Srimannarayana Grandhi

arXiv2026

Information Technology Curriculum: General or Specialized? An Australia's Census Study

Despite the strong employment prospect for information technology (IT) graduates, a comprehensive study investigating the status quo of offering different types of IT degree by Australian universities does not exist. To address this issue, this paper investigates how Australian universities offer three different types of IT degree: general, specialized, and those with majors. Using effect size analysis, we have observed some interesting phenomena about the correlation between how Australian universities offer their IT degrees and different factors, including, for example, type and reputation of universities, degree level, research component, supporting infrastructure, and industry engagement. Our census study painted the status quo of offering different types of IT degree by Australian universities, and provided insights into whether these IT degrees properly address the IT industry's needs. Based on the findings, we have also highlighted some insights and made recommendations on how to improve IT students' learning outcomes and graduates' employability.

cs.CY↗

arXiv2026

Interpretable Unsupervised Deformable Image Registration via Confidence-bound Multi-Hop Visual Reasoning

Unsupervised deformable image registration requires aligning complex anatomical structures without reference labels, making interpretability and reliability critical. Existing deep learning methods achieve considerable accuracy but often lack transparency, leading to error drift and reduced clinical trust. We propose a novel Multi-Hop Visual Chain of Reasoning (VCoR) framework that reformulates registration as a progressive reasoning process. Inspired by the iterative nature of clinical decision-making, each visual reasoning hop integrates a Localized Spatial Refinement (LSR) module to enrich feature representations and a Cross-Reference Attention (CRA) mechanism that leads the iterative refinement process, preserving anatomical consistency. This multi-hop strategy enables robust handling of large deformations and produces a transparent sequence of intermediate predictions with a theoretical bound. Beyond accuracy, our framework offers built-in interpretability by estimating uncertainty via the stability and convergence of deformation fields across hops. Extensive evaluations on two challenging public datasets, DIR-Lab 4D CT (lung) and IXI T1-weighted MRI (brain), demonstrate that VCoR achieves competitive registration accuracy while offering rich intermediate visualizations and confidence measures. By embedding an implicit visual reasoning paradigm, we present an interpretable, reliable, and clinically viable unsupervised medical image registration.

cs.CV↗