Machine Learning and Survey-based Predictors of InfoSec Non-Compliance

Survey items developed in behavioral Information Security (InfoSec) research should be practically useful in identifying individuals who are likely to create risk by failing to comply with InfoSec guidance. The literature shows that attitudes, beliefs, and perceptions drive compliance behavior and has influenced the creation of a multitude of training programs focused on improving ones’ InfoSec behaviors. While automated controls and directly observable technical indicators are generally preferred by InfoSec practitioners, difficult-to-monitor user actions can still compromise the effectiveness of automatic controls. For example, despite prohibition, doubtful or skeptical employees often increase organizational risk by using the same password to authenticate corporate and external services. Analysis of network traffic or device configurations is unlikely to provide evidence of these vulnerabilities but responses to well-designed surveys might. Guided by the relatively new IPAM model, this study administered 96 survey items from the Behavioral InfoSec literature, across three separate points in time, to 217 respondents. Using systematic feature selection techniques, manageable subsets of 29, 20, and 15 items were identified and tested as predictors of non-compliance with security policy. The feature selection process validates IPAM's innovation in using nuanced self-efficacy and planning items across multiple time frames. Prediction models were trained using several ML algorithms. Practically useful levels of prediction accuracy were achieved with, for example, ensemble tree models identifying 69% of the riskiest individuals within the top 25% of the sample. The findings indicate the usefulness of psychometric items from the behavioral InfoSec in guiding training programs and other cybersecurity control activities and demonstrate that they are promising as additional inputs to AI models that monitor networks for security events.

Implementing The Multi Expression Programming, Nonlinear Regression, Artificial Neural Network, and M5P-Tree Models to Predict The Long-Term of Compressive Strength of Cement- Mortar Modified With Fly Ash

In the recent decade, supplementary cementing ingredients have become an essential part of various strength ranges of concrete and cement-mortar mix design. Examples are natural materials, by-products, industrial wastes, and materials that require less energy and time to generate. Fly ash is one of the most widely utilized additional cementing ingredients. Fly ash is a by-product substance produced by coal combustion. It's being used in cement mortar and concrete as a pozzolanic substance. It has demonstrated significant influence in improving liquid and solid properties of cement mortar, such as compressive strength. Multi Expression Programming (MEP) is employed in this study to estimate the compressive strength (CS) of cement mortar modified with fly ash. The outcomes of this model were compared and evaluated with several other models such as the Nonlinear Regression model (NLR), Artificial Neural Network (ANN), and M5P-tree models that have been used in the construction fields. The input parameters included water/cement ratio (w/c), curing time (t days), and fly ash content (FA %), while the target property was compressive strength up to 360 days of curing. Four hundred fifty (450) data are collected from previous literature on modifying cement mortar with fly ash for that purpose. The water/cement ratio ranged from 0.24 to 1.2, and the fly ash was used to replace cement up to 55% (%wt. of dry cement). Based on the Coefficient of Determination (R2), Root Mean Squared Error (RMSE), Scatter Index (SI), Objective (OBJ), Mean Absolute Error (MAE), t-test value, the uncertainty of 95%, Performance Index (ρ), and boxplot for actual and predicted compressive strength. The MEP model performed better than other developed models according to evaluation tools. The compressive strength was also correlated with flexural and splitting tensile strengths using different nonlinear models.

Machine learning models of tobacco susceptibility and current use among adolescents from 97 countries in the Global Youth Tobacco Survey, 2013-2017

Adolescents are particularly vulnerable to tobacco initiation and escalation. Identifying factors associated with adolescent tobacco susceptibility and use can guide tobacco prevention efforts. Novel machine learning (ML) approaches efficiently identify interactive relations among factors of tobacco risks and identify high-risk subpopulations that may benefit from targeted prevention interventions. Nationally representative cross-sectional 2013–2017 Global Youth Tobacco Survey (GYTS) data from 97 countries (28 high-income and 69 low-and middle-income countries) from 342,481 adolescents aged 13–15 years (weighted N = 52,817,455) were analyzed using ML regression tree models, accounting for sampling weights. Predictors included demographics (sex, age), geography (region, country-income), and self-reported exposure to tobacco marketing, secondhand smoke, and tobacco control policies. 11.9% (95% CI 11.1%-12.6%) of tobacco-naïve adolescents were susceptible to tobacco use and 11.7% (11.0%-12.5%) of adolescents reported using any tobacco product (cigarettes, other smoked tobacco, smokeless tobacco) in the past 30 days. Regression tree models found that exposure or receptivity to tobacco industry promotions and secondhand smoke exposure predicted increased risks of susceptibility and use, while support for smoke-free air policies predicted decreased risks of tobacco susceptibility and use. Anti-tobacco school education and health warning messages on product packs predicted susceptibility or use, but their protective effects were not evident across all adolescent subgroups. Sex, region, and country-income moderated the effects of tobacco promotion and control factors on susceptibility or use, showing higher rates of susceptibility and use in males and high-income countries, Africa and the Americas (susceptibility), and Europe and Southeast Asia (use). Tobacco policy-related factors robustly predicted both tobacco susceptibility and use in global adolescents, and interacted with adolescent characteristics and other environments in complex ways that stratified adolescents based on their tobacco risk. These findings emphasize the importance of efficient ML modeling of interactions in tobacco risk prediction and suggest a role for targeted prevention strategies for high-risk adolescents.

VISmaF: Synthetic Tree for Immersive Virtual Visualization in Smart Farming. Part I: Scientific Background Review and Model Proposal

Computer-Generated Imagery (CGI) has received increasing interest in both research and the entertainment industry. Recent advancements in computer graphics allowed researchers and companies to create large-scale virtual environments with growing resolution and complexity. Among the different applications, the generation of biological assets is a relevant task that implies challenges due to the extreme complexity associated with natural structures. An example is represented by trees, whose composition made by thousands of leaves, branches, branchlets, and stems with oriented directions is hard to be modeled. Realistic 3D models of trees can be exploited for a wide range of applications including decision-making support, visualization of ecosystem changes over time, and for simple visualization purposes. In this review, we give an overview of the most common approaches used to generate 3D tree models, discussing both methodologies and available commercial software. We focus on strategies for modeling and rendering of plants, highlighting their accordance or not with botanical knowledge and biological models. We also present a proof of concept to link biological models and 3D rendering engines through Ordinary Differential Equations.

Modeling natural mortality for different plant densities in dendroenergetic trials

Mortality was modelled for three species (Acacia melanoxylon, Eucalyptus camaldulensis, Eucalyptus nitens) at three plantation densities (5000, 7500, and 10000 trees ha-1) in an trial of biomass production for purposes of dendroenergetic. One modelling based on individual tree level and two mortality modelling alternatives were evaluated: four survival probability equations and eight difference equations. The individual tree survival modelling considered a logistic model, is a linear combination of variables to individual tree at current time and the previous time as estimator, being the main variables the variation of the competition index and the variation of basal area growth between the current growth period and the previous growth period. The survival probability alternative used state variables of the stand (age, dominant height, average square diameter) as predictors, whereas the difference equations were adjusted according to age-based changes only. The models to stand levels showed better result than individual tree models, and in general, the mortality models based on difference equations presented better indicators of precision and parsimony. The rate of relative mortality was constant, i.e., (dN/dE)/N, and varied between species, revealing greater mortality, consecutively, in E. nitens, A. melanoxylon, and E. camaldulensis. Although mortality tended to be higher at greater plantation densities, stand density did not significantly affect the parameters of the adjusted models. Highlights The mortality stand level models showed better results than the individual tree models for dendroenergetic crops, and in general, the mortality models based on difference equations presented better precision indicators and parsimony. The survival probability alternative involved state variables of the stand like age, dominant height, and average square diameter as predictors, while the difference equations were fitted according to age-based changes only. Mortality tended to be higher at greater plantation densities, however stand density did not significantly affect the parameters of the mortality equations.

Different Statistical Approaches to Develop a Guideline for Improvement of Caregiver’s Mental Health

Caregiver burden is common, and improvement of caregivers’ mental health could lead to better quality of care and well-being for both caregivers and care recipients. We investigate ways to develop a guideline to enhance caregiver’s mental well-being by applying and comparing regression tree and ensemble tree models. Data comes from the 2017 National Health and Aging Trends Study and National Study of Caregiving. Dementia caregivers’ (n=945) aspects of caregiving, care activities, support environment, and participation along with basic demographics and health are considered. First, insignificant predictors are preselected using linear regression with backward selection, which will not be included in the tree models. Using the predetermined predictors that are not excluded in the backward selection method, regression tree and ensemble tree models are generated to predict emotional difficulty of caregivers. The regression tree with the preselected predictors predicts caregivers with low to moderate levels of overload and high levels of joy being with their care recipient associated with the lowest level of emotional difficulty. On the other hand, if caregivers have high levels of overload and low to moderately high levels of positive affect, this is linked with the highest level of emotional difficulty. Ensemble tree models showed similar results with lower error measures. Using tree-based methods can help determine the most important predictors of caregiver mental health. Easily interpretable results with applicable decision rules can provide a guideline for intervention developers.

Money laundering transaction detection with classification tree models

The detection of money laundering is a very important problem, especially in the financial sector. We propose a mathematical specification of the problem in terms of a classification tree model that ”automates” expert based manual decisions. We operationally validate the model on a concrete application that originates from a large Italian bank. The application of the model to the data shows a good predictive accuracy and, even more importantly, the reduction of false positives, with respect to the ”manual” expert based activity. From an interpretational viewpoint, while some drivers of suspicious laundering activity are in line with the daily business practices of the bank’s anti money laundering operations, some others are new discoveries.