arXiv subjects

Daniel Crawford

Publications and source records attributed to Daniel Crawford.

15 recordsLinked to original sources

arXiv2026

Enhancing User Resilience Against AI-Augmented Phishing: A Two-Stage Framework for Detection and Personalized Training

The rapid development of artificial intelligence, including agents and deepfake techniques, has accelerated phishing attacks and lowered the threshold for attackers. Modern phishing attacks now blend multiple tactics, including social engineering, URL spoofing, and AI deepfakes enabling adversaries to craft highly convincing messages that exploit human vulnerabilities and bypass traditional detection systems. At the same time, current security awareness education struggles to keep up with the speed, sophistication, and complexity of these evolving threats. To address this challenge, we propose a two-stage anti-phishing framework, CyberGLA, that combines technical defense and user-centered security education. In the Detection stage, we introduce EmailKnight, a spoof detection tool that performs multi-level email analysis. To enhance user awareness, the Training stage incorporates a large language model (LLM)-based security coach that dynamically selects personalized training modules based on the outcomes of the Detection stage. This dual purpose design philosophy enables effective protection against the evolving threats of modern email phishing attacks.

cs.CR↗

arXiv2026

Multi-Tier Mentorship with AI-Assisted Development: Authentic Engineering for K-12 and Undergraduates

K-12 students often possess creative engineering ideas but lack technical skills to build them, while undergraduates have coding expertise but few opportunities to lead real-world projects or mentor others. The rapid development of AI-assisted tools offers a potential bridge to connect these groups, yet the structure for effective K-12 and university collaborations remains underexplored. This paper introduces a multi-tiered mentorship framework enabling high school students to engage in authentic engineering through AI-assisted development using large language models and AI agents, while undergraduate mentors provide architectural oversight. We test this framework through LuckyTag, a privacy-preserving NFC-based lost-and-found system. The model positions high schoolers as product leads, undergraduates as technical architects, and faculty as minimal-intervention advisors. A pilot with four high school students, three undergraduates and two faculty yielded survey data showing high perceived barrier removal and gains in system architecture understanding. Thematic analysis reveals that AI amplifies rather than supplants mentoring demands, requiring human oversight for logic and security. These findings suggest a hybrid model for equitable K-12 and university collaboration on computing integration that emphasizes "AI micromanagement" and architectural reasoning over traditional syntax.

cs.CY↗

arXiv2026

LearnAI: Just-in-Time AI Co-Creation Across Disciplines at a University

As generative AI reshapes professional and educational practice, institutions face a challenge: how to support diverse learners, from non-coders to advanced students, in building confidence and practice with AI-supported problem solving. Most institutional responses bifurcate into conceptual workshops for general audiences or technical courses for computer science majors, leaving few spaces where mixed-ability learners can engage common AI tasks at levels matched to their prior experience. This experience report presents the LearnAI Framework, a two-layer model for just-in-time AI co-creation piloted at a comprehensive teaching university. The Wide-Exposure Layer embeds short presentations in existing courses to build AI awareness at scale, reaching students and faculty across 18 courses in five disciplines. The Customized Co-Creation Layer provides opt-in, one-on-one sessions where clients work with trained undergraduate tutors through a 5-Stage Pedagogical Script: Problem Framing, Tool-Task Mapping, Iterative Co-Prompting, Deployment and Verification, and Ethical Reflection. Over two semesters, 35 clients co-created 36 portfolio websites and over 20 deployed web applications. Interviews with five clients and two tutors suggest a recurring change in how clients described AI use, shifting from treating AI as a passive answer machine to engaging it as a collaborative tool under human direction. A small paired pre/post AI readiness dataset (N = 7) provides preliminary descriptive context, and tutor accounts document how the pedagogical script was enacted and adapted across client types. We report on boundary cases including clients who felt overwhelmed and respondents who deliberately rejected AI use. This paper contributes a practical, adoptable framework with initial evidence from a single institution.

cs.CY↗

arXiv2025

2024 NSF CSSI-Cybertraining-SCIPE PI Meeting August 12 to 13, 2024, Charlotte, NC

The second annual NSF, OAC CSSI, CyberTraining and related programs PI meeting was held August 12 to 13 in Charlotte, NC, with participation from PIs or representatives of all major awards. Keynotes, panels, breakouts, and poster sessions allowed PIs to engage with each other, NSF staff, and invited experts. The 286 attendees represented 292 awards across CSSI, CyberTraining, OAC Core, CIP, SCIPE CDSE, and related programs, and presented over 250 posters. This report documents the meetings structure, findings, and recommendations, offering a snapshot of current community perspectives on cyberinfrastructure. A key takeaway is a vibrant, engaged community advancing science through CI. AI-driven research modalities complement established HPC and data centric tools. Workforce development efforts align well with the CSSI community.

cs.ET↗

arXiv2025

Robustness of Majorana modes to potential disorder in Fe chains on a superconducting Rashba alloy

Majorana modes offer great potential for fault-tolerant quantum computation due to their topological protection. However, for superconductor-semiconductor nanowire hybrids, intrinsic disorder makes the unambiguous detection of Majorana modes difficult. Here, we construct 1D spin chains from individual Fe atoms on the Rashba surface alloy BiAg2/Ag(111) with proximity-induced superconductivity from a Nb(110) substrate. While the Fe chains exhibit perfect crystalline order, we observe nano-scale potential disorder of the BiAg2/Ag(111)/Nb(110) heterostructure by scanning tunneling microscopy. However, this does not prevent the emergence of zero-energy modes at both ends of the Fe chains, in agreement with tight-binding calculations showing that they are only found in the topologically non-trivial regime of the phase diagram. These Majorana modes are indeed robust against potential disorder.

cond-mat.supr-con↗

arXiv2025

Gate-tunable polarity inversions and three-fold rotation symmetry of the superconducting diode effect

The superconducting diode effect is an asymmetry in the critical current with respect to the supercurrent polarity. One impetus driving recent interest in the effect is its dependence on intrinsic or microscopic symmetry breaking mechanisms. Here, we study the superconducting diode effect in gated planar Josephson junctions fabricated on a superconductor--semiconductor heterostructure under an in-plane magnetic field. We observe two gate-driven inversions of the diode polarity in the vicinity of zero field, as well as a third-harmonic component in the dependence of the diode efficiency on the in-plane field angle. We analyze the Lifshitz invariant for an arbitrary spin--orbit coupling and show that multiple polarity inversions are possible in the presence of both linear and cubic Dresselhaus terms, where the Rashba parameter varies monotonically with gate voltage. Numerical calculations of the diode efficiency further reveal the presence of higher harmonics in its field-angle dependence in the presence of spin--orbit coupling.

cond-mat.mes-hall↗

arXiv2024

Superconducting diode effect in diffusive superconductors and Josephson junctions with Rashba spin-orbit coupling

We characterize the superconducting diode effect (SDE) in two-dimensional diffusive structures with Rashba spin-orbit coupling using the quasiclassical formalism. We consider both homogeneous superconductors and Josephson junctions. In the first case, the diode effect monotonically increases as the magnetic field is increased and the temperature is reduced, in contrast to the non-monotonic behavior found in clean structures. In Josephson junctions, SDE dramatically increases and changes its sign close to the $0-\pi$ transition of the junction, which occurs at specific junction lengths and strengths of the magnetic field. We show that the SDE is strongly suppressed in narrow junctions. Our results are relevant for understanding recent experiments that measure SDE in mesoscopic nanostructures, where significant disorder is unavoidable.

cond-mat.supr-con↗

arXiv2023

Increased localization of Majorana modes in antiferromagnetic chains on superconductors

Magnet-superconductor hybrid (MSH) systems are a key platform for custom-designed topological superconductors. Ideally, the ends of a one-dimensional MSH structure will host Majorana zero-modes (MZMs), the fundamental unit of topological quantum computing. However, some of the experiments with ferromagnetic chains show a more complicated picture. Due to tiny gap sizes and hence long coherence lengths MZMs might hybridize and lose their topological protection. Recent experiments on a niobium surface have shown that both ferromagnetic and antiferromagnetic chains may be engineered, with the magnetic order depending on the crystallographic direction of the chain. While ferromagnetic chains are well understood, antiferromagnetic chains are less so. Here we study two models inspired by the niobium surface: a minimal model to elucidate the general topological properties of antiferromagnetic chains, and an extended model to more closely simulate a real system by mimicking the proximity effect. We find that in general for antiferromagnetic chains the topological gap is larger than for ferromagnetic ones and thus coherence lengths are shorter for antiferromagnetic chains, yielding more pronounced localization of MZMs in these chains. While topological phases for both ferromagnetic and antiferromagnetic chains both depend on the magnetic moment of the adatoms and the chemical potential, we find that antiferromagnetic chains also have a strong dependence on the magnitude of Rashba spin-orbit coupling at the surface.

cond-mat.supr-con↗

arXiv2021

Majorana modes with side features in magnet-superconductor hybrid systems

Magnet-superconductor hybrid (MSH) systems represent promising platforms to host Majorana zero modes (MZMs), the elemental building blocks for fault-tolerant quantum computers. Theoretical description of such MSH structures is mostly based on simplified models, not accounting for the complexity of real materials. Here, based on density functional theory, we derive a superconducting 80-band model to study an MSH system consisting of a magnetic manganese chain on the s wave superconductor niobium. For a wide range of values of the superconducting order parameter, the system is a topological superconductor, with MZMs exhibiting non-universal spatial patterns and a drastic accumulation of spectral weight on both sides along the magnetic chain. These side feature states can be explained by an effective model which is guided by the ab initio results. Performing scanning tunneling spectroscopy experiments on the same system, we observe a spatial structure in the low-energy local density of states that is consistent with the theoretical findings. Our results open a first-principle approach to the discovery of topological superconductors.

cond-mat.supr-con↗

arXiv2021

Evidence of topological Shiba bands in artificial spin chains on superconductors

A major challenge in developing topological superconductors for implementing topological quantum computing is their characterization and control. It has been proposed that a p-wave gapped topological superconductor can be fabricated with single-atom precision by assembling chains of magnetic atoms on s-wave superconductors with spin-orbit coupling. Here, we analyze the Bogoliubov quasiparticle interference in atom-by-atom constructed Mn chains on Nb(110) and for the first time reveal the formation of multi-orbital Shiba bands using momentum resolved measurements. We find evidence that one band features a topologically non-trivial p-wave gap as inferred from its shape and particle-hole asymmetric intensity. Our work is an important step towards a distinct experimental determination of topological phases in multi-orbital systems by bulk electron band structure properties only.

cond-mat.supr-con↗

arXiv2020

High-Temperature Majorana Fermions in Magnet-Superconductor Hybrid Systems

Magnet-superconductor hybrid (MSH) structures represent one of the most promising platforms to realize, control and manipulate Majorana modes using scanning tunneling methods. By depositing either chains or islands of magnetic atoms on the surface of a conventional, elemental superconductor such as Pb or Re, topological superconducting phases can emerge. They feature either localised Majorana bound states at the chain ends or dispersing chiral Majorana modes at the island's boundary. Yet some of these experiments have not reached the spectral resolution to clearly distinguish between topological Majorana and trivial Shiba states due to very small superconducting gap sizes and experiments performed at sub-Kelvin temperatures. Here we consider superconducting substrates with unconventional spin-singlet pairing, including high-temperature d-wave and extended s-wave superconductors. We derive topological phase diagrams and compute edge states for cylinder and island geometries and discuss their properties. Several time-reversal invariant topological superconducting phases of the Zhang-Kane-Mele type are found and discussed. Moreover, we study one-dimensional MSH structures and show that parameters to realize topologically non-trivial magnetic chains embedded into a larger, two-dimensional substrate differ from the purely one-dimensional case. Quite generally we find that unconventional superconducting substrates work as well as the conventional s-wave substrates to realize topological phases. In particular, iron-based pnictide and chalcogenide superconductors are the most promising class of substrates for future high-temperature MSH systems.

cond-mat.supr-con↗

arXiv2019

Adiabatic Quantum Kitchen Sinks for Learning Kernels Using Randomized Features

Quantum information processing is likely to have far-reaching impact in the field of artificial intelligence. While the race to build an error-corrected quantum computer is ongoing, noisy, intermediate-scale quantum (NISQ) devices provide an immediate platform for exploring a possible quantum advantage through hybrid quantum--classical machine learning algorithms. One example of such a hybrid algorithm is "quantum kitchen sinks", which builds upon the classical algorithm known as "random kitchen sinks" to leverage a gate model quantum computer for machine learning applications. We propose an alternative algorithm called "adiabatic quantum kitchen sinks", which employs an adiabatic quantum device to transform data features into new features in a non-linear manner, which can then be employed by classical machine learning algorithms. We present the effectiveness of our algorithm for performing binary classification on both a synthetic dataset and a real-world dataset. In terms of classification accuracy, our algorithm significantly enhances the performance of a classical linear classifier on the studied binary classification tasks and can potentially be implemented on a current adiabatic quantum device to solve practical problems.

quant-ph↗

arXiv2019

Multi-Community Detection in Signed Graphs Using Quantum Hardware

Signed graphs serve as a primary tool for modelling social networks. They can represent relationships between individuals (i.e., nodes) with the use of signed edges. Finding communities in a signed graph is of great importance in many areas, for example, targeted advertisement. We propose an algorithm to detect multiple communities in a signed graph. Our method reduces the multi-community detection problem to a quadratic binary unconstrained optimization problem and uses state-of-the-art quantum or classical optimizers to find an optimal assignment of each individual to a specific community.

quant-ph↗

arXiv2019

Reinforcement Learning Using Quantum Boltzmann Machines

We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin Hamiltonian with a layout of qubits similar to that of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to numerically simulate quantum sampling from this system. We design a reinforcement learning algorithm in which the set of visible nodes representing the states and actions of an optimal policy are the first and last layers of the deep network. In absence of a transverse field, our simulations show that DBMs are trained more effectively than restricted Boltzmann machines (RBM) with the same number of nodes. We then develop a framework for training the network as a quantum Boltzmann machine (QBM) in the presence of a significant transverse field for reinforcement learning. This method also outperforms the reinforcement learning method that uses RBMs.

quant-ph↗

arXiv2017

Free energy-based reinforcement learning using a quantum processor

Recent theoretical and experimental results suggest the possibility of using current and near-future quantum hardware in challenging sampling tasks. In this paper, we introduce free energy-based reinforcement learning (FERL) as an application of quantum hardware. We propose a method for processing a quantum annealer's measured qubit spin configurations in approximating the free energy of a quantum Boltzmann machine (QBM). We then apply this method to perform reinforcement learning on the grid-world problem using the D-Wave 2000Q quantum annealer. The experimental results show that our technique is a promising method for harnessing the power of quantum sampling in reinforcement learning tasks.

cs.LG↗