Proceedings of the 2021 7th International Conference on Computing and Data Engineering最新文献

Attribute Network Alignment Based on Network Embedding 基于网络嵌入的属性网络对齐

Proceedings of the 2021 7th International Conference on Computing and Data Engineering Pub Date : 2021-01-15 DOI: 10.1145/3456172.3456217

Fan Yang, Wenxin Liang, Linlin Zong

{"title":"Attribute Network Alignment Based on Network Embedding","authors":"Fan Yang, Wenxin Liang, Linlin Zong","doi":"10.1145/3456172.3456217","DOIUrl":"https://doi.org/10.1145/3456172.3456217","url":null,"abstract":"Nodes with similar network structure and attribute features probably distribute across different networks. For instance, people tend to have accounts across various social networks. In recent years, network alignment to identify potential correspondences between nodes across networks has been research focus on social computing. In this paper, we propose an attribute network alignment method ANANE based on network embedding, which uses the network structure and node attributes together. Different from the previous embedding method based only on network structure and the existing iterative process based on structure and attributes, the proposed ANANE integrates heterogeneous network structure and attribute features into a unified embedding for node similarity measurement. We solve both the attribute network embedding and the network alignment simultaneously under a unified framework. In particular, we use neighbor approximation to generate the structure embedding and an auto-coder to obtain the attribute embedding. Then the attention mechanism is used to get the unified embedding for alignment. Empirically, we evaluate our proposed model ANANE over several real-world datasets, and it demonstrates effectiveness compared with several state-of-the-art methods on network alignment tasks.","PeriodicalId":133908,"journal":{"name":"Proceedings of the 2021 7th International Conference on Computing and Data Engineering","volume":"204 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120885358","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

Local stability analysis of mathematical model of Tuberculosis disease in Thailand 泰国结核病数学模型的局部稳定性分析

Proceedings of the 2021 7th International Conference on Computing and Data Engineering Pub Date : 2021-01-15 DOI: 10.1145/3456172.3456206

P. Pongsumpun

引用次数: 0

Energy Efficient Multistandard Decompressor ASIP 节能多标准减压器ASIP

Proceedings of the 2021 7th International Conference on Computing and Data Engineering Pub Date : 2021-01-15 DOI: 10.1145/3456172.3456218

J. Hoozemans, Kati Tervo, P. Jääskeläinen, Z. Al-Ars

{"title":"Energy Efficient Multistandard Decompressor ASIP","authors":"J. Hoozemans, Kati Tervo, P. Jääskeläinen, Z. Al-Ars","doi":"10.1145/3456172.3456218","DOIUrl":"https://doi.org/10.1145/3456172.3456218","url":null,"abstract":"Many applications make extensive use of various forms of compression techniques for storing and communicating data. As decompression is highly regular and repetitive, it is a suitable candidate for acceleration. Examples are offloading (de)compression to a dedicated circuit on a heterogeneous System-on-Chip, or attaching FPGAs or ASICs directly to storage so they can perform these tasks on-the-fly and transparently to the application. ASIC or FPGA implementations will usually result in higher energy-efficiency compared to CPUs. Various ASIC and FPGA accelerators have been developed, but they typically target a single algorithm. However, supporting different compression algorithms could be desirable in many situations. For example, the Apache Parquet file format popular in Big Data analytics supports using different compression standards, even between blocks in a single file. This calls for a more flexible software based co-processor approach. To this end, we propose a compiler-supported Application-Specific Instruction-set Processor (ASIP) design that is able to decompress a range of lossless compression standard without FPGA reconfiguration. We perform a case study of searching a compressed database dump of the entire English Wikipedia.","PeriodicalId":133908,"journal":{"name":"Proceedings of the 2021 7th International Conference on Computing and Data Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129907179","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

Strategies for Heterogeneous Multi-Core Processing Based on Graph Programming 基于图编程的异构多核处理策略

Proceedings of the 2021 7th International Conference on Computing and Data Engineering Pub Date : 2021-01-15 DOI: 10.1145/3456172.3456196

J. Fryer, Paulo Garcia

{"title":"Strategies for Heterogeneous Multi-Core Processing Based on Graph Programming","authors":"J. Fryer, Paulo Garcia","doi":"10.1145/3456172.3456196","DOIUrl":"https://doi.org/10.1145/3456172.3456196","url":null,"abstract":"In this paper, we explore strategies for automated parallelization and reconfiguration across heterogeneous multi-core processor, based on a programming paradigm and an associated model of computation designed for efficient and automated parallelization across processing elements, efficient reconfiguration (i.e., mapping of computational tasks across processing elements), and combining synchronous and asynchronous I/O handling within the same conceptual programming model. We introduce an analytical model of parallelization, unlocked by graph programming, that can effectively reason about power and performance tradeoffs in heterogeneous multi-core, and inform reconfiguration strategies. We analyze the implications of our model through an analysis of reconfiguration scenarios given a program’s characteristics; our analysis quantifies the benefits of reconfiguring software for higher levels of parallelism, given an amount of data left to process. We empirically validate the performance advantage of our automatic parallelism capabilities through Horde, an open source graph programming interpreter; in our experiments, automatic parallelization from one to four cores improves average case execution time by a factor of 2 and worst case execution time by a factor of 3. When reconfiguring across heterogeneous processors, our model can predict execution time with an average error of 9.45%.","PeriodicalId":133908,"journal":{"name":"Proceedings of the 2021 7th International Conference on Computing and Data Engineering","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124787188","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

Monitoring and Controlling Electrical Appliances through Rule Engine in the Smart Office 利用规则引擎对智能办公室的电器进行监控

Proceedings of the 2021 7th International Conference on Computing and Data Engineering Pub Date : 2021-01-15 DOI: 10.1145/3456172.3456219

K. Namee, Rudsada Kaewsaeng-On, J. Polpinij, G. Albadrani

{"title":"Monitoring and Controlling Electrical Appliances through Rule Engine in the Smart Office","authors":"K. Namee, Rudsada Kaewsaeng-On, J. Polpinij, G. Albadrani","doi":"10.1145/3456172.3456219","DOIUrl":"https://doi.org/10.1145/3456172.3456219","url":null,"abstract":"In recent years, there has been an increasing interest in Internet of Things (IoT). Rule Engine is a developer tool to connect hardware devices to APIs (Application Programming Interface), a flow-based programming development with a UI for developers to use through a Web Browser. Node-RED runs on Node.js, making it ideal for use with Raspberry Pi as it consumes less resources. The file size is not large, and Node.js also acts as an intermediary for the Raspberry Pi to communicate with the web browser and other devices. This research is a development of the rule engine to monitor and operate office equipment. It was also shown that electrical appliances can be more easily controlled and operated by sensors. It can control the office environment in the desired conditions to become a smart office perfectly such as air conditioner, room temperature, room climate which includes the moisture value Carbon dioxide, TVOC, PM 2.5, and carbon monoxide. The values are stored in the Cloud and Edge Computing, and the results are very satisfactory. The results of this study indicate that the Rule Engine is the medium that connects sensors and office appliances, and effective. This finding has important implications for developing the smart office.","PeriodicalId":133908,"journal":{"name":"Proceedings of the 2021 7th International Conference on Computing and Data Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129201162","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

Facebook Customer Engagement Graph Analysis Using K-core, M-core and Km-core Methods 使用K-core, M-core和Km-core方法分析Facebook用户粘性图表

Proceedings of the 2021 7th International Conference on Computing and Data Engineering Pub Date : 2021-01-15 DOI: 10.1145/3456172.3456215

Thidawan Klaysri

引用次数: 0

Dynamical model of Asthma from effect of smog in upper northern Thailand 泰国北部上部雾霾影响下哮喘的动力学模型

Proceedings of the 2021 7th International Conference on Computing and Data Engineering Pub Date : 2021-01-15 DOI: 10.1145/3456172.3456207

P. Pongsumpun

引用次数: 0

Graph-AutoFS: Auto Feature Selection in Graph Neural Graph- autofs:图神经网络中的自动特征选择

Proceedings of the 2021 7th International Conference on Computing and Data Engineering Pub Date : 2021-01-15 DOI: 10.1145/3456172.3456191

Siyu Xiong, Rengyang Liu, Chao Yi

{"title":"Graph-AutoFS: Auto Feature Selection in Graph Neural","authors":"Siyu Xiong, Rengyang Liu, Chao Yi","doi":"10.1145/3456172.3456191","DOIUrl":"https://doi.org/10.1145/3456172.3456191","url":null,"abstract":"Graph embedding is an effective method to represent graph data in low-dimensional space for graph analysis. Based on the inspiration of graph convolutional network (GCN), researchers have made significant progress by learning the vector representation of graph topology and node features in this task. However, it is challenging to select the feature of nodes in the real-world data. Traditional feature selection method like one-hot encoding or description of the node brings large memory and computation cost. Even worse, useless features may introduce noise and complicate the training process. In this paper, we propose a two-stage algorithm for graph automatic feature selection (Graph-AutoFS) to improve existing models. Graph-AutoFS can automatically select important features as training inputs, and the computational cost is precisely equal to the convergence of the training target model. We did not introduce discrete candidate feature sets in the search stage but relax the choices to be continuous by introducing the architecture parameters. By implementing a regularized optimizer on the architecture parameters, the model can automatically identify and delete redundant features during the model's training process. In the re-train phase, we keep the architecture parameters serving as an attention unit to boost the performance. We use three public benchmark data sets and two popular graph embedding methods to conduct experiments to verify the performance of Graph-AutoFS in node clustering and link prediction tasks. Experimental results show that Graph-AutoFS consistently outperforms original graph embedding methods considerably on these tasks.","PeriodicalId":133908,"journal":{"name":"Proceedings of the 2021 7th International Conference on Computing and Data Engineering","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114629243","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 Improved Random Walk Algorithm for Resource Scheduling in Cloud Datacenter 一种改进的随机漫步算法用于云数据中心的资源调度

Proceedings of the 2021 7th International Conference on Computing and Data Engineering Pub Date : 2021-01-15 DOI: 10.1145/3456172.3456212

Mingjie Sun, Xiaoyong Li, Yali Gao, Jie Yuan, Wenping Kong, Hai-feng Chang

{"title":"An Improved Random Walk Algorithm for Resource Scheduling in Cloud Datacenter","authors":"Mingjie Sun, Xiaoyong Li, Yali Gao, Jie Yuan, Wenping Kong, Hai-feng Chang","doi":"10.1145/3456172.3456212","DOIUrl":"https://doi.org/10.1145/3456172.3456212","url":null,"abstract":"Resource scheduling plays a crucial role in improving resource utilization rate and user service quality of cloud datacenter. An efficient resource scheduling algorithm enables the datacenter to achieve load balancing, becoming the core of enterprise development. However, at present, the scheduling algorithm of cloud datacenter is usually lack of dynamics, and the calculation is relatively complex. When searching for the optimal scheme, it is easy to fall into the local optimal value, resulting in a large amount of calculation, high energy consumption, low QoS (Quality of Service) and low resource utilization. In this paper, we focus on the prevalent problems of lacking of dynamics, the high makespan and energy consumption in cloud datacenter and design a dynamic load balancing schedule framework. In this framework, we propose an improved random walk algorithm which searches the global optimal scheme with simpler computing. We compare our proposed improved random walk algorithm with Round Rabin algorithm and Particle Swarm Optimization (PSO) algorithm. The experimental results prove that our proposed algorithm improves the utilization rate of resources. Particularly, the makespan of our proposed random walk algorithm is 7% lower than PSO's and the overall energy consumption of ours algorithm is about 15% lower than PSO's.","PeriodicalId":133908,"journal":{"name":"Proceedings of the 2021 7th International Conference on Computing and Data Engineering","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130867779","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

Experiment and Performance Analysis for Streaming Data on Multicore Platform 多核平台上流数据的实验与性能分析

Proceedings of the 2021 7th International Conference on Computing and Data Engineering Pub Date : 2021-01-15 DOI: 10.1145/3456172.3456220

D. Ren, Hao Liu

引用次数: 0