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Assessing Mental Stress Based on Smartphone Sensing data: An Empirical Study

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Assessing Mental Stress Based on Smartphone Sensing Data: An...

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Feng Wang Yasha Wang Jiangtao Wang Haoyi Xiong Junfeng Zhao Daqing Zhang Key Laboratory of High Confdence Software Technologies Ministry of Education Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Peking University Information Technology Institute Tianjin Binhai China National Engineering Research Center of Software Engineering Beijing China Big Data Lab Baidu Research Beijing

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Mental stress is a critical factor affecting one's physical and mental well-being. At the early stage, the effect of stress is often underestimated, while it usually leads to serious issue Lateran. Therefore, it is crucial to detect stress before it evolves into severe problems. Traditional stress detection methods are based on either questionnaires or professional devices, which are time-consuming, costly and intrusive. With the popularity of smartphones embedded with a rich set of sensors, which can capture people's context, such as movement, sound, location and so on, it is an alternative way to access people's behavior by smartphones. Through an empirical study, this paper proposes an automatic and non-intrusive stress detection framework based on smartphone sensing data. First, we construct various discriminative features from multi-modality phone sensing data, in which both absolute and relative features are considered to make the model more personalized. Then, to tackle the challenge of label insufficiency, we further develop a co-training based method for stress level classification. Finally, we evaluate our model based on an open dataset, and the experimental results verify its advantages over other baselines.

关键词： Sensors Stress Feature extraction Psychology Mobile handsets data models Biomedical monitoring

Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency

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arXiv 2020年

作者： Luo, Xiangde Liao, Wenjun Chen, Jieneng Song, Tao Chen, Yinan Zhang, Shichuan Chen, Nianyong Wang, Guotai Zhang, Shaoting School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu Chengdu China West China Hospital Sichuan University Chengdu China College of Electronics and Information Technology Tongji University Shanghai China SenseTime Research Shanghai China Sichuan Cancer Hospital & Institute China West China Hospital-SenseTime Joint Lab West China Biomedical Big Data Center Sichuan University West China Hospital Chengdu China

Gross Target Volume (GTV) segmentation plays an irreplaceable role in radiotherapy planning for Nasopharyngeal Carcinoma (NPC). Despite that Convolutional Neural Networks (CNN) have achieved good performance for this task, they rely on a large set of labeled images for training, which is expensive and time-consuming to acquire. In this paper, we propose a novel framework with Uncertainty Rectified Pyramid Consistency (URPC) regularization for semisupervised NPC GTV segmentation. Concretely, we extend a backbone segmentation network to produce pyramid predictions at different scales. The pyramid predictions network (PPNet) is supervised by the ground truth of labeled images and a multi-scale consistency loss for unlabeled images, motivated by the fact that prediction at different scales for the same input should be similar and consistent. However, due to the different resolution of these predictions, encouraging them to be consistent at each pixel directly has low robustness and may lose some fine details. To address this problem, we further design a novel uncertainty rectifying module to enable the framework to gradually learn from meaningful and reliable consensual regions at different scales. Experimental results on a dataset with 258 NPC MR images showed that with only 10% or 20% images labeled, our method largely improved the segmentation performance by leveraging the unlabeled images, and it also outperformed five state-of-the-art semi-supervised segmentation methods. Moreover, when only 50% images labeled, URPC achieved an average Dice score of 82.74% that was close to fully supervised learning. © 2020, CC BY-NC-ND.

关键词： Forecasting

Time irreversibility and amplitude irreversibility measures for nonequilibrium processes in complex systems

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arXiv 2020年

作者： Yao, Wenpo Wang, Jun Perc, Matjaž Yao, Wenli Dai, Jiafei Guo, Daqing Yao, Dezhong Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroInformation Center for Information in Medicine School of Life Science and Technology University of Electronic Science and Technology of China Chengdu611731 China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China Faculty of Natural Sciences and Mathematics University of Maribor Koroška cesta 160 Maribor2000 Slovenia Complexity Science Hub Vienna Josefstädterstraße 39 Vienna1080 Austria School of Mines China University of Mining and Technology Xuzhou221116 China Department of Neurology Jinling Hospital Medical School of Nanjing University NanjingJiangsu210002 China Sichuan Institute for Brain Science and Brain-Inspired Intelligence University of Electronic Science and Technology of China Chengdu611731 China Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu2019RU035 China

Time irreversibility, which characterizes nonequilibrium processes, can be measured based on the probabilistic differences between symmetric vectors. To simplify the quantification of time irreversibility, symmetric permutations instead of symmetric vectors have been employed in some studies. However, although effective in practical applications, this approach is conceptually incorrect. Time irreversibility should be measured based on the permutations of symmetric vectors rather than symmetric permutations, whereas symmetric permutations can instead be employed to determine the quantitative amplitude irreversibility – a novel parameter proposed in this paper for nonequilibrium calculated by means of the probabilistic difference in amplitude fluctuations. Through theoretical and experimental analyses, we highlight the strong similarities and close associations between the time irreversibility and amplitude irreversibility measures. Our paper clarifies the connections of and the differences between the two types of permutation-based parameters for quantitative nonequilibrium, and by doing so, we bridge the concepts of amplitude irreversibility and time irreversibility and broaden the selection of quantitative tools for studying nonequilibrium processes in complex systems. Copyright © 2020, The Authors. All rights reserved.

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On privacy protection of latent dirichlet allocation model training

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arXiv 2019年

作者： Zhao, Fangyuan Ren, Xuebin Yang, Shusen Yang, Xinyu School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi'an Jiaotong University China

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive information of the training datasets and bring significant privacy risks. To mitigate the privacy issues in LDA, we focus on studying privacy-preserving algorithms of LDA model training in this paper. In particular, we first develop a privacy monitoring algorithm to investigate the privacy guarantee obtained from the inherent randomness of the collapsed gibbs sampling (CGS) process in a typical LDA training algorithm on centralized curated datasets. Then, we further propose a locally private LDA training algorithm on crowdsourced data to provide local differential privacy for individual data contributors. The experimental results on real-world datasets demonstrate the effectiveness of our proposed algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistics

Advanced Variations of Two-Dimensional Principal Component Analysis for Face Recognition

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arXiv 2019年

作者： Zhao, Meixiang Jia, Zhigang Cai, Yunfeng Chen, Xiao Gong, Dunwei School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Mathematics and Statistics Jiangsu Key Laboratory of Education Big Data Science and Engineering Jiangsu Normal University Xuzhou221116 China Cognitive Computing Lab Baidu Research Beijing100193 China

The two-dimensional principal component analysis (2DPCA) has become one of the most powerful tools of artificial intelligent algorithms. In this paper, we review 2DPCA and its variations, and propose a general ridge regression model to extract features from both row and column directions. To enhance the generalization ability of extracted features, a novel relaxed 2DPCA (R2DPCA) is proposed with a new ridge regression model. R2DPCA generates a weighting vector with utilizing the label information, and maximizes a relaxed criterion with applying an optimal algorithm to get the essential features. The R2DPCA-based approaches for face recognition and image reconstruction are also proposed and the selected principle components are weighted to enhance the role of main components. Numerical experiments on well-known standard databases indicate that R2DPCA has high generalization ability and can achieve a higher recognition rate than the state-of-the-art methods, including in the deep learning methods such as CNNs, DBNs, and DNNs. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

Coevolution spreading in complex networks

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arXiv 2019年

作者： Wang, Wei Liu, Quan-Hui Liang, Junhao Hu, Yanqing Zhou, Tao Cybersecurity Research Institute Sichuan University Chengdu610065 China Big Data Research Center University of Electronic Science and Technology of China Chengdu610054 China Compleχ Lab University of Electronic Science and Technology of China Chengdu610054 China College of Computer Science Sichuan University Chengdu610065 China School of Mathematics Sun Yat-Sen University Guangzhou510275 China School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519082 China

The propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic-awareness, and epidemic-resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed. Copyright © 2019, The Authors. All rights reserved.

关键词： Complex networks

Donut Modes in Space Wavelength Division Multiplexing: Multimode Optical Fiber Transmission based on Electrical Feedback Equalizer

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Journal of Physics: Conference Series 2021年第1期1755卷

作者： Alaan Ghazi S. A. Aljunid Syed Zulkarnain Syed Idrus R. Endut N. Ali Angela Amphawan Alaa Fareed Aras Al-dawoodi Awab Noori School of Human Development & Techno-communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia. Computer Technical Engineering Al-Qalam University College Iraq Advanced Communication Engineering (ACE) Centre of Excellence Universiti Malaysia Perlis Malaysia. School of Microelectronic Engineering (SCCE) Universiti Malaysia Perlis Perlis Malaysia Lightwave Laboratory School of Engineering and Technology Sunway University 47500 Petaling Jaya Selangor Malaysia. School of Computing University Utara Malaysia Malaysia. Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Data Science Research Lab School of Computing Universiti Utara Malaysia Malaysia

This study demonstrates the implementation of multi-input-multi-output (MIMO) transmission donut modes in space wavelength division multiplexing (SWDM) over multi-mode optical fiber (MMOF) and simulating the effect of mode coupling of the transmitted signal. However, during the data transmitted over MMF by using SWDM an issue is encountered, which is the inter symbol interference (ISI) due to mode coupling. ISI has been recognized as a major obstacle to high-speed data transmission. The signal processing in SDM of donut modes over MMOF with electrical feedback equalizer is used to improve the performance for increasing the bandwidth. A data rate 80 Gbit/s up for a distance of 1600 meters is achieved. SWDM topology was designed emitted from a spatial vertical cavity surface-emitting laser (VCSEL) through MMOF in conjunction with the configurations of electrical feedback equalizer. The parameters and values used in Opt-Sim simulation topology were listed. The performance measurement of this study based on bit-error rates (BER) and eye diagram before and after electrical feedback equalization were analysed.

关键词：

DBFT: A Byzantine Fault Tolerant Protocol with Graceful Performance Degradation

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DBFT: A Byzantine Fault Tolerant Protocol with Graceful Perf...

Reliable Distributed Systems

作者： Jingjing Zhang Yingyao Rong Jiannong Cao Chunming Rong Jing Bian Weigang Wu School of Data and Comp. Sci. Sun Yat-Sen University Guangzhou China Department of Computing Hong Kong Polytechnic University HongKong Electrical Engineering & Computer Science Department University of Stavanger Stavanger Norway Guangdong Province Key Lab of Big Data Analysis and Proc. Sun Yat-sen University Guangzhou China

ISBN: (数字)9781728142227

ISBN: (纸本)9781728142234

The surging interest in blockchain has revitalized the search for efficient Byzantine fault-tolerant (BFT) protocols, which are used for blockchains to achieve consensus among replicated data blocks. Most existing BFT protocols perform well in fault-free cases, but they usually suffer from serious performance degradation when faults occur. In this paper, we present DBFT, a BFT protocol that realizes graceful performance degradation in normal cases. The major novelty of DBFT lies in the double-response mechanism, which lets replica nodes deterministically respond to clients twice: one is after the speculative execution phase and the other is after the commitment phase. The double-response mechanism can handle inconsistency in speculative execution, so as to alleviate performance degradation caused by faults. Moreover, DBFT does not involve clients in critical consensus operations so as to reduce the load of clients. The correctness properties, i.e., safety and liveness, of DBFT is rigorously proved. The performance of DBFT is evaluated via experiments and the results show that, DBFT outperforms similar BFT protocols obviously in normal cases.

关键词： cryptographic protocols fault tolerance degradation of performance Fault tolerance cryptographic protocols speculative execution protocols customers Clients Reaction Mechanisms

Supramolecular engineering of Cathode Materials for Aqueous Zinc-ion Energy Storage Devices: Novel Benzothiadiazole Functionalized Two-Dimensional Olefin-Linked COFs

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Angewandte Chemie 2023年第10期135卷

作者： Dr. Haijun Peng Senhe Huang Dr. Verónica Montes-García Dr. Dawid Pakulski Dr. Haipeng Guo Dr. Fanny Richard Prof. Xiaodong Zhuang Prof. Paolo Samorì Dr. Artur Ciesielski Université de Strasbourg CNRS Institut de Science et d'Ingénierie Supramoléculaires 8 allée Gaspard Monge 67000 Strasbourg France Contribution: Data curation (lead) Investigation (lead) Writing - original draft (lead) Writing - review & editing (lead) The Soft2D Lab State Key Laboratory of Metal Matrix Composites Shanghai Key Laboratory of Electrical Insulation and Thermal Ageing School of Chemistry and Chemical Engineering Shanghai Jiao Tong University 200240 Shanghai China Centre for Advanced Technologies Adam Mickiewicz University Uniwersytetu Poznańskiego 10 61-614 Poznań Poland Adam Mickiewicz University Foundation Poznań Science and Technology Park Rubież 46 61-612 Poznań Poland

Two-dimensional covalent organic frameworks (COFs) have emerged as promising materials for energy storage applications exhibiting enhanced electrochemical performance. While most of the reported organic cathode materials for zinc-ion batteries use carbonyl groups as electrochemically-active sites, their high hydrophilicity in aqueous electrolytes represents a critical drawback. Herein, we report a novel and structurally robust olefin-linked COF-TMT-BT synthesized via the aldol condensation between 2,4,6-trimethyl-1,3,5-triazine (TMT) and 4,4′-(benzothiadiazole-4,7-diyl)dibenzaldehyde (BT), where benzothiadiazole units are explored as novel electrochemically-active groups. Our COF-TMT-BT exhibits an outstanding Zn 2+ storage capability, delivering a state-of-the-art capacity of 283.5 mAh g −1 at 0.1 A g −1 . Computational and experimental analyses reveal that the charge-storage mechanism in COF-TMT-BT electrodes is based on the supramolecularly engineered and reversible Zn 2+ coordination by the benzothiadiazole units.

关键词： Charge Storage Mechanism Covalent Organic Frameworks Energy Storage Devices Functional Porous Materials Zinc-Ion Batteries