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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jingpeng Sun Rongxiao Wang Gangming Zhao Chen Chen Yixiao Qu Jinpeng Li Xiyuan Hu Yizhou Yu School of Computer Science and Engineering Nanjing University of Science and Technology Institute of Automation Chinese Academy of Sciences College of Mathematics and System Science Xinjiang University Department of Computer Science The University of Hong Kong Ningbo Institute of Life and Health Industry University of Chinese Academy of Sciences

Automatic sleep staging is vital to scale up sleep assessment and diagnosis to serve millions experiencing sleep deprivation and disorders and enable longitudinal sleep monitoring in home environments. However, how to learn from multi-channel raw physiological signal inputs (e.g., EEG and EOG) to capture the sleep stage and physiological signal relations remains a big challenge. In this paper, we propose a sleep staging model, named Sleep Staging Cross-modal Transformer (START), which is a transformer-only method for sleep stage classification. Our model is capable of learning a joint representation from both EEG and EOG signals by using a cross-modal fusion strategy. Experimental results show that our model outperforms the state-of-the-art methods on two public datasets. Furthermore, our model provides considerable reductions in parameters and training time compared to previous methods.

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PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization

arXiv

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arXiv 2024年

作者： Ye, Yuyang Tang, Lu-An Wang, Haoyu Yu, Runlong Yu, Wenchao He, Erhu Chen, Haifeng Xiong, Hui Department of Management Science and Information Systems Rutgers Business School Rutgers University NewarkNJ United States Department of Data Science and System Security NEC Laboratories PrincetonNJ United States Department of Computer Science University of Pittsburgh PittsburghPA United States Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou China

Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity for operational optimization in industry 4.0. In recent years, Deep Reinforcement Learning (DRL) based methods offer promising enhancements for sequential optimization processes and can be used for reducing carbon emissions. However, existing DRL methods need a pre-defined reward function to assess the impact of each action on the final sustainable development goals (SDG). In many real applications, such a reward function cannot be given in advance. To address the problem, this study proposes a Performance based Adversarial Imitation Learning (PAIL) engine. It is a novel method to acquire optimal operational policies for carbon neutrality without any pre-defined action rewards. Specifically, PAIL employs a Transformer-based policy generator to encode historical information and predict following actions within a multi-dimensional space. The entire action sequence will be iteratively updated by an environmental simulator. Then PAIL uses a discriminator to minimize the discrepancy between generated sequences and real-world samples of high SDG. In parallel, a Q-learning framework based performance estimator is designed to estimate the impact of each action on SDG. Based on these estimations, PAIL refines generated policies with the rewards from both discriminator and performance estimator. PAIL is evaluated on multiple real-world application cases and datasets. The experiment results demonstrate the effectiveness of PAIL comparing to other state-of-the-art baselines. In addition, PAIL offers meaningful interpretability for the optimization in carbon neutrality. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Precise Timing over Beyond 5G Networks for Intelligent Transport in the Smart City

Precise Timing over Beyond 5G Networks for Intelligent Trans...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Hongxing Li Guochu Shou Yaqiong Liu Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

The need for precise timing is increasing with the development of the smart city, and intelligent transport is a key application that often relies on the distribution of accurate timing. Existing time synchronization solutions cannot adequately meet the precise timing needs for practical applications in intelligent transportation. Global Navigation Satellite systems (GNSS) signals, for instance, can be obstructed, weakened, or deflected in urban canyon environments. While certain sectors like telecommunications and smart grids have successfully adopted IEEE 1588 for high-precision time synchronization, its dependence on standalone networks and limited application within local area networks poses challenges when catering to the wide-ranging and mobile demands of intelligent transportation applications. This paper categorizes the time synchronization requirements of intelligent transport and proposes a time synchronization scheme over converged networks, which separates time transmission and precision compensation based on software-defined networking (SDN) principles, and makes it possible to provide precise timing for intelligent transport devices, including on-board units (OBUs) and roadside units (RSUs), over wired and wireless communication networks. Experimental results indicate that the proposed scheme achieves a relative time error of less than 1.2 microseconds over the converged networks.

关键词： Wireless communication Global navigation satellite system Accuracy Smart cities Transportation Telecommunications Smart grids Synchronization Software defined networking Local area networks

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Few-Shot Gearbox Fault Diagnosis Based on Semi-Supervised Prototypical Network and Contrastive Learning

Few-Shot Gearbox Fault Diagnosis Based on Semi-Supervised Pr...

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Chinese Automation Congress (CAC)

作者： Shulie Cheng Yingjie Liang Zuowei Ping Xuechao Liao Maolin Wang Yong Zhang School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China National Key Laboratory of Science and Technology on Vessel Integrated Power System Naval University of Engineering Wuhan China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Due to the scarcity of labeled faulty data in industrial practice, fault diagnosis models often face challenges related to overfitting and limited accuracy. This article introduces a novel solution to tackle the problem—a fault diagnosis method that leverages a combination of a semi-supervised prototype network and contrastive learning. Firstly, a limited number of labeled samples is employed to construct pairs of positive and negative samples. And the training method of contrastive learning is used to provide appropriate initial parameters for the autoencoder. Secondly, the autoencoder is employed as a feature mapping function within the prototype network and obtains the prototype using a limited number of labeled samples. Finally, a prototype refinement method fine-tunes the prototype through unlabeled data, reducing the impact of anomalous data and obtaining a more stable and accurate prototype. The effectiveness of the proposed method is evaluated using gearbox data as a validation dataset. Through comparative analysis with other methods, the results demonstrate that the proposed approach achieves superior diagnostic accuracy.

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RKSeg+: make full use of Runge–Kutta methods in medical image segmentation

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Multimedia systems 2024年第2期30卷 65-65页

作者： Zhu, Mai Fu, Chong Wang, Xingwei School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China

The dynamical system perspective has been used to build efficient image classification networks and semantic segmentation networks. Furthermore, the Runge–Kutta (RK) methods are powerful tools for building networks from the dynamical systems perspective. Hence, the Runge–Kutta segmentation network (RKSeg) for medical image segmentation was born. Skip connections and multiple scaling are often used in common models but lack mathematical explanations. RKSeg interprets and uses skip connections based on the RK methods. Therefore, RKSeg greatly improves segmentation efficiency. However, it does not explain and use multiple scales from a dynamical system perspective but only inherits the multi-scale scheme of existing models. We compensate for this shortcoming by interpreting and using multiple scales based on the RK methods. In addition, the network structure also limits the excellent image classification networks as the backbones of RKSegs. Therefore, we modify the network structure to support more image classification networks as backbones. As a result, we propose a novel network structure RKSeg+. Our proposed RKSeg+ achieves better segmentation results with fewer parameters than RKSeg. Furthermore, RKSeg+, well configured with few parameters, outperforms state-of-the-art models on six of the ten organ datasets in the Medical Segmentation Decathlon. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Semantic Segmentation

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Working Smarter Not Harder: Hybrid Cooling for Deep Learning in Edge Datacenters

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年

作者： Pei, Qiangyu Yuan, Yongjie Hu, Haichuan Wang, Lin Zhang, Dong Yan, Bingheng Yu, Chen Liu, Fangming Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology 1037 Luoyu Road Wuhan430074 China Paderborn University TU Darmstadt Germany Jinan Inspur Data Co. Ltd. China Huazhong University of Science and Technology Peng Cheng Laboratory China

The proliferation of deep-learning-based mobile and IoT applications has driven the increasing deployment of edge datacenters equipped with domain-specific accelerators. The unprecedented computing power offered by these accelerators puts a heavy burden on the cooling system, motivating more potent cooling techniques like cold water cooling. However, we observe that cold water cooling results in significant energy waste in edge datacenters due to the fluctuating resource utilization both spatially and temporally. To tackle this issue, we propose the concept of "working smarter"by slowing down accelerators deliberately whenever possible and enabling warm water cooling during these times to achieve cooling efficiency. Based on this concept, we develop Hyco - a hybrid water cooling system tailored for edge datacenters running deep learning workloads. First, Hyco features a zone-based cooling architecture enabling dynamic switching between cold water and warm water cooling. Then, based on a lightweight latency estimation method, Hyco incorporates a learning-based scheduling scheme to determine "which"accelerator workers and "when"to slow down through an adaptive and intelligent power-latency trade-off for deep learning models. The simulation with real-world traces shows that Hyco reduces the cooling energy consumption by up to 34.74× while satisfying latency constraints more than 99% of the time for deep-learning-based applications. © 2016 IEEE.

关键词： Deep reinforcement learning

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption

arXiv

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arXiv 2024年

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2024, CC BY.

关键词： Bamboo

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Multi-Agent Based Stochastic Dynamical Model to Measure Community Resilience

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Journal of Social Computing 2022年第3期3卷 262-286页

作者： Jaber Valinejad Lamine Mili C.Natalie Van Der Wal Data and System Science Lab Harvard Medical SchoolHarvard UniversityCambridgeMA 02138USA Bradley Department of Electrical and Computer Engineering Virginia TechNorthern Virginia CenterFalls ChurchVA 22043USA Department of Multi-Actor Systems Faculty of TechnologyPolicy and ManagementDelft University of TechnologyDelft2600 AAthe Netherlands.

Emergency services and utilities need appropriate planning tools to analyze and improve infrastructure and community resilience to *** as a key metric of community resilience is the social well-being of a community during a disaster,which is made up of mental and physical social *** factors influencing community resilience directly or indirectly are emotional health,emergency services,and the availability of critical infrastructures services,such as food,agriculture,water,transportation,electric power,and communications *** turns out that in computational social science literature dealing with community resilience,the role of these critical infrastructures along with some important social characteristics is not *** address these weaknesses,we develop a new multi-agent based stochastic dynamical model,standardized by overview,design concepts,details,and decision(ODD+D)protocol and derived from neuro-science,psychological and social sciences,to measure community resilience in terms of mental and physical *** this model,we analyze the micro-macro level dependence between the emergency services and power systems and social characteristics such as fear,risk perception,informationseeking behaviour,cooperation,flexibility,empathy,and experience,in an artificial ***,we simulate this model in two case studies and show that a high level of flexibility,experience,and cooperation enhances community *** for both theory and practice are discussed.

关键词： community resilience collective behavior emergency services power systems critical infrastructures artificial society overview,design concepts,details,and decision(ODD+D) cyber-physical-social system

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A Website Fingerprinting Attack Based on Parameter Prediction Model for Conceptual Drift

A Website Fingerprinting Attack Based on Parameter Predictio...

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Software system and Information Processing (ICSSIP), IEEE International Conference on

作者： Jian Cui Taizhong Xu Li Zhao Shuai Han College of Computer Science and Technology Harbin Engineering University Harbin China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China School of Electrical and Information Engineering Yunnan Minzu University Yunnan Key Laboratory of Unmanned Autonomous System Kunming China

ISBN: (数字)9798331506131

ISBN: (纸本)9798331506148

Website content updates and changes in the network environment can lead to concept drift, significantly impacting the performance of existing website fingerprinting methods. Recent research has focused on transfer learning-based approaches for concept drift adaptation to address this issue with limited samples. These methods involve pre-training a robust feature extractor and fine-tuning the classifier with a few samples. However, the effectiveness of these classifiers is highly dependent on the distributional gap between pre-training and fine-tuning datasets. As the divergence increases, more fine-tuning data is required to maintain classification performance. Consequently, existing methods struggle to achieve effective website identification tasks under severe concept drift with limited tuning data. To overcome these limitations, we propose a model parameter prediction approach, which adapts to new domains from data and model perspectives to enhance website identification performance. The proposed method predicts future model parameters to mitigate the effects of distributional changes, effectively adapting to evolving network environments. Experimental results demonstrate that our approach achieves significant improvements in accuracy, particularly under scenarios with limited tuning data and substantial concept drift.

关键词： Adaptation models Accuracy Concept drift Predictive models Fingerprint recognition Feature extraction Data models Robustness Long short term memory Tuning

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Optimized Design of a Haptic Unit for Vibrotactile Amplitude Modulation

Optimized Design of a Haptic Unit for Vibrotactile Amplitude...

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IEEE International Conference on Robotics and Biomimetics

作者： Jingchen Huang Yun Fang Weichao Guo Zhijian Qiao Xinjun Sheng State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering and the Meta Robotics Institute Shanghai Jiao Tong University Shanghai China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Communicating information to users is a crucial aspect of human-machine interaction. Vibrotactile feedback encodes information into spatiotemporal vibrations, enabling users to perceive tactile sensations. It offers advantages such as lightweight, wearability, and high stability, with broad applications in sensory substitution, virtual reality, education, and healthcare. However, existing haptic unit designs lack amplitude modulation capabilities, which limits their applications. This paper proposed an optimized design of the haptic unit from the perspective of vibration amplitude modulation. A modified elastic model was developed to describe the propagation and attenuation mechanisms of vibration in the skin. Based on the model, two types of hierarchical architectural design were proposed. The design incorporated various materials arranged in multiple layers to amplify or attenuate the vibration amplitude as it traveled through the structure. An experimental platform was built to evaluate the performance of the optimized design.

关键词： Vibrations Solid modeling Virtual reality Amplitude modulation Robot sensing systems Motors Stability analysis Skin Haptic interfaces Spatiotemporal phenomena

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