2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第5页

LE-CapsNet: A Light and Enhanced Capsule Network LE-CapsNet:一个轻型和增强胶囊网络

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00280

Pouya Shiri, A. Baniasadi

引用次数: 1

Detecting Offensive Content on Twitter During Proud Boys Riots 在骄傲男孩骚乱期间检测Twitter上的攻击性内容

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00253

M. Fahim, S. Gokhale

{"title":"Detecting Offensive Content on Twitter During Proud Boys Riots","authors":"M. Fahim, S. Gokhale","doi":"10.1109/ICMLA52953.2021.00253","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00253","url":null,"abstract":"Hateful and offensive speech on online social media platforms has seen a rise in the recent years. Often used to convey humor through sarcasm or to emphasize a point, offensive speech may also be employed to insult, deride and mock alternate points of view. In turbulent and chaotic circumstances, insults and mockery can lead to violence and unrest, and hence, such speech must be identified and tagged to limit its damage. This paper presents an application of machine learning to detect hateful and offensive content from Twitter feeds shared after the protests by Proud Boys, an extremist, ideological and violent hate group. A comprehensive coding guide, consolidating definitions of what constitutes offensive content based on the potential to trigger and incite people is developed and used to label the tweets. Linguistic, auxiliary and social features extracted from these labeled tweets were used to train machine learning classifiers, which detect offensive content with an accuracy of about 92%. An analysis of the importance scores reveals that offensiveness is pre-dominantly a function of words and their combinations, rather than meta features such as punctuations and quotes. This observation can form the foundation of pre-trained classifiers that can be deployed to automatically detect offensive speech in new and unforeseen circumstances.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"44 3","pages":"1582-1587"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72631341","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

Deep Learning Methods for the Prediction of Information Display Type Using Eye Tracking Sequences 基于眼动追踪序列的信息显示类型预测的深度学习方法

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00100

Yuehan Yin, Yahya Alqahtani, Jinjuan Feng, J. Chakraborty, M. P. McGuire

引用次数: 0

Predicting YOLO Misdetection by Learning Grid Cell Consensus 学习网格单元一致性预测YOLO误检

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00107

B. Paudel, Danushka Senarathna, Haibo Wang, S. Tragoudas, Yao Hu, Shengbing Jiang

引用次数: 2

PROV-GEM: Automated Provenance Analysis Framework using Graph Embeddings gem:使用图嵌入的自动化来源分析框架

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00273

Maya Kapoor, Joshua Melton, Michael Ridenhour, S. Krishnan, Thomas Moyer

{"title":"PROV-GEM: Automated Provenance Analysis Framework using Graph Embeddings","authors":"Maya Kapoor, Joshua Melton, Michael Ridenhour, S. Krishnan, Thomas Moyer","doi":"10.1109/ICMLA52953.2021.00273","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00273","url":null,"abstract":"Data provenance graphs, detailed traces of system behavior, are a popular construct to analyze and forecast malicious cyber activity like advanced persistent threats (APT). A critical limitation of existing analysis techniques is the lack of an automated analytic framework to predict APTs. In this work, we address that limitation by augmenting efficient capture and storage mechanisms to include automated analysis. Specifically, we propose PROV-GEM, a deep graph learning framework to identify malicious anomalous behavior from provenance data. Since data provenance graphs are complex datasets often expressed as heterogeneous attributed multiplex networks, we use a unified relation-aware embedding framework to capture the necessary contexts and associated interactions between the various entities manifest in the data. Furthermore, provenance graphs by nature are rich detailed structures that are heavily attributed compared to other complex systems that have been used traditionally in graph machine learning applications. Towards that end, our framework uniquely captures “multi-embeddings” that can represent varied contexts of nodes and their multi-faceted nature. We demonstrate the efficacy of our embeddings by applying PROV-GEM to two publicly available APT provenance graph datasets from StreamSpot and Unicorn. PROV-GEM achieves strong performance on both datasets with a 99% accuracy and 97% F1-score on the StreamSpot dataset, and a 97% accuracy and 89% F1-score on the Unicorn dataset, equaling or outperforming comparable state-of-the-art APT threat detection models. Unlike other frameworks, PROV-GEM utilizes an efficient graph convolutional approach coupled with relational self-attention to generate rich graph embeddings that capture the complex topology of data provenance graphs, providing an effective automated analytic framework for APT detection.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"115 1","pages":"1720-1727"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77194918","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

Kernel ridge reconstruction for anomaly detection: general and low computational reconstruction 异常检测的核脊重建:一般和低计算重建

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00036

Yasutaka Furusho, Shuhei Nitta, Y. Sakata

{"title":"Kernel ridge reconstruction for anomaly detection: general and low computational reconstruction","authors":"Yasutaka Furusho, Shuhei Nitta, Y. Sakata","doi":"10.1109/ICMLA52953.2021.00036","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00036","url":null,"abstract":"Autoencoders (AEs) have been widely used for anomaly detection because models trained to reconstruct a normal data are expected to have a higher reconstruction error for anomalous data than that for normal data, and the higher error is adopted as a criterion for identifying anomalies. However, the high capacity of AEs is sometimes able to reconstruct anomalous data even when trained only on normal data, which leads to overlooked anomalies. To remedy this problem, we propose a kernel ridge reconstruction (KRR) approach for general, high-performance, and low computational anomaly detection. KRR replaces the non-linear decoder network of the AE with a linear regressor, which uses the weighted sum of training normal data for reconstruction, and thus prevents the reconstruction of anomalous data. We also reveal the desired property of the encoder for KRR to achieve high anomaly detection performance and propose an effective training algorithm to realize such property by instance discrimination and feature decorrelation. In addition, KRR reduces the computational cost because it replaces the non-linear decoder network with a linear regressor. Our experiments on MNIST, CIFAR10, and KDDCup99 datasets prove its applicability, high performance, and low computational cost. In particular, KRR achieved an area under the curve (AUC) of 0.670 with 12 millions multiply-accumulate operations (MACs) on the CIFAR10 dataset, outperforming a recent reconstruction-based anomaly detection method (MemAE) with a 1.1-fold higher AUC and 0.291 as many MACs.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"73 1","pages":"185-190"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76213444","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

Depression Detection Using Combination of sMRI and fMRI Image Features 结合sMRI和fMRI图像特征的抑郁症检测

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00092

Marzieh Mousavian, Jianhua Chen, S. Greening

引用次数: 0

Detecting SSH and FTP Brute Force Attacks in Big Data 大数据环境下SSH、FTP暴力破解检测

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00126

John T. Hancock, T. Khoshgoftaar, Joffrey L. Leevy

引用次数: 0

GAN Based Approach for Drug Design 基于GAN的药物设计方法

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00136

Aninditha Ramesh, Anusha S. Rao, Sanjana Moudgalya, K. S. Srinivas

引用次数: 1

Batch and Online Variational Learning of Hierarchical Pitman-Yor Mixtures of Multivariate Beta Distributions 多变量Beta分布的分层Pitman-Yor混合的批处理和在线变分学习

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00053

Narges Manouchehri, N. Bouguila, Wentao Fan

引用次数: 4