Skill UI

This guide provides a comprehensive framework for researchers to assess the scientific value, originality, and scope of their research findings. It helps determine if a topic is suitable for high-level journals like Scientia Sinica Informationis (SSI), guides the choice between Chinese and English versions, and advises on the appropriate journal fit and article column (e.g., Review, Paper, Letter). Ideal for pre-submission planning and refining research focus.

A comprehensive, end-to-end guide for researchers aiming to publish high-impact manuscripts in top-tier journals like SCI. It meticulously maps the entire journey, from initial topic brainstorming and framework building to rigorous experimentation, submission auditing, revision, and final publication preparation. This skill minimizes common publication pitfalls and guides authors through the entire academic life cycle.

This guide clarifies the role of computation in theoretical computer science submissions, particularly for venues that prioritize theorems over empirical data. It distinguishes between legitimate forms of evidence—such as certified proof steps, constructive object discoveries, and illustrative examples—and inappropriate performance benchmarking. It helps researchers structure their work to align with theorem-based research, ensuring computational elements enhance the core theoretical contribution.

Guidelines for ensuring academic proofs are completely verifiable by third parties, especially when theorems are distributed across multiple documents (e.g., extended abstract and full version). It emphasizes single-source builds, preventing statement drift, and detailing computational determinism requirements for maximum rigor in theoretical computer science research.

This guide helps researchers evaluate the scope and general significance of their theoretical computer science work. It introduces the 'breadth test' (corridor, lineage, technique) and a detailed routing map to advise whether the paper is best suited for a flagship, broad-scope venue like STOC, or a more specialized conference (e.g., SODA, ITCS, CCC). It provides a structured decision procedure for optimizing academic publication success.

This guide outlines the rigorous process for submitting and evaluating research artifacts for academic conferences (TACAS/ETAPS). It details the mandatory two-round evaluation system, covering tool-demonstration papers and general research case studies. Authors must package their work to achieve badges like Available, Functional, and Reusable, ensuring maximum reproducibility and environmental compatibility on a clean virtual machine.

This comprehensive guide details the mandatory production steps required after a research paper is accepted for publication in Springer LNCS. Key tasks include de-anonymizing the manuscript (restoring true names, affiliations, and funding details), applying the specific llncs.cls format, finalizing metadata with ORCIDs, integrating reviewer feedback, permanently linking artifacts using DOIs, and completing the CC-BY open-access licensing forms.

A comprehensive guide for authors submitting technical papers, detailing methods to ensure that all reported claims, figures, and benchmark numbers can be independently regenerated. This involves creating VM-ready, self-contained artifacts, mapping every claim to a specific script, and defining clear reproducibility levels (Turnkey, Scripted) to satisfy conference requirements.

A comprehensive pre-submission checklist for authors preparing papers for TACAS/ETAPS. It guides users through selecting the correct paper category (research, tool, case-study), adhering to specific Springer LNCS page limits, managing double-blind vs. single-blind review rules, and correctly staging mandatory tool artifacts to ensure acceptance readiness.

This guide provides comprehensive rules for authors submitting research papers to verification experts (TACAS/LNCS). It emphasizes stating the core contribution immediately, explicitly claiming soundness or completeness, supporting empirical claims with fair and reproducible benchmarks, and aligning the paper structure with its specific category (research, tool, case study).

A comprehensive guide for academic authors on how to transition raw research findings into a clear, explicit, and highly significant statement of knowledge. It teaches authors how to frame their contribution—whether it's a new theory, a refined measurement, or a policy implication—to satisfy journal requirements and reviewer skepticism, ensuring the paper clearly answers the question: 'What does the field now know?'

This guide instructs researchers on how to build robust, theoretically grounded economic mechanisms and derive testable predictions for accounting and finance research. It covers the methodologies required for archival, analytical, and experimental papers, ensuring claims move beyond mere correlation to establish clear causal economic channels.