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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Zhang, Zhe Liu, Jie Chi, Lihua Chen, Xinhai Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781728186351

Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been only limited attempts for Chinese punctuation restoration. Due to the differences between Chinese and English in terms of grammar and basic semantic units, existing methods for English is not suitable for Chinese punctuation restoration. To tackle this problem, we propose a hybrid model combining the kernel of Bidirectional Encoder Representations from Transformers (BERT), Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). This model employs a flexible structure and special CNN design which can extract word-level features for Chinese language. We compared the performance of the hybrid model with five widely-used punctuation restoration models on the public dataset. Experimental results demonstrate that our hybrid model is simple and efficient. It outperforms other models and achieves an accuracy of 69.1%. © 2020 IEEE.

关键词： Recurrent neural networks

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MixedCache: Enabling Flow Directed Rule-Caching Scheme based on Heterogeneous Cache for OpenFlow 3

MixedCache: Enabling Flow Directed Rule-Caching Scheme based...

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2020 3rd International Conference on Computer Information Science and Artificial Intelligence, CISAI 2020

作者： Yang, Jialun Yang, Yi Liu, Jiahao Liu, Rulin Qiao, Peng Wang, Baosheng College of Computer Science and Technology National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

OpenFlow switches in SDN use Multiple Flow Tables (MFTs) for fine-grained flow control. Commodity switches integrate hardware storage resources such as SRAM and TCAM to store flow tables to achieve high-speed lookups. Many increased flow tables are rapidly exhausting these hardware storage resources, which makes the switches have to balance high-speed search and massive storage. The rule-caching scheme is a popular method to solve this problem, which caches the most commonly used rules into hardware storage resources. The existing rule-caching schemes are based on single hardware storage resources, and they cannot flexibly adjust the caching strategy according to the traffic characteristics. Simultaneously, the deployed commodity switches face the problem of difficulty in changing the size of SRAM and TCAM. This paper innovatively proposes the MixedCache scheme, which makes full use of the hardware storage resources in the switch according to the skewed characteristics of network traffic. MixedCache stores the large flows in SRAM by exact match and stores the small flows in the TCAM by wildcard match. MixedCache does not need to change the size of the deployed switch hardware storage resources, but makes full use of existing resources. Compared with the rule-caching scheme based on the exact match, the cache hit rate can increase by up to 15.61%. Compared with the rule-caching scheme based on the wildcard match, the cache hit rate can increase by up to 29.69%. © Published under licence by IOP Publishing Ltd.

关键词： Static random access storage

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A Novel Interactive Recurrent Attention Network for Emotion-Cause Pair Extraction

A Novel Interactive Recurrent Attention Network for Emotion-...

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作者： Xiangyu Jia Xinhai Chen Qian Wan Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

Unlike Emotion Cause Extraction(ECE) task which consists of pre-annotate emotions and passage, emotion-cause pair extraction(ECPE) aims at extracting potential emotions and corresponding causes in the document without the need for pre-annotations. Traditional ECPE solutions divide the extracting emotions and causes operation into two separate parts. However, separating the bidirectional dependence between emotion and cause may lose a lot of potentially useful information. In this paper, we propose a novel interactive recurrent attention network(IRAN). Our approach focuses on the bidirectional impact between emotions and causes, and extracts emotions and causes simultaneously. The information in the document can be fully exploited through multiple modeling and information extraction. Our emotion-specific transformation and distance fusion correlation can adaptively focus on the emotions and the distance, gracefully incorporate them into a distinguishable neural network attention framework. The experimental results show that our proposed model achieves better performance than other widely-used models on the ECPE corpus.

关键词： Natural language processing Deep learning Sentiment analysis Text mining Emotion-cause pair extraction

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A Novel Deep Neural Network Model for Credit Risk Prediction of Chinese Farmers

A Novel Deep Neural Network Model for Credit Risk Prediction...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yalong Xie Aiping Li Ziniu Liu Kai Chen Hongkui Tu College of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665418164

China is a big agricultural county with more than 500 million rural population. In China, farmers usually loan from rural commercial banks or rural credit cooperatives. It is crucial for the national economic development and the improvement of people's standard of living that how to reasonably use funds to subsidize the agricultural population and reduce the risk of rural loans. At present, credit risk prediction of farmers mainly depends on the experience of experts in the business field, and there is little published research on using artificial intelligence methods to solve this problem. This paper presents a complete set of methods, including data collection, feature selection, etc. We propose a novel deep neural network model named DNN-CRP for credit risk prediction of Chinese framers. Experiments on an actual credit loan dataset of Chinese farmers are presented, and experimental results show that the comprehensive performance of the DNN-CRP model is better than current state-of-the-art models. It is believed that the DNN-CRP model proposed in this paper can help banks improve the efficiency of the credit loan business of farmers and reduce credit risks.

关键词： Deep learning Sensitivity Neural networks Sociology Predictive models Data collection Data models

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Neutronic calculations of the China dual-functional lithium–lead test blanket module with the parallel discrete ordinates code Hydra

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Nuclear Science and Techniques 2020年第8期31卷 1-12页

作者： Guang-Chun Zhang Jie Liu Liang-Zhi Cao Hong-Chun Wu Xian-Bao Yuan Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China School of Nuclear Science and Technology Xi’an Jiaotong UniversityXi’an 710049China College of Mechanical and Power Engineering China Three Gorges UniversityYichang 443002China

The China dual-functional lithium–lead test blanket module(DFLL-TBM) is a liquid Li Pb blanket concept developed by the Institute of Nuclear Energy Safety Technology of the Chinese Academy of Sciences for testing in ITER to validate relevant tritium breeding and shielding technologies. In this study, neutronic calculations of DFLL-TBM were carried out using a massively parallel three-dimensional transport code, Hydra, with the Fusion Evaluated Nuclear Data Library/MG. Hydra was developed by the Nuclear Engineering Computational Physics Lab based on the discrete ordinates method and has been devoted to neutronic analysis and shielding evaluation for nuclear facilities. An in-house Monte Carlo code(MCX) was employed to verify the discretized calculation model used by Hydra for the DFLL-TBM calculations. The results showed two key aspects:(1) In most material zones,Hydra solutions are in good agreement with the reference MCX results within 1%, and the maximal relative difference of the neutron flux is merely 3%, demonstrating the correctness of the calculation model;(2) while the current DFLL-TBM design meets the operation shielding requirement of ITER for 4 years, it does not satisfy the tritium self-sufficiency requirement. Compared to the two-step approach, Hydra produces higher accuracies as it does not rely on the homogenization technique during the calculation process. The parallel efficiency tests of Hydra using the DFLL-TBM model also showed that this code maintains a high parallel efficiency on O(100) processors and, as a result, is able to significantly improve computing performance through parallelization. Parameter studies have been carried out by varying the thickness of the beryllium armor layer and the tritium breeding zone to understand the influence of the beryllium layer and breeding zone thickness on tritium breeding performance. This establishes a foundation for further improvement in the tritium production performance of DFLL-TBM.

关键词： Discrete ordinates method DFLL-TBM Neutronic analysis Tritium breeding performance

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MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Symmetric Encryption for Cloud Storage

MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Sy...

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Annual Conference on Privacy, Security and Trust, PST

作者： Panyu Wu Zhenfu Cao Jiachen Shen Xiaolei Dong Yihao Yang Jun Zhou Liming Fang Zhe Liu Chunpeng Ge Chunhua Su Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Research Center for Basic Theories of Intelligent Computing Research Institute of Basic Theories Zhejiang Lab Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China Shandong University Jinan China University of Aizu Fukushima Japan

Since data outsourcing poses privacy concerns with data leakage, searchable symmetric encryption (SSE) has emerged as a powerful solution that enables clients to perform query operations on encrypted data while preserving their privacy. Dynamic SSE schemes have been proposed to handle update operations. However, it is shown that updates might increase the risk of information leakage. Meanwhile, to meet the requirement of real-world applications, it is desirable to have the searchable encryption scheme which supports both multiple clients and multi-keyword queries. To address these issues, this paper proposes MMDSSE, a multi-client forward secure dynamic SSE scheme that supports multi-keyword queries. MMDSSE allows the clients narrow down the results by providing an arbitrary subset of the entire archive, and thus suitable for cloud storage environment. Security analysis and experimental evaluations show that MMDSSE is secure and efficient.

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Multi-task Pre-training Language Model for Semantic Network Completion

arXiv

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arXiv 2022年

作者： Li, Da Yang, Sen Xu, Kele Yi, Ming He, Yukai Wang, Huaimin Tencent Beijing100084 China Beijing Institute of Microbiology and Epidemiology Beijing100071 China State Key Laboratory of Pathogen and Biosecurity Beijing100071 China National University of Defense Technology Changsha410073 China National Key Lab of Parallel and Distributed Processing Changsha410073 China

Semantic networks, such as the knowledge graph, can represent the knowledge leveraging the graph structure. Although the knowledge graph shows promising values in natural language processing, it suffers from incompleteness. This paper focuses on knowledge graph completion by predicting linkage between entities, which is a fundamental yet critical task. Semantic matching is a potential solution for link prediction as it can deal with unseen entities, while the translational distance based methods struggle with the unseen entities. However, to achieve competitive performance as translational distance based methods, semantic matching based methods require large-scale datasets for the training purpose, which are typically unavailable in practical settings. Therefore, we employ the language model and introduce a novel knowledge graph architecture named LP-BERT, which contains two main stages: multi-task pre-training and knowledge graph fine-tuning. In the pre-training phase, three tasks are taken to drive the model to learn the relationship information from triples by predicting either entities or relations. While in the fine-tuning phase, inspired by contrastive learning, we design a triple-style negative sampling in a batch, which greatly increases the proportion of negative sampling while keeping the training time almost unchanged. Furthermore, we propose a new data augmentation method utilizing the inverse relationship of triples to improve the performance and robustness of the model. To demonstrate the effectiveness of our proposed framework, we conduct extensive experiments on three widely-used knowledge graph datasets, WN18RR, FB15k-237, and UMLS. The experimental results demonstrate the superiority of our methods, and our approach achieves state-of-the-art results on the WN18RR and FB15k-237 datasets. Significantly, the Hits@10 indicator is improved by 5% from the previous state-of-the-art result on the WN18RR dataset while reaching 100% on the UMLS dataset. Copy

关键词： Inverse problems

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PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain

PPSS: A Privacy-Preserving Supervision Scheme Based on Block...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Ming Chen Peichang Shi Xiang Fu Feng Jiang Fei Gao Penghui Ma Jinzhu Kong National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Kylin Software Co.LTD Changsha China Zhejiang Lab Hangzhou China

In JointCloud Computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud Computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud Computing, and combines the “double encryptions” and “threshold encryption” technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.

关键词： Privacy Uncertainty Decision making Buildings Reliability engineering Blockchains Complexity theory

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Modeling Full Information with Graph Network for Joint Entity-Relation Extraction

Modeling Full Information with Graph Network for Joint Entit...

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2021年第四届算法、计算和人工智能国际会议

作者： Qian Wan Luona Wei Xinhai Chen Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology College of Systems Engineering National University of Defense Technology Laboratory of Software Engineering for Complex Systems National University of Defense Technology

Fully capturing contextual information and analyzing the association between entity semantics and type is helpful for joint extraction task: 1) The context can reflect the part of speech and semantics of entity. 2) The entity type is closely related to the relation between entities. Previous research used to simply embed the contextual information into shallow layer of the model, ignoring the association between entity semantics and type. In this paper, we propose a graph network with full-information modeling to explicitly model different-level information in the text. The contextual information of entity is dynamically embedded in each span representation to improve the reasoning ability. To capture the fine-grained association between the semantics and type of entity, the graph network uses the feature of entity types to generate edge information between different nodes. Experimental results show that our model outperforms previous models on the Co NLL04 dataset and obtains competitive results on the SciERC dataset in both entity recognition and relation extraction. Extensive additional experiments further verify the effectiveness of the model.

关键词： Named Entity Recognition Relation Extraction Natural Language processing Graph Network Deep Learning

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Towards Precise End-to-end Semi-Supervised Human Head Detection Network

Towards Precise End-to-end Semi-Supervised Human Head Detect...

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International Joint Conference on Neural Networks (IJCNN)

作者： Rongchun Li Junjie Zhang Yuntao Liu Yong Dou National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Head detection, as a fundamental task in practice for many head-related problems, requires an enormous number of annotated boxes to maintain the performance. To alleviate the time and cost of labeling each image in the dataset, we propose an end-to-end semi-supervised head detection frame-work, which shows competitive results with only a small set of data. Specifically, under the setting of semi-supervised, we introduce a weak boxes generate branch and a weak boxes refine branch to produce pseudo ground truth label for unlabeled images with the guidance of annotated images. The weak boxes generate branch is embedded in the detection framework taking the proposals as input and outputting the initial weak boxes that coarsely locate the place of the head. Then, the weak boxes refine branch adjusts the weak boxes more accurate gradually by training a transferred sub-network with the established relation between proposals, weak boxes and labeled boxes. In the training process, we jointly train the two branches in an end-to-end manner, which can generate better pseudo bounding boxes with a small dataset online to avoid over-fitting and obtain a more precise head detector. The results on the public head detection benchmark Brainwash and SCUT-HEAD show the effectiveness of our method.

关键词： Head Proposals Training Detectors Object detection Feature extraction Task analysis

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