Skill UI

This comprehensive skill guides authors submitting research to The Burlington Magazine. It outlines the rigorous academic standards required for object-based art history, focusing on connoisseurship, attribution, and provenance. It ensures manuscripts are tightly argued using primary source evidence, technical findings, and close visual examination, helping authors align their work with the journal's specific evidentiary bar and house style.

This guide provides a strategic framework for drafting author responses to challenging peer reviews in academic conferences. It teaches authors how to structure replies by addressing four key axes (Correctness, Novelty, Backing, Clarity), ensuring technical precision, maintaining anonymity, and framing commitments that survive the subsequent Area Chair (AC) discussion phase.

A comprehensive guide for authors submitting to UAI (Uncertainty in Artificial Intelligence). It details how to position research by ensuring coverage across diverse academic communities, including Machine Learning, Statistics, Causal Inference, and foundational mathematics. Focuses on establishing the technical 'delta' rather than merely listing citations, helping authors avoid common review failures.

This comprehensive pre-upload audit guide ensures submissions meet the strict technical and anonymity requirements for UAI OpenReview. It covers critical aspects like the 8-page main body constraint, combining all content into a single PDF, handling file size caps (15MB/50MB), and performing an identity sweep to maintain double-blind review standards.

This guide outlines the crucial, multi-stage process of converting a conditionally accepted academic paper into the final, publishable camera-ready version for top-tier conferences. It covers fulfilling rebuttal promises, meeting strict ACM accessibility standards (e.g., alt text for every figure and table), managing the ACM TAPS workflow, and finalizing all technical and logistical assets.

Provides comprehensive guidelines for designing rigorous technical evaluations for interactive systems, specifically tailored for top-tier venues like UIST. It emphasizes matching evaluation methods to specific artifact claims, reporting distributions over means, closing the interaction loop, and performing thorough ablations against real-world baselines. This framework ensures the scientific rigor and credibility of submitted research.

This guide provides a rigorous methodology for achieving scientific reproducibility in technical and user experience papers (e.g., UIST). It details three essential 'ledgers': the Build Ledger (pinning software, hardware, and environment dependencies), the Measurement Ledger (defining precise protocols for metrics like latency and accuracy), and the Study Ledger (documenting human evaluation tasks). Following these protocols ensures that external researchers can accurately rebuild the artifact and validate reported results.

A comprehensive guide for researchers determining if their Human-Computer Interaction (HCI) project contribution is an 'interface artifact' suitable for UIST, or if it belongs to a different venue like CHI or CSCW. It employs the 'artifact-subtraction test' to distinguish between technical system novelty and empirical user findings, ensuring proper conference submission routing.

A comprehensive guide for authors submitting interactive system papers to UIST. It details the required narrative arc (walkthrough before mechanism), emphasizes technical specificity in implementation details, and provides rules for designing figures and structuring the argument within strict page limits.

A specialized guide for authors aiming to submit to the Conference on Uncertainty in Artificial Intelligence (UAI). It provides expert advice on framing the core contribution—such as probabilistic reasoning, causal inference, and uncertainty modeling—and calibrating the manuscript for highly technical, statistical ML reviewers. Use this skill to refine your submission strategy, identify evidence gaps, and properly position your work relative to other top-tier AI venues like AISTATS and IJCAI.

A comprehensive guide for optimizing computer science manuscripts targeting the USENIX Annual Technical Conference (ATC). It covers core systems research fields (OS, networking, storage, security), defining the required evidence bar (built artifacts, clean evaluation), and re-framing the paper's contribution to align with top-tier systems venue expectations.

A comprehensive guide detailing the mandatory and optional evaluation phases for academic security artifacts, such as those required for USENIX Security. It covers Phase-1 (availability) checks and Phase-2 (functionality, reproducibility) requirements. Provides best practices for structuring code, managing technical hazards (e.g., dangerous-by-default behavior), and ensuring verifiable, reproducible results for peer review.