2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第7页

Similarity Estimation for Classical Indian Music 印度古典音乐的相似性估计

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00130

Anusha Sridharan, M. Moh, Teng-Sheng Moh

{"title":"Similarity Estimation for Classical Indian Music","authors":"Anusha Sridharan, M. Moh, Teng-Sheng Moh","doi":"10.1109/ICMLA.2018.00130","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00130","url":null,"abstract":"Music is a complicated form of communication, where creators and cultures communicate and expose their individualities. Thanks to music digitalization, recommendation systems and other online services have become indispensable in the field of Music Information Retrieval (MIR). Classification of music is essential for music recommendation systems. In this paper, we propose an approach for finding similarity between music. Our approach is based on mid-level attributes like pitch, midi value, interval, contour, and duration, and applying text-based classification techniques. Performance evaluation has been done using the accuracy score of scikit-learn. As a preliminary study, our system first predicted jazz, metal, and ragtime for western music. The genre prediction system has been tested on 476 music files with a maximum accuracy of 95.8% across different n-grams. Then, we have analyzed and classified the Indian classical Carnatic music based on their raga. Our system has predicted Sankarabharam, Mohanam, and Sindhubhairavi ragas. The raga prediction system was tested on 68 music files with a maximum accuracy of 90.14% across different n-grams.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"10 1","pages":"814-819"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82040337","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

Fault Diagnosis Method Based on Scaling Law for On-line Refrigerant Leak Detection 基于标度律的制冷剂泄漏在线检测故障诊断方法

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00177

Shun Takeuchi, Takahiro Saito

{"title":"Fault Diagnosis Method Based on Scaling Law for On-line Refrigerant Leak Detection","authors":"Shun Takeuchi, Takahiro Saito","doi":"10.1109/ICMLA.2018.00177","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00177","url":null,"abstract":"Early fault detection using instrumented sensor data is one of the promising application areas of machine learning in industrial facilities. However, it is difficult to improve the generalization performance of the trained fault-detection model because of the complex system configuration in the target diagnostic system and insufficient fault data. It is not trivial to apply the trained model to other systems. Here we propose a fault diagnosis method for refrigerant leak detection considering the physical modeling and control mechanism of an air-conditioning system. We derive a useful scaling law related to refrigerant leak. If the control mechanism is the same, the model can be applied to other air-conditioning systems irrespective of the system configuration. Small-scale off-line fault test data obtained in a laboratory are applied to estimate the scaling exponent. We evaluate the proposed scaling law by using real-world data. Based on a statistical hypothesis test of the interaction between two groups, we show that the scaling exponents of different air-conditioning systems are equivalent. In addition, we estimated the time series of the degree of leakage of real process data based on the scaling law and confirmed that the proposed method is promising for early leak detection through comparison with assessment by experts.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"13 1","pages":"1087-1094"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78443018","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

Towards Affect Recognition through Interactions with Learning Materials 透过与学习资料的互动，迈向情感认知

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00062

E. Ghaleb, Mirela C. Popa, E. Hortal, S. Asteriadis, Gerhard Weiss

{"title":"Towards Affect Recognition through Interactions with Learning Materials","authors":"E. Ghaleb, Mirela C. Popa, E. Hortal, S. Asteriadis, Gerhard Weiss","doi":"10.1109/ICMLA.2018.00062","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00062","url":null,"abstract":"Affective state recognition has recently attracted a notable amount of attention in the research community, as it can be directly linked to a student's performance during learning. Consequently, being able to retrieve the affect of a student can lead to more personalized education, targeting higher degrees of engagement and, thus, optimizing the learning experience and its outcomes. In this paper, we apply Machine Learning (ML) and present a novel approach for affect recognition in Technology-Enhanced Learning (TEL) by understanding learners' experience through tracking their interactions with a serious game as a learning platform. We utilize a variety of interaction parameters to examine their potential to be used as an indicator of the learner's affective state. Driven by the Theory of Flow model, we investigate the correspondence between the prediction of users' self-reported affective states and the interaction features. Cross-subject evaluation using Support Vector Machines (SVMs) on a dataset of 32 participants interacting with the platform demonstrated that the proposed framework could achieve a significant precision in affect recognition. The subject-based evaluation highlighted the benefits of an adaptive personalized learning experience, contributing to achieving optimized levels of engagement.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"68 1","pages":"372-379"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79511995","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: 10

On Developing a UAV Pursuit-Evasion Policy Using Reinforcement Learning 基于强化学习的无人机追逃策略研究

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00138

Bogdan I. Vlahov, Eric Squires, Laura Strickland, Charles Pippin

引用次数: 29

Automatic Recognition of Mild Cognitive Impairment and Alzheimers Disease Using Ensemble based 3D Densely Connected Convolutional Networks 基于集成三维密集连接卷积网络的轻度认知障碍和阿尔茨海默病自动识别

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00083

Shuqiang Wang, Hongfei Wang, Yanyan Shen, Xiangyu Wang

引用次数: 40

Inner Attention Based bi-LSTMs with Indexing for non-Factoid Question Answering 基于内注意的双lstm索引非虚构问答

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00009

Akshay Sharma, Chetan Harithas

{"title":"Inner Attention Based bi-LSTMs with Indexing for non-Factoid Question Answering","authors":"Akshay Sharma, Chetan Harithas","doi":"10.1109/ICMLA.2018.00009","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00009","url":null,"abstract":"In this paper, we focussed on non-factoid question answering problem using a bidirectional LSTM with an inner attention mechanism and indexing for better accuracy. Non factoid QA is an important task and can be significantly applied in constructing useful knowledge bases and extracting valuable information. The advantage of using Deep Learning frameworks in solving these kind of problems is that it does not require any feature engineering and other linguistic tools. The proposed approach is to extend a LSTM (Long Short Term Memory) model in two directions, one with a Convolutional layer and other with an inner attention mechanism, proposed by Bingning Wang, et al., to the LSTMs, to generate answer representations in accordance with the question. On top of this Deep Learning model we used an information retrieval model based on indexing to generate answers and improve the accuracy. The proposed methodology showed an improvement in accuracy over the referred model and respective baselines and also with respect to the answer lengths used. The models are tested with two non factoid QA data sets: TREC-QA and InsuranceQA.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77102057","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

Teacher/Student Deep Semi-Supervised Learning for Training with Noisy Labels 带噪声标签训练的师生深度半监督学习

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00147

Zeyad Hailat, Xue-wen Chen

{"title":"Teacher/Student Deep Semi-Supervised Learning for Training with Noisy Labels","authors":"Zeyad Hailat, Xue-wen Chen","doi":"10.1109/ICMLA.2018.00147","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00147","url":null,"abstract":"Deep learning methods are at the forefront of leading state-of-the-art methods in a wide range of machine learning applications. In particular, convolutional neural networks (CNNs) attain topmost performance assuming a sufficiently large number of labeled training examples. Unfortunately, labeled data is artificially curated, and it requires human labor, which consequently makes it expensive and time-consuming. Moreover, there are no guarantees that the obtained labels are noise-free. In fact, the performance of CNNs is influenced by the level of noisy labels in the training dataset. Although the literature lacks attention to train learning methods with noisy labels, few semi-supervised learning methods mitigate this obstacle. In this paper, we propose a new teacher/student deep semi-supervised learning (TS-DSSL) method that employs self-training on noisy labels training dataset. We measure the efficiency of TS-DSSL on semi-supervised visual object classification tasks on the benchmark datasets CIFAR10 and MNIST. TS-DSSL achieves impressive results even in the presence of high-level noisy labels. It also sets a record on datasets with various levels of noisy labels created from the previous datasets with uniform and non-uniform noise distributions.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"182 6","pages":"907-912"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72577643","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

Reinforcement Learning Algorithms for Uncertain, Dynamic, Zero-Sum Games 不确定、动态、零和博弈的强化学习算法

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00015

S. Mukhopadhyay, Omkar J. Tilak, S. Chakrabarti

引用次数: 11

Multimodal Sentiment Analysis Using Deep Learning 使用深度学习的多模态情感分析

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00240

Rakhee Sharma, Ngoc Tan, F. Sadat

引用次数: 3

ROI Detection in Mammogram Images Using Wavelet-Based Haralick and HOG Features 基于小波Haralick和HOG特征的乳房x线图像ROI检测

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00023

Sena Busra Yengec Tasdemir, Kasim Tasdemir, Z. Aydın

引用次数: 16