Mori K.,Bunkyo University |
Ohmori T.,Tama University
Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016 | Year: 2016
In this study, we derive the Bayesian estimators of polytomous item response theory (IRT) models with an approximated conditional likelihood function and consider their mathematical optimalities. First, we derive Bayesian estimators of the rating scale model (RSM) and related IRT models with a conditional type approximated likelihood function proposed by . Then, we evaluate the admissibility and minimaxity of these estimators. We then show the range of the approximation by the conditional likelihood method using a simulation study. An application is also presented. © 2016 IEEE.
Tsutsumi E.,Tama University
Journal for Geometry and Graphics | Year: 2011
A full-year elective course of Computer Graphics is conducted using High-end 3D-CG software. Lectures and exercises on projection drawing methods and sketch drawing are given at the beginning of the course. MCT was performed at the beginning and the end of the course. This paper examines the results, especially with respect to the relationship between scores on MCTs and scores on assignments of the 2008 class. MCT increased at the end of the course especially in case of students who got higher total subject scores. It became clear that the higher the score on assignments, the more improvement there was in the student's spatial abilities. In assignments significantly correlating with MCT scores, students had to model objects by creating a picture in their mind, because examples were given only by orthogonal projection views or they had to compose by using some abstract conditions like "to include Boolean operation", etc. Thus, the students those who could create clear images of the target objects both geomet-rically and dimensionally and the students who intuitively understood when they had created the wrong form of the model received good results on assignments and could gain greater understanding of spatial recognition. In conclusion, if we define abilities measured by MCT in terms of cognitive abilities relating to three dimensional objects drawn in a two dimensional plane, completing assignments by making images of objects improves the space sense of students. © 2011 Heldermann Verlag.
Ichimura S.,Tama University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2017
Gamification is the concept that utilizing elements and ideas of video games in non-gaming fields. It aims at improving user experience, user engagement and users’ motivation by utilizing elements and mechanisms by which video games entertain many people. In the paper, we explain some basic ideas to let people enjoy housekeeping work, and propose a vacuum cleaner with gamification elements as an example of the ideas. As a result of the experiments, it turned out that our vacuum cleaner with game elements could provide more enjoyable experience to users than usual. © Springer International Publishing AG 2017.
Hadavandi E.,Amirkabir University of Technology |
Shahrabi J.,Amirkabir University of Technology |
Hayashi Y.,Tama University
Soft Computing | Year: 2015
In this paper, we focus on modeling multi-target regression problems with high-dimensional feature spaces and a small number of instances that are common in many real-life problems of predictive modeling. With the aim of designing an accurate prediction tool, we present a novel mixture of experts (MoE) model called subspace-projected MoE (SPMoE). Training the experts of the SPMoE is done using a boosting-like manner by a combination of ideas from subspace projection method and the negative correlation learning algorithm (NCL). Instead of using whole original input space for training the experts, we develop a new cluster-based subspace projection method to obtain projected subspaces focused on the difficult instances at each step of the boosting approach for training the diverse experts. The experts of the SPMoE are trained on the obtained subspaces using a new NCL algorithm called sequential NCL. The SPMoE is compared with the other ensemble models using three real cases of high-dimensional multi-target regression problems; the electrical discharge machining, energy efficiency and an important problem in the field of operations strategy called the practice–performance problem. The experimental results show that the prediction accuracy of the SPMoE is significantly better than the other ensemble and single models and can be considered to be a promising alternative for modeling the high-dimensional multi-target regression problems. © 2015 Springer-Verlag Berlin Heidelberg
Hayashi Y.,Tama University
International Journal of Computational Intelligence and Applications | Year: 2013
This paper presents theoretical and historical backgrounds related to neural network rule extraction. It also investigates approaches for neural network rule extraction by ensemble concepts. Bologna pointed out that although many authors had generated comprehensive models from individual networks, much less work had been done to explain ensembles of neural networks. This paper carefully surveyed the previous work on rule extraction from neural network ensembles since 1988. We are aware of three major research groups i.e., Bologna' group, Zhou' group and Hayashi' group. The reason of these situations is obvious. Since the structures of previous neural network ensembles were quite complicated, the research on the efficient rule extraction algorithm from neural network ensembles was few although their learning capability was extremely high. Thus, these issues make rule extraction algorithm for neural network ensemble difficult task. However, there is a practical need for new ideas for neural network ensembles in order to realize the extremely high-performance needs of various rule extraction problems in real life. This paper successively explain nature of artificial neural networks, origin of neural network rule extraction, incorporating fuzziness in neural network rule extraction, theoretical foundation of neural network rule extraction, computational complexity of neural network rule extraction, neuro-fuzzy hybridization, previous rule extraction from neural network ensembles and difficulties of previous neural network ensembles. Next, this paper address three principles of proposed neural network rule extraction: to increase recognition rates, to extract rules from neural network ensembles, and to minimize the use of computing resources. We also propose an ensemble-recursive-rule extraction (E-Re-RX) by two or three standard backpropagation to train multi-layer perceptrons (MLPs), which enabled extremely high recognition accuracy and the extraction of comprehensible rules. Furthermore, this enabled rule extraction that resulted in fewer rules than those in previously proposed methods. This paper summarizes experimental results of rule extraction using E-Re-RX by multiple standard backpropagation MLPs and provides deep discussions. The results make it possible for the output from a neural network ensemble to be in the form of rules, thus open the black box of trained neural networks ensembles. Finally, we provide valuable conclusions and as future work, three open questions on the E-Re-RX algorithm. © 2013 Imperial College Press.
Murata-Soraci K.,Tama University
Psychology of Mindfulness | Year: 2014
In our daily attempt to make sense of life and the world, we come to realize that appropriation of life hinges upon our mindfulness. Being alert to how we dwell in the interior and exterior landscapes of our existence may yet turn around the present modes of living into a salutary condition and/or a better direction. And yet, in what sense(s) does "mindfulness" matter to us for genuinely experiencing our mortal time and for (re-)creation of the world? How successfully does "mindfulness" intervene the psycho-somatic experience of predicaments and enable us to better cope with times of distress and suffering? This book is an interdisciplinary collection of studies on mindfulness explored and discussed by the authors from different walks of life, disciplines and interests. It offers a rich set of interventions related to the practice of mindfulness meditations which effectively reduces human predicaments of menstrual-stress, neurosis, loneliness, anxiety, trauma, forgetfulness, and distress over cancer, fear of dying alone, mourning, etc., gathered by the authors through their research, teaching, and practice from the fields of philosophy, psychology, medicine, therapy, social work, education and fine arts. Most of the authors, even those of hard-nosed empirical scientists, share their concern and disquietude about the dualistic stance of the representational subject and the discriminatory thinking and language of substance and intentionality. They address, covertly or overtly, the difference between the Western mode of reflection and Eastern mode of meditation in the human subject's experience of life and try to integrate various forms of Christian, Hindu, Taoist and Zen meditations into their contexts. The authors in this volume are thus attuned to the menace of recognizable objectivism and objectification of intrinsically interconnected lives of things, and are seeking, through their studies, ways of overcoming a tyranny of the "I" under a zenith of its objectivism in our cultural climate. Accordingly, this volume includes also three informative essays on the Mahayana Buddhist traditions in India and China, Japanese Sôtô Zen practice of Dôgen, and a comparative study of meditation between the Western and the Eastern traditions of spirituality, so as to shed light on the historical and philosophical backgrounds of mindfulness meditation of the far East. This collection of essays closes with mindfulness as an issue of "interspecies, human-animal relations." In order to find a step beyond the epoch of anthropocentrism, this book will extend our alertness to nonhuman animals wherein the essential traits of mankind have been repeatedly drawn and appropriated in the history of man. In conclusion, the book will examine new possibilities in the mind of the reader for seeking a way of authentic co-belonging with other species and of building a new çthos beyond the age of objectivism and anthropocentrism. © 2014 by Nova Science Publishers, Inc. All rights reserved.
Mori H.,Meiji University |
Nakano K.,Tama University
IEEE Symposium on Computational Intelligence Applications in Smart Grid, CIASG | Year: 2015
In this paper, a new method is proposed for Locational Marginal Pricing (LMP) forecasting in Smart Grid. The marginal cost is required to supply electricity to incremental loads in case where a certain node increases power demands in a balanced power system. LMP plays an important role to maintain economic efficiency in power markets in a way that electricity flows from a low-cost area to high-cost one and the transmission network congestion in alleviated. The power market players are interested in maximizing the profits and minimizing the risks through selling and buying electricity. As a result, it is of importance to obtain accurate information on electricity pricing forecasting in advance so that their desire is reflected. This paper presents the Gaussian Process (GP) technique that comes from the extension of Support Vector Machine (SVM) that hierarchical Bayesian estimation is introduced to express the model parameters as the probabilistic variables. The advantage is that the model accuracy of GP is better than others. In this paper, GP is integrated with the k-means method of clustering to improve the performance of GP. Also, this paper makes use of the Mahalanobis kernel in GP rather than the Gaussian one so that GP is generalized to approximate nonlinear systems. The proposed method is successfully applied to real data of ISO New England in USA. © 2014 IEEE.
Okada A.,Tama University
Studies in Classification, Data Analysis, and Knowledge Organization | Year: 2012
Car switching or car trade-in data among car categories were analyzed by a procedure of asymmetric multidimensional scaling. The procedure, which deals with one-mode two-way asymmetric similarities, has originally been introduced to derive the centrality of the asymmetric social network. In the present procedure, the similarity from a car category to the other car category is represented not by the distance in a multidimensional space like the conventional multidimensional scaling, but is represented by the weighted sum of areas with the positive or negative sign along dimensions of a multidimensional space. The result of the analysis shows that attributes which already have been revealed in previous studies accounted for car switching among car categories by a different manner from previous studies, and can more easily be interpreted than the previous studies. © 2012 Springer-Verlag Berlin Heidelberg.
Okada A.,Tama University
Studies in Classification, Data Analysis, and Knowledge Organization | Year: 2014
Brand switching data among eight soft drink brands were analyzed. The data are represented by an 8×8 brand switching matrix. The brand switching matrix is inevitably asymmetric, because the relationship from brand j to brand k is not necessarily equal to the relationship from brand k to brand j . The brand switching matrix was analyzed by asymmetric multidimensional scaling based on singular value decomposition. The four-dimensional result was chosen as the solution. The solution gives the outward tendency,which represents the strength of switching from a corresponding brand to the other brands along each dimension, and the inward tendency, which represents the strength of switching to a corresponding brand from the other brands along each dimension. The solution disclosed that the differences between diet and non-diet brands as well as between cola and lemon-lime brands played important roles in the brand switching. © Springer International Publishing Switzerland 2014.
Komiyama A.,Tama University
Neuro-Ophthalmology Japan | Year: 2015
It is well known that the primary feature of nystagmus is a drift of the eyes from the desired position of the eyes; saccadic intrusions, however, are characterized by inappropriate saccadic movements that interfere with steady fixation. Several types of saccadic intrusions have their own salient features including the presence or absence or duration of their saccadic intervals. In this article, we aim to update readers on the latest advances in understanding square-wave jerks (SWJs) and macrosaccadic oscillations (MSOs), in which the abnormal eye movements have saccadic intervals, and present video clips and electrooculograms of the typical eye movements for a comparison. SWJs are small, conjugated saccades, ranging from 0.5 to 5 degrees (usually less than 2 degrees) in size, which take the eyes away from the fixation position, subsequently returning to the original position after a period of about 200 ms. SWJs are commonly found in healthy subjects, especially the elderly; they occur frequently in cerebral disease, spinocerebellar degeneration, and progressive supranuclear palsy. SWJs may likely result from abnormally enlarged microsaccades that play a pivotal role in optimizing the perception of an object by shifting the image on the retina in small portions of approximately 0.5 degrees. On the other hand, MSOs are a rare form of large-sized saccadic oscillations around the fixation point that wax and wane, reportedly with a "normal" saccadic interval of 200 ms, and were documented in cerebellar disorders and myasthenia gravis with edrophonium administration. MSOs, however, are considered to be an enhanced variation of saccadic hypermetria with the saccadic gain over 2.0, and therefore, the saccadic interval of MSOs should be the same as that of short-latency corrective saccades after saccadic hypermetria, which is around 125 ms in reported cases.