Skill UI

A comprehensive guide for authors submitting to top-tier electronics journals. It provides detailed methodologies on structuring experimental chapters, ensuring comparison fairness, achieving statistical rigor, and presenting reproducible results to withstand demanding peer review scrutiny.

This guide provides a comprehensive checklist for evaluating Electronic Design Automation (EDA) research manuscripts for top-tier conferences like DAC. It emphasizes the necessity of using standard, recognized benchmark suites (e.g., ISPD, EPFL, TAU), comparing results against the true State-of-the-Art (SOTA), reporting full PPA metrics and runtime, and implementing ablation studies to ensure fairness, reproducibility, and generalizability, especially for ML-based methods.

A detailed model of the ACM/IEEE Design Automation Conference (DAC) research manuscript review process. This guide explains the double-blind, TPC-driven, and single-shot nature of the evaluation, emphasizing the critical balance between genuine novelty and measured Quality-of-Results (QoR). It outlines the high standards for evidence fairness, benchmark credibility, and ablation studies required for acceptance, differentiating DAC's approach from architecture and software venues.

This guide meticulously models the rigorous decision-making machinery of top-tier academic conferences, using EuroSys as a detailed example. It covers the two-round submission cycle, the 'Accept/Revise/Reject' outcome logic, double-blind review mechanics, and critical criteria (like deployment plausibility and verifiable fairness) that determine a paper's success. Ideal for researchers and graduate students preparing systems research submissions.

A comprehensive guide for designing and reporting rigorous experimental results in robotics research. It details the 'evidence ladder' (from simulation to real-world deployment), statistical best practices (e.g., sample size, confidence intervals), ensuring baseline fairness, and quantifying sim-to-real transfer gaps. Essential reading for researchers submitting to top-tier robotics conferences.

This guide details the unique two-deadline review model of SenSys. It provides authors with a comprehensive roadmap for interpreting complex rejection outcomes, structuring substantive revisions, formulating effective responses to reviewers, and understanding critical systems review criteria, such as energy measurement and baseline fairness. Essential reading for systems researchers.

This comprehensive guide outlines best practices for designing data systems evaluations, ensuring results are robust, reproducible, and convincing to expert reviewers. It covers workload realism, baseline fairness, necessary ablation studies, and advanced metric visualization techniques (e.g., sweeping curves, failure analysis) required for top-tier data systems research.