Skill UI

This comprehensive guide teaches researchers how to conduct an exhaustive "reinvention audit" for academic papers, specifically in the database field. It covers searching deep historical literature (pre-2000), modern conference venues (SIGMOD, ICDE), and industrial open-source systems (PostgreSQL, RocksDB) to ensure true novelty and properly position the contribution delta.

This comprehensive guide outlines the rigorous standards required for achieving experimental reproducibility in systems research papers (e.g., VLDB). It instructs authors on disclosing critical context, including hardware specifications, software configurations, data provenance, workload patterns, and handling measurement variance. Adhering to these guidelines ensures that all performance claims are fully traceable from raw data to published figures, meeting the highest standards of academic review.

This guide provides a comprehensive overview of the VLDB submission and review pipeline. It details the rolling monthly structure, the decision outcomes (Accept, Revise, Reject), and the specific expectations for authors at each stage. Use this to set realistic expectations regarding review timelines, the nature of expert feedback, and optimizing your revision strategy, contrasting it with traditional batch venues.

A comprehensive checklist and guide for authors submitting research papers to PVLDB (Proceedings of the VLDB Endowment). This resource details critical steps, including adherence to multiple deadlines (abstract, full paper), category budget selection, single-blind formatting rules, and author co-contribution caps, ensuring submissions avoid desk rejection.

A comprehensive guide for authors submitting to academic conferences, addressing strict page limitations where appendices count against the core budget. It details best practices for distributing supplementary evidence across three external homes: the main paper, dedicated arXiv technical reports, and artifact repositories. The guide emphasizes maintaining self-containment, managing version discipline, and strategically cutting content to ensure the core claims are reviewable.

This comprehensive guide helps researchers determine if their project qualifies as a core data-management primitive, which specific PVLDB category (Regular, EA&B, Scalable Data Science, Vision) it belongs to, and whether a major data conference like VLDB or SIGMOD is the optimal submission venue. It provides a rigorous primitive test and detailed routing advice for top-tier data systems research.

This comprehensive guide manages the entire project lifecycle for academic conference submissions, such as VLDB. It provides detailed methodologies for initial volume planning, computing optimal submission timelines (month-picking), coordinating group author contributions, and managing the revision bandwidth. It is essential for maintaining rigorous scheduling and mitigating the risk of missed deadlines in fast-paced research environments.

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.

A comprehensive guide for creating the two required artifacts for a WACV paper: an anonymous package for double-blind review, and a public release post-acceptance. It emphasizes reproducing deployed system claims (latency, power, robustness) and maintaining consistency across revision rounds.

A comprehensive guide for authors submitting to WACV, detailing the strategic differences between the optional one-page Round 1 rebuttal and the mandatory change summary accompanying a Revise-and-Resubmit. It provides best practices for addressing reviewer concerns, maintaining anonymity, and accurately documenting revisions.

A comprehensive guide for transforming an accepted, anonymous conference paper into the final, public version of record for WACV. It covers critical steps including de-anonymization, incorporating final revisions, ensuring dual publication compliance (IEEE Xplore & CVF), fulfilling data/code release obligations, and preparing the presentation material.

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.