{"title":"Knowledge reduction in interval-valued information systems","authors":"D. Miao, N. Zhang, Xiaodong Yue","doi":"10.1109/COGINF.2009.5250721","DOIUrl":"https://doi.org/10.1109/COGINF.2009.5250721","url":null,"abstract":"In this paper, the concept of α -maximal consistent blocks is proposed to formulate the new rough approximations to an arbitrary object set in interval-valued information systems. The a -maximal consistent blocks can provide the simpler discernibility matrices and discernibility functions in reduction of interval-valued information systems. This means that they can provide a more efficient computation for knowledge acquisitions. Numerical examples are employed to substantiate the conceptual arguments.","PeriodicalId":420853,"journal":{"name":"2009 8th IEEE International Conference on Cognitive Informatics","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125570542","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}
{"title":"Evaluating a method to detect temporal trends of phrases in research documents","authors":"H. Abe, S. Tsumoto","doi":"10.1109/COGINF.2009.5250711","DOIUrl":"https://doi.org/10.1109/COGINF.2009.5250711","url":null,"abstract":"In text mining processes, the importance indices of the technical terms play a key role in finding valuable patterns from various documents. Further, methods for finding emergent terms have attracted considerable attention as an important issue called temporal text mining. However, many conventional methods are not robust against changes in technical terms. In order to detect remarkable temporal trends of technical terms in given textual datasets robustly, we propose a method based on temporal changes in several importance indices by assuming the importance indices of the terms to be a dataset. Empirical studies show that two representative importance indices are applied to the documents from two research areas. After detecting the temporal trends, we compared the emergent trend of the technical phrases to some emergent phrases given by a domain expert.","PeriodicalId":420853,"journal":{"name":"2009 8th IEEE International Conference on Cognitive Informatics","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125594636","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}
{"title":"Multifractal analysis and feature extraction of DNA sequences","authors":"W. Kinsner, Hong Zhang","doi":"10.1109/COGINF.2009.5250696","DOIUrl":"https://doi.org/10.1109/COGINF.2009.5250696","url":null,"abstract":"This paper presents feature extraction and estimations of multifractal measures for deoxyribonucleic acid (DNA) sequences, and demonstrates the intriguing possibility of identifying biological functionality using information contained within the DNA sequence. We have developed a technique that seeks patterns or correlations in the DNA sequence at a higher level. The technique has three main steps: (i) transforms the DNA sequence symbols into a modified Lévy walk, (ii) transforms the Lévy walk into a signal spectrum, and (iii) breaks the spectrum into subspectra and treats each of these as an attractor from which the multifractal dimension spectrum is estimated. An optimal minimum window size and volume element size are found for estimation of the multifractal measures. Experimental results show that DNA is a multifractal, and that the multifractality changes depending upon the location (coding or noncoding region) in the sequence.","PeriodicalId":420853,"journal":{"name":"2009 8th IEEE International Conference on Cognitive Informatics","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133127432","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}
{"title":"Conventionalized cognition conventionalizes cognition","authors":"Chu-Ren Huang","doi":"10.1109/COGINF.2009.5250718","DOIUrl":"https://doi.org/10.1109/COGINF.2009.5250718","url":null,"abstract":"Language may be viewed as a system of conventionalized cognition in the sense that it conventionally conceptualizes a range of perceived realities shared by a community of speakers. In this talk, I focus on how language, and Chinese in particular, conventionalizes cognition in order to contribute to a macro-theory of cognitive informatics. Based on distributional data from a large-scale corpus, I will show that Chinese conventionally and selectively represents important aspects of cognition, such as transition vs. state, and production vs. perception. I will also draw attention to the fact that these conventions lead to the packaging of cognitive events, i.e., following Pustejovsky's event coercion, which can be seen as an extension of Aristotle's four causes of knowledge. Lastly, relying on this intuition that language conventionalizes cognition, I will propose a simple yet robust approach to text-based emotion detection and classification.","PeriodicalId":420853,"journal":{"name":"2009 8th IEEE International Conference on Cognitive Informatics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126950745","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}
{"title":"Unified hierarchical iterate model of human conceptualization and cognition","authors":"Jiayu Zhou, Lifeng Jin, Sherwin Han","doi":"10.1109/COGINF.2009.5250677","DOIUrl":"https://doi.org/10.1109/COGINF.2009.5250677","url":null,"abstract":"In this paper, we propose a hierarchically iterate model of human conceptualization and cognition. Based on the theory of dual structure of cognition and related physiological evidence, we firstly propose the notion of concept space in human brain and a corresponding conceptualization model. Conceptualization is the process of forming concepts in our brain. The concept of cognition process is then defined to introduce our unified iterative cognition model. The hierarchy within the iterative cognition is the logic of human cognition. Some visual cognition examples are employed to investigate our cognition model. With our conceptualization and cognition model, the processes of auditory cognition, visual cognition and more over, language understanding could be explained under a unified theoretical framework. Separate papers describing specific usages of the framework will be published.","PeriodicalId":420853,"journal":{"name":"2009 8th IEEE International Conference on Cognitive Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130521585","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}
{"title":"There is no royal road to computer vision","authors":"Bo Zhang","doi":"10.1109/COGINF.2009.5250748","DOIUrl":"https://doi.org/10.1109/COGINF.2009.5250748","url":null,"abstract":"To endow computers with human visual capability is one of the main goals of artificial intelligence (AI) although there still is a long way to go. Taking object recognition as an example in 1980s, a main approach addressing the problem is the 3D reconstruction one, i.e., the reconstruction of 3D object from 2D images. In 1990s since the 3D reconstruction method was confronted with extreme difficulty, most researchers abandoned the attempts and turned to the 2D based approach, i.e., object recognition from 2D images directly. However, the new road is still uneven. In this talk, I will address the main principles of the new approach, its seedtime and the difficulty faced recently. When a huge amount of 2D-image data are obtained by digital cameras in object recognition (or classification), they should be transformed into an object invariant representation. In order to solve the problem, we need two key techniques, i.e., a robust detector and an object invariant describer. A number of great efforts have been made on these techniques, but so far few efficient solutions have been found. A new direction emerged to solve the problems of computer vision is that computer science may learn some things from neuron science or brain science. This talk will discuss what computer vision can learn from human visual principles and how it will be affected by the new interdisciplinary research on computer vision.","PeriodicalId":420853,"journal":{"name":"2009 8th IEEE International Conference on Cognitive Informatics","volume":"65-66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134458566","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}
{"title":"On hierarchical content-based image retrieval by dynamic indexing and guided search","authors":"J. You, K. Cheung, J. Liu, L. Guo","doi":"10.1117/12.529100","DOIUrl":"https://doi.org/10.1117/12.529100","url":null,"abstract":"This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending date mining and data warehousing techniques. The proposed algorithms include: a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing, an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features.","PeriodicalId":420853,"journal":{"name":"2009 8th IEEE International Conference on Cognitive Informatics","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127378344","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}