Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence最新文献_第3页

Manifold Learning for Financial Market Visualization 金融市场可视化的多元学习

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395297

Y. Huang

引用次数: 1

Using The Ternary Closed-loop Model to Research on pterosaurs' flight capability 利用三元闭环模型研究翼龙的飞行能力

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395261

Maida Wang, Zhong-xiang Zhang

引用次数: 0

Finger Vein Recognition Based on Multi-Task Learning 基于多任务学习的手指静脉识别

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395277

Zhiang Hao, P. Fang, Hanwen Yang

引用次数: 5

Discrete Intelligible Recognition Network 离散可理解识别网络

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395285

S. Mei, Lei Zhang, Yan Wang

{"title":"Discrete Intelligible Recognition Network","authors":"S. Mei, Lei Zhang, Yan Wang","doi":"10.1145/3395260.3395285","DOIUrl":"https://doi.org/10.1145/3395260.3395285","url":null,"abstract":"We present a new approach to recognize object and we test it in MNIST data set. The main purpose of this method is to solve some problems encountered by most current artificial intelligence. Firstly, most current artificial intelligence is incomprehensible. Second, most AI algorithm use a large number of additions and multiplications, but in our brain, it will be difficult to implement or learn multipliers automatically, and gradient descent is also difficult to achieve. In our method, we hope that we can implement a kind of algorithm that does not use gradient descent and may not use adders and multipliers and the algorithm is comprehensible. We propose a new method. The method does not use multipliers and adders as much as possible. For intelligibility, we discretize the parameters of the entire algorithm. The parameters can be regarded as an encoding. Algorithm recognizes objects by means of comparators. It first needs to remember the identified object and decompose this object into smaller objects through continuous learning, and grasp more changes of objects by creating more branches. Because the algorithm is identified by comparing similarities, we can learn how the object is identified by the path of the algorithm's execution, and thus understand how the algorithm makes decisions. You can get the code from: https://github.com/msq17/Discrete-Intelligible-Recognition-Network","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"49 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":"131003662","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

Fully Convolutional Network based on Contrast Information Integration for Dermoscopic Image Segmentation 基于对比度信息集成的全卷积网络皮肤镜图像分割

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395284

Shuyuan Chen, Chaojie Ji, Ruxin Wang, Hongyan Wu

引用次数: 1

Joint Prediction of Group-Level Emotion and Cohesiveness with Multi-Task Loss 多任务损失对群体情绪和凝聚力的联合预测

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395294

Bochao Zou, Zhifeng Lin, Haoyi Wang, Yingxue Wang, Xiang-wen Lyu, Haiyong Xie

{"title":"Joint Prediction of Group-Level Emotion and Cohesiveness with Multi-Task Loss","authors":"Bochao Zou, Zhifeng Lin, Haoyi Wang, Yingxue Wang, Xiang-wen Lyu, Haiyong Xie","doi":"10.1145/3395260.3395294","DOIUrl":"https://doi.org/10.1145/3395260.3395294","url":null,"abstract":"This paper presents a hybrid deep learning network for the prediction of group-level emotion and cohesiveness. In this work, we first train deep models individually on face, pose, whole image, as well as fusion of them on Group Affect Dataset to predict group-level emotion, then feed the classification results into additional regression layer to regress group cohesiveness. Thus, our model combines group emotion and group cohesiveness and achieves better results. The best result we obtained on the test set is an ensemble of best models we trained on the validation set, and this model achieve a MSE of 0.4849. In order to further improve the performance, a multi-task loss model which combines classification of group emotion with regression of cohesiveness is adopted. Prior work on group cohesiveness usually fulfill the task of cohesiveness regression based on the output of emotion classification network. However, the two characteristics are believed to be correlated but one cannot necessarily predict the other. Hence, both information sources are important. Thus, the proposed multi-task loss setting combines the classification and regression tasks. The results prove that estimation of group emotion and cohesiveness is correlated and can be benefited by joint training of the two tasks.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"31 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":"124489552","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

Semi-supervised Mixed Sparse Representation based Classification for Face Recognition 基于半监督混合稀疏表示的人脸识别分类

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395289

Yikun Wang, Kai Zheng

{"title":"Semi-supervised Mixed Sparse Representation based Classification for Face Recognition","authors":"Yikun Wang, Kai Zheng","doi":"10.1145/3395260.3395289","DOIUrl":"https://doi.org/10.1145/3395260.3395289","url":null,"abstract":"The purpose of this paper is to solve the problem that the local optimum is prone to arise in Semi-Supervised Sparse Representation based Classification (S3RC), so as to improve the classification effect. Although S3RC can solve the issue of face recognition in the case of insufficient samples and corruption (including linear and non-linear) by using a Gaussian Mixture Model, due to the defect of Expectation-Maximization (EM) algorithm, which can only get local extremum but not global extremum, prototype dictionary (which contains only class-specific information) has a great contribution to the final recognition effect. This paper introduces a Semi-supervised Mixed Sparse Representation (SMSR) method based on dictionary decomposition to construct a prototype dictionary to solve this problem. We decompose the training image multiple times to ensure a more suitable prototype dictionary on the basis of reducing the corruption of the training data. The experiments results on AR and FERET databases demonstrate the effectiveness that, the proposed approach yields improved results compared to S3RC and other state-of-the-art algorithms.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"91 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":"134569638","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 of particle drilling emergency decision system based on logical reasoning rule 基于逻辑推理规则的颗粒钻井应急决策系统研究

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395302

Lei Li, Weicheng Li, Xiaolin Zhang

引用次数: 0

Improved Reference Vector Guided Differential Evolution Algorithm for Many-Objective Optimization 多目标优化的改进参考向量引导差分进化算法

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395268

Jie Lin, S. Zheng, Y. Long

{"title":"Improved Reference Vector Guided Differential Evolution Algorithm for Many-Objective Optimization","authors":"Jie Lin, S. Zheng, Y. Long","doi":"10.1145/3395260.3395268","DOIUrl":"https://doi.org/10.1145/3395260.3395268","url":null,"abstract":"Most of the existing evolutionary algorithms to deal with many-objective problems are based on the enhancing of selection strategy. Among them, the reference vector-guided evolutionary algorithm (RVEA) achieves excellent performance. In this paper, a new search engine is combined with RVEA to achieve further performance enhancement of the differential evolutionary (DE) algorithm. In the optimization process of differential evolution algorithm on many-objective problems, improving convergence and maintaining diversity are two different optimization directions, and it is usually difficult to maintain a balance between them. To solve this problem, a new search engine based on DE is proposed. The proposed search engine is implemented based on a cooperative scheme of local and global search strategies. In the local search, the population is divided into several sub-populations, each of which evolves independently using the proposed mutation strategy. The distance between the individuals in each sub-population is relatively close. Therefore, it has a strong exploitation capability, and will not make the population lose diversity. Meanwhile, the selection strategy of RVEA enables the population to maintain diversity, and the DE/rand/1 utilized in global search is sufficient to keep a strong exploration capability. Therefore, the proposed approach can achieve a good balance between exploration and exploitation. The experimental results show that the proposed algorithm performs well in many-objective optimizations up to more than 10 objectives.","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":"129506604","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

Mutual effect of the direct and indirect pathways on the basal ganglia network 基底神经节网络中直接通路和间接通路的相互作用

Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence Pub Date : 2020-04-10 DOI: 10.1145/3395260.3395264

Keping Zhang, Xia Shi, Yuan Wang

{"title":"Mutual effect of the direct and indirect pathways on the basal ganglia network","authors":"Keping Zhang, Xia Shi, Yuan Wang","doi":"10.1145/3395260.3395264","DOIUrl":"https://doi.org/10.1145/3395260.3395264","url":null,"abstract":"The main cause of Parkinson's disease is that the loss of dopamine makes the direct and indirect pathways of striatum lose their original balance, which eventually leads to abnormality of basal ganglia network. At the same time, various structures of the basal ganglia network show abnormal synchronous discharge. Thalamus also can't respond to cortex normally, making Parkinson's patients tremble abnormally. As the key structure of basal ganglia network, striatum has been always put much attention in the research. However, due to the complexity of the two pathways, a simplified method was always used in the research. Namely, The input of the striatum is set as a constant and the input current of the two pathways is analyzed individually. However, it's obvious that the simplified method can't truly reflect the influence of the two pathways on the basal ganglia network. In this paper, we will use the modified Hodgkin-Huxley model to simulate the influence of the direct path and the indirect path on the network. Corresponding parameters are adjusted in normal state. Then sensitivity characteristics of the two pathways on the network were observed by analyzing the relay ability of thalamus to the signals from the cerebral cortex, which will have much significance in diagnosis in Parkinson's disease.","PeriodicalId":103490,"journal":{"name":"Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence","volume":"25 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120866752","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