This paper presents a maximum entropy model approach to identifying conjuncts of conjunctive structures in questions of financial domain from on-line discussion *** avoid phrasal ambiguity, only features in lexical an...
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This paper presents a maximum entropy model approach to identifying conjuncts of conjunctive structures in questions of financial domain from on-line discussion *** avoid phrasal ambiguity, only features in lexical and shallow syntactic level are *** conjunct detection problem is converted into a stepwise boundary identification task, reducing the search space of a n-word sentence from O(n2) to O(n), The best performance on the test set achieves 85.88% recall and 96% *** approach itself is domain-independent and can be used for conjunct identification in questions universally.
This paper presents an approach to combining VoIP auditing and speaker recognition,and an application of using speaker recognition on VoIP voice data is *** this paper,it deals with how to obtain VoIP voice data when ...
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This paper presents an approach to combining VoIP auditing and speaker recognition,and an application of using speaker recognition on VoIP voice data is *** this paper,it deals with how to obtain VoIP voice data when audio communication:signaling protocol analysis and voice data traffic *** converting the raw voice data to wave files,the speaker recognition can use them to identify the speaker and the result shows it is an effective *** paper also discusses the influence by codec compression and packet loss of VoIP communication.
We consider a parallel method for solving generalized eigen-value problems that arise from molecular orbital computations. We use a moment-based method that finds several eigenvalues and their corresponding eigenvecto...
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Existing video research incorporates the use of relevance feedback based on user-dependent interpretations to improve the retrieval results. In this paper, we segregate the process of relevance feedback into 2 distinc...
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ISBN:
(纸本)9781595937025
Existing video research incorporates the use of relevance feedback based on user-dependent interpretations to improve the retrieval results. In this paper, we segregate the process of relevance feedback into 2 distinct facets: (a) recall-directed feedback;and (b) precision-directed feedback. The recall-directed facet employs general features such as text and high level features (HLFs) to maximize efficiency and recall during feedback, making it very suitable for large corpuses. The precision-directed facet on the other hand uses many other multimodal features in an active learning environment for improved accuracy. Combined with a performance-based adaptive sampling strategy, this process continuously re-ranks a subset of instances as the user annotates. Experiments done using TRECVID 2006 dataset show that our approach is efficient and effective. Copyright 2007 ACM.
Current mammographic screeningfor breast cancer is less effective for younger women. To complement mammography for premenopausal women, we investigated the feasibility screening test using 98 blood serum proteins. Bec...
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Intrusion detection systems (IDSs) have been substantially improved in recent past. However, network attacks have become more sophisticated and increasingly complex: many of current attacks are coordinated and origina...
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Intrusion detection systems (IDSs) have been substantially improved in recent past. However, network attacks have become more sophisticated and increasingly complex: many of current attacks are coordinated and originated in multiple networks. To detect these attacks, IDSs need to obtain information on network events from multiple networks or administrative domains. This work demonstrates that a Distributed IDS (DIDS) can be composed of existing IDSs, improving the detection of misuses in a multiple network environment. We use a grid middleware for creating a service-based intrusion detection grid. We demonstrate through experimental results that the proposed DIDS allows the integration of heterogeneous existing IDSs and improves the detection of attacks by exploring the synergy between existing IDSs.
Owing to the growth of Internet and computer technology, pattern recognition for large-scale datasets has become one of the hot research topics. The major challenges are to reduce the human efforts involved and to imp...
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Owing to the growth of Internet and computer technology, pattern recognition for large-scale datasets has become one of the hot research topics. The major challenges are to reduce the human efforts involved and to improve the efficiency. Traditional passive learning methods require labeling of all training samples may not be feasible in large-scale recognition problems because of the requirement of large-scale class labeling for the huge number of training samples. In the literatures, there are many studies on active learning methods, which does not require all training samples to be labeled and it selects training samples for labeling based on the knowledge of the current classifier. In this paper, we present an active learning method using localized generalization error of candidate sample as selection criterion. Our method uses the generalization error of candidate sample, so theoretically it should have a better performance than other methods. From the experiment results, our method outperforms other methods in both yielding higher prediction accuracy on testing dataset and selecting fewer training samples. Furthermore, we propose a heuristics improvement based on the Q -neighborhood idea of the localized generalization error model to reduce the number of samples being selected and the computational time.
The massive amount of multimedia information especially video available on the Web requires a more precise and interactive retrieval. Current operational video retrieval systems do not make use of the implicit visual ...
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The massive amount of multimedia information especially video available on the Web requires a more precise and interactive retrieval. Current operational video retrieval systems do not make use of the implicit visual features but rely only on textual metadata supplied by the user during uploading. This greatly affects the retrieval performance as the metadata may not be comprehensive or consistent. In this paper, we describe the use of a spatio-temporal visual map (STVM) model to supplement Web video retrieval. This is done by employing the spatio-temporal visual similarity to rerank the text-retrieval results and find new results. Experimental results on a dynamic Web video corpus show significant improvement based on STVM model, with good usability scores based on human users.
The coding performance can be further improved when the hierarchical B-picture coding is introduced into H.264/AVC. However, the existing rate control schemes can not work efficiently in such new coding framework. Thi...
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The coding performance can be further improved when the hierarchical B-picture coding is introduced into H.264/AVC. However, the existing rate control schemes can not work efficiently in such new coding framework. This paper proposes a novel rate control algorithm when hierarchical B-picture coding is used in H.264/AVC. Firstly, a set of scaling-factors applied in designing cascaded quantizer for the B frames at different temporal levels is introduced. Based on the designed scaling-factors, an efficient bit-allocation strategy for hierarchical B-picture coding is presented. The experiments show that the proposed rate control algorithm can further improve PSNR up to 0.7dB compared to the existing hierarchical B-picture coding in H.264/AVC, while the mismatch of target bit rate and real bit rate does not exceed 2%.
In this paper, we propose a method to rank the highlights of broadcast racquet sports videos. Compared with previous work, we integrate relevance feedback into highlight ranking framework to effectively capture the us...
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In this paper, we propose a method to rank the highlights of broadcast racquet sports videos. Compared with previous work, we integrate relevance feedback into highlight ranking framework to effectively capture the user's interest in attention subspaces and generate personalized ranking result. First, we establish three user attention subspaces and extract audio, visual, temporal affective features to represent the human perception of highlight in each subspace. Then, the highlight ranking models are constructed using support vector regression (SVR) for the three subspaces respectively. Finally, the three submodels are linearly combined to generate the final ranking model. Relevance feedback technique is employed to adjust the weights of each submodel to obtain the result which is suitable to the user's preference. Experimental results demonstrate our approach is effective.
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