Skill UI

A comprehensive guide for designing, auditing, and reporting advanced statistical and causal inference experiments. It emphasizes moving beyond simple accuracy metrics to quantify uncertainty, model robustness, and causal mechanisms. Covers metrics like coverage, KL divergence, and regret, and outlines best practices for seeding, compute fairness, and creating standardized reporting blocks (e.g., UAI standards).

This guide details advanced best practices for achieving scientific reproducibility in probabilistic modeling and deep learning research. It mandates rigorous disclosure of seeds, hyperparameters, diagnostic traces (e.g., R-hat, ELBO), and compute environments to ensure that all claims made in academic papers are verifiably backed by traceable evidence. It elevates reproducibility from a simple code run to a comprehensive audit.

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 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 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.

This comprehensive guide provides researchers with structured methodologies for designing and auditing Computer Vision (CV) experiments for major conferences like WACV. It details the critical differences between the Applications and Algorithms tracks, emphasizing the need to report performance under real deployment constraints (e.g., power, latency) and ensuring fair, rigorous comparisons with baselines. Ideal for revising submissions to meet high review standards.

A comprehensive guide for structuring the 'Related Work' section in academic papers, especially in Computer Vision (CV). It teaches authors how to strategically position their research contribution by covering multiple literature shelves: vision methods, domain constraints, concurrent arXiv work, and prior art. This ensures the paper clearly defines a unique gap rather than merely listing citations, adhering to double-blind review standards.

This guide models the comprehensive two-round, journal-style peer review process used at major computer vision conferences like WACV. It details the distinct stages: Round 1 outcomes (Accept, Revise and Resubmit, Reject), the optional R1 rebuttal phase, and the final R2 decision. It emphasizes that the most critical phase for authors is executing high-quality revisions during the Revise-and-Resubmit stage, rather than solely relying on rebuttals.

This guide helps researchers determine the optimal venue for their Computer Vision paper, specifically addressing the unique identity of WACV (Applications-first) versus major conferences like CVPR, ICCV, and ECCV. It guides authors on whether their contribution is driven by real-world applications/constraints or by advancing core algorithms, and how to choose between the Applications or Algorithms track within WACV.

This guide provides comprehensive advice for positioning applied computer vision research for the IEEE/CVF WACV conference. It helps authors assess the manuscript's fit, re-frame the narrative from journal or arXiv styles, and ensure the contribution highlights practical systems, strong empirical evaluation, and application grounding required by top CV venues.

Use for live Windows or macOS GUI tasks that structured tools cannot complete. Discover approved apps and windows, observe current state, act via semantic elements or shortcuts, and verify every requested outcome. Includes an operating loop for reliable desktop automation and visual verification.

This skill facilitates the computation of Electronic Clinical Quality Measures (eCQMs) by leveraging CQL (Clinical Quality Language) logic against the QDM (Quality Data Model). It is designed to extract crucial facts—such as counseling provided, symptoms, or documented exclusions—from unstructured clinical notes. By lifting these facts, it supplements structured EHR data, ensuring that numerators and valid exclusions are accurately captured for quality measure evaluation. Note that this tool augments data capture and does not replace certified measure engines.