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Incorporating reviewer and product information for review rating prediction

Incorporating reviewer and product information for review ra...

引用

22nd international Joint conference on Artificial Intelligence, IJCAI 2011

作者： Li, Fangtao Liu, Nathan Jin, Hongwei Zhao, Kai Yang, Qiang Zhu, Xiaoyan Tsinghua National Laboratory for Information Science and Technology Dept. of Computer Science and Technology Tsinghua University Beijing China Dept. of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong NEC Labs China Beijing China

ISBN: (纸本)9781577355120

Traditional sentiment analysis mainly considers binary classifications of reviews, but in many real-world sentiment classification problems, non-binary review ratings are more useful. This is especially true when consumers wish to compare two products, both of which are not negative. Previous work has addressed this problem by extracting various features from the review text for learning a predictor. Since the same word may have different sentiment effects when used by different reviewers on different products, we argue that it is necessary to model such reviewer and product dependent effects in order to predict review ratings more accurately. In this paper, we propose a novel learning framework to incorporate reviewer and product information into the text based learner for rating prediction. The reviewer, product and text features are modeled as a three-dimension tensor. Tensor factorization techniques can then be employed to reduce the data sparsity problems. We perform extensive experiments to demonstrate the effectiveness of our model, which has a significant improvement compared to state of the art methods, especially for reviews with unpopular products and inactive reviewers.

关键词： Sentiment analysis

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learning an Atlas of a Cognitive Process in Its Functional Geometry

引用

22nd international conference on Information Processing in Medical Imaging (IPMI)

作者： Langs, Georg Lashkari, Danial Sweet, Andrew Tie, Yanmei Rigolo, Laura Golby, Alexandra J. Golland, Polina MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA Brigham & Womens Hosp Dept Neurosurg Harvard Med Sch Boston MA USA Med Univ Vinenna Computat Image Analysis & Radiol Lab Dept Radiol Vienna Austria

ISBN: (纸本)9783642220913;9783642220920

In this paper we construct an atlas that captures functional characteristics of a cognitive process from a population of individuals. The functional connectivity is encoded in a low-dimensional embedding space derived from a diffusion process on a graph that represents correlations of fMRI time courses. The atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution. We derive an algorithm for fitting this generative model to the observed data in a population. Our results in a language fMRI study demonstrate that the method identifies coherent and functionally equivalent regions across subjects.

关键词： Population statistics

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22nd international conference on Modelling, Monitoring and Management of Air Pollution, AIR 2014

22nd International Conference on Modelling, Monitoring and M...

引用

25th Annual IFIP WG 11.3 conference on data and Applications Security and Privacy, DBSec 2011

ISBN: (纸本)9783642223471

The proceedings contain 26 papers. The special focus in this conference is on Modelling, monitoring and management of air pollution. The topics include: Information flow containment;re-designing the web’s access control system;integrated management of security policies;cooperative data access in multi-cloud environments;multiparty authorization framework for data sharing in online social networks;enforcing confidentiality and data visibility constraints;public-key encrypted bloom filters with applications to supply chain integrity;an optimization model for the extended role mining problem;dynamics in delegation and revocation schemes;history-dependent inference control of queries by dynamic policy adaption;multilevel secure data stream processing;query processing in private data outsourcing using anonymization;private database search with sublinear query time;efficient distributed linear programming with limited disclosure;privacy-preserving data mining: a game-theoretic approach;enhancing cardspace authentication using a mobile device;verifiable secret sharing with comprehensive and efficient public verification;a robust remote user authentication scheme against smart card security breach;N-Gram based secure similar document detection;an index structure for private data outsourcing;selective disclosure on encrypted documents;a new leakage-resilient IBE scheme in the relative leakage model;accurate accident reconstruction in VANET;cyber situation awareness: modeling the security analyst in a cyber-attack scenario through instance-based learning;leveraging UML for security engineering and enforcement in a collaboration on duty and adaptive workflow model that extends NIST RBAC and preserving privacy in structural neuroimages.

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Discovery of Gait Anomalies from Motion Sensor data

Discovery of Gait Anomalies from Motion Sensor Data

引用

22nd international conference on Tools with Artificial Intelligence

作者： Pogorelc, Bogdan Gams, Matja Spica Int Doo Dept Intelligent Syst Jozef Stefan Inst Ljubljana Slovenia

ISBN: (纸本)9780769542638

A tool for discovery of gait anomalies of elderly from motion sensor data is proposed. The gait of the user is captured with the motion capture system, which consists of tags attached to the body and sensors situated in the apartment. Position of the tags is acquired by the sensors and the resulting time series of position coordinates are analyzed with dynamic time warping and machine learning algorithms in order to identify the specific gait anomaly. We designed medically oriented features for training a machine learning classifier that classifies the user's gait into: i) normal, ii) with hemiplegia, iii) with Parkinson's disease, iv) with pain in the back and v) with pain in the leg. Experimental results show that the proposed tool is usable for discovery of gait anomalies.

关键词： Parkinson disease biosensors data mining diseases dynamic time warping elderly gait analysis gait analysis gait anomaly geriatrics hemiplegia learning (artificial intelligence) leg pain machine learning classifier medical computing motion capture system motion recognition motion sensor data patient care pattern classification tag position

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Attribute Selection and Imbalanced data: Problems in Software Defect Prediction

Attribute Selection and Imbalanced Data: Problems in Softwar...

引用

22nd international conference on Tools with Artificial Intelligence

作者： Khoshgoftaar, Taghi M. Gao, Kehan Seliya, Naeem Florida Atlantic Univ Boca Raton FL 33431 USA Eastern Connecticut State Univ Willimantic CT 06226 USA Univ Michigan Dearborn MI 48128 USA

ISBN: (纸本)9780769542638

The data mining and machine learning community is often faced with two key problems: working with imbalanced data and selecting the best features for machine learning. This paper presents a process involving a feature selection technique for selecting the important attributes and a data sampling technique for addressing class imbalance. The application domain of this study is software engineering, more specifically, software quality prediction using classification models. When using feature selection and data sampling together, different scenarios should be considered. The four possible scenarios are: (1) feature selection based on original data, and modeling (defect prediction) based on original data;(2) feature selection based on original data, and modeling based on sampled data;(3) feature selection based on sampled data, and modeling based on original data;and (4) feature selection based on sampled data, and modeling based on sampled data. The research objective is to compare the software defect prediction performances of models based on the four scenarios. The case study consists of nine software measurement data sets obtained from the PROMISE software project repository. Empirical results suggest that feature selection based on sampled data performs significantly better than feature selection based on original data, and that defect prediction models perform similarly regardless of whether the training data was formed using sampled or original data.

关键词： feature selection data sampling software measurements defect prediction

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The Penalty Avoiding Rational Policy Making Algorithm in Continuous Action Spaces

The Penalty Avoiding Rational Policy Making Algorithm in Con...

引用

11th international conference on intelligent data engineering and automated learning

作者： Miyazaki, Kazuteru Natl Inst Acad Degrees & Univ Evaluat Kodaira Tokyo 1878587 Japan

ISBN: (纸本)9783642153808

Reinforcement learning involves learning to adapt to environments through the presentation of rewards - special input - serving as clues. To obtain quick rational policies, profit sharing, rational policy making algorithm, penalty avoiding rational policy making algorithm (PARP), PS-r* and PS-r# are used. They are called Exploitation-oriented learning (XoL). When applying reinforcement learning to actual problems, treatment of continuous-valued input and output are sometimes required. A method based on PARP is proposed as a XoL method corresponding to the continuous-valued input, but continuous-valued output cannot be treated. We study the treatment of continuous-valued output suitable for a XoL method in which the environment includes both a reward and a penalty. We extend PARP in the continuous-valued input to continuous-valued output. We apply our proposal to the pole-cart balancing problem and confirm its validity.

关键词： Reinforcement learning

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Software traceability with topic modeling 10

Software traceability with topic modeling

引用

32nd ACM/IEEE international conference on Software engineering, ICSE 2010

作者： Asuncion, Hazeline U. Asuncion, Arthur U. Taylor, Richard N. Institute for Software Research University of California Irvine CA United States Center for Machine Learning and Intelligent Systems University of California Irvine CA United States

ISBN: (纸本)9781605587196

Software traceability is a fundamentally important task in software engineering. The need for automated traceability increases as projects become more complex and as the number of artifacts increases. We propose an automated technique that combines traceability with a machine learning technique known as topic modeling. Our approach automatically records traceability links during the software development process and learns a probabilistic topic model over artifacts. The learned model allows for the semantic categorization of artifacts and the topical visualization of the software system. To test our approach, we have implemented several tools: an artifact search tool combining keyword-based search and topic modeling, a recording tool that performs prospective traceability, and a visualization tool that allows one to navigate the software architecture and view semantic topics associated with relevant artifacts and architectural components. We apply our approach to several data sets and discuss how topic modeling enhances software traceability, and vice versa. © 2010 ACM.

关键词： Software architecture

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Dependency Discovery in data Quality

Dependency Discovery in Data Quality

引用

22nd international conference on Advanced Information Systems engineering

作者： Barone, Daniele Stella, Fabio Batini, Carlo Univ Toronto Dept Comp Sci Toronto ON Canada Univ Milan Dept Informat Syst & Commun Milan Italy

ISBN: (纸本)9783642130939

A conceptual framework for the automatic discovery of dependencies between data quality dimensions is described. Dependency discovery consists in recovering the dependency structure for a set of data quality dimensions measured on attributes of a database. This task is accomplished through the data mining methodology, by learning a Bayesian Network from a database. The Bayesian Network is used to analyze dependency between data quality dimensions associated with different attributes. The proposed framework is instantiated on a real world database. The task of dependency discovery is presented in the case when the following data quality dimensions are considered;accuracy, completeness, and consistency. The Bayesian Network model shows how data quality can be improved while satisfying budget constraints.

关键词： data quality Bayesian networks data mining

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intelligent software agent design issues with extensions to the descartes specification language

Intelligent software agent design issues with extensions to ...

引用

作者： Subburaj, Vinitha H. Urban, Joseph E. Computer Science Department Texas Tech University Lubbock TX 79409-3104 United States

ISBN: (纸本)1891706268

Development of intelligent software agents that include learning and reasoning has recently challenged the software industry. The complexity involved in the development of such systems demands a specific methodology be used. To tackle this problem, this paper presents a formal approach to specification for intelligent software agents. The paper describes the different types of agents and the existing methodologies to specify those agent categories. The paper includes an analysis of intelligent software agents, capabilities, characteristics, and design issues. This paper also discusses the Descartes specification language, an executable formal method which was extended to derive the design of intelligent software agents. The extensions made to the Descartes specification language and the appropriateness towards the development of intelligent systems are also stated.

关键词： Specification languages

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Reengineering the learning process in a transport company

Reengineering the learning process in a transport company

引用

6th international Workshop on Enterprise and Organizational Modeling and Simulation, EOMAS 2010 - In Conjunction with the CAISE 2010, 22nd international conference on Advanced Information Systems engineering

作者： De Vries, Pieter Lukosch, Heide Delft University of Technology Jaffalaan 5 2628 BX Delft Netherlands

Swift changes in the regulations and the organization of the transport sector make innovation an absolute necessity. A company's ability to cope with these changes depends largely on the learning capacity of the organization. Therefore in the transport case described in this paper the company decided that reengineering of the learning strategy was needed to handle the needs of their mobile workforce also in the long term. The strategy is based on a multi dimensional design using sound educational concepts and recent insights in the changing knowledge landscape in combination with learning 2.0 elements to handle the ever changing learning demands. The multi faceted solution is an integrated cross functional business portal with information, learning and performance improvement as the essential processes in the design. The portal is online and acceptance is clearly growing. Process monitoring and evaluation supply data on the usage and success of this integrated approach.

关键词： Reengineering

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