High-level Cryptographic Abstractions

The interfaces exposed by commonly used cryptographic libraries are clumsy, complicated, and assume an understanding of cryptographic algorithms. The challenge is to design high-level abstractions that require minimum knowledge and effort to use while also allowing maximum control when needed. This paper proposes such high-level abstractions consisting of simple cryptographic primitives and full declarative configuration. These abstractions can be implemented on top of any cryptographic library in any language. We have implemented these abstractions in Python, and used them to write a wide variety of well-known security protocols, including Signal, Kerberos, and TLS. We show that programs using our abstractions are much smaller and easier to write than using low-level libraries, where size of security protocols implemented is reduced by about a third on average. We show our implementation incurs a small overhead, less than 5 microseconds for shared key operations and less than 341 microseconds (< 1%) for public key operations. We also show our abstractions are safe against main types of cryptographic misuse reported in the literature

The Internal Consistency of the Moral Injury Event Scale: A Reliability Generalisation Meta-Analysis and Systematic Review

© Hogrefe. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1027/1015-5759/a000824The Moral Injury Event Scale (MIES) is a tool for measuring exposure to potentially morally injurious event(s) and distress. Although it reported acceptable psychometric properties in its initial development studies, it has since been used in multiple contexts and populations without assessment of its changing properties. A reliability generalization of the MIES and its Sub-Scales was therefore undertaken. A systematic search of electronic databases (PsychINFO; PTSD Pubs; MEDLINE; Scopus; Web of Science) identified 42 studies reporting internal consistencies (Cronbach’s α) up to April 2022. Unfortunately, few studies reported any other form of reliability or validity metric (e.g., test-retest, inter-rater reliability). A random effects model with a Bayesian analytic framework and the DerSimonian-Laird (1986) estimate was used. The review found the MIES to be an internally consistent tool based on α estimates at both Full-scale (α = .88; 95% CI [.87–.89]) and Sub-scales (α = .82–.92; 95% CI [.79–.93]). The review uncovered high heterogeneity and inconsistencies in its administration and modification although figures generally remained above acceptable levels (α .70). Based on the review, the MIES represents an internally reliably tool for measuring potentially morally injurious events and distress at both Full and Sub-Scales according to pooled Cronbach’s α estimates.Peer reviewe

The Total Synthesis of (–)-Scabrolide A

The first total synthesis of the norcembranoid diterpenoid scabrolide A is disclosed. The route begins with the synthesis of two chiral pool-derived fragments, which undergo a convergent coupling to expediently introduce all 19 carbon atoms of the natural product. An intramolecular Diels–Alder reaction and an enone–olefin cycloaddition/fragmentation sequence are then employed to construct the fused [5–6–7] linear carbocyclic core of the molecule and complete the total synthesis

Entrepreneurial Motivation

Recent research on entrepreneurship has focused largely on macro-level environmental forces. Although researchers adopting this focus have rightly criticized much of the existing empirical research on the role of human motivation in entrepreneurship, we believe that the development of entrepreneurship theory requires consideration of the motivations of people making entrepreneurial decisions. To provide a road map for researchers interested in this area, we discuss the major motivations that prior researchers have suggested should influence the entrepreneurial process, as well as suggest some motivations that are less commonly studied in this area. In addition to outlining the major reasons for exploring these motivations, we identify the major weaknesses that have limited the predictive power of previous research on this topic. We offer explicit solutions for future research to adopt to overcome these problems