Skill UI

A comprehensive guide for authors targeting the IEEE CASE conference. This tool helps assess manuscript fit by focusing on automation science, manufacturing, logistics, and cyber-physical systems. It guides authors on how to re-frame algorithmic designs with practical operational evidence, structure the contribution, and anticipate reviewer expectations for top-tier automation venues.

This guide helps authors targeting top-tier conferences on Constraint Programming (CP) to strategically position their manuscripts. It provides deep insights into the required scope, evidence bar, technical focus (e.g., propagation, optimization), and necessary re-framing techniques. Use this skill to enhance algorithmic clarity and ensure your work aligns precisely with the specialized community standards of the venue.

A comprehensive framework for analyzing and presenting algorithmic contributions in academic papers. It guides users beyond mere computational speed, requiring rigorous proof of complexity (Big O), convergence (rate and conditions), or approximation bounds (rho). Essential for optimizing and validating complex computational models in scientific fields.

This comprehensive guide teaches how to structure and write professional rebuttal letters (R&R) for top-tier academic journals, particularly in management science. It focuses on methodically addressing reviewer concerns regarding assumptions, proof gaps, algorithmic complexity, and empirical evidence, requiring precise, point-by-point references to the revised manuscript to ensure academic rigor.

A comprehensive guide for rigorously positioning an Operations Research (OR) manuscript against prior art. It teaches authors to move beyond vague 'gap' statements by constructing a precise, technical delta against 3-5 closest papers. Focus areas include weakening assumptions, improving bounds, enhancing algorithmic complexity, and defining new operational regimes, often presented in a structured comparison table to satisfy technical reviewers.

This guide outlines the rigorous analytical methodology required for Operations Research (OR) manuscripts. It instructs users on selecting the appropriate mathematical machinery—such as duality, dynamic programming, convergence analysis, or concentration inequalities—to establish provable guarantees, approximation factors, and algorithmic complexity. It ensures that claims are backed by rigorous proofs, moving beyond mere heuristics.

This comprehensive guide helps authors determine the suitability and optimal presentation of their robotics scientific manuscripts for the Robotics: Science and Systems (RSS) conference. It provides strategic advice on conference positioning, defining the evidence bar, re-framing algorithmic or theoretical contributions, and addressing the specific needs of top-tier embodied systems reviewers.

This skill guides authors on strategically positioning data mining research for the SIAM International Conference on Data Mining (SDM). It helps adjust manuscript framing, emphasizing mathematical rigor, statistical depth, and algorithmic contribution suitable for top Computer Science venues. Use it for venue selection, understanding reviewer expectations, and structuring evidence to meet the high standards of SIAM-style research.

This skill provides comprehensive guidance for authors submitting highly rigorous theoretical computer science manuscripts to top-tier venues like SIAM Journal on Computing (SICOMP). It covers required standards for algorithmic design, computational complexity, formal proofs, house style, and the high bar for theoretical contribution necessary for foundational academic publishing.

This guide provides researchers with a critical framework for determining if their work on algorithms, data structures, and discrete mathematics is suitable for SODA (ACM-SIAM Symposium on Discrete Algorithms). It outlines a rigorous three-question fit test, offers a routing map to comparable specialized venues (like STOC or FOCS), and clarifies the specific academic pattern required for a submission to be considered novel and impactful to an algorithms expert.

A comprehensive guide for designing and auditing visualization experiments for high-stakes academic submissions (e.g., IEEE VIS). It details how to match evidence—such as controlled studies, power analyses, and benchmarks—to the specific type of claim (perceptual, algorithmic, system). Learn best practices for statistical rigor, CVD-safe encoding, and maintaining full provenance to ensure your findings are scientifically sound and trustworthy.