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Software ranking and analysis based on mining market requirements and characteristics 15

Software ranking and analysis based on mining market require...

引用

7th Asia-Pacific Symposium on Internetware, Internetware 2015

作者： Liu, Bingxun Yin, Gang Wang, Tao Zhang, Fang Wang, Huaimin National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781450336413

As the rapid growth of open source software, how to choose software from many alternatives becomes a great challenge. Traditional ranking approaches mainly focus on the characteristics of the software themselves, such as qualities, security, reliable and so on. In this paper we investigate the market demands for software engineers, and propose a novel approach for ranking software by analyzing the market requirements for special software. At the same time we conclude the characteristics of software advertisements and analyze the reasons that why these situations emerge and tendency of software market requirements. As industries always need to balance several different factors for selecting software, the market demands can be a good indicator for ranking software and software evaluating. This paper provides quite a different perspective and some interesting inferences on software market requirements, and it can be a valuable supplement for traditional ranking methods, as well as software evaluating. © 2015 ACM.

关键词： Open source software

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Collaborative search in large-scale unstructured peer-to-peer networks

Collaborative search in large-scale unstructured peer-to-pee...

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36th International Conference on parallel processing in Xi'an, ICPP

作者： Zhang, Yiming Li, Dongsheng Chen, Lei Lu, Xicheng National Laboratory for Parallel and Distributed Processing NUDT Department of Computer Science Hong Kong University of Science and Technology

ISBN: (纸本)076952933X

Searching in large-scale unstructured peer-to-peer networks is challenging due to the lack of effective hint information to guide queries. In this paper, we propose POP, a parallel, cOllaborative and Probabilistic search mechanism, in which query messages are viewed as search units to collaborate with each other and aggregate the distributed hints during the search process. A scheme called distributed Bloom Filter (DBF) is presented to propagate the hints with a bandwidth-aware manner, in which a node divides the received Bloom filter vector into subvectors and disseminates the fragments to its neighbors according to their bandwidth capacity. The effectiveness of POP is demonstrated through theoretical analysis and extensive simulations. © 2007 IEEE.

关键词： Bandwidth

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An example-based decoder for spoken language machine translation 6

An example-based decoder for spoken language machine transla...

引用

6th SIGHAN Workshop on Chinese Language processing, SIGHAN 2008

作者： Li, Zhou-Jun Chao, Wen-Han Chen, Yue-Xin National Laboratory for Parallel and Distributed Processing Changsha China School of Computer Science and Engineering Beihang University China

In this paper, we propose an example-based decoder for a statistical machine translation (SMT) system, which is used for spoken language machine translation. In this way, it will help to solve the re-ordering problem and other problems for spoken language MT, such as lots of omissions, idioms etc. Through experiments, we show that this approach obtains improvements over the baseline on a Chinese-English spoken language translation task. © IJCNLP 2008. All rights reserved.

关键词： Machine translation

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Word Embedding-based Context-sensitive Network Flow Payload Anomaly Detection 3

Word Embedding-based Context-sensitive Network Flow Payload ...

引用

3rd International Conference on Applied Machine Learning, ICAML 2021

作者： Li, Yizhou Wang, Yijie Cheng, Li Xu, Hongzuo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665421256

Payload anomaly detection can discover malicious beliaviors tiidden in network packets. It is liard to liandle payload due to its various possible characters and complex semantic context, and tlius identifying abnormal payload is also a non-trivial task. Prior art only uses the n-gram language model to extract features, which directly leads to ultra-high-dimensional feature space and also fails to capture the context semantics fully. Accordingly, this paper proposes a word embedding-based context-sensitive network flow payload anomaly detection method (termed WECAD). First, WECAD obtains the initial feature representation of the payload through the word embedding-based method. Then, we propose a corpus pruning algorithm, which appUes the cosine similarity clustering and frequency distribution to prune inconsequential characters. We only keep the essential characters to reduce the calculation space. Subsequently, we propose a context learning algorithm. It employs the co-occurrence matrix transformation technology and introduces the backward step size to consider the order relationship of essential characters. Comprehensive experiments on real-world intrusion detection datasets validate the effectiveness of our method. © 2021 IEEE

关键词： Embeddings

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A Novel Deep Neural Network Model for Credit Risk Prediction of Chinese Farmers 6

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

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6th IEEE International Conference on Data Science in Cyberspace, DSC 2021

作者： Xie, Yalong Li, Aiping Liu, Ziniu Chen, Kai Tu, Hongkui College Of Computer National University Of Defense Technology Science And Technology On Parallel And Distributed Processing Laboratory Changsha China

ISBN: (纸本)9781665418157

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. © 2021 IEEE.

关键词： Neural network models

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Controllable Template Generation for Document-level Event Extraction 4

Controllable Template Generation for Document-level Event Ex...

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4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Fang, Quntian Liu, Feng Huang, Zhen Guo, Zhenliang Wang, Changjian Li, Dongsheng Hu, Minghao National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha China Academy of Military Sciences Information Research Center Beijing China

ISBN: (纸本)9798350394375

Document-level event extraction task has achieved significant progress based on template generation methods. However, there is no reasonable regulation and restriction in the existing template-based generation methods, which results in the uncontrollability of the generation results. In some scenarios, model generates entities that do not belong to the input text, or generate template content repeatedly. It is determined by the nature of the extraction task and the generation task. To this end, we propose a controllable template generation event extraction model. According to the characteristics of template generation and event extraction tasks, the model devises copy mechanism, inhibition mechanism and rejection mechanism under the appropriately constructed template. Our model achieves state-of-the-art result on MUC-4 dataset, and finally through experimental analysis, it demonstrates the effectiveness of each mechanism we proposed. © 2024 IEEE.

关键词： Extraction

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Guided Spatio-Temporal Learning Method for 4K Video Super-Resolution 5

Guided Spatio-Temporal Learning Method for 4K Video Super-Re...

引用

5th ACM International Conference on Multimedia in Asia, MMAsia 2023

作者： Jiang, Qin Wang, Qinglin Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology China

ISBN: (纸本)9798400702051

4K Video Super-Resolution (VSR) presents a challenging task in video processing, as most existing VSR models have high computational complexity, limiting their application to high-resolution videos, particularly for 4K resolution videos. To address this issue, we propose a novel Guided Spatio-Temporal Video Super-Resolution network (GST-VSR) designed to perform 4K VSR on a single GPU. The proposed method comprises two key components: the Spatio-Temporal Alignment Network (STAN) and the Super-resolution Reconstruction Network (SRN), which work together to enhance the quality of the output frames. The STAN is responsible for extracting highly relevant features in frames and aligning the reference frame with the neighboring frames at the feature level to maintain temporal consistency. The SRN fuses high-quality features into the final high-resolution frames. Unlike existing methods, our proposed approach does not require explicit optical flow estimation, making it more efficient and less computationally demanding. To facilitate the training and testing of the compared models, we have established a new dataset, Pixabay-Set, consisting of 145 videos suitable for the 4K VSR task. Experimental results on the test dataset show that the proposed method achieves competitive performance compared to state-of-the-art models. In summary, our proposed GST-VSR network provides an effective solution to the challenging task of 4K VSR. © 2023 Copyright held by the owner/author(s).

关键词： Statistical tests

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A distributed class-incremental learning method based on neural network parameter fusion 21

A distributed class-incremental learning method based on neu...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Guan, Hongtao Wang, Yijie Ma, Xingkong Li, Yongmou National Laboratory for Parallel and Distributed Processing College of Computer Science and Technology National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781728120584

Class-incremental learning has received considerable attention due to the better adaptability to constantly changing characteristic of online learning. The neural network is suitable for class-incremental learning because of batch training style and fine-tuning. However, as the rapid growth of data centers and data category, data isolated island problem presents a new challenge to the current machine learning model: how to provide an accurate training method in a distributed and incremental manner. Existing solutions cannot adapt to incremental classes without aggregating data. In this paper, we propose a distributed class-incremental learning framework, called DCIL, which iteratively updates new class parameters and fixes existing class parameters to achieve high training accuracy. Especially, we propose MF, a parameter fusion method to integrate the parameters of multi-nodes into a new global parameter. In order to match the requirements of identical columns corresponding to the known classes, we propose a single node class-incremental learning, called SCIL. To evaluate the performance of DCIL, we conduct experiments on 5 datasets under various parameter settings on both 2 and 4 nodes distributed scenarios. Extensive experiments confirm that DCIL outperform the general baselines and achieve classification accuracy closes to the method of data aggregating. © 2019 IEEE.

关键词： Iterative methods

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NVLAN: A Novel VLAN Technology for Scalable Multi-tenant Datacenter Networks 2

NVLAN: A Novel VLAN Technology for Scalable Multi-tenant Dat...

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2nd International Conference on Advanced Cloud and Big Data, CBD 2014

作者： Hu, Yang Li, Huiba Peng, Yuxing Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Computer Science Department China

ISBN: (纸本)9781479980857

Nowadays, cloud providers of 'Infrastructure as a service' require datacenter networks to support virtualization and multi-tenancy at large scale, while it brings a grand challenge to datacenters. Traditional network technologies of datacenters and common commodity devices of network can not meet these new needs. In this paper, we present NVLAN, a novel VLAN technology for scalable multi-tenant datacenter networks. NVLAN eliminates the quantity limitation of logic isolated networks on VLAN through a slight modification. NVLAN can be applied to very large-scale datacenter networks, without the need of expensive and dedicated switches. Through encapsulation of tenant's packets, NVLAN provides tenants with full address space virtualization to configure their own logic network. We implemented NVLAN on a test bed, and demonstrated that it can be scaled up to large numbers of multi-tenant networks with negligible overhead. © 2014 IEEE.

关键词： Virtualization

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Labelwalking nonnegative matrix factorization 40

Labelwalking nonnegative matrix factorization

引用

40th IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2015

作者： Lan, Long Guan, Naiyang Zhang, Xiang Huang, Xuhui Luo, Zhigang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781467369978

Semi-supervised learning (SSL) utilizes plenty of unlabeled examples to boost the performance of learning from limited labeled examples. Due to its great discriminant power, SSL has been widely applied to various real-world tasks such as information retrieval, pattern recognition, and speech separa- tion. Label propagation (LP) is a popular SSL method which propagates labels through the dataset along high density areas defined by unlabeled examples, LP assumes nearby examples should share the same label, thus, it unavoidably pushes the labels to the wrong examples, especially when different la- beled examples are not strictly separated. Seed K-means uses labeled examples to initialize class centers, and avoid getting stuck in poor local optima comparing to traditional K-means, however the hard constraint of each example's membership makes Seed K-means failed in many real world applications. This paper proposes a novel label walking nonnegative matrix factorization method (LWNMF) to handle labeled examples in SSL based on the framework of NMF. LWNMF decomposes the whole dataset into the product of a basis matrix and a coefficient matrix, and to travel labels to unlabeled examples, LWNMF regards the class indicators of labeled examples as their coefficients and iteratively updates both basis matrix and coefficients of unlabeled examples. Since LWNMF learns comprehensive class centroids, labels iteratively walk to unlabeled examples through these significant centroids. © 2015 IEEE.

关键词： K-means Label propagation Nonnegative matrix factorization Semisupervised learning

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