Skill UI

This guide helps researchers determine the optimal conference or journal venue for their software engineering contribution. It specifically addresses whether the core of the work is a testing or analysis technique evaluated on real subjects (ISSTA), or if it fits better within broader Software Engineering (ICSE/FSE), formal verification (PLDI/CAV), or applied domains.

This guide details the rigorous review process for papers in Mathematical Finance, covering single-blind peer review standards, from editor screening to line-by-line proof verification by referees. Learn what reviewers prioritize—methodological novelty, proof correctness, and financial applicability—to avoid common submission pitfalls and improve your chances of acceptance.

A comprehensive guide for executing and reporting advanced analysis for high-level academic manuscripts, particularly in management science. It mandates rigorous verification across two lanes: the analytical lane (proving theorems and comparative statics) and the empirical lane (identifying and estimating causal effects using methods like DiD, IV, and RDD). It emphasizes adherence to Data-and-Code-Disclosure policies, ensuring all results are reproducible, verifiable, and meet high methodological standards.

A comprehensive guide for positioning security research submissions (e.g., for NDSS, USENIX). It teaches how to write the 'Related Work' section not as a mere literature review, but as a critical security argument that establishes genuine novelty (the 'delta') by distinguishing mechanisms, layers, and preconditions from prior art. Covers citation verification, handling concurrent work, and double-blind anonymity rules.

This comprehensive guide provides expert methodologies for authors writing the 'Related Work' section for top-tier networked systems research papers (e.g., NSDI, SIGCOMM, OSDI). It details the required literature coverage gaps, strategic positioning techniques (e.g., delta by mechanism), academic verification protocols, and double-blind submission rules necessary to ensure the work is academically rigorous and highly competitive.

This guide provides critical advice on structuring technical research papers for major academic conferences. It emphasizes the crucial distinction between 'decision-critical' content (must appear in the first 10 pages) and 'verification-detail' content (can be deferred to the appendix). It also advises on choosing the correct submission track (Regular Paper vs. Brief Announcement).

This guide provides strict guidelines for authors writing the "Related Work" section for top-tier programming language theory conferences like POPL. It emphasizes moving beyond general literature reviews to pinpoint precise, technical "delta sentences" that prove novelty against neighboring formal systems. It covers citation standards, venue mapping, and attribution verification for maximum academic impact.

A comprehensive checklist detailing the final pre-submission steps for Social Psychology Quarterly (SPQ) via SageTrack/ScholarOne Manuscript Central. It guides authors through masking, ASA style compliance, abstract formatting, and fee verification to ensure the manuscript meets all journal requirements and avoids temporary rejection.

This guide details best practices for structuring supplementary material in highly technical academic submissions (e.g., STOC). It teaches authors how to design an appendix that facilitates selective verification by reviewers, ensuring that complex proofs, deferred results, and extended discussions are navigable. Key techniques include optimizing the Table of Contents, maintaining strict pointer discipline, and distinguishing between material required for core understanding versus material saved for the full version.

A detailed guide for planning and executing research submissions for top-tier theoretical computer science conferences (STOC/FOCS). It provides a backward-planning timeline, covering all milestones from initial result completion (T-16 weeks) through drafting, verification, submission, peer review management, and final presentation preparation.

This comprehensive guide outlines best practices for designing and auditing empirical evaluations of formal verification tools and algorithms. It stresses the use of shared, community-accepted benchmarks, ensuring fair comparison against the strongest baselines with equal resources, meticulous reporting of all metrics, validating results for soundness, and guaranteeing full reproducibility on clean virtual environments.

This guide provides detailed guidelines for authors submitting to top-tier formal methods conferences (like TACAS). It emphasizes moving beyond simple literature summaries by employing a "delta-first" approach. Authors must precisely position their algorithm or tool by contrasting its novel features, guarantees, or scaling benefits against the nearest prior work, while also crediting underlying infrastructure and covering major verification venues.