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检索条件"任意字段=2014 IEEE Symposium on Computational Intelligence in Big Data, CIBD 2014"

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Recursive Possibilistic Fuzzy Modeling

Recursive Possibilistic Fuzzy Modeling

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2014 ieee symposium on Evolving and Autonomous Learning Systems (EALS)

作者： Maciel, Leandro Gomide, Fernando Ballini, Rosangela Univ Estadual Campinas Sch Elect & Comp Engn Campinas SP Brazil Univ Estadual Campinas Inst Econ Campinas SP Brazil

ISBN: (纸本)9781479944941

This paper suggests a recursive possibilistic approach for fuzzy modeling of time-varying processes. The approach is based on an extension of the possibilistic fuzzy c-means clustering and functional fuzzy rule-based modeling. Recursive possibilistic fuzzy modeling (rPFM) employs memberships and typicalities to cluster data. Functional fuzzy models uses affine functions in the fuzzy rule consequents. The parameters of the consequent functions are computed using the recursive least squares. Two classic benchmarks, Mackey-Glass time series and Box & Jenkins furnace data, are studied to illustrate the rPFM modeling and applicability. data produced by a synthetic model with parameter drift is used to show the usefulness of rPFM to model time-varying processes. Performance of rPFM is compared with well established recursive fuzzy and neural fuzzy modeling and identification. The results show that recursive possibilistic fuzzy modeling produces parsimonious models with comparable or better accuracy than the alternative approaches.

关键词： fuzzy set theory least mean squares methods pattern clustering possibility theory time series Box & Jenkins furnace data Mackey-Glass time series affine function functional fuzzy rule-based modeling possibilistic fuzzy c-means clustering rPFM recursive least squares recursive possibilistic fuzzy modeling time-varying process Adaptation models Biological system modeling Clustering algorithms computational modeling data models Dispersion Gold fuzzy modeling Least squares method recursive least squares Biological system modeling data models possibility theory Clustering algorithms Fuzzy Set Theory computational modeling Cluster Analysis Recursive Time series time varying Gold Fuzzy rules

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User Identification Through Command History Analysis

User Identification Through Command History Analysis

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ieee symposium on computational intelligence in Cyber Security

作者： Khosmood, Foaad Nico, Phillip L. Woolery, Jonathan Calif Polytech State Univ San Luis Obispo Dept Comp Sci San Luis Obispo CA 93407 USA

ISBN: (纸本)9781479945214

As any veteran of the editor wars can attest, Unix users can be fiercely and irrationally attached to the commands they use and the manner in which they use them. In this work, we investigate the problem of identifying users out of a large set of candidates (25-97) through their command-line histories. Using standard algorithms and feature sets inspired by natural language authorship attribution literature, we demonstrate conclusively that individual users can be identified with a high degree of accuracy through their command-line behavior. Further, we report on the best performing feature combinations, from the many thousands that are possible, both in terms of accuracy and generality. We validate our work by experimenting on three user corpora comprising data gathered over three decades at three distinct locations. These are the Greenberg user profile corpus (168 users), Schonlau masquerading corpus (50 users) and Cal Poly command history corpus (97 users). The first two are well known corpora published in 1991 and 2001 respectively. The last is developed by the authors in a year-long study in 2014 and represents the most recent corpus of its kind. For a 50 user configuration, we find feature sets that can successfully identify users with over 90% accuracy on the Cal Poly, Greenberg and one variant of the Schonlau corpus, and over 87% on the other Schonlau variant.

关键词： Unix information analysis learning (artificial intelligence) natural language processing Cal Poly command history corpus Schonlau corpus Schonlau masquerading corpus Schonlau variant Unix user command history analysis command-line behavior command-line history editor war feature set natural language authorship attribution literature standard algorithm user configuration user corpora user identification user profile corpus Accuracy Computer science Decision trees Entropy Feature extraction History Semantics user id Unix operating system information analysis computer science Decision Trees Natural Language Processing Semantics Feature extraction Consumers entropy commands Command line

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Scalable big data Computing for the Personalization of Machine Learned Models and its Application to Automatic Speech Recognition Service 2

Scalable Big Data Computing for the Personalization of Machi...

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ieee International Conference on big data

作者： Ahnn, Jong Hoon Samsung Res Amer Silicon Valley Cloud Res Lab Mountain View CA 94043 USA

ISBN: (纸本)9781479956661

We observe that the recent advances in big data computing have empowered model-based services such as speech recognition, face recognition, context-aware service, and many other services. Various sources of user's logs can be utilized in remodeling or adapting existing models to improve the quality of service. We propose a system that can support store/retrieve data and process them in a scalable manner. Recently advances in ASR and big data technologies drive more personalized services in many areas of services. A speaker adaptation is one good example which requires huge computation cost in creating a personalized acoustic model and corresponding language model over 100s millions of Samsung product users. We propose a personalized and scalable ASR system powered by the big data infrastructure which brings data-driven personalized opportunities to voice-enabled services such as voice-to-text transcriber, voice-enabled web search in a peta bytes scale. We verify the feasibility of speaker adaptation based on 107 testers' recordings and obtain about 10% of recognition accuracy. We study an optimal set of execution environments by executing jobs running either on Hadoop 1 or Hadoop 2 cluster, and move forward performance optimization strategies: workflow compaction, file compression, best file system selection among several distributed file systems. We devise a metric for the cost of personalized model creation to compare the efficiency of one cluster with the other cluster, and it provides the estimated total execution time for the given number of machines. We finally introduce our in-house object storage and data storage design, and their high performance compared to state-of-the art systems, optimized for voice-enabled services to effectively support small and large files.

关键词： big data learning (artificial intelligence) parallel processing speech recognition big data computing big data infrastructure Hadoop 1 cluster Hadoop 2 cluster Samsung product users automatic speech recognition service best file system selection computation cost context-aware service data storage design distributed file systems face recognition file compression in-house object storage language model machine learned models model-based services performance optimization strategies personalized ASR system personalized acoustic model personalized model creation quality of service scalable ASR system speaker adaptation voice-enabled Web search voice-enabled services voice-to-text transcriber workflow compaction Acoustics Adaptation models big data computational modeling Memory Speech Speech recognition

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Real-time Deformation, Registration and Tracking of Solids Based on Physical Simulation 13

Real-time Deformation, Registration and Tracking of Solids B...

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ieee International symposium on Mixed and Augmented Reality (ISMAR) - Science and Technology

作者： Leizea, Ibai Alvarez, Hugo Aguinaga, Iker Borro, Diego Univ Navarra CEIT E-31080 Pamplona Spain

ISBN: (纸本)9781479961849

This paper proposes a novel approach to registering deformations of 3D non-rigid objects for Augmented Reality applications. Our prototype is able to handle different types of objects in real-time regardless of their geometry and appearance (with and without texture) with the support of an RGB-D camera. During an automatic offline stage, the model is processed in order to extract the data that serves as input for a physics-based simulation. Using its output, the deformations of the model are estimated by considering the simulated behaviour as a constraint. Furthermore, our framework incorporates a tracking method based on templates in order to detect the object in the scene and continuously update the camera pose without any user intervention. Therefore, it is a complete solution that extends from tracking to deformation formulation for either textured or untextured objects regardless of their geometrical shape. Our proposal focuses on providing a correct visual with a low computational cost. Experiments with real and synthetic data demonstrate the visual accuracy and the performance of our approach.

关键词： I.4.8 [IMAGE PROCESSING AND COMPUTER VISION]: Scene Analysis I.6.8 [SIMULATION AND MODELING]: Types of Simulation [I.2.10]: ARTIFICIAL intelligence Vision and Scene Understanding

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A Hybrid computational intelligence Approach for Efficiently Evaluating Customer Sentiments in E-Commerce Reviews

A Hybrid Computational Intelligence Approach for Efficiently...

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2014 ieee symposium on Intelligent Agents (IA)

作者： Acampora, Giovanni Cosma, Georgina Nottingham Trent Univ Sch Sci & Technol Nottingham NG11 8NS England

ISBN: (纸本)9781479944880

The Internet has opened new interesting scenarios in the fields of e-commerce, marketing and on-line transactions. In particular, thanks to mobile technologies, customers can make purchases in a faster and cheaper way than visiting stores, and business companies can increase their sales volume due to a world-wide visibility. Moreover, online trading systems allow customers to gather all the required information about product quality and characteristics, via customer reviews, and make an informed purchase. Due to the fact that these reviews are used to determine the extent of customers acceptance and satisfaction of a product or service, they can affect the future selling performance and market share of a company because they can also be used by companies to determine the success of a product, and predict its demand. As a consequence, tools for efficiently classifying textual customer reviews are becoming a key component of each e-commerce development framework to enable business companies to define the most suitable selling strategies and improve their market capabilities. This paper introduces an innovative framework for efficiently analysing customer sentiments in textual reviews in order to compute their corresponding numerical rating to allow companies to better plan their future business activities. The proposed approach addresses different issues involved in this significant task: the dimension and imprecision of ratings data. As shown in experimental results, the proposed hybrid approach yields better learning performance than other state of the art rating predictors.

关键词： Internet customer satisfaction electronic commerce business activities business companies customer acceptance customer sentiments e-commerce development framework e-commerce reviews hybrid computational intelligence innovative framework marketing mobile technologies online trading systems online transactions sales volume textual customer reviews world-wide visibility Companies databases Feature extraction Genetic algorithms Matrix decomposition Natural language processing Vectors electronic commerce Consumer Satisfaction online transactions Internets Companies Mobile Technologies matrix decomposition Natural Language Processing Feature extraction Genetic Algorithm (GA) Telemarketing database Online business Purchasing

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A Combined MapReduce-Windowing Two-Level Parallel Scheme for Evolutionary Prototype Generation

A Combined MapReduce-Windowing Two-Level Parallel Scheme for...

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ieee Congress on Evolutionary Computation (CEC)

作者： Triguero, Isaac Peralta, Daniel Bacardit, Jaume Garcia, Salvador Herrera, Francisco Univ Granada CITIC UGR Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Jaen Dept Comp Sci Jaen 23071 Spain Newcastle Univ Sch Comp Sci Newcastle Upon Tyne NE1 7RU Tyne & Wear England

ISBN: (纸本)9781479914883

Evolutionary prototype generation techniques have demonstrated their usefulness to improve the capabilities of the nearest neighbor classifier. They act as data reduction algorithms by generating representative points of a given problem. Their main purposes are to speed up the classification process and to reduce the storage requirements and sensitivity to noise of the nearest neighbor rule. Nowadays, with the increment of available data, the use of this kind of reduction techniques becomes more important. However, their applicability can be limited to problems with no more than tens of thousands of instances. In order to address this limitation, in this work we develop a two-level parallelization scheme for evolutionary prototype generation methods. Firstly, it distributes the functioning of these algorithms in several tasks based on a MapReduce framework. Then, for each one of these tasks (mappers), we accelerate the prototype generation process by using a windowing approach. This model enables evolutionary prototype generation algorithms to be applied over large-scale classification problems without accuracy loss. Our preliminary experiments using a dataset of 1 million instances show that this proposal is an appropriate tool to improve the performance of the nearest neighbor classifier with big data.

关键词： Acceleration big data computational modeling data mining Prototypes Runtime Training

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Reports on the 2013 AAAI fall symposium series

Reports on the 2013 AAAI fall symposium series

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作者： Burns, Gully A.P.C. Gil, Yolanda Villanueva-Rosales, Natalia Liu, Yan Risi, Sebastian Lehman, Joel Clune, Jeff Lebiere, Christian Rosenbloom, Paul S. Van Harmelen, Frank Hendler, James A. Hitzler, Pascal Janowicz, Krzysztof Swarup, Samarth Biomedical Knowledge Engineering Group Information Sciences Institute University of Southern California United States Interactive Knowledge Capture Research Group Information Sciences Institute United States Department of Computer Science University of Southern California United States Department of Computer Science and Cyber-ShARE Center of Excellence University of Texas El Paso United States Center for Computer Games Research University of Copenhagen Denmark Department of Computer Science University of Texas Austin United States University of Wyoming United States Department of Psychology Carnegie Mellon University United States Institute for Creative Technologies United States Vrije Universiteit Amsterdam Netherlands Rensselaer Polytechnic Institute United States Wright State University United States University of California Santa Barbara United States Virginia Bioinformatics Institute Virginia Tech. United States

The Association for the Advancement of Artificial intelligence was pleased to present the 2013 Fall symposium Series, held Friday through Sunday, November 15-17, at the Westin Arlington Gateway in Arlington, Virginia, near Washington, D.C., USA. The titles of the five symposia were Discovery Informatics: AI Takes a Science- Centered View on big data (FS-13-01);How Should intelligence Be Abstracted in AI Research: MDPs, Symbolic Representations, Artificial Neural Networks, or - ? (FS-13-02);Integrated Cognition (FS-13-03);Semantics for big data (FS- 13-04);and Social Networks and Social Contagion: Web Analytics and computational Social Science (FS-13-05). The highlights of each symposium are presented in this report. Copyright © 2014, Association for the Advancement of Artificial intelligence. All rights reserved.

关键词： big data

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Detecting and profiling sedentary young men using machine learning algorithms 5

Detecting and profiling sedentary young men using machine le...

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ieee symposium on computational intelligence and data Mining (CIDM)

作者： Siirtola, Pekka Pyky, Riitta Ahola, Riikka Koskimaki, Heli Jamsa, Timo Korpelainen, Raija Roning, Juha Univ Oulu Comp Sci & Engn Dept POB 4500 FI-90014 Oulu Finland Oulu Deaconess Inst Dept Sports & Exercise Med Oulu Finland Oulu Univ Hosp Med Res Ctr Oulu Oulu Finland Univ Oulu SF-90100 Oulu Finland Univ Oulu Fac Med Inst Hlth Sci Oulu Finland Univ Oulu Fac Med Dept Med Technol SF-90100 Oulu Finland

ISBN: (纸本)9781479945184

Many governments and institutions have guidelines for health-enhancing physical activity. Additionally, according to recent studies, the amount of time spent on sitting is a highly important determinant of health and wellbeing. In fact, sedentary lifestyle can lead to many diseases and, what is more, it is even found to be associated with increased mortality. In this study, a data set consisting of self-reported questionnaire, medical diagnoses and fitness tests was studied to detect sedentary young men from a large population and to create a profile of a sedentary person. The data set was collected from 595 young men and contained altogether 678 features. Most of these are answers to multi-choice close-ended questions. More precisely, features were mostly integers with a scale from 1 to 5 or from 1 to 2, and therefore, there was only a little variability in the values of features. In order to detect and profile a sedentary young man, machine learning algorithms were applied to the data set. The performance of five algorithms is compared (quadratic discriminant analysis (QDA), linear discriminant analysis (LDA), C4.5, random forests, and k nearest neighbours (kNN)) to find the most accurate algorithm. The results of this study show that when the aim is to detect a sedentary person based on medical records and fitness tests, LDA performs better than the other algorithms, but still the accuracy is not high. In the second part of the study the differences between highly sedentary and non-sedentary young men are searched, recognition can be obtained with high accuracy with each algorithm.

关键词： learning (artificial intelligence) medical diagnostic computing fitness test health-enhancing physical activity machine learning algorithms medical diagnosis sedentary young men Accuracy Diseases Educational institutions Feature extraction Machine learning algorithms Medical diagnostic imaging Sociology Machine learning algorithms medical diagnostic computing Medical diagnosis Sociology Educational institutions Feature extraction Fitness Testing maladjustment health diagnostic imaging Disease diagnosis

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Interference Suppression using CPP Adaptive Notch Filters for UWB Synchronization in Stochastic Non-Linear Channels

Interference Suppression using CPP Adaptive Notch Filters fo...

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ieee symposium on computational intelligence for Communication Systems and Networks (CIComms)

作者： Begum, Farhana Sarma, Manash Pratim Sarma, Kandarpa Kumar Mastorakis, Nikos Bulucea, Aida Gauhati Univ Dept Elect & Commun Engn Gauhati 781014 Assam India Tech Univ Sofia Sofia 1000 Bulgaria Univ Craiova Craiova Romania

ISBN: (纸本)9781479945245

For precise synchronization of ultra wide band (UWB) signals in wireless channels, narrow band interference (NBI) suppression is a challenging issue. This is more relevant for real time channels which are non-linear in nature and are coexisted by narrow band wireless systems. An interference suppression scheme coupled to an energy detection based synchronization approach designed using second-order complex adaptive notch filter (ANF) is reported in this paper. This ANF uses a gradient descent algorithm for tracking the filter coefficients. The use of the same exploits the correlation difference property of signals received and suppresses NBIs in UWB signals. Consequently, it significantly reduces the computational complexity compared to the existing adaptive filters and requires lower power for an efficient hardware implementation. The data rate of UWB is usually high, so a combined pipelined-parallelism (CPP) approach is proposed which effectively simplifies the hardware design unlike direct and cascade forms. Detailed analysis suggest that the proposed scheme provides better performance in terms of power, speed, convergence and stability. Moreover, this scheme when used in conjunction with energy detection receivers significantly improves the interference tolerance margin, thereby raising the performance levels of synchronization with energy detection approach in non-coherent energy detection receivers.

关键词： adaptive filters computational complexity interference suppression notch filters synchronisation ultra wideband communication wireless channels CPP adaptive notch filters CPP approach NBI suppression UWB signals UWB synchronization combined pipelined-parallelism approach energy detection receivers filter coefficients interference tolerance margin narrow band interference suppression real time channels stochastic nonlinear channels ultra wide band signals Capacitance Clocks Interference Power demand Propagation delay Receivers Synchronization suppression of interference wireless channels complexity classes Trap Filter Filter coefficient adaptive filters Propagation delay Ultra wideband communication Ultra wideband technologies Power demand Receivers timing channel Timing devices capacitance Adaptive notch filter

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Reports of the 2014 AAAI spring symposium series

Reports of the 2014 AAAI spring symposium series

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作者： Jain, Manish Jiang, Albert Xin Kido, Takashi Takadama, Keiki Mercer, Eric G. Rungta, Neha Waser, Mark Wagner, Alan Burke, Jennifer Sofge, Don Lawless, William Sridharan, Mohan Hawes, Nick Hwang, Tim Computer Science Department Virginia Polytechnic Institute State University United States Computer Science Department University of Southern California United States Rikengenesis Japan University of Electro-Communications Japan Computer Science Department Brigham Young University ProvoUT United States NASA Ames Research Center Moffet FieldCA United States Digital Wisdom Institute United States Georgia Tech Research Institute Georgia Institute of Technology United States Boeing Research and Technology United States Naval Research Laboratory United States Department of Mathematics and Psychology Pain College AugustaGA United States Department of Computer Science Texas Tech University United States School of Computer Science University of Birmingham United Kingdom Pacific Social Architecting Corporation United Kingdom Data and Society Research Institute New York City United States

The Association for the Advancement of Artificial intelligence was pleased to present the AAAI 2014 Spring symposium Series, held Monday through Wednesday, March 24- 26, 2014. The titles of the eight symposia were Applied computational Game Theory, big data Becomes Personal: Knowledge into Meaning, Formal Verification and Modeling in Human-Machine Systems, Implementing Selves with Safe Motivational Systems and Self-Improvement, The Intersection of Robust intelligence and Trust in Autonomous Systems, Knowledge Representation and Reasoning in Robotics, Qualitative Representations for Robots, and Social Hacking and Cognitive Security on the Internet and New Media). This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium. Copyright © 2014, Association for the Advancement of Artificial intelligence. All rights reserved.

关键词： Personal computing

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