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检索条件"任意字段=22nd International Conference on Intelligent Data Engineering and Automated Learning"

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Short term load prediction of a distribution network based on an artificial intelligent method

Short term load prediction of a distribution network based o...

引用

22nd international conference and Exhibition on Electricity Distribution, CIRED 2013

作者： Faiazy, Mahdi Ebtehaj, Mahdi Ardebil Province Power Distribution Utility Iran

ISBN: (纸本)9781849197328

Accurate load forecasting play a key role in economical use of energy and real time security analysis of system. Artificial Neural Network (ANN) model have been extensively implemented to produce accurate results for shortterm load forecasting with time lead ranging from an hour to a week. In this paper a practical case of the small load area of a town getting supplied by 19 distribution feeders is considered with dominant residential-type of load. Historical load and temperature data is collected from January-2010 to December- *** weather seasons are defined by the Meteorological Department, India. Each season includes the group of month .Representative months are selected from each season by observing the variation in load behavior patterns. An input vector composed of load and temperature values at previous instants, is employed to train ANN designed for each selected month by using Back-Propagation algorithm with Momentum learning rule. ANN testing is carried out and their performance is evaluated using mean absolute percentage error (MAPE) criterion. Finally, error values are compared for each month and hence the deviation in forecasting ability of ANN is observed for each month and season.

关键词： Neural networks

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A model-based evaluation of trust and situation awareness in the Diner's Dilemma game

A model-based evaluation of trust and situation awareness in...

引用

22nd Annual conference on Behavior Representation in Modeling and Simulation, BRiMS 2013 - Co-located with the international conference on Cognitive Modeling

作者： O'Donovan, John Jones, Rashaad E.T. Marusich, Laura R. Teng, Yun Gonzalez, Cleotilde Höllerer, Tobias Department of Computer Science University of California Santa Barbara United States S.A. Technologies Inc. Marietta GA 30066 United States Human Research and Engineering Directorate U.S Army Research Laboratory Aberdeen Proving Ground MD United States Department of Social and Decision Sciences Carnegie Mellon University United States

ISBN: (纸本)9781627484701

This paper describes a machine learning approach to evaluate the relationship between trust behavior and Situation Awareness (SA) in the context of a 3-player Iterated Diner's Dilemma game. Our experimental setup consisted of a supervised study in which 24 participants played against computer opponents with different cooperation strategies. Three user interfaces were evaluated in the study, each corresponding to a specific level of SA (Perception, Comprehension, and Projection). Two concepts of trust are explored in the study: Empirical trust behavior was recorded as the degree of cooperation imparted by the participant in a given condition, and Self-reported trust assessments were also collected at regular intervals throughout the study. To explore the relations that exist between these two concepts of trust, and different SA Level conditions, a machine learning approach was applied to train a variety of models to accurately predict trust behavior in each condition. Our best performing algorithm was a J48 rule-based learner, which leveraged SA Level, strategy, and self-reported survey data to predict trust behavior to 76% accuracy, and 67% accuracy using only SA Level and opponent strategy. This is a relative increase of 43% and 24% respectively over a benchmark majority class predictor. These results indicate that support at a level of SA and opponent strategy are good predictors of trust behavior in the Iterated Diner's Dilemma game. Our results also show that trust-prediction models perform best when they are trained on a combination of self-reported data, and strategy / SA Level information.

关键词： User interfaces

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Robot recommender system using affection-based episode ontology for personalization

Robot recommender system using affection-based episode ontol...

引用

22nd IEEE international Symposium on Robot and Human Interactive Communication: "Living Together, Enjoying Together, and Working Together with Robots!", IEEE RO-MAN 2013

作者： Lim, Gi Hyun Hong, Seung Woo Lee, Inhee Suh, Il Hong Beetz, Michael Intelligent Autonomous Systems Technische Universität München Munich Germany Department of Computer Science and Engineering Hanyang University Seoul Korea Republic of Institute for Artificial Intelligence University of Bremen Bremen Germany

ISBN: (纸本)9781479905072

This paper proposes a robot recommender system, which uses a hybrid filtering method based on n-gram affective event model. Nowadays there are strong tendency to utilize a robot for educational services, which can provide educational contents to enhances individual student's motivation. However, the current service robots can be more holistic systems to offer personalized robotic services to satisfy every individuals by reflecting their preferences. Here, robotic service can be another field to meet personal need. Hybrid approaches of personalization technology that combine collaborative filtering approaches and content-based approaches are proposed over the last decade. Especially, n-gram based approaches are proposed to utilize sequential information from very large data sets. This paper suggests an extends affective event model and its n-gram model combining fact semantic knowledge, event episodic knowledge and emotion. To show the validity of the proposed approach, we applied the scenario of English learning. The experiment results shows that an educational service robot recommend two students as different content types, even though they miss same question. © 2013 IEEE.

关键词： Recommender systems

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Researcher Homepage Classification using Unlabeled data 13

Researcher Homepage Classification using Unlabeled Data

引用

international conference on World Wide Web

作者： Sujatha Das G. Cornelia Caragea Prasenjit Mitra C. Lee Giles Computer Science and Engineering The Pennsylvania State University University Park PA-16803 Computer Science and Engineering University of North Texas Denton TX-76203 Computer Science and Engineering Information Sciences and TechnologyThe Pennsylvania State University University Park PA-16803

ISBN: (纸本)9781450322942

A classifier that determines if a webpage is relevant to a specified set of topics comprises a key component for focused crawling. Can a classifier that is tuned to perform well on training datasets continue to filter out irrelevant pages in the face of changed content on the Web? We investigate this question in the context of researcher homepage crawling. We show experimentally that classifiers trained on existing datasets for homepage identification underperform while classifying "irrelevant" pages on current-day academic websites. As an alternative to obtaining datasets to retrain the classifier for the new content, we propose to use effectively unlimited amounts of unlabeled data readily available from these websites in a co-training scenario. To this end, we design novel URL-based features and use them in conjunction with content-based features as complementary views of the data to obtain remarkable improvements in accurately identifying homepages from the current-day university websites. In addition, we propose a novel technique for "learning a conforming pair of classifiers" using mini-batch gradient descent. Our algorithm seeks to minimize a loss (objective) function quantifying the difference in predictions from the two views afforded by co-training. We demonstrate that tuning the classifiers so that they make "similar" predictions on unlabeled data strongly corresponds to the effect achieved by co-training algorithms. We argue that this loss formulation provides insight into understanding the co-training process and can be used even in absence of a validation set.

关键词： co-training consensus maximization gradient descent

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intelligent State-based XML Firewall

Intelligent State-based XML Firewall

引用

22nd international conference on Computer Theory and Applications (ICCTA)

作者： Thabet, Gihan Khattab, Sherif Hassan, Hesham A. Omara, Fatma A. Cairo Univ Fac Comp & Informat Giza Egypt

ISBN: (纸本)9781467328241;9781467328234

Service-oriented architecture (SOA) is an architectural style of software systems, in which a system is composed of isolated or independent services. XML has become the most common way for information exchange and data representation in SOA. Electronic bill presentment and payment (EBPP) is one of the most critical SOA applications for which security a major concern. State-based XML firewall systems have been introduced as one of the major means for SOA security. The work of this paper introduces a new learning mechanism to extend State-based XML Firewall to improve security level. The proposed extension is justified using a case study.

关键词： service-oriented architecture (SOA) XML firewall rule-based learning systems

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A Mediation Layer for Connecting data-Intensive Applications to Reconfigurable data Nodes

A Mediation Layer for Connecting Data-Intensive Applications...

引用

international conference on Computer Communications and Networks

作者： Mohamad Jomaa Khaleel Mershad Noor Abbani Yaman Sharaf-Dabbagh Bashar Romanous Hassan Artail Mazen A. R. Saghir Hazem Hajj Haitham Akkary Mariette Awad Department of Electrical and Computer Engineering American University of Beirut Beirut Lebanon Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

A novel and rapidly growing area of research concerns data-intensive applications and the technical challenges that accompany it. One of those challenges is developing approaches and mechanisms that render high performance in processing and storing data. We joined this research effort by proposing a reconfigurable active solid state drives (RASSD) system that deals with such applications, through employing basic hardware, namely FPGA's connected to SSD's, to service the above applications as processing nodes, and take advantage of the close proximity between storage and processing. In this paper, we propose an intelligent middleware system for interfacing workstation-based and mobile applications to the distributed RASSD system. In order to provide high performance in terms of time and functionality, the middleware manages the data processing on the RASSD nodes through special pieces of code that we call drivelets, along with FPGA configuration files (bitstreams). Another important responsibility of the proposed middleware architecture lies in the unguided management of applications' flows, where it uses an intelligent script-parsing mechanism to turn one general request from the client into a sequence of operations needed to generate the required results. The middleware design allows for the integration of mobile applications into the overall architecture of the RASSD system, and allowing them to run data intensive applications that otherwise it is unfeasible for them to execute. We validate our design by comparing it to an existing middleware architecture, and present two use-cases with their results and discussion.

关键词： Middleware mobile applications FPGA solid state drives distributed computing drivelet bitstream

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Short Term Load Prediction of a Distribution Network based on an Artificial intelligent Method

Short Term Load Prediction of a Distribution Network based o...

引用

international conference and Exhibition on Electricity Distribution

作者： Mahdi FAIAZY Mahdi EBTEHAJ Ardebil Province Power Distribution Utility

ISBN: (纸本)9781629931333

Accurate load forecasting play a key role in economical use of energy and real time security analysis of system. Artificial Neural Network (ANN) model have been extensively implemented to produce accurate results for shortterm load forecasting with time lead ranging from an hour to a week. In this paper a practical case of the small load area of a town getting supplied by 19 distribution feeders is considered with dominant residential-type of load. Historical load and temperature data is collected from January-2010 to December-2010. Four weather seasons are defined by the Meteorological Department, India. Each season includes the group of month. Representative months are selected from each season by observing the variation in load behavior patterns. An input vector composed of load and temperature values at previous instants, is employed to train ANN designed for each selected month by using Back-Propagation algorithm with Momentum learning rule. ANN testing is carried out and their performance is evaluated using mean absolute percentage error (MAPE) criterion. Finally, error values are compared for each month and hence the deviation in forecasting ability of ANN is observed for each month and season.

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Neural networks for proof-pattern recognition

Neural networks for proof-pattern recognition

引用

22nd international conference on Artificial Neural Networks, ICANN 2012

作者： Komendantskaya, Ekaterina Lichota, Kacper School of Computing University of Dundee United Kingdom

ISBN: (纸本)9783642332654

We propose a new method of feature extraction that allows to apply pattern-recognition abilities of neural networks to data-mine automated proofs. We propose a new algorithm to represent proofs for first-order logic programs as feature vectors;and present its implementation. We test the method on a number of problems and implementation scenarios, using three-layer neural nets with backpropagation learning. © 2012 Springer-Verlag.

关键词： data mining

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Machine learning technologies for the requirements analysis in complex systems

Machine learning technologies for the requirements analysis ...

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22nd Annual international Symposium of the international Council on Systems engineering, INCOSE 2012 and the 8th Biennial European Systems engineering conference 2012, EuSEC 2012

作者： Nardini, M. Ciambra, F. Garzoli, F. Croce, D. De Cao, D. Basili, R. Finmeccanica SELEX Sistemi Integrati Italy University of Roma Tor Vergata Italy

ISBN: (纸本)9781622769162

Requirement Analysis is a relevant application area for a variety of Semantic Technologies related to the extraction, disambiguation and exploitation of the knowledge derived from technical requirement documents. Most methods rely on shallow language processing technologies for the automatic extraction of core concepts (e.g. components/devices, their parts and functionalities) and norms (e.g. constraints on the use of components). Few works have been devoted to study paraphrasing (i.e. textual equivalence between requirement definitions) for consistency checking and redundancy elimination. We propose here a distributional method to train a kernel-based learning algorithm (i.e. SVM), as a cost-effective approach to validate requirements from text in support of Requirement Analysis in the design of a Complex Systems, i.e. Naval Combat Systems. These latter are complex systems based on software components able to manage all the Combat System Equipment in different mission scenarios. We will describe the application of Recognition of Textual Entailment (RTE) techniques based on data-driven learning methods to this scenario. While modeling the asymmetric logical relation of entailment between two textual descriptions (i.e. an hypothesis and its thesis), RTE can be here applied to the validation step of compositions between system functionalities described in the requirement specification texts. Early evidences are here discussed, as a proof of the strong applicability of the method to the general case and to other realistic scenarios in System engineering. ©2012. Published and used by INCOSE with permission.

关键词： Extraction

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learning how to select an action: A computational model

Learning how to select an action: A computational model

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22nd international conference on Artificial Neural Networks, ICANN 2012

作者： Denizdurduran, Berat Sengor, Neslihan Serap Istanbul Technical University Electronics and Communications Engineering Department Maslak Istanbul 34469 Turkey

ISBN: (纸本)9783642332685

Neurophysiological experimental results suggest that basal ganglia plays crucial role in action selection while dopamine modifies this process. There are computational models based on these experimental results for action selection. This work focuses on modification of action selection by dopamine release. In the model, a dynamical system is considered for action selection and modification of action selection process is realized by reinforcement learning. The ability of the proposed dynamical system is investigated by bifurcation analysis. Based on the results of this bifurcation analysis, the effect of reinforcement learning on action selection is discussed. The model is implemented on a mobile robot and a foraging task is realized where an exploration in an unfamiliar environment with training in the world is accomplished. Thus, this work fulfills its aim of showing the efficiency of brain-inspired computational models in controlling intelligent agents. © 2012 Springer-Verlag.

关键词： Reinforcement learning

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