bluuebunny/crossref_metadata_2025 huggingface dataset:
Hybrid Module of the IACVIRTUAL Project
Authors: Brasil Lourdes M. ,   Master’s Program in Knowledge and Information Technology Management/Biomedical Engineering Course, Catholic University of Brasília, Campus II, SGAN 916, Módulo B, Sala A 206 Asa Norte, Brasília DF 70790-160, Brazil, Rojas Jean C. C. , Azevedo Fernando M. de , Almeida Carlos W. D. de ,   Computer Science Course, Catholic University of Brasília, Campus II, SGAN 916, Módulo B, Sala A 206, Asa Norte, Brasília DF 70790-160, Brazil,   Biomedical Engineering Institute, Federal University of Santa Catarina, Trindade, Florianópolis, SC 88040-900, Brazil,   Technology Center, Department of Electronics and Systems, Federal University of Pernambuco, DES/CTG-UFPE, Rua Acadêmico Hélio Ramos s/n, sala 416, Cidade Universitária, Recife, PE 50740-530, Brazil
Published: April 2016

This work represents the hybrid module of the IACVIRTUAL meta-environment. In this context we will basically approach the Hybrid Expert System (HES), which is composed by the Neural Networks Based Expert System (NNES) and by the Rule-Based Expert System (RBES). The HES is destined to support the decision of a clinical-surgical team, in the area of cardiology, in the definition of a therapeutic conduct in patients with coronary heart disease. The implementation process starts with the Knowledge Acquisition (KA), which comes from the analysis of a series of clinical parameters, which are used as input data for the NNES. This way, knowledge acquired during elicitation is converted in fuzzy rules. Through these rules, elicitated knowledge is mapped in AND/OR graphs, which then represent the starting structure of the NNES. Learning and optimization of the RBES are made through the Genetic-Backpropagation Based Learning Algorithm (GENBACK). This algorithm can, during the learning process, modify the weight of the connections, as well as the network structure. Knowledge abstracted from the RBES, being already refined, as well as trained and tested, is used to form the Knowledge Base of the RBES.
Intelligent Human-Computer Communication of Engineers at Extended Companies
Authors: Horváth László ,   Institute of Intelligent Engineering Systems, John von Neumann Faculty of Informatics, Budapest Tech Polytechnical Institution, Bécsi u. 96/b, Budapest, H-1034, Hungary, Rudas Imre J.
Published: April 2016

During the past decades, prevailing method of engineering changed from manual drawing to computer modeling. By now, capabilities of modeling and analysis in virtual engineering systems give better results and more quickly than expensive physical prototypes. The next step in advancement of engineering is development of modeling systems for utilization of intelligent computing. The authors propose in this paper a methodology for intelligent communication and change management for engineering modeling. They were motivated by increasing of importance of change management because of continuous product development and high number of custom tailored product variants. Configuration and open architecture features of engineering modeling, portal management, and product data management (PDM) systems are available for implementation of the proposed modeling in the form of an extension to complex industrial engineering system. In this paper, the authors first give a general schema of advanced engineering activities and define role and place of the proposed modeling. Following this, modeling by behavior, adaptivity and design intent descriptions are explained as an integrated method to enhance intelligence of shape centered models. Next computer assisted activity of an active engineer, and procedure of change processing at decision on change information are detailed. Finally, flexible and controlled definition of characteristics of modeled engineering objects and behaviors are emphasized and discussed.
Two-Dimensional Copulas as Important Binary Aggregation Operators
Authors: Pap Endre ,   Department of Mathematics and Informatics, Faculty of Natural Sciences and Mathematics in Novi Sad, Trg Dositeja Obradovića 4, 21000 Novi Sad, Serbia and Montenegro, Takács Marta ,   Budapest Tech, John von Neumann Faculty of Informatics, H-1034 Budapest, Bécsi út 96.b, Hungary
Published: April 2016

We introduce 2-copulas (copulas, shortly) and recent related research results. We present invariant copulas and their application in the theory of aggregation operators. Copulas are transformed by increasing bijections at the unit interval and discuss copula attractors. We also present results on the approximation of associative copulas by strict and nilpotent triangular norms.
Geometric Identification and Control of Nonlinear Dynamic Systems Based on Floating Basis Vector Representation
Authors: Tar József K. ,   Institute of Intelligent Engineering Systems, John von Neumann Faculty of Informatics, Budapest Tech, H-1034 Budapest, Bécsi út 96/b., Hungary, Rudas Imre J. , Rontó Miklós ,   Faculty of Science, Eötvös Loránd University, H-1117 Budapest, Pázmány Péter sétány 1/A., Hungary
Published: April 2016

In this paper a simple adaptive controller is outlined that creates only temporal and situation-dependent system model. It may be a plausible alternative of the more sophisticated soft computing approaches that aim the identification of permanent and complete models. The temporal model can be built up and maintained step-by-step on the basis of slow elimination of fading information by the use of simple updating rules consisting of finite algebraic steps of lucid geometric interpretation. It may be used for filling in the “lookup tables” or rule bases of the more sophisticated representations experimentally. The method applies simple elimination of the the casual algebraic singularities the occurrence of which cannot be evaded in the practice. The operation of the method is illustrated by the control of a 2 Degrees Of Freedom dynamic system as a typical paradigm via simulation.
Generalizing Possibility-Based Fuzzy Relational Models
Authors: Nakata Michinori ,   Department of Management Science, Faculty of Management and Information Science, Josai International University, 1 Gumyo, Togane, Chiba 283-8555, Japan
Published: April 2016

The generalized possibility-based fuzzy relational model we propose frees possibility-based fuzzy relational models from the semantic ambiguity and the indistinguishability of membership attribute values. We demonstrate extended relational algebra in this data model. To prevent the semantic ambiguity, a membership attribute is attached to every attribute. This clarifies where each membership attribute value comes from. What each membership attribute value means depends on the property of that attribute. To prevent the indistinguishability of membership attribute values, the value is expressed in a possibility distribution in interval [0,1]. This clarifies what effects the imprecise data value allowed for an attribute has on the membership attribute value. No semantic ambiguity and no indistinguishability of membership attribute values therefore exists in the generalized possibility-based fuzzy relational model.
Reduction of Attribute Values for Kansei Representation
Authors: Muto Yuji ,   Division of Computer Science, Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo 060-0814, Japan, Kudo Mineichi , Murai Tetsuya
Published: April 2016

In this paper, we discuss attribute-value reduction for Kansei analysis using information granulation. In the traditional “reduction” sense, the goal is to find the smallest number of attributes enabling us to discern each tuple or each decision class. Once we focus on the number of attribute values, i.e., the size and/or resolution of each attribute domain, another criterion is needed. We must ask ourselves which is better: 1) discerning them with a single attribute described in detail, or 2) discerning them with a few attributes described roughly. Our study answers this question. If we evaluate this difference in the light of understandability or of Kansei representation, we may prefer working with a few attributes described roughly because they yield simpler descriptions. To do this, we propose a criterion and an algorithm to find near-optimal solutions for the criterion, detailing results for databases in the UCI Machine Learning Repository.
An Application of Discernibility Functions to Generating Minimal Rules in Non-Deterministic Information Systems
Authors: Sakai Hiroshi ,   Department of Mathematics and Computer Aided Science, Faculty of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu 804-8550, Japan, Nakata Michinori ,   Faculty of Management and Information Science, Josai International University, Gumyo, Togane, Chiba 283-8555, Japan
Published: April 2016

Minimal rule generation in Non-deterministic Information Systems (NISs), which follows rough sets based rule generation in Deterministic Information Systems (DISs), is presented. According to certain rules and possible rules in NISs, minimal certain rules and minimal possible rules are defined. Discernibility functions are also introduced into NISs for generating minimal certain rules. Like minimal rule generation in DISs, the condition part of a minimal certain rule is given as a solution of an introduced discernibility function. As for generating minimal possible rules, there may be lots of discernibility functions to be solved. So, an algorithm based on an order of attributes is proposed. A tool, which generates minimal certain rules and minimal possible rules, has also been implemented.
QoS Constrains Model: Establishing QoS Association for Ontology-Based Web Service Selection Mechanism
Authors: Taher Laila ,   Information Technology Department, Roads & Transport Authority, Dubai, P.O.Box 118899, United Arab Emirates, Basha Rawshan , Khatib Hazem El ,   Department of Computer Science, University of Sharjah, Sharjah, P.O.Box 27272, United Arab Emirates,   College of Information Technology, UAE University, Al Ain, P.O.Box 17555, United Arab Emirates
Published: April 2016

Quality of Service (QoS) is an important criterion for Web service selection. The work in this paper is based on QoS Information & Computation framework (QoS-IC) [1]. QoS-IC framework supports QoS-based service selection in Service Oriented Architecture (SOA), namely Web services. In this paper, we extend the functionality of QoS-IC framework with QoS-Constraints Model. QoS-Constraints establish an association relationship between different QoS properties and are used to govern the QoS-based service selection in the underlying algorithm. We have enhanced the algorithm to accommodate QoS-Constraints and discussed an experimental evaluation based on an implementation prototype that shows how QoS-Constraints introduce efficiency to the QoS-based service selection mechanism and save valuable time for consumers.
Efficient Merging for Heterogeneous Domain Ontologies Based on WordNet
Authors: Kong Hyunjang ,   Department of Computer Science & Engineering, Chosun University, #375 Seosuk-dong, Dong-gu, Gwangju 501-759, South Korea, Hwang Myunggwon , Kim Pankoo
Published: April 2016

As semantic web study progresses, domain ontologies built by engineers are based on writer’s academic background and research interests, preventing a uniform or standardized ontology using standard ontology building tools and ontology languages and restricting interoperability. Current ontology building tools focus on creating, editing and inferencing ontology efficiently but do not offer a way for merging heterogeneous domain ontologies. This paper presents a way for merging heterogeneous ontologies efficiently and correctly.
Minimization of XML Tree Pattern Queries in the Presence of Integrity Constraints
Authors: Chen Yangjun ,   Department of Applied Computer Science, University of Winnipeg, 515 Portage Avenue, Winnipeg, Manitoba R3B 2E9, Canada, Che Dunren ,   Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, USA
Published: April 2016

In this paper, we provide a polynomial-time tree pattern query minimization algorithm whose efficiency stems from two key observations: (i) Inherent redundant “components” usually exist inside the rudimentary query provided by the user. (ii) Irredundant nodes may become redundant when constraints such as co-occurrence and required child/descendant are given. We show the result that the algorithm obtained by first augmenting the input tree pattern using the constraints, and then applying minimization, always finds the unique minimal equivalent to the original query. We complement our analytical results with an experimental study that shows the effectiveness of our tree pattern minimization techniques.
Computational Models of Language Within Context and Context-Sensitive Language Understanding
Authors: Ito Noriko ,   Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe, Kyoto 610-0394, Japan, Sugimoto Toru , Takahashi Yusuke , Iwashita Shino , Sugeno Michio ,   Brain Science Institute, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan,   Justsystem Corporation, Aoyama bldg. 1-2-3 Kita-Aoyama, Minato-ku, Tokyo 107-8640, Japan,   Kyushu University, 4-9-1 Shiobaru, Minami-ku, Fukuoka 815-8540, Japan
Published: April 2016

We propose two computational models - one of a language within context based on systemic functional linguistic theory and one of context-sensitive language understanding. The model of a language within context called the Semiotic Base characterizes contextual, semantic, lexicogrammatical, and graphological aspects of input texts. The understanding process is divided into shallow and deep analyses. Shallow analysis consists of morphological and dependency analyses and word concept and case relation assignment, mainly by existing natural language processing tools and machine-readable dictionaries. Results are used to detect the contextual configuration of input text in contextual analysis. This is followed by deep analyses of lexicogrammar, semantics, and concepts, conducted by referencing a subset of resources related to the detected context. Our proposed models have been implemented in Java and verified by integrating them into such applications as dialog-based question-and-answer (Q&A).
New Similarity Measure Between Two Fuzzy Sets
Authors: Rezaei Hassan ,   Department of Computer Science, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki-shi 214-8571, Japan, Emoto Masashi , Mukaidono Masao
Published: April 2016

We propose a new similarity measure between two fuzzy sets based on their relative sigma count and extend it to define two other measures, one a similarity measure between elements in fuzzy sets and the second a similarity measure between fuzzy sets in which all elements in the universe of discourse are weighted. We compare our proposal to several previous measures proposed in [1-6].
Alternate Genetic Network Programming with Association Rules Acquisition Mechanisms Between Attribute Families
Authors: Shimada Kaoru ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka 808-0135, Japan, Hirasawa Kotaro , Hu Jinglu
Published: April 2016

A method of association rule mining with chi-squared test using Alternate Genetic Network Programming (aGNP) is proposed. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as genes. aGNP is an extended GNP in terms of including two kinds of node function sets. The proposed system can extract important association rules whose antecedent and consequent are composed of the attributes of each family defined by users. Rule extraction is done without identifying frequent itemsets used in Apriori-like methods. The method can be applied to rule extraction from dense database, and can extract dependent pairs of the sets of attributes in the database. Extracted rules are stored in a pool all together through generations and reflected in genetic operators as acquired information. In this paper, we describe the algorithm capable of finding the important association rules and present some experimental results.
Applying Fuzzy Logic and Neural Network to Rheumatism Treatment in Oriental Medicine
Authors: Thang Cao ,   Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan, Cooper Eric W. , Hoshino Yukinobu , Kamei Katsuari ,   College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan,   Dept. of Electronic and Photonic System Engineering, Kochi University of Technology, 185 Miyanoguchi, Tosayamada, Kami, Kochi 782-8502, Japan
Published: April 2016

In this paper, we present an application of soft computing into a decision support system RETS: Rheumatic Evaluation and Treatment System in Oriental Medicine (OM). Inputs of the system are severities of observed symptoms on patients and outputs are a diagnosis of rheumatic states, its explanations and herbal prescriptions. First, an outline of the proposed decision support system is described after considering rheumatic diagnoses and prescriptions by OM doctors. Next, diagnosis by fuzzy inference and prescription by neural networks are described. By fuzzy inference, RETS diagnoses the most appropriate rheumatic state in which the patient appears to be infected, then it gives a prescription written in suitable herbs with reasonable amounts based on neural networks. Training data for the neural networks is collected from experienced OM physicians and OM text books. Finally, we describe evaluations and restrictions of RETS.
A Regularization Approach to Fuzzy Clustering with Nonlinear Membership Weights
Authors: Honda Katsuhiro ,   Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Nakaku, Sakai, Osaka 599-8531, Japan, Ichihashi Hidetomo
Published: April 2016

Fuzzyc-means (FCM) is the fuzzy version ofc-means clustering, in which memberships are fuzzified by introducing an additional parameter into the linear objective function of the weighted sum of distances between datapoints and cluster centers. Regularization of hardc-means clustering is another approach to fuzzification, in which regularization terms such as entropy and quadratic terms have been adopted. We generalized the fuzzification concept and propose a new approach to fuzzy clustering in which linear weights of hardc-means clustering are replaced by nonlinear ones through regularization. Numerical experiments demonstrated that the proposed algorithm has the characteristic features of the standard FCM algorithm and of regularization approaches. One of the proposed nonlinear weights makes it possible to both to attract data to clusters and to repulse different clusters. This feature derives different types of fuzzy classification functions in both probabilistic and possibilistic models.
Landscape Image Retrieval with Query by Sketch and Icon
Authors: Hayashi Takahiro ,   The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan, Ishikawa Atsushi , Onai Rikio
Published: April 2016

This paper reports a new method for retrieving landscape images using a sketch and icons as a query. Based on the proposal, first, a user sketches lines expressing contours of landscape elements such as mountains and forests and attaches icons expressing landscape elements to the sketch. Second, whether individual images in a database match with the layout expressed by the sketch and icons is judged with principal component analysis and pattern recognition. From experimental results, we have confirmed that the proportion of the correct images ranked within top 10 of retrieval results is 80% in an average.
An Incremental Neural Network for Online Supervised Learning and Topology Learning
Authors: Kamiya Youki ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, R2-52, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan, Furao Shen , Hasegawa Osamu ,   Imaging Science and Engineering Lab., Tokyo Institute of Technology,   PRESTO, Japan Science and Technology Agency (JST)
Published: April 2016

A new self-organizing incremental network is designed for online supervised learning. During learning of the network, an adaptive similarity threshold is used to judge if new nodes are needed when online training data are introduced into the system. Nodes caused by noise are deleted to decrease the misclassification. The proposed network, which is robust to noisy training data, suits the following tasks: (1) online or even life-long supervised learning; (2) incremental learning, i.e., learning new information without destroying old learned information; (3) learning without any predefined optimal condition; (4) representing the topology structure of inputting online data; and (5) learning the number of nodes needed to represent every class. Experiments of artificial data and high-dimension real-world data show that the proposed method can achieve classification with a high recognition ratio, high speed, and low memory.
Prior’s Three-Valued Modal Logic Q and its Possible Applications
Authors: Akama Seiki ,   Dept. of Information Science, Teikyo Heisei University, 2289 Uruido, Ichihara, Chiba 290-0193, Japan, Nagata Yasunori ,   Dept. of Elect’l & Electronics Eng., University of the Ryukyus, 1 Senbaru, Nishihara, Okinawa 903-0213, Japan
Published: April 2016

Prior proposed a three-valued modal logicQas a “correct” modal logic from his philosophical motivations. Unfortunately, Prior’sQand many-valued modal logic have been neglected in the tradition of many-valued and modal logic. In this paper, we introduce a version of three-valued Kripke semantics forQ, which aims to establish Prior’s ideas based on possible worlds. We investigate formal properties ofQand prove the completeness theorem ofQ. We also compare our approach with others and suggest possible applications.
Image Thresholding Computation Using Atanassov’s Intuitionistic Fuzzy Sets
Authors: Bustince H. ,   Departamento de Automática y Computación, Universidad Pública de Navarra, Campus de Arrosadía, s/n, 31006 Pamplona, Navarra, Spain, Barrenechea E. , Pagola M. , Orduna R.
Published: April 2016

In this paper, a new thresholding technique using Atanassov’s intuitionistic fuzzy sets (A-IFSs) and restricted dissimilarity functions is introduced. In recent years, various thresholding techniques ([18, 24]) based on fuzzy set theory have been introduced to overcome the problem of non-uniform illumination and inherent image vagueness. In this paper we analyze this task and propose a new method for handling the grayness ambiguity and vagueness during the process of threshold selection.
New Similarity Measures of Intuitionistic Fuzzy Sets
Authors: Rezaei Hassan ,   Department of Computer Science, Meiji University, 1-1-1 Higashi-Mita, Tama-Ku, Kawasaki-shi 214-8571, Japan, Mukaidono Masao
Published: April 2016

In this review of existing similarity measures of intuitionistic fuzzy sets (IFSs), we apply some numerical examples showing that not all existing similarity measures are effective and reasonable in some cases. We propose properties based on intuition and the human thinking process that should be satisfied by all dissimilarity and similarity measures, together with new similarity measures of IFSs that overcome drawbacks of existing measures. In examples, we compare the proposed similarity measures to existing measures and present two propositions on the relationship between similarity and dissimilarity measures of IFSs. We also propose that a suitable combination of similarity measures may be a similarity measure.
An Approach in Designing Hierarchy of Fuzzy Behaviors for Mobile Robot Navigation
Authors: Ngo Long Thanh ,   Center of Simulation Technology, Le Quy Don Technical University, 100-Hoang Quoc Viet Rd., Cau Giay Dist., Hanoi, Vietnam, Pham Long The , Nguyen Phuong Hoang , Hirota Kaoru ,   Center of Health Information Technology, Ministry of Health, 3-Phuong Mai St., Dong Da Dist., Hanoi, Vietnam,   Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Modori-ku, Yokohama 226-8502, Japan
Published: April 2016

We propose an approach to the design hierarchical behaviors for mobile robot navigation in which robot perceives information about the environment from sensor then computes fuzzy output for individual behavior. Each behavior involves a fuzzy controller with the same output. The behavior hierarchy combines commands from fuzzy behavior output and defuzzifies it to archive crisp values for controlling the direction in which robot moves. Simulation results and statistics demonstrate the feasibility of our proposal.
Toward Natural Communication: Human-Robot Gestural Interaction Using Pointing
Authors: Sato Eri ,   Tokyo Metropolitan Institute of Technology, Yamaguchi Lab., 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Nakajima Aika , Nakazato Jun , Yamaguchi Toru ,   Tokyo Metropolitan University, Yamaguchi Lab., 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan
Published: April 2016

We are studying human and robot interaction based on interpersonal communication, focusing on pointing. Pointing, while useful in communicating with others, is highly context-dependent, making it difficult for robots to interpret accurately. We conducted three experiments on robot behavior, creating basic motion using a virtual robot because using a real robot requires much time. We then had the virtual robot interact with two real robots having different degrees of freedom and ranges of movement.
Abstract Image Generation Based on Local Similarity Pattern
Authors: Takama Yasufumi ,   Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Shigemori Keisuke
Published: April 2016

The method for generating abstract images from a set of images is proposed. The method selects a representative image from a given set of images, in which the common features in terms of the composition are highlighted with image processing techniques. Common features are extracted based on Local Similarity Pattern (LSP), which has been originally proposed for image retrieval. The selection of representative images is performed based on the difference between the color histogram calculated from a set of regions, of which color features are common, and that calculated from the remaining regions. The experimental results show the performance of the proposed method, in terms of its effectiveness for image classification, as well as the accuracy of selecting representative images. The concept of abstract images is expected to be useful for developing a directory service for searching images on the Web.
Cross-Resolution Image Similarity Modeling
Authors: Jović Mladen ,   Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Hatakeyama Yutaka , Hirota Kaoru
Published: April 2016

A cross-resolution image similarity model employing probabilistic interpretations of the similarity values turned into fuzzy values combined with suitable merge functions is presented. Masking the negative particularities of individual region-based image similarity models, five region-based image similarity models are used at the same time when calculating the overall image similarity. By employing aggregation operators, capturing of a variety of conjunctive, disjunctive, and other non-linear combinations of similarity criteria is allowed. Empirical evaluation of the proposed model on four test databases, containing 4,444 images in 150 semantic categories was carried out. The results obtained from the evaluation revealed that cross-resolution image similarity modeling results in optimal retrieval performance. Compared to two well-known image retrieval systems, SIMPLicity and WBIIS, the proposed model brings an increase of 1.7% and 22% respectively in average retrieval precision. The experimental evaluation presented may thus be helpful and suggest possible further improvements can be achieved along the same line of research directions in various computer vision tasks.
Classification of Online Game Players Using Action Transition Probability and Kullback Leibler Entropy
Authors: Thawonmas Ruck ,   Intelligent Computer Entertainment Laboratory, Graduate School of Science and Engineering, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan, Ho Ji-Young ,   SAMSUNG ELECTRONICS CO.,LTD.
Published: April 2016

Online game players are more satisfied with contents tailored to their preferences. Player classification is necessary for determining which classes players belong to. In this paper, we propose a new player classification approach using action transition probability and Kullback Leibler entropy. In experiments with two online game simulators, Zereal and Simac, our approach performed better than an existing approach based on action frequency and comparably to another existing approach using the Hidden Markov Model (HMM). Our approach takes into account both the frequency and order of player action. While HMM performance depends on its structure and initial parameters, our approach requires no parameter settings.
Adaptation and Self-Adaptation Mechanisms in Genetic Network Programming for Mining Association Rules
Authors: Taboada Karla ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Gonzales Eloy , Shimada Kaoru , Mabu Shingo , Hirasawa Kotaro , Hu Jinglu
Published: April 2016

In this paper we propose a method of association rule mining using Genetic Network Programming (GNP) with adaptive and self-adaptive mechanisms of genetic operators in order to improve the performance of association rule extraction systems. GNP is one of the evolutionary methods, whose directed graphs are evolved to find a solution as individuals. Adaptation behavior in GNP is related to adjust the setting of control parameters such as the proportion of crossover and mutation. The aim is not only to find suitable adjustments but to do it efficiently. Regarding to self-adaptation, the algorithm controls the setting of these parameters themselves – embedding them into an individual’s genome and evolving them, and it usually changes the structure of the evolution which is typically static. Specifically, self-adaptation of crossover and mutation operators in GNP aiming to change the rate of them by evolution is studied in this paper. Our method based on GNP can measure the significance of the association via the chi-squared test and obtain a sufficient number of important association rules. Extracted association rules are stored in a pool all together through generations and reflected in three genetic operators as acquired information.
Selected Papers from TAAI2005
Authors: Lin Wen-Yang ,   National University of Kaohsiung, Taiwan, Hong Tzung-Pei , Chen Shyi-Ming ,   National Taiwan University of Science and Technology, Taiwan
Published: April 2016

The annual Conference on Artificial Intelligence and Applications (TAAI), held in Taiwan and hosted by the Taiwanese Association of Artificial Intelligence and other academic institution, has expanded to an international forum following the success of its previous nine conferences. It hopes to bring together researchers and scholars in all areas of artificial intelligence from leading institutes and industries worldwide to exchange ideas on these hi-tech fields. Over 200 papers and 6 special issues consisting of 30 invited papers were presented, making TAAI 2005 the greatest in the TAAI series. This special issue presents 11 papers from this prestigious event, each of which has received outstanding recommendations from the reviewers and has been revised by authors for this publication. These papers detail a variety of applications of different artificial intelligent technologies, ranging from nonlinear programming, data classification, dynamic knowledge acquisition, and complex time-series prediction to liver disease classification and automatic Chinese text summarization. Some also deal with theoretical aspect of intelligent technologies, including genetic algorithms, neural networks, support vector machines, ant-colony algorithms, and pseudo-Boolean functions. This issue thus concisely summarizes recent advances in artificial intelligence and its applications. We are sure that you will find it a valuable resource in the AI research community, and hope that it will inspire our readers in the development of new applications and advanced intelligent systems. As guest editors of this special issue, we would like to express our sincere gratitude to all of the authors for contributing their work to this special issue and to thank the referees for their untiring efforts to complete reviews within a limited time. Thanks also go to Mr. Kazuki Ohmori of Fuji Technology Press for his kind assistance in the publication of this special issue. Last but not least, we thank JACIII Editor-in-Chief Kaoru Hirota for the enthusiastic hospitality with which JACIII has supported this special issue.
Capturing Evolutional Knowledge Using Time Interval Tracing
Authors: Lin Shun-Chieh ,   Department of Computer Science, National Chiao Tung University, 1001, Ta-hsueh Rd., Hsinchu, Taiwan, Teng Chia-Wen , Tseng Shian-Shyong ,   Department of Info. Sci. & App., Asia University, 500, Liu-Feng Rd., Wufeng, Taichung, Taiwan
Published: April 2016

Knowledge acquisition is a critical bottleneck in building a knowledge-based system. Much research and many tools have been developed to acquire domain knowledge with embedded rules that may be ignored in constructing the initial prototype. Due to different backgrounds and dynamic knowledge changing over time, domain knowledge constructed at one time may be degraded at any time thereafter. Here, we propose knowledge acquisition, called enhanced embedded meaning capturing under uncertainty deciding (enhanced EMCUD), which constructs a domain ontology and traces information over time to efficiently update time-related domain knowledge based on the current environment. We enrich the knowledge base and ease the construction of domain knowledge that changes with times and the environment.
Dynamically Adjusting Migration Rates for Multi-Population Genetic Algorithms
Authors: Hong Tzung-Pei ,   Dept. of Computer Sci. and Info. Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan, Lin Wen-Yang , Liu Shu-Min , Lin Jiann-Horng ,   Dept. of Information Management, I-Shou University, Kaohsiung 840, Taiwan
Published: April 2016

In this paper, the issue of adapting migration parameters for MGAs is investigated. We examine, in particular, the effect of adapting the migration rates on the performance and solution quality of MGAs. Thereby, we propose an adaptive scheme to adjust the appropriate migration rates for MGAs. If the individuals from a neighboring sub-population can greatly improve the solution quality of a current population, then the migration from the neighbor has a positive effect. In this case, the migration rate from the neighbor should be increased; otherwise, it should be decreased. According to the principle, an adaptive multi-population genetic algorithm which can adjust the migration rates is proposed. Experiments on the 0/1 knapsack problem are conducted to show the effectiveness of our approach. The results of our work have illustrated the effectiveness of self-adaptation for MGAs and paved the way for this unexplored area.
Automatic Extraction of Key Sentences via Word Sense Identification for Chinese Text Summarization
Authors: Kuo Yau-Hwang ,   CREDIT, Department of Computer Science and Information Engineering, National Cheng Kung University, No.1, Ta-Hsueh Rd., Tainan, Taiwan, Huang Hsun-Hui
Published: April 2016

In this paper, a novel method of key sentences extraction is proposed for automatic Chinese text summarization. Key-senses/sense-patterns discovery and key sentences extraction are its two main components. Since there is no Chinese lexical database like WordNet available to the authors, a compromise is to word-segment, POS-tag a target Chinese text and translate all the nouns/verbs into English for sense disambiguation using WordNet. The characteristic of the proposed method is that each sentence is represented by senses and the key senses in each sentence form a fuzzy transaction. Each entry of the fuzzy transaction is the maximum similarity degree of the corresponding key sense with each of the senses in the sentence. A prototype of this automatic Chinese text summarization scheme is constructed and an intrinsic method with the information-retrieval criteria is used for measuring the summary quality. The results of applying the prototype to datasets with manually-generated summaries are shown.
Improving Ant Colony Optimization Algorithms for Solving Traveling Salesman Problems
Authors: Hung Kuo-Sheng ,   Dept. of Electrical Eng., National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Rd. Taipei, 106, Taiwan, Su Shun-Feng , Lee Zne-Jung ,   Dept. of Electrical Eng., National Taipei University of Technology, No. 1, Sec. 3, Chung-Hsiao E. Rd., Taipei, 106, Taiwan,   Dept. of Information Management, Huafan University, Taipei, Taiwan
Published: April 2016

Ant colony optimization (ACO) has been successfully applied to solve combinatorial optimization problems, but it still has some drawbacks such as stagnation behavior, long computational time, and premature convergence. These drawbacks will be more evident when the problem size increases. In this paper, we reported the analysis of using a lower pheromone trail bound and a dynamic updating rule for the heuristic parameters based on entropy to improve the efficiency of ACO in solving Traveling Salesman Problems (TSPs). TSPs are NP-hard problem. Even though the problem itself is simple, when the number of city is large, the search space will become extremely large and it becomes very difficult to find the optimal solution in a short time. From our experiments, it can be found that the proposed algorithm indeed has superior search performance over traditional ACO algorithms do.
Color Restoration Algorithm Based on Color Instance Under Low Illumination
Authors: Hatakeyama Yutaka ,   Center of Medical Information Science, Medical School, Kochi University, Kohasu, Oko-cho, Nankoku city, Kochi 783-8505, Japan, Mitsuta Akimichi , Hirota Kaoru ,   Customer Solutions Development Co.,Ltd., 3-2-1 Sakado, Takatsu-ku, Kawasaki-city, Kanagawa 213-0012, Japan,   Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
Published: April 2016

Color restoration algorithm using color instance-based reasoning is proposed. The proposed system does not employ a physical model but color instance. The color instance, Color Change Vector, is defined based on distribution of color values for 175 color scheme cards taken under low and standard illuminations. Modification of CCV is proposed based on color value and linguistic expression. The given image is restored by adding the appropriate color instances for a given color value. A prototype system for color restoration is constructed. An experiment is done with dynamic images of an outdoor walking person, to evaluate the performance of the proposed system in terms of color-difference and processing time. The proposed method presents a foundation for identifying a person captured by a practical security system using a low cost CCD camera.
Expanding Transformation: A Basis for Correctness Verification of Rewriting Rules
Authors: Nantajeewarawat Ekawit ,   Computer Science Program, Sirindhorn International Institute of Technology, Thammasat University, Rangsit Campus, P.O. Box 22, Thammasat-Rangsit Post Office, Pathumthani 12121, Thailand, Akama Kiyoshi , Koike Hidekatsu ,   Division of Large-Scale Computational Systems, Information Initiative Center, Hokkaido University, Kita 11 Nishi 5, Kita-ku, Sapporo, Hokkaido 060-0811, Japan,   Faculty of Social Information, Sapporo Gakuin University, 11 Bunkyodai, Ebetsu, Hokkaido 060-8555, Japan
Published: April 2016

Unfolding transformation has long been used for computation and program transformation both in functional programming and logic programming paradigms. In this paper, we clarify that an unfolding step can be regarded as the composition of two simpler operations, i.e., expanding transformation and unification, and show that expanding transformation, rather than unfolding transformation, is a suitable basis for verifying the correctness of rewriting rules by pattern manipulation, which in turn provides a basis for systematically generating rewriting rules from a given problem description. We verify the correctness of expanding transformation and demonstrate the correctness of a basic class of rewriting rules, called general rewriting rules, based on expanding transformation. Applying expanding transformation and its correctness result, we demonstrate correctness verification of a larger class of rewriting rules, called expanding-based rewriting rules, by transformation of clause patterns.
Selected Papers from SCIS & ISIS 2006 – No.1
Authors: Kubota Naoyuki ,   Associate Professor,
Published: April 2016

SCIS & ISIS is a biennial international joint conference in the field of soft computing and intelligent systems, including branches of researches from fuzzy systems, neural networks, evolutionary computation, multi-agent systems, artificial intelligence or robotics. SCIS & ISIS 2006 falls on the 3rd International Conference on Soft Computing and Intelligent Systems (SCIS) and the 7th International Symposium on Advanced Intelligent Systems (ISIS) held at Tokyo Institute of Technology, in Tokyo, Japan, on September 20-24, 2006. In this conference, 464 original papers were accepted for presentation and the number of attendees was 526. After preliminary selection and review made by the session chairs and the International Program Committees of SCIS & ISIS 2006, we have selected more than 50 papers to be published in extended form in the Special Issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics. The accepted papers are published as the special issues in Vol.11, No.6, 7, and 8 in 2007. This current issue presents 23 papers and covers most of the topics of the conference including fuzzy theories, self-organizing maps, and the optimization of neural networks. The learning and search methods in computational intelligence and real-world applications to image processing, robotics and manufacturing systems are highlighted in this current issue. I would like to thank all the authors and reviewers for their contribution to make this special issue possible. I am also grateful to Prof. Toshio Fukuda, Nagoya University and Prof. Kaoru Hirota, Tokyo Institute of Technology, Editors-in-chief, for inviting me to serve as Guest Editor of this Journal.
A Minimal Neural Network Ensemble Construction Method: A Constructive Approach
Authors: Akhand M. A. H. ,   Department of Human and Artificial Intelligence Systems, Graduate School of Engineering, University of Fukui, Murase Kazuyuki ,   Research and Education Program for Life Science, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan
Published: April 2016

This paper presents a neural network ensemble (NNE) construction method for classification problems. The proposed method automatically determines a minimal NNE architecture and thus called the Minimal Neural Network Ensemble Construction (MNNEC) method. To determine minimal architecture, it starts with a single neural network (NN) with a minimal number of hidden units. During training process, it adds additional NN(s) with cumulative number(s) of hidden units. In conventional methods, in contrast, the number of NNs for NNE and the number of hidden nodes for each NN should be predetermined. At the time of NN addition in MNNEC, the added NN specializes in the previously unsolved portion of the input space. Finally all the NNs are trained simultaneously to improve the generalization ability. Therefore, for easy problems when multiple NNs are not required and a single NN is sufficient, the MNNEC can generate a single NN with a minimal number of hidden units. The MNNEC has been tested extensively on several benchmark problems of machine learning and NNs. The results exhibit that the MNNEC is able to construct NNEs of much smaller size than conventional methods.
Self-Organizing Fusion Neural Networks
Authors: Wang Jung-Hua ,   Electrical Engineering Department, National Taiwan Ocean University, 2 Peining Rd. Keelung, Taiwan, Tseng Chun-Shun , Shen Sih-Yin , Jheng Ya-Yun
Published: April 2016

This paper presents a self-organizing fusion neural network (SOFNN) effective in performing fast clustering and segmentation. Based on a counteracting learning scheme, SOFNN employs two parameters that together control the training in a counteracting manner to obviate problems of over-segmentation and under-segmentation. In particular, a simultaneous region-based updating strategy is adopted to facilitate an interesting fusion effect useful for identifying regions comprising an object in a self-organizing way. To achieve reliable merging, a dynamic merging criterion based on both intra-regional and inter-regional local statistics is used. Such extension in adjacency not only helps achieve more accurate segmentation results, but also improves input noise tolerance. Through iterating the three phases of simultaneous updating, self-organizing fusion, and extended merging, the training process converges without manual intervention, thereby conveniently obviating the need of pre-specifying the terminating number of objects. Unlike existing methods that sequentially merge regions, all regions in SOFNN can be processed in parallel fashion, thus providing great potentiality for a fully parallel hardware implementation.
Self-Organizing Map with Generating and Moving Neurons in Visible Space
Authors: Tachibana Kanta ,   Dept. of Computational Science and Engineering, Graduate School of Engineering, Nagoya University, Furou-cho, Chikusa, Nagoya 464-8603, Japan, Furuhashi Takeshi
Published: April 2016

Kohonen’s Self-Organizing feature Map (SOM) is used to obtain topology-preserving mapping from high-dimensional feature space to visible space of two or fewer dimensions. The SOM algorithm uses a fixed structure of neurons in visible space and learns a dataset by updating reference points in feature space. The mapping result depends on mapping parameters fixed, which are the number and visible positions of neurons, and parameters of learning, which are the learning rate, total iteration, and the setting of neighboring radii. To obtain a satisfactory result, the user usually must try many combinations of parameters. It is wasteful, however, to set up every possible combination of parameters and to repeatedly run the algorithm from the beginning because the computation cost for learning is large, especially for a large-scale dataset. These problems arise due to the fixing of two types of mapping parameters, i.e., the number and visible positions of neurons. The high computation cost is mainly in the calculation of distances from each sample to all reference points. At the beginning of learning, reference points should be adjusted globally to preserve the topology well because they are initially set far from optimal positions in feature space, e.g. randomly. Such many reference points subdivides feature space into unnecessarily fine Voronoi regions. To avoid this computational waste, it is natural to start learning with a small number of neurons and increase the number of neurons during learning. We propose a new SOM method that varies the number and visible positions of neurons, and thus is applicable also to visible torus and sphere spaces. We apply our proposal to spherical visible space. We use central Voronoi tessellation to move visible positions for two reasons: to tessellate visible space evenly for easy visualization and to level the number of neighboring neurons and better preserve topology. We demonstrate the effect of generating neurons to reduce computation cost and of moving visible positions in visualization and topology preservation.