Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence最新文献

A Scale-adaptive Color Preservation Neural Style Transfer Method 一种尺度自适应色彩保存神经风格转移方法

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395286

Qing Shen, Lu Zou, Fangjun Wang, Zhangjin Huang

{"title":"A Scale-adaptive Color Preservation Neural Style Transfer Method","authors":"Qing Shen, Lu Zou, Fangjun Wang, Zhangjin Huang","doi":"10.1145/3395260.3395286","DOIUrl":"https://doi.org/10.1145/3395260.3395286","url":null,"abstract":"Since the feature maps of deep neural networks were adopted to compute the representation of style and content information, neural style transfer (NST) methods have sprung up like mushrooms. But the existing methods ignore a fundamental fact that a style or an artistic image not only contains style information but also contains content information. And we find that there may be a conflict between style and content. Motivated by this idea, we propose a novel method, which only adopts the detail layer of the style image to compute the style loss. To avoid the potential conflicts between the style loss and the content loss, we just abandon the latter. The smooth base layer of the content image will be added to the intermediate results to keep the semantic content invariant. Our ablation studies show that this strategy can make the results scale-adaptive to the style image. Furthermore, we use an interpolation method so that the overall color of our results remains unchanged and our results have a colorful stroke. The qualitative and quantitative analyses show that our results have a better visual effect than the existing methods.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117240652","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: 2

Insect Detection Research in Natural Environment Based on Faster-R-CNN Model 基于Faster-R-CNN模型的自然环境昆虫检测研究

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395265

Yunpan Du, Yang Liu, Nianqiang Li

引用次数: 0

Neural Question Generation based on Seq2Seq 基于Seq2Seq的神经问题生成

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395275

Bingran Liu

引用次数: 9

Multi-Task Spatial-Temporal Graph Attention Network for Taxi Demand Prediction 出租车需求预测的多任务时空图注意网络

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395266

Mingming Wu, Chaochao Zhu, Lianliang Chen

{"title":"Multi-Task Spatial-Temporal Graph Attention Network for Taxi Demand Prediction","authors":"Mingming Wu, Chaochao Zhu, Lianliang Chen","doi":"10.1145/3395260.3395266","DOIUrl":"https://doi.org/10.1145/3395260.3395266","url":null,"abstract":"Taxi demand prediction is of much importance, which enables the building of intelligent systems and smart city. It is necessary to predict taxi demand accurately to schedule taxi fleet in a reasonable and efficient way and to reduce the pressure of traffic jam. However, the taxi demand involves complex and non-linear spatial-temporal impacts. The superiority of deep learning makes people explore the possibility to apply it to traffic prediction. State-of-the-art methods on taxi demand prediction only capture static spatial correlations between regions (e.g., Using static graph embedding) and only take taxi demand data into consideration. We propose a Multi-Task Spatial-Temporal Graph Attention Network (MSTGAT-Net) framework which models the correlations between regions dynamically with graph-attention network and captures the correlation between taxi pick up and taxi drop off with multi-task training. To the best of our knowledge, it is the first paper to address the taxi demand prediction problem with graph attention network and multi-task learning. Experiments on real-world taxi data show that our model is superior to state-of-the-art methods.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129018968","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: 5

DACNN

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395292

Yitong Pang, Jianing Tong, Yiming Zhang, Zhihua Wei

引用次数: 1

Keywords Extraction Based on Word Relevance Degrees 基于词关联度的关键词提取

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395262

Chaoxian Chen, Bo Yang, Changjian Zhao

引用次数: 1

Research on Chinese Intent Recognition Based on BERT pre-trained model 基于BERT预训练模型的汉语意图识别研究

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395274

P. Zhang, Li Huang

{"title":"Research on Chinese Intent Recognition Based on BERT pre-trained model","authors":"P. Zhang, Li Huang","doi":"10.1145/3395260.3395274","DOIUrl":"https://doi.org/10.1145/3395260.3395274","url":null,"abstract":"As a sub-task in natural language understanding, intent recognition research plays an important role in it. The accuracy of intent recognition is directly related to the performance of semantic slot filling, the choice of data set, and the research that will affect subsequent dialogue systems. Considering the diversity in text representation, traditional machine learning has been unable to accurately understand the deep meaning of user texts. This paper uses a BERT pre-trained model in deep learning based on Chinese text knots, and then adds a linear classification to it. Using the downstream classification task to fine-tune the pre-trained model so that the entire model together maximizes the performance of the downstream task. This paper performs domain intent classification experiments on the Chinese text THUCNews dataset.Compared with recurrent neural network(RNN) and convolutional neural network(CNN) methods, this method can improve performance by 3 percentage points. Experimental results show that the BERT pre-trained model can provide better accuracy and recall of Chinese news text domain intent classification.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115812342","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

Impact Analysis of Mobile Medical APPs Based on AHP-TOPSIS: A Case Study of China 基于AHP-TOPSIS的移动医疗app影响分析——以中国为例

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395272

Xin Li, Jianxin You, Wang Zhao

引用次数: 1

Vision-based UAV Positioning Method Assisted by Relative Attitude Classification 基于视觉的相对姿态分类辅助无人机定位方法

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395263

S. Zhang, Jie Li, Chengwei Yang, Yu Yang, Xiaoling Hu

{"title":"Vision-based UAV Positioning Method Assisted by Relative Attitude Classification","authors":"S. Zhang, Jie Li, Chengwei Yang, Yu Yang, Xiaoling Hu","doi":"10.1145/3395260.3395263","DOIUrl":"https://doi.org/10.1145/3395260.3395263","url":null,"abstract":"When the Unmanned Aerial Vehicle(UAV) is flying in formation, the common communication method is radio frequency(RF) communication. However, in practical applications, the way of RF communication is susceptible to interference from other factors such as electromagnetism. Therefore, in order to improve the anti-interference of the UAV cluster flight, it's necessary to use a positioning method which is based on visual information. Based on the above analysis, this paper proposes a vision-based UAV positioning method assisted by attitude classification. Firstly, the problem of solving the relative attitude of the UAV is transformed into a classification problem by the object recognition method, and a preliminary classification of the relative attitude of the friendly UAV is realized. Based on the principle of camera calibration, the pixel size and coordinates of the target UAV can be transform to the body coordinate system. Since the camera and the carrier UAV are fixedly connected, when the latitude and longitude coordinates of the carrier UAV are known, relative coordinate conversion can be performed to calculate the coordinates of the target UAV in the world coordinate system. Realize the positioning task of the target UAV. Simulation results are performed on the proposed method of the UAV relative attitude recognition accuracy exceeds 90%, and the average error in the distance simulation system of 2.56%. The final coordinate positioning accuracy exceeds 90% without losing the target.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130277261","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: 2

Application of Temperature Prediction Model Based on LSTNet in Telecommunication Room 基于LSTNet的温度预测模型在电信机房中的应用

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395270

Chun-Hsiang Lee, Saisai Yang, Yongqiang Fan, X. Lin, Hongfeng Tao, Chao Wu

引用次数: 2