Skill UI

This guide assists authors with positioning their computer science manuscripts for the IEEE International Conference on Software Maintenance and Evolution (ICSME). It details the core scope, including software maintenance, evolution, refactoring, and technical debt, and provides best practices for structuring submissions. It emphasizes the need for real repository evidence, actionable engineering insights, and proper differentiation from related venues like SANER or ICSE.

This guide provides rigorous standards for designing, validating, and reporting security research, particularly for high-stakes academic conferences like IEEE S&P. It covers critical areas including adaptive adversary modeling, measurement validity (sampling, ethics), statistical reporting, establishing strong baselines/ablations, and adhering to ethical constraints (IRB, authorization) to ensure evidence is robust and reproducible.

This guide provides rigorous methodology for authors submitting security and machine learning research to top conferences. It emphasizes the need to 'freeze the world' of measurement by pinning target versions, microarchitectural states, and network conditions. It details how to report probabilistic evidence (like attack success rates) and manage ethical data release, ensuring findings remain valid and verifiable over time.

A comprehensive guide on structuring academic robustness checks for rigorous economic papers. This strategy advises authors to organize checks by the specific threat to the core assumptions (e.g., functional form, calibrated parameters, sample limitations) rather than simply listing multiple re-runs. Learn how to report findings as bounded ranges and present the argument with scholarly confidence.

This guide provides rigorous standards for authors, detailing how to convert dense statistical outputs (like complex regression coefficients or parameter estimates) into clear, single-question exhibits for top economics journals. It emphasizes shifting focus from mere statistical reporting (e.g., significance stars) to communicating core economic mechanisms, comparative statics, and parameter identification, ensuring the argument is immediately legible to a broad, expert audience.

This guide provides comprehensive standards for designing, auditing, and documenting AI experiments for major conferences like IJCAI. It covers establishing strong baselines, conducting ablation studies, and reporting meticulous details on hyperparameters, computational infrastructure, and ethical risks, ensuring maximum reproducibility and academic credibility for AI research.

This comprehensive guide provides best practices for designing, running, and reporting computational experiments for academic journals. It teaches authors how to make performance claims scientifically defensible by addressing potential referee criticisms (e.g., tuning artifacts, seed luck). It covers advanced statistical techniques (performance profiles, nonparametric tests) and details the full structure required for a complete, verifiable GitHub data deposit.

A comprehensive guide to designing rigorous, reproducible, and fair experimental protocols for computational science research, particularly for top-tier academic journals. It covers critical elements such as selecting standard benchmark instances, establishing strong baselines, ensuring fair parameter tuning, reporting hardware constraints, and performing robust statistical validation, helping researchers avoid common pitfalls and maximize the scientific validity of their claims.

This framework provides a comprehensive methodology for designing, auditing, and reporting empirical measurements about large-scale systems like the Internet. It guides users to ensure measurements are scientifically rigorous, by matching claims to evidence, quantifying coverage bias, designing for temporal changes, and adhering to strict ethical guidelines and data provenance standards.

This guide provides comprehensive best practices for authors to enhance the reproducibility of technical research papers (especially in networking and computer science). It covers documenting proofs, pinning simulation setups, reporting parameters, and best practices for releasing code and datasets, transforming a paper from a claim into a verifiable scientific artifact.

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.