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2017 International Conference on Data Mining, Communications and Information Technology, DMCIT 2017

作者： Yang, Kehua Liu, Youfang Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronics Hunan University Changsha China

ISBN: (纸本)9781450352185

Dempster-Shafer theory is a useful method in data fusion. To avoid counter-intuitive result of classical Dempster-Shafer's combination rule dealing with highly conflict evidences, many improved combination methods have been proposed through modifying the classical Dempster-Shafer's rule or original evidence by using a certain measure of the degree of conflict or uncertain data. However, usually the frame of discernment is incomplete, the current methods have limitation in handling this *** order to solve this problem, an improved combination method for conflicting evidence in open frame of discernment is proposed. In this paper, according to information theory the information entropy of each evidence is calculated and assigned to the unknown proposition which is added into the frame of discernment. Then reliability degree of evidences obtained by the Jousselme distance is used to revise source evidences. Finally, Dempster-Shafer's rule is applied to combine the mean of revised evidences several times. One numerical example is employed and in comparison with the existing alternatives, the proposed approach can effectively solve the conflicting problem and the incompleteness of frame. © 2017 Association for computing Machinery.

关键词： Information theory

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An Evidence Combination Method based on DBSCAN Clustering

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Computers, Materials & Continua 2018年第11期57卷 269-281页

作者： Kehua Yang Tian Tan Wei Zhang College of Computer Science and Electronic Engineering and Key Laboratory for Embedded and Network Computing of Hunan ProvinceHunan UniversityChangshaHunan410082China Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State UniversityVirginiaBlacksburg24060USA

Dempster-Shafer(D-S)evidence theory is a key technology for integrating uncertain information from multiple ***,the combination rules can be paradoxical when the evidence seriously conflict with each *** the paper,we propose a novel combination algorithm based on unsupervised Density-Based Spatial Clustering of Applications with Noise(DBSCAN)density *** the proposed mechanism,firstly,the original evidence sets are preprocessed by DBSCAN density clustering,and a successfully focal element similarity criteria is used to mine the potential information between the evidence,and make a correct measure of the conflict ***,two different discount factors are adopted to revise the original evidence sets,based on the result of DBSCAN density ***,we conduct the information fusion for the revised evidence sets by D-S combination *** results show that the proposed method can effectively solve the synthesis problem of high-conflict evidence,with better accuracy,stability and convergence speed.

关键词： D-S evidence theory information fusion DBSCAN combination rules

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Energy optimization on dynamic multiple functions automotive cyber-physical systems 21

Energy optimization on dynamic multiple functions automotive...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Liu, Yan Xie, Guoqi Jin, Linlin Li, Renfa Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha Hunan China

ISBN: (纸本)9781728120584

A new generation of automotive electronic systems is a typical cyber-physical systems (CPS) based on an integrated architecture, in which multiple functions can be executed on one electronic control unit (ECU) and one function can be assigned to different ECU. Heterogeneity and dynamics are essential characteristics of automotive CPS (ACPS). Considering heterogeneity and dynamics of ACPS, optimizing functional safety and energy consumption in system design is still a challenge. In this paper, we focus on the collaborative optimization of functional safety and energy consumption with global method in the context of prioritizing functional safety. Considering the requirements of functional safety, we propose a deadline driven processor merging for multiple dynamic functions (DPMMDF) algorithm to maximizes the number of functions that meet deadlines when a function arrives dynamically from the perspective of global optimization. Further, we use a global energy saving for multiple dynamic functions (GESMDF) algorithm, combined with the integrated architecture features, to optimize the energy consumption of ACPS by selecting multiple candidate executable ECUs for a task. Experimental results show that combination of the proposed two algorithms can effectively reduce deadline missed ratio (DMR), and minimize energy consumption compared with existing methods. © 2019 IEEE.

关键词： Global optimization

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Diverse Image Colorizaiton via Deep Color Prior and Triplet Latent Regularization

Diverse Image Colorizaiton via Deep Color Prior and Triplet ...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Jiang, Bin Dai, Jiawu Zeng, Weiyuan Wang, Renjun College of Computer Science and Electronic Engineering Hunan University China Key Laboratory for Embedded and Network Computing of Hunan Province China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Automatic image colorization has made tremendous progress in recent years. However, previous methods rarely consider the inherent diversity of natural images, and existing diverse colorization approaches still suffer from the following two limitations. i) The diversity of generated images is limited. ii) Some inevitable artifacts substantially degrade the quality of colorization results. To cope with such problems, we propose a novel diverse image colorization network based on vanilla GAN, which extracts the deep color priors through an initial colorization network first, and then utilizes these priors to modulate the diverse generation to achieve high-fidelity colorization outputs. In addition, a novel triplet latent regularization is proposed to constrain the correspondence between latent codes and generated images, which efficiently alleviates mode collapse and encourages more diverse colorization results. Extensive experiments on three benchmark datasets demonstrate the superiority of our method over existing diverse image colorization models. © 2022 IEEE.

关键词： Generative adversarial networks

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Locally differentially private high-dimensional data synthesis

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Science China(Information Sciences) 2023年第1期66卷 25-42页

作者： Xue CHEN Cheng WANG Qing YANG Teng HU Changjun JIANG Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University

In local differential privacy(LDP), a challenging problem is the ability to generate highdimensional data while efficiently capturing the correlation between attributes in a dataset. Existing solutions for low-dimensional data synthesis, which partition the privacy budget among all attributes, cease to be effective in high-dimensional scenarios due to the large-scale noise and communication cost caused by the high dimension. In fact, the high-dimensional characteristics not only bring challenges but also make it possible to apply some technologies to break this bottleneck. This paper presents Sam Priv Syn for high-dimensional data synthesis under LDP, which is composed of a marginal sampling module and a data generation *** marginal sampling module is used to sample from the original data to obtain two-way marginals. The sampling process is based on mutual information, which is updated iteratively to retain, as much as possible,the correlation between attributes. The data generation module is used to reconstruct the synthetic dataset from the sampled two-way marginals. Furthermore, this study conducted comparison experiments on the real-world datasets to demonstrate the effectiveness and efficiency of the proposed method, with results proving that Sam PrivSyn can not only protect privacy but also retain the correlation information between the attributes.

关键词： local differential privacy high-dimensional data synthesis sampling data privacy privacy-preserving protocols

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A Novel Neighborhood-Weighted Sampling Method for Imbalanced Datasets

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Chinese Journal of Electronics 2022年第5期31卷 969-979页

作者： GUANG Mingjian YAN Chungang LIU Guanjun WANG Junli JIANG Changjun Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University

The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However,they usually ignore the positional relationship between a sample and the heterogeneous samples in its neighborhood when calculating sample weight. This paper proposes a novel neighborhood-weighted based sampling method named NWBBagging to improve the Bagging algorithm's performance on imbalanced datasets. It considers the positional relationship between the center sample and the heterogeneous samples in its neighborhood when identifying critical samples. And a parameter reduction method is proposed and combined into the ensemble learning framework, which reduces the parameters and increases the classifier's diversity. We compare NWBBagging with some state-of-the-art ensemble learning algorithms on 34 imbalanced datasets, and the result shows that NWBBagging achieves better performance.

关键词： parameter reduction method neighborhood-weighted sampling method pattern classification class imbalance problem NWBBagging k-nearest neighbors sampling methods imbalanced datasets nearest neighbour methods ensemble learning algorithm Bagging algorithm

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A Sentiment Classification Model Based on Multiple Multi-classifier Systems 1

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5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Yang, Kehua Liao, Chenglin Zhang, Wei College of Computer Science and Electronic Engineering Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University ChangshaHunan410082 China

ISBN: (数字)9783030242657

ISBN: (纸本)9783030242640

With the fast development of the Internet, especially the development of the Web technique, more and more people express their views on the Internet. The content of these views involve various aspects of our life, such as books, movies, musics, policies, commodities, technical services, etc. Since these views are usually subjective and possess great potential commercial and social value, the study of sentiment analysis is followed. At the early stage of sentiment analysis, researchers used lexicon based method to solve sentiment analysis problem. With the improvement in machine learning and Natural Language Processing technology, machine learning based method are becoming more and more popular in the field of sentiment analysis. In this study, we propose an ensemble sentiment classification model based on three multi-classifier systems. The proposed model uses majority voting as an ensemble method of multiple multi-classifier systems. The effectiveness of the classification model is verified on five public datasets. Experimental results show that the proposed model is superior to traditional sentiment classification methods and simple multi-classifier systems in the performance of classification accuracy. By using the sentiment classification model proposed in this paper, the accuracy improvement in different domains also show that our proposed model has certain generalization ability. © 2019, Springer Nature Switzerland AG.

关键词： Sentiment analysis

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Object-oriented program tailoring based on model 4

Object-oriented program tailoring based on model

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4th International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation, CDMMS2014

作者： Jin, Qing Kai Li, Jun Yi Zhang, Yan College of Information Science and Engineering Hunan University ChangshaHunan China Key Laboratory for Embedded and Network Computing of Hunan Province ChangshaHunan China

ISBN: (纸本)9783038351528

For the redundant codes caused by software reuse and rapid development in object-oriented program, the paper proposes a cutting model and designs a cutting scheme based on this model. Firstly, a cutting model is established. Then, static analysis is made for object-oriented codes and the cutting information base of objects to be clipped is established. Lastly, the cutting information base and cutting model is matched to cut the program. A test on a practical java project indicates that the cutting scheme can make the volume of the project reduce by about 21%. © (2014) Trans Tech Publications, Switzerland.

关键词： Static analysis

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Optimizing Word Embedding for Fine-Grained Sentiment Analysis 1

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5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Zhang, Wei Zhang, Yue Yang, Kehua Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (数字)9783030242657

ISBN: (纸本)9783030242640

Word embeddings have been extensively used for various Natural Language Processing tasks. However, word vectors trained based on corpus context information fail to distinguish words with the same context but different semantics, which may lead to a similar word vector with opposite semantic terms. This will affect some Natural Language Processing tasks, such as fine-grained sentiment analysis tasks. In this paper, a new word vectors optimization model is proposed. This model can be applied to any pre-trained word vectors. Within a certain range, it can make the opposite semantic words away from each other, and the same semantic words are close to each other. The experimental results show that our model can improve the traditional word embedding in the fine-grained emotional analysis task of Chinese Weibo, and the optimized word vector using our model outperforms the unoptimized word vector. © 2019, Springer Nature Switzerland AG.

关键词： Embeddings

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A novel graph kernel algorithm for improving the effect of text classification

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Computer Speech & Language 2026年 95卷

作者： Fan Yang Tan Zhu Jing Huang Zhilin Huang Guoqi Xie College of Computer and Information Engineering Central South University of Forestry and Technology Changsha Hunan 41004 PR China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan 411201 PR China Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha Hunan 410082 PR China

Text classification is an important topic in natural language processing. In recent years, both graph kernel methods and deep learning methods have been widely employed in text classification tasks. However, previous graph kernel algorithms focused too much on the graph structure itself, such as the shortest path subgraph,while focusing limited attention to the information of the text itself. Previous deep learning methods have often resulted in substantial utilization of computational resources. Therefore,we propose a new graph kernel algorithm to address the disadvantages. First,we extract the textual information of the document using the term weighting scheme. Second,we collect the structural information on the document graph. Third, graph kernel is used for similarity measurement for text classification. We compared eight baseline methods on three experimental datasets, including traditional deep learning methods and graph-based classification methods, and tested our algorithm on multiple indicators. The experimental results demonstrate that our algorithm outperforms other baseline methods in terms of accuracy. Furthermore, it achieves a minimum reduction of 69% in memory consumption and a minimum decrease of 23% in runtime. Furthermore, as we decrease the percentage of training data, our algorithm continues to achieve superior results compared to other deep learning methods. The excellent experimental results show that our algorithm can improve the efficiency of text classification tasks and reduce the occupation of computer resources under the premise of ensuring high accuracy.

关键词： Document similarity measure Graph kernel Machine learning Term weighting Text classification

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