Deep Wavelet Convolutional Neural Networks for Multimodal Human Activity Recognition Using Wearable Inertial Sensors.

Recent advances in wearable systems have made inertial sensors, such as accelerometers and gyroscopes, compact, lightweight, multimodal, low-cost, and highly accurate. Wearable inertial sensor-based multimodal human activity recognition (HAR) methods utilize the rich sensing data from embedded multimodal sensors to infer human activities. However, existing HAR approaches either rely on domain knowledge or fail to address the time-frequency dependencies of multimodal sensor signals. In this paper, we propose a novel method called deep wavelet convolutional neural networks (DWCNN) designed to learn features from the time-frequency domain and improve accuracy for multimodal HAR. DWCNN introduces a framework that combines continuous wavelet transforms (CWT) with enhanced deep convolutional neural networks (DCNN) to capture the dependencies of sensing signals in the time-frequency domain, thereby enhancing the feature representation ability for multiple wearable inertial sensor-based HAR tasks. Within the CWT, we further propose an algorithm to estimate the wavelet scale parameter. This helps enhance the performance of CWT when computing the time-frequency representation of the input signals. The output of the CWT then serves as input for the proposed DCNN, which consists of residual blocks for extracting features from different modalities and attention blocks for fusing these features of multimodal signals. We conducted extensive experiments on five benchmark HAR datasets: WISDM, UCI-HAR, Heterogeneous, PAMAP2, and UniMiB SHAR. The experimental results demonstrate the superior performance of the proposed model over existing competitors.

Considering similarity and the rating conversion of neighbors on neural collaborative filtering.

One of the most popular recommender system techniques is collaborative filtering (CF). Nowadays, many researchers apply a neural network with CF, but few focus on the neighbors' concept of CF. This work needs to consider two major issues: the similarity levels between the neighbors and the target user and the user's rating pattern conversion. Because different neighbors have a different influence on the target user and different users usually have a different rating pattern, the ratings directly utilized by the neighbor's preference pattern may be incorrect for the target user. Under two main issues, we try to accomplish three main ideas of CF's prediction: the similarity between users' levels, the neighbor's rating, and the rating conversion. Thus, we propose three main modules, the rating conversion module, the similarity module, and the prediction module, to solve the two issues mentioned above. The rating conversion module projects the neighbor's rating into the target user's aspect. The similarity module uses the users' attentions to compute similarity levels between users. Finally, these similarity levels and the converted ratings are integrated to perform the prediction. The proposed method is compared with the current CF with friends and latent factor model using two types of datasets: real-world and synthetic datasets. We evaluate N neighbors and all neighbors on real-world datasets to prove the number of neighbor is important. Moreover, the performance of the rating conversion module is also evaluated. The proposed method simulates the full rating datasets and the partial rating dataset to compare the effectiveness of using different types of distribution and dataset size. The experimental results demonstrate that the proposed method effectively outperformed the baselines using ranking evaluation and prediction accuracy on real-world and synthetic datasets. Besides, The effectiveness of using different the number of neighbors depends on the quality of neighbors.

A clique-based method using dynamic programming for computing edit distance between unordered trees.

Many kinds of tree-structured data, such as RNA secondary structures, have become available due to the progress of techniques in the field of molecular biology. To analyze the tree-structured data, various measures for computing the similarity between them have been developed and applied. Among them, tree edit distance is one of the most widely used measures. However, the tree edit distance problem for unordered trees is NP-hard. Therefore, it is required to develop efficient algorithms for the problem. Recently, a practical method called clique-based algorithm has been proposed, but it is not fast for large trees. This article presents an improved clique-based method for the tree edit distance problem for unordered trees. The improved method is obtained by introducing a dynamic programming scheme and heuristic techniques to the previous clique-based method. To evaluate the efficiency of the improved method, we applied the method to comparison of real tree structured data such as glycan structures. For large tree-structures, the improved method is much faster than the previous method. In particular, for hard instances, the improved method achieved more than 100 times speed-up.

A clique-based method for the edit distance between unordered trees and its application to analysis of glycan structures.

BACKGROUND: Measuring similarities between tree structured data is important for analysis of RNA secondary structures, phylogenetic trees, glycan structures, and vascular trees. The edit distance is one of the most widely used measures for comparison of tree structured data. However, it is known that computation of the edit distance for rooted unordered trees is NP-hard. Furthermore, there is almost no available software tool that can compute the exact edit distance for unordered trees. RESULTS: In this paper, we present a practical method for computing the edit distance between rooted unordered trees. In this method, the edit distance problem for unordered trees is transformed into the maximum clique problem and then efficient solvers for the maximum clique problem are applied. We applied the proposed method to similar structure search for glycan structures. The result suggests that our proposed method can efficiently compute the edit distance for moderate size unordered trees. It also suggests that the proposed method has the accuracy comparative to those by the edit distance for ordered trees and by an existing method for glycan search. CONCLUSIONS: The proposed method is simple but useful for computation of the edit distance between unordered trees. The object code is available upon request.