Paper Digest: Recent Papers on Anomaly Detection
Paper Digest Team extracted all recent Anomaly Detection related papers on our radar, and generated highlight sentences for them. The results are then sorted by relevance & date. In addition to this ‘static’ page, we also provide a real-time version of this article, which has more coverage and is updated in real time to include the most recent updates on this topic.
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TABLE 1: Paper Digest: Recent Papers on Anomaly Detection
| Title | Author(s) | Highlight | Category | Date | |
|---|---|---|---|---|---|
| 1 | The Building Data Genome Project 2, Energy Meter Data From The ASHRAE Great Energy Predictor III Competition | CLAYTON MILLER et. al. | The Building Data Genome Project 2, Energy Meter Data From The ASHRAE Great Energy Predictor III Competition | Scientific data | 2020-10-27 |
| 2 | On Synthetic Data Generation For Anomaly Detection In Complex Social Networks | ANDREEA SISTRUNK et. al. | This paper studies the feasibility of synthetic data generation for mission-critical applications. | CS.arxiv | 2020-10-24 |
| 3 | Model Extraction Attacks On Graph Neural Networks: Taxonomy And Realization | BANG WU et. al. | In this paper, we explore and develop model extraction attacks against GNN models. | CS.arxiv | 2020-10-23 |
| 4 | Low-rank On Graphs Plus Temporally Smooth Sparse Decomposition For Anomaly Detection In Spatiotemporal Data | SEYYID EMRE SOFUOGLU et. al. | We introduce an unsupervised tensor-based anomaly detection method that takes the sparse and temporally continuous nature of anomalies into account. | CS.arxiv | 2020-10-23 |
| 5 | Early Anomaly Detection In Time Series: A Hierarchical Approach For Predicting Critical Health Episodes | VITOR CERQUEIRA et. al. | In this paper we deal with critical health events, which represent a significant cause of mortality in intensive care units of hospitals. | STATISTICS.arxiv | 2020-10-22 |
| 6 | Anomaly Detection In A Large-scale Cloud Platform | MOHAMMAD SAIFUL ISLAM et. al. | In this paper, we share our solutions’ architecture, implementation notes, and best practices that emerged while evolving the monitoring system. | CS.arxiv | 2020-10-21 |
| 7 | MicroNets: Neural Network Architectures For Deploying TinyML Applications On Commodity Microcontrollers | COLBY BANBURY et. al. | In this paper, we observe an intriguing property of NAS search spaces for MCU model design: on average, model latency varies linearly with model operation (op) count under a uniform prior over models in the search space. | CS.arxiv | 2020-10-21 |
| 8 | Astronomaly: Personalised Active Anomaly Detection In Astronomical Data | MICHELLE LOCHNER et. al. | Here we introduce Astronomaly: a general anomaly detection framework with a novel active learning approach designed to provide personalised recommendations. | PHYSICS.arxiv | 2020-10-21 |
| 9 | Anomaly Detection For Multivariate Time Series Of Exotic Supernovae | V. ASHLEY VILLAR et. al. | Here we present an unsupervised method to search for anomalous time series in real time for transient, multivariate, and aperiodic signals. | PHYSICS.arxiv | 2020-10-21 |
| 10 | Early Anomaly Detection By Learning And Forecasting Behavior | TONG ZHAO et. al. | In this work, we propose Eland, a novel framework that uses behavior data augmentation for early anomaly detection. | CS.arxiv | 2020-10-20 |
| 11 | A Federated Learning Approach To Anomaly Detection In Smart Buildings | RAED ABDEL SATER et. al. | We propose a novel privacy-by-design federated deep learning model based on a recurrent neural network architecture, and we demonstrate that it is more than twice as fast during training convergence compared to its centralized counterpart. | CS.arxiv | 2020-10-20 |
| 12 | Anomaly Detection With Variational Quantum Generative Adversarial Networks | DANIEL HERR et. al. | We introduce variational quantum-classical Wasserstein GANs to address these issues and embed this model in a classical machine learning framework for anomaly detection. | PHYSICS.arxiv | 2020-10-20 |
| 13 | Thresholds Versus Anomaly Detection For Surveillance Of Pneumonia And Influenza Mortality | TIMOTHY L WIEMKEN et. al. | Thresholds Versus Anomaly Detection For Surveillance Of Pneumonia And Influenza Mortality | Emerging infectious diseases | 2020-10-20 |
| 14 | Mitigating Sybil Attacks On Differential Privacy Based Federated Learning | YUPENG JIANG et. al. | In this work, we implement the first Sybil attacks on differential privacy based federated learning architectures and show their impacts on model convergence. | CS.arxiv | 2020-10-20 |
| 15 | Graph Fairing Convolutional Networks For Anomaly Detection | MAHSA MESGARAN et. al. | In this paper, we introduce a simple, yet very effective graph convolutional network with skip connections for semi-supervised anomaly detection. | CS.arxiv | 2020-10-20 |
| 16 | Error-Bounded Graph Anomaly Loss For GNNs | TONG ZHAO et. al. | In this work, we propose a novel loss function to train GNNs for anomaly-detectable node representations. | CS.cikm | 2020-10-19 |
| 17 | TdGraphEmbed: Temporal Dynamic Graph-Level Embedding | MORAN BELADEV et. al. | In this study, we present tdGraphEmbed, a novel temporal graph-level embedding approach that extend the random-walk based node embedding methods to globally embed both the nodes of the graph and its representation at each time step, thus creating representation of the entire graph at each step. | CS.cikm | 2020-10-19 |
| 18 | Generative Adversarial Attributed Network Anomaly Detection | ZHENXING CHEN et. al. | In this paper, we propose a generative adversarial attributed network (GAAN) anomaly detection framework. | CS.cikm | 2020-10-19 |
| 19 | Few-shot Insider Threat Detection | SHUHAN YUAN et. al. | In this work, we propose a novel framework combining the idea of self-supervised pre-training and metric-based few-shot learning to detect insiders. | CS.cikm | 2020-10-19 |
| 20 | Application Performance Anomaly Detection With LSTM On Temporal Irregularities In Logs | XAVIER BARIL et. al. | In this paper, we develop a new model for performance anomaly detection that captures temporal deviations from the nominal model, by means of a sliding window data representation. | CS.cikm | 2020-10-19 |
| 21 | ITAD: Integrative Tensor-based Anomaly Detection System For Reducing False Positives Of Satellite Systems | YOUJIN SHIN et. al. | In this work, we introduce an Integrative Tensor-based Anomaly Detection (ITAD) framework to detect anomalies in a satellite system with the goal of minimizing false positives. | CS.cikm | 2020-10-19 |
| 22 | Anomaly : Detection Of Anomalous Structure In Time Series Data | ALEX FISCH et. al. | This article describes the methods implemented whilst also highlighting their application to simulated data as well as real data examples contained in the package. | STATISTICS.arxiv | 2020-10-19 |
| 23 | Anomaly Subgraph Detection With Feature Transfer | YING SUN et. al. | In this paper, to detect anomaly in a graph with insufficient anomalous features, we propose a pioneering approach ASD-FT (Anomaly Subgraph Detection with Feature Transfer) based on a strategy of anomalous feature transfers between different layers of a multilayer graph. | CS.cikm | 2020-10-19 |
| 24 | A View-Adversarial Framework For Multi-View Network Embedding | DONGQI FU et. al. | In this paper, we propose a view-adversarial framework to generate comprehensive and robust multi-view network representations named VANE, which is based on two adversarial games. | CS.cikm | 2020-10-19 |
| 25 | OPHiForest: Order Preserving Hashing Based Isolation Forest For Robust And Scalable Anomaly Detection | HAOLONG XIANG et. al. | In this paper, we propose a novel anomaly detection method named OPHiForest with the use of the order preserving hashing based isolation forest. | CS.cikm | 2020-10-19 |
| 26 | Anomaly Detection On X-Rays Using Self-Supervised Aggregation Learning | BEHZAD BOZORGTABAR et. al. | In this paper, we present SALAD, an end-to-end deep self-supervised methodology for anomaly detection on X-Ray images. | CS.arxiv | 2020-10-19 |
| 27 | LogBug: Generating Adversarial System Logs In Real Time | JINGYU SUN et. al. | In this paper, to our best knowledge, we take the first step to propose a novel real-time black-box attack framework LogBug in which attackers slightly modify the logs to deviate the analysis result (i.e., evading the anomaly detection) without knowing the learning model and parameters of the log parser. | CS.cikm | 2020-10-19 |
| 28 | Addressing Variance Shrinkage In Variational Autoencoders Using Quantile Regression | HALEH AKRAMI et. al. | We describe an alternative approach that avoids this variance shrinkage problem by using quantile regression. | CS.arxiv | 2020-10-18 |
| 29 | EgoDetect: Visual Detection And Exploration Of Anomaly In Social Communication Network | JIANSU PU et. al. | EgoDetect: Visual Detection And Exploration Of Anomaly In Social Communication Network | Sensors (Basel, Switzerland) | 2020-10-18 |
| 30 | HIV Testing By Public Health Centers And Municipalities, And New HIV Cases During The COVID-19 Pandemic In Japan | EJIMA, K. et. al. | As sensitivity analyses, we performed the same analysis for two subgroups: men who have sex with men (MSM) and non-Japanese. | HEALTH.medrxiv | 2020-10-18 |
| 31 | On The Usage Of Generative Models For Network Anomaly Detection In Multivariate Time-Series | GASTÓN GARCÍA GONZÁLEZ et. al. | In this paper we introduce Net-GAN, a novel approach to network anomaly detection in time-series, using recurrent neural networks (RNNs) and generative adversarial networks (GAN). | CS.arxiv | 2020-10-16 |
| 32 | Detecting Technical Anomalies In High-Frequency Water-Quality Data Using Artificial Neural Networks | JAVIER RODRIGUEZ-PEREZ et. al. | Detecting Technical Anomalies In High-Frequency Water-Quality Data Using Artificial Neural Networks | Environmental science & technology | 2020-10-16 |
| 33 | A Spatiotemporal Deep Learning Approach For Unsupervised Anomaly Detection In Cloud Systems | ZILONG HE et. al. | A Spatiotemporal Deep Learning Approach For Unsupervised Anomaly Detection In Cloud Systems | IEEE transactions on neural networks and learning systems | 2020-10-16 |
| 34 | Anomaly Detection Based On Zero-Shot Outlier Synthesis And Hierarchical Feature Distillation | ADIN RAMIREZ RIVERA et. al. | Anomaly Detection Based On Zero-Shot Outlier Synthesis And Hierarchical Feature Distillation | IEEE transactions on neural networks and learning systems | 2020-10-16 |
| 35 | Unsupervised Video Anomaly Detection Via Flow-based Generative Modeling On Appearance And Motion Latent Features | MYEONGAH CHO et. al. | We propose an implicit two-path AutoEncoder (ITAE) that exploits the structure of a SlowFast network and focuses on spatial and temporal information through appearance (slow) and motion (fast) encoders, respectively. | CS.arxiv | 2020-10-15 |
| 36 | Combining Outlier Analysis Algorithms To Identify New Physics At The LHC | MELISSA VAN BEEKVELD et. al. | In this study, we compare the anomaly scores of a variety of anomaly detection techniques: an isolation forest, a Gaussian mixture model, a static autoencoder, and a $\beta$-variational autoencoder (VAE), where we define the reconstruction loss of the latter as a weighted combination of regression and classification terms. | PHYSICS.arxiv | 2020-10-15 |
| 37 | Against The Others! Detecting Moral Outrage InSocial Media Networks | WIENKE STRATHERN et. al. | In this paper we examine these challenges developing a method to detect the point of change systematically spotting on linguistic cues of tweets. | CS.arxiv | 2020-10-14 |
| 38 | Reconstruct Anomaly To Normal: Adversarial Learned And Latent Vector-constrained Autoencoder For Time-series Anomaly Detection | CHUNKAI ZHANG et. al. | In this paper, we propose RAN based on the idea of Reconstruct Anomalies to Normal and apply it for unsupervised time series anomaly detection. | CS.arxiv | 2020-10-14 |
| 39 | Securing Manufacturing Using Blockchain | ZAHRA JADIDI et. al. | In this paper, we propose an anomaly detection framework for ICSs that consists of two stages: i) blockchain-based log management where the logs of ICS devices are collected in a secure and distributed manner, and ii) multi-source anomaly detection where the blockchain logs are analysed using multi-source deep learning which in turn provides a system wide anomaly detection method. | CS.arxiv | 2020-10-14 |
| 40 | Anomaly Detection For Bivariate Signals | MARIE COTTRELL et. al. | In this paper we propose a simple and empirical approach to detect anomalies in the behavior of multivariate time series. | MATH.arxiv | 2020-10-14 |
| 41 | Understanding Deep Learning In Land Use Classification Based On Sentinel-2 Time Series | MANUEL CAMPOS-TABERNER et. al. | Understanding Deep Learning In Land Use Classification Based On Sentinel-2 Time Series | Scientific reports | 2020-10-14 |
| 42 | Scalable Changepoint And Anomaly Detection In Cross-correlated Data With An Application To Condition Monitoring | MARTIN TVETEN et. al. | Motivated by a condition monitoring application arising from subsea engineering we derive a novel, scalable approach to detecting anomalous mean structure in a subset of correlated multivariate time series. | STATISTICS.arxiv | 2020-10-14 |
| 43 | Detecting Anomalies From Video-Sequences: A Novel Descriptor | GIULIA ORRÙ et. al. | We present a novel descriptor for crowd behavior analysis and anomaly detection. | CS.arxiv | 2020-10-13 |
| 44 | Cloze Test Helps: Effective Video Anomaly Detection Via Learning To Complete Video Events | GUANG YU et. al. | Inspired by frequently-used cloze test in language study, we propose a brand-new VAD solution named Video Event Completion (VEC) to bridge gaps above: First, we propose a novel pipeline to achieve both precise and comprehensive enclosure of video activities. | CS.mm | 2020-10-12 |
| 45 | Cluster Attention Contrast For Video Anomaly Detection | ZIMING WANG et. al. | To avoid these problems, we introduce a novel contrastive representation learning task, Cluster Attention Contrast, to establish subcategories of normality as clusters. | CS.mm | 2020-10-12 |
| 46 | Global Information Guided Video Anomaly Detection | HUI LV et. al. | In this paper, we propose an end-to-end Global Information Guided (GIG) anomaly detection framework for anomaly detection using the video-level annotations (i.e., weak labels). | CS.mm | 2020-10-12 |
| 47 | PANDA — Adapting Pretrained Features For Anomaly Detection | TAL REISS et. al. | In this work, we propose two methods for combating collapse: i) a variant of early stopping that dynamically learns the stopping iteration ii) elastic regularization inspired by continual learning. | CS.arxiv | 2020-10-12 |
| 48 | Anomaly Detection With Conditional Variational Autoencoders | ADRIAN ALAN POL et. al. | In this work, we exploit the deep conditional variational autoencoder (CVAE) and we define an original loss function together with a metric that targets hierarchically structured data AD. | CS.arxiv | 2020-10-12 |
| 49 | PyAnomaly: A Pytorch-based Toolkit For Video Anomaly Detection | YUHAO CHENG et. al. | In this paper, we present a PyTorch-based video anomaly detection toolbox, namely PyAnomaly that contains high modular and extensible components, comprehensive and impartial evaluation platforms, a friendly manageable system configuration, and the abundant engineering deployment functions. | CS.mm | 2020-10-12 |
| 50 | Large Scale Hierarchical Anomaly Detection And Temporal Localization | SOUMIL KANWAL et. al. | In this paper, we propose a multiple feature-based approach for CitySCENE challenge-based anomaly detection. | CS.mm | 2020-10-12 |
| 51 | Modularized Framework With Category-Sensitive Abnormal Filter For City Anomaly Detection | JIE WU et. al. | In this paper, we propose a modularized framework to perform general and specific anomaly detection. | CS.mm | 2020-10-12 |
| 52 | Anomaly Detection In Discrete Manufacturing Systems By Pattern Relation Table Approaches | XINMIAO SUN et. al. | Anomaly Detection In Discrete Manufacturing Systems By Pattern Relation Table Approaches | Sensors (Basel, Switzerland) | 2020-10-12 |
| 53 | Enhancing Anomaly Detection In Surveillance Videos With Transfer Learning From Action Recognition | KUN LIU et. al. | In this paper, we propose to utilize transfer learning to leverage the good results from action recognition for anomaly detection in surveillance videos. | CS.mm | 2020-10-12 |
| 54 | AnomalyBench: An Open Benchmark For Explainable Anomaly Detection | VINCENT JACOB et. al. | In this report, we present AnomalyBench, the first comprehensive benchmark for explainable AD over high-dimensional (2000+) time series data. | CS.arxiv | 2020-10-10 |
| 55 | Anomaly Detection Based On Zero-Shot Outlier Synthesis And Hierarchical Feature Distillation | ADÍN RAMÍREZ RIVERA et. al. | In this paper, we propose a two-level hierarchical latent space representation that distills inliers’ feature-descriptors (through autoencoders) into more robust representations based on a variational family of distributions (through a variational autoencoder) for zero-shot anomaly generation. | CS.arxiv | 2020-10-10 |
| 56 | Unsupervised 3D Brain Anomaly Detection | JAIME SIMARRO VIANA et. al. | As such, AD systems can help physicians to determine the presence, severity, and extension of a pathology. | EESS.arxiv | 2020-10-09 |
| 57 | Algorithmic Frameworks For The Detection Of High Density Anomalies | RALPH FOORTHUIS | In order to be able to identify high-density anomalies, this study introduces several non-parametric algorithmic frameworks for unsupervised detection. | CS.arxiv | 2020-10-09 |
| 58 | Anomaly Detection Of Energy Consumption In Buildings: A Review, Current Trends And New Perspectives | YASSINE HIMEUR et. al. | In this regard, this paper is proposed to indepthly review existing frameworks of anomaly detection in power consumption and provide a critical analysis of existing solutions. | CS.arxiv | 2020-10-09 |
| 59 | Real-time Anomaly Detection With Superexperts | RAISA DZHAMTYROVA et. al. | We propose to apply the prediction with expert advice (PEA) framework to a real-time anomaly detection problem. | STATISTICS.arxiv | 2020-10-08 |
| 60 | Anomaly Detection In Large Labeled Multi-Graph Databases | HUNG T. NGUYEN et. al. | We introduce CODETECT, the first approach that addresses the anomaly detection task for graph databases with such complex nature. | CS.arxiv | 2020-10-07 |
| 61 | Deep Learning Models For Predictive Maintenance: A Survey, Comparison, Challenges And Prospect | OSCAR SERRADILLA et. al. | This work aims at facilitating this task by reviewing state-of-the-art deep learning architectures, and how they integrate with predictive maintenance stages to meet industrial companies’ requirements (i.e. anomaly detection, root cause analysis, remaining useful life estimation). | CS.arxiv | 2020-10-07 |
| 62 | Flow-based Anomaly Detection | ŁUKASZ MAZIARKA et. al. | We propose OneFlow – a flow-based one-class classifier for anomaly (outliers) detection that finds a minimal volume bounding region. | CS.arxiv | 2020-10-06 |
| 63 | A New Benchmark And Method For The Evaluation Of Chest Wall Detection In Digital Mammography | GERSON AFRICANO et. al. | A New Benchmark And Method For The Evaluation Of Chest Wall Detection In Digital Mammography | Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference | 2020-10-06 |
| 64 | GLOSS: A Tensor Decomposition Approach For Anomaly Detection In Spatiotemporal Data | SEYYID EMRE SOFUOGLU et. al. | In this paper, we introduce an unsupervised tensor based anomaly detection method for spatiotemporal urban traffic data. | EESS.arxiv | 2020-10-06 |
| 65 | Global Soil Moisture From In-situ Measurements Using Machine Learning — SoMo.ml | SUNGMIN O et. al. | Here we present a global, long-term dataset of soil moisture generated from in-situ measurements using machine learning, SoMo.ml. | PHYSICS.arxiv | 2020-10-05 |
| 66 | Deep Anomaly Detection By Residual Adaptation | LUCAS DEECKE et. al. | In this paper we propose a novel approach to deep anomaly detection based on augmenting large pretrained networks with residual corrections that adjusts them to the task of anomaly detection. | CS.arxiv | 2020-10-05 |
| 67 | Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets And Context Mining | CHONGKE WU et. al. | Many video anomaly detection approaches using deep learning methods focus on a single camera video stream with a fixed scenario. | CS.arxiv | 2020-10-05 |
| 68 | Unsupervised Region-based Anomaly Detection In Brain MRI With Adversarial Image Inpainting | BAO NGUYEN et. al. | In contrast, this paper proposes a fully automatic, unsupervised inpainting-based brain tumour segmentation system for T1-weighted MRI. | EESS.arxiv | 2020-10-05 |
| 69 | Medical Fraud And Abuse Detection System Based On Machine Learning | CONGHAI ZHANG et. al. | Medical Fraud And Abuse Detection System Based On Machine Learning | International journal of environmental research and public health | 2020-10-05 |
| 70 | Marker-Based Structural Displacement Measurement Models With Camera Movement Error Correction Using Image Matching And Anomaly Detection | JISUNG KIM et. al. | Marker-Based Structural Displacement Measurement Models With Camera Movement Error Correction Using Image Matching And Anomaly Detection | Sensors (Basel, Switzerland) | 2020-10-05 |
| 71 | TimeAutoML: Autonomous Representation Learning For Multivariate Irregularly Sampled Time Series | YANG JIAO et. al. | To this end, we propose an autonomous representation learning approach for multivariate time series (TimeAutoML) with irregular sampling rates and variable lengths. | CS.arxiv | 2020-10-04 |
| 72 | Equipment Anomaly Detection For Semiconductor Manufacturing By Exploiting Unsupervised Learning From Sensory Data | CHIEH-YU CHEN et. al. | Equipment Anomaly Detection For Semiconductor Manufacturing By Exploiting Unsupervised Learning From Sensory Data | Sensors (Basel, Switzerland) | 2020-10-02 |
| 73 | Transform-Based Multiresolution Decomposition For Degradation Detection In Cellular Networks | SERGIO FORTES et. al. | Transform-Based Multiresolution Decomposition For Degradation Detection In Cellular Networks | Sensors (Basel, Switzerland) | 2020-10-02 |
| 74 | Data-Driven Anomaly Detection Approach For Time-Series Streaming Data | MINGHU ZHANG et. al. | Data-Driven Anomaly Detection Approach For Time-Series Streaming Data | Sensors (Basel, Switzerland) | 2020-10-02 |
| 75 | The CAMELS Project: Cosmology And Astrophysics With MachinE Learning Simulations | FRANCISCO VILLAESCUSA-NAVARRO et. al. | The goal of the CAMELS project is to provide theory predictions for different observables as a function of cosmology and astrophysics, and it is the largest suite of cosmological (magneto-)hydrodynamic simulations designed to train machine learning algorithms. | PHYSICS.arxiv | 2020-10-01 |
| 76 | ResGCN: Attention-based Deep Residual Modeling For Anomaly Detection On Attributed Networks | YULONG PEI et. al. | We propose Residual Graph Convolutional Network (ResGCN), an attention-based deep residual modeling approach that can tackle these issues: modeling the attributed networks with GCN allows to capture the sparsity and nonlinearity; utilizing a deep neural network allows to directly learn residual from the input, and a residual-based attention mechanism reduces the adverse effect from anomalous nodes and prevents over-smoothing. | CS.arxiv | 2020-09-30 |
| 77 | Driver Anomaly Detection: A Dataset And Contrastive Learning Approach | OKAN KÖPÜKLÜ et. al. | For this task, we introduce a new video-based benchmark, the Driver Anomaly Detection (DAD) dataset, which contains normal driving videos together with a set of anomalous actions in its training set. | CS.arxiv | 2020-09-30 |
| 78 | A Robust Interpretable Deep Learning Classifier For Heart Anomaly Detection Without Segmentation | THEEKSHANA DISSANAYAKE et. al. | A Robust Interpretable Deep Learning Classifier For Heart Anomaly Detection Without Segmentation | IEEE journal of biomedical and health informatics | 2020-09-30 |
| 79 | A Survey On Deep Learning Techniques For Video Anomaly Detection | JESSIE JAMES P. SUAREZ et. al. | This paper provides a categorization of the different Deep Learning approaches with respect to their objectives. | CS.arxiv | 2020-09-29 |
| 80 | A Comparison Of Classical And Variational Autoencoders For Anomaly Detection | FABRIZIO PATUZZO | This paper analyzes and compares a classical and a variational autoencoder in the context of anomaly detection. | CS.arxiv | 2020-09-29 |
| 81 | Current Time Series Anomaly Detection Benchmarks Are Flawed And Are Creating The Illusion Of Progress | RENJIE WU et. al. | In this work we make a surprising claim. | CS.arxiv | 2020-09-29 |
| 82 | Privacy Preserving Anomaly Detection Based On Local Density Estimation | CHUN KAI ZHANG et. al. | Privacy Preserving Anomaly Detection Based On Local Density Estimation | Mathematical biosciences and engineering : MBE | 2020-09-29 |
| 83 | Unsupervised Clustering For Collider Physics | VINICIUS MIKUNI et. al. | We propose a new method for Unsupervised clustering in particle physics named UCluster, where information in the embedding space created by a neural network is used to categorise collision events into different clusters that share similar properties. | PHYSICS.arxiv | 2020-09-28 |
| 84 | Anomaly Detection And Sampling Cost Control Via Hierarchical GANs | CHEN ZHONG et. al. | In this work, we study anomaly detection by considering the detection of threshold crossings in a stochastic time series without the knowledge of its statistics. | CS.arxiv | 2020-09-28 |
| 85 | STAN: Synthetic Network Traffic Generation Using Autoregressive Neural Models | SHENGZHE XU et. al. | This paper presents our approach called STAN, Synthetic Network Traffic Generation using Autoregressive Neural models, to generate realistic synthetic network traffic data. | CS.arxiv | 2020-09-27 |
| 86 | Deep Autoencoding GMM-based Unsupervised Anomaly Detection In Acoustic Signals And Its Hyper-parameter Optimization | HARSH PUROHIT et. al. | In this work, we propose a new method based on a deep autoencoding Gaussian mixture model with hyper-parameter optimization (DAGMM-HO). | EESS.arxiv | 2020-09-25 |
| 87 | A Unifying Review Of Deep And Shallow Anomaly Detection | LUKAS RUFF et. al. | In this review we aim to identify the common underlying principles as well as the assumptions that are often made implicitly by various methods. | CS.arxiv | 2020-09-24 |
| 88 | Goals And Measures For Analyzing Power Consumption Data In Manufacturing Enterprises | SÖREN HENNING et. al. | In this paper, we follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes. | CS.arxiv | 2020-09-22 |
| 89 | Regularizing Attention Networks For Anomaly Detection In Visual Question Answering | DOYUP LEE et. al. | In this study, we evaluate the robustness of state-of-the-art VQA models to five different anomalies, including worst-case scenarios, the most frequent scenarios, and the current limitation of VQA models. | CS.arxiv | 2020-09-21 |
| 90 | Enhanced Conformational Sampling Method Based On Anomaly Detection Parallel Cascade Selection Molecular Dynamics: Ad-PaCS-MD | RYUHEI HARADA et. al. | Enhanced Conformational Sampling Method Based On Anomaly Detection Parallel Cascade Selection Molecular Dynamics: Ad-PaCS-MD | Journal of chemical theory and computation | 2020-09-20 |
| 91 | Unsupervised Anomaly Detection On Temporal Multiway Data | DUC NGUYEN et. al. | We focus our investigation on two-way data, in which a data matrix is observed at each time step. | CS.arxiv | 2020-09-20 |
| 92 | Out-Of-Bag Anomaly Detection | EGOR KLEVAK et. al. | In this paper, we propose a novel model-based anomaly detection method, that we call Out-of- Bag anomaly detection, which handles multi-dimensional datasets consisting of numerical and categorical features. | CS.arxiv | 2020-09-20 |
| 93 | Design Of 2D Sparse Array Transducers For Anomaly Detection In Medical Phantoms | XIAOTONG LI et. al. | Design Of 2D Sparse Array Transducers For Anomaly Detection In Medical Phantoms | Sensors (Basel, Switzerland) | 2020-09-19 |
| 94 | On Multi-Session Website Fingerprinting Over TLS Handshake | AIDA RAMEZANI et. al. | In this paper, we propose a deep learning approach to generate a multi-label classifier that can predict the websites visited by a user in a certain period. | CS.arxiv | 2020-09-19 |
| 95 | Learning Latent Representation For IoT Anomaly Detection | LY VU et. al. | Learning Latent Representation For IoT Anomaly Detection | IEEE transactions on cybernetics | 2020-09-18 |
| 96 | Building Power Consumption Datasets: Survey, Taxonomy And Future Directions | YASSINE HIMEUR et. al. | Starting from the necessity of a comprehensive review of existing databases, this work is proposed to survey, study and visualize the numerical and methodological nature of building energy consumption datasets. | CS.arxiv | 2020-09-17 |
| 97 | MStream: Fast Streaming Multi-Aspect Group Anomaly Detection | SIDDHARTH BHATIA et. al. | Our work aims to define a streaming multi-aspect data anomaly detection framework, termed MStream, which can detect unusual group anomalies as they occur, in a dynamic manner. | CS.arxiv | 2020-09-17 |
| 98 | Real-Time Streaming Anomaly Detection In Dynamic Graphs | SIDDHARTH BHATIA et. al. | In this work, we propose MIDAS, which focuses on detecting microcluster anomalies, or suddenly arriving groups of suspiciously similar edges, such as lockstep behavior, including denial of service attacks in network traffic data. | CS.arxiv | 2020-09-17 |
| 99 | A Neural Network For Image Anomaly Detection With Deep Pyramidal Representations And Dynamic Routing | PANKAJ MISHRA et. al. | A Neural Network For Image Anomaly Detection With Deep Pyramidal Representations And Dynamic Routing | International journal of neural systems | 2020-09-16 |
| 100 | TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks | ALEXANDER GEIGER et. al. | In this paper, we propose TadGAN, an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs). | CS.arxiv | 2020-09-16 |
| 101 | Anomaly And Fraud Detection In Credit Card Transactions Using The ARIMA Model | GIULIA MOSCHINI et. al. | The results show that the ARIMA model presents a better detecting power than the benchmark models. | CS.arxiv | 2020-09-16 |
| 102 | Large-Scale Intelligent Microservices | MARK HAMILTON et. al. | We introduce an Apache Spark-based micro-service orchestration framework that extends database operations to include web service primitives. | CS.arxiv | 2020-09-16 |
| 103 | Meta-AAD: Active Anomaly Detection With Deep Reinforcement Learning | DAOCHEN ZHA et. al. | In this work, we propose Active Anomaly Detection with Meta-Policy (Meta-AAD), a novel framework that learns a meta-policy for query selection. | CS.arxiv | 2020-09-15 |
| 104 | Deep Reinforcement Learning For Unknown Anomaly Detection | GUANSONG PANG et. al. | We propose here instead a deep reinforcement-learning-based approach that actively seeks novel classes of anomaly that lie beyond the scope of the labeled training data. | CS.arxiv | 2020-09-14 |
| 105 | DAICS: A Deep Learning Solution For Anomaly Detection In Industrial Control Systems | MAGED ABDELATY et. al. | This paper presents DAICS, a novel deep learning framework with a modular design to fit in large ICSs. | CS.arxiv | 2020-09-14 |
| 106 | Where Is Waldo (and His Friends)? A Comparison Of Anomaly Detection Algorithms For Time-domain Astronomy | JUAN RAFAEL MARTÍNEZ-GALARZA et. al. | In this paper, we investigate strategies to identify novel objects and to contextualize them within large time-series data sets to facilitate the discovery of new objects, new classes of objects, and the physical interpretation of their anomalous nature. | PHYSICS.arxiv | 2020-09-14 |
| 107 | Real Time Anomaly Detection And Categorisation | ALEXANDER T. M. FISCH et. al. | This work extends recently proposed post-hoc (offline) anomaly detection methodology to the sequential setting. | STATISTICS.arxiv | 2020-09-14 |
| 108 | Generator Versus Segmentor: Pseudo-healthy Synthesis | ZHANG YUNLONG et. al. | In this paper, we discuss the problems of these approaches, which are the style transfer and artifacts respectively. | CS.arxiv | 2020-09-11 |
| 109 | Machine Learning And Data Science Approach Towards Trend And Predictors Analysis Of CDC Mortality Data For The USA | YASIR NADEEM et. al. | The purpose of conducting this project was to derive conclusions based on the statistics from the provided dataset and predict label(s) of the dataset using supervised or unsupervised learning algorithms. | CS.arxiv | 2020-09-11 |
| 110 | Robust Clustering With Normal Mixture Models: A Pseudo $β$-Likelihood Approach | SOUMYA CHAKRABORTY et. al. | We propose a robust alternative to the ordinary likelihood approach for this estimation problem which performs simultaneous estimation and data clustering and leads to subsequent anomaly detection. | STATISTICS.arxiv | 2020-09-10 |
| 111 | Understanding Coarsening For Embedding Large-Scale Graphs | TAHA ATAHAN AKYILDIZ et. al. | In this work, we thoroughly analyze the impact of the coarsening quality on the embedding performance both in terms of speed and accuracy. | CS.arxiv | 2020-09-10 |
| 112 | Detection Of Behavioral Anomalies In Medication Adherence Patterns Among Patients With Serious Mental Illness Engaged With A Digital Medicine System | JONATHAN KNIGHTS et. al. | Detection Of Behavioral Anomalies In Medication Adherence Patterns Among Patients With Serious Mental Illness Engaged With A Digital Medicine System | JMIR mental health | 2020-09-10 |
| 113 | A Multitiered Solution For Anomaly Detection In Edge Computing For Smart Meters | DARMAWAN UTOMO et. al. | A Multitiered Solution For Anomaly Detection In Edge Computing For Smart Meters | Sensors (Basel, Switzerland) | 2020-09-10 |
| 114 | Detection Of Anomalies And Faults In Industrial IoT Systems By Data Mining: Study Of CHRIST Osmotron Water Purification System | MOHAMMAD SADEGH SADEGHI GARMAROODI et. al. | Given the data, we propose two anomaly detection approaches to build up our edge fault detection system. However, since we cannot collect all possible faults data, an anomaly detection approach is proposed based on normal system identification which models the system components by artificial neural networks. | CS.arxiv | 2020-09-08 |
| 115 | Label-Free Segmentation Of COVID-19 Lesions In Lung CT | QINGSONG YAO et. al. | To alleviate the burden of data annotation, we herein present a label-free approach for segmenting COVID-19 lesions in CT via pixel-level anomaly modeling that mines out the relevant knowledge from normal CT lung scans. | EESS.arxiv | 2020-09-08 |
| 116 | Unsupervised Learning Based Robust Multivariate Intrusion Detection System For Cyber-Physical Systems Using Low Rank Matrix | ANEET K. DUTTA et. al. | In this paper, we propose a robust multivariate intrusion detection system called RAD for detecting attacks in the cyber-physical systems in O(d) space and time complexity, where d is the number parameters in the system state vector. | CS.arxiv | 2020-09-07 |
| 117 | Anomaly Detection In Stationary Settings: A Permutation-Based Higher Criticism Approach | IVO V. STOEPKER et. al. | In this work we take a non-parametric stance, and propose a permutation-based variant of the higher criticism statistic not requiring knowledge of the null distribution. | STATISTICS.arxiv | 2020-09-07 |
| 118 | Anomaly Detection With Partitioning Overfitting Autoencoder Ensembles | BORIS LORBEER et. al. | In this paper, we propose POTATOES (Partitioning OverfiTting AuTOencoder EnSemble) a new type of autoencoder ensembles for unsupervised outlier detection. | CS.arxiv | 2020-09-06 |
| 119 | Deep Learning For The Analysis Of Disruption Precursors Based On Plasma Tomography | DIOGO R. FERREIRA et. al. | In this work, we focus on bolometer tomography to reconstruct the plasma radiation profile and, on top of it, we apply anomaly detection to identify the radiation patterns that precede major disruptions. | PHYSICS.arxiv | 2020-09-06 |
| 120 | PySAD: A Streaming Anomaly Detection Framework In Python | SELIM F. YILMAZ et. al. | PySAD serves various state-of-the-art methods for streaming anomaly detection. | CS.arxiv | 2020-09-05 |
| 121 | Simulation-Assisted Decorrelation For Resonant Anomaly Detection | KEES BENKENDORFER et. al. | We explore two solutions to this challenge by minimally incorporating simulation into the learning. | PHYSICS.arxiv | 2020-09-04 |
| 122 | Towards The Development Of Entropy-Based Anomaly Detection In An Astrophysics Simulation | DREW SCHMIDT et. al. | To that end, we present an anomaly problem which arises from a core-collapse supernovae simulation. | CS.arxiv | 2020-09-04 |
| 123 | Multivariate Time-series Anomaly Detection Via Graph Attention Network | HANG ZHAO et. al. | In this paper, we propose a novel self-supervised framework for multivariate time-series anomaly detection to address this issue. | CS.arxiv | 2020-09-04 |
| 124 | Real-time Monitoring Of COVID-19 Dynamics Using Automated Trend Fitting And Anomaly Detection | JOMBART, T. et. al. | We introduce ASMODEE (Automatic Selection of Models and Outlier Detection for Epidemics), a new tool for detecting sudden changes in COVID-19 incidence. | HEALTH.medrxiv | 2020-09-03 |
| 125 | Fetal Brain Age Estimation And Anomaly Detection Using Attention-based Deep Ensembles With Uncertainty | WEN SHI et. al. | Fetal Brain Age Estimation And Anomaly Detection Using Attention-based Deep Ensembles With Uncertainty | NeuroImage | 2020-09-02 |
| 126 | Building And Interpreting Deep Similarity Models | OLIVER EBERLE et. al. | Building And Interpreting Deep Similarity Models | IEEE transactions on pattern analysis and machine intelligence | 2020-09-01 |
| 127 | Chimbuko: A Workflow-Level Scalable Performance Trace Analysis Tool | SUNGSOO HA et. al. | This work introduces Chimbuko, a performance analysis framework that provides real-time, distributed, in situ anomaly detection. | CS.arxiv | 2020-08-31 |
| 128 | Connecting Web Event Forecasting With Anomaly Detection: A Case Study On Enterprise Web Applications Using Self-Supervised Neural Networks | XIAOYONG YUAN et. al. | In this paper, we present a web event forecasting approach, DeepEvent, in enterprise web applications for better anomaly detection. | CS.arxiv | 2020-08-31 |
| 129 | Anomaly Detection By Recombining Gated Unsupervised Experts | J. -P. SCHULZE et. al. | Inspired by mixture-of-experts models and the analysis of the hidden activations of neural networks, we introduce a novel unsupervised anomaly detection method called ARGUE. | CS.arxiv | 2020-08-31 |
| 130 | Multi-Scale One-Class Recurrent Neural Networks For Discrete Event Sequence Anomaly Detection | ZHIWEI WANG et. al. | To address these challenges, in this paper, we propose OC4Seq, a multi-scale one-class recurrent neural network for detecting anomalies in discrete event sequences. | CS.arxiv | 2020-08-31 |
| 131 | Blackmarket-driven Collusion On Online Media: A Survey | HRIDOY SANKAR DUTTA et. al. | We present a broad overview of the problem, definitions of the related problems and concepts, the taxonomy of the proposed approaches, description of the publicly available datasets and online tools, and discuss the outstanding issues. | CS.arxiv | 2020-08-30 |
| 132 | Deep Anomaly Detection For CNC Machine Cutting Tool Using Spindle Current Signals | GUANG LI et. al. | Deep Anomaly Detection For CNC Machine Cutting Tool Using Spindle Current Signals | Sensors (Basel, Switzerland) | 2020-08-29 |
| 133 | Real-time Discriminant Analysis In The Presence Of Label And Measurement Noise | IWEIN VRANCKX et. al. | We present a novel QDA method based on a recent real-time robust algorithm. | STATISTICS.arxiv | 2020-08-29 |
| 134 | Improved Anomaly Detection By Training An Autoencoder With Skip Connections On Images Corrupted With Stain-shaped Noise | ANNE-SOPHIE COLLIN et. al. | In the common scenario where only clean images are available for training, we propose to corrupt them with a synthetic noise model to prevent the convergence of the network towards the identity mapping, and introduce an original Stain noise model for that purpose. | EESS.arxiv | 2020-08-29 |
| 135 | Puzzle-AE: Novelty Detection In Images Through Solving Puzzles | MOHAMMADREZA SALEHI et. al. | To this end, we propose a new AE framework that is trained based on solving puzzles on randomly permuted image patches. | CS.arxiv | 2020-08-29 |
| 136 | Unsupervised Spectral Mapping And Feature Selection For Hyperspectral Anomaly Detection | WEIYING XIE et. al. | Unsupervised Spectral Mapping And Feature Selection For Hyperspectral Anomaly Detection | Neural networks : the official journal of the International Neural Network Society | 2020-08-28 |
| 137 | Online Anomaly Detection With Bandwidth Optimized Hierarchical Kernel Density Estimators | MINE KERPICCI et. al. | Online Anomaly Detection With Bandwidth Optimized Hierarchical Kernel Density Estimators | IEEE transactions on neural networks and learning systems | 2020-08-27 |
| 138 | Training Robust Anomaly Detection Using ML-Enhanced Simulations | PHILIP FELDMAN | This paper describes the use of neural networks to enhance simulations for subsequent training of anomaly-detection systems. | CS.arxiv | 2020-08-27 |
| 139 | The Impact Of Discretization Method On The Detection Of Six Types Of Anomalies In Datasets | RALPH FOORTHUIS | Numerous algorithms use discretization of numerical data in their detection processes. | CS.arxiv | 2020-08-27 |
| 140 | A Self-Reasoning Framework For Anomaly Detection Using Video-Level Labels | MUHAMMAD ZAIGHAM ZAHEER et. al. | In the current work, we propose a weakly supervised anomaly detection framework based on deep neural networks which is trained in a self-reasoning fashion using only video-level labels. | CS.arxiv | 2020-08-26 |
| 141 | Applying Surface Normal Information In Drivable Area And Road Anomaly Detection For Ground Mobile Robots | HENGLI WANG et. al. | Therefore, we develop a novel module named the Normal Inference Module (NIM), which can generate surface normal information from dense depth images with high accuracy and efficiency. | CS.arxiv | 2020-08-26 |
| 142 | Hybrid Deep Neural Networks To Infer State Models Of Black-Box Systems | MOHAMMAD JAFAR MASHHADI et. al. | To overcome the above issues, in this paper, we propose a hybrid deep neural network that accepts as input a set of time series, one per input/output signal of the system, and applies a set of convolutional and recurrent layers to learn the non-linear correlations between signals and the patterns, over time. | CS.arxiv | 2020-08-26 |
| 143 | Efficient Hierarchical Clustering For Classification And Anomaly Detection | ISHITA DOSHI et. al. | We propose a class of hierarchical clustering algorithms that can be used both for efficient and scalable real-time multiclass classification as well as in detecting new anomalies in user-generated content. | CS.arxiv | 2020-08-25 |
| 144 | Spectroscopic Observations Of The Machine-learning Selected Anomaly Catalogue From The AllWISE Sky Survey | A. SOLARZ et. al. | Designed to detect sources deviating from a training set composed of known classes, this algorithm was used to create a model for the expected data based on WISE objects with spectroscopic identifications in the Sloan Digital Sky Survey (SDSS). | PHYSICS.arxiv | 2020-08-24 |
| 145 | Multiple Network Embedding For Anomaly Detection In Time Series Of Graphs | GUODONG CHEN et. al. | This paper considers the graph signal processing problem of anomaly detection in time series of graphs. | STATISTICS.arxiv | 2020-08-23 |
| 146 | Indoor Scene Change Captioning Based On Multimodality Data | YUE QIU et. al. | Indoor Scene Change Captioning Based On Multimodality Data | Sensors (Basel, Switzerland) | 2020-08-23 |
| 147 | Automated And Model-Free Bridge Damage Indicators With Simultaneous Multiparameter Modal Anomaly Detection | THANH T X TRAN et. al. | Automated And Model-Free Bridge Damage Indicators With Simultaneous Multiparameter Modal Anomaly Detection | Sensors (Basel, Switzerland) | 2020-08-22 |
| 148 | Attention Guided Anomaly Localization In Images | SHASHANKA VENKATARAMANAN et. al. | Without the need of anomalous training images, we propose Convolutional Adversarial Variational autoencoder with Guided Attention (CAVGA), which localizes the anomaly with a convolutional latent variable to preserve the spatial information. | CS.eccv | 2020-08-21 |
| 149 | Isolation Distributional Kernel: A New Tool For Kernel Based Anomaly Detection | KAI MING TING et. al. | We introduce Isolation Distributional Kernel as a new way to measure the similarity between two distributions. | CS.kdd | 2020-08-21 |
| 150 | Neural Batch Sampling With Reinforcement Learning For Semi-Supervised Anomaly Detection | WEN-HSUAN CHU et. al. | In particular, we propose a novel semi-supervised learning algorithm for anomaly detection and segmentation using an anomaly classifier that uses as input the extit{loss profile} of a data sample processed through an autoencoder. | CS.eccv | 2020-08-21 |
| 151 | Scaling Choice Models Of Relational Social Data | JAN OVERGOOR et. al. | Given the importance of negative sampling, in this work we introduce a model simplification technique for mixed logit models that we call "de-mixing”, whereby standard mixture models of network formation—particularly models that mix local and global link formation—are reformulated to operate their modes over disjoint choice sets. | CS.kdd | 2020-08-21 |
| 152 | USAD: UnSupervised Anomaly Detection On Multivariate Time Series | JULIEN AUDIBERT et. al. | In this paper, we propose a fast and stable method called UnSupervised Anomaly Detection for multivariate time series (USAD) based on adversely trained autoencoders. | CS.kdd | 2020-08-21 |
| 153 | Automatic Incident Detection On Freeways Based On Bluetooth Traffic Monitoring | PEDRO MERCADER et. al. | Automatic Incident Detection On Freeways Based On Bluetooth Traffic Monitoring | Accident; analysis and prevention | 2020-08-21 |
| 154 | Backpropagated Gradient Representations For Anomaly Detection | GUKYEONG KWON et. al. | Hence, we propose the utilization of backpropagated gradients as representations to characterize model behavior on anomalies and, consequently, detect such anomalies. | CS.eccv | 2020-08-21 |
| 155 | Self-Attentive Classification-Based Anomaly Detection In Unstructured Logs | SASHO NEDELKOSKI et. al. | We propose Logsy, a classification-based method to learn log representations in a way to distinguish between normal data from the system of interest and anomaly samples from auxiliary log datasets, easily accessible via the internet. | CS.arxiv | 2020-08-21 |
| 156 | Fast RobustSTL: Efficient And Robust Seasonal-Trend Decomposition For Time Series With Complex Patterns | QINGSONG WEN et. al. | In this paper, we extend RobustSTL to handle multiple seasonality. | CS.kdd | 2020-08-21 |
| 157 | Encoding Structure-Texture Relation With P-Net For Anomaly Detection In Retinal Images | KANG ZHOU et. al. | Motivated by this, we propose to leverage the relation between the image texture and structure to design a deep neural network for anomaly detection. | CS.eccv | 2020-08-21 |
| 158 | CLAWS: Clustering Assisted Weakly Supervised Learning With Normalcy Suppression For Anomalous Event Detection | MUHAMMAD ZAIGHAM ZAHEER et. al. | In this work, we propose a weakly supervised anomaly detection method which has manifold contributions including 1) a random batch based training procedure to reduce inter-batch correlation, 2) a normalcy suppression mechanism to minimize anomaly scores of the normal regions of a video by taking into account the overall information available in one training batch, and 3) a clustering distance based loss to contribute towards mitigating the label noise and to produce better anomaly representations by encouraging our model to generate distinct normal and anomalous clusters. | CS.eccv | 2020-08-21 |
| 159 | Synthesize Then Compare: Detecting Failures And Anomalies For Semantic Segmentation | YINGDA XIA et. al. | In this paper, we systematically study failure and anomaly detection for semantic segmentation and propose a unified framework, consisting of two modules, to address these two related problems. | CS.eccv | 2020-08-21 |
| 160 | Clustering Driven Deep Autoencoder For Video Anomaly Detection | YUNPENG CHANG et. al. | Since the abnormal events are usually different from normal events in appearance and/or in motion behavior, we address this issue by designing a novel convolution autoencoder architecture to separately capture spatial and temporal informative representation. | CS.eccv | 2020-08-21 |
| 161 | ADIC: Anomaly Detection Integrated Circuit In 65nm CMOS Utilizing Approximate Computing | BAPI KAR et. al. | In this paper, we present a low-power anomaly detection integrated circuit (ADIC) based on a one-class classifier (OCC) neural network. | EESS.arxiv | 2020-08-21 |
| 162 | AutoKnow: Self-Driving Knowledge Collection For Products Of Thousands Of Types | XIN LUNA DONG et. al. | We describe AutoKnow, our automatic (self-driving) system that addresses these challenges. | CS.kdd | 2020-08-21 |
| 163 | Laplacian Change Point Detection For Dynamic Graphs | SHENYANG HUANG et. al. | In this paper, we focus on change point detection in dynamic graphs and address two main challenges associated with this problem: I) how to compare graph snapshots across time, II) how to capture temporal dependencies. | CS.kdd | 2020-08-21 |
| 164 | Few-Shot Scene-Adaptive Anomaly Detection | YIWEI LU et. al. | In this paper, we propose a novel few-shot scene-adaptive anomaly detection problem to address the limitations of previous approaches. | CS.eccv | 2020-08-21 |
| 165 | A Data-Driven Graph Generative Model For Temporal Interaction Networks | DAWEI ZHOU et. al. | To address these challenges, we propose an end-to-end deep generative framework named TagGen. | CS.kdd | 2020-08-21 |
| 166 | TAnoGAN: Time Series Anomaly Detection With Generative Adversarial Networks | MD ABUL BASHAR et. al. | In this paper, we propose a novel GAN-based unsupervised method called TAnoGan for detecting anomalies in time series when a small number of data points are available. | CS.arxiv | 2020-08-21 |
| 167 | Image Quality Assessment For Closed-loop Computer-assisted Lung Ultrasound | ZACHARY M C BAUM et. al. | We describe a novel, two-stage computer assistance system for lung anomaly detection using ultrasound imaging in the intensive care setting to improve operator performance and patient stratification during coronavirus pandemics. | EESS.arxiv | 2020-08-20 |
| 168 | Anomaly Detection On Seasonal Metrics Via Robust Time Series Decomposition | TIANWEI LI et. al. | In this paper, we proposed a robust anomaly detection algorithm (MEDIFF) to monitor online business metrics in real time. | STATISTICS.arxiv | 2020-08-20 |
| 169 | Localizing Anomalies From Weakly-Labeled Videos | HUI LV et. al. | In this paper, we propose a Weakly Supervised Anomaly Localization (WSAL) method focusing on temporally localizing anomalous segments within anomalous videos. | CS.arxiv | 2020-08-20 |
| 170 | Using Ensemble Classifiers To Detect Incipient Anomalies | BAIHONG JIN et. al. | We show in this paper that ensemble learning methods can give improved performance on incipient anomalies and identify common pitfalls in these models through extensive experiments on two real-world datasets. | CS.arxiv | 2020-08-19 |
| 171 | Generalizing Fault Detection Against Domain Shifts Using Stratification-Aware Cross-Validation | YINGSHUI TAN et. al. | We show in this paper that ensemble learning methods can give improved performance on incipient anomalies and identify common pitfalls in these models through extensive experiments on two real-world datasets. | CS.arxiv | 2020-08-19 |
| 172 | Transferring Complementary Operating Conditions For Anomaly Detection | GABRIEL MICHAU et. al. | In this paper, we propose a framework for the transfer of complementary operating conditions between different units, to train more robust anomaly detectors. | CS.arxiv | 2020-08-18 |
| 173 | Anomaly Detection With Convolutional Autoencoders For Fingerprint Presentation Attack Detection | JASCHA KOLBERG et. al. | To address this issue, we propose a new PAD technique based on autoencoders (AEs) trained only on bona fide samples (i.e. one-class), which are captured in the short wave infrared domain. | CS.arxiv | 2020-08-18 |
| 174 | SECODA: Segmentation- And Combination-Based Detection Of Anomalies | RALPH FOORTHUIS | This study introduces SECODA, a novel general-purpose unsupervised non-parametric anomaly detection algorithm for datasets containing continuous and categorical attributes. | CS.arxiv | 2020-08-16 |
| 175 | Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description | XIAOGANG DENG et. al. | Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description | Sensors (Basel, Switzerland) | 2020-08-16 |
| 176 | A Deep Learning Ensemble For Network Anomaly And Cyber-Attack Detection | VIBEKANANDA DUTTA et. al. | A Deep Learning Ensemble For Network Anomaly And Cyber-Attack Detection | Sensors (Basel, Switzerland) | 2020-08-15 |
| 177 | Loghub: A Large Collection Of System Log Datasets Towards Automated Log Analytics | SHILIN HE et. al. | In this paper, we summarize the statistics of these datasets, introduce some practical log usage scenarios, and present a case study on anomaly detection to demonstrate how loghub facilitates the research and practice in this field. | CS.arxiv | 2020-08-14 |
| 178 | Statistical Evaluation Of Anomaly Detectors For Sequences | ERIK SCHARWÄCHTER et. al. | In this work, we formalize a notion of precision and recall with temporal tolerance for point-based anomaly detection in sequential data. | CS.arxiv | 2020-08-13 |
| 179 | Detecting Abnormal Traffic In Large-Scale Networks | MAHMOUD SAID ELSAYED et. al. | In this paper, we provide a detailed and systematic analysis of the existing Machine Learning (ML) approaches that can tackle most of these attacks. | CS.arxiv | 2020-08-13 |
| 180 | Learning To Detect Anomalous Wireless Links In IoT Networks | GREGOR CERAR et. al. | In this paper, motivated by a real-world experimental IoT deployment, we introduce four types of wireless network anomalies that are identified at the link layer. | CS.arxiv | 2020-08-12 |
| 181 | Anomaly Localization By Modeling Perceptual Features | DAVID DEHAENE et. al. | Here, we argue that those issues can emerge from having a simplistic model of the anomaly distribution and we propose a new VAE-based model expressing a more complex anomaly model that is also closer to human perception. | CS.arxiv | 2020-08-12 |
| 182 | Rule-based Anomaly Detection For Railway Signalling Networks | MARKUS HEINRICH et. al. | We propose a rule-based anomaly detection system for railway signalling that mitigates attacks by a Dolev-Yao attacker who is able to inject control commands and to perform semantic attacks. | CS.arxiv | 2020-08-12 |
| 183 | Detection Of Abnormal Vessel Behaviours From AIS Data Using GeoTrackNet: From The Laboratory To The Ocean | DUONG NGUYEN et. al. | This paper analyses a novel neural network we have recently introduced — GeoTrackNet — regarding operational contexts. | CS.arxiv | 2020-08-12 |
| 184 | A Novel Approach For Continuous Health Status Monitoring And Automatic Detection Of Infection Incidences In People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism | ASHENAFI ZEBENE WOLDAREGAY et. al. | A Novel Approach For Continuous Health Status Monitoring And Automatic Detection Of Infection Incidences In People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism | Journal of medical Internet research | 2020-08-12 |
| 185 | Toward Detecting Infection Incidence In People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework For A Personalized Digital Infectious Disease Detection System | ASHENAFI ZEBENE WOLDAREGAY et. al. | Toward Detecting Infection Incidence In People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework For A Personalized Digital Infectious Disease Detection System | Journal of medical Internet research | 2020-08-12 |
| 186 | Exposing Deep-faked Videos By Anomalous Co-motion Pattern Detection | GENGXING WANG et. al. | In this paper, we propose an fully-interpretable video forensic method that is designed specifically to expose deep-faked videos. | CS.arxiv | 2020-08-11 |
| 187 | Unsupervised Machine Learning For Transient Discovery In Deeper, Wider, Faster Light Curves | SARA WEBB et. al. | We present an unsupervised method for transient discovery using a clustering technique and the Astronomaly package. | PHYSICS.arxiv | 2020-08-11 |
| 188 | ARCADe: A Rapid Continual Anomaly Detector | AHMED FRIKHA et. al. | We define this novel learning problem of continual anomaly detection (CAD) and formulate it as a meta-learning problem. | CS.arxiv | 2020-08-10 |
| 189 | Methods For Step Count Data: Determining Valid Days And Quantifying Fragmentation Of Walking Bouts | LISA REIDER et. al. | Methods For Step Count Data: Determining Valid Days And Quantifying Fragmentation Of Walking Bouts | Gait & posture | 2020-08-07 |
| 190 | A Causal-based Framework For Multimodal Multivariate Time Series Validation Enhanced By Unsupervised Deep Learning As An Enabler For Industry 4.0 | CEDRIC SCHOCKAERT | An advanced conceptual validation framework for multimodal multivariate time series defines a multi-level contextual anomaly detection ranging from an univariate context definition, to a multimodal abstract context representation learnt by an Autoencoder from heterogeneous data (images, time series, sounds, etc.) associated to an industrial process. | CS.arxiv | 2020-08-05 |
| 191 | Bayesian Optimization With Machine Learning Algorithms Towards Anomaly Detection | MOHAMMADNOOR INJADAT et. al. | In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique to tune the parameters of Support Vector Machine with Gaussian Kernel (SVM-RBF), Random Forest (RF), and k-Nearest Neighbor (k-NN) algorithms. | CS.arxiv | 2020-08-05 |
| 192 | Interpretable Anomaly Detection With Mondrian P{ó}lya Forests On Data Streams | CHARLIE DICKENS et. al. | Our experiments show that these methods achieves state-of-the-art performance while providing statistically interpretable anomaly scores. | CS.arxiv | 2020-08-04 |
| 193 | Forecasting Risk Of Crop Disease With Anomaly Detection Algorithms | PETER SKELSEY | Forecasting Risk Of Crop Disease With Anomaly Detection Algorithms | Phytopathology | 2020-08-04 |
| 194 | Learning Based Methods For Traffic Matrix Estimation From Link Measurements | SHENGHE XU et. al. | In this paper, we consider the TM estimation problem where we have information about the distribution of the demand sizes. | CS.arxiv | 2020-08-03 |
| 195 | Geometric Anomaly Detection In Data | BERNADETTE J STOLZ et. al. | Geometric Anomaly Detection In Data | Proceedings of the National Academy of Sciences of the United States of America | 2020-08-03 |
| 196 | Locally Masked Convolution For Autoregressive Models | AJAY JAIN et. al. | To generate data in arbitrary orders, we introduce LMConv: a simple modification to the standard 2D convolution that allows arbitrary masks to be applied to the weights at each location in the image. | CS.uai | 2020-08-03 |
| 197 | Multi-Temporal Analysis And Scaling Relations Of 100,000,000,000 Network Packets | JEREMY KEPNER et. al. | Building on prior work in interactive supercomputing and GraphBLAS hypersparse hierarchical traffic matrices, we have developed an efficient method for computing a wide variety of streaming network quantities on diverse time scales. | CS.arxiv | 2020-08-01 |
| 198 | Anomaly Detection At Scale: The Case For Deep Distributional Time Series Models | FADHEL AYED et. al. | This paper introduces a new methodology for detecting anomalies in time series data, with a primary application to monitoring the health of (micro-) services and cloud resources. | CS.arxiv | 2020-07-30 |
| 199 | On The Nature And Types Of Anomalies: A Review | RALPH FOORTHUIS | By means of an extensive literature review this study therefore offers the first theoretically principled and domain-independent typology of data anomalies, and presents a full overview of anomaly types and subtypes. | CS.arxiv | 2020-07-30 |
| 200 | Revisiting Crowd Behaviour Analysis Through Deep Learning: Taxonomy, Anomaly Detection, Crowd Emotions, Datasets, Opportunities And Prospects | FRANCISCO LUQUE SÁNCHEZ et. al. | Revisiting Crowd Behaviour Analysis Through Deep Learning: Taxonomy, Anomaly Detection, Crowd Emotions, Datasets, Opportunities And Prospects | An international journal on information fusion | 2020-07-29 |
| 201 | A Deep Learning Framework For Generation And Analysis Of Driving Scenario Trajectories | ANDREAS DEMETRIOU et. al. | We propose a unified deep learning framework for generation and analysis of driving scenario trajectories, and validate its effectiveness in a principled way. | CS.arxiv | 2020-07-28 |
| 202 | Cassandra: Detecting Trojaned Networks From Adversarial Perturbations | XIAOYU ZHANG et. al. | We propose a method to verify if a pre-trained model is Trojaned or benign. | CS.arxiv | 2020-07-28 |
| 203 | DeScarGAN: Disease-Specific Anomaly Detection With Weak Supervision | JULIA WOLLEB et. al. | In this work, we present a weakly supervised and detail-preserving method that is able to detect structural changes of existing anatomical structures. | EESS.arxiv | 2020-07-28 |
| 204 | Anomaly Detection In Context-aware Feature Models | JACOPO MAURO | In this paper, we formalize the anomaly analysis in Context-aware Feature Models and we show how Quantified Boolean Formula (QBF) solvers can be used to detect anomalies without relying on iterative calls to a SAT solver. | CS.arxiv | 2020-07-28 |
| 205 | Few-Shot Bearing Anomaly Detection Based On Model-Agnostic Meta-Learning | SHEN ZHANG et. al. | In this paper, we propose a few-shot learning approach for bearing anomaly detection based on model-agnostic meta-learning (MAML), which targets for training an effective fault classifier using limited data. | CS.arxiv | 2020-07-25 |
| 207 | Insightful Assistant: AI-compatible Operation Graph Representations For Enhancing Industrial Conversational Agents | BEKIR BAYRAK et. al. | In the light of these deficits, this paper presents Insightful Assistant—a pipeline concept based on a novel operation graph representation resulting from the intents detected. We further collected a unique crowd-sourced set of 869 requests, each with four different variants expected visualization, for an industrial dataset. | CS.arxiv | 2020-07-25 |
| 208 | Improved Slice-wise Tumour Detection In Brain MRIs By Computing Dissimilarities Between Latent Representations | ALEXANDRA-IOANA ALBU et. al. | Recently, in Albu et al., 2020, we have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder, trained on unlabelled data. | CS.arxiv | 2020-07-24 |
| 209 | MADGAN: Unsupervised Medical Anomaly Detection GAN Using Multiple Adjacent Brain MRI Slice Reconstruction | CHANGHEE HAN et. al. | Therefore, we propose unsupervised Medical Anomaly Detection Generative Adversarial Network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 L1 loss-trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones-reconstructs unseen healthy/abnormal scans; (Diagnosis) Average L2 loss per scan discriminates them, comparing the ground truth/reconstructed slices. | CS.arxiv | 2020-07-24 |
| 210 | Unsupervised KPIs-Based Clustering Of Jobs In HPC Data Centers | MOHAMED S HALAWA et. al. | Unsupervised KPIs-Based Clustering Of Jobs In HPC Data Centers | Sensors (Basel, Switzerland) | 2020-07-23 |
| 211 | Annotation-free Learning Of Plankton For Classification And Anomaly Detection | VITO P PASTORE et. al. | Annotation-free Learning Of Plankton For Classification And Anomaly Detection | Scientific reports | 2020-07-22 |
| 212 | Inductive Anomaly Detection On Attributed Networks | KAIZE DING et. al. | In this study, we propose to tackle the problem of inductive anomaly detection on attributed networks with a novel unsupervised framework: Aegis (adversarial graph differentiation networks). | CS.ijcai | 2020-07-21 |
| 213 | Robustness Of Autoencoders For Anomaly Detection Under Adversarial Impact | ADAM GOODGE et. al. | In this work, we study the effect of adversarial attacks on the performance of anomaly-detecting autoencoders using real data from a Cyber physical system (CPS) testbed with intervals of controlled, physical attacks as anomalies. | CS.ijcai | 2020-07-21 |
| 214 | An Anomaly Detection And Explainability Framework Using Convolutional Autoencoders For Data Storage Systems | ROY ASSAF et. al. | We demonstrate the application of this framework along with an intuitive interactive web interface which was developed for data storage system anomaly detection. | CS.ijcai | 2020-07-21 |
| 215 | Unsupervised Representation Learning By Predicting Random Distances | HU WANG et. al. | To enable unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected space. | CS.ijcai | 2020-07-21 |
| 216 | Interpretable Anomaly Detection With DIFFI: Depth-based Feature Importance For The Isolation Forest | MATTIA CARLETTI et. al. | In this paper we propose effective, yet computationally inexpensive, methods to define feature importance scores at both global and local level for the Isolation Forest. | CS.arxiv | 2020-07-21 |
| 217 | Towards A Hierarchical Bayesian Model Of Multi-View Anomaly Detection | ZHEN WANG et. al. | In this paper, we propose a novel inductive semi-supervised Bayesian multi-view anomaly detector. | CS.ijcai | 2020-07-21 |
| 218 | Handling Black Swan Events In Deep Learning With Diversely Extrapolated Neural Networks | MAXIME WABARTHA et. al. | To avoid this pitfall, we introduce DENN, an ensemble approach building a set of Diversely Extrapolated Neural Networks that fits the training data and is able to generalize more diversely when extrapolating to novel data points. | CS.ijcai | 2020-07-21 |
| 219 | Class Prior Estimation In Active Positive And Unlabeled Learning | LORENZO PERINI et. al. | In this paper, we explore how to tackle this problem when the observed labels were acquired via active learning. | CS.ijcai | 2020-07-21 |
| 220 | Unsupervised Anomaly Detection For Discrete Sequence Healthcare Data | VICTORIA SNOROVIKHINA et. al. | We propose a machine learning model that automates fraud detection in an unsupervised way. | CS.arxiv | 2020-07-20 |
| 221 | CU-BEMS, Smart Building Electricity Consumption And Indoor Environmental Sensor Datasets | MANISA PIPATTANASOMPORN et. al. | CU-BEMS, Smart Building Electricity Consumption And Indoor Environmental Sensor Datasets | Scientific data | 2020-07-20 |
| 222 | Deep Anomaly Detection For Time-series Data In Industrial IoT: A Communication-Efficient On-device Federated Learning Approach | YI LIU et. al. | With this focus, this paper proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. | CS.arxiv | 2020-07-19 |
| 223 | DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition For Anomaly Detection | DONGYUN LIN et. al. | In this paper, we propose an AD method called dual deep reconstruction networks based image decomposition (DDR-ID). | CS.arxiv | 2020-07-18 |
| 224 | Few-Shot Defect Segmentation Leveraging Abundant Normal Training Samples Through Normal Background Regularization And Crop-and-Paste Operation | DONGYUN LIN et. al. | We present two effective regularization techniques via incorporating abundant defect-free images into the training of a UNet-like encoder-decoder defect segmentation network. | CS.arxiv | 2020-07-18 |
| 225 | Wearable Epileptic Seizure Prediction System With Machine-Learning-Based Anomaly Detection Of Heart Rate Variability | TOSHITAKA YAMAKAWA et. al. | Wearable Epileptic Seizure Prediction System With Machine-Learning-Based Anomaly Detection Of Heart Rate Variability | Sensors (Basel, Switzerland) | 2020-07-17 |
| 226 | Digital Cell Sorter (DCS): A Cell Type Identification, Anomaly Detection, And Hopfield Landscapes Toolkit For Single-cell Transcriptomics | DOMANSKYI, S. et. al. | Here, we introduce: (i) two methods for automatic cell type identification (i.e. without expert curator) based on a voting algorithm and a Hopfield classifier, (ii) a method for cell anomaly quantification based on isolation forest, and (iii) a tool for the visualization of cell phenotypic landscapes based on Hopfield energy-like functions. | BIOLOGY.biorxiv | 2020-07-17 |
| 227 | Detecting Out-of-distribution Samples Via Variational Auto-encoder With Reliable Uncertainty Estimation | XUMING RAN et. al. | Inspired by these ideas, We propose an improved noise contrastive prior (INCP) to acquire reliable uncertainty estimate for standard VAEs. | CS.arxiv | 2020-07-16 |
| 228 | Hierarchical Anomaly Detection Model For In-Vehicle Networks Using Machine Learning Algorithms | SEUNGHYUN PARK et. al. | Hierarchical Anomaly Detection Model For In-Vehicle Networks Using Machine Learning Algorithms | Sensors (Basel, Switzerland) | 2020-07-15 |
| 229 | H-VGRAE: A Hierarchical Stochastic Spatial-Temporal Embedding Method For Robust Anomaly Detection In Dynamic Networks | CHENMING YANG et. al. | In this paper, a stochastic neural network, named by Hierarchical Variational Graph Recurrent Autoencoder (H-VGRAE), is proposed to detect anomalies in dynamic networks by the learned robust node representations in the form of random variables. | CS.arxiv | 2020-07-14 |
| 230 | ADSAGE: Anomaly Detection In Sequences Of Attributed Graph Edges Applied To Insider Threat Detection At Fine-grained Level | MATHIEU GARCHERY et. al. | To address these issues we introduce ADSAGE to detect anomalies in audit log events modeled as graph edges. | CS.arxiv | 2020-07-14 |
| 231 | ReAD: A Regional Anomaly Detection Framework Based On Dynamic Partition | HUAISHAO LUO et. al. | In this paper, we proposed a dynamic region partition method to address the above issues. | CS.arxiv | 2020-07-13 |
| 232 | GAN-based Anomaly Detection In Multi-modal MRI Images | SEAN BENSON et. al. | We describe and demonstrate a method to use GANs trained from multi-modal magnetic resonance images as a 3-channel input. | BIOLOGY.biorxiv | 2020-07-12 |
| 233 | Anomaly Detection-Based Unknown Face Presentation Attack Detection | YASHASVI BAWEJA et. al. | In this paper, we present a deep-learning solution for anomaly detection-based spoof attack detection where both classifier and feature representations are learned together end-to-end. | CS.arxiv | 2020-07-11 |
| 234 | Small-GAN: Speeding Up GAN Training Using Core-Sets | SAMRATH SINHA et. al. | Thus, it would be nice if there were some trick by which we could generate batches that were effectively big though small in practice. In this work, we propose such a trick, inspired by the use of Coreset-selection in active learning. | CS.icml | 2020-07-11 |
| 235 | Interpretable, Multidimensional, Multimodal Anomaly Detection With Negative Sampling For Detection Of Device Failure | JOHN SIPPLE | In this paper we propose a scalable, unsupervised approach for detecting anomalies in the Internet of Things (IoT). | CS.icml | 2020-07-11 |
| 236 | ID-Conditioned Auto-Encoder For Unsupervised Anomaly Detection | SŁAWOMIR KAPKA | In this paper, we introduce ID-Conditioned Auto-Encoder for unsupervised anomaly detection. | EESS.arxiv | 2020-07-10 |
| 237 | Learning Retrospective Knowledge With Reverse Reinforcement Learning | SHANGTONG ZHANG et. al. | We present a Reverse Reinforcement Learning (Reverse RL) approach for representing retrospective knowledge. | CS.arxiv | 2020-07-09 |
| 238 | Brain Tumor Anomaly Detection Via Latent Regularized Adversarial Network | NAN WANG et. al. | Aiming at the imbalance of brain tumor data and the rare amount of labeled data, we propose an innovative brain tumor abnormality detection algorithm. | EESS.arxiv | 2020-07-09 |
| 239 | Artificial Intelligence And Machine Learning In 5G Network Security: Opportunities, Advantages, And Future Research Trends | NOMAN HAIDER et. al. | This article presents AI and ML driven applications for 5G network security, their implications and possible research directions. | CS.arxiv | 2020-07-08 |
| 240 | Deep Convolutional Neural Network-based Anomaly Detection For Organ Classification In Gastric X-ray Examination | REN TOGO et. al. | Deep Convolutional Neural Network-based Anomaly Detection For Organ Classification In Gastric X-ray Examination | Computers in biology and medicine | 2020-07-08 |
| 241 | ADA: Adaptive Deep Log Anomaly Detector | Y. YUAN et. al. | In this paper, we propose ADA: Adaptive Deep Log Anomaly Detector, an unsupervised online deep neural network framework that leverages LSTM networks and regularly adapts to newer log patterns to ensure accurate anomaly detection. | CS.infocom | 2020-07-06 |
| 242 | Evaluation Of Mental Workload During Automobile Driving Using One-class Support Vector Machine With Eye Movement Data | TAKANORI CHIHARA et. al. | Evaluation Of Mental Workload During Automobile Driving Using One-class Support Vector Machine With Eye Movement Data | Applied ergonomics | 2020-07-06 |
| 243 | HiFi: Hybrid Rule Placement For Fine-Grained Flow Management In SDNs | G. ZHAO et. al. | In this paper, we design and implement HiFi, a system that achieves fine-grained management with a minimal number of flow entries. | CS.infocom | 2020-07-06 |
| 244 | GAN-based Hyperspectral Anomaly Detection | SERTAC ARISOY et. al. | In this paper, we propose a generative adversarial network (GAN)-based hyperspectral anomaly detection algorithm. | EESS.arxiv | 2020-07-05 |
| 245 | Deep Learning For Anomaly Detection: A Review | GUANSONG PANG et. al. | We review their key intuitions, objective functions, underlying assumptions, advantages and disadvantages, and discuss how they address the aforementioned challenges. | CS.arxiv | 2020-07-05 |
| 246 | Annotated Dataset For Anomaly Detection In A Data Center With IoT Sensors | LAURA VIGOYA et. al. | Annotated Dataset For Anomaly Detection In A Data Center With IoT Sensors | Sensors (Basel, Switzerland) | 2020-07-04 |
| 247 | Efficient Anomaly Detection With Generative Adversarial Network For Breast Ultrasound Imaging | TOMOYUKI FUJIOKA et. al. | Efficient Anomaly Detection With Generative Adversarial Network For Breast Ultrasound Imaging | Diagnostics (Basel, Switzerland) | 2020-07-04 |
| 248 | Multiple Instance-Based Video Anomaly Detection Using Deep Temporal Encoding-Decoding | AMMAR MANSOOR KAMOONA et. al. | In this paper, we propose a weakly supervised deep temporal encoding-decoding solution for anomaly detection in surveillance videos using multiple instance learning. | CS.arxiv | 2020-07-03 |
| 249 | LSTM-Based VAE-GAN For Time-Series Anomaly Detection | ZIJIAN NIU et. al. | LSTM-Based VAE-GAN For Time-Series Anomaly Detection | Sensors (Basel, Switzerland) | 2020-07-03 |
| 250 | Explainable Deep One-Class Classification | PHILIPP LIZNERSKI et. al. | In this paper we present an explainable deep one-class classification method, Fully Convolutional Data Description (FCDD), where the mapped samples are themselves also an explanation heatmap. | CS.arxiv | 2020-07-03 |
| 251 | Novel Semi-metrics For Multivariate Change Point Analysis And Anomaly Detection | NICK JAMES et. al. | Novel Semi-metrics For Multivariate Change Point Analysis And Anomaly Detection | Physica D. Nonlinear phenomena | 2020-07-02 |
| 252 | A Comparative Study Of Network Traffic Representations For Novelty Detection | KUN YANG et. al. | This work explores different data representations for novelty detection in the Internet of Things, studying the effect of different representations of network traffic flows on the performance of a wide range of machine learning algorithms for novelty detection for problems arising in IoT, including malware detection, the detection of rogue devices, and the detection of cyberphysical anomalies. | CS.arxiv | 2020-06-30 |
| 253 | Abnormal Activity Capture From Passenger Flow Of Elevator Based On Unsupervised Learning And Fine-grained Multi-label Recognition | CHUNHUA JIA et. al. | More specifically in our implementation we propose GraftNet, a solution for fine-grained multi-label recognition task, to recognize human attributes, e.g. gender, age, appearance, and occupation. | CS.arxiv | 2020-06-29 |
| 254 | Patch SVDD: Patch-level SVDD For Anomaly Detection And Segmentation | JIHUN YI et. al. | In this paper, we address the problem of image anomaly detection and segmentation. | CS.arxiv | 2020-06-29 |
| 255 | Power-Based Non-Intrusive Condition Monitoring For Terminal Device In Smart Grid | GUOMING ZHANG et. al. | Power-Based Non-Intrusive Condition Monitoring For Terminal Device In Smart Grid | Sensors (Basel, Switzerland) | 2020-06-28 |
| 256 | Leveraging Siamese Networks For One-Shot Intrusion Detection Model | HANAN HINDY et. al. | The use of supervised Machine Learning (ML) to enhance Intrusion Detection Systems has been the subject of significant research. | CS.arxiv | 2020-06-27 |
| 257 | Few-Shot Anomaly Detection For Polyp Frames From Colonoscopy | YU TIAN et. al. | In this paper, we address the inappropriate sensitivity to outliers by also learning from inliers. | CS.arxiv | 2020-06-26 |
| 258 | Generative Damage Learning For Concrete Aging Detection Using Auto-flight Images | TAKATO YASUNO et. al. | This paper proposes an anomaly detection method using unpaired image-to-image translation mapping from damaged images to reverse aging fakes that approximates healthy conditions. | EESS.arxiv | 2020-06-26 |
| 259 | Anomaly Detection Using Deep Reconstruction And Forecasting For Autonomous Systems | NADARASAR BAHAVAN et. al. | We propose self-supervised deep algorithms to detect anomalies in heterogeneous autonomous systems using frontal camera video and IMU readings. | CS.arxiv | 2020-06-25 |
| 260 | Solving The Phantom Inventory Problem: Near-optimal Entry-wise Anomaly Detection | VIVEK F. FARIAS et. al. | So motivated, we propose a conceptually simple entry-wise approach to anomaly detection in low-rank Poisson matrices. | STATISTICS.arxiv | 2020-06-23 |
| 261 | Anomaly Detection With Deep Perceptual Autoencoders | NINA TULUPTCEVA et. al. | To address this problem, we introduce a new powerful method of image anomaly detection. | CS.arxiv | 2020-06-23 |
| 262 | Locally Masked Convolution For Autoregressive Models | AJAY JAIN et. al. | To generate data in arbitrary orders, we introduce LMConv: a simple modification to the standard 2D convolution that allows arbitrary masks to be applied to the weights at each location in the image. | CS.arxiv | 2020-06-22 |
| 263 | A Neural Network For Determination Of Latent Dimensionality In Nonnegative Matrix Factorization | BENJAMIN T. NEBGEN et. al. | We utilize a supervised machine learning approach in combination with a recent method for model determination, called NMFk, to determine the number of hidden features automatically. | CS.arxiv | 2020-06-22 |
| 264 | G2D: Generate To Detect Anomaly | MASOUD POURREZA et. al. | In this paper, we propose a novel method for irregularity detection. | CS.arxiv | 2020-06-20 |
| 265 | Manifolds For Unsupervised Visual Anomaly Detection | LOUISE NAUD et. al. | To this end, we propose constant curvature manifolds for embedding data distributions in unsupervised visual anomaly detection. | CS.arxiv | 2020-06-19 |
| 266 | Lightweight Collaborative Anomaly Detection For The IoT Using Blockchain | YISROEL MIRSKY et. al. | In this paper, we propose (1) a novel approach for anomaly detection and (2) a lightweight framework that utilizes the blockchain to ensemble an anomaly detection model in a distributed environment. | CS.arxiv | 2020-06-18 |
| 267 | Lipschitz Continuous Autoencoders In Application To Anomaly Detection | YOUNG-GEUN KIM et. al. | In this work, we formalize current practices, build a theoretical framework of anomaly detection algorithms equipped with an objective function and a hypothesis space, and establish a desirable property of the anomaly detection algorithm, namely, admissibility. | CS.aistats | 2020-06-18 |
| 268 | The Clever Hans Effect In Anomaly Detection | JACOB KAUFFMANN et. al. | Therefore, this paper will contribute an explainable AI (XAI) procedure that can highlight the relevant features used by popular anomaly detection models of different type. | CS.arxiv | 2020-06-18 |
| 269 | Deep Dense And Convolutional Autoencoders For Unsupervised Anomaly Detection In Machine Condition Sounds | ALEXANDRINE RIBEIRO et. al. | This technical report describes two methods that were developed for Task 2 of the DCASE 2020 challenge. | EESS.arxiv | 2020-06-18 |
| 270 | Use Of In-the-wild Images For Anomaly Detection In Face Anti-spoofing | LATIFAH ABDUH et. al. | Here, we explore the use of in-the-wild images, and images from non-specialized face databases, to train one-class classifiers for face anti-spoofing. | CS.arxiv | 2020-06-18 |
| 271 | The Area Of The Convex Hull Of Sampled Curves: A Robust Functional Statistical Depth Measure | GUILLAUME STAERMAN et. al. | In this paper, we propose a novel notion of functional depth based on the area of the convex hull of sampled curves, capturing gradual departures from centrality, even beyond the envelope of the data, in a natural fashion. | CS.aistats | 2020-06-18 |
| 272 | Understanding Anomaly Detection With Deep Invertible Networks Through Hierarchies Of Distributions And Features | ROBIN TIBOR SCHIRRMEISTER et. al. | To remove the negative impact of model bias and domain prior on detecting high-level differences, we propose two methods, first, using the log likelihood ratios of two identical models, one trained on the in-distribution data (e.g., CIFAR10) and the other one on a more general distribution of images (e.g., 80 Million Tiny Images). | CS.arxiv | 2020-06-18 |
| 273 | The Machine Learning Bazaar: Harnessing The ML Ecosystem For Effective System Development | MICAH J. SMITH et. al. | To address these problems, we introduce the Machine Learning Bazaar, a new framework for developing machine learning and automated machine learning software systems. | CS.sigmod | 2020-06-17 |
| 274 | Self-Supervised Representation Learning For Visual Anomaly Detection | RABIA ALI et. al. | We consider the problem of anomaly detection in images and videos, and present a new visual anomaly detection technique for videos. | CS.arxiv | 2020-06-17 |
| 275 | PROUD: PaRallel OUtlier Detection For Streams | THEODOROS TOLIOPOULOS et. al. | We introduce PROUD, standing for PaRallel OUtlier Detection for streams, which is an extensible engine for continuous multi-parameter parallel distance-based outlier (or anomaly) detection tailored to big data streams. | CS.sigmod | 2020-06-17 |
| 276 | Analytical Probability Distributions And EM-Learning For Deep Generative Networks | RANDALL BALESTRIERO et. al. | These findings enable us to derive an analytical Expectation-Maximization (EM) algorithm that enables gradient-free DGN learning. | CS.arxiv | 2020-06-17 |
| 277 | Plug-and-Play Anomaly Detection With Expectation Maximization Filtering | MUHAMMAD UMAR KARIM KHAN et. al. | We tackle all these constraints with our approach in this paper. | CS.arxiv | 2020-06-16 |
| 278 | A Specifically Designed Machine Learning Algorithm For GNSS Position Time Series Prediction And Its Applications In Outlier And Anomaly Detection And Earthquake Prediction | M. KIANI | We present a simple yet efficient supervised machine learning algorithm that is designed for the GNSS position time series prediction. | EESS.arxiv | 2020-06-16 |
| 279 | Learning Latent Space Energy-Based Prior Model | BO PANG et. al. | In this paper, we develop the maximum likelihood learning algorithm and its variation based on short-run Markov chain Monte Carlo sampling from the prior and the posterior distributions of the latent vector, and we show that the learned model exhibits strong performance in terms of image and text generation and anomaly detection. | STATISTICS.arxiv | 2020-06-15 |
| 280 | Robust Variational Autoencoder For Tabular Data With Beta Divergence | HALEH AKRAMI et. al. | In this work, we derive a novel form of a variational autoencoder for tabular data sets with categorical and continuous features that is robust to outliers in training data. | CS.arxiv | 2020-06-15 |
| 281 | A Model-Based Approach To Anomaly Detection Trading Detection Time And False Alarm Rate | CHARLES F. GONÇALVES et. al. | In this paper, we propose a new methodology that addresses the practical challenges to implement anomaly detection approaches. | CS.arxiv | 2020-06-15 |
| 282 | Anomalous Motion Detection On Highway Using Deep Learning | HARPREET SINGH et. al. | We evaluate state-of-the-art deep learning anomaly detection models and propose novel variations to these methods. This paper presents a new anomaly detection dataset – the Highway Traffic Anomaly (HTA) dataset – for the problem of detecting anomalous traffic patterns from dash cam videos of vehicles on highways. | CS.arxiv | 2020-06-15 |
| 283 | Anomaly Detection In Astrophysics: A Comparison Between Unsupervised Deep And Machine Learning On KiDS Data | MAURIZIO D’ADDONA et. al. | The former method, working directly on images, is considered potentially able to identify peculiar objects like interacting galaxies and gravitational lenses. | PHYSICS.arxiv | 2020-06-15 |
| 284 | Categorical Anomaly Detection In Heterogeneous Data Using Minimum Description Length Clustering | JAMES CHENEY et. al. | Fast and effective unsupervised anomaly detection algorithms have been proposed for categorical data based on the minimum description length (MDL) principle. | CS.arxiv | 2020-06-14 |
| 285 | Anomaly Detection Neural Network With Dual Auto-Encoders GAN And Its Industrial Inspection Applications | TA-WEI TANG et. al. | Anomaly Detection Neural Network With Dual Auto-Encoders GAN And Its Industrial Inspection Applications | Sensors (Basel, Switzerland) | 2020-06-12 |
| 286 | Non-Negative Bregman Divergence Minimization For Deep Direct Density Ratio Estimation | MASAHIRO KATO et. al. | In this paper, we introduce a non-negative correction for empirical risk using only the prior knowledge of the upper bound of the density ratio. | CS.arxiv | 2020-06-12 |
| 287 | Pseudo-healthy Synthesis With Pathology Disentanglement And Adversarial Learning | TIAN XIA et. al. | Pseudo-healthy Synthesis With Pathology Disentanglement And Adversarial Learning | Medical image analysis | 2020-06-12 |
| 288 | Joint Training Of Variational Auto-Encoder And Latent Energy-Based Model | TIAN HAN et. al. | This paper proposes a joint training method to learn both the variational auto-encoder (VAE) and the latent energy-based model (EBM). | CS.arxiv | 2020-06-10 |
| 289 | Finding New Physics Without Learning About It: Anomaly Detection As A Tool For Searches At Colliders | M. CRISPIM ROMAO et. al. | In this paper we propose a new strategy, based on anomaly detection methods, to search for New Physics phenomena at colliders independently of the details of such new events. | PHYSICS.arxiv | 2020-06-09 |
| 290 | Detecting Coal Pulverizing System Anomaly Using A Gated Recurrent Unit And Clustering | ZIAN CHEN et. al. | Detecting Coal Pulverizing System Anomaly Using A Gated Recurrent Unit And Clustering | Sensors (Basel, Switzerland) | 2020-06-08 |
| 291 | Learning Memory-Guided Normality For Anomaly Detection | HYUNJONG PARK et. al. | To address this problem, we present an unsupervised learning approach to anomaly detection that considers the diversity of normal patterns explicitly, while lessening the representation capacity of CNNs. | CS.cvpr | 2020-06-08 |
| 292 | Self-Trained Deep Ordinal Regression For End-to-End Video Anomaly Detection | GUANSONG PANG et. al. | By formulating a surrogate two-class ordinal regression task we devise an end-to-end trainable video anomaly detection approach that enables joint representation learning and anomaly scoring without manually labeled normal/abnormal data. | CS.cvpr | 2020-06-08 |
| 293 | Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings | PAUL BERGMANN et. al. | We introduce a powerful student-teacher framework for the challenging problem of unsupervised anomaly detection and pixel-precise anomaly segmentation in high-resolution images. | CS.cvpr | 2020-06-08 |
| 294 | Graph Embedded Pose Clustering For Anomaly Detection | AMIR MARKOVITZ et. al. | We propose a new method for anomaly detection of human actions. | CS.cvpr | 2020-06-08 |
| 295 | Hybrid Model For Anomaly Detection On Call Detail Records By Time Series Forecasting | ARYAN MOKHTARI et. al. | This paper’s primary goal is to study subscribers’ behavior in a cellular network, mainly predicting the number of calls in a region and detecting anomalies in the network traffic. | CS.arxiv | 2020-06-07 |
| 296 | A Novel Algorithm For Optimized Real Time Anomaly Detection In Timeseries | KRISHNAM KAPOOR | In this paper, we have evaluated and compared the performance of popular algorithms from domains of Machine Learning and Statistics in detecting anomalies on both offline data as well as real time data. | STATISTICS.arxiv | 2020-06-07 |
| 297 | Uncertainty-Aware Deep Classifiers Using Generative Models | MURAT SENSOY et. al. | In this work we develop a novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions of the feature space. | CS.arxiv | 2020-06-07 |
| 298 | Inception Augmentation Generative Adversarial Network | SAMAN MOTAMED et. al. | In this work, we propose a new GAN architecture for semi-supervised augmentation of chest X-rays for the detection of pneumonia. | CS.arxiv | 2020-06-05 |
| 299 | Acoustic Anomaly Detection For Machine Sounds Based On Image Transfer Learning | ROBERT MÜLLER et. al. | In this paper, we consider acoustic malfunction detection via transfer learning. | EESS.arxiv | 2020-06-05 |
| 300 | Unsupervised Abnormality Detection Using Heterogeneous Autonomous Systems | SAYEED SHAFAYET CHOWDHURY et. al. | To that effect, in this paper, a heterogeneous system is proposed which estimates the degree of abnormality of an unmanned surveillance drone, analyzing real-time image and IMU (Inertial Measurement Unit) sensor data in an unsupervised manner. | CS.arxiv | 2020-06-05 |
| 301 | Anomaly Detection On Streamed Data | THOMAS COCHRANE et. al. | We introduce powerful but simple methodology for identifying anomalous observations against a corpus of `normal’ observations. | CS.arxiv | 2020-06-05 |
| 302 | Anomaly Detection With Domain Adaptation | ZIYI YANG et. al. | We study the problem of semi-supervised anomaly detection with domain adaptation. | CS.arxiv | 2020-06-05 |
| 303 | LSTM-based Anomaly Detection For Non-linear Dynamical System | YUE TAN et. al. | In this paper, we propose a novel anomaly detection scheme in non-linear dynamical system based on Long Short-Term Memory (LSTM) to capture complex temporal changes of the time sequence and make multi-step predictions. | EESS.arxiv | 2020-06-04 |
| 304 | Nontechnical Losses Detection Through Coordinated BiWGAN And SVDD | TIANYU HU et. al. | Nontechnical Losses Detection Through Coordinated BiWGAN And SVDD | IEEE transactions on neural networks and learning systems | 2020-06-04 |
| 305 | Dyslexia Diagnosis By EEG Temporal And Spectral Descriptors: An Anomaly Detection Approach | ANDRÉS ORTIZ et. al. | Dyslexia Diagnosis By EEG Temporal And Spectral Descriptors: An Anomaly Detection Approach | International journal of neural systems | 2020-06-04 |
| 306 | The Building Data Genome Project 2, Energy Meter Data From The ASHRAE Great Energy Predictor III Competition | CLAYTON MILLER et. al. | This paper describes an open data set of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). | STATISTICS.arxiv | 2020-06-03 |
| 307 | Semi-supervised And Unsupervised Methods For Heart Sounds Classification In Restricted Data Environments | BALAGOPAL UNNIKRISHNAN et. al. | In this study, we conduct a comprehensive study of heart sounds classification by using various supervised, semi-supervised and unsupervised approaches on the PhysioNet/CinC 2016 Challenge dataset. | CS.arxiv | 2020-06-03 |
| 308 | Anomaly Detection With Tensor Networks | JINHUI WANG et. al. | In this paper, the prowess of tensor networks is demonstrated on the particular task of one-class anomaly detection. | CS.arxiv | 2020-06-03 |
| 309 | Comparing And Integrating US COVID-19 Data From Multiple Sources With Anomaly Detection And Repairing | GUANNAN WANG et. al. | To address these detected issues, we propose the corresponding repairing methods and procedures if corrections are necessary. | STATISTICS.arxiv | 2020-06-01 |
| 310 | Anomaly Detection Trusted Hardware Sensors For Critical Infrastructure Legacy Devices | APOSTOLOS P FOURNARIS et. al. | Anomaly Detection Trusted Hardware Sensors For Critical Infrastructure Legacy Devices | Sensors (Basel, Switzerland) | 2020-05-30 |
| 311 | Rethinking Assumptions In Deep Anomaly Detection | LUKAS RUFF et. al. | In this paper we present results demonstrating that this intuition surprisingly does not extend to deep AD on images. | CS.arxiv | 2020-05-30 |
| 312 | Machine Learning Methods To Detect Money Laundering In The Bitcoin Blockchain In The Presence Of Label Scarcity | JOANA LORENZ et. al. | Here, we address money laundering detection assuming minimal access to labels. | CS.arxiv | 2020-05-29 |
| 313 | 3D-Convolutional Neural Network With Generative Adversarial Network And Autoencoder For Robust Anomaly Detection In Video Surveillance | WONSUP SHIN et. al. | 3D-Convolutional Neural Network With Generative Adversarial Network And Autoencoder For Robust Anomaly Detection In Video Surveillance | International journal of neural systems | 2020-05-28 |
| 314 | Modeling The Distribution Of Normal Data In Pre-Trained Deep Features For Anomaly Detection | OLIVER RIPPEL et. al. | Popular AD algorithms commonly try to learn a model of normality from scratch using task specific datasets, but are limited to semi-supervised approaches employing mostly normal data due to the inaccessibility of anomalies on a large scale combined with the ambiguous nature of anomaly appearance. | CS.arxiv | 2020-05-28 |
| 315 | Log-stable Probability Density Functions, Non-stationarity Evaluation, And Multi-feature Autocorrelation Analysis Of The $γ$-ray Light Curves Of Blazars | JAROSLAW DUDA et. al. | In this work, we study the probability density functions that best describe the $\gamma$-ray flux distribution of decade-long light curves of a sample of blazars. | PHYSICS.arxiv | 2020-05-28 |
| 316 | Unsupervised Anomaly Detection In Journal-Level Citation Networks | BAANI LEEN KAUR JOLLY et. al. | In this paper, we present a novel approach to detect the anomalies in a journal-level scientific citation network, and compare the results with the existing graph anomaly detection algorithms. Due to the lack of proper ground-truth, we introduce a journal-level citation anomaly dataset which consists of synthetically injected citation anomalies and use it to evaluate our methodology. | CS.arxiv | 2020-05-28 |
| 317 | Anomaly Detection Based On Deep Learning Using Video For Prevention Of Industrial Accidents | SATOSHI HASHIMOTO et. al. | This paper proposes an anomaly detection method for the prevention of industrial accidents using machine learning technology. | CS.arxiv | 2020-05-27 |
| 318 | CPAC-Conv: CP-decomposition To Approximately Compress Convolutional Layers In Deep Learning | YINAN WANG et. al. | The contributions of our work could be summarized into three aspects: 1) we adapt CP-decomposition to compress convolutional kernels and derive the expressions of both forward and backward propagations for our proposed CPAC-Conv layer; 2) compared with the original convolutional layer, the proposed CPAC-Conv layer can reduce the number of parameters without decaying prediction performance. | CS.arxiv | 2020-05-27 |
| 319 | Factor Analysis Of Mixed Data For Anomaly Detection | MATTHEW DAVIDOW et. al. | We propose a kurtosis-weighted Factor Analysis of Mixed Data for anomaly detection, FAMDAD, to obtain a continuous embedding for anomaly scoring. | STATISTICS.arxiv | 2020-05-25 |
| 320 | SunDown: Model-driven Per-Panel Solar Anomaly Detection For Residential Arrays | MENGHONG FENG et. al. | In this paper, we present SunDown, a sensorless approach designed to detect per-panel faults in residential solar arrays. | EESS.arxiv | 2020-05-25 |
| 321 | Unsupervised Online Anomaly Detection On Irregularly Sampled Or Missing Valued Time-Series Data Using LSTM Networks | OGUZHAN KARAAHMETOGLU et. al. | We study anomaly detection and introduce an algorithm that processes variable length, irregularly sampled sequences or sequences with missing values. | STATISTICS.arxiv | 2020-05-25 |
| 322 | Master-Auxiliary: An Efficient Aggregation Strategy For Video Anomaly Detection | ZHIGUO WANG et. al. | This paper proposes an efficient strategy to aggregate multiple detectors together. | CS.arxiv | 2020-05-23 |
| 323 | Tractometry-based Anomaly Detection For Single-subject White Matter Analysis | MAXIME CHAMBERLAND et. al. | We present a framework that operates on the manifold of white matter (WM) pathways to learn normative microstructural features, and discriminate those at genetic risk from controls in a paediatric population. | BIOLOGY.arxiv | 2020-05-22 |
| 324 | A Robust Interpretable Deep Learning Classifier For Heart Anomaly Detection Without Segmentation | THEEKSHANA DISSANAYAKE et. al. | A Robust Interpretable Deep Learning Classifier For Heart Anomaly Detection Without Segmentation | CS.arxiv | 2020-05-21 |
| 325 | An Incremental Clustering Method For Anomaly Detection In Flight Data | WEIZUN ZHAO et. al. | To address this problem, we propose a novel incremental anomaly detection method to identify common patterns and detect outliers in flight operations from FDR data. | CS.arxiv | 2020-05-20 |
| 326 | A Metric Learning Approach To Anomaly Detection In Video Games | BENEDICT WILKINS et. al. | With the aim of designing automated tools that assist in the video game quality assurance process, we frame the problem of identifying bugs in video games as an anomaly detection (AD) problem. | CS.arxiv | 2020-05-20 |
| 327 | Detection Of Anomalous High-frequency Events In Human Intracranial EEG | KRIT CHARUPANIT et. al. | Detection Of Anomalous High-frequency Events In Human Intracranial EEG | Epilepsia open | 2020-05-20 |
| 328 | Informative Path Planning For Anomaly Detection In Environment Exploration And Monitoring | ANTOINE BLANCHARD et. al. | We introduce a number of novel criteria that guide the UAV towards regions of strong anomalies by leveraging previously collected information in a mathematically elegant and computationally tractable manner. | STATISTICS.arxiv | 2020-05-20 |
| 329 | Anomaly Detection In Cloud Components | MOHAMMAD SAIFUL ISLAM et. al. | Our goal is to improve the quality of Cloud services through early detection of such failures by analyzing resource utilization metrics. | CS.arxiv | 2020-05-18 |
| 330 | Transformation Based Deep Anomaly Detection In Astronomical Images | ESTEBAN REYES et. al. | In this work, we propose several enhancements to a geometric transformation based model for anomaly detection in images (GeoTranform). | CS.arxiv | 2020-05-15 |
| 331 | Structural Temporal Graph Neural Networks For Anomaly Detection In Dynamic Graphs | LEI CAI et. al. | In this paper, we propose StrGNN, an end-to-end structural temporal Graph Neural Network model for detecting anomalous edges in dynamic graphs. | CS.arxiv | 2020-05-15 |
| 332 | Time Series To Images: Monitoring The Condition Of Industrial Assets With Deep Learning Image Processing Algorithms | GABRIEL RODRIGUEZ GARCIA et. al. | Inspired by the success of deep learning methods in computer vision, several studies have proposed to transform time-series into image-like representations, leading to very promising results. | CS.arxiv | 2020-05-14 |
| 333 | Anomaly Detection And Classification In Time Series With Kervolutional Neural Networks | OLIVER AMMANN et. al. | In this paper, we explore the potential of kervolutional neural networks applied to time series data. | CS.arxiv | 2020-05-14 |
| 334 | Estimate The Implicit Likelihoods Of GANs With Application To Anomaly Detection | SHAOGANG REN et. al. | In this paper, we propose an approach to estimate the implicit likelihoods of GAN models. | CS.www | 2020-05-13 |
| 335 | Modeling Heterogeneous Statistical Patterns In High-dimensional Data By Adversarial Distributions: An Unsupervised Generative Framework | HAN ZHANG et. al. | To address the above issues, we propose a novel unsupervised generative framework called FIRD, which utilizes adversarial distributions to fit and disentangle the heterogeneous statistical patterns. | CS.www | 2020-05-13 |
| 336 | Nonparametric Anomaly Detection On Time Series Of Graphs | OFORI-BOATENG, D. et. al. | One of the particular objectives of the anomaly detection task from the neuroscience perspective is the reconstruction of the dynamic manner of brain region interactions. | BIOLOGY.biorxiv | 2020-05-12 |
| 337 | Unsupervised Anomaly Detection Via Deep Metric Learning With End-to-End Optimization | SELIM F. YILMAZ et. al. | We present a novel data distillation method through self-supervision to remedy the conventional practice of assuming all data as normal. | CS.arxiv | 2020-05-12 |
| 338 | Integrated Methodology To Cognitive Network \& Slice Management In Virtualized 5G Networks | XENOFON VASILAKOS et. al. | To address this need, we take a use case-driven approach to design and present a novel Integrated Methodology for CNSM in virtualized 5G networks based on a concrete eHealth use case, and elaborate on it to derive a generic approach for 5G slice management use cases. | CS.arxiv | 2020-05-10 |
| 339 | A Showcase Of The Use Of Autoencoders In Feature Learning Applications | DAVID CHARTE et. al. | This work presents these applications and provides details on how autoencoders can perform them, including code samples making use of an R package with an easy-to-use interface for autoencoder design and training, \texttt{ruta}. | CS.arxiv | 2020-05-08 |
| 340 | Learning Generalized Spoof Cues For Face Anti-spoofing | HAOCHENG FENG et. al. | In this paper, we reformulate FAS in an anomaly detection perspective and propose a residual-learning framework to learn the discriminative live-spoof differences which are defined as the spoof cues. | CS.arxiv | 2020-05-08 |
| 341 | Dijet Resonance Search With Weak Supervision Using Sqrt[s]=13 TeV Pp Collisions In The ATLAS Detector | G AAD et. al. | Dijet Resonance Search With Weak Supervision Using Sqrt[s]=13 TeV Pp Collisions In The ATLAS Detector | Physical review letters | 2020-05-07 |
| 342 | A Review Of Computer Vision Methods In Network Security | JIAWEI ZHAO et. al. | In this paper, we provide a comprehensive survey of such work under three topics; i) phishing attempt detection, ii) malware detection, and iii) traffic anomaly detection. | CS.arxiv | 2020-05-07 |
| 343 | Dijet Resonance Search With Weak Supervision Using $\sqrt{s}=13$ TeV $pp$ Collisions In The ATLAS Detector | ATLAS COLLABORATION | This Letter describes a search for resonant new physics using a machine-learning anomaly detection procedure that does not rely on a signal model hypothesis. | PHYSICS.arxiv | 2020-05-06 |
| 344 | Personalized Early Stage Alzheimer’s Disease Detection: A Case Study Of President Reagan’s Speeches | WANG, N. et. al. | In this paper, we show that machine learning-based unsupervised clustering of and anomaly detection with linguistic biomarkers are promising approaches for intuitive visualization and personalized early stage detection of Alzheimer’s disease. | HEALTH.medrxiv | 2020-05-06 |
| 345 | Sub-Image Anomaly Detection With Deep Pyramid Correspondences | NIV COHEN et. al. | In this work we present a novel anomaly segmentation approach based on alignment between an anomalous image and a constant number of the similar normal images. | CS.arxiv | 2020-05-05 |
| 346 | Classification-Based Anomaly Detection For General Data | LIRON BERGMAN et. al. | In this work, we present a unifying view and propose an open-set method, GOAD, to relax current generalization assumptions. | CS.arxiv | 2020-05-05 |
| 347 | Informative Ranking Of Stand Out Collections Of Symptoms: A New Data-Driven Approach To Identify The Strong Warning Signs Of COVID 19 | ALMOMANI, A. A. R. et. al. | We develop here a data-driven approach for disease recognition based on given symptoms, to be efficient tool for anomaly detection. | HEALTH.medrxiv | 2020-05-04 |
| 348 | Sum-Product-Transform Networks: Exploiting Symmetries Using Invertible Transformations | TOMAS PEVNY et. al. | In this work, we propose Sum-Product-Transform Networks (SPTN), an extension of sum-product networks that uses invertible transformations as additional internal nodes. | STATISTICS.arxiv | 2020-05-04 |
| 349 | Adversarially Learned Anomaly Detection On CMS Open Data: Re-discovering The Top Quark | OLIVER KNAPP et. al. | We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton-proton collisions at the Large Hadron Collider. | PHYSICS.arxiv | 2020-05-04 |
| 350 | Interpreting Rate-Distortion Of Variational Autoencoder And Using Model Uncertainty For Anomaly Detection | SEONHO PARK et. al. | We show empirically the competitive performance of our approach on benchmark datasets. | CS.arxiv | 2020-05-04 |
| 351 | Pattern-Based Analysis Of Time Series: Estimation | ELYAS SABETI et. al. | In this paper, we propose a novel method, called pattern tree, to learn patterns in the times-series using a binary-structured tree. | STATISTICS.arxiv | 2020-05-02 |
| 352 | Open Set Wireless Transmitter Authorization: Deep Learning Approaches And Dataset Considerations | SAMER HANNA et. al. | In this paper, we consider the problem of recognizing authorized transmitters and rejecting new transmitters. | EESS.arxiv | 2020-05-02 |
| 353 | Learning Compliance Adaptation In Contact-Rich Manipulation | JIANFENG GAO et. al. | In this paper, we propose a novel approach for learning predictive models of force profiles required for contact-rich tasks. | CS.arxiv | 2020-05-01 |
| 354 | The 4th AI City Challenge | MILIND NAPHADE et. al. | The 4th AI City Challenge | CS.arxiv | 2020-04-30 |
| 355 | Dynamic Anomaly Detection With High-fidelity Simulators: A Convex Optimization Approach | KAIKAI PAN et. al. | The main objective of this article is to develop scalable dynamic anomaly detectors when high-fidelity simulators of power systems are at our disposal. | MATH.arxiv | 2020-04-28 |
| 356 | Anomaly Detection-Based Recognition Of Near-Native Protein Structures | SIVANI TADEPALLI et. al. | Anomaly Detection-Based Recognition Of Near-Native Protein Structures | IEEE transactions on nanobioscience | 2020-04-27 |
| 357 | Multi-IF : An Approach To Anomaly Detection In Self-Driving Systems | KUN CHENG et. al. | This paper proposes a fast method called Multi-IF, using multiple invocation features of system calls to detect anomalies in self-driving systems. | CS.arxiv | 2020-04-27 |
| 358 | Anomalydae: Dual Autoencoder For Anomaly Detection On Attributed Networks | H. FAN et. al. | In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings. | CS.icassp | 2020-04-26 |
| 359 | Quickest Detection Of Growing Dynamic Anomalies In Networks | G. ROVATSOS et. al. | The problem of quickest growing dynamic anomaly detection in sensor networks is studied. | CS.icassp | 2020-04-26 |
| 360 | Mahalanobis Distance Based Adversarial Network For Anomaly Detection | Y. HOU et. al. | This paper proposes an efficient method, known as Mahalanobis Distance-based Adversarial Network (MDAN), for anomaly detection. | CS.icassp | 2020-04-26 |
| 361 | Anomaly Detection In Mixed Time-Series Using A Convolutional Sparse Representation With Application To Spacecraft Health Monitoring | B. PILASTRE et. al. | This paper introduces a convolutional sparse model for anomaly detection in mixed continuous and discrete data. | CS.icassp | 2020-04-26 |
| 362 | Data-Driven Construction Of Data Center Graph Of Things For Anomaly Detection | HAO ZHANG et. al. | This work proposes a data-driven pipeline (ts2graph) to build a DC graph of things (sensor graph) from the time series measurements of sensors. | CS.arxiv | 2020-04-26 |
| 363 | Image Segmentation Based Privacy-Preserving Human Action Recognition For Anomaly Detection | J. YAN et. al. | In this paper, we prove that human action recognition accuracy mostly depends on contextual data, rather than on privacy-related data. | CS.icassp | 2020-04-26 |
| 364 | Anomaly Detection With Training Data In Hyperspectral Imagery | J. LIU et. al. | In this paper, we investigate the anomaly detection problem for multi-pixel targets in hyperspectral imagery when training data are available. | CS.icassp | 2020-04-26 |
| 365 | SPIDERnet: Attention Network For One-Shot Anomaly Detection In Sounds | Y. KOIZUMI et. al. | We propose a similarity function for one-shot anomaly detection in sounds (ADS) called SPecific anomaly IDentifiER network (SPI- DERnet). | CS.icassp | 2020-04-26 |
| 366 | Anomaly Detection For Time Series Using VAE-LSTM Hybrid Model | S. LIN et. al. | In this work, we propose a VAE-LSTM hybrid model as an unsupervised approach for anomaly detection in time series. | CS.icassp | 2020-04-26 |
| 367 | Adversarial Anomaly Detection For Marked Spatio-Temporal Streaming Data | S. ZHU et. al. | How to efficiently detect anomalies in these dynamic systems using these streaming event data? This work proposes a novel anomaly detection framework for such event data combining the Long Short-Term Memory (LSTM) and marked spatio-temporal point processes. | CS.icassp | 2020-04-26 |
| 368 | Robust And Computationally-Efficient Anomaly Detection Using Powers-Of-Two Networks | U. MUNEEB et. al. | We propose a technique to increase robustness and reduce computational complexity in a Convolutional Neural Network (CNN) based anomaly detector that utilizes the optical flow information of video data. | CS.icassp | 2020-04-26 |
| 369 | Unseen Face Presentation Attack Detection With Hypersphere Loss | Z. LI et. al. | In this paper, we formulate the face presentation attack detection task under an open-set setting and address with our proposed deep anomaly detection based method. | CS.icassp | 2020-04-26 |
| 370 | Semi-Supervised Optimal Transport Methods For Detecting Anomalies | A. ALAOUI-BELGHITI et. al. | Building upon advances on optimal transport and anomaly detection, we propose a generalization of an unsupervised and automatic method for detection of significant deviation from reference signals. | CS.icassp | 2020-04-26 |
| 371 | Training LSTM For Unsupervised Anomaly Detection Without A Priori Knowledge | Y. CHERDO et. al. | In contrast, we propose a novel anomaly detector, coined as LSTM-Decomposed (LSTM-D), that does not require this normality knowledge. | CS.icassp | 2020-04-26 |
| 372 | LogEvent2vec: LogEvent-to-Vector Based Anomaly Detection For Large-Scale Logs In Internet Of Things | JIN WANG et. al. | LogEvent2vec: LogEvent-to-Vector Based Anomaly Detection For Large-Scale Logs In Internet Of Things | Sensors (Basel, Switzerland) | 2020-04-26 |
| 373 | How To Find A Unicorn: A Novel Model-free, Unsupervised Anomaly Detection Method For Time Series | ZSIGMOND BENKŐ et. al. | In this paper, we present a new anomaly concept called unicorn or unique event and present a new, model-independent, unsupervised detection algorithm to detect unicorns. | CS.arxiv | 2020-04-23 |
| 374 | Real-time Detection Of Clustered Events In Video-imaging Data With Applications To Additive Manufacturing | HAO YAN et. al. | In this paper, we propose an integrated spatio-temporal decomposition and regression approach for anomaly detection in video-imaging data. | STATISTICS.arxiv | 2020-04-23 |
| 375 | Discovering Imperfectly Observable Adversarial Actions Using Anomaly Detection | OLGA PETROVA et. al. | We propose two algorithms for solving such games — a direct extension of existing algorithms based on discretizing the feature space and linear programming and the second algorithm based on constrained learning. | CS.arxiv | 2020-04-22 |
| 377 | Sequential Anomaly Detection Using Inverse Reinforcement Learning | MIN-HWAN OH et. al. | We propose an end-to-end framework for sequential anomaly detection using inverse reinforcement learning (IRL), whose objective is to determine the decision-making agent’s underlying function which triggers his/her behavior. | CS.arxiv | 2020-04-22 |
| 378 | Automated Anomaly Detection In Large Sequences | P. BONIOL et. al. | In this work, we address these problems, and propose NorM, a novel approach, suitable for domain-agnostic anomaly detection. | CS.icde | 2020-04-20 |
| 379 | Online Anomalous Trajectory Detection With Deep Generative Sequence Modeling | Y. LIU et. al. | To this end, we propose a novel model, namely Gaussian Mixture Variational Sequence AutoEncoder (GM-VSAE), to tackle these challenges. | CS.icde | 2020-04-20 |
| 380 | Automatic Anomaly Detection On In-Production Manufacturing Machines Using Statistical Learning Methods | FEDERICO PITTINO et. al. | Automatic Anomaly Detection On In-Production Manufacturing Machines Using Statistical Learning Methods | Sensors (Basel, Switzerland) | 2020-04-20 |
| 381 | Privacy-preserving Real-time Anomaly Detection Using Edge Computing | S. MEHNAZ AND E. BERTINO | We propose a privacy-preserving framework that enables efficient anomaly detection on encrypted data by leveraging a lightweight and aggregation optimized encryption scheme to encrypt the data before off-loading the data to the edge. | CS.icde | 2020-04-20 |
| 382 | Network Anomaly Detection Based On Tensor Decomposition | ANANDA STREIT et. al. | We propose a new approach for online tensor decomposition where changes in the normal subspace can be tracked efficiently. | CS.arxiv | 2020-04-20 |
| 383 | Pseudo-healthy Synthesis With Pathology Disentanglement And Adversarial Learning | TIAN XIA et. al. | In this paper, we present a model that is encouraged to disentangle the information of pathology from what seems to be healthy. | EESS.arxiv | 2020-04-20 |
| 384 | Querying Streaming System Monitoring Data For Enterprise System Anomaly Detection | P. GAO ET AL. | In the demo, we aim to show the complete usage scenario of SAQL by (1) performing an APT attack in a controlled environment, and (2) using SAQL to detect the abnormal behaviors in real time by querying the collected stream of system monitoring data that contains the attack traces. | CS.icde | 2020-04-20 |
| 385 | SAD: An Unsupervised System For Subsequence Anomaly Detection | P. BONIOL et. al. | In this demonstration, we present a system for unsupervised Subsequence Anomaly Detection (SAD) that uses the NorM method. | CS.icde | 2020-04-20 |
| 386 | Matrix Factorization With Interval-Valued Data | M. LI et. al. | In this paper, we propose matrix decomposition techniques that consider the existence of interval-valued data. | CS.icde | 2020-04-20 |
| 387 | User-driven Error Detection For Time Series With Events | K. LE AND P. PAPOTTI | In this work, we exploit active learning to detect both errors and events in a single solution that aims at minimizing user interaction. | CS.icde | 2020-04-20 |
| 388 | An Anomaly Detection System For The Protection Of Relational Database Systems Against Data Leakage By Application Programs | D. FADOLALKARIM et. al. | In this paper, we propose AD-PROM, an Anomaly Detection system that aims at protecting relational database systems against malicious/compromised applications PROgraMs aiming at stealing data. | CS.icde | 2020-04-20 |
| 389 | Informative Ranking Of Stand Out Collections Of Symptoms: A New Data-Driven Approach To Identify The Strong Warning Signs Of COVID 19 | ABD ALRAHMAN ALMOMANI et. al. | We develop here a data-driven approach for disease recognition based on given symptoms, to be efficient tool for anomaly detection. | BIOLOGY.arxiv | 2020-04-19 |
| 390 | Neural Memory Plasticity For Medical Anomaly Detection | THARINDU FERNANDO et. al. | Neural Memory Plasticity For Medical Anomaly Detection | Neural networks : the official journal of the International Neural Network Society | 2020-04-18 |
| 391 | Exploring Time-series Motifs Through DTW-SOM | MARIA INÊS SILVA et. al. | In this paper, we argue that visually exploring time-series motifs computed by motif discovery algorithms can be useful to understand and debug results. | CS.arxiv | 2020-04-17 |
| 392 | Motion And Region Aware Adversarial Learning For Fall Detection With Thermal Imaging | VINEET MEHTA et. al. | We introduce a technique to track the region of interest, a region-based difference constraint, and a joint discriminator to compute the reconstruction error. | CS.arxiv | 2020-04-17 |
| 393 | Old Is Gold: Redefining The Adversarially Learned One-Class Classifier Training Paradigm | MUHAMMAD ZAIGHAM ZAHEER et. al. | In this study, we propose a framework that effectively generates stable results across a wide range of training steps and allows us to use both the generator and the discriminator of an adversarial model for efficient and robust anomaly detection. | CS.arxiv | 2020-04-16 |
| 394 | Contextual-Bandit Anomaly Detection For IoT Data In Distributed Hierarchical Edge Computing | MAO V. NGO et. al. | In this paper, we propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems to solve this problem, for both univariate and multivariate IoT data. | CS.arxiv | 2020-04-15 |
| 395 | A Survey Of Single-Scene Video Anomaly Detection | BHARATHKUMAR RAMACHANDRA et. al. | This survey article summarizes research trends on the topic of anomaly detection in video feeds of a single scene. | CS.arxiv | 2020-04-13 |
| 396 | Quantifying And Detecting Collective Motion In Crowd Scenes | XUELONG LI et. al. | Quantifying And Detecting Collective Motion In Crowd Scenes | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society | 2020-04-13 |
| 397 | Anomaly Detection In Trajectory Data With Normalizing Flows | MADSON L. D. DIAS et. al. | We aim to tackle such a problem from a probability density estimation point of view, since it provides an unsupervised procedure to identify out of distribution samples. | STATISTICS.arxiv | 2020-04-13 |
| 398 | Hybrid Attention Networks For Flow And Pressure Forecasting In Water Distribution Systems | ZIQING MA et. al. | To address this issue, we proposed a hybrid dual-stage spatial-temporal attention-based recurrent neural networks (hDS-RNN). | CS.arxiv | 2020-04-13 |
| 399 | Anomaly Detection Of Time Series With Smoothness-Inducing Sequential Variational Auto-Encoder | LONGYUAN LI et. al. | Anomaly Detection Of Time Series With Smoothness-Inducing Sequential Variational Auto-Encoder | IEEE transactions on neural networks and learning systems | 2020-04-13 |
| 400 | Towards Anomaly Detection In Dashcam Videos | SANJAY HARESH et. al. | We propose to apply data-driven anomaly detection ideas from deep learning to dashcam videos, which hold the promise of bridging this gap. To counter this issue, we present a large and diverse dataset of truck dashcam videos, namely RetroTrucks, that includes normal and anomalous driving scenes. | CS.arxiv | 2020-04-10 |
| 401 | Anomaly Detection With SDAE | BENJAMIN SMITH et. al. | Given the restricted parameters under which the models were trained, the Deep Autoencoder perfoms the best, however, the Supervised Deep Autoencoder outperforms the other models in total anomalies detected when considerations for the test datasets are given. | CS.arxiv | 2020-04-09 |
| 402 | Towed Overhauser Marine Magnetometer For Weak Magnetic Anomaly Detection In Severe Ocean Conditions | J GE et. al. | Towed Overhauser Marine Magnetometer For Weak Magnetic Anomaly Detection In Severe Ocean Conditions | The Review of scientific instruments | 2020-04-09 |
| 403 | On Anomaly Interpretation Via Shapley Values | NAOYA TAKEISHI et. al. | In this paper, we investigate the use of the Shapley value for anomaly interpretation. | CS.arxiv | 2020-04-09 |
| 404 | A Mathematical Assessment Of The Isolation Tree Method For Outliers Detection In Big Data | FERNANDO A. MORALES et. al. | In this paper, the mathematical analysis of the Isolation Random Forest Method (IRF Method) for anomaly detection is presented. | STATISTICS.arxiv | 2020-04-09 |
| 405 | Bayesian Classification, Anomaly Detection, And Survival Analysis Using Network Inputs With Application To The Microbiome | NATHANIEL JOSEPHS et. al. | In this paper, we propose a Bayesian modeling framework that provides a unified approach to binary classification, anomaly detection, and survival analysis with network inputs. | STATISTICS.arxiv | 2020-04-09 |
| 406 | Challenges In Vessel Behavior And Anomaly Detection: From Classical Machine Learning To Deep Learning | LUCAS MAY PETRY et. al. | In this paper, we discuss challenges and opportunities in classical machine learning and deep learning for vessel event and anomaly detection. | CS.arxiv | 2020-04-07 |
| 407 | Deep End-to-End One-Class Classifier | MOHAMMAD SABOKROU et. al. | Deep End-to-End One-Class Classifier | IEEE transactions on neural networks and learning systems | 2020-04-07 |
| 408 | PAElla: Edge-AI Based Real-Time Malware Detection In Data Centers | ANTONIO LIBRI et. al. | In this paper, we focus on Data Centers (DCs) and Supercomputers (SCs), where a new generation of high-resolution monitoring systems is being deployed, opening new opportunities for analysis like anomaly detection and security, but introducing new challenges for handling the vast amount of data it produces. | CS.arxiv | 2020-04-07 |
| 409 | Autoencoders For Unsupervised Anomaly Segmentation In Brain MR Images: A Comparative Study | CHRISTOPH BAUR et. al. | The main principle behind these works is to learn a model of normal anatomy by learning to compress and recover healthy data. | EESS.arxiv | 2020-04-07 |
| 410 | When, Where, And What? A New Dataset For Anomaly Detection In Driving Videos | YU YAO et. al. | This paper proposes traffic anomaly detection with a \textit{when-where-what} pipeline to detect, localize, and recognize anomalous events from egocentric videos. We introduce a new dataset called Detection of Traffic Anomaly (DoTA) containing 4,677 videos with temporal, spatial, and categorical annotations. | CS.arxiv | 2020-04-06 |
| 411 | Correction To Exploiting Epistemic Uncertainty Of Anatomy Segmentation For Anomaly Detection In Retinal OCT | PHILIPP SEEBOCK et. al. | Correction To Exploiting Epistemic Uncertainty Of Anatomy Segmentation For Anomaly Detection In Retinal OCT | IEEE transactions on medical imaging | 2020-04-06 |
| 412 | Anomaly Detection And Prototype Selection Using Polyhedron Curvature | BENYAMIN GHOJOGH et. al. | We propose a novel approach to anomaly detection called Curvature Anomaly Detection (CAD) and Kernel CAD based on the idea of polyhedron curvature. | CS.arxiv | 2020-04-05 |
| 413 | Moving Metric Detection And Alerting System At EBay | ZEZHONG ZHANG et. al. | In the first phase, we developed an efficient anomaly detection algorithm, called Moving Metric Detector (MMD), to identify potential alerts among metrics with distribution agnostic criteria. | CS.arxiv | 2020-04-05 |
| 414 | ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach For Time Series | MING-CHANG LEE et. al. | With these goals in mind, this paper introduces ReRe, which is a Real-time Ready-to-go proactive Anomaly Detection algorithm for streaming time series. | CS.arxiv | 2020-04-05 |
| 415 | Any-Shot Sequential Anomaly Detection In Surveillance Videos | KEVAL DOSHI et. al. | Motivated by these research gaps, we propose an online anomaly detection method for surveillance videos using transfer learning and any-shot learning, which in turn significantly reduces the training complexity and provides a mechanism that can detect anomalies using only a few labeled nominal examples. | CS.arxiv | 2020-04-04 |
| 416 | Using Large-Scale Anomaly Detection On Code To Improve Kotlin Compiler | TIMOFEY BRYKSIN et. al. | In this work, we apply anomaly detection to source code and bytecode to facilitate the development of a programming language and its compiler. | CS.arxiv | 2020-04-03 |
| 417 | Deep Generative Models-Based Anomaly Detection For Spacecraft Control Systems | HYOJUNG AHN et. al. | Deep Generative Models-Based Anomaly Detection For Spacecraft Control Systems | Sensors (Basel, Switzerland) | 2020-04-02 |
| 418 | Video Anomaly Detection For Smart Surveillance | SIJIE ZHU et. al. | In this paper, we provide a brief overview of the recent research progress on video anomaly detection and highlight a few future research directions. | CS.arxiv | 2020-04-01 |
| 419 | Anomaly Detection In Univariate Time-series: A Survey On The State-of-the-Art | MOHAMMAD BRAEI et. al. | Seminal work on anomaly detection methods has been focussing on statistical approaches. | CS.arxiv | 2020-04-01 |
| 420 | Improve Aggressive Driver Recognition Using Collision Surrogate Measurement And Imbalanced Class Boosting | KE WANG et. al. | Improve Aggressive Driver Recognition Using Collision Surrogate Measurement And Imbalanced Class Boosting | International journal of environmental research and public health | 2020-03-31 |
| 421 | Introduction To Rare-Event Predictive Modeling For Inferential Statisticians — A Hands-On Application In The Prediction Of Breakthrough Patents | DANIEL HAIN et. al. | In this paper, we introduce to a machine learning (ML) approach to quantitative analysis geared towards optimizing the predictive performance, contrasting it with standard practices inferential statistics which focus on producing good parameter estimates. | CS.arxiv | 2020-03-30 |
| 422 | Anomaly Detection Using Electric Impedance Tomography Based On Real And Imaginary Images | IMAM SAPUAN et. al. | Anomaly Detection Using Electric Impedance Tomography Based On Real And Imaginary Images | Sensors (Basel, Switzerland) | 2020-03-30 |
| 423 | Deep Learning-Based Anomaly Detection In Cyber-Physical Systems: Progress And Opportunities | YUAN LUO et. al. | In this paper, we review state-of-the-art DLAD methods in CPSs. | CS.arxiv | 2020-03-30 |
| 424 | Pattern Recognition And Anomaly Detection By Self-Organizing Maps In A Multi Month E-nose Survey At An Industrial Site | SABINA LICEN et. al. | Pattern Recognition And Anomaly Detection By Self-Organizing Maps In A Multi Month E-nose Survey At An Industrial Site | Sensors (Basel, Switzerland) | 2020-03-29 |
| 425 | Time Series Data Cleaning: From Anomaly Detection To Anomaly Repairing (Technical Report) | AOQIAN ZHANG et. al. | Instead of simply discarding anomalies, we propose to (iteratively) repair them in time series data, by creatively bonding the beauty of temporal nature in anomaly detection with the widely considered minimum change principle in data repairing. | CS.arxiv | 2020-03-27 |
| 426 | One-Shot GAN Generated Fake Face Detection | HADI MANSOURIFAR et. al. | In this paper, we propose a universal One-Shot GAN generated fake face detection method which can be used in significantly different areas of anomaly detection. | CS.arxiv | 2020-03-27 |
| 427 | ABBA: Adaptive Brownian Bridge-based Symbolic Aggregation Of Time Series | STEVEN ELSWORTH et. al. | We show that the reconstruction error of this representation can be modelled as a random walk with pinned start and end points, a so-called Brownian bridge. | CS.arxiv | 2020-03-27 |
| 428 | Viral Pneumonia Screening On Chest X-ray Images Using Confidence-Aware Anomaly Detection | JIANPENG ZHANG et. al. | In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module. | EESS.arxiv | 2020-03-27 |
| 429 | FastDTW Is Approximate And Generally Slower Than The Algorithm It Approximates | RENJIE WU et. al. | In this work, we make a surprising claim. | CS.arxiv | 2020-03-25 |
| 430 | MIM-Based Generative Adversarial Networks And Its Application On Anomaly Detection | RUI SHE et. al. | In this paper, we adopt the exponential form, referred from the Message Importance Measure (MIM), to replace the logarithm form of the original GAN. | CS.arxiv | 2020-03-25 |
| 431 | A One-Stage Approach For Surface Anomaly Detection With Background Suppression Strategies | GAOKAI LIU et. al. | A One-Stage Approach For Surface Anomaly Detection With Background Suppression Strategies | Sensors (Basel, Switzerland) | 2020-03-25 |
| 432 | Unsupervised Anomaly Detection With Adversarial Mirrored AutoEncoders | G. SOMEPALLI et. al. | In this work, we propose Adversarial Mirrored AutoEncoder (AMA), a simple modification to Adversarial Autoencoder where we use a Mirrored Wasserstein loss in the Discriminator to enforce better semantic-level reconstruction. | CS.arxiv | 2020-03-24 |
| 433 | Dividing Deep Learning Model For Continuous Anomaly Detection Of Inconsistent ICT Systems | KENGO TAJIRI et. al. | To solve this problem, we propose an ICT-systems-monitoring method with deep learning models divided based on the correlation of log data. | CS.arxiv | 2020-03-24 |
| 434 | Low Power Unsupervised Anomaly Detection By Non-Parametric Modeling Of Sensor Statistics | AHISH SHYLENDRA et. al. | This work presents AEGIS, a novel mixed-signal framework for real-time anomaly detection by examining sensor stream statistics. | EESS.arxiv | 2020-03-23 |
| 435 | Unsupervised Automatic Seizure Detection For Focal-onset Seizures Recorded With Behind-the-ear EEG Using An Anomaly-detecting Generative Adversarial Network | SUNGMIN YOU et. al. | Unsupervised Automatic Seizure Detection For Focal-onset Seizures Recorded With Behind-the-ear EEG Using An Anomaly-detecting Generative Adversarial Network | Computer methods and programs in biomedicine | 2020-03-23 |
| 436 | Attention-Based Self-Supervised Feature Learning For Security Data | I-TA LEE et. al. | In this paper, we design a self-supervised sequence-to-sequence model with attention to learn an embedding for data routinely used in cyber-security applications. | CS.arxiv | 2020-03-23 |
| 437 | Bayesian Models Applied To Cyber Security Anomaly Detection Problems | JOSÉ A. PERUSQUÍA et. al. | In this review we provide a description of some typical cyber security challenges, typical types of data and statistical methods, paying special attention to Bayesian approaches for these problems. | CS.arxiv | 2020-03-23 |
| 438 | Monocular Visual-inertial SLAM Algorithm Combined With Wheel Speed Anomaly Detection | PENG GANG et. al. | To address the weak observability of monocular visual-inertial odometers on ground-based mobile robots, this paper proposes a monocular inertial SLAM algorithm combined with wheel speed anomaly detection. | CS.arxiv | 2020-03-22 |
| 439 | Unsupervised Phase Discovery With Deep Anomaly Detection | KORBINIAN KOTTMANN et. al. | We demonstrate how to discover unknown quantum phases in many body systems using automated and unsupervised machine learning. | PHYSICS.arxiv | 2020-03-22 |
| 440 | Kontrol Edilebilir PtSTL Formulu Sentezi — Synthesis Of Controllable PtSTL Formulas | IRMAK SAGLAM et. al. | In this work, we develop an approach to anomaly detection and prevention problem using Signal Temporal Logic (STL). | CS.arxiv | 2020-03-22 |
| 441 | Visual Structural Assessment And Anomaly Detection For High-Velocity Data Streams | PUNIT RATHORE et. al. | Visual Structural Assessment And Anomaly Detection For High-Velocity Data Streams | IEEE transactions on cybernetics | 2020-03-19 |
| 442 | Synthesize Then Compare: Detecting Failures And Anomalies For Semantic Segmentation | YINGDA XIA et. al. | In this paper, we systematically study failure and anomaly detection for semantic segmentation and propose a unified framework, consisting of two modules, to address these two related problems. | CS.arxiv | 2020-03-18 |
| 443 | Deep Neural Network For Respiratory Sound Classification In Wearable Devices Enabled By Patient Specific Model Tuning | JYOTIBDHA ACHARYA et. al. | Deep Neural Network For Respiratory Sound Classification In Wearable Devices Enabled By Patient Specific Model Tuning | IEEE transactions on biomedical circuits and systems | 2020-03-18 |
| 444 | Self-Supervised Log Parsing | SASHO NEDELKOSKI et. al. | We propose a novel parsing technique called NuLog that utilizes a self-supervised learning model and formulates the parsing task as masked language modeling (MLM). | CS.arxiv | 2020-03-17 |
| 445 | Anomaly Detection In Video Data Based On Probabilistic Latent Space Models | GIULIA SLAVIC et. al. | This paper proposes a method for detecting anomalies in video data. | CS.arxiv | 2020-03-17 |
| 446 | Anomalous Instance Detection In Deep Learning: A Survey | SAIKIRAN BULUSU et. al. | Our goal in this survey is to provide an easier yet better understanding of the techniques belonging to different categories in which research has been done on this topic. | CS.arxiv | 2020-03-15 |
| 447 | Hybrid Cryptocurrency Pump And Dump Detection | HADI MANSOURIFAR et. al. | In this paper, we propose a novel hybrid pump and dump detection method based on distance and density metrics. | CS.arxiv | 2020-03-14 |
| 448 | A Software Deep Packet Inspection System For Network Traffic Analysis And Anomaly Detection | WENGUANG SONG et. al. | A Software Deep Packet Inspection System For Network Traffic Analysis And Anomaly Detection | Sensors (Basel, Switzerland) | 2020-03-14 |
| 449 | Building And Interpreting Deep Similarity Models | OLIVER EBERLE et. al. | In this paper, we propose to make similarities interpretable by augmenting them with an explanation in terms of input features. | CS.arxiv | 2020-03-11 |
| 450 | Anomaly Detection In Beehives Using Deep Recurrent Autoencoders | PADRAIG DAVIDSON et. al. | In this position paper we present an autoencoder, a deep learning model, which detects any type of anomaly in data independent of its origin. | CS.arxiv | 2020-03-10 |
| 451 | Hardware Architecture Proposal For TEDA Algorithm To Data Streaming Anomaly Detection | LUCILEIDE M. D. DA SILVA et. al. | As a possible solution to this problem, this paper proposes a hardware architecture for Typicality and Eccentricity Data Analytic (TEDA) algorithm implemented on Field Programmable Gate Arrays (FPGA) for use in data streaming anomaly detection. | CS.arxiv | 2020-03-08 |
| 452 | Isolation Mondrian Forest For Batch And Online Anomaly Detection | HAORAN MA et. al. | We propose a new method, named isolation Mondrian forest (iMondrian forest), for batch and online anomaly detection. | CS.arxiv | 2020-03-07 |
| 453 | Machine Learning Based Anomaly Detection For 5G Networks | JORDAN LAM et. al. | This paper proposes SDS (Software Defined Security) as a means to provide an automated, flexible and scalable network defence system. | CS.arxiv | 2020-03-06 |
| 454 | A Survey Of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG) | HONG ZU LI et. al. | A Survey Of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG) | Sensors (Basel, Switzerland) | 2020-03-06 |
| 455 | Malicious Network Traffic Detection Based On Deep Neural Networks And Association Analysis | MINGHUI GAO et. al. | Malicious Network Traffic Detection Based On Deep Neural Networks And Association Analysis | Sensors (Basel, Switzerland) | 2020-03-06 |
| 456 | A Survey On Big Data For Network Traffic Monitoring And Analysis | ALESSANDRO D’ALCONZO et. al. | This survey mainly focuses on approaches and technologies to manage the big NTMA data, additionally briefly discussing big data analytics (e.g., machine learning) for the sake of NTMA. | CS.arxiv | 2020-03-03 |
| 457 | $\text{A}^3$: Activation Anomaly Analysis | PHILIP SPERL et. al. | Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. | CS.arxiv | 2020-03-03 |
| 458 | CRATOS: Cognition Of Reliable Algorithm For Time-series Optimal Solution | ZILING WU et. al. | In this paper, we propose CRATOS which is a self-adapt algorithms that extract features from time series, and then cluster series with similar features into one group. | CS.arxiv | 2020-03-03 |
| 459 | Why Is The Mahalanobis Distance Effective For Anomaly Detection? | RYO KAMOI et. al. | This work analyzes why this method exhibits such strong performance in practical settings while imposing an implausible assumption; namely, that class conditional distributions of pre-trained features have tied covariance. | STATISTICS.arxiv | 2020-02-29 |
| 460 | Unsupervised Dictionary Learning For Anomaly Detection | PAUL IROFTI et. al. | We present new results of our recent semi-supervised online algorithm, TODDLeR, on a anti-money laundering application. | CS.arxiv | 2020-02-29 |
| 461 | DROCC: Deep Robust One-Class Classification | SACHIN GOYAL et. al. | In this work, we propose Deep Robust One-Class Classification (DROCC) that is both applicable to most standard domains without requiring any side-information and robust to representation collapse. | CS.arxiv | 2020-02-28 |
| 462 | Time Series Data Augmentation For Deep Learning: A Survey | QINGSONG WEN et. al. | In this paper, we systematically review different data augmentation methods for time series. | CS.arxiv | 2020-02-27 |
| 463 | Semi-supervised Anomaly Detection On Attributed Graphs | ATSUTOSHI KUMAGAI et. al. | We propose a simple yet effective method for detecting anomalous instances on an attribute graph with label information of a small number of instances. | STATISTICS.arxiv | 2020-02-27 |
| 464 | An On-Device Federated Learning Approach For Cooperative Anomaly Detection | REI ITO et. al. | In this paper, as an on-device learning algorithm, we focus on OS-ELM (Online Sequential Extreme Learning Machine) and combine it with autoencoder for anomaly detection. | CS.arxiv | 2020-02-27 |
| 465 | Wavelet-based Temporal Forecasting Models Of Human Activities For Anomaly Detection | MANUEL FERNANDEZ-CARMONA et. al. | This paper presents a novel approach for temporal modelling of long-term human activities based on wavelet transforms. | CS.arxiv | 2020-02-26 |
| 466 | Unsupervised Discovery, Control, And Disentanglement Of Semantic Attributes With Applications To Anomaly Detection | WILLIAM PAUL et. al. | For (a), we propose a network architecture that exploits the combination of multiscale generative models with mutual information (MI) maximization. | CS.arxiv | 2020-02-25 |
| 467 | Low-Rank And Sparse Decomposition With Mixture Of Gaussian For Hyperspectral Anomaly Detection | LU LI et. al. | Low-Rank And Sparse Decomposition With Mixture Of Gaussian For Hyperspectral Anomaly Detection | IEEE transactions on cybernetics | 2020-02-25 |
| 468 | Deep Nearest Neighbor Anomaly Detection | LIRON BERGMAN et. al. | In this work, we investigate whether the recent progress can indeed outperform nearest-neighbor methods operating on an Imagenet pretrained feature space. | CS.arxiv | 2020-02-24 |
| 469 | Modeling Aerial Gamma-Ray Backgrounds Using Non-negative Matrix Factorization | M. S. BANDSTRA et. al. | We propose using Non-negative Matrix Factorization (NMF) as a data-driven approach to spectral decomposition. | PHYSICS.arxiv | 2020-02-24 |
| 470 | One-Class Graph Neural Networks For Anomaly Detection In Attributed Networks | XUHONG WANG et. al. | In this work, we propose One Class Graph Neural Network (OCGNN), a one-class classification framework for graph anomaly detection. | CS.arxiv | 2020-02-21 |
| 471 | RobustPeriod: Time-Frequency Mining For Robust Multiple Periodicities Detection | QINGSONG WEN et. al. | In this paper, we propose a robust periodicity detection algorithm to address these challenges. | CS.arxiv | 2020-02-21 |
| 472 | RobustTAD: Robust Time Series Anomaly Detection Via Decomposition And Convolutional Neural Networks | JINGKUN GAO et. al. | In this paper, we propose RobustTAD, a Robust Time series Anomaly Detection framework by integrating robust seasonal-trend decomposition and convolutional neural network for time series data. | CS.arxiv | 2020-02-21 |
| 473 | Decision Triggered Data Transmission And Collection In Industrial Internet Of Things | JIGUANG HE et. al. | We propose a decision triggered data transmission and collection (DTDTC) protocol for condition monitoring and anomaly detection in the industrial Internet of things (IIoT). | EESS.arxiv | 2020-02-20 |
| 474 | Delegated Regressor, A Robust Approach For Automated Anomaly Detection In The Soil Radon Time Series Data | MUHAMMAD RAFIQUE et. al. | Delegated Regressor, A Robust Approach For Automated Anomaly Detection In The Soil Radon Time Series Data | Scientific reports | 2020-02-20 |
| 475 | Resource Usage And Performance Trade-offs For Machine Learning Models In Smart Environments | DAVY PREUVENEERS et. al. | Resource Usage And Performance Trade-offs For Machine Learning Models In Smart Environments | Sensors (Basel, Switzerland) | 2020-02-20 |
| 476 | A Flexible Outlier Detector Based On A Topology Given By Graph Communities | O. RAMOS TERRADES et. al. | This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. | CS.arxiv | 2020-02-18 |
| 477 | Correlation-aware Deep Generative Model For Unsupervised Anomaly Detection | HAOYI FAN et. al. | In this paper, we propose a method of Correlation aware unsupervised Anomaly detection via Deep Gaussian Mixture Model (CADGMM), which captures the complex correlation among data points for high-quality low-dimensional representation learning. | CS.arxiv | 2020-02-17 |
| 478 | Heterogeneous Sensing Data Analysis For Commercial Waste Collection | FOUED MELAKESSOU et. al. | Heterogeneous Sensing Data Analysis For Commercial Waste Collection | Sensors (Basel, Switzerland) | 2020-02-12 |
| 479 | A Review On Outlier/anomaly Detection In Time Series Data | ANE BLÁZQUEZ-GARCÍA et. al. | This review aims to provide a structured and comprehensive state-of-the-art on outlier detection techniques in the context of time series. | CS.arxiv | 2020-02-11 |
| 480 | AnomalyDAE: Dual Autoencoder For Anomaly Detection On Attributed Networks | HAOYI FAN et. al. | In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings. | CS.arxiv | 2020-02-10 |
| 481 | Out-of-Distribution Detection With Distance Guarantee In Deep Generative Models | YUFENG ZHANG et. al. | Based on our theorem and the key observation, we propose an easy-to-perform method both for group and point-wise anomaly detection via estimating the total correlation of representations in DGM. | CS.arxiv | 2020-02-09 |
| 482 | Multi-Scale Anomaly Detection On Attributed Networks | LEONARDO GUTIÉRREZ-GÓMEZ et. al. | Here, we propose a principled way to uncover outlier nodes simultaneously with the context with respect to which they are anomalous, at all relevant scales of the network. | CS.aaai | 2020-02-07 |
| 483 | Detecting Semantic Anomalies | FARUK AHMED et. al. | We critically appraise the recent interest in out-of-distribution (OOD) detection and question the practical relevance of existing benchmarks. | CS.aaai | 2020-02-07 |
| 484 | Memory Augmented Generative Adversarial Networks For Anomaly Detection | ZIYI YANG et. al. | In this paper, we present a memory-augmented algorithm for anomaly detection. | CS.arxiv | 2020-02-07 |
| 485 | Deep Unsupervised Binary Coding Networks For Multivariate Time Series Retrieval | DIXIAN ZHU et. al. | To this end, we present Deep Unsupervised Binary Coding Networks (DUBCNs) to perform multivariate time series retrieval. | CS.aaai | 2020-02-07 |
| 486 | Transfer Learning For Anomaly Detection Through Localized And Unsupervised Instance Selection | VERCRUYSSEN VINCENT et. al. | This paper proposes a novel transfer learning algorithm for anomaly detection that selects and transfers relevant labeled instances from a source anomaly detection task to a target one. | CS.aaai | 2020-02-07 |
| 487 | Anomaly Detection Using Deep Autoencoders For In-situ Wastewater Systems Monitoring Data | STEFANIA RUSSO et. al. | This paper proposes an anomaly detection method based on a deep autoencoder for in-situ wastewater systems monitoring data. | EESS.arxiv | 2020-02-07 |
| 488 | MixedAD: A Scalable Algorithm For Detecting Mixed Anomalies In Attributed Graphs | MENGXIAO ZHU et. al. | In this paper, we propose the scalable algorithm called MixedAD. | CS.aaai | 2020-02-07 |
| 489 | Butler Enables Rapid Cloud-based Analysis Of Thousands Of Human Genomes | SERGEI YAKNEEN et. al. | Butler Enables Rapid Cloud-based Analysis Of Thousands Of Human Genomes | Nature biotechnology | 2020-02-05 |
| 490 | Anomaly Detection By One Class Latent Regularized Networks | CHENGWEI CHEN et. al. | Given a wide range of images belonging to the normal class, emerging from some distribution, the objective of this task is to construct the model to detect out-of-distribution images belonging to abnormal instances. | CS.arxiv | 2020-02-04 |
| 491 | Acoustic Anomaly Detection Via Latent Regularized Gaussian Mixture Generative Adversarial Networks | CHENGWEI CHEN et. al. | In this paper, a novel Gaussian Mixture Generative Adversarial Network (GMGAN) is proposed under semi-supervised learning framework, in which the underlying structure of training data is not only captured in spectrogram reconstruction space, but also can be further restricted in the space of latent representation in a discriminant manner. | EESS.arxiv | 2020-02-03 |
| 492 | Prediction Of Chronic Disease-Related Inpatient Prolonged Length Of Stay Using Machine Learning Algorithms | HASAN SYMUM et. al. | Prediction Of Chronic Disease-Related Inpatient Prolonged Length Of Stay Using Machine Learning Algorithms | Healthcare informatics research | 2020-01-31 |
| 493 | Real-Time Nonparametric Anomaly Detection In High-Dimensional Settings | MEHMET NECIP KURT et. al. | Real-Time Nonparametric Anomaly Detection In High-Dimensional Settings | IEEE transactions on pattern analysis and machine intelligence | 2020-01-30 |
| 494 | Ensemble Grammar Induction For Detecting Anomalies In Time Series | YIFENG GAO et. al. | In this paper, we introduce a grammar-induction-based anomaly detection method utilizing ensemble learning. | CS.arxiv | 2020-01-29 |
| 495 | Estimating Descriptors For Large Graphs | ZOHAIR RAZA HASSAN et. al. | In this work, we propose streaming algorithms to efficiently approximate descriptors by estimating counts of sub-graphs of order $k\leq 4$, and thereby devise extensions of two existing graph comparison paradigms: the Graphlet Kernel and NetSimile. | CS.arxiv | 2020-01-28 |
| 496 | Real-time Out-of-distribution Detection In Learning-Enabled Cyber-Physical Systems | FEIYANG CAI et. al. | We use variational autoencoders and deep support vector data description to learn models that can be used efficiently compute the nonconformity of new inputs relative to the training set and enable real-time detection of out-of-distribution high-dimensional inputs. | CS.arxiv | 2020-01-28 |
| 497 | Comprehensive Analysis Of Time Series Forecasting Using Neural Networks | MANIE TADAYON et. al. | The massive volume of data and recent advancements in the processing power of the computers enable researchers to develop more sophisticated machine learning algorithms such as neural networks to forecast the time series data. | CS.arxiv | 2020-01-26 |
| 498 | Development And Use Of A Clinical Decision Support System For The Diagnosis Of Social Anxiety Disorder | SINA FATHI et. al. | Development And Use Of A Clinical Decision Support System For The Diagnosis Of Social Anxiety Disorder | Computer methods and programs in biomedicine | 2020-01-24 |
| 499 | Detection Of Thin Boundaries Between Different Types Of Anomalies In Outlier Detection Using Enhanced Neural Networks | RASOUL KIANI et. al. | Basic domain-independent methods are introduced to detect these defined anomalies in both unsupervised and supervised datasets. | CS.arxiv | 2020-01-24 |
| 500 | Security In Process: Visually Supported Triage Analysis In Industrial Process Data | ANNA-PIA LOHFINK et. al. | Security In Process: Visually Supported Triage Analysis In Industrial Process Data | IEEE transactions on visualization and computer graphics | 2020-01-24 |
| 501 | RePAD: Real-time Proactive Anomaly Detection For Time Series | MING-CHANG LEE et. al. | During the past decade, many anomaly detection approaches have been introduced in different fields such as network monitoring, fraud detection, and intrusion detection. | CS.arxiv | 2020-01-24 |
| 502 | Kernel-based Gaussian Process For Anomaly Detection In Sparse Gamma-ray Data | GREGORY R ROMANCHEK et. al. | Kernel-based Gaussian Process For Anomaly Detection In Sparse Gamma-ray Data | PloS one | 2020-01-23 |
| 503 | Universal Data Anomaly Detection Via Inverse Generative Adversary Network | KURSAT RASIM MESTAV et. al. | A semi-supervised deep learning technique based on an inverse generative adversary network is proposed. | CS.arxiv | 2020-01-23 |
| 504 | Simple And Effective Prevention Of Mode Collapse In Deep One-Class Classification | PENNY CHONG et. al. | In this work, we consider two regularizers to prevent hypersphere collapse in deep SVDD. | CS.arxiv | 2020-01-23 |
| 505 | Safe Robot Navigation Via Multi-Modal Anomaly Detection | LORENZ WELLHAUSEN et. al. | In this work, we evaluate multiple anomaly detection methods with a combination of uni- and multi-modal images in their performance on data from different environmental conditions. | CS.arxiv | 2020-01-22 |
| 506 | A Versatile Anomaly Detection Method For Medical Images With A Flow-based Generative Model In Semi-supervision Setting | H. SHIBATA et. al. | In this study, we present an anomaly detection method based on two trained flow-based generative models. | EESS.arxiv | 2020-01-21 |
| 507 | An Intelligent And Time-Efficient DDoS Identification Framework For Real-Time Enterprise Networks SAD-F: Spark Based Anomaly Detection Framework | AWAIS AHMED et. al. | In this paper, we will be exploring security analytic techniques for DDoS anomaly detection using different machine learning techniques. | CS.arxiv | 2020-01-21 |
| 508 | Randomized Algorithms For Computation Of Tucker Decomposition And Higher Order SVD (HOSVD) | SALMAN AHMADI-ASL et. al. | In this paper, we review recent advances in randomization for computation of Tucker decomposition and Higher Order SVD (HOSVD). | MATH.arxiv | 2020-01-20 |
| 509 | Transparently Capturing Request Execution Path For Anomaly Detection | YONG YANG et. al. | We analyze a comprehensive list of execution scenarios, and propose principles and algorithms for generating the end-to-end request execution path for all the scenarios. | CS.arxiv | 2020-01-20 |
| 510 | Unsupervised GEI-Based Gait Disorders Detection From Different Views | AMR ELKHOLY et. al. | Unsupervised GEI-Based Gait Disorders Detection From Different Views | Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference | 2020-01-18 |
| 511 | OIAD: One-for-all Image Anomaly Detection With Disentanglement Learning | SHUO WANG et. al. | In this paper, we propose a One-for-all Image Anomaly Detection system (OIAD) based on disentangled learning using only clean samples. | CS.arxiv | 2020-01-18 |
| 512 | Neighborhood Structure Assisted Non-negative Matrix Factorization And Its Application In Unsupervised Point Anomaly Detection | IMTIAZ AHMED et. al. | To address that shortcoming, we propose to consider and incorporate the neighborhood structural similarity information within the NMF framework by modeling the data through a minimum spanning tree. | CS.arxiv | 2020-01-17 |
| 513 | Regularized Cycle Consistent Generative Adversarial Network For Anomaly Detection | ZIYI YANG et. al. | In this paper, we investigate algorithms for anomaly detection. | CS.arxiv | 2020-01-17 |
| 514 | Unsupervised Learning Of The Set Of Local Maxima | LIOR WOLF et. al. | We present an algorithm, show an example where it is more efficient to use local maxima as an indicator function than to employ conventional classification, and derive a suitable generalization bound. | CS.arxiv | 2020-01-14 |
| 515 | Simulation Assisted Likelihood-free Anomaly Detection | ANDERS ANDREASSEN et. al. | This paper introduces a hybrid method that makes the best of both approaches. | PHYSICS.arxiv | 2020-01-14 |
| 516 | Unsupervised Distribution Learning For Lunar Surface Anomaly Detection | ADAM LESNIKOWSKI et. al. | In this work we show that modern data-driven machine learning techniques can be successfully applied on lunar surface remote sensing data to learn, in an unsupervised way, sufficiently good representations of the data distribution to enable lunar technosignature and anomaly detection. | PHYSICS.arxiv | 2020-01-14 |
| 517 | Anomaly Detection With Density Estimation | BENJAMIN NACHMAN et. al. | We leverage recent breakthroughs in neural density estimation to propose a new unsupervised anomaly detection technique (ANODE). | PHYSICS.arxiv | 2020-01-14 |
| 518 | Deep Learning Achieves Perfect Anomaly Detection On 108,308 Retinal Images Including Unlearned Diseases | AYAKA SUZUKI et. al. | As long as there is a risk of overlooking a disease with a technique, ophthalmologists must double-check even those images that the technique classifies as normal. | CS.arxiv | 2020-01-13 |
| 519 | Anomaly Detection In EEG Signals: A Case Study On Similarity Measure | GUANGYUAN CHEN et. al. | Anomaly Detection In EEG Signals: A Case Study On Similarity Measure | Computational intelligence and neuroscience | 2020-01-10 |
| 520 | Adaptive Anomaly Detection For IoT Data In Hierarchical Edge Computing | MAO V. NGO et. al. | In this paper, we propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. | CS.arxiv | 2020-01-10 |
| 521 | Utility Of Follow-up Standard Sonography For Fetal Anomaly Detection | JOHN J BYRNE et. al. | Utility Of Follow-up Standard Sonography For Fetal Anomaly Detection | American journal of obstetrics and gynecology | 2020-01-10 |
| 522 | Towards Automatic Threat Detection: A Survey Of Advances Of Deep Learning Within X-ray Security Imaging | SAMET AKCAY et. al. | This paper aims to review computerised X-ray security imaging algorithms by taxonomising the field into conventional machine learning and contemporary deep learning applications. The paper further explores well-established X-ray datasets and provides a performance benchmark. | CS.arxiv | 2020-01-05 |
| 523 | Root Cause Detection Among Anomalous Time Series Using Temporal State Alignment | SAYAN CHAKRABORTY et. al. | In this paper, we propose a method that isolates the root cause of an anomaly by analyzing the patterns in time series fluctuations. | CS.arxiv | 2020-01-04 |
| 524 | Automated Segmentation Of Acute Stroke Lesions Using A Data-driven Anomaly Detection On Diffusion Weighted MRI | SANAZ NAZARI-FARSANI et. al. | Automated Segmentation Of Acute Stroke Lesions Using A Data-driven Anomaly Detection On Diffusion Weighted MRI | Journal of neuroscience methods | 2020-01-02 |
| 525 | A General Anomaly Detection Framework For Fleet-based Condition Monitoring Of Machines | KILIAN HENDRICKX et. al. | This work proposes an unsupervised, generic, anomaly detection framework for fleet-based condition monitoring. | CS.arxiv | 2019-12-30 |
| 526 | Low-Rate DoS Attacks Detection Based On MAF-ADM | SIJIA ZHAN et. al. | Low-Rate DoS Attacks Detection Based On MAF-ADM | Sensors (Basel, Switzerland) | 2019-12-29 |
| 527 | History-based Anomaly Detector: An Adversarial Approach To Anomaly Detection | PIERRICK CHATILLON et. al. | In this article, we propose a simple yet new adversarial method to tackle this problem, denoted as History-based anomaly detector (HistoryAD). | CS.arxiv | 2019-12-26 |
| 528 | A Machine Learning-Based Method For Automated Blockchain Transaction Signing Including Personalized Anomaly Detection | BLAŽ PODGORELEC et. al. | A Machine Learning-Based Method For Automated Blockchain Transaction Signing Including Personalized Anomaly Detection | Sensors (Basel, Switzerland) | 2019-12-25 |
| 529 | Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services | YUANSHENG LUO et. al. | Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services | Sensors (Basel, Switzerland) | 2019-12-24 |
| 530 | Detecting Stuffing Of A User’s Credentials At Her Own Accounts | KE COBY WANG et. al. | We propose a framework by which websites can coordinate to detect credential stuffing on individual user accounts. | CS.arxiv | 2019-12-23 |
| 531 | AEGR: A Simple Approach To Gradient Reversal In Autoencoders For Network Anomaly Detection | KASRA BABAEI et. al. | In this paper, a new gradient-reversal method is proposed to overcome the influence of anomalies on the training phase for the purpose of detecting network anomalies. | CS.arxiv | 2019-12-21 |
| 532 | Robust Anomaly Detection And Backdoor Attack Detection Via Differential Privacy | MIN DU et. al. | This paper shows that differential privacy could improve the utility of outlier detection, novelty detection and backdoor attack detection, through both a theoretical analysis and extensive experimental results (constructed and real-world). | CS.iclr | 2019-12-21 |
| 533 | Deep Semi-Supervised Anomaly Detection | LUKAS RUFF et. al. | We introduce Deep SAD, a deep method for general semi-supervised anomaly detection that especially takes advantage of labeled anomalies. | CS.iclr | 2019-12-21 |
| 534 | Optimal Strategies Against Generative Attacks | ROY MOR et. al. | We cast the problem as a maximin game, characterize the optimal strategy for both attacker and authenticator in the general case, and provide the optimal strategies in closed form for the case of Gaussian source distributions. | CS.iclr | 2019-12-21 |
| 535 | Normalizing Flows For Deep Anomaly Detection | ARTEM RYZHIKOV et. al. | In this work, weconsider cases with missing certain kinds of anomalies in the training dataset, while significant statistics for the normal class isavailable. | CS.arxiv | 2019-12-19 |
| 536 | Adaptive Scheduling For Efficient Execution Of Dynamic Stream Workflows | MUTAZ BARIKA et. al. | Therefore, we propose a two-phase adaptive scheduling technique to efficiently schedule dynamic workflow application in Multicloud environment that can respond to changes in the velocity of data at runtime. | CS.arxiv | 2019-12-18 |
| 537 | Unsupervised Anomaly Detection In Stream Data With Online Evolving Spiking Neural Networks | PIOTR S. MACIĄG et. al. | In this work, we propose a novel OeSNN-UAD (Online evolving Spiking Neural Networks for Unsupervised Anomaly Detection) approach for online anomaly detection in univariate time series data. | CS.arxiv | 2019-12-18 |
| 538 | Probabilistic Software Modeling: A Data-driven Paradigm For Software Analysis | HANNES THALLER et. al. | In this work, we present the modeling methodologies, an empirical study of the runtime behavior of software systems, and a comprehensive study on PSM modeled systems. | CS.arxiv | 2019-12-17 |
| 539 | MimicGAN: Robust Projection Onto Image Manifolds With Corruption Mimicking | RUSHIL ANIRUDH et. al. | To address this, we propose corruption mimicking — a new robust projection technique, that utilizes a surrogate network to approximate the unknown corruption directly at test time, without the need for additional supervision or data augmentation. | CS.arxiv | 2019-12-16 |
| 540 | Mathematical Methods For Visualization And Anomaly Detection In Telemetry Datasets | MANUCHEHR AMINIAN et. al. | Mathematical Methods For Visualization And Anomaly Detection In Telemetry Datasets | Interface focus | 2019-12-13 |
| 541 | TopoAct: Visually Exploring The Shape Of Activations In Deep Learning | ARCHIT RATHORE et. al. | With a large number of inputs, we aim to obtain a global view of what neurons detect by studying their activations. | CS.arxiv | 2019-12-13 |
| 542 | Peek Inside The Closed World: Evaluating Autoencoder-Based Detection Of DDoS To Cloud | HANG GUO et. al. | Our primary contribution is to improve our understanding for why ML-based AD works on some malicious flows but not others. | CS.arxiv | 2019-12-11 |
| 543 | Using Deep Learning To Solve Computer Security Challenges: A Survey | YOON-HO CHOI et. al. | In particular, the review covers eight computer security problems being solved by applications of Deep Learning: security-oriented program analysis, defending return-oriented programming (ROP) attacks, achieving control-flow integrity (CFI), defending network attacks, malware classification, system-event-based anomaly detection, memory forensics, and fuzzing for software security. | CS.arxiv | 2019-12-11 |
| 544 | Minority Report Detection In Refugee-authored Community-driven Journalism Using RBMs | BOGDANA RAKOVA et. al. | In this paper, we propose the following 1) a method of data collection through stakeholder organizations experienced in working with displaced and refugee communities, 2) a method of topic modeling based on Deep Boltzmann Machines that identifies topics and issues of interest within the population, to help enable mapping of human rights violations, and 3) a secondary analysis component that will use the probability of fit to isolate minority reports within these stories using anomaly detection techniques. | CS.arxiv | 2019-12-10 |
| 545 | Security In Process: Visually Supported Triage Analysis In Industrial Process Data | ANNA-PIA LOHFINK et. al. | We present a tailored visualization system that utilizes inherent features of measurements from industrial processes to full capacity to provide insight into the data and support triage analysis by laymen and experts. | CS.arxiv | 2019-12-10 |
| 546 | Oversampling Log Messages Using A Sequence Generative Adversarial Network For Anomaly Detection And Classification | AMIR FARZAD et. al. | In this paper, a model is proposed to generate text log messages using a SeqGAN network. | CS.arxiv | 2019-12-09 |
| 547 | Deep Autoencoders With Value-at-Risk Thresholding For Unsupervised Anomaly Detection | ALBERT AKHRIEV et. al. | We consider an incremental-learning approach, wherein a deep-autoencoding (DAE) model of what is normal is trained and used to detect anomalies at the same time. | CS.arxiv | 2019-12-09 |
| 548 | PIDForest: Anomaly Detection Via Partial Identification | PARIKSHIT GOPALAN et. al. | We propose a framework called Partial Identification which captures the intuition that anomalies are easy to distinguish from the overwhelming majority of points by relatively few attribute values. | CS.arxiv | 2019-12-07 |
| 549 | Transfer Learning From An Auxiliary Discriminative Task For Unsupervised Anomaly Detection | URWA MUAZ et. al. | This study aims to investigate the transferability of features learned by network classification to unsupervised anomaly detection. | CS.arxiv | 2019-12-05 |
| 550 | Deep Anomaly Detection In Packet Payload | JIAXIN LIU et. al. | To overcome these limitations and adaptively detect anomalies from the packet payload, we propose a deep learning based framework which consists of two steps. | EESS.arxiv | 2019-12-05 |
| 551 | ATEMPO: Pathway-Specific Temporal Anomalies For Precision Therapeutics | CHRISTOPHER MICHAEL PIETRAS et. al. | ATEMPO: Pathway-Specific Temporal Anomalies For Precision Therapeutics | Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing | 2019-12-05 |
| 552 | Computing The Statistical Significance Of Optimized Communities In Networks | JOHN PALOWITCH | Computing The Statistical Significance Of Optimized Communities In Networks | Scientific reports | 2019-12-05 |
| 553 | Copula-based Anomaly Scoring And Localization For Large-scale, High-dimensional Continuous Data | GÁBOR HORVÁTH et. al. | Since the rare events are present in the tails of the probability distributions, we use copula functions, that are able to model the fat-tailed distributions well. | CS.arxiv | 2019-12-04 |
| 554 | ADEPOS: A Novel Approximate Computing Framework For Anomaly Detection Systems And Its Implementation In 65nm CMOS | SUMON KUMAR BOSE et. al. | In this paper, we present an approximate computing method to reduce the computation energy of a specific type of IoT system used for anomaly detection (e.g. in predictive maintenance, epileptic seizure detection, etc). | CS.arxiv | 2019-12-04 |
| 555 | Semi-Supervised Learning Of Bearing Anomaly Detection Via Deep Variational Autoencoders | SHEN ZHANG et. al. | In this paper, we thus propose a semi-supervised learning approach for bearing anomaly detection using variational autoencoder (VAE) based deep generative models, which allows for effective utilization of dataset when only a small subset of data have labels. | CS.arxiv | 2019-12-02 |
| 556 | Anomaly Detection In Particulate Matter Sensor Using Hypothesis Pruning Generative Adversarial Network | YEONGHYEON PARK et. al. | In this paper, we call the overall malfunction as an anomaly, and we aim to detect anomalies for the maintenance of PM measuring sensors. | CS.arxiv | 2019-12-02 |
| 557 | ACE — An Anomaly Contribution Explainer For Cyber-Security Applications | XIAO ZHANG et. al. | In this paper, we introduce Anomaly Contribution Explainer or ACE, a tool to explain security anomaly detection models in terms of the model features through a regression framework, and its variant, ACE-KL, which highlights the important anomaly contributors. | CS.arxiv | 2019-11-30 |
| 558 | Free-riders In Federated Learning: Attacks And Defenses | JIERUI LIN et. al. | In this paper, we are the first to propose the notion of free rider attacks, to explore possible ways that an attacker may construct gradient updates, without any local training data. | CS.arxiv | 2019-11-28 |
| 559 | Online And Unsupervised Anomaly Detection For Streaming Data Using An Array Of Sliding Windows And PDDs | LINGYU ZHANG et. al. | Online And Unsupervised Anomaly Detection For Streaming Data Using An Array Of Sliding Windows And PDDs | IEEE transactions on cybernetics | 2019-11-28 |
| 560 | High- And Low-level Image Component Decomposition Using VAEs For Improved Reconstruction And Anomaly Detection | DAVID ZIMMERER et. al. | We propose to alleviate these issues by adding a new branch to conditional hierarchical VAEs. | CS.arxiv | 2019-11-27 |
| 561 | Sparse-GAN: Sparsity-constrained Generative Adversarial Network For Anomaly Detection In Retinal OCT Image | KANG ZHOU et. al. | To address the limitation, we propose a novel anomaly detection framework termed Sparsity-constrained Generative Adversarial Network (Sparse-GAN) for disease screening where only healthy data are available in the training set. | CS.arxiv | 2019-11-27 |
| 562 | Annotation-free Learning Of Plankton For Classification And Anomaly Detection | PASTORE, V. P. et. al. | In this paper we present a novel set of algorithms to perform accurate detection and classification of plankton species with minimal supervision. In this paper, we present a novel set of algorithms to perform accurate detection and classification of plankton species with minimal supervision. | BIOLOGY.biorxiv | 2019-11-27 |
| 563 | Host-based Anomaly Detection Using Eigentraces Feature Extraction And One-class Classification On System Call Trace Data | EHSAN AGHAEI et. al. | This paper proposes a methodology for host-based anomaly detection using a semi-supervised algorithm namely one-class classifier combined with a PCA-based feature extraction technique called Eigentraces on system call trace data. | CS.arxiv | 2019-11-25 |
| 564 | Latent Space Conditioning For Improved Classification And Anomaly Detection | ERIK NORLANDER et. al. | We propose a new type of variational autoencoder to perform improved pre-processing for clustering and anomaly detection on data with a given label. | CS.arxiv | 2019-11-24 |
| 565 | AnoNet: Weakly Supervised Anomaly Detection In Textured Surfaces | MANPREET SINGH MINHAS et. al. | We tackle the problem of teaching a network with a low number of training samples with a system we call AnoNet. | CS.arxiv | 2019-11-24 |
| 566 | Attribute Restoration Framework For Anomaly Detection | CHAOQIN HUANG et. al. | We here propose to break this equivalence by erasing selected attributes from the original data and reformulate it as a restoration task, where the normal and the anomalous data are expected to be distinguishable based on restoration errors. | CS.arxiv | 2019-11-24 |
| 567 | Regularized Siamese Neural Network For Unsupervised Outlier Detection On Brain Multiparametric Magnetic Resonance Imaging: Application To Epilepsy Lesion Screening | ZARUHI ALAVERDYAN et. al. | Regularized Siamese Neural Network For Unsupervised Outlier Detection On Brain Multiparametric Magnetic Resonance Imaging: Application To Epilepsy Lesion Screening | Medical image analysis | 2019-11-21 |
| 568 | Progressive Temporal-Spatial-Semantic Analysis Of Driving Anomaly Detection And Recounting | RIXING ZHU et. al. | Progressive Temporal-Spatial-Semantic Analysis Of Driving Anomaly Detection And Recounting | Sensors (Basel, Switzerland) | 2019-11-21 |
| 569 | EvAn: Neuromorphic Event-based Anomaly Detection | LAKSHMI ANNAMALAI et. al. | In this paper, for the first time in event data analytics community, we leverage these advantages of an event camera towards a critical vision application – video anomaly detection. Since there is no existing dataset for anomaly detection in event domain, we also provide an anomaly detection event dataset with an exhaustive set of anomalies. | STATISTICS.arxiv | 2019-11-21 |
| 570 | Generalizing Information To The Evolution Of Rational Belief | JED A. DUERSCH et. al. | We derive a general theory of information from first principles that accounts for evolving belief and recovers all of these measures. | CS.arxiv | 2019-11-21 |
| 571 | Rule Extraction In Unsupervised Anomaly Detection For Model Explainability: Application To OneClass SVM | ALBERTO BARBADO et. al. | Together with that, we propose algorithms to compute metrics related with XAI regarding the comprehensivility, representativeness, stability and diversity of the rules extracted. | CS.arxiv | 2019-11-21 |
| 572 | Log Message Anomaly Detection And Classification Using Auto-B/LSTM And Auto-GRU | AMIR FARZAD et. al. | In this paper, Deep Learning (DL) methods called Auto-LSTM, Auto-BLSTM and Auto-GRU are developed for anomaly detection and log classification. | CS.arxiv | 2019-11-20 |
| 573 | A Framework For End-to-End Deep Learning-Based Anomaly Detection In Transportation Networks | NEEMA DAVIS et. al. | Experiments on seven diverse real-world data sets demonstrate the superior anomaly detection performance of our proposed model over the other models considered in the comparison study. | CS.arxiv | 2019-11-20 |
| 574 | Deep Anomaly Detection With Deviation Networks | GUANSONG PANG et. al. | This paper introduces a novel anomaly detection framework and its instantiation to address these problems. | CS.arxiv | 2019-11-19 |
| 575 | Seq2Seq RNN Based Gait Anomaly Detection From Smartphone Acquired Multimodal Motion Data | RICCARDO BONETTO et. al. | Seq2Seq RNN Based Gait Anomaly Detection From Smartphone Acquired Multimodal Motion Data | EESS.arxiv | 2019-11-19 |
| 576 | A Promotion Method For Generation Error Based Video Anomaly Detection | ZHIGUO WANG et. al. | To address these problems, we propose a promotion method: utilize the maximum of block-level GEs on the frame to detect anomaly. | CS.arxiv | 2019-11-19 |
| 577 | On The Impact Of Object And Sub-component Level Segmentation Strategies For Supervised Anomaly Detection Within X-ray Security Imagery | NEELANJAN BHOWMIK et. al. | Here we address this problem considering varying segmentation strategies to enable the use of both object level and sub-component level anomaly detection via the use of secondary convolutional neural network (CNN) architectures. | CS.arxiv | 2019-11-19 |
| 578 | Online Adaptive Asymmetric Active Learning With Limited Budgets | YIFAN ZHANG et. al. | In this paper, we propose a novel Online Adaptive Asymmetric Active learning algorithm, based on a new asymmetric strategy (merging both asymmetric losses and queries strategies), and second-order optimization. | CS.arxiv | 2019-11-18 |
| 579 | Deep Verifier Networks: Verification Of Deep Discriminative Models With Deep Generative Models | TONG CHE et. al. | In this paper, we propose a novel framework — deep verifier networks (DVN) to verify the inputs and outputs of deep discriminative models with deep generative models. | CS.arxiv | 2019-11-17 |
| 580 | Finding Skewed Subcubes Under A Distribution | PARIKSHIT GOPALAN et. al. | In this work we focus on the special but important case where the space is the Boolean hypercube, and the expected marginals are uniform. We propose the problem of enumerating/list-decoding all large subcubes where the distribution $\psi$ deviates markedly from what we expect; we refer to such subcubes as skewed subcubes. | CS.arxiv | 2019-11-17 |
| 581 | RSM-GAN: A Convolutional Recurrent GAN For Anomaly Detection In Contaminated Seasonal Multivariate Time Series | FARZANEH KHOSHNEVISAN et. al. | This paper presents a novel unsupervised deep learning architecture for multivariate time series anomaly detection, called Robust Seasonal Multivariate Generative Adversarial Network (RSM-GAN). | CS.arxiv | 2019-11-16 |
| 582 | Learning Representations For Time Series Clustering | QIANLI MA et. al. | Here we propose a novel unsupervised temporal representation learning model, named Deep Temporal Clustering Representation (DTCR), which integrates the temporal reconstruction and K-means objective into the seq2seq model. | CS.nips | 2019-11-15 |
| 583 | Statistical Analysis Of Nearest Neighbor Methods For Anomaly Detection | XIAOYI GU et. al. | In this paper we are concerned with investigating the performance of NN-based methods for anomaly detection. | CS.nips | 2019-11-15 |
| 584 | Transfer Anomaly Detection By Inferring Latent Domain Representations | ATSUTOSHI KUMAGAI et. al. | We propose a method to improve the anomaly detection performance on target domains by transferring knowledge on related domains. | CS.nips | 2019-11-15 |
| 585 | BIVA: A Very Deep Hierarchy Of Latent Variables For Generative Modeling | LARS MAAL�E et. al. | In this paper we close the performance gap by constructing VAE models that can effectively utilize a deep hierarchy of stochastic variables and model complex covariance structures. | CS.nips | 2019-11-15 |
| 586 | High-dimensional Multivariate Forecasting With Low-rank Gaussian Copula Processes | DAVID SALINAS et. al. | We propose to combine an RNN-based time series model with a Gaussian copula process output model with a low-rank covariance structure to reduce the computational complexity and handle non-Gaussian marginal distributions. | CS.nips | 2019-11-15 |
| 587 | Uncertainty On Asynchronous Time Event Prediction | BERTRAND CHARPENTIER et. al. | In this work, we tackle the task of predicting the next event (given a history), and how this prediction changes with the passage of time. | CS.nips | 2019-11-15 |
| 588 | Real-time Anomaly Detection And Classification In Streaming PMU Data | CHRISTOPHER HANNON et. al. | This work presents a general interpretable framework for analyzing real-time PMU data, and thus enabling grid operators to understand the current state and to identify anomalies on the fly. | EESS.arxiv | 2019-11-14 |
| 589 | Uncertainty On Asynchronous Time Event Prediction | MARIN BILOŠ et. al. | In this work, we tackle the task of predicting the next event (given a history), and how this prediction changes with the passage of time. | CS.arxiv | 2019-11-13 |
| 590 | Real-time Ultra-low Power ECG Anomaly Detection Using An Event-driven Neuromorphic Processor | FELIX CHRISTIAN BAUER et. al. | In this work we propose a compact and sub-mW low power neural processing system that can be used to perform on-line and real-time preliminary diagnosis of pathological conditions, to raise warnings for the existence of possible pathological conditions, or to trigger an off-line data recording system for further analysis by a medical professional. | EESS.arxiv | 2019-11-13 |
| 591 | Anomaly Detection In Large Scale Networks With Latent Space Models | WESLEY LEE et. al. | We develop a real-time anomaly detection algorithm for directed activity on large, sparse networks. | STATISTICS.arxiv | 2019-11-13 |
| 592 | Machine Learning Based Network Vulnerability Analysis Of Industrial Internet Of Things | MAEDE ZOLANVARI et. al. | Following that, a literature review of the available intrusion detection solutions using machine learning models is presented. | CS.arxiv | 2019-11-13 |
| 593 | Real-Time Anomaly Detection For Advanced Manufacturing: Improving On Twitter’s State Of The Art | CAITRÍONA M. RYAN et. al. | This paper presents a novel algorithm to detect anomalies in streaming time series data via statistical learning. | EESS.arxiv | 2019-11-13 |
| 594 | Enabling Efficient Privacy-Assured Outlier Detection Over Encrypted Incremental Datasets | SHANGQI LAI et. al. | In this paper, we present a privacy-preserving outlier detection protocol (PPOD) for incremental datasets. | CS.arxiv | 2019-11-13 |
| 595 | Local Distinguishability Aggrandizing Network For Human Anomaly Detection | MAOGUO GONG et. al. | Local Distinguishability Aggrandizing Network For Human Anomaly Detection | Neural networks : the official journal of the International Neural Network Society | 2019-11-13 |
| 596 | Data Anomaly Detection For Internet Of Vehicles Based On Traffic Cellular Automata And Driving Style | NAN DING et. al. | Data Anomaly Detection For Internet Of Vehicles Based On Traffic Cellular Automata And Driving Style | Sensors (Basel, Switzerland) | 2019-11-12 |
| 597 | Detecting Network Disruptions At Colocation Facilities | ALEXANDROS MILOLIDAKIS et. al. | In this paper we devise a methodology to identify collocation facilities in traceroute data and to monitor delay and routing patterns between facilities. | CS.arxiv | 2019-11-12 |
| 598 | Anomaly Detection For Industrial Control Systems Using Sequence-to-Sequence Neural Networks | JONGUK KIM et. al. | This study proposes an anomaly detection method for operational data of industrial control systems (ICSs). | CS.arxiv | 2019-11-12 |
| 599 | Deep Variational Semi-Supervised Novelty Detection | TAL DANIEL et. al. | In this work, we propose two variational methods for training VAEs for SSAD. | CS.arxiv | 2019-11-12 |
| 600 |