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FDA3: Federated defense against adversarial attacks for cloud-based iiot applications

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arXiv 2020年

作者： Song, Yunfei Liu, Tian Wei, Tongquan Wang, Xiangfeng Tao, Zhe Chen, Mingsong MoE Engineering Research Center of Software Hardware Co-design Technology and Application East China Normal University Shanghai200062 China Shanghai Institute of Intelligent Science and Technology Tongji University Godel Lab Shanghai China

—Along with the proliferation of Artificial Intelligence (AI) and Internet of Things (IoT) techniques, various kinds of adversarial attacks are increasingly emerging to fool Deep Neural Networks (DNNs) used by Industrial IoT (IIoT) applications. Due to biased training data or vulnerable underlying models, imperceptible modifications on inputs made by adversarial attacks may result in devastating consequences. Although existing methods are promising in defending such malicious attacks, most of them can only deal with limited existing attack types, which makes the deployment of large-scale IIoT devices a great challenge. To address this problem, we present an effective federated defense approach named FDA3 that can aggregate defense knowledge against adversarial examples from different sources. Inspired by federated learning, our proposed cloud-based architecture enables the sharing of defense capabilities against different attacks among IIoT devices. Comprehensive experimental results show that the generated DNNs by our approach can not only resist more malicious attacks than existing attack-specific adversarial training methods, but also can prevent IIoT applications from new attacks. Copyright © 2020, The Authors. All rights reserved.

关键词： Network security

End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19

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arXiv 2020年

作者： Belkacem, Abdelkader Nasreddine Ouhbi, Sofia Lakas, Abderrahmane Benkhelifa, Elhadj Chen, Chao Department of Computer and Network Engineering CIT UAEU UAE Department of Computer Science and Software Engineering CIT UAEU UAE Cloud Computing and Applications Reseach Lab Staffordshire University United Kingdom Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

DPA-2: a large atomic model as a multi-task learner

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

Agricultural Production Evaluation and Spatial Correlation Analysis of Townships in Hainan Island

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Agricultural Production Evaluation and Spatial Correlation A...

2018 International Conference on Mathematics, Modeling, Simulation and Statistics application（MMSSA2018）

作者： Lu Ye Maofen Li Xiaoli Qin Yuping Li Weihong Liang Institute of Scientific and Technical Information Chinese Academy of Tropical Agricultural Sciences Key Lab of Tropical Crops Information Technology Application Research of Hainan Province School of Software Engineering Beijing University of Posts and Telecommunications

ISBN: (纸本)9781510880504

Using principal component analysis, natural breaks, spatial correlation analysis methods, based on the status evaluation, spatial differentiation and correlation of agricultural production of 195 townships in Hainan Island were analyzed, so as to provide a reference for formulating scientific township agricultural plans and policies. The results show that:(1) Townships with better agricultural production are located in cities and counties with better agricultural development. The major factors that affect agricultural production are area of cultivated land, area of facility agriculture, and the proportion of employees in primary industry.(2) Above 45% of townships in the first three levels of agricultural production are distributed in eastern and western cities and counties, and 50% in the fourth level are distributed in central cities and counties.(3) There are spatial agglomerations in areas with similar agricultural production. Meanwhile, some suggestions were put forward to promote the development of agricultural production in Hainan Island.

关键词： agriclutural production evaluation of townships principal component analysis natural breaks spatial correlation analysis suggestions

Deeply supervised depth map super-resolution as novel view synthesis

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arXiv 2018年

作者： Song, Xibin Dai, Yuchao Qin, Xueying School of Computer Science and Technology Shandong University China Baidu Research Beijing China National Engineering Laboratory of Deep Learning Technology and Application China Shaanxi Key Lab of Information Acquisition and Processing School of Electronics and Information Northwestern Polytechnical University School of Software Shandong University China

Deep convolutional neural network (DCNN) has been successfully applied to depth map super-resolution and outperforms existing methods by a wide margin. However, there still exist two major issues with these DCNN based depth map super-resolution methods that hinder the performance: i) The low-resolution depth maps either need to be up-sampled before feeding into the network or substantial deconvolution has to be used;and ii) The supervision (high-resolution depth maps) is only applied at the end of the network, thus it is difficult to handle large up-sampling factors, such as ×8;×16. In this paper, we propose a new framework to tackle the above problems. First, we propose to represent the task of depth map super-resolution as a series of novel view synthesis sub-tasks. The novel view synthesis sub-task aims at generating (synthesizing) a depth map from different camera pose, which could be learned in parallel. Second, to handle large up-sampling factors, we present a deeply supervised network structure to enforce strong supervision in each stage of the network. Third, a multi-scale fusion strategy is proposed to effectively exploit the feature maps at different scales and handle the blocking effect. In this way, our proposed framework could deal with challenging depth map super-resolution efficiently under large up-sampling factors (e.g.×8;×16). Our method only uses the low-resolution depth map as input, and the support of color image is not needed, which greatly reduces the restriction of our method. Extensive experiments on various benchmarking datasets demonstrate the superiority of our method over current state-of-the-art depth map super-resolution methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Optical resolving power

AdaRate: A rate-adaptive traffic measurement method in software defined networks

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International Journal of Performability engineering 2017年第6期13卷 937-944页

作者： Tang, Jixing Zhang, Yue Li, Yan MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai200062 China Shanghai Key Lab for Trustworthy Computing East China Normal University Shanghai200062 China

Traffic measurement is the basis of analysis and prediction of network traffic. Its accuracy directly affects the reliability of upper applications. This paper proposes a rate-adaptive traffic measurement method called AdaRate. It adjusts the polling period according to fluctuations of flow rates which is calculated with a sliding window queue. We noticed that some fluctuations of flow rates may be dropped in AdaRate while the polling period is too long. Then we propose RAdaRate. It adjusts the polling period according to AdaRate or sets the polling period to minimum randomly. We compared the performance of AdaRate and RAdaRate with SWT in CeMon and Payless in Mininet when monitoring those VBR flows with frequent and large fluctuation. The results show that the accuracy of RAdaRate is the best while the overhead of AdaRate is the least. The overhead of AdaRate is 68.75% less than periodic polling. © 2017 Totem Publisher, Inc. All rights reserved.

关键词： software defined networking

Parallel implementation of dense optical flow computation on many-core processor 1

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Chen, Wenjie Yu, Jin Zhang, Weihua Jiang, Linhua Zhang, Guanhua Chai, Zhilei MoE Engineering Research Center for Software/Hardware Co-design Technology and Application East China Normal University Shanghai200061 China School of IoT Engineering Jiangnan University Wuxi214122 China Parallel Processing Institute Fudan University Shanghai200433 China Shanghai Key Lab of Modern Optical Systems University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (数字)9783319271194

ISBN: (纸本)9783319271187

Computation of optical flow is a fundamental step in computer vision applications. However, due to its high complexity, it is difficult to compute a high-accuracy optical flow field in real time. This paper proposes a parallel computing approach for fast computation of high-accuracy optical flow field. It is specially designed for Tilera, a typical many-core processor with 36 tiles. By efficiently exploiting the advantages of the mesh architecture of Tilera, and by appropriately handling the parallelism inherent in the optical flow computation, the proposed implemention is able to significantly reduce the computation time while keep a low power consumption. Experiment shows that, for a 640×480 image, the computation time is only 0. 80 seconds per frame. It is 2. 56 times faster than on a typical CPU i3-3240 (3. 4GHz), and the power consumption as less as 1/6. Experimental results also show that the proposed parallel approach is highly scalable for variable requirements on computation speeds and power consumptions, since it can flexibly selects a proper number of computing cores. © Springer International Publishing Switzerland 2015.

关键词： Parallel processing systems

An approach for discovering user similarity in social networks based on the Bayesian network and MapReduce 1

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An approach for discovering user similarity in social networ...

2014 1st IEEE International Conference on Behavioral, Economic, Socio-Cultural Computing, BESC 2014

作者： Xu, Juan Yue, Kun Li, Jin Wang, Feng Liu, Weiyi School of Information Science and Engineering Yunnan University Kunming China School of Software Yunnan University Kunming China Yunnan Computer Technology Application Key Lab Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781479969807

Adopting Bayesian network (BN) as the effective framework for representing and inferring dependencies and uncertainties among variables, in this paper, we established a BN-based model to discover user similarities in social networks. First, we built a BN to describe the direct similarity relationships between users, called social user BN and abbreviated as SUBN. Second, we proposed a distributed storage method based on Hbase to store the SUBN and support the efficient probabilistic inferences. Consequently, we proposed a SUBN-based method to find indirect similarity relationships between users. Experimental results show the efficiency and accuracy of our method. © 2014 IEEE.

关键词： MapReduce

Coalgebras for fuzzy transition systems

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Coalgebras for fuzzy transition systems

作者： Wu, Hengyang Chen, Yixiang Information Engineer College Hangzhou Dianzi University Hangzhou 310018 China MoE Engineering Research Center for Software/Hardware Co-design Technology and Application Shanghai Key Lab for Trustworthy Computing East China Normal University Shanghai 200062 China

This paper studies a coalgebraic theory of fuzzy transition systems. Main conclusions include: the functor FA for deterministic fuzzy transition systems and the functor (Pa°F)A for nondeterministic fuzzy transition systems preserve weak pullbacks, and the functor FA has a final coalgebra under some restricted conditions. Moreover, we show how to get a concrete (fuzzy) bisimulation from a coalgebraic bisimulation. Crown Copyright © 2014 Published by Elsevier B.V. All rights reserved.

关键词： Algebra