2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE)最新文献

Isolate-Set-Based In-Memory Parallel Subgraph Matching Framework 基于隔离集的内存并行子图匹配框架

2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2022-10-21 DOI: 10.1145/3569966.3570004

Hang Qie, Y. Dou

{"title":"Isolate-Set-Based In-Memory Parallel Subgraph Matching Framework","authors":"Hang Qie, Y. Dou","doi":"10.1145/3569966.3570004","DOIUrl":"https://doi.org/10.1145/3569966.3570004","url":null,"abstract":"Subgraph matching is a classical graph task, which finds all subgraphs that are isomorphic to the query graph in a labeled data graph. According to the theoretical basis, there are three types of subgraph matching methods: exploration-based methods, state-space representation methods, and multi-way join methods. Each of these methods has its advantages, but their common feature is a large amount of computation. In order to improve the computation efficiency, some representative subgraph matching methods have their own parallel optimization methods, but these parallel optimization methods are not universal. To overcome the drawbacks and improve the efficiency of parallel computing, we propose an isolate-set-based parallel subgraph matching framework. The parallel framework can flexibly adapt different subgraph matching methods without changing the sequential methods themselves. We partition the search tree into several decoupled subtrees and prune the subtrees to reduce computation overheads. And then, parallel subgraph matching is implemented on the decoupled pruned subtrees. To demonstrate the proposed method’s effectiveness empirically, we parallelize several representative subgraph matching methods in our framework and compare their performance with the sequential methods. The results show that our parallel framework is adapted to representative sequential methods, outperforms the sequential methods by a large margin, and achieves a speedup of 10x-50x on a 64-core x86 machine.","PeriodicalId":6460,"journal":{"name":"2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87549640","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 Fast Attitude Estimation Method Using Homography Matrix 一种基于单应矩阵的快速姿态估计方法

2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2020-01-01 DOI: 10.1145/3403746.3403922

YongYong Duan, Lei Zhao, Zongling Wu, Yujie Yang

引用次数: 0

Model-based engineering for microservice architectures using Enterprise Integration Patterns for inter-service communication 微服务架构的基于模型的工程，使用企业集成模式进行服务间通信

2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2017-07-12 DOI: 10.1109/JCSSE.2017.8025912

R. Petrasch

引用次数: 24

Design and development of lane detection based on FPGA 基于FPGA的车道检测系统的设计与开发

2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2017-07-12 DOI: 10.1109/JCSSE.2017.8025909

Piyatap Promrit, W. Suntiamorntut

引用次数: 5

Parallelized FPA-SVM: Parallelized parameter selection and classification using Flower Pollination Algorithm and Support Vector Machine 并行化FPA-SVM:基于授粉算法和支持向量机的并行化参数选择和分类

2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2017-07-12 DOI: 10.1109/JCSSE.2017.8025899

Jean-Charles Coetsier, Rachsuda Jiamthapthaksin

引用次数: 3

OSV: OSPF vulnerability checking tool OSV: OSPF漏洞检测工具

2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2017-07-12 DOI: 10.1109/JCSSE.2017.8025919

Poonyavee Kasemsuwan, V. Visoottiviseth

引用次数: 2

ARSA-the pervasive Rescuer Supporting System for the Pre-hospital Emergency Medical Service arsa——院前紧急医疗服务的普遍救援支持系统

2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2017-07-12 DOI: 10.1109/JCSSE.2017.8025920

Manachai Toahchoodee

引用次数: 3

Enemy evaluation AI for 2D action-platform game 2D动作平台游戏的敌人评估AI

2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2017-07-12 DOI: 10.1109/JCSSE.2017.8025906

Pichit Promsutipong, Vishnu Kotrajaras

引用次数: 1

Northern Thai Dialect Text to Speech 泰国北部方言从文本到语音

2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2017-07-12 DOI: 10.1109/JCSSE.2017.8025905

Pannakorn Chao-angthong, A. Suchato, P. Punyabukkana

{"title":"Northern Thai Dialect Text to Speech","authors":"Pannakorn Chao-angthong, A. Suchato, P. Punyabukkana","doi":"10.1109/JCSSE.2017.8025905","DOIUrl":"https://doi.org/10.1109/JCSSE.2017.8025905","url":null,"abstract":"Each of the dialects of Thai Language has a distinct identity associated with its accents. The conversation between different native speakers of these dialects despite their standard language origination cannot be avoided when visiting each region. Communication with people who understand only the Northern Thai Dialect (NTD) brought us to the idea of inventing the Northern Thai Dialect Text to Speech (NTD-TTS). This idea derives from the same concept as a translating program; after getting text input in the Center Thai Dialect (CTD), the TTS system will translate and synthesize speech output in NTD. TTS used a software structure and modified two components: Grapheme to Phoneme (G2P) and Speech models. The NTD-G2P conversion was created by using rule-based and dictionary-based approaches. It was evaluated by 100 randomly selected sentences from ORCHID. The NTD-G2P reports a conversion accuracy of 83.19% on the syllable level and it is used for implementing the NTD-corpus. The sentence selections were presented to train the NTD speech model. The selection chosen covers 95.32% in the first percentile of phoneme distribution in the NTD-corpus. After connecting the speech models to the TTS system, the whole system was evaluated with Mean Opinion Score (MOS) and the comprehension on the syllable level by the native speakers. The NTD-MOS evaluations indicated that the accent, naturalness, and intelligibility of synthetic speech ranged from “acceptable” to “good”. The test set of the NTD-TTS system earned a good MOS and high comprehension percentage from the NTD native listeners. The results are 3.73 in the accent, 3.68 in the naturalness, 3.63 in the intelligibility, and the comprehension percentage is 97.16%.","PeriodicalId":6460,"journal":{"name":"2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79641865","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

IOT for smart farm: A case study of the Lingzhi mushroom farm at Maejo University 物联网应用于智慧农场:以前城大学灵芝蘑菇农场为例

2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2017-07-12 DOI: 10.1109/JCSSE.2017.8025904

O. Chieochan, Anukit Saokaew, E. Boonchieng

{"title":"IOT for smart farm: A case study of the Lingzhi mushroom farm at Maejo University","authors":"O. Chieochan, Anukit Saokaew, E. Boonchieng","doi":"10.1109/JCSSE.2017.8025904","DOIUrl":"https://doi.org/10.1109/JCSSE.2017.8025904","url":null,"abstract":"This research aims to prototype a smart Lingzhi mushroom farm. This research applied the use of IOT with a sensor to measure and monitors the humidity in the Lingzhi mushroom farm. The humidity data processed through NETPIE was developed and provided by NECTEC as a free service for IOT. Humidity data was stored into a NET FEED (a sub service from NETPIE) and displayed on mobile devices and computers through NET FREEBOARD (another sub service of NETPIE). This research also controlled sprinkler and fog pumps automatically and the functional status (switching on and off for periods of time) pushes notifications through LINE API on the LINE Application. The equipment and tools used in this research were NodeMCU, humidity sensor, RTC (real time clock), relay module, sprinkler and fog pumps. C++ and Node.JS were used as programming. The services and protocol used were NETPIE (Network Platform for internet of everything) with subservices such as NETPIE FEED, NETPIE FREEBOARD, and NETPIE REST API. The results of the research showed that using IOT with the sensor enhanced the prototype of smart farming.","PeriodicalId":6460,"journal":{"name":"2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"182 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76045312","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: 50