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

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

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

Design and Implementation of Interactive PyQt5-Based Air Pollutant Spectral Analysis Software 基于交互式pyqt5的大气污染物光谱分析软件的设计与实现

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201308

Chuming Wang

{"title":"Design and Implementation of Interactive PyQt5-Based Air Pollutant Spectral Analysis Software","authors":"Chuming Wang","doi":"10.1109/CCAI57533.2023.10201308","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201308","url":null,"abstract":"Air pollution is an important issue in the environmental field. To control air pollution, it is particularly critical to clarify the composition of air pollutants in corresponding areas. Using atmospheric spectral data and machine learning methods, the composition of pollutants in the atmosphere can be quickly predicted. This process requires the processing of a large amount of spectral data and file management. In response to these requirements, multiple functional interfaces were designed in this work, and interface development and logic writing were carried out by combining the QT designer with code. Data management was carried out in combination with the MySQL database, and spectral diagrams were drawn in combination with the Matplotlib library. Interactive air pollution analysis software based on PyQt5 was created. The software can realize data storage, retrieval, modification, and deletion and facilitate the processing and visualization of a large amount of spectral data. Combined with the model of spectral component prediction and concentration identification, the corresponding interactive operation can be realized.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"10 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":"128599707","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

Design of Cloud Service Customer Experience Management System Enhanced by AI 基于AI的云服务客户体验管理系统设计

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201285

Qiaoling Li, Qingping Cao, Chunyu Shi, Yi Liu, Gang Lu, Zhilan Huang

引用次数: 0

Review of Network Anomaly Detection in the High-speed Railway Signal System Based on Artificial Intelligence 基于人工智能的高速铁路信号系统网络异常检测研究综述

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201304

Siyuan Li, Jing Wang

引用次数: 0

Error-Driven Triplet-Based Online Fine-Tuning for Cross-Background Image Classification 基于误差驱动三元组的跨背景图像分类在线微调

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201306

Sheng-Luen Chung, Wei-Ting Guo

{"title":"Error-Driven Triplet-Based Online Fine-Tuning for Cross-Background Image Classification","authors":"Sheng-Luen Chung, Wei-Ting Guo","doi":"10.1109/CCAI57533.2023.10201306","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201306","url":null,"abstract":"Image classification is a fundamental task in computer vision with numerous real-world applications. However, classification models trained on one set of images may not perform well when tested on another set, especially when the two sets differ in terms of the background environment. To address this challenge, we propose an error-driven triplet-based online fine-tuning approach that leverages misclassified samples to refine the classification model at intervals when enough misclassified samples are collected. Our approach builds on the triplet network architecture, which learns to represent images in a low-dimensional space where images with the same label are clustered together. We use a pre-trained classification model that was trained on a collection of 180 types of images from one background scene. However, when we apply the model to a new background scene with additional types of images, its performance is compromised due to the domain shift. Our proposed approach leverages the misclassified samples by contrast them with positive and negative samples as triplet data to fine-tune the model in new background scene. We use a loss function that combines the triplet loss and the classification loss to update the model weights. We evaluate our approach on two challenging image classification datasets with different background environments. The experimental results demonstrate that our approach achieves significant improvements in accuracy compared to the baseline classification model without fine-tuning. Overall, our error-driven triplet-based online fine-tuning approach shows promising results for adapting classification models to changing background with additionally new classification types, where the performance of pre-trained models is limited.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"16 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":"128381443","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-Agent Reinforcement Learning Investigation Based on Football Games 基于足球比赛的多智能体强化学习研究

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201281

Danping Wu

{"title":"Multi-Agent Reinforcement Learning Investigation Based on Football Games","authors":"Danping Wu","doi":"10.1109/CCAI57533.2023.10201281","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201281","url":null,"abstract":"Games are classic scenarios for reinforcement learning, and the support of a variety of standard tasks and experimental platforms is one of the reasons for the success of reinforcement learning. In the actual environment, interacting with other individuals is often necessary to be considered, such as in games Go, Poker, football and tennis, which require multi-agent participation. This paper implemented some multi-agent investigation based on Google Research Football to show the performance gap between the centralized trained policy, which produces one action when called; and a unified “super agent”, which produces multiple actions simultaneously. Then it compared the sensitivity of the learned policies by perturbing the policy parameters / observations / initial states / cooperative agent. In this case, Proximal Policy Optimization (PPO) method has faster convergence than IMPALA, and using mean provides higher episode mean reward than using the max operator for aggregation. Besides, a multi-player experiment was also tried out, the results show that the less the episode difference is, the greater the expected return it gets.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"13 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":"123125257","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

Research on Motion Trend Enhanced 2D Detection on Drones 无人机运动趋势增强二维检测研究

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201284

Hao Wu

引用次数: 0

Transformer-based Temporal Knowledge Graph Completion 基于转换器的时态知识图补全

2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2023-05-26 DOI: 10.1109/CCAI57533.2023.10201286

Simin Hu, Boyue Wang, Jiapu Wang, Yujian Ma, Lan Zhao

{"title":"Transformer-based Temporal Knowledge Graph Completion","authors":"Simin Hu, Boyue Wang, Jiapu Wang, Yujian Ma, Lan Zhao","doi":"10.1109/CCAI57533.2023.10201286","DOIUrl":"https://doi.org/10.1109/CCAI57533.2023.10201286","url":null,"abstract":"A structured semantic knowledge base called a temporal knowledge graph contains several quadruple facts that change throughout time. To infer missing facts is one of the main challenges with temporal knowledge graph, i.e., temporal knowledge graph completion (TKGC). Transformer has strong modeling abilities across a variety of domains since its self-attention mechanism makes it possible to model the global dependencies of input sequences, while few studies explore Transformer encoders for TKGC tasks. To address this problem, we propose a novel end-to-end TKGC model named Transbe-TuckERTT that adopts an encoder-decoder architecture. Specifically, t he proposed model employs the Transformer-based encoder to facilitate interaction between entities, relations, and temporal information within the quadruple to generate highly expressive embeddings. The TuckERTT decoder uses encoded embeddings to predict missing facts in the knowledge graph. Experimental results demonstrate that our proposed model outperforms several state-of-the-art TKGC methods on three public benchmark datasets, verifying the effectiveness of the self-attention mechanism in the Transformer-based encoder for capturing dependencies in the temporal knowledge graph.","PeriodicalId":285760,"journal":{"name":"2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"38 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":"133812087","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