Thermal and mechanical development of extremely high heat flux cooling modules based on jet array technology

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1997.Includes bibliographical references.by Rudy S. Dahbura.M.S

Understanding Security Behavior of Real Users: Analysis of a Phishing Study

This paper presents a set of statistical analyses on an empirical study of phishing email sorting by real online users. Participants were assigned to multitasking and/or incentive conditions in unattended web-based tasks that are the most realistic in any comparable study to date. Our three stages of analyses included logistic regression models to identify individual phishing “cues” contributing to successful classifications, statistical significance tests assessing the links between participants’ training experience and self-assessments of success to their actual performance, significance tests searching for significant demographic factors influencing task completion performance, and lastly k-means clustering based on a range of performance measures and utilizing participants’ demographic attributes. In particular, the results indicate that multitasking and incentives create complex dynamics while demographic traits and cybersecurity training can be informative predictors of user security behavior. These findings strongly support the benefits of security training and education and advocate for customized and differentiated interventions to increase users’ success of correctly identifying phishing emails

An integrated search-based approach for automatic testing from extended finite state machine (EFSM) models

This is the post-print version of the Article - Copyright @ 2011 ElsevierThe extended finite state machine (EFSM) is a modelling approach that has been used to represent a wide range of systems. When testing from an EFSM, it is normal to use a test criterion such as transition coverage. Such test criteria are often expressed in terms of transition paths (TPs) through an EFSM. Despite the popularity of EFSMs, testing from an EFSM is difficult for two main reasons: path feasibility and path input sequence generation. The path feasibility problem concerns generating paths that are feasible whereas the path input sequence generation problem is to find an input sequence that can traverse a feasible path. While search-based approaches have been used in test automation, there has been relatively little work that uses them when testing from an EFSM. In this paper, we propose an integrated search-based approach to automate testing from an EFSM. The approach has two phases, the aim of the first phase being to produce a feasible TP (FTP) while the second phase searches for an input sequence to trigger this TP. The first phase uses a Genetic Algorithm whose fitness function is a TP feasibility metric based on dataflow dependence. The second phase uses a Genetic Algorithm whose fitness function is based on a combination of a branch distance function and approach level. Experimental results using five EFSMs found the first phase to be effective in generating FTPs with a success rate of approximately 96.6%. Furthermore, the proposed input sequence generator could trigger all the generated feasible TPs (success rate = 100%). The results derived from the experiment demonstrate that the proposed approach is effective in automating testing from an EFSM

Governing AI safety through independent audits

Highly automated systems are becoming omnipresent. They range in function from self-driving vehicles to advanced medical diagnostics and afford many benefits. However, there are assurance challenges that have become increasingly visible in high-profile crashes and incidents. Governance of such systems is critical to garner widespread public trust. Governance principles have been previously proposed offering aspirational guidance to automated system developers; however, their implementation is often impractical given the excessive costs and processes required to enact and then enforce the principles. This Perspective, authored by an international and multidisciplinary team across government organizations, industry and academia, proposes a mechanism to drive widespread assurance of highly automated systems: independent audit. As proposed, independent audit of AI systems would embody three ‘AAA’ governance principles of prospective risk Assessments, operation Audit trails and system Adherence to jurisdictional requirements. Independent audit of AI systems serves as a pragmatic approach to an otherwise burdensome and unenforceable assurance challenge