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23rd USENIX conference on File and Storage Technologies, FAST 2025

作者： Li, Changlong Zhu, Zongwei Wang, Chao Liu, Fangming Xu, Fei Sha, Edwin H.-M. Zhou, Xuehai School of Computer Science and Technology East China Normal University China Jianghuai Advance Technology Center Hefei230026 China MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application China School of Software Engineering University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Huazhong University of Science and Technology China Peng Cheng Laboratory China

ISBN: (纸本)9781939133458

In mobile systems, memory can be compressed page-by-page to save space. This approach is widely adopted because memory data is accessed by page. However, this paper shows that the system response speed is significantly limited by page-grained compression. In this paper, we observe that approximately a quarter of anonymous memory pages are highly correlated, even though the association is implicit. Inspired by this, we propose Archer, an association-rule-aware memory compression framework in mobile systems. Archer demonstrates that memory in mobile devices should be compressed using flexible granularity, rather than relying solely on traditional page compression. To further integrate association-rule mining techniques into system design, we redesign the LRU mechanism and propose an adaptive memory compression region. Experimental results show that the average app launching speed is 1.55x faster when enabling Archer, and the average photographic speed and frame rate increase by 1.42x and 1.31x, respectively, compared to the state-of-the-art. © 2025 FAST. All Rights Reserved.

关键词： Portable equipment

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OpenNet: Incremental Learning for Autonomous Driving Object Detection with Balanced Loss

OpenNet: Incremental Learning for Autonomous Driving Object ...

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2023 IEEE International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Zezhou Cao, Guitao Xi, Xidong Wang, Jiangtao East China Normal University MoE Engineering Research Center of SW/HW Co-design Technology and Application Shanghai China Software Engineering Institute East China Normal University Shanghai China

ISBN: (纸本)9798350337020

Automated driving object detection has always been a challenging task in computer vision due to environmental uncertainties. These uncertainties include significant differences in object sizes and encountering the class unseen. It may result in poor performance when traditional object detection models are directly applied to automated driving detection. Because they usually presume fixed categories of common traffic participants, such as pedestrians and cars. Worsely, the huge class imbalance between common and novel classes further exacerbates performance degradation. To address the issues stated, we propose OpenNet to moderate the class imbalance with the Balanced Loss, which is based on Cross Entropy Loss. Besides, we adopt an inductive layer based on gradient reshaping to fast learn new classes with limited samples during incremental learning. To against catastrophic forgetting, we employ normalized feature distillation. By the way, we improve multi-scale detection robustness and unknown class recognition through FPN and energy-based detection, respectively. The Experimental results upon the coDA dataset show that the proposed method can obtain better performance than that of the existing methods. © 2023 IEEE.

关键词： Class Imbalance Incremental Learning Meta-Learning Open World Object Detection

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NL2CTL: Automatic Generation of Formal Requirements Specifications via Large Language Models 1

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25th International conference on Formal engineering Methods, ICFEM 2024

作者： Zhao, Mengyan Tao, Ran Huang, Yanhong Shi, Jianqi Qin, Shengchao Yang, Yang National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai China Hardware/Software Co-Design Technology and Application Engineering Research Center East China Normal University Shanghai China Guangzhou Institute of Technology Xidian University Xi’an China ICTT and ISN Laboratory Xidian University Xi’an China Software Engineering Institute East China Normal University Shanghai China

ISBN: (数字)9789819606177

ISBN: (纸本)9789819606160

Reducing the gap between natural language requirements and precise formal specifications is a critical task in requirements engineering. In recent years, requirement engineering is becoming increasingly complex alongside the growing intricacy of system engineering. Most requirements are expressed in natural language, which can be incomplete and ambiguous. However, formal languages with strict semantics can accurately represent certain temporal logic properties and allow for automated verification and analysis. This often limits the application of verification techniques, as writing formal specifications is a manual, error-prone, and time-consuming task. To address this, this paper proposes a framework that leverages Large Language Models (LLMs) to achieve automated conversion of natural language requirements to computation Tree Logic (CTL). To address the issue of dataset scarcity, we leveraged the interactive and generative capabilities of LLMs. By constructing a random generation algorithm and utilizing prompt engineering, we generated an NL-CTL dataset using LLMs. The generated dataset was then used to fine-tune the T5-Large model, enhancing its generative capacity. To improve generalization, this paper proposes the use of the GPT-3.5 Atomic Proposition (AP) Recognition method, which eliminates the constraints of using the framework across different domains. A series of experimental evaluations showed that the fine-tuned LLM achieved an accuracy of 46.4%, whereas the LLM with few-shot learning using only prompt engineering achieved only 2% accuracy, demonstrating the feasibility of this approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Semantics

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MACEdge: Real-Time Video Analytics Based on Multi-Access collaborative Edge computing

MACEdge: Real-Time Video Analytics Based on Multi-Access Col...

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IEEE International conference on Systems, Man and Cybernetics

作者： Dian Zhong Minghua Zhu Binbin Huang MOE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

Video analysis typically requires a significant amount of computing resources and energy. Traditional cloud-based video analysis relies on concentrating computing resources in the cloud, which puts a tremendous load on network bandwidth and introduces poor user experience due to latency. Edge computing offers a solution by offloading a portion of the video analysis tasks to the edge, significantly reducing the pressure on bandwidth and latency. However, the fixed video configurations (resolution, frame rate, etc.) uploaded to the edge may not be optimal for specific situations. Moreover, using a low video configuration makes it difficult for edge video analysis systems to recognize environmental changes, while using a high video configuration poses challenges to latency and bandwidth. Therefore, in this paper, we propose the MACEdge-an edge video analysis framework by leveraging the multi-access capability of the edge gateway to enhance edge perception by accessing heterogeneous sensors. We offer an algorithm based on Q Learning for dynamic adaptive adjustment of sensor threshold and video configurations. In experiments using real-world data, our collaborative solution improved accuracy and reduced high bandwidth costs compared to other video configuration adjustment benchmarks.

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QueryEdge: Real-Time Muti-Video Query in Edge-Cloud collaborative System

QueryEdge: Real-Time Muti-Video Query in Edge-Cloud Collabor...

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IEEE International conference on Systems, Man and Cybernetics

作者： Jihua Zhong Yannian Niu Minghua Zhu MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

The real-time query of surveillance video plays a significant role in many fields such as public safety, smart city, and abnormality monitoring. However, with the exponential growth of surveillance video data, traditional cloud-based intelligent video processing faces significant challenges in terms of latency and bandwidth, while the pure edge computing approach is deficient in query accuracy due to its lack of computational power. Existing edge cloud collaboration approaches, such as SurveilEdge, focus on real-time target queries within a single video stream and do not show promising results during target queries across multiple video streams. For this reason, this paper proposes Query Edge, an edge-cloud collaborative real-time query system for multiple video streams. Specifically, we design a real-time query system based on an edge-cloud collaboration framework to achieve highly accurate and low-latency target query services in multiple video streams. In addition, we introduce a prioritization mechanism and a load-balancing strategy in the query task scheduling process to further improve query efficiency. The evaluation proves that QueryEdge has a significant improvement in query latency and bandwidth consumption compared with pure cloud computing, pure edge computing, and SurveilEdge.

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Slim-MY: A Lightweight Network for Power Line Defect Detection with FPGA Acceleration

Slim-MY: A Lightweight Network for Power Line Defect Detecti...

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International conference on Parallel and Distributed Systems (ICPADS)

作者： Xiaoyang Zhang Wenjie Chen Zihao Zhou Zhuowen Duan MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

The power line is a crucial component of the power system. However, long-term exposure to the natural environment can lead to various defects, such as burrs, corrosion, and corona. These defects seriously threaten the safe operation of the power system. Traditional manual inspections and image processing methods are time-consuming, labor-intensive, and have lower accuracy. In light of these, we propose a lightweight network called Slim-MY. It utilizes lightweight feature extraction and fusion modules, significantly reducing computational and parameter overhead while ensuring defect detection accuracy. Furthermore, to accelerate the post-processing process, we propose the FP-NMS algorithm. It parallelizes the sorting and overlapping box removal parts of the NMS algorithm, making it more suitable for deployment on devices that support parallel computing, such as FPGA. By testing on our dataset of power line defects, the Slim-MY network achieves a detection accuracy and computation of 92.7% and 1.1 GFLOPs respectively, which outperforms most other networks. By accelerating on the KV260 FPGA, the Slim-MY network achieves an inference speed of 94 fps, and the FP-NMS algorithm’s execution speed is 14.8 times that of the GTX 1080Ti. It indicates the feasibility and efficiency of the method we propose in power line defect detection tasks.

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LWSA: A Learning-Based Workflow Scheduling Algorithm for Energy-Efficient UAV Delivery System

LWSA: A Learning-Based Workflow Scheduling Algorithm for Ene...

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International conference on Parallel and Distributed Systems (ICPADS)

作者： Jiali Wang Yutong Ye Ting Wang Mingsong Chen MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

Due to their fast speed and easy deployment, Unmanned Aerial Vehicles (UAVs) have been widely used across various sectors, such as earthquake rescue, medical assistance, and smart agriculture. However, UAVs in delivery networks face significant challenges due to limited battery life and computational capabilities, particularly for tasks that entail intensive computing workflows. In this context, Multi-access Edge computing (MEC), which provides computing resources in close proximity to mobile terminal devices, has emerged as a promising solution. UAVs can offload computing tasks to MEC resources across diverse Internet of Things (IoT) environments. Although task offloading can enhance their task processing capability, it simultaneously brings additional costs, encompassing data transmission time and energy consumption. To address these issues, this paper proposes a novel workflow scheduling method based on the Proximal Policy Optimization (PPO) algorithm, aimed at optimizing UAV energy consumption within MEC environments. The proposed approach establishes a learning-based workflow scheduling strategy harnessing the adaptability of the PPO algorithm to manage dynamic and intricate scenarios, which facilitates efficient task allocation to optimal computational resources while accounting for flight time constraints. Extensive experiments conducted on various well-known scientific workflow benchmarks in real-world UAV delivery networks validate the effectiveness of our method. compared with state-of-the-art methods, our approach significantly reduces UAV energy consumption and task completion time, simultaneously increasing UAV’s effective payload capacity.

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Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning

arXiv

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arXiv 2025年

作者： Chen, Xi Li, Qin Cai, Haibin Wang, Ting MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China

Federated Learning (FL) empowers multiple clients to collaboratively train machine learning models without sharing local data, making it highly applicable in heterogeneous Internet of Things (IoT) environments. However, intrinsic heterogeneity in clients’ model architectures and computing capabilities often results in model accuracy loss and the intractable straggler problem, which significantly impairs training effectiveness. To tackle these challenges, this paper proposes a novel Heterogeneity-aware Personalized Federated Learning method, named HAPFL, via multi-level Reinforcement Learning (RL) mechanisms. HAPFL optimizes the training process by incorporating three strategic components: 1) An RL-based heterogeneous model allocation mechanism. The parameter server employs a Proximal Policy Optimization (PPO)-based RL agent to adaptively allocate appropriately sized, differentiated models to clients based on their performance, effectively mitigating performance disparities. 2) An RL-based training intensity adjustment scheme. The parameter server leverages another PPO-based RL agent to dynamically fine-tune the training intensity for each client to further enhance training efficiency and reduce straggling latency. 3) A knowledge distillation-based mutual learning mechanism. Each client deploys both a heterogeneous local model and a homogeneous lightweight model named LiteModel, where these models undergo mutual learning through knowledge distillation. This uniform LiteModel plays a pivotal role in aggregating and sharing global knowledge, significantly enhancing the effectiveness of personalized local training. Experimental results across multiple benchmark datasets demonstrate that HAPFL not only achieves high accuracy but also substantially reduces the overall training time by 20.9%-40.4% and decreases straggling latency by 19.0%-48.0% compared to existing solutions. © 2025, CC BY.

关键词： Federated learning

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A Range/Angle-based Localization Framework in WSN: A Clustering Approach

A Range/Angle-based Localization Framework in WSN: A Cluster...

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Sensing, Measurement, communication and Internet of Things Technologies (SMC-IoT), International conference on

作者： Sijie Kong Bo Xiao MOE Research Center for Software/Hardware Co-Design Engineering and Application Software Engineering Institute East China Normal University Shanghai China

ISBN: (数字)9798350373257

ISBN: (纸本)9798350373264

In this paper, we propose a localization algorithm framework for wireless sensor networks (WSNs) based on cluster implementation. The algorithm addresses the issue of information locality in traditional distributed localization algorithms by utilizing clustering. In contrast to centralized algorithms, this algorithm achieves a more balanced node energy consumption without the need for additional special nodes. Through clustering, nodes pass appropriate information to their targets, and through optimizations within and between clusters, relatively accurate location information is obtained. After multiple rounds of clustering-localization iterations, the accuracy of node localization significantly improves. Importantly, simulation results demonstrate that the algorithm can adapt to various deployment scenarios by adjusting clustering parameters to balance energy consumption and localization accuracy.

关键词： Location awareness Wireless sensor networks Energy consumption Simulation Clustering algorithms Sensors Internet of Things

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Flipping consistent and counterfactual Attention Network for Facial Expression Recognition

Flipping Consistent and Counterfactual Attention Network for...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wenjie Liu Xinlong Shi Xianzhong Liu School of Software Engineering East China Normal University Shanghai China Software/Hardware Co-Design Engineering Research Center MoE

Facial expression recognition (FER) has recently made great process with attention mechanisms. However, existing methods mostly treat the network as a black box and ignore the causal relationship between modules. In this paper, we propose a flipping consistent and counterfactual attention network (FCCAN), which innovatively uses two causal inferences to guide the learning process. First, we construct an attention network with a multi-head attention learning (MAL) module to learn attention for different local features. Then, we measure and improve the quality of each component with causal reasoning. According to the consistency of features from flipped images, we design flipping consistency learning (FCL) module to force the network to extract comprehensive expression features. Based on counterfactual causality, we propose counterfactual attention learning (CAL) module to encourage the network to learn more effective attention. Extensive experiments implemented on FER datasets (including RAF-DB, FERPlus and AffectNet) show that our FCCAN outperforms state-of-the-art methods.

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