Skill UI

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.

Provides comprehensive guidelines for packaging supplementary materials, including code, data, model weights, and benchmarks, for major machine learning conferences like ICML. It details the requirements for both the double-blind review stage (focusing on anonymization and minimal replication commands) and the final public release package (ensuring permanent public links and clear licensing).

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.

This comprehensive guide details the entire ICSE conference paper review lifecycle, from initial submission to final decision. It analyzes the four core scoring criteria—Novelty, Rigor, Relevance, and Verifiability—and maps out the Accept/Major Revision/Reject decision model. Use this to strategically plan your revisions and understand the academic publishing landscape.

A comprehensive guide for authors targeting the IEEE International Requirements Engineering Conference (RE). This skill helps assess if a computer science manuscript is a strong fit for the venue, focusing on requirements elicitation, modeling, traceability, and stakeholder-centered validation. It provides guidance on re-framing submissions and structuring evidence for academic conference acceptance.

A comprehensive strategic guide for authors targeting the IEEE Symposium on Security and Privacy (S&P). This skill helps evaluate if a computer science manuscript is a proper fit for the venue by covering rigorous threat modeling, ethical considerations, evidence requirements, and how to strategically frame the contribution. Use this when determining conference submission strategy or refining security-focused academic research.

This guide provides rigorous standards for designing, validating, and reporting security research, particularly for high-stakes academic conferences like IEEE S&P. It covers critical areas including adaptive adversary modeling, measurement validity (sampling, ethics), statistical reporting, establishing strong baselines/ablations, and adhering to ethical constraints (IRB, authorization) to ensure evidence is robust and reproducible.

This guide outlines best practices for packaging research artifacts (code, data, proofs, models) submitted for major AI conferences like IJCAI. It helps researchers create convincing, anonymous, and reproducible evidence to support their claims, even when formal artifact evaluation tracks are unavailable.

This comprehensive guide outlines the necessary theoretical rigor for algorithms and models submitted to top-tier computational journals. It instructs authors on how to formally state mathematical formulations, rigorously prove model validity, establish convergence, and quantify complexity (e.g., approximation ratios, finite termination). It provides specialized paths for exact methods, metaheuristics, and ML-enhanced OR, ensuring that the theoretical advancements fully support and rationalize the experimental findings.

A detailed guide modeling the rigorous multi-round review process for major academic conferences, such as IMC. It covers the unique two-deadline cycle, the distinction between 'One-Shot-Revision' (a bounded, actionable contract) and general revisions, and emphasizes key review criteria including ethical treatment, artifact availability, and vantage-point representativeness. Essential for authors submitting measurement-heavy research.

This guide helps researchers navigate the complex landscape of theoretical computer science conferences. It explains the crucial distinction between 'conceptual novelty' (favored by ITCS) and 'deep technical results' (favored by STOC/FOCS). Use this to determine if your research contribution is a new idea, model, or connection, or if it is the deepest, most polished theorem on an established problem.

This comprehensive guide provides a rigorous framework for developing original theoretical contributions in Information Systems (IS) research, particularly for top-tier academic journals. It outlines various theoretical genres—including behavioral mechanisms, economic models, and design theories—and teaches authors how to move beyond mere description. Key principles include constructing novel, well-defined constructs; identifying clear boundary conditions; and executing deep 'round-trip' theoretical integrations to ensure the contribution is genuinely original.