2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第9页

Pilot Skill Level and Workload Prediction for Sliding-Scale Autonomy 滑动尺度自治的飞行员技能水平和工作量预测

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00188

Sai K. R. Nittala, Colin P. Elkin, J. M. Kiker, R. Meyer, James Curro, Ali K. Reiter, Kevin S. Xu, V. Devabhaktuni

{"title":"Pilot Skill Level and Workload Prediction for Sliding-Scale Autonomy","authors":"Sai K. R. Nittala, Colin P. Elkin, J. M. Kiker, R. Meyer, James Curro, Ali K. Reiter, Kevin S. Xu, V. Devabhaktuni","doi":"10.1109/ICMLA.2018.00188","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00188","url":null,"abstract":"An emerging topic in human-computer interaction research involves optimal collaboration between humans and machines to achieve a particular goal. One approach to such a goal involves sliding-scale autonomy, in which a machine dynamically adjusts between different levels of autonomy based on a variety of measurements. In this paper, we propose a system to predict skill level and workload for aircraft pilots using machine learning algorithms. Our proposed system uses the pilot's heart rate variability and flight control data, including pilot inputs such as throttle and aileron, and flight sensor data such as latitude and longitude. We conduct a user study on 15 pilots, each flying the same 5 pre-defined routes on a flight simulator. Our results indicate that the flight control data alone are sufficient to provide a near-perfect classification of a pilot's skill level into expert or novice. On the other hand, predicting mental workload is much more difficult, and a combination of flight control and heart rate data is required to obtain an accurate estimate of mental workload. Our findings provide the first step towards a sliding-scale autonomous system for aviation.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"13 1","pages":"1166-1173"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83358571","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: 9

Density-Based Fuzzy C-Means Multi-center Re-clustering Radar Signal Sorting Algorithm 基于密度的模糊c均值多中心重聚类雷达信号分选算法

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00144

Sheng Cao, Shucheng Wang, Yan Zhang

引用次数: 5

A Comparative Evaluation of Machine Learning Methods for Robot Navigation Through Human Crowds 机器人在人群中导航的机器学习方法比较评价

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00089

Anastasia Gaydashenko, D. Kudenko, A. Shpilman

引用次数: 7

Machine Learning for Classification of Inhibitors of Hepatic Drug Transporters 肝脏药物转运体抑制剂分类的机器学习

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00034

Natalia Khuri, Shantanu Deshmukh

{"title":"Machine Learning for Classification of Inhibitors of Hepatic Drug Transporters","authors":"Natalia Khuri, Shantanu Deshmukh","doi":"10.1109/ICMLA.2018.00034","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00034","url":null,"abstract":"Interactions between drugs may occur when drugs are administered together. These interactions can increase or decrease the efficacy of one of the drugs or can cause a new therapeutic effect which cannot be attributed to either drug alone. An important mechanism underlying drug-drug interactions is inhibition of proteins that mediate transport of drugs across cellular membranes. We developed five machine learning models, including deep learning, for predicting which drugs may inhibit transporter proteins in the liver, and assessed their performance in internal and external validation. Three out of five methods, k-nearest Neighbors, Support Vector Machines, and Recursive Neural Networks have not been previously applied in this domain. The area under the Receiver Operating Curve statistic for the five models ranged between 67% and 78%. Random forest and Support Vector Machines models showed the highest performance in external validation as assessed by the F1 metric. Our modeling approach and results demonstrate a practical application of machine learning techniques in an important application domain.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"61 1","pages":"181-186"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73397243","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

Deep Reinforcement Learning for Fairness in Distributed Robotic Multi-type Resource Allocation 分布式机器人多类型资源分配公平性的深度强化学习

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00075

Qinyun Zhu, J. Oh

{"title":"Deep Reinforcement Learning for Fairness in Distributed Robotic Multi-type Resource Allocation","authors":"Qinyun Zhu, J. Oh","doi":"10.1109/ICMLA.2018.00075","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00075","url":null,"abstract":"As autonomous robots are becoming a reality, we discover new challenges in coordination among these robots. We present a unique new problem that each robot makes decisions in achieving tasks that require multiple robots with different capabilities. Fair resource allocation is essential to ensure that all resource requesters acquire adequate robot resources and accomplish their tasks. We propose solutions to the fairness problem in multi-type resource allocation for multi-robot systems that have multiple resource requesters requiring heterogeneous robots with different capabilities to accomplish tasks. In particular, this work focuses on systems of single-tasking robots with multi-robot tasks (STR-MRT). In STR-MRT, the capability of a robot is the resource for accomplishing a specific task. In this problem, 1) each robot can perform only one task at a time, 2) tasks are divisible, and 3) accomplishing each task requires resources from one or more robots. We model the decentralized resource allocation in STR-MRT as a coordination game between the robots. Each robot strategically selects one resource requester. Then a consensus-based algorithm conducts formation of a robotic team for each task. We leverage the Dominant Resource Fairness (DRF) and Deep Q-learning Network (DQN) to support requester selection. The results suggest that the DQN outperforms the common Q-learning.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"387 1","pages":"460-466"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79566588","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

Sonar-to-Satellite Translation using Deep Learning 使用深度学习的声纳到卫星翻译

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00074

G. G. Giacomo, M. Santos, Paulo L. J. Drews-Jr, S. Botelho

引用次数: 3

A Persona-Based Multi-turn Conversation Model in an Adversarial Learning Framework 对抗性学习框架中基于角色的多回合会话模型

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00079

O. Olabiyi, Anish Khazane, Erik T. Mueller

引用次数: 9

Neural Adaptive Controller Applied to a VTOL Plant Using Takagi-Sugeno Fuzzy Model 基于Takagi-Sugeno模糊模型的垂直起降装置神经自适应控制器

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00186

Andres Morocho-Caiza, J. Rodríguez-Flores, J. Hernández-Ambato

引用次数: 1

A Pipeline for Optimizing F1-Measure in Multi-label Text Classification 多标签文本分类中一种优化f1测度的管道

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00148

Bingyu Wang, Cheng Li, Virgil Pavlu, J. Aslam

{"title":"A Pipeline for Optimizing F1-Measure in Multi-label Text Classification","authors":"Bingyu Wang, Cheng Li, Virgil Pavlu, J. Aslam","doi":"10.1109/ICMLA.2018.00148","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00148","url":null,"abstract":"Multi-label text classification is the machine learning task wherein each document is tagged with multiple labels, and this task is uniquely challenging due to high dimensional features and correlated labels. Such text classifiers need to be regularized to prevent severe over-fitting in the high dimensional space, and they also need to take into account label dependencies in order to make accurate predictions under uncertainty. Many classic multi-label learning algorithms focus on incorporating label dependencies in the model training phase and optimize for the strict set-accuracy measure. We propose a new pipeline which takes such algorithms and improves their F1-performance with careful training regularization and a new prediction strategy based on support inference, calibration and GFM, to the point that classic multi-label models are able to outperform recent sophisticated methods (PDsparse, SPEN) and models (LSF, CFT, CLEMS) designed specifically to be multi-label F-optimal. Beyond performance and practical contributions, we further demonstrate that support inference acts as a strong regularizer on the label prediction structure.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"913-918"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86240434","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

Trademark Design Code Identification Using Deep Neural Networks 利用深度神经网络识别商标设计代码

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00017

Girish Showkatramani, Nidhi Khatri, Arlene Landicho, Darwin Layog

{"title":"Trademark Design Code Identification Using Deep Neural Networks","authors":"Girish Showkatramani, Nidhi Khatri, Arlene Landicho, Darwin Layog","doi":"10.1109/ICMLA.2018.00017","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00017","url":null,"abstract":"Trademark review and approval is a complex process that involves thorough analysis and review of the design components of the marks including the visual characteristics as well as the textual mark description data specifying the significant aspects of the mark. One of the crucial aspect in review of the trademark application is determining the design codes of the trademarks based on their mark description. Currently, the process of identifying the design codes for a trademark is performed manually in the United States Patent and Trademark Office (USPTO) and takes substantial amount of time. Recently, word embeddings and deep neural networks (DNNs) have demonstrated excellent performance in computer vision and various natural language processing (NLP) tasks such as machine translation, speech recognition, sentence and document classification etc. to name a few. In this study, we explored fastText and different neural networks such as Convolution Neural Networks (CNN), Long Short Term Memory (LSTM), bidirectional versions of both LSTM and Gated Recurrent Unit (GRU) and Recurrent Convolutional Neural Network (RCNN) to automate trademark design code classification based on their mark description. Overall, it was found that the trademark word embeddings with RCNN model outperformed other models. Our study thereby seeks to provide a solution towards the time intensive and laborious process of identifying design codes of the trademarks.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"2 1","pages":"61-65"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86515380","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