M. Korytkowski, R. Scherer, P. Staszewski, Piotr Woldan
{"title":"Bag-of-features image indexing and classification in microsoft SQL server relational database","authors":"M. Korytkowski, R. Scherer, P. Staszewski, Piotr Woldan","doi":"10.1109/CYBConf.2015.7175981","DOIUrl":"https://doi.org/10.1109/CYBConf.2015.7175981","url":null,"abstract":"This paper presents a novel relational database architecture aimed to visual objects classification and retrieval. The framework is based on the bag-of-features image representation model combined with the Support Vector Machine classification and is integrated in a Microsoft SQL Server database.","PeriodicalId":177233,"journal":{"name":"2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122932518","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":"Translation of MathML formulas to Polish text, example applications in teaching the blind","authors":"A. Salamończyk, J. Brzostek-Pawlowska","doi":"10.1109/CYBConf.2015.7175939","DOIUrl":"https://doi.org/10.1109/CYBConf.2015.7175939","url":null,"abstract":"Blind people require special means of reading and writing of mathematical content. They require of alternative user interfaces in accessing math formulas. In this paper a tool helps surpass the difficulty in reading online documents containing mathematical expressions is presented. The translator from a MathML form to text in Polish take into account a mathematical context of symbols and neighboring nodes names in MathML structure.","PeriodicalId":177233,"journal":{"name":"2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127956695","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 conception for use of user profile to prediction learning effects in Intelligent Tutoring Systems","authors":"Adrianna Kozierkiewicz-Hetmanska, J. Bernacki","doi":"10.1109/CYBConf.2015.7175913","DOIUrl":"https://doi.org/10.1109/CYBConf.2015.7175913","url":null,"abstract":"Intelligent Tutoring Systems (ITS) offer adaptivity to user abilities, personal character trait, learning styles and preferences. The user modelling is one of the major factors that can influence an adaptivity. The content and structure of user profile should allow to recommend learning material suitable for student's needs. In this work a user profile is designed and a method for predicting learner's abilities is proposed. We use a Naive Bayes classifier in order to predict student's learning results. A prediction of user's abilities could be very useful for determining an initial learning scenario or assigning a student to a suitable collaborative learning group.","PeriodicalId":177233,"journal":{"name":"2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132323529","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":"Binarization of spectral histogram models: An application to efficient biometric identification","authors":"A. Pflug, C. Rathgeb, U. Scherhag, C. Busch","doi":"10.1109/CYBConf.2015.7175985","DOIUrl":"https://doi.org/10.1109/CYBConf.2015.7175985","url":null,"abstract":"Feature extraction techniques such as local binary patterns (LBP) or binarized statistical image features (BSIF) are crucial components in a biometric recognition system. The vast majority of relevant approaches employs spectral histograms as feature representation, i.e. extracted biometric reference data consists of sequences of histograms. Transforming these histogram sequences to a binary representation in an accuracy-preserving manner would offer major advantages w.r.t. data storage and efficient comparison. We propose a generic binarization for spectral histogram models in conjunction with a Hamming distance-based comparator. The proposed binarization and comparison technique enables a compact storage and a fast comparison of biometric features at a negligible cost of biometric performance (accuracy). Further, we investigate a serial combination of the binary comparator and histogram model-based comparator in a biometric identification system. Experiments are carried out for two emerging biometric characteristics, i.e. palmprint and ear, confirming the soundness of the presented technique.","PeriodicalId":177233,"journal":{"name":"2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129665802","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":"Polynomial speed control of electric drive with flexible coupling: Preliminary experiments","authors":"S. Dodds, Jacob L. Pedersen, P. Derugo, K. Szabat","doi":"10.1109/CYBConf.2015.7175978","DOIUrl":"https://doi.org/10.1109/CYBConf.2015.7175978","url":null,"abstract":"The generic polynomial controller for linear, time invariant (LTI) plants is applied to a DC electric drive with significant flexibility of the coupling between the motor and the mechanical load for its speed control. In contrast with a traditional PI controller, the polynomial controller permits independent placement of the closed loop poles to achieve the desired closed loop dynamics. An original contribution is the simple design procedure based on the Settling Time Formula that relates the location of a dominant multiple pole to the settling time of the step response. The model of the flexible drive is presented and expressed as a transfer function. This is used to introduce the general polynomial controller design method. Then step response simulations are presented followed by corresponding experimental runs and comparisons made.","PeriodicalId":177233,"journal":{"name":"2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129803854","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":"3D segmentation of the cerebrospinal fluid from CT brain scans using local histogram similarity map","authors":"A. Fabijańska, J. Gocławski","doi":"10.1109/CYBConf.2015.7175916","DOIUrl":"https://doi.org/10.1109/CYBConf.2015.7175916","url":null,"abstract":"This paper considers the problem of segmentation of regions filled with the cerebrospinal fluid (CSF) from CT brain images. In particular a 3D segmentation method is proposed. The method performs segmentation by comparing intensity distribution within the user defined seed region with local intensity distributions around all image voxels. In order to speed up the computations, GPU computing is used. Results of applying the introduced approach to segmentation of CSF accumulated in different brain regions are presented and discussed. Issues regarding accuracy and execution time of the method are also taken into account.","PeriodicalId":177233,"journal":{"name":"2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131234081","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":"Bio-inspired clustering: Basic features and future trends in the era of Big Data","authors":"David Camacho","doi":"10.1109/CYBConf.2015.7175897","DOIUrl":"https://doi.org/10.1109/CYBConf.2015.7175897","url":null,"abstract":"Clustering is perhaps one of the most popular approaches used in unsupervised machine learning. There's a huge number of different methods and algorithms that have been designed in the last decades related to this “blind pattern search”, some of these approaches are based on bio-inspired methods such as Evolutionary Computation, Swarm Intelligence or Neural Networks among others. In the last years, and due to the fast growing of Big Data problems, some interesting advances and new approaches are currently being developed in this area, new algorithms like online clustering and streaming clustering are appearing. These new algorithms try to solve classical problems in Clustering and deal with the new features of these new kind of problems. This keynote lecture will provide some basics on both, Clustering methods and bio-inspired computation, and how they have been combined to improve the quality of these algorithms, to later show the main features that Big Data needs to obtain reliable clustering approaches. Finally, some practical examples and applications will be described to show how these new algorithms are evolving to be used in the near future in complex and dynamic environments.","PeriodicalId":177233,"journal":{"name":"2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126404186","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":"Positivity and stability of discrete-time nonlinear systems","authors":"T. Kaczorek","doi":"10.1109/CYBConf.2015.7175924","DOIUrl":"https://doi.org/10.1109/CYBConf.2015.7175924","url":null,"abstract":"The positivity and asymptotic stability of the discrete-time nonlinear systems are addressed. Necessary and sufficient conditions for the positivity of the systems and sufficient conditions for asymptotic stability of the positive systems are established. The proposed stability tests are based on Lyapunov method. The effectiveness of the tests are demonstrated on examples.","PeriodicalId":177233,"journal":{"name":"2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127869201","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":"Extracting of temporal patterns from data for hierarchical classifiers construction","authors":"M. Szpyrka, A. Szczur, Jan G. Bazan, Lukasz Dydo","doi":"10.1109/CYBConf.2015.7175955","DOIUrl":"https://doi.org/10.1109/CYBConf.2015.7175955","url":null,"abstract":"A method of automatic extracting of temporal patterns from learning data for constructing hierarchical behavioral patterns based classifiers is considered in the paper. The presented approach can be used to complete the knowledge provided by experts or to discover the knowledge automatically if no expert knowledge is accessible. Formal description of temporal patterns is provided and an algorithm for automatic patterns extraction and evaluation is described. A system for packet-based network traffic anomaly detection is used to illustrate the considered ideas.","PeriodicalId":177233,"journal":{"name":"2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133262699","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 new online clustering approach for data in arbitrary shaped clusters","authors":"Richard Hyde, P. Angelov","doi":"10.1109/CYBConf.2015.7175937","DOIUrl":"https://doi.org/10.1109/CYBConf.2015.7175937","url":null,"abstract":"In this paper we demonstrate a new density based clustering technique, CODSAS, for online clustering of streaming data into arbitrary shaped clusters. CODAS is a two stage process using a simple local density to initiate micro-clusters which are then combined into clusters. Memory efficiency is gained by not storing or re-using any data. Computational efficiency is gained by using hyper-spherical micro-clusters to achieve a micro-cluster joining technique that is dimensionally independent for speed. The micro-clusters divide the data space in to sub-spaces with a core region and a non-core region. Core regions which intersect define the clusters. A threshold value is used to identify outlier micro-clusters separately from small clusters of unusual data. The cluster information is fully maintained on-line. In this paper we compare CODAS with ELM, DEC, Chameleon, DBScan and Denstream and demonstrate that CODAS achieves comparable results but in a fully on-line and dimensionally scale-able manner.","PeriodicalId":177233,"journal":{"name":"2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)","volume":"51 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127566490","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}