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30th IEEE International Conference on Parallel and Distributed systems. ICPADS 2024

作者： Xiong, Tianchen Peng, Bo Shanghai Jiao Tong University School of Software Shanghai China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai China

ISBN: (纸本)9798331515966

Serverless computing is extensively employed in distributed storage computing scenarios owing to its dynamic responsiveness and on-demand allocation capabilities. Within distributed storage computing, data integrity computation holds significant importance. Nevertheless, current serverless systems.fail to consider the CPU-intensive nature of data integrity computation, leading to prolonged CPU occupancy, increased latency, and substantial performance deterioration, particularly in parallel scenarios. Therefore, there is an imperative need for a solution that can efficiently handle this kind of computation in serverless systems. In this paper, we introduce DICFaaS, a high-performance serverless system specialized for parallel and scalable data integrity computation. DICFaaS can recognize the offloading potential of servers and automatically offload CPU-intensive data integrity computation. The offloading function of DICFaaS can be easily integrated into other existing serverless systems. We evaluate the effectiveness of DICFaaS for data integrity computation compared to state-of-the-art serverless systems. For example, DICFaaS delivers up to 17 × latency improvement over AWS Lambda and up to 42 × latency improvement over OpenFaaS when the data size is 200M. It also saves up to 6 0% CPU overhead at runtime compared to OpenFaaS. © 2024 IEEE.

关键词： Data Integrity Computation Lossless Hardware Offloading Serverless systems Storage Computation

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DICFaaS: Parallel and scalable Data Integrity Computation for Serverless systems

DICFaaS: Parallel and Scalable Data Integrity Computation fo...

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International Conference on Parallel and Distributed systems.(ICPADS)

作者： Tianchen Xiong Bo Peng School of Software Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Serverless computing is extensively employed in distributed storage computing scenarios owing to its dynamic responsiveness and on-demand allocation capabilities. Within distributed storage computing, data integrity computation holds significant importance. Nevertheless, current serverless systems.fail to consider the CPU-intensive nature of data integrity computation, leading to prolonged CPU occupancy, increased latency, and substantial performance deterioration, particularly in parallel scenarios. Therefore, there is an imperative need for a solution that can efficiently handle this kind of computation in serverless systems. In this paper, we introduce DICFaaS, a high-performance serverless system specialized for parallel and scalable data integrity computation. DICFaaS can recognize the offloading potential of servers and automatically offload CPU-intensive data integrity computation. The offloading function of DICFaaS can be easily integrated into other existing serverless systems. We evaluate the effectiveness of DICFaaS for data integrity computation compared to state-of-the-art serverless systems. For example, DICFaaS delivers up to $17 \times$ latency improvement over AWS Lambda and up to $42 \times$ latency improvement over OpenFaaS when the data size is 200M. It also saves up to $\mathbf{6 0 \%}$ CPU overhead at runtime compared to OpenFaaS.

关键词： Runtime Data integrity Heuristic algorithms Scalability Serverless computing Distributed databases Quality of service Parallel processing Dynamic scheduling Hardware

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MONETA: Prior-free and Truthful Auctions with Adaptive Reserve Price for High Revenue in Dynamic Spectrum Access Networks

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Chinese Journal of Electronics 2023年第2期21卷 355-361页

作者： Xiaobing Wu Guihai Chen Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University Shanghai China

Auction is efficient for spectrum allocation in future dynamic spectrum access networks. Truthful or strategy-proof auction is favorable since every bidder only needs to bid his true valuation and the auctioneer assigns spectra to bidders who value them most. Existing truthful spectrum auction schemes however either generate very low revenue for the auctioneer or need extra prior distribution information on the true valuation of bidders. Low revenue generation could discourage the auctioneers from leasing their spectra and it is usually hard to get prior information on bidders' true evaluations on the spectra. In this paper, we propose a class of truthful spectrum auction schemes which bring higher revenue for the spectrum owners and do not need any prior information at the same time. We present both theoretical and simulation results of our proposed auction schemes.

关键词： Upper bound Adaptive systems Simulation Dynamic spectrum access Probability Resource management Cost accounting

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

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Frontiers of Medicine 2022年第3期16卷 496-506页

作者： Yaxin Chen Tianyi Yang Xiaofeng Gao Ajing Xu Department of Pharmacy Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai 200240China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240China Clinical Research Unit Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai 200240China

The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia,usage of diabetes drugs,changes in insulin levels,and excretion,and this risk begins as early as *** factors including demographic data(such as age,height,weight,and gender),medical history(such as smoking,drinking,and menopause),and examination(such as bone mineral density,blood routine,and urine routine)may be related to bone metabolism in patients with ***,most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of *** addition,the fracture risk of patients with diabetes and osteoporosis has not been further studied *** this paper,a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis,and investigate the effect of patients’physiological factors on fracture risk.A total of 147 raw input features are considered in our *** presented model is compared with several benchmarks based on various metrics to prove its ***,the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost deep neural network healthcare risk prediction

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AdaSkip: Adaptive Sublayer Skipping for Accelerating Long-Context LLM Inference 39

AdaSkip: Adaptive Sublayer Skipping for Accelerating Long-Co...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： He, Zhuomin Yao, Yizhen Zuo, Pengfei Gao, Bin Li, Qinya Zheng, Zhenzhe Wu, Fan Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University China Huawei Cloud China School of Computing National University of Singapore Singapore

ISBN: (纸本)157735897X

Long-context large language models (LLMs) inference is increasingly critical, motivating a number of studies devoted to alleviating the substantial storage and computational costs in such scenarios. Layer-wise skipping methods are promising optimizations but rarely explored in long-context inference. We observe that existing layer-wise skipping strategies have several limitations when applied in long-context inference, including the inability to adapt to model and context variability, disregard for sublayer significance, and inapplicability for the prefilling phase. This paper proposes AdaSkip, an adaptive sublayer skipping method specifically designed for long-context inference. AdaSkip adaptively identifies less important layers by leveraging on-the-fly similarity information, enables sublayer-wise skipping, and accelerates both the prefilling and decoding phases. The effectiveness of AdaSkip is demonstrated through extensive experiments on various long-context benchmarks and models, showcasing its superior inference performance over existing baselines. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

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Biophysics Reports 2023年第2期9卷 57-66页

作者： Peng Chen Zhenlei Li Zhaolin Hong Haoran Zheng Rong Zeng School of Computer Science and Technology University of Science and Technology of China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Department of Systems Biology University of Science and Technology of China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences School of Life Science and Technology ShanghaiTech University

Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasetsbased semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles:(1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning;(2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality;(3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas(TCGA) and the Gene Expression Comprehensive Database(GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.

关键词： Deep learning Tumor type classification Cancer-specific biomarkers MSSL

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On the Security of Double-Block-Length Hash Functions with Rate 1

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Chinese Journal of Electronics 2023年第3期21卷 566-570页

作者： GONG Zheng LUO Yiyuan LAI Xuejia CHEN Kefei School of Computer Science South China Normal University Guangzhou China Department of Computer Science Shanghai Jiaotong University Shanghai China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai China

The security of double-block-length hash functions with rate 1, which are based on a block cipher with a block length of n bits and a key length of 2n bits, was analyzed by Satoh et al. and Hirose. In this paper, we reconsider the security of this general class of hash functions (named FDBL-II for brevity). The new counter-examples and attacks are presented on FDBL-II, which reveal some flaws in the necessary conditions proposed by Satoh et al. and Hirose. Moreover, our analysis shows that all rate-1 hash functions in FDBL-II fail to be optimally (second) preimage resistant. Finally, the necessary conditions are revised for ensuring that a subclass of hash functions in FDBL-II can be optimally secure against collision attacks.

关键词： Hash functions Resistance Ciphers Education Security

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Ciphertext-only fault analysis on the Midori lightweight cryptosystem

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Science China(Information Sciences) 2020年第3期63卷 288-292页

作者： Wei LI Linfeng LIAO Dawu GU Shan CAO Yixin WU Jiayao LI Zhihong ZHOU Zheng GUO Ya LIU Zhiqiang LIU School of Computer Science and Technology Donghua University Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Key Laboratory of Integrate Administration Technologies for Information Security School of Microelectronics Shanghai Jiao Tong University Department of Computer Science and Engineering University of Shanghai for Science and Technology

Dear editor,The Midori lightweight cipher can be applied in the RFID tags and other low-resource devices to protect information on the Internet. This cipher was proposed by Banik et al. and presented at the ASIACRYPT conference [1]. It has a Substitution Permutation Network structure with a 128-bit keysize, and two block-size variants, 64 bits

关键词： CFA Ciphertext-only fault analysis on the Midori lightweight cryptosystem

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SMedBERT: A knowledge-enhanced pre-trained language model with structured semantics for medical text mining 59

SMedBERT: A knowledge-enhanced pre-trained language model wi...

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Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Zhang, Taolin Cai, Zerui Wang, Chengyu Qiu, Minghui Yang, Bite He, Xiaofeng School of Software Engineering East China Normal University China Alibaba Group Shanghai Key Laboratory of Trsustworthy Computing China School of Computer Science and Technology East China Normal University China DXY Shanghai Research Institute for Intelligent Autonomous Systems China

ISBN: (纸本)9781954085527

Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding. For medical domains, the background knowledge sources are especially useful, due to the massive medical terms and their complicated relations are difficult to understand in text. In this work, we introduce SMedBERT, a medical PLM trained on large-scale medical corpora, incorporating deep structured semantics knowledge from neighbours of linked-entity. In SMedBERT, the mention-neighbour hybrid attention is proposed to learn heterogeneous-entity information, which infuses the semantic representations of entity types into the homogeneous neighbouring entity structure. Apart from knowledge integration as external features, we propose to employ the neighbors of linked-entities in the knowledge graph as additional global contexts of text mentions, allowing them to communicate via shared neighbors, thus enrich their semantic representations. Experiments demonstrate that SMedBERT significantly outperforms strong baselines in various knowledge-intensive Chinese medical tasks. It also improves the performance of other tasks such as question answering, question matching and natural language inference. © 2021 Association for Computational Linguistics

关键词： Knowledge graph

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(Mostly) Exitless VM protection from untrusted hypervisor through disaggregated nested virtualization 20

(Mostly) Exitless VM protection from untrusted hypervisor th...

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Proceedings of the 29th USENIX Conference on Security Symposium

作者： Zeyu Mi Dingji Li Haibo Chen Binyu Zang Haibing Guan Shanghai Key Laboratory for Scalable Computing Systems School of Software Shanghai Jiao Tong University

ISBN: (纸本)9781939133175

Today's cloud tenants are facing severe security threats such as compromised hypervisors, which forces a strong adversary model where the hypervisor should be excluded out of the TCB. Previous approaches to shielding guest VMs either suffer from insufficient protection or result in suboptimal performance due to frequent VM exits (especially for I/O operations). This paper presents CloudVisor-D, an efficient nested hypervisor design that embraces both strong protection and high performance. The core idea of CloudVisor-D is to disaggregate the nested hypervisor by separating major protection logics into a protected Guardian-VM alongside each guest VM. The Guardian-VM is securely isolated and protected by the nested hypervisor and provides secure services for most privileged operations like hypercalls, EPT violations and I/O operations from guest VMs. By leveraging recent hardware features, most privileged operations from a guest VM require no VM exits to the nested hypervisor, which are the major sources of performance slowdown in prior designs. We have implemented CloudVisor-D on a commercially available machine with these recent hardware features. Experimental evaluation shows that CloudVisor-D incurs negligible performance overhead even for I/O intensive benchmarks and in some cases outperforms a vanilla hypervisor due to the reduced number of VM exits.

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