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

Journal of Physics: Conference Series 2020年第1期1693卷

作者： Jialun Yang Yi Yang Jiahao Liu Rulin Liu Peng Qiao Baosheng Wang College of Computer Science and Technology National University of Defense Technology Changsha 410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 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%.

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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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Enhancing 2D Representation via Adjacent Views for 3D Shape Retrieval

Enhancing 2D Representation via Adjacent Views for 3D Shape ...

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International Conference on Computer Vision (ICCV)

作者： Cheng Xu Zhaoqun Li Qiang Qiu Biao Leng Jingfei Jiang School of Computer Science & Engineering Beihang University Duke University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beihang University National Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Multi-view shape descriptors obtained from various 2D images are commonly adopted in 3D shape retrieval. One major challenge is that significant shape information are discarded during 2D view rendering through projection. In this paper, we propose a convolutional neural network based method, CenterNet, to enhance each individual 2D view using its neighboring ones. By exploiting cross-view correlations, CenterNet learns how adjacent views can be maximally incorporated for an enhanced 2D representation to effectively describe shapes. We observe that a very small amount of, e.g., six, enhanced 2D views, are already sufficient for a panoramic shape description. Thus, by simply aggregating features from six enhanced 2D views, we arrive at a highly compact yet discriminative shape descriptor. The proposed shape descriptor significantly outperforms state-of-the-art 3D shape retrieval methods on the ModelNet and ShapeNetCore55 benchmarks, and also exhibits robustness against object occlusion.

关键词： Shape Three-dimensional displays Correlation Two dimensional displays Feature extraction Solid modeling Computational modeling

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Correction to: A compressed matrix sequence method for solving normal equations of bundle adjustment

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Machine Vision and Applications 2021年第4期32卷 1-1页

作者： Peng, Jiaxin Liu, Jie Wei, Hua Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Changsha China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Changsha China Algorithm Lab Changsha Morale Network Technology Co. Ltd. Changsha China Water and Land Resources Research Center of the Middle Reaches of Yangtze River School of Resources Environment Science and Engineering Hubei University of Science and Technology Xianning China

A correction to this paper has been published: https://***/10.1007/s00138-021-01222-5

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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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A novel hybrid architecture for high speed regular expression matching 1

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1st International Symposium on Mobile Internet Security, MobiSec 2016

作者： Xu, Chengcheng Zhao, Baokang Chen, Shuhui Su, Jinshu College of Computer National University of Defense Technology No. 137 Yanwachi Street ChangshaHunan410073 China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha410073 China

ISBN: (数字)9789811078507

ISBN: (纸本)9789811078491

Mobile devices play an important role in our everyday lives, but they also bring great security threats. Deep packet inspection (DPI) is one of the most efficient methods to detect the malicious information hidden in the mobile traffic, and regular expression matching is widely used in DPI for its powerful expressive ability. However, with the increasing complexity of regular expressions, traditional solutions cannot meet the requirements of both storage and high performance. In this paper, we propose a novel hybrid matching architecture and two-stage memory architecture for the state of the art hybrid FA to solve this problem. Experiment results confirm that our architecture is scalable to complex rule sets, and the matching performance outperforms state of the art memory centric solution by up to 15x. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Memory architecture

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Exploiting a depth context model in visual tracking with correlation filter

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Frontiers of Information technology & Electronic Engineering 2017年第5期18卷 667-679页

作者： Zhao-yun CHEN Lei LUO Da-fei HUANG Mei WEN Chun-yuan ZHANG College of Computer National University of Defense TechnologyChangsha 410073China National Key Laboratory of Parallel and Distributed Processing Changsha 410073China

Recently correlation filter based trackers have attracted considerable attention for their high computational efficiency. However, they cannot handle occlusion and scale variation well enough. This paper aims at preventing the tracker from failure in these two situations by integrating the depth information into a correlation filter based tracker. By using RGB-D data, we construct a depth context model to reveal the spatial correlation between the target and its surrounding regions. Furthermore, we adopt a region growing method to make our tracker robust to occlusion and scale variation. Additional optimizations such as a model updating scheme are applied to improve the performance for longer video sequences. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracker performs favourably against state-of-the-art algorithms.

关键词： Visual tracking Depth context model Correlation filter Region growing

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Using Document Embedding Techniques for Similar Bug Reports Recommendation

Using Document Embedding Techniques for Similar Bug Reports ...

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

作者： Ming Chen Dongyang Hu Tao Wang Jun Long Gang Yin Yue Yu Yang Zhang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781538665664;9781538665657

In the software development, it is common to find that several bug reports are related to many common code files, i.e., similar bugs. Similar bug recommendation is a meaningful task that could assist developers in bug triaging and fixing. If developers can focus on fewer code files, they can fix similar bugs in less time with higher quality. Therefore, building a similar bug recommendation system is a meaningful task that can improve development efficiency. As the state of the art, Yang et al.'s work presented an approach that combines TF-IDF method with Word Embedding model and achieved a good result. To further improve the performance of their approach, in this paper, we propose a novel approach using Document Embedding model. In our preliminary evaluation, we conduct the experiment on the datasets of J dt, Birt and Eclipse platform. And the results show that our approach outperforms Yang et ale `s, with 2.67-9.50% of improvement.

关键词： Computer bugs Task analysis Predictive models Measurement Context modeling Training Software

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Asia-Pacific Conference on Software Engineering

作者： Dongyang Hu Ming Chen Tao Wang Junsheng Chang Gang Yin Yue Yu Yang Zhang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781728119717;9781728119700

In the software development, it is not uncommon to find that several bug reports are related to many common code files, i.e., similar bugs. Similar bug recommendation is a meaningful task which can assist developers in bug triaging and fixing. As the state of the art, Yang et al.'s work presented an approach that combines TF-IDF method with word embedding model and achieved a good result. To further improve the performance of their approach, in this paper, we propose a novel approach using Document Embedding model. In our preliminary evaluation, we conduct the experiment on 13,090 bug reports from the Eclipse platform and the results show that our approach outperforms Yang et al.'s, with 7.89-8.96% of improvement.

关键词： Computer bugs Software engineering Task analysis Measurement Software Information retrieval Natural language processing

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Fisher Discriminant Analysis Random Forest for Online Class Incremental Learning

Fisher Discriminant Analysis Random Forest for Online Class ...

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

作者： Wang Xiong Yijie Wang Li Cheng Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781728111421;9781728111414

Class incremental learning needs to deal with a dynamic environment where data class appears incrementally, it is a challenge to learn new knowledge while preserving what has already been learned. On the other hand, due to the limited storage of the online scenario, algorithm is usually obstructed to frequently scan or simply store all historical data, it is another challenge to reduce the historical data storage for algorithm. Few existing work have addressed above challenges simultaneously. In this paper, we propose Fisher Discriminant Analysis Random Forest (FDARF), which consists of two parts, GHS (Generate Hierarchical Split) and RRS (Random Reform Subtree), that cooperatively operate. GHS combines FDA (Fisher Discriminant Analysis) with tree hierarchy to learn a hierarchical split of data space that provides strong ability for classification. The statistics in leaves (i.e. historical data) can be described by covariance matrix and further optimized by matrix sketching algorithm to reduce storage; for every tree initialized by GHS, RRS randomly reforms certain state subtree, which creates diversity that can be ensemble for ensuring effectiveness of class incremental learning. Extensive experiments on diverse datasets validate that FDARF can well adapt to the online class incremental learning.

关键词： Vegetation Training Covariance matrices Memory Forestry Learning systems

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