BibSonomyburst/tag/patternBibSonomy RSS feed for /tag/pattern2012-02-15T09:02:00+01:00http://www.bibsonomy.org/bibtex/2f720bd1339d243f8c35eb79a831b1d28/muhemuhe2012-01-27T14:10:42+01:00Analysis; Blind Classification Components Pattern Principal Separation; Source Traffic noise; recognition; <span class="authorEditorList"><a href="/author/Mato-Méndez">Fernando J. Mato-Méndez</a>, and <a href="/author/Sobreira-Seoane">Manuel A. Sobreira-Seoane</a> </span><em>Applied Acoustics</em> (<em>2011</em>)Fri Jan 27 14:10:42 CET 2012Applied Acoustics590-598Blind separation to improve classification of traffic noise722011Analysis; Blind Classification Components Pattern Principal Separation; Source Traffic noise; recognition; In order to apply noise mapping to traffic noise prediction, a knowledge of several information about traffic characteristics is required to predict the noise levels emitted by the roads involved. In the European case, the CNOSSOS-EU calculation method for traffic-noise level prediction is now under discussion, to be agreed in response to the European Directive relating to the Assessment and Management of Environmental Noise (2002/49/EC). In this application context, standard ISO 1996-2:2007 Determination of Environmental Noise Levels, in its Section 6.2, specifically mentions that during Leq measurements of road traffic noise the number of vehicle pass-bys shall be counted during the measurement time interval. This information is often not available in many roads, so it is typically registered by means of casual counts, often through manual procedures. Besides, if the measurement result is converted to other traffic conditions, a categorization of the vehicles involved is also required. Some additional information, such as the traffic density and the average speed, should be registered if a calculation method is used to build a noise map. In this paper a new automatic classification system of traffic noise covering these requirements is presented. The portable system processes a two channel audio recording to provide information of the average speed and the number of vehicles, which are classified in six categories during the measurement period. After several evaluations of the possibilities to get a good classification of the noise emission of a road from audio recordings, it is shown that increasing the within-class separation, as well as introducing a novel BSS–PCA-based classifier, the precision achieved in the final results is substantially improved.http://www.bibsonomy.org/bibtex/2d6cc798d5a11afdf5671e3df9a753561/muhemuhe2012-01-27T14:10:42+01:00Environmental hidden markov models monitoring; noise pattern recognition; <span class="authorEditorList"><a href="/author/Couvreur">Christophe Couvreur</a>, <a href="/author/Fontaine">Vincent Fontaine</a>, <a href="/author/Gaunard">Paul Gaunard</a>, and <a href="/author/Mubikangiey">Corine Ginette Mubikangiey</a> </span><em>Applied Acoustics</em> (<em>1998</em>)Fri Jan 27 14:10:42 CET 2012Applied Acoustics187-206Automatic Classification of Environmental Noise Events by Hidden Markov Models541998Environmental hidden markov models monitoring; noise pattern recognition; The automatic classification of environmental noise sources from their acoustic signatures recorded at the microphone of a noise monitoring system (NMS) is an active subject of research nowadays. This paper shows how hidden Markov models (HMMs) can be used to build an environmental noise recognition system based on a time-frequency analysis of the noise signal. The theory of HMMs is briefly reviewed in the context of automatic noise recognition. The performance of the HMM-based approach is evaluated experimentally for the classification of five types of noise events (car, truck, moped, aircraft, train).With more than 95% of correct classifications, the HMM-based approach is found to outperform previously proposed classifiers which were based on the average spectrum of noise events. A classification test performed with human listeners for the same data shows that the best HMM-based classifier also outperforms the “average” human listener who achieves only 91.8% of correct classifications for the same task.http://www.bibsonomy.org/bibtex/2bdf265245cc7e74fb0d85912330ee976/lwa_2011lwa_20112012-01-11T13:01:15+01:002011 LWA LWA_2011 binary classification evolving fuzzy pattern streams trees <span class="authorEditorList"><a href="/author/Shaker">Ammar Shaker</a>, <a href="/author/Senge">Robin Senge</a>, and <a href="/author/Hüllermeier">Eyke Hüllermeier</a> </span><em>Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation, </em>(<em>2011</em>)Wed Jan 11 13:01:15 CET 2012Working Notes of the LWA 2011 - Learning, Knowledge, AdaptationEvolving Fuzzy Pattern Trees for Binary Classification on Data Streams20112011 LWA LWA_2011 binary classification evolving fuzzy pattern streams trees http://www.bibsonomy.org/bibtex/27c5adcd5ac6d71e5166c430deaa672cf/lwa_2011lwa_20112012-01-11T13:01:15+01:002011 LWA LWA_2011 demonstration discovery environment intelligent mining paper pattern richclient subgroup vikamine <span class="authorEditorList"><a href="/author/Atzmueller">Martin Atzmueller</a>, and <a href="/author/Lemmerich">Florian Lemmerich</a> </span><em>Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation, </em>(<em>2011</em>)Wed Jan 11 13:01:15 CET 2012Working Notes of the LWA 2011 - Learning, Knowledge, AdaptationVIKAMINE - A Rich-Client Environment for Intelligent Pattern Mining and Subgroup Discovery (Demonstration Paper)20112011 LWA LWA_2011 demonstration discovery environment intelligent mining paper pattern richclient subgroup vikamine http://www.bibsonomy.org/bibtex/27a0616f715b10a301680e8cd5e914929/lwa_2011lwa_20112012-01-11T13:01:15+01:002011 LWA LWA_2011 direct frequency pattern respect sampling <span class="authorEditorList"><a href="/author/Boley">Mario Boley</a>, <a href="/author/Lucchese">Claudio Lucchese</a>, <a href="/author/Paurat">Daniel Paurat</a>, and <a href="/author/Gärtner">Thomas Gärtner</a> </span><em>Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation, </em>(<em>2011</em>)Wed Jan 11 13:01:15 CET 2012Working Notes of the LWA 2011 - Learning, Knowledge, AdaptationDirect Pattern Sampling with Respect to Pattern Frequency20112011 LWA LWA_2011 direct frequency pattern respect sampling http://www.bibsonomy.org/bibtex/25da5aa6b09f73be90a692e0ed493702e/portaporta2011-12-27T21:19:01+01:00model task based modeling user interface design pattern notation <span class="authorEditorList"><a href="/author/Radeke">Frank Radeke</a>, and <a href="/author/Forbrig">Peter Forbrig</a> </span><em>Proceedings of the 6th international conference on Task models and diagrams for user interface design, </em><em>page 184--197. </em><em>Berlin/Heidelberg, </em><em>Springer-Verlag, </em>(<em>2007</em>)Tue Dec 27 21:19:01 CET 2011Berlin/HeidelbergProceedings of the 6th international conference on Task models and diagrams for user interface design184--197TAMODIA'07Patterns in task-based modeling of user interfaces2007model task based modeling user interface design pattern notation The development of user interfaces is influenced by various challenges in recent years. These are foremost caused by increasing complexity of the underlying applications and the use of these applications on different devices, by different user types and in changing environments. Model-based user interface development approaches have been shown to be suitable to face these challenges. However, creating, transforming and linking the various included models are complex tasks. Employing patterns can avoid these disadvantages and provide an advanced concept of reuse. In this paper a general framework is introduced that describes how model-based approaches can be extended with patterns. The implementation of the framework is exemplarily shown in order to derive a concrete pattern-driven model-based approach for user interface development. A case study is used to illustrate the derived approach.http://www.bibsonomy.org/bibtex/24ddf1978d85537893b30ac2d4f5f2acb/portaporta2011-12-27T21:13:50+01:00design pattern multimodal web interface voice <span class="authorEditorList"><a href="/author/Neto">Americo T. Neto</a>, <a href="/author/Fortes">Renata P. Fortes</a>, and <a href="/author/da Silva Filho">Adalberto G. da Silva Filho</a> </span><em>Proceedings of the 26th annual ACM international conference on Design of communication, </em><em>page 277--278. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Tue Dec 27 21:13:50 CET 2011New York, NY, USAProceedings of the 26th annual ACM international conference on Design of communication277--278SIGDOC '08Multimodal interfaces design issues: the fusion of well-designed voice and graphical user interfaces2008design pattern multimodal web interface voice This poster describes a novel approach that is intended to combine graphical user interfaces ({GUI}) and voice interaction in the Web throughout the design and the development of intuitive and user-friendly multimodal interfaces. We are considering the user interface design modeling aspects, principles and guidelines that are being used to design voice and {GUI}. Our goal is to get an organized process to the design of multimodal interfaces that will let the designers to evaluate the interfaces with real users, find out usability issues and report the rationale to correct those issues. Moreover it will capture and store the acquired knowledge gathered during the multimodal interfaces design by using the design rationale ({DR}) and design patterns techniques.http://www.bibsonomy.org/bibtex/220ced51227aff62f6912b4a641d5de54/flint63flint632011-12-16T10:53:19+01:00information knowledge zzz.spm paper recognition data semantic web analysis ieee ai pattern v1010 temporal processing management <span class="authorEditorList"><a href="/author/Valle">Emanuele Della Valle</a>, <a href="/author/Ceri">Stefano Ceri</a>, <a href="/author/van Harmelen">Frank van Harmelen</a>, and <a href="/author/Fensel">Dieter Fensel</a> </span><em>IEEE Intelligent Systems</em> <em>24(6):83-89</em> (<em>2009</em>)Fri Dec 16 10:53:19 CET 2011IEEE Intelligent Systems683-89It's a Streaming World! Reasoning upon Rapidly Changing Information242009information knowledge zzz.spm paper recognition data semantic web analysis ieee ai pattern v1010 temporal processing management Data streams occur in modern applications such as sensor network monitoring, traffic engineering, {RFID} tags applications, telecom call recording, medical record management, financial applications, and clickstreams. On the Web, many sites distribute and present information in real-time streams. In many of these application areas, the ability to perform complex reasoning tasks that combine streaming data with evolving knowledge would be of great benefit. Stream reasoning---an unexplored, yet high-impact research area---is a new multidisciplinary approach that will build on the Semantic Web and provide the abstractions, foundations, methods, and tools required to integrate data streams and reasoning systems.http://www.bibsonomy.org/bibtex/252851b4a02b609951059c9602485679d/flint63flint632011-12-15T16:06:24+01:00springer adaptive multimodal semantic web interaction zzz.th.c4 interface requirements ai pattern v1010 book dialog user design <span class="authorEditorList"><a href="/author/Bezold">Matthias Bezold</a>, and <a href="/author/Minker">Wolfgang Minker</a> </span><em>Springer, </em><em>New York, </em>(<em>2011</em>)Thu Dec 15 16:06:24 CET 2011New YorkAdaptive Multimodal Interactive Systems2011springer adaptive multimodal semantic web interaction zzz.th.c4 interface requirements ai pattern v1010 book dialog user design The book introduces a general framework for adapting multimodal interactive systems and comprises a detailed discussion of each of the steps required for adaptation. This book also investigates how interactive systems may be improved in terms of usability and user friendliness while describing the exhaustive user tests employed to evaluate the presented approaches. After introducing general theory, a generic approach for user modeling in interactive systems is presented, ranging from an observation of basic events to a description of higher-level user behavior. Adaptations are presented as a set of patterns similar to those known from software or usability engineering. These patterns describe recurring problems and present proven solutions. The authors include a discussion on when and how to employ patterns and provide guidance to the system designer who wants to add adaptivity to interactive systems. In addition to these patterns, the book introduces an adaptation framework, which exhibits an abstraction layer using Semantic Web technology. Adaptations are implemented on top of this abstraction layer by creating a semantic representation of the adaptation patterns. The patterns cover both graphical interfaces as well as speech-based and multimodal interactive systems.http://www.bibsonomy.org/bibtex/2a47a41658592202811f0139d4bb65871/hothohotho2011-11-30T14:47:12+01:002011 bookmarking mining myown pattern semantic social <span class="authorEditorList"><a href="/author/Atzmueller">Martin Atzmueller</a>, <a href="/author/Benz">Dominik Benz</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Proceedings of the 4th international workshop on Social Data on the Web SDoW2011, </em>(<em>2011</em>)Wed Nov 30 14:47:12 CET 2011Proceedings of the 4th international workshop on Social Data on the Web (SDoW2011)Towards Mining Semantic Maturity in Social Bookmarking Systems20112011 bookmarking mining myown pattern semantic social http://www.bibsonomy.org/bibtex/2f67d3599f5282425b8e0e5b383d436a0/victorinostarvictorinostar2011-11-25T12:01:56+01:00collaborative homonymy level_variation pattern polysemy sensemaking social synonymy tagging taxonomy usage <span class="authorEditorList"><a href="/author/Golder">Scott A. Golder</a>, and <a href="/author/Huberman">Bernardo A. Huberman</a> </span><em>Journal of Information Science</em> <em>32(2):198--208</em> (<em>2006</em>)Fri Nov 25 12:01:56 CET 2011Journal of Information Science2198--208Usage patterns of collaborative tagging systems322006collaborative homonymy level_variation pattern polysemy sensemaking social synonymy tagging taxonomy usage Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamic aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given URL. We also present a dynamic model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.http://www.bibsonomy.org/bibtex/22341696dc975a82cd17e1ae9721e5292/khilgenbergkhilgenberg2011-10-29T18:21:58+02:00design imported kde pattern projekt sdcframework software ss11 <span class="authorEditorList"><a href="/author/Gamma">Erich Gamma</a>, <a href="/author/Helm">Richard Helm</a>, <a href="/author/Johnson">Ralph Johnson</a>, and <a href="/author/John">Vlissides John</a> </span><em>Addison-Wesley, </em><em>Bonn, </em><em>1.~Auf\/l. edition, </em>(<em>1996</em>)<em>Design Patterns, 1995, Deutsche Übersetzung von Dirk Riehle . </em>Sat Oct 29 18:21:58 CEST 2011Bonn1.~{A}uf\/l.Design Patterns, 1995, Deutsche {\"U}bersetzung von Dirk Riehle{Entwurfsmuster: Elemente wiederverwendbarer objektorientierter Software}1996design imported kde pattern projekt sdcframework software ss11 http://www.bibsonomy.org/bibtex/2222ccbe9b3a900091c4148e2a93fff81/atzmuelleratzmueller2011-10-14T23:14:53+02:002011 data discovery itegpub knowledge mining myown pattern social <span class="authorEditorList"><a href="/author/Lemmerich">Florian Lemmerich</a>, and <a href="/author/Atzmueller">Martin Atzmueller</a> </span><em>Proc. IEEE SocialCom 2011, Workshop on Modeling Social Media MSM 2011, </em><em>IEEE Computer Society, </em>(<em>2011</em>)Fri Oct 14 23:14:53 CEST 2011Proc. IEEE SocialCom 2011, Workshop on Modeling Social Media (MSM 2011)Modeling Location-Based Profiles of Social Image Media using Explorative Pattern Mining20112011 data discovery itegpub knowledge mining myown pattern social http://www.bibsonomy.org/bibtex/2a47a41658592202811f0139d4bb65871/atzmuelleratzmueller2011-10-14T23:12:03+02:002011 bookmarking itegpub maturity mining myown pattern semantic social <span class="authorEditorList"><a href="/author/Atzmueller">Martin Atzmueller</a>, <a href="/author/Benz">Dominik Benz</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Proceedings of the 4th international workshop on Social Data on the Web SDoW2011, </em>(<em>2011</em>)Fri Oct 14 23:12:03 CEST 2011Proceedings of the 4th international workshop on Social Data on the Web (SDoW2011)Towards Mining Semantic Maturity in Social Bookmarking Systems20112011 bookmarking itegpub maturity mining myown pattern semantic social http://www.bibsonomy.org/bibtex/256521f54efa8a332927a512bafcd8042/atzmuelleratzmueller2011-10-14T23:10:21+02:002011 client data discovery environment intelligent itegpub mining myown pattern rich subgroup vikamine <span class="authorEditorList"><a href="/author/Atzmueller">Martin Atzmueller</a>, and <a href="/author/Lemmerich">Florian Lemmerich</a> </span><em>Proceedings of the LWA 2011 - Learning, Knowledge, Adaptation, </em>(<em>2011</em>)Fri Oct 14 23:10:21 CEST 2011Proceedings of the LWA 2011 - Learning, Knowledge, AdaptationVIKAMINE - A Rich-Client Environment for Intelligent Pattern Mining and Subgroup Discovery20112011 client data discovery environment intelligent itegpub mining myown pattern rich subgroup vikamine http://www.bibsonomy.org/bibtex/24ec771b9b88fb5a1fd7b2d575d158ca8/flint63flint632011-09-30T14:16:51+02:00pattern acm search v1010 recognition paper enterprise analysis business database <span class="authorEditorList"><a href="/author/Chaudhuri">Surajit Chaudhuri</a>, <a href="/author/Dayal">Umeshwar Dayal</a>, and <a href="/author/Narasayya">Vivek Narasayya</a> </span><em>Communications of the ACM</em> (<em>August 2011</em>)Fri Sep 30 14:16:51 CEST 2011Communications of the ACM#aug#88-98An Overview of Business Intelligence Technology542011pattern acm search v1010 recognition paper enterprise analysis business database BI technologies are essential to running today's businesses and this technology is going through sea changes. Business intelligence (BI) software is a collection of decision support technologies for the enterprise aimed at enabling knowledge workers such as executives, managers, and analysts to make better and faster decisions.http://www.bibsonomy.org/bibtex/2ce57271181e2932d981edb23f405d55e/vojvoj2011-09-08T22:51:45+02:00ontology pattern <span class="authorEditorList"><a href="/author/Tang">Yan Tang</a>, <a href="/author/Baer">Peter De Baer</a>, <a href="/author/Zhao">Gang Zhao</a>, <a href="/author/Meersman">Robert Meersman</a>, and <a href="/author/Pudney">Kevin Pudney</a> </span><em>4th International Workshop on Ontology Content, </em><em> 5872, </em><em>page 514--523. </em><em>Springer, </em>(<em>2009</em>)Thu Sep 08 22:51:45 CEST 20114th International Workshop on Ontology Content5872514--523LNCSTowards a Pattern-Driven Topical Ontology Modeling Methodology in Elderly Care Homes2009ontology pattern This paper presents a pattern-driven ontology modeling methodology, which is used to create topical ontologies in the human resource management (HRM) domain. An ontology topic is used to group concepts from different contexts (or even from different domain ontologies). We use the Organization for Economic Co-operation and Development (OECD) and the National Vocational Qualification (NVQ) as the resource to create the topical ontologies in this paper. The methodology is implemented in a tool called PAD-ON suit. The paper approach is illustrated with a use case from elderly care homes in UK.http://www.bibsonomy.org/bibtex/26ac7d7989859c2c4eeeb62ca30e5d496/ecml_pkdd_2011ecml_pkdd_20112011-09-03T13:18:16+02:002011 constrained discriminative ecml_pkdd_2011 frequent logistic mining pattern patterns regression <span class="authorEditorList"><a href="/author/Anand">Rajul Anand</a>, and <a href="/author/Reddy">Chandan K. Reddy</a> </span><em>Proceedings of the ECML/PKDD 2011, </em>(<em>2011</em>)Sat Sep 03 13:18:16 CEST 2011Proceedings of the ECML/PKDD 2011Constrained Logistic Regression for Discriminative Pattern Mining20112011 constrained discriminative ecml_pkdd_2011 frequent logistic mining pattern patterns regression http://www.bibsonomy.org/bibtex/2e6c4126450fdb2fece6995f159376bbb/ecml_pkdd_2011ecml_pkdd_20112011-09-03T13:18:16+02:002011 ecml_pkdd_2011 ensemble fusion learning methods novel pattern recognition <span class="authorEditorList"><a href="/author/Awais">Muhammad Awais</a>, <a href="/author/Yan">Fei Yan</a>, <a href="/author/Mikolajczyk">Krystian Mikolajczyk</a>, and <a href="/author/Kittler">Josef Kittler</a> </span><em>Proceedings of the ECML/PKDD 2011, </em>(<em>2011</em>)Sat Sep 03 13:18:16 CEST 2011Proceedings of the ECML/PKDD 2011Novel Fusion Methods for Pattern Recognition20112011 ecml_pkdd_2011 ensemble fusion learning methods novel pattern recognition http://www.bibsonomy.org/bibtex/2060145def9718094fb99e27deba13446/procomunprocomun2011-09-01T13:26:03+02:003D abilities; corona; degradation; dimension; discharge discharges; discriminating features; fractal fractals; insulation lacunarity; life partial pattern patterns; recognition; surface testing; <span class="authorEditorList"><a href="/author/Krivda">A. Krivda</a>, <a href="/author/Gulski">E. Gulski</a>, <a href="/author/Satish">L. Satish</a>, and <a href="/author/Zaengl">W.S. Zaengl</a> </span><em>Dielectrics and Electrical Insulation, IEEE Transactions on</em> <em>2(5):889 -892</em> (<em>October 1995</em>)Thu Sep 01 13:26:03 CEST 2011Dielectrics and Electrical Insulation, IEEE Transactions onoct5889 -892The use of fractal features for recognition of 3-D discharge patterns219953D abilities; corona; degradation; dimension; discharge discharges; discriminating features; fractal fractals; insulation lacunarity; life partial pattern patterns; recognition; surface testing;