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

Sensitivity-based (p, α, k) - Anonymity Privacy Protection Algorithm 基于灵敏度的(p， α， k) -匿名隐私保护算法

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201294

Suming Chen, Bin Wang, Yuquan Chen, Yuhui Ma, Tao Xing, Jianli Zhao

{"title":"Sensitivity-based (p, α, k) - Anonymity Privacy Protection Algorithm","authors":"Suming Chen, Bin Wang, Yuquan Chen, Yuhui Ma, Tao Xing, Jianli Zhao","doi":"10.1109/CCAI57533.2023.10201294","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201294","url":null,"abstract":"Medical data itself has extremely high research value, but how to protect its privacy and security in the process of sharing medical data has attracted widespread attention from researchers. Aiming at the problems of homogeneity attack, background knowledge attack and high-sensitivity similarity attack in data sharing of k -anonymity privacy protection algorithm, a sensitivity-based (p, α, k) -anonymity privacy protection algorithm is proposed. The concept of semantic similarity tree is introduced, which can resist background knowledge attacks. The improved clustering method of equivalence classes can solve homogeneity attacks and high-sensitivity similarity attacks. Thus, the security of medical data sharing can be realized. Experiments show that (p, α, k) - anonymity privacy protection algorithm has the best performance when α is equal to 0.5. In addition, compared with k -anonymity privacy protection algorithm, although (p, α, k) - anonymity privacy protection algorithm has higher execution time and information loss, it effectively solves the problems of k - anonymity algorithm and improves the security of medical data sharing.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"60 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":"134431626","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

Hyperbolic Graph Convolutional Networks for Aspect-Based Sentiment Analysis 基于方面的情感分析的双曲图卷积网络

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201311

Xueda Li, C. Min, H. Zhang, Liang Yang, Dongyu Zhang, Hongfei Lin

{"title":"Hyperbolic Graph Convolutional Networks for Aspect-Based Sentiment Analysis","authors":"Xueda Li, C. Min, H. Zhang, Liang Yang, Dongyu Zhang, Hongfei Lin","doi":"10.1109/CCAI57533.2023.10201311","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201311","url":null,"abstract":"Aspect-based sentiment analysis is a fine-grained sentiment analysis task that aims to predict the sentiment polarity of a specific aspect. Recent work adopts graph convolutional networks over dependency trees to capture the syntactic connections of aspects and opinion words while introducing the BiAffine to jointly refine syntax structures and semantic correlations. However, in the Euclidean space, the neural network models can’t well capture the syntactic connections of aspects and opinion words due to the inaccurate dependency trees representation, and the original structures and correlations are affected due to the BiAffine exchange method. Fortunately, dependency trees can be represented well since hyperbolic space can be viewed as continuous simulations of trees, so we propose a hyperbolic graph convolutional networks (HyperGCN) model to handle these challenges. We employ hyperbolic graph convolution with the dependency tree to model syntactic connections between aspects and opinion words, additionally, we also capture the semantic correlations with a hyperbolic graph convolutional network incorporating self-attention mechanism. Particularly, to exchange the relevant features without original syntax structures and semantic correlations being affected, we leverage an attention mechanism with residual structure to exchange relevant features of syntactic and semantic information. The experimental results on three datasets verify the effectiveness of our model.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"12 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":"131938654","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

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

An Effective Online Stream Feature Selection Auxiliary Method for High-Dimensional Unbalanced Data 一种有效的高维不平衡数据在线流特征选择辅助方法

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201246

Xingtong Qian, Yinghua Zhou

{"title":"An Effective Online Stream Feature Selection Auxiliary Method for High-Dimensional Unbalanced Data","authors":"Xingtong Qian, Yinghua Zhou","doi":"10.1109/CCAI57533.2023.10201246","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201246","url":null,"abstract":"In the area of feature selection from highdimensional data, online streaming feature selection methods have received extensive attention in the past few decades due to their online selection abilities. Existing online stream feature selection methods perform well on many balanced datasets, But the real datasets are usually high-dimensional and unbalanced. For example, in medical examination data, the proportion of the sick people is much smaller than that of the healthy people. In the face of unbalanced data, traditional stream feature selection algorithms confront problems such as few selected features and low classification accuracy. Therefore, how to perform online stream feature selection under high-dimensional and unbalanced conditions is a challenge. In this paper, a general and easy-toimplement auxiliary algorithm is proposed, which can supplement the existing stream feature selection methods and dig out feature subsets effectively. Finally, the experiments are carried out on seven high-dimensional and unbalanced datasets and the results show that the auxiliary method can improve the traditional online stream feature selection methods and enable the classifiers to achieve better classification performance.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"14 1 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":"116789914","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 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

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