With the steady growth of sensing technology and wearable devices, sensor-based human activity recognition has become essential in widespread applications, such as healthcare monitoring and fitness tracking, where accurate and real-time systems are required. To achieve real-time response, recent studies have focused on lightweight neural network models. Specifically, they designed the network architectures by restricting the number of layers shallowly or connections of each layer. However, these approaches suffer from limited accuracy because the classifier only uses the features at the last layer. In this study, we propose a cheap all-layer aggregation network, CALANet, for accuracy improvement while maintaining the efficiency of existing real-time HAR models. Specifically, CALANet allows the classifier to aggregate the features for all layers, resulting in a performance gain. In addition, this work proves that the theoretical computation cost of CALANet is equivalent to that of conventional networks. Evaluated on seven publicly available datasets, CALANet outperformed existing methods, achieving state-of-the-art performance. The source codes of the CALANet are publicly available at https://github.com/jgpark92/CALANet.

Human activity recognition is an essential task for human-centered intelligent systems such as healthcare and smart vehicles, which can be accomplished by analyzing time-series signals collected from sensors in wearable devices. In these applications, real-time response is vital because prompt action is necessary for urgent events such as an elderly person falling or driving while drowsy. Although recurrent neural networks have been widely used owing to their temporal modeling capabilities, recent studies have focused on convolutional neural networks (CNNs) that are suitable for real-time responses because they incur lower computational costs. However, CNNs with a manual design may fail to achieve optimal accuracy due to varying computational budgets with applications or devices. In this paper, we propose a novel design framework that uses a mathematical approach to derive a CNN architecture suitable for a given computational budget. As a result, we introduce a grouped temporal shift network (GTSNet) with the network architecture to be flexibly modified by predefining the theoretical computation cost. We demonstrate the effectiveness of our framework in experiments, achieving the best performance for well-known public benchmark datasets under limited computational budgets. The source codes of the GTSNet are publicly available at https://github.com/jgpark92/GTSNet.

Summary Owing to their autonomous nature, computer‐aided pronunciation training systems are regarded as a useful tool for pronunciation training for beginner‐level students, particularly those who feel uncomfortable having their pronunciation corrected in front of other students or who have difficulty with face‐to‐face training for other reasons. In this study, we propose a computer‐assisted pronunciation training system targeting unacceptable pronunciation due to confusion among contextual allophones, a problem that often emerges from phoneme‐based feedback provided during pronunciation training. To consider the different pronunciation of phonemes according to position, the proposed system is implemented to recommend a set of words focusing on phoneme pairs. Experimental results show that the proposed system results in the improvement of pronunciation skills through training sessions that use recommended words containing phoneme pairs that were initially pronounced incorrectly.

We propose a convex optimization approach for multi-label feature selection. The effective feature subset can be obtained through finding a global optima of a convex objective function for multi-label feature selection. However conventional greedy approaches are prone to suboptimal result. In this paper, the mathematical procedures and considerations for the optimization approach are presented for multi-label feature selection based on mutual information. We compared the proposed method with conventional greedy search based methods to show the potential of optimization based multi-label feature selection.

The chord progression is a fundamental building block in music which sketches the overall mood of a song. Many composers compose music by first deciding chord progressions as a structure and then adding melody and details. Despite its importance, it is rarely used as an emotional feature in music emotion recognition. Few previous works considered chords or intervals as features but the progression or transition of chords were ignored. In this work, we explore the effect of chord progressions in music emotion recognition. We collected music database and extracted features to form an emotion recognition model. The chord progression is then detected from each song, and its effectiveness is showed using cross-validation. The results show that chord progressions have influence in music emotion, especially valence.

The research criticizes constructively that just positive facial expressions of robot have a decisive influence on its social interactions with human, and proposes that the aesthetic interactions of robot like an innocent play make it look sociable, even if it expresses any negative emotion. We replace positively or negatively emotional expression with aesthetic one, and then present our participants it in the form of aesthetic imitative interaction. They respond positively to positive expressions of robot, negatively to the negative. But on the condition of aesthetic imitative interactions they respond positively to even negative expressions of robot.

SUMMARY: Despite the importance of using the semantic distance to improve the performance of conventional expression-based clustering, there are few freely available software that provides a clustering algorithm using the ontology-based semantic distances as prior knowledge. Here, we present the SICAGO (SemI-supervised Cluster Analysis using semantic distance between gene pairs in Gene Ontology) system that helps to discover the groups of genes more effectively using prior knowledge extracted from Gene Ontology. AVAILABILITY: http://ai.cau.ac.kr/sicago.html CONTACT: [email protected]

The task of automatically determining the concepts referred to in chief complaint (CC) data from electronic medical records (EMRs) is an essential component of many EMR applications aimed at biosurveillance for disease outbreaks. Previous approaches that have been used for this concept mapping have mainly relied on term-level matching, whereby the medical terms in the raw text and their synonyms are matched with concepts in a terminology database. These previous approaches, however, have shortcomings that limit their efficacy in CC concept mapping, where the concepts for CC data are often represented by associative terms rather than by synonyms. Therefore, herein we propose a concept mapping scheme based on a two-phase matching approach, especially for application to Korean CCs, which uses term-level complete matching in the first phase and concept-level matching based on concept learning in the second phase. The proposed concept-level matching suggests the method to learn all the terms (associative terms as well as synonyms) that represent the concept and predict the most probable concept for a CC based on the learned terms. Experiments on 1204 CCs extracted from 15,618 discharge summaries of Korean EMRs showed that the proposed method gave significantly improved F-measure values compared to the baseline system, with improvements of up to 73.57%.

MOTIVATION: Clustering technique is used to find groups of genes that show similar expression patterns under multiple experimental conditions. Nonetheless, the results obtained by cluster analysis are influenced by the existence of missing values that commonly arise in microarray experiments. Because a clustering method requires a complete data matrix as an input, previous studies have estimated the missing values using an imputation method in the preprocessing step of clustering. However, a common limitation of these conventional approaches is that once the estimates of missing values are fixed in the preprocessing step, they are not changed during subsequent processes of clustering; badly estimated missing values obtained in data preprocessing are likely to deteriorate the quality and reliability of clustering results. Thus, a new clustering method is required for improving missing values during iterative clustering process. RESULTS: We present a method for Clustering Incomplete data using Alternating Optimization (CIAO) in which a prior imputation method is not required. To reduce the influence of imputation in preprocessing, we take an alternative optimization approach to find better estimates during iterative clustering process. This method improves the estimates of missing values by exploiting the cluster information such as cluster centroids and all available non-missing values in each iteration. To test the performance of the CIAO, we applied the CIAO and conventional imputation-based clustering methods, e.g. k-means based on KNNimpute, for clustering two yeast incomplete data sets, and compared the clustering result of each method using the Saccharomyces Genome Database annotations. The clustering results of the CIAO method are more significantly relevant to the biological gene annotations than those of other methods, indicating its effectiveness and potential for clustering incomplete gene expression data. AVAILABILITY: The software was developed using Java language, and can be executed on the platforms that JVM (Java Virtual Machine) is running. It is available from the authors upon request.

The purpose of data description is to give a compact description of the target data that represents most of its characteristics. In a support vector data description (SVDD), the compact description of target data is given in a hyperspherical model, which is determined by a small portion of data called support vectors. Despite the usefulness of the conventional SVDD, however, it may not identify the optimal solution of target description especially when the support vectors do not have the overall characteristics of the target data. To address the issue in SVDD methodology, we propose a new SVDD by introducing new distance measurements based on the notion of a relative density degree for each data point in order to reflect the distribution of a given data set. Moreover, for a real application, we extend the proposed method for the protein localization prediction problem which is a multiclass and multilabel problem. Experiments with various real data sets show promising results.

MOTIVATION: Clustering has been used as a popular technique for finding groups of genes that show similar expression patterns under multiple experimental conditions. Many clustering methods have been proposed for clustering gene-expression data, including the hierarchical clustering, k-means clustering and self-organizing map (SOM). However, the conventional methods are limited to identify different shapes of clusters because they use a fixed distance norm when calculating the distance between genes. The fixed distance norm imposes a fixed geometrical shape on the clusters regardless of the actual data distribution. Thus, different distance norms are required for handling the different shapes of clusters. RESULTS: We present the Gustafson-Kessel (GK) clustering method for microarray gene-expression data. To detect clusters of different shapes in a dataset, we use an adaptive distance norm that is calculated by a fuzzy covariance matrix (F) of each cluster in which the eigenstructure of F is used as an indicator of the shape of the cluster. Moreover, the GK method is less prone to falling into local minima than the k-means and SOM because it makes decisions through the use of membership degrees of a gene to clusters. The algorithmic procedure is accomplished by the alternating optimization technique, which iteratively improves a sequence of sets of clusters until no further improvement is possible. To test the performance of the GK method, we applied the GK method and well-known conventional methods to three recently published yeast datasets, and compared the performance of each method using the Saccharomyces Genome Database annotations. The clustering results of the GK method are more significantly relevant to the biological annotations than those of the other methods, demonstrating its effectiveness and potential for clustering gene-expression data. AVAILABILITY: The software was developed using Java language, and can be executed on the platforms that JVM (Java Virtual Machine) is running. It is available from the authors upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at http://dragon.kaist.ac.kr/gk.

The goal of information retrieval system is to search the documents which user wants to obtain in a fast and efficient way. Many information retrieval models, including Boolean models, vector models, and fuzzy models based on the traditional fuzzy set theory, have been proposed to achieve these kinds of objectives. The previous models, however, have a limitation on the fact that they do not consider the user's preference in the document search process. In this paper, we proposed a new extended fuzzy information retrieval system which can handle the shortcomings of the previous ones. In the proposed model, a new similarity measure was applied in order to calculate the relevance degree among documents which can exploit the user's preference.