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

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Dynamic Graph Neural Network for Fake News Detection 基于动态图神经网络的假新闻检测
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754681
Chenguang Song, Yiyang Teng, Bin Wu
{"title":"Dynamic Graph Neural Network for Fake News Detection","authors":"Chenguang Song, Yiyang Teng, Bin Wu","doi":"10.1109/CCIS53392.2021.9754681","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754681","url":null,"abstract":"The majority of existing propagation-based fake news detection algorithms are overwhelmingly depend on static networks, supposing the entire information propagation graph is readily available before performing fake news detection algorithms. However, real-world information diffusion networks are dynamic as new nodes joining the network and new edges being created. To deal with the problem, we proposed a dynamic propagation graph-based fake news detection method to capture the missing dynamic propagation information in static networks. Specifically, the proposed fake news detection algorithm models each news propagation graph as a series of graph snapshots recorded at discrete time stamps. We evaluate our approach on two bench datasets, and the experimental results demonstrate the effectiveness of the proposed method.","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":"129260707","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: 16
Learn Fine-grained Sharing Network for Multiple Tasks 学习多任务的细粒度共享网络
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754608
Yanbao Ma, Hao Xu, Junzhou He, Kun Qian
{"title":"Learn Fine-grained Sharing Network for Multiple Tasks","authors":"Yanbao Ma, Hao Xu, Junzhou He, Kun Qian","doi":"10.1109/CCIS53392.2021.9754608","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754608","url":null,"abstract":"Conventional Multi-Task Learning (MTL) models, such as hard sharing, adopt handcrafted network architecture, which shares entire layers for all tasks, and thus have two shortcomings: 1) negative transfer phenomenon and 2) low parameter efficiency. This paper proposes a novel neural network model, which allows different tasks to share a network at the parameter level. Specifically, the model defines a subnet for each task by adopting task-specific binary masks. The masks are trainable and can be learned together with network weights using standard back-propagation. Benefit from the fine-grained sharing mechanism, the negative transfer phenomenon can be alleviated, and the parameter efficiency is greatly improved. According to the experiments on a public dataset, our model outperforms the single-task baseline model even when only 0.8% of parameters remained in the subnets. Compared with the multi-task baseline model using fixed masks, our model is much more robust to changes in network sparsity.","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":"126195613","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
Knowledge Transfer-Based Multiple Data Sets Collaborative Analysis for Hyperspectral Band Selection 基于知识转移的高光谱波段选择多数据集协同分析
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754661
Jiao Shi, Xi Zhang, Zeping Zhang, Xiaoyang Li, Deyun Zhou, Yu Lei
{"title":"Knowledge Transfer-Based Multiple Data Sets Collaborative Analysis for Hyperspectral Band Selection","authors":"Jiao Shi, Xi Zhang, Zeping Zhang, Xiaoyang Li, Deyun Zhou, Yu Lei","doi":"10.1109/CCIS53392.2021.9754661","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754661","url":null,"abstract":"The paper presents a knowledge transfer based collaborative analysis method for hyperspectral band selection. This collaborative analysis method establishes different band selection tasks for multiple data sets, cooperatively analyzes the shared spectral spatial structure between hyperspectral data sets, so as to improve the performance of band selection tasks for each data set. The transfer probability is adjusted dynamically to realize spectral knowledge transfer effectively and improve the cooperation ability of collaborative analysis. Experiments indicate that the proposed collaborative analysis method works more efficiently than the comparison methods.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"2015 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":"121565699","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
Federated Learning Method Based on Knowledge Distillation and Deep Gradient Compression 基于知识蒸馏和深度梯度压缩的联邦学习方法
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754651
Haiyan Cui, Junping Du, Yang Jiang, Yue Wang, Runyu Yu
{"title":"Federated Learning Method Based on Knowledge Distillation and Deep Gradient Compression","authors":"Haiyan Cui, Junping Du, Yang Jiang, Yue Wang, Runyu Yu","doi":"10.1109/CCIS53392.2021.9754651","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754651","url":null,"abstract":"Federated learning is a new type of multi-agency collaborative training model paradigm, which is widely used in many fields, among which communication overhead is a key issue. In order to reduce the amount of data transmitted in the communication process, we propose a federated learning algorithm based on knowledge distillation and deep gradient compression (Fed-KDDGC-SGD). First, we use local data on the client to train the teacher network, and then use the soft labels generated by the teacher network to train the student network and upload the gradient to the central server during the training process. In order to further reduce the communication bandwidth occupied by sending the gradient, the deep gradient compression algorithm is used to compress the gradient vector, and only the gradient value of the top R% of the absolute value is sent. The experimental results show that the improved federated learning algorithm effectively reduces the communication overhead and has certain practical significance.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"74 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114035529","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
Android Unit Test Case Generation Based on the Strategy of Multi-Dimensional Coverage 基于多维覆盖策略的Android单元测试用例生成
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754637
Jie Cao, Han Huang, Fangqing Liu
{"title":"Android Unit Test Case Generation Based on the Strategy of Multi-Dimensional Coverage","authors":"Jie Cao, Han Huang, Fangqing Liu","doi":"10.1109/CCIS53392.2021.9754637","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754637","url":null,"abstract":"Although Android apps are becoming more and more popular, apps tend to contain defects which can ultimately manifest as failures (or crashes) to end-users. In order to improve software quality, people have proposed different automated tools for testing Android applications. The automatic generation of test cases is an important means to improve the efficiency of software testing, but many tools related to test case generation do not seem to be fully adapted to the problem of Android test case generation. Therefore, this article aims to design and evaluate the automatic generation method of Android application test cases based on the multi-dimensional coverage. We conducted test experiments on 48 self-developed Android apps through the Gradle plugin, and the final average target coverage reached about 32%. Considering that the current component modules are not yet complete, we believe that tools will reach even higher coverage in the future.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"37 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114052667","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
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization 基于流水线矩阵乘法加速设计和非均匀量化的深度学习推理方案
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-10-10 DOI: 10.1109/CCIS53392.2021.9754668
Yuyang Zhang, Dik Hin Leung, Min Guo, Yijia Xiao, Haoyue Liu, Yunfei Li, Jiyuan Zhang, Guan Wang, Zhen Chen
{"title":"A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization","authors":"Yuyang Zhang, Dik Hin Leung, Min Guo, Yijia Xiao, Haoyue Liu, Yunfei Li, Jiyuan Zhang, Guan Wang, Zhen Chen","doi":"10.1109/CCIS53392.2021.9754668","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754668","url":null,"abstract":"Matrix multiplication is the bedrock in Deep Learning inference application. When it comes to hardware acceleration on edge computing devices, matrix multiplication often takes up a great majority of the time. To achieve better performance in edge computing, we introduce a low-power Multi-layer Perceptron (MLP) accelerator based on a pipelined matrix multiplication scheme and a nonuniform quantization methodology. The implementation is running on Field-programmable Gate Array (FPGA) devices and tested its performance on handwritten digit classification and Q-learning tasks. Results show that our method can achieve better performance with fewer power consumption.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130288508","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
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