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检索条件"任意字段=13th IEEE International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2022"

共 21 条记录，以下是11-20 订阅

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Comparative Study on Data Sovereignty Guarantee Technology

Comparative Study on Data Sovereignty Guarantee Technology

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international symposium on parallel architectures, algorithms and programming (paap)

作者： Yaodong Tao Shuai Yang Hongmei Ge Industrial Internet Security Research Center/Director Shenyang Ligong University School of Information Science and Engineering Beijing Jiaotong University Beijing China School of Information Science and Engineering Shenyang Ligong University Shenyang China Xingyuan Certification Center Co. Ltd Beijing China

ISBN: (纸本)9781665452199

As an economic commodity, data sharing, circulation and trading can not only reduce the maintenance and management costs of enterprises, but also tap the potential value of data, improve the internal workflow of enterprises and the cooperation among enterprises. the marketization of data elements and the clarification of data sovereignty are the current difficulties hindering data flow. this paper addresses one of the current data circulation issues: how to maintain data sovereignty, and makes exploration and research in combination with the current era background. For the current research projects and products, compare and analyze the techniques used to maintain data sovereignty. Finally, based on the current technology, it gives recommendations for the future development of data sovereignty protection technology.

关键词： Costs Data security programming Maintenance engineering parallel architectures

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Fake news detection method based on multi-feature fusion of entity and structured text

Fake news detection method based on multi-feature fusion of ...

引用

international symposium on parallel architectures, algorithms and programming (paap)

作者： Tianqi Ren Ke Ji Zhenxiang Chen Runyuan Sun Kun Ma Jin Zhou School of Information Science and Engineering University of Jinan Jinan China

ISBN: (纸本)9781665452199

Internet technology has facilitated the development of social media, which has become the main way for people to get news. the low cost of information generation has allowed unconfirmed fake news to be noticed and reposted, which can easily cause social and economic harm. Much of the existing research on fake news detection has focused solely on the accuracy of detection, but has neglected the time efficiency of detection. We propose a fake news detection method called MFEAST, which simplifies article content and speeds up model training by extracting key information from articles. Compared with existing real fake news detection methods, our method substantially reduces the training time and achieves high detection accuracy.

关键词： Training Costs Text recognition Social networking (online) Text categorization programming parallel architectures

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Passivity-Based Finite-Time Consensus for Nonlinear Fractional-Order Multi-Agent Systems

Passivity-Based Finite-Time Consensus for Nonlinear Fraction...

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international symposium on parallel architectures, algorithms and programming (paap)

作者： Zeng-Ran Zhang Jin-Liang Wang Chen-Guang Liu Jigang Wu Yan-Li Huang School of Software Tiangong University Tianjin China School of Computer Science and Technology Tiangong University Tianjin China Institute of Artificial Intelligence Beihang University Beijing China School of Computer Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781665452199

this paper utilizes the finite-time passivity (FTP) to settle the finite-time consensus (FTC) problem for a class of nonlinear fractional-order multi-agent systems (FOMASs). By selecting appropriate state feedback controller, a FTP criterion for FOMAS is derived on the basis of inequality techniques, and a sufficient condition to ensure FOMAS can achieve FTC is also given by exploiting the FTP result. Moreover, to testify the validity of the FTP and FTC criteria, a numerical example is presented.

关键词： Sufficient conditions State feedback programming parallel architectures Multi-agent systems

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Progressive Differentiable Architecture Search Based Automatic Modulation Classification Method

Progressive Differentiable Architecture Search Based Automat...

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international symposium on parallel architectures, algorithms and programming (paap)

作者： Xiaofeng Chen Xixi Zhang Yu Wang Jie Yang Guan Gui Hikmet Sari College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781665452199

Automatic modulation classification (AMC) is a key step of signal demodulation that determines whether the receiver can correctly receive the modulation type of the transmitted signal without prior knowledge. Deep learning (DL) based AMC methods have achieved excellent performances. However, these methods highly rely on expert experience to design network structures. these hand-designed networks have fixed structures and lack flexibility, which often leads to insufficient model generalization. Neural architecture search (NAS) is a vital direction for automatic machine learning (AutoML) which can solve the shortcomings of hand-designed networks. In this paper, we propose a lightweight progressive differentiable architecture search-based AMC (PDARTS-AMC) method to search for a very lightweight network with good performance. Experimental results show that the proposed PDARTS-AMC method both improves the accuracy and reduces the computational cost when compared with existing methods.

关键词： Deep learning Machine learning algorithms Receivers programming parallel architectures Computational efficiency Classification algorithms

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the Fast Paillier Decryption with Montgomery Modular Multiplication Based on OpenMP

The Fast Paillier Decryption with Montgomery Modular Multipl...

引用

international symposium on parallel architectures, algorithms and programming (paap)

作者： Decong Lin Hongbo Cao Chunzi Tian Yongqi Sun Beijing Jiaotong University Beijing China

ISBN: (纸本)9781665452199

With the increasing awareness of privacy protection and data security, people’s concerns over the confidentiality of sensitive data still limit the application of distributed artificial intelligence. In fact, a new encryption form, called homomorphic encryption(HE), has achieved a balance between security and operability. In particular, one of the HE schemes named Paillier has been adopted to protect data privacy in distributed artificial intelligence. However, the massive computation of modular multiplication in Paillier greatly affects the speed of encryption and decryption. In this paper, we propose a fast CRT-Paillier scheme to accelerate its decryption process. We first introduce the Montgomery algorithm to the CRT-Paillier to improve the process of the modular exponentiation, and then compute the modular exponentiation in parallel by using OpenMP. the experimental results show that our proposed scheme has greatly heightened its decryption speed while preserving the same security level. Especially, when the key length is 4096-bit, its speed of decryption is about 148 times faster than CRT-Paillier.

关键词： Data privacy Data security Distributed databases programming parallel architectures Homomorphic encryption Artificial intelligence

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Multi-selection Attention for Multimodal Aspect-level Sentiment Classification

Multi-selection Attention for Multimodal Aspect-level Sentim...

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international symposium on parallel architectures, algorithms and programming (paap)

作者： Yuqing Miao Ronghai Luo Tonglai Liu Wanzhen Zhang Guoyong Cai Ming Zhou School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China College of Information Science and Technology Zhongkai University of Agriculture and Engineering Guangzhou China Guilin Hivision Technology Company Guilin China

ISBN: (纸本)9781665452199

Multimodal aspect-level sentiment classification aims to utilize images to recognize the sentiment polarity of target aspects in text. To address the issues of low utilization of inter-modal complementary information and vanishing gradients, a multimodal aspect-level sentiment based on multi-selection attention mechanism is proposed. Multi-selection attention mechanism explicitly considers the contribution of different modalities to aspects and utilizes shared features and private features of image modality to enhance sentiment expression of target aspects. On this basis, inspired by residual connections in ResNet and encoder-decoders in U-Net, a simple and effective residual encoder-decoder is proposed to mine deep information and avoid vanishing gradients. the experimental results on two public sentiment datasets show that the proposed model can better utilize images to supplement textual modality and improve the accuracy of sentiment classification.

关键词： Deep learning Image recognition Text recognition Target recognition Bit error rate programming parallel architectures

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Graph-based Multi-view Partial Multi-label Learning

Graph-based Multi-view Partial Multi-label Learning

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international symposium on parallel architectures, algorithms and programming (paap)

作者： Wei Liu Songhe Feng Hui Tian School of Computer and Information Technology Beijing Jiaotong University Beijing China China Mobile Xiongan Information Communication Technologies Ltd China Mobile System Integration Communication. Ltd Beijing China

ISBN: (纸本)9781665452199

In multi-view partial multi-label learning (MVPML) problem, each instance is described by several heterogeneous feature representations and associated with a set of candidate labels, which include both ground-truth and noisy labels. the key to learn from MVPML data lies in how to deal with multi-view data and how to select the ground-truth labels from candidate label set. In this paper, we propose a Graph-based Multi-view Partial Multi-label method, which integrates exploiting multi-view information, noisy label disambiguation and training predictor model into a whole framework. Specifically, we first exploit the consensus information across different views by learning the similarity graph of each view and fuses these similarity graphs into a unified graph. Secondly, we decompose the observed label set into a ground-truth label matrix and a noisy label matrix, where the noisy label matrix is assumed to be sparse. then, we embed the learned unified similarity graph into the process of label disambiguation to obtain a more reliable ground-truth label matrix. Finally, the predictive model is learned by the ground-truth label matrix. Extensive experiments indicate that our proposed method can achieve superior or comparable performance against state-of-the-art methods.

关键词： Training Fuses Predictive models programming Prediction algorithms parallel architectures Sparse matrices

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An Efficient Ambient Noise Cross-Correlation Algorithm on Heterogeneous CPU-GPU Cluster

An Efficient Ambient Noise Cross-Correlation Algorithm on He...

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international symposium on parallel architectures, algorithms and programming (paap)

作者： Chao Wu Xing Tan Huikun Li Guangzhong Sun Network and Information Center Supercomputing Center University of Science and Technology of China Hefei China School of Earth and Space Sciences University of Science and Technology of China Hefei China School of Software Engineering University of Science and Technology of China Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781665452199

Calculation of noise cross-correlation functions (NCF) plays a vital role in ambient noise seismology. However high computation and storage requirements of NCF become huge obstacles in development of ambient noise seismology. Nowadays heterogeneous clusters show great impact in scientific computing. In this paper, we propose a parallel NCF algorithm based on heterogeneous cluster. Firstly, input SAC files are partitioned by dimension of dates and distributed to different nodes of heterogeneous cluster. Secondly, during NCF computation in each node, workloads are divided by station pairs, and calculated in different graphics processing units (GPUs) embedded in computing node. Finally, all NCF results of different dates are gathered to one computing node and stacked using all central processing units (CPUs) of that node. Experimental results demonstrate that the parallel NCF computing can be accomplished within much less time than that of the CPU counterpart, and the speedup remains almost linear when increasing number of computing nodes.

关键词： Correlation Scientific computing Seismology Clustering algorithms Graphics processing units programming parallel processing

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parallel Accelerating Ultra-Long Read Alignment by Vertical Partitioning Data

Parallel Accelerating Ultra-Long Read Alignment by Vertical ...

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international symposium on parallel architectures, algorithms and programming (paap)

作者： Deng Pan Cheng Zhong Danyang Chen Jinxiong Zhang Feng Yang School of Computer Electronics and Information Key Laboratory of Parallel Distributed Computing Technology in Guangxi Universities Guangxi University Nanning Guangxi China School of Computer Electronics and Information Key Laboratory of Parallel Distributed Computing Guangxi University Nanning Guangxi China

ISBN: (纸本)9781665452199

the alignment between sequencing reads and genome is a basic work in biological big data analysis. Each read of the third generation sequencing data is getting longer, and the data size is getting larger. To effectively solve the ultra-long read alignment problem with high requirements for computing and memory capacity, a strategy for vertical partitioning ultra-long reads on hybrid CPU/GPU cluster is proposed, and a heap data structure is used to filter the local aligned results in all computing nodes of the parallel cluster system according to the alignment score to reduce the data transmission size. the methods for early termination and parallel merging-splicing are used to accelerate splicing local aligned results. the local aligned results among all computing nodes are collected and extended to obtain the final alignment results. the experimental results on datasets of simulated and real ultra-long reads show that the proposed parallel alignment algorithm can obtain high alignment accuracy, sensitivity and base-level sensitivity as a whole, and accelerate completing alignment between ultra-long reads and reference genome.

关键词： Sequential analysis Sensitivity Splicing Merging Memory management Genomics programming

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MCVAE: Multi-channel Variational Autoencoder for Anomaly Detection

MCVAE: Multi-channel Variational Autoencoder for Anomaly Det...

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international symposium on parallel architectures, algorithms and programming (paap)

作者： Wanzhen Zhang Liheng Xu Zhihui Yu Zikai Zhang Tonglai Liu Shuangyin Liu Engineering Trainning Center Guilin University of Aerospace Technology (College of Information Science and Technology Zhongkai University of Agriculture and Engineering) Guilin China College of Information Science and Technology Zhongkai University of Agriculture and Engineering Guangzhou China State Key Lab of Rail Traffic Control & Safety Beijing Jiaotong University Beijing China

ISBN: (纸本)9781665452199

Unsupervised anomaly detection is a very important problem due to its wide applications in many systems such as the network communication system, the Internet of things, and the ICS system. Interpretable modeling of heterogeneous data channels is also essential in anomaly detection, due to the intrinsic multi-modality in multi-channel multi-dimension data. Some existing works use variational autoencoder (VAE) for anomaly detection with a single Gaussian distribution model. However, these VAE can not learn the complex distribution between features, and hence cannot make accurate detections. To tackle this challenge, in this paper, we propose a multi-channel VAE model to jointly account for latent relationships across multiple channels. Moreover, we explore the training loss function combining the reconstruction loss of each channel. We also explore the test loss combining the reconstruction probability and reconstruction ratio between global channel information and each channel information. the proposed detector reports an anomaly when the test loss is below a certain threshold. We conduct extensive simulations on a real world dataset and find that our proposed scheme outperforms the state-of-the-art anomaly detection schemes compared with existing methods.

关键词： Training Detectors programming Gaussian distribution Feature extraction parallel architectures Internet of things

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