Measuring Computer Forensics Skill

Computer forensic analysts combine their technical skills with their forensic aptitude to recover information from computers and storage devices. Most technology professionals demonstrate expertise through the acquisition of different professional certifications. Certifications, however, are not always a valid judge of skill, because certifications are formatted as written and applicable tests. It is common for people to forget knowledge and skills when they are not routinely practiced. The same applies with technology certifications. One must practice the skills learned for the certification test consistently in order to convert them to long-term memory. “Cognitive processes play a prominent role in the acquisition and retention of new behavior patterns” (Bandura 1977, p. 192). As a skill is practiced, it is better retained. Due to the current inability to accurately measure an individual’s skills and understanding of computer forensics principles, this research will investigate how to measure proficiency amongst professionals and novices. Recent research utilized conceptual expertise within the context of computer security (Giboney et al. 2016). This study utilized a technique to quickly measure the difference between novices and experts. Following their guidelines, we propose to do the same for computer forensics expertise with the following research question: What knowledge, skills and abilities are needed to be demonstrated in a measure to assess computer forensics expertise? Conceptual expertise is the understanding about the theoretical concepts and their relationship in a topic area. The SEAM process (Giboney et al. 2016) aims to gauge the practical application of situations to the goal wherein experts can show their conceptual expertise. The conceptual expertise task is based on the idea that those who have surface level knowledge will group scenarios by surface features while experts will be able to group the same scenarios by deep features (Giboney et al. 2016). The assessment has been designed to measure the understanding of basic computer forensics processes. It consists of twenty-five situations created to highlight different stages of the digital forensic process. These situations focus on a gender-neutral individual, Jordan and the tasks they perform given certain parameters. Survey takers will group the situations by stage of forensics or by what crime the task is involved with. We will show that the assessment can accurately determine an individual’s understanding of computer forensics. When this is shown, this assessment could be used in a variety of ways including initial assessments of job candidates and pre- and post- tests for computer forensic classes

Machine-learning to Stratify Diabetic Patients Using Novel Cardiac Biomarkers and Integrative Genomics

Background: Diabetes mellitus is a chronic disease that impacts an increasing percentage of people each year. Among its comorbidities, diabetics are two to four times more likely to develop cardiovascular diseases. While HbA1c remains the primary diagnostic for diabetics, its ability to predict long-term, health outcomes across diverse demographics, ethnic groups, and at a personalized level are limited. The purpose of this study was to provide a model for precision medicine through the implementation of machine-learning algorithms using multiple cardiac biomarkers as a means for predicting diabetes mellitus development. Methods: Right atrial appendages from 50 patients, 30 non-diabetic and 20 type 2 diabetic, were procured from the WVU Ruby Memorial Hospital. Machine-learning was applied to physiological, biochemical, and sequencing data for each patient. Supervised learning implementing SHapley Additive exPlanations (SHAP) allowed binary (no diabetes or type 2 diabetes) and multiple classifcation (no diabetes, prediabetes, and type 2 diabetes) of the patient cohort with and without the inclusion of HbA1c levels. Findings were validated through Logistic Regression (LR), Linear Discriminant Analysis (LDA), Gaussian Naïve Bayes (NB), Support Vector Machine (SVM), and Classifcation and Regression Tree (CART) models with tenfold cross validation. Results: Total nuclear methylation and hydroxymethylation were highly correlated to diabetic status, with nuclear methylation and mitochondrial electron transport chain (ETC) activities achieving superior testing accuracies in the predictive model (~84% testing, binary). Mitochondrial DNA SNPs found in the D-Loop region (SNP-73G, -16126C, and -16362C) were highly associated with diabetes mellitus. The CpG island of transcription factor A, mitochondrial (TFAM) revealed CpG24 (chr10:58385262, P=0.003) and CpG29 (chr10:58385324, P=0.001) as markers correlating with diabetic progression. When combining the most predictive factors from each set, total nuclear methylation and CpG24 methylation were the best diagnostic measures in both binary and multiple classifcation sets. Conclusions: Using machine-learning, we were able to identify novel as well as the most relevant biomarkers associated with type 2 diabetes mellitus by integrating physiological, biochemical, and sequencing datasets. Ultimately, this approach may be used as a guideline for future investigations into disease pathogenesis and novel biomarker discover

The Evolution of Black of Black Freedom Through Political Power

Through the examination African American office holders during reconstruction, my independent study centers around an understanding of the evolution of Black freedom during the time frame from the 17th to the 21st Century. Examining Black Political Power, specifically, I am examining the lives of John Roy Lynch, W.E.B Dubois, and Fredrick Douglas to uncover how individual historical subjects featured the project of Black freedom. I plan to argue the process that took place for the evolution of Black freedom through Political Power. One of my theories is that Black Reconstruction has never ended and that there is a non-consistent progression towards equality. So, examining the lives of the major historians that had an impact in the evolution of African American freedom, including a focus on the state of Georgia during the founding and reconstruction era

Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders:A Longitudinal Observational Study

BACKGROUND: Mood disorders are burdensome illnesses that often go undetected and untreated. Sensor technologies within smartphones may provide an opportunity for identifying the early changes in circadian rhythm and social support/connectedness that signify the onset of a depressive or manic episode. OBJECTIVE: Using smartphone sensor data, this study investigated the relationship between circadian rhythm, which was determined by GPS data, and symptoms of mental health among a clinical sample of adults diagnosed with major depressive disorder or bipolar disorder. METHODS: A total of 121 participants were recruited from a clinical setting to take part in a 10-week observational study. Self-report questionnaires for mental health outcomes, social support, social connectedness, and quality of life were assessed at 6 time points throughout the study period. Participants consented to passively sharing their smartphone GPS data for the duration of the study. Circadian rhythm (ie, regularity of location changes in a 24-hour rhythm) was extracted from GPS mobility patterns at baseline. RESULTS: Although we found no association between circadian rhythm and mental health functioning at baseline, there was a positive association between circadian rhythm and the size of participants' social support networks at baseline (r=0.22; P=.03; R2=0.049). In participants with bipolar disorder, circadian rhythm was associated with a change in anxiety from baseline; a higher circadian rhythm was associated with an increase in anxiety and a lower circadian rhythm was associated with a decrease in anxiety at time point 5. CONCLUSIONS: Circadian rhythm, which was extracted from smartphone GPS data, was associated with social support and predicted changes in anxiety in a clinical sample of adults with mood disorders. Larger studies are required for further validations. However, smartphone sensing may have the potential to monitor early symptoms of mood disorders

Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus.

Speckle tracking echocardiography (STE) has been utilized to evaluate independent spatial alterations in the diabetic heart, but the progressive manifestation of regional and segmental cardiac dysfunction in the type 2 diabetic (T2DM) heart remains understudied. Therefore, the objective of this study was to elucidate if machine learning could be utilized to reliably describe patterns of the progressive regional and segmental dysfunction that are associated with the development of cardiac contractile dysfunction in the T2DM heart. Non-invasive conventional echocardiography and STE datasets were utilized to segregate mice into two pre-determined groups, wild-type and Db/Db, at 5, 12, 20, and 25 weeks. A support vector machine model, which classifies data using a single line, or hyperplane, that best separates each class, and a ReliefF algorithm, which ranks features by how well each feature lends to the classification of data, were used to identify and rank cardiac regions, segments, and features by their ability to identify cardiac dysfunction. STE features more accurately segregated animals as diabetic or non-diabetic when compared with conventional echocardiography, and the ReliefF algorithm efficiently ranked STE features by their ability to identify cardiac dysfunction. The Septal region, and the AntSeptum segment, best identified cardiac dysfunction at 5, 20, and 25 weeks, with the AntSeptum also containing the greatest number of features which differed between diabetic and non-diabetic mice. Cardiac dysfunction manifests in a spatial and temporal fashion, and is defined by patterns of regional and segmental dysfunction in the T2DM heart which are identifiable using machine learning methodologies. Further, machine learning identified the Septal region and AntSeptum segment as locales of interest for therapeutic interventions aimed at ameliorating cardiac dysfunction in T2DM, suggesting that machine learning may provide a more thorough approach to managing contractile data with the intention of identifying experimental and therapeutic targets

Influenza-induced immune suppression to methicillin-resistant Staphylococcus aureus is mediated by TLR9.

Bacterial lung infections, particularly with methicillin-resistant Staphylococcus aureus (MRSA), increase mortality following influenza infection, but the mechanisms remain unclear. Here we show that expression of TLR9, a microbial DNA sensor, is increased in murine lung macrophages, dendritic cells, CD8+ T cells and epithelial cells post-influenza infection. TLR9-/- mice did not show differences in handling influenza nor MRSA infection alone. However, TLR9-/- mice have improved survival and bacterial clearance in the lung post-influenza and MRSA dual infection, with no difference in viral load during dual infection. We demonstrate that TLR9 is upregulated on macrophages even when they are not themselves infected, suggesting that TLR9 upregulation is related to soluble mediators. We rule out a role for elevations in interferon-γ (IFNγ) in mediating the beneficial MRSA clearance in TLR9-/- mice. While macrophages from WT and TLR9-/- mice show similar phagocytosis and bacterial killing to MRSA alone, following influenza infection, there is a marked upregulation of scavenger receptor A and MRSA phagocytosis as well as inducible nitric oxide synthase (Inos) and improved bacterial killing that is specific to TLR9-deficient cells. Bone marrow transplant chimera experiments and in vitro experiments using TLR9 antagonists suggest TLR9 expression on non-hematopoietic cells, rather than the macrophages themselves, is important for regulating myeloid cell function. Interestingly, improved bacterial clearance post-dual infection was restricted to MRSA, as there was no difference in the clearance of Streptococcus pneumoniae. Taken together these data show a surprising inhibitory role for TLR9 signaling in mediating clearance of MRSA that manifests following influenza infection