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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Cao, Hongye Yang, Shangdong Huo, Jing Chen, Xingguo Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781643684369

Offline Reinforcement Learning (RL) is an important research domain for real-world applications because it can avert expensive and dangerous online exploration. Offline RL is prone to extrapolation errors caused by the distribution shift between offline datasets and states visited by behavior policy. Existing offline RL methods constrain the policy to offline behavior to prevent extrapolation errors. But these methods limit the generalization potential of agents in Out-Of-Distribution (OOD) regions and cannot effectively evaluate OOD generalization behavior. To improve the generalization of the policy in OOD regions while avoiding extrapolation errors, we propose an Energy-Based Policy Optimization (EBPO) method for OOD generalization. An energy function based on the distribution of offline data is proposed for the evaluation of OOD generalization behavior, instead of relying on model discrepancies to constrain the policy. The way of quantifying exploration behavior in terms of energy values can balance the return and risk. To improve the stability of generalization and solve the problem of sparse reward in complex environment, episodic memory is applied to store successful experiences that can improve sample efficiency. Extensive experiments on the D4RL datasets demonstrate that EBPO outperforms the state-of-the-art methods and achieves robust performance on challenging tasks that require OOD generalization. © 2023 The Authors.

关键词： Extrapolation

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Time and Cost-Efficient Cloud Data Transmission based on Serverless Computing Compression 42

Time and Cost-Efficient Cloud Data Transmission based on Ser...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Gu, Rong Chen, Xiaofei Dai, Haipeng Wang, Shulin Wang, Zhaokang Tu, Yaofeng Huang, Yihua Chen, Guihai Nanjing University State Key Laboratory for Novel Software Technology China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Zte Cooperation China

ISBN: (纸本)9798350334142

Nowadays, there exists a lot of cross-region data transmission demand on cloud. It is promising to use serverless computing for compressing data to save the transmission data amount. However, it is challenging to estimate the data transmission time and monetary cost with serverless compression. In addition, minimizing the data transmission cost is non-trivial due to enormous parameter space and joint optimization. This paper focuses on this problem and makes the following contributions: (1) We propose empirical data transmission time and monetary cost models based on serverless compression. (2) For single-task cloud data transmission, we propose two efficient parameter search methods based on Sequential Quadratic Programming (SQP ) and Eliminate then Divide and Conquer (EDC), which are theoretically proven with error upper bounds. (3) Furthermore, for multi-task cloud data transmission, a parameter search method based on dynamic programming and numerical computation is proposed to reduce the algorithm complexity from exponential to linear complexity. We have implemented the entire actual system and evaluated it with various workloads and application cases on the real-world AWS serverless computing platform. Experimental results on cross-region public cloud show that the proposed approach can improve the parameter search efficiency by more than 3× compared with the state-of-art parameter search methods and achieves better parameter quality. Compared with other competing cloud data transmission approaches, our approach is able to achieve higher time efficiency and lower monetary cost. © 2023 IEEE.

关键词： Efficiency

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SOFTENED SYMBOL GROUNDING FOR NEUROSYMBOLIC SYSTEMS 11

SOFTENED SYMBOL GROUNDING FOR NEUROSYMBOLIC SYSTEMS

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11th International Conference on Learning Representations, ICLR 2023

作者： Li, Zenan Yao, Yuan Chen, Taolue Xu, Jingwei Cao, Chun Ma, Xiaoxing Lü, Jian State Key Lab of Novel Software Technology Nanjing University China Department of Computer Science Birkbeck University of London United Kingdom

Neuro-symbolic learning generally consists of two separated worlds, i.e., neural network training and symbolic constraint solving, whose success hinges on symbol grounding, a fundamental problem in *** paper presents a novel, softened symbol grounding process, bridging the gap between the two worlds, and resulting in an effective and efficient neuro-symbolic learning ***, the framework features (1) modeling of symbol solution states as a Boltzmann distribution, which avoids expensive state searching and facilitates mutually beneficial interactions between network training and symbolic reasoning;(2) a new MCMC technique leveraging projection and SMT solvers, which efficiently samples from disconnected symbol solution spaces;(3) an annealing mechanism that can escape from sub-optimal symbol *** with three representative neuro-symbolic learning tasks demonstrate that, owing to its superior symbol grounding capability, our framework successfully solves problems well beyond the frontier of the existing proposals. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Boltzmann equation

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Hierarchical Sketch: An Efficient Solution for Threshold-t Flows Measurement in High-Speed Networks 21

Hierarchical Sketch: An Efficient Solution for Threshold-t F...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Han, Yifan Huang, He Du, Yang Sun, Yu-E Liu, Jia Xu, Hongli Soochow University School of Computer Science and Technology China Soochow University School of Rail Transportation China Nanjing University State Key Laboratory for Novel Software Technology China University of Science and Technology of China School of Computer Science and Technology China

ISBN: (纸本)9798350329223

Per-flow size measurement has wide applications in data center networks, such as flow scheduling, anomaly detection, and traffic engineering. To fit the module into limited on-chip memory and catch up with the line speed, existing works either use the bit sharing scheme to compress the flow information or require complex calculations to screen out large flows, resulting in substantial accuracy loss or heavy memory accesses, especially failing to measure medium-sized flows accurately, which are meaningful for practical applications such as usage accounting and billing. In light of this, we propose a new Hierarchical Sketch that adopts a hybrid on-chip/off-chip architecture to detect and estimate the flows whose size exceeds the threshold t (threshold-t flows for short) to cover both large and medium-sized flows. In Hierarchical Sketch, we design an on-chip bucket array together with an adaptive update scheme to record potential candidates for threshold-t flows within one memory access as well as filter out unwanted flows. Besides, the flow records are downloaded to off-chip space for high memory efficiency and supporting online queries. We also implement a prototype on the NetFPGA development platform. Experimental results show that Hierarchical Sketch is up to 72.61% more accurate and 245% faster than the best baseline. © 2023 IEEE.

关键词： HIgh speed networks

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CA-DTS:A Distributed and Collaborative Task Scheduling Algorithm for Edge Computing Enabled Intelligent Road Network

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Journal of computer science & technology 2023年第5期38卷 1113-1131页

作者：胡世红罗渠元李光辉施巍松叶保留 Key Laboratory of Water Big Data Technology of Ministry of Water Resources Houhai UniversityNanjing 210098China School of Computer and Information Houhai UniversityNanjing 210098China School of Information Science and Technology Southwest Jiaotong UniversityChengdu 611756China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuci 214122China Department of Computer and Information Sciences University of DelawareNewarkDE 19716U.S.A National Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210093China

Edge computing enabled Intelligent Road Network(EC-IRN)provides powerful and convenient computing services for vehicles and roadside sensing *** continuous emergence of transportation applications has caused a huge burden on roadside units(RSUs)equipped with edge servers in the Intelligent Road Network(IRN).Collaborative task scheduling among RSUs is an effective way to solve this ***,it is challenging to achieve collaborative scheduling among different RSUs in a completely decentralized *** this paper,we first model the interactions involved in task scheduling among distributed RSUs as a Markov *** that multi-agent deep reinforcement learning(MADRL)is a promising approach for the Markov game in decision optimization,we propose a collaborative task scheduling algorithm based on MADRL for EC-IRN,named CA-DTS,aiming to minimize the long-term average delay of *** reduce the training costs caused by trial-and-error,CA-DTS specially designs a reward function and utilizes the distributed deployment and collective training architecture of counterfactual multi-agent policy gradient(COMA).To improve the stability of performance in large-scale environments,CA-DTS takes advantage of the action semantics network(ASN)to facilitate cooperation among multiple *** evaluation results of both the testbed and simulation demonstrate the effectiveness of our proposed *** with the baselines,CA-DTS can achieve convergence about 35%faster,and obtain average task delay that is lower by approximately 9.4%,9.8%,and 6.7%,in different scenarios with varying numbers of RSUs,service types,and task arrival rates,respectively.

关键词： edge computing deep reinforcement learning task scheduling vehicular edge computing

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Crowdsourcing aggregation with deep Bayesian learning

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science China(Information sciences) 2021年第3期64卷 46-56页

作者： Shao-Yuan LI Sheng-Jun HUANG Songcan CHEN College of Computer Science and Technology College of Artificial IntelligenceNanjing University of Aeronautics and Astronautics State Key Laboratory for Novel Software Technology Nanjing University

In this study, we consider a crowdsourcing classification problem in which labeling information from crowds is aggregated to infer latent true labels. We propose a fully Bayesian deep generative crowdsourcing model(Bayes DGC), which combines the strength of deep neural networks(DNNs) on automatic representation learning and the interpretable probabilistic structure encoding of probabilistic graphical models. The model comprises a DNN classifier as a prior for the true labels and a probabilistic model for the annotation generation process. The DNN classifier and annotation generation process share the latent true label variables. To address the inference challenge, we developed a natural-gradient stochastic variational inference, which combines variational message passing for conjugate parameters and stochastic gradient descent for DNN and learns the distribution of latent true labels and workers' confusion matrix via end-to-end training. We illustrated the effectiveness of the proposed model using empirical results on 22 real-world datasets.

关键词： crowdsourcing classification fully Bayesian deep generative models natural gradient stochastic variational inference

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AoI-aware Incentive Mechanism for Mobile Crowdsensing using Stackelberg Game 42

AoI-aware Incentive Mechanism for Mobile Crowdsensing using ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Xiao, Mingjun Xu, Yin Zhou, Jinrui Wu, Jie Zhang, Sheng Zheng, Jun University of Science and Technology of China School of Computer Science and Technology China Suzhou Institute for Advanced Research University of Science and Technology of China China Temple Universityy Department of Computer and Information Sciences United States Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350334142

Mobile CrowdSensing (MCS) is a mobile computing paradigm, through which a platform can coordinate a crowd of workers to accomplish large-scale data collection tasks using their mobile devices. Information freshness has attracted much focus on MCS research worldwide. In this paper, we investigate the incentive mechanism design in MCS systems that take the freshness of collected data and social benefits into concerns. First, we introduce the Age of Information (AoI) metric to measure the freshness of data. Then, we model the incentive mechanism design with AoI guarantees as a novel incomplete information two-stage Stackelberg game with multiple constraints. Next, we derive the optimal strategies of this game so as to determine the optimal reward paid by the platform and the optimal data update frequency for each worker. Moreover, we prove that these optimal strategies form a unique Stackelberg equilibrium. Based on the optimal strategies, we propose an AoI-Aware Incentive (AIAI) mechanism for the MCS system, whereby the platform and all workers can maximize their utilities simultaneously. Meanwhile, the system can ensure that the AoI values of all data uploaded to the platform are not larger than a given threshold to achieve high data freshness. Extensive simulations on real-world traces are conducted to demonstrate the significant performance of AIAI. © 2023 IEEE.

关键词： Optimal systems

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SoVAR: Build Generalizable Scenarios from Accident Reports for Autonomous Driving Testing 24

SoVAR: Build Generalizable Scenarios from Accident Reports f...

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39th ACM/IEEE International Conference on Automated software Engineering, ASE 2024

作者： Guo, An Zhou, Yuan Tian, Haoxiang Fang, Chunrong Sun, Yunjian Sun, Weisong Gao, Xinyu Luu, Anh Tuan Liu, Yang Chen, Zhenyu State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798400712487

Autonomous driving systems (ADSs) have undergone remarkable development and are increasingly employed in safety-critical applications. However, recently reported data on fatal accidents involving ADSs suggests that the desired level of safety has not yet been fully achieved. Consequently, there is a growing need for more comprehensive and targeted testing approaches to ensure safe driving. Scenarios from real-world accident reports provide valuable resources for ADS testing, including critical scenarios and high-quality seeds. However, existing scenario reconstruction methods from accident reports often exhibit limited accuracy in information extraction. Moreover, due to the diversity and complexity of road environments, matching current accident information with the simulation map data for reconstruction poses significant *** this paper, we design and implement SoVAR, a tool for automatically generating road-generalizable scenarios from accident reports. SoVAR utilizes well-designed prompts with linguistic patterns to guide the large language model (LLM) in extracting accident information from textual data. Subsequently, it formulates and solves accident-related constraints in conjunction with the extracted accident information to generate accident trajectories. Finally, SoVAR reconstructs accident scenarios on various map structures and converts them into test scenarios to evaluate its capability to detect defects in industrial ADSs. We experiment with SoVAR, using the accident reports from the National Highway Traffic Safety Administration's (NHTSA) database to generate test scenarios for the industrial-grade ADS Apollo. The experimental findings demonstrate that SoVAR can effectively generate generalized accident scenarios across different road structures. Furthermore, the results confirm that SoVAR identified 5 distinct safety violation types that contributed to the crash of Baidu Apollo. © 2024 Copyright is held by the owner/author(s). Publication ri

关键词： Highway administration

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Fully distributed identity-based threshold signatures with identifiable aborts

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Frontiers of computer science 2023年第5期17卷 179-195页

作者： Yan JIANG Youwen ZHU Jian WANG Xingxin LI College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China State Key Laboratory of Integrated Services Networks(Xidian University) Xi’an 710071China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China Department of Mathematical Informatics University of TokyoTokyo 113-8654Japan

Identity-based threshold signature(IDTS)is a forceful primitive to protect identity and data privacy,in which parties can collaboratively sign a given message as a signer without reconstructing a signing ***,most IDTS schemes rely on a trusted key generation center(KGC).Recently,some IDTS schemes can achieve escrow-free security against corrupted KGC,but all of them are vulnerable to denial-of-service attacks in the dishonest majority setting,where cheaters may force the protocol to abort without providing any *** this work,we present a fully decentralized IDTS scheme to resist corrupted KGC and denialof-service *** this end,we design threshold protocols to achieve distributed key generation,private key extraction,and signing generation which can withstand the collusion between KGCs and signers,and then we propose an identification mechanism that can detect the identity of cheaters during key generation,private key extraction and signing ***,we formally prove that the proposed scheme is threshold unforgeability against chosen message *** experimental results show that the computation time of both key generation and signing generation is<1 s,and private key extraction is about 3 s,which is practical in the distributed environment.

关键词： threshold signatures single points of failure identifiable aborts

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Verifying Contextual Refinement with Ownership Transfer

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Journal of computer science & technology 2021年第6期36卷 1342-1366页

作者： Zhao-Hui Li Xin-Yu Feng School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China Department of Computer Science and Technology Nanjing UniversityNanjing 210023China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

Contextual refinement is a compositional approach to compositional verification of concurrent *** has been much work designing program logics to prove the contextual refinement between the object implementation and its abstract ***,these program logics for contextual refinement verification cannot support objects with resource ownership transfer,which is a common pattern in many concurrent objects,such as the memory management module in OS kernels,which transfers the allocated memory block between the object and *** this paper,we propose a new approach to give abstract and implementation independent specifications to concurrent objects with ownership *** also design a program logic to verify contextual refinement of concurrent objects *** abstract *** have successfully applied our logic to verifying an implementation of the memory management module,where the implementation is an appropriately simplified version of the original version from a real-world preemptive OS kernel.

关键词： contextual refinement program logic concurrent object ownership transfer verification

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