cs papers | Gist.Science

Explainable artificial intelligence in interface design influences user outcomes and cybersecurity literacy

This study demonstrates that integrating explainable artificial intelligence (XAI) into interface design tools significantly enhances user outcomes, including creative confidence, perceived learning, trust, autonomy, and usability, during UI design tasks, suggesting that XAI fosters critical engagement with AI recommendations while highlighting the need for future research to directly measure its impact on cybersecurity and privacy literacy behaviors.

Ahmed Al-sa'di, Mahmoud Al-Sarayreh, Esraa Ahmad2026-09-25

💻 computer science

Using LLMs for Ontology generation by video summarization

This paper presents a two-phase algorithm that leverages Large Language Models to automatically generate RDF ontologies from YouTube video URLs by first creating a textual summary and then converting it into a structured domain representation, achieving 90% accuracy on a sports dataset.

Khaled Omar2026-09-25

💻 computer science

Right-to-Sell Verification for Industrial Supply Chains via DKIM and Zero-Knowledge Proof on a Public Blockchain

This paper proposes a portable, privacy-preserving industrial blockchain system that uses DKIM-signed emails and zero-knowledge proofs to verify a reseller's right to sell products across marketplaces and borders without requiring platform integration, specialized hardware, or the disclosure of sensitive trade history.

Shinsaku Ashizawa2026-09-25

💻 computer science

Automated Dimensional Verification of Textile Cuts Using Improved U-Net and ResUnet Models

This study presents a fully automated, scalable system for millimeter-scale dimensional verification of textile cuts that leverages improved U-Net and ResUnet models with a composite loss function and an adaptive selection mechanism to overcome the challenges of fabric deformation and outperform conventional manual and fixed-architecture measurement methods.

Robert Falcon, Marcelo Barrionuevo, Carlos H. Inga Espinoza2026-09-25

💻 computer science

Separating AI-assisted authoring from governed execution through specification-driven composition A design framework and industrial experience report for explainable automation in regulated data transformation

This paper proposes a specification-driven composition framework that enables the safe use of AI in regulated data workflows by separating AI-assisted authoring from governed execution, ensuring that only validated, versioned, and fully traceable artifacts are composed and executed.

Rostislav Markov2026-09-25

💻 computer science

ScaleCal: Scale-Aware Confidence Calibration for Small Language Models

ScaleCal is a training-free framework that addresses confidence-signal degradation in small language models by combining temperature-calibrated logit-based signals with selective semantic consistency sampling, significantly improving calibration error and failure detection while maintaining low computational costs.

Wei Chen2026-09-25

💻 computer science

Rethinking Learning with Noisy Labels: A CriticalReview of Structured Supervision, RepresentationDynamics, and Robust-Learning Pipelines

This critical narrative review redefines learning with noisy labels by moving beyond random error assumptions to analyze structured supervision mismatches, representation dynamics, and robust pipelines, ultimately connecting noise assumptions with risk correction and evaluation protocols to address challenges in modern foundation-model and open-world ecosystems.

Wenxiao Fan, Kan Li2026-09-25

💻 computer science

Explainable Machine Learning Framework to Prioritize Predictors of Type 2 Diabetes Risk Among Adult Males Using Lifestyle and Clinical Indicators

This study developed an explainable machine learning framework using NHANES 2017–2018 data to demonstrate that while age, fasting glucose, and family history are top predictors of Type 2 diabetes in adult males, the inclusion of waist-to-hip ratio significantly enhances risk prediction accuracy when fasting glucose is excluded.

Siva Nanthini Shanmugam, Uchenna Esther Okpete, Md Ariful Islam Mozumder, Hee-Cheol Kim2026-09-25

💻 computer science

Lightweight Deep Learning for Processing 3D Medical Images: A Systematic Review

This systematic review examines lightweight deep learning techniques for 3D medical imaging, highlighting their ability to reduce computational demands for resource-constrained settings while identifying key research gaps and outlining future directions for robust, efficient, and clinically integrated models.

Rahim ullah, Hasan Abbas, Hazrat Ali, Syed Waqar Nabi2026-09-25

💻 computer science

When Unseen Attacks Look Normal: Open-Set Evaluation, Feature Observability, and Protocol-Invariant Detection in Mobile Ad Hoc Networks

This paper demonstrates that while standard machine learning models fail to detect unseen attacks in Mobile Ad Hoc Networks due to their reliance on variable distributions, a simple protocol-invariant feature—counting routing neighbors with no decoded frames—achieves perfect detection of wormhole attacks by identifying structural violations rather than statistical anomalies.

Amruth V, Devaraj Verma C2026-09-25

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