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IEEE Transactions on Sustainable Computing 2024年

作者： Ding, Jiepin Xia, Jun Yang, Yaning Zhou, Junlong Chen, Mingsong Li, Keqin East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai200062 China Ningxia Normal University School of Physics and Electronic Information Engineering Guyuan756500 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China State University of New York Department of Computer Science New PaltzNY12561 United States

Since Industry 5.0 emphasizes that manufacturing enterprises should raise awareness of social contribution to achieve sustainable development, more and more meta-heuristic algorithms are investigated to save energy in manufacturing systems. Although non-dominated sorting-based meta-heuristics have been recognized as promising multi-objective optimization methods for solving the energy-efficient flexible job shop scheduling problem (EFJSP), it is hard to guarantee the quality of the Pareto front (e.g., total energy consumption, makespan) due to the lack of population diversity. This is mainly because an improper individual comparison inevitably reduces population diversity, thus limiting exploration and exploitation abilities during population updates. To achieve efficient population evolution, this paper introduces a novel space-cooperation multi-objective optimization (SCMO) method that can effectively solve EFJSP to obtain scheduling schemes with better trade-offs. By cooperatively evaluating the similarity among individuals in both the decision space and objective space, we propose a space-cooperation population update method based on a three-vector representation that can accurately eliminate repetitive individuals to derive higher-quality Pareto solutions. To further improve search efficiency, we propose a difference-driven local search, which selectively changes the positions of operations with higher differences to search for neighbors effectively. Based on the Taguchi method, we conduct experiments to obtain a suitable parameter combination of SCMO. Comprehensive experimental results show that, compared to state-of-the-art methods, our SCMO method achieves the highest HV and NR and the lowest IGD, with an average of 0.990, 0.952, and 0.001, respectively. Meanwhile, compared to traditional local search approaches, our difference-driven local search obtains twice the HV on instance Mk12 and reduces the solving time from 1521s to 475s. © 2016 IEEE.

关键词： Job shop scheduling

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Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT applications

arXiv

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arXiv 2022年

作者： Liu, Tian Ding, Jiahao Wang, Ting Pan, Miao Chen, Mingsong The MoE Engineering Research Center of Software Hardware Co-Design Technology and Application East China Normal University Shanghai200062 China The Department of Information Science and Engineering Zaozhuang University Zaozhuang277160 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States

As a promising method of central model training on decentralized device data while securing user privacy, Federated Learning (FL) is becoming popular in Internet of Things (IoT) design. However, when the data collected by IoT devices are highly skewed in a non-independent and identically distributed (non-IID) manner, the accuracy of vanilla FL method cannot be guaranteed. Although there exist various solutions that try to address the bottleneck of FL with non-IID data, most of them suffer from extra intolerable communication overhead and low model accuracy. To enable fast and accurate FL, this paper proposes a novel data-based device grouping approach that can effectively reduce the disadvantages of weight divergence during the training of non-IID data. However, since our grouping method is based on the similarity of extracted feature maps from IoT devices, it may incur additional risks of privacy exposure. To solve this problem, we propose an improved version by exploiting similarity information using the Locality-Sensitive Hashing (LSH) algorithm without exposing extracted feature maps. Comprehensive experimental results on well-known benchmarks show that our approach can not only accelerate the convergence rate, but also improve the prediction accuracy for FL with non-IID data. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

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FPGA-Based Parallel Implementation of SURF Algorithm

FPGA-Based Parallel Implementation of SURF Algorithm

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Wenjie Chen Shuaishuai Ding Zhilei Chai Daojing He Weihua Zhang Guanhua Zhang Qiwei Peng Wang Luo MoE Engineering Research Center for Software/Hardware Co-design Technology and Application East China Normal University Shanghai China School of loT Engineering Jiangnan University Wuxi China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Parallel Processing Institute Fudan University Shanghai China

ISBN: (纸本)9781509053827

SURF (Speeded up robust features) detection is used extensively in object detection, tracking and matching. However, due to its high complexity, it is usually a challenge to perform such detection in real time on a general-purpose processor. This paper proposes a parallel computing algorithm for the fast computation of SURF, which is specially designed for FPGAs. By efficiently exploiting the advantages of the architecture of an FPGA, and by appropriately handling the inherent parallelism of the SURF computation, the proposed algorithm is able to significantly reduce the computation time. Our experimental results show that, for an image with a resolution of 640x480, the processing time for computing using SURF is only 0.047 seconds on an FPGA (XC6SLX150T, 66.7 MHz), which is 13 times faster than when performed on a typical i3-3240 CPU (with a 3.4 GHz main frequency) and 249 times faster than when performed on a traditional ARM system (CortexTM-A8, 1 GHz).

关键词： Feature extraction Parallel processing Field programmable gate arrays Hardware Algorithm design and analysis Interpolation Real-time systems

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When Adversarial Example Attacks Meet Vertical Federated Learning

When Adversarial Example Attacks Meet Vertical Federated Lea...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Dan Meng Zhihui Fu Chao Kong Yue Qi Guitao Cao OPPO Research Institute Shenzhen China School of Computer and Information Anhui Polytechnic University Wuhu China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China MoE Engineering Research Center of SH/HW Co-design Technology and Application ECNU Shanghai China

Federated learning (FL) is proposed to enable efficient machine learning while protecting the privacy of user data. In non-federated scenarios, machine learning models are vulnerable to adversarial attacks. The attacker can construct elaborate adversarial examples, which can greatly reduce the performance of the victim model. It is worth exploring how FL models differ from traditional models in face of complex adversarial attacks. We are also curious about the robustness of different FL modeling methods against adversarial attacks, providing suggestions for selecting suitable FL models in practical applications. Consequently, we perform adversarial example attacks on vertical federated learning and design experiments to study how vertical federated models perform under adversarial example attacks. The simulation results show that federated models are more robust than non-federated models. Besides, in vertical federation learning, decision trees and XGBoost are much more robust than logistic regression and neural networks.

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Tournament selection based artificial bee colony algorithm with elitist strategy

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8916卷 387-396页

作者： Zhang, Meng-Dan Zhan, Zhi-Hui Li, Jing-Jing Zhang, Jun Department of Computer Science Sun Yat-sen University 510275 China Key Lab. Machine Intelligence and Advanced Computing Ministry of Education China Engineering Research Center of Supercomputing Engineering Software MOE China Key Lab. Software Technology Education Department of Guangdong Province China School of Computer Science South China Normal University China

Artificial bee colony (ABC) algorithm is a novel heuristic algorithm inspired from the intelligent behavior of honey bee swarm. ABC algorithm has a good performance on solving optimization problems of multivariable functions and has been applied in many fields. However, traditional ABC algorithm chooses solutions on the onlooker stage with roulette wheel selection (RWS) strategy which has several disadvantages. Firstly, RWS is suitable for maximization optimization problem. The fitness value has to be converted when solving minimization optimization problem. This makes RWS difficult to be generally used in real-world applications. Secondly, RWS has no any parameter that can control the selection pressure. Therefore, RWS is not easy to adapt to various optimization problems. This paper proposes a tournament selection based ABC (TSABC) algorithm to avoid these disadvantages of RWS based ABC. Moreover, this paper proposes an elitist strategy that can be applied to traditional ABC, TSABC, and any other ABC variants, so as to avoid the phenomenon that ABC algorithm may abandon the globally best solution in the scout stage. We compare the performance of traditional ABC and TSABC on a set of benchmark functions. The experiment results show that TSABC is more flexible and can be efficiently adapted to solve various optimization problems by controlling the selection pressure. © Springer International Publishing Switzerland 2014.

关键词： Optimization

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Evolutionary algorithm for a genetic robot’s personality

Evolutionary algorithm for a genetic robot’s personality

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Kang-Hee Lee Hyun-Sik Shim Woo-Sup Han Kwang-Choon Kim Jong-Hwan Kim Fundamental Technology Team Mechatronics & Manufacturing Technology Center Corporate Technology Operations Samsung Electronics Company Limited South Korea Application Technology Laboratory Telecommunication Research and Development Center Telecommunication Network Business Samsung Electronics Company Limited South Korea Faculty of Department of Electrical Engineering and Computer Science Korea Advanced Institute of Science and Technology South Korea

This paper proposes a new concept of the genetic robot which has its own robot genome, in which each chromosome consists of many genes that contribute to defining the robotpsilas personality. The large number of genes also allows for a highly complex system, however it becomes increasingly difficult and time-consuming to ensure reliability, variability and consistency for the robotpsilas personality while manually initializing values for the individual genes. To overcome this difficulty, this paper proposes an evolutionary algorithm for a genetic robotpsilas personality (EAGRP). EAGRP evolves a gene pool that customizes the robotpsilas genome so that it closely matches a simplified set of features desired by the user. It does this using several new techniques. It acts on a 2 dimensional individual upon which a new masking method, the Eliza-Meme scheme, is used to derive a plausible individual given the restricted preference settings desired by the user. The proposed crossover method allows reproduction for the 2-dimensional genome. Finally, the evaluation procedure for individuals is carried out in a virtual environment using tailored perception scenarios.

关键词： Robots

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Generating software test data by particle swarm optimization

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8886卷 37-47页

作者： Jia, Ya-Hui Chen, Wei-Neng Zhang, Jun Li, Jing-Jing School of Information Science and Technology Sun Yat-sen University Guangzhou China School of Advanced Computing Sun Yat-sen University Guangzhou China Key Lab. Machine Intelligence and Advanced Computing Ministry of Education China Engineering Research Center of Supercomputing Engineering Software MOE China School of Computer Science South China Normal University Guangzhou China

Search-based method using meta-heuristic algorithms is a hot topic in automatic test data generation. In this paper, we develop an automatic test data generating tool named particle swarm optimization data generation tool (PSODGT). The PSODGT is characterized by the following two features. First, the PSODGT adopts the condition-decision coverage (C/DC) as the criterion of software testing, aiming to build an efficient test data set that covers all conditions. Second, the PSODGT uses a particle swarm optimization (PSO) approach to generate test data set. In addition, a new position initialization technique is developed for PSO. Instead of initializing the test data randomly, the proposed technique uses the previously-found test data that can reach the target condition as the initial positions so that the search speed of PSODGT can be further accelerated. The PSODGT is tested on four practical programs. Experimental results show that the proposed PSO approach is promising. © Springer International Publishing Switzerland 2014.

关键词： Software testing

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Determination of responses of liquid xenon to low energy electron and nuclear recoils using a PandaX-Ⅱ detector

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Chinese Physics C 2021年第7期45卷 273-282页

作者： Binbin Yan Abdusalam Abdukerim Wei Chen Xun Chen Yunhua Chen Chen Cheng Xiangyi Cui Yingjie Fan Deqing Fang Changbo Fu Mengting Fu Lisheng Geng Karl Giboni Linhui Gu Xuyuan Guo Ke Han Changda He Di Huang Peiyao Huang Yan Huang Yanlin Huang Zhou Huang Xiangdong Ji Yonglin Ju Shuaijie Li Jianglai Liu Zhuoqun Lei Wenbo Ma Yugang Ma Yajun Mao Yue Meng Kaixiang Ni Jinhua Ning Xuyang Ning Xiangxiang Ren Changsong Shang Lin Si Guofang Shen Andi Tan Anqing Wang Hongwei Wang Meng Wang Qiuhong Wang Siguang Wang Wei Wang Xiuli Wang Zhou Wang Mengmeng Wu Shiyong Wu Weihao Wu Jingkai Xia Mengjiao Xiao Pengwei Xie Rui Yan Jijun Yang Yong Yang Chunxu Yu Jumin Yuan Ying Yuan Jianfeng Yue Xinning Zeng Dan Zhang Tao Zhang Li Zhao Qibin Zheng Jifang Zhou Ning Zhou Xiaopeng Zhou School of Physics and Astronomy Shanghai Jiao Tong UniversityMOE Key Laboratory for Particle Astrophysics and CosmologyShanghai Key Laboratory for Particle Physics and CosmologyShanghai 200240China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China Key Laboratory of Nuclear Physics and Ion-beam Application(MOE) Institute of Modern PhysicsFudan UniversityShanghai 200433China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213China Yalong River Hydropower Development Company Ltd.288 Shuanglin RoadChengdu 610051China School of Physics Sun Yat-Sen UniversityGuangzhou 510275China Tsung-Dao Lee Institute Shanghai 200240China School of Physics Nankai UniversityTianjin 300071China School of Physics Peking UniversityBeijing 100871China School of Physics Beihang UniversityBeijing 100191China International Research Center for Nuclei and Particles in the Cosmos&Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang UniversityBeijing 100191China School of Medical Instrument and Food Engineering University of Shanghai for Science and TechnologyShanghai 200093China Department of Physics University of MarylandCollege ParkMaryland 20742USA School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Physics and Key Laboratory of Particle Physics and Particle Irradiation(MOE) Shandong UniversityJinan 250100China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China Center for High Energy Physics Peking UniversityBeijing 100871China

We present a systematic determination of the responses of PandaX-Ⅱ,a dual phase xenon time projection chamber detector,to low energy *** electron recoil(ER) and nuclear recoil(NR) responses are calibrated,respectively,with injected tritiated methane or 220Rn source,and with 241Am-Be neutron source,in an energy range from 1-25 keV(ER) and 4-80 keV(NR),under the two drift fields,400 and 317 V/*** empirical model is used to fit the light yield and charge yield for both types of *** best fit models can describe the calibration data *** systematic uncertainties of the fitted models are obtained via statistical comparison to the data.

关键词： dark matter liquid xenon time projection chamber calibration electron recoil nucleon recoil NEST2.0

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Accelerating Homomorphic Encryption by Using Representation of Lower Hamming Weight

SSRN

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SSRN 2022年

作者： Wu, Junfei Meng, Dan Cao, Guitao Wei, Tongquan Cao, Wenming Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China MoE Engineering Research Center of SH/HW Co-design Technology and Application East China Normal University Shanghai200062 China OPPO Research Institute Shenzhen China School of Computer Science and Technology China College of Information Engineering Shenzhen University Shenzhen518060 China

How to simultaneously guarantee data processing and data hiding has become a topical issue because of the increasing needs for enhancing privacy protection. Homomorphic encryption is a privacy computing technology that can satisfy the above requirements and strike a balance between security and operability. However, homomorphic encryption suffers from severe computational inefficiency. Although many works attempt to address the problem, few works has noticed that the computational bottleneck of homomorphic encryption is closely related to the Hamming weight of the exponents. In this paper, we investigate the computational efficiency issue of homomorphic encryption and propose a signed binary representation based solution that significantly improves the state of the art Paillier schemes. We first use signed binary representation to reduce the Hamming weight of the exponent, thereby decreasing the number of modular multiplications in homomorphic encryption. We then combine signed binary representation with precomputation techniques to further accelerate homomorphic encryption. Our proposed approach can be integrated into all Paillier schemes and computing platforms without significant modification. The experimental results exhibit that our technique can considerably improve the encryption speed, in particular, it can accelerate the state of the arts by 5.25\times when signed binary representation and precomputation are jointly utilized. © 2022, The Authors. All rights reserved.

关键词： Cryptography

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t-PSI: Efficient Multi-party Private Set Intersection with Threshold

t-PSI: Efficient Multi-party Private Set Intersection with T...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Dan Meng Zhihui Fu Chao Kong Yue Qi Guitao Cao OPPO Research Institute Shenzhen China School of Computer and Information Anhui Polytechnic University Wuhu China Shanghai Key Laboratory of Trustworthy Computing1 East China Normal University Shanghai China MoE Engineering Research Center of SH/HW Co-design Technology and Application ECNU Shanghai China

Multi-party privacy set intersection (MPSI) enables multiple parties to compute the intersection of their datasets without leaking data privacy. Among existing MPSI protocols, e.g., KMPRT-based protocols, oblivious evaluation of programmable pseudo-random function (OPPRF) or oblivious pseudo-random function (OPRF) is typically used to generate pseudo random numbers, requiring frequent online interactions. To mitigate such communication overheads, we propose the threshold Privacy Set Intersection (t-PSI) protocol for multi-party, leveraging the Shamir Secret Sharing (SSS) protocol and the masking construction mechanism. We propose two types of masking construction mechanisms: Polynomial and Garbled Bloom-filter. To the best of our knowledge, this is the first Secret Sharing-based MPSI protocol. With the significant reduction of inter-party communication, the t-PSI protocol is highly scalable and can handle datasets ranging from 2 6 to 2 20 . Further, with built-in fault tolerance, t-PSI works even if some parties went offline during protocol execution. As shown in the experiments, for five parties with datasets of 2 20 items each, the proposed t-PSI takes only 63 seconds, outperforming the state-of-the-art in [1].

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