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International Conference on Asian Language processing

作者： Wang, Hongling Zhou, Guodong Jiangsu Provincial Key Lab. for Computer Information Processing Technology School of Computer Science and Technology Soochow University Suzhou 215006 China

ISBN: (纸本)9780769542881

This paper presents a topic-driven framework for generating a generic summary from multi-documents. Our approach is based on the intuition that, from the statistical point of view, the summary's probability distribution over the topics should be consistent with the multi-documents'probability distribution over the inherent topics. Here, the topics are defined as weighted "bag-of-words" and derived by Latent Dirichlet Allocation from a collection of documents, either the given multi-documents or a related large-scale corpus. In this sense, we could represent various kinds of text units, such as word, sentence, summary, document and multi-documents, using a single vector space model via their corresponding probability distributions over the derived topics. Therefore, we are able to extract a sentence or summary by calculating the similarity between a sentence/summary and the given multi-documents via their topic probability distributions. In particular, we propose two methods in similarity measurement: the static method and the dynamic method. While the former is employed to detect the salience of information in a static way, the later further controls redundancy in a dynamic way. In addition, we integrate various popular features to improve the performance. Evaluation on the TAC 2008 update summarization task shows encouraging results. © 2010 IEEE.

关键词： Probability distributions

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Employing the centering theory in pronoun resolution from the semantic perspective

Employing the centering theory in pronoun resolution from th...

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2009 Conference on Empirical Methods in Natural Language processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009

作者： Kong, Fang Zhou, Guo Dong Zhu, Qiaoming JiangSu Provincial Key Lab. for Computer Information Processing Technology School of Computer Science and Technology Soochow University Suzhou 215006 China

In this paper, we employ the centering theory in pronoun resolution from the semantic perspective. First, diverse semantic role features with regard to different predicates in a sentence are explored. Moreover, given a pronominal anaphor, its relative ranking among all the pronouns in a sentence, according to relevant semantic role information and its surface position, is incorporated. In particular, the use of both the semantic role features and the relative pronominal ranking feature in pronoun resolution is guided by extending the centering theory from the grammatical level to the semantic level in tracking the local discourse focus. Finally, detailed pronominal subcategory features are incorporated to enhance the discriminative power of both the semantic role features and the relative pronominal ranking feature. Experimental results on the ACE 2003 corpus show that the centering-motivated features contribute much to pronoun resolution. © 2009 ACL and AFNLP.

关键词： Semantics

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Improved MFCC feature extraction combining symmetric ICA algorithm for robust speech recognition

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Journal of Multimedia 2012年第1期7卷 74-81页

作者： Zhao, Huan Zhao, Kai Liu, He Yu, Fei School of Information Science and Engineering Hunan University Changsha China Jiangsu Provincial Key Laboratory of Computer Information Processing Technology Suzhou China

Independent component analysis (ICA), instead of the traditional discrete cosine transform (DCT), is often used to project log Mel spectrum in robust speech feature extraction. The paper proposed using symmetric orthogonalization in ICA for projecting log Mel spectrum into a new feature space as a substitute in extracting speech features to solve the problem of cumulative error and unequal weights that deflation orthogonalization brings, so as to improve the robustness of speech recognition systems, and increase the efficiency of estimation at the same time. Furthermore, the paper studied the nonlinearities of the objective function in ICA and their coefficients, tested them in all kinds of environments, finding that they influenced the recognition rate greatly in speech recognition systems, and applied a new coefficient in the proposed method. Experiments based on HMM and Aurora-2 speech corpus suggested that the new method was superior to deflation- based ICA and MFCC. © 2012 ACADEMY PUBLISHER.

关键词： Feature extraction

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Developing software using a novel event-condition-state pattern framework: Taking mobile input method system As A Case Study

Journal of Software

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Journal of Software 2011年第8期6卷 1506-1512页

作者： Zhu, Fei Diao, Hongjun Huang, Wei School of Computer Science and Technology Soochow University Suzhou Jiangsu 215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou Jiangsu 215006 China

Design patterns add more reliability, flexibility and reusability to a software system. Taking advantage of design patterns is usually beneficial to software design and makes software development relatively easier. The state pattern proposed by GoF is not a determined design and can hardly be transformed into implementation code directly. It is disable to cope with sophisticated response event either. Hereby we propose a novel event-conditionstate pattern framework, an event-based finite state machine. The framework improves GoF state pattern by resegmenting Context into two classes, one for management and the other for operation interface. Essential components for implementation, such as management, state transferring, and event triggering mechanism, are also taken into consideration in the framework. We use pattern framework to develop a thirty party input method system which has a Windows Mobile system version and a Symbian system version separately. We rewrote Context part related with system interfaces in the two different systems, and encapsulated the part related with internal logic of input method by concrete subclasses, thus avoiding redundant developing work in both systems and providing convenience for later synchronous updating and maintenance of two input methods. With the pattern framework, much effort during software development is saved. The development logic seems simpler and clearer. As a result, the whole development process is simplified. © 2011 ACADEMY PUBLISHER.

关键词： Software design

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Multi-Scale Cascaded with Cross-Attention Network-Based Deformation Vector Field Estimation for Motion-Compensated 4D-CBCT Reconstruction

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年

作者： Yuan, Peng Lyu, Fei Gao, Zhiqiang Yang, Chunfeng Hu, Dianlin Zhu, Jian Wu, Zhan Lyu, Tianling Zhao, Wei Dong, Jianmin Chen, Yang Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Ministry of Education Southeast University Nanjing210096 China Laboratory of Image Science and Technology Southeast University Nanjing210096 China Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong Academy for Advanced Interdisciplinary Science and Technology Zhejiang University of Technology Hangzhou China School of Physics Beihang University Beijing China Beihang Hangzhou Innovation Institute Zhejiang Hangzhou China School of Shandong Cancer Hospital and Institute Shandong First Medical University Shandong Academy of Medical Sciences Jinan Shandong China School of Information Engineering Xizang Minzu University Xianyang712082 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology The School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China

Four-Dimensional Cone Beam Computed Tomography (4D-CBCT) imaging technology offers enhanced image quality and spatial resolution for intraoperative guidance, facilitating real-time tracking of tumor position changes during radiotherapy. However, this is still a task of great challenges due to insufficient projections at each respiratory phase after phase-sorting, and the image phases reconstructed directly from phase-sorted data are discrete and discontinuous. To generate high-quality 4D-CBCT deformation vector fields (DVFs), this paper leverages the preoperative static prior image to guide intraoperative dynamic sparse-view reconstruction images for reducing anatomical structure differences, ultimately achieving continuous and dynamic 4D-CBCT imaging. In this paper, we propose a Multi-scale Cascaded residual deformable vector field estimation framework based on Cross-attention in Motioncompensated 4D-CBCT reconstruction (MCCM), which combines Multi-Scale Cascaded residual registration network (MSCNet), Cross-Attention Enhanced feature Fusion (CAEF) module and Structure-Enhanced Motion-Compensated (SEMC) module: 1) the MCCM employs a multi-scale cascaded residual network strategy, merging multi-receptive fields and multi-resolution feature maps for large-scale internal changes. 2) the CAEF is embedded into MSC-Net to facilitate effective communication and learning between features and promote the flow of information. 3) the SEMC is developed to reduce artifacts after intraoperative CBCT sparse-view reconstruction, restore global lung motion changes and local details, and enhance structural information through image fusion. The proposed method has been evaluated using simulated and clinical datasets and has shown promising results by comparative experiment. Our approach exhibits significant improvements across various evaluation metrics. © 2024 IEEE.

关键词： Image fusion

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Privacy preservation in role-based access control model

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Journal of Networks 2011年第8期6卷 1106-1113页

作者： Chen, Zuo Yang, Qiuwei Wan, Xin Tu, Yuanyan Yu, Fei Xu, Chen School of Computer and Communication Hunan University Changsha China Jiangsu Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China

Privacy preservation is a crucial problem in resource sharing and collab.rating among multi-domains. Based on this problem, we propose a role-based access control model for privacy preservation. This scheme avoided the privacy leakage of resources while implementing access control, and it has the advantage of lower communication overhead. We demonstrate this scheme meets the IND-CCA2 semantic security by using random oracle. The simulation result shows this scheme has better execution efficiency and application effects. © 2011 ACADEMY PUBLISHER.

关键词： Cryptography

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Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning

arXiv

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arXiv 2025年

作者： He, Yuting Wang, Boyu Ge, Rongjun Chen, Yang Yang, Guanyu Li, Shuo Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Nanjing China School of Instrument Science and Engineering Southeast University Nanjing China Department of Computer Science Western University LondonONN6A 3K7 Canada Department of Biomedical Engineering Department of Computer and Data Science Case Western Reserve University ClevelandOH44106 United States

Dense contrastive representation learning (DCRL) has greatly improved the learning efficiency for image dense prediction tasks, showing its great potential to reduce the large costs of medical image collection and dense annotation. However, the properties of medical images make unreliable correspondence discovery, bringing an open problem of large-scale false positive and negative (FP&N) pairs in DCRL. In this paper, we propose GEoMetric vIsual deNse sImilarity (GEMINI) learning which embeds the homeomorphism prior to DCRL and enables a reliable correspondence discovery for effective dense contrast. We proposes a deformable homeomorphism learning (DHL) which models the homeomorphism of medical images and learns to estimate a deformable mapping to predict the pixels’ correspondence under the condition of topological preservation. It effectively reduces the searching space of pairing and drives an implicit and soft learning of negative pairs via gradient. We also proposes a geometric semantic similarity (GSS) which extracts semantic information in features to measure the alignment degree for the correspondence learning. It will promote the learning efficiency and performance of deformation, constructing positive pairs reliably. We implement two practical variants on two typical representation learning tasks in our experiments. Our promising results on seven datasets which outperform the existing methods show our great superiority. We will release our code at a companion website. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Enriched format text categorization using a component similarity approach

Journal of Software

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Journal of Software 2011年第9期6卷 1713-1720页

作者： Zhu, Fei Yang, Jiong Zhou, Yong School of Computer Science and Technology Soochow University Suzhou Jiangsu 215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

Text categorization has been widely studied for years. However, conventional plain text categorization approaches which work good in plain text behave poor when they are simply applied to enriched format texts. An categorization approach that is applicable to enriched format text is proposed. During feature selection, we get feature structure distribution weight by using extended structure model so that structure affections to categorization are fully considered. Text formats are also taken into account in feature weighting. The combined feature weighting approach strengthens important parts and weakens less important ones. The text categorization is fulfilled by document component similarity, which first decomposes document, gathers features by components and other user-defined rules, completes document component tree, and then achieves categorization by it. We implement a CSBC based Naïve Bayes classifier in which the final result is the combination of all classifiers of component tree. Finally we parse *** document, draw components that are most related to classification from *** documents, and then use the classifier to categorize *** documents. The experiment results show that the classifier can automatically classify *** documents and work quite well. © 2011 ACADEMY PUBLISHER.

关键词： Classifiers

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Towards termination criteria of ant colony optimization

Towards termination criteria of ant colony optimization

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3rd International Conference on Natural Computation, ICNC 2007

作者： Lv, Qiang Xia, Oyan School of Computer Science and Technology Suzhou University Suzhou Jiangsu 215006 China Provincial Key Lab. for Computer Information Processing Technology Suzhou Jiangsu 215006 China

ISBN: (纸本)0769528759

Ant colony optimization (ACO for short) has been proved a successful meta-heuristic by a huge of empirical studies. This paper discusses the termination criteria of ACO and therefore provides research ideas to other meta-heuristics. A general discussion about four types of termination criteria has been firstly discussed in this article. Then some measures of convergence based on pheromone have been introduced. And a new termination criterion based on Bayesian approach is presented. Finally a new convergence proof for a class of ACO algorithms is presented. © 2007 IEEE.

关键词： Heuristic methods

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Dependency-driven feature-based learning for extracting protein-protein interactions from biomedical text

Dependency-driven feature-based learning for extracting prot...

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23rd International Conference on Computational Linguistics, Coling 2010

作者： Liu, Bing Qian, Longhua Wang, Hongling Zhou, Guodong Jiangsu Provincial Key Lab for Computer Information Processing Technology School of Computer Science and Technology Soochow University China

Recent kernel-based PPI extraction systems achieve promising performance because of their capability to capture structural syntactic information, but at the expense of computational complexity. This paper incorporates dependency information as well as other lexical and syntactic knowledge in a feature-based framework. Our motivation is that, considering the large amount of biomedical literature being archived daily, feature-based methods with comparable performance are more suitable for practical applications. Additionally, we explore the difference of lexical characteristics between biomedical and newswire domains. Experimental evaluation on the AIMed corpus shows that our system achieves comparable performance of 54.7 in F1-Score with other state-of-the-art PPI extraction systems, yet the best performance among all the feature-based ones.

关键词： Syntactics

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