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28th Text REtrieval Conference, TREC 2019

作者： Ding, Yuyang Lian, Xiaoying Zhou, Houquan Liu, Zhaoge Ding, Hanxing Hou, Zhongni University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology China

This paper describes our work in the background linking task and entity ranking task in TREC 2018 News Track. We explore four methods in background linking task and two methods in entity ranking task. All of our methods largely rely on off-the-shell open-source components(e.g., Solr for indexing the documents), and follow the basic thoughts-BM25 and relevance feedback. These methods differ in how they analyze the given input to obtain a query and to what extent the returned results are re-ranked taking meta data of the document(e.g., publish dates) into account. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

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ICTNET at Trec 2019 Incident Streams Track 28

ICTNET at Trec 2019 Incident Streams Track

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28th Text REtrieval Conference, TREC 2019

作者： Guangsheng, Kuang Kun, Zhang Jiabao, Zhang Xin, Zheng University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology China

Social medial become our public ways to share our information in our lives. Crisis management via social medial is becoming indispensable for its tremendous information. While deep learning shows surprising outcome in many tasks. So in this paper, we cope this learning task with neural network in the view of classification problem. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

关键词： Deep learning

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Sparse Word Embeddings Using 1 Regularized Online Learning

Sparse Word Embeddings Using 1 Regularized Online Learning

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Sun, Fei Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

Recently, Word2Vec tool has attracted a lot of interest for its promising performances in a variety of natural language processing (NLP) tasks. However, a critical issue is that the dense word representations learned in Word2Vec are lacking of interpretability. It is natural to ask if one could improve their interpretability while keeping their performances. Inspired by the success of sparse models in enhancing interpretability, we propose to introduce sparse constraint into Word2Vec. Specifically, we take the Continuous Bag of Words (CBOW) model as an example in our study and add the l regularizer into its learning objective. One challenge of optimization lies in that stochastic gradient descent (SGD) cannot directly produce sparse solutions with 1 regularizer in online training. To solve this problem, we employ the Regularized Dual Averaging (RDA) method, an online optimization algorithm for regularized stochastic learning. In this way, the learning process is very efficient and our model can scale up to very large corpus to derive sparse word representations. The proposed model is evaluated on both expressive power and interpretability. The results show that, compared with the original CBOW model, the proposed model can obtain state-of-the-art results with better interpretability using less than 10% non-zero elements. © 2016 International Joint Conferences on Artificial Intelligence. All rights reserved.

关键词： Optimization

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An efficient automated trust negotiation framework supporting adaptive policies

An efficient automated trust negotiation framework supportin...

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2nd International Workshop on Education technology and Computer science, ETCS 2010

作者： Liu, Bailing Lu, Hongwei Zhao, Yizhu College of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Key Lab of Information Network Security Ministry of Public Security Shanghai China

ISBN: (纸本)9780769539874

Automated trust negotiation (ATN) is an approach that establishes mutual trust between strangers wishing to share resources or conduct business by gradually requesting and disclosing digitally signed credentials. Previous work on improving negotiation efficiency mainly focuses on using history negotiation information, which may leak much unnecessary information and cannot improve the negotiation efficiency if both negotiators do not have appropriate history information. Thus in this paper, we enhance the negotiation efficiency from a new aspect adaptive policies. An ATN framework is designed to enhance the negotiation efficiency, which can not only simplify the negotiation process and reduce the number of exchanged credentials and credential validations whenever possible, but also avoid to revealing unnecessary information during negotiations. This framework presents a number of innovative features, such as the support for overlapping virtual organizations (VOs for short) to evaluate trustworthiness level, and the use of fine-grained adaptive policies to adjust policies based on the trustworthiness levels. © 2010 IEEE.

关键词： Efficiency

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A Survey on the Privacy-Preserving Data Aggregation in Wireless Sensor networks

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China Communications 2015年第5期12卷 162-180页

作者： XU Jian YANG Geng CHEN Zhengyu WANG Qianqian Jiangsu High Technology Research Key Lab for WSNs College of Computer Science & TechnologyNanjing University of Posts & Telecommunications Communication and Sensor Network Technology of Ministry of Education Nanjing University of Posts & Telecommunications School of Electrical & Information Engineering Jinling Institute of Technology School of Software Engineering Jinling Institute of Technology

Wireless sensor networks(WSNs)consist of a great deal of sensor nodes with limited power,computation,storage,sensing and communication *** aggregation is a very important technique,which is designed to substantially reduce the communication overhead and energy expenditure of sensor node during the process of data collection in a ***,privacy-preservation is more challenging especially in data aggregation,where the aggregators need to perform some aggregation operations on sensing data it *** present a state-of-the art survey of privacy-preserving data aggregation in *** first,we classify the existing privacy-preserving data aggregation schemes into different categories by the core privacy-preserving techniques used in each *** then compare and contrast different algorithms on the basis of performance measures such as the privacy protection ability,communication consumption,power consumption and data accuracy ***,based on the existing work,we also discuss a number of open issues which may intrigue the interest of researchers for future work.

关键词： wireless sensor networks data aggregation privacy-preserving

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Community discovery method based on complex network of data fusion based on the super network perspective

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International Journal of Computers and Applications 2021年第4期43卷 383-390页

作者： Pei, Li School of Computer Science & Technology Xi‘an University of Posts and Telecommunications Xi‘an China Shaanxi Key Laboratory of Network Data Intelligent Processing Xi‘an University of Posts and Telecommunications Xi‘an China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi‘an Jiaotong University Xi‘an China

To enhance the computational efficiency and precision of community discovery, a community discovery algorithm with the mixed label based on the minimum description length (MDI) of information compression is proposed in this paper. Firstly, the community detection is converted to the information compression problem of seeking for an effective network structure and the quality evaluation function is constructed based on the MDL criterion. Secondly, the community discovery algorithm with heuristic mixed label movement is constructed based on the label node movement algorithm and Louvain community addition algorithm so as to reduce the quality evaluation function. Finally, the simulation experiment in the standard test set and API capture Sina microblog dataset shows that the proposed algorithm is superior to the selected comparison algorithm in computational efficiency and precision. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Computational efficiency

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Nested Event Extraction upon Pivot Element Recognition 30

Nested Event Extraction upon Pivot Element Recognition

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ren, Weicheng Li, Zixuan Jin, Xiaolong Bai, Long Su, Miao Liu, Yantao Guan, Saiping Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively. Nested events involve a kind of Pivot Elements (PEs) that simultaneously act as arguments of outer-nest events and as triggers of inner-nest events, and thus connect them into nested structures. This special characteristic of PEs brings challenges to existing NEE methods, as they cannot well cope with the dual identities of PEs. Therefore, this paper proposes a new model, called PerNee, which extracts nested events mainly based on recognizing PEs. Specifically, PerNee first recognizes the triggers of both inner-nest and outer-nest events and further recognizes the PEs via classifying the relation type between trigger pairs. The model uses prompt learning to incorporate information from both event types and argument roles for better trigger and argument representations to improve NEE performance. Since existing NEE datasets (e.g., Genia11) are limited to specific domains and contain a narrow range of event types with nested structures, we systematically categorize nested events in the generic domain and construct a new NEE dataset, called ACE2005-Nest. Experimental results demonstrate that PerNee consistently achieves state-of-the-art performance on ACE2005-Nest, Genia11, and Genia13. The ACE2005-Nest dataset and the code of the PerNee model are available at https://***/waysonren/PerNee. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Character recognition

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A Contrastive Pre-training Approach to Discriminative Autoencoder for Dense Retrieval 22

A Contrastive Pre-training Approach to Discriminative Autoen...

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31st ACM International Conference on Information and Knowledge Management, CIKM 2022

作者： Ma, Xinyu Zhang, Ruqing Guo, Jiafeng Fan, Yixing Cheng, Xueqi Cas Key Lab of Network Data Science and Technology Ict Cas University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450392365

Dense retrieval (DR) has shown promising results in information retrieval. In essence, DR requires high-quality text representations to support effective search in the representation space. Recent studies have shown that pre-trained autoencoder-based language models with a weak decoder can provide high-quality text representations, boosting the effectiveness and few-shot ability of DR models. However, even a weak autoregressive decoder has the bypass effect on the encoder. More importantly, the discriminative ability of learned representations may be limited since each token is treated equally important in decoding the input texts. To address the above problems, in this paper, we propose a contrastive pre-training approach to learn a discriminative autoencoder with a lightweight multi-layer perception (MLP) decoder. The basic idea is to generate word distributions of input text in a non-autoregressive fashion and pull the word distributions of two masked versions of one text close while pushing away from others. We theoretically show that our contrastive strategy can suppress the common words and highlight the representative words in decoding, leading to discriminative representations. Empirical results show that our method can significantly outperform the state-of-the-art autoencoder-based language models and other pre-trained models for dense retrieval. © 2022 ACM.

关键词： Decoding

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Application of D-S Evidence theory to uncertainty of assessment result in E-government

Application of D-S Evidence theory to uncertainty of assessm...

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7th Web Information Systems and Applications Conference, WISA 2010, 5th Workshop on Semantic Web and Ontology, SWON 2010, 4th Workshop on Electronic Government technology and Application, EGTA 2010

作者： Xia, Bing Pan, Heng Zheng, Qiusheng Miao, Fengjun School of Computer Science Zhongyuan University of Technology Zhengzhou Key Lab of Computer Network Security Assessment Zhengzhou China

ISBN: (纸本)9780769541938

How to solve uncertainty of assessment result is an important issue in E-government. Current qualitative assessment are based on subjective qualitative evaluation and the rigid division of the quantitative data, however both methods are less objective ,precise and impartiality, as a result cause accuracy of assessment poorly. D-S Evidence theory is fit for processing the subjective judgment and synthesizing the uncertain knowledge. So, this theory is applied to the uncertainty of assessment results. Firstly used the classic 2/8 rule to build a dynamic contribution matrix. Then after weighted and normalized, we got a basic probability assignment MASS matrix. Final chosen one of the largest number of synthetic probability as assessment result. The algorithm has a strong versatility and an example is used to demonstrate its effectively in this text. © 2010 IEEE.

关键词： Matrix algebra

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Spam filtering by stages

Spam filtering by stages

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2nd International Conference on Convergent Information technology, ICCIT 07

作者： Liu, Ming Li, Yunchun Li, Wei Network Technology Key Lab. of Beijing School of Computer Science and Engineering Beihang University Beijing 100083 China

ISBN: (纸本)0769530389

At the present time, the typical spam filtering technique is content filtering, but the content filtering belongs to after the event precaution, and cannot avoid network resources waste due to spam. Therefore, this paper used a method of spam behaviour recognition filtering. The method identify the spam according to the behaviour of mail sent, set up the model by Bayes technique, filter spam by the least risk of Bayes technique, and in the mail filtering application filtering spam by stages in order to recognizes the spam email as soon as possible. Experiments show that this method could get a better effect, and could identify the spam as early as possible, so that this approach could solve the problem of resources wasting. © 2007 IEEE.

关键词： Grading

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