bluuebunny/crossref_metadata_2025 huggingface dataset:
Real Time Color Object Tracking on Cell Broadband Engine Using Particle Filters
Authors: Ikoma Norikazu ,   Faculty of Engineering, Kyushu Institute of Technology, Kita-Kyushu, Fukuoka 804-8550, Japan, Asahara Akihiro ,   Fixstars Corporation, 1-8-27-3F Kounan, Minato-ku, Tokyo 108-0075, Japan
Published: April 2016

Real time visual tracking by particle filter has been implemented on Cell Broadband Engine in parallel. Major problem for the implementation is small size of Local Store (LS) in SPEs (Synergistic PEs), which are computational cores, to deal with image of large size. As a first step for the implementation, we focus on color single object tracking, which is one of the most simple case of visual tracking. By elaborating to compress the color extracted image into bit-wise representation of binary image, all information of the color extracted image can be stored in LS for 640×480 size of original image. By applying our previous implementation of general particle filter algorithm on Cell/B.E. to this specific case, we have achieved real time performance of visual tracking on PlayStation®3 about 7 fps with a camera of maximum 15 fps.
Interactive Data Mining for Large-Scale Image Databases Based on Formal Concept Analysis
Authors: Tanabata Takanari ,   National Institute of Agrobiological Sciences, 2-1-2 Kannondai, Tsukuba, Ibaraki 305-8602, Japan, Sawase Kazuhito , Nobuhara Hajime , Bede Barnabas ,   Department of Intelligent Interaction Technologies, University of Tsukuba, 1-1-1 Tenodai, Tsukuba Science City, Ibaraki 305-8573, Japan,   Department of Mathematics, The University of Texas-Pan American, 1201 West University, Edinburg, Texas 78539, USA
Published: April 2016

In order to perform an interactive data-mining for huge image databases efficiently, a visualization interface based on Formal Concept Analysis (FCA) is proposed. The proposed interface system provides an intuitive lattice structure enabling users freely and easily to select FCA attributes and to view different aspects of the Hasse diagram of the lattice of a given image database. The investigation environment is implemented using C++ and the OpenCV library on a personal computer (CPU = 2.13 GHz, MM = 2 GB). In visualization experiments using 1,000 Corel Image Gallery images, we test image features such as color, edge, and face detectors as FCA attributes. Experimental analysis confirms the effectiveness of the proposed interface and its potential as an efficient datamining tool.
Human Behavior Measurement Based on Sensor Network and Robot Partners
Authors: Kubota Naoyuki ,   Dept. of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan, Obo Takenori , Liu Honghai ,   University of Portsmouth, Eldon Building, Winston Churchill Avenue, Portsmouth, England PO1 2DJ, UK
Published: April 2016

This paper proposes to measure human behavior based on a sensor network. It starts by explaining informationally structured space, robot partners, and sensor networks developed in this study. Next, it discusses the applicability of sensor network and robot partner to rehabilitation. Growing neural gas is applied to extract an image of a person from a background image and a steady-state genetic algorithm is used to extract human movement from threedimensional distance images. We confirm the effectiveness of our proposal through experiments.
SN Ratio Estimation and Speech Segment Detection of Extracted Signals Through Independent Component Analysis
Authors: Koya Takeshi ,   Solutions Development Laboratory, Advanced Solutions Technology Japan, Shinjyuku, Tokyo 169-0051, Japan, Iwasaki Nobuo , Ishibashi Takaaki , Hirano Go , Shiratsuchi Hiroshi , Gotanda Hiromu ,   Graduate School of Advanced Technology, Kinki University, 11-6 Kayanomori, Iizuka-shi, Fukuoka 820-8555, Japan,   Kumamoto National College of Technology, Koshi-shi, Kumamoto 861-1102, Japan
Published: April 2016

In real world environments where acoustic signals are contaminated with various noises, it is difficult to estimate the Signal-to-Noise Ratio (SNR) only from signals observed at microphones; the knowledge of acoustic transfer functions and original source signals is inevitable for SNR estimation. The present paper proposes a method to estimate SNR approximately in the real world environments without the knowledge of transfer functions and source signals: SNR is estimated after application of Independent Component Analysis (ICA) to the signals observed at microphones. Our proposed method also works as a speech segment detector since detection of speech segments are necessarily carried out in the course of SNR estimation. From several experimental results, the proposed method has been confirmed to be valid.
Web-Based Intelligent Photograph Management System Enhancing Browsing Experience
Authors: Orii Yuki ,   Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo 184-8588, Japan, Nozawa Takayuki , Kondo Toshiyuki
Published: April 2016

We developed a web-based intelligent photograph browsing system that enables automatic clustering of unstructured digital photograph collection. We conducted a user study to assess the effectiveness of developed photograph browsing system em APC and APCCT). The user task adopted here was finding some target photographs indicated by the experimenter. The results suggest that the clustering method of the photograph browsing systems should be changed according to whether or not the photographs had been taken by the user.
Learning Similarity Matrix from Constraints of Relational Neighbors
Authors: Okabe Masayuki ,   Information and Media Center, Toyohashi University of Technology, 1-1 Tenpaku, Toyohashi, Aichi 441-8580, Japan, Yamada Seiji ,   National Institute of Informatics, the Graduate University for Advanced Studies (SOKENDAI), 2-1-2 Hitotsubashi, Chiyoda, Tokyko 101-8430, Japan
Published: April 2016

This paper describes a method of learning similarity matrix from pairwise constraints assumed used under the situation such as interactive clustering, where we can expect little user feedback. With the small number of pairwise constraints used, our method attempts to use additional constraints induced by the affinity relationship between constrained data and their neighbors. The similarity matrix is learned by solving an optimization problem formalized as semidefinite programming. Additional constraints are used as complementary in the optimization problem. Results of experiments confirmed the effectiveness of our proposed method in several clustering tasks and that our method is a promising approach.
A Novel Taxi Dispatch System Integrating a Multi-Customer Strategy and Genetic Network Programming
Authors: Meng QingBiao ,   Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan, Mabu Shingo , Yu Lu , Hirasawa Kotaro
Published: April 2016

Taxi service usually benefits people by providing comfortable and flexible ride experiences. However, an inherent problem, the insufficient number of taxis at traffic peak, has baffled taxi service ever since it existed. This paper hereby proposes a Multi-Customer Taxi Dispatch System (MCTDS), where taxis are granted a right to take customers with different Origin-Destination (OD) pairs simultaneously, to shorten the total waiting time and traveling time. In addition, to mitigate the damage of detours, MCTDS is built based on Genetic Network Programming, a graph-based evolutionary algorithm that has shown excellent performances previously in some complicated applications. We also modify the structure of GNP to achieve an improvement in performance. In the simulation part, we demonstrate that MCTDS outperforms the conventional GNP and some heuristic taxi dispatch approaches.
Evolving Asset Portfolios by Genetic Relation Algorithm
Authors: Parque Victor ,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 803-0135, Japan, Mabu Shingo , Hirasawa Kotaro
Published: April 2016

Global financial development have opened innumerable risks and opportunities for investments. A global view of the portfolio allocation through diversification brings advantages for the risk allocation in investments. In this paper, an asset allocation framework under the return, risk and liquidity considerations is proposed for short term investment using Genetic Relation Algorithm. Simulations using the stocks, bonds and currencies from relevant financial markets in USA, Europe and Asia show that the proposed framework is effective and robust. The efficacy of the proposed method is compared against the relevant constructs in finance and computational fields.
Integrating Process and Work Breakdown Structure with Design Structure Matrix
Authors: Lee Jonathan ,   Department of Computer Science and Information Engineering, National Central University, Jhongli, Taiwan, Deng Whan-Yo , Lee Wen-Tin , Lee Shin-Jie , Hsu Kuo-Hsun , Ma Shang-Pin ,   Department of Computer and Information Science, National Taichung University, Taichung, Taiwan,   Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan
Published: April 2016

In software development, project plans document scope, cost, effort, and schedule, guide project managers, and control project execution. Developing a project plan without incorporating how an organization doing things - i.e., organizational culture - may lead to project failure. To ensure stable process performance and to benefit from organizational culture, it is crucial that organizational processes be taken into account in project planning. Organizational processes enable stable process performance across an organization and provide a basis for cumulative, long term benefits to the organization. In proposing a systematic approach that supports bi-directional transformation between processes and the Work Breakdown Structure (WBS), we propose Process2WBS and WBS2Process to assist project managers in project planning with an organization’s set of standard processes. Process2WBS consumes processes and transforms them into a WBS with Design Structure Matrix (DSM) analysis, and WBS2Process transforms the WBS with project-specific information into executable processes expressed in XPDL.
A BPEL-Based Fault-Handling Architecture for Telecom Operation Support Systems
Authors: Chen Ing-Yi ,   Department of Computer Science and Information Engineering, National Taipei University of Technology, Taiwan, Ni Guo-Kai , Kuo Cheng-Hwa , Lin Chau-Young ,   Department of Commerce Technology and Management, Chihlee Institute of Technology, Taiwan,   Telecommunication Laboratories, ChungHwa Telecom Co., Ltd., Taiwan
Published: April 2016

Exception handling is an important issue in dependable software computing. This paper presents a system architecture and explains the role of responsibility of each component that is introduced in the architecture. The design is used to ensure the constructed service-oriented applications remain in a stable state. Service-Oriented Architecture is a paradigm for constructing current enterprise applications. It uses service composition technology to induce existing service components to provide advanced services. The most important concept in this methodology is that of reusing existing components. Business Process Execution Language (BPEL) is widely used for achieving service composition, providing basic capability for developers to deal with faults using defined activities standard in BPEL. No sufficient design, however, supports developers in implementing fault-handling components efficiently. The architecture proposed in this paper provides the design approach to explain how to reuse the fault-handling components. Additionally, the paper also provides an empirical case study that illustrates how this design was used by the Chunghwa Telecom Company in an effort to improve efficiency in implementing fault-handling components and in constructing service-oriented applications. The number of fault-handling components required was reduced from 38 to 3, implying a 92.10% efficiency rate.
Using Planning and Case-Based Reasoning for Service Composition
Authors: Lee Chiung-Hon Leon ,   Department of Computer Science & Information Engineering, Nanhua University, Taiwan, Liu Alan , Huang Huan-Hsian ,   Department of Electrical Engineering, National Chung-Cheng University, Taiwan
Published: April 2016

Planning commonly applied to automating Web Service composition involves two problems - (i) overlooked user needs combined with services provided by the systems themselves and outside services providing a much more flexible service model. (ii) “Speeding up” and “facilitating” services by not recording information about service providers already having served users and about planning already processed. We propose merging internal and external services to meet user needs. Internal services include system functions designed to meet user needs. External services mean Web services provided by outside service providers. We plan to combine both types of services to create planning to meet user needs. We apply case-based reasoning to store planning and related information in a case base to make planning much faster when users have similar needs.
Interactive Learning of Verbal Descriptors Meanings for Face Drawing System
Authors: Benhidour Hafida ,   Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8573, Japan, Onisawa Takehisa
Published: April 2016

This paper presents an approach for online learning of descriptors meanings in the Face Drawing System with verbal descriptors. The targeted descriptors are those describing the size of the facial features. The users of the system have the possibility to draw faces using linguistic terms and retouch the features of the drawn face using a combination of an adverb and a linguistic descriptor until the desired face is obtained. The system learns interactively the meanings of the terms from the retouches made by the user on the drawn face. For the current user, the system interactively learns the meanings of the descriptors using the proposed algorithm for online learning of Gaussian fuzzy sets approach based on the Maximum Likelihood Estimation (MLE) of Gaussian mixture. The system then adjusts the drawn face according to the new meaning of the descriptors.
Special Issue on Advances in Computational Intelligence, Learning and Their Applications
Authors: Chakraborty Goutam , Liu Yong
Published: April 2016

Algorithms based on Computational Intelligence (CI) and algorithms for learning, attracted a large community of researchers in the last four decades or more. In the beginning, such algorithms were motivated to realize human intelligence in machines. They were to explore novelties in research rather than to implement them for products or services. By the end of the 1990s, CI already had a strong theoretical basis. By then, advanced hardware technologies, cheap chip design, miniaturization and reduced power requirement for hardware, encouraged intelligent algorithm applications to proliferate in real products. This trend is growing, and will continue for long in future. When this special issue was proposed for publication, we were almost certain that submissions on applications would outnumber those on new algorithms on CI or learning per se - and we were right! The September 2009 Intelligent System Symposium, FAN2009 - the nineteenth in the series - organized at the University of Aizu in Japan, attracted over 100 submissions. Reviewers were asked to comment on the suitability of individual submissions for consideration for this journal issue. We personally checked all recommended submissions, finally settling on just 12 for publication. Authors were asked to expand on their work. Each paper, submitted for this special issue, was further judged by 3 reviewers. Finally, 3 papers were rejected and 9 accepted. Of the 9 selected, the first deals with Optimization Algorithms. The remaining 8 are on applications. The second and the third papers are on document clustering and recommendation. The fourth and the fifth cover applications on image recognition - posture estimation of human body, and object segmentation using stereo images. The remaining four papers are applications of CI in different ways. The sixth is on similarity computation using collaborative filtering, the seventh deals with interesting stock-market investor behavior, and the eighth on collecting environmental information from wireless ZigBee sensor networks for use in indoor positioning systems. The final paper is an engineering application of CI, tracker for photovoltaic systems using online learning neural networks. All papers selected are interesting and novel in their application areas and approaches. We expect this issue to encourage young researchers to apply CI to novel problems. We thank the authors for submitting their work and the reviewers for their selfless service. We also thank Reiko Ohta of Fuji Technology Press for her unflagging aid in making this issue a success.
Applying Naive Bayes Classifier to Document Clustering
Authors: Ji Jie ,   System Intelligence Lab., The University of Aizu, Tsuruga, Ikki-machi, Aizu-wakamatsu, Fukushima 965-8580, Japan, Zhao Qiangfu
Published: April 2016

Document clustering partitions sets of unlabeled documents so that documents in clusters share common concepts. A Naive Bayes Classifier (BC) is a simple probabilistic classifier based on applying Bayes’ theorem with strong (naive) independence assumptions. BC requires a small amount of training data to estimate parameters required for classification. Since training data must be labeled, we propose an Iterative Bayes Clustering (IBC) algorithm. To improve IBC performance, we propose combining IBC with Comparative Advantage-based (CA) initialization method. Experimental results show that our proposal improves performance significantly over classical clustering methods.
Spatial Object Segmentation Using Stereo Images
Authors: Hao Yong ,   Department of Computer Science and Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan, He Lifeng , Nakamura Tsuyoshi , Chao Yuyan , Itoh Hidenori ,   Graduate School of Information Science and Technology, Aichi Prefectural University, Nagakute-cho, Aichi 480-1198, Japan,   Graduate School of Environment Management, Nagoya Sangyo University, Owariasahi-city, Aichi 488-8711, Japan
Published: April 2016

The framework of our proposed for segmenting objects using spatial location information from stereo images. An efficient graph-based image segmentation algorithm within this framework for combining changes in optical features and physical location to segment reality scenes into perceptually and semantically uniform regions. Optical and physical location are extracted usingk-means clustering, and we propose a rules table for combining optical and spatial features together. The performance of our proposed framework is demonstrated in a series of reality-scene images using experimental data from the Middlebury stereo image data

(http://vision.middlebury.edu/stereo/data/).
Similarity Computation Method for Collaborative Filtering Based on Optimization
Authors: Yamashita Akihiro ,   Graduate School of Information Science and Technology, Hokkaido University, Kita14, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan, Kawamura Hidenori , Suzuki Keiji
Published: April 2016

The recommender system provides personalized recommendations at many e-commerce websites. Collaborative filtering is one of the most popular and effective recommendation algorithms. User-based collaborative filtering, the conventional approach in collaborative filtering, uses user similarity computed based on user item rating. Recommendations are provided by calculating rating predictions based on similarity. Pearson’s correlation coefficient or cosign distance is used as similarity. Until now, a lot of discussions for efficient similarity computation were given by many researchers. Despite active discussion of similarity computation, little computation has been made for optimal similarity in collaborative filtering. In this research, similarity optimization problem was formulated by defining similarities between an active user and other users as a vector variable. The quasi-optimal solution was obtained based on Particle Swarm Optimization (PSO) approach, compared to Pearson’s correlation coefficient. Experimental results based on agentbased simulation and sample dataset show that similarity based on PSO improves recommendation accuracy. We also found that PSO-based similarity computation provides rating predictions for unknown ratings more accurately than conventional similarity computation.
An Indoor Positioning System Based on Probabilistic Model with ZigBee Sensor Networks
Authors: Tsuji Junpei ,   Laboratory of Harmonious Systems Engineering, Hokkaido University, Sapporo, Japan, Kawamura Hidenori , Suzuki Keiji , Ikeda Takeshi , Sashima Akio , Kurumatani Koichi ,   National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan,   Core Research for Evolutional Science and Technology (CREST), Tokyo, Japan
Published: April 2016

In large indoor commercial spaces constructed recently, services are improved if indoor locations of visitors and employees can be detected. The indoor positioning using ZigBee network based on preobservation of RSSI at positioning areas we propose presents experiments on positioning a mobile ZigBee node on a laboratory floor, including multipath effect. Using our proposal, we determine mobile node location within an accuracy of 3.0 m.
Special Issue on AISAT 2009 - Selected Papers
Authors: Negnevitsky Michael
Published: April 2016

Artificial Intelligence (AI) is the key to success in novel applications ranging from robotics to speech- and image-recognition systems, and from stock forecast investment systems to informal communication between robots and humans. The last two decades have seen AIfs focus shift from philosophical arguments to practical applications in both science and technology. AI researchers and educators have simultaneously recognized mutual commonality in their work and have initiated much-needed multidisciplinary approaches to AI. Guest Editor, Michael Negnevitsky, organized the 3rd International Workshop on Artificial Intelligence in Science and Technology (AISAT 2009) in Hobart, Tasmania, Australia last year, bringing together a globally diverse group of scientists and engineers to discuss issues related to practical AI applications in science and technology. The workshop featured presentations from the US, Japan, Malaysia, Spain, Kuwait, and Australia. All papers were peer reviewed by two experts for technical content, contribution, and originality to ensure high presentation quality. Fewer than 30% of submissions were accepted for presentation at the Workshop. Authors of the most outstanding presentations at AISAT 2009 were encouraged to submit manuscripts to this special issue, whose papers present innovative approaches and promising practical applications for AI. Submissions were reviewed for relevance, originality, significance, and presentation based on JACIII review criteria. We are sure that readers will find these papers both interesting and inspiring. We hope also that they will motivate researchers to expand their studies on AI applications in science and technology.
How AI-Type Uncertainty Ideas Can Improve Inter-Disciplinary Collaboration and Education: Lessons from a Case Study
Authors: Silva Paulo Pinheiro da ,   University of Texas at El Paso, El Paso, TX 79968, USA, Velasco Aaron , Kosheleva Olga , Kreinovich Vladik
Published: April 2016

In many application areas, there is a need for interdisciplinary collaboration and education. However, such collaboration and education are not easy. On the example of our participation in a cyberinfrastructure project, we show that many obstacles on the path to successful collaboration and education can be overcome if we take into account that each person’s knowledge of a statement is often a matter ofdegree– and that we can therefore use appropriate degree-based ideas and techniques.
Intelligent Intrusion Detection Based on Genetically Tuned Artificial Neural Networks
Authors: Reznik Leon ,   Department of Computer Science, Rochester Institute of Technology, 102 Lomb Memorial Drive, Rochester, NY 14623, USA, Adams Michael J. , Woodard Bryan
Published: April 2016

Artificial intelligence techniques and in particular neural networks (ANN) have been widely employed in an Intrusion Detection System (IDS) design. Due to their high learning ability, their application allows achieving higher performance in various applications. However, they can improve the resource consumption at the same time. This design goal, which is very important in a real life IDS design has received much less attention so far. The paper investigates the optimization methods of improving the ANN-based IDS performance along with the resource consumption. A particular consideration is given to partially connected neural networks that open another way of tuning the ANN structure toward the application. The study examines the choice of the connectivity ratio and its optimization with genetic algorithms. Various genetic algorithms parameters are tested in computer network attacks detection and recognition problems. The results are analyzed and IDS design recommendations are provided.
An Analysis of Group Recommendation Strategies
Authors: Berkovsky Shlomo ,   Tasmanian ICT Center, CSIRO, GPO Box 1538, Hobart, Tasmania 7001, Australia, Freyne Jill
Published: April 2016

Collaborative filtering recommender systems often suffer from a data sparsity problem, where systems have insufficient data to generate accurate recommendations. To partially resolve this, the use of group aggregated data in the collaborative filtering recommendations process has been suggested. Although group recommendations are typically less accurate than personalized recommendations, they can be more accurate than generic ones, which are the natural fall back when personalized recommendations cannot be generated. This work presents a study that exploits a dataset of recipe ratings from families of users, in order to evaluate the accuracy of several group recommendation strategies and weighting models.
A Dual-Axis Liquid-Rate Microgyroscope Using Electro-Conjugate Fluid
Authors: Yokota Shinichi ,   Precision and Intelligence Lab., Tokyo Institute of Technology, R2-41, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8503, Japan, Ogawa Yoshitsugu , Takemura Kenjiro , Edamura Kazuya ,   Department of Mechanical Engineering, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan,   New Technology Management Co., Ltd.
Published: April 2016

The dual-axis liquid-rate gyroscope using Electro-Conjugate Fluid (ECF) proposed here follows a previously proposed single-axis liquid-rate gyroscope using ECF [1], a dielectric “smart fluid” generating a powerful jet flow (ECF jet) under a strong Direct-Current (DC) electric field. Although the proposed gyroscope’s sensing principle is based on that of a conventional gas rate sensor [2-4], the gyroscope itself is much more sensitivity because its density is generally higher than that of a gas. The gyroscope is more suitable for a microsensor than a gas rate sensor having a pump with piezoelectric actuator because the proposed gyroscope’s pump consists of a pair of tiny electrodes. We fabricated a 40×40×21 mm3dual-axis liquid-rate gyroscope prototype applying a single-axis liquid-rate microgyroscope and confirmed performance in experiments. Scale factors for theXandY-axes measured in the gyroscope are -24 mV/°/s and -21 mV/°/s. Experimental results confirmed the dual-axis liquid-rate gyroscope potential.
Directional Intention Identification for Running Control of an Omnidirectional Walker
Authors: Jiang Yinlai ,   Department of Intelligent Mechanical Systems Engineering, Kochi University of Technology, 185 Miyanokuti, Tosayamada, Kami, Kochi 782-8502, Japan, Wang Shuoyu , Ishida Kenji , Ando Takeshi , Fujie Masakatsu G. ,   Department of Physical Medicine and Rehabilitation, Kochi University, 2-5-1 Akebono-cho, Kochi 780-8520, Japan,   Department of Modern Mechanical Engineering, Waseda University, 59-309, 3-4-1 Okubo, Shinjyuku, Tokyo 169-8555, Japan
Published: April 2016

Walking is a vital exercise for health promotion and a fundamental ability necessary for everyday life. In previous work, we developed an OmniDirectional Walker (ODW) for walking rehabilitation and walking support. In walking support, it is necessary for the ODW to know the direction the user intends to go based on user manipulation. Actual directional intent must, however, be identified from physical manipulation because a user’s directional intent and physical manipulation are not always mutually consistent. In this paper, a novel interface is proposed to recognize a user’s directional intention according to the forearm pressures exerted to the ODW by the user with wrists and elbows. The forearm pressures are measured by 4 sensors embedded in the ODW’s armrest. The relationship between forearm pressure and directional intention was extracted as fuzzy rules and an algorithm was proposed for directional intention identification based on distance-type fuzzy reasoning method. The effectiveness of the algorithm was verified by experimental reasoning results demonstrated to be consistent with intended directions.
Observed Body Clustering for Imitation Based on Value System
Authors: Tamura Yoshihiro ,   Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan, Takahashi Yasutake , Asada Minoru ,   Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan,   JST ERATO Asada Synergistic Intelligence Project, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
Published: April 2016

In order to develop skills, actions, and behavior in a human symbiotic environment, a robot must learn something from behavior observation of predecessors or humans. Recently, robotic imitation methods based on many approaches have been proposed. We have proposed reinforcement learning based approaches for the imitation and investigated them under an assumption that an observer recognizes the body parts of the performer and maps them to the ones of its own. However, the assumption is not always applicable because of physical differences between the performer and the observer. In order to learn various behaviors from the observation, the robot has to cluster the observed body area of the performer on the camera image and maps the clustered parts to its own body parts based on reasonable criterion for itself and feedback the data for the imitation. This paper shows that the clustering the body area on the camera image into the body parts of its own based on the estimation of the state value in a framework of reinforcement learning as well as it imitates the observed behavior based on the state value estimation. Clustering parameters are updated based on the temporal difference error analogously so the parameters of the state value function of the behavior are updated based on the temporal difference error. The validity of the proposed method is investigated by applying it to an imitation of a dynamic throwing motion of an inverted pendulum robot and human.
A Method for Using Discounted Utterances in Spontaneous Conversation
Authors: Yamaguchi Hiroki ,   Dept. of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan, Ohsawa Yukio , Nishihara Yoko
Published: April 2016

Using Discounted Utterances (DUs) in spontaneous conversation by applying text mining technology, extraction, and evaluation, we focused on DUs where values were buried in previous conversations. We discovered DU potentials by reconsidering them through human-computer interaction. Onlinechat experiments clarified DU features and demonstrated our system’s importance. We found DUs involving (1) experiences shared by the subjects, (2) subjects’ unique experiences, concerns, or beliefs, and (3) apparent unimportance or unrecognized potential. Results of the experiments showed our evaluation method to be appropriate for calculating DU importance when DUs involving (3) were valued significantly lower than (1) and (2). Experiments also suggested that most DUs extracted by the system were not indeed completely ignored but included subjects’ unique stories involving main contexts. Such stories were based on subjects’ unique experiences and may be useful for helping subjects’ metacognition. The system may also enable nonsubjects to infer subjects and their thinking.
Psychological Effects of Color in Overview+Detail Document Interface
Authors: Ichino Junko ,   The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan, Takeuchi Kazuhiro , Isahara Hitoshi ,   Osaka Electro-Communication University,   Toyohashi University of Technology
Published: April 2016

Online documents have two major advantages over paper documents: hypertext links and the larger volume of data space made possible by online references. However, electronic media are not necessarily preferable to paper media in all circumstances. Many people print out online documents before reading. Here, we attempt to create a document interface to support online reading. After analyzing cue information used to accelerate the reading of online documents, how the system should present the documents, and how the documents should be managed by users, we propose an online document interface that removes hindrances and supports active reading. We also employ the psychological effects of color in order to realize this interface. Moreover, we confirm the effectiveness of our proposed color interface by comparing it with both a randomly colored and colorless interface.
Psychological Effects of a Self-Sufficiency Model Based on Urge System
Authors: Ando Teruaki ,   School of Information Science and Technology, Chukyo University, 101 Tokodachi, Kaizu-cho, Toyota 470-0393, Japan, Kanoh Masayoshi
Published: April 2016

In recent years, robots to coexist with humans have been developed. Their ability to communicate is indispensable for their coexistence with humans, so studies on the interaction between humans and robots are important. This paper proposes a model of the selfsufficiency system of a robot, in which we apply the urge system to the autonomous system of emotion. In this model, a robot expresses its changing psychological and physiological conditions (physiological load condition) and conveys them sensitively to the user. This is expected to result in a mental interaction effect between the user and the agent. We carry out simulation experiments on this model and verify the psychological interaction between the software robot (agent) and the user. As a result of these experiments, it is recognized that the agents with the ability to properly express physiological load among those with this model implemented have a tendency to receive higher evaluations from their users.
No Time Limit and Time Limit Model of Multiple Round Dutch Auction Based on Genetic Network Programming
Authors: Mabu Shingo ,   Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135, Japan, Yu Donggeng , Yue Chuan , Hirasawa Kotaro
Published: April 2016

Nowadays, Dutch auction is used widely at online auction sites. To make online Dutch auction more efficient and more intelligent, it is useful to develop an agent using evolutionary computation which will be adaptive to different auction environments. In this paper, a Genetic Network Programming (GNP) based strategy for auction agents has been proposed to do auctions in multiple round Dutch Auction environments under two types of auction models, no time limit model and time limit model. GNP is a graph-based evolutionary method extended from Genetic Algorithms (GA) and Genetic Programming (GP), which can create optimal solutions by evolution. Although the application of GNP to English auction has been done already, here, a new GNP structure is used for Dutch auction. The simulation results show that the GNP based strategy can also make the agents work well in Dutch auction and the advanced GNP structure makes the agents perform better than that in English auction.
Predictor Using an Error-Convergence Neuron Network and its Application to Electrocardiograms
Authors: Kobayakawa Shunsuke ,   Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka 808-0196, Japan, Yokoi Hirokazu
Published: April 2016

The output error of a neuron network cannot converge at zero, even if the training for a neuron network is iterated many times. “Error-convergence neuron network” in which the output error of a singleoutput system uses neuron networks with multiplestep convergence, has been designed to resolve this problem. The output error is converged at zero by setting infinite steps of the neuron network. Three types of neuron network systems also have been designed. They are “Error-convergence parallel neuron network,” “Error-convergence recurrent neuron network,” and “Error-convergence parallel recurrent neuron network.” A subsequent prediction can be obtained by recurring the prediction of a predictor to its input if the predictor is free of prediction error. An error-convergence neuron network can be applied to realize this predictor. “Error-convergence neuron network predictor” has been proposed as such a predictor. In this study, its feasibility is investigated by performing prediction training for the errorconvergence neuron network predictor constructed of second-order Volterra neuron networks with two steps, using the nonlinear time series signal of a normal sinus rhythm electrocardiogram. Predictions without any error were obtained.
Common Testbed Generating Tool Based on XML for Multiple Interdependent Issues Negotiation Problems
Authors: Fujita Katsuhide ,   Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan, Ito Takayuki , Klein Mark ,   Sloan School of Management, Massachusetts Institute of Technology, 5 Cambridge Center, Cambridge 02139, USA
Published: April 2016

Multiple interdependent issues negotiations have been widely studied since most real-world negotiation involves multiple interdependent issues. Our work focuses on negotiation with multiple interdependent issues in which agent utility functions are nonlinear. In the field of multiple issue negotiations, there are no established common testbeds for evaluating protocols. In this paper, we propose a common testbed creating tool based on XML that mainly covers the utility functions based on cube-constraints and cone-constraints. First, we propose a testbed generating tool that inputs configuration data and outputs XML formatted files that represent agent utility spaces. The current tool can produce four types of utility spaces: Random, A Single Hill, Two-Hills, and SeveralHills. These types are observed in real negotiation settings. Also we define the agent’s utility space information based on XML formats. By defining the testbed data as XMLs, users can easily read the files and change the data structure.
Risk Analysis of Portfolios Under Uncertainty: Minimizing Average Rates of Falling
Authors: Yoshida Yuji ,   Faculty of Economics and Business Administration, University of Kitakyushu, 4-2-1 Kitagata, Kokuraminami, Kitakyushu 802-8577, Japan
Published: April 2016

A portfolio model to minimize the risk of falling under uncertainty is discussed. The risk of falling is represented by the value-at-risk of rate of return. Introducing the perception-based extension of the average value-at-risk, this paper formulates a portfolio problem to minimize the risk of falling with fuzzy random variables. In the proposed model, randomness and fuzziness are evaluated respectively by the probabilistic expectation and the mean with evaluation weights and λ-mean functions. The analytical solutions of the portfolio problem regarding the risk of falling are given. This paper gives formulae to show the explicit relations among the following important parameters in portfolio: the expected rate of return, the risk probability of falling and bankruptcy, and the average rate of falling regarding the asset prices. A numerical example is given to explain how to obtain the optimal portfolio and these parameters from the asset prices in the stock market.
Kernel Functions Derived from Fuzzy Clustering and Their Application to Kernel Fuzzyc-Means
Authors: Hwang Jeongsik ,   Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Ibaraki 305-8573, Japan, Miyamoto Sadaaki
Published: April 2016

Among widely used kernel functions, such as support vector machines, in data analysis, the Gaussian kernel is most often used. This kernel arises in entropy-based fuzzyc-means clustering. There is reason, however, to check whether other types of functions used in fuzzyc-means are also kernels. Using completely monotone functions, we show they can be kernels if a regularization constant proposed by Ichihashi is introduced. We also show how these kernel functions are applied to kernel-based fuzzyc-means clustering, which outperform the Gaussian kernel in a typical example.
Variable Precision Rough Set Model in Information Tables with Missing Values
Authors: Kusunoki Yoshifumi ,   Department of Systems Innovation, Graduate School of Engineering Sciences, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan, Inuiguchi Masahiro
Published: April 2016

In this paper, we study rough set models in information tables with missing values. The variable precision rough set model proposed by Ziarko tolerates misclassification error using a membership function in complete information tables. We generalize the variable precision rough set in information tables with missing values. Because of incompleteness, the membership degree of each objects becomes an interval value. We define six different approximate regions using the lower and upper bounds of membership functions. The properties of the proposed rough set model are investigated. Moreover we show that the proposed model is a generalization of rough set models based on similarity relations.
Integrated Decision-Making System for Robot Soccer
Authors: Vačák Ján ,   Technical University of Košice, Letná 9, 042 00 Košice, Slovakia, Hirota Kaoru ,   Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
Published: April 2016

This paper deals with the design of an integrated decision-making system for robot soccer. Three main tasks groups of decision-making are discussed based on an analysis of artificial intelligence means used by individual teams – choice of playing strategy, navigation, and kicking. The paper shows that crisp splitting of these decision-making groups is suspicious, and, instead, the use of a convenient implementation means to keep them together is recommended. In this case, fuzzy cognitive maps are introduced whose role is to integrate decision tasks, which is the main contribution of this paper. The paper describes a new design for kicking decisions. The system was successfully tested in the simulator Webots and some concluding remarks are made.
Special Issue on New Trends in Agent-Based Simulation
Authors: Takadama Keiki , Izumi Kiyoshi
Published: April 2016

Agent-Based Simulation (ABS), an interdisciplinary area embracing both the computer science and the social science, has attracted much attention and aided the understanding of socially complex phenomena. A current important issue in this research area is how to improve ABS effectiveness and comprehension, which makes further mutual influence between the computer science and the social sciences indispensable - e.g., (1) agent modeling involving learning mechanisms in the computer science and (2) social dynamics analysis needed in the social science. Such integration of these two areas would help fulfill the great potential of ABS, first in solving complex engineering problems using agent-based technology and second in developing and testing new theories on socially complex systems. This special issue features ABS papers from both of these important areas exploring new trends in ABS. The 10 papers composing this special issue start with papers by Nobutada Fujii and Hiroyasu Inoue analyzing the relationship between the network structure and system dynamics. In these papers, an agent-based computational economics approach has been active in applying agent-based technologies to financial and economic systems. Papers by Biliana Alexandrova-Kabadjova, Isamu Okada, TomokoOhi, and Nariaki Nishino cover consumer and financial markets using agent-based models. They test economic theory and examine market phenomena for market design. Agent-based simulation is increasingly used in application fields in the social sciences. Papers by Kiyoshi Izumi, Hideki Fujii, Hiromitsu Hattori, and Shigeo Sagai propose solutions for actual social problems such as injury prevention, traffic, and electrical power. Models are created based on behavior data, and the integration of an agent-based model and real data is a hot topic in this area. As the beginning of these technical papers, this issue starts by a position paper to give an ABS overview for understanding important issues in ABS from an overall viewpoint and for understanding state-of-the-art ABS. The information presented is invaluable in helping readers grasp the important features of ABS.
Social and Group Simulation Based on Real Data Analysis
Authors: Izumi Kiyoshi ,   School of Engineering, The University of Tokyo, PRESTO, JST, Tokyo, Japan, Takadama Keiki , Hattori Hiromitsu , Nishino Nariaki , Noda Itsuki ,   Department of Informatics, The University of Electro-Communications, Tokyo, Japan,   Graduate School of Informatics, Kyoto University, Kyoto, Japan,   Information Technology Research Institute, National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan
Published: April 2016

Recently, social simulation research based on real data has appeared in various fields. This paper introduces studies of Agent-Based Simulation (ABSs) based on real data, focusing on introducing studies in the fields of financial marketing, traffic, pedestrians, and a sustainable society. We also introduce some approaches to establish a general method and/or theory about linking social simulation to real data. Finally, we categorize ABS research for understanding ABS research features.
Agent-Based Simulation of Product Diffusion with Network Externality in a Heterogeneous Consumer Network
Authors: Fujii Nobutada ,   Graduate School of System Informatics, Kobe University, Kaihara Toshiya , Eda Takashi ,   Graduate School of Engineering, Kobe University, Rokkodai 1-1, Nada, Kobe 657-8501, Japan
Published: April 2016

In a product market with network externality, outperforming products are not always disseminated. Product markets are often modeled and examined by simulation to clarify product diffusion processes. In earlier work, where multiagent systems and complex networks were used to model the product market, consumer heterogeneity was not considered despite its influence on the product diffusion process. This paper introduces threshold models into multiagent-systembased simulation in complex networks to realize consumer heterogeneity. The feasibility of the proposed method is discussed using computer simulation results, and consumer heterogeneity and network structure affect product diffusion processes.