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Gravity and other geophysical studies of the crust of Southern BritainGenc, Halit Tugrul January 1988 (has links)
No description available.
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Immunological studies in the 22q11 deletion syndromeBillingham, Joanne Louise January 2000 (has links)
No description available.
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The epidemiology of anophthalmos / microphthalmos in EnglandBusby, Aracelie Lorraine January 2002 (has links)
No description available.
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Time-variant normal profiling for anomaly detection systemsKim, Jung Yeop. January 2008 (has links)
Thesis (Ph.D.)--University of Wyoming, 2008. / Title from PDF title page (viewed on August 3, 2009). Includes bibliographical references (p. 73-84).
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Anomaly detection with Machine learning : Quality assurance of statistical data in the Aid communityBlomquist, Hanna, Möller, Johanna January 2015 (has links)
The overall purpose of this study was to find a way to identify incorrect data in Sida’s statistics about their contributions. A contribution is the financial support given by Sida to a project. The goal was to build an algorithm that determines if a contribution has a risk to be inaccurate coded, based on supervised classification methods within the area of Machine Learning. A thorough data analysis process was done in order to train a model to find hidden patterns in the data. Descriptive features containing important information about the contributions were successfully selected and used for this task. These included keywords that were retrieved from descriptions of the contributions. Two Machine learning methods, Adaboost and Support Vector Machines, were tested for ten classification models. Each model got evaluated depending on their accuracy of predicting the target variable into its correct class. A misclassified component was more likely to be incorrectly coded and was also seen as an anomaly. The Adaboost method performed better and more steadily on the majority of the models. Six classification models built with the Adaboost method were combined to one final ensemble classifier. This classifier was verified with new unseen data and an anomaly score was calculated for each component. The higher the score, the higher the risk of being anomalous. The result was a ranked list, where the most anomalous components were prioritized for further investigation of staff at Sida.
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Incremental Anomaly Detection Using Two-Layer Cluster-based StructureBigdeli, Elnaz January 2016 (has links)
Anomaly detection algorithms face several challenges, including processing
speed and dealing with noise in data. In this thesis, a two-layer cluster-
based anomaly detection structure is presented which is fast, noise-resilient
and incremental. In this structure, each normal pattern is considered as
a cluster, and each cluster is represented using a Gaussian Mixture Model
(GMM). Then, new instances are presented to the GMM to be labeled as
normal or abnormal.
The proposed structure comprises three main steps. In the first step, the
data are clustered. The second step is to represent each cluster in a way
that enables the model to classify new instances. The Summarization based
on Gaussian Mixture Model (SGMM) proposed in this thesis represents each
cluster as a GMM.
In the third step, a two-layer structure efficiently updates clusters using
GMM representation while detecting and ignoring redundant instances. A
new approach, called Collective Probabilistic Labeling (CPL) is presented
to update clusters in a batch mode. This approach makes the updating
phase noise-resistant and fast. The collective approach also introduces a new
concept called 'rag bag' used to store new instances. The new instances
collected in the rag bag are clustered and summarized by GMMs. This
enables online systems to identify nearby clusters in the existing and new
clusters, and merge them quickly, despite the presence of noise to update the model.
An important step in the updating is the merging of new clusters with ex-
isting ones. To this end, a new distance measure is proposed, which is a mod-
i ed Kullback-Leibler distance between two GMMs. This modi ed distance
allows accurate identi cation of nearby clusters. After finding neighboring
clusters, they are merged, quickly and accurately. One of the reasons that
GMM is chosen to represent clusters is to have a clear and valid mathematical
representation for clusters, which eases further cluster analysis.
In most real-time anomaly detection applications, incoming instances are
often similar to previous ones. In these cases, there is no need to update
clusters based on duplicates, since they have already been modeled in the
cluster distribution. The two-layer structure is responsible for identifying
redundant instances. In this structure, redundant instance are ignored, and
the remaining new instances are used to update clusters. Ignoring redundant
instances, which are typically in the majority, makes the detection phase fast.
Each part of the general structure is validated in this thesis. The experiments include, detection rates, clustering goodness, time, memory usage
and the complexity of the algorithms. The accuracy of the clustering and
summarization of clusters using GMMs is evaluated, and compared to that of
other methods. Using Davies-Bouldin (DB) and Dunn indexes, the distances
for original and regenerated clusters using GMMs is almost zero with SGMM
method while this value for ABACUS is around 0:01. Moreover, the results
show that the SGMM algorithm is 3 times faster than ABACUS in running
time, using one-third of the memory used by ABACUS.
The CPL method, used to label new instances, is found to collectively
remove the effect of noise, while increasing the accuracy of labeling new
instances. In a noisy environment, the detection rate of the CPL method
is 5% higher than other algorithms such as one-class SVM. The false alarm rate is decreased by 10% on average. Memory use is 20 times lesser that that
of the one-class SVM.
The proposed method is found to lower the false alarm rate, which is
one of the basic problems for the one-class SVM. Experiments show the false
alarm rate is decreased from 5% to 15% among different datasets, while the
detection rate is increased from 5% to 10% in di erent datasets with two-
layer structure. The memory usage for the two-layer structure is 20 to 50
times less than that of one-class SVM. One-class SVM uses support vectors in
labeling new instances, while the labeling of the two-layer structure depends
on the number of GMMs. The experiments show that the two-layer structure
is 20 to 50 times faster than the one-class SVM in labeling new instances.
Moreover, the updating time of two-layer structure is 2 to 3 times less than
one-layer structure. This reduction is the direct result of ignoring redundant
instances and using two-layer structure.
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Knowing, but not telling: Do skilled analysts understand the net operating assets anomaly?January 2021 (has links)
archives@tulane.edu / Using hand-collected, professional designations of equity analysts as proxies for analyst accounting skills, this paper finds that analysts with strong accounting skills understand the net operating assets (NOA) anomaly. They provide more accurate and less optimistic earnings forecasts for firms with higher NOA. However, at the same time, they issue more optimistic recommendations for the same high NOA firms. This paper argues that conflicts of interests and the ways skilled analysts strategically incorporate the information into their outputs to achieve incentive-based goals drive this inconsistency. This strategic behavior of skilled analysts tends to create price discrepancies and might suggest that the NOA anomaly is more behavior-driven and less risk-oriented. / 1 / Miao He
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Anomaly Detection in Univariate Time Series Data in the Presence of Concept DriftZamani Alavijeh, Soroush January 2021 (has links)
Digital applications and devices record data over time to enable the users and managers to monitor their activity. Errors occur in data, including the time series data, for various reasons including software system failures and human errors. The problem of identifying errors, also referred to as anomaly detection, in time series data is a well studied topic by the data management and systems researchers. Such data are often recorded in dynamic environments where a change in the standard or the recording hardware can result in different and novel patterns arising in the data. Such novel patterns are caused by what is referred to as concept drifts. Concept drift occurs when there is a pattern change in the statistical properties of the data, e.g. the distribution of the data, over time. The problem of identifying anomalies in time series data recorded and stored in dynamic environments has not been extensively studied. In this study, we focus on this problem. We propose and implement a unified framework that is able to identify drifts in univariate time series data and incorporate information gained from the data to train a learning model that is able to detect anomalies in unseen univariate time series data. / Thesis / Master of Science (MSc)
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An information based approach to anomaly detection in dynamic systemsOh, Ki-Tae January 1995 (has links)
No description available.
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Design and Implementation of Parallel Anomaly DetectionShanbhag, Shashank 01 January 2007 (has links) (PDF)
The main objective of the thesis is to show that multiple anomaly detection algorithms can be implemented in parallel to effectively characterize the type of traffic causing the abnormal behavior. The logs are obtained by running six anomaly detection algorithms in parallel on the Network Processor. Further, a hierarchical tree representation is defined which illustrates the state of traffic in real-time. The nodes represent a particular subset of traffic and each of the nodes calculate the aggregate for the traffic represented by the node, given the output from all the algorithms. The greater the aggregate, the darker the node indicating an anomaly. The visual representation makes it easy for an operator to distinguish between anomalous and non-anomalous nodes.
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