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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Wei Gao Mingzhong Wang Liehuang Zhu Xiaoping Zhang Language Information Processing and Cloud Computing Application Beijing Institute of Technology National Key Lab of Vehicular Transmission China North Vehicle Research Institute

ISBN: (纸本)9781479965144

Messages spreading inside vehicular ad hoc networks (VANETs) generally need to achieve the property of verifiability and content integrity, while preserving user privacy. Otherwise, VANETs will either fall into chaos, or prevent users from embracing it. To achieve this goal, we propose a protocol, which contains a priori and posteriori countermeasures, to guarantee these features. The a priori process firstly verifies that each message is sent by a vehicle only once. Then it collects and checks whether the count of the message exceeds the threshold value to improve the trustworthiness of the message. The posteriori process verifies the integrity of the message, ensuring it is unchanged during transmission between the vehicle and the road side unit. The privacy is preserved by applying group signature. In case of disruptive events, the proposed solution can trace back to the source vehicle which generates the message.

关键词： Vehicles Privacy Authentication Protocols Public key Servers

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CrowdX: Enhancing automatic construction of indoor floorplan with opportunistic encounters

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018年第4期2卷 1–21页

作者： Chen, Huijie Li, Fan Hei, Xiaojun Wang, Yu School of Computer Science Beijing Institute of Technology Beijing Engineering Research Center High Volume Language Information Processing and Cloud Computing Applications Beijing China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Department of Computer Science University of North Carolina at Charlotte Charlotte United States

The lack of floorplan limits the spread of pervasive indoor location-based services. Existing crowdsourcing based approaches mostly rely on identifying, locating landmarks in the environment and utilizing the spatial relationship between the landmarks and traces for efficiently constructing fine-grained floorplan. However, these methods are always restricted by the sparse landmark distribution or may cause privacy leakage. In this paper, we propose CrowdX, a crowdsourcing system for accurate, low-cost indoor floorplan construction enhanced with opportunistic encounters among mobile users. The key insight is that the spatial relation (i.e., the displacement of each user and the distance between each other during the encounter) will be extracted from the audio and inertia data, which are aligned by the proposed vibration event-based method. Such information can be used to calibrate the drift of encounter position. The calibrated encounter position is beneficial to most of the floorplan generation steps, such as trace drift elimination, landmark positioning, hallway assembling, and room area estimation. Our experiments in three shopping malls show that CrowdX achieves an average F-measure around 89.4%. In addition, the average estimated room area error within about 20%. The evaluation results demonstrate a significant improvement of accuracy enhanced with opportunistic encounters. © 2018 Association for computing Machinery.

关键词： Crowdsourcing

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Topical sentence embedding for query focused document summarization

Topical sentence embedding for query focused document summar...

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2017 IJCAI Workshop on Semantic Machine Learning, SML 2017

作者： Gao, Yang Wei, Linjing Huang, Heyan Liu, Qian Beijing Eng. Res. Ctr. of High Vol. Language Information Processing and Cloud Computing Applications China BIT Beijing Advanced Innovation Center for Imaging Technology Capital Normal University China

Distributed vector representation for sentences have been utilized in summarization area, since it simplifies semantic cosine calculation between sentence to sentence as well as sentence to document. Many extension works have been done to incorporate latent topics and word embedding, however, few of them assign sentences with explicit topics. Besides, much sentence embedding framework follows the same spirit of prediction task about a word in the sentence, which omits the sentence-to-sentence coherence. To address these problems, we proposed a novel sentence embedding framework to collaborate the current sentence representation, word-based content and topic assignment of the sentence to predict the next sentence representation. The experiments on summarization tasks show our model outperforms state-of-the-art methods. Copyright © by the paper's authors. Copying permitted for private and academic purposes.

关键词： Semantics

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Unsupervised Question Answering via Answer Diversifying 29

Unsupervised Question Answering via Answer Diversifying

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29th International Conference on Computational Linguistics, COLING 2022

作者： Nie, Yuxiang Huang, Heyan Chi, Zewen Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China Beijing Institute of Technology Southeast Academy of Information Technology Fujian China

Unsupervised question answering is an attractive task due to its independence on labeled data. Previous works usually make use of heuristic rules as well as pre-trained models to construct data and train QA models. However, most of these works regard named entity (NE) as the only answer type, which ignores the high diversity of answers in the real world. To tackle this problem, we propose a novel unsupervised method by diversifying answers, named DiverseQA. Specifically, the proposed method is composed of three modules: data construction, data augmentation and denoising filter. Firstly, the data construction module extends the extracted named entity into a longer sentence constituent as the new answer span to construct a QA dataset with diverse answers. Secondly, the data augmentation module adopts an answer-type dependent data augmentation process via adversarial training in the embedding level. Thirdly, the denoising filter module is designed to alleviate the noise in the constructed data. Extensive experiments show that the proposed method outperforms previous unsupervised models on five benchmark datasets, including SQuADv1.1, NewsQA, TriviaQA, BioASQ, and DuoRC. Besides, the proposed method shows strong performance in the few-shot learning setting. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Natural language processing systems

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The Adaptive Multicast Data Origin Authentication Protocol

The Adaptive Multicast Data Origin Authentication Protocol

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第二届中国互联网学术年会

作者： Liehuang Zhu Hao Yang Zhenghe Yang Beijing Engineering Research Center of Massive Language Information Processing and Cloud Computing Application School of Computer Science and Technology Beijing Institute of Technology China Beijing

Most multicast data origin authentication schemes work under the fixed parameters without taking the problem of changeable network environment into ***,the network conditions will obviously influence the efficiency of a protocol such as the time delay and the overhead considering the packet loss rate or something *** adjusting the parameters adaptively to achieve the ideal state with the dynamic network is *** achieve a high authentication rate and adapt to the changeable and unstable network environment,we proposed a multicast data origin authentication protocol which is adaptive depending on the packet error rates as well as the time delay,and is robust against the packet loss and *** model can estimate a more appropriate packet error rate and make the time delay lower according to the feedback values got from the receivers using the Markov chain so that it can be ***’s more,the IDA (information Dispersal Algorithm) and the Merkle HASH tree are also combined to resist the packet loss or *** strategy is especially efficient in terms of not only the adaptation of dynamic network but also the shortcut of overhead and delay,because the reliable intelligent algorithm is included in and only the necessary parts are involved in to authenticate.

关键词： Authentication adaptive estimate packet error rate time delay Markov chain

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Singleton detection for coreference resolution via multi-window and multi-filter CNN 1

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13th China Workshop on Machine Translation, CWMT 2017

作者： Li, Kenan Huang, Heyan Guo, Yuhang Jian, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Application 5 South Zhongguancun Street Beijing100081 China Beijing Advanced Innovation Center for Imaging Technology Capital Normal University Beijing100048 China

ISBN: (数字)9789811071348

ISBN: (纸本)9789811071331

Mention detection is the first and a key stage in most of coreference resolution systems. Singleton mentions are the ones which appear only once and are not mentioned in the following texts. Singleton mentions always affect the performance of coreference resolution systems. To remove the singleton ones from the automatically predicted mentions, we propose a novel singleton detection method based on multi-window and multi-filter convolutional neural network (MMCNN). The MMCNN model can detect singleton mentions with less use of hand-designed features and more sentence information. Experiments show that our system outperforms all the existing singleton detection systems. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Convolution

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The Span-Based Transformer Classifier for Named Entity Recognition

The Span-Based Transformer Classifier for Named Entity Recog...

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International Conference on Autonomous Unmanned Systems, ICAUS 2021

作者： Li, Yangfan Sun, Xin Li, Hongao Yang, Kaige Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Southeast Academy of Information Technology Fujian Putian351100 China

ISBN: (纸本)9789811694912

Named entity recognition is a basic task in the field of natural language processing. The existing named entity recognition model has the problem of poor recognition effect on specific domain data sets. Aiming at the inconsistency of sequence labeling caused by the shortcomings of the traditional BIO (B-begin, I-inside, O-outside) annotation method, a named entity recognition model based on transformer is proposed, which uses the feature extraction of encoder in transformer and the labeling method of limited interval. Firstly, the BERT pre-training model is used to perform word-level dynamic vector representation of the input text. Then the vector representation of the text is intercepted by the span-based labeling method, and the feature of text and candidate named entity is extracted by self-attention mechanism. The results of experiment on the dataset in the industrial informatization field show the effectiveness of the model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Natural language processing systems

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A Denoising Method for Distant Supervised Relation Extraction Based on Deep Clustering

A Denoising Method for Distant Supervised Relation Extractio...

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International Conference on Autonomous Unmanned Systems, ICAUS 2021

作者： Li, Hongao Sun, Xin Yang, Kaige Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Southeast Academy of Information Technology Fujian Putian351100 China

ISBN: (纸本)9789811694912

Distant supervision for relation extraction uses an external knowledge base as supervision signals to automatically label corpus and has attracted more and more attention. However, this method has an ideal hypothesis that all instances containing the same entity pairs represent the same relation, which will lead to a lot of noisy data and affect the training effect of classifier. Aiming at the noise problem of distant supervised dataset, we propose a denoising method based on deep clustering. First, the deep clustering method is used to train the text features and clustering centers, and the effect of clustering is improved by using the information of mask entity pairs. Then high quality training samples are obtained by removing the noisy data, so as to achieve the effect of denoising. Experimental results show that the proposed model can effectively improve the performance of relation extraction by comparing with the traditional denoising methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Knowledge based systems

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A Parallel Cross-language Retrieval System for Patent Documents

A Parallel Cross-Language Retrieval System for Patent Docume...

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2015 6th IEEE International Conference on Software Engineering and Service Science(ICSESS 2015)

作者： Xin Shen Heyan Huang Lingzhi Li Yonggang Huang Beijing Engineering Research Center of High Volume Language Information Processing & Cloud Computing Application Beijing Institute of Technology Overhaul Branch Beijing Electric Power Company

In order to help people obtain useful information from patent documents in different languages. This paper proposes a cross-language retrieval system to search Chinese and English patent documents simultaneously. This system consists of query translation module, document retrieval module and user interaction module. Query translation module is used to translate query based on bilingual dictionaries. Document retrieval module consists of monolingual retrieval system using standard vector space model. In order to retrieve in highly parallel, we use the Map Reduce model to calculate the similarity. User interaction module provides users with interactive mechanism used to improve the retrieval accuracy in the system. It contains two parts: the second translation and relevance feedback. The experimental results show that our system has good performance.

关键词： Patent document cross-language information retrieval parallel retrieval Hadoop

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Capturing Global Structural information in Long Document Question Answering with Compressive Graph Selector Network

Capturing Global Structural Information in Long Document Que...

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2022 Conference on Empirical Methods in Natural language processing, EMNLP 2022

作者： Nie, Yuxiang Huang, Heyan Wei, Wei Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Beijing Institute of Technology Southeast Academy of Information Technology China Huazhong University of Science and Technology China

Long document question answering is a challenging task due to its demands for complex reasoning over long text. Previous works usually take long documents as non-structured flat texts or only consider the local structure in long documents. However, these methods usually ignore the global structure of the long document, which is essential for long-range understanding. To tackle this problem, we propose Compressive Graph Selector Network (CGSN) to capture the global structure in a compressive and iterative manner. The proposed model mainly focuses on the evidence selection phase of long document question answering. Specifically, it consists of three modules: local graph network, global graph network and evidence memory network. Firstly, the local graph network builds the graph structure of the chunked segment in token, sentence, paragraph and segment levels to capture the short-term dependency of the text. Secondly, the global graph network selectively receives the information of each level from the local graph, compresses them into the global graph nodes and applies graph attention to the global graph nodes to build the long-range reasoning over the entire text in an iterative way. Thirdly, the evidence memory network is designed to alleviate the redundancy problem in the evidence selection by saving the selected result in the previous steps. Extensive experiments show that the proposed model outperforms previous methods on two datasets. © 2022 Association for Computational Linguistics.

关键词： Iterative methods

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