2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)最新文献_第3页

Parallel Accelerating Ultra-Long Read Alignment by Vertical Partitioning Data 垂直分区数据并行加速超长读对齐

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010526

Deng Pan, Cheng Zhong, Danyang Chen, Jinxiong Zhang, Feng Yang

{"title":"Parallel Accelerating Ultra-Long Read Alignment by Vertical Partitioning Data","authors":"Deng Pan, Cheng Zhong, Danyang Chen, Jinxiong Zhang, Feng Yang","doi":"10.1109/PAAP56126.2022.10010526","DOIUrl":"https://doi.org/10.1109/PAAP56126.2022.10010526","url":null,"abstract":"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.","PeriodicalId":336339,"journal":{"name":"2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128346148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Information Diffusion with SIRP and SEIRP in Social Networks 基于SIRP和SEIRP的社交网络信息扩散

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010567

Peihuang Huang, Xi Chen, Bingna Wu

引用次数: 0

The Fast Paillier Decryption with Montgomery Modular Multiplication Based on OpenMP 基于OpenMP的Montgomery模乘法快速Paillier解密

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010630

Decong Lin, Hongbo Cao, Chunzi Tian, Yongqi Sun

{"title":"The Fast Paillier Decryption with Montgomery Modular Multiplication Based on OpenMP","authors":"Decong Lin, Hongbo Cao, Chunzi Tian, Yongqi Sun","doi":"10.1109/PAAP56126.2022.10010630","DOIUrl":"https://doi.org/10.1109/PAAP56126.2022.10010630","url":null,"abstract":"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.","PeriodicalId":336339,"journal":{"name":"2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)","volume":"37 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114117175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A HeteSim-Measured algorithm fused semantic features in heterogeneous social networks 一种融合异构社交网络语义特征的HeteSim-Measured算法

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010560

Pingfan He, Shiyi Wang, Huaying Qi

引用次数: 0

Passivity-Based Finite-Time Consensus for Nonlinear Fractional-Order Multi-Agent Systems 非线性分数阶多智能体系统的无源有限时间一致性

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010520

Zeng-Ran Zhang, Jin-Liang Wang, Chen-Guang Liu, W. Jigang, Yanli Huang

引用次数: 0

Progressive Differentiable Architecture Search Based Automatic Modulation Classification Method 基于渐进式可微结构搜索的自动调制分类方法

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010492

Xiaofeng Chen, Xixi Zhang, Yu Wang, Jie Yang, Guan Gui, H. Sari

引用次数: 0

Hypergraphs: Concepts, Applications and Analysis 超图:概念、应用和分析

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010428

Wenping Zheng, Meilin Liu, Liang Jiye

引用次数: 0

Deep Just-In-Time Consistent Comment Update via Source Code Changes 通过源代码更改实现深度及时一致的注释更新

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010469

Shikai Guo, Xihui Xu, Hui Li, Rong Chen

{"title":"Deep Just-In-Time Consistent Comment Update via Source Code Changes","authors":"Shikai Guo, Xihui Xu, Hui Li, Rong Chen","doi":"10.1109/PAAP56126.2022.10010469","DOIUrl":"https://doi.org/10.1109/PAAP56126.2022.10010469","url":null,"abstract":"During software development and maintenance, code comments are often missing, inadequate, or they do not match the actual code content. In response to this problem, the research community has proposed a method for updating natural language comments based on code changes. However, there are two major limitations of this method that must be addressed: the long-term and non-temporal dependencies in the source code. To address these limitations, we propose a new model called code semantic learning–comment update (CSL2CU). The code semantic learning component of CSL2CU uses a self-attention mechanism and a positional encoding mechanism. It also uses a relative positional representation to model pairwise relationships between source code tags, thereby improving its ability to capture long-term dependencies and non-temporal dependencies of source code tagging ability. The comment-update component of CSL2CU is used to generate new comments based on old comments and code editing. The experimental results show that the CSL2CU model outperforms the three baselines used in exact match, BLEU, METEOR, and SARI.","PeriodicalId":336339,"journal":{"name":"2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127344753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph-based Multi-view Partial Multi-label Learning 基于图的多视图部分多标签学习

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010429

Wei Liu, Songhe Feng, Hui Tian

{"title":"Graph-based Multi-view Partial Multi-label Learning","authors":"Wei Liu, Songhe Feng, Hui Tian","doi":"10.1109/PAAP56126.2022.10010429","DOIUrl":"https://doi.org/10.1109/PAAP56126.2022.10010429","url":null,"abstract":"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.","PeriodicalId":336339,"journal":{"name":"2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129617413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1