2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)最新文献_第8页

Causal Discovery of Cyber Attack Phases 网络攻击阶段的因果发现

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00219

W. G. Mueller, Alex Memory, Kyle Bartrem

引用次数: 4

Stochastic Coordinate Descent for 01 Loss and Its Sensitivity to Adversarial Attacks 01损失的随机坐标下降及其对抗性攻击的敏感性

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00056

Meiyan Xie, Yunzhe Xue, Usman Roshan

{"title":"Stochastic Coordinate Descent for 01 Loss and Its Sensitivity to Adversarial Attacks","authors":"Meiyan Xie, Yunzhe Xue, Usman Roshan","doi":"10.1109/ICMLA.2019.00056","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00056","url":null,"abstract":"The 01 loss while hard to optimize is least sensitive to outliers compared to its continuous differentiable counterparts, namely hinge and logistic loss. Recently the 01 loss has been shown to be most robust compared to surrogate losses against corrupted labels which can be interpreted as adversarial attacks. Here we propose a stochastic coordinate descent heuristic for linear 01 loss classification. We implement and study our heuristic on real datasets from the UCI machine learning archive and find our method to be comparable to the support vector machine in accuracy and tractable in training time. We conjecture that the 01 loss may be harder to attack in a black box setting due to its non-continuity and infinite solution space. We train our linear classifier in a one-vs-one multi-class strategy on CIFAR10 and STL10 image benchmark datasets. In both cases we find our classifier to have the same accuracy as the linear support vector machine but more resilient to black box attacks. On CIFAR10 the linear support vector machine has 0% on adversarial examples while the 01 loss classifier hovers about 10%. On STL10 the linear support vector machine has 0% accuracy whereas 01 loss is at 10%. Our work here suggests that 01 loss may be more resilient to adversarial attacks than the hinge loss and further work is required.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117020413","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: 8

Understanding Early Childhood Obesity via Interpretation of Machine Learning Model Predictions 通过解释机器学习模型预测来理解早期儿童肥胖

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00235

Xueqin Pang, C. Forrest, F. Lê-Scherban, A. Masino

{"title":"Understanding Early Childhood Obesity via Interpretation of Machine Learning Model Predictions","authors":"Xueqin Pang, C. Forrest, F. Lê-Scherban, A. Masino","doi":"10.1109/ICMLA.2019.00235","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00235","url":null,"abstract":"Obesity, as an independent risk factor for increased morbidity and mortality throughout the lifecycle, is a major health issue in the United States. Pediatric obesity is a strong risk factor for adult obesity, as it tends to be stable and tracks into adulthood. Therefore, prevention of childhood obesity is urgently required for reduction in obesity prevalence and obesity related comorbidities. In this paper, the general pediatric obesity development pattern and the onset time period of early childhood obesity was identified via analysis of approximately 11 million pediatric clinical encounters of 860,510 unique individuals. XGBoost model was developed to predict at age 2 years if individuals would develop obesity in early childhood. The model is generalized to both males and females, and achieved an AUC of 81% (± 0.1%). Obesity associated risk factors were further analyzed via interpretation of the XGBoost model predictions. Besides known predictive factors such as weight, height, race, and ethnicity, new factors such as body temperature and respiratory rate were also identified. As body temperature and respiratory rate are related to human metabolism, novel physiologic mechanisms that cause these associations might be discovered in future research. We decomposed model recall to different age ranges when obesity incidence occurred. The model recall for individuals with obesity incidence between 24–36 months was 97.63%, while recall for obesity incidence between 72–84 months was 48.96%, suggesting obesity is less predictable further in the future. Since obesity is largely affected by evolving factors such as life style, diet, and living environment, it is possible that obesity prevention may be achieved via changes in adjustable factors.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131694589","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

Design of a Cost-Effective Deep Convolutional Neural Network–Based Scheme for Diagnosing Faults in Smart Grids 基于深度卷积神经网络的智能电网故障诊断方案设计

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00232

Hossein Hassani, Maryam Farajzadeh-Zanjani, R. Razavi-Far, M. Saif, V. Palade

{"title":"Design of a Cost-Effective Deep Convolutional Neural Network–Based Scheme for Diagnosing Faults in Smart Grids","authors":"Hossein Hassani, Maryam Farajzadeh-Zanjani, R. Razavi-Far, M. Saif, V. Palade","doi":"10.1109/ICMLA.2019.00232","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00232","url":null,"abstract":"There has been a growing interest in using smart grids due to their capability in delivering automated and distributed energy level to the consumption units. However, in order to guarantee the safe and reliable delivery of the high-quality power from the generation units to the consumers, smart grids need to be equipped with diagnostic systems. This paper presents an efficient data-driven scheme for diagnosing faults in smart grids. In order to reduce the computational burden and monitor the state of the system with a lower number of smart meters, a method based on the affinity propagation clustering algorithm is suggested for the placement of meters, that makes use of the graph-based representation of the system. The collected voltage data measurements from the installed meters are then decomposed by matching pursuit decomposition in order to generate informative features. Extracted features are then used to train a convolutional neural network, and the constructed deep learning model is then tested using unseen samples of normal and faulty conditions. Simulation results based on the IEEE 39–Bus System demonstrate the effectiveness of the proposed data-driven fault diagnostic system.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134103142","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

Acoustic Scene Classification Using Deep Mixtures of Pre-trained Convolutional Neural Networks 基于深度混合预训练卷积神经网络的声学场景分类

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00151

Truc The Nguyen, Alexander Fuchs, F. Pernkopf

{"title":"Acoustic Scene Classification Using Deep Mixtures of Pre-trained Convolutional Neural Networks","authors":"Truc The Nguyen, Alexander Fuchs, F. Pernkopf","doi":"10.1109/ICMLA.2019.00151","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00151","url":null,"abstract":"We propose a heterogeneous system of Deep Mixture of Experts (DMoEs) models using different Convolutional Neural Networks (CNNs) for acoustic scene classification (ASC). Each DMoEs module is a mixture of different parallel CNN structures weighted by a gating network. All CNNs use the same input data. The CNN architectures play the role of experts extracting a variety of features. The experts are pre-trained, and kept fixed (frozen) for the DMoEs model. The DMoEs is post-trained by optimizing weights of the gating network, which estimates the contribution of the experts in the mixture. In order to enhance the performance, we use an ensemble of three DMoEs modules each with different pairs of inputs and individual CNN models. The input pairs are spectrogram combinations of binaural audio and mono audio as well as their pre-processed variations using harmonic-percussive source separation (HPSS) and nearest neighbor filters (NNFs). The classification result of the proposed system is 72.1% improving the baseline by around 12% (absolute) on the development data of DCASE 2018 challenge task 1A.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134398568","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

Hypergraph Link Prediction: Learning Drug Interaction Networks Embeddings 超图链接预测:学习药物相互作用网络嵌入

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00299

M. Vaida, Kevin Purcell

{"title":"Hypergraph Link Prediction: Learning Drug Interaction Networks Embeddings","authors":"M. Vaida, Kevin Purcell","doi":"10.1109/ICMLA.2019.00299","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00299","url":null,"abstract":"Graph neural networks (GNNs) have revolutionized deep learning on non-Euclidean data domains, and are extensively used in fields such as social media and recommendation systems. However, complex relational data structures such as hypergraphs, pose challenges for GNNs in terms of their ability to model, embed, and learn relational complexities of multigraphs. Most GNNs focus on capturing flat local neighborhoods of a node thus failing to account for structural properties of multi-relational graphs. This paper introduces Hypergraph Link Prediction (HLP), a novel approach of encoding the multilink structure of graphs. HLP allows pooling operations to incorporate a 360 degrees overview of a node interaction profile, by learning local neighborhood and global hypergraph structure simultaneously. Global graph information is injected into node representations, such that unique global structural patterns of every node are encoded at the node level. HLP leverages the augmented hypergraph adjacency matrix to incorporate the depth of the hypergraph in the convolutional layers. The model is applied to the task of predicting multi-drug interactions, by modeling relations between pairs of drugs as a hypergraph. The existence and the type of drug interactions between the same pair of drugs are mapped as multiple edges, and can be inferred by learning the multigraph local and global structure concurrently. To account for molecular graph properties of a drug, additional drug chemical graph structural fingerprints are included as node attributes.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132927363","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: 8

Lean Training Data Generation for Planar Object Detection Models in Unsteady Logistics Contexts 非定常物流环境下平面目标检测模型的精益训练数据生成

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00062

Laura Dörr, Felix Brandt, Anne Meyer, Martin Pouls

{"title":"Lean Training Data Generation for Planar Object Detection Models in Unsteady Logistics Contexts","authors":"Laura Dörr, Felix Brandt, Anne Meyer, Martin Pouls","doi":"10.1109/ICMLA.2019.00062","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00062","url":null,"abstract":"Supervised deep learning has become the state of the art method for object detection and is used in many application areas such as autonomous driving, manufacturing industries or security systems. The acquisition of annotated data sets for the training of neural networks is highly time-consuming and error-prone. Thus, the supervised training of such object detection models is not feasible in some cases. This holds for the task of logistics transport label detection, as this use-case stands out by requiring highly specialized, quickly adapting models whilst allowing for little manual efforts in the data preparation and training process. We propose an easy training data generation method enabling the fully automated training of specialized models for the task of logistics transport label detection. For data synthesis, we stitch instances of the transport labels to be detected into background images whilst using image degradation and augmentation methods. We evaluate the employment of both use-case-specific, carefully selected background images and randomly selected real-world background images. Further, we compare two different data generation approaches: one generating realistically looking images and a simpler one making do without any manual image annotation. We examine and evaluate the introduced method on a new and publicly available example data set relevant for logistics transport label detection. We show that accurate models can be trained exclusively on synthetic training data and we compare their performance to models trained on real, manually annotated images.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133253828","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

Detection of False Data Injection Attacks in Cyber-Physical Systems using Dynamic Invariants 利用动态不变量检测网络物理系统中的虚假数据注入攻击

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00173

K. Nakayama, N. Muralidhar, Chenrui Jin, Ratnesh K. Sharma

{"title":"Detection of False Data Injection Attacks in Cyber-Physical Systems using Dynamic Invariants","authors":"K. Nakayama, N. Muralidhar, Chenrui Jin, Ratnesh K. Sharma","doi":"10.1109/ICMLA.2019.00173","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00173","url":null,"abstract":"Modern cyber-physical systems are increasingly complex and vulnerable to attacks like false data injection aimed at destabilizing and confusing the systems. We develop and evaluate an attack-detection framework aimed at learning a dynamic invariant network, data-driven temporal causal relationships between components of cyber-physical systems. We evaluate the relative performance in attack detection of the proposed model relative to traditional anomaly detection approaches. In this paper, we introduce Granger Causality based Kalman Filter with Adaptive Robust Thresholding (G-KART) as a framework for anomaly detection based on data-driven functional relationships between components in cyber-physical systems. In particular, we select power systems as a critical infrastructure with complex cyber-physical systems whose protection is an essential facet of national security. The system presented is capable of learning with or without network topology the task of detection of false data injection attacks in power systems. Kalman filters are used to learn and update the dynamic state of each component in the power system and in-turn monitor the component for malicious activity. The ego network for each node in the invariant graph is treated as an ensemble model of Kalman filters, each of which captures a subset of the node's interactions with other parts of the network. We finally also introduce an alerting mechanism to surface alerts about compromised nodes.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121255693","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

Web User Authentication Using Chosen Word Keystroke Dynamics 使用选定单词击键动力学的Web用户身份验证

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00188

K. Rahman, Deepak Neupane, Abdulrahman Zaiter, M. Hossain

引用次数: 5

Pattern and Anomaly Localization in Complex and Dynamic Data 复杂动态数据中的模式与异常定位

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00285

Sid Ryan, Roberto Corizzo, I. Kiringa, N. Japkowicz

{"title":"Pattern and Anomaly Localization in Complex and Dynamic Data","authors":"Sid Ryan, Roberto Corizzo, I. Kiringa, N. Japkowicz","doi":"10.1109/ICMLA.2019.00285","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00285","url":null,"abstract":"Following a series of deep learning breakthroughs in the area of image segmentation, multiple objects in an image input can be finely sub-categorized. Although Convolutional Neural Networks (CNNs) are known for their state-of-the-art performance in image classification, they present drawbacks when used to analyze different data types, such as time series. In this paper, we propose the Sequential Mask Convolutional Neural Network (SMCNN), a method that overcomes such drawbacks, and leverages CNNs for sequential data analysis. Our method transforms sequential data into an image representation by means of a specialized filter that produces flexible shape forms, and detects multiple types of outliers simultaneously. We evaluate the effectiveness of our method on data containing a variety of anomaly types combined with different concept drifts. The solution shows to significantly outperform prior endeavors and to provide high generalization capabilities on a wide array of data characteristics. We attribute its success to its ability to pinpoint the exact location of patterns and anomalies in parallel and to the invariance of CNNs, which allows them to adapt seamlessly to concept drifts.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128594980","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: 8