Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence最新文献_第2页

On Finding the Best Learning Model for Assessing Confidence in Speech 寻找评估语音自信的最佳学习模型

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426838

Shruti S. Nair, Madhumita Mohan, Jemima Rajesh, P. Chandran

{"title":"On Finding the Best Learning Model for Assessing Confidence in Speech","authors":"Shruti S. Nair, Madhumita Mohan, Jemima Rajesh, P. Chandran","doi":"10.1145/3426826.3426838","DOIUrl":"https://doi.org/10.1145/3426826.3426838","url":null,"abstract":"The human mind is naturally conditioned to assess the confidence of another speaker. Hence, confidence while speaking is crucial for success across most domains and situations. Confidence in speech is a highly useful trait to have when engaged in interactions and discussions. In the right amounts, it can often sound pleasant or reassuring to the listener. For a person striving to achieve a note of confidence in his/her voice, finding a human evaluator to give relevant feedback on the tone and voice is not always possible. Given the growing power of neural networks and other machine learning tools today, a machine could potentially serve as an evaluator for assessing the confidence in the user's speech, and provide scores as feedback for the user's improvement. In this paper, we present the descriptions, results and analysis of our experiments in predicting the confidence of a speaker using machine learning and audio processing tools. The project involved the building and scoring of an unbiased dataset of audio recordings based on the confidence of the speaker. The audio clips were recorded by the peers in the campus and graded based on clarity, modulation, pace, and volume. Three models were trained and tested on the built dataset: a multilayer perceptron (MLP) neural network, a support vector machine (SVM) and a convolutional neural network (CNN) to predict the confidence of a speaker. Our results show that convolutional neural networks produce scores with the highest accuracy, 86.3%, where accuracy is measured with respect to the closeness to the scores awarded by human assessment.","PeriodicalId":202857,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126758384","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

Cooperation of Neural Networks for Spoken Digit Classification 语音数字分类中神经网络的协同作用

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426830

Nan Wu, Jing-Min Dai, Ziling Wei, Xueqi Duan, Shih-Chieh Su

{"title":"Cooperation of Neural Networks for Spoken Digit Classification","authors":"Nan Wu, Jing-Min Dai, Ziling Wei, Xueqi Duan, Shih-Chieh Su","doi":"10.1145/3426826.3426830","DOIUrl":"https://doi.org/10.1145/3426826.3426830","url":null,"abstract":"Notably, all neural network models are trained by using gradient descent, and by far, the most successful approach for machine learning is to use gradient descent. However, this is a greedy algorithm and hits some of the biggest open problems in the neural networks. By using gradient descent, it is not guaranteed that a better solution cannot be found. Here, this article has presented an empirical study of the performance of two hidden layers’ neural networks. It gives practical methods to improve the accuracy of neural networks: cooperation method of neural network. In this study, our group applied the data augmentation method by adding noise into the training data set and compared 3 kinds of training methods: batch gradient descent (BGD), stochastic gradient descent (SGD), and batch stochastic gradient descent (BSGD). According to cooperating the neural networks, the performance of these neural networks has improved compared to baseline neural networks by 47% (PEG (generalization classification error probability) of 9 neural networks in cooperation is 0.071). Finally, the real-time classification using a cooperation method which has PEG equals 0.04 (single neural networks’ PEG is 0.104), further proves the results that cooperation improves the performance of neural networks.","PeriodicalId":202857,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122217874","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

Stock Selection Strategy Based on Support Vector Machine 基于支持向量机的股票选择策略

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426829

Runhuan Liu

{"title":"Stock Selection Strategy Based on Support Vector Machine","authors":"Runhuan Liu","doi":"10.1145/3426826.3426829","DOIUrl":"https://doi.org/10.1145/3426826.3426829","url":null,"abstract":"Stock traders nowadays attach increasing importance to artificial intelligence and machine learning techniques to construct better-performing stock portfolios. In this paper, a stock-selection model based on support vector machine (SVM) is applied to the data of selected technical indicators. Also, principal component analysis (PCA) is brought into the SVM model in order to cancel out the correlation and reduce the complexity of technical indicators. The model is carried out weekly on 12 years of historical data from 2008 to 2020, based on the component stocks of the Shanghai and Shenzhen 300 Index (CSI 300). Experimental results show that the annualized return yielded by our model reaches 14.5%, which significantly outperforms the return of the CSI 300. By comparing the results before and after employing PCA, the study suggests that PCA performs well when dealing with complex and non-linear data regarding investment securities, and PCA is especially beneficial for investments with relatively higher risk tolerance. It can be concluded that the proposed stock-selection model, which combines SVM with PCA, is of practical value for investors.","PeriodicalId":202857,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence","volume":"127 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114052749","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

Robust Neural Network Training Using Inverted Probability Distribution 基于倒概率分布的鲁棒神经网络训练

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426827

Teerapaun Tanprasert, T. Tanprasert

{"title":"Robust Neural Network Training Using Inverted Probability Distribution","authors":"Teerapaun Tanprasert, T. Tanprasert","doi":"10.1145/3426826.3426827","DOIUrl":"https://doi.org/10.1145/3426826.3426827","url":null,"abstract":"This paper presents strategies to tweak the probability distribution of the data set to bias the training process of a neural network for a better learning outcome. For a real-world problem, provided that the probability distribution of the population can be assumed, the training set can be sampled from the population in such a way that its probability distribution satisfies certain targeted characteristics. For example, if the boundary between classes is critical to the training outcome, a larger proportion of training data may be drawn from the area around the boundaries. On the other hand, if the learning outcome is aimed at resembling a common concept encoded in the training set, learning from the data near the norm may be more effective. In order to explore the effectiveness of the various strategies, the concept was applied to two problems: 3-spiral and wine quality. Experimental results suggest that, whether the problem requires an emphasis on classifying boundary or recognizing the central pattern, our novel sampling strategy – inverted probability distribution – performs exceptionally well.","PeriodicalId":202857,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122112265","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

Machine Learning in Tourism 旅游业中的机器学习

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426837

Fatemeh Afsahhosseini, Y. Al-Mulla

引用次数: 8

NDWI-DeepLabv3+: High-Precision Extraction of Water Bodies from Remote Sensing Images NDWI-DeepLabv3+:遥感影像水体的高精度提取

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426847

Yuhao Peng, Houcheng Su, Chao Xu, Ao Feng, Tao Liu

{"title":"NDWI-DeepLabv3+: High-Precision Extraction of Water Bodies from Remote Sensing Images","authors":"Yuhao Peng, Houcheng Su, Chao Xu, Ao Feng, Tao Liu","doi":"10.1145/3426826.3426847","DOIUrl":"https://doi.org/10.1145/3426826.3426847","url":null,"abstract":"How to efficiently and accurately extract water bodies from remote sensing images is the focus of scholars' research. Current research often does not make full use of the unique multi-band data of remote sensing images. This paper proposes an improved NDWI-DeepLabv3+ network to improve the accuracy of water body extraction, especially from urban remote sensing images. We improve the network from two main aspects: multi-scale input and multi-band data feature fusion. And for the critical parts of the network, we put forward a variety of feasible solutions to compare and select the best. In the end, we chose to convert the feature map calculated by NDWI into an input adapted to the neural network, and at the same time, develop a parallel convolution structure to fuse and extract the band data features. We verify the effectiveness of this method by comparing other multi-scale architecture networks in the same period. The NDWI-DeepLabV3+ network proposed in this paper can extract water from the L2A level data of Sentinel-2, which can slightly increase the computational consumption and obtain better performance. It provides new ideas for intelligently extracting hydrological information from remote sensing images.","PeriodicalId":202857,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127815109","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

Power Transmission Line Foreign Object Detection based on Improved YOLOv3 and Deployed to the Chip 基于改进YOLOv3的输电线路异物检测及芯片部署

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426845

Jing Li, Yuhu Nie, WenpengCui Cui, R. Liu, Zhe Zheng

引用次数: 2

Wavelet-Aided Stock Forecasting Model based on Ensembled Machine Learning 基于集成机器学习的小波辅助股票预测模型

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426834

Yuanyuan Qu, Zhongkai Zhang, Zhiliang Qin

引用次数: 2

Character-level Recurrent Neural Network for Text Classification Applied to Large Scale Chinese News Corpus 字符级递归神经网络文本分类在大型中文新闻语料中的应用

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426842

Xin Wu, Jiang He

引用次数: 3

Improved Secure Lightweight RFID Authentication Protocol 改进的安全轻量级RFID认证协议

Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence Pub Date : 2020-09-18 DOI: 10.1145/3426826.3426850

Famei He, Zhou Fang, Dong Wang, Qiuyun Wang, Yueyuan Hao, Xuren Wang

引用次数: 1