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Differential Private Preservation Multi-core DBScan Clustering for Network User Data

Procedia Computer Science 2018年 129卷 257-262页

作者： Lina Ni Chao Li Haoran Liu Anu G. Bourgeois Jiguo Yu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Computer Science Georgia State University University Plaza Atlanta GA 30303 USA School of Information Science and Engineering Qufu Normal University Shandong China

Mining rich network user data to extract valuable information becomes the focus and commercial interest of researchers. Differential privacy protection is a new paradigm based on data distortion, which protects sensitive data while maintains a certain statistical properties by adding random noise, and makes up for the shortcomings that traditional schemas require knowledge background assumptions and cannot analyze quantitatively. In this paper, we propose a DP-MCDBScan schema based on the powerful differential privacy. We perform simulation to evaluate our schema, whose results show that our schema has better efficiency, accuracy, and privacy protection effect than previous schemas.

关键词： Private preserving differential privacy MCDBScan clustering data mining Laplace noise

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Optimal Linear Crossover for Mitigating Negative Transfer in Evolutionary Multitasking

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IEEE Transactions on Evolutionary Computation 2024年

作者： Liu, Zhaobo Yuan, Jianhua Zhang, Haili Zeng, Tao Zhu, Zexuan Shenzhen University Institute for Advanced Study Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen Polytechnic University School of Undergraduate Education Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University Shenzhen City Key Laboratory of Embedded System Design Shenzhen518060 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of China Chongqing400044 China

Evolutionary multitasking algorithms use information exchange among individuals in a population to solve multiple optimization problems simultaneously. Negative transfer is a critical factor that affects the performance of evolutionary multitasking algorithms. In this study, we propose an innovative approach to mitigate negative transfer in evolutionary multitasking algorithms. The proposed approach is grounded in rigorous theoretical analysis, which provides valuable theoretical insights into the design of an optimal linear crossover operator for mitigating negative transfer. By identifying interpretable conditions, we establish a solid theoretical foundation to prevent negative transfer in diverse scenarios. Building upon these findings, we theoretically derive a closed-form expression for the optimal crossover operator and propose practical design methods based on approximations. Furthermore, we integrate the proposed optimal crossover operator into a fundamental evolutionary multitasking algorithm framework. The resultant algorithm is comparable or superior to other state-of-the-art methods. Empirical validation through comprehensive experiments confirms the effectiveness of our theoretical findings. © 1997-2012 IEEE.

关键词： Optimization algorithms

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A Transformation Algorithm for Optimal Admissible Generalized Mutual Exclusion Constraints on Petri Nets with Uncontrollable Transitions

A Transformation Algorithm for Optimal Admissible Generalize...

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2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

作者： ShouGuang Wang ChengYing Wang MengChu Zhou College of Information & Electronic Engineer ngineering ing Zhe Zhejiang Gongshang UniversityHangzh College of Information & Electronic Engineer ngineering ing Zhejiang Gongshang Univ UniversityHangz The MoE Key Laboratory of Embedded System and Service Computing Tongji UniversityShanghai 200092Ch

For a class of Petri nets whose uncontrollable influence subnets are forward synchronization and backward conflict free (FSBCF) nets, this work studies their structure and properties and proposes an algorithm to transform a given generalized mutual exclusion constraint (GMEC) into an optimal admissible one. Compared with the path-based constraint transformation approaches, the proposed method has much higher computational efficiency. An example is used to illustrate the application of the proposed method.

关键词： Automated manufacturing systems discrete event systems forbidden state problem mutual exclusion Petri nets supervisory control

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Modeling and analysis of dual-arm cluster tools for wafer fabrication with revisiting

Modeling and analysis of dual-arm cluster tools for wafer fa...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Yan Qiao NaiQi Wu MengChu Zhou Department of Industrial Engineering School of Mechatronics Engineering Guangdong University of Technology Guangzhou China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Some wafer fabrication processes are repeated processes, e.g. atomic layer deposition (ALD) process. For such processes, the wafers need to visit some processing modules for a number of times, which complicates the cycle time analysis. This paper studies the cycle time analysis problem for such processes. With a Petri net model, it is found that such processes contain local cycles involving only the revisiting PMs and global cycles involving both revisiting and non-revisiting PMs. The process switches between these two types of cycles such that the process never reaches a steady state. Based on this finding, the mechanism underlying such processes is revealed and analytical expressions are given for the calculation of their cycle time. Illustrative examples are presented to show the application of the proposed approach.

关键词： Robots Semiconductor device modeling Fabrication Schedules Analytical models system recovery Load modeling

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Classification of facial diagnosis gloss in Chinese medicine based on different algorithms

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Chinese journal of integrative medicine 2016年 2016 Apr 4页

作者： Fu-Feng Li Rui Zhou Guo-Zheng Li Rui-Wei Zhao Four Diagnosis Laboratory of Traditional Chinese Medicine Shanghai University of Traditional Chinese Medicine Shanghai 201203 China. li_fufeng@***. Four Diagnosis Laboratory of Traditional Chinese Medicine Shanghai University of Traditional Chinese Medicine Shanghai 201203 China. The Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Control Science & Engineering Tongji University Shanghai 201804 China.

OBJECTIVE:To design a face gloss classification model and to provide an automatic and quantitative approach for the diagnosis of Chinese medicine (CM) based on the face images. METHODS:To classify the face gloss images into two groups (gloss and non-gloss), feature extraction methods were applied to the original images. The original images were supposed to obtain a more ideal representation in which gloss information was better revealed in four color spaces [including red, green, blue (RGB), hue, saturation, value (HSV), Gray and Lab]. Principal component analysis (PCA), 2-dimensional PCA (2DPCA), 2-directional 2-dimensional PCA [(2D)PCA], linear discriminant analysis (LDA), 2-dimensional LDA (2DLDA), and partial least squares (PLS) were used as the feature extraction methods of face gloss. k nearest neighbor was used as the classifification method. RESULTS:All the six feature extraction methods were useful in extracting information of face gloss, especially LDA, which had the best prediction accuracy in the 4 color spaces. The average accuracy of LDA in the Lab was 7%-10% higher than that of PCA, 2DPCA, (2D)PCA and 2DLDA P<0.05). The prediction accuracy of LDA reached 98% in the Lab color space and showed practical usage in clinical diagnosis. The consistent rate between the CM experts and the facial diagnosis system was 81%. CONCLUSION:A computer-assisted classifification model was designed to provide an automatic and quantitative approach for the gloss diagnosis of CM based on the face images.

关键词： Chinese medicine computer-assisted face gloss diagnosis feature extraction image processing

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Parallelizing abnormal event detection in crowded scenes with GPU

Parallelizing abnormal event detection in crowded scenes wit...

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Mohammadreza Yavari Maozhen Li Siguang Li Man Qi Department of Electronic and Computer Engineering Brunel University London Uxbridge UB8 3PH UK The Key Laboratory of Embedded Systems and Service Computing Tongji University Shanghai China

Analyzing human activities in surveillance videos a challenging task due to the high volume of data that needs to be processed in a timely manner. This paper presents a GPU based Gaussian Mixture Model (GMM) to detect abnormal activities in crowded scenes. GMM is a fully unsupervised method that predicts abnormal crowd behaviors based on the processing of normal crowd behaviors. Specifically, we use crowd distribution and GMM to estimate the speed and to predict the behaviors of the crowd. The performance of the parallel GMM is evaluated from the aspects of computation efficiency and accuracy in terms of area under the curve.

关键词： Videos Graphics processing units Computational modeling Computer vision Surveillance Image motion analysis Pattern recognition

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StoreApp: A shared storage appliance for efficient and scalable virtualized Hadoop clusters

StoreApp: A shared storage appliance for efficient and scala...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yanfei Guo Jia Rao Dazhao Cheng Changjun Jiang Cheng-Zhong Xu Xiaobo Zhou Department of Computer Science University of Colorado Colorado Springs Colorado USA The Key Lab of Embedded System and Service Computing Tongji University Shanghai China Electrical and Computer Engineering Wayne State University Detroit Michigan USA

Virtualizing Hadoop clusters provides many benefits, including rapid deployment, on-demand elasticity and secure multi-tenancy. However, a simple migration of Hadoop to a virtualized environment does not fully exploit these benefits. The dual role of a Hadoop worker, acting as both a compute node and a data node, makes it difficult to achieve efficient IO processing, maintain data locality, and exploit resource elasticity in the cloud. We find that decoupling per-node storage from its computation opens up opportunities for IO acceleration, locality improvement, and on-the-fly cluster resizing. To fully exploit these opportunities, we propose StoreApp, a shared storage appliance for virtual Hadoop worker nodes co-located on the same physical host. To completely separate storage from computation and prioritize IO processing, StoreApp pro-actively pushes intermediate data generated by map tasks to the storage node. StoreApp also implements late-binding task creation to take the advantage of prefetched data due to mis-aligned records. Experimental results show that StoreApp achieves up to 61% performance improvement compared to stock Hadoop and resizes the cluster to the (near) optimal degree of parallelism.

关键词： Prefetching Benchmark testing Cloud computing Computers Conferences Elasticity Parallel processing

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Robust Adaptive Control of Nonlinear systems with Asymmetric Non-smooth Saturation

Robust Adaptive Control of Nonlinear Systems with Asymmetric...

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第28届中国控制与决策会议

作者： Xuehai Tu Junfeng Lai Kai Zhao Institute of Smart System and Renewable Energy School of AutomationChongqing University The Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing Universit

ISBN: (纸本)9781467397155

In this paper,the robust adaptive control is proposed for a class of parametric uncertain nonhnear system with asymmetric non-smooth input *** introducing a well-defined smooth function,a robust adaptive control algorithms are developed to deal with the problem of asymmetric non-smooth *** most of the existing control schemes for the uncertain nonhnear system,which requires an assumption that the control gain is an unknown constant,the developed controller only needs that the control gain is a bounded and unknown time-varying *** this work,the robust adaptive controller can guarantee global uniform boundedness of the closed-loop system signals as rigorously proved by Lyapunov analysis.A simulation results is presented to illustrate the effectiveness of the proposed adaptive control technique.

关键词： Robust Adaptive Control Asymmetric Non-Smooth Saturation Uniformly Bounded

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New reachability trees for unbounded Petri nets

New reachability trees for unbounded Petri nets

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IEEE International Conference on Robotics and Automation (ICRA)

作者： ShouGuang Wang MengChu Zhou MengDi Gan Dan You Yue Li School of Information & Electronic Engineering Zhejiang Gongshang University Hangzhou China State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an China MoE Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (纸本)9781479969241

Reachability is an important dynamic property of Petri nets. Its determination given an unbounded net and initial marking has remained an open problem since 1960s. Due to its extreme difficulty, a great deal of research has been focused on some subclasses of unbounded nets. For arbitrary ones, this work intends to propose a new reachability tree (NRT), which consists of only but all reachable markings from its initial marking. Furthermore, an NRT-based method to decide deadlocks of unbounded nets is presented. An example is provided to show the new results.

关键词： system recovery Petri nets Manufacturing systems Solids Gallium nitride embedded systems

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Distributed Computation Offloading using Deep Reinforcement Learning in Internet of Vehicles

Distributed Computation Offloading using Deep Reinforcement ...

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IEEE International Conference on Communications in China (ICCC)

作者： Chen Chen Zheng Wang Qingqi Pei Ci He Zhibin Dou State Key Laboratory of Integrated Services Networks Xidian University Xi'an P.R.China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing China The Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China

ISBN: (数字)9781728173276

ISBN: (纸本)9781728173283

In this paper, we first take the moving vehicles as a RP (resource pool), by which we proposed a distributed computation offloading scheme to fully utilize the available resources and reduce task execution time in I0V (Internet of Vehicles). After that, we divide a complex task into many sub-tasks and indicate that how to assign these small tasks to satisfy the task execution time in RP is a NP problem. The executing time of a task is modeled as the longest calculation time among all small tasks, which is actually a min-max problem. For a dynamically vehicular environment, a distributed computing offloading strategy based on deep reinforcement learning is proposed to find the best offloading scheme to minimize the execution time of a task. Numerical results demonstrate that our scheme is better than Partial Flooding Algorithm and can make full use of the available computing resources of surrounding vehicles by considering the mobility of vehicles, the delay of communication transmission, and the separability of the tasks, thus greatly reducing the execution time of the computing tasks.

关键词： Computational modeling Simulation Reinforcement learning User experience Delays Internet Task analysis

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