{"title":"Two decades'research on decision-theoretic rough sets","authors":"Dun Liu, Huaxiong Li, Xianzhong Zhou","doi":"10.1109/COGINF.2010.5599770","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599770","url":null,"abstract":"The decision-theoretic rough set model (DTRSM) was proposed two decades ago. In this paper, the development of DTRSM, including the theories and the potential applications, are reviewed. With respect to the two semantic issues of DTRSM, three-way decision procedure, attribute reduction methods and potential applications of DTRSM are examined. This paper reviews the history of DTRSM over the last twenty years with a view to its future.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115347506","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":"Structural optimization of lennard-jones clusters by hybrid social cognitive optimization algorithm","authors":"Yongjing Chen, Z. Cui, J. Zeng","doi":"10.1109/COGINF.2010.5599739","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599739","url":null,"abstract":"Structural optimization of Lennard-Jones (LJ) clusters is a classical NP problem. There are many local minima locating near the global minimum, and the local optima number is increased exponentially. Social cognitive optimization. algorithm (SCOA) is a new swarm intelligent technique by simulating the human society. However, its local search capability is still weak. Therefore, in this paper, a novel local search strategy, Limited memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) is employed to enhance the exploitation capability of SCOA. Simulation results show the proposed hybrid algorithm has successfully found the lowest-energy structures of the LJ2-LJ5, LJ7-LJ9 and LJ12 comparing with PSO and the standard SCOA.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115830045","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":"A handwriting textline extraction approach based on connected domain","authors":"Wei Gao, Fuchun Sun, Zhonghang Yin","doi":"10.1109/COGINF.2010.5599738","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599738","url":null,"abstract":"This paper describes an approach for extracting words, textlines and text blocks by analyzing the spatial configuration of connected domain and word contour rectangles on a given document image. The basic idea is that connected components of black pixels and contours can be used as computational units in document image analysis. In this paper, we try to find a spatial feature and overlapped relationships for every contour rectangle, and we call this feature rectangle “Standard Rectangle”(SR). Then we calculate the split line of every textline according to a series of operations of SRs, and separate the word contour rectangles to different lines. In the next step we estimate that if the adjacent textlines is overlapped. If it is, we calculate the overlap distance and move the word contour rectangles according to it. Our experiment show the approach does good work on both overlapped textlines and detached textlines.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131093036","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":"Extend argumentation frameworks based on degree of attack","authors":"Bingsheng Wang, G. Luo","doi":"10.1109/COGINF.2010.5599810","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599810","url":null,"abstract":"To capture the quantity and quality properties of attacks in argumentation process, a fuzzy argumentation framework is presented in this paper. Degree of attack is introduced into argumentation, which contributes to formalizing the internal structure of arguments. Then based on probability theory, total attack is proposed to calculate the effect of all attacks. In addition, the extensional semantics for fuzzy argumentation frameworks are explored in the same way as for extended argumentation frameworks. Finally, the application of FAF in investigation is stated.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123991952","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":"Discrimination of the imaging quality based on the sharpness and illuminance factor","authors":"Wang Xue, Ou Yangqi","doi":"10.1109/COGINF.2010.5599771","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599771","url":null,"abstract":"In the visual inspection with the machine system, the prerequisite for the detection success is to get a clear and stable image with high signal to noise ratio. For the current lack of quality assessment on the collected images, an experimental evaluation method is proposed for the quality of the collected image based on the image sharpness parameters and the illumination factor of the CCD target surface. Heavy rail surface was selected as the research object, and a flag was set as an object. The first thing was to get the best image sharpness point of the object, followed by employing wavelet parameters and the least squares method (LS-SVM) to identify the object. Meanwhile, the loss rate of the object size was analyzed to get the best illumination factor of the CCD target surface, and thus obtaining the best spots for global capture of image quality. This method gave a more comprehensive consideration on various factors on CCD imaging, and proven to be quite practical.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115508506","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":"Access control for Web Services Based on feedback and decay","authors":"Shangzhu Jin, Jun Peng","doi":"10.1109/COGINF.2010.5599687","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599687","url":null,"abstract":"Security is a very important requirement for any Web application. In order to avoid free-rider behaviors and reduce requesters' malicious accesses in Web services, a kind of trust management based on feedback and time decay in Web Services is presented in this paper. According to the service providers' feedback, honest accesses will be rewarded while dishonest ones will get punishment by such mechanism, which would effectively encourage the service requesters to take part in access control honestly and avoid free-rider behaviors by giving those who are dishonest and inactive a kind of time decay punishment. After simulated experiments, the effectiveness of the trust mechanism is verified, which indicates that this kind of trust management is able to encourage the service requesters to take part in access control much more actively with an honest attitude so that the security of Web services gets improved.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115618608","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":"Feedforward neural networks for automated classification","authors":"I. Jordanov, A. Georgieva","doi":"10.1109/COGINF.2010.5599673","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599673","url":null,"abstract":"We investigate an intelligent computer vision system that incorporates feedforward neural networks (NN) for recognition and classification of commercially available cork tiles. The system is capable of acquiring and processing gray images using several feature generation and analysis techniques. Its functionality includes image acquisition, feature extraction and preprocessing, and feature classification with NN. We also discuss system test and validation results from the recognition and classification tasks. The system investigation also includes statistical feature processing (features number and dimensionality reduction techniques) and classifier design and training. The NN are trained with our genetic low-discrepancy search method for global optimization (GLPτS), and demonstrate very good generalisation abilities when tested on unseen samples. In our view, the reported success rate of up to 95% is due to several factors: combination of feature generation techniques; application of Analysis of Variance (ANOVA) and Principal Component Analysis (PCA), which appeared to be very efficient for preprocessing the data; and also the use of suitable NN design and learning method.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129450849","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":"Prion protein and human cognition","authors":"W. Zou, Jue Yuan","doi":"10.1109/COGINF.2010.5599805","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599805","url":null,"abstract":"Prion protein (PrP) is probably the only known infectious protein that possesses chameleon-like features, as reflected not only in its conformation but also in its function. Cellular PrP (PrPC), rich in α-helical structures, is believed to be functionally involved in neurotransmitter metabolism, immune cell activation, cell adhesion, signal transduction, copper metabolism, antioxidant activity, and programmed cell death. Its pathologic conformer (PrPSc), rich in β-sheet structures, plays an essential role in the pathogenesis of prion diseases, a group of age-related transmissible neurodegenerative disorders which, as in Alzheimer's disease, inevitably attack human cognition. Histologically, PrP is highly concentrated in the central nervous system although it is also widely expressed in the peripheral tissues and organs at lower levels. Observations indicating that a pronounced cognitive deficit is often evident in the early stage of prion diseases strongly imply that PrP is associated with human cognition. Indeed, cognitive deficit is one of a few phenotypes identified in mice lacking PrP. This article highlights evidence of the involvement of PrPC in human cognition. In addition, it puts forth the hypothesis that two potential PrP-implicated pathways are operative in human cognition.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129479031","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":"SISCHMAX: Discovering common contour patterns","authors":"Xiaobing Liu, Bo Zhang","doi":"10.1109/COGINF.2010.5599740","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599740","url":null,"abstract":"Discovering common shape contour is a promising topic. However, local position and scale variance always leads to the mismatch of similar contours. In this study, we propose Shift Invariant Sparse Coding HMAX (SISCHMAX)to address this problem. Shift Invariant Sparse Coding is used to learn the configuration of line responses on the output of HMAX C1 layer. And we test the proposed method on Caltech101 dataset for discovering object contour. Due to the tolerance of local shift of the lines, sparse coding extracts better object contours using HMAX C1 outputs instead of gray value.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127897803","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":"Efficient missing data imputation for supervised learning","authors":"Shichao Zhang, Xindong Wu, Manlong Zhu","doi":"10.1109/COGINF.2010.5599826","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599826","url":null,"abstract":"In supervised learning, missing values usually appear in the training set. The missing values in a dataset may generate bias, affecting the quality of the supervised learning process or the performance of classification algorithms. These imply that a reliable method for dealing with missing values is necessary. In this paper, we analyze the difference between iterative imputation of missing values and single imputation in real-world applications. We propose an EM-style iterative imputation method, in which each missing attribute-value is iteratively filled using a predictor constructed from the known values and predicted values of the missing attribute-values from the previous iterations. Meanwhile, we demonstrate that it is reasonable to consider the imputation ordering for patching up multiple missing attribute values, and therefore introduce a method for imputation ordering. We experimentally show that our approach significantly outperforms some standard machine learning methods for handling missing values in classification tasks.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116361936","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}