Anomaly detection from time-changing environmental sensor data streams

This thesis stems from the project with real-time environmental monitoring company EMSAT Corporation. They were looking for methods to automatically ag spikes and other anomalies in their environmental sensor data streams. The problem presents several challenges: near real-time anomaly detection, absence of labeled data and time-changing data streams. Here, we address this problem using both a statistical parametric approach as well as a non-parametric approach like Kernel Density Estimation (KDE). The main contribution of this thesis is extending the KDE to work more effectively for evolving data streams, particularly in presence of concept drift. To address that, we have developed a framework for integrating Adaptive Windowing (ADWIN) change detection algorithm with KDE. We have tested this approach on several real world data sets and received positive feedback from our industry collaborator. Some results appearing in this thesis have been presented at ECML PKDD 2015 Doctoral Consortium.

Data mining and visualization for anomaly detection and diagnosis in civil structures

Peer reviewed

Antarctic free-air and complete Bouguer gravity anomaly grid

Gravity surveying is challenging in Antarctica because of its hostile environment and inaccessibility. Nevertheless, many ground-based, airborne and shipborne gravity campaigns have been completed by the geophysical and geodetic communities since the 1980s. We present the first modern Antarctic-wide gravity data compilation derived from 13 million data points covering an area of 10 million km**2, which corresponds to 73% coverage of the continent. The remove-compute-restore technique was applied for gridding, which facilitated levelling of the different gravity datasets with respect to an Earth Gravity Model derived from satellite data alone. The resulting free-air and Bouguer gravity anomaly grids of 10 km resolution are publicly available. These grids will enable new high-resolution combined Earth Gravity Models to be derived and represent a major step forward towards solving the geodetic polar data gap problem. They provide a new tool to investigate continental-scale lithospheric structure and geological evolution of Antarctica.

Seafloor spreading magnetic anomaly isochron map compilation for the Weddell Sea and Scotia Sea

The deep sea sedimentary record is an archive of the pre-glacial to glacial development of Antarctica and changes in climate, tectonics and ocean circulation. Identification of the pre-glacial, transitional and full glacial components in the sedimentary record is necessary for ice sheet reconstruction and to build circum-Antarctic sediment thickness grids for past topography and bathymetry reconstructions, which constrain paleoclimate models. A ~3300 km long Weddell Sea to Scotia Sea transect consisting of multichannel seismic reflection data from various organisations, were used to interpret new horizons to define the initial basin-wide seismostratigraphy and to identify the pre-glacial to glacial components. We mapped seven main units of which three are in the inferred Cretaceous-Paleocene pre-glacial regime, one in the Eocene-Oligocene transitional regime and three units in the Miocene-Pleistocene full glacial climate regime. Sparse borehole data from ODP leg 113 and SHALDRIL constrain the ages of the upper three units. Compiled seafloor spreading magnetic anomalies constrain the basement ages and the hypothetical age model. In many cases, the new horizons and stratigraphy contradict the interpretations in local studies. Each seismic sedimentary unit and its associated base horizon are continuous and traceable for the entire transect length, but reflect a lateral change in age whilst representing the same deposition process. The up to 1240 m thick pre-glacial seismic units form a mound in the central Weddell Sea basin and, in conjunction with the eroded flank geometry, support the interpretation of a Cretaceous proto-Weddell Gyre. The base reflector of the transitional seismic unit, which marks the initial ice sheet advances to the outer shelf, has a lateral model age of 26.6-15.5 Ma from southeast to northwest. The Pliocene-Pleistocene glacial deposits reveals lower sedimentations rates, indicating a reduced sediment supply. Sedimentation rates for the pre-glacial, transitional and full glacial components are highest around the Antarctic Peninsula, indicating higher erosion and sediment supply of a younger basement. We interpret an Eocene East Antarctic Ice Sheet expansion, Oligocene grounding of the West Antarctic Ice Sheet and Early Miocene grounding of the Antarctic Peninsula Ice Sheet.

Video anomaly detection in real-time on a Power-Aware Heterogeneous Platform

FPGAs and GPUs are often used when real-time performance in video processing is required. An accelerated processor is chosen based on task-specific priorities (power consumption, processing time and detection accuracy), and this decision is normally made once at design time. All three characteristics are important, particularly in battery-powered systems. Here we propose a method for moving selection of processing platform from a single design-time choice to a continuous run time one.We implement Histogram of Oriented Gradients (HOG) detectors for cars and people and Mixture of Gaussians (MoG) motion detectors running across FPGA, GPU and CPU in a heterogeneous system. We use this to detect illegally parked vehicles in urban scenes. Power, time and accuracy information for each detector is characterised. An anomaly measure is assigned to each detected object based on its trajectory and location, when compared to learned contextual movement patterns. This drives processor and implementation selection, so that scenes with high behavioural anomalies are processed with faster but more power hungry implementations, but routine or static time periods are processed with power-optimised, less accurate, slower versions. Real-time performance is evaluated on video datasets including i-LIDS. Compared to power-optimised static selection, automatic dynamic implementation mapping is 10% more accurate but draws 12W extra power in our testbed desktop system.

Contextual anomaly detection in crowded surveillance scenes

This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour.

The changing epidemiology of Ebstein's anomaly and its relationship with maternal mental health conditions: a European registry-based study

OBJECTIVES: The aim of this study was to describe the epidemiology of Ebstein's anomaly in Europe and its association with maternal health and medication exposure during pregnancy.

DESIGN: We carried out a descriptive epidemiological analysis of population-based data.

SETTING: We included data from 15 European Surveillance of Congenital Anomalies Congenital Anomaly Registries in 12 European countries, with a population of 5.6 million births during 1982-2011. Participants Cases included live births, fetal deaths from 20 weeks gestation, and terminations of pregnancy for fetal anomaly. Main outcome measures We estimated total prevalence per 10,000 births. Odds ratios for exposure to maternal illnesses/medications in the first trimester of pregnancy were calculated by comparing Ebstein's anomaly cases with cardiac and non-cardiac malformed controls, excluding cases with genetic syndromes and adjusting for time period and country.

RESULTS: In total, 264 Ebstein's anomaly cases were recorded; 81% were live births, 2% of which were diagnosed after the 1st year of life; 54% of cases with Ebstein's anomaly or a co-existing congenital anomaly were prenatally diagnosed. Total prevalence rose over time from 0.29 (95% confidence interval (CI) 0.20-0.41) to 0.48 (95% CI 0.40-0.57) (p<0.01). In all, nine cases were exposed to maternal mental health conditions/medications (adjusted odds ratio (adjOR) 2.64, 95% CI 1.33-5.21) compared with cardiac controls. Cases were more likely to be exposed to maternal β-thalassemia (adjOR 10.5, 95% CI 3.13-35.3, n=3) and haemorrhage in early pregnancy (adjOR 1.77, 95% CI 0.93-3.38, n=11) compared with cardiac controls.

CONCLUSIONS: The increasing prevalence of Ebstein's anomaly may be related to better and earlier diagnosis. Our data suggest that Ebstein's anomaly is associated with maternal mental health problems generally rather than lithium or benzodiazepines specifically; therefore, changing or stopping medications may not be preventative. We found new associations requiring confirmation.

Feature Selection for Anomaly Detection Using Optical Emission Spectroscopy

To maintain the pace of development set by Moore's law, production processes in semiconductor manufacturing are becoming more and more complex. The development of efficient and interpretable anomaly detection systems is fundamental to keeping production costs low. As the dimension of process monitoring data can become extremely high anomaly detection systems are impacted by the curse of dimensionality, hence dimensionality reduction plays an important role. Classical dimensionality reduction approaches, such as Principal Component Analysis, generally involve transformations that seek to maximize the explained variance. In datasets with several clusters of correlated variables the contributions of isolated variables to explained variance may be insignificant, with the result that they may not be included in the reduced data representation. It is then not possible to detect an anomaly if it is only reflected in such isolated variables. In this paper we present a new dimensionality reduction technique that takes account of such isolated variables and demonstrate how it can be used to build an interpretable and robust anomaly detection system for Optical Emission Spectroscopy data.

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work.

Monoamniotic twins discordant for body stalk anomaly

Body stalk anomaly is a rare malformation. This anomaly in monozygotic twins is extremely unusual. We describe a case of monoamniotic pregnancy discordant for body stalk anomaly diagnosed at 11 weeks. Ultrasound showed a fetus with a large anterior abdominal wall defect, anomaly of the spine and no evidence of lower extremities and other with a normal morphology. As far as our concern, only three monoamniotic pregnancies discordant for this malformation were reported. Our case represents the fourth reported monoamniotic pregnancy discordant for body stalk anomaly with diagnosis made by ultrasound and the second diagnosed in the first trimester.

'Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection'

Abstract. Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.

Low-Volatility Anomaly in Helsinki Stock Exchange

International research shows that low-volatility stocks have beaten high-volatility stocks in terms of returns for decades on multiple markets. This abbreviation from traditional risk-return framework is known as low-volatility anomaly. This study focuses on explaining the anomaly and finding how strongly it appears in NASDAQ OMX Helsinki stock exchange. Data consists of all listed companies starting from 2001 and ending close to 2015. Methodology follows closely Baker and Haugen (2012) by sorting companies into deciles according to 3-month volatility and then calculating monthly returns for these different volatility groups. Annualized return for the lowest volatility decile is 8.85 %, while highest volatility decile destroys wealth at rate of -19.96 % per annum. Results are parallel also in quintiles that represent larger amount of companies and thus dilute outliers. Observation period captures financial crisis of 2007-2008 and European debt crisis, which embodies as low main index annual return of 1 %, but at the same time proves the success of low-volatility strategy. Low-volatility anomaly is driven by multiple reasons such as leverage constrained trading and managerial incentives which both prompt to invest in risky assets, but behavioral matters also have major weight in maintaining the anomaly.