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

Output Consensus for Second-Order Nonlinear Multi-Agent Systems 二阶非线性多智能体系统的输出一致性

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010496

Zheng Xuanyuan, Jin-Liang Wang, W. Jigang, Yanli Huang

引用次数: 0

Accelerating GNN Inference by Soft Channel Pruning 利用软信道剪枝加速GNN推断

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010603

Wenbo Zhang, Jingwei Sun, Guangzhong Sun

{"title":"Accelerating GNN Inference by Soft Channel Pruning","authors":"Wenbo Zhang, Jingwei Sun, Guangzhong Sun","doi":"10.1109/PAAP56126.2022.10010603","DOIUrl":"https://doi.org/10.1109/PAAP56126.2022.10010603","url":null,"abstract":"Graph Neural Networks (GNNs) are effective models for processing graph-structured data. With the continuous growth of graph data scale and the deepening of graph neural network layers, the heavy cost of GNN inference has greatly limited its application in real-time tasks. This paper focus on accelerating the performance of GNN inference. We first measures the execution time ratio of each stage in the inference process for commonly used GNN models, and analyzes the performance characteristics of different stages. We find out that the critical performance factor of GNN inference is the feature dimension, which is different to CNN and NLP models. Therefore, we propose a soft channel pruning method with a ladder pruning pattern. It reduces the calculation from unimportant graph node features and achieve performance acceleration. Meanwhile, it reserves inference accuracy of GNNs. According to experimental validation on graph datasets of different scales, our method can effectively reduce the inference latency and achieve 2×–7× speedup. Also, compared with existing pruning methods, higher inference accuracy can be obtained with comparable speedup ratio.","PeriodicalId":336339,"journal":{"name":"2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)","volume":"96 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":"127726854","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

Do Not Have Enough Data? An Easy Data Augmentation for Code Summarization 没有足够的数据?一个简单的数据增强代码汇总

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010698

Zixuan Song, Xiuwei Shang, Mengxuan Li, Rong Chen, Hui Li, Shikai Guo

{"title":"Do Not Have Enough Data? An Easy Data Augmentation for Code Summarization","authors":"Zixuan Song, Xiuwei Shang, Mengxuan Li, Rong Chen, Hui Li, Shikai Guo","doi":"10.1109/PAAP56126.2022.10010698","DOIUrl":"https://doi.org/10.1109/PAAP56126.2022.10010698","url":null,"abstract":"Code comments improve the readability and intelligibility of codes, which can help developers understand programs and improve the efficiency of the software maintenance and evolution process. Unfortunately, code comments are often mismatched, missing, or outdated in software projects, which negatively affects the efficiency of developers to infer the functionality from source code and affect the efficiency of software maintenance and evolution. To solve this problem, many source code summarization algorithms have been proposed. However, these methods usually try to collect a large data set which contains the mapping between code comments and source code to train models. Hence, the effectiveness of the models often rely on the quality of the training data. There are two limitations for the training sets: the insufficient data collection limitation (i.e., generate a large amount of noises-free training data) and data distribution bias limitation (i.e., generate training data for infrequently used methods). To address this issues, we have proposed a data augmentation method for code comments, named CDA-CS. Extensive experiments on Java and Python projects collected from GitHub are conducted to evaluate the performance of CDA-CS. Training models on the augmented dataset, the state-of-the-art algorithms can easily get a further 1.37% to 2.24% improvement in terms of different evaluation metrics (i.e., BLEU-4, METEOR, ROUGH_L) with no additional cost.","PeriodicalId":336339,"journal":{"name":"2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)","volume":"87 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":"133952374","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

Multi-Modal Virtual-Real Fusion based Transformer for Collaborative Perception 基于多模态虚实融合的协同感知变压器

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010640

Hui Zhang, Guiyang Luo, Yuanzhouhan Cao, Yi Jin, Yidong Li

{"title":"Multi-Modal Virtual-Real Fusion based Transformer for Collaborative Perception","authors":"Hui Zhang, Guiyang Luo, Yuanzhouhan Cao, Yi Jin, Yidong Li","doi":"10.1109/PAAP56126.2022.10010640","DOIUrl":"https://doi.org/10.1109/PAAP56126.2022.10010640","url":null,"abstract":"Automobile intelligence and networking have become the inevitable trend in the future development of the automotive industry. Existing intelligent and connected vehicles rely on single-agent intelligence to perform the basic perception, which is still weak in dealing with the problem of accurate recognition and positioning in complex traffic scenes such as small and far away objects. To tackle this issue, we propose a multi-model virtual-real fusion Transformer for collaborative perception. Specifically, to possess the complementary information from both RGB images and LiDAR point clouds, we propose the multi-model virtual-real fusion (MVRF) method, which generates virtual points and compensates for the lack of point information on sparse locations. Furthermore, the heterogeneous graph attention network (HGAN) is constructed to capture the inter-agent interaction and adaptively incorporate multiple agents’ features. The HGAN contains a series of encoder layers, each of which has a heterogeneous inter-agent attention module and a multi-scale self-attention module, which motivates to learn different relationships based on various agents’ types and simultaneously capture the global and local spatial attention. Extensive experiments demonstrate that the proposed method gains superior performance as compared with 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":"128922551","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: 3

Multi-selection Attention for Multimodal Aspect-level Sentiment Classification 多模态层面情感分类的多选择注意

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010454

YuQing Miao, Ronghai Luo, Tonglai Liu, Wanzhen Zhang, Guoyong Cai, M. Zhou

引用次数: 0

Leveraging Graph to Improve Lexicon Enhanced Chinese Sequence Labelling 利用图改进词典增强中文序列标注

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010525

Kailan Zhang, Baopeng Zhang, Zhu Teng

引用次数: 0

Traffic Speed Prediction of Road Cluster with Heterogeneous Sampling Frequency 非均匀采样频率下道路簇交通速度预测

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010538

Guiyuan Jiang, Peilan He, Jigang Wu, Yidan Sun, T. Srikanthan

{"title":"Traffic Speed Prediction of Road Cluster with Heterogeneous Sampling Frequency","authors":"Guiyuan Jiang, Peilan He, Jigang Wu, Yidan Sun, T. Srikanthan","doi":"10.1109/PAAP56126.2022.10010538","DOIUrl":"https://doi.org/10.1109/PAAP56126.2022.10010538","url":null,"abstract":"Accurate short-term road traffic prediction is essential for achieving intelligent transportation systems, such as traffic management, travel route planning, and navigation. The existing works typically provide the prediction for an individual road segment each time. Even though some models aim to simultaneously predict the traffic of a cluster of road segments, they usually assume that the road cluster has a regular network topology (e.g., ring network or grid network). These methods cannot be easily extended to road networks of arbitrary graph topology. This paper addresses the problem of traffic speed prediction for a cluster of road segments with arbitrary topology and heterogeneous sampling frequency of traffic states. We propose a novel prediction framework consisting of three modules: network partitioning, feature extraction, and traffic prediction modules. The first module divides the entire traffic network into several disjoint clusters with high intra-clusters similarity and low intercluster similarity, based on our proposed measurement metrics for measuring the similarity of time series with heterogeneous sampling frequency. The second module extract features that capture temporal correlations of speed series and contextual factors (e.g., road network characteristics and extrinsic factors) while considering the heterogeneity in data frequency. The third module relies on the obtained features to simultaneously predict the traffic states of all road segments in a cluster, where the spatial correlations among roadways are captured via an attention mechanism. The performance is evaluated using large-scale real-world traffic data involving 42 bus services.","PeriodicalId":336339,"journal":{"name":"2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)","volume":"9 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":"115663360","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

Structuring Meaningful Code Changes in Developer Community 在开发人员社区中构建有意义的代码更改

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010364

Mengxuan Li, Shikai Guo, X. Ge, Hui Li, Rong Chen

{"title":"Structuring Meaningful Code Changes in Developer Community","authors":"Mengxuan Li, Shikai Guo, X. Ge, Hui Li, Rong Chen","doi":"10.1109/PAAP56126.2022.10010364","DOIUrl":"https://doi.org/10.1109/PAAP56126.2022.10010364","url":null,"abstract":"The rapid development of Open-Source Software (OSS) has resulted in a significant demand for code changes to maintain OSS. Symptoms of poor design and implementation choices in code changes often occur, thus heavily hindering code reviewers to verify correctness and soundness of code changes. Researchers have investigated how to learn meaningful code changes to assist developers in anticipating changes that code reviewers may suggest for the submitted code. However, there are two main limitations to be addressed, including the limitation of long-range dependencies of the source code and the missing syntactic structural information of the source code. To solve these limitations, we propose a novel method named GTCT. GTCT comprises two components: code graph embedding and code transformation learning. To address the missing syntactic structural information, we encoding the source code into a code graph structure from the lexical and syntactic representations of the source code. Subsequently, we uses the multi-head attention mechanism and positional encoding mechanism to address the long-range dependencies limitation. Extensive experiments are conducted to evaluate the performance of GTCT by both quantitative and qualitative analyses. For the quantitative analysis, GTCT relatively outperforms the baseline on six datasets by 210%, 342.86%, 135%, 29.41%, 109.09%, and 91.67% in terms of perfect prediction. Meanwhile, the qualitative analysis shows that each type of code change by GTCT outperforms that of the baseline method in terms of bug fixed, refactoring code, and others’ taxonomy of code changes.","PeriodicalId":336339,"journal":{"name":"2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)","volume":"66 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":"124335154","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

Identity-based Secure and Efficient Intelligent Inference Framework for IoT-Cloud System 基于身份的物联网云系统安全高效智能推理框架

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010411

Jingyi Li, Yidong Li, Chuntao Ding, Jinhui Yu, Yan Ren

引用次数: 0

MCVAE: Multi-channel Variational Autoencoder for Anomaly Detection 用于异常检测的多通道变分自编码器

2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) Pub Date : 2022-11-25 DOI: 10.1109/PAAP56126.2022.10010358

Wanzhen Zhang, Liheng Xu, Zhihui Yu, Zikai Zhang, Tonglai Liu, Shuangyin Liu

{"title":"MCVAE: Multi-channel Variational Autoencoder for Anomaly Detection","authors":"Wanzhen Zhang, Liheng Xu, Zhihui Yu, Zikai Zhang, Tonglai Liu, Shuangyin Liu","doi":"10.1109/PAAP56126.2022.10010358","DOIUrl":"https://doi.org/10.1109/PAAP56126.2022.10010358","url":null,"abstract":"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.","PeriodicalId":336339,"journal":{"name":"2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)","volume":"29 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":"116702180","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