bluuebunny/crossref_metadata_2025 huggingface dataset:
Relative Relaxation and Weighted Information Loss to Simplify and Stabilize Feature Detection
Authors: Kamimura Ryotaro ,   IT Education Center, Tokai University, Kanagawa, Japan
Published: April 2016

In this paper, we propose new information-theoretic methods to stabilize feature detection. We have introduced information-theoretic methods to realize competitive learning. It turned out that mutual information maximization corresponds to a process of competition among neurons. This means that mutual information can be effective in describing competitive processes. Thus, by using this mutual information, we have introduced information loss to interpret internal representations. By relaxing competitive units by some components such as units and connection weights, a neural network’s information is decreased. If the information loss is sufficiently large, the components play important roles. However, with the information loss, there have been some problems, such as the instability of final representations. This means that final outputs are significantly dependent upon chosen parameters. To stabilize final representations, we introduce two computational methods, that is, relative relaxation and weighted information loss. The relative relaxation is introduced because mutual information is dependent upon the Gaussian width. Thus, we can relax competitive units or softly delete some components, relative only to a predetermined base state. In addition, we introduce weighted information loss to take into account information on related components. We applied the methods to the well-known Iris problem and a problem regarding the extinction of animals and plants. In the Iris problem, experimental results confirmed that final representations were significantly stable if we appropriately chose the parameter for the base state. On the other hand, in the extinction problem, weighted information losses showed better performance, where final outputs were significantly more stable than those by the other methods.
Text-Style Conversion of Speech Transcript into Web Document for Lecture Archive
Authors: Ito Masashi ,   Graduate School of Information Science, Nagoya University, Ohno Tomohiro , Matsubara Shigeki ,   Graduate School of International Development, Nagoya University,   Information Technology Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
Published: April 2016

It is very significant to the knowledge society to accumulate spoken documents on the web. However, because of the high redundancy of spontaneous speech, the faithfully transcribed text is not readable on an Internet browser, and therefore not suitable as a web document. This paper proposes a technique for converting spoken documents into web documents for the purpose of building a speech archiving system. The technique edits automatically transcribed texts and improves their readability on the browser. The readable text can be generated by applying technology such as paraphrasing, segmentation, and structuring transcribed texts. Editing experiments using lecture data demonstrated the feasibility of the technique. A prototype system of spoken document archiving was implemented to confirm its effectiveness.
Modelling Numerical and Spatial Uncertainty in GrayscaleImage Capture Using Fuzzy Set Theory
Authors: Nachtegae Mike ,   Dept. of Applied Mathematics and Computer Science, Ghent University, Fuzziness and Uncertainty Modelling Research Unit, Sussner Peter , Mélange Tom , Kerre Etienne E. ,   Dept. of Applied Mathematics, University of Campinas, Campinas, SP 13083 859, Brazil
Published: April 2016

In this paper, we will discuss interval-valued and intuitionistic fuzzy sets as a model for grayscale images, taking into account the uncertainty regarding the measured grayscale values, which in some cases is also related to the uncertainty regarding the spatial position of an object in an image. We will demonstrate the practical potential of this image model by introducing an interval-valued morphological theory and by illustrating its application with some examples. The results show that the uncertainty that is present during the image capture not only can be modelled, but can also be propagated such that the information regarding the uncertainty is never lost.
Bias of Standard Errors in Latent Class Model Applications Using Newton-Raphson and EM Algorithms
Authors: Camilleri Liberato ,   Department of Statistics and Operations Research, University of Malta
Published: April 2016

The EM algorithm is a popular method for computing maximum likelihood estimates. It tends to be numerically stable, reduces execution time compared to other estimation procedures and is easy to implement in latent class models. However, the EM algorithm fails to provide a consistent estimator of the standard errors of maximum likelihood estimates in incomplete data applications. Correct standard errors can be obtained by numerical differentiation. The technique requires computation of a complete-data gradient vector and Hessian matrix, but not those associated with the incomplete data likelihood. Obtaining first and second derivatives numerically is computationally very intensive and execution time may become very expensive when fitting Latent class models using a Newton-type algorithm. When the execution time is too high one is motivated to use the EM algorithm solution to initialize the Newton Raphson algorithm. We also investigate the effect on the execution time when a final Newton-Raphson step follows the EM algorithm after convergence. In this paper we compare the standard errors provided by the EM and Newton-Raphson algorithms for two models and analyze how this bias is affected by the number of parameters in the model fit.
Comparison of Traditional 2D and Virtual Patterns Design in 3D
Authors: Cichocka Agnieszka ,   Laboratoire GEnie et Matériaux TEXtiles, 'Ecole Nationale des Arts et Industries Textiles (ENSAIT), University Nord of Lille, Lille, 2 allée Louise et Victor Champier BP 30329 59056 ROUBAIX CEDEX 1, Bruniaux Pascal ,   Faculty of Material Technologies and Textile Design, Department of Clothing Science and Technology, Ul. .Zwirki 36, 90-942 L'od'z, Poland
Published: April 2016

This paper presents a garment pattern generation process and modelling of virtual garment design method in 3D. This work characterize our global project on virtual clothing design contains of the conception of virtual adaptive mannequin, and also of the creation and modelling of garment in 3D. According to the ideas of mass customization and e-commerce, as well the need of numerical innovations in garment industry we employ our model of virtual garment and methodology enabling to conceive the virtual clothing directly on a mannequin morphotype in 3D. This method gives us possibility to create a perfect garment and taking into account all peculiarities of human body. In 2D method we made two patterns: left and right side separately to obtain best result and to get possibility to compare two cases: when symmetric pattern is used and when we use pattern consisted of two parts specially made for each side of the body. In the present context we use the example of a basic women's shirt: bodice and sleeve. We used the method of making pattern employed in Russia compared with French method. Finally the superposition of virtual and real pattern was done in order to visualise the right results.
Q Value-Based Dynamic Programming with Boltzmann Distribution for Global Optimal Traffic Routing Strategy
Authors: Yu Shanqing ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Mabu Shingo , Ye Fengming , Wang Hongqiang , Shimada Kaoru , Hirasawa Kotaro
Published: April 2016

In this paper, we propose a heuristic method -- Boltzmann Optimal Route Method trying to find a good approximation to the global optimum route for Origin-Destination pairs through iterations until the total traveling time converges. The overall idea of our method is to update the traveling time of each route section iteratively according to its corresponding traffic volume, and continuously generate a new global route by Q value-based Dynamic Programming combined with Boltzmann distribution. Finally, we can get the global optimum route considering the traffic volumes of the road sections. The new proposed method is compared with the conventional shortest-path method- Greedy strategy both in the static traffic system where the volumes of all the given Origin-Destination pairs of road networks are constant and in the dynamic traffic system in which changing traffic volumes are constantly provided. The results demonstrate that the proposed method performs better than the conventional method in global perspective.
Special Issue on New Trends in Reinforcement Learning
Authors: Miyazaki Kazuteru ,   Associate Professor, Department of Assessment and Research for degree  Awarding, National Institution for Academic Degrees and University Evaluation, Japan, Takadama Keiki ,   Associate Professor, Department of Human Communication,    Faculty of Electro-Communications, The University of Electro-Comunications, Japan
Published: April 2016

Recently, the tailor-made system that grants an individual request has been recognized as the important approach. Such a system requires the ggoal-directed learningh through interaction between user and system, which is mainly addressed in greinforcement learningh domain. This special issue on gNew Trends in Reinforcement Learningh called for papers on the cuttingedge research exploring the goal-directed learning, which represents reinforcement learning. Many contributions were forthcoming, but we finally selected 12 works for publication. Although greinforcement learningh is included in the title of this special issue, the research works do not necessarily have to be on reinforcement learning itself, so long as the theme coincides with that of this special issue. In making our final selections, we gave special consideration to the kinds of research which can actively lead to new trends in reinforcement learning. Of the 12 papers in this special issue, the first four mainly deal with the expansion of the reinforcement learning method in single agent environments. These cover a broad range of research, from works based on dynamic programming to exploitation-oriented methods. The next two works deal with the Learning Classifier System (LCS), which applies the rule discovery mechanism to reinforcement learning. LCS is a technique with a long history, but for this issue, we were able to publish two theoretical works. We are also grateful to Prof. Toshio Fukuda, Nagoya University, and Prof. Kaoru Hirota, Tokyo Institute of Technology, the editors-in-chief, and the NASTEC 2008 conference staff for inviting us to guest-edit this Journal. The next four papers mainly deal with multi agent environments. We were able to draw from a wide range of research: from measuring interaction, through the expansion of techniques incorporating simultaneous learning, to research leading to application in multi agent environments. The last two contributions mainly deal with application. We publish one paper on exemplar generalization and another detailing the successful application to government bond trading. Each of these researches can be considered to be at the cutting-edge of reinforcement learning. We would like to end by saying that we hope this special issue constitutes a large contribution to the development of the field while holding a wide international appeal.
Construction of Semi-Markov Decision Process Models of Continuous State Space Environments Using Growing Cell Structures and Multiagentk-Certainty Exploration Method
Authors: Tateyama Takeshi ,   Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Kawata Seiichi , Shimomura Yoshiki ,   Advanced Institute of Industrial Technology, 1-10-40 Higashiohi , Shinagawa-ku, Tokyo 140-0011, Japan,   Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan
Published: April 2016

k-certainty exploration method, an efficient reinforcement learning algorithm, is not applied to environments whose state space is continuous because continuous state space must be changed to discrete state space. Our purpose is to construct discrete semi-Markov decision process (SMDP) models of such environments using growing cell structures to autonomously divide continuous state space then usingk-certainty exploration method to construct SMDP models. Multiagentk-certainty exploration method is then used to improve exploration efficiency. Mobile robot simulation demonstrated our proposal's usefulness and efficiency.
Exploitation-Oriented Learning PS-r#
Authors: Miyazaki Kazuteru ,   Department of Assessment and Research for degree Awarding, National Institution for Academic Degrees and University Evaluation, 1-29-1 Gakuennishimachi, Kodaira, Tokyo 187-8587, Japan, Kobayashi Shigenobu ,   Graduate School of Interdisciplinary Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8502, Japan
Published: April 2016

Exploitation-oriented learning (XoL) is a novel approach to goal-directed learning from interaction. Reinforcement learning is much more focused on learning and ensures optimality in Markov decision process (MDP) environments, XoL involves learning a rational policy that obtains rewards continuously and very quickly. PS-r*, a form of XoL, involves learning a useful rational policy not inferior to the random walk in the partially observed Markov decision process (POMDP) where reward types number one. PS-r*, however, requires O(MN2) memory where N is the number of sensory input types and M is an action. We propose PS-r#for learning a useful rational policy in the POMDP using O(MN) memory. PS-r#effectiveness is confirmed in numerical examples.
A New Improved Penalty Avoiding Rational Policy Making Algorithm for Keepaway with Continuous State Spaces
Authors: Watanabe Takuji ,   Platform Software Div., Software Unit, Fujitsu, Limited, Shinyokohama TECH Building, 3-9-18 Shinyokohama Kohoku-ku, Yokohama, Kanagawa 222-0033, Japan, Miyazaki Kazuteru , Kobayashi Hiroaki ,   Department of Assessment and Research for degree Awarding, National Institution for Academic Degrees and University Evaluation, 1-29-1 Gakuennishimachi, Kodaira, Tokyo 187-8587, Japan,   Dept. of Mechanical Engineering Informatics, Meiji University, 1-1-1 Higashimita Tama-ku,  Kawasaki, Kanagawa 214-8571, Japan
Published: April 2016

The penalty avoiding rational policy making algorithm (PARP) [1] previously improved to save memory and cope with uncertainty, i.e., IPARP [2], requires that states be discretized in real environments with continuous state spaces, using function approximation or some other method. Especially, in PARP, a method that discretizes state using a basis functions is known [3]. Because this creates a new basis function based on the current input and its next observation, however, an unsuitable basis function may be generated in some asynchronous multiagent environments. We therefore propose a uniform basis function and range extent of the basis function is estimated before learning. We show the effectiveness of our proposal using a soccer game task called “Keepaway.”
Exemplar Generalization in Reinforcement Learning: Improving Performance with Fewer Exemplars
Authors: Matsushima Hiroyasu ,   The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu, Tokyo 182-8585, Japan, Hattori Kiyohiko , Takadama Keiki ,   PRESTO, Japan Science and Technology Agency (JST), 4-1-8 Honcho Kawaguchi, Saitama 332-0012, Japan
Published: April 2016

This paper focuses on the generalization of exemplars (i.e., good rules) in the reinforcement learning framework and proposes Exemplar Generalization in Reinforcement Learning (EGRL) that extracts usual exemplars from a lot of exemplars provided as a prior knowledge and generalizes them by deleting unnecessary exemplars (some exemplars overlap) as much as possible. Through intensive simulation of a simple cargo layout problem to validate EGRL effectiveness, the following implications have been revealed: (1) EGRL derives good performance with fewer exemplars than using the efficient numbers of exemplars and randomly selected exemplars and (2) integration of covering, deletion, and subsumption mechanisms in EGRL is critical for improving EGRL performance and generalization.
Acquiring a Government Bond Trading Strategy Using Reinforcement Learning
Authors: Matsui Tohgoroh ,   Tohgoroh Machine Learning Research Institute, Chiba, Japan, Goto Takashi , Izumi Kiyoshi ,   The Bank of Tokyo-Mitsubishi UFJ, Ltd., Tokyo, Japan,   National Institute of Advanced Industrial Science and Technology, Tokyo, Japan
Published: April 2016

This paper proposes using reinforcement learning to acquire a government bond trading strategy. We applied this method to the 10-year Japanese government bond (JGB) market and confirmed that it acquires profitable trading even in extrapolation.
Product-Impression Analysis Using Fuzzy C4.5 Decision Tree
Authors: Tokumaru Masataka ,   Faculty of Engineering Science, Kansai University, Osaka, Japan, Muranaka Noriaki
Published: April 2016

Product design requires careful consideration of many factors. However, it is difficult for many developers to design a product that will satisfy everyone, because they do not know the answers to questions such as “What causes large number of people to like a specific product?” “What fascinates them about the product?” or “Which part or element of the design makes it attractive?” In general, people like “usable” products, but it is difficult to know what makes certain products easy to use. This paper proposes a method for investigating “ease-of-use” by means of a fuzzy decision tree. It builds a decision tree based on the results of a questionnaire, which determines user's product impressions, and helps to uncover the major factors that affect the product's “ease-of-use.” In this paper, we analyzed golf clubs and investigated the factors that determine their particular ease-of-hitting, which is concerned with the ability to hit golf balls easily. As a result, we obtained some reliable rules affecting user impressions using the fuzzy decision tree built from the questionnaire results.
Concept of Neighborhood Degree and its Application to Switching Plural Optimization Methods in Scheduling
Authors: Dong Fangyan ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Chen Kewei , Hirota Kaoru ,   The Computing, Designing and Integration Co., Ltd. 474-5 Tsukakoshi 3, Saiwai-ku, Kawasaki, Kanagawa 212-0024, Japan
Published: April 2016

A concept of neighborhood degree is proposed to evaluate the quality of solutions to scheduling problems such as vehicle routing, scheduling, and dispatching problems. It is possible to apply it to the optimization process of scheduling problems in order to switch between various optimization methods by considering convergence speed and solution quality. In the experiments on TSP benchmark data, two optimization methods, i.e., tabu search and simulated annealing, are switched effectively by observing the variation of the neighborhood degree. Directions for Practical applications are also mentioned.
Human Behavior Classification Using Thinning Algorithm and Support Vector Machine
Authors: Widyanto M. Rahmat ,   Faculty of Computer Science, University of Indonesia Depok 16424, Indonesia, Endah Sukmawati Nur , Hirota Kaoru ,   Faculty of Mathematics and Natural Science, Diponegoro University Prof. Soedarto, S.H. street, Tembalang, Semarang, Indonesia,   Dept. Computational Intelligence & System Science, Tokyo Institute of Technology G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
Published: April 2016

This paper proposes a skeleton-based human behavior classification system using thinning algorithm and Support Vector Machine (SVM). The proposed system consists of two phases, skeletonization phase where main human body part is constructed using thinning algorithm, and classification phase where the skeleton constructed by previous phase is classified into certain human behavior pose using SVM. Experiment using 44 training and 44 testing data of real human poses shows that the system achieves 81.06% accuracy. This system can be further developed for early detection of criminal action.
Fuzzy Relevance Feedback in Image Retrieval for Color Feature Using Query Vector Modification Method
Authors: Widyanto M. Rahmat ,   Faculty of Computer Science, University of Indonesia Depok Campus, Depok 16424, West Java, Indonesia, Maftukhah Tatik ,   Indonesian Institute of Science Puspiptek, Serpong, Banten 15314, Indonesia
Published: April 2016

Fuzzy relevance feedback using Query Vector Modification (QVM) method in image retrieval is proposed. For feedback, the proposed six relevance levels are: “very relevant”, “relevant”, “few relevant”, “vague”, “not relevant”, and “very non relevant”. For computation of user feedback result, QVM method is proposed. The QVM method repeatedly reformulates the query vector through user feedback. The system derives the image similarity by computing the Euclidean distance, and computation of color parameter value by Red, Green, and Blue (RGB) color model. Five steps for fuzzy relevance feedback are: image similarity, output image, computation of membership value, feedback computation, and feedback result. Experiments used QVM method for six relevance levels. Fuzzy relevance feedback using QVM method gives higher precision value than conventional relevance feedback method. Experimental results show that the precision value improved by 28.56% and recall value improved 3.2% of conventional relevance feedback. That indicated performance Image Retrieval System can be improved by fuzzy relevance feedback using QVM method.
Omnidirectional Vision for Mobile Robot Navigation
Authors: Taha Zahari ,   Department of Engineering Design and Manufacture, University Malaya, 50603 Kuala Lumpur, Malaysia, Chew Jouh Yeong , Yap Hwa Jen
Published: April 2016

Machine vision has been widely studied, leading to the discovery of many image-processing and identification techniques. Together with this, rapid advances in computer processing speed have triggered a growing need for vision sensor data and faster robot response. In considering omnidirectional camera use in machine vision, we have studied omnidirectional image features in depth to determine correlation between parameters and ways to flatten 3-dimensional images into 2 dimensions. We also discuss ways to process omnidirectional images based on their individual features.
Pyramidal Structure Algorithm for Fingerprint Classification Based on Artificial Neural Networks
Authors: Ezin Eugène C. ,   Unité de Recherche en Informatique et Sciences Appliquées Institut de Mathématiques et de Sciences Physiques Université d’Abomey-Calavi, Benin
Published: April 2016

Feature extraction plays a primary role in pattern recognition classification. Many context-based and problem-based algorithms have been proposed providing good performance in high-quality fingerprint imaging but fail when declining with poor-quality fingerprints. The pyramidal algorithm we present in this paper operates on an image matrix layer for extracting features from ink-and-paper fingerprints. The effectiveness of the pyramidal algorithm compared to the consolidation algorithm is demonstrated using a backpropagation neural network experiment to test preprocessed data.
Relative Magnitude of Gaussian Curvature via Self-Calibration
Authors: Ding Yi ,   Nagoya Institute of Technology, Gokiso-cho Showa-ku Nagoya 466-8555, Japan, Iwahori Yuji , Nakagawa Takashi , Nakamura Tsuyoshi , He Lifeng , Woodham Robert J. , Itoh Hidenori ,   Chubu University, Matsumoto-cho 1200 Kasugai 487-8501, Japan,   Faculty of Information Science and Technology, Aichi Prefectural University, Aichi 480-1198, Japan,   University of British Columbia, Vancouver, B.C. Canada, V6T 1Z4
Published: April 2016

Gaussian curvature encodes important information about object shape. This paper presents a technique to recover the relative magnitude of Gaussian curvature from multiple images acquired under different conditions of illumination. Previous approaches make use of a separate calibration sphere. Here, we require no distinct calibration object. The novel idea is to use controlled motion of the target object itself for self-calibration. The target object is rotated in fixed steps in both the vertical and the horizontal directions. A distinguished point on the object serves as a marker. Neural network training data are obtained from the predicted geometric positions of the marker under known rotations. Four light sources with different directions are used. An RBF neural network learns the mapping of image intensities to marker position coordinates along a virtual sphere. Neural network maps four image irradiances on the target object onto a point on a virtual sphere. The area value surrounded by four mapped points onto a sphere gives an approximate value of Gaussian curvature. The modification neural network is learned for the basis function to obtain more accurate Gaussian curvature. Spatially varying albedo is allowed since the effect of albedo can be removed. It is shown that self-calibration makes it possible to recover the relative magnitude of Gaussian curvature at each point without a separate calibration object. No particular functional model of surface reflectance is assumed. Experiments with real data are demonstrated. Quantitative error analysis is provided for a synthetic example.
Concurrent Societies Based on Genetic Algorithm and Particle Swarm Optimization
Authors: Markovic Hrvoje ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Dong Fangyan , Hirota Kaoru
Published: April 2016

A parallel multi-population based metaheuristic optimization framework, called Concurrent Societies, inspired by human intellectual evolution, is proposed. It uses population based metaheuristics to evolve its populations, and fitness function approximations as representations of knowledge. By utilizing iteratively refined approximations it reduces the number of required evaluations and, as a byproduct, it produces models of the fitness function. The proposed framework is implemented as two Concurrent Societies: one based on genetic algorithm and one based on particle swarm optimization both using k -nearest neighbor regression as fitness approximation. The performance is evaluated on 10 standard test problems and compared to other commonly used metaheuristics. Results show that the usage of the framework considerably increases efficiency (by a factor of 7.6 to 977) and effectiveness (absolute error reduced by more than few orders of magnitude). The proposed framework is intended for optimization problems with expensive fitness functions, such as optimization in design and interactive optimization.
Acoustic Analysis of Breathy and Rough Voice Characterizing Elderly Speech
Authors: Miyazaki Takeshi ,   Department of Electrical Engineering, Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kitakyushu-shi, Fukuoka 804-8550, Japan, Mizumachi Mitsunori , Niyada Katsuyuki
Published: April 2016

This paper aims at investigating acoustic features, which can objectively explain breathiness and roughness of elderly speech, respectively. In this paper, acoustic analysis was carried out using word sequences, which were uttered by 153 male speakers in the age range of between 20 and 89 years old. Concerning the breathiness, we confirmed that elderly breathy voices caused energy lift in higher frequency region over 4 kHz in average power spectra during the stationary parts in the uttered vowels. Concerning roughness, we observed the slight fluctuations, which synchronized with vocal cord vibration, in amplitude spectra during stationary parts of vowels. Based on acoustic analysis results, we propose physical parameters for measuring breathiness and roughness, respectively. In this paper, listening tests were carried out to quantitatively give the subject degrees of breathiness and roughness, respectively. It was confirmed that the proposed physical parameters had correlation with each of subjective degrees. Relationships between age and acoustic characteristics of breathiness and roughness were investigated using the proposed parameters. It is confirmed that the degree of breathiness and roughness increased in proportion to age, especially in age ranges over 60 years old.
Improving Classification Performance of Nursing-Care Text Classification System by Using GA-Based Term Selection
Authors: Nii Manabu ,   Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo, Japan, Yamaguchi Takafumi , Takahashi Yutaka , Uchinuno Atsuko , Sakashita Reiko ,   WPI Immunology Frontier Research Center, Osaka University,   College of Nursing Art & Science, University of Hyogo, 13-71 Kitaouji-cho, Akashi, Hyogo, Japan
Published: April 2016

In order to reduce evaluation workloads for nursingcare experts, we have proposed a Support Vector Machine (SVM) based classification system. In this paper, for improving the classification performance, we propose a Genetic Algorithm (GA) based attribute selection method. First, we extract nouns and verbs from nursing-care texts by using of the morphological analysis software and store the extracted terms into a “term list.” Next, some combinations of terms in the term list are selected by a GA with two objectives; (1) maximizing the number of correctly classified texts and (2) minimizing the number of selected terms. Then, we classify the nursing-care texts with these selected terms by using of a SVM-based classification system. From computer simulations, we show the effectiveness of a GA-based attribute selection method for classifying the nursing-care texts.
Fuzzy few-Nearest Neighbor Method with a Few Samples for Personal Authentication
Authors: Arai Yoshinori ,   Dept. Eng., C.S., Tokyo Polytechnic Univ., Lien Nguyen Thi Huong , Ishigaki Kazuma , Satoh Hiroyuki , Hayashi Teruhiko , Dong Fangyan , Hirota Kaoru ,   Dept. C.I. & S.S., Tokyo Institute of Technology,   Schlumberger K.K.,   Hitachi Automotive Systems Co., Ltd.,   Soliton Systems K.K.
Published: April 2016

The Fuzzy few-Nearest Neighbor (Ff-NN) method, which is an extended version of k-Nearest Neighbor algorithm (k-NN) and one of case-based learning methods, is proposed. Ff-NN intends to achieve stable identification performance even if the number of learning samples is as small as two. Applied to personal authentication systems such as enter/exit authorizations, Ff-NN reduces the user dictionary creation burden. Using 26 kinds of feature (face images and voices) data from 66 test objects, we conducted experiments on a PC to verify the feasibility of our proposed method. Forced recognition rate of conventional single-NN is 79.2% (standard deviation 2.83), and that of Ff-NN is 87.6% (SD 1.97). Recognition rates of dictionary data with 14, 17, and 26 features, are 90.6%, 92.5%, and 97.5%, respectively. We collect a very small number of nonintrusive samples so that two or more features are used to improve recognition performance. We present applicability of this method to personal authentication systems through experiments using 66 registrants, corresponding to 30 households.
Structure Extraction from Decorated Characters by Graph Spectral Decomposition and Component Selection Criterion
Authors: Kawano Hideaki ,   Faculty of Engineering, Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kitakyushu 804-8550, Japan, Shimamura Akito , Maeda Hiroshi , Orii Hideaki , Ikoma Norikazu
Published: April 2016

Recognizing decorated characters using conventional Optical Character Recognition (OCR) requires that the structure of the decorated characters be extracted. The method that Omachi et al. proposed to do this could not extract characters including both thin and thick lines. To improve the performance of Omachi’s approach, we proposed using graph spectral decomposition, demonstrating our previous proposal’s applicability to wider range of decorated characters. Here we propose a new criterion for selecting structure extraction components in the graph spectral decomposition framework, demonstrating the feasibility of the criterion through experimental results.
Robust Estimation of Sound Source Direction with Deterministic Background Noise and Stochastic Source Dynamics Models
Authors: Mizumachi Mitsunori ,   Department of Electrical Engineering, Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kitakyushu-shi, Fukuoka 804-8550, Japan, Niyada Katsuyuki
Published: April 2016

Direction of Arrival (DOA), a type of auxiliary information used in acoustic signal processing, is vulnerable to acoustical noise, so we want to male the estimation of DOA in noisy environments, relying on spectral sparseness. The energy of acoustic signals such as speech is wide-band, with individual signals localized in specific but different frequency regions. Our proposal involves filtering out spatial features provisionally from subband frequency components at the dominant frequency of the target signal using particle filtering with a sound source dynamics model. The feasibility of our proposal is confirmed by estimating a sound source direction in noisy conditions, also confirming that frequency selectivity and state estimation using particle filters help improve DOA estimation robustness against noise in noisy conditions.
An Evolutionary Negotiation Model Using Genetic Network Programming
Authors: Hossain Md. Tofazzal ,   Graduate school of Information, Production, and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Mabu Shingo , Hirasawa Kotaro
Published: April 2016

In a common two-party price negotiation, the buyer and the seller try to maximize inherently conflicting objectives regarding the price of a product. Generating an intelligent solution for both the buyer and the seller is often difficult, time-consuming and inefficient under such conditions. In this work, we therefore devise an intelligent solution for such complex situations in Business-to-Business E-business activities. The main contribution of this paper is the design of an intelligent decision support mechanism for an intelligent and automated negotiation solution based on a newly developed evolutionary computing technique called Genetic Network Programming (GNP). In addition, we devise a new Negotiation Protocol aiming to develop a set of rules for agents’ behavior during the negotiation process. Moreover, to reach a negotiation solution, concession that is usually predetermined in the conventional system is needed. To the contrary, in this research, concession is determined automatically by evolution that makes our system intelligent, automated, and efficient. Extensive simulations are emphasized to study the characteristics and behaviors of agents obtained by evolution.
Automatic Generation of Musical Tone Row and Rhythm Based on the Twelve-Tone Technique Using Genetic Algorithm
Authors: Maeda Yoichiro ,   Dept. of Human and Artificial Intelligent Systems, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui-shi, Fukui 910-8507, Japan, Kajihara Yusuke
Published: April 2016

Genetic Algorithms (GA) and Interactive Genetic Algorithms (IGA) used to generate sound in computer applications generating music are difficult to use, as is, in directly composing music. We propose music composition based on the 12-Tone Technique (TTT). In TTT composition, the melody and rhythm are usually created separately. The melody is created first to determine the musical subject and atmosphere. We design a fitness function based on the relationship between consonant and dissonant intervals that are a part of general musical theory and generate the 12-Tone (TT) row automatically by searching for consonant tone rows using the GA. We then set a fitness function for evaluating the rhythm we define, and obtained musical rhythm using the GA.
Investigation on Robot User Interface for Information Access
Authors: Takama Yasufumi ,   Graduate School of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Namba Hiroki
Published: April 2016

This paper investigates the characteristics of robots for non-industrial use such as home robots, when those are used as an interface for accessing information. Although information support is one of important capabilities home robots should have, the merits of accessing information via a robot compared with the access via PC or a mobile phone have yet to be fully explored. This paper focuses on the physical presence of robots, which is supposed to be important for robots to provide users with information. In order to investigate the merits, two experiments with participants are performed in this paper. The main contributions of the paper are the following points. First, it is shown that a robot can effectively attract the participants through movements, even though they pay less attention to it. Second, the possibility of using robot actions for providing additional information about information to be accessed by the participant is also investigated. Finally, the effect of a robot User Interface (UI) prototype on communication among users when providing information to them is also investigated. The obtained results support the significance of information support by home robots, which will be used for designing home robots with information support facility.
An Extension Approach for Neural Networks by Introducing a Nearest Neighbor Algorithm in Relative Coordinates
Authors: Suzaki Hirofumi ,   Department of Bioinformatics, Graduate School of Systems Life Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan, Kuhara Satoru ,   Faculty of Agriculture, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan
Published: April 2016

Computational models known as neural networks discriminate among different types of nonlinear data, enabling the design of flexible calculation through machine-learning algorithms. Thanks to the simplicity of calculation, the nearest neighbor algorithm is a well-studied classification method. If the nearest neighbor algorithm inference is shown by a network model consisting of a neuron model representing data, it may become deterministic with adjustable parameters. We propose a new neuron model using the generalized mean and have designed a practical neural network framework based on the nearest neighbor algorithm. Because our proposed parallel distributed processing model is not simply a distance comparison between two points, it uses information from a whole body of data. This makes our classification superior to the nearest neighbor algorithm for algorithmic principles.
Neural Network Implementation of Image Rendering via Self-Calibration
Authors: Ding Yi ,   Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan, Iwahori Yuji , Nakamura Tsuyoshi , He Lifeng , Woodham Robert J. , Itoh Hidenori ,   Dept. of Computer Science, Chubu University, 1200 Matsumoto-cho, Kasugai 487-8501, Japan,   Faculty of Information Science and Technology, Aichi Prefectural University, 1522-3 Kumabari-Ibaragabasama, Nagakute, Aichi-gun, Aichi 480-1198, Japan,   University of British Columbia, Vancouver, B.C. V6T 1Z4, Canada
Published: April 2016

This paper proposes a new approach for selfcalibration and color image rendering using Radial Basis Function (RBF) neural network. Most empirical approaches make use of a calibration object. Here, we require no calibration object to both shape recovery and color image rendering. The neural network learning data are obtained through the rotations of a target object. The approach can generate realistic virtual images without any calibration object which has the same reflectance properties as the target object. The proposed approach uses a neural network to obtain both surface orientation and albedo, and applies another neural network to generate virtual images for any viewpoint and any direction of light source. Experiments with real data are demonstrated.
Extraction of Web Site Evaluation Criteria and Automatic Evaluation
Authors: Li Peng ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, J2, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, Yamada Seiji ,   National Institute of Informatics, SOKENDAI, 2-1-2 Hitotsubashi, Chiyoda, Tokyo 101-8430, Japan
Published: April 2016

This paper proposes an automated web site evaluation using machine learning to extract evaluation criteria from existing evaluation data. Web site evaluation is a significant task because evaluated web sites provide information useful to users in estimating sites validation and popularity. Although many practical approaches have been taken to present possible measuring sticks for web sites, their evaluation criteria are manually determined. We developed a method to obtain evaluation criteria automatically and rank web sites with the learned classifier. Evaluation criteria are discriminant functions learned from a set of ranking information and evaluation features collected automatically by web robots. Experiments confirmed the effectiveness of our approach and its potential in high-quality web site evaluation.
A Model for Generating Facial Expressions Using Virtual Emotion Based on Simple Recurrent Network
Authors: Matsui Yuki ,   Graduate School of Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan, Kanoh Masayoshi , Kato Shohei , Nakamura Tsuyoshi , Itoh Hidenori ,   School of Information Science and Technology, Chukyo University, 101 Tokodachi, Kaizu-cho, Toyota 470-0393, Japan
Published: April 2016

We propose an interactive facial expression model using the Simple Recurrent Network (SRN) for achieving interactions through facial expressions between robots and human beings. The proposed model counts humans in the root system as receivers of facial expressions to achieve a dynamic system bi-directionally affecting humans and robots. Robots typically generate only static changes in facial expression using motion files, so they seem bored, unnatural, and strange to their users. We use interactions between robots and people to diversity the inputs of robots and use emotional state transitions of robots to reduce uniformities in output facial expressions. This paper discusses a dynamic system that causes the proposed model to learn emotional facial expressions based on those of humans. Next, we regard internal states generated by the proposed model as virtual emotions and show that mixed emotions can be expressed by users’ inputs from the virtual emotional space. Moreover, based on the results of a questionnaire, we see that facial expressions adopted in the virtual emotional space of the proposed model received high rates of approval from the users.
The Search for a Search: Measuring the Information Cost of Higher Level Search
Authors: Dembski William A. ,   Center for Science & Culture, Discovery Institute, Seattle, WA 98104, USA, II Robert J. Marks ,   Dept. of Electrical & Computer Engineering, Baylor University, Waco, TX 76798, USA
Published: April 2016

Needle-in-the-haystack problems look for small targets in large spaces. In such cases, blind search stands no hope of success. Conservation of information dictates any search technique will work, on average, as well as blind search. Success requires an assisted search. But whence the assistance required for a search to be successful? To pose the question this way suggests that successful searches do not emerge spontaneously but need themselves to be discovered via a search. The question then naturally arises whether such a higher-level “search for a search” is any easier than the original search. We prove two results: (1) The Horizontal No Free Lunch Theorem, which shows that average relative performance of searches never exceeds unassisted or blind searches, and (2) The Vertical No Free Lunch Theorem, which shows that the difficulty of searching for a successful search increases exponentially with respect to the minimum allowable active information being sought.
Genetic Network Programming with Estimation of Distribution Algorithms for Class Association Rule Mining in Traffic Prediction
Authors: Li Xianneng ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Mabu Shingo , Zhou Huiyu , Shimada Kaoru , Hirasawa Kotaro
Published: April 2016

Genetic Network Programming (GNP) is one of the evolutionary optimization algorithms, which uses directed-graph structures to represent its solutions. It has been clarified that GNP works well to find class association rules in traffic prediction systems. In this paper, a novel evolutionary paradigm named GNP with Estimation of Distribution Algorithms (GNP-EDAs) is proposed and used to find important class association rules in traffic prediction systems. In GNP-EDAs, a probabilistic model replaces crossover and mutation to enhance the evolution. The new population of individuals is produced from the probabilistic distribution estimated from the selected elite individuals of the previous generation. The probabilistic information on the connections and transitions of GNP-EDAs is extracted from its population to construct the probabilistic model. In this paper, two methods are described to build the probabilistic model for producing the offspring. In addition, a classification mechanism is introduced to estimate the traffic prediction based on the extracted class association rules. We compared GNPEDAs with the conventional GNP and the simulation results showed that GNP-EDAs can extract the class association rules more effectively, when the number of the candidate class association rules increase. And the classification accuracy of the proposed method shows good results in traffic prediction systems.
A Signal-Representation-Based Parser to Extract Text-Based Information from the Web
Authors: Su Mu-Chun ,   Department of Computer Science & Information Engineering, National Central University, Taiwan, Wang Shao-Jui , Huang Chen-Ko , Pa-ChunWang  , Hsu Fu-Hau , Lin Shih-Chieh , Hsieh Yi-Zeng ,   Chunghwa Telecom Co., Ltd., Taiwan,   COMPAL ELECTRONIC, INC, Taiwan,   Quality Management Center, Cathay General Hospital, Taiwan, R.O.C.,   School of Medicine, Fu Jen Catholic University, Taiwan
Published: April 2016

Most of the dramatically increased amount of information available on the World Wide Web is provided via HTML and formatted for human browsing rather than for software programs. This situation calls for a tool that automatically extracts information from semistructured Web information sources, increasing the usefulness of value-added Web services. We present a signal-representation-based parser (SIRAP) that breaks Web pages up into logically coherent groups - groups of information related to an entity, for example. Templates for records with different tag structures are generated incrementally by a Histogram-Based Correlation Coefficient (HBCC) algorithm, then records on a Web page are detected efficiently using templates generated by matching. Hundreds of Web pages from 17 state-of-the-art search engines were used to demonstrate the feasibility of our approach.
A New Trajectory Generation Scheme for Direction Turning in Biped Walking
Authors: Kim Man-Seok ,   Department of Electronics Engineering, Dong-A University, 840 Hadan 2-dong, Saha-gu, Busan 604-714, Korea, Kim Jo-Hwan , Choi Min-Goo , Kim Jong-Wook
Published: April 2016

This paper proposes a new trajectory generation scheme for direction turning during biped walking based on our previous study and experiences. For turning in walking, two joints in the sagittal plane should be added in the robot model, and each joint should rotate appropriately considering stability and landing precision. All the angular trajectories for 12 joints in the lower body are approximated by blending polynomials optimized by univariate Dynamic Encoding Algorithm for Searches (uDEAS). The proposed walking scheme is validated via simulation.