Skill UI

This guide helps structure, audit, and refine the exhibits (tables and figures) for academic manuscripts, particularly in Human Relations. It ensures that the inference path from raw data to theoretical claims is visible. It covers best practices for both qualitative exhibits (data-structure models, evidence tables) and quantitative exhibits (regression models, interaction plots), while adhering to submission constraints like anonymization and word limits.

A comprehensive guide for generating and packaging reproducible evidence for signal processing submissions to ICASSP. It details what reviewers prioritize (scoring scripts, dataset splits, logs) and outlines best practices to ensure that headline metrics are easily regenerated, thereby maximizing review credibility and post-publication impact.

A comprehensive guide for researchers submitting signal processing papers, detailing how to achieve rigorous reproducibility for conferences like ICASSP. It emphasizes that simply providing a model is insufficient; authors must pin down the 'scoring ruler,' data versions, front-end parameters, and computational seeds to ensure every reported metric can be independently verified.

A comprehensive guide detailing the ICASSP peer review pipeline, covering single-blind review mechanics, technical committee evaluation criteria, and the strategic importance of evidence, significance, and reproducibility in signal processing papers. Use this to craft submission strategies and decode decision letters.

A comprehensive guide modeling the ICCV review pipeline, covering the entire cycle from registration to final decision (March-June). It details author responsibilities, the enforcement of reviewer accountability, the prohibition of LLM use in reviews, and strategies for authors to interpret and leverage diverse review feedback, including utilizing the 'Findings' workshop.

A comprehensive guide for positioning database research papers for top-tier conferences like ICDE. It teaches authors how to distinguish genuine novelty (new mechanisms) from engineering delta (implementation improvements), conduct deep literature audits, and compare their work against both academic venues and real-world production systems. Essential for improving manuscript eligibility and impact.

This guide outlines comprehensive standards for scientific reproducibility in data engineering. It details best practices for pinning hardware, software, datasets, and variance protocols, ensuring that all performance claims are verifiable and re-runnable by peers, making reproducibility a scored component of academic submission.

A comprehensive guide to the rigorous, multi-stage peer review process for top-tier data engineering conferences like ICDE. It details the two-round structure, area-chair coordination, single-blind dynamics, and the high standards set by 'builder-heavy' reviewers. Use this to strategize paper submission, understand key evaluation dimensions (novelty, soundness, evaluation), and anticipate common review objections.

A comprehensive guide for authors preparing submissions for ICDE conferences. It details how to properly segment content into the main 12-page body versus the supplementary material, ensuring the core argument remains self-contained while providing reviewers with runnable artifacts, extended experiments, and full technical documentation.

A comprehensive guide for authors preparing submissions for top-tier data mining conferences (e.g., ICDM). It emphasizes the critical strategy of preemptively addressing potential weaknesses—such as baselines, variance, scaling, and data leakage—directly within the submitted manuscript, as author rebuttals are often not permitted. Also provides strict guidelines for drafting anonymous, high-impact responses if a rebuttal window is available.

A comprehensive guide detailing how to structure the 'Related Work' section for submissions to top data mining conferences like ICDM. It emphasizes positioning the research based on core mechanisms and mining tasks, rather than simply listing results. The guide also provides critical advice on maintaining triple-blind anonymity, verifying citation venues (avoiding misattribution traps), and ensuring the novelty claim is distilled into a single, quotable mechanism-contrast sentence.

A comprehensive guide for ensuring the reproducibility of data mining research for top-tier conferences like ICDM. It addresses the unique challenges of triple-blind review and strict page limits by detailing necessary artifacts, such as configuration files, seed lists, and anonymized packages. Learn how to achieve Rerunnable, Rebuildable, or Attested reproducibility tiers to solidify your scientific claims.