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A comparative study of autonomous learning outlier detection methods applied to fault detection

A comparative study of autonomous learning outlier detection...

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IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015

作者： Bezerra, Clauber Gomes Costa, Bruno Sielly Jales Guedes, Luiz Affonso Angelov, Plamen Parvanov Campus EaD Federal Institute of Rio Grande Do Norte-IFRN Natal Brazil Department of Computer Engineering and Automation Federal University of Rio Grande Do Norte-UFRN Brazil Lancaster University Data Science Group School of Computing and Communications LA1 4WA United Kingdom

ISBN: (纸本)9781467374286

Outlier detection is a problem that has been largely studied in the past few years due to its great applicability in real world problems (e.g. financial, social, climate, security). Fault detection in industrial processes is one of these problems. In that context, several methods have been proposed in literature to address fault detection. In this paper we propose a comparative analysis of three recently introduced outlier detection methods: RDE, RDE with Forgetting and TEDA. Such methods were applied to the data set provided in DAMADICS benchmark, a very well-known real data tool for fault detection applications. The results, however, can be extended to similar problems of the area. Therewith, in this work we compare the main features of each method as well as the results obtained with them. © 2015 IEEE.

关键词： Benchmarking

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Mapping representation based on meta-data and spin for localization workflows 2

Mapping representation based on meta-data and spin for local...

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Joint 2nd International Workshop on Semantic Web Enterprise Adoption and Best Practice, WaSABi 2014 and 2nd International Workshop on Finance and Economics on the Semantic Web, FEOSW 2014 - Co-located with 11th European Semantic Web Conference, ESWC 2014

作者： Meehan, Alan Brennan, Rob Lewis, Dave O'Sullivan, Declan CNGL Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland

The localization industry currently deploys language translation workflows based on heterogeneous tool-chains. Standardized tool interchange formats such as XLIFF (XML Localization Interchange File Format) have had some impact on enabling more agile translation workflows. However the rise of new tools based on machine translation technology and the growing demand for enterprise linked data applications has created new interoperability challenges as workflows need to encompass a broader range of tools. In this paper we present an approach of representing mappings between RDF-based representations of multilingual content and meta-data. To represent the mappings, we use a combination of SPARQL Inferencing Notation (SPIN) and meta-data. Our approach allows the mapping representation to be published as Linked data. In contrast to other frameworks such as R2R, the mappings are executed via a standard SPARQL processor. The objective is to provide a more agile approach to translation workflows and greater interoperability between software tools by leveraging the ongoing innovation in the Multilingual Web field. Our use case is a Language Technology retraining workflow where publishing mappings leads to new opportunities for interoperability and end-to-end tool-chain analytics. We present the results from an initial experiment which compared our approach of executing and representing mappings to that of a similar approach - The R2R Framework.

关键词： Mapping

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Search result visualization with characters for children 14

Search result visualization with characters for children

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13th International Conference on Interaction Design and Children, IDC 2014

作者： Gossen, Tatiana Müller, Rene Stober, Sebastian Nürnberger, Andreas Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany

ISBN: (纸本)9781450322720

In this paper, we explore alternative ways to visualize search results for children. We propose a novel search result visualization using characters. The main idea is to represent each web document as a character where a character visually provides clues about the webpage's content. We focused on children between six and twelve as a target user group. Following the user-centered development approach, we conducted a preliminary user study to determine how children would represent a webpage as a sketch based on a given template of a character. Using the study results the first prototype of a search engine was developed. We evaluated the search interface on a touchpad and a touch table in a second user study and analyzed user's satisfaction and preferences. © Copyright is held by the owner/author(s).

关键词： Search engines

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An exploratory search user interface concept supporting vague querying and a novel result representation

An exploratory search user interface concept supporting vagu...

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Workshop

作者： Lüddecke, Timo Jüttner, Markus Nitsche, Marcus Nürnberger, Andreas Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany

Common search engines deliver quite good results when the user has a precise notion of what he is looking for. However, the user might have in mind additional prior information regarding the importance of specific terms. Consequently, it seems desirable to avoid the latter and incorporate the knowledge into the query instead. Therefore, we propose a search user interface concept that supports users in modelling their uncertainty in a comfortable way, foster exploratory search and provide a compact yet informative representation of results. An implemented prototype demonstrates the feasibility of the concept. We also present results of a first two-step usability study. The results indicate a good usability of the concept and show that even this novel concept meets user's expectations. Copyright © 2014 for the individual papers by the paper's authors.

关键词： Search engines

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Persistent e-mail identification is viable! 4

Persistent e-mail identification is viable!

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4th International Workshop on Semantic Digital Archives, SDA 2014. Held as part of the International Digital Libraries Conference, DL 2014

作者： Haun, Stefan Nürnberger, Andreas Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke-University Magdeburg Germany

Persistent identification of entities in Personal Information Management (PIM) is necessary to enable stable, long-term references in archives and semantic applications. In the case of e-mails, the standard offers Message-IDs (MID), which are widely deployed. However, stores do not use the MID but rather rely on a path, which is likely to change, to refer to e-mails and thus do not offer a stable identification. We show that MIDs are viable to identify and retrieve e-mails from an IMAP store in real-world scenarios. The presented concept can be integrated into any store, but we also offer a software solution that serves as an additional layer above the store and allows real-time access over MID. We propose a validation method to prove that the concept is working and some applications that are enabled by e-mail identification are sketched.

关键词： Semantics

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NEI:A framework for dynamic news event exploration and visualization 14

NEI:A framework for dynamic news event exploration and visua...

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7th International Symposium on Visual Information Communication and Interaction, VINCI 2014

作者： Guo, Xiaofei Li, Juanzi Yang, Ruibing Ma, Xiaoli Knowledge Engineering Group Department of Computer Science and Technology Tsinghua University 10084 China

ISBN: (纸本)9781450327657

Nowadays, there are many events reported by News Media everyday, which contains a massive number of news. People are getting more and more interested in understanding how an event evolves after it happens. News related to the same event or similar events usually has more common entities and stronger topic correlations, which is a new perspective to study news event. Due to the complexity of event evolving process, event visualization has been a big challenge for a long time. In this paper, we design a novel four-phase framework NEI(News Event Insight) that focuses on visualizing a news event properly and clearly, namely (1)Entity Topic Modeling. We extract topics and entities through timeline. (2)Temporal Topic Correlation Analysis. Based on the topic modeling result, we design two methods to select hot topics and build links for them. (3)Keyword Extraction. Specially, we combine string frequency with syntax features and use language models to acquire candidate keywords for representing topics. (4)Visualization. Visualization demonstrates the quantifying properties of topics related to a certain event. A case study shows our framework achieves promising results on both single event and similar events. Copyright 2014 ACM.

关键词： Visualization

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Reports of the AAAI 2014 conference workshops

Reports of the AAAI 2014 conference workshops

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作者： Albrecht, Stefano V. Barreto, André M. S. Braziunas, Darius Buckeridge, David L. Cuayáhuitl, Heriberto Dethlefs, Nina Endres, Markus Farahmand, Amir-Massoud Fox, Mark Frommberger, Lutz Ganzfried, Sam Guillet, Sébastien Gil, Yolanda Hunter, Lawrence E. Jhala, Arnav Kersting, Kristian Konidaris, George Lecue, Freddy McIlraith, Sheila Natarajan, Sriraam Noorian, Zeinab Poole, David Ronfard, Rémi Saffiotti, Alessandro Shaban-Nejad, Arash Srivastava, Biplav Tesauro, Gerald Uceda-Sosa, Rosario Van Den Broeck, Guy Van Otterlo, Martijn Wallace, Byron C. Weng, Paul Wiens, Jenna Zhang, Jie School of Informatics University of Edinburgh United Kingdom Department of Applied and Computational Mathematics Brazilian National Laboratory Brazil Big Data Group Kobo Inc. Brazil Department of Epidemiology and Biostatistics McGill University Canada School of Mathematical and Computer Sciences Heriot-Watt University in Edinburgh United Kingdom University of Augsburg Germany Carnegie Mellon University United States University of Toronto Canada Cognitive Systems Group University of Bremen Germany LIARA UQAC Chicoutimi Canada Information Sciences Institute University of Southern California Computer Science Department United States Center for Computational Pharmacology University of Colorado School of Medicine United States Department of Computational Media University of California Santa Cruz United States Computer Science Department Technical University of Dortmund Germany MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA United States IBM Research - Smarter Cities Technology Centre Dubline Ireland School of Informatics and Computing Indiana University United States Department of Computer Science University of Saskatchewan Canada University of British Columbia Canada INRIA University of Grenoble France Orebro University Sweden McGill Clinical and Health Informatics Group McGill University Canada IBM Research United States Cognitive Computing Department IBM Research Yorktown HeightsNY United States Computer Science Department Katholieke Universiteit Leuven Belgium Cognitive Artificial Intelligence Department Radboud University Nijmegen Netherlands University of Texas Austin United States Pierre and Marie Curie University Paris France University of Michigan United States School of Computer Engineering Nanyang Technological University Singapore

The AAAI-14 Workshop program was held Sunday and Monday, July 27-28, 2014, at the Québec City Convention Centre in Québec, Canada. The AAAI-14 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Artificial Intelligence and Robotics;Artificial Intelligence Applied to Assistive Technologies and Smart Environments;Cognitive Computing for Augmented Human Intelligence;computer Poker and Imperfect Information;Discovery Informatics;Incentives and Trust in Electronic Communities;Intelligent Cinematography and Editing;Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action, and Communication;Modern Artificial Intelligence for Health Analytics;Multiagent Interaction Without Prior Coordination;Multidisciplinary Workshop on Advances in Preference Handling;Semantic Cities - Beyond Open data to Models, Standards, and Reasoning;Sequential Decision Making with Big data;Statistical Relational AI;and the World Wide Web and Public Health Intelligence. This article presents short summaries of those events. © 2015, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Decision making

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The linked data mining challenge 2014 results and experiences 3

The linked data mining challenge 2014 results and experience...

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3rd International Workshop on knowledge Discovery and data Mining Meets Linked Open data, Know@LOD 2014, Co-located with 11th Extended Semantic Web Conference, ESWC 2014

作者： Svátek, Vojtěch Mynarz, Jindřich Paulheim, Heiko University of Economics Department of Information and Knowledge Engineering Prague Czech Republic University of Mannheim Germany Research Group Data and Web Science Germany

The 2014 edition of the Linked data Mining Challenge, conducted in conjunction with Know@LOD 2014, has been the third edition of this challenge. The underlying data came from two domains: public procurement, and researcher collaboration. Like in the previous year, when the challenge was held at the data Mining on Linked data workshop co-located with the European Conference on Machine Learning and Principles and Practice of knowledge Discovery in databases (ECML PKDD 2013), the response to the challenge appeared lower than expected, with only one solution submitted for the predictive task this year. We have tried to track the reasons for the continuously low participation in the challenge via a questionnaire survey, and principles have been distilled that could help organizers of future similar challenges.

关键词： Linked data

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A fast noise resilient anomaly detection using GMM-based collective labelling

A fast noise resilient anomaly detection using GMM-based col...

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science and Information Conference (SAI)

作者： Elnaz Bigdeli Bijan Raahemi Mahdi Mohammadi Stan Matwin School of Electrical Engineering and Computer Science University of Ottawa Ottawa ON Canada Telfer School of Management Knowledge Discovery and Data Mining lab University of Ottawa E. Ottawa ON Canada Department of Computing Dalhousie University Halifax NB Canada Polish Academy of Sciences Warsaw Poland

Anomaly detection algorithms face several challenges including computational complexity and resiliency to noise in input data. In this paper, we propose a fast and noise-resilient cluster-based anomaly detection method using collective labelling approach. In the proposed Collective Probabilistic Anomaly Detection method, first instead of labelling each new sample (as normal or anomaly) individually, the new samples are clustered, then labelled. This collective labelling mitigates the negative impact of noise by relying on group behaviour rather than individual characteristics of incoming samples. Second, since grouping and labelling new samples may be time-consuming, we summarize clusters using Gaussian Mixture Model (GMM). Not only does GMM offer faster processing speed; it also facilitates summarizing clusters with arbitrary shape, and consequently, reducing the memory space requirement. Finally, a modified distance measure, based on Kullback-Liebner method, is proposed to calculate the similarity among clusters represented by GMMs. We evaluate the proposed method on various datasets by measuring its false alarm rate, detection rate and memory requirement. We also add different levels of noise to the input datasets to demonstrate the performance of the proposed collective anomaly detection method in the presence of noise. The experimental results confirm superior performance of the proposed method compared to individually-based labelling techniques in terms of memory usage, detection rate and false alarm rate.

关键词： Noise Shape Training Gaussian mixture model Clustering algorithms Support vector machines

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Online fault detection based on Typicality and Eccentricity data Analytics

Online fault detection based on Typicality and Eccentricity ...

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International Joint Conference on Neural Networks, IJCNN 2015

作者： Costa, Bruno Sielly Jales Bezerra, Clauber Gomes Guedes, Luiz Affonso Angelov, Plamen Parvanov Campus Natal - Zona Norte Federal Institute of Rio Grande Do Norte - IFRN Natal Brazil Campus EaD Federal Institute of Rio Grande Do Norte - IFRN Natal Brazil Department of Computer Engineering and Automation Federal University of Rio Grande Do Norte - UFRN Brazil Lancaster University Data Science Group School of Computing and Communications LancasterLA1 4WA United Kingdom Excellence Carlos III University Madrid Spain

ISBN: (纸本)9781479919604

Fault detection is a task of major importance in industry nowadays, since that it can considerably reduce the risk of accidents involving human lives, in addition to production and, consequently, financial losses. Therefore, fault detection systems have been largely studied in the past few years, resulting in many different methods and approaches to solve such problem. This paper presents a detailed study on fault detection on industrial processes based on the recently introduced eccentricity and typicality data analytics (TEDA) approach. TEDA is a recursive and non-parametric method, firstly proposed to the general problem of anomaly detection on data streams. It is based on the measures of data density and proximity from each read data point to the analyzed data set. TEDA is an online autonomous learning algorithm that does not require a priori knowledge about the process, is completely free of user- and problem-defined parameters, requires very low computational effort and, thus, is very suitable for real-time applications. The results further presented were generated by the application of TEDA to a pilot plant for industrial process. © 2015 IEEE.

关键词： Fault detection

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