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2019 11th International Conference on Knowledge and Systems Engineering (KSE)最新文献

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KSE 2019 Keynote Abstract KSE 2019主题演讲摘要
2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/kse.2019.8919260
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引用次数: 0
KSE 2019 Author Index KSE 2019作者索引
2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/kse.2019.8919461
{"title":"KSE 2019 Author Index","authors":"","doi":"10.1109/kse.2019.8919461","DOIUrl":"https://doi.org/10.1109/kse.2019.8919461","url":null,"abstract":"","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130030697","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
KSE 2019 Table of Content KSE 2019目录
2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/kse.2019.8919261
{"title":"KSE 2019 Table of Content","authors":"","doi":"10.1109/kse.2019.8919261","DOIUrl":"https://doi.org/10.1109/kse.2019.8919261","url":null,"abstract":"","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130415101","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
Sentence Selective Neural Extractive Summarization with Reinforcement Learning 基于强化学习的句子选择性神经提取摘要
2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919490
Laifu Chen, M. Nguyen
{"title":"Sentence Selective Neural Extractive Summarization with Reinforcement Learning","authors":"Laifu Chen, M. Nguyen","doi":"10.1109/KSE.2019.8919490","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919490","url":null,"abstract":"In this work we employed a common Recurrent Neural Network (RNN) based sequence model for single document summarization, composed of encoder-extractor hierarchical network architecture. We develop a sentence level selective encoding mechanism to select important feature before extracting sentences, and use a novel reinforcement learning based training algorithm to extend the sequence model. Besides, for single document extractive summarization task, most of researchers only pay attention to the main part of document. We analyze and explore the side information such as the headline and image caption in both CNN and Daily Mail news datasets. Empirical experiment results show the effect that our model outperforms the baseline model, and can be comparable with the state-of-the-art extractive systems when automatically evaluated in the ROUGE metric. The statistics analysis of the data set verifies our experiment results.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132642604","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: 14
The vehicle routing problem with time windows: A case study of fresh food distribution center 具有时间窗的车辆路径问题——以生鲜配送中心为例
2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919443
P. Anh, Chau Tuan Cuong, Phan Nguyen Ky Phuc
{"title":"The vehicle routing problem with time windows: A case study of fresh food distribution center","authors":"P. Anh, Chau Tuan Cuong, Phan Nguyen Ky Phuc","doi":"10.1109/KSE.2019.8919443","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919443","url":null,"abstract":"Inefficient routing leading to an increase in cost for outbound is a common problem many distribution centers encounter. This paper introduces an approach to establish a routing system to be cost saving and sustainable, ensuring better utilization of truckload comparing to the current situation as well as strictly following retailer time for receiving orders. Vehicle Routing Problem (VRP) would be reviewed and a mathematical model will be proposed in order to find optimal set of routes with minimum total cost. To satisfy the requirements of orders, VRP with time window (VRPTW) is applied to consider upper time and lower time constraints, and capacity of vehicles constraints. However, this model follows a NP-hard problem, the new approach is introduced divided into 2 stages to reduce size of the model. The approach includes a heuristic-based clustering algorithm which was applied to group of locations into a certain number of clusters, and then VRPTW is used to solve for each cluster to find optimal set of routes.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114339864","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: 4
The Repository of Web Document Summarization using Social Information 基于社会信息的Web文档摘要资源库
2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919378
Minh-Tien Nguyen, Van-Hau Nguyen, Duc-Vu Tran
{"title":"The Repository of Web Document Summarization using Social Information","authors":"Minh-Tien Nguyen, Van-Hau Nguyen, Duc-Vu Tran","doi":"10.1109/KSE.2019.8919378","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919378","url":null,"abstract":"Summarization using social information is a task which extracts summary sentences and relevant user posts of a Web document by integrating its relevant social information. Prior studies introduced several strong models for this task; however, there are gaps from papers to the reproduction of such models. This paper leverages the gaps by investigating summa-rization algorithms to facilitate next studies. The investigation was conducted by implementing traditional and state-of-the-art methods, from unsupervised to supervised learning fashion. We used three datasets in English and Vietnamese to confirm the efficiency of the methods. Experimental results indicate that sophisticated models obtain improvements of ROUGE-scores compared to the basic ones, which do not use social information. However, in some cases, simple methods comparably perform state-of-the-art methods, suggesting that the performance of summarization methods can be still improved.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114686942","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 Speaker-Adaptive HMM-based Vietnamese Text-to-Speech System 基于说话人自适应hmm的越南语文本转语音系统
2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919326
Duy Khanh Ninh
{"title":"A Speaker-Adaptive HMM-based Vietnamese Text-to-Speech System","authors":"Duy Khanh Ninh","doi":"10.1109/KSE.2019.8919326","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919326","url":null,"abstract":"This paper describes the first attempt in developing a Vietnamese HMM-based Text-to-Speech system using the speaker-adaptive approach. Although speaker-dependent systems have been built widely, no speaker-adaptive system has been developed for Vietnamese so far. We collected speech data from several Vietnamese native speakers and employed state-of-the-art speech analysis, model training and speaker adaptation techniques to develop the system. Besides, we performed perceptual experiments to compare the quality of speaker-adapted (SA) voices built on the average voice model and speaker-dependent (SD) voices built on SD models, and to confirm the effects of contextual features including word boundary (WB) and part-of-speech (POS) on the quality of synthetic speech. Evaluation results show that SA voices have significantly higher naturalness than SD voices when the same limited contextual feature set excluding WB and POS was used. In addition, SA voices trained with limited contextual features excluding WB and POS still have better quality than SD voices trained with full contextual features including WB and POS. These results show the robustness of the speaker-adaptive over the speaker-dependent approach for Vietnamese statistical parametric speech synthesis.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116062224","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: 4
Transfer Learning for Predicting Software Faults 基于迁移学习的软件故障预测
2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919351
V. Phan, Khanh Duy Tung Nguyen, L. V. Pham
{"title":"Transfer Learning for Predicting Software Faults","authors":"V. Phan, Khanh Duy Tung Nguyen, L. V. Pham","doi":"10.1109/KSE.2019.8919351","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919351","url":null,"abstract":"This paper investigates a transfer learning application for predicting software faults. Detecting faulty modules in software projects is challenging due to two main issues 1) the low quality of existing handcrafted features leads to the bad performance of traditional learning models and 2) the shortage of annotated data hinders applying deep neural networks. Recently, transfer learning is a good solution to train deep neural networks with insufficient data. Our experiments for tasks of within-project and cross-project software fault prediction have shown the transferable possibility among project data. As a result, the performance of the base model is significantly improved and achieves competitive results with the state of the art method.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114080365","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: 2
Transfer learning for a Vietnamese dialogue system 越南语对话系统的迁移学习
2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919425
Dang Pham, Huy Q. Le, T. Quan
{"title":"Transfer learning for a Vietnamese dialogue system","authors":"Dang Pham, Huy Q. Le, T. Quan","doi":"10.1109/KSE.2019.8919425","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919425","url":null,"abstract":"Training data plays an essential role in the neural network-based dialogue system. Although most of the recent works researching about dialogue system base on a deep neural network show a significant improvement to traditional methods, they required many labeled data for both training and evaluating. In this paper, we present the result of using transfer learning in a Vietnamese dialogue system for the classification task, and we further show benefits of transfer learning on named entity recognition task by experiment results on public VLSP dataset and meets the performance of training from scratch with only 10% of training data.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131519326","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
Message from the KSE’19 General & TPC Chairs KSE第19届一般主席和TPC主席的讲话
2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/kse.2019.8919272
{"title":"Message from the KSE’19 General & TPC Chairs","authors":"","doi":"10.1109/kse.2019.8919272","DOIUrl":"https://doi.org/10.1109/kse.2019.8919272","url":null,"abstract":"","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129155901","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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