2013 Conference on Technologies and Applications of Artificial Intelligence

6-8 Dec. 2013

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  • [Front cover]

    Publication Year: 2013, Page(s): C4
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  • [Title page i]

    Publication Year: 2013, Page(s): i
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  • [Title page iii]

    Publication Year: 2013, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2013, Page(s): iv
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  • Table of contents

    Publication Year: 2013, Page(s):v - xii
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  • Preface - TAAI 2013

    Publication Year: 2013, Page(s):xiii - xiv
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  • Sponsors and Co-Organizers

    Publication Year: 2013, Page(s): xv
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  • TAAI 2013 Organizing Committees

    Publication Year: 2013, Page(s):xvi - xxii
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  • Special session: Organization - AIIS 2013

    Publication Year: 2013, Page(s): xxiii
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  • Special Session: Organization - IHB 2013

    Publication Year: 2013, Page(s):xxiv - xxv
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  • Workshop Organization - CCB 2013

    Publication Year: 2013, Page(s):xxvi - xxvii
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  • Workshop Organization - HCC 2013

    Publication Year: 2013, Page(s): xxviii
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  • IWCG 2013 Workshop: Organization

    Publication Year: 2013, Page(s): xxix
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  • TAEA 2013 Workshop: Organization

    Publication Year: 2013, Page(s): xxx
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  • Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances

    Publication Year: 2013, Page(s):1 - 6
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (295 KB) | HTML iconHTML

    We consider the unsupervised learning problem of assigning labels to unlabeled data. A naive approach is to use clustering methods, but this works well only when data is properly clustered and each cluster corresponds to an underlying class. In this paper, we first show that this unsupervised labeling problem in balanced binary cases can be solved if two unlabeled datasets having different class b... View full abstract»

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  • Key Stroke Profiling for Data Loss Prevention

    Publication Year: 2013, Page(s):7 - 12
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (698 KB) | HTML iconHTML

    Data leakage has become a serious problem to many organizations. To provide visibility into what data is confidential and where it's stored, many of current data leakage prevention (DLP) solutions depend on scanning file content. This approach needs the capability of parsing various file formats, but for those unsupported file formats there still exist risks of data breach. To address this issue, ... View full abstract»

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  • Active Learning for Multiclass Cost-Sensitive Classification Using Probabilistic Models

    Publication Year: 2013, Page(s):13 - 18
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (378 KB) | HTML iconHTML

    Multiclass cost-sensitive active learning is a relatively new problem. In this paper, we derive the maximum expected cost and cost-weighted minimum margin strategies for multiclass cost-sensitive active learning. The two strategies can be viewed as extended versions of the classical cost-insensitive active learning strategies. The experimental results demonstrate that the derived strategies are pr... View full abstract»

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  • Pairwise Regression with Upper Confidence Bound for Contextual Bandit with Multiple Actions

    Publication Year: 2013, Page(s):19 - 24
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (299 KB) | HTML iconHTML

    The contextual bandit problem is typically used to model online applications such as article recommendation. However, the problem cannot fully meet certain needs of these applications, such as performing multiple actions at the same time. We defined a new Contextual Bandit Problem with Multiple Actions (CBMA), which is an extension of the traditional contextual bandit problem and fits the online a... View full abstract»

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  • Data Selection Techniques for Large-Scale Rank SVM

    Publication Year: 2013, Page(s):25 - 30
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (406 KB) | HTML iconHTML

    Learning to rank has become a popular research topic in several areas such as information retrieval and machine learning. Pair-wise ranking, which learns all the order preferences between pairs of examples, is a typical method for solving the ranking problem. In pair-wise ranking, Rank SVM is a widely-used algorithm and has been successfully applied to the ranking problem in the previous work. How... View full abstract»

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  • Lossless Vector-Quantised Index Coding with Index-Pattern Coding Scheme

    Publication Year: 2013, Page(s):31 - 36
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1087 KB) | HTML iconHTML

    In this paper, an efficient new on-line lossless compression algorithm is presented to encode image VQ. The proposed coding technique exploits the correlation of neighboring index-pairs not in the original vector-quantized index map but in the principal index-pattern table which is generated from the two dimensional histogram of index-patterns in the training stage. Simulation results indicate tha... View full abstract»

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  • Energy Disaggregation via Clustered Regression Models: A Case Study in the Convenience Store

    Publication Year: 2013, Page(s):37 - 42
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1065 KB) | HTML iconHTML

    Global warming and the depletion of natural resources are two of the most difficult problems we have ever faced. To address this problem, people have begun paying more attention to carbon emission reduction and energy saving. For the residential electricity use, many studies have demonstrated that feedbacks, such as energy consumption of each appliance in the home, can help consumers reduce electr... View full abstract»

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  • On Package Organization for Willingness Satisfaction in Social Networks

    Publication Year: 2013, Page(s):43 - 48
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    Studies show that both the personal preference and social tightness between friends play important roles in the decision process of activity participation for a person. Considering the preference of a person and the social tightness among friends, in this work, we formulate a new research problem, called Package Organization for Willingness satisfaction (POWA), to effectively select items into a p... View full abstract»

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  • Preference-Aware Community Detection for Item Recommendation

    Publication Year: 2013, Page(s):49 - 54
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (566 KB) | HTML iconHTML

    In recent years, researches on recommendation systems based on social information have attracted a lot of attentions. Although a number of social-based recommendation techniques have been proposed in the literature, most of their concepts are only based on the individual or friends' rating behaviors. It leads to the problem that the recommended item list is usually constrained within the users' or... View full abstract»

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  • Using Attribute Construction to Improve the Predictability of a GP Financial Forecasting Algorithm

    Publication Year: 2013, Page(s):55 - 60
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (259 KB) | HTML iconHTML

    Financial forecasting is an important area in computational finance. EDDIE 8 is an established Genetic Programming financial forecasting algorithm, which has successfully been applied to a number of international datasets. The purpose of this paper is to further increase the algorithm's predictive performance, by improving its data space representation. In order to achieve this, we use attribute c... View full abstract»

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  • Frequent Pattern Based User Behavior Anomaly Detection for Cloud System

    Publication Year: 2013, Page(s):61 - 66
    Cited by:  Papers (2)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (249 KB) | HTML iconHTML

    Cloud Computing is a hot topic in the global IT industry, which is considered as the main part of the network and computing service provider in recent years. Some security issues will be more threatening in cloud computing, such as account theft and insider threat. We propose a framework to utilize anomaly detection and random re-sampling techniques for profiling user's behaviors via the frequent ... View full abstract»

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