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24th IEEE International conference on High Performance computing and communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wei, Shuai Cao, Guitao Dai, Cheng Dai, Shengxin Guo, Bing College of Computer Science Sichuan University Chengdu China MoE Engineering Research Center of SW/HW Co-design Technology and Application MoE East China Normal University Shanghai China MoE Engineering Research Center of SW/HW Co-design Technology and Application MoE East China Normal University Chengdu China Sichuan University Chengdu China

ISBN: (纸本)9798350319934

Federated learning(FL) enables multiple partici-pants to build a common, robust machine learning model without sharing data, which is a key technique to address data privacy, security, access rights, and access to heterogeneous data. However, existing FL algorithms still fall short of expectations in unsupervised learning and Non-iid data, which are two major challenges that limit the applicability and accuracy. For example, the lack of data labels or skewed feature distributions leads to loss of representativeness of the model, at the same time, tagging client data will increase a lot of costs in real application scenarios, therefore, supervised federated learning greatly limits the applicability of FL. In this paper, we propose a new unsupervised FL algorithm Fedco. In particular, at the parameter aggregation phase, we apply the momentum update queue to improve the training performance, enhance the model accuracy, and lower the labeling costs. At the local client training phase, the common queue that could be regarded as a big dictionary solve the data heterogeneity. The experiment on the benchmark data set shows that our method can be compared to other supervised and semi-supervised federated learning models, which proves the effectiveness of Fedco. © 2022 IEEE.

关键词： Unsupervised learning

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Towards Efficient Task Offloading at the Edge Based on Meta-Reinforcement Learning with Hybrid Action Space

Towards Efficient Task Offloading at the Edge Based on Meta-...

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IEEE International conference on communications (ICC)

作者： Zhao Yang Yuxiang Deng Ting Wang Haibin Cai MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China

As a critical concern of multi-access edge computing (MEC), task offloading has received extensive attention. Although deep reinforcement learning (DRL) has achieved great success in resolving the task offloading problem, most existing DRL-based offloading schemes only consider either continuous action space or discrete action space, which results in the loss of optimality of decisions. Moreover, the generalization ability of the existing schemes is still far from adaptive to dynamic changes in the environment. This leads to offloading strategies having to conduct re-sampling and re-training, which largely impairs the offloading efficiency. To address these issues, we propose a novel efficient MEC task offloading scheme based on parameterized meta-reinforcement learning taking hybrid action space into account. We first formulate this problem as a non-convex multi-objective optimization problem. Then, we design a parameterized meta-reinforcement learning algorithm, named Meta-Hybrid-PPO, with hybrid action space to solve the optimization problem. comprehensive experimental results show that our Meta-Hybrid-PPO not only performs better than existing state-of-the-art methods in reducing task processing latency and computational energy consumption but also achieves better adaptability.

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Prophet: SSD Failure Analysis and Prediction Guided by Flash Reliability Characteristics in Data centers

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IEEE Transactions on computers 2025年

作者： Song, Yunpeng Liang, Yujiong Liu, Jialin Shi, Liang East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai China East China Normal University School of Computer Science and Technology Shanghai China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan China

Solid-state drives (SSDs) are massively deployed in various fields, especially in data centers, for their excellent cost-effectiveness. However, SSDs may fail due to their imperfect manufacturing processes, resulting in system-level failures and even downtime in data centers. This makes SSD failure prediction critical. Current studies focus on dealing with data missing, numerical normalization, and other statistical issues in using machine learning methods, but the consideration of the reliability characteristics of the underlying flash media of SSDs and the timeliness (time duration between predicted failure and real failure) of SSD failure prediction result is missing. In this work, we study the failure characteristics of over 200,000 drives from industry data centers over a 4-year period, as well as daily data. The relationship between SSD attribute values and failures is first investigated. Then, we analyzed the SSD failure characteristics from several aspects (causes, differences between failures, and timeliness of prediction results) relying on flash reliability characteristics. Based on these, a novel SSD failure prediction method (Prophet) is proposed. Specifically, Prophet contains the following two components. First, to cope with the differences between failures, a diff-state method is proposed for differential machine learning modeling of SSDs in different "States". We define the "State" of an SSD, which represents the range of values in which the SSD currently lies in terms of some key attributes. Through flash reliability characteristics, we distinguish between different failures before training the model to obtain accurate predictions of different failure behaviors. Second, a recovery period method is proposed to enhance the timeliness of SSD failure prediction result by designing the sample selection method. The enhanced timeliness can be utilized by operations personnel to handle failed SSDs, such as replacement and repair. The evaluation results of

关键词： Failure rate

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Buffer with Feature Tokens: combating confusion in Medical Image continual Learning

Buffer with Feature Tokens: Combating Confusion in Medical I...

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Zheming Fan Weiting Chen Jiahao Fan Ruizhi Yu Shanghai Key Laboratory of Trustworthy Computing MOE Research Center of Software/Hardware Co-Design Engineering East China Normal University

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Due to its complexity and privacy concerns, medical data is often difficult to collect fully at once. New data emerges with the discovery of new diseases and advances in medical technology, but privacy concerns limit its storage. When deep learning models require training on datasets of new diseases, they often suffer from catastrophic forgetting, where the models’ performance on previously learned data degrades significantly. Recently, pre-trained continual learning methods have been proposed. Unlike earlier continual learning methods, these new approaches do not require learning excessive amounts of new knowledge, furthermore, they can still achieve good performance even when the pre-trained feature extractor is completely frozen. For these approaches, the performance degradation is not primarily due to catastrophic forgetting, but rather confusion. To combat confusion, we propose a method using a buffer with feature tokens (BWFT) which is cheaper and safer than directly storing the original data. Additionally, we propose a feature merge method to further reduce storage and computation costs. In the class-incremental scenario, our method outperforms state-of-the-art methods on multiple medical datasets as well as natural image datasets. code is available at https://***/1345475677/BWFT.

关键词： continuing education Degradation Training Data privacy Privacy costs Feature extraction Data models Biomedical imaging Diseases

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Bounded-DWA: An Efficient Local Planner for Ackermann-driven Vehicles on Sandy Terrain

Bounded-DWA: An Efficient Local Planner for Ackermann-driven...

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IEEE International conference on Real-time computing and Robotics (RCAR)

作者： Ke Gong Zhiyuan Xu Xinyu Zhang Shanghai Key Laboratory of Trustworthy Computing Engineering Research Center of Software/Hardware Co-Design Technology and Application (MoE) and School of Software Engineering East China Normal University Shanghai Midea Group China

We present a new dynamic window approach (DWA) for mobile vehicles equipped with Ackermann steering geometry that adheres to Ackermann kinematic constraints. By integrating these constraints with the sampling window in DWA, we can further reduce and bound the sampling range and enhance the efficiency of the DWA when a mobile vehicle moves on sandy terrain. Furthermore, we improve the evaluation function to optimize the selected trajectory. Our algorithm is successfully validated in ROS and Gazebo through comparison with other existing local planner such as the original DWA and TEB algorithms. We also successfully deploy our Bounded-DWA in the application of coverage path planning, where tree-planting robots traverse on sandy terrain.

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Multi-granularity Information Flow Enhanced Skeleton-based Infant Seizure Detection

Multi-granularity Information Flow Enhanced Skeleton-based I...

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2023 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Huiyuan Chen, Weiting Zhang, Yiqing Li, Na East China Normal University Moe Research Center of Software/Hardware Co-Design Engineering Shanghai Key Laboratory of Trustworthy Computing Shanghai China Shanghai Jiao Tong University School of Medicine Children's Hospital Shanghai China

ISBN: (纸本)9798350337488

Automatic video seizure detection can assist in the diagnosis of seizures. Existing works usually rely on the information of single granularity for learning seizure patterns and the performance of these works is often constrained by the limited annotated data. To overcome these problems, we introduce a multi-granularity information flow method to enhance skeleton-based infant seizure detection from different perspectives. A forward-backward consistency constraint is proposed to use the reverse temporal sequences as augmented data, helping make better predictions. Besides, an importance-aware module is proposed to adaptively pay more attention to important frames. Experimental results indicate that our method can improve the performance of seizure detection and achieve high performance with a relatively low amount of parameters. © 2023 IEEE.

关键词： forward-backward consistency multi-granularity information seizure detection skeleton-based action recognition

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Predicting Liner Arrival Time Based on Deep Learning 3

Predicting Liner Arrival Time Based on Deep Learning

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3rd IEEE International conference on Civil Aviation Safety and Information technology, ICCASIT 2021

作者： Huang, Chao Huang, Yuqi Yu, Yang Xiao, Bo MoE Engineering Research Center for Software/Hardware Co-Design Technology East China Normal University Shanghai China

ISBN: (纸本)9781665425186

Sea transportation has become the principal mode of transportation. It is of great significance to accurately predict the estimated time of arrival (ETA) of the liner carriage. This paper proposes a model based on deep learning algorithm to deal with liner arrival time prediction in sea transportation. Two data cleaning algorithms and one data enhancement algorithm are presented, with data cleaning effectively cleaning the GPS data generated by the liner and data enhancement increasing the diversity of data samples. A method based on deep learning to predict liner arrival time is provided, using the Factorization Machine (FM) model to generate second-order crossover features, and grouped convolution and attention mechanisms to enhance the representation ability of the model. Experiments show that the method proposed control the prediction error better than traditional machine learning models. © 2021 IEEE

关键词： Forecasting

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A Robust and Generalized Gauss-Seidel Solver for Physically-correct Simultaneous collisions

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Proceedings of the ACM on computer Graphics and Interactive Techniques 2025年第1期8卷 1-17页

作者： Huanbo Zhou Zhenyu Xu Xijun Liu Xinyu Zhang Software Engineering Institute East China Normal University Shanghai Shanghai China East China Normal University Shanghai Shanghai China Software Engineering Institute East China Normal University Shanghai Shanghai China Shanghai Key Laboratory of Trustworthy Computing Shanghai Shanghai China and Engineering Research Center of Software/Hardware Co-design Technology and Application MoE) Shanghai Shanghai China

Simulating multi-object collisions in real-time environments remains a significant challenge, particularly when handling simultaneous collisions in a physically accurate manner. Traditional Gauss-Seidel solvers, widely employed in physics engines, often fail to preserve the symmetry and consistency of multi-object interactions that are often observed in physics. In this paper, we present a generalized and robust Gauss-Seidel solver to overcome the difficulties in simultaneous collisions. Using spatial and temporal collision states to classify and resolve constraints, our algorithm ensures correct collision propagation and symmetry, addressing the limitations commonly found in existing solvers. Moreover, our algorithm can mitigate jitters caused by floating-point errors, ensuring smooth and stable collision responses. Our approach demonstrates fast convergence and improved accuracy in scenarios involving complex multi-object collisions.

关键词： Physically-based simulation Gauss-Seidel solver Simultaneous collisions

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Brief Industry Paper: Towards Efficient Task Scheduling for AUTOSAR using Parallel Pruning 44

Brief Industry Paper: Towards Efficient Task Scheduling for ...

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44th IEEE Real-Time Systems Symposium, RTSS 2023

作者： Yang, Yanxing Zhang, Nan Yan, Dengke Wei, Xian Zhou, Junlong Liu, Hong Chen, Mingsong MoE Engineering Research Center of SW/HW Co-Design Technology and Application East China Normal University China School of Computer Science and Engineering Nanjing University of Science and Technology China Shanghai Uni-Sentry Intelligent Technology Co. Ltd. China

ISBN: (纸本)9798350328578

As a standardized software framework and open E/E system architecture, the AUTomotive Open System ARchitecture (AUTOSAR) has been widely applied to autonomous driving systems to enable real-time control. However, due to the increasing design complexity and the lack of efficient algorithms and design automation tools, it is difficult to quickly figure out an optimal task scheduling scheme for an AUTOSAR-based system. To address this problem, we introduce a novel task scheduling method that can parallelly search for an optimal solution with the help of our proposed pruning strategy. Experimental results on a real-world AUTOSAR-based autonomous driving system demonstrate that our approach can achieve much better task scheduling solutions than the ones obtained manually and significantly reduce the overall task scheduling time. © 2023 IEEE.

关键词： Real time control

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Brief Industry Paper: RTLight: Digital Twin-Based Real-Time Federated Traffic Signal control 44

Brief Industry Paper: RTLight: Digital Twin-Based Real-Time ...

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44th IEEE Real-Time Systems Symposium, RTSS 2023

作者： Ye, Yutong Ling, Zhiwei Yang, Yaning Wei, Xian Cheng, Chen Chen, Su Chen, Mingsong East China Normal University MoE Engineering Research Center of SW/HW Co-Design Technology and Application China Wuxi Huiwang Smart Technology Co. Ltd. China China Mobile Iot Co. Ltd. China

ISBN: (纸本)9798350328578

Although Reinforcement Learning (RL)-based methods have been widely researched in Traffic Signal control (TSC), they still suffer from the problems of poor adaptation to real-world traffic scenarios and slow convergence to optimized solutions. This is because RL-based TSC methods have a high dependency on accurate modeling of the environment. With transportation infrastructure constraints, some vehicle dynamic information in the road network is difficult to obtain in real-time, which strongly limits the capability of RL agents. To address this problem, we propose a novel real-time federated traffic signal control system named RTLight, which can efficiently control traffic lights in real-time for multi-intersection scenarios. Based on the digital twin, the RL agent can obtain sufficient traffic information and interact with the environment in real time. Inspired by federated learning, our system supports knowledge sharing among intersections, which improves the overall convergence rate and control performance. Note that, we have deployed our RTLight system for large-scale application validation in Xishan district, Wuxi, China. Experimental results obtained from various real-world traffic scenarios demonstrate that RTLight can significantly improve the control performance. © 2023 IEEE.

关键词： Reinforcement learning

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