2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)最新文献_第4页

Dimensional music emotion recognition by valence-arousal regression 基于价-唤醒回归的空间音乐情感识别

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862063

Junjie Bai, Jun Peng, Jinliang Shi, Dedong Tang, Ying Wu, Jianqing Li, Kan Luo

引用次数: 14

Experiments on the supervised learning algorithm for formal concept elicitation by cognitive robots 认知机器人形式概念启发的监督学习算法实验

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862015

Omar A. Zatarain, Yingxu Wang

{"title":"Experiments on the supervised learning algorithm for formal concept elicitation by cognitive robots","authors":"Omar A. Zatarain, Yingxu Wang","doi":"10.1109/ICCI-CC.2016.7862015","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862015","url":null,"abstract":"Concept elicitation is a fundamental methodology for knowledge extraction and representation in cognitive robot learning. Traditional machine learning technologies deal with object identification, cluster classification, functional regression, and behavior acquisition. This paper presents a supervised machine knowledge learning methodology for concept elicitation from sample dictionaries in natural languages. Formal concepts are autonomously generated based on collective intention of attributes and collective extension of objects elicited from informal definitions in dictionaries. A system of formal concept generation for a cognitive robot is implemented by the Algorithm of Machine Concept Elicitation (AMCE) in MATLAB. Experiments on machine learning for creating a set of twenty formal concepts reveal that the cognitive robot is able to learn synergized concepts in human knowledge in order to build its own cognitive knowledge base. The results of machine-generated concepts demonstrate that the AMCE algorithm can over perform human knowledge expressions in dictionaries in terms of relevance, accuracy, quantitativeness, and cohesiveness.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121667551","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: 13

Deductive reasoning and computing based on propositional logic 基于命题逻辑的演绎推理和计算

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862050

G. Luo, Chongyuan Yin

引用次数: 0

Considering eye movement type when applying random forest to detect cognitive distraction 应用随机森林检测认知分心时考虑眼球运动类型

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862064

Hiroaki Koma, Taku Harada, Akira Yoshizawa, H. Iwasaki

引用次数: 5

Feature extraction of video using deep neural network 基于深度神经网络的视频特征提取

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862078

Yoshihiro Hayakawa, Takanori Oonuma, Hideyuki Kobayashi, Akiko Takahashi, Shinji Chiba, N. M. Fujiki

引用次数: 7

Cognitive visual analytics of multi-dimensional cloud system monitoring data 多维云系统监测数据的认知可视化分析

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.4018/IJSSCI.2017010102

G. Baciu, Yungzhe Wang, Chenhui Li

{"title":"Cognitive visual analytics of multi-dimensional cloud system monitoring data","authors":"G. Baciu, Yungzhe Wang, Chenhui Li","doi":"10.4018/IJSSCI.2017010102","DOIUrl":"https://doi.org/10.4018/IJSSCI.2017010102","url":null,"abstract":"Hardware virtualization has enabled large scale computational service delivery models with significant cost leverage and has improved resource utilization of cloud computing platforms. This has completely changed the landscape of computing in the last decade. It has enabled very large-scale data analytics through distributed, high performance computing. However, due to the infrastructure complexity, end-users and administrators of cloud platforms can rarely obtain a complete picture of the state of cloud computing systems and data centers. Recent monitoring tools enable users to obtain large amounts of data with respect to many utilization parameters of cloud platforms. However, they often fall short of maximizing the overall insight into the resource utilization dynamics of cloud platforms. Furthermore, existing tools make it difficult to observe large scale patterns making it difficult to learn from the past behavior of cloud system dynamics. New operating platforms for cloud management and service provisioning allow live migration and dynamic resource re-allocation at multiple levels of the hardware virtualization layers. Hence, it has become necessary to provide cognitive visualizing tools for monitoring the activities in an active cloud environment. In this work, we describe a perceptual-based interactive visualization platform that gives users and administrators a cognitive view of cloud computing system dynamics. We define machine states and aggregate states at multiple levels of detail to construct a multiview presentation of the resource utilization according to the scalability and the elasticity features of a cloud computing system.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115156228","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

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862048

Kaizhong Zhang

引用次数: 0

Logic of natural language: Through the eyes of ontological semantics 自然语言的逻辑:本体语义学的视角

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862084

Julia Taylor Rayz, V. Raskin

引用次数: 2

Soft sensing of the burning through point in iron-making process 炼铁过程中烧透点的软测量

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862070

Jingliang Shi, Ying Wu, Lu Liao, Xin-ping Yan, J. Zeng, Rusen Yang

引用次数: 2

A copula based method for fish species classification 一种基于交配体的鱼类分类方法

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862079

Raj Singh Dhawal, Liang Chen

{"title":"A copula based method for fish species classification","authors":"Raj Singh Dhawal, Liang Chen","doi":"10.1109/ICCI-CC.2016.7862079","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862079","url":null,"abstract":"The proposed work develops a method for classification of the species of a fish given in an image, which is a sub-ordinate level classification problem. Sub-ordinate classification is complex as it relies on identifying the notable distinction among the part level characteristics of subjects rather than relying on presence or absence of parts for classification, as done in basic level categorization. Fish image categorization is unique and challenging as the images of same fish species can show significant differences in the fish's attributes when taken in different conditions. Our approach analyses the local patches of images, cropped based on specific body parts, and hence keep comparison more specific to grab more finer details rather than comparing global postures. We have used state-of-the-art multidimensional image descriptor HOG (Histogram of Oriented Gradients) and, colour histograms to create representative feature vectors; feature vectors are summarized using Copula theory which has not been used in many applications in analysing multi-dimensional space despite being one of the most used tools to analyse bivariate data from complex industries like finance and medical science. Our method is very simple yet we have matched the classification accuracy of other proposed complex work for such problems.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121390549","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