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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin Qian Yu-Hui Chang Ping Lv Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science & Technology Nanjing China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou China

ISBN: (纸本)9781509003914

Attribute reduction is one of the key issues for data preprocess in data mining. Many heuristic attribute reduction algorithms based on discernibility matrix have been proposed for inconsistent decision tables. However, these methods are usually computationally time-consuming. To address this issue, the derived consistent decision tables are defined for different definitions of relative reducts. The computations for different reducts of the original inconsistent decision tables are converted into the computations for their corresponding reducts of the derived consistent datasets. The relative discernibility object pair and the more optimal relative discernibility degree from view of the boundary region are designed to accelerate the attribute reduction process. An efficient attribute reduction framework using relative discernibility degree is proposed for large datasets. Experimental results show that our attribute reduction algorithms are effective and feasible for large inconsistent datasets.

关键词： Cybernetics Acceleration Time complexity Rough sets Heuristic algorithms data mining Algorithm design and analysis

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Parallel knowledge acquisition algorithm for big data using MapReduce

Parallel knowledge acquisition algorithm for big data using ...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin Qian Min Xia Ping Lv Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science & Technology Nanjing China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou China

ISBN: (纸本)9781467372220

With the rapid growth of data volume, knowledge acquisition for big data has become a new challenge. To address this issue, the hierarchical decision table is defined and implemented in this work. The properties of different hierarchical decision tables are discussed under the different granularity of conditional attributes. A novel knowledge acquisition algorithm for big data using MapReduce is proposed. Experimental results demonstrate that the proposed algorithm is able to deal with big data and mine hierarchical decision rules under the different granularity.

关键词： big data Hierarchical Rough Set Knowledge Acquisition MapReduce big data Information Extraction Decision tables

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Robust unsupervised feature selection by nonnegative sparse subspace learning

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Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

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Automatic salient object segmentation via shape prior based active contour model

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ICIC Express Letters, Part B: Applications 2016年第11期7卷 2491-2496页

作者： Gao, Shangbing Zhang, Youdong Zhou, Jun Zheng, Hao The Key Laboratory for Traffic and Transportation Security of Jiangsu Province Faculty of Computer and Software Engineering No. 1 Meicheng Rd. Huai’an223003 China Jiangsu Provincial Key Laboratory for Advanced Manufacturing Technology Huaiyin Institute of Technology No. 1 Meicheng Rd. Huai’an223003 China Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University No. 3601 Hongjing Ave. Jiangning Dist. Nanjing211171 China

In this paper, we propose a novel model for unsupervised segmentation of viewer's attention object from natural images based on localizing region-based active con-tour (LRAC). Firstly, we proposed the saliency detection model via the multi-scale super-pixel. Then, object-level shape prior is extracted combining saliency with object boundary information. Finally, this contour is improved by the edge-preserving to generate the initial contour for our automatic object segmentation system. In contrast with localiz-ing region-based active contours that require considerable user interaction, the proposed method does not require it, i.e., the segmentation task is ful_lled in a fully automatic manner. Extensive experiments results on a large variety of natural images demonstrate that our algorithm consistently outperforms the popular existing salient object segmenta-tion methods, yielding higher precision and better recall rates. © 2016 ICIC International.

关键词： Image segmentation

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Smoothed multi-view subspace clustering

arXiv

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arXiv 2021年

作者： Chen, Peng Liu, Liang Ma, Zhengrui Kang, Zhao Jangsu Automation Research Institute Jiangsu Lianyungang China University of Electronic Science and Technology of China Sichuan Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Sichuan Chengdu China

In recent years, multi-view subspace clustering has achieved impressive performance due to the exploitation of complementary imformation across multiple views. However, multi-view data can be very complicated and are not easy to cluster in real-world applications. Most existing methods operate on raw data and may not obtain the optimal solution. In this work, we propose a novel multi-view clustering method named smoothed multi-view subspace clustering (SMVSC) by employing a novel technique, i.e., graph filtering, to obtain a smooth representation for each view, in which similar data points have similar feature values. Specifically, it retains the graph geometric features through applying a low-pass filter. Consequently, it produces a "clustering-friendly" representation and greatly facilitates the downstream clustering task. Extensive experiments on benchmark datasets validate the superiority of our approach. analysis shows that graph filtering increases the separability of classes. © 2021, CC BY-NC-ND.

关键词： Low pass filters

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Weighted Discriminative Dictionary Learning based on Low-rank Representation

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Journal of Physics: Conference Series 2017年第1期787卷

作者： Heyou Chang Hao Zheng Key Laboratory of Trusted Cloud Computing and Big Data Analysis School of Information Engineering Nanjing XiaoZhuang University Nanjing China

Low-rank representation has been widely used in the field of pattern classification, especially when both training and testing images are corrupted with large noise. Dictionary plays an important role in low-rank representation. With respect to the semantic dictionary, the optimal representation matrix should be block-diagonal. However, traditional low-rank representation based dictionary learning methods cannot effectively exploit the discriminative information between data and dictionary. To address this problem, this paper proposed weighted discriminative dictionary learning based on low-rank representation, where a weighted representation regularization term is constructed. The regularization associates label information of both training samples and dictionary atoms, and encourages to generate a discriminative representation with class-wise block-diagonal structure, which can further improve the classification performance where both training and testing images are corrupted with large noise. Experimental results demonstrate advantages of the proposed method over the state-of-the-art methods.

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Budgeted Persuasion on User Opinions via Varying Susceptibility

Budgeted Persuasion on User Opinions via Varying Susceptibil...

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IEEE International Conference on Performance, computing and Communications (IPCCC)

作者： Wenyi Tang Xinrui Xu Guangchun Luo Zaobo He Kaiming Zhan School of Computer Science and Engineering University of Electronic Science and Technology of China School of Information and Software Engineering University of Electronic Science and Technology of China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Miami University Sichuan Jiuyuan Yinhai Software Co. Ltd

ISBN: (数字)9781728198293

ISBN: (纸本)9781728198309

Nowadays, the social network becomes an indispensable part of people's daily life, meanwhile offers an unprecedentedly convenient access for purposive individuals to influence the opinions of network users. Current studies present a subtle persuasion approach that finds a number of key users meanwhile varies their susceptibility extent to impact the public opinion. Such persuasion is significantly critical for public security, as it could facilitate both the spreading and dispelling of malicious rumors. However, the major body of these studies enclose impractical assumptions, such that persuaders have an unlimited budget, or the costs of varying different users' susceptibilities are the same, thus rendering these works unsuitable for realistic scenarios. Therefore, this work originally proposes a more practical and generalized problem of persuasion, where varying the susceptibilities of different users holds different costs. The analysis of its non-convexity, non-submodularity and complexity shows that solving the proposed problem is nontrivial, thus inspiring us to provide an intuitive greedy algorithm. Furthermore, we design an accelerated algorithm based on the community property, which reduces the time consumption more than one order of magnitude. The acceleration is based on the intuition that the impact of a user within a proper community could be a good estimation of the impact in the whole network, while the computation of the former one is much more efficient. The relationship between two algorithms is fully analyzed, which shows the community-based algorithm can degenerate to the intuitive greedy algorithm under a specific setting. Finally, comprehensive evaluations on real-world datasets show the superiority of proposed algorithms on both effectiveness and efficiency.

关键词： Greedy algorithms Social networking (online) Heuristic algorithms Search problems Rendering (computer graphics) Acceleration Security

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Edge computing service deployment and task offloading based on multi-task high-dimensional multi-objective optimization

arXiv

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arXiv 2023年

作者： Guo, Yanheng Zhang, Yan Wu, Linjie Li, Mengxia Cai, Xingjuan Chen, Jinjun Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Shanxi Taiyuan030024 China State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia

The Mobile Edge computing (MEC) system located close to the client allows mobile smart devices to offload their computations onto edge servers, enabling them to benefit from low-latency computing services. Both cloud service providers and users seek more comprehensive solutions, necessitating judicious decisions in service deployment and task offloading while balancing multiple objectives. This study investigates service deployment and task offloading challenges in a multi-user environment, framing them as a multi-task high-dimensional multi-objective optimization (MT-HD-MOO) problem within an edge environment. To ensure stable service provisioning, beyond considering latency, energy consumption, and cost as deployment objectives, network reliability is also incorporated. Furthermore, to promote equitable usage of edge servers, load balancing is introduced as a fourth task offloading objective, in addition to latency, energy consumption, and cost. Additionally, this paper designs a MT-HD-MOO algorithm based on a multi-selection strategy to address this model and its solution. By employing diverse selection strategies, an environment selection strategy pool is established to enhance population diversity within the high-dimensional objective space. Ultimately, the algorithm’s effectiveness is verified through simulation experiments. © 2023, CC BY-NC-ND.

关键词： Multiobjective optimization

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Resource-Aware Multi-Criteria Vehicle Participation for Federated Learning in Internet of Vehicles

SSRN

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SSRN 2023年

作者： Wen, Jie Zhang, Jingbo Zhang, Zhixia Cui, Zhihua Cai, Xingjuan Chen, Jinjun The Shanxi Key Laboratory of Advanced Control and Equipment intelligence Taiyuan University of Science and Technology Shanxi Taiyuan China The Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Shanxi Taiyuan China The State Key Lab for Novel Software Technology Nanjing University China Department of Computing Technologies Swinburne University of Technology Melbourne Australia

Federated learning (FL), as a safe distributed training mode, provides strong support for the edge intelligence of the Internet of Vehicles (IoV) to realize efficient collaborative control and safe data sharing. However, due to the resource limitation and the instability of training environment in the complex IoV, ideal performance of FL cannot be achieved. Since considering the actual resource constraints and federated task requirements, the diversified device selection criteria make the resource-aware vehicle selection problem become a multi-criteria selection problem. To effectively support FL for IoV, the resource-aware multi-criteria vehicle selection problem was described as a many-objective optimization problem, and proposed a resource-aware many-objective vehicle selection model (RA-MaOVSM) to optimize resource efficiency. The RA-MaOVSM considering heterogeneous resources (like computation resources, communication resources, energy resources and data resources) of on-board devices in IoV, and realizes the joint optimization of learning efficiency, energy cost and global performance. Additionally, a novel probability distribution combination game strategy is applied to many-objective evolutionary algorithm (MaOEA) for improving the model solving performance. Simulation results demonstrate that RA-MaOVSM can effectively optimize the IoV resources and FL model performance, and the designed algorithm exhibits good convergence and distribution, achieving a good balance among multiple device selection criteria. © 2023, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Malicious Family Identify Combining Multi-channel Mapping Feature Image and Fine-Tuned CNN

Malicious Family Identify Combining Multi-channel Mapping Fe...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Chenghua Tang Chen Zhou Min Hu Mengmeng Yang Baohua Qiang Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Strategic Centre for Research in Privacy-Preserving Technologies & Systems Nanyang Technological University Singapore Guangxi Cloud Computing and Big Data Collaborative Innovation Center Guilin University of Electronic Technology Guilin China

Using the features of malicious family to detect malicious code can improve the analysis efficiency and reduce the workload. Aiming at the problems of low efficiency and low accuracy of classifiers in analyzing and detecting malicious code using traditional machine learning, a new malicious family identification method 2MFI-FT is proposed. Firstly, the more comprehensive and essential features of malicious code are obtained by extracting local feature information, assembly instruction set information and visible string information. Secondly, in order to solve the problem of inconsistent images generated by feature sequences, different feature grayscale images are fused into a multi-channel mapping feature image (2MFI) based on the signature matrix. Thirdly, in view of the high cost of collecting and labeling enough data, a classification model with high recognition accuracy and strong generalization ability is realized, based on the fine-tuned Convolutional Neural Network (CNN). The regularization technique is also used to improve the robustness of the model. It is tested on Microsoft Malware Classification Challenge dataset. The experimental results show that the proposed 2MFI method for multi-channel feature image visualization has good identification effect and low time consumption when used as the input of the network model. At the same time, the 2MFI-FT method combined with fine-tuned CNN can accurately identify malicious families, and the accuracy on small training sets and large training sets can reach about 98.25% and 99.68% respectively, which provides a feasible solution for effective identification of malicious families.

关键词： Training Codes Instruction sets Semantics Feature extraction Malware Robustness

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