Skill UI

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 guide provides a comprehensive framework for authors to rigorously defend their research design in manuscripts concerning Global Environmental Change (GEC). It covers methodological pluralism, including quantitative causal inference, qualitative case studies, experiments, and mixed-methods integration. Learn how to strengthen your design, justify causal claims, address confounders, and link local evidence to multi-scale global processes.

A comprehensive guide for authors responding to technical reviews and revising conference papers during a limited, combined rebuttal window. It teaches how to strategically triage objections, run decisive experiments, craft arguments for a program committee champion, and ensure minimal, faithful revisions to maximize acceptance chances.

A comprehensive guide for auditing and improving the scientific rigor of hardware architectural evaluations (e.g., HPCA style). It emphasizes establishing a clear 'fidelity contract' defining tools, scope, and configurations. It requires that all performance claims are backed by credible instruments, mandates capturing full machine state provenance, ensuring honest baseline tuning, and reporting results via per-workload distributions rather than single means.

This guide provides comprehensive methodological standards for conducting high-quality human-subjects studies in Human-Robot Interaction (HRI). It covers the entire research lifecycle, including choosing optimal designs (within/between-subjects), ensuring statistical power, reporting effect sizes, and adhering to ethical guidelines (e.g., Wizard-of-Oz disclosure). It ensures research meets the rigorous academic standards required for top-tier publications.

A comprehensive guide for structuring rigorous research designs in Human Resource Management. It addresses advanced methodological challenges such as common-method bias (CMB), endogeneity, cross-level data analysis, and multi-wave longitudinal modeling. Learn how to align theoretical claims with appropriate designs (e.g., field experiments, panel data, multi-source surveys) to ensure causal inference and methodological rigor.

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.

A methodology guide for structuring high-stakes academic research experiments, particularly for top-tier CV conferences (like ICCV). It emphasizes systematic planning under strict deadlines, covering benchmark drift audits, ensuring fairness by accounting for pretraining confounds, structuring ablations to isolate mechanisms, and strategically sequencing runs to maximize impact before the submission deadline.

This guide provides comprehensive standards for designing, executing, and reporting systems experiments for top-tier academic venues (like ICDE). It emphasizes methodological rigor, including mandatory ablation studies, reporting variance and tail latency (p99), demonstrating scaling trends (curves over points), disclosing cost trade-offs, and ensuring full hardware and software reproducibility to withstand adversarial review.

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 researchers designing and auditing empirical evaluations for data mining papers (e.g., ICDM). It teaches how to structure a trustworthy evaluation by defining operational tasks, conducting ablation studies to isolate mechanisms, proving scalability, and implementing known-truth checks to ensure findings are valid and not mere artifacts.