In the e-learning context, how much the learner is concentrated and engaged, or the learners’ efficiency, is essential for providing adaptive and flexible materials, timely suggestions, etc., which can lead to efficient learning. In this work, we explore to predict learners’ efficiency with a realistic configuration, in which we use a webcam or a laptop PC’s built-in camera. Specifically, we first provide a feasible definition of the learners’ efficiency, and based on this definition, we predict one’s efficiency from facial behavior. We predict the learners’ efficiency using various convolutional neural networks. Results are discussed using different evaluation metrics.

Electroencephalogram (EEG) signal has been widely applied in detecting human emotion. Individual differences limit the generalization in cross-subject classification since the release of emotion will be definitely different across persons even with the same emotion stimuli. Previous research utilizes domain adaptation to solve this problem in a leave-one-subject-out training that is learning common emotion-related features from many subjects and testing on a new subject, which requires abundant labeled data. This paper proposed a novel one-to-one domain adaptation method, the Task-specific Domain Adversarial Neural Network (T-DANN) which transfers knowledge from either one subject to predict on another subject or knowledge from one phase to predict on another phase within the same subject. Therefore, T-DANN is more flexible and requires much less data during training. T-DANN is an adversarial training method which adapts the conditional distribution between domains and adapts classification boundaries between classes simultaneously. Compared with data from different subjects, phase data from the same subject has much deeper correlation thus enhances the prediction of emotion in new phase. We evaluated our method on EEG emotion benchmark dataset SEED. The experiments showed that our proposed method outperformed other baseline methods in cross-subject adaptation. By cross-phase adaptation, our method achieved accuracy that approximately 3% lower than the state-of-the-art method but only used 1/14 labeled data, indicating the priority of our proposed method in real-time application.

Recently, researchers have utilized neural networks to accurately solve partial differential equations (PDEs), enabling the mesh-free method for scientific computation. Unfortunately, the network performance drops when encountering a high nonlinearity domain. To improve the generalizability, we introduce the novel approach of employing multi-task learning techniques, the uncertainty-weighting loss and the gradients surgery, in the context of learning PDE solutions. The multi-task scheme exploits the benefits of learning shared representations, controlled by cross-stitch modules, between multiple related PDEs, which are obtainable by varying the PDE parameterization coefficients, to generalize better on the original PDE. Encouraging the network pay closer attention to the high nonlinearity domain regions that are more challenging to learn, we also propose adversarial training for generating supplementary high-loss samples, similarly distributed to the original training distribution. In the experiments, our proposed methods are found to be effective and reduce the error on the unseen data points as compared to the previous approaches in various PDE examples, including high-dimensional stochastic PDEs.

In order to improve Remaining Useful Life (RUL) prediction accuracy for rolling bearings under defect progressing, robustness for individual difference and fluctuation of vibration features are challenging issues. In this research, we propose a novel RUL prediction method that uses a hierarchical Bayesian method to consider the individual difference of RUL, and uses an intermediate variable indicating the defect condition instead of predicting RUL directly from vibration features. The proposed method can perform a monotonous RUL prediction curve and improved prediction accuracy especially for early stage of defect progression.

Metric learning algorithms create distance metrics to capture the important relationships among features. These algorithms have been successful in low dimensional data. However, it is a challenge to handle high dimensional problems in real-world applications. This paper applies in high dimensional clustering problems a Reinforcement Learning (RL) based metric filtering approach in Evolutionary Distance Metric Learning (EDML), which relies on an evolutionary approach to optimize its distance metric. The proposed framework called High Dimensional Reinforced (HDR)-EDML has two novelties. The first is using a function approximation RL method called Least-Squares Policy Iteration (LSPI) to create a feature selection control strategy in high-dimensional input space to filter the metric. The second is adopting a two-way information exchange approach between LSPI and EDML Evolutionary Algorithm (EA) in metric learning. In the first way, LSPI will learn and send feedback that will affect the EDML metric creation. In the second way, the evolutionary feature prioritizing of EDML is utilized by LSPI in its learning process. These two novelties aim to reduce the number of selected features while maintaining the clustering performance. HDR-EDML is compared to conventional K-means, Information-Theoretic Metric Learning (ITML), normal EDML, and EDML with l1norm regularization for feature selection as baselines. Moreover, 3 different HDR-EDML approaches are examined to explore different ways of information exchange. Results show a significant decrease in the number of features while maintaining accuracy as well as reduced computational time and memory compared to another RL-based filtering method.

We proposed a method to extract causal relations of clusters from multi-dimensional event sequence data, along with another method to detect the changes of the extracted relations over time. The proposed Granger Cluster Sequence Mining (GCSM) algorithm identifies the pairs of spatial data clusters that have causality over time with each other. It extends the Cluster Sequence Mining algorithm, which utilized a statistical inference technique to identify occurrence relation, with a causality based on Granger causality. We also proposed a statistical model to infer the changes over time of each extracted causal relation. With experiments using synthetic data and semi-real data, we confirmed that the algorithm works correctly, and able to extract the embedded causal relations with high F-score and high accuracy of change points.

In this paper, we propose two modified neural networks based on dual path multi-scale fusion networks (SFANet) and SegNet for accurate and efficient crowd counting. Inspired by SFANet, the first model, which is named M-SFANet, is attached with atrous spatial pyramid pooling (ASPP) and context-aware module (CAN). The encoder of M-SFANet is enhanced with ASPP containing parallel atrous convolutional layers with different sampling rates and hence able to extract multi-scale features of the target object and incorporate larger context. To further deal with scale variation throughout an input image, we leverage the CAN module which adaptively encodes the scales of the contextual information. The combination yields an effective model for counting in both dense and sparse crowd scenes. Based on the SFANet decoder structure, M-SFANet`s decoder has dual paths, for density map and attention map generation. The second model is called M-SegNet, which is produced by replacing the bilinear upsampling in SFANet with max unpooling that is used in SegNet. This change provides a faster model while providing competitive counting performance. Designed for high-speed surveillance applications, M-SegNet has no additional multi-scale-aware module in order to not increase the complexity. Both models are encoder-decoder based architectures and are end-to-end trainable. We conduct extensive experiments on five crowd counting datasets and one vehicle counting dataset to show that these modifications yield algorithms that could improve state-of-the-art crowd counting methods. Codes are available at https://github.com/Pongpisit-Thanasutives/Variations-of-SFANet-for-Crowd-Counting.

This paper proposed a method to detect changes in causal relations over a multi-dimensional sequence of events. Cluster Sequence Mining algorithm was modified to extract causal relations in the form of g-patterns: a pair of clusters of events that have their occurrence time determined by Granger causality. This paper also proposed the pattern time signature, a probabilistic density function of the cluster sequence occurring at any given time. Synthetic data were used for validation. The result shows that the proposed algorithm can correctly identify the changes in causal relations even under noisy data.

The performance of human workers can be fluctuated due to changes in the cognitive state during sustained work. Though past researches have made human performance monitoring possible by utilizing physiological signals, little attention has been paid to the context of office works. This research proposes a multimodal approach to estimate office task performance. A transcription typing experiment was conducted to simulate the real working environment while typing speed and error rate represented as performance metrics. Physiological data collected during the experiment, together with conventional machine learning algorithms showed feasibility to accurately predict two levels (good/bad) of task performance. More importantly, a comprehensive comparison between choices of modality suggests that using data from particular sources could gain predictive performance comparable to the complete set of modalities.

Data collection plays an important role in business agility; data can prove valuable and provide insights for important features. However, conventional data collection methods can be costly and time-consuming. This paper proposes a hybrid system R-EDML that combines a sequential feature selection performed by Reinforcement Learning (RL) with the evolutionary feature prioritization of Evolutionary Distance Metric Learning (EDML) in a clustering process. The goal is to reduce the features while maintaining or increasing the accuracy leading to less time complexity and future data collection time and cost reduction. In this method, features represented by the diagonal elements of EDML matrices are prioritized using a differential evolution algorithm. Further, a selection control strategy using RL is learned by sequentially inserting and evaluating the prioritized elements. The outcome offers the best accuracy R-EDML matrix with the least number of elements. Diagonal R-EDML focusing on the diagonal elements is compared with EDML and conventional feature selection. Full Matrix R-EDML focusing on the diagonal and non-diagonal elements is tested and compared with Information-Theoretic Metric Learning. Moreover, R-EDML policy is tested for each EDML generation and across all generations. Results show a significant decrease in the number of features while maintaining or increasing accuracy.

In this research, we combine relational learning with multi-domain to develop a formal framework for a recommendation system. The design of our framework aims at: (i) constructing general rules for recommendations, (ii) providing suggested items with clear and understandable explanations, (iii) delivering a broad range of recommendations including novel and unexpected items. We use relational learning to find all possible relations, including novel relations, and to form the general rules for recommendations. Each rule is represented in relational logic, a formal language, associating with probability. The rules are used to suggest the items, in any domain, to the user whose preferences or other properties satisfy the conditions of the rule. The information described by the rule serves as an explanation for the suggested item. It states clearly why the items are chosen for the users. The explanation is in if-then logical format which is unambiguous, less redundant and more concise compared to a natural language used in other explanation recommendation systems. The explanation itself can help persuade the user to try out the suggested items, and the associated probability can drive the user to make a decision easier and faster with more confidence. Incorporating information or knowledge from multiple domains allows us to broaden our search space and provides us with more opportunities to discover items which are previously unseen or surprised to a user resulting in a wide range of recommendations. The experiment results show that our proposed algorithm is very promising. Although the quality of recommendations provided by our framework is moderate, our framework does produce interesting recommendations not found in the primitive single-domain based system and with simple and understandable explanations.

In this paper, we focus on a method that integrates a physical model into a neural network. This study proposes a neural network that can predict two components, namely outputs based on a physical model and its model discrepancy. To achieve such a goal, we propose a novel neural network architecture and associated loss functions designed based on a target physical model. The physical model is used as a regularizer of spatial behavior where output from the neural network is used as an intermediate variable. Then, the model discrepancy is defined as its residual to the observation value. We also propose a network architecture which has Shared and Non-Shared networks, and the neural network can be trained by alternate optimization. We constructed the proposed method with wind prediction in the upper troposphere based on thermal wind equations as an example. The experimental results demonstrate that the proposed method can achieve higher predictive accuracy than normal convolutional neural network or using thermal wind equation, also the obtained model discrepancy expresses convergence and divergence of wind vectors.

This study proposes a novel distance metric learning method called evolutionary distance metric learning (EDML) to improve clustering quality that simultaneously evaluates inter- and intra-clusters. While we also provide an extension which integrates kernelization technique to the proposed method n amely kernelized evolutionary distance metric learning (K-EDML). Hence, the non-linear transformation of distance metric can be performed while maintaining all properties of EDML. The proposed methods are able to handle either class label or pairwise constraints and directly improve any clustering index as an objective function. Both can be viewed as utilization of cluster-level soft constraints, unlike other instance-level hard constraints which sometimes collapse the clustering. Also, maintaining neighbor relation of clusters can lead to better visualization of the clustering result. For multimodality problem of the objective function, an evolutionary algorithm (EA), differential evolution with self-adapting control parameters and generalized opposition-based learning (GOjDE), is employed to optimize a metric transform matrix based on the Mahalanobis distance. We empirically demonstrate the drawback of EDML in non-linearly separable input space and illustrate the benefit of kernel function to extension K-EDML method by showing its superior result benefits to other clustering algorithms in the semi-supervised clustering on various real-world datasets.

We propose a novel mining algorithm called cluster sequence mining (CSM) to extract cluster pairs with occurrence correlation from event sequence data. CSM extracts patterns with a pair of clusters that satisfies space proximity of the individual clusters and temporal proximity between events from different clusters in time intervals. CSM extends a unique co-occurring cluster mining (CCM) algorithm by considering the order of event occurrences and distribution of time intervals. The probability density of time intervals is inferred using Bayesian inference for robustness against uncertainty. To improve inference accuracy of the density function of time intervals, we utilize the idea of dynamic programming (DP) matching to obtain the correspondence between multiple event occurrences. With an experiment using synthetic data, we confirm that CSM is capable of extracting clusters with a high F-measure and low estimation error of the time interval distribution even under uncertainty. In addition, we find that DP matching can improve the inference accuracy of the density function of time intervals. Finally, CSM is applied to a real-world acoustic emission event sequence data set to evaluate damage interactions in a fuel cell.

We propose a method to generate explainable recommendation rules on cross-domain problems. Our two main contributions are: i) using relational learning to generate the rules which are able to explain clearly why the items were recommended to the particular user, ii) using the user's preferences of items on different domains and item attributes to generate novel or unexpected recommendations for the user. To illustrate that our method is indeed feasible and applicable, we conducted experiments on music and movie domains.

When musicians are asked to create music with a special theme or for a particular use, it is important for them to meet the client's demands and express their original creativity. As this task is a little more difficult than their free creative activities, an automatic music composition system may support them. Requirements for the system are the ease of expressing the musicians' intention and the shortness of the processing time. In this paper, a scene where musicians create a song using an automatic composition system is assumed, and a method of generating a melody for the system is proposed. In the proposed method, two kinds of sensibility models for melody are induced from the existing music. A melody template is generated on the basis of those models using a symbiotic evolution algorithm. A melody is completed by determining pitch of each note in the melody template. The results of the subjective evaluation experiment show the effectiveness of the proposed method. In addition, two case studies of works with professional musicians are also presented. The results of questionnaire surveys show that the composed music reflects the musicians' intentions and is not inferior to music composed by human composers.

Many research studies on distance metric learning (DML) reiterate that the definition of distance between two data points substantially affects clustering tasks. Recently, variety of DML methods have been proposed to improve the accuracy of clustering by learning a distance metric; however, most of them only perform a linear transformation, which yields insignificant to non-linear separable data. This study proposes a DML method which provides an integration of kernelization technique with Mahalanobis-based DML. Thus, non-linear transformation of the distance metric can be performed. Moreover, a cluster validity index is optimized by an evolutionary algorithm. The empirical results on semi-supervised clustering suggest the promising result on both synthetic and real-world data set.

Multimodality has been recently exploited to overcome the challenges of emotion recognition. In this paper, we present a study of fusion of electroencephalogram (EEG) features and musical features extracted from musical stimuli at decision level in recognizing the time-varying binary classes of arousal and valence. Our empirical results demonstrate that EEG modality was suffered from the non-stability of EEG signals, yet fusing with music modality could alleviate the issue and enhance the performance of emotion recognition.

ART-2a has been shown to be effective against stream data clustering with unknown number of cluster in nature. As data grows, ART-2a running time become a major problem. We proposed a new algorithm, ART-2b, whose runtime performance is linear to the number of input instances, while still maintaining similar clustering result to ART-2a.

Data stream classifiers that can withstand unusual phenomena in an evolving data stream, such as concept drift and concept evolution, are highly desirable for data stream mining. Most existing methods deal with such phenomena in a supervised manner, which is costly in a real-world scenario. To address this shortcoming, we propose a concept drift detection approach that combines our approach with a semi-supervised adaptive incremental neural gas (A2ING) classifier. Our approach makes use of A2ING's graph topology structure to detect changes in a data stream. We derive a graph's instability around its decision boundary and find the difference in prior and posterior distributions of the criteria. The empirical results show the effectiveness of our method. The classifier requires a relatively low number of true labels compared to existing approaches and shows high effectiveness in change detection.

In our society, people engage in a variety of interactions. To analyze such interactions, we consider these interactions as a game and people as agents equipped with reinforcement learning algorithms. Reinforcement learning algorithms are widely studied with a goal of identifying strategies of gaining large payoffs in games; however, existing algorithms learn slowly because they require a large number of interactions. In this work, we constructed an algorithm that both learns quickly and maximizes payoffs in various repeated games. Our proposed algorithm combines two different algorithms that are used in the early and later stages of our algorithm. We conducted experiments in which our proposed agents played ten kinds of games in self-play and with other agents. Results showed that our proposed algorithm learned more quickly than existing algorithms and gained sufficiently large payoffs in nine games.

In the ocean around the world, depth series ocean data of temperature and salinity have being measured. However, it is difficult to discriminate the errors from the normal data since the variation of ocean areas are different. In this research, using hierarchical clustering, we partitioned the ocean into some areas so that the ocean data have the same variation in each area. Then, transforming the ocean data into sets of sliding windows in consideration of depth series, we applied some anomaly detection methodologies. Finally, we succeeded in assigning high anomaly scores on errors that seemed to be normal.

Estimating emotional states in music listening based on electroencephalogram (EEG) has been capturing the attention of researchers in the past decade. Although deep belief network (DBN) has witnessed the success in various domains including early works in emotion recognition based on EEG, it remains unclear whether DBN could improve emotion classification in music domains, especially in dynamic strategy that considers time-varying characteristics of emotion. This paper presents an early study of applying DBNs to improve emotion recognition in music listening where emotions were annotated continuously in time by subjects. Our subject-dependent results using stratified 10-fold cross-validation strategy suggested that DBNs could improve performance in valence classification with fractal dimension (FD), power spectral density (PSD), and discrete wavelet transform (DWT) features and improve performance in arousal classification with FD and DWT features. Furthermore, we found that the size of sliding window affected classification accuracies when using features in time (FD) and time-frequency (DWT) domains, while smaller window (1-4 seconds) could achieve higher performance compared with a larger window (5-8 seconds).

As emotional responses of a human to stimuli could evolve over the course of time, continuous emotion reporting is essential for the construction of a computational model to capture the temporal evolution of the human emotions. However, continuous emotion assessment is confronting various challenges, especially when using the continuous arousal-valence space. Manipulating emotion annotation data prior to performing emotion recognition is, therefore, necessary. In this paper, we present a study of applying three different signal filtering techniques to smooth annotation data; moving average filter, Savitzky-Golay filter, and median filter. We performed experiments of arousal and valence recognition in music listening tasks employing signals from electroencephalogram (EEG). Fractal dimension approach was adopted to extract informative features from brain dynamics and emotional states were then derived by classification and regression techniques. Our empirical results suggested the promise of the moving average filter that could enhance the performance of emotion classifying and tracking.

To gain a better understanding of how neural ensembles communicate and process information, neural decoding algorithms are used to extract information encoded in their spiking activity. Bayesian decoding is one of the most used neural population decoding approaches to extract information from the ensemble spiking activity of rat hippocampal neurons. Recently it has been shown how Bayesian decoding can be implemented without the intermediate step of sorting spike waveforms into groups of single units. Here we extend the approach in order to make it suitable for online encoding/decoding scenarios that require real-time decoding such as brain-machine interfaces. We propose an online algorithm for the Bayesian decoding that reduces the time required for decoding neural populations, resulting in a real-time capable decoding framework. More specifically, we improve the speed of the probability density estimation step, which is the most essential and the most expensive computation of the spike-sorting-less decoding process, by developing a kernel density compression algorithm. In contrary to existing online kernel compression techniques, rather than optimizing for the minimum estimation error caused by kernels compression, the proposed method compresses kernels on the basis of the distance between the merging component and its most similar neighbor. Thus, without costly optimization, the proposed method has very low compression latency with a small and manageable estimation error. In addition, the proposed bandwidth matching method for Gaussian kernels merging has an interesting mathematical property whereby optimization in the estimation of the probability density function can be performed efficiently, resulting in a faster decoding speed. We successfully applied the proposed kernel compression algorithm to the Bayesian decoding framework to reconstruct positions of a freely moving rat from hippocampal unsorted spikes, with significant improvements in the decoding speed and acceptable decoding error.

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.

An upcoming trend of affective gaming is where a player's emotional state is used to manipulate game play. This is an interesting field to explore especially for the survival horror genre that is excellent at producing player's intense emotions. In this research, we analyzed different player affective states prior to (i.e., Neutral, Anxiety, Suspense) and after (i.e., Low-Fear, Mid-Fear, High-Fear) a scary event using an affect annotation tool to collect player self-reports of their affective states during the game. Brainwave signals, heart rate and keyboard-mouse activity were also collected for analyzing the potential of automatically detecting horror-related affect. Results indicated that players were more likely to experience fear from a scary event when they were in a suspense state compared to when they were in a neutral state. In this state, players only experienced fear after experiencing surprise. Heart rate data gave the best result in classifying player affect, which achieved up to 90% overall accuracy. This highlights the potential of using player affect in survival horror games to adapt a scary event to evoke more fear from players.

Self-reflection and self-evaluation are effective processes for identifying good learning behavior. These are essential in self-directed learning scenarios because students have to be responsible for their own learning. Although students benefit from doing fine-grained analysis of their own behavior, which we observed in our previous work, asking them to perform tasks such as analysis and making annotations are tedious and take significant amount of time and effort. In this paper, we present our work on the development of incremental affect models that can be used to minimize effort in analyzing and annotating behavior. Incremental models have an added benefit of adaptability to new information, which can be used by future systems to provide up-to-date affect-related feedback in real time.

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.

In ambient information systems, not only extracting human behavior with a sensor network but also adaptive autonomous interaction between the environment and humans is an important function. In this paper, we propose a reinforcement learning methodology for acquiring suitable interaction for each person's daily behavior. This time, we used vital sensors to detect and classify a user's condition. In an experiment, we show the feasibility of the proposed methodology.

In recent years, highly-functional mobile devices such as smart phones and car navigation systems are widely used. These are important for our daily life because we use their applications anywhere and anytime. With the variety of applications available on these devices, however, it becomes more difficult to choose an appropriate application. Therefore we need a mechanism that recommends us suitable applications, which should depend on a user's context because he/she uses his/her devices differently in every context. This paper shows that it follows a power law what applications a user executes in daily life, and proposes a novel approach to find context-aware applications in the mobile devices. This approach is based on the term frequency - inverse document frequency (TF-IDF), which is used for extracting important keywords in a document. Moreover, we propose an application recommendation mechanism using this approach. Experimental results show that this recommendation mechanism is more effective than the mechanism using Naive Bayes.

Learning is commonly associated with knowledge transfer involving guidance from a teacher. However, as people grow older they are expected to know how to learn by themselves. In this research, we analyzed student learning in an unsupervised learning environment, i.e., performing academic research, wherein students have complete control over their learning thus requiring them to manage it. Transition likelihood metrics were used to analyze the interplay between emotion, learning and non-learning related activities while students did research. Several observations were seen from students learning in this environment such as students experiencing cognitive disequilibrium but experiencing disengagement faster. Non-learning related activities were also shown to have the potential of motivating students to resume learning. Lastly, user-specific traits and context seem to affect the interplay between learning and non-learning activities in an unsupervised learning environment. This highlights the need to not only create general models to predict student behavior but also user-specific models to allow future systems to provide appropriate feedback in this environment.

Commonly attributed to digital natives is the ability to quickly, yet effectively, shift from one task to another. However, several works have debunked this assumption by showing that multitasking even among digital natives led to poor learning performance and productivity. Our aim is to provide a tool to help digital natives be self-aware of desirable, while curbing undesirable, learning behaviors. Our tool is infused with self-annotation and feedback mechanisms that allow students to reflect upon their entire learning history. Our results indicate that the annotation process with the tool helped students understand their learning behaviors better and identify ways in which their behaviors can be improved.

In ambient information systems, not only extracting human behavior by sensor network but also adaptive autonomous interaction between the environment and humans is an important function. In this paper we propose a reinforcement learning framework to extract suitable interaction for each person from daily behavior. In the experiment, we show the feasibility of the proposed methodology.

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.

Data-centric affect modeling may render itself restrictive in practical applications for three reasons, namely, it falls short of feature optimization, infers discrete affect classes, and deals with relatively small to average sized datasets. Though it seems practical to use the feature combinations already associated to commonly investigated sensors, there may be other potentially optimal features that can lead to new relations. Secondly, although it seems more realistic to view affect as continuous, it requires using continuous labels that will increase the difficulty of modeling. Lastly, although a large scale dataset reflects a more precise range of values for any given feature, it severely hinders computational efficiency. We address these problems when inferring physiology-affect relations from datasets that contain 2-3 million feature vectors, each with 49 features and labelled with continuous affect values. We employ automatic feature selection to acquire near optimal feature subsets and a fast approximate kNN algorithm to solve the regression problem and cope with the challenge of a large scale dataset. Our results show that high estimation accuracy may be achieved even when the selected feature subset is only about 7% of the original features. May the results here motivate the HCI community to pursue affect modeling without being deterred by large datasets and further the discussions on acquiring optimal features for accurate continuous affect approximation.

The basic framework of next generation intelligent transport systems (ITSs) is discussed. We propose a new congestion forecast system, which reacts to dynamically changing traffic conditions based on a coordination mechanism using pheromone communication models. We evaluate and verify the basic effectiveness of this method using simple simulations.

In this study, we proposed the use of grant application data to acquire knowledge of the relationships among scientific research subjects. We modeled grant application data to construct a method of capturing the relationships among research subjects, then conducted experiments using actual grant application data. The results indicated that our method successfully elucidated the relationships among research subjects.

Recalls and defective parts are a major problem for manufacturers and a major challenge in information management. Increasingly complex supply chains have created a situation where parts' data is distributed across a volatile network of suppliers' databases. The challenge of parts tracking in this environment requires maintaining data views across constantly-restructuring networks of information systems as new suppliers are contracted and subassemblies are outsourced. In this paper we present a method for manufacturers to efficiently and automatically index all component parts in their products, allowing for complex changes to the supply chain, and enabling highly efficient reverse-lookups for recalls. Our method improves parts lookup and search time by subdividing all component part indexes into a distributed, versioned matrix of tables which models the structure of the supply chain. We introduce a method of reverse-indexing component parts using manufacturing time to pinpoint products for selective recalls, running in a fraction of the time of a traditional database search. We evaluate our method for applicability to the automotive industry using statistically re-generated data from a major automobile manufacturer to simulate a supply chain 7 layers deep, comprising 1000 individual component parts, indexing a distributed dataset totaling approximately 1TB in size.

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.

The consideration of human feelings in automated music generation by intelligent music systems, albeit a compelling theme, has received very little attention. This work aims to computationally specify a system's music compositional intelligence that tightly couples with the listener's affective perceptions. First, the system induces a model that describes the relationship between feelings and musical structures. The model is learned by applying the inductive logic programming paradigm of FOIL coupled with the Diverse Density weighting metric over a dataset that was constructed using musical score fragments that were hand-labeled by the listener according to a semantic differential scale that uses bipolar affective descriptor pairs. A genetic algorithm, whose fitness function is based on the acquired model and follows basic music theory, is then used to generate variants of the original musical structures. Lastly, the system creates chordal and non-chordal tones out of the GA-obtained variants. Empirical results show that the system is 80.6% accurate at the average in classifying the affective labels of the musical structures and that it is able to automatically generate musical pieces that stimulate four kinds of impressions, namely, favorable-unfavorable, bright-dark, happy-sad, and heartrending-not heartrending.

This paper describes a learning process for the tutor of an intelligent tutoring system (ITS) to automatically learn models of student categories and self-improve its instructional plans on the level of these categories. Using real-world teaching scenarios as experiment data, we empirically show that for every category the tutor is able to efficiently learn effective instructional plans. Our experiment results also show that the absence of category background knowledge decreases the tutor's learning performance as well the effectiveness of the learned instructional plans.

Inductive learning of first-order theory based on examples has serious bottleneck in the enormous hypothesis search space needed, making existing learning approaches perform poorly when compared to the propositional approach. Moreover, in order to choose the appropiate candidates, all Inductive Logic Programming (ILP) systems only use quantitive information, e.g. number of examples covered and length of rules, which is insufficient for search space having many similar candidates. This paper introduces a novel approach to improve ILP by incorporating the qualitative information into the search heuristics by focusing only on a kind of data where one instance consists of several parts, as well as relations among parts. This approach aims to find the hypothesis describing each class by using both individual and relational characteristics of parts of examples. This kind of data can be found in various domains, especially in representing chemical compound structure. Each compound is composed of atoms as parts, and bonds as relations between two atoms. We apply the proposed approach for discovering rules describing the activity of compounds from their structures from two real-world datasets: mutagenicity in nitroaromatic compounds and dopamine antagonist compounds. The results were compared to the existing method using ten-fold cross validation, and we found that the proposed method significantly produced more accurate results in prediction.

We propose a geometric method for document image processing. This research focuses on document understanding and classification by applying the Winnow algorithm, an online machine learning method. This application makes the document image processing more flexible with various kind of documents since the meaningful knowledge can be extracted from training examples and the model for document type can be updated when there is a new example. This research aims to analyze and classify scientific papers. We conduct the experiments on documents from the proceedings of various conferences to show the performance of the proposed method. The experimental results are compared with the WISDOM++ system and also show the advantages of using the online machine learning method.

We designed a secure key registration system based on the proactive secret-sharing scheme. A user can register important data such as a session key to a distributed system in a (t, n)-threshold scheme, which means that the data can be recovered if t sewers cooperate (in other words, that the data cannot be revealed unless t sewers collude). The proactive scheme provides stronger security against an active adversary. We designed the protocol to generate an implicit secret, to distribute shares of it, and to reconstruct the secret for proactive secret-sharing without a dealer. We also developed a prototype of a data archiving service framework on the Internet. To allow users to access the system via a Web browser, we implemented a system based on the PKI (public key infrastructure), where the client/server authentication is done by means of X.509 certification. We also used the publish/subscribe communication model to realize interaction between key management servers, because it is easy to implement the broadcasting channels used in the share update phase.

Authoring work for multimedia contents is knowledge-lean work. Elementary school pupils frequently have difficult acquiring authoring skills, i.e. how to develop the idea or materials to be expressed, how to compose the materials of the outcome or contents. We focus on the problems of how to support authoring work to arrange materials in order to formulate ideas as the stories develop. The basic ideas behind the method are that (1) a basic story is automatically generated by genetic algorithm (GA) based operations and shown to a user as sequences of pictures, (2) an interactive GA is used to evaluate and select a preferred story, (3) the results are compared with previously stored stories using case-based techniques. Based on these ideas, we have developed a computer supported environment for this purpose and conducted related experiments.

The authors have developed a constraint-based method of describing features. Every feature is controlled by geometric constraints, which naturally specify the dimensioning information that reflects the designer's intent. An ATMS-based geometric reasoning system is proposed that efficiently evaluates the constraints in order to determine geometric attributes, and detects over-constrained situations in order to resolve conflicts among the constraints.>