arXiv subjects

William Hughes

Publications and source records attributed to William Hughes.

4 recordsLinked to original sources

arXiv2026

Gamifying Cyber Governance: A Virtual Escape Room to Transform Cybersecurity Policy Education

Serious games are gaining popularity as effective teaching and learning tools, providing engaging, interactive, and practical experiences for students. Gamified learning experiences, such as virtual escape rooms, have emerged as powerful tools in bridging theory and practice, fostering deeper understanding and engagement among students. This paper presents the design, implementation, and evaluation of a virtual escape room tailored specifically for cybersecurity governance and policy education. Developed as a 3D immersive environment, the escape room simulates a virtual company scenario to facilitate risk-informed cyber policy development. It consists of three interactive zones, each offering distinct sets of scenario-based problems that target specific educational objectives. Through these zones, students analyze cybersecurity risks, match security frameworks, and draft appropriate policies, thereby developing critical thinking, decision-making skills, and practical cybersecurity competencies. The primary contribution of this work lies in its innovative integration of game-based learning and immersive technology to create robust, interactive learning materials that are also resilient to generative AI interventions, thereby maintaining academic integrity. Additionally, the escape room provides students with exposure to real-world cybersecurity scenarios in a virtual office environment that meets industry expectations. Results from a student survey indicated strong positive feedback, highlighting significant improvements in students engagement, practical understanding, and enthusiasm toward cybersecurity governance and policy concepts, underscoring the effectiveness and potential of gamification in cybersecurity education.

cs.CY↗

arXiv2021

The genomic architecture and evolutionary fates of supergenes

Supergenes are genomic regions containing sets of tightly linked loci that control multi-trait phenotypic polymorphisms under balancing selection. Recent advances in genomics have uncovered significant variation in both the genomic architecture as well as the mode of origin of supergenes across diverse organismal systems. Although the role of genomic architecture for the origin of supergenes has been much discussed, differences in the genomic architecture also subsequently affect the evolutionary trajectory of supergenes and the rate of degeneration of supergene haplotypes. In this review, we synthesize recent genomic work and historical models of supergene evolution, highlighting how the genomic architecture of supergenes affects their evolutionary fate. We discuss how recent findings on classic supergenes involved in governing ant colony social form, mimicry in butterflies, and heterostyly in flowering plants relate to theoretical expectations. Furthermore, we use forward simulations to demonstrate that differences in genomic architecture affect the degeneration of supergenes. Finally, we discuss implications of the evolution of supergene haplotypes for the long-term fate of balanced polymorphisms governed by supergenes.

q-bio.PE↗

arXiv2020

An optically-heated atomic source for compact ion trap vacuum systems

We present a design for an atomic oven suitable for loading ion traps, which is operated via optical heating with a continuous-wave multimode diode laser. The absence of the low-resistance electrical connections necessary for Joule heating allows the oven to be extremely well thermally isolated from the rest of the vacuum system, and for an oven filled with calcium we achieve a number density suitable for rapid ion loading in the target region with ~200 mW of laser power, limited by radiative losses. With simple feedforward to the laser power, the turn-on time for the oven is less than 20 s, while the oven contains enough calcium to operate continuously for many thousands of years without replenishment.

physics.atom-ph↗

arXiv2019

Optimizing Design Verification using Machine Learning: Doing better than Random

As integrated circuits have become progressively more complex, constrained random stimulus has become ubiquitous as a means of stimulating a designs functionality and ensuring it fully meets expectations. In theory, random stimulus allows all possible combinations to be exercised given enough time, but in practice with highly complex designs a purely random approach will have difficulty in exercising all possible combinations in a timely fashion. As a result it is often necessary to steer the Design Verification (DV) environment to generate hard to hit combinations. The resulting constrained-random approach is powerful but often relies on extensive human expertise to guide the DV environment in order to fully exercise the design. As designs become more complex, the guidance aspect becomes progressively more challenging and time consuming often resulting in design schedules in which the verification time to hit all possible design coverage points is the dominant schedule limitation. This paper describes an approach which leverages existing constrained-random DV environment tools but which further enhances them using supervised learning and reinforcement learning techniques. This approach provides better than random results in a highly automated fashion thereby ensuring DV objectives of full design coverage can be achieved on an accelerated timescale and with fewer resources. Two hardware verification examples are presented, one of a Cache Controller design and one using the open-source RISCV-Ariane design and Google's RISCV Random Instruction Generator. We demonstrate that a machine-learning based approach can perform significantly better on functional coverage and reaching complex hard-to-hit states than a random or constrained-random approach.

cs.LG↗