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作者： Shi, Dianxi Wu, Zhendong Ding, Bo Yan, Huining National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9780769546698

Context situation, which means a snapshot of the status of the real world, is formed by integrating a large amount of contexts collected from various resources. How to get the context situation and use the situation to provide better services is a challenging issue. In this paper, we focused on this challenge on the basis of the mobile cloud computing architecture. An abstract model is proposed in this paper to uniformly collect the context and send them to cloud. A rule-based large-scale context aggregation algorithm is also proposed which utilizes the MapReduce computing paradigm. Finally, a large-scale context management framework based on the abstract model and the context aggregation algorithm is proposed, and a real-time traffic demo is implemented to verify the validity of the framework. © 2012 IEEE.

关键词： MapReduce

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Resource Allocation with Dynamic Substrate Network in Data Centre Networks

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China Communications 2013年第9期10卷 130-142页

作者：李小玲王怀民丁博李小勇刘惠 National Key Laboratory for Parallel and Distributed Processing School of Computer ScienceNational University of Defense Technology

The resource allocation problem in data centre networks refers to a map of the workloads provided by the cloud users/tenants to the Substrate Network(SN)which are provided by the cloud *** studies consider the dynamic arrival and departure of the workloads,while the dynamics of the substrate are *** this paper,we first propose the resource allocation with the dynamic SN,and denote it as ***,we propose an efficient mapping algorithm for *** performance of the proposed algorithm is evaluated by performing simulation *** results show that the proposed algorithm can effectively solve the GraphMap-DS.

关键词： resource allocation data centrenetwork workload substrate algorithm

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FedEAE: Federated Learning Based Privacy-Preserving Event Argument Extraction 12th

FedEAE: Federated Learning Based Privacy-Preserving Event Ar...

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12th national CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Hu, Fei Dong, Shenpo Chang, Tao Zhou, Jie Li, Haili Wang, Jingnan Chen, Rui Liu, Haijiao Wang, Xiaodong National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783031446955

Benefiting from Pre-trained Language Model (PLM), Event Argument Extraction (EAE) methods have achieved SOTA performance in general scenarios of Event Extraction (EE). However, with increasing concerns and regulations on data privacy, aggregating distributed data among different institutions in some privacy-sensitive territories (e.g., medical record analysis, financial statement analysis, etc.) becomes very difficult, and it’s hard to train an accurate EAE model with limited local data. Federated Learning (FL) provides promising methods for a large number of clients to collaboratively learn a shared global model without the need to exchange privacy-sensitive data. Therefore, we propose a privacy-preserving EAE method named FedEAE based on FL to solve the current difficulties. To better adapt to federated scenarios, we design a dataset named FedACE generated from the ACE2005 dataset under IID and Non-IID for our experiments. Extensive experiments show that FedEAE achieves promising performance compared to existing baselines, thus validates the effectiveness of our method. To the best of our knowledge, FedEAE is the first to apply FL in the EAE task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Extraction

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BCE: A privacy-preserving common-friend estimation method for distributed online social networks without cryptography

BCE: A privacy-preserving common-friend estimation method fo...

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2012 7th International ICST Conference on Communications and Networking in China, CHINACOM 2012

作者： Fu, Yongquan Wang, Yijie National Key Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Hunan province 410073 China

ISBN: (纸本)9781467326995

distributed online social networks (DOSN) have emerged recently. Nevertheless, recommending friends in the distributed social networks has not been exploited fully. We propose BCE (Bloom Filter based Common-Friend Estimation), a scalable and privacy-preserving common-friend estimation scheme that estimates the set of common friends without the need of cryptography techniques. First, BCE denotes each user using the identifiers created by the Peer-to-Peer underlay that are robust against the dictionary attacks. Second, BCE uses a Bloom filter to represent a friend list for scalability. Third, BCE estimates common friends of two users using the intersection of Bloom filters computed by one of their common friends, which ensures the privacy of friend lists against unknown users. Our privacy analysis shows that BCE hides the privacy of each user with a high probability. Simulations over real-world social-network data sets confirms that BCE is both accurate and scalable. © 2012 IEEE.

关键词： Social networking (online)

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On the feasibility of common-friend measurements for the distributed online social networks

On the feasibility of common-friend measurements for the dis...

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2012 7th International ICST Conference on Communications and Networking in China, CHINACOM 2012

作者： Fu, Yongquan Wang, Yijie National Key Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Hunan province 410073 China

ISBN: (纸本)9781467326995

distributed social networks have emerged recently. Nevertheless, recommending friends in the distributed social networks has not been exploited fully. We propose FDist, a distributed common-friend estimation scheme that estimates the number of common-friends between any pair of users without disclosing the friends' information. FDist uses privacy-preserving common-friend measurements to collect a small number of common-friend samples, and uses low-dimensional coordinates to estimate the number of common friends to other users. Simulation results on real-world social networks confirm that FDist is both scalable and accurate. © 2012 IEEE.

关键词： Social networking (online)

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parallelization of the training for face detection with transactional memory

Parallelization of the training for face detection with tran...

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2011 International Conference on Automation and Robotics, ICAR 2011

作者： Zeng, Kun Key Laboratory and Technology for National Defence of Parallel and Distributed Processing School of Computer National Univiersity of Defense Technology ChangSha Hunan China

ISBN: (纸本)9783642256455

The development of multi-core processor makes the parallelization of traditional sequential algorithms increasingly important. Meanwhile, transactional memory serves a good parallel programming model. This paper takes the advantage of software transactional memory to parallelize the Multi-Exit Asymmetric Adaboost algorithm for face detection. The parallel version is evaluated on three different implementations of software transactional memory. The experiment results show that the transactional memory based parallelization outperforms the traditional lock based approach. A speedup of nearly seven is achieved on a eight-core machine on an eight-core system. © 2011 Springer-Verlag.

关键词： Face recognition

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AUModel: A conceptual model for adaptive software 5

AUModel: A conceptual model for adaptive software

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2014 5th IEEE International Conference on Software Engineering and Service Science, ICSESS 2014

作者： Liu, Hui Ding, Bo Shi, Dianxi Wang, Huaimin National Key Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha Hunan410073 China

ISBN: (纸本)9781479932788

Pervasive software should be able to adapt itself to the changing environments and user requirements. Obviously, it will bring great challenges to the software engineering practice. This paper proposes AUModel, a conceptual model for adaptive software, which takes adaptability as an inherent feature and can act as the foundation of the engineering process. By introducing AUModel, the reuse of software adaptation infrastructure as well as the separation of adaptation concerns are enabled, which can facilitate both the development and maintenance of adaptive software. This paper also presents our initial attempts to realize this model, including a middleware prototype to support this model and an application to validate its effectiveness. © 2014 IEEE.

关键词： Middleware

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Research on Fine-Grained Sentiment Classification 8th

Research on Fine-Grained Sentiment Classification

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8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019

作者： Wang, Zhihui Wang, Xiaodong Chang, Tao Lv, Shaohe Guo, Xiaoting National Key Laboratory of Parallel and Distributed Processing College of Computer Science and Technology National University of Defence Technology Changsha410073 China

ISBN: (纸本)9783030322359

Aiming at the fine-grained sentiment classification that distinguishes the emotional intensity, the commonly used dataset SST-1 is analyzed in depth. Through the analysis, it is found that the dataset has serious problems such as data imbalance and small overall scale, which seriously restricts the classification effect. In order to solve the related problems, data augmentation method is adopted to realize the optimization of the dataset. The IMDB and other data which are relatively homologous to the original dataset are annotated, and the focus is to expand the categories with fewer numbers. By this way, the problem of data imbalance is effectively alleviated and the original data scale is expanded. Then, based on the Bidirectional Encoder Representations from Transformers (BERT) model, which has good overall performance on natural language processing, the benchmark classification model is built. Through multiple comparison experiments on the original dataset and the enhanced data, the influence of the deficiency of the original dataset on the classification effect is verified. And, it is fully demonstrated that the enhanced data can effectively improve the test results and solve the problem of large differences in performance between different categories well. © 2019, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Unified Contextualized Knowledge Embedding Method for Static and Temporal Knowledge Graph

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 33卷 82-95页

作者： Gao, Yifu Qiao, Linbo Huang, Zhen Kan, Zhigang He, Yongquan Li, Dongsheng National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology Changsha410000 China Intelligent Game and Decision Lab Beijing China Meituan Beijing100102 China

Recent years, there is a growing interest in knowledge graph embedding (KGE), which maps symbolic entities and relations into low-dimensional vector space to effectively represent structured data from the knowledge graph. In addition, the concept of temporal knowledge graph is proposed to document dynamically changing facts in the real world. Existing works attempt to incorporate temporal information into static KGE methods to accomplish temporal knowledge representations. However, existing static or temporal KGE approaches focus on the single query fact and ignore the query-relevant contextual information in the graph structure. This paper moves beyond the traditional way of scoring facts in distinct vector space and proposes a unified framework with pre-trained language models (PLM) to learn dynamic contextualized static/temporal knowledge graph embeddings, called CoS/TKGE. Given the query-specific subgraph, our model transforms it into an input sequence and uses the PLM to obtain the contextualized knowledge representations, which is flexible adaptive to the input graph contexts. We reformulate the link prediction task as a mask prediction problem to fine-tune the pre-trained language model. And the contrastive learning technique is employed to align dynamic contextual embeddings with static global embeddings. Experimental results on three widely used static and temporal KG datasets show the superiority of our model. © 2014 IEEE.

关键词： Knowledge graph

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RScam: Cloud-based anti-malware via reversible sketch 11th

RScam: Cloud-based anti-malware via reversible sketch

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11th International Conference Security and Privacy in Communication Networks, SecureComm 2015

作者： Sun, Hao Wang, Xiaofeng Su, Jinshu Chen, Peixin College of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9783319288642

Cybercrime caused by malware becomes a persistent and damaging threat which makes the trusted security solution urgently demanded, especially for resource-constrained ends. The existing industry and academic approaches provide available anti-malware systems based on different perspectives. However, it is hard to achieve high performance detection and data privacy protection simultaneously. This paper proposes a cloud-based anti-malware system, called RScam, which provides fast and trusted security service for the resource-constrained ends. In RScam, we present suspicious bucket filtering, a novel signature-based detection mechanism based on the reversible sketch structure, which provides retrospective and accurate orientations of malicious signature fragments. Then we design a lightweight client which utilizes the digest of signature fragments to sharply reduce detection range. Finally, we design balanced interaction mechanism, which transmits sketch coordinates of suspicious file fragments and transformation of malicious signature fragments between the client and cloud server to protect data privacy and reduce traffic volume. We evaluate the performance of RScam with campus suspicious traffic and normal files. The results demonstrate validity and veracity of the proposed mechanism. Our system can outperform other existing systems with less time and traffic consumption. © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015.

关键词： Malware

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