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Wearable motion sensor-based chewing side detection

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Smart Health 2021年 21卷

作者： Wang, Shuangquan Zhou, Gang Watson, Amanda Xie, Lei Sun, Minglong Jung, Woosub Department of Mathematics & Computer Science Salisbury University United States Computer Science Department William & Mary United States PRECISE Center University of Pennsylvania United States State Key Laboratory for Novel Software Technology Nanjing University China

Chewing side preference means a tendency to use one side to chew food more frequently than the other. Medical studies show that chewing side preference can result in lateral facial asymmetry, teeth abrasion, temporomandibular disorders, malocclusion, and stomach illness. To continuously detect chewing side preference and quantify its severity in daily life, several wearable sensor-based methods have been proposed in recent years. However, these methods are either intrusive or not fine-grained enough. In this paper, we propose a wearable motion sensor-based chewing side detection method. We observe that chewing activity generates mastication muscle bulge and skull vibration, which can be sensed by motion sensors worn on the mastication muscles. In addition, the muscle bulge and skull vibration of the chewing side are different from those of the non-chewing side. These observations motivate us to deploy motion sensors on the left and right temporalis muscles to detect chewing sides. We propose a heuristic-rules based method to exclude non-chewing data and segment each chew accurately. The relative difference series of the left and right sensors are then calculated to characterize the difference of muscle bulge and skull vibration between the chewing side and the non-chewing side. A two-class classifier is trained using long short-term memory (LSTM), an artificial recurrent neural network, to model the data samples and classify chewing sides. A real-world evaluation dataset of eight food types is collected from eight human subjects. The average detection accuracy reaches 84.8%. The highest detection accuracy for a single subject is up to 97.4%. © 2021 Elsevier Inc.

关键词： Chewing side preference LSTM Muscle bulge Skull vibration Wearable motion sensors

TEA: Temporal excitation and aggregation for action recognition

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arXiv 2020年

作者： Li, Yan Ji, Bin Shi, Xintian Zhang, Jianguo Kang, Bin Wang, Limin Tencent State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Engineering Southern University of Science and Technology China

Temporal modeling is key for action recognition in videos. It normally considers both short-range motions and long-range aggregations. In this paper, we propose a Temporal Excitation and Aggregation (TEA) block, including a motion excitation (ME) module and a multiple temporal aggregation (MTA) module, specifically designed to capture both short- and long-range temporal evolution. In particular, for short-range motion modeling, the ME module calculates the feature-level temporal differences from spatiotemporal features. It then utilizes the differences to excite the motion-sensitive channels of the features. The long-range temporal aggregations in previous works are typically achieved by stacking a large number of local temporal convolutions. Each convolution processes a local temporal window at a time. In contrast, the MTA module proposes to deform the local convolution to a group of sub-convolutions, forming a hierarchical residual architecture. Without introducing additional parameters, the features will be processed with a series of sub-convolutions, and each frame could complete multiple temporal aggregations with neighborhoods. The final equivalent receptive field of temporal dimension is accordingly enlarged, which is capable of modeling the long-range temporal relationship over distant frames. The two components of the TEA block are complementary in temporal modeling. Finally, our approach achieves impressive results at low FLOPs on several action recognition benchmarks, such as Kinetics, Something-Something, HMDB51, and UCF101, which confirms its effectiveness and efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity

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arXiv 2022年

作者： Li, Jiangmeng Qiang, Wenwen Zheng, Changwen Su, Bing Razzak, Farid Wen, Ji-Rong Xiong, Hui The University of Chinese Academy of Sciences Beijing China The Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China The New York University Columbia University New York United States The Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China Guangzhou China Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong

While self-supervised learning techniques are often used to mine hidden knowledge from unlabeled data via modeling multiple views, it is unclear how to perform effective representation learning in a complex and inconsistent context. To this end, we propose a new multi-view self-supervised learning method, namely consistency and complementarity network (CoCoNet), to comprehensively learn global inter-view consistent and local cross-view complementarity-preserving representations from multiple views. To capture crucial common knowledge which is implicitly shared among views, CoCoNet employs a global consistency module that aligns the probabilistic distribution of views by utilizing an efficient discrepancy metric based on the generalized sliced Wasserstein distance. To incorporate cross-view complementary information, CoCoNet proposes a heuristic complementarity-aware contrastive learning approach, which extracts a complementarity-factor jointing cross-view discriminative knowledge and uses it as the contrast to guide the learning of view-specific encoders. Theoretically, the superiority of CoCoNet is verified by our information-theoretical-based analyses. Empirically, our thorough experimental results show that CoCoNet outperforms the state-of-the-art self-supervised methods by a significant margin, for instance, CoCoNet beats the best benchmark method by an average margin of 1.1% on ImageNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Supervised learning

Balancing communication overhead and accuracy in compression integration: a survey

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Journal of Supercomputing 2025年第8期81卷

作者： Yang, Aiqiang Liu, Jie Yang, Bo Mo, Zeyao Li, Keqin College of Computer Science and Technology National University of Defense Technology Changsha410000 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha410073 China China Academy of Engineering Physics Beijing China Department of Computer Science State University of New York New PaltzNY12561 United States

In large-scale distributed training, communication compression techniques are widely used to reduce the significant communication overhead caused by the frequent exchange of model parameters or gradients between training nodes. However, these techniques often introduce additional computational complexity and may lead to data loss, thereby affecting model convergence and performance. This review examines key optimization methods in communication compression, including pruning techniques that remove irrelevant weights, quantization techniques that convert floating-point parameters to low-precision representations, and sparsification techniques that transmit only critical gradients. Low-rank approximation techniques, which compress parameters through matrix factorization, are particularly useful for large-scale models. These techniques have also been widely applied in various application scenarios, demonstrating their effectiveness in different environments. Application scenarios include distributed training, federated learning, and edge computing, where bottlenecks are carefully identified and evaluated in common scenarios, providing a basis for further optimization. Future development directions emphasize co-design of hardware and algorithms, dynamic strategies, and cross-layer optimization. This study provides valuable comparisons of key methods and theoretical analysis for efficient communication compression in distributed systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Edge computing

Quantum Random Access Stored-Program Machines

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arXiv 2020年

作者： Wang, Qisheng Ying, Mingsheng The Department of Computer Science and Technology Tsinghua University China The State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China

Random access machines (RAMs) and random access stored-program machines (RASPs) are models of computing that are closer to the architecture of real-world computers than Turing machines (TMs). They are also convenient in complexity analysis of algorithms. The relationships between RAMs, RASPs and TMs are well-studied. However, clear relationships between their quantum counterparts are still missing in the literature. We fill in this gap by formally defining the models of quantum random access machines (QRAMs) and quantum random access stored-program machines (QRASPs) and clarifying the relationships between QRAMs, QRASPs and quantum Turing machines (QTMs). In particular, we show that P ⊆ EQRAMP ⊆ EQP ⊆ BQP = BQRAMP, where EQRAMP and BQRAMP stand for the sets of problems that can be solved by polynomial-time QRAMs with certainty and bounded-error, respectively. At the heart of our proof, is a standardisation of QTM with an extended halting scheme, which is of independent *** Codes 68Q05 (Primary) 81P68, 68Q12 (Secondary) Copyright © 2020, The Authors. All rights reserved.

关键词： Turing machines

A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact Removal

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arXiv 2022年

作者： Wang, Tao Zhang, Kaihao Chen, Xuanxi Luo, Wenhan Deng, Jiankang Lu, Tong Cao, Xiaochun Liu, Wei Li, Hongdong Zafeiriou, Stefanos State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China College of Engineering and Computer Science Australian National University CanberraACT Australia Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China Department of Computing Imperial College London London United Kingdom Tencent Shenzhen518107 China

Face Restoration (FR) aims to restore High-Quality (HQ) faces from Low-Quality (LQ) input images, which is a domain-specific image restoration problem in the low-level computer vision area. The early face restoration methods mainly use statistic priors and degradation models, which are difficult to meet the requirements of real-world applications in practice. In recent years, face restoration has witnessed great progress after stepping into the deep learning era. However, there are few works to study deep learning-based face restoration methods systematically. Thus, this paper comprehensively surveys recent advances in deep learning techniques for face restoration. Specifically, we first summarize different problem formulations and analyze the characteristic of the face image. Second, we discuss the challenges of face restoration. Concerning these challenges, we present a comprehensive review of existing FR methods, including prior based methods and deep learning-based methods. Then, we explore developed techniques in the task of FR covering network architectures, loss functions, and benchmark datasets. We also conduct a systematic benchmark evaluation on representative methods. Finally, we discuss future directions, including network designs, metrics, benchmark datasets, applications, etc. We also provide an open-source repository for all the discussed methods, which is available at https://***/TaoWangzj/Awesome-Face-Restoration. Copyright © 2022, The Authors. All rights reserved.

关键词： Restoration

Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN

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Epilepsy Classification for Mining Deeper Relationships betw...

2020 International Conference on computer Vision, Image and Deep Learning, CVIDL 2020

作者： Chen, Xin Zheng, Yuanjie Niu, Yi Li, Chengiiang Shandong Normal University School of Information Science and Engineering Jinan250358 China Key Lab of Intelligent Computing and Information Security Universities of Shandong Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Institute of Biomedical Sciences Jinan250358 China Department of Electrical Engineering Information Technology Shandong University of Science and Technology Jinan250031 China

ISBN: (纸本)9781728194813

Epilepsy is a brain disorder caused by abnormal discharges of neurons in brain. It is one of the most commonly studied disorders in neurology. The research of epilepsy electroencephalogram (EEG) has become a hot research topic. We find that in epilepsy EEG detection task, many previous methods focused on directly collecting the data of each channel, but these methods seldom analyse relationships between signals. Therefore, we propose the Epilepsy EEG Graph Convolutional Network EGCN, which makes full use of correlations between channels to deeply mine data information. We specifically design 5-layer graph convolutional network structure for classification of healthy and epileptic patients. The method is applied to public data set (Boon and CHB-MIT) to establish a reasonable classification model. And we compare it with some advanced algorithms. The experimental results show that the E-GCN method is superior to many existing methods in classification accuracy. In brief, the E-GCN method can be effectively used in classification and detection for epilepsy. This provides new ideas for colleagues, who study epilepsy EEG. In addition, this also provides richer experience for diagnosis of epilepsy. © 2020 IEEE.

关键词： Electroencephalography

Mining Knowledge within Categories in Global and Local Fashion for Multi-Label Text Classification

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Mining Knowledge within Categories in Global and Local Fashi...

International Joint Conference on Neural Networks (IJCNN)

作者： Sheng Bi Peng Shi Yuntao Du Bin Jin Lingshuang Yu Department of Computer Science and Technology National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Multi-label text classification (MLTC) is an important task in natural language processing, which assigns multiple labels to each text in the dataset. Typical method like Binary Relevance (BR) is arguably the most intuitive solution for the task. It works by decomposing the multi-label learning task into a number of independent binary learning tasks while ignoring the correlation between labels. Recently, neural network models attract much attention. Researchers view the MLTC task as a sequence generation problem. Although some new methods based on generative model (e.g. sequence-to-sequence), such as novel decoder structure and various attention mechanisms, can improve the performance. These methods still have some short-comings, such as unreasonable loss function, unclear ordering of target labels. To address these limitations, we propose a simple and effective novel model, which combines the merits of neural network and BRs methods. Our model also takes into account the categories and levels of labels. We decompose the MLTC problem to binary classification, together with global and local extractor to avoid the impact of label ordering and cumulative error. Experimental results show that our model achieves an improvement of 3.0% micro-F1 and a reduction of 6.0% hamming loss on AAPD dataset compared with the state-of-the-art work. And obtained good performance on RCV1-V2 dataset.

关键词： Task analysis Data mining Encoding Neural networks Decoding computer science Natural language processing

Multi-label text classification: Select distinct semantic understanding for different labels 3rd

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Multi-label text classification: Select distinct semantic un...

3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019

作者： Sun, Wei Ran, Xiangying Luo, Xiangyang Xu, Yunlai Wang, Chongjun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783030260743

Multi-label classification is a challenging task in natural language processing. Most of existing methods tend to ignore the semantic information of the text. Besides, different parts of the text contribute differently to each label, which is not considered by most of existing methods. In this paper, we propose a novel model for multi-label text classification. This model generates high-level semantic understanding representations with a multi-level dilated convolution. The multi-level dilated convolution effectively reduces dimension and expands the receptive fields without loss of information. Moreover, a hybrid attention mechanism is designed to capture most relevant information of the text based on trainable label embeddings and semantic understanding. Experimental results on the dataset AAPD and RCV1-V2 show that our model has significant advantages over baseline methods. © 2019, Springer Nature Switzerland AG.

关键词： Convolution