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2010 International Conference on Intelligent Computation Technology and Automation, ICICTA 2010

作者： Liu, Rui Xiong, Rui Gao, Kun State Key Lab of Software Development Environment Beihang University No.37 Xueyuan Road Haidian District Beijing 100191 China

ISBN: (纸本)9780769540771

Currently, a large number of Web information on the Internet is presented in structured objects. Mining object information from Web is of great importance for Web data management. This paper presents a Web object block mining method based on tag similarity. It first constructs a DOM tree for the Web page and calculates the similarity of all possible generalized nodes. Then a pruning method is used to filter the redundant information based on the features of noise data and find the Web object region. Finally the Web objects are identified in the Web object region. The experiment results show that, comparing to IEPAD, our method got a higher precision. © 2010 IEEE.

关键词： Websites

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CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual labeled Sequence Translation

CROP: Zero-shot Cross-lingual Named Entity Recognition with ...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Yang, Jian Huang, Shaohan Ma, Shuming Yin, Yuwei Dong, Li Zhang, Dongdong Guo, Hongcheng Li, Zhoujun Wei, Furu State Key Lab of Software Development Environment Beihang University China Microsoft Research Asia The University of Hong Kong Hong Kong

Named entity recognition (NER) suffers from the scarcity of annotated training data, especially for low-resource languages without labeled data. Cross-lingual NER has been proposed to alleviate this issue by transferring knowledge from high-resource languages to low-resource languages via aligned cross-lingual representations or machine translation results. However, the performance of cross-lingual NER methods is severely affected by the unsatisfactory quality of translation or label projection. To address these problems, we propose a Cross-lingual Entity Projection framework (CROP) to enable zero-shot cross-lingual NER with the help of a multilingual labeled sequence translation model. Specifically, the target sequence is first translated into the source language and then tagged by a source NER model. We further adopt a labeled sequence translation model to project the tagged sequence back to the target language and label the target raw sentence. Ultimately, the whole pipeline is integrated into an end-to-end model by the way of self-training. Experimental results on two benchmarks demonstrate that our method substantially outperforms the previous strong baseline by a large margin of +3∼7 F1 scores and achieves state-of-the-art performance. © 2022 Association for Computational Linguistics.

关键词： Benchmarking

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Social-aware relay node selection in delay tolerant networks

Social-aware relay node selection in delay tolerant networks

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2013 IEEE 2013 22nd International Conference on Computer Communication and Networks, ICCCN 2013

作者： Wei, Kaimin Zeng, Deze Guo, Song Xu, Ke State Key Lab. of Software Development Environment Beihang University China School of Computer Science and Engineering University of Aizu Japan

ISBN: (纸本)9781467357746

In Delay Tolerant Networks (DTNs), the connections between mobile nodes are intermittent and constant end-to-end paths rarely exist. In order to achieve high delivery ratio, most existing DTN routing algorithms favor the ''multi-hop forwarding'' fashion where a message can be forwarded by multiple relay nodes in the hope that one of the employed relay nodes can deliver the message to the destination node. Since aggressively employing relay nodes may incur intolerable delivery cost to resource-constrained mobile nodes in DTNs, it is significant to design a cost-efficient routing protocol that can achieves high delivery ratio. In this paper, we first utilize the small-world feature to limit the maximum forwarding hops in a reasonable way and then propose a greedy relay node selection strategy. A cost-efficient social-aware forwarding algorithm called TBSF (the-best-so-far) is then presented. Extensive simulations on real mobility traces are conducted to evaluate the performance of TBSF. Simulation results show that, in comparison with several well-known routing algorithms, TBSF can achieve high performance in terms of delivery ratio and delivery delay while with much lower delivery cost. © 2013 IEEE.

关键词： Delay tolerant networks

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PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation 29

PAEG: Phrase-level Adversarial Example Generation for Neural...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Wan, Juncheng Yang, Jian Ma, Shuming Zhang, Dongdong Zhang, Weinan Yu, Yong Li, Zhoujun Shanghai Jiao Tong University China State Key Lab of Software Development Environment Beihang University China Microsoft Research Asia

While end-to-end neural machine translation (NMT) has achieved impressive progress, noisy input usually leads models to become fragile and unstable. Generating adversarial examples as the augmented data has been proved to be useful to alleviate this problem. Existing methods for adversarial example generation (AEG) are word-level or character-level, which ignore the ubiquitous phrase structure. In this paper, we propose a Phrase-level Adversarial Example Generation (PAEG) framework to enhance the robustness of the translation model. Our method further improves the gradient-based word-level AEG method by adopting a phrase-level substitution strategy. We verify our method on three benchmarks, including LDC Chinese-English, IWSLT14 German-English, and WMT14 English-German tasks. Experimental results demonstrate that our approach significantly improves translation performance and robustness to noise compared to previous strong baselines. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Neural machine translation

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A meaningful learning method for zero-shot semantic segmentation

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Science China(Information Sciences) 2023年第11期66卷 35-53页

作者： Xianglong LIU Shihao BAI Shan AN Shuo WANG Wei LIU Xiaowei ZHAO Yuqing MA State Key Lab of Software Development Environment Beihang University JD Health International Inc.

Zero-shot semantic segmentation, which is developed to segment unseen categories, has attracted increasing attention due to its strong practicability. Previous approaches usually applied semantic-visual mapping based on seen categories to unseen categories, and thus failed to generate meaningful unseen visual representations and struggled to balance the seen and unseen concepts in the classifier. To overcome the above limitations, we propose a novel meaningful learning method that could be embedded into any generationbased zero-shot semantic segmentation model, borrowing the idea from the educational psychology field. The proposed meaningful learning method refers to the process that the new concepts could be learned by relating to existing comprehensible concepts and harmoniously incorporated into the concept schema. Specifically, we introduce a generator with conjugate conceptual correlation(G3C) which generates meaningful unseen visual information through anchoring into existing concepts. Moreover, simulating the rational thinking mechanism,we introduce a fast-slow concept modulator to alleviate the noisy over-correlation problem introduced by G3C and further construct a comprehensive concept schema. Extensive experiments conducted on three benchmarks demonstrate the superior performance of our method, especially according to the commonlyacknowledged h-m Io U(e.g., 4% improvement on the Pascal-VOC dataset).

关键词： meaningful learning zero-shot learning semantic segmentation conjugate conceptual correlation fast-slow conceptual modulator

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A method for placing traceroute-like topology discovery instrumentation

A method for placing traceroute-like topology discovery inst...

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The 11th IEEE International Conference on Communications Systems(第11届电子和电气工程师协会国际通信系统会议)(IEEE ICCS 2008)

作者： Wei Han Ke Xu State Key Lab.of Software Develop Environment Beihang University Beijing China

An accurate map of the Internet is very important for studying the network’s internal structure and network management. The main approach to map the Internet is to collect information from a set of sources by using traceroute-like probes. In a typical mapping project, active measurement sources are relatively scarce while traceroute destinations are plentiful, which makes the sampled graph quite different from the original one. So, it becomes very important to determine how to place these sources such that the sampled graph can be closer to the original one, especially in the case that the number of sources is limited. In this paper, we investigate the relationship between the placement of traceroute sources and their sampled result, which, to our knowledge, has not been systematically studied before. Based on the relationship, we propose a method on how to place the traceroute sources. We show that the graph sampled from sources selected by our method is more accurate than the ones randomly selected. We also validate our conclusion using the raw trace data of skitter project.

关键词： topology discovery traceroute sources placement

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A sentiment classification method for chinese document

A sentiment classification method for chinese document

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International Conference on Computer Science and Education

作者： Liu, Rui Xiong, Rui Song, Lang State Key Lab. of Software Development Environment Beihang University No.37 Xueyuan Road Haidian District Beijing 100191 China

ISBN: (纸本)9781424460052

For the great demand of analyzing and mining user's opinion, this paper presents a novel sentiment classification method for Chinese document. Firstly, the subjective documents are pre-processed and the sentiment features are extracted;then it calculates the basic polarity of the sentiment features based on a sentiment word dictionary, meanwhile dynamic sentiment word's polarity is judged and polarity adjustment is carried out according to the context information;finally, the overall polarity of document is determined by all sentiment words in it. Experiment of comparing with BaseLine and SVM shows the effectiveness of the proposed method. ©2010 IEEE.

关键词： Sentiment analysis

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An adaptive heuristic approach for distributed QoS-based service composition

An adaptive heuristic approach for distributed QoS-based ser...

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15th IEEE Symposium on Computers and Communications, ISCC 2010

作者： Li, Jing Zhao, Yongwang Liu, Min Sun, Hailong Ma, Dianfu State Key Lab of Software Development Environment Beihang University Beijing China Institute of Advanced Computing Technology Beihang University Beijing China

ISBN: (纸本)9781424477555

QoS-based service selection becomes a commonly accepted procedure to support rapid and dynamic web service composition. In this paper, we study the problem of QoS-based service selection in distributed QoS management environments where QoS values of alternative services are maintained by distributed QoS registries. A distributed heuristic approach is proposed to solve the problem efficiently with a high approximation ratio, and enable adaptability in distributed cross-organization environments with data privacy protection and a low cost of communication. The proposed approach consists of four stages in which variable elimination is used to reduce the size of the problem;constraint decomposition allows performing service selection independently on each QoS registry;supplementary service selection and concentrated optimization improve the approximation ratio. Performance analyses and simulation experiments show that the proposed approach performs efficiently with close-to-optimal results and fits well to distributed QoS management environments. © 2010 IEEE.

关键词： Web services

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Compact hashing for mixed image-keyword query over multi-label images 12

Compact hashing for mixed image-keyword query over multi-lab...

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2nd ACM International Conference on Multimedia Retrieval, ICMR 2012

作者： Liu, Xianglong Mu, Yadong Lang, Bo Chang, Shih-Fu State Key Lab of Software Development Environment Beihang University Beijing China Columbia University New York NY 10027 United States

ISBN: (纸本)9781450313292

Recently locality-sensitive hashing (LSH) algorithms have attracted much attention owing to its empirical success and theoretic guarantee in large-scale visual search. In this paper we address the new topic of hashing with multi-label data, in which images in the database are assumed to be associated with missing or noisy multiple labels and each query consists of a query image and several textual search terms, similar to the new "Search with Image" function introduced by the Google Image Search. The returned images are judged based on the combination of visual similarity and semantic information conveyed by search terms. In most of the state-of-the-art approaches, the learned hashing functions are universal for all labels. To further enhance the hashing efficiency for such multi-label data, we propose a novel scheme "boosted shared hashing". Our basic observation is that image labels typically form cliques in the feature space. Hashing efficacy can be greatly improved by making each hashing function more targeted at and only shared across such cliques instead of all labels in conventional hashing methods. In other words, each hashing function is deliberately designed such that it is especially effective for a subset of labels. The targeted, but sparse association between labels and hash bits reduces the computation and storage when indexing a new datum, since only a small number of relevant hashing functions become active given the labels. We develop a Boosting-style algorithm for simultaneously optimizing the label subset and hashing function in a unified framework. Experimental results on standard image benchmarks like CIFAR-10 and NUS-WIDE show that the proposed hashing scheme achieves substantially superior performances over conventional methods in terms of accuracy under the same hash bit budget. Copyright © 2012 ACM.

关键词： Budget control

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A SVM based method for active relevance feedback

A SVM based method for active relevance feedback

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2nd International Conference on Computer and Automation Engineering, ICCAE 2010

作者： Chen, Zilong Lu, Yang State Key Lab. of Software Development Environment BeiHang University HaiDian District Beijing China School of Software and Microelectronics Peking University HaiDian District Beijing China

ISBN: (纸本)9781424455850

In vector space models, traditional relevance feedback techniques, which utilize the terms in the relevant documents to enrich the user's initial query, is an effective method to improve retrieval performance. However, in this process, it also brings some non-relevance terms in the relevant documents in the new query. The number of non-relevance terms will increase according to the repeat of feedback process;it will damage the retrieval performance finally. This paper introduces a SVM Based method for relevance feedback. We train a classifier on the feedback documents and classify the rest of the documents. Thus, in the result list, the relevant documents are in front of the non-relevant documents. The new approach avoids modifying the query via text classification algorithm in the relevance feedback process, and it is a new direction for the relevance feedback techniques. Experiments with TREC dataset demonstrate the effectiveness of this method. ©2010 IEEE.

关键词： Classification (of information)

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