Human error remains the primary vector for privacy breaches, with users frequently exposing sensitive personal data through unsafe behaviors, such as granting excessive permissions, neglecting screen locks, or falling for social engineering. Yet, existing information security awareness (ISA) programs often adopt a one-size-fits-all approach, neglecting users’ personality traits and risk perception. In this work, we conducted a comprehensive five-week study with 105 participants to evaluate how personality traits and Passive Risk-Taking (PRT) interact with different ISA training methodologies. Prior research has primarily examined direct associations between personality traits and self-reported ISA measures, producing mixed findings and providing limited insight into how individual differences shape responsiveness to ISA training interventions. To address these limitations, we used a longitudinal sensor-based framework that captures real-world security behaviors over time, enabling a more reliable assessment of ISA training outcomes. Our results show that individual differences moderate training efficacy: users with high Agreeableness achieved greater gains through active-risk training (social engineering simulations), whereas those with lower Agreeableness benefited more from passive-risk training targeting omission-based risks. Furthermore, we find that Agreeableness moderates the relationships between both baseline PRT and changes in PRT and subsequent ISA improvements, while Conscientiousness moderates how resource-related PRT translates into ISA gains. Overall, these findings show that personality traits shape how users benefit from different training strategies and influence the alignment between risk reduction and awareness gains, supporting a shift toward adaptive, privacy-enhancing ISA training.

MOTIVATION: Correlated events of gains and losses enable inference of co-evolution relations. The reconstruction of the co-evolutionary interactions network in prokaryotic species may elucidate functional associations among genes. RESULTS: We developed a novel probabilistic methodology for the detection of co-evolutionary interactions between pairs of genes. Using this method we inferred the co-evolutionary network among 4593 Clusters of Orthologous Genes (COGs). The number of co-evolutionary interactions substantially differed among COGs. Over 40% were found to co-evolve with at least one partner. We partitioned the network of co-evolutionary relations into clusters and uncovered multiple modular assemblies of genes with clearly defined functions. Finally, we measured the extent to which co-evolutionary relations coincide with other cellular relations such as genomic proximity, gene fusion propensity, co-expression, protein-protein interactions and metabolic connections. Our results show that co-evolutionary relations only partially overlap with these other types of networks. Our results suggest that the inferred co-evolutionary network in prokaryotes is highly informative towards revealing functional relations among genes, often showing signals that cannot be extracted from other network types. AVAILABILITY AND IMPLEMENTATION: Available under GPL license as open source. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: The evolutionary analysis of presence and absence profiles (phyletic patterns) is widely used in biology. It is assumed that the observed phyletic pattern is the result of gain and loss dynamics along a phylogenetic tree. Examples of characters that are represented by phyletic patterns include restriction sites, gene families, introns and indels, to name a few. Here, we present a user-friendly web server that accurately infers branch-specific and site-specific gain and loss events. The novel inference methodology is based on a stochastic mapping approach utilizing models that reliably capture the underlying evolutionary processes. A variety of features are available including the ability to analyze the data with various evolutionary models, to infer gain and loss events using either stochastic mapping or maximum parsimony, and to estimate gain and loss rates for each character analyzed. AVAILABILITY: Freely available for use at http://gloome.tau.ac.il/.

This paper presents a sensor fusion framework for mapping unknown environments for mobile robots. The proposed framework enables on-line selection of the most reliable logical sensors and the most suitable fusion algorithm. The framework is rule-based, employing the “simplest sensor fusion algorithm with the most reliable sensors” concept. This goal is achieved through measures that were developed to quantify on-line the performance of the sensors. The framework was evaluated in an experiment consisting of a mobile robot equipped with five logical sensors. The framework was compared to four other algorithms. The advantages of this new framework are presented using statistical, histogram, time series and graphical analyses.