Skill UI

This guide assists authors targeting the ACM SIGMOD conference. It provides expert advice on positioning, framing, and structuring database systems papers. It emphasizes strong algorithmic or systems contributions, realistic workloads, and necessary evidence, while also advising on routing relative to similar venues like VLDB and ICDE.

A comprehensive strategy guide for authors preparing manuscripts for the ACM Symposium on Theory of Computing (STOC). It helps researchers determine the ideal fit, required evidence bar, and narrative framing for major theoretical contributions in algorithms, complexity, cryptography, and theoretical computer science.

This guide provides comprehensive best practices for ensuring scientific reproducibility in technical papers, particularly for electronics and information processing fields. It covers the critical pillars of documentation: environment setup, data sourcing and preprocessing, full algorithmic disclosure (including pseudo-code and hyperparameters), and robust statistical evaluation. By adopting these standards, authors can transform their work from potentially unverifiable results into credible, verifiable scientific contributions suitable for top-tier journals.

This guide outlines the stringent reproducibility and data requirements for submitting manuscripts to Agricultural Systems (AgSy). Because AgSy treats code, models, and algorithms as research data, authors must learn how to deposit all materials in a recognized repository (e.g., Zenodo, OSF), cite them permanently, and document the model structure, parameters, and versions. It provides a checklist for building a reproducible research workflow.

A comprehensive guide for academics submitting AI and statistical research papers. It details how to map every claim (theoretical, algorithmic, or empirical) to verifiable evidence, covering necessary details like datasets, hyperparameters, random seeds, compute resources, and uncertainty estimates. It outlines the standards for achieving high levels of reproducibility, from descriptive to turnkey, ensuring scientific rigor for top-tier conferences.

A comprehensive guide for authors aiming to submit machine learning manuscripts to the Asian Conference on Machine Learning (ACML). This skill helps evaluate manuscript fit, re-frame research from journal or arXiv styles, understand required contribution types (e.g., algorithms, theorems, benchmarks), and align the paper's evidence bar with the expectations of a top-tier, regional AI/ML venue. Use it for venue selection and submission strategy development.

This comprehensive skill guides researchers through the submission process for the Chinese Journal of Management Science. It covers the journal's core academic positioning (operations research, decision analysis, complex systems), required methodological rigor (model setting, algorithms, proofs), structural guidelines, and essential formatting checks. It helps authors assess manuscript fit and identify common rejection risks before official submission.

A comprehensive guide for structuring the algorithm section of academic papers. It details the crucial steps for deciding on the solution route—whether to use commercial solvers, exact algorithms, or meta-heuristics. It emphasizes the requirement for rigorous pseudo-code, property analysis, and multi-level baseline comparisons to prove algorithmic novelty and contribution.

Formats tables, figures, formulas and pseudocode for a Chinese Journal of Management Science manuscript: three-line tables with bolded optima, uniformly numbered equation-editor formulas, algorithm boxes, convergence and sensitivity plots, all inside the 12-page limit, plus a pre-submission checklist.

This comprehensive workflow guides researchers through the entire academic publication lifecycle for submissions to the Chinese Journal of Management Science. It acts as a critical router, ensuring manuscripts cover essential stages: topic selection, advanced model formulation, algorithm design, empirical validation (out-of-sample testing), and generating actionable managerial insights. It helps prevent common pitfalls like lacking empirical depth or misaligning with the journal's core focus on applied modeling.

This comprehensive guide assists researchers and practitioners in selecting the optimal venue and track (e.g., DAC, ICCAD, ISCA) for their electronic design automation (EDA) or chip-design project. It clarifies the critical distinction between the archival Research Manuscript track and the industry-facing Engineering Track at DAC, emphasizing the need to frame contributions using measurable Quality of Results (QoR) improvements, novel algorithms, or real-world industrial lessons.

This skill guides authors on strategically positioning their AI/ML manuscripts for the European Conference on Artificial Intelligence (ECAI). It provides deep insights into the conference's scope, required contribution framing (e.g., algorithm, system, theorem), evidence standards, and how to tailor the narrative for a general European AI readership. Use it when targeting ECAI or needing to refine a paper from a journal or arXiv style into a selective conference submission.