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International Conference on Tools with Artificial Intelligence

作者： Lijuan Cui Changjian Wang Ziyang Li Yuxing Peng Science and Technology on Parallel and distributed Processing Laboratory National University of Defence Technology

ISBN: (纸本)9781509044603

Support Vector Machines (SVMs) are powerful classification tools. However, the model training is very time-consuming when meeting large scale data sets. Some efforts have been devoted to screening out non-support vectors (non-SVs) to accelerate the training. But their processes rely on prior knowledge of other classifiers with different parameters to screen out non-SVs. In this paper, we propose Directional Indicator Support Vector Machines (DISVMs) to efficiently identify non-SVs. DISVMs employs a directional indicator, which points to the approximately orthogonal direction of the separating hyperplane, to qualitatively define the location of different samples and thus identify non-SVs. Furthermore, DISVMs leverages a two-stage algorithm: the first stage is to compute the directional indicator. The second stage is to identify non-SVs using the indicator. To avoid misjudgement, we propose CnSV method for non-SVs based on the majority rule. DISVMs screens out non-SVs with light computation and little accuracy loss. Experiments show that our approach significantly reduces the total computation cost.

关键词： Support vector machines Non-support vector Directional indicator Computation efficiency Data preprocessing

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A load balancing-supported constant degree DHT ID assignment method

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2010年第11期38卷 2649-2654页

作者： Wang, Xiao-Hai Peng, Yu-Xing Li, Dong-Sheng Key Laboratory of Science and Technology for Parallel and Distributed Processing National University of Defense Technology Changsha Hunan 410073 China

A Load Balancing-Supported ID assignment method is the foundation to implement and maintain DHT overlays, realized constant degree DHTs usually use simple pure centralized or distributed ID management strategies, which cannot resolve the contradiction between cost of maintaining topologies' information and topologies' balance. Analyzing the universal tree structures in the topologies, an ID Assignment method RFIDAM based on the internal structure Routing Forest is proposed, which regularly aggregates local balancing information to guide new nodes' joining for overall balance. The experimental results show, with low maintenance and routing message overhead, the system's loading balance is efficiently ensured with the length of IDs differ by at most 2.

关键词： Topology

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A secure light-weight public auditing scheme in cloud computing with potentially malicious third party auditor

A secure light-weight public auditing scheme in cloud comput...

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作者： Wu, Yilun Lin, Xinye Lu, Xicheng Su, Jinshu Chen, Peixin College of Computer National University of Defense Technology China School of Computer Science McGill University Montreal Canada National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology China

Public auditing is a new technique to protect the integrity of outsourced data in the remote cloud. Users delegate the ability of auditing to a third party auditor (TPA), and assume that each result from the TPA is correct. However, the TPA is not always trustworthy in reality. In this paper, we consider a scenario in which the TPA may lower the reputation of the cloud server by cheating users, and propose a novel public auditing scheme to address this security issue. The analyses and the evaluation prove that our scheme is both secure and efficient. © 2016 The Institute of Electronics, Information and Communication Engineers.

关键词： Cloud security

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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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The International Conference on Automation and Robotics(ICAR 2011)

作者： Kun Zeng Key Laboratory and technology for National Defence of Parallel and Distributed Processing School of Computer National Univiersity of Defense Technology

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.

关键词： Transactional Memory Face Detection Machine Learning Optimistic Algorithm Pessimistic Algorithm

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Implementation of encrypted data for outsourced database

Implementation of encrypted data for outsourced database

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2010 2nd International Conference on Computational Intelligence and Natural Computing, CINC 2010

作者： Wang, Zheng-Fei Tang, Ai-Guo Department of Computer Hunan Business College Changsha 410205 China National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781424477036

Encryption technology has become an important mechanism of securing data stored in the outsourced database. However, it is a difficulty to query efficiently the encrypted data and many researchers take it into consideration. To solve the problem, an encrypted schema, based on the Postgresql DBMS, is proposed Through the security dictionary and the extended SQL, the approach implements the encrypted storage and efficiently query over the encrypted data in the outsourced databases. Results of experiments validate the efficiency and feasibility of our approach. ©2010 IEEE.

关键词： Cryptography

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Understanding User Interests Acquisition in Personalized Online Course Recommendation

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Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, APWeb-WAIM 2018

作者： Li, Xiao Wang, Ting Wang, Huaimin Tang, Jintao Information Center National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9783030012977

MOOCs have attracted a large number of learners with different education background all over the world. Despite its increasing popularity, MOOCs still suffer from the problem of high drop-out rate. One important reason may be due to the difficulty in understanding learning demand and user interests. To helper users find the most suitable courses, personalized course recommendation technology has become a hot research topic in e-learning and data mining community. One of the keys to the success of personalized course recommendation is a good user modeling method. Previous works in course recommendation often focus on developing user modeling methodology which learns latent user interests from historic learning data. Recently, interactive course recommendation has become more and more popular. In this paradigm, recommender systems can directly query user interests through survey tables or questionnaires and thus the learned interests may be more accurate. In this paper, we study the user interest acquisition problem based on the interactive course recommendation framework (ICRF). Under this framework, we systematically discuss different settings on querying user interests. To reduce performance-cost score, we propose the ICRF user interest acquisition algorithm that combines representative sampling and interest propagation algorithm to acquire user interests in a cost-effective way. With extensive experiments on real-world MOOC course enrollment datasets, we empirically demonstrate that our selective acquisition strategy is very effective and it can reduce the performance-cost score by 30.25% compared to the traditional aggressive acquisition strategies. © 2018, Springer Nature Switzerland AG.

关键词： Surveys

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Character feature learning for named entity recognition

Character feature learning for named entity recognition

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作者： Zeng, Ping Tan, Qingping Zhang, Haoyu Meng, Xiankai Zhang, Zhuo Xu, Jianjun Lei, Yan College of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing Changsha China School of Software Engineering Chongqing University Chongqing China

The deep neural named entity recognition model automatically learns and extracts the features of entities and solves the problem of the traditional model relying heavily on complex feature engineering and obscure professional knowledge. This issue has become a hot topic in recent years. Existing deep neural models only involve simple character learning and extraction methods, which limit their capability. To further explore the performance of deep neural models, we propose two character feature learning models based on convolution neural network and long short-term memory network. These two models consider the local semantic and position features of word characters. Experiments conducted on the CoNLL-2003 dataset show that the proposed models outperform traditional ones and demonstrate excellent performance. © Copyright 2018 The Institute of Electronics Information and Communication Engineers.

关键词： Extraction

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Using NMFAS to identify key biological pathways associated with human diseases

Using NMFAS to identify key biological pathways associated w...

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2012 IEEE 6th International Conference on Systems Biology, ISB 2012

作者： Guo, Hao Zhu, Yunping Li, Dong He, Fuchu Liu, Qijun State Key Laboratory of Proteomics Beijing Proteome Research Centre Beijing Institute of Radiation Medicine Beijing China National Laboratory for Parallel and Distributed Processing National University of Deference and Technology Changsha China

ISBN: (纸本)9781467343985

Gene expression microarray enables us to measure the gene expression levels for thousands of genes at the same time. Here, we constructed the non-negative matrix factorization analysis strategy (NMFAS) to dig the underlying biological pathways related with various diseases by factorizing the pathway expression matrix, which was extracted from microarray matrix using pathway membership information, into the product of row and column vectors. We defined row vector as the pathway activity and column vector as the gene contribution weight. Via comparing the pathway activity of two different sample groups, we can identify significantly expressed pathways. We applied this strategy on two different cases: smoking and type 2 diabetes (DM2). We found 152 differentially expressed pathways by the comparison of pathway activity between smoker and never smoker, including pathways that have been validated in literature, such as "O-Glycans biosynthesis" and "Glutathione metabolism". We also found important genes related to smoking phenotype, such as NQO, HSPA1A, ALDH3A1. As for DM2 analysis, our results suggested 9 pathways were significantly expressed, including typical pathways like "Oxidative phosphorylation" and "mTOR signaling pathway", and found genes like CAPNS1, APP, COX7A1, COX7B, which might play important roles in the cellular regulations of DM2. In conclusion, Our strategy can be efficiently used to integrate gene expression profiles and biological pathway information to identify the key processes underlying human disease and can identify gene pathways missed by alternative approaches. © 2012 IEEE.

关键词： Non-negative matrix factorization

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CRAWL: A Trace Routing Algorithm Based on Hybrid Two-Layer Topology 33rd

CRAWL: A Trace Routing Algorithm Based on Hybrid Two-Layer T...

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33rd International Conference on Advanced Information Networking and Applications, AINA 2019

作者： Zheng, Li-ming Long, Wen-feng Liu, Yu-Jia Sun, Wei-dong Department of Message Communication Armed Police Officer Academy Chengdu China National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783030150341

Data distribution is a key technology for resources convergence and sharing in distributed environment. To better meet the requirement for real time data distribution in the dynamic network, a trace routing algorithm named CRAWL based on the hybrid two-layered topology is put forward. The algorithm contains an overlay topology named CBDLO, upper of which consists of multiple distributed balanced binary trees corresponding to different properties and the lower of which is an unstructured topology. CRAWL forwards the data on the lower unstructured topology in the form of random walk, so that the data can be sent to the corresponding upper topology entry. It also includes a matching algorithm named CDM for the parallel matching data properties on the upper distributed and balanced binary tree and transmitting the matched data to the nodes that are interested in the data. The experimental results show that the algorithm can effectively support large-scale data distribution in a dynamical network, reduce distribution overhead and matching delays. © 2019, Springer Nature Switzerland AG.

关键词： Routing algorithms

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Software Effective Evaluating technology: SWEET

Software Effective Evaluating Technology: SWEET

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Yaozong Li Tao Wang Yue Yu Dongyang Hu National Laboratory for Parallel and Distributed Processing National University of Defence Technology Changsha China

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

In open source community, there are a large number of software resources existing. Such software resources distribute in different societies or storehouses, which require different software characteristic. This phenomenon results in difficult to evaluate software quality using traditional methods. In this case, a novel open-source software sorting algorithm may be an effective solution. Considering both subjective and objective levels, we propose a new method on software sorting and retrieving. In the subjective level, metrics are selected from the corresponding collaborative development community based on software topic. In the objective level, metrics are obtained from the group emotional evaluation value of the knowledge sharing community. We have proved the effectiveness of the method through comparison experiments. Combining with the Solrcloud tool, this method has been integrated into the OSSEAN platform.

关键词： Open source software Sorting Collaboration Software algorithms Measurement Tools

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