Ken-ichi Fukui

An analysis of player affect transitions in survival horror games

The trend of multimodal interaction in interactive gaming has grown significantly as demonstrated for example by the wide acceptance of the Wii Remote and the Kinect as tools not just for commercial games but for game research as well. Furthermore, using the player’s affective state as an additional input for game manipulation has opened the realm of affective gaming. In this paper, we analyzed the affective states of players prior to and after witnessing a scary event in a survival horror game. Player affect data were collected through our own affect annotation tool that allows the player to report his affect labels while watching his recorded gameplay and facial expressions. The affect data were then used for training prediction models with the player’s brainwave and heart rate signals, as well as keyboard–mouse activities collected during gameplay. Our results show that (i) players are likely to get more fearful of a scary event when they are in the suspense state and that (ii) heart rate is a good candidate for detecting player affect. Using our results, game designers can maximize the fear level of the player by slowly building tension until the suspense state and showing a scary event after that. We believe that this approach can be applied to the analyses of different sets of emotions in other games as well.

Combining Burst Extraction Method and Sequence-Based SOM for Evaluation of Fracture Dynamics in Solid Oxide Fuel Cell

In this paper, we propose a novel, exact border-based approach that provides an optimal solution for the hiding of sensitive frequent itemsets by (i) minimally extending the original database by a synthetically generated database part - the database extension, (ii) formulating the creation of the database extension as a constraint satisfaction problem that is solved by using binary integer programming, and (Hi) providing an approximate solution close to the optimal one when an ideal solution does not exist. Extending the original database for sensitive itemset hiding is proved to provide optimal solutions to an extended set of hiding problems compared to previous approaches and to provide solutions of higher quality.A crucial issue inputting solid oxide fuel cells (SOFCs) into practical use is the establishment of a technique for evaluating the deterioration of SOFCs. We attempted to capture fracture dynamics measured by acoustic emission (AE) method, employing the following approaches: (1) detection of AE waves utilizing burst extraction method, (2) clustering of AE waves based on the burst level to identify AE types, and (3) visualization of fracture dynamics using adopted self-organizing map. We empirically validated our approach, and obtained a map that can interpret fracture dynamics as physical phenomenon.

Evolutionary Distance Metric Learning Approach to Semi-supervised Clustering with Neighbor Relations

This study proposes a distance metric learning method based on a clustering index with neighbor relation that simultaneously evaluates inter-and intra-clusters. Our proposed method optimizes a distance transform matrix based on the Mahalanobis distance by utilizing a self-adaptive differential evolution (jDE) algorithm. Our approach directly improves various clustering indices and in principle requires less auxiliary information compared to conventional metric learning methods. We experimentally validated the search efficiency of jDE and the generalization performance.

Kullback-Leibler Divergence Based Kernel SOM for Visualization of Damage Process on Fuel Cells

The present work developed a basis to explore numerous damage events utilizing Self-Organizing Map (SOM) introducing Kullback-Leibler (KL) divergence as an appropriate similarity for frequency spectra of damage events. Firstly, we validated the use of KL divergence to frequency spectra of damage events. The experiment using the datasets of damage related sounds showed that the kernel SOM using KL kernel generates accurate cluster map compared to using general kernel functions and the standard SOM. Afterward, we demonstrated our approach can clarify damage process of Solid Oxide Fuel Cells (SOFC) from acoustic emission (AE) events observed by damage test of SOFC. The damage process was inferred by occurrence frequency of AE events upon the cluster map of SOM, where the occurrence density change was obtained by kernel density estimation (KDE). The presented approach can be a common foundation for the domain experts to clarify fracture mechanism of SOFC and/or to monitor SOFC operation.

Neighborhood-Based smoothing of external cluster validity measures

This paper proposes a methodology for introducing a neighborhood relation of clusters to the conventional cluster validity measures using external criteria, that is, class information. The extended measure evaluates the cluster validity together with connectivity of class distribution based on a neighborhood relation of clusters. A weighting function is introduced for smoothing the basic statistics to set-based measures and to pairwise-based measures. Our method can extend any cluster validity measure based on a set or pairwise of data points. In the experiment, we examined the neighbor component of the extended measure and revealed an appropriate neighborhood radius and some properties using synthetic and real-world data.

Sequence-based SOM: Visualizing transition of dynamic clusters

We have proposed neural-network based visualization approach, called sequence-based SOM (self-organizing map) that visualizes transition of dynamic clusters by introducing the sequencing weight function onto the neuron topology. This approach mitigates the problems with a sliding window-based method. In this paper, we confirmed the properties of the proposed method via artificial data sets, and a real news articles data set by showing the topicspsila derivation and diversification/convergence. Visualization of cluster transition aids in the comprehension of such phenomena which come useful in various domains such as fault diagnosis and medical check-up, among others.

Cluster-based predictive modeling to improve pedagogic reasoning

This paper discusses a cluster knowledge-based predictive modeling framework actualized in a learning agent that leverages on the capability of a clustering algorithm to discover in logged tutorial interactions unknown structures that may exhibit predictive characteristics. The learned cluster models are described along learner-system interaction attributes, i.e., in terms of the learners knowledge state and behaviour and systems tutoring actions. The agent utilizes the knowledge of its various clusters to learn predictive models of high-level student information that can be utilized to support fine-grained individualized adaptation. We investigated on utilizing the Self-Organizing Map as clustering algorithm, and the naive Bayesian classifier and perception as weighting algorithms to learn the predictive models. Though the agent faced the difficulty imposed by the experimentation dataset, empirical results show that utilizing cluster knowledge has the potential to improve coarse-grained prediction for a more informed and improved pedagogic decision-making.

Familiarity effects in EEG-based emotion recognition

Although emotion detection using electroencephalogram (EEG) data has become a highly active area of research over the last decades, little attention has been paid to stimulus familiarity, a crucial subjectivity issue. Using both our experimental data and a sophisticated database (DEAP dataset), we investigated the effects of familiarity on brain activity based on EEG signals. Focusing on familiarity studies, we allowed subjects to select the same number of familiar and unfamiliar songs; both resulting datasets demonstrated the importance of reporting self-emotion based on the assumption that the emotional state when experiencing music is subjective. We found evidence that music familiarity influences both the power spectra of brainwaves and the brain functional connectivity to a certain level. We conducted an additional experiment using music familiarity in an attempt to recognize emotional states; our empirical results suggested that the use of only songs with low familiarity levels can enhance the performance of EEG-based emotion classification systems that adopt fractal dimension or power spectral density features and support vector machine, multilayer perceptron or C4.5 classifier. This suggests that unfamiliar songs are most appropriate for the construction of an emotion recognition system.

Evolutionary multi-objective distance metric learning for multi-label clustering

In data mining and machine learning, the definition of the distance between two data points substantially affects clustering and classification tasks. We propose a distance metric learning (DML) method for multi-label clustering, that uses evolutionary multi-objective optimization and a cluster validity measure with a neighbor relation that simultaneously evaluates inter- and intra-clusters. The proposed method produces clustering results considering multiple class labels and allows the induction of knowledge regarding relations between class labels in multi-label clustering or between objective functions and elements in transform matrix. Experimental results have shown that the proposed DML method produces better transform matrices than single-objective optimization and is helpful in finding the attributes that affect the trade-off relationship among objective functions.

Visualizing dynamics of the hot topics using sequence-based self-organizing maps

We are currently working on a SOM-based method for temporal analysis and visualization of “hot topic” trends in news articles. Hot topics are extracted from a document collection by applying PCA to term frequency bag-of-words vectors. Evaluative experiments on three data sets, the largest expands across ten years, show that SBSOM induces a sequential analysis and that the use of label confidence mitigates the performance loss.