Skill UI

A comprehensive guide for authors submitting networking research to CoNEXT. It outlines best practices for achieving technical reproducibility, focusing on pinning traces, configurations, environments, and pipelines. Learn the critical timeline for optional ACM badging and how to maintain an honest data-availability posture for robust academic review.

This detailed guide teaches authors how to structure and position their research paper within the highly interdisciplinary field of robot learning. It covers essential best practices, including reviewing multiple concurrent literature streams (ML methods, classical robotics, robot lineage, VLA models) and adhering to strict academic citation hygiene (e.g., handling PMLR year offsets).

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.

This comprehensive guide outlines best practices for achieving rigorous scientific reproducibility, especially for research involving storage systems and hardware dependencies. It mandates documenting not only the code but also the specific device models, firmware versions, operating states, and full measurement protocols to ensure verifiable results by peer readers.

A comprehensive guide for researchers preparing academic artifacts for voluntary, post-acceptance evaluation (e.g., HPCA). It outlines best practices for structuring submissions for cold-start evaluators, defining reproducibility tiers (full reproduction, regeneration from logs, inspection), and managing computational budgets to successfully earn IEEE reproducibility badges.

This comprehensive guide outlines the full project management lifecycle for submitting research papers to the ICASSP conference (IEEE Signal Processing Society). It details the annual timeline, critical milestones, backward planning heuristics, and best practices across various submission tracks to ensure timely and successful publication.

This comprehensive guide provides best practices for packaging supplemental materials and artifacts for top-tier academic conferences like ICDE. It instructs authors on how to build a robust, reproducible evidence chain, including parameterized workload generators, configuration scripts, and detailed logs. The goal is to ensure that reviewers can quickly and accurately validate all performance claims and technical contributions, making the work 'turnkey' and verifiable.

A comprehensive guide for data engineers and researchers determining the best venue (ICDE, SIGMOD, VLDB, etc.) for their data-intensive systems paper. It outlines the foundational "data-management-primitive test" and provides strategic advice on navigating the overlaps and differences between major database conferences based on research focus and submission timeline.

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.

A comprehensive guide to strategically routing robotics research projects. This skill helps determine whether a project is best suited for ICRA, IROS, CoRL, RA-L, or other specialized venues. It evaluates your work based on the physical embodiment test, contribution type (e.g., HRI, manipulation, perception), and provides a decision framework between direct conference submission and journal publication.

A comprehensive methodology guide for designing and auditing empirical studies in software engineering, specifically focused on maintenance, evolution, and change impact. It outlines best practices for ensuring evidence is proportional to the claim, managing data provenance (SHAs, criteria), handling statistical confounds (survivorship, effect sizes), and integrating rigorous LLM evaluation protocols.

This guide details the comprehensive process for preparing security research artifacts for post-acceptance evaluation at major conferences like IEEE S&P. It covers the three types of evaluation badges (Available, Functional, Results Reproduced), strict handling constraints for exploits and malware, and best practices for packaging the submission (e.g., Dockerfiles, README structure) to ensure external evaluators can reliably reproduce findings within a limited time frame.