Agreeable Smellers and Sensitive Neurotics – Correlations among Personality Traits and Sensory Thresholds

Correlations between personality traits and a wide range of sensory thresholds were examined. Participants (N = 124) completed a personality inventory (NEO-FFI) and underwent assessment of olfactory, trigeminal, tactile and gustatory detection thresholds, as well as examination of trigeminal and tactile pain thresholds. Significantly enhanced odor sensitivity in socially agreeable people, significantly enhanced trigeminal sensitivity in neurotic subjects, and a tendency for enhanced pain tolerance in highly conscientious participants was revealed. It is postulated that varied sensory processing may influence an individual's perception of the environment; particularly their perception of socially relevant or potentially dangerous stimuli and thus, varied with personality

FraudDroid: Automated Ad Fraud Detection for Android Apps

Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a single UI state is involved and can be identified based on static information such as the size or location of ad views. Other types of fraud exist that involve multiple UI states and are performed dynamically while users interact with the app. Such dynamic interaction frauds, although now widely spread in apps, have not yet been explored nor addressed in the literature. In this work, we investigate a wide range of mobile ad frauds to provide a comprehensive taxonomy to the research community. We then propose, FraudDroid, a novel hybrid approach to detect ad frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI state transition graphs and collects their associated runtime network traffics, which are then leveraged to check against a set of heuristic-based rules for identifying ad fraudulent behaviours. We show empirically that FraudDroid detects ad frauds with a high precision (93%) and recall (92%). Experimental results further show that FraudDroid is capable of detecting ad frauds across the spectrum of fraud types. By analysing 12,000 ad-supported Android apps, FraudDroid identified 335 cases of fraud associated with 20 ad networks that are further confirmed to be true positive results and are shared with our fellow researchers to promote advanced ad fraud detectionComment: 12 pages, 10 figure

Participant's demographic data.

<p>Data of the NEO-FFI is provided converted to t-scores, provided by the German normative sample described in the manual. T-scores shown are standardized scores with a mean of 50 and a standard deviation of 10.</p

Predictors of Neuroticism.

<p>Only the inclusion of the variables olfactory detection, trigeminal chemosensory and electrical cutaneous detection and pain led to significant regression models and thus only those models are shown.</p><p><i>Note.</i> N = 103. CI = confidence interval.</p><p>*p<0.05.</p><p>**p>0.01.</p

Predictors of Agreeableness.

<p>Only the inclusion of the variables olfactory detection, trigeminal chemosensory detection and pain led to significant regression models, therefore only those models are presented.</p><p><i>Note.</i> N = 103. CI = confidence interval.</p><p>*p<0.05.</p><p>**p>0.01.</p

Pearson's correlation coefficients between personality traits and sensory detection and pain thresholds.

<p><i>Note.</i> In each line first correlation coefficients are presented, followed by level of significance. If p<0.05 the level of significance corrected for multiple comparison is presented in brackets. Correction was performed with the Bonferroni-Holm-method adjusted for dependent measurements (k = 10) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0018701#pone.0018701-Holm1" target="_blank">[32]</a>. n.s. … not significant. k … correction coefficient.</p