2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)最新文献_第2页

The Research on Lightweight Traffic Sign Recognition Algorithm Based on Improved YOLOv5 Model 基于改进YOLOv5模型的交通标志轻量化识别算法研究

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201317

Tiande Liu, Changlei Dongye

{"title":"The Research on Lightweight Traffic Sign Recognition Algorithm Based on Improved YOLOv5 Model","authors":"Tiande Liu, Changlei Dongye","doi":"10.1109/CCAI57533.2023.10201317","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201317","url":null,"abstract":"Traffic sign detection is an important research direction in object detection, which has been widely used in intelligent transportation system, driving assistance, automatic driving and other fields. In practical applications, traffic sign detection algorithms are required to complete detection and recognition tasks quickly and accurately, which requires the algorithm model to be lightweight to meet the deployment conditions. Aiming at the existing traffic sign detection problems, a lightweight traffic sign detection network based on YOLOv5s model was constructed, which improved the detection performance of the network model on the premise of guaranteeing the computing speed. In order to ensure lightweight, YOLOv5s model was selected. Firstly, Dense CSP Module (DCM) was designed to enhance the effect of feature fusion. At the same time, the feature pyramid is improved, and reduced the number of parameters in the model. Experimental results show that compared with the original algorithm, the detection efficiency of the proposed algorithm is improved by 5.28%, and the experimental results on multiple data sets show obvious improvement effect. This is a lightweight model that works well in the area of traffic sign detection.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123712655","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

Multi-level Adversarial Training for Stock Sentiment Prediction 股票情绪预测的多级对抗训练

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201295

Zimu Wang, Hong-Seng Gan

{"title":"Multi-level Adversarial Training for Stock Sentiment Prediction","authors":"Zimu Wang, Hong-Seng Gan","doi":"10.1109/CCAI57533.2023.10201295","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201295","url":null,"abstract":"Stock sentiment prediction is a task to evaluate whether the investors are expecting or gaining a positive or negative return from a stock, which has a high correlation with investors’ sentiments towards the business. However, as the nature of social media, the textual information posted by ordinary people is usually noisy, inconsistent, and even grammatically incorrect, leading the model to generate unsatisfied predictions. In this paper, we improve the performance of stock sentiment prediction by applying and comparing adversarial training at multiple levels, including character, word, and sentence levels, with the utilization of three novel adversarial attack models: DeepWordBug, BAE, and Generative Adversarial Network (GAN). We also propose an effective pre-processing technique and a novel adversarial examples incorporation method to improve the prediction results. To make an objective evaluation, we select three backbone models: Embedding Bag, BERT, and RoBERTa-Twitter, and validate the models before and after adversarial training on the TweetFinSent dataset. Experimental results demonstrate remarkable improvements in the models after adversarial training, and the RoBERTa-Twitter model with word-level adversarial training performs optimally among the experimented models. We conclude that sentence-level and word-level adversarial training are the most appropriate for deep learning and pre-trained language models, respectively, and we further conduct ablation studies to highlight the usefulness of our data pre-processing and adversarial examples incorporation approaches and a case study to display the adversarial examples generated by the proposed adversarial attack models.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123366158","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

Towards Accurate Crowd Counting Via Smoothed Dilated Convolutions and Transformer 通过平滑扩展卷积和变压器实现准确的人群计数

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201260

Xin Zeng, Huake Wang, Gaoyi Zhu, Yunpeng Wu

引用次数: 0

Optimization UUV Self-localization Method Based on Distributed Network 基于分布式网络的优化UUV自定位方法

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201264

Kaixuan Cong, Genjia Xu, Lezhong Wang, Juan Hui

{"title":"Optimization UUV Self-localization Method Based on Distributed Network","authors":"Kaixuan Cong, Genjia Xu, Lezhong Wang, Juan Hui","doi":"10.1109/CCAI57533.2023.10201264","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201264","url":null,"abstract":"With the proposal of carbon capture and storage technology, seabed carbon storage technology has become an effective way to change climate change. This paper proposes an optimization UUV self-localization method to solve the problem of less accurate feedback leakage location of seabed carbon sequestration. The method employs a hyperbolic intersection model and uses an improved generalized cross correlation algorithm based on PATH weighting for time delay estimation. The localization model is also solved using the joint Chan&Taylor algorithm. The method enables accurate feedback on the leak location, ensuring timely and efficient repair. Simulation results show that the improved delay detection algorithm is more accurate than the traditional PATH-weighted generalized correlation algorithm in a low signal-to-noise environment; the selection of suitable initial values for Taylor expansion also improves the accuracy and efficiency of the joint Chan&Taylor algorithm. The experimental test results show that the maximum error in a 1.5km*1.5km localization area is less 20m, indicating that the method has superior localization accuracy and is more valuable to be utilized.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"390 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114241364","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

Machine Learning Approach to Sentiment Recognition from Periodic Reports 基于周期报告的情感识别的机器学习方法

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201329

Junfeng Zhu, Xiaopeng Ren

引用次数: 0

A Simulation-Based Multi-CPU Architecture Virtual Machine Management System for OpenStack 基于仿真的OpenStack多cpu架构虚拟机管理系统

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201301

Yuting Wu, Wei Zhou, Dongliang Zhao

引用次数: 0

Study on the Assessment of Chinese Sentence Difficulty for Second Language Teaching 面向第二语言教学的汉语句子难度评价研究

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201252

Shuqin Zhu, Ziyao Xiao, Wei Wei

引用次数: 0

Performance and Application Scenario Evaluation of Network Hardware Queue 网络硬件队列性能及应用场景评估

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201305

Xiang Gao, Rongkai Liu, Xiancheng Lin

引用次数: 0

Challenges and Solutions of Public Cloud Carrying 5GC Network 公有云承载5GC网络的挑战与解决方案

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201256

Qingping Cao, Zhilan Huang, Qiaoling Li, Yi Liu, Yangchun Li, Gang Lu

引用次数: 0

Neural Abstractive Summarization: A Brief Survey 神经抽象文摘:简要综述

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201274

Lu Qian, Haiyang Zhang, Wen Wang, Dawei Liu, Xin Huang

引用次数: 1