2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)最新文献_第2页

Generating High Quality Titles in StackOverflow via Data Denoising Method 通过数据去噪方法在StackOverflow中生成高质量的标题

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010656

Shikai Guo, Bowen Ping, Zixuan Song, Hui Li, Rong Chen

{"title":"Generating High Quality Titles in StackOverflow via Data Denoising Method","authors":"Shikai Guo, Bowen Ping, Zixuan Song, Hui Li, Rong Chen","doi":"10.1109/PAAP56126.2022.10010656","DOIUrl":"https://doi.org/10.1109/PAAP56126.2022.10010656","url":null,"abstract":"StackOverflow is one of the most popular question-and-answer platforms on the internet and whether posts on StackOverflow will be answered largely depends on their titles’ quality. Based on recurrent neural networks (RNN) or transformers, previous studies have attempted to use real posts from StackOverflow to generate better titles. However, the challenge of noise in existing data has been ignored, leading models can’t generate higher quality titles. To address this issue, we propose the K-clusters confidence learning for code titles (KCL-CT) model, which contains code clustering and confident learning (CL) denoising components. Specifically, the code clustering component is used to capture the word order and semantic information in code and classify code into different functional categories. The CL denoising component receives the output from the code clustering component and employs a heuristic method based on a confidence threshold to prune raw datasets. We conducted experiments based on Java, Python, JavaScript, SQL and C# datasets, the results of which indicated that in terms of the BLEU and ROUGE scores, the proposed KCL-CT model can outperform previous state-of-the-art models by 2.0%–11.1% and 2.5%–14.0%, respectively.","PeriodicalId":336339,"journal":{"name":"2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)","volume":"58 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":"127082144","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

Fake news detection method based on multi-feature fusion of entity and structured text 基于实体与结构化文本多特征融合的假新闻检测方法

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010377

Tianqi Ren, Ke Ji, Zhenxiang Chen, R. Sun, Kun Ma, Jin Zhou

引用次数: 0

A MPI programming model for fast bird nest detection on the railway catenary 铁路接触网快速鸟巢检测的MPI规划模型

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010395

Hongwei Zhao, Yidong Li, Siquan Wu, Zhen Tian, Junbo Liu

引用次数: 0

Cluster-based Federated Learning Framework for Intrusion Detection 基于聚类的入侵检测联邦学习框架

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010553

Luxin Cai, Naiyue Chen, Yuanmeng Wei, Huaping Chen, Yidong Li

{"title":"Cluster-based Federated Learning Framework for Intrusion Detection","authors":"Luxin Cai, Naiyue Chen, Yuanmeng Wei, Huaping Chen, Yidong Li","doi":"10.1109/PAAP56126.2022.10010553","DOIUrl":"https://doi.org/10.1109/PAAP56126.2022.10010553","url":null,"abstract":"With the rapid development of Industrial Internet, the network intrusion detection has become particularly important. In the Industrial Internet, large-scale data is distributed in the edge nodes caused the joint analysis of network intrusion detection at each edge node has become necessary. Federated learning structure can avoid data out of local nodes to protect user privacy data. However, the data distribution is different for each edge nodes, which limits the effectiveness of federated learning models. We focus on the non-IID data features and propose a new cluster-based federated learning framework for network intrusion detection. In this method, we cluster clients into different communities by data labels, which the clients contain the similar proportion of data labels in the same community. Based on the clustering results, we decompose federated learning model aggregation into cluster aggregation and global aggregation by leveraging similarities both within and between clusters. We conduct extensive experiments based on UNSW_NB15 dataset. The results show that our method has better performance than FedAvg and FedProx. It can work well in scenarios with different distributions of data samples while ensuring data security and privacy protection.","PeriodicalId":336339,"journal":{"name":"2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)","volume":"284 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":"127556019","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

Comparative Study on Data Sovereignty Guarantee Technology 数据主权保障技术的比较研究

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010593

Yaodong Tao, shuai Yang, Hongmei Ge

引用次数: 0

An Efficient Ambient Noise Cross-Correlation Algorithm on Heterogeneous CPU-GPU Cluster 一种高效的异构CPU-GPU集群环境噪声互相关算法

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010612

Chao Wu, Xing Tan, Huikun Li, Guangzhong Sun

引用次数: 1

Joint Original Space and Latent Space for Multi-view Spectral Clustering 多视点光谱聚类的联合原始空间和潜在空间

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010419

Ruiting Hu, Zhibin Gu, Songhe Feng

引用次数: 0

A Multi-object Detection Sampling Algorithm For Large Scenes 大场景下的多目标检测采样算法

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010614

Liang Jin, Xiaochuan Li, Baoyu Fan, Zhenhua Guo, Ruidong Li, Li Wang, Yanwei Wang, Yaqian Zhao, Rengang Li

{"title":"A Multi-object Detection Sampling Algorithm For Large Scenes","authors":"Liang Jin, Xiaochuan Li, Baoyu Fan, Zhenhua Guo, Ruidong Li, Li Wang, Yanwei Wang, Yaqian Zhao, Rengang Li","doi":"10.1109/PAAP56126.2022.10010614","DOIUrl":"https://doi.org/10.1109/PAAP56126.2022.10010614","url":null,"abstract":"Multi-object detection in large scenes aims to find objects in images, which usually contain more than one billion pixels. Based on the concept of dividing and conquering, the state-of-the-art (SOTA) methods slice the super-resolution image into patches first and then lower the image solution to detect objects later. The advantage of this method is that it can adapt quickly to regular detection algorithms. However, a set of parameters needs to be set manually, such as the size of sliding windows and overlap, which is quite hard to fit all scenarios. It may result in a loss of samples located at the boundary of the sliding window and the oversampling of inefficient samples that appear within the overlap. In this paper, we propose a object-oriented image sampling algorithm based on anchor boxes during training and multi-scale pyramids during inference. Inspired by the mature object detection baseline Scale-YOLOv4, we present more tricks to fit large scenes. The accuracy can reach 66%, which is 24 points higher than the CascadeRCNN model of the official backbone network ResNet50. Finally, we have won first place in the PANDA object detection tracking using this method.","PeriodicalId":336339,"journal":{"name":"2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)","volume":"65 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":"127974016","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

Efficient Heuristic Algorithm to Speed up GraphCut in GPU for Image Stitching 图形处理器中GraphCut图像拼接的高效启发式算法

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010453

Tai Nguyen, Minh Bui, Huong Ninh, T. Nguyen, H. Tran

引用次数: 0

PAAP 2022 Cover Page PAAP 2022封面

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/paap56126.2022.10010604

引用次数: 0