Algorithmic Diversity for Software Security

Software diversity protects against a modern-day exploits such as code-reuse attacks. When an attacker designs a code-reuse attack on an example executable, it relies on replicating the target environment. With software diversity, the attacker cannot reliably replicate their target. This is a security benefit which can be applied to massive-scale software distribution. When applied to large-scale communities, an invested attacker may perform analysis of samples to improve the chances of a successful attack (M. Franz). We present a general NOP-insertion algorithm which can be expanded and customized for security, performance, or other costs. We demonstrate an improvement in security so that a code-reuse attack based on any one variant has minimal chances of success on another and analyse the costs of this method. Alternately, the variants may be customized to meet performance or memory overhead constraints. Deterministic diversification allows for the flexibility to balance these needs in a way that doesn't exist in a random online method

Bridging the gap: a novel approach to mathematics support

The ever growing gap between secondary and university level mathematics is now becoming a major concern to higher education institutions. The increase in diversity of students’ background in mathematics, from students who have studied the more traditional A-level programmes to students with BTEC or international qualifications and part-time students who have been out of education for long periods, means that they are often unprepared for the marked shift in levels and catering for all abilities is difficult in the normal lecture, tutorial format. Lack of sufficient mathematical knowledge not only affects students’ success on courses but also leads to disengagement and thus a high drop-out rate in the first 2 years of study. Many universities now offer a maths support service in an attempt to overcome this but their success is varied. This paper presents a novel approach to maths support designed and adopted by the University of Lincoln, School of Engineering, to bridge this transition gap for students, offer continued support through assessment for learning (AFL) and Individual Learning Plans (ILP’s) and ultimately increase student success, engagement and retention. The paper then extends this proven approach and discusses proposed enhancements through the use of on-line diagnostic testing and implementation of a ‘student expert’ system to harness mathematical knowledge held by those gifted and talented students often overlooked by higher education institutions and to promote peer-to-peer mentoring. The paper shows that with the current support system in place, there is a marked increase in student retention, compared with national benchmark data, and an increase in student engagement and success measured through student feedback and presented retention data

Increasing the impact of mathematics support on aiding student transition in higher education

The ever growing gap between secondary and university level mathematics is a major concern to higher education institutions. The increase in diversity of students’ background in mathematics, with entry qualifications ranging from the more traditional A-level programmes to BTEC or international qualifications is compounded where institutions attempt to widen participation. For example, work-based learners may have been out of education for prolonged periods, and consequently, are often unprepared for the marked shift in levels, and catering for all abilities is difficult in the normal lecture, tutorial format. Lack of sufficient mathematical knowledge not only affects students’ achievement on courses but also leads to disengagement and higher drop-out rates during the first two years of study. Many universities now offer a maths support service in an attempt to overcome these issues, but their success is varied. This paper presents a novel approach to maths support designed and adopted by the University of Lincoln, School of Engineering, to bridge this transition gap for students, offer continued support through assessment for learning (AFL) and Individual Learning Plans (ILP’s), and ultimately increase student achievement, engagement and retention. The paper then extends this proven approach and discusses recently implemented enhancements through the use of on-line diagnostic testing and a ‘student expert’ system to harness mathematical knowledge held by those gifted and talented students (often overlooked by higher education institutions) and to promote peer-to-peer mentoring. The paper shows that with the proven system in place, there is a marked increase in student retention compared with national benchmark data, and an increase in student engagement and achievement measured through student feedback and assessments. Although the on-line enhancements are in the early stages of implementation it is expected, based on these results, that further improvements will be shown

Obligation, Permission, and Bayesian Orgulity

This essay has two aims. The first is to correct an increasingly popular way of misunderstanding Belot's Orgulity Argument. The Orgulity Argument charges Bayesianism with defect as a normative epistemology. For concreteness, our argument focuses on Cisewski et al.'s recent rejoinder to Belot. The conditions that underwrite their version of the argument are too strong and Belot does not endorse them on our reading. A more compelling version of the Orgulity Argument than Cisewski et al. present is available, however---a point that we make by drawing an analogy with de Finetti's argument against mandating countable additivity. Having presented the best version of the Orgulity Argument, our second aim is to develop a reply to it. We extend Elga's idea of appealing to finitely additive probability to show that the challenge posed by the Orgulity Argument can be met

The Yin and Yang of Kinship and Business: Complementary or Contradictory Forces?

Are the social domains of kinship and business on balance complementary or contradictory? Do ventures that invest heavily in both – conventionally referred to as “family firms” – bear a net gain or net loss? We are scarcely the first to raise these questions. How then will we try to contribute to an answer? We try this in five ways, all of them based on previous literature. First, we develop the dichotomy of kinship and business by taking seriously the metaphor of yin and yang, merging it with the anthropological constructs of structural domains such as “domestic” and “public.” This metaphor proves to shed light on the relevant literature. Second, we provide a qualitative survey of the costs and benefits of kinship in business. Third, we summarize the empirical work that addresses the performance outcomes from family involvement. Fourth, we consider the practitioner implications of these studies. Finally, we ask if scholars are as yet in a position to answer these questions

Why Can’t a Family Business Be More Like a Nonfamily Business? Modes of Professionalization in Family Firms

The authors survey arguments that family firms should behave more like nonfamily firms and “professionalize.” Despite the apparent advantages of this transition, many family firms fail to do so or do so only partially. The authors reflect on why this might be so, and the range of possible modes of professionalization. They derive six ideal types: (a) minimally professional family firms; (b) wealth dispensing, private family firms; (c) entrepreneurially operated family firms; (d) entrepreneurial family business groups; (e) pseudoprofessional, public family firms; and (f) hybrid professional family firms. The authors conclude with suggestions for further research that is attentive to such variation

Another Approach to Consensus and Maximally Informed Opinions with Increasing Evidence

Merging of opinions results underwrite Bayesian rejoinders to complaints about the subjective nature of personal probability. Such results establish that sufficiently similar priors achieve consensus in the long run when fed the same increasing stream of evidence. Initial subjectivity, the line goes, is of mere transient significance, giving way to intersubjective agreement eventually. Here, we establish a merging result for sets of probability measures that are updated by Jeffrey conditioning. This generalizes a number of different merging results in the literature. We also show that such sets converge to a shared, maximally informed opinion. Convergence to a maximally informed opinion is a (weak) Jeffrey conditioning analogue of Bayesian “convergence to the truth” for conditional probabilities. Finally, we demonstrate the philosophical significance of our study by detailing applications to the topics of dynamic coherence, imprecise probabilities, and probabilistic opinion pooling