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Research shows that phishing emails often utilize persuasion techniques, such as social proof, liking, consistency, authority, scarcity, and reciprocity to gain trust to obtain sensitive information or maliciously infect devices. The link between principles of persuasion and social engineering attacks, particularly in phishing email attacks, is an important topic in cyber security as they are the common and effective method used by cybercriminals to obtain sensitive information or access computer systems. This survey paper concluded that spear phishing, a targeted form of phishing, has been found to be specifically effective as attackers can tailor their messages to the specific characteristics, interests, and vulnerabilities of their targets. Understanding the uses of the principles of persuasion in spear phishing is key to the effective defence against it and eventually its elimination. This survey paper systematically summarizes and presents the current state of the art in understanding the use of principles of persuasion in phishing. Through a systematic review of the existing literature, this survey paper identifies a significant gap in the understanding of the impact of principles of persuasion as a social engineering strategy in phishing attacks and highlights the need for further research in this area., Comment: 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

Background: Effective use of mobile health technologies requires high participant adherence and retention. However, remote digital health studies often face high attrition and low adherence, potentially introducing bias and limiting generalizability. Objective: This study aims to identify longitudinal indicators of participant retention and adherence to develop strategies for improving data collection in digital health studies and understanding how cohorts are shaped by participant withdrawal and non-adherence. Methods: We conducted analyses on the Brighten study, a smartphone-based randomized controlled trial evaluating apps for depression treatment. Participants were asked to complete seven digital questionnaires regularly. Outcomes included adherence (questionnaire completion), engagement (post-baseline participation), and retention (continued participation over time). We analyzed relationships between these outcomes, static factors (e.g., demographics, average questionnaire scores) and dynamic factors (e.g., questionnaire score changes over time). Results: Of 2,201 participants, 1,093 completed at least one non-baseline questionnaire (median completion rate: 37.6%). Adherence was higher among participants with lower average depression severity (P<.001) and those perceiving improvement (P=.001). Demographic factors significantly influenced adherence and engagement. Participants with greater baseline depressive symptoms were more likely to withdraw before completing non-baseline questionnaires (t=-2.53, P=.01). However, symptom improvement was linked to better adherence (U=127,084; P<.001) and retention (HR=0.78, P=.002). Conclusion: Clinical trajectories and perceived improvements in depressive symptoms are key indicators of engagement, adherence, and retention. These findings may enhance data interpretation and inform strategies to boost retention and adherence in future trials.

We construct continuum directed polymer measures corresponding to the critical 2d stochastic heat flow (2d SHF) introduced by Caravenna, Sun, and Zygouras in their recent article [Inventiones mathematicae 233, 325--460 (2023)]. For this purpose, we prove a Chapman-Kolmogorov relation for the 2d SHF along with a related elementary conditional expectation formula. We explore some basic properties of the continuum polymer measures, with our main focus being on their second moments. In particular, we show that the form of their second moments is consistent with the family of continuum polymer measures, indexed by a disorder strength parameter, having a conditional Gaussian multiplicative chaos distributional interrelationship similar to that previously found in an analogous hierarchical toy model., Comment: 39 pages

T1 mapping is a quantitative magnetic resonance imaging (qMRI) technique that has emerged as a valuable tool in the diagnosis of diffuse myocardial diseases. However, prevailing approaches have relied heavily on breath-hold sequences to eliminate respiratory motion artifacts. This limitation hinders accessibility and effectiveness for patients who cannot tolerate breath-holding. Image registration can be used to enable free-breathing T1 mapping. Yet, inherent intensity differences between the different time points make the registration task challenging. We introduce PCMC-T1, a physically-constrained deep-learning model for motion correction in free-breathing T1 mapping. We incorporate the signal decay model into the network architecture to encourage physically-plausible deformations along the longitudinal relaxation axis. We compared PCMC-T1 to baseline deep-learning-based image registration approaches using a 5-fold experimental setup on a publicly available dataset of 210 patients. PCMC-T1 demonstrated superior model fitting quality (R2: 0.955) and achieved the highest clinical impact (clinical score: 3.93) compared to baseline methods (0.941, 0.946 and 3.34, 3.62 respectively). Anatomical alignment results were comparable (Dice score: 0.9835 vs. 0.984, 0.988). Our code and trained models are available at https://github.com/eyalhana/PCMC-T1., Comment: Accepted to MICCAI 2023

We discuss a family of time-inhomogeneous two-dimensional diffusions, defined over a finite time interval $[0,T]$, having transition density functions that are expressible in terms of the integral kernels for negative exponentials of the two-dimensional Schr\"odinger operator with a point potential at the origin. These diffusions have a singular drift pointing in the direction of the origin that is strong enough to enable the possibly of visiting there, in contrast to a two-dimensional Brownian motion. Our main focus is on characterizing a local time process at the origin analogous to that for a one-dimensional Brownian motion and on studying the law of its process inverse., Comment: 75 pages. This version includes a few small changes in the introduction

Ultrasound is the second most used modality in medical imaging. It is cost effective, hazardless, portable and implemented routinely in numerous clinical procedures. Nonetheless, image quality is characterized by granulated appearance, poor SNR and speckle noise. Specific for malignant tumors, the margins are blurred and indistinct. Thus, there is a great need for improving ultrasound image quality. We hypothesize that this can be achieved, using neural networks, by translation into a more realistic display which mimics an anatomical cut through the tissue. In order to achieve this goal, the preferable approach would be to use a set of paired images. However, this is practically impossible in our case. Therefore, Cycle Generative Adversarial Network (CycleGAN) was used, in order to learn each domain properties separately and enforce cross domain cycle consistency. The two datasets which were used for training the model were "Breast Ultrasound Images" (BUSI) and a set of optic images of poultry breast tissue samples acquired at our lab. The generated pseudo anatomical images provide improved visual discrimination of the lesions with clearer border definition and pronounced contrast. In order to evaluate the preservation of the anatomical features, the lesions in the ultrasonic images and the generated pseudo anatomical images were both automatically segmented and compared. This comparison yielded median dice score of 0.91 for the benign tumors and 0.70 for the malignant ones. The median lesion center error was 0.58% and 3.27% for the benign and malignancies respectively and the median area error index was 0.40% and 4.34% for the benign and malignancies respectively. In conclusion, these generated pseudo anatomical images, which are presented in a more intuitive way, enhance tissue anatomy and can potentially simplify the diagnosis and improve the clinical outcome., Comment: 9 pages, 5 figures

Artificial intelligence algorithms are increasingly adopted as decisional aides by public bodies, with the promise of overcoming biases of human decision-makers. At the same time, they may introduce new biases in the human-algorithm interaction. Drawing on psychology and public administration literatures, we investigate two key biases: overreliance on algorithmic advice even in the face of warning signals from other sources (automation bias), and selective adoption of algorithmic advice when this corresponds to stereotypes (selective adherence). We assess these via three experimental studies conducted in the NetherlandsWe discuss the implications of our findings for public sector decision making in the age of automation. Overall, our study speaks to potential negative effects of automation of the administrative state for already vulnerable and disadvantaged citizens., Comment: Paper published in Journal of Public Administration Research and Theory (2022)

Motivated by the work of J. S\'andor [19], in this paper we establish a new Wilker type and Huygens type inequalities involving the trigonometric and hyperbolic functions. Moreover, in terms of hyperbolic functions, the upper and lower bounds of sin(x)/x and tan(x)/x are given.