2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)最新文献

Convergence of optimal allocation sequence in regression models with cost consideration 考虑成本的回归模型中最优分配序列的收敛性

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00017

Xiaoxi Sun, Ying Xu, Yujing Du

引用次数: 1

Reliability Analysis and Optimization of Computer Communication Network Based on Machine Learning Algorithm 基于机器学习算法的计算机通信网络可靠性分析与优化

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00018

Dai Liu

{"title":"Reliability Analysis and Optimization of Computer Communication Network Based on Machine Learning Algorithm","authors":"Dai Liu","doi":"10.1109/FAIML57028.2022.00018","DOIUrl":"https://doi.org/10.1109/FAIML57028.2022.00018","url":null,"abstract":"Machine learning is to find laws from observed data and use these laws to predict future data or unobservable data. Network measurement and routing optimization strategy are critical components in the routing optimization problem. Due to the continuous progress of information technology, computer information technology is widely used in various fields, so its security and reliability will be paid more and more attention. The unsupervised learning classification is carried out through the fast density clustering algorithm to classify the importance of nodes, which can be effectively applied to the important evaluation of communication network nodes and support the planning of the communication network. Given the progress of communication technology, optical fiber technology and computer internet technology, the network's functions have been strengthened daily, and the research on the reliability of computer communication networks has been promoted to develop in depth. Furthermore, optimization theory can realize the bandwidth allocation of a communication network. The important is that computer communication network reliability based on machine learning algorithm has great economic value, social value and social benefit.","PeriodicalId":307172,"journal":{"name":"2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123462899","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

Less-Last Number Hyperparameter Algorithm for MCMC on Online Scheme: In Updating Hyperparameter Online Gaussian Process 在线方案上MCMC的少尾数超参数算法:更新超参数在线高斯过程

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00011

S. S. Sholihat, S. Indratno, U. Mukhaiyar

{"title":"Less-Last Number Hyperparameter Algorithm for MCMC on Online Scheme: In Updating Hyperparameter Online Gaussian Process","authors":"S. S. Sholihat, S. Indratno, U. Mukhaiyar","doi":"10.1109/FAIML57028.2022.00011","DOIUrl":"https://doi.org/10.1109/FAIML57028.2022.00011","url":null,"abstract":"The exciting topic on the Gaussian process is hyper-parameter estimation. The simple and powerful method for the hyper-parameter estimation is Markov Chain Monte Carlo (MCMC). In the internet era, we need to consider the influence of new data coming to hyper-parameter of the Gaussian Process in online manner, updating is needed. Meanwhile, the main problem of running hyper-parameter estimation using MCMC is time-computation. The large size of covariance matrices on the Gaussian process has an expensive time-computing if should be combined to MCMC. We proposed the Less-Last Number Hyper-parameter (LLNH) algorithm for less time computing MCMC. The idea is merging two Markov Chain Monte Carlo (MCMC) sub-posterior algorithm iteratively. The first sub-posterior results recent hyper-parameter estimation. Meanwhile, the second sub-posterior run MCMC for $m$ last data points including new data coming iteratively, $m$-online sub-data ($m$ « data size), using the recent hyper-parameter estimation. The merging is to estimates new hyper-parameter estimation. Technically, MCMC runs m-data size on every iteration for less time-computing. Moreover, MCMC is started by the recent estimation to represent the MCMC involving left data. The algorithm is an efficient method for the hyper-parameter estimation of Gaussian process regression on iterative real-time data. It is applicable to the online scheme. The result showed that the LLNH algorithm performs well on hyper-parameter estimation and has less time-computation comparing to the offline MCMC.","PeriodicalId":307172,"journal":{"name":"2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127679510","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 Comprehensive Survey and Outlook for Cross-Resolution Person Re-Identification 跨分辨人再识别的综合调查与展望

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00047

Qiongqian Yang, Yehansen Chen, Jianfeng Zhang, Zhenting Li

引用次数: 0

The Resolution of English Pronoun by Chinese-speaking Learners: Evidence from Eye Movement 汉语学习者对英语代词的分辨:来自眼动的证据

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00025

X. Peng

引用次数: 0

Aspect-level Sentiment Analysis Research based on XLN et-LCF 基于XLN et-LCF的方面级情感分析研究

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00040

Donglin Ma, Qingqing Chen, Ce Yang

{"title":"Aspect-level Sentiment Analysis Research based on XLN et-LCF","authors":"Donglin Ma, Qingqing Chen, Ce Yang","doi":"10.1109/FAIML57028.2022.00040","DOIUrl":"https://doi.org/10.1109/FAIML57028.2022.00040","url":null,"abstract":"At present, the commonly used aspect-level sentiment analysis method is to combine the neural network model and the attention mechanism, use the neural network to mine the semantic features of the sentence, and the attention mechanism assigns the weight of the emotional words in the sentence. However, when there are multiple aspects in a sentence, and the aspect words are uncertain, methods that rely solely on the attention mechanism cannot effectively distinguish sentiment words from different aspects. Therefore, an aspect-level sentiment analysis model based on XLNet-LCF is proposed. The model obtains contextual semantic features bidirectionally through XLNet pre-training, and introduces a context focus mechanism to capture the local context of the context with the aspect word as the focus, which can effectively reduce the impact of different aspects. To solve the problem of mutual interference between emotional words, we combined the multi-head self-attention mechanism to deeply extract the semantic features in the global context to construct an emotional weight matrix. Finally, the matrix was normalized to improve the training speed and input the emotional analysis layer to judge the emotional polarity. The model is tested on three public datasets, Laptop, Restaurant, and Twitter. The results show that the accuracy rates of sentiment analysis of the XLNet-LFC model reach 80.12%, 85.24%, and 81.2%, and the F1 values reach 76.59%, 76.9%, and 73.37%, the overall performance is better than the comparison model.","PeriodicalId":307172,"journal":{"name":"2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)","volume":"58 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125128688","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

The Building Intelligent Isolation and Lightning Protection System Based on Labview 基于Labview的建筑智能隔离防雷系统

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00034

Zhijun Peng, Yunge Wang

引用次数: 0

Spatial-Temporal Separable Attention for Video Action Recognition 视频动作识别的时空可分离注意

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00050

Xi Guo, Yikun Hu, Fang Chen, Yuhui Jin, Jian Qiao, Jian Huang, Qin Yang

引用次数: 0

An Anchor-Free Target Detection Algorithm Combining Attention and Dilation Convolution 一种结合注意卷积和扩张卷积的无锚目标检测算法

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00016

Lei Xiong, Fengsui Wang, Yaping Qian, Yue Xu

{"title":"An Anchor-Free Target Detection Algorithm Combining Attention and Dilation Convolution","authors":"Lei Xiong, Fengsui Wang, Yaping Qian, Yue Xu","doi":"10.1109/FAIML57028.2022.00016","DOIUrl":"https://doi.org/10.1109/FAIML57028.2022.00016","url":null,"abstract":"Aiming at the problem of insufficient target detection capability in CenterNet, an improved target detection model combining attention and cavity convolution is proposed. Firstly, in order to improve the ability of the network to obtain the semantic and location features of the target, an improved nonlocal attention mechanism module (CANL) is designed to capture the remote dependence of the target in the image along the channel domain and the spatial domain, respectively. Secondly, a multi-scale feature extraction network based on dilation convolution (MSNet) is designed to improve the expression ability of the network to different scale targets, the residual structure is used to fuse the receptive field features of multiple scales in parallel, and the feature information obtained by the target in the image at multiple scales is retained. Finally, the proposed algorithm is verified on PASCAL VOC dataset. The detection accuracy of the proposed algorithm is 2.65 % higher than that of the baseline algorithm CenterNet, which effectively improves the performance of the anchorless object detection algorithm.","PeriodicalId":307172,"journal":{"name":"2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127450331","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

Deep Learning-Based Approach for Object Detection in Robot Football Competition 基于深度学习的机器人足球比赛目标检测方法

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00042

Zhao-lei Wang

{"title":"Deep Learning-Based Approach for Object Detection in Robot Football Competition","authors":"Zhao-lei Wang","doi":"10.1109/FAIML57028.2022.00042","DOIUrl":"https://doi.org/10.1109/FAIML57028.2022.00042","url":null,"abstract":"Robot football competition is a complex and emerging field of artificial intelligence research involving object detection technology, robotics, intelligent control, and other technologies. However, object detection is one of the most core technologies, supporting robots to realize tactical cooperation and real-time actions such as shooting, passing, and obstacle avoidance behavior. With precise accuracy and detection speed requirements, fast-moving robots and footballs must be recognized accurately by object detection algorithms under environments of changing backgrounds and lighting conditions. In this paper, to propose a reliable detection method for robot football competition, an end-to-end training approach is applied based on the YOLOv3 algorithm. K-means reclustering is used to calculate more appropriate bounding box priors to adapt to the size of detected objects. Besides, the smooth L1 loss function is adopted for the loss of the bounding box instead of MSE loss to reduce the model's sensitivity to outliers. With the framework of Pytorch, the proposed method can reach the mAP up to 96.5%, recognizing specific targets under the Standard Platform League(SPL) of Robocup. Accurate object detection algorithms can improve the capabilities of robot behavioral decision-making and positioning. In the future, superior lightweight algorithms can also be deployed on edge devices to meet the visual needs of real-time intelligent services.","PeriodicalId":307172,"journal":{"name":"2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131577023","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