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You Can Wash Hands Better: Accurate Daily Handwashing Assessment with a Smartwatch

IEEE Transactions on Mobile Computing 2025年

作者： Wang, Fei Zhang, Tingting Wu, Xilei Wang, Pengcheng Wang, Xin Ding, Han Shi, Jingang Han, Jinsong Huang, Dong Xi'an Jiaotong University School of Software Engineering Xi'an 710049 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an 710071 China Kuaishou Technology Inc. Local Life Research and Development Center Beijing 100085 China Xi'an Jiaotong University School of Computer Science and Technology Xi'an 710049 China Zhejiang University College of Computer Science and Technology Hangzhou 310027 China Carnegie Mellon University Robotics Institute Pittsburgh 15213 United States

Hand hygiene is among the most effective daily practices for preventing infectious diseases such as influenza, malaria, and skin infections. While professional guidelines emphasize proper handwashing to reduce the risk of viral infections, surveys reveal that adherence to these recommendations remains low. To address this gap, we propose UWash, a wearable solution leveraging smartwatches to evaluate handwashing procedures, aiming to raise awareness and cultivate high-quality handwashing habits. We frame the task of handwashing assessment as an action segmentation problem, similar to those in computer vision, and introduce a simple yet efficient two-stream UNet-like network to achieve this goal. Experiments involving 51 subjects demonstrate that UWash achieves 92.27% accuracy in handwashing gesture recognition, an error of < 0.5 seconds in onset/offset detection, and an error of < 5 points in gesture scoring under user-dependent settings. The system also performs robustly in user-independent and user-independent-location-independent evaluations. Remarkably, UWash maintains high performance in real-world tests, including evaluations with 10 random passersby at a hospital 9 months later and 10 passersby in an in-the-wild test conducted 2 years later. UWash is the first system to score handwashing quality based on gesture sequences, offering actionable guidance for improving daily hand hygiene. © 2002-2012 IEEE.

关键词： deep neural network gesture recognition handwashing assessment smartwatch

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Multi-sample based contrastive loss for top-k recommendation

arXiv

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arXiv 2021年

作者： Tang, Hao Zhao, Guoshuai Wu, Yuxia Qian, Xueming The School of Information and Communication Engineering Xi’an Jiaotong University Xi’an710049 China The School of Software Engineering Xi’an Jiaotong University Xi’an710049 China The Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Information and Communication Engineering SMILES LAB Xi’an Jiaotong University Xi’an710049 China

The top-k recommendation is a fundamental task in recommendation systems which is generally learned by comparing positive and negative pairs. The Contrastive Loss (CL) is the key in contrastive learning that has received more attention recently and we find it is well suited for top-k recommendations. However, it is a problem that CL treats the importance of the positive and negative samples as the same. On the one hand, CL faces the imbalance problem of one positive sample and many negative samples. On the other hand, positive items are so few in sparser datasets that their importance should be emphasized. Moreover, the other important issue is that the sparse positive items are still not sufficiently utilized in recommendations. So we propose a new data augmentation method by using multiple positive items (or samples) simultaneously with the CL loss function. Therefore, we propose a Multi-Sample based Contrastive Loss (MSCL) function which solves the two problems by balancing the importance of positive and negative samples and data augmentation. And based on the graph convolution network (GCN) method, experimental results demonstrate the state-of-the-art performance of MSCL. The proposed MSCL is simple and can be applied in many methods. We will release our code on GitHub upon the acceptance. © 2021, CC BY.

关键词： Convolution

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A portable rotational sensor based on the dual-polarization fiber optic gyroscope

A portable rotational sensor based on the dual-polarization ...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Yuwen Cao Lanxin Zhu Tong Zhou Chunxia Yang Yanjun Chen Zhengbin Li State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing China School of Software and Microelectronics Peking University Beijing China Youfu Technology Co. Ltd Hangzhou China

A portable rotational sensor based on a modified dual-polarization fiber optic gyroscope is demonstrated, in which the excess relative intensity noise is effectively com-pensated achieving an enhanced sensitivity of 7... 详细信息

ISBN: (纸本)9781957171005

关键词： Optical fiber sensors Sensitivity Seismology Prototypes Optical fiber communication Thermal noise Gyroscopes

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Joint Space-Time Sparsity Based Jamming Detection for Mission-Critical mMTC networks

arXiv

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arXiv 2023年

作者： Wang, Shao-Di Wang, Hui-Ming Li, Zhetao Leung, Victor C.M. The School of Information and Communication Engineering Xi'an Jiaotong University Shaanxi Xi'An710049 China The Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'An710049 China The Key Laboratory of Hunnan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent Network School of Computer Science Xiangtan University Xiangtan411105 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

For mission-critical massive machine-type communications (mMTC) applications, the messages are required to be delivered in real-time. However, due to the weak security protection capabilities of the low-cost and low-complexity machine-type devices, active jamming attack in the uplink access is a serious threat. Uplink access jamming (UAJ) can increase the number of dropped/retransmitted packets and restrict or prevent the normal device access. To tackle this vital and challenging problem, we propose a novel UAJ detection method based on the joint space-time sparsity (JSTS). Our key insight is that the JSTS-based feature will be significantly impacted if UAJ happens, since only a small fraction of the devices are active and the traffic pattern for each device is sporadic in the normal state. Unlike the existing detection methods under batch mode (i.e., all sample observations are collected before making a decision), the JSTS-based detection is performed in a sequential manner by processing the received signals one by one, which can detect UAJ as quickly as possible. Moreover, the proposed JSTS-based method does not rely on the prior knowledge of the attackers, since it only cares the abrupt change in the JSTS-based feature on each frame. Numerical results evaluate and confirm the effectiveness of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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Granger-Causality Mining in Atmospheric Visibility Based on Deep Learning 9th

Granger-Causality Mining in Atmospheric Visibility Based on ...

引用

9th International Conference on Frontier Computing, FC 2019

作者： Liu, Bo He, Xi Li, Jianqiang Qu, Guangzhi Lang, Jianlei Gu, Rentao Faculty of Information Technology School of Software Engineering Beijing University of Technology Beijing100124 China Computer Science and Engineering Department Oakland University RochesterMI48309 United States Key Laboratory of Beijing on Regional Air Pollution Control College of Environmental and Energy Engineering Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789811532498

Causal relationship mining of multi-dimensional meteorological time series data can reveal the potential connection between visibility and other influencing factors, and it is meaningful in terms of environmental management, air pollution chasing, and haze control. However, because causality analysis based on statistical methods or traditional machine learning techniques cannot represent the complex relationship between visibility and its influencing factors, as an extension of traditional Granger-causality analysis, we propose a causality mining method based on the seq2seq-LSTM deep learning model. The method can profoundly reveal the hidden relationship between different features and visibility and the regular pattern of bad weather, which can provide theoretical support for air pollution control. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Time series

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DVO + LCLMF: A web service recommendation mechanism with QoS privacy preservation

引用

Concurrency and Computation: Practice and Experience 2022年第18期34卷 e7003-e7003页

作者： Li, Kui Ji, Yi-mu Liu, Shang-dong Wu, Fei Yao, Hai-chang He, Jing Liu, Qiang Liu, Yan-lan Shao, Si-si You, Shuai School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Nantong Bigdata Research Key Laboratory for Rehabilitation Medicine The Second People's Hospital of Nantong Nantong China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China Institute of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC Australia

QoS-aware based web service recommendation is one of the crucial solutions to help users find high-quality web services. To accurately predict the QoS values of candidate services, it is usually required to collect historical QoS data of users (QoS data for short). If these collected QoS data are improperly processed, QoS data privacy may be threatened. However, how to accurately predict the QoS values of candidate services while protecting QoS data privacy has not been well studied. In response to the situation, we propose a hybrid web service recommendation mechanism, which is divided into three parts. In the first part, the QoS data privacy preservation algorithm, which called DVO, is proposed based on keeping the cosine similarity of QoS data unchanged, that is, to realize the confusion of QoS data while ensuring the availability of QoS data remains unchanged. In the second part, a hybrid matrix factorization model based on location information and service features, which called LCLMF, is proposed to improve the accuracy of QoS values prediction. According to DVO and LCLMF, the DVO + LCLMF is designed in the third part, which can accurately predict QoS values while protecting QoS data privacy. The experimental results show that DVO + LCLMF can accurately predict the QoS values of candidate services on the basis of attaining QoS data privacy protection. © 2022 John Wiley & Sons, Ltd.

关键词： Data privacy

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Traceable Multiauthority Attribute-Based Encryption with Outsourced Decryption and Hidden Policy for CIoT

引用

Wireless Communications and Mobile Computing 2021年第1期2021卷

作者： Liu, Suhui Yu, Jiguo Hu, Chunqiang Li, Mengmeng School of Computer Science Qufu Normal University Rizhao Shandong276826 China Jinan Shandong250353 China Jinan Shandong250014 China Shandong Laboratory of Computer Networks Jinan250014 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University China

Cloud-assisted Internet of Things (IoT) significantly facilitate IoT devices to outsource their data for high efficient management. Unfortunately, some unsettled security issues dramatically impact the popularity of IoT, such as illegal access and key escrow problem. Traditional public-key encryption can be used to guarantees data confidentiality, while it cannot achieve efficient data sharing. The attribute-based encryption (ABE) is the most promising way to ensure data security and to realize one-to-many fine-grained data sharing simultaneously. However, it cannot be well applied in the cloud-assisted IoT due to the complexity of its decryption and the decryption key leakage problem. To prevent the abuse of decryption rights, we propose a multiauthority ABE scheme with white-box traceability in this paper. Moreover, our scheme greatly lightens the overhead on devices by outsourcing the most decryption work to the cloud server. Besides, fully hidden policy is implemented to protect the privacy of the access policy. Our scheme is proved to be selectively secure against replayable chosen ciphertext attack (RCCA) under the random oracle model. Some theory analysis and simulation are described in the end. © 2021 Suhui Liu et al.

关键词： Internet of things

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Dual Representation Learning for One-Step Clustering of Multi-View Data

arXiv

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arXiv 2022年

作者： Zhang, Wei Deng, Zhaohong Choi, Kup-Sze Wang, Jun Wang, Shitong The School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Key Laboratory of Media Design and Software Technology Wuxi214122 China The School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China ZJLab Shanghai200433 China The Centre for Smart Health The Hong Kong Polytechnic University Hong Kong The Key laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai200444 China

Multi-view data are commonly encountered in data mining applications. Effective extraction of information from multi-view data requires specific design of clustering methods to cater for data with multiple views, which is non-trivial and challenging. In this paper, we propose a novel one-step multi-view clustering method by exploiting the dual representation of both the common and specific information of different views. The motivation originates from the rationale that multi-view data contain not only the consistent knowledge between views but also the unique knowledge of each view. Meanwhile, to make the representation learning more specific to the clustering task, a one-step learning framework is proposed to integrate representation learning and clustering partition as a whole. With this framework, the representation learning and clustering partition mutually benefit each other, which effectively improves the clustering performance. Results from extensive experiments conducted on benchmark multi-view datasets clearly demonstrate the superiority of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Cluster analysis

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Effective and Efficient Graph Learning for Multi-view Clustering

arXiv

引用

arXiv 2021年

作者： Gao, Quanxue Xia, Wei Gao, Xinbo Zhang, Xiangdong Li, Qin Tao, Dacheng The State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China School of Software Engineering Shenzhen Institute of Information Technology Shenzhen518172 China The School of Electronic Engineering Xidian University Xi'An710071 China The Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China The UBTECH Sydney Artificial Intelligence Centre The School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney DarlingtonNSW2008 Australia

Despite the impressive clustering performance and efficiency in characterizing both the relationship between data and cluster structure, existing graph-based multi-view clustering methods still have the following drawbacks. They suffer from the expensive time burden due to both the construction of graphs and eigen-decomposition of Laplacian matrix, and fail to explore the cluster structure of large-scale data. Moreover, they require a post-processing to get the final clustering, resulting in suboptimal performance. Furthermore, rank of the learned view-consensus graph cannot approximate the target rank. In this paper, drawing the inspiration from the bipartite graph, we propose an effective and efficient graph learning model for multi-view clustering. Specifically, our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm, which well characterizes both the spatial structure and complementary information embedded in graphs of different views. We learn view-consensus graph with adaptively weighted strategy and connectivity constraint such that the connected components indicates clusters directly. Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size. Extensive experimental results indicate that our method is superior to state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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LCL-AKA: Lightweight Authentication and Key Agreement Protocol for Power IoT

引用

IEEE Transactions on Smart Grid 2025年

作者： Liu, Zewei Hu, Chunqiang Ruan, Conghao Pu, Yuwen Hu, Pengfei Yu, Jiguo Cyber Physical Society Ministry of Education Key Laboratory of Dependable Service Computing Chongqing University China Chongqing University School of Big Data and Software Engineering Chongqing China China Southern Power Grid Joint Laboratory on Cyberspace Security China Shandong University School of Computer Science and Technology Shandong China Qilu University of Technology Shandong Academy of Sciences School of Computer Science and Technology Jinan250353 China Shandong Laboratory of Computer Networks Jinan250014 China

The rapid adoption of power Internet of Things (PIoT) systems has made security a critical concern, particularly as existing certificateless authentication and key agreement (CL-AKA) protocols face three fundamental limitations that hinder their practical deployment: excessive computational overhead from multi-round interactions, inadequate protection against advanced cryptanalytic attacks, and inflexible architectures unsuitable for dynamic grid environments. To tackle the previously mentioned challenges, this paper proposes lightweight authentication and key agreement protocol (LCL-AKA) for PIoT. Firstly, this protocol efficiently completes the process of identity authentication and session key agreement within a single round of communication interaction, significantly reducing computational costs and improving communication efficiency. Security is rigorously validated through formal proofs under the eCK model and comprehensive analysis showing resistance to key compromise impersonation, unknown key-share attack. Ultimately, comparative experiments are executed to delve into the security and performance characteristics of the proposed protocol. Compared to benchmark protocols deployed in PIoT, the authentication delay of LCL-AKA is decreased by at least 14.88%;in addition, the energy consumption of LCL-AKA is decreased by at least 11.28%. © 2010-2012 IEEE.

关键词： certificateless authentication and key agreement communication efficiency PIoT security proofs

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