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7th Asia-Pacific Symposium on Internetware, Internetware 2015

作者： Meng, Zhanshuai Jiang, Yanyan Xu, Chang State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781450336413

Mobile apps are prevalent in everyone's daily life. However, apps are oftentimes defective, undermining their convenience, and therefore automated testing and analysis of apps are developed to enhance apps' quality. As these advanced technologies become increasingly effective and complicated, prototyping such a tool also becomes a challenge. To facilitate easy development of high-quality testing and analysis tools that work with real-world apps, we in this paper present the design and implementation of ATT (Android Testing Toolkit), for crafting reusable and scalable testing and analysis on Android apps. ATT consists of integrated tools and APIs for event generation, profiling and program instrumentation, and can be distributed over cloud platforms for scalable testing and analysis. We qualitatively evaluated the applicability of ATT by demonstrating implementation of a series of existing and enhanced work (RERAN, Monkey+ and UGA) upon ATT, and quantitatively studied the scalability of parallelized UGA on a cloud platform with five real-world apps. We believe that our ATT tool would facilitate development of more advanced testing and analysis approaches for Android apps. © 2015 ACM.

关键词： Quality control

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Mobile-agent-based web service composition

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4th International Conference on Grid and Cooperative Computing - GCC 2005

作者： Qian, Zhuzhong Lu, SangLu Xie, Li State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)3540305106

In this paper we propose a mobile-agent-based web service composition (MAWSC) model for the dynamic web service composition (WSC). As compared with the traditional WSC models, our model avoids bottleneck of data transfer by peer-to-peer approach. As it achieves the composition without a centralized control, there is no center needed and every WSC portal can offer services for a large quantity of clients. In MAWSC model, WSC client inputs the specification and the initial parameters through WSC portal;then, system creates mobile agents to achieve the task and return the final results back to the client. In this paper, we define "planning" to specify the logic actions of mobile agent so as to execute the WSC automatically. And we also give the process mapping rules to convert the specification of WSC to the planning, and the behavior matching algorithms to map the planning to the physical behaviors of agents. © Springer-Verlag Berlin Heidelberg 2005.

关键词： World Wide Web

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DAAP: privacy-preserving model accuracy estimation on unlabeled datasets through distribution-aware adversarial perturbation 24

DAAP: privacy-preserving model accuracy estimation on unlabe...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Guodong Cao Zhibo Wang Yunhe Feng Xiaowei Dong School of Cyber Science and Engineering Wuhan University P. R. China The State Key Laboratory of Blockchain and Data Security Zhejiang University P. R. China and School of Cyber Science and Technology Zhejiang University P. R. China Department of Computer Science and Engineering University of North Texas

ISBN: (纸本)9781939133441

In the dynamic field of deep learning, accurately estimating model performance while ensuring data privacy against diverse and unlabeled test datasets presents a critical challenge. This is primarily due to the significant distributional shifts between training and test datasets, which complicates model evaluation. Traditional methods for assessing model accuracy often require direct access to the entire test dataset, posing significant risks of data leakage and model theft. To address these issues, we propose a novel approach: Distribution-Aware Adversarial Perturbation (DAAP). This method is designed to estimate the accuracy of deep learning models on unlabeled test datasets without compromising privacy. Specifically, DAAP leverages a publicly available dataset as an intermediary to bridge the gap between the model and the test data, effectively circumventing direct interaction and mitigating privacy concerns. By strategically applying adversarial perturbations, DAAP minimizes the distributional discrepancies between datasets, enabling precise estimation of model performance on unseen test data. We present two specialized strategies for white-box and black-box model contexts: the former focuses on reducing output entropy disparities, while the latter manipulates distribution discriminators. Overall, the DAAP introduces a novel framework for privacy-preserving accuracy estimation in model evaluation. This novel approach not only addresses critical challenges related to data privacy and distributional shifts but also enhances the reliability and integrity of model performance assessments. Our extensive evaluation on the CIFAR-10-C, CIFAR-100-C, and CelebA datasets demonstrates the effectiveness of DAAP in accurately estimating performance while safeguarding both data and model privacy.

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Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Sun, Geng Ma, Weilong Li, Jiahui Sun, Zemin Wang, Jiacheng Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Software Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The computation task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV. In this paper, we consider a UAV-assisted MEC system where the UAV carries the edge servers to facilitate task offloading for ground devices (GDs), and formulate a calculation delay and energy consumption multi-objective optimization problem (CDECMOP) to simultaneously improve the performance and reduce the cost of the system. Then, by modeling the formulated problem as a multi-objective Markov decision process (MOMDP), we propose a multi-objective deep reinforcement learning (DRL) algorithm within an evolutionary framework to dynamically adjust the weights and obtain non-dominated policies. Moreover, to ensure stable convergence and improve performance, we incorporate a target distribution learning (TDL) algorithm. Simulation results demonstrate that the proposed algorithm can better balance multiple optimization objectives and obtain superior non-dominated solutions compared to other methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Some Contributions to Nonmonotonic Consequence

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Journal of computer science & technology 2001年第4期16卷 297-314页

作者：朱朝晖张东摩陈世福朱梧槚 Department of Computer Science Nanjing University of Aeronautics and Astronautics Nanjing P.R. China State Key Laboratory for Novel Software Technology Nanjing University Nanjing P.R. China

This paper introduces a non-Horn rule WRM which is a weak form of rational monotony. We explore the effects of adding this non-Horn rule to the rules for the preferential inference. In this paper, a relation |~ is said to be P + WRM iff it is a preferential inference and satisfies the rule WRM. We establish the representation theorem for P + WRM, and compare the strength of WRM with some non-Horn rules appearing in literatures. Moreover, we explore the relation between P + WRM and conditional logic, and demonstrate that P + WRM is equivalent to 'flat' fragment of conditional logic CS4.2. Another contribution of this paper is to explore the relation between two special kinds of preferential models, i.e., PRC model and quasi-linear model. Main result reveals that the latter is a special form of the former.

关键词： nonmonotonic consequence relation conditional logic rational mo- notony injective PRC model quasi-linear model

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A New Approach for Multi-Document Update Summarization

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Journal of computer science & technology 2010年第4期25卷 739-749页

作者：龙翀黄民烈朱小燕李明 State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and TechnologyDepartment of Computer Science and TechnologyTsinghua University School of Computer Science University of Waterloo

Fast changing knowledge on the Internet can be acquired more efficiently with the help of automatic document summarization and updating techniques. This paper describes a novel approach for multi-document update summarization. The best summary is defined to be the one which has the minimum information distance to the entire document set. The best update summary has the minimum conditional information distance to a document cluster given that a prior document cluster has already been read. Experiments on the DUC/TAC 2007 to 2009 datasets （http：//***/, http：//***/tac/） have proved that our method closely correlates with the human summaries and outperforms other programs such as LexRank in many categories under the ROUGE evaluation criterion.

关键词： data mining text mining Kolmogorov complexity information distance

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A novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and K-Means

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Chinese Journal of Electronics 2015年第4期24卷 694-701页

作者： TRAN Dang Cong WU Zhijian WANG Zelin DENG Changshou State Key Laboratory of Software Engineering School of Computer Wuhan University Vietnam Academy of Science and Technology School of Information Science and Technology Jiujiang University

To improve the performance of K-means clustering algorithm, this paper presents a new hybrid approach of Enhanced artificial bee colony algorithm and Kmeans(EABCK). In EABCK, the original artificial bee colony algorithm(called ABC) is enhanced by a new mutation operation and guided by the global best solution(called EABC). Then, the best solution is updated by Kmeans in each iteration for data clustering. In the experiments, a set of benchmark functions was used to evaluate the performance of EABC with other comparative ABC variants. To evaluate the performance of EABCK on data clustering, eleven benchmark datasets were utilized. The experimental results show that EABC and EABCK outperform other comparative ABC variants and data clustering algorithms, respectively.

关键词： Data clustering K-means Artificial bee colony Mutation operation

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Graph-Neural-Network-Based Transaction Link Prediction Method for Public Blockchain in Heterogeneous Information Networks

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Blockchain: Research and Applications 2025年

作者： Zening Zhao Jinsong Wang Jiajia Wei School of Computer Science and Engineering Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin 300384 China National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin 300457 China

Public blockchain has outstanding performance in transaction privacy protection because of its anonymity. The data openness brings feasibility to transaction behavior analysis. At present, the transaction data of the public chain are huge, including complex trading objects and relationships. It is difficult to extract attributes and predict transaction behavior by traditional methods. To solve the problems, we extract the transaction features to construct the Ethereum transaction heterogeneous information network (HIN), and propose graph-neural-network-based transaction prediction method for public blockchain in HINs, which can divide the network into subgraphs according to connectivity and make the prediction results of transaction behavior more accurate. Experiments show that the execution time consumption of the proposed transaction subgraph division method is reduced by 70.61% on average compared with the search method. The accuracy of the proposed behavior prediction method also improve compared with the traditional random walk method, with an average accuracy of 83.82%.

关键词： Blockchain Link prediction Heterogeneous information networks Graph neural networks Random walk

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An Efficient Bitcoin Network Topology Discovery Algorithm for Dynamic Display

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Blockchain: Research and Applications 2025年

作者： Zening Zhao Jinsong Wang Miao Yang Haitao Wang School of Computer Science and Engineering Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin 300384 China National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin 300457 China

The Bitcoin network comprises numerous nodes, necessitating users to invest significant network requests and time in comprehending its network topology. In this paper, we propose a Bitcoin network topology discovery algorithm that utilizes lightweight probe nodes to facilitate rapid transmission of network protocols. Building upon this, we introduce a node layer clustering algorithm based on filtering stable network nodes, enabling parallel discovery of the network topology. Additionally, we present an adaptive method for dynamically displaying the layered structure of the network topology. Experimental results demonstrate that our proposed method reduces communication overhead by approximately 72.16% when achieving a 95% similarity in network topology. Furthermore, the algorithm is applicable for discovering the network topology in other blockchain networks with similar structures.

关键词： Blockchain network topology discovery Bitcoin stability coefficient dynamic updates

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A constructive model for collective intelligence

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National science Review 2020年第8期7卷 1273-1277页

作者： Wei Zhang Hong Mei Key Laboratory of High Confidence Software Technology (Peking University) Ministry of Education Institute of Software Department of Computer Science and Technology Peking University

Natural phenomena of collective intelligence(CI) occurring in physical space show a potential approach to effective large-scale human collaboration in cyberspace. Based on existing explanatory understanding of CI, this perspective proposes a constructive model for building artificial CI systems, i.e., problem-oriented CI phenomena with AI-powered information integration and feedback.

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