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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...

引用

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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A programming language approach to internet-based virtual environment computing

A programming language approach to internet-based virtual en...

引用

作者： Ji, Wang Shen, Rui Wang, Huai-Min National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha 410073 China

There is an increasing need to build scalable distributed systems over the Internet infrastructure. However, the development of distributed scalable applications suffers from lack of a wide accepted virtual computing environment. Users have to take great efforts on the management and sharing of the involved resources over Internet, whose characteristics are intrinsic growth, autonomy and diversity. To deal with this challenge, Internet-based Virtual Computing Environment (iVCE) is proposed and developed to serve as a platform for distributed scalable applications over the open infrastructure, whose kernel mechanisms are on-demand aggregation and autonomic collaboration of resources. In this paper, we present a programming language for iVCE named Owlet. Owlet conforms with the conceptual model of iVCE, and exposes the iVCE to application developers. As an interaction language based on peer-to-peer content-based publish/subscribe scheme, Owlet abstracts the Internet as an environment for the roles to interact, and uses roles to build a relatively stable view of resources for the on-demand resource aggregation. It provides language constructs to use 1) distributed event driven rules to describe interaction protocols among different roles, 2) conversations to correlate events and rules into a common context, and 3) resource pooling to do fault tolerance and load balancing among networked nodes. We have implemented an Owlet compiler and its runtime environment according to the architecture of iVCE, and built several Owlet applications, including a peer-to-peer file sharing application. Experimental results show that, with iVCE, the separation of resource aggregation logic and business logic significantly eases the process of building scalable distributed applications. © 2011 Springer Science+Business Media, LLC & Science Press, China.

关键词： Fault tolerance

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Communication delay analysis based on network calculus

Communication delay analysis based on network calculus

引用

2010 2nd International Conference on Software Technology and Engineering, ICSTE 2010

作者： Lin, Yufei Xu, Xinhai Lin, Yisong National Laboratory for Parallel and Distributed Processing Computer School National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781424486656

Network calculus is a promising theory for analyzing and modeling networks based on min-plus algebra. Using network calculus theory, we propose formulas of arrival curve and service curve for end-to-end communication, build the corresponding time model, and derive the communication delay formulas for two scenarios of the model respectively. Then we take fat tree topology, which is widely used in Infiniband interconnection, as an example to analyze the delay of one-to-all broadcast. This paper, as a groundwork, provides a new approach for the network researchers to delve communication delay in future researches. © 2010 IEEE.

关键词： Calculations

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Unsupervised feature learning for human activity recognition using smartphone sensors 2

Unsupervised feature learning for human activity recognition...

引用

2nd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2014

作者： Li, Yongmou Shi, Dianxi Ding, B. Liu, Dongbo National Key Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783319138169

Feature representation has a significant impact on human activity recognition. While the common used hand-crafted features rely heavily on the specific domain knowledge and may suffer from nonadaptability to the particular dataset. To alleviate the problems of handcrafted features, we present a feature extraction framework which exploits different unsupervised feature learning techniques to learning useful feature representation from accelerometer and gyroscope sensor data for human activity recognition. The unsupervised learning techniques we investigate include sparse auto-encoder, denoising auto-encoder and *** evaluate the performance on a public human activity recognition dataset and also compare our method with traditional features and another way of unsupervised feature learning. The results show that the learned features of our framework outperform the other two methods. The sparse auto-encoder achieves better results than other two techniques within our framework. © Springer International Publishing Switzerland 2014.

关键词： Signal encoding

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A task-mapping algorithm for performance simulation based on simulated annealing

A task-mapping algorithm for performance simulation based on...

引用

2011 International Conference on Computational and Information Sciences, ICCIS 2011

作者： Guo, Xiaowei Lin, Yufei Xu, Xinhai Zhang, Xin National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9780769545011

Nowadays, the scale of parallel computer systems is increasing, and simulation technology has become an important tool for performance prediction in the system development process. Task mapping approach is an important aspect affecting the performance of simulation. In this paper, in order to solve the task mapping problem in performance simulation, a task mapping algorithm based on simulated annealing is proposed, and we verified the correctness and effectiveness of the algorithm by experiments. Experimental results show that the algorithm has high efficiency, and can solve the large-scale problem with lower time cost. © 2011 IEEE.

关键词： Simulated annealing

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SemanticCast: Content-based data distribution over self-organizing semantic overlay networks

SemanticCast: Content-based data distribution over self-orga...

引用

11th International Conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2010

作者： Zheng, Zhong Wang, Yijie National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9780769542874

Many applications demand distributing data with different contents efficiently in the network environment with unreliable links and a high node churn. Existing approaches mostly focus on optimizing either efficiency or robustness of data distribution, and fail to ensure both of them simultaneously. In this paper, we propose SemanticCast - a content-based data distribution approach over self-organizing semantic overlay networks. SemanticCast maintains a self-organizing semantic overlay based on view exchange (called Crowd). In Crowd, each node seeks neighbors with more similar interests by periodically exchanging its neighbor list (called view) with a chosen neighbor. Through these nodes' self-organizing behavior, various interest communities emerge in the overlay. For data distribution over Crowd, SemanticCast adopts random walk to route data between interest communities, and adopts flooding to disseminate data inside the interested communities. The experimental results show that compared to existing approaches, SemanticCast can support efficient content-based data distribution in the unreliable and highly dynamic network environment. © 2010 IEEE.

关键词： Semantics

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Kernel fusion: An effective method for better power efficiency on multithreaded GPU

Kernel fusion: An effective method for better power efficien...

引用

2010 IEEE/ACM International Conference on Green Computing and Communications, GreenCom 2010, 2010 IEEE/ACM International Conference on Cyber, Physical and Social Computing, CPSCom 2010

作者： Wang, Guibin Lin, YiSong Yi, Wei National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9780769543314

As one of the most popular accelerators, Graphics processing Unit (GPU) has demonstrated high computing power in several application fields. On the other hand, GPU also produces high power consumption and has been one of the most largest power consumers in desktop and supercomputer systems. However, software power optimization method targeted for GPU has not been well studied. In this work, we propose kernel fusion method to reduce energy consumption and improve power efficiency on GPU architecture. Through fusing two or more independent kernels, kernel fusion method achieves higher utilization and much more balanced demand for hardware resources, which provides much more potential for power optimization, such as dynamic voltage and frequency scaling (DVFS). Basing on the CUDA programming model, this paper also gives several different fusion methods targeted for different situations. In order to make judicious fusion strategy, we deduce the process of fusing multiple independent kernels as a dynamic programming problem, which could be well solved with many existing tools and be simply embedded into compiler or runtime system. To reduce the overhead introduced by kernel fusion, we also propose effective method to reduce the usage of shared memory and coordinate the thread space of the kernels to be fused. Detailed experimental evaluation validates that the proposed kernel fusion method could reduce energy consumption without performance loss for several typical kernels. © 2010 IEEE.

关键词： Graphics processing unit

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Accelerating Sample-based GNN Training by Feature Caching on GPUs 7

Accelerating Sample-based GNN Training by Feature Caching on...

引用

7th IEEE International Conference on Smart Cloud, SmartCloud 2022

作者： He, Yuqi Lai, Zhiquan Ran, Zhejiang Zhang, Lizhi Li, Dongsheng National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing College of Computer Changsha China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

The existing graph neural network (GNN) systems adopt sample-based training on large-scale graphs over multiple GPUs. Although they support large-scale graph training, large data loading overhead is still a bottleneck. In this work, we propose SCGraph, a method that supports GPU high-speed feature caching. We classify the graph vertices sorted by out-degrees. For high out-degree vertices, we set grading caches via different GPUs to increase the overall cache content through NVLink high-speed data transmission between them. For low out-degree vertices, we expand training vertices' neighborhood in advance to regenerate cache. We evaluate SCGraph against two state-of-the-art industrial GNN frameworks, i.e., DGL and PaGraph on two datasets Reddit and ogbn-products. Experimental results show that SCGraph achieves up to 1.83× performance speedup over the state-of-the-art baselines. © 2022 IEEE.

关键词： Graph neural networks

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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...

引用

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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Detecting null dereference with a game

Detecting null dereference with a game

引用

2012 International Conference on Information Technology and Software Engineering, ITSE 2012

作者： Chen, Jie National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology 410073 Changsha China

ISBN: (纸本)9783642345302

As one of the most dangerous and common software vulnerabilities, null dereference often leads to program crashes. In this chapter we propose a human computation method to detect null dereference in a "frog and bug" game. The purpose of the game is to utilize the rich distributed human resource to do the complex science work in a simple and pleasant way. Programs are transformed into visual puzzles first, and then the server collects information from the people playing the game. Finally the recorded metadata is translated back into the positions in the programs where null dereference may happen. The game has shown to be effective. © 2013 Springer-Verlag.

关键词： Computers

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