2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)最新文献_第4页

Research on Traffic Flow Forecasting Method Based on Graph Neural Network 基于图神经网络的交通流预测方法研究

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832353

Xiao Wenjuan, Liu Jianfeng

引用次数: 2

2022 2nd IEEE International Conference on Software Engineering and Artificial Intelligence (SEAI 2022) 第二届IEEE软件工程与人工智能国际会议(SEAI 2022)

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832300

引用次数: 0

An Automated Essay Scoring model Based on Stacking Method 基于堆叠法的自动作文评分模型

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832246

Chenchen Li, Lin Lin, Wei Mao, Liu Xiong, Yongping Lin

引用次数: 1

Evolutionary Multitasking with Scale-Adaptive Subspace for Large-Scale Optimization 基于尺度自适应子空间的进化多任务大规模优化

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832312

Chunxiang Song, Xiaojun Wang, Yiqiao Cai

{"title":"Evolutionary Multitasking with Scale-Adaptive Subspace for Large-Scale Optimization","authors":"Chunxiang Song, Xiaojun Wang, Yiqiao Cai","doi":"10.1109/seai55746.2022.9832312","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832312","url":null,"abstract":"Large-scale optimization is an important research topic in the field of evolutionary computation which has been widely applied in many scientific and engineering fields. In large-scale optimization, many research studies on simplifying the search space for large-scale optimization problems (LSOPs) have been proposed, such as cooperative co-evolution, and dimension reduction. However, the validity of simplified solution spaces with different scales in the evolutionary process is not fully explored to guide the search direction. Therefore, this paper puts forward an evolutionary multitasking method with scale-adaptive subspace (EMT-SAS) for LSOPs, which uses a set of subspaces with different accuracy scales to cooperatively assist original problem search. The basic idea is to adopt the low-accuracy scale subspace to fast locate promising regions and utilize the high-accuracy scale subspace to refine the solution accuracy. Moreover, an archive management strategy is also proposed to select training samples, which can further enhance the diversity of the algorithm. The performance of the proposed EMT-SAS is evaluated on the CEC2013 large-scale benchmark problems. The experimental results have demonstrated the superiority of EMT-SAS when compared with other advanced algorithms.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121107847","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

Natural Language Processing-based Model for Log Anomaly Detection 基于自然语言处理的日志异常检测模型

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832400

Zezhou Li, Jing Zhang, Xianbo Zhang, Feng Lin, Chao Wang, Xingye Cai

{"title":"Natural Language Processing-based Model for Log Anomaly Detection","authors":"Zezhou Li, Jing Zhang, Xianbo Zhang, Feng Lin, Chao Wang, Xingye Cai","doi":"10.1109/seai55746.2022.9832400","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832400","url":null,"abstract":"Logs are widely used in IT industry and the anomaly detection of logs is essential to identify the running status of systems. Conventional methods solving this problem require sophisticated rule-based regulations and intensive labor input. In this paper, we propose a new model based on natural language processing techniques. In order to modify the feature extraction and to improve the vector quality of log templates, Part-of-Speech (PoS) and Named Entity Recognition (NER) are employed in our model, which leads to the less involvement of regulation-based rule and a modification of the template vector thanks to the weight vector by NER. The PoS property of each token in the template is firstly analyzed, which also reduces labor involvement and helps for better weight allocation. The weight investigation on tokens of the template is introduced to modify the template vector. And the final detection based on the modified vector of templates is realized by deep neural networks (DNNs). The effectiveness of our model is tested on three datasets, and compared with two state-of-the-art models. The evaluation results prove that our model achieves better log anomaly detection.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115324772","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

Machine Learning Algorithms for Privacy Policy Classification: A Comparative Study 隐私策略分类的机器学习算法比较研究

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832027

Abdullah R Alshamsan, S. A. Chaudhry

引用次数: 1

Generating face images from fine-grained sketches based on GAN with global-local joint discriminator 基于全局-局部联合鉴别器的GAN从细粒度草图生成人脸图像

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832103

Huachao Gao, Wei Mao, Yongping Lin

{"title":"Generating face images from fine-grained sketches based on GAN with global-local joint discriminator","authors":"Huachao Gao, Wei Mao, Yongping Lin","doi":"10.1109/seai55746.2022.9832103","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832103","url":null,"abstract":"This paper explores the face image generating with clear details from fine-grained sketches. Edge maps are usually used as sketches in face image generation tasks. However, there are some problems such as discontinuous lines and a lack of detailed information in the edge map, so the generated face image is not clear enough and lacks details. To address this problem, a face sketch dataset with rich details is made. The discriminator combines a global discriminator and a local discriminator, which ensures that the generated face image has a complete face structure and generates clearer face details. A self-attention mechanism is employed in the generative model to establish long-term dependencies on partial feature information. The model is quantitatively evaluated by using the valuation criteria IS, FID, and KID. Among them, the evaluation scores of the model on IS, FID, and KID are 1.956 ± 0.053, 17.941 ± 0.970, and 0.009 ± 0.001, respectively. The evaluation results show that the model achieves good performance. Furthermore, the visual effects of the model are analyzed by comparing with the pix2pixHD and CycleGAN models on the generated images. The final results show that our model outperforms the other two models and can generate high-quality face images with sharp details.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":" 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132041295","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

Severity Prediction for Bug Reports using Tree-based Ensemble Models: A Comparative Study 使用基于树的集成模型预测Bug报告的严重性:比较研究

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832212

Anh-Hien Dao, Yue-lin Yu, Cheng-Zen Yang

引用次数: 2

Semi-supervised Video Deraining Based on Enhanced Spatio- Temporal Interaction Network 基于增强型时空交互网络的半监督视频训练

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832166

Juhao Jiang, Bingxiang Yang, Guannan Chen

{"title":"Semi-supervised Video Deraining Based on Enhanced Spatio- Temporal Interaction Network","authors":"Juhao Jiang, Bingxiang Yang, Guannan Chen","doi":"10.1109/seai55746.2022.9832166","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832166","url":null,"abstract":"Video rain removal is an important task in computer vision. Although the video rain removal method based on deep learning has achieved great success in recent years, there are still two main challenges: how to use the large amount of information between consecutive frames to extract cross-space and the powerful spatio-temporal characteristics of the time domain, and how to restore the use of high-speed methods to produce high-quality rain-removing videos. In this article, we propose a new end-to-end video rain removal framework called Enhanced Spatio-temporal Interaction Network (ESTINet), which greatly improves the current state-of-the-art video rain removal quality and speed. The enhanced spatio-temporal interaction network uses the advantages of deep residual networks and convolutional long and short-term memory to capture the spatial characteristics and temporal correlation between consecutive frames at the cost of few computing resources. In addition, Different prior formats are designed for labeled synthetic data and unlabeled real data, and a semi-supervised learning mechanism is designed through different prior formats.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126206933","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

Point Pattern Feature Matching Method Based on Polar Coordinate 基于极坐标的点模式特征匹配方法

2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2022-06-10 DOI: 10.1109/seai55746.2022.9832322

Qing Xu, Lei Zhang

{"title":"Point Pattern Feature Matching Method Based on Polar Coordinate","authors":"Qing Xu, Lei Zhang","doi":"10.1109/seai55746.2022.9832322","DOIUrl":"https://doi.org/10.1109/seai55746.2022.9832322","url":null,"abstract":"Infrared label point feature matching is one of the basic problems in the application direction of computer vision, pattern recognition and robot navigation and positioning. First, the center of the label point set and the weighted distance center are obtained in Cartesian coordinates, then the Cartesian coordinates are converted into polar coordinates to construct the position information feature vector of the label point set. Finally, using the method of spatial geometric constraints to compare each point in the label code to the feature vector, the matching result of the label code point set is obtained. The contributions of this paper are as follows: 1. A feature vector of label point set based on polar coordinate transformation is constructed, which makes the feature vector invariant to rotation, translation and illumination. 2. Using the combination of feature vector and spatial geometric constraints to screen label point pairs can effectively overcome the adverse effects of outlier points and noise. The final experiments demonstrate the effectiveness and robustness of the polar coordinate feature matching of label codes.","PeriodicalId":290416,"journal":{"name":"2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126815856","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