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

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

Positive emotion enhances association-memory

The influence of emotion on association-memory is often attributed to arousal, but negative stimuli are typically used to test for these effects. While prior studies of negative emotion on association-memory have found impairments, theories suggest that positive emotion may have a distinct effect on memory, and may lead to enhanced association-memory. Here we tested participants’ memory for pairs of positive and neutral words using cued recall, supplemented with a mathematical modeling approach designed to disentangle item- versus association-memory effects that may otherwise confound cued-recall performance. We consistently found enhanced association-memory due to positive emotion. These results provide further evidence that positive information is processed differently than negative and that, when examining association formation, valence as well as arousal must be considered