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2018 IEEE International Conference on Progress in Informatics and Computing (PIC)最新文献

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An Overview of Geospatial Information Visualization 地理空间信息可视化概述
2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706332
T. Zou, Wenshu Li, P. Liu, Xianchuang Su, Hai Huang, Yang Han, Xiaoying Guo
{"title":"An Overview of Geospatial Information Visualization","authors":"T. Zou, Wenshu Li, P. Liu, Xianchuang Su, Hai Huang, Yang Han, Xiaoying Guo","doi":"10.1109/PIC.2018.8706332","DOIUrl":"https://doi.org/10.1109/PIC.2018.8706332","url":null,"abstract":"The geospatial data nowadays are usually large and complex, which call for innovative and easy-to-understand visualization platforms that can reveal meaningful information. Compared with the traditional visual analysis methods, today’s visualization technologies have been able to deal with large amount of complex data. This paper reviews the technologies and applications of geospatial information visualization. Finally, the problems and challenges of geospatial visualization are expounded, and the possible future directions are discussed.","PeriodicalId":236106,"journal":{"name":"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117307781","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
Emotion Recognition using Sequence Mining 基于序列挖掘的情感识别
2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706312
Lei Wang, Yiwei Song, Jingqiang Chen, Guozi Sun, Huakang Li
{"title":"Emotion Recognition using Sequence Mining","authors":"Lei Wang, Yiwei Song, Jingqiang Chen, Guozi Sun, Huakang Li","doi":"10.1109/PIC.2018.8706312","DOIUrl":"https://doi.org/10.1109/PIC.2018.8706312","url":null,"abstract":"With the development of human-computer interaction technology, user emotion recognition, as an important factor in the process of natural language communication, has become a hot research topic. Current studies mainly analyze the emotional within a single long sentence, but in real communication, the transmission of information and emotion is more often achieved by multi-round dialogue. In this paper, we construct the emotional sequence between people and people in different scenarios to identify their multi-round conversational emotions, and analyze their emotional changes through sequence mining. This article takes several novels as the analysis corpus, and proceeds the scene segmentation according to the chapters of the novel. Then we analyze the emotional bias of each conversation between different people in each scene, and then construct the emotional matrix between people in each scene. Finally, the LSTM algorithm is used to mine different emotional patterns and changing trends between people compared with machine learning algorithm. Experimental results show that the proposed sequential-based emotion recognition method can recognize the emotions between people very well and predict the future emotional patterns.","PeriodicalId":236106,"journal":{"name":"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124796522","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
TaSRD: Task Scheduling Relying on Resource and Dependency in Mobile Edge Computing 基于资源和依赖的移动边缘计算任务调度
2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706333
Yuting Cao, Hao-peng Chen, Jian-wei Jiang, Fei Hu
{"title":"TaSRD: Task Scheduling Relying on Resource and Dependency in Mobile Edge Computing","authors":"Yuting Cao, Hao-peng Chen, Jian-wei Jiang, Fei Hu","doi":"10.1109/PIC.2018.8706333","DOIUrl":"https://doi.org/10.1109/PIC.2018.8706333","url":null,"abstract":"Offloading computation-intensive tasks from mobile to nearby resource-rich surrogates, called edge servers, is proposed recently because traditional mobile cloud computing has a bottleneck of bandwidth and resource limitation for devices. The primary performance concern of offloading is how to maximize energy saving under task delay and task dependency limitation. Besides, edge servers that mobile perceived are changeable and heterogeneous in the process of offloading. In this paper, we formalize this problem, reduce it into knapsack problem and propose a task scheduling scheme, named TaSRD, including independent sub-task scheduling for tasks without dependencies and dependent sub-task scheduling for dependent tasks. We implement TaSRD and evaluate it by case study and simulation on CloudSim framework developed by Melbourne University. We use time model and energy model to measure results and recommend suitable parameters for TaSRD. The experimental results demonstrate that TaSRD can effectively save energy and reduce makespan for mobile while offloading tasks to edge servers.","PeriodicalId":236106,"journal":{"name":"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123319975","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
Context-Aware Correlation Filter for Visual Tracking with Deep Convolution Features 基于深度卷积特征的上下文感知视觉跟踪相关滤波器
2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706135
Leyi Zhang, Huicong Wu, Jie Song
{"title":"Context-Aware Correlation Filter for Visual Tracking with Deep Convolution Features","authors":"Leyi Zhang, Huicong Wu, Jie Song","doi":"10.1109/PIC.2018.8706135","DOIUrl":"https://doi.org/10.1109/PIC.2018.8706135","url":null,"abstract":"Visual object tracking is a challenging problem due to appearance variation of target. Correlation filter (CF) -based trackers have shown competing results for visual object tracking. However, they perform poorly in the case of abrupt motion and heavy background clutter due to less use of contextual information. In this paper, we solve this problem by explicitly incorporating contextual information into a context-aware (CA) framework. Under this framework, deep features from higher convolutional layers encode more semantic information of target which are robust to appearance variations, and features from lower layers locate the target more precise. Compared with handcrafted features, DL-based representation learning require less human interventions and provide much better performance. Extensive experimental results on largescale benchmark datasets show that the proposed algorithm performs favorably against state-of-the-art methods.","PeriodicalId":236106,"journal":{"name":"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114814386","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
Evaluation of Unsupervised Clustering Methods on Hyperspectral Image Data Sets 高光谱图像数据集的无监督聚类方法评价
2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706315
Wei Zhang, Z. Lian, Chanying Huang
{"title":"Evaluation of Unsupervised Clustering Methods on Hyperspectral Image Data Sets","authors":"Wei Zhang, Z. Lian, Chanying Huang","doi":"10.1109/PIC.2018.8706315","DOIUrl":"https://doi.org/10.1109/PIC.2018.8706315","url":null,"abstract":"Classification and clustering of hyper spectral remote sensing images are keys to extract abundant information. Researchers have developed several popular clustering algorithms in the past years, and many learning based methods have been developed nowadays. Conventional clustering algorithms showed good performance with low dimensional data, like RGB images and database records. In hyperspectral image clustering, methods are usually composed of two steps, feature extraction and conventional clustering. This paper attempted to evaluate performances of conventional clustering methods based hyperspectral images without any feature extraction step.","PeriodicalId":236106,"journal":{"name":"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126560311","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
Cross-Domain Recommendation Method in Tourism 旅游业中的跨领域推荐方法
2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706265
Qing Qi, Jian Cao, Yudong Tan, Quan-Wu Xiao
{"title":"Cross-Domain Recommendation Method in Tourism","authors":"Qing Qi, Jian Cao, Yudong Tan, Quan-Wu Xiao","doi":"10.1109/PIC.2018.8706265","DOIUrl":"https://doi.org/10.1109/PIC.2018.8706265","url":null,"abstract":"In recent years, online travel service has become increasingly popular and effective. Its development has become a hotspot of tourism. Considering the particularity of tourism products, this paper aimed at hotel recommendation service. However, the sparsity of tourism data is an unavoidable problem in recommender systems. In order to solve this problem, we introduce the data from air ticket area, and then builds the cross-domain user profile. In this way, hotel recommendation problem turns to become user profile analysis problem. In addition, we proposed a recommendation method based on transformation matrix, on the one hand, it can solve the cold start problem, on the other hand, users with unsatisfactory results based on user profile recommendation method can be improved. Experiments show that the recommendation method based on cross-domain user portrait is much better than the traditional recommendation method based on popularity or price. Finally, we prove that the recommendation method based on transformation matrix can effectively improve the accuracy of the recommendation method based on user portrait.","PeriodicalId":236106,"journal":{"name":"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132257086","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: 5
MuG-QA: Multilingual Grammatical Question Answering for RDF Data RDF数据的多语言语法问答
2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706310
E. Zimina, J. Nummenmaa, K. Jarvelin, J. Peltonen, K. Stefanidis
{"title":"MuG-QA: Multilingual Grammatical Question Answering for RDF Data","authors":"E. Zimina, J. Nummenmaa, K. Jarvelin, J. Peltonen, K. Stefanidis","doi":"10.1109/PIC.2018.8706310","DOIUrl":"https://doi.org/10.1109/PIC.2018.8706310","url":null,"abstract":"We introduce Multilingual Grammatical Question Answering (MuG-QA), a system for answering questions in the English, German, Italian and French languages over DBpedia. The natural language modelling and parsing is implemented using Grammatical Framework (GF), a grammar formalism having natural support for multilinguality. The question analysis is based on forming an abstract conceptual grammar from the questions, and then using linearisation of the abstract grammar into different languages to parse the questions. Once a natural language question is parsed, the resulting abstract grammar tree is matched with the knowledge base schema and contents to formulate a SPARQL query. A particular strength of our approach is that once the abstract grammar has been designed, implementation for a new concrete language is relatively quick, supposing that the language has basic support in the GF Resource Grammar Library. MuG-QA has been tested with data from the QALD-7 benchmark and showed competitive results.","PeriodicalId":236106,"journal":{"name":"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"441 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133557675","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: 3
Dynamic Least-cost Task Scheduling for Enabling Ubiquitous Sensing Service in Edge Computing 边缘计算中实现泛在感知服务的动态最小成本任务调度
2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706314
Jiamei Shen, Hao-peng Chen
{"title":"Dynamic Least-cost Task Scheduling for Enabling Ubiquitous Sensing Service in Edge Computing","authors":"Jiamei Shen, Hao-peng Chen","doi":"10.1109/PIC.2018.8706314","DOIUrl":"https://doi.org/10.1109/PIC.2018.8706314","url":null,"abstract":"Sensor as a Service(SaaS) is introduced by providing ubiquitous sensing services along with prosperity of Internet of Things (IoT) [1]. It comprises billions of mobile devices equipped with sensors which can sense, communicate and compute. With more capabilities, devices could share some computation tasks which are used to be taken by the cloud servers or the edge servers. To achieve the most benefit of energy consumption and delay, we propose Dynamic Least-cost Task Scheduling(DLCTS) mechanism for enabling on-demand ubiquitous sensing service by leveraging edge computing which brings compute and storage resources on or close to devices. Through virtualization of sensors required in a region, we could adapt task assignment by changing mappings between virtual sensors and devices dynamically to the movement of devices and incidents. Simulation results show that the proposed algorithm achieves great cost performance without paying extra expense of quality and decision-making time. And it proves that the scheme is suitable for densely-distributed devices and large-scaled for multiple sensing tasks.","PeriodicalId":236106,"journal":{"name":"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114479207","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 Emotion Recognition Method Based on Selective Gated Recurrent Unit 一种基于选择性门控循环单元的情绪识别方法
2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706140
Qidong Yang, Jian Zhou, Chunling Cheng, Xianwei Wei, Shujie Chu
{"title":"An Emotion Recognition Method Based on Selective Gated Recurrent Unit","authors":"Qidong Yang, Jian Zhou, Chunling Cheng, Xianwei Wei, Shujie Chu","doi":"10.1109/PIC.2018.8706140","DOIUrl":"https://doi.org/10.1109/PIC.2018.8706140","url":null,"abstract":"Electroencephalogram (EEG) signals can intuitively reflect the slight variations in human emotions. Consequently, they are the first choice for emotion recognition media. However, EEG signals at different time steps have different emotion representing abilities. By filtering out EEG signals with low representing abilities, the efficacy of extracted EEG features will increase. Thus emotion recognition accuracy can be improved. Therefore, a new feature extraction method called Selective Gated Recurrent Unit (SGRU) is proposed in this paper. From SGRU, we design a new method for emotion recognition. Firstly, SGRU is constructed to extract features from EEG signals. Secondly, a Fully Connected Neural Network (FCNN) is built to classify emotions with the features obtained by SGRU. Finally, the experiment results on DEAP dataset indicate that the method proposed can achieve better performance on emotion recognition compared with other similar methods.","PeriodicalId":236106,"journal":{"name":"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122099510","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: 5
Registration of Lung CT Images Using B-spline Based Free-Form Deformation 基于b样条自由变形的肺CT图像配准
2018 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2018-12-01 DOI: 10.1109/PIC.2018.8706300
Limei Zhang, Min Li, Wensheng Hou, Panyun Fan
{"title":"Registration of Lung CT Images Using B-spline Based Free-Form Deformation","authors":"Limei Zhang, Min Li, Wensheng Hou, Panyun Fan","doi":"10.1109/PIC.2018.8706300","DOIUrl":"https://doi.org/10.1109/PIC.2018.8706300","url":null,"abstract":"Image registration of lung CT images plays a crucial role in estimating lung motion that is of great significance in image guided radiation therapy. However, the respiratory-induced deformation makes registration of lung CT images rather difficult and challenging. In this study, we present a deformable image registration framework in which the B-spline method is taken for transformation of registration and the compressible flow theory with L2 norm of displacement vectors is used for similarity measure. Results show that our method obtains more accurate registration of lung CT images than the often used SSD, CC and some other state-of-the-art methods.","PeriodicalId":236106,"journal":{"name":"2018 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121240058","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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