2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)最新文献_第6页

A support system for remote brainstorming sessions 远程头脑风暴会议的支持系统

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI: 10.1109/ICCI-CC.2015.7259402

Yuki Kaeri, K. Sugawara, Y. Manabe, C. Moulin

引用次数: 0

Automated Knowledge Appreciation: A relevant reasoning approach to expand our knowledge and increase its value automatically 自动化知识增值:一种相关的推理方法，可以自动扩展我们的知识并增加其价值

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI: 10.1109/ICCI-CC.2015.7259383

Jingde Cheng

引用次数: 0

Optimal performance for communication networked systems with QoS constraints 具有QoS约束的通信网络系统的最优性能

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI: 10.1109/ICCI-CC.2015.7259374

Xin-Xiang Sun, Xisheng Zhan, Jie Wu, Tao Jiang

引用次数: 0

Cloudets: Cloud-based cognition for large streaming data Cloudets:基于云的大型流数据认知

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI: 10.1109/ICCI-CC.2015.7259407

G. Baciu, Chenhui Li, Yunzhe Wang, Xiujun Zhang

{"title":"Cloudets: Cloud-based cognition for large streaming data","authors":"G. Baciu, Chenhui Li, Yunzhe Wang, Xiujun Zhang","doi":"10.1109/ICCI-CC.2015.7259407","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2015.7259407","url":null,"abstract":"Big data cognition has become a dominant problem in interactive visual analytics for event detection and response, metereology, cosmology, and large smart city applications including traffic monitoring and management, search and rescue operations, crowd management and logistics. The main problems are mainly due to big data volume and velocity and, in some cases, variety in both dimension and type. A practical approach to understanding and viewing big data features is through streaming operations. Streaming allows for both volume and velocity characteristics of big data, and often, for variety as well. However, performing analytics at interactive rates is currently an open challenge in most big data applications. Cloud computing platforms provide practical support and leverage to solving some of the big data and visual analytics problems, especially when dealing with the volume and velocity characteristics of current data generation. In order to interact with streaming data patterns in an elastic cloud environment, we present a new elastic framework for big data visual analytics in the cloud, the Cloudet. The Cloudet is a self-adaptive cloud-based platform that treats both data and compute nodes as elastic objects. The main objective is to readily achieve the scalability and elasticity of cloud computing platforms in order to process large streaming data and adapt to potential interactions between data stream features. Our main contributions include a robust cloud-based framework, the Cloudet, which can flexibly process the streaming data and applications to illustrate the setup and operations of this framework. The framework includes a cloud profile manager that attempts to optimize the cloudet parameters in order to achieve expressivity, scalability, reliability, and the proper aggregation of the data streams into several density maps for the purpose of dynamic visualization of data features.","PeriodicalId":328695,"journal":{"name":"2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132614373","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

A new computer for cognitive computing 一种用于认知计算的新型计算机

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI: 10.1109/ICCI-CC.2015.7259363

J. Frost, M. W. Numan, M. Liebelt, B. Phillips

引用次数: 7

A formal model inspired on human decision-making process 一个受人类决策过程启发的正式模型

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI: 10.1109/ICCI-CC.2015.7259413

José-Antonio Cervantes, Félix F. Ramos, Yingxu Wang

引用次数: 1

MapReduce based content searching of surveillance system videos 基于MapReduce的监控系统视频内容搜索

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-01 DOI: 10.1109/ICCI-CC.2015.7259393

Zheng Xu, Haiyan Chen

引用次数: 1

How the bridging inference links unordered sentences for semantic coherence 桥接推理如何连接无序句子以达到语义连贯

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-01 DOI: 10.1109/ICCI-CC.2015.7259392

Weidong Liu, Xiangfeng Luo, Jun Shu

{"title":"How the bridging inference links unordered sentences for semantic coherence","authors":"Weidong Liu, Xiangfeng Luo, Jun Shu","doi":"10.1109/ICCI-CC.2015.7259392","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2015.7259392","url":null,"abstract":"With social media becomes increasing popular, volumes of short texts appear in Web, such as Tweets and Micro-blogs. Since these short texts have vast decentralized topics, weak associate relations, large redundancy and abundant noise, how to link this large scale of unordered short text with semantic coherence is a challenge problem. The challenging issues includes: how to represent and measure the semantic coherence state of sentences; how to guild the linking process of short text for semantic coherence via different schemas. To solve the above issues, bridging inference is developed, which simulates the discourse process to narrow semantic gaps between short texts. Bridging inference links unordered short texts by coherence detection and bridging inference schemas. We evaluate our method by measuring semantic coherence in bridging inference process. Experimental results show that bridging inference increases the semantic coherence of unordered short text. The proposed method can be used in short-text origination, e-learning, e-science, web semantic search, and online question-answering system in future works, etc.","PeriodicalId":328695,"journal":{"name":"2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126909678","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

Predicting citation counts of papers 预测论文的引用次数

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-01 DOI: 10.1109/ICCI-CC.2015.7259421

Junpeng Chen, Chunxia Zhang

{"title":"Predicting citation counts of papers","authors":"Junpeng Chen, Chunxia Zhang","doi":"10.1109/ICCI-CC.2015.7259421","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2015.7259421","url":null,"abstract":"The task of citation counts prediction is to predict the citation counts of a paper after a given time period. Future citation counts of papers are an important metric to estimate potential influences of published papers, and will be helpful for researchers to choose representative literatures. This task can be treated as a regression problem. This paper proposes two types of predictive features to represent fundamental characteristics of papers and authors: six content features and ten author features. We introduce the IBM Model 1 to calculate the association probabilities between paper topics which are employed to extract content features, and use the bipartite network projection to obtain the author collaboration network which is utilized to extract author features. Further, we introduce the Gradient Boosted Regression Trees to predict citation counts of papers. Our approach combines contents and topics of papers and multi-dimensional measures of author collaborations in one learning process. Experimental results on the KDD CUP dataset demonstrate that our predicting features and models are effective to solve the problem of citation counts prediction of papers.","PeriodicalId":328695,"journal":{"name":"2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128590227","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: 18