Skill UI

A comprehensive guide for writing the evaluation section of a systems paper for top-tier conferences. It teaches authors how to map every technical claim to verifiable experiments, establish fair state-of-the-art baselines, utilize mixed workloads (microbenchmarks, E2E applications, and traces), and honestly report critical metrics like performance tails, variance, and overheads. The goal is to maximize credibility and withstand rigorous peer review.

This comprehensive guide outlines best practices for designing and auditing empirical evaluations of formal verification tools and algorithms. It stresses the use of shared, community-accepted benchmarks, ensuring fair comparison against the strongest baselines with equal resources, meticulous reporting of all metrics, validating results for soundness, and guaranteeing full reproducibility on clean virtual environments.

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 skill provides a comprehensive guide for authors targeting the USENIX Symposium on Operating Systems Design and Implementation (OSDI). It helps in positioning complex systems research, covering core scope areas like operating systems, distributed systems, and cloud infrastructure. It details how to frame the contribution, build evidence (artifacts, baselines), and distinguish the work from similar top-tier venues like SOSP and NSDI.

A detailed guide for revising academic papers, specifically tailored for systems and database conferences like VLDB. It emphasizes establishing a strong 'page-one contract,' quantifying all performance claims with scoped metrics and baselines, and maintaining rigorous candor regarding design trade-offs and terminology throughout the manuscript.

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.