2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)最新文献_第3页

Improved YOLOv5 Algorithm Based on CBAM Attention Mechanism 基于CBAM注意机制的改进YOLOv5算法

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00051

Ruixiang Fan, Zhongpan Qiu

引用次数: 1

Predict the Water Level of the Lake Mead for the Next 30 Years Based on ARIMA 基于ARIMA的米德湖未来30年水位预测

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00022

Yixin Li

引用次数: 0

Informatization of Constructive English Learning Platform Based on Artificial Intelligence Algorithm 基于人工智能算法的建构式英语学习平台信息化

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00023

Ting Li, Xuemei Zou

{"title":"Informatization of Constructive English Learning Platform Based on Artificial Intelligence Algorithm","authors":"Ting Li, Xuemei Zou","doi":"10.1109/FAIML57028.2022.00023","DOIUrl":"https://doi.org/10.1109/FAIML57028.2022.00023","url":null,"abstract":"In recent years, intelligence is the new direction of today's social development, and it is also a new feature of the development of informatization. With the rapid development of technologies such as artificial intelligence, the Internet of Things, big data and cloud computing, smart products and devices such as smart homes, smart robots, smart toys, and smart buildings have emerged, which have brought great changes to people's life and work styles. Through the constructive research on artificial intelligence algorithms, this paper designs a relevant information-based English learning platform, which can greatly improve the efficiency of English learning in the traditional textbook mode, and cultivate the interest and enthusiasm of college students to learn English. Through the research of artificial intelligence algorithm, this paper finds that informatization of constructive English learning platform focuses on building the ecological environment of students' autonomous learning, which can greatly improve students' autonomous learning ability, it take the student as the center, to provide learning resources and learning methods, at the time let the students actively constructing autonomous learning awareness, respect and encourage students to have different progress and development. The final results of the study show that the number of students who choose learning interaction is the largest, accounting for 41%, followed by the number of students who choose independent learning, accounting for 31%. It can be concluded that students attach most importance to learning interaction, which can greatly improve students' enthusiasm and initiative in learning.","PeriodicalId":307172,"journal":{"name":"2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)","volume":"188 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":"115482510","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

Imitation Learning Based on Visual-text Fusion for Robotic Sorting Tasks 基于视觉文本融合的模仿学习在机器人分类任务中的应用

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00038

Mei-hua Shi, Shuling Dai, Yongjia Zhao

{"title":"Imitation Learning Based on Visual-text Fusion for Robotic Sorting Tasks","authors":"Mei-hua Shi, Shuling Dai, Yongjia Zhao","doi":"10.1109/FAIML57028.2022.00038","DOIUrl":"https://doi.org/10.1109/FAIML57028.2022.00038","url":null,"abstract":"In this paper, we propose an imitation learning method based visual-text fusion for manipulation task. Manipulation is predicted based on text instructions by abstracting the manipulation into text instructions, learning the semantic concepts in the text instructions, and combining them with spatial features for visual inference. The construction process and demonstration content of the expert demonstration dataset is described in detail, which is focused on the process of decomposing the operation task through text. In addition, we present the learning process and demonstrate the network structure of functional modules to highlight the fusion of text features with visual features. The effectiveness of this method is verified by a simulated learning experiment on a multi-step manipulation task. The results show that the behavioral strategy achieved a 92.19% task completion rate on known objects and 80.03% on unknown objects. It is proved that, owing to the introduction of text, the decomposition of the operational task in terms of abstract semantics is realized and the difficulty of learning is reduced. Meanwhile, the behavioral strategy can perform accurate spatial location inference based on text features, thereby achieving accurate action prediction.","PeriodicalId":307172,"journal":{"name":"2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)","volume":"106 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":"123081902","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

Moving Target Detection Algorithm Based on SIFT Feature Matching 基于SIFT特征匹配的运动目标检测算法

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00045

Kunwei Song, Fangrong Zhu, Linlin Song

{"title":"Moving Target Detection Algorithm Based on SIFT Feature Matching","authors":"Kunwei Song, Fangrong Zhu, Linlin Song","doi":"10.1109/FAIML57028.2022.00045","DOIUrl":"https://doi.org/10.1109/FAIML57028.2022.00045","url":null,"abstract":"After the computer technology is mature, the sequence images obtained from the camera are processed by the computer, and the images converted into digital signals are processed by the computer, so the new discipline of computer vision is born. The reason why we should pay attention to computer vision is to use computers to replace the human brain and human eyes to extract, identify, track and other series of understanding and analysis of moving targets in specific scenes. The purpose of this paper is to research the moving target detection algorithm based on SIFT feature matching. According to the current video surveillance requirements for moving target detection, reduce the number of candidate samples in the input detection module in the TLD algorithm, so as to optimize the TLD algorithm and make it meet the real-time requirements. A tracking module that uses SIFT algorithm to optimize the TLD algorithm is proposed, and the TLD algorithm based on SIFT feature matching is tested. The experimental results show that the tracking accuracy of the algorithm reaches more than 90%, and the algorithm has better robustness to the rotation of moving objects.","PeriodicalId":307172,"journal":{"name":"2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)","volume":"53 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":"116781705","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

Arbitrary-Oriented Ship Detection based on Deep Learning 基于深度学习的任意方向船舶检测

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00046

Xingyu Chen, Chaoying Tang

引用次数: 0

The architecture design of training data microservice based on blockchain technology 基于区块链技术的训练数据微服务架构设计

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00032

He Bai, Xin Liu, Wenjiang Wang

引用次数: 0

Named Entity Annotation Corpus for Commercial Opportunity Mining 用于商业机会挖掘的命名实体标注语料库

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00028

Lulu Shi, Yongjie Qi, Hongchao Ma, Kunli Zhang, Hongying Zan, Q. Zhou

{"title":"Named Entity Annotation Corpus for Commercial Opportunity Mining","authors":"Lulu Shi, Yongjie Qi, Hongchao Ma, Kunli Zhang, Hongying Zan, Q. Zhou","doi":"10.1109/FAIML57028.2022.00028","DOIUrl":"https://doi.org/10.1109/FAIML57028.2022.00028","url":null,"abstract":"With the rising number of enterprises in China, enterprise data present explosive growth. Mining valuable commercial opportunity information from massive enterprise data is called commercial opportunity mining. Commercial opportunity information has rich investment reference value, it can help enterprises and investors to make better decisions, so it has important research significance. In order to solve the problem of the lack of Chinese named entity annotation corpus suitable for commercial opportunity mining research, this paper constructs the Commercial Opportunity Information named entity annotation Corpus (COIC). Based on the demand of commercial opportunity mining, we collect six types of commercial information. By deeply analyzing the characteristics of commercial texts, we define nine categories of commercial entities, and formulate the entity annotation specification. We use entity annotation platform to conduct pre-annotation, manual annotation, and manual proofreading of entities in commercial texts with more than 720,000 characters. The COIC we constructed contains 36,787 entities, and the annotation consistency reaches 0.8613. The current mainstream algorithms in the field of named entity recognition are selected for preliminary experiments, and various entities in the corpus are evaluated, which lays the data foundation for the follow-up research on commercial opportunity mining.","PeriodicalId":307172,"journal":{"name":"2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)","volume":"29 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":"129595990","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

SARAH: Semantic-Aware Representation Balance Hashing for Image Retrieval SARAH:用于图像检索的语义感知表示平衡哈希

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00039

Changlin Fan, Fengming Liang, Bo Xiao, Yuqiong Wu, Jincheng Yu, Shifei Zhou, Ye Li, Chunjie Sheng

{"title":"SARAH: Semantic-Aware Representation Balance Hashing for Image Retrieval","authors":"Changlin Fan, Fengming Liang, Bo Xiao, Yuqiong Wu, Jincheng Yu, Shifei Zhou, Ye Li, Chunjie Sheng","doi":"10.1109/FAIML57028.2022.00039","DOIUrl":"https://doi.org/10.1109/FAIML57028.2022.00039","url":null,"abstract":"Deep hashing is vitally important for large-scale image retrieval. Recently, central similarity based deep hashing approaches have shown great advantages for category-level image retrieval; in the existing approaches, however, categories are typically represented by a set of predefined binary vectors which are generated from Hadamard matrix or entry-wisely sampled from Bernoulli distribution. Unfortunately, such kind of category representations lack of discriminativity and semantic information. In this paper, we propose a novel Semantic-Aware Representation bAlance Hashing framework, dubbed SARAH, for category-level image retrieval. Specifically, in SARAH, the category representations are learned to preserve semantic similarities and to maximize pairwise distance; whereas the continuous code of each image is extracted by convolutional network and supervised via a central similarity loss with the corresponding semantic representation which is constructed by the learned category representations. As a consequence, the semantically similar images can be encoded to hash codes with small Hamming distance.","PeriodicalId":307172,"journal":{"name":"2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)","volume":"1 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":"129570127","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

Tawa Pukllay Proof: New Method for Solving Arithmetic Operations with The Inca Yupana Using Pattern Recognition and Parallelism Tawa Pukllay证明:用模式识别和并行性解决印加尤帕那算术运算的新方法

2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Pub Date : 2022-06-01 DOI: 10.1109/FAIML57028.2022.00048

Dhavit Prem, Rosario Guzman-Jimenez, Fernando Sotomayor, Alvaro Saldivar

引用次数: 0