Skill UI

This comprehensive guide provides a detailed, step-by-step checklist for authors preparing manuscripts for high-level academic journals, specifically modeled after the requirements of China Rural Economy. It covers all critical stages, including formatting standards, double-blinding rules, reference citation completeness, and rigorous empirical data verification, ensuring submissions meet professional scholarly standards.

A comprehensive guide for researchers submitting NLP papers, detailing best practices for achieving high reproducibility. It instructs users to pin model versions, log data provenance, report compute costs, and adhere rigorously to the Responsible NLP checklist. Essential for ensuring academic integrity and enabling rigorous verification of ML experiments.

A guide for writing theoretical computer science papers, detailing how to structure proofs and correctly incorporate computational evidence. It emphasizes matching arguments (e.g., upper/lower bounds) to claim shapes and ensures that computation (like SMT verification or exhaustive checks) is rigorous, reproducible, and serves only to certify a step, not replace the main theorem.

This guide details best practices for achieving reproducibility in highly theoretical computer science papers, such as those submitted to ICALP. Since artifact evaluation is absent, reproducibility hinges on the ability for any reader to independently verify every step of the proof. Key requirements include writing self-contained proofs, providing verifiable computational certificates, maintaining consistency between submissions, and optionally using formal verification tools like Coq or Lean.

A comprehensive guide detailing the academic standards for ensuring research reproducibility at ICSE. Covers open-science policies, mandatory data availability statements, techniques for pinning provenance (especially for mining and LLMs), and best practices for maintaining verifiability while ensuring anonymity. Essential for academic submissions.

This comprehensive guide serves as the mandatory final audit checklist for papers submitted to top-tier security conferences (like S&P). It covers crucial, non-negotiable steps—including meticulous formatting, ORCID verification, deep anonymization techniques, and ethics considerations—to prevent preventable desk-rejects during the submission workflow.

This comprehensive guide details the strict content partitioning rules for preparing papers for top-tier security conferences like IEEE S&P. It advises authors on differentiating between core claims (which must live in the limited body pages) and verification material (which belongs in the appendix), ensuring adherence to strict page limits and review expectations.

A comprehensive checklist for auditing and preparing main-track conference submissions for top-tier AI venues like IJCAI. This guide covers critical compliance points, including double-blind anonymity enforcement, strict page limits, LLM usage policies, author details verification, and identifying potential desk-reject risks before final submission.

This skill guides authors on positioning their computer science manuscripts for the International Conference on Computer Aided Verification (CAV). It provides a comprehensive framework for assessing conference fit, framing the contribution, strengthening evidence, and navigating the specific requirements (like formal correctness and tool benchmarks) of top-tier formal verification venues, ensuring submissions meet community expectations.

This comprehensive guide outlines best practices for designing, evaluating, and reporting experimental results in speech processing research (ASR, TTS, Speaker Verification, etc.). It details required metrics (WER, MOS, EER), establishing robust baselines, conducting significance testing (bootstrap, CI), and ensuring comprehensive condition coverage (speakers, noise, languages) to enhance the scientific rigor of academic submissions.

A comprehensive guide for conducting empirical data analysis in International Relations (IR). This skill addresses specific econometric challenges inherent to IR data, such as dyadic dependence, state selection bias, and gravity modeling. It guides users through rigorous reporting standards, advanced statistical methods, and verification-readiness protocols required by top-tier academic journals.

This guide provides a detailed walkthrough of the rigorous review process at International Organizations (IO). It covers aspects like double-blind peer review, specialized expert assessment, and the distinctive verification stage where quantitative results and formal proofs are re-run. Use this to pre-empt potential failure modes and structure your manuscript for maximum impact before submission.