2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第5页

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

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

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

Mitigating IoT-based Cyberattacks on the Smart Grid 缓解基于物联网的智能电网网络攻击

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-109

Y. Yilmaz, S. Uludag

引用次数: 15

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

Multidisciplinary Optimization in Decentralized Reinforcement Learning 分散强化学习中的多学科优化

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-63

T. Nguyen, S. Mukhopadhyay

引用次数: 0

Recognition of Acoustic Events Using Masked Conditional Neural Networks 基于掩模条件神经网络的声事件识别

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-158

Fady Medhat, D. Chesmore, John A. Robinson

{"title":"Recognition of Acoustic Events Using Masked Conditional Neural Networks","authors":"Fady Medhat, D. Chesmore, John A. Robinson","doi":"10.1109/ICMLA.2017.0-158","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-158","url":null,"abstract":"Automatic feature extraction using neural networks has accomplished remarkable success for images, but for sound recognition, these models are usually modified to fit the nature of the multi-dimensional temporal representation of the audio signal in spectrograms. This may not efficiently harness the time-frequency representation of the signal. The ConditionaL Neural Network (CLNN) takes into consideration the interrelation between the temporal frames, and the Masked ConditionaL Neural Network (MCLNN) extends upon the CLNN by forcing a systematic sparseness over the network’s weights using a binary mask. The masking allows the network to learn about frequency bands rather than bins, mimicking a filterbank used in signal transformations such as MFCC. Additionally, the Mask is designed to consider various combinations of features, which automates the feature hand-crafting process. We applied the MCLNN for the Environmental Sound Recognition problem using the Urbansound8k, YorNoise, ESC-10 and ESC-50 datasets. The MCLNN have achieved competitive performance compared to state-of-the-art Convolutional Neural Networks and hand-crafted attempts.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"43 1","pages":"199-206"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80555502","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: 5

Application of Decision Trees for Detection of Student Dropout Profiles 决策树在学生辍学档案检测中的应用

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-107

R. T. Pereira, Javier Caicedo Zambrano

引用次数: 16

Modelling of Fuzzy Logic Controller of a Maximum Power Point Tracker Based on Artificial Neural Network 基于人工神经网络的最大功率跟踪器模糊控制器建模

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-114

R. Benkercha, S. Moulahoum, I. Colak

{"title":"Modelling of Fuzzy Logic Controller of a Maximum Power Point Tracker Based on Artificial Neural Network","authors":"R. Benkercha, S. Moulahoum, I. Colak","doi":"10.1109/ICMLA.2017.0-114","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-114","url":null,"abstract":"The Grid Connected Photovoltaic System (GCPV) has become more used system in renewable energy. Several researches have been carried out to improve the efficiency and the decrease of energy losses. One of the important components used to increase the efficiency is the DC/DC boost converter. In this paper, a new hybrid model is proposed to control the DC/DC converter, this new controller is built on the fuzzy logic controller (FLC) and artificial neural network (ANN). The pathway taken to build the model is divided into three steps, the first step is to generate a data based on the FLC, the next step is to choose an ANN structure for modeling the FLC and the last step is the test and the validation of the obtained model. The phase of building an ANN is achieved by supervised learning based on back-propagation algorithm. This algorithm is used to train the ANN model by searching of the optimal weights and thresholds that has been a minimal root mean square error between the FLC output and the ANN model. The validation test was performed with various irradiation values between the both intelligent controllers and classical P&O algorithm simultaneously.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"68 1","pages":"485-492"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78644867","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