Skill UI

Provides comprehensive guidelines for authors submitting papers to Acta Automatica Sinica (AAS). It details how to organize and present anonymous, reproducible code, data, and experimental artifacts to enhance the credibility and rigor of research in control, automation, and pattern recognition, supporting the double-blind review process.

A comprehensive guide for designing and presenting rigorous experiments and simulations for top-tier journals in Control Theory, Robotics, and Smart Systems. Covers RQ-driven design, establishing fair baselines, providing theoretical proofs of stability/convergence, performing ablation studies, and ensuring statistical significance, helping authors build an undeniable evidence chain for double-blind external review.

Provides comprehensive guidelines for achieving scientific reproducibility in academic research, particularly for control systems, automation, and pattern recognition. It covers fixing environment dependencies, data handling, random seeds, and process pipelines, helping authors withstand rigorous peer review questioning and build verifiable research artifacts.

A comprehensive audit checklist designed to guide authors through the rigorous preparation process for submitting academic manuscripts to Acta Automatica Sinica (AAS). It covers critical steps such as adopting double-blind anonymity, mandatory author commitment forms, ensuring originality, and meeting the high formatting standards required for top-tier publications in control and automation theory.

This skill guides researchers to determine if their topic fits the scope of Acta Automatica Sinica (AAS). It provides detailed boundaries for control theory, pattern recognition, robotics, and smart systems, helping authors distinguish their contribution from pure algorithms or system software, and advising on appropriate article type (Review/Paper/Short) to avoid initial rejection.

Provides comprehensive guidance for researchers deciding the optimal conference venue for Software Engineering (SE) research. It distinguishes between contributions that focus on *automation* (techniques or tools that automate SE tasks) suitable for ASE, versus those that are broad empirical findings, theoretical results, or pure ML advances, directing them to venues like ICSE, FSE, or ISSTA.

Provides comprehensive guidelines for structuring and writing academic papers for Automated Software Engineering (ASE) conferences. It teaches authors how to present an automated task, emphasize the automation design over the deep learning model, provide evidence from real subjects, and adhere to strict formatting and argument presentation rules to maximize acceptance chances.

Provides guidelines for creating robust, verifiable, and reusable Electronic Design Automation (EDA) artifacts for submission to academic conferences like DAC. The focus shifts from chasing conference badges to ensuring community trust and scientific reproducibility. Key elements include pinning the toolchain, using standard benchmarks, providing clear setup instructions, and archiving the release in a DOI-issuing repository.

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 comprehensive guide for establishing research reproducibility in Electronic Design Automation (EDA). It details best practices for pinning benchmark suites, disclosing specific tool versions (open/commercial), documenting technology nodes (PDKs), and handling stochastic data flows (seeds, variance). Essential for ensuring that semiconductor design claims and ML-for-EDA results are credible and verifiable by the academic community.

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 comprehensive guide provides a detailed audit checklist for submitting research manuscripts to major design automation conferences (e.g., DAC). It covers critical steps such as the two-stage abstract/manuscript timeline, adherence to the strict 6+1 page format, ensuring double-blind anonymization (removing all identifying metadata), declaring TPC conflicts, and mitigating common submission risks. Essential reading for researchers preparing for high-stakes academic submissions.