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2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)最新文献

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On the Convergence of Federated Learning with Stochastic Quantization 随机量化下联邦学习的收敛性
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754534
Wenling Li, Yuhao Li, Junping Du
{"title":"On the Convergence of Federated Learning with Stochastic Quantization","authors":"Wenling Li, Yuhao Li, Junping Du","doi":"10.1109/CCIS53392.2021.9754534","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754534","url":null,"abstract":"This paper studies the distributed federated learning problem when the exchanged information between the server and the workers is quantized. A novel quantized federated averaging algorithm is developed by applying stochastic quantization scheme to the local and global model parameters. Specifically, the server broadcasts the quantized global model parameter to the workers; the workers update local model parameters using their own datasets and upload the quantized version to the server; then the server updates the global model parameter by aggregating all the quantized local model parameters and its previous global model parameter. This algorithm can be interpreted as a quantized variant of the federated averaging algorithm. Extensive experiments using realistic data are provided to show the effectiveness of the proposed algorithm.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129746700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Classification-based Mixture-of-Kriging Assisted Evolutionary Algorithm for Expensive Many-objective Optimization 一种基于分类的kriging混合辅助进化算法用于昂贵的多目标优化
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754528
Ga-Min Kang, Xunfeng Wu, Qiuzhen Lin
{"title":"A Classification-based Mixture-of-Kriging Assisted Evolutionary Algorithm for Expensive Many-objective Optimization","authors":"Ga-Min Kang, Xunfeng Wu, Qiuzhen Lin","doi":"10.1109/CCIS53392.2021.9754528","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754528","url":null,"abstract":"Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used to solve expensive optimization problems (EOPs). However, most studies only focus on solving single or multiobjective EOPs. The study of using SAEAs to solve many-objective EOPs has not received much attention. To fill this research gap, this paper presents a new SAEA by using mixture-of-Kriging as a surrogate to approximate the objective values in many-objecitve EOPs. In this algorithm, a fitness-based classification method is employed for choosing data to train the models. Experimental results demonstrate that the proposed algorithm is very promising in performance comparison with the state-of-the-art SAEAs on a number of benchmark problems.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128635698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Novel Radial Basis Function (RBF) Network for Bayesian Optimization 一种新的径向基函数网络用于贝叶斯优化
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754629
Jianping Luo, Wei Xu, Jiao Chen
{"title":"A Novel Radial Basis Function (RBF) Network for Bayesian Optimization","authors":"Jianping Luo, Wei Xu, Jiao Chen","doi":"10.1109/CCIS53392.2021.9754629","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754629","url":null,"abstract":"Gaussian process (GP) is the most popular surrogate model used in Bayesian optimization for solving computationally expensive problems. However, the computation time for constructing GP may become excessively long when the number of training samples increases. This study investigates multi-task learning with the radial basis function (RBF) network and proposes a multi-task learning network models based on RBF. Moreover, the proposed multi-task-RBF networks are applied to a Bayesian optimization framework and used to replace the GP for avoiding the covariance calculation. Experimental studies under several scenarios indicate that the proposed algorithm is competitive in performance compared with GP- and single-task-based Bayesian optimizations.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124631686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Regularized Hypergraph Recommendation Algorithm with Attention Mechanism 一种具有注意机制的正则化超图推荐算法
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754648
Tingting Zhu, Jianrui Chen, Zhihui Wang, Meixia He
{"title":"A Regularized Hypergraph Recommendation Algorithm with Attention Mechanism","authors":"Tingting Zhu, Jianrui Chen, Zhihui Wang, Meixia He","doi":"10.1109/CCIS53392.2021.9754648","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754648","url":null,"abstract":"Collaborative filtering is one of the most commonly used recommendation technologies in recommender systems. However, it has the problems of sparse score data and low recommendation accuracy. To address these problems, we propose a regularized hypergraph recommendation algorithm with attention mechanism. We can fully mine the high-order relationships in the hyperedges without loss of information. In addition, we apply users’ attributes information to calculate the attraction of an item to the attributes, and then get the similarity between items. In order to calculate the similarity more accurately, the relations of items to attributes are divided into two cases: likes and dislikes. Moreover, according to the different attention of users to different items, we introduce the attention mechanism. Finally, we build and optimize the regularization function to obtain the predictive scores and recommendation lists. Experimental results on Movielens-100K and Movielens-1M demonstrate the effectiveness of our proposed algorithm.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127635051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Prediction of Heart Disease based on Enhanced Random Forest 基于增强随机森林的心脏病预测
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754669
Xianzhen Huang, H. Fan, Hongjin Zhu, Xiangping Zhang
{"title":"Prediction of Heart Disease based on Enhanced Random Forest","authors":"Xianzhen Huang, H. Fan, Hongjin Zhu, Xiangping Zhang","doi":"10.1109/CCIS53392.2021.9754669","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754669","url":null,"abstract":"In recent years, in order to better predict heart disease, researchers have proposed algorithms such as Bayesian network, neural network, random forest, K-Means clustering and so on. For improving the prediction accuracy of the model, this paper optimizes and improves the model through the following two aspects: (1) Synthetic Minority Oversampling Technique (SMOTE) is used to deal with the uneven distribution of data sets and small number of data samples. (2) based on the complexity of samples, the similarity method is used to improve the classification accuracy of random forests. Our analysis has shown that the proposed model based on enhanced random forest has higher accuracy than the traditional method. In the prediction of heart disease, the optimized algorithm improves the accuracy of 5.96% compared with random forest.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127803038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
DRSGN: Dual Revised Semantic Graph Structured Network for Image-Text Matching DRSGN:用于图像-文本匹配的双重修正语义图结构网络
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754625
Xiao Yang, Xiaojun Wu, Tianyang Xu
{"title":"DRSGN: Dual Revised Semantic Graph Structured Network for Image-Text Matching","authors":"Xiao Yang, Xiaojun Wu, Tianyang Xu","doi":"10.1109/CCIS53392.2021.9754625","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754625","url":null,"abstract":"Image-Text matching has made significant contributions to bridge the gap in multi-modal retrieval for advanced pattern recognition systems. The key point of this task is mapping images and text semantics into a common space, where the correlations of similar pairs and dissimilar ones are distinguishable. To concentrate on obtaining semantics, most of existing methods utilize an external object detector to extract compatible region-level visual representations to match the word-level textual clues in captions. In addition to perform the local region matching, several recent studies design a classification task using global information in the final matching layer, but such global supervision signal is difficult to transmitted to the shallow feature descriptor learning layer. To address this issue, in this paper, we propose a novel Dual Revised Semantic Graph Structured Network (DRSGN) to supplement global supervision to the regional semantics adaptively in the shallow layers. In principle, DRSGN integrates regional and global descriptors to formulate an attention supervising mechanism, aiming to simultaneously highlight the regional instances and global scene to obtain complementary visual clues. A dual semantic supervising module is then established to interact between two modalities to extract the genuine matching pairs. Finally, a semantic graph consisting of the obtained multi-modal clues is designed to perform similarity reasoning between the positional relation embedded textual nodes and semantic related visual nodes. The dedicated global signals provide complementary supervision against local regions to support improved matching capacity. The experimental results on Flikr30K and MSCOCO demonstrate the effectiveness of the proposed DRSGN, with improved matching performance against the local region-based approaches","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132674278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Evolution of Cooperation in Signed Networks Under a Cheating Strategy 欺骗策略下签名网络合作的演化
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754647
Jingkuan Zhang, Zhenguo Liu, Ziwen Hong, Lijia Ma, Yanli Yang, Jianqiang Li
{"title":"Evolution of Cooperation in Signed Networks Under a Cheating Strategy","authors":"Jingkuan Zhang, Zhenguo Liu, Ziwen Hong, Lijia Ma, Yanli Yang, Jianqiang Li","doi":"10.1109/CCIS53392.2021.9754647","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754647","url":null,"abstract":"Evolutionary game theory tries to analyze the underlying stochastic and nonlinear decision-making processes of individuals, which provides a comprehensive understanding of the emergence of individual cooperative behaviors. The existing works mainly focus on the evolutionary game of players with undirected relationships, while neglecting conflicting relationships between players. In this paper, we study the evolution of cooperation in signed networks with conflicting relationships. Moreover, we propose a cheating strategy to promote the cooperation of individuals in the evolutionary game. In this strategy, to gain a maximum payoff, individuals will provide reliable payoff information to his friends, whereas they give unreliable payoff information to his opponents. Experiments on both simulated and real-world signed networks show that this cheating strategy can effectively promote the cooperation of players in signed networks.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116787716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Adaptive Asynchronous Transfer Evolutionary Framework Towards Many-Task Optimization 面向多任务优化的自适应异步迁移进化框架
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754633
Baihao Chen, Huiniei Tang, Qiuzhen Lin
{"title":"An Adaptive Asynchronous Transfer Evolutionary Framework Towards Many-Task Optimization","authors":"Baihao Chen, Huiniei Tang, Qiuzhen Lin","doi":"10.1109/CCIS53392.2021.9754633","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754633","url":null,"abstract":"Multi-task optimization (MTO) has emerged as a new growing field and has elicited numerous related studies. However, most existing MTO algorithms are overwhelmed by many-task optimization (MaTO) problems due to the complex inter-task relationships. To overcome this challenge, a novel evolutionary framework towards MaTO namely MaTEA-AAT is proposed in this paper. First, a new transfer paradigm called adaptive asynchronous transfer is used to improve the transfer efficiency. Second, a selection strategy is devised to choose the proper transfer task pair from the plethora of inter-task relationships. Finally, an experiment is designed to compare with four different types of algorithms on the CEC2021 many-task test suite and the results demonstrate the advantage and compatibility of MaTEA-AAT.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116841093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Device-Free Indoor Localization Based on Supervised Dictionary Learning 基于监督字典学习的无设备室内定位
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754635
Kangkang Zhang, Benying Tan, Shuxue Ding
{"title":"Device-Free Indoor Localization Based on Supervised Dictionary Learning","authors":"Kangkang Zhang, Benying Tan, Shuxue Ding","doi":"10.1109/CCIS53392.2021.9754635","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754635","url":null,"abstract":"As a promising intelligent localization technology, device-free localization (DFL) is an area to be developed urgently. We propose a supervised dictionary learning algorithm to model DFL. The supervised dictionary learning algorithm can accurately update the columns in the dictionary and train a linear transformation matrix for target localization. In the regularization item of dictionary learning, we use generalized minimax-concave (GMC) regularization to replace the l0-norm to obtain accurate and tractable solutions. We deploy a sensor network in the laboratory environment to perform localization experiments. In the current experimental environment, our proposed algorithm can achieve 100% localization accuracy. We add Gaussian-distributed noise to all experimental data to test the anti-noise performance of the proposed algorithm. When the signal-to-noise ratio (SNR) is 10dB, our proposed algorithm can still achieve 100% accuracy which outperforms the state-of-the-art algorithms. Moreover, we show the performance improvement of the supervised model to the unsupervised model.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132849991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Research on Multi-task Deep Learning Approach Based on Hybrid Sharing and Network Optimization 基于混合共享和网络优化的多任务深度学习方法研究
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754530
Hui Guo, Jingchun Guo
{"title":"Research on Multi-task Deep Learning Approach Based on Hybrid Sharing and Network Optimization","authors":"Hui Guo, Jingchun Guo","doi":"10.1109/CCIS53392.2021.9754530","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754530","url":null,"abstract":"Multi-task learning which is a branch of deep learning has received extensive attention and in-depth research. However, there are still some difficult problems such as unclear feature sharing, indistinguishable related tasks and overly complex network structure. Therefore, a multi-task learning approach based on hybrid sharing and network optimization is presented. Firstly, training data is fed into the hard parameter sharing network for hybrid training without distinguishing tasks, then the similarity of tasks is measured according to the gradient changes of each task in sharing network layers. Secondly, similar tasks are divided into the same group which is represented by a hard parameter sharing network, while tasks with weak correlation or large differences are divided into different groups which are characterized by soft parameter sharing network. Moreover, it gives a new network training method combining hybrid and alternating, so as to take full advantages of approaches based on the task-level and feature-level. Thirdly, according to the differences of features extracted from the shared layers and the gradient changes in deep layers, the relevant activation value is adjusted and the network is optimized, which not only maintain the conciseness of the network structure, but also help to solve the non-equilibrium problem of data during multi-task learning. Finally, the feasibility and effectiveness of this approach is verified through the applications of MNIST data set and iris and balance data in the UCI data set.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131863837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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