Skill UI

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.

This skill enables rigorous tracking of physical units and the systematic propagation of measurement uncertainty in scientific calculations. It supports unit conversions, building comprehensive GUM uncertainty budgets, and performing dimensional and physical plausibility checks. Use it whenever a calculation requires adherence to strict metrological standards and accurate error reporting.

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 positioning computer science manuscripts for top-tier security conferences like USENIX Security. It covers core topics (systems, network, privacy, software security), required evidence (rigorous artifact, measurement, vulnerability analysis), and strategic advice on framing the work to meet adversarial reviewer expectations. Ideal for authors preparing high-stakes academic submissions.

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.

A comprehensive guide for positioning research papers for top-tier security conferences like USENIX Security. It covers defining coverage against 'Big-Four' and specialty venues, ensuring novelty across multi-cycle academic pipelines, identifying mechanism-level differentiators, and mastering double-blind citation mechanics for maximum impact.

This guide covers the mandatory Open Science appendix for high-level security research. It instructs authors on enumerating all artifacts (code, datasets, configurations), documenting data withholding decisions, and implementing engineering controls (like environment pinning and version tracking) to ensure that published results are independently and reliably reproducible.

This guide outlines the strict formatting and structural guidelines for submitting security conference papers (like USENIX Security). It helps authors determine which material—core mechanisms, proofs, or supplementary data—must remain in the main body to guarantee review visibility, versus what can be safely demoted to optional appendices or external artifacts.

This comprehensive guide provides an end-to-end planning framework for USENIX Security Symposium submissions. It assists researchers in selecting the optimal submission cycle, mapping backward timelines from key milestones (like registration), assigning ownership for venue-specific risks (e.g., ethics, reproducibility), and coordinating through the complex post-submission decision process (Accept/Reject).

A comprehensive guide detailing the standards for scientific reproducibility in visualization research, particularly for IEEE VIS submissions. It outlines the Graphics Replicability Stamp Initiative (GRSI) requirements, covering how independent volunteers must reproduce results using provided code, data, and environments (e.g., Docker containers). Learn how to package your work—including determinism aids, mapping tables, and licensed data—to pass rigorous, clean-machine testing.

This guide details best practices for achieving high standards of reproducibility in visual analytics papers, focusing on open materials, verifiable code, and rigorous documentation. It covers mapping figures to data sources, preregistering studies, documenting data provenance, and defining the level of reproducibility achieved (e.g., turnkey, scripted).

A comprehensive guide for authors submitting visualization papers to major conferences like IEEE VIS. It outlines the strict rules for partitioning content, ensuring the core argument remains judgeable from the main body while leveraging supplemental materials (video, code, full data) to demonstrate interaction and reproducibility. Essential reading for academic researchers in the field.