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

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Automatic Summarization Method of Technical Literature Based On Domain Ontonogy 基于领域本体的技术文献自动摘要方法
Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395278
Pan Cao, Junwei Luo, Lin Lu
{"title":"Automatic Summarization Method of Technical Literature Based On Domain Ontonogy","authors":"Pan Cao, Junwei Luo, Lin Lu","doi":"10.1145/3395260.3395278","DOIUrl":"https://doi.org/10.1145/3395260.3395278","url":null,"abstract":"In the field of automatic summarization technology, it is hot but difficult to make full use of the semantic information of text. Owing to the semantic reasoning of ontology, we proposed an automatic summarization method based on domain ontology. The domain ontology is constructed by using knowledge graph as the initial template and the FP-Growth association rule to mine the relations between conceptions. After mapping sentences to RDF triples, the final triples will be given by semantic reasoning based on domain ontology. The summary of technical literature based on domain ontology express the themes more accurately. Taking ROUGE as the evaluation, the method we give is better than the traditional auto-summarization method in average, the ROUGE value of multiple documents summarization is 15% higher than single document summarization.","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":"130504174","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
Key Technology of Artificial Intelligence in Hull Form Intelligent Optimization 船体形态智能优化中的人工智能关键技术
Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395296
Zhang Li, Weimin Chen
{"title":"Key Technology of Artificial Intelligence in Hull Form Intelligent Optimization","authors":"Zhang Li, Weimin Chen","doi":"10.1145/3395260.3395296","DOIUrl":"https://doi.org/10.1145/3395260.3395296","url":null,"abstract":"Hull form optimization is an important aspect of ship hydrodynamic research, and the primary target of ship design. More and more researchers are focusing on hull form intelligent optimization in recent years. First of all, this paper summarizes the development of ship hull form optimization, as well as the technology used. Then the principle of ship optimization is summarized, and on this basis, the function and framework of ship intelligent optimization are summarized. The key technologies in the process of intelligent optimization are contributed: 1) Non-Uniform Rational B-Splines(NURBS) surface generation technology, including parametric modeling technology and non-parametric modeling technology; 2) surrogate model technology, including artificial neural network(ANN), machine learning(ML) and deep learning(DL); 3) optimization algorithm, including genetic algorithm(GA), ant colony algorithm(ACA) and artificial bee colony algorithm(ABC). Finally, the difficulties and challenges of the key technologies are analyzed. Based on artificial intelligence technology, hull form optimization can effectively improve its efficiency and provide key technical support for ship intelligent optimization.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"50 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":"114490836","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
A Concept Drift Detection Algorithm based on Fuzzy Marginal Density 一种基于模糊边缘密度的概念漂移检测算法
Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395301
Jing Yang, Jie Zhang, Sujuan Qin
{"title":"A Concept Drift Detection Algorithm based on Fuzzy Marginal Density","authors":"Jing Yang, Jie Zhang, Sujuan Qin","doi":"10.1145/3395260.3395301","DOIUrl":"https://doi.org/10.1145/3395260.3395301","url":null,"abstract":"With the continuous emergence of data, concept drift in data streams is becoming more and more common. In the past, concept drift detection was regarded as a task based on supervised learning, but it is of practical significance to study concept drift in semi-supervised or unsupervised learning since it is difficult to get all the labels of the data streams in real time. Existing algorithms based on semi-supervised learning to detect concept drift show good performance, but there is still room for improvement in terms of detection delay and false alarm rate. In this paper, we propose an algorithm named as Fuzzy Margin Density Drift Detection suitable for semi-supervised learning. This method explores the membership function of the fuzzy marginal dataset to more accurately describe and quantify the classification confidence of samples in the data stream, which takes full advantage of the classification confidence of each samples. This method is more accurate for concept drift detection, and can avoid the false alarm in some degree. We verified the effectiveness of the proposed algorithm through experiments on synthetic and real data set.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"4 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":"125132807","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
Face Restoration Based on GANs and NST 基于gan和NST的人脸恢复
Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395304
Yanshun Zhao, Jinda Hu, Xindong Zhang
{"title":"Face Restoration Based on GANs and NST","authors":"Yanshun Zhao, Jinda Hu, Xindong Zhang","doi":"10.1145/3395260.3395304","DOIUrl":"https://doi.org/10.1145/3395260.3395304","url":null,"abstract":"Image restoration technology is a research hotspot in the field of deep learning computer vision. However, due to the complexity of facial textures, the existing face restoration algorithms lack visual coherence. We propose a face restoration algorithm (GNST) based on Generative Adversarial Networks (GANs) and Neural Style Transfer (NST), which uses the generative network to repair the damaged facial contents and then uses the style transfer to adjust the overall style. At the stage of repairing content, we design four losses from different aspects. We add two total variation losses (tv loss) besides the traditional content loss and adversarial loss. The first tv loss can make the generated image smoother and cleaner. The second tv loss can effectively prevent the generator's gradient collapse when generator \"cheat\" the discriminator. In addition, it is used to highlight facial important features. Experiments show that G-NST achieves better results than existing methods.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"110 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":"127950914","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
Chinese relation extraction based on lattice network improved with BERT model 基于BERT模型改进的格网中文关系抽取
Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395276
Zheng-sheng Zhang, Qingsong Yu
{"title":"Chinese relation extraction based on lattice network improved with BERT model","authors":"Zheng-sheng Zhang, Qingsong Yu","doi":"10.1145/3395260.3395276","DOIUrl":"https://doi.org/10.1145/3395260.3395276","url":null,"abstract":"Relation classification is a basic and important task in the field of natural language processing(NLP). There are already many researches on English dataset, but the researches on Chinese dataset are very few. Due to the particularity of Chinese language, most existing methods suffer from the two main problems of segmentation error and polysemy. To sum up, the problem of segmentation error can be solved fairly well by many models, take lattice model for example, which can segment Chinese word precisely. But the problem of polysemy has not received enough attention. In this paper, we take advantage of BERT model to deal with the problem of polysemy. The experimental results show that our model achieves good result and outperforms baseline model.","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":"124389822","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: 3
Prediction and counting of field wheat based on LC-DcVgg 基于LC-DcVgg的大田小麦产量预测与计数
Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395299
Zhang Yiwen, Shu Baiyi, Xu Ziwei, Wang Yue, Mu Jiong
{"title":"Prediction and counting of field wheat based on LC-DcVgg","authors":"Zhang Yiwen, Shu Baiyi, Xu Ziwei, Wang Yue, Mu Jiong","doi":"10.1145/3395260.3395299","DOIUrl":"https://doi.org/10.1145/3395260.3395299","url":null,"abstract":"The number of wheat spikes per unit area is an important parameter for assessing wheat yield and wheat planting density. At present, the methods of intelligent counting of wheat include remote sensing technology and machine learning technology, but all have shortcomings such as poor stability, strong limitations, and poor versatility. And however, the existing object detection neural network algorithm requires a large amount of manpower to produce data sets. And it is not possible to identify too dense wheat spikes. In this paper, a new structure called direct connection is proposed to improve the algorithm of Vggnet, which makes it better combined with localization-based counting loss. Direct connection can fuse the features of shallow layer with those of deep layers, which can make the network retain the original picture information and make the localization-based counting loss play a better role in wheat spike counting. The model has a good recognition effect on the wheat spikes that are stuck together. For dense wheat spikes, the model can achieve MAE of 11.857, an accuracy rate of 90.4%, RMSE of 16.985.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"50 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":"133192079","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: 4
An Improved Collaborative Filtering Algorithm Based on User Interest Diffusion and Time Correlation 一种基于用户兴趣扩散和时间相关的改进协同过滤算法
Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395267
Kangle Hui, Hong Hou, Siyu Xue
{"title":"An Improved Collaborative Filtering Algorithm Based on User Interest Diffusion and Time Correlation","authors":"Kangle Hui, Hong Hou, Siyu Xue","doi":"10.1145/3395260.3395267","DOIUrl":"https://doi.org/10.1145/3395260.3395267","url":null,"abstract":"This paper proposes an improved collaborative filtering recommendation algorithm based on user interest diffusion and the time correlation. Firstly, the algorithm improves user synthesis similarity calculation method based on user interest diffusion, calculates the direct similarity of user interest and the similarity of user interest diffusion, and obtains the synthesis similarity of user interest through parameter adjustment. Then, for the user interest change with time, the time correlation function is applied to the similarity calculation between users. Finally, the recommendation weight is divided into the time correlation data weight and the synthesis similarity data weight, so that a more accurate prediction score is obtained.. The comparison experiments showed that the algorithm can reduce the sparseness of the data set effectively when the data is sparse, and improves the precision of the recommendation algorithm.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"50 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":"131677408","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
Attention model with multi-layer supervision for text Classification 基于多层监督的文本分类注意模型
Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395290
Chunyi Yue, Hanqiang Cao, Guoping Xu, Youli Dong
{"title":"Attention model with multi-layer supervision for text Classification","authors":"Chunyi Yue, Hanqiang Cao, Guoping Xu, Youli Dong","doi":"10.1145/3395260.3395290","DOIUrl":"https://doi.org/10.1145/3395260.3395290","url":null,"abstract":"Text classification is a classic topic in natural language processing. In this study, we propose an attention model with multi-layer supervision for this task. In our model, the previous context vector is directly used as attention to select the required features, and multi-layer supervision is used for text classification, i.e., the prediction losses are combined across all layers in the global cost function. The main contribution of our model is that the context vector is not only used as attention but also as a representation of an input text for classification at each layer. We conducted experiments based on five benchmark text classification data sets and the results indicate that our model can improve classification performance when applied to most of the data sets.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"8 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134544167","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
Image Compression Algorithm Optimization Based on Android Application 基于Android应用的图像压缩算法优化
Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395295
Pingping Chen, Qingming Xu, Xiaoran Geng, Min Zou, Huiling Liu, Dingying Tan
{"title":"Image Compression Algorithm Optimization Based on Android Application","authors":"Pingping Chen, Qingming Xu, Xiaoran Geng, Min Zou, Huiling Liu, Dingying Tan","doi":"10.1145/3395260.3395295","DOIUrl":"https://doi.org/10.1145/3395260.3395295","url":null,"abstract":"In Android mobile applications, the transmission of pictures in the network is one of the main causes of data traffic consumption. In order to make the pictures transmit in small size in the network and obtain good picture quality, a new compression strategy is designed. By investigating the image compression algorithms commonly used in the market, two optimization schemes based on Luban algorithm to calculate the aspect ratio of the image and prioritize the size of the image and then consider the size are proposed. The experimental results show that the optimization algorithm is less than twice the compression of the commonly used Luban algorithm within 15ms of the compression time. The optimized algorithm can quickly and efficiently compress the image and preserve good image quality.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"7 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":"133402584","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
Chinese Text Classification Method Based on BERT Word Embedding 基于BERT词嵌入的中文文本分类方法
Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395273
Ziniu Wang, Zhilin Huang, Jianling Gao
{"title":"Chinese Text Classification Method Based on BERT Word Embedding","authors":"Ziniu Wang, Zhilin Huang, Jianling Gao","doi":"10.1145/3395260.3395273","DOIUrl":"https://doi.org/10.1145/3395260.3395273","url":null,"abstract":"In this paper, we enhance the semantic representation of the word through the BERT pre-training language model, dynamically generates the semantic vector according to the context of the character, and then inputs the character vector embedded as a character-level word vector sequence into the CapsNet.We builted the BiGRU module in the capsule network for text feature extraction, and introduced attention mechanism to focus on key information.We use the corpus of baidu's Chinese question and answer data set and only take the types of questions as classified samples to conduct experiments.We used the separate BERT network and the CapsNet as a comparative experiment. Finally, the experimental results show that the model effect is better than using one of the models alone, and the effect is improved.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"40 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":"131460491","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: 6
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