2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第9页

Robust Modeling of Continuous 4-D Affective Space from EEG Recording 基于脑电记录的连续四维情感空间鲁棒建模

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0188

Rakib Al-Fahad, M. Yeasin

{"title":"Robust Modeling of Continuous 4-D Affective Space from EEG Recording","authors":"Rakib Al-Fahad, M. Yeasin","doi":"10.1109/ICMLA.2016.0188","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0188","url":null,"abstract":"The inherent intangible nature, complexity, context-specific interpretations of emotions make it difficult to quantify and model affective space. Dimensional theory is one of the effective methods to describe and model emotions. Despite recent advances in affective computing, modeling continuous affective space remains a challenge. Here, we present a computational framework to study the role of functional areas of brain and band frequencies in modeling 4-D continuous affective space (Valence, Arousal, Like and Dominance). In particular, we used Electroencephalogram (EEG) recordings and adopted a recursive feature elimination (RFE) approach to select band frequencies and electrode locations (functional areas) that are most relevant for predicting affective space. Empirical analyses on DEAP dataset [1] reveals that only a small number of locations (7-12) and certain band frequencies carry most discriminative information. Using the selected features, we modeled 4-D affective space using Support Vector Regression (SVR). Regression analysis show that Root Mean Square Error (RMSE) for Valence, Arousal, Dominance, Like are 1.40, 1.23, 1.24 and, 1.24, respectively. Besides SVR, the performance of feature fusion and ensemble classifiers were also compared to determine the robust model against technical noise and individual variations. It was observed that the prediction accuracy of the final model is up to 37% better than human judgment evaluated on same data set. Spillover effect of our approach may include design of task-specific (i.e., emotion, memory capacity) EEG headset with a minimal number of electrodes.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122033613","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: 7

An Empirical Study on Machine Learning Models for Wind Power Predictions 风电预测机器学习模型的实证研究

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0135

Yiqian Liu, Huajie Zhang

引用次数: 10

Identifying Nontechnical Power Loss via Spatial and Temporal Deep Learning 通过时空深度学习识别非技术功率损失

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0052

Rajendra Rana Bhat, R. Trevizan, Rahul Sengupta, Xiaolin Li, A. Bretas

引用次数: 58

Inferring Hearing Loss from Learned Speech Kernels 从习得语言核推断听力损失

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0014

Bonny Banerjee, Masoumeh Heidari Kapourchali, S. Najnin, L. L. Mendel, Sungmin Lee, Chhayakanta Patro, Monique Pousson

{"title":"Inferring Hearing Loss from Learned Speech Kernels","authors":"Bonny Banerjee, Masoumeh Heidari Kapourchali, S. Najnin, L. L. Mendel, Sungmin Lee, Chhayakanta Patro, Monique Pousson","doi":"10.1109/ICMLA.2016.0014","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0014","url":null,"abstract":"Does a hearing-impaired individual's speech reflect his hearing loss, and if it does, can the nature of hearing loss be inferred from his speech? To investigate these questions, at least four hours of speech data were recorded from each of 37 adult individuals, both male and female, belonging to four classes: 7 normal, and 30 severely-to-profoundly hearing impaired with high, medium or low speech intelligibility. Acoustic kernels were learned for each individual by capturing the distribution of his speech data points represented as 20 ms duration windows. These kernels were evaluated using a set of neurophysiological metrics, namely, distribution of characteristic frequencies, equal loudness contour, bandwidth and Q10 value of tuning curve. Our experimental results reveal that a hearing-impaired individual's speech does reflect his hearing loss provided his loss of hearing has considerably affected the intelligibility of his speech. For such individuals, the lack of tuning in any frequency range can be inferred from his learned speech kernels.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124927270","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

Cyberbullying Detection with a Pronunciation Based Convolutional Neural Network 基于语音卷积神经网络的网络欺凌检测

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0132

Xiang Zhang, Jonathan Tong, Nishant Vishwamitra, E. Whittaker, Joseph P. Mazer, Robin M. Kowalski, Hongxin Hu, Feng Luo, J. Macbeth, Edward C. Dillon

{"title":"Cyberbullying Detection with a Pronunciation Based Convolutional Neural Network","authors":"Xiang Zhang, Jonathan Tong, Nishant Vishwamitra, E. Whittaker, Joseph P. Mazer, Robin M. Kowalski, Hongxin Hu, Feng Luo, J. Macbeth, Edward C. Dillon","doi":"10.1109/ICMLA.2016.0132","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0132","url":null,"abstract":"Cyberbullying can have a deep and long lasting impact on its victims, who are often adolescents. Accurately detecting cyberbullying helps prevent it. However, the noise and errors in social media posts and messages make detecting cyberbullying very challenging. In this paper, we propose a novel pronunciation based convolutional neural network (PCNN) to address this challenge. Upon observing that the pronunciation of misspelled words in informal online conversations is often unchanged, we used the phoneme codes of the text as the features for a convolutional neural network. This procedure corrects spelling errors that did not alter the pronunciation, thereby alleviating the problem of noise and bullying data sparsity. To overcome class imbalance, a common problem in cyberbullying datasets, we implement three techniques that include threshold-moving, cost function adjusting, and a hybrid solution in our model. We evaluate the performance of our models using two cyberbullying datasets collected from Twitter and Formspring.me. The results of our experiment show that PCNN can achieve improved recall and precision compared to baseline convolutional neural networks.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129704547","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: 78

Short-Term Urban Rail Passenger Flow Forecasting: A Dynamic Bayesian Network Approach 城市轨道交通短期客流预测:动态贝叶斯网络方法

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0187

J. Roos, S. Bonnevay, G. Gavin

引用次数: 27

Automatic Object Detection Using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay 利用DBSCAN自动目标检测计数佛罗里达实验中的中毒蝇

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0133

Christian Bodenstein, Markus Goetz, Annika Jansen, H. Scholz, M. Riedel

引用次数: 4

A Privacy-Preserving Solution for the Bipartite Ranking Problem 二部排序问题的隐私保护解

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0067

N. Faramarzi, Erman Ayday, H. Altay Güvenir

{"title":"A Privacy-Preserving Solution for the Bipartite Ranking Problem","authors":"N. Faramarzi, Erman Ayday, H. Altay Güvenir","doi":"10.1109/ICMLA.2016.0067","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0067","url":null,"abstract":"In this paper, we propose an efficient solution for the privacy-preserving of a bipartite ranking algorithm. The bipartite ranking problem can be considered as finding a function that ranks positive instances (in a dataset) higher than the negative ones. However, one common concern for all the existing schemes is the privacy of individuals in the dataset. That is, one (e.g., a researcher) needs to access the records of all individuals in the dataset in order to run the algorithm. This privacy concern puts limitations on the use of sensitive personal data for such analysis. The RIMARC (Ranking Instances by Maximizing Area under the ROC Curve) algorithm solves the bipartite ranking problem by learning a model to rank instances. As part of the model, it learns weights for each feature by analyzing the area under receiver operating characteristic (ROC) curve. RIMARC algorithm is shown to be more accurate and efficient than its counterparts. Thus, we use this algorithm as a building-block and provide a privacy-preserving version of the RIMARC algorithm using homomorphic encryption and secure multi-party computation. Our proposed algorithm lets a data owner outsource the storage and processing of its encrypted dataset to a semi-trusted cloud. Then, a researcher can get the results of his/her queries (to learn the ranking function) on the dataset by interacting with the cloud. During this process, neither the researcher nor the cloud learns any information about the raw dataset. We prove the security of the proposed algorithm and show its efficiency via experiments on real data.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127901713","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

Detecting Smooth Cluster Changes in Evolving Graphs 进化图中平滑聚类变化的检测

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0066

Sohei Okui, Kaho Osamura, Akihiro Inokuchi

引用次数: 1

Demographic Group Prediction Based on Smart Device User Recognition Gestures 基于智能设备用户识别手势的人口统计群体预测

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0025

Adel R. Alharbi, M. Thornton

{"title":"Demographic Group Prediction Based on Smart Device User Recognition Gestures","authors":"Adel R. Alharbi, M. Thornton","doi":"10.1109/ICMLA.2016.0025","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0025","url":null,"abstract":"We propose a novel demographic group prediction mechanism for smart device users based upon the recognition of user gestures. The core idea of our proposed approach is to utilize data from a variety of the internal environmental sensors in the device to predict useful demographics information. In order to achieve this objective, an application with several intuitive user interfaces was implemented and used to capture user data. The results presented here are based upon the data from fifty users. These captured data are integrated or fused, pre-processed, analyzed, and used as training data for a supervised machine learning predictive approach. The data reduction methods are based upon principal component analysis (PCA) and linear discriminant analysis (LDA). PCA/LDA were implemented to reduce the data feature dimensions and to improve the k-nearest neighbors (KNN) supervised classification predictions. The results of our experiment indicate that high accuracy is achieved from this method. To the best of our knowledge, this is the first research that uses user recognition gestures to predict multiple demographic groups.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123241604","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