<p>The increase in the use of digital devices, has vastly increased the amount of data used and consequently, has increased the availability and relevance of digital evidence. Typically, digital evidence helps to establish the identity of an offender by identifying the username or the user account logged into the device at the time of offense. Investigating officers need to establish the link between that user and an actual person. This is difficult in the case of computers that are shared or compromised. Also, the increasing amount of data in digital investigations necessitates the use of advanced data analysis approaches like machine learning, while keeping pace with the constantly evolving techniques. It also requires reporting on known error rates for these advanced techniques. There have been several research studies exploring the use of behavioral biometrics to support this user attribution in digital forensics. However, the use of the state-of-the-art XGBoost algorithm, hasn’t been explored yet. This study builds on previously conducted research by modeling user interaction using the XGBoost algorithm, based on features related to keystroke and mouse usage, and verifying the performance for user attribution. With an F1 score and Area Under the Receiver Operating Curve (AUROC) of .95, the algorithm successfully attributes the user event to the right user. The XGBoost model also outperforms other classifiers based on algorithms such as Support Vector Machines (SVM), Boosted SVM and Random Forest.</p>

No description available.

Iris recognition is a validated and non-invasive human identification technology currently implemented for the purposes of surveillance and security (i.e. border control, schools, military). Similar to deoxyribonucleic acid (DNA), irises are a highly individualizing component of the human body. Based on a lack of genetic penetrance, irises are unique between an individual’s left and right iris and between identical twins, proving to be more individualizing than DNA. At this time, little to no research has been conducted on the use of postmortem iris scanning as a biometric measurement of identification. The purpose of this pilot study is to explore the use of iris recognition as a tool for postmortem identification. Objectives of the study include determining whether current iris recognition technology can locate and detect iris codes in postmortem globes, and if iris scans collected at different postmortem time intervals can be identified as the same iris initially enrolled. Data from 43 decedents involving 148 subsequent iris scans demonstrated a subsequent match rate of approximately 80%, supporting the theory that iris recognition technology is capable of detecting and identifying an individual’s iris code in a postmortem setting. A chi-square test of independence showed no significant difference between match outcomes and the globe scanned (left vs. right), and gender had no bearing on the match outcome. There was a significant relationship between iris color and match outcome, with blue/gray eyes yielding a lower match rate (59%) compared to brown (82%) or green/hazel eyes (88%), however, the sample size of blue/gray eyes in this study was not large enough to draw a meaningful conclusion. An isolated case involving an antemortem initial scan collected from an individual on life support yielded an accurate identification (match) with a subsequent scan captured at approximately 10 hours postmortem. Falsely rejected subsequent iris scans or "no match" results occurred in about 20% of scans; they were observed at each PMI range and varied from 19-30%. The false reject rate is too high to reliably establish non-identity when used alone and ideally would be significantly lower prior to implementation in a forensic setting; however, a "no match" could be confirmed using another method. Importantly, the data showed a false match rate or false accept rate (FAR) of zero, a result consistent with previous iris recognition studies in living individuals. The preliminary results of this pilot study demonstrate a plausible role for iris recognition in postmortem human identification. Implementation of a universal iris recognition database would benefit the medicolegal death investigation and forensic pathology communities, and has potential applications to other situations such as missing persons and human trafficking cases.

Biometric identification uses individuals' characteristics to attempt to match a sample to a database of existing samples. An increasingly commonly used characteristic is the iris section of the eye, which is valued for its uniqueness among individuals and stability over time. One key concern with iris recognition systems is the time required to find a test sample's match in a database of subjects. This work considers methods for categorizing irises within a database, so that a search for a match to a test sample can be focused on the test sample's category. The main method for categorization used in this work is texton learning. Texton learning involves creating a vocabulary of features and determining how much of each feature a given sample has. Once images are represented by textons, they are clustered in an unsupervised process. Success of the system is assessed as its ability to take a previously unseen image from a subject and classify it the same as the database reference for the subject. This work improves upon the past applications of texton learning with more thorough experiments to determine the optimal number of textons and image clusters. This system also investigates different accuracy metrics, with this work detailing two key methods and their relative benefits. Additionally, more in depth analysis is given for potential time saving impacts for finding a database match. Beyond the improvements to texton learning, symbolic features (ethnicity and gender) have been incorporated into the categorization process using a probabilistic metric. This is an innovative combination of using the numerical representation of the iris along with demographic information.

No description available.

Black walnut (Juglans nigra L.) is a valued, Mississippi tree species with very little published mensurational data. Tree profile equations are effective tree volume predictors but are typically developed from measurements on destructively sampled trees, an impractical method on valuable species. This study developed black walnut and green ash (Fraxinus pennsylvanica) profile equations from non-destructive measurements using a Barr & Stroud FP15 optical dendrometer. Accuracy of the dendrometer was validated by taking both optical dendrometer and felled, direct measurements on green ash trees. Two profile models were evaluated for measured tree data. Separate equations were created from optical dendrometer tree profile data for black walnut and green ash and felled tree profile data for green ash. The Barr & Stroud allowed tree profile equations to be developed from standing tree measurements with acceptable accuracy, thus providing useful tools towards the valuation and management of southeastern black walnut and green ash.

Red spruce (Picea rubens Sarg.) is a commercially and ecologically important conifer species that primarily exists at northern latitudes of eastern North America. During the last glaciation, its range extended down the Appalachian Mountain chain into North Carolina and Tennessee. Since the planet warmed over the subsequent millennia, only small, sky-island populations remain at the highest peaks of the southern Appalachians where their habitat continues to be threatened by a warming climate. While they have been recognized for the rare wildlife habitat they provide in the region, these populations remain understudied. This thesis aimed to provide additional quantitative methods for managing red spruce stands through regionally fitted stem taper equations and to examine differences in stem form between the northern and southern populations of red spruce. In Chapter 1, five stem taper equations were evaluated for their ability to predict upper stem diameters and total volume in southern Appalachian red spruce: a simple linear, a quadratic polynomial, a segmented, a variable exponent, and a geometric model. Based on past studies and our results, we found that the best equations to use were the variable exponent and segmented polynomial models. Users should consider their own objectives and practical limitations in choosing which equation to use. In Chapter 2, we examined differences in stem form using three methods: a sectional rate of change in diameter, a sectional form class ratio, and a region variable added to two taper equations. The results were mixed, with the rates of change showing significant differences (p<0.05), but the form class ratios showing a mix of significant and insignificant differences. The two equations also had contrasting significance results. This made it unclear whether there were significant differences in stem form between the two populations but supported the idea that localized taper equations would provide the best results. / Master of Science / Red spruce (Picea rubens Sarg.) is a conifer native to eastern North America. It primarily exists in cold, moist climates found in the northeastern US and eastern Canada. Additionally, remnant populations of red spruce exist along the highest peaks of the Appalachian Mountains southward into North Carolina and Tennessee. These trees have been separated from northern red spruce populations for thousands of years and subjected to different growing conditions at high elevations that may have affected their stem characteristics. Stem taper is the rate at which the diameter of a tree's stem changes from the ground to the tip. Many equations have been developed to estimate diameters throughout the stem using simple measurements like total height and diameter at breast height (1.37 m or 4.5 ft). These equations can be used to estimate diameters, heights, and volumes of trees which is helpful for valuing trees for wood production, carbon accounting, or wildlife habitat. Stem taper can vary among species and within a species, so tailoring equations to local populations is important for obtaining the most accurate estimates. Currently, there are no known taper equations fitted specifically for these red spruce trees in the southern Appalachians. This thesis aimed to develop stem taper equations specific to this population of trees and to analyze whether the stem forms of southern red spruce trees differed from their northern counterparts. The results showed that there is evidence both for and against the hypothesis that these populations have different stem forms, and further research is necessary to confirm differences. We also showed that a variable exponent equation and a segmented polynomial equation provided the most accurate estimates of diameter and volume for the southern spruce populations.

Background: Biometric identification plays a crucial role in various industries such as retail, and banking. Among the different biometric traits, iris patterns have become a reliable means of identification due to their unique features. In our thesis, we focus on evaluating and comparing different machine learning algorithms for irisrecognition. The main aim is to identify the algorithm that achieves the highestperformance for iris recognition. Objectives: The main objective of the thesis is to train, test, and evaluate the best performing model using the iris image dataset among the selected algorithmsthrough a literature review. Additionally, the goal is to compare different algorithms for a biometric recognition system that relies on iris features. Methods: Our research is supported by an extensive literature review that usesa wide range of scholarly articles specifically focused on iris recognition. Experimentation is also used to determine the most accurate machine-learning algorithm interms of accuracy. Results: Our experimentation results revealed that the accuracy rates for all themodels were as follows: CNN obtained the highest accuracy at 98.7%, while SVM and the SVM combination with hamming distance achieved 86% and 80%, respectively. Based on our research findings, we conclude that including hamming distancewith SVM did not result in improved accuracy compared to other classification algorithms. Finally, CNN achieved high accuracy in comparison to different algorithmsfor iris recognition. Conclusions: To achieve our research goals, we divided the dataset into three parts: 60% for training, 20% for testing, and another 20% for validation. Different techniques were used to train the algorithm with the training dataset. The results aretested for every algorithm to determine its accuracy. Among the selected algorithms, the convolutional neural network delivered an accurate performance with an accuracy of 98.7%. By employing performance metrics, we have effectively addressed theresearch questions and identified the most accurate algorithm for the iris recognitionsystem.

Biometrics is a unique physical or behavioral characteristic of a person. This unique attribute, such as fingerprints or gait, can be used for identification or verification purposes. Gait is an emerging biometrics with great potential. Gait recognition is based on recognizing a person by the manner in which they walk. Its potential lays in that it can be captured at a distance and does not require the cooperation of the subject. This advantage makes it a very attractive tool for forensic cases and applications, where it can assist in identifying a suspect when other evidence such as DNA, fingerprints, or a face were not attainable. Gait can be used for recognition in a direct manner when the two samples are shot from similar camera resolution, position, and conditions. Yet in some cases, the only sample available is of an incomplete gait cycle, low resolution, low frame rate, a partially visible subject, or a single static image. Most of these conditions have one thing in common: static measurements. A gait signature is usually formed from a number of dynamic and static features. Static features are physical measurements of height, length, or build; while dynamic features are representations of joint rotations or trajectories. The aim of this thesis is to study the potential of predicting dynamic features from static features. In this thesis, we have created a database that utilizes a 3D laser scanner for capturing accurate shape and volumes of a person, and a motion capture system to accurately record motion data. The first analysis focused on analyzing the correlation between twenty-one 2D static features and eight dynamic features. Eleven pairs of features were regarded as significant with the criterion of a P-value less than 0.05. Other features also showed a strong correlation that indicated the potential of their predictive power. The second analysis focused on 3D static and dynamic features. Through the correlation analysis, 1196 pairs of features were found to be significantly correlated. Based on these results, a linear regression analysis was used to predict a dynamic gait signature. The predictors chosen were based on two adaptive methods that were developed in this thesis: "the top-x" method and the "mixed method". The predictions were assessed for both for their accuracy and their classification potential that would be used for gait recognition. The top results produced a 59.21% mean matching percentile. This result will act as baseline for future research in predicting a dynamic gait signature from static features. The results of this thesis bare potential for applications in biomechanics, biometrics, forensics, and 3D animation.

This dissertation examines border security practices in West Africa, with emphasis on the effects of practices of international intervention. The dissertation advances an understanding of borders as institutional spaces, eschewing a view of borders as geographical features alone. It leverages this view of borders to examine the everyday practices of border control, focusing in particular on the security professionals who cooperate and compete over the meaning and enactment of border security. The dissertation draws from ethnographic fieldwork in Senegal and Mauritania to advance three case studies. First, it examines Spanish police cooperation with Senegal and Mauritania on the prevention of irregular migration by sea and land routes. Second, it analyzes Mauritania’s construction of new border posts in response to migration and terrorism. Third, it looks into the adoption of biometric identification at airports and in official documents in Senegal and Mauritania. In each of these cases, the dissertation argues, everyday border security practices are framed in terms of capacity, with border control taking on the practical characteristics of statebuilding. This dissertation makes three key contributions to knowledge. First, by focusing on the quotidian social and technical aspects of borders, it provides a view into the concrete knowledge practices and organizational politics that drive border control, even if they are of complex causality. Second, this dissertation contributes to security studies a theorization of the movement of security practices and understandings between global contexts. Third, by relying on fieldwork in closed and rarely accessible contexts, it provides a view into the functioning and social relations of West African fields of security. / Thesis / Doctor of Philosophy (PhD) / This dissertation examines efforts to boost border security in Senegal and Mauritania—two states on the Atlantic coast of West Africa—with emphasis on the international cooperation and knowledge transmission that emerges as part of these efforts. The dissertation argues that borders are not only lines on a map, but institutions in which security professionals compete and cooperate over questions such as who should carry out border control and how. It also argues that with security framed as a question of development and state capacity, securing borders becomes a question of statebuilding. To show this, the dissertation draws on data from interviews in law enforcement and national security agencies, embassies, and international organizations to provide a mapping of actors in the field of border security and their relations. Its empirical cases focus on joint migration patrols, border post construction, and the use of biometric identification