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

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

Learning Convolutional Neural Networks from Ordered Features of Generic Data 从通用数据的有序特征学习卷积神经网络

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00145

Eric Golinko, Thomas Sonderman, Xingquan Zhu

引用次数: 6

Financial Markets Prediction with Deep Learning 基于深度学习的金融市场预测

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00022

Jia Wang, Tong Sun, Benyuan Liu, Yu Cao, Degang Wang

{"title":"Financial Markets Prediction with Deep Learning","authors":"Jia Wang, Tong Sun, Benyuan Liu, Yu Cao, Degang Wang","doi":"10.1109/ICMLA.2018.00022","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00022","url":null,"abstract":"Financial markets are difficult to predict due to its complex systems dynamics. Although there have been some recent studies that use machine learning techniques for financial markets prediction, they do not offer satisfactory performance on financial returns. We propose a novel one-dimensional convolutional neural networks (CNN) model to predict financial market movement. The customized one-dimensional convolutional layers scan financial trading data through time, while different types of data, such as prices and volume, share parameters (kernels) with each other. Our model automatically extracts features instead of using traditional technical indicators and thus can avoid biases caused by selection of technical indicators and pre-defined coefficients in technical indicators. We evaluate the performance of our prediction model with strictly backtesting on historical trading data of six futures from January 2010 to October 2017. The experiment results show that our CNN model can effectively extract more generalized and informative features than traditional technical indicators, and achieves more robust and profitable financial performance than previous machine learning approaches.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"70 1","pages":"97-104"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90476904","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: 27

User-Centered Development of a Pedestrian Assistance System Using End-to-End Learning 基于端到端学习的以用户为中心的行人辅助系统开发

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00129

Hasham Shahid Qureshi, T. Glasmachers, Rebecca Wiczorek

{"title":"User-Centered Development of a Pedestrian Assistance System Using End-to-End Learning","authors":"Hasham Shahid Qureshi, T. Glasmachers, Rebecca Wiczorek","doi":"10.1109/ICMLA.2018.00129","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00129","url":null,"abstract":"In this paper, we propose an algorithm developed to detect the curbstone and its surroundings. This work is a part of the user-centered development of an assistance system currently being developed to support the older pedestrians crossing the road. For the development of this algorithm, an end-to-end learning approach was chosen. The convolutional neural network was selected to process raw pixels from a mono camera and the network was trained on a dataset to detect the curb. The use of end-to-end learning with a convolutional neural network proved remarkably powerful in distinguishing the curbstone. In order to train the network, images of curb and their surroundings were essential. For this purpose, a new dataset was created where multiple requirements, for example, different approach angles to the curbstone, weather and light conditions etc, were considered. As this system is currently being developed for Berlin (Germany), an analysis was carried out to determine the types and frequencies of pavements in Berlin pathways. Based on this analysis and the requirements, a dataset was created which comprises the images of the pavements, for example, cobblestone, concrete slabs etc, in diverse sets of weather and light conditions. This dataset was developed using the videos taken at 10 frames per second from a mono camera. For the collection of dataset and for testing purposes, a prototype in the form of a walker was built which has sensors, Leddar and camera mounted on it. This paper gives an overview of the development of the algorithm and describes the procedures, such as district analysis of Berlin and data collection, needed to develop the algorithm.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"22 1","pages":"808-813"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90685168","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

A Crowdsourcing Semi-Supervised LSTM Training Approach to Identify Novel Items in Emerging Artificial Intelligent Environments 新兴人工智能环境中识别新项目的一种众包半监督LSTM训练方法

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00241

Edoardo Serra, Haritha Akella, A. Cuzzocrea

{"title":"A Crowdsourcing Semi-Supervised LSTM Training Approach to Identify Novel Items in Emerging Artificial Intelligent Environments","authors":"Edoardo Serra, Haritha Akella, A. Cuzzocrea","doi":"10.1109/ICMLA.2018.00241","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00241","url":null,"abstract":"Nowadays always new kinds of cuisines appear on the market. Even though main cuisines such as French, Italian, Japanese, Chinese and Indian are always appreciated, they are not anymore the most popular. The new trend is fusion cuisine. A fusion cuisine is a combination of different main cuisines, this combination makes this cuisine new. The opening of a new restaurant proposing a new kind of cuisine produces a lot of excitement and people feel the need to try it and be part of this new culture. Yelp is a platform which publishes crowd-sourced reviews about different businesses, in particular, restaurants. Yelp allows the possibility to declare for each restaurant the kind of cuisine. Unfortunately, since the restaurants in the Yelp database are not often generated by the owners but by the users creating the reviews, there is no much information about the kind of cuisine, especially for restaurants providing fusion ones. In this paper, we address the problem of identifying restaurants proposing new kinds of cuisines by using their Yelp reviews. These new cuisines can be completely new or fusion cuisines. Discriminating between main cuisines and fusion cuisines is very difficult because fusion cuisines are similar to the main ones even if they are conceptually different. We propose 4Phase, a semi-supervised procedure that trains Long Short-Term Memory with only the text reviews of the restaurants providing main cuisines. The trained LSTM is ultimately used as a feature generator in combination with a standard novelty detection model (e.g., Gaussian Mixture Models). We perform experiments on Yelp to separate restaurants providing main cuisines from the ones providing completely new cuisines or fusion ones. In this experiments, our 4Phase procedure outperforms all the baselines (term frequency, Doc2Vec, autoencoder LSTM, etc.) and reaches 0.91 of both AUROC and MAP.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"14 1","pages":"1479-1485"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85570925","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

Canonical ELM: Improving the Performance of Extreme Learning Machines on Multivariate Regression Tasks Using Canonical Correlations 典型ELM:利用典型相关提高极限学习机在多元回归任务上的性能

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00116

B. O. Odelowo, David V. Anderson

引用次数: 1

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

Analysis of Memory Capacity for Deep Echo State Networks 深回声状态网络的存储容量分析

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00072

Xuanlin Liu, Mingzhe Chen, Changchuan Yin, W. Saad

{"title":"Analysis of Memory Capacity for Deep Echo State Networks","authors":"Xuanlin Liu, Mingzhe Chen, Changchuan Yin, W. Saad","doi":"10.1109/ICMLA.2018.00072","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00072","url":null,"abstract":"In this paper, the echo state network (ESN) memory capacity, which represents the amount of input data an ESN can store, is analyzed for a new type of deep ESNs. In particular, two deep ESN architectures are studied. First, a parallel deep ESN is proposed in which multiple reservoirs are connected in parallel allowing them to average outputs of multiple ESNs, thus decreasing the prediction error. Then, a series architecture ESN is proposed in which ESN reservoirs are placed in cascade that the output of each ESN is the input of the next ESN in the series. This series ESN architecture can capture more features between the input sequence and the output sequence thus improving the overall prediction accuracy. Fundamental analysis shows that the memory capacity of parallel ESNs is equivalent to that of a traditional shallow ESN, while the memory capacity of series ESNs is smaller than that of a traditional shallow ESN. In terms of normalized root mean square error, simulation results show that the parallel deep ESN achieves 38.5% reduction compared to the traditional shallow ESN while the series deep ESN achieves 16.8% reduction.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"40 1","pages":"443-448"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90783690","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

Network Traffic Prediction Using Recurrent Neural Networks 基于递归神经网络的网络流量预测

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00035

Nipun Ramakrishnan, Tarun Soni

{"title":"Network Traffic Prediction Using Recurrent Neural Networks","authors":"Nipun Ramakrishnan, Tarun Soni","doi":"10.1109/ICMLA.2018.00035","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00035","url":null,"abstract":"The network traffic prediction problem involves predicting characteristics of future network traffic from observations of past traffic. Network traffic prediction has a variety of applications including network monitoring, resource management, and threat detection. In this paper, we propose several Recurrent Neural Network (RNN) architectures (the standard RNN, Long Short Term Memory (LSTM) networks, and Gated Recurrent Units (GRU)) to solve the network traffic prediction problem. We analyze the performance of these models on three important problems in network traffic prediction: volume prediction, packet protocol prediction, and packet distribution prediction. We achieve state of the art results on the volume prediction problem on public datasets such as the GEANT and Abilene networks. We also believe this is the first work in the domain of protocol prediction and packet distribution prediction using RNN architectures. In this paper, we show that RNN architectures demonstrate promising results in all three of these domains in network traffic prediction, outperforming standard statistical forecasting models significantly.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"852 ","pages":"187-193"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91464337","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: 75

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