement protocols are proposed. The advantage of our protocols is that the authentication of the sender and receiver is established at the same time although the key distribution is only one pass. In addition, transmitted data size in our schemes is very small and the parameter for key agreement consists of just one. In this paper, an additional security attribute for key agreement protocols is defined and the rigorous security of our protocols is estimated. The performance evaluation is also analyzed by comparing our schemes with the previous schemes. Keywords: Cryptography, authentication, ID-based, key agreement, pairing [ABSTRACT FROM AUTHOR]

This paper describes human centered systems in which human evaluation, decision, interpretation, feelings, subjectivity play important roles. One of useful approaches to human centered systems thinking is soft computing techniques that permit uncertainty of information since a real world is complex and it is difficult and wasteful to deal with its information strictly. This paper also introduces some examples of human centered systems, a music composition support system, a story generation system from pictures, a facial caricature drawing system and a pedestrian navigation system. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce pairing over elliptic curve and apply it to ID based key agreement schemes and privacy preserving key agreement schemes. The key words here are ID based scheme, pairing over elliptic curve and privacy reserving scheme. Elliptic curve is one of the popular research topics in cryptography because it has some useful features. One of them is a bilinear function such as Tate pairing. Bilinear function has excellent properties suitable to applications in cryptography. Some schemes are proposed using the bilinear function so far and ID based cryptosystems are the one of the most important applications. ID is any information like name, mail address, phone number, any public information can be used. Another recent problem is privacy. Careless use of ID may leak privacy of the person corresponding to the ID. In this paper we introduce pairing over elliptic curve and propose ID based key agreement schemes and privacy preserving key agreement scheme. Keywords: ID based cryptosystem, pairing, privacy, key agreement scheme [ABSTRACT FROM AUTHOR]

Input-Output data modelling using fully tuned radial basis function networks(RBF) for a tilt rotor aircraft experimental platform is presented in this paper. The behavior of the four degree-of-freedom platform exemplifies a high order nonlinear system with significant cross-coupling between longitudinal, latitudinal directional motions, and tilt rotor nacelles rolling movement. This paper develops a practical algorithm coupled with model validity tests for identifying nonlinear autoregressive moving average model with exogenous inputs(NARMAX). It is proved that input-output data modelling using fully tuned algorithm is suitable for modelling novelty configuration air vehicles. A procedure for system modelling was proposed in the beginning of this paper and the subsequent sections provided detailed descriptions on how each stage in the procedure could be realized. The effectiveness of this modelling procedure is demonstrated through the tilt rotor aircraft platform. The estimated model can be utilized for nonlinear flight simulation and control studies. [ABSTRACT FROM AUTHOR]

Robots can be greatly benefited from the integration of artificial senses in order to adapt to changing worlds. To be effective in complex unstructured environments robots have to perceive the environment and adapt accordingly. In this paper, it is introduced a biology inspired multimodal architecture called M2ARTMAP which is based on the biological model of sensorial perception and has been designed to be a more versatile alternative to data fusion techniques and non-modular neural architectures. Besides the computational overload compared to FuzzyARTMAP, M2ARTMAP reaches similar performance. This paper reports the results found in simulated environments and also the observed results during assembly operations using an industrial robot provided with vision and force sensing capabilities. [ABSTRACT FROM AUTHOR]

Industrial manufacturing cells involving fixtureless environments require more efficient methods to achieve assembly tasks. This paper introduces an approach for Robotic Fixtureless Assembly (RFA). The approach is based on the Fuzzy ARTMAP neural network and learning strategies to acquire the skill from scratch without knowledge about the assembly system. The vision system provides the necessary information to accomplish the assembly task such as pose, orientation and type of component. Different ad-hoc input vectors were used as input to the assembly and the vision systems through several experiments which are described. The paper also describes the task knowledge acquisition and the followed strategies to solve the problem of automating the peg-in-hole assembly using 2D images. The approach is validated through experimental work using an industrial robot. [ABSTRACT FROM AUTHOR]

In this paper we describe the work done to build a new voice based on diphone concatenation in the Spanish spoken in Mexico. This voice is compatible with the Text to Speech Synthesis System Festival. In the development of each module of the system the own features of Spanish were taken into account. In this work we hope to enhance the naturalness of the synthesized voice by including a prosodic model. The prosodic factors taken into consideration by the model are: phrasing, accentuation, duration and F0 contour. Duration and F0 prediction models were trained from natural speech corpora. We found the best prediction models by testing several machine learning methods and two different corpora. The paper describes the building, and training process as well as the results and their respective interpretation. [ABSTRACT FROM AUTHOR]

This paper presents a Grey-Markov forecasting model for forecasting the electric power requirement in China. This method takes into account the general trend series and random fluctuations about this trend. It has the merits of both simplicity of application and high forecasting precision. This paper is based on historical data of the electric power requirement in China, and forecasts and analyzes the electric power requirement in China by the Grey-Markov forecasting model. The forecasting precisions of Grey-Markov forecasting model from 2002 to 2004 are 99.42%, 98.05% and 97.56%, and those of GM(1,1) grey forecasting model are 98.53%, 94.02% and 88.48%. It shows that the Grey-Markov forecasting models have higher precision than GM(1,1) grey forecasting model. The results provides scientific basis for the planned development of the electric power supply in China. [ABSTRACT FROM AUTHOR]

In this paper we proposed an approach to maintain large case library, which based on the idea that a large case library can be transformed to a compact one by using a set of case-specific weights. A linear programming technique is being used to obtain case-specific weights. By learning such local weights knowledge, many of redundant or similar cases can be removed from the original case library or stored in a secondary case library. This approach is useful for case library with a large number of redundant or similar cases and the retrieval efficiency is a real concern of the user. This method of maintaining case library from scratch, as proposed in this paper, consists of two main steps. First, a linear programming technique for learning case-specific weights is used to evaluate the importance of different features for each case. Second, a case selection strategy based on the concepts of case coverage and reachability is carried out to select representative cases. Furthermore, a case retrieval strategy of the compact case library we built is discussed. The effectiveness of the approach is demonstrated experimentally by using two sets of testing data, and the results are promising. [ABSTRACT FROM AUTHOR]

Based on the idea that the cut sets of fuzzy sets are indeed crisp, but facilitate a normative theory for formalizing fuzzy set theory, this paper introduces cut sets of the fuzzy concepts and fuzzy roles as atomic concepts and atomic roles to build , a new fuzzy extension of . This paper gives the definition of syntax, semantics and knowledge base of and discusses the comparison among and other fuzzy extensions of . In addition, this paper defines the acyclic TBox form of , presents sound and complete algorithms for reasoning tasks w.r.t acyclic TBox, and proves the complexity of them is PSPACE-complete. [ABSTRACT FROM AUTHOR]

It is shown in this paper a way of representing deductive systems using the situation calculus. The situation calculus is a family of first order languages with induction that allows the specification of evolving worlds and reasoning about them and has found a number of applications in AI. A method for the representation of formulae and of proofs is presented in which the induction axiom on states is used to represent structural induction on formulae and proofs. This paper's formalizations are relevant for the purpose of meta reasoning and of automated or manual deduction in the context of situation calculus specifications. An example proof is given for the fact that no deductive system is complete for arbitrary situation calculus specifications (an expectable result). [ABSTRACT FROM AUTHOR]

In this paper, we extend Moss and Parikh's bi-modal language for knowledge and effort by an additional modality describing improvement. Like the source language, the new one too has a natural interpretaion in spatial contexts. The result of this is that concepts like the comparison of topologies can be captured within the framework of modal logic now. The main technical issue of the paper is a completeness theorem for the tri-modal system arising from the new language. Keywords: Reasoning about knowledge, modal logic and topology, spatial reasoning, completeness. [ABSTRACT FROM AUTHOR]