2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第6页

Computable Expert Knowledge in Computer Games 计算机游戏中的可计算专家知识

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-69

K. Fujii, F. Hsieh, Cho-Jui Hsieh

引用次数: 0

Cybersecurity Automated Information Extraction Techniques: Drawbacks of Current Methods, and Enhanced Extractors 网络安全自动信息提取技术:现有方法的缺点，以及增强的提取器

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-122

R. A. Bridges, Kelly M. T. Huffer, Corinne L. Jones, Michael D. Iannacone, J. Goodall

{"title":"Cybersecurity Automated Information Extraction Techniques: Drawbacks of Current Methods, and Enhanced Extractors","authors":"R. A. Bridges, Kelly M. T. Huffer, Corinne L. Jones, Michael D. Iannacone, J. Goodall","doi":"10.1109/ICMLA.2017.0-122","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-122","url":null,"abstract":"We address a crucial element of applied information extraction—accurate identification of basic security entities in text-—by evaluating previous methods and presenting new labelers. Our survey reveals that the previous efforts have not been tested on documents similar to the targeted sources (news articles, blogs, tweets, etc.) and that no sufficiently large publicly available annotated corpus of these documents exists. By assembling a representative test corpus, we perform a quantitative evaluation of previous methods in a realistic setting, revealing an overall lack of recall, and giving insight to the models' beneficial and inhibiting elements. In particular, our results show that many previous efforts overfit to the non-representative test corpora in this domain. Informed by this evaluation, we present three novel cyber entity extractors, which seek to leverage the available labeled data but remain worthwhile on the more diverse documents encountered in the wild. Each new model increases the state of the art in recall, with maximal or near maximal F1 score. Our results establish that the state of the art in cyber entity tagging is characterized by F1 = 0.61.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"126 1","pages":"437-442"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73727840","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: 17

A Novel Application of Naive Bayes Classifier in Photovoltaic Energy Prediction 朴素贝叶斯分类器在光伏能量预测中的新应用

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-108

R. Bayindir, M. Yesilbudak, Medine Colak, N. Genç

引用次数: 29

Anomaly Detection in Earth Dam and Levee Passive Seismic Data Using Multivariate Gaussian 基于多元高斯分布的土坝、堤被动地震数据异常检测

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-81

W. Fisher, B. Jackson, T. Camp, V. Krzhizhanovskaya

引用次数: 3

Detection of Exfiltration and Tunneling over DNS DNS的泄漏和隧道检测

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-71

Anirban Das, Min-Yi Shen, M. Shashanka, Jisheng Wang

引用次数: 40

Performance and Security Strength Trade-Off in Machine Learning Based Biometric Authentication Systems 基于机器学习的生物识别认证系统的性能和安全强度权衡

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-12

Koosha Sadeghi, Ayan Banerjee, Javad Sohankar, S. Gupta

引用次数: 6

Predicting Waiting Times in Radiation Oncology Using Machine Learning 利用机器学习预测放射肿瘤学的等待时间

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-16

Akash Joseph, T. Hijal, J. Kildea, L. Hendren, D. Herrera

{"title":"Predicting Waiting Times in Radiation Oncology Using Machine Learning","authors":"Akash Joseph, T. Hijal, J. Kildea, L. Hendren, D. Herrera","doi":"10.1109/ICMLA.2017.00-16","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00-16","url":null,"abstract":"We describe a method for predicting waiting times in radiation oncology using machine learning. The patient waiting experience remains one of the most vexing challenges facing healthcare. At our comprehensive cancer centre, waiting periods that arise throughout a patient’s course of treatment are generally difficult for staff to predict and only rough estimates are typically provided based on personal experience. To the patient, waiting times feel long and are seemingly unpredictable. Delays for treatment at our centre depend on the durations of preceding patients scheduled in the queue. To that end, we have incorporated the treatment records of all previously-treated patients into a machine learning framework in order to predict treatment durations to infer an overall waiting time. We found that the Random Forest Regression model provides the best predictions for daily fractionated radiotherapy treatment durations. Using this model, we achieved a median residual (actual minus predicted duration) of 0.25 minutes and a standard deviation residual of 6.1 minutes to retrospective treatment data. Waiting times are derived by summing the predicted durations. The main features that generated the best fit model (from most to least significant) are: Allocated appointment time, radiotherapy fraction number, median past duration of treatments, the number of treatment fields, and previous treatment duration.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"51 1","pages":"1024-1029"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72829418","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: 12

Component Based Architecture for the Control of Crossing Regions in Railway Networks 基于组件的铁路网络跨区控制体系结构

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-105

F. Ahmad, A. Sadiq, A. Enríquez, A. Muhammad, M. Anwar, Usama Ujaz Bajwa, M. Naseer, S. Khan

{"title":"Component Based Architecture for the Control of Crossing Regions in Railway Networks","authors":"F. Ahmad, A. Sadiq, A. Enríquez, A. Muhammad, M. Anwar, Usama Ujaz Bajwa, M. Naseer, S. Khan","doi":"10.1109/ICMLA.2017.0-105","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-105","url":null,"abstract":"The research work in this paper discusses an improved Petri net model of railway crossing regions which are important and critical components of a railway networks. A control algorithm has been developed showing the interaction of the controller to other component of the system. A formal approach viz. Petri net (PN) is applied to model the safety requirement of trains along the crossing regions in railway networks. For the modeling, the component based modeling and the state-oriented modeling approaches have been integrated. First the track components and the control component are identified. The interaction of identified components, satisfying the safety requirements, is also defined in the high level architecture. Finally, state-oriented modeling approach has been adopted to design the detailed model of crossing region. Further, this paper uses the coverability tree to verify the specifications of crossing regions. By taking the crossing point as center, a circular region is introduced for safety purpose. Further, safety properties have been defined in term of place-invariants which have been verified by the state-space analysis.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"25 1","pages":"540-545"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87392926","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

UoI-NMF Cluster: A Robust Nonnegative Matrix Factorization Algorithm for Improved Parts-Based Decomposition and Reconstruction of Noisy Data UoI-NMF聚类:一种改进的基于部件的噪声数据分解与重构鲁棒非负矩阵分解算法

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-152

Shashanka Ubaru, Kesheng Wu, K. Bouchard

{"title":"UoI-NMF Cluster: A Robust Nonnegative Matrix Factorization Algorithm for Improved Parts-Based Decomposition and Reconstruction of Noisy Data","authors":"Shashanka Ubaru, Kesheng Wu, K. Bouchard","doi":"10.1109/ICMLA.2017.0-152","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-152","url":null,"abstract":"With the ever growing collection of large volumes of scientific data, development of interpretable machine learning tools to analyze such data is becoming more important. However, robust, interpretable machine learning tools are lacking, threatening extraction of scientific insight and discovery. Nonnegative Matrix Factorization (NMF) algorithms decompose an m × n nonnegative data matrix A into a k × n basis matrix H and an m × k weight matrix W, such that A ≈ WH, where k is the desired rank. In this paper, we present a novel two stage algorithm, UoI-NMF_cluster for NMF, which is based on three innovations: (i) completely separate bases learning from weight estimation, (ii) learn bases by clustering NMF results across bootstrap resamples of the data, and (iii) use the recently introduced Union of Intersections (UoI) framework to estimate ultra-sparse weights that maximize data reconstruction accuracy. We deploy our algorithm on various synthetic and scientific data to illustrate its performance, with a focus on neuroscience data. Compared to other NMF algorithms, UoI-NMF_cluster yields: a) more accurate parts-based decompositions of noisy data, b) a sparse and accurate weight matrix, and c) high accuracy reconstructions of the de-noised data. Together, these improvements enhance the performance and interpretability of NMF application to noisy data, and suggest similar approaches may benefit other matrix decomposition algorithms.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"6 1","pages":"241-248"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82272011","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

A Machine Learning Approach to Detecting Sensor Data Modification Intrusions in WBANs 基于机器学习的传感器数据修改入侵检测方法

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-163

A. Verner, Dany Butvinik

引用次数: 12