Dimensionality reduction techniques such as t-SNE and UMAP are useful both for overview of high-dimensional datasets and as part of a machine learning pipeline. These techniques create a non-parametric model of the manifold by fitting a density kernel about each data point using the distances to its k-nearest neighbors. In dense regions, this approach works well, but in sparse regions, it tends to draw unrelated points into the nearest cluster. Our work focuses on a homotopy method which imposes graph-based regularization over the manifold parameters to update the embedding. As the homotopy parameter increases, so does the cost of modeling different scales between adjacent neighborhoods. This gradually imposes a more uniform scale over the manifold, resulting in a more faithful embedding which preserves structure in dense areas while pushing sparse anomalous points outward.

abstract: Imagery data has become important for civil infrastructure operation and maintenance because imagery data can capture detailed visual information with high frequencies. Computer vision can be useful for acquiring spatiotemporal details to support the timely maintenance of critical civil infrastructures that serve society. Some examples include: irrigation canals need to maintain the leaking sections to avoid water loss; project engineers need to identify the deviating parts of the workflow to have the project finished on time and within budget; detecting abnormal behaviors of air traffic controllers is necessary to reduce operational errors and avoid air traffic accidents. Identifying the outliers of the civil infrastructure can help engineers focus on targeted areas. However, large amounts of imagery data bring the difficulty of information overloading. Anomaly detection combined with contextual knowledge could help address such information overloading to support the operation and maintenance of civil infrastructures. Some challenges make such identification of anomalies difficult. The first challenge is that diverse large civil infrastructures span among various geospatial environments so that previous algorithms cannot handle anomaly detection of civil infrastructures in different environments. The second challenge is that the crowded and rapidly changing workspaces can cause difficulties for the reliable detection of deviating parts of the workflow. The third challenge is that limited studies examined how to detect abnormal behaviors for diverse people in a real-time and non-intrusive manner. Using video andii relevant data sources (e.g., biometric and communication data) could be promising but still need a baseline of normal behaviors for outlier detection. This dissertation presents an anomaly detection framework that uses contextual knowledge, contextual information, and contextual data for filtering visual information extracted by computer vision techniques (ADCV) to address the challenges described above. The framework categorizes the anomaly detection of civil infrastructures into two categories: with and without a baseline of normal events. The author uses three case studies to illustrate how the developed approaches can address ADCV challenges in different categories of anomaly detection. Detailed data collection and experiments validate the developed ADCV approaches. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2020

<p>In the first chapter, we illustrate a big picture of the printing systems and the concentration of this dissertation. </p><p><br></p><p>In the second chapter, we present a tone-dependent fast error diffusion algorithm for color images, in which the quantizer is based on a simulated linearized printer space and the filter weight function depends on the ratio of the luminance of the current pixel to the maximum luminance value. The pixels are processed according to a serpentine scan instead of the classic raster scan. We compare the results of our algorithm to those achieved using</p> <p>the fixed Floyd-Steinberg weights and processing the image according to a raster scan ordering. In the third chapter, we first design a defect generator to generate the synthetic abnormal</p> <p>printer sounds, and then develop or explore three features for sound-based anomaly detection. In the fourth chapter, we explore six classifiers as our anomaly detection models, and explore or develop six augmentation methods to see whether or not an augmented dataset can improve the model performance. In the fifth chapter, we illustrate the data arrangement and the evaluation methods. Finally, we show the evaluation results based on</p> <p>different inputs, different features, and different classifiers.</p> <p><br></p><p>In the last chapter, we summarize the contributions of this dissertation.</p>

In the era of Internet of Things (IoT) and big data, collecting, processing and analyzing enormous data faces unprecedented challenges even when being stored in preprocessed form. Anomaly detection, statistically viewed as identifying outliers having low probabilities from the modelling of data distribution p(x), becomes more crucial. In this Master thesis, two (supervised and unsupervised) novel deep anomaly detection frameworks are presented which can achieve state-of-art performance on a range of datasets. Capsule net is an advanced artificial neural network, being able to encode intrinsic spatial relationship between parts and a whole. This property allows it to work as both a classifier and a deep autoencoder. Taking this advantage of CapsNet, a new anomaly detection technique named AnoCapsNet is proposed and three normality score functions are designed: prediction-probability-based (PP-based) normality score function, reconstruction-error-based (RE-based) normality score function, and a normality score function that combines prediction-probability-based and reconstruction-error-based together (named as PP+RE-based normality score function) for evaluating the "outlierness" of unseen images. The results on four datasets demonstrate that the PP-based method performs consistently well, while the RE-based approach is relatively sensitive to the similarity between labeled and unlabeled images. The PP+RE-based approach effectively takes advantages of both methods and achieves state-of-the-art results. In many situations, neither the domain of anomalous samples can be fully understood, nor the domain of the normal samples is straightforward. Thus deep generative models are more suitable than supervised methods in such cases. As a variant of variational autoencoder (VAE), beta-VAE is designed for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner. The t-Distributed Stochastic Neighbor Embedding (t-SNE), an unsupervised non-linear technique primarily used for data exploration and visualizing high-dimensional data, has advantages at creating a single map that reveals local and important global structure at many different scales. Taking advantages of both disentangled representation learning (using beta-VAE as an implementation) and low-dimensional neighbor embedding (using t-SNE as an implementation), another novel anomaly detection approach named AnoDM (stands for Anomaly detection based on unsupervised Disentangled representation learning and Manifold learning) is presented. A new anomaly score function is defined by combining (1) beta-VAE's reconstruction error, and (2) latent representations' distances in the t-SNE space. This is a general framework, thus any disentangled representation learning and low-dimensional embedding techniques can be applied. AnoDM is evaluated on both image and time-series data and achieves better results than models that use just one of the two measures and other existing advanced deep learning methods.

The ability to look outward from your vehicle and assess dangerous peer behavior is typically a trivial task for humans, but not always. Distracted driving is an issue that has been seen on our roadways ever since cars have been invented, but even more so after the wide spread use of cell phones. This thesis introduces a new system for monitoring the surrounding vehicles with outside facing cameras that detect in real time if the vehicle being followed is engaging in distracted behavior. This system uses techniques from image processing, signal processing, and machine learning. It’s ability to pick out drivers with dangerous behavior is shown to be accurate with a hit count of 87.5%, and with few false positives. It aims to help make either the human driver or the machine driver more aware and assist with better decision making.

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Anomaly detection generally refers to algorithmic procedures aimed at identifying relatively rare events in data sets that differ substantially from the majority of the data set to which they belong. In the context of data series generated by sensors mounted on mobile devices for non-destructive inspection and monitoring, anomalies typically identify defects to be detected, therefore defining the main task of this class of devices. In this case, a useful way of operationally defining anomalies is to look at their information content with respect to the background data, which is typically noisy and therefore easily masking the relevant events if unfiltered. In this thesis, a Kullback-Leibler (KL) Divergence filter is proposed to detect signals with relatively high information content, namely anomalies, within data series. The data is generated by using the model of a broad class of proximity sensors that apply to devices commonly used in engineering practice. This includes, for example, sensory devices mounted on mobile robotic devices for the non-destructive inspection of hazardous or other environments that may not be accessible to humans for direct inspection. The raw sensory data generated by this class of sensors is often challenging to analyze due to the prevalence of noise over the signal content that reveals the presence of relevant features, as for example damage in gas pipelines. The proposed filter is built to detect the difference of information content between the data series collected by the sensor and a baseline data series, with the advantage of not requiring the design of a threshold. Moreover, differing from the traditional filters which need the prior knowledge or distribution assumptions about the data, this KL Divergence filter is model free and suitable for all kinds of raw sensory data. Of course, it is also compatible with classical signal distribution assumptions, such as Gaussian approximation, for instance. Also, the robustness and sensitivity of the KL Divergence filter are discussed under different scenarios with various signal to noise ratios of data generated by a simulator reproducing very realistic scenarios and based on models of real sensors provided by manufacturers or widely accepted in the literature.

Anomaly detection is a relevant problem in the field of Mechanical Engineering, because the analysis of mechanical systems often relies on identifying deviations from what is considered "normal". The mechanical sciences are represented by a heterogeneous collection of data types: some systems may be highly dimensional, may contain exclusively spatial or temporal data, may be spatiotemporally linked, or may be non-deterministic and best described probabilistically. Given the broad range of data types in this field, it is not possible to propose a single processing method that will be appropriate, or even usable, for all data types. This has led to human observation remaining a common, albeit costly and inefficient, approach to detecting anomalous signals or patterns in mechanical data. The advantages of automated anomaly detection in mechanical systems include reduced monitoring costs, increased reliability of fault detection, and improved safety for users and operators. This dissertation proposes a hierarchical framework for anomaly detection through machine learning, and applies it to three distinct and heterogeneous data types: state-based data, parameter-driven data, and spatiotemporal sensor network data. In time-series data, anomaly detection results were robust in synthetic data generated using multiple simulation algorithms, as well as experimental data from rolling element bearings, with highly accurate detection rates (>99% detection, <1% false alarm). Significant developments were shown in parameter-driven data by reducing the sample sizes necessary for analysis, as well as reducing the time required for computation. The event-space model extends previous work into a geospatial sensor network and demonstrates applications of this type of event modeling at various timescales, and compares the model to results obtained using other approaches. Each data type is processed in a unique way relative to the others, but all are fitted to the same hierarchical structure for system modeling. This hierarchical model is the key development proposed by this dissertation, and makes both novel and significant contributions to the fields of mechanical analysis and data processing. This work demonstrates the effectiveness of the developed approaches, details how they differ from other relevant industry standard methods, and concludes with a proposal for additional research into other data types. / Ph. D.

Background: With the advent of the information age, there are many large numbers of services rising which run on several clusters of computers.  Maintaining such large complex systems is a very difficult task. Developers use one tool which is common for almost all software systems, they are the console logs. To troubleshoot problems, developers refer to these logs to solve the issue. Identifying anomalies in the logs would lead us to the cause of the problem, thereby automating the analysis of logs. This study focuses on anomaly detection in logs. Objectives: The main goal of the thesis is to identify different algorithms for anomaly detection in logs, implement the algorithms and compare them by doing an experiment. Methods: A literature review had been conducted for identifying the most suitable algorithms for anomaly detection in logs. An experiment was conducted to compare the algorithms identified in the literature review. The experiment was performed on a dataset of logs generated by Hadoop Data File System (HDFS) servers which consisted of more than 11 million lines of logs. The algorithms that have been compared are K-means, DBSCAN, Isolation Forest, and Local Outlier Factor algorithms which are all unsupervised learning algorithms. Results: The performance of all these algorithms has been compared using metrics precision, recall, accuracy, F1 score, and run time. Though DBSCAN was the fastest, it resulted in poor recall, similarly Isolation Forest also resulted in poor recall. Local Outlier Factor was the fastest to predict. K-means had the highest precision and Local Outlier Factor had the highest recall, accuracy, and F1 score. Conclusion: After comparing the metrics of different algorithms, we conclude that Local Outlier Factor performed better than the other algorithms with respect to most of the metrics measured.

In this project work the basis for an anomaly detection system in ARM processors was researched on. Specifically, the focus was set to determine the performance monitoring units (PMU) in the processor which allow the reliable detection of anomalies. This was achieved by injecting targeted faults on the assembly level into the binary file to represent attacks on a physical level in a consistent way. A set of three PMUs was determined to reach a detection rate of 56.67% to 66.67% (depending on the test scenario) in the selected scenarios. However, the expected detection rate is higher for real-world attacks, due to the broad nature of the executed tests. In addition, it was observed that the readout frequency of these PMUs is critical, and in general, it is advisable to expose the values after each function call, or in the case of security-sensitive sections, multiple times within functions.