Skill UI

Provides a comprehensive guide on structuring the 'Related Work' section for top-tier computer graphics conferences like SIGGRAPH. It teaches authors how to position a new contribution based on 'capability delta' (what prior methods fail to do) rather than chronology, covering specialized technical lanes (rendering, geometry, AI for graphics) and managing complex academic attribution.

A comprehensive guide for computer graphics researchers detailing how to achieve result-level reproducibility, especially for major conferences like SIGGRAPH. It emphasizes pinning all provenance—including assets, random seeds, hardware stacks, and hyperparameters—and methodologies for handling non-determinism in floating-point math and stochastic simulations, ensuring that the reported figures and timings can be regenerated by any reader.

This skill assists researchers in strategically positioning their computer graphics and visualization research. It evaluates whether a contribution is truly "graphics-shaped" (demanding visual results and advancing graphics axes) and recommends the optimal venue (e.g., SIGGRAPH, CVPR, CHI, EGSR) and submission track (Dual-track or Journal-only) based on the nature and maturity of the work. It provides a crucial, high-leverage decision framework before committing to a submission.

A detailed guide for managing the entire academic paper submission process for major graphics conferences like SIGGRAPH and SIGGRAPH Asia. It outlines the two annual cycles, backward planning from deadlines, key milestones (results video, baselines), rebuttal strategies, and final camera-ready submission steps, ensuring researchers can manage complex timelines effectively.

This comprehensive guide provides the optimal structure and academic writing discipline for submitting high-quality technical papers to major computer graphics conferences, such as SIGGRAPH. It emphasizes building a compelling 'first-page arc,' leveraging the teaser figure to immediately demonstrate results, maintaining academic honesty by detailing limitations, and adhering to strict page budget constraints.

This guide provides a comprehensive framework for authors responding to peer reviews in major NLP/IR conferences. It covers strategies for active rebuttal periods, pre-emptive revisions when no window exists, and planning resubmissions after rejection. Key advice includes technical rebuttal tactics (e.g., addressing baselines, significance testing), maintaining professional tone, and efficiently using existing computational evidence to maximize acceptance chances.

This guide details the critical steps and changes required to transform a reviewed SIGIR submission into the final Version of Record published via ACM Digital Library. It covers technical requirements, metadata standardization (CCS, keywords), conflict resolution, and adhering to publishing timelines and copyright protocols (e-rights, TAPS).

This comprehensive guide details the rigorous methodology for designing, executing, and reporting experimental results in Information Retrieval (IR). It covers best practices for selecting appropriate test collections, choosing defensible metrics, performing proper statistical significance testing (e.g., paired t-tests), ensuring baseline fairness, and proactively addressing modern pitfalls like LLM contamination. Essential for publishing high-quality, auditable research in IR/NLP.

A comprehensive guide for packaging and evaluating research artifacts for academic conferences like SIGMETRICS. It details the requirements for achieving ACM Badges (Functional, Reusable, Reproduced) by ensuring the packaged simulation can regenerate key figures and results on a clean machine, moving beyond mere tool execution.

A comprehensive guide detailing how to construct the critical 'one-shot revision letter' for SIGMETRICS submissions. This process requires transforming the response into a systematic 'change ledger,' ensuring every required change from the reviewers' list is explicitly addressed, justified, or demonstrably resolved in the revised paper or accompanying artifacts. Essential for authors aiming for conference acceptance.

A comprehensive guide detailing the mandatory steps for converting an accepted, double-anonymous conference paper into a final, journal-ready (camera-ready) version for academic publication. This process covers de-anonymization, completing journal metadata (DOI, ORCID, CCS), incorporating shepherd-required revisions, and ensuring permanent reproducibility.

A comprehensive guide for researchers developing systems or algorithms, detailing the necessary level of evidence required for performance claims (e.g., bounds, real-world performance, learning guarantees). It emphasizes scientific rigor, statistical reporting (confidence intervals), assumption testing, and data provenance to ensure findings are robust and reproducible in academic settings.