Skill UI

This guide provides essential structural advice for authors writing high-impact research papers for top-tier AI conferences. It covers the 'first-page arc,' ensuring the contribution is generalizable (not model-specific), matching evidence types (proofs vs. empirical data) to claims, and maintaining narrative density over a limited page count. Perfect for researchers preparing submissions.

A comprehensive workflow guide for routing clinical medicine manuscripts. It classifies studies by specialty, study design (RCT, observational, review), and evidence strength, helping authors select the optimal journal venue (e.g., JAMA, Lancet) for submission and re-framing. It emphasizes matching the manuscript to specialized society guidelines and evidence hierarchy.

This guide outlines the rigorous methodological standards for empirical work in AI fairness, accountability, and transparency (FAccT). It advises researchers on matching specific claims (e.g., disparate harm) to appropriate evidence types—including disaggregated metrics, coded qualitative data, and documented ethical procedures. It stresses the importance of ethical review, handling protected attributes, and acknowledging limitations to ensure high scientific and ethical rigor.

A comprehensive methodology for designing scientific and reproducible storage evaluations, particularly for high-stakes submissions. This guide covers controlling device state (aging, preconditioning, TRIM), selecting credible workloads (fio, YCSB), matching metrics (P99.9 latency, bytes-written) to claims, and rigorously testing durability and crash consistency on real hardware.

Provides rigorous guidelines for analyzing experimental game data and constructing numerical examples for high-level theoretical journals. Emphasizes that analysis must be tightly linked to theoretical claims, such as matching observed outcomes to predicted equilibria, rather than serving as general causal evidence. Covers session-level clustering, structural fitting, and ensuring computational reproducibility.

This comprehensive guide helps authors navigate the complex decision of where to submit their Human-Robot Interaction (HRI) research. It outlines critical criteria, including the robot-embodiment and human-in-the-loop tests, to determine the correct venue (e.g., HRI, CHI, ICRA/IROS). Furthermore, it details the five specialized tracks within HRI and advises authors on matching their primary technical contribution to the most suitable academic domain.

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.

A comprehensive guide for designing and auditing empirical experiments in signal processing, audio, and speech domains. It emphasizes matching standard metrics (e.g., WER, SI-SDR, PSNR) to specific tasks, establishing current strong baselines, and systematically sweeping operating conditions (like SNR or reverberation time) to ensure robust, reliable, and publication-ready results.

This guide outlines the standards for ensuring the verifiability and rigor of theoretical computer science papers, particularly for venues like ICDT. It mandates complete proofs, precise pinning of models and assumptions, explicit matching of upper and lower bounds, and strict internal consistency across all claims and full versions. Use this checklist before submission to elevate academic quality.

This guide provides essential instructions for authors submitting research to the ECAI conference, focusing on creating credible and reproducible artifacts. It strongly cautions authors about misconceptions regarding separate artifact badges, emphasizing that credibility must be built into the paper and supplement for direct reviewer judgment. Guidelines are provided for matching the artifact (code, proofs, data) to the specific contribution type (Theory, ML, Optimization).

A comprehensive guide for designing, executing, and reporting high-stakes research studies in Software Engineering (SE), tailored to meet the stringent standards of top academic conferences like ICSE. It covers best practices for evidence-claim matching, advanced statistical rigor (non-parametric tests, effect sizes), handling AI model ablations, and implementing threats-driven design to ensure findings are robust, valid, and maximally reproducible.

A comprehensive guide for authors targeting IEEE Transactions on Information Theory. It assesses whether a manuscript meets the journal's extremely high theoretical standards, focusing on theorem-driven proofs, matching achievability and converse arguments, and fundamental information-theoretic limits (e.g., channel capacity, rate-distortion). Ideal for authors needing a fit check or re-framing applied results into foundational theory.