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design, Automation and Test in Europe conference and Exhibition

作者： Guangyu Wei Changlong Li Rui Xu Qingfeng Zhuge Edwin H.-M. Sha School of Computer Science and Technology East China Normal University MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application Department of Computer Science City University of Hong Kong

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Mobile devices have become ubiquitous in daily life. In contrast to traditional servers, mobile devices suffer from limited memory resources, leading to a significant degradation in the user experience. This paper demonstrates that the primary cause of memory consumption lies in anonymous pages associated with application heaps. Existing schemes are ineffective in deduplicating these pages due to the limited occurrence of the same anonymous pages. This paper presents Sparrow, a similar-page aware deduplication solution for mobile systems. Sparrow shows that memory pages still have the potential to deduplicate, even though the same pages are rare. An interesting observation inspires this, that is, a high number of pages having the partially-same contents. We have implemented Sparrow on real-life smartphones. Experimental results indicate that 30.45% more space can be saved with Sparrow.

关键词： Degradation Memory management User experience Mobile applications Servers Smart phones

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CacheTrimmer: Adaptive Cache File Trimming for Optimized Performance and Lifetime on Mobile Devices

CacheTrimmer: Adaptive Cache File Trimming for Optimized Per...

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IEEE International conference on computer design: VLSI in computers and Processors, (ICCD)

作者： Yi Zhang Yunpeng Song Wentong Li Yiyang Huang Dingcui Yu Mengyang Ma Liang Shi MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

Mobile devices always cache numerous files during application runtime, which can be trimmed to improve the user experience. However, existing cache file trimming methods are unaware of the cleaning cost within the file system and storage devices, which degrades the system performance and storage lifetime, resulting in low benefits of trimming cache files. Motivated by this, an adaptive cache file trimming (CacheTrimmer) scheme is proposed to trim cache files for performance and lifetime improvement. The basic idea is to determine the trimming timing based on the cleaning cost of the file system and storage device, maximizing the benefit of trimming cache files. Specifically, CacheTrimmer includes two components: First, a cleaning cost-aware trimming method is proposed to trim cache files by recording the index information of cache files in a list and determining the timing and size of file trimming. Second, to avoid trimming-induced intra-segment fragmentation and improve trimming efficiency, a log-structured cache scheme is further proposed to maintain the cache files in separate segments. We prototype CacheTrimmer with a real mobile platform. Experimental results under real workloads show that CacheTrimmer achieves encourage performance and lifetime improvement compared to the state-of-the-art.

关键词： Performance evaluation costs Runtime File systems System performance Cleaning Mobile handsets User experience Timing Recording

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Static code Analysis of IEC 61131–3 ST Programs via Symbolic Execution

Static Code Analysis of IEC 61131–3 ST Programs via Symboli...

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IEEE International conference on Systems, Man and Cybernetics

作者： Mengyan Zhao Yanhong Huang Jianqi Shi Yinghao Chen Yang Yang East China Normal University Shanghai China National Trusted Embedded Software Engineering Technology Research Center Shanghai China Hardware/Software Co-Design Technology and Application Engineering Research Center Shanghai China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

A Programmable Logic controller (PLC) is an essentially domain-specific computer used to control physical equipment and is widely used in industrial control fields. It plays a crucial role in automating complex processes for industrial automation systems, requiring high reliability as code vulnerabilities can potentially lead to disasters. Therefore, vulnerability detection in PLC programs is of significant importance. However, the availability of tools supporting vulnerability detection in PLC programming languages is limited. This paper attempts to improve industrial security from the perspective of code security and proposes a static code analysis approach specifically designed for IEC 61131–3 Structured Text (ST) programs. This approach uses structural pattern matching and symbolic execution technology to identify program defects and improve quality by detecting problematic code structures and potential issues early in the development process, thereby reducing the debugging effort required during developments. considering the characteristic of periodic loop execution in PLCs, we introduce the loop unwinding technique to collect constraints from subsequent execution cycles for detection purposes. Based on the aforementioned approach, we implement a static code analysis tool, ST-Checker and make a series of evaluations. The experimental results show that this method is feasible and can detect potential defects that existing PLC compilers cannot detect, improving the precision of defect detection with data dependencies.

关键词： codes Industrial control Programmable logic devices Merging Focusing Security Reliability IEC Standards Pattern matching Defect detection

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Protein Functional Family Classification Based on Multilevel Feature Information

Protein Functional Family Classification Based on Multilevel...

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2022 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Zhou, Guokai Chen, Weiting East China Normal University MOE Research Center of Software/Hardware Co-Design Engineering Shanghai Key Laboratory of Trustworthy Computing Shanghai China

ISBN: (纸本)9781665468190

Next-generation sequencing technologies generated a large number of new sequenced proteins. Classifying these proteins into functional families is an important task in the field of biological protein property research. The high-cost and time-consuming drawbacks of bioassays promote the development of machine learning-based protein classification studies. However, previous work only used single level features, which were not informative enough and had classification bias. In this paper, we propose a new model named PCNM to alleviate the above problems. Different from previous work that extracts only a single level of features, the PCNM model uses a convolutional Neural Network (CNN) module and a Global and Local feature (GL) module to extract feature at three levels: conserved domains, amino acids, and protein sequences. The experimental results of the Phages of orthologous groups dataset (POG) and the G-protein coupled receptor superfamily dataset (GPCR) show that our proposed PCNM outperforms currently available methods in five metrics: accuracy, precision, recall, F1 score, and MCC. PCNM achieves a high accuracy of 85.12% on the GPCR sub-subfamily dataset and 81.42% on the POG dataset. The robustness experiments of PCNM further demonstrate that the model alleviates the classification bias. © 2022 IEEE.

关键词： Proteins

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SLSNet: Weakly-Supervised Skin Lesion Segmentation Network with Self-attentions 20th

SLSNet: Weakly-Supervised Skin Lesion Segmentation Network w...

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20th Pacific Rim International conference on Artificial Intelligence, PRICAI 2023

作者： Pei, Songwen Huang, Junjie School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education East China Normal University Shanghai200062 China

ISBN: (纸本)9789819970247

computer-aided skin lesion segmentation with high precision is crucial to diagnose skin cancers in the early stage. However, the lack of pixel-level labels makes the skin lesion segmentation tasks challenging. To tackle this problem, a new weakly-supervised skin lesion segmentation network with self-attentions named SLSNet is proposed. SLSNet contains two modules and uses image-level labels as supervision information. One module named Intra-image Self-attention Seed Expansion (ISE) digs intra-image self-attentions with an expansion loss and a confidence loss to expand seed areas. The other module named Inter-image Affinity-based Noise Suppression (IAS) suppresses the noise pixels in attention maps via inter-image correlations. Extensive experiments conducted on ISIC-2017 dataset show that SLSNet achieves relatively high performance while reducing human labeling efforts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Expansion

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A Heatmap-Based Weighted Multi-stream Fusion Network in Skeleton Modality for Action Recognition

A Heatmap-Based Weighted Multi-stream Fusion Network in Skel...

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Intelligent control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), International conference on

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

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Human Action Recognition (HAR) has received widespread attention in recent years. For skeleton modality, the representation of spatio-temporal motion and the weight allocation of different streams are still under discussion. Therefore, we propose a Heatmap-based Weighted Multi-Stream Fusion Network, HW-MSFN, which attempts to represent spatiotemporal motion in a novel way, prompting the adaptability of 3D-CNN to motion. Additionally, compared with the slow motion obtained frame by frame, we use fast motion which is captured at a larger time scale, in order to attain more general motion characteristics. Finally, to make full use of inter-frame information and intra-frame information, we explore the best weight allocation of multiple stream fusion for skeleton modality. Experiments implemented on 3 databases including HMDB51, UCF101 and FineGYM99 show that our model outperforms state-of-the-art methods in single skeleton modality.

关键词： Heating systems Adaptation models Signal processing Feature extraction Skeleton Spatiotemporal phenomena Resource management Human activity recognition Streams Intelligent control

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An efficient group key agreement protocol with administrator 3

An efficient group key agreement protocol with administrator

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3rd International conference on Internet of Things and Smart City, IoTSC 2023

作者： Fang, Qihao Zhang, Lei Zhang, Mingrui Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Science and Technology on Communication Security Laboratory Chengdu610041 China Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education Shanghai200062 China

ISBN: (纸本)9781510666375

Group key agreement technology is one of the most popular solution to establish a reliable secure channel between communicating entities of a group. Group key agreement protocol with administrator (GKAA) can handle a large number of group operations in sequence to resolve disorder problem and it is widely used in practical applications. However, in most of existing protocol, the administrator has to compute materials for each participant in the group which brings huge calculate overhead. In this paper, we propose an efficient group key agreement protocol with Administrator. Our GKAA protocol is able to perform group operation in a high efficacy way with the help of a key tree. It is also flexible and scalable since it suits for any network communication environment like IoT, video meeting, social applications, etc. Our protocol satisfies the properties including forward secrecy, PCS and key control resistance. © 2023 SPIE.

关键词： Network security

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FlexFL: Heterogeneous Federated Learning via APoZ-Guided Flexible Pruning in Uncertain Scenarios

arXiv

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arXiv 2024年

作者： Chen, Zekai Jia, Chentao Hu, Ming Xie, Xiaofei Li, Anran Chen, Mingsong The MoE Engineering Research Center of Hardware/Software Co-design Technology and Application East China Normal University Shanghai200062 China Singapore Management University Singapore Yale University United States

Along with the increasing popularity of Deep Learning (DL) techniques, more and more Artificial Intelligence of Things (AIoT) systems are adopting federated learning (FL) to enable privacy-aware collaborative learning among AIoT devices. However, due to the inherent data and device heterogeneity issues, existing FL-based AIoT systems suffer from the model selection problem. Although various heterogeneous FL methods have been investigated to enable collaborative training among heterogeneous models, there is still a lack of i) wise heterogeneous model generation methods for devices, ii) consideration of uncertain factors, and iii) performance guarantee for large models, thus strongly limiting the overall FL performance. To address the above issues, this paper introduces a novel heterogeneous FL framework named FlexFL. By adopting our Average Percentage of Zeros (APoZ)-guided flexible pruning strategy, FlexFL can effectively derive best-fit models for heterogeneous devices to explore their greatest potential. Meanwhile, our proposed adaptive local pruning strategy allows AIoT devices to prune their received models according to their varying resources within uncertain scenarios. Moreover, based on self-knowledge distillation, FlexFL can enhance the inference performance of large models by learning knowledge from small models. comprehensive experimental results show that, compared to state-of-the-art heterogeneous FL methods, FlexFL can significantly improve the overall inference accuracy by up to 14.24%. copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Practical privacy-preserving mixing protocol for Bitcoin 26

Practical privacy-preserving mixing protocol for Bitcoin

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26th International conference on computer Supported cooperative Work in design, CSCWD 2023

作者： Chang, Qianqian Xu, Lin Zhang, Lei Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Science and Technology on Communication Security Laboratory Chengdu610041 China Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education Shanghai200062 China

ISBN: (纸本)9798350331684

The privacy of Cryptocurrencies are of great concern in various fields. researches has shown that pseudonyms, which are used in Bitcoin, only provide weak privacy. The privacy of users may be put at risk under deanonymization attacks. The exisiting schemes typically require a trusted-third party to achieve anonymity, however this usually faces a single-point fault. In addition, existing schemes suffer from high communication complexity and impracticality. This paper proposes a practical privacy-preserving mixing protocol for Bitcoin to achieve unlink-ability of input and output address of transactions. compared to existing schemes, our protocol improves practicality. The communication complexity of our protocol is linearly related to the number of peers. Moreover, our protocol is scalable as it works not only for Bitcoin, but also for other cryptocurrencies. © 2023 IEEE.

关键词： computational complexity

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SuperDetector: A Framework for Performance Detection on Vulnerabilities of Smart contracts 6

SuperDetector: A Framework for Performance Detection on Vuln...

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2022 6th International conference on Data Mining, communications and Information technology, DMCIT 2022

作者： Dai, Meiyi Yang, Zhe Guo, Jian Software/Hardware Co-design Technology and Application Engineering Research Center East China Normal University China Shanghai Key Lab for Trustworthy Computing East China Normal University China

The technology of Ethereum blockchain enables the implementation of smart contracts. Nowadays smart contracts are one of the most successful applications of blockchain technology, which are widely used in many fields, such as finance, energy and services. The decentralization and immutability properties of Ethereum blockchain provide security for transactions from smart contracts. However, these properties are possible to lead to unfixable vulnerabilities of smart contracts. In recent years, vulnerability detection on smart contracts has attracted more attention from researchers and many related tools have emerged. Nevertheless, the existing vulnerability detection tools have not yet been put into formal use. Due to the lack of suitable smart contract sets and fair metrics with other factors, it remains complex work to conduct authoritative performance tests on these tools. In this paper, we first summarize eight common vulnerabilities of smart contracts, and then divide them into call-related and call-irrelated vulnerabilities according to whether they involve caller functions or not. In addition, we propose a detection framework called SuperDetector, which combines a variety of vulnerability detection tools based on static analysis technology. And the framework mainly measures the detection performance, which contains vulnerability coverage, detection accuracy, usability and execution time on the two types of vulnerabilities. The results indicate that the framework can analyze the performance from different perspectives, and the performance of vulnerability coverage and detection accuracy on call-related vulnerabilities is much better than that on call-irrelated vulnerabilities. Finally, we design a selector based on the detection results in the framework for improving the vulnerability coverage and detection accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Smart contract

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