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Siamese Generative Adversarial Predicting network for Extremely Sparse Data in Recommendation System

Siamese Generative Adversarial Predicting Network for Extrem...

引用

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

作者： Qingxian Wang Renjian Zhang Kangkang Ma Bo Chen Jiufang Chen Xiaoyu Shi School of Computer Science and Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China Intelligent Terminal Key Laboratory of SiChuan Province Yibin China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

In the recommendation system, user-item preferences are described by a High-Dimensional and Sparse (HiDS) matrix. Collaborative Filtering (CF)-based models have been widely adopted to solve unknown entries estimation. However, a CF-based model does not take the data distribution characteristic of an HiDS matrix into account, thereby its representation ability limited. To address this issue, this paper proposes to build a Siamese Generative Adversarial Predicting network (SGAPN) to estimate the unknown entries in an HiDS matrix. Firstly, we build a model to learn the data distribution characteristic of an HiDS matrix. Secondly, based on the learned data distribution and observed entries, we build a model to estimate the unknown entries. Compared with the CF-based model, our model takes the data distribution of an HiDS matrix into account to address the unknown entries estimation. Experimental results over four HiDS matrices generated by industrial applications demonstrate that compared with several state-of-the-art models, the proposed model achieves competitive prediction accuracy.

关键词： Training Collaborative filtering Estimation Predictive models Data models Sparse matrices

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Video classification technology based on deep learning

Video classification technology based on deep learning

引用

2020 International conference on Engineering Management of Communication and Technology, EMCTECH 2020

作者： Liu, Mengyun College of Information Studies University of Maryland College Park United States

ISBN: (纸本)9781665404488

It takes a large amount of time for artificial video classification and analysis, and the processing efficiency is low, so it is difficult to better meet the practical needs for the era of big data. Therefore, it is extremely urgent to form an intelligent video analysis and classification method, which is worth exploring. In this paper, experiments on feature extraction, feature fusion and similarity measurement are carried out to determine methods to improve video classification accuracy. The results show that the classification accuracy decreases with the increase of pyramid layers. The performance of the cascade SRU method is better, which can improve the classification accuracy. In addition, two-level cascading coding can better complete the extraction of effective information in depth characteristics and promote the accuracy of classification. The classification accuracy of the network increases, after the characteristic aggregation is accomplished by means of average pooling. © 2020 IEEE.

关键词： Classification (of information)

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Optimal FPGA-oriented lightweight network architecture search under multi-objective constraints

Optimal FPGA-oriented lightweight network architecture searc...

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

作者： Xiaobin Li Hongxu Jiang Fangzheng Tian Yonghua Zhang Rui Miao Donghuan Xu Beijing Key Laboratory of Digital Media of Beihang University Hangzhou Innovation Institute of Beihang University Beijing China Shanghai Aerospace Control Technology Institute Shanghai China

The neural architecture search (NAS) technology has developed rapidly, but most algorithms mainly target the GPUs, causing the searched models to lack consideration of the target hardware platform. This paper proposes a novel NAS algorithm specific for the unique calculation and storage character of FPGA to accelerate the deployment efficiency of neural networks and automated build the FPGA-oriented neural structure. Firstly, we design the search space based on the lightweight candidate blocks. Then, we measure the real simulation latency of each candidate block on the FPGA platform and for the first time using it to direct the NAS process. Eventually, this paper proposed a differentiable representation function of hardware latency and built a multi-objective NAS framework based on real simulation latency and FLOPS. In addition, by analyzing the relationship between the network convergence process and the model size, we achieve an optimal trade-off balance between precision and latency and build a more suitable network structure for deployment on FPGA. Compared with state-of-the-art manual and automated lightweight models, our method has relatively better accuracy and latency performance. Extensive experiments on the ImageNet dataset show that the proposed method can achieve 2x speedup compared with the similar accuracy manual lightweight network (MobileNet V2) and even get 3x speedup compared with the equivalent auto lightweight network (DARTS).

关键词： Analytical models Neural networks Manuals network architecture Hardware Time measurement Real-time systems

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A User Behavior-based Random Distribution Scheme for Adversarial Example Generated CAPTCHA

A User Behavior-Based Random Distribution Scheme for Adversa...

引用

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

作者： Wenwen Zheng Weiqi Wang Wei Ren Shiyuan Feng Shiqin Liu Yi Ren School of Computer Science China University of Geosciences Wuhan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computing Science University of East Anglia Norwich U.K

The widespread and mature application of deep learning technology on human behavior analysis leads to the possibility that the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) may not be able to accurately determine whether or not the user is human, which makes the distributed denial-of-service (DDoS) attacks launched by deep learning-based malicious automatic computer programs become possible. The CAPTCHA generated by adversarial examples can defend against the automatic identification by the deep learning-based method, however, if the deployed CAPTCHAs are all adversarial examples solely, the adversary has the chance to collect sufficient adversarial examples, which also causes security issues. In this paper, a user behavior-based random distribution scheme for adversarial example generated CAPTCHA is proposed to tackle the above sole distribution problem. Specifically, we generated two kinds of CAPTCHAs, one is normal and the other is generated by the fast gradient signed method (FGSM), i.e., the adversarial examples. Meanwhile, user behaviors are analyzed and associated with a reasonable probability model. According to the probability selection, the normal CAPTCHAs or strong CAPTCHAs will be delivered to the users. The experimental results illustrate that our scheme has an excellent ability to distinguish computers from humans. Thus, it can protect the CAPTCHA systems from DDoS attacks.

关键词： Computers Learning systems Deep learning Analytical models Computational modeling Denial-of-service attack Security

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Workload Prediction in Edge Computing based on Graph Neural network

Workload Prediction in Edge Computing based on Graph Neural ...

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

作者： WeiWei Miao Zeng Zeng Mingxuan Zhang Siping Quan Zhen Zhang Shihao Li Li Zhang Qi Sun Information and Telecommunication Branch State Grid Jiangsu Electric Power CO.LTD Nanjing China State Grid Taizhou Power Supply Company Nanjing China

In a typical edge computing paradigm, multiple edge servers are located near the end users to provide augmented computation and bandwidth. As the resources of the edge servers are limited, precisely predicting the workload of different edge servers can be of great importance in efficiently utilizing the edges. However, due to the dynamics of end users, the workload in the edge servers have lots of spikes. In this paper, we consider a deep learning method to predict the resource usage of edge servers. The main idea is to use graph neural network (GNN) to capture the interconnected topology of the edge servers. The edge servers that are close in proximity often have similar resource load patterns. The GNN is then concatenated with a LSTM layer to output the prediction value. The algorithm is evaluated in a real edge computing data set. The results show that our algorithm have high accuracy in predicting the resource usage. In addition, it outperforms other SOTA algorithms significantly.

关键词： Deep learning network topology Aggregates Bandwidth Prediction algorithms Graph neural networks Topology

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Off-chain Anonymous Payment Channel Scheme based on Mimblewimble

Off-chain Anonymous Payment Channel Scheme based on Mimblewi...

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

作者： Zhenyu Guan Lixin Zhang Yingpeng Zhang Dawei Li Yu Sun Jian Cui School of Cyber Science and Technology Beihang University Beijing China

Mimblewimble, proposed by an anonymous author in 2016, combines several privacy protection technologies to enhance the Bitcoin blockchain. However, Mimblewimble does not support fast micropayments, and there are still problems such as high transaction fees and poor scalability. The payment channel allows all parties to perform transactions off-chain while maintaining the security of transactions on-chain to reduce the burden of the blockchain. It has been used in the Lightning network to expand the blockchain. On the basis of Mimblewimble, our paper exploits a method of adding a random blinding factor to the amount of additively homomorphic commitments, and describes a scheme of using payment channels for fast off-chain transactions. The scheme can implement fast off-chain payments and improve scalability while protecting transaction privacy.

关键词： Privacy Scalability Lightning Bitcoin Throughput Blockchains

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FastTune: Timely and Precise Congestion Control in Data Center network

FastTune: Timely and Precise Congestion Control in Data Cent...

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

作者： Renjie Zhou Dezun Dong Shan Huang Yang Bai National University of Defense Technology Changsha China

Modern data center networks (DCNs) exhibit high dynamics in both time and space dimensions, which poses challenges for congestion control protocols to achieve low latency, fast convergence, and high throughput. Existing methods have leveraged fine-grained link load information to achieve precise congestion control, but it still suffers from untimely control in highly dynamic DCNs. In this paper, we propose a timely and precise congestion control method called FastTune. FastTune employs fine-grained network status to achieve accurate feedback, uses switch feedback to control the first RTT, and leverages ACK-padding to shorten the feedback path and regulate congestion in time. FastTune develops a multiplicative increase/decrease (MI/MD) algorithm to achieve fast convergence based on timely and precise feedback. Large-scale evaluations show that, compared with state-of-the-art work, FastTune significantly reduces the feedback delay by up to 87%, reduces the average flow completion time by 40%, and the 99th percentile flow completion time by 51%. Besides, FastTune maintains near-zero queueing and reasonable throughput.

关键词： Data centers Protocols Switches Aerospace electronics Throughput Control systems Delays

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The Random Content Poisoning Attack in NDN

The Random Content Poisoning Attack in NDN

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

作者： Pengfei Yue Ru Li Bin Pang College of Computer Inner Mongolia University Hohhot China

The Information-Centric network (ICN) was proposed to offer a clean state paradigm of the Internet by replacing the host-centric paradigm of today’s Internet. As one realization of the ICN, the Named Data networking (NDN) binds the security directly to the content in a network and the pervasive cache mechanism provides better support for the content distribution. Since there is no address in NDN, attacks that target victims by addresses fail to work and the security of content is guaranteed by verifying the digital signatures of data packets. However, as proved by some previous research works, some DDoS attacks, e.g., the Interest Flooding Attack (IFA) and the Content Poisoning Attack (CPA) hazard and even paralyze the NDN. In this paper, existing DDoS attack patterns in NDN are discussed and a new attack pattern named the Random Content Poisoning Attack (RCPA) is proposed and modeled. By observing the results of simulations with a real network topology, it is found that the RCPA is more hidden than CPA for its trick attack model and passive nature. To detect the RCPA, an Information Gain Ratio-based feature selection and three machine learning algorithms are introduced in this paper. By evaluating the accuracies of predictions with selected features, it is observed that a way of offline detection works well against the RCPA.

关键词： Machine learning algorithms network topology Information-centric networking Feature extraction Denial-of-service attack Hazards Internet

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FedGroup: Efficient Federated Learning via Decomposed Similarity-based Clustering

FedGroup: Efficient Federated Learning via Decomposed Simila...

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

作者： Moming Duan Duo Liu Xinyuan Ji Renping Liu Liang Liang Xianzhang Chen Yujuan Tan College of Computer Science Chongqing University Chongqing China School of Computer Science and Technology Xi’an Jiaotong University Xi’an China

Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally. Unlike the centralized training setting, the non-IID and imbalanced (statistical heterogeneity) training data of FL is distributed in the federated network, which will increase the divergences between the local models and the global model, further degrading performance. In this paper, we propose a novel clustered federated learning (CFL) framework FedGroup, in which we 1) group the training of clients based on the similarities between the clients’ optimization directions for high training performance; 2) construct a new data-driven distance measure to improve the efficiency of the client clustering procedure. 3) implement a newcomer device cold start mechanism based on the auxiliary global model for framework scalability and *** can achieve improvements by dividing joint optimization into groups of sub-optimization and can be combined with FL optimizer FedProx. The convergence and complexity are analyzed to demonstrate the efficiency of our proposed framework. We also evaluate FedGroup and FedGrouProx (combined with FedProx) on several open datasets and made comparisons with related CFL frameworks. The results show that FedGroup can significantly improve absolute test accuracy by +14.1% on FEMNIST compared to FedAvg, +3.4% on Sentiment140 compared to FedProx, +6.9% on MNIST compared to FeSEM.

关键词： Training Performance evaluation Scalability Neural networks Training data Linear approximation Collaborative work

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

作者： Lei Zhang Xuefei Chen Yuxiang Ma School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Mobile devices face the problem of handover signal sources in mobile communication networks. It is particularly difficult to address this problem in 5G networks, because the coverage area of 5G base stations is smaller than that of traditional mobile communication networks, e.g., 4G networks. Therefore, when a device is moving, it needs to frequently handover base stations to maintain the connection with the network in the 5G network. Given that 5G networks have high requirements for low latency and high reliability, new methods need to be proposed to optimize the selection of gNBs for devices. The prediction of the UEs’ trajectory can reduce redundant handovers between devices and base stations. However, the prediction of the UEs’ movement trajectory requires sufficient user movement data to achieve the desired prediction accuracy. For users with less trajectory data, inaccurate predictions may cause negative effects and even increase the number of handovers. In this paper, we propose a gNB handover model based on user similarity. The evaluation results show that our method can reduce the number of handovers by 50% compared with existing related solutions, which means that the proposed method will effectively reduce the transmission delay and enhance the robustness of the gNB.

关键词： Base stations 5G mobile communication Handover Predictive models Prediction algorithms Data models Robustness

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