Skill UI

A comprehensive guide on structuring empirical research papers for top-tier conferences, such as FSE. It outlines strict partitioning rules, ensuring that all decision-critical evidence, core arguments, and key findings reside within the primary reviewed body, while supplementary artifacts and appendices provide necessary, but non-judgmental, support. Covers page budget discipline, double-anonymity requirements, and optimizing the paper's flow.

This guide helps researchers determine the optimal academic venue for their software engineering work. It compares major conferences (FSE, ICSE, ASE, ISSTA) and journals, advising on contribution shape, evidence maturity, and community fit to maximize publication chances and fit the research into the correct academic community.

A comprehensive guide for revising empirical papers submitted to top-tier Software Engineering conferences (like FSE). This framework emphasizes structuring the paper around a clear SE contribution, treating research questions as contracts, backing every claim with proportional evidence, and rigorously arguing threats to validity, rather than merely listing them. It ensures the paper maintains maximum academic rigor and focus.

This detailed project management skill provides a comprehensive, backward-planning guide for submitting to highly structured academic conferences, such as HPCA. It maps out critical milestones—from initial venue fit and evidence locking to review, revision, and final camera-ready submission—while identifying common failure modes and coordination requirements to maximize acceptance chances.

This guide details the strategic process of writing a rebuttal for top-tier academic conferences (such as HRI). It provides a framework for triaging reviewer critiques, addressing methodological concerns (e.g., sample size, confounding), and making credible, actionable promises for the final camera-ready version, all while maintaining academic integrity and anonymity.

A comprehensive guide on how to structure the 'Related Work' section for top-tier theoretical computer science conferences (like ICALP). It teaches authors how to prove their results are genuinely new and better by precisely comparing their bounds against the best known literature, correctly crediting prior venues (STOC, FOCS, etc.), and maintaining academic rigor under double-blind guidelines.

A comprehensive guide detailing the high-stakes, single-round, correctness-centered peer review process for top-tier theoretical computer science conferences like ICALP. It outlines the unique two-track system (Track A and Track B), review criteria, and provides actionable advice on interpreting reviewer feedback and structuring submissions compared to other major venues.

This guide provides structural best practices for writing highly technical academic papers, particularly for top-tier conferences. It teaches authors how to critically distinguish between the core material that must be visible in the main body (for the referee to follow and judge) and the auxiliary details, complete proofs, or routine calculations that can be safely deferred to an appendix. Following this structure prevents common writing pitfalls, such as 'hollow bodies,' ensuring the paper is both highly readable and technically rigorous.

This comprehensive guide provides a detailed, backward-planning workflow for submitting research papers to major academic conferences like ICALP. It outlines every critical stage—from initial theorem proof and abstract registration to anonymous submission, rebuttal periods, and camera-ready preparation—ensuring authors manage the strict, single annual deadlines required for academic publication.

A comprehensive guide for structuring high-level theoretical computer science papers for top-tier conferences like ICALP. It dictates how to present the model, state the theorem, demonstrate the improvement over prior bounds, and manage proof complexity within strict page limits, ensuring maximum clarity and verifiability for expert reviewers.

A comprehensive guide for authors submitting to major signal processing conferences like ICASSP. It details how to audit, define, and precisely position a paper's novelty by comparing it against specific literature lanes (SPS journals, conferences, and ML venues). Learn to articulate the technical delta against the nearest work in a constrained, four-page format.

A comprehensive guide for researchers submitting signal processing papers, detailing how to achieve rigorous reproducibility for conferences like ICASSP. It emphasizes that simply providing a model is insufficient; authors must pin down the 'scoring ruler,' data versions, front-end parameters, and computational seeds to ensure every reported metric can be independently verified.