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29th International Conference on Geoinformatics, Geoinformatics 2022

作者： Wang, Yi'an Cai, Fangyi Cheng, Shih-Fen Wu, Bo Cao, Kai School of Geographic Sciences East China Normal University Shanghai China East China Normal University Key Laboratory of Geographic Information Science Ministry of Education Shanghai China East China Normal University Key Lab. of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources Shanghai China National University of Singapore Department of Geography Singapore School of Computing and Information Systems Singapore Management University Singapore School of Geography and Environment Jiangxi Normal University Jiangxi Nanchang China

ISBN: (数字)9798350309881

ISBN: (纸本)9798350309881

In this research, a taxi travel time based Geographically Weighted Regression model (GWR) is proposed and utilized to model the public housing price in the case study of Singapore. In addition, a comparison between the proposed taxi data driven GWR and other models, such as ordinary least squares model (OLS), GWR model based on Euclidean distance and GWR model based on public transport travel time, have also been carried out. Results indicates that taxi travel time based GWR model has better fitting performance than the OLS model, and slightly better than the Euclidean distance-based GWR model, however, it is not as good as the GWR model based on public transport travel time according to the metric of Adjusted R2. These experiments indicate that the public transport travel time may has a major part to play in modeling the public housing resale prices compared to taxi travel time or driving time, and both the taxi travel time and public transport travel time can better explain the public housing resale prices in Singapore compared to Euclidean distance in the GWR modeling. © 2022 IEEE.

关键词： Travel time

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Task Offloading and Dispatching for MEC with Selfish Mobile Devices and Access Points

Task Offloading and Dispatching for MEC with Selfish Mobile ...

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GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Lu Fang Tong Liu Yanmin Zhu Yuanyuan Yang School of Computer Engineering and Science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Shanghai Key Lab of Scalable Computing and Systems Stony Brook University

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

Multi-access Edge computing (MEC) comes forth as a promising computing paradigm to meet the low-delay requirements of computation-intensive applications. In this work, we focus on the task offloading and dispatching problem in MEC, where mobile devices (MDs) can decide to execute tasks locally or offload them to access points (APs), and each AP can dispatch its received tasks to other APs. Specially, we consider both APs and MDs are selfish, which aim to minimize their respective task completion delay. The problem is particularly difficult, as there exists computing resource competition among MDs and APs, respectively. Furthermore, the offloading decisions made by MDs and the dispatching decisions made by APs are interactive. To overcome the challenges, we first formulate the problem as a multi-leader multi-follower Stackelberg game, and rigorously prove the existence of a Stackelberg equilibrium. Then, we propose an efficient approach to achieve a Stackelberg equilibrium, which includes a Q-learning based offloading strategy for MDs and a best response based dispatching strategy for APs. We also demonstrate an upper bound of the total completion delay achieved by our approach with a constant approximation ratio. Extensive simulations are also conducted to show the performance of our approach, compared with baselines.

关键词： Upper bound Simulation Games Dispatching Mobile handsets Delays Task analysis

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Accuracy-Guaranteed Event Detection via Collab.rative Mobile Crowdsensing with Unreliable Users 15th

Accuracy-Guaranteed Event Detection via Collaborative Mobile...

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15th EAI International Conference on Collab.rative computing: Networking, Applications and Worksharing, Collab.rateCom 2019

作者： Liu, Tong Wu, Wenbin Zhu, Yanmin Tong, Weiqin School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Lab of Scalable Computing and Systems Shanghai China

ISBN: (纸本)9783030301453

Recently, mobile crowdsensing has become a promising paradigm to collect rich spatial sensing data, by taking advantage of widely distributed sensing devices like smartphones. Based on sensing data, event detection can be conducted in urban areas, to monitor abnormal incidents like traffic jam. However, how to guarantee the detection accuracy is still an open issue, especially when unreliable users who may report wrong observations are considered. In this work, we focus on the problem of user recruitment in collab.rative mobile crowdsensing, aiming to optimize the fine-grained detection accuracy in a large urban area. Unfortunately, the problem is proved to be NP-hard, which means there is no polynomial-time algorithm to achieve the optimal solution unless P NP. To meet the challenge, we first employ a probabilistic model to characterize the unreliability of users, and measure the uncertainty of inferring event occurrences given collected observations by Shannon entropy. Then, by leveraging the properties of adaptive monotonicity and adaptive submodularity, we propose an adaptive greedy algorithm for user recruitment, which is theoretically proved to achieve a constant approximation ratio guarantee. Extensive simulations are conducted, which show our proposed algorithm outperforms baselines under different settings. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Polynomial approximation

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Exploring architectural ingredients of adversarially robust deep neural networks 21

Exploring architectural ingredients of adversarially robust ...

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Proceedings of the 35th International Conference on Neural Information Processing systems

作者： Hanxun Huang Yisen Wang Sarah Erfani Quanquan Gu James Bailey Xingjun Ma School of Computing and Information Systems The University of Melbourne Victoria Australia Key Lab. of Machine Perception School of Artificial Intelligence Peking University Beijing China and Institute for Artificial Intelligence Peking University Beijing China University of California Los Angeles School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9781713845393

Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks. A range of defense methods have been proposed to train adversarially robust DNNs, among which adversarial training has demonstrated promising results. However, despite preliminary understandings developed for adversarial training, it is still not clear, from the architectural perspective, what configurations can lead to more robust DNNs. In this paper, we address this gap via a comprehensive investigation on the impact of network width and depth on the robustness of adversarially trained DNNs. Specifically, we make the following key observations: 1) more parame- ters (higher model capacity) does not necessarily help adversarial robustness; 2) reducing capacity at the last stage (the last group of blocks) of the network can actually improve adversarial robustness; and 3) under the same parameter budget, there exists an optimal architectural configuration for adversarial robustness. We also provide a theoretical analysis explaning why such network configuration can help robustness. These architectural insights can help design adversarially robust DNNs.

关键词：

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Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks

arXiv

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arXiv 2021年

作者： Huang, Hanxun Wang, Yisen Erfani, Sarah Gu, Quanquan Bailey, James Ma, Xingjun School of Computing and Information Systems The University of Melbourne VIC Australia Key Lab. of Machine Perception School of Artificial Intelligence Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China University of California Los Angeles United States School of Computer Science Fudan University Shanghai China

Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks. A range of defense methods have been proposed to train adversarially robust DNNs, among which adversarial training has demonstrated promising results. However, despite preliminary understandings developed for adversarial training, it is still not clear, from the architectural perspective, what configurations can lead to more robust DNNs. In this paper, we address this gap via a comprehensive investigation on the impact of network width and depth on the robustness of adversarially trained DNNs. Specifically, we make the following key observations: 1) more parameters (higher model capacity) does not necessarily help adversarial robustness;2) reducing capacity at the last stage (the last group of blocks) of the network can actually improve adversarial robustness;and 3) under the same parameter budget, there exists an optimal architectural configuration for adversarial robustness. We also provide a theoretical analysis explaning why such network configuration can help robustness. These architectural insights can help design adversarially robust DNNs. Code is availab.e at https://***/HanxunH/RobustWRN. © 2021, CC BY.

关键词： Deep neural networks

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Improved impossible differential cryptanalysis of large-block Rijndael

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Science China(Information Sciences) 2019年第3期62卷 5-18页

作者： Ya LIU Yifan SHI Dawu GU Bo DAI Fengyu ZHAO Wei LI Zhiqiang LIU Zhiqiang ZENG Engineering Research Center of Optical Instrument and System Ministry of Education Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University School of Computer Science and Technology Donghua University Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Science and Technology on Information Assurance Laboratory

Rijndael is a substitution-permutation network（SPN） block cipher for the AES development process. Its block and key sizes range from 128 to 256 bits in steps of 32 bits, which can be denoted by Rijndael-b-k, where b and k are the block and key sizes, respectively. Among them, Rijndael-128-128/192/256,that is, AES, has been studied by many researchers, and the security of other large-block versions of Rijndael has been exploited less frequently. However, more attention has been paid to large-block versions of block ciphers with the fast development of quantum computers. In this paper, we propose improved impossible differential attacks on 10-round Rijndael-256-256, 10-round Rijndael-224-256, and 9-round Rijndael-224-224 using precomputation tables, redundancies of key schedules, and multiple impossible differentials. For 10-round Rijndael-256-256, the data, time, and memory complexities of our attack were approximately 2244.4chosen plaintexts, 2240.1encryptions, and 2181.4blocks, respectively. For 10-round Rijndael-224-256, the data, time, and memory complexities of our attack were approximately 2214.4chosen plaintexts, 2241.3encryptions, and 2183.4blocks, respectively. For 9-round Rijndael-224-224, the data, time, and memory complexities of our attack are approximately 2214.4chosen plaintexts, 2113.4encryptions, and 287.4blocks,respectively, or 2206.6chosen plaintexts, 2153.6encryptions, and 2111.6blocks, respectively. To the best of our knowledge, our results are currently the best on Rijndael-256-256 and Rijndael-224-224/256.

关键词： block cipher Rijndael precomputation tables impossible di?erentials multiple impossible differential attacks

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Espresso: Brewing Java for More Non-Volatility with Non-volatile Memory

arXiv

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arXiv 2017年

作者： Wu, Mingyu Zhao, Ziming Li, Haoyu Li, Heting Chen, Haibo Zang, Binyu Guan, Haibing Shanghai Key Lab. of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University

Fast, byte-addressable non-volatile memory (NVM) em-braces both near-DRAM latency and disk-like persistence, which has generated considerable interests to revolutionize system software stack and programming models. However, it is less understood how NVM can be combined with managed runtime like Java virtual machine (JVM) to ease persistence management. This paper proposes Espresso1, a holistic ex-tension to Java and its runtime, to enable Java programmers to exploit NVM for persistence management with high perfor-mance. Espresso first provides a general persistent heap de-sign called Persistent Java Heap (PJH) to manage persistent data as normal Java objects. The heap is then strengthened with a recoverable mechanism to provide crash consistency for heap metadata. It then provides a new abstraction called Persistent Java Object (PJO) to provide an easy-to-use but safe persistent programming model for programmers to per-sist application data. The evaluation confirms that Espresso significantly outperforms state-of-art NVM support for Java (i.e., JPA and PCJ) while being compatible to existing data structures in Java programs. Copyright © 2017, The Authors. All rights reserved.

关键词： Nonvolatile storage

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Accelerating Traditional File systems on Non-volatile Main Memory

Accelerating Traditional File Systems on Non-volatile Main M...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Weitong Jin Yanmin Zhu Linpeng Huang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Key Lab of Scalable Computing and Systems

ISBN: (纸本)9781538632086

New non-volatile memory (NVM) technologies, e.g., Phase Change Memory (PCM), are emerging with main features such as byte addressability, cheapness and large size, and will be deployed to serve as main memory. For fast processing of big data, block device drivers have been developed for NVM such that traditional file systems (e.g., EXT4) operated on non-volatile main memory. However, the existing block driver blindly caches pages, which incurs unnecessary access delays. To overcome this drawback, this paper develops a novel block device driver, called NVMBD, for byte-addressable non-volatile main memory. It adopts two methods of processing block I/O requests, i.e., traditional block I/O and BMAP. On the one hand, traditional block I/O reads/writes 512B sectors with multiple load/store instructions. On the other hand, BMAP inserts NVM pages into the page cache tree when processing block I/O requests, which removes the page movement between the page cache and the NVM. BMAP involves in the management of page cache, but it is transparent to file systems. BMAP provides file systems with the DAX-like feature, improving the file system performance. Through comprehensive experiments, we demonstrate that the developed block device driver significantly improves the file access performance.

关键词： Nonvolatile memory Performance evaluation Random access memory Device drivers Kernel System performance

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Topology and survivability analysis for flight flow networks: Case studies of three China airline companies 11

Topology and survivability analysis for flight flow networks...

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11th International Conference on Ubiquitous Information Management and Communication, IMCOM 2017

作者： Gao, Xiaofeng Dang, Yaru Chen, Guihai Gu, Jian Wang, Yongjian Shanghai Key Lab of Scalable Computing and Systems Dept. of Computer Science Shanghai Jiao Tong University Shanghai China Civil Aviation SandT Educational Evaluation Research Center Civil Aviation University of China Tianjin China Key Laboratory of Information Network Security Ministry of Public Security Third Research Institute of Ministry of Public Security Shanghai China

ISBN: (纸本)9781450348881

In this paper we study the survivability feature of airline networks. To measure survivability, we comprehensively consider the network structure measures such as degree distribution, average path length, clustering coefficient, and density;the centrality metrics such as degree centrality and betweenness centrality;as well as the connectivity measures such as maximum connected subgraph size and overall efficiency. Taking three listed China airline companies as examples, namely Air China, China Southern Airlines, and China Eastern Airlines, we provide mass number of simulations to test the survivability of the three companies under random attacks and malicious attacks. The results depict the scalefree features of such networks without small-world characteristics, while the attack simulations reflect the survivability of airline networks clearly. To the best of our knowledge, we are the first to provide case studies for airline networks of individual airline companies in China, which has both theoretical and practical significance in related areas. © 2017 ACM.

关键词： Complex networks

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Multiple influence maximization in social networks 10

Multiple influence maximization in social networks

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10th International Conference on Ubiquitous Information Management and Communication, IMCOM 2016

作者： Sun, Honglin Gao, Xiaofeng Chen, Guihai Gu, Jian Wang, Yongjian Shanghai Key Lab. of Scalable Computing and Systems Dept. of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of Information Network Security of Ministry of Public Security Third Research Institute of Ministry of Public Security Shanghai China

ISBN: (纸本)9781450341424

Influence Maximization, a technique of social analysis to help marketers select a small group of users to promote their products with the goal of maximizing their influence spread, has been studied for a long time. However, previous researches did not consider the scenario that a company may intend to promote serval kinds of products in the same social network, where each user has different preferences for different categories. Considering this scenario in this paper, we propose the Multiple Influence Maximization (MIM) problem based on the assumption that one seed user can accept serval kinds of goods for free, and at the same time non-seed users have enough purchasing power to accept different promotions from their social friends. To address this issue, we design the corresponding greedy algorithm framework with an approximate radio of 1-1/e if the influence function is submodular. Besides, to simplify the computation of influence spread and considering the properties of real social influence propagation, we propose a novel influence quantitative computation model in which influence function fortunately is submodular. Applying this model into our MIM problem, the experiment results show that the greedy algorithm out-performs serval traditional heuristics in influence spread without loss of performance. © 2015 ACM.

关键词： Method of moments

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