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2013 IEEE International Conference on Automation Science and Engineering, CASE 2013

作者： Zhu, Qinghua Wu, Naiqi Qiao, Yan Zhou, Mengchu School of Computer Guangdong University of Technology Guangzhou 510006 China Macau University of Science and Technology China Department of Industrial Engineering School of Electro-Mechanical Engineering Guangdong University of Technology Guangzhou 510006 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102-1982 United States

ISBN: (纸本)9781479915156

It is very challenging to schedule a multi-cluster tool for it needs to coordinate the activities of multiple robots. This paper studies one-wafer cyclic scheduling for multi-cluster tools whose bottleneck cluster tool is process-bound. A Petri net model is developed to describe the system. Based on the model, it is shown that, for any multi-cluster tool whose bottleneck cluster tool is process-bound, there is always a one-wafer cyclic schedule. Then, a method is presented to find the minimal cycle time and the optimal one-wafer cyclic schedule. The method requires only simple calculation. An example illustrates the application and effectiveness of the proposed method. © 2013 IEEE.

关键词： Scheduling

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Query-Centered Temporal Community Search via Time-Constrained Personalized PageRank

arXiv

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arXiv 2023年

作者： Lin, Longlong Yuan, Pingpeng Li, Rong-Hua Zhu, Chunxue Qin, Hongchao Jin, Hai Jia, Tao College of Computer and Information Science Southwest University Chongqing400715 China National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technololgy Beijing Institute of Technology Beijing100081 China

Existing temporal community search suffers from two defects: (i) they ignore the temporal proximity between the query vertex q and other vertices but simply require the result to include q. Thus, they find many temporal irrelevant vertices (these vertices are called query-drifted vertices) to q for satisfying their cohesiveness, resulting in q being marginalized;(ii) their methods are NP-hard, incurring high costs for exact solutions or compromised qualities for approximate/heuristic algorithms. Inspired by these, we propose a novel problem named query-centered temporal community search to circumvent query-drifted vertices. Specifically, we first present a novel concept of Time-Constrained Personalized PageRank to characterize the temporal proximity between q and other vertices. Then, we introduce a model called β-temporal proximity core, which can combine temporal proximity and structural cohesiveness. Subsequently, our problem is formulated as an optimization task that finds a β-temporal proximity core with the largest β. To solve our problem, we first devise an exact and near-linear time greedy removing algorithm that iteratively removes unpromising vertices. To improve efficiency, we then design an approximate two-stage local search algorithm with bound-based pruning techniques. Finally, extensive experiments on eight real-life datasets and nine competitors show the superiority of the proposed solutions. © 2023, CC BY.

关键词： Iterative methods

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Next-Generation Simulation Illuminates Scientific Problems of Organised Complexity

arXiv

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arXiv 2024年

作者： Wang, Cheng Wang, Chuwen Zhang, Wang Zhao, Yu Zeng, Shirong Ning, Ronghui Jiang, Changjun Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China

As artificial intelligence becomes increasingly prevalent in scientific research, data-driven methodologies appear to overshadow traditional approaches in resolving scientific problems. In this Perspective, we revisit a classic classification of scientific problems and acknowledge that a series of unresolved problems remain. Throughout the history of researching scientific problems, scientists have continuously formed new paradigms facilitated by advances in data, algorithms, and computational power. To better tackle unresolved problems, especially those of organised complexity, a novel paradigm is necessitated. While recognising that the strengths of new paradigms have expanded the scope of resolvable scientific problems, we aware that the continued advancement of data, algorithms, and computational power alone is hardly to bring a new paradigm. We posit that the integration of paradigms, which capitalises on the strengths of each, represents a promising approach. Specifically, we focus on next-generation simulation (NGS), which can serve as a platform to integrate methods from different paradigms. We propose a methodology, sophisticated behavioural simulation (SBS), to realise it. SBS represents a higher level of paradigms integration based on foundational models to simulate complex systems, such as social systems involving sophisticated human strategies and behaviours. NGS extends beyond the capabilities of traditional mathematical modelling simulations and agent-based modelling simulations, and therefore, positions itself as a potential solution to problems of organised complexity in complex systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Simulation platform

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Scaling-Up Item-Based Collaborative Filtering Recommendation Algorithm Based on Hadoop

Scaling-Up Item-Based Collaborative Filtering Recommendation...

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IEEE World Congress on services (serviceS)

作者： Jing Jiang Jie Lu Guangquan Zhang Guodong Long Decision Systems & E-Service Intelligence Research Laboratory Center of Quantum Computing and Intelligent System School of Software Faculty of Engineering and Information Technology Sydney University of Technology Sydney NSW Australia

Collaborative filtering (CF) techniques have achieved widespread success in E-commerce nowadays. The tremendous growth of the number of customers and products in recent years poses some key challenges for recommender systems in which high quality recommendations are required and more recommendations per second for millions of customers and products need to be performed. Thus, the improvement of scalability and efficiency of collaborative filtering (CF) algorithms become increasingly important and difficult. In this paper, we developed and implemented a scaling-up item-based collaborative filtering algorithm on MapReduce, by splitting the three most costly computations in the proposed algorithm into four Map-Reduce phases, each of which can be independently executed on different nodes in parallel. We also proposed efficient partition strategies not only to enable the parallel computation in each Map-Reduce phase but also to maximize data locality to minimize the communication cost. Experimental results effectively showed the good performance in scalability and efficiency of the item-based CF algorithm on a Hadoop cluster.

关键词： Scalability Prediction algorithms Algorithm design and analysis Collaboration Filtering Partitioning algorithms Clustering algorithms

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Interval set cluster analysis: A re-formulation 1

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12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular computing, RSFDGrC 2009

作者： Yao, Yiyu Lingras, Pawan Wang, Ruizhi Miao, Duoqian Department of Computer Science University of Regina Regina SK S4S 0A2 Canada Department of Mathematics and Computing Science Saint Mary's University Halifax NS B3H 3C3 Canada Dept. of Comp. Science and Technology Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804 China

ISBN: (数字)9783642106460

ISBN: (纸本)3642106455

A new clustering strategy is proposed based on interval sets, which is an alternative formulation different from the ones used in the existing studies. Instead of using a single set as the representation of a cluster, each cluster is represented by an interval set that is defined by a pair of sets called the lower and upper bounds. Elements in the lower bound are typical elements of the cluster and elements between the upper and lower bounds are fringe elements of the cluster. A cluster is therefore more realistically characterized by a set of core elements and a set of boundary elements. Two types of interval set clusterings are proposed, one is non-overlapping lower bound interval set clustering and the other is overlapping lower bound interval set clusterings, corresponding to the standard partition based and covering based clusterings. © 2009 Springer-Verlag Berlin Heidelberg.

关键词： Cluster analysis

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Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

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Security and Safety 2022年第1期1卷 121-141页

作者： Zhong Li Xianke Wu Changjun Jiang College of Information Science and Technology Donghua University Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science Tongji University Shanghai Network Financial Security Collaborative Innovation Center Tongji University

Nowadays, large numbers of smart sensors(e.g., road-side cameras) which communicate with nearby base stations could launch distributed denial of services(DDoS) attack storms in intelligent transportation systems. DDoS attacks disable the services provided by base stations. Thus in this paper, considering the uneven communication traffic flows and privacy preserving, we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future. However, in the federated learning,we need to consider the problem of poisoning attacks due to malicious participants. The poisoning attacks will lead to the intelligent transportation systems paralysis without security protection. Traditional poisoning attacks mainly apply to the classification model with labeled data. In this paper, we propose a reinforcement learning-based poisoning method specifically for poisoning the prediction model with unlabeled data. Besides, previous related defense strategies rely on validation datasets with labeled data in the server. However, it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving, and our datasets are also unlabeled. Furthermore, we give a validation dataset-free defense strategy based on Dempster–Shafer(D–S) evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS prediction. In our experiments, we simulate 3000 points in combination with DARPA2000 dataset to carry out evaluations. The results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short time. Meanwhile, we compare our proposed defense algorithm with three popularly used defense algorithms. The results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability.

关键词： Poisoning attacks Defenses Multi-step DDoS prediction Unlabeled data Intelligent transportation systems

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Improved asymptotic multicast throughput for random extended networks

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Computer Communications 2010年第18期33卷 2195-2202页

作者： Cheng Wang Changjun Jiang Xiang-Yang Li Jiujun Cheng Department of Computer Science and Technology Tongji University Shanghai China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science Illinois Institute of Technology Chicago IL 60616 USA

We study the asymptotic throughput for random extended networks , where n ad hoc nodes are randomly deployed in a square region R ( n ) = 0 , n 2 . We directly consider the multicast throughput to unify the unicast and broadcast throughput, and design a new multicast scheme under the generalized physical model based on the so-called secondary highways system . Taking account of all possible cases of n s = ω (1) and 1 ⩽ n d ⩽ n − 1, we derive the achievable multicast throughput, where n s and n d denote the number of sessions and the number of destinations of each session. We prove that for some cases in terms of n s and n d , our scheme achieves better throughput than the existing schemes.

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Adaptive social network construction using Gaussian mixture model

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Information technology Journal 2011年第12期10卷 2252-2259页

作者： Guo, Xin Xiang, Yang Chen, Qian Wei, Wei Department of Computer Science and Technology School of Electronic and Information Engineering Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China

A Social network graph shows social interactions and relationships between individuals in a specific social environment, which is very helpful for analyzing social relationships, activities, structures, etc. The author quantized the strengths of social objects' relationships in social environment using an improved vector space model. Gaussian mixture model was employed to set the threshold for identifying social relationships adaptively and divide social subgroups automatically. According to the threshold, social network graph would be constructed based on performance measures. It is concluded that hidden social relationships can be discovered effectively by using this approach which is very flexible and adaptive for dynamic information feedback mechanism. © 2011 Asian Network for Scientific Information.

关键词： Gaussian distribution

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Virtual-Source and Virtual-Swarm-Based Particle Swarm Optimizer for Large-Scale Multi-Source Location via Robot Swarm

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Zhang, JunQi Lin, YuXuan Zhou, MengChu Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Multi-source location is a significant application in the field of robot swarm and is required to find all sources whose number and distribution are unknown in advance. With few parameters and fast search, Particle Swarm Optimizer (PSO) variants that have certain grouping capability have been applied to address multi-source location problems by dividing a swarm such that every source has robots to locate. However, they are difficult to predetermine the exact number of groups, require a big number of robots, and are easily trapped in the no-signal areas when the proportion of no-signal areas is high. This work proposes a Virtual-source and Virtual-swarm-based PSO (VVPSO) to divide a search area into multiple cells equally, each of which has a virtual source in its center. Then, instead of robots grouping, only one group of robots is employed to traverse all virtual sources, and search their corresponding cells to locate real sources by a new PSO called Real-virtual mapping PSO (RMPSO). RMPSO asymmetrically maps a robot into a particle swarm with multiple virtual particles to perform PSO, which greatly reduces the requirements for the number of robots. Experimental results show that VVPSO has great search scalability and can solve large-scale multi-source location problems than two state-of-the-art grouping methods and three representative multimodal PSO variants, even with only one robot. Hence, this work greatly advances the field of multi-source location by using mobile robot swarm. IEEE

关键词： Robots

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Decomposition based efficient algorithms for knowledge reduction

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ICIC Express Letters 2010年第3期4卷 1025-1032页

作者： Jiao, Na Miao, Duoqian Zhang, Hongyun Department of Computer Science and Technology Ministry of Education of China Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education of China Tongji University Shanghai 201804 China

Knowledge reduction, one of essential issues for data mining, has alwaysbeen a, hot topic due to the explosive growth of inform,a,tion. However, whenhandling large-scale data, m,any current knowledge reduction m,ethod,s based, onrough set theory are incapable. In this paper, efficient algorithms forknowledge reduction are put forward, based, on decomposition principle. Theidea, of decomposition is to break a, complex problem, down into a,m,a,ster-problem, and, several sub-problem,s that are simpler, m,orem,a,na,gea,ble and, m,ore solvable by using existing induction methods, thenjoining them, together in order to solve the original problem,. Compared, withsom,e traditional methods, the efficiency of the proposed algorithms can beillustrated by experiments with standard datasets from, UCI database. ICIC International © 2010.

关键词： Rough set theory

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