Lofty Hindukush, Karakoram and Himalayan (HKH) mountain ranges centered in the Northern Pakistan are host to some of the world’s largest glaciers outside the Polar Regions and are a source of water for drinking and irrigation to the millions of people living downstream. With the increase in the global temperatures, glaciers are reported as retreating globally. However, some of the glaciers in the Karakoram mountain ranges are reported as surging with positive mass balance, especially since the 1990s. This phenomenon is described as "The Karakoram Anomaly". Various efforts have been made to explain the state and fate of the HKH glaciers in the recent past. However, they are limited to quantification of the change in temperature, precipitation and river runoff, or through their impact on future climate projections. For the HKH region, temperature fluctuations have been out of the phase with hemispheric trends for past several centuries. Therefore, climate change in this region is not solely the temperature effect on melting as compared to other glaciated regions. To identify the reasons for the establishment of the Karakoram Anomaly, monthly mean climatic variables for last five decades, reported from meteorological observatories at the valley floors in HKH region, are analyzed. In addition to the climatic variables of temperature and precipitation, monthly mean synoptic observations reported by meteorological observatories in both morning and afternoon, along with monthly mean radiosonde data are used. From these data the role of different near-surface and upper atmospheric meteorological variables in maintaining the positive mass balance of the glaciers and the development of the Karakoram Anomaly can be explained. An overall warming in the region is observed. The trends in the summer temperatures, which were reported as decreasing a decade ago, are now found as increasing in updated time series. However, the overall gradient is still negative. The winter mean and maximum temperatures are increasing with accelerated trends. Both maximum and minimum temperatures in summer are not diverging anymore and the diurnal temperature range is decreasing in the most recent decade. The afternoon cloudiness is found as increasing throughout the year except for spring, which is indicative of an increase in convective uplifting. Moreover, humidity is increasing all over the region; due to evaporation in the spring, from monsoon moisture advection in summer, and due to the recycling of monsoon moisture in autumn. Furthermore, near-surface wind speed and net radiation in the region are decreasing, explaining the decrease in the summer minimum temperature and the presence of the cloudy skies. The decrease in near-surface wind speed, and net radiation, and increase in water vapor pressure put a limit on the evapotranspiration process. In addition, winter and summer precipitation is increasing. The aridity index, which is based on the ratio of precipitation and reference evaporation, indicates that region is turning moisture surplus and energy deficient. Surface atmospheric pressure and 700 hPa geopotential height is increasing due to warming in the bottom layers of the troposphere. Nighttime inversion in the lower tropospheric layers is decreasing due to warming. Analysis of gridded observed and reanalysis datasets indicates that they are not presenting a signal of change in accordance with the instrumental record. Furthermore, it is found that meteorological conditions during the summer season are still favorable for the sustenance of glaciers whereas more melting may occur in the spring season that may increase the early season river flows and may affect lower lying portions of the debris-free glaciers.

Abstract. Security and privacy in digital communications is the need of the hour. SSL/TLS has become widely adopted to provide the same. Multiple application layer protocols can be layered on top of it. However protection is this form results in all the data being encrypted causing problems for an intrusion detection system which relies on a sniffer that analyses packets on a network. We thus hypothesise that a host based intrusion detection system that analyses packets after decryption would be able to detect attacks against security protocols. To this effect we conduct two experiments where we attack a web server and a mail server, collect data, analyse it and conclude with methods to detect such attacks. These methods are in the form of peudocode.

Outliers are a major concern in data quality as it limits the reliability of any data. The objective of our investigation was to examine the presence and cause of outliers in the system for controlling and recording the feed intake of dairy cows in Lovsta farm, Uppsala Sweden. The analyses were made on data recorded as a timestamp of each visit of the cows to the feeding troughs from the period of August 2015 to January 2016. A three step methodology was applied to this data. The first step was fitting a mixed model to the data then the resulting residuals was used in the second step to fit a model based clustering for Gaussian mixture distribution which resulted in clusters of which 2.5% of the observations were in the outlier cluster. Finally, as the third step, a logistic regression was then fit modelling the presence of outliers versus the non-outlier clusters. It appeared that on early hours of the morning between 6am to 11.59am, there is a high possibility of recorded values to be outliers with odds ratio of 1.1227 and this is also the same time frame noted to have the least activity in feed consumption of the cows with a decrease of 0.027 kilograms as compared to the other timeframes. These findings provide a basis for further investigation to more specifically narrow down the causes of the outliers.

Abnormal behaviour may indicate important objects and events in a wide variety of domains. One such domain is intelligence and surveillance, where there is a clear trend towards more and more advanced sensor systems producing huge amounts of trajectory data from moving objects, such as people, vehicles, vessels and aircraft. In the maritime domain, for example, abnormal vessel behaviour, such as unexpected stops, deviations from standard routes, speeding, traffic direction violations etc., may indicate threats and dangers related to smuggling, sea drunkenness, collisions, grounding, hijacking, piracy etc. Timely detection of these relatively infrequent events, which is critical for enabling proactive measures, requires constant analysis of all trajectories; this is typically a great challenge to human analysts due to information overload, fatigue and inattention. In the Baltic Sea, for example, there are typically 3000–4000 commercial vessels present that are monitored by only a few human analysts. Thus, there is a need for automated detection of abnormal trajectory patterns. In this thesis, we investigate algorithms appropriate for automated detection of anomalous trajectories in surveillance applications. We identify and discuss some key theoretical properties of such algorithms, which have not been fully addressed in previous work: sequential anomaly detection in incomplete trajectories, continuous learning based on new data requiring no or limited human feedback, a minimum of parameters and a low and well-calibrated false alarm rate. A number of algorithms based on statistical methods and nearest neighbour methods are proposed that address some or all of these key properties. In particular, a novel algorithm known as the Similarity-based Nearest Neighbour Conformal Anomaly Detector (SNN-CAD) is proposed. This algorithm is based on the theory of Conformal prediction and is unique in the sense that it addresses all of the key properties above. The proposed algorithms are evaluated on real world trajectory data sets, including vessel traffic data, which have been complemented with simulated anomalous data. The experiments demonstrate the type of anomalous behaviour that can be detected at a low overall alarm rate. Quantitative results for learning and classification performance of the algorithms are compared. In particular, results from reproduced experiments on public data sets show that SNN-CAD, combined with Hausdorff distance  for measuring dissimilarity between trajectories, achieves excellent classification performance without any parameter tuning. It is concluded that SNN-CAD, due to its general and parameter-light design, is applicable in virtually any anomaly detection application. Directions for future work include investigating sensitivity to noisy data, and investigating long-term learning strategies, which address issues related to changing behaviour patterns and increasing size and complexity of training data.

Anomaly detection is a general theory of detecting unusual patterns or events in data. This master thesis investigates the subject of anomaly detection in two different applications. The first application is product inspection using a camera and the second application is surveillance using a 2D laser scanner. The first part of the thesis presents a system for automatic visual defect inspection. The system is based on aligning the images of the product to a common template and doing pixel-wise comparisons. The system is trained using only images of products that are defined as normal, i.e. non-defective products. The visual properties of the inspected products are modelled using three different methods. The performance of the system and the different methods have been evaluated on four different datasets. The second part of the thesis presents a surveillance system based on a single laser range scanner. The system is able to detect certain anomalous events based on the time, position and velocities of individual objects in the scene. The practical usefulness of the system is made plausible by a qualitative evaluation using unlabelled data.

Nous étudions les conséquences cosmologiques des anomalies de Weyl qui émergent de la renormalisation des opérateurs composés des champs, y compris la métrique. Ces anomalies sont codifiées dans les habillements gravitationnels des opérateurs dans une action effective quantique non-locale. Nous obtenons les équations d'évolution qui découlent de cette action et nous en cherchons des solutions cosmologiques. Par simplicité on se limite à la gravité d'Einstein-Hilbert avec une constante cosmologique. Nous initions par considérer la gravité en deux dimensions, où la théorie de Liouville nous permet de calculer l'habillement gravitationnel de la constant cosmologique. Avec une formulation invariante de Weyl, nous déterminons l'action effective et le tenseur de moment correspondant, qui deviennent non-locaux. Les anomalies de Weyl modifient le tenseur entier, pas seulement sa trace, et nous trouvons une énergie du vide qui décline avec le temps et un ralentissement de l'expansion de de Sitter à une de quasi-de Sitter. En quatre dimensions, motivés par nos résultats en deux dimensions, nous paramétrisons l'action effective avec des habillements gravitationnels générales. Dans le cas des dimensions anormales constantes, le tenseur de moment conduit encore à une énergie du vide qui décline et une expansion de quasi-de Sitter de roulement lent. Les dimensions anormales sont calculables à priori dans une certaine théorie microscopique avec des méthodes semi-classiques. Même si les dimensions anormales sont petites en théorie des perturbations, leur contribution intégrée le long des plusieurs e-folds pourrait mener à des effets significatifs pendant la cosmologie primordiale. / In this thesis we study the cosmological consequences of Weyl anomalies arising from the renormalization of composite operators of the fundamental fields, including the metric. These anomalies are encoded in the gravitational dressings of the operators in a non-local quantum effective action. We derive the evolution equations that follow from this action and look for cosmological solutions. For simplicity, we focus on Einstein-Hilbert gravity with a cosmological constant. We first consider two-dimensional gravity, where Liouville theory allows us to compute the gravitational dressing of the cosmological constant operator. Using a Weyl-invariant formulation, we determine the gauge-invariant but non-local effective action, and compute the corresponding non-local momentum tensor. The Weyl anomalies modify the full quantum momentum tensor, not only its trace, and hence lead to interesting effects in the cosmological dynamics. In particular, we find a decaying vacuum energy and a slow-down of the de Sitter expansion. In four dimensions, motivated by our results in two dimensions, we parametrize the effective action with scale-dependent gravitational dressings, and compute the general evolution equations. In the approximation of constant anomalous dimensions, the momentum tensor leads to a decaying vacuum energy and a slow-roll quasi-de Sitter expansion, just as in two dimensions. The anomalous dimensions are in principle computable in a given microscopic theory using semiclassical methods. Even though the anomalous dimensions are small in perturbation theory, their integrated effect over several e-folds could add up to something significant during primordial cosmology.

Human operators of modern surveillance systems are confronted with an increasing amount of trajectory data from moving objects, such as people, vehicles, vessels, and aircraft. A large majority of these trajectories reflect routine traffic and are uninteresting. Nevertheless, some objects are engaged in dangerous, illegal or otherwise interesting activities, which may manifest themselves as unusual and abnormal trajectories. These anomalous trajectories can be difficult to detect by human operators due to cognitive limitations. In this thesis, we study algorithms for the automated detection of anomalous trajectories in surveillance applications. The main results and contributions of the thesis are two-fold. Firstly, we propose and discuss a novel approach for anomaly detection, called conformal anomaly detection, which is based on conformal prediction (Vovk et al.). In particular, we propose two general algorithms for anomaly detection: the conformal anomaly detector (CAD) and the computationally more efficient inductive conformal anomaly detector (ICAD). A key property of conformal anomaly detection, in contrast to previous methods, is that it provides a well-founded approach for the tuning of the anomaly threshold that can be directly related to the expected or desired alarm rate. Secondly, we propose and analyse two parameter-light algorithms for unsupervised online learning and sequential detection of anomalous trajectories based on CAD and ICAD: the sequential Hausdorff nearest neighbours conformal anomaly detector (SHNN-CAD) and the sequential sub-trajectory local outlier inductive conformal anomaly detector (SSTLO-ICAD), which is more sensitive to local anomalous sub-trajectories. We implement the proposed algorithms and investigate their classification performance on a number of real and synthetic datasets from the video and maritime surveillance domains. The results show that SHNN-CAD achieves competitive classification performance with minimum parameter tuning on video trajectories. Moreover, we demonstrate that SSTLO-ICAD is able to accurately discriminate realistic anomalous vessel trajectories from normal background traffic.

The goal of this thesis is to explore some variations of neural networks. The thesis is mainly split into two parts: a variation of the shaping functions in neural networks and a variation of learning rules in neural networks. In the first part, we mainly investigate polynomial perceptrons - a perceptron with a polynomial shaping function instead of a linear one. We prove the polynomial perceptron convergence theorem and illustrate the notion by showing that a higher order perceptron can learn the XOR function through empirical experiments with implementation. In the second part, we propose three models (SMLP, SA, SA2) for stream learning and anomaly detection in streams. The main technique allowing these models to perform at a level comparable to the state-of-the-art algorithms in stream learning is the learning rule used. We employ mini-batch gradient descent algorithm and stochastic gradient descent algorithm to speed up the models. In addition, the use of parallel processing with multi-threads makes the proposed methods highly efficient in dealing with streaming data. Our analysis shows that all models have linear runtime and constant memory requirement. We also demonstrate empirically that the proposed methods feature high detection rate, low false alarm rate, and fast response. The paper on the first two models (SMLP, SA) is published in the 29th Canadian AI Conference and won the best paper award. The invited journal paper on the third model (SA2) for Computational Intelligence is under peer review.

We consider the problem of extracting a specific feature from a noisy signal generated by a multi-channels Remote Field Eddy Current Sensor. The sensor is installed on a mobile robot whose mission is the detection of anomalous regions in metal pipelines. Given the presence of noise that characterizes the data series, anomaly signals could be masked by noise and therefore difficult to identify in some instances. In order to enhance signal peaks that potentially identify anomalies we consider an entropy filter built on a-posteriori probability density functions associated with data series. Thresholds based on the Neyman-Pearson criterion for hypothesis testing are derived. The algorithmic tool is applied to the analysis of data from a portion of pipeline with a set of anomalies introduced at predetermined locations. Critical areas identifying anomalies capture the set of damaged locations, demonstrating the effectiveness of the filter in detection with Remote Field Eddy Current Sensor.

This paper discusses the use of diagnostics based on machine learning (ML) within a flight test context. The paper begins by discussing some of the problems associated with instrumenting a test aircraft and how they could be ameliorated using ML-based diagnostics. We then describe a number of types of supervised ML algorithms which can be used in this context. In addition, key practical aspects of applying these algorithms, such as feature engineering and parameter selection, are also discussed. The paper then outlines a real-world application developed by Curtiss-Wright, called Machine Learning for Advanced System Diagnostics (MLASD). This description includes key challenges that were encountered during the development process and how suitable input features were identified. Real-world results are also presented. Finally, we suggest some further applications of ML techniques, in addition to describing other areas of development.