Awesome-Journal-Skills

Use when preparing the accountability artifacts that accompany an ACM FAccT paper — datasheets for datasets, model cards, data statements, audit and impact-assessment documentation, and released code/data — since FAccT's culture centers documentation and accountability infrastructure rather than a formal ACM artifact-badging track; covers what makes each genre credible, anonymized-review versus public-release versions, and consistency with the paper's harm claims.

Use when drafting ACM FAccT author responses — the short factual-correction rebuttal after preliminary reviews, and, distinctively for the 2026-new process, the Revise-and-resubmit round where you address Area-Chair-prioritized concerns from a mixed CS+law+social-science panel, map each to a concrete change, and survive a re-review while keeping mutual anonymity.

Use when preparing an accepted ACM FAccT paper for camera-ready — de-anonymizing and adding the withheld Positionality/Acknowledgements statements, switching to the acmart sigconf proceedings format, ACM metadata (DOI, ORCID, CCS concepts, rights), the two-round camera-ready calendar for Accept vs Revise papers, ACM Open Access, finalizing datasheets/model cards, and integrating required changes without scope creep.

Use when designing or auditing ACM FAccT empirical work — quantitative fairness audits with disaggregated metrics and fair baselines, qualitative and participatory studies with coding and reflexivity, mixed-methods designs, sound handling of protected attributes and proxies, consent and IRB for human-subjects and community-facing work, and matching evidence to the shape of a fairness/accountability/transparency claim.

Use when positioning an ACM FAccT submission across its many disciplinary lanes — algorithmic fairness/ML, HCI, law and policy, STS and critical theory, and prior FAccT/FAT* proceedings — writing delta-first contrast that a mixed reviewer pool will accept, citing borrowed constructs to their real origin, keeping self-citations mutually anonymous, and declaring overlap with workshops, preprints, and prior versions.

Use when strengthening ACM FAccT transparency and reproducibility — releasing code, data, and analysis for quantitative audits; documenting datasets and models with datasheets, model cards, and data statements; making qualitative and participatory work auditable without breaking confidentiality; pinning provenance for scraped and model-generated data; and keeping the paper, the supplementary material, and any released artifact consistent.

Use when reasoning about how an ACM FAccT submission is evaluated — mutually-anonymous review by a mixed CS+law+social-science pool matched via author-selected focus areas, Area Chairs, the short factual-correction rebuttal, the new Accept/Revise/Reject decision with a revise-and-resubmit round, and how FAccT's interdisciplinary process differs from a pure-ML conference's single-shot rebuttal.

Use when auditing an ACM FAccT submission for OpenReview readiness — the mandatory abstract-registration gate with focus-area selection, the acmart anonymous/review PDF, the 14-page (+1 endmatter) budget, mutual anonymity, the required Generative AI Usage Statement and optional ethics/adverse-impacts statements, archival vs non-archival choice, and desk-reject triage before the deadline.

Use when deciding what belongs in an ACM FAccT paper body versus its endmatter statements, appendices, and supplementary material — the 14-page acmart budget, the extra endmatter page for ethics/adverse-impacts statements, the rule that decision-critical evidence stays in the reviewed pages, mutually-anonymous supplementary material, and how to split an interdisciplinary paper between body, endmatter, and artifact.

Use when deciding whether a responsible-AI project belongs at ACM FAccT or should route to a pure-ML venue (NeurIPS/ICML/ICLR), an HCI venue (CHI/CSCW), a law/policy venue, or an AI-ethics venue (AIES), by testing whether fairness, accountability, or transparency is a first-class contribution and whether the interdisciplinary framing is native rather than bolted on.

Use when planning an ACM FAccT project timeline from venue fit through abstract registration, OpenReview submission, the rebuttal window, the new Accept/Revise/Reject revise-and-resubmit round, two-round camera-ready, and in-person presentation, with backward-planning offsets for an interdisciplinary responsible-AI paper and honest handling of the single annual deadline.

Use when revising an ACM FAccT paper so that a fairness/accountability/transparency contribution is legible on the first page to a mixed CS+law+social-science reviewer pool, claims are scoped to who is affected, limitations and harms are argued rather than recited, the interdisciplinary framing is native, and the acmart page budget and endmatter statements are used well.

Use when preparing the "artifact" of an ICDT (International Conference on Database Theory) paper, which at a pure-theory venue is the complete-proofs full version rather than a code package — covering why ICDT has no ACM-style artifact-badging or code-artifact track, what the marked appendix and the archived arXiv full version must contain, and (only for the rare algorithmic paper) how any code should be handled.

Use when responding to ICDT (International Conference on Database Theory) referees — principally the first-cycle Revise-and-resubmit round, where you return a corrected paper that closes each identified proof gap, plus any short author-comment phase, keeping the response anonymous and mapping every referee point to a concrete change in the revised PDF.

Use when preparing the final LIPIcs camera-ready for an accepted ICDT (International Conference on Database Theory) paper — the lipics-v2021 final document, mandatory ACM CCS concepts and keywords, author ORCIDs, de-anonymization, complete proofs and the full-version link, CC-BY licensing, and passing the Dagstuhl/DROPS production checks that mint the DOI.

Use when deciding what counts as evidence for an ICDT (International Conference on Database Theory) result — matching-bound complexity analysis as the primary evidence, worked examples and counterexamples that establish separations, and, only for papers with an algorithmic contribution, a proportional and honestly-scoped experimental evaluation that does not pretend to be the contribution.

Use when positioning an ICDT (International Conference on Database Theory) paper against the database-theory literature — covering the ICDT/PODS lineage, finite model theory and complexity, delta-first positioning against the nearest prior theorem, honest treatment of overlapping bounds, and keeping self-citations anonymous under the since-2024 rule.

Use when strengthening the verifiability of an ICDT (International Conference on Database Theory) paper — the theory-venue analogue of reproducibility — covering complete and self-contained proofs, exact models and assumptions, claim-to-proof mapping, matching upper and lower bounds, consistency between the LIPIcs paper and the arXiv full version, and honest labeling of what is proved versus conjectured.

Use when reasoning about how an ICDT (International Conference on Database Theory) submission is evaluated, covering the two-submission-cycle model, the first-cycle revision (Accept / Revise / Reject) decision, anonymous review since 2024, the cross-cycle resubmission restriction, proof-correctness scrutiny by a database-theory PC, and how ICDT's process differs from PODS and from the co-located EDBT systems track.

Use when auditing an ICDT (International Conference on Database Theory) regular-paper submission for Microsoft CMT readiness, covering the two-step abstract-then-paper deadline in the correct submission cycle, the lipics-v2021 15-page limit excluding references, the clearly-marked appendix read at the PC's discretion, anonymous submission since 2024, the complete-proofs / full-version expectation, and which submission problems are unfixable after the AoE cutoff.

Use when deciding how to split an ICDT (International Conference on Database Theory) paper between the 15-page lipics-v2021 body and the clearly-marked appendix that referees read at their discretion, given that online/external appendices are not allowed and every proof needed to certify the result must live inside the single submitted PDF.

Use when deciding whether a database-theory project belongs at ICDT (International Conference on Database Theory) or should be routed to PODS, the co-located EDBT systems track, a pure-TCS venue (LICS, ICALP, STACS), or a journal (ACM TODS, LMCS, TheoretiCS), and when distinguishing ICDT from its sibling database-theory flagship PODS by calendar, publisher, and community.

Use when planning an ICDT (International Conference on Database Theory) campaign end to end, running the two-submission-cycle calendar backward from a chosen cycle deadline through abstract registration, the first-cycle revision round, LIPIcs camera-ready, and presentation at the co-located EDBT/ICDT event.

Use when shaping the prose of an ICDT (International Conference on Database Theory) paper — the theorem-proof structure, stating the data model and computation model precisely, leading with the result and its data-management consequence, proof-sketch-then-appendix discipline within the lipics-v2021 15-page budget, and the notation conventions a database-theory referee expects.

当你在为《自动化学报》(Acta Automatica Sinica, AAS) 稿件准备代码与数据可用性材料、判断本刊是否设有独立制品评审(artifact evaluation)徽章制度时调用。讲清本刊现状(以同行评议为主、无独立徽章制度的现况为待核实)、如何主动提供匿名可复现的代码/数据以增强说服力、控制仿真与实物实验制品的组织，帮助控制/自动化/模式识别方向的中文稿在 Acta Automatica Sinica 外审中用可用制品提升可信度。

当你在收到《自动化学报》(Acta Automatica Sinica, AAS) 的外审意见、需要撰写退修说明与逐条答复信时调用。给出逐条对应审稿意见的答复结构、修改对照(改前→改后)、大修/小修的不同策略、对分歧意见的礼貌据理回应、保持双盲的匿名口径、以及按编辑部期限组织修改稿，帮助作者在 Acta Automatica Sinica 的多轮退修与重新送审中最大化被录用的机会。

当你在稿件被《自动化学报》(Acta Automatica Sinica, AAS) 录用后、需要完成定稿、清样校对、补齐署名与基金、按本刊体例排版并处理稿酬与出版排期时调用。覆盖录用后从匿名稿恢复作者信息、套用本刊定稿模板、中英文摘要与参考文献终校、校样(清样)逐处核对、样刊与稿酬安排，帮助作者在 Acta Automatica Sinica 见刊前把清样打磨到零差错并理解稿酬与出版流程。

当你在为《自动化学报》(Acta Automatica Sinica, AAS) 设计与呈现实验、仿真或实物验证时调用。覆盖研究问题(RQ)驱动的实验设计、公平基线与对照、控制类的稳定性/收敛性/鲁棒性分析、模式识别类的指标与统计显著性、消融实验、仿真与实物平台的报告要素，帮助为控制理论、机器人、智能系统方向的中文稿搭建经得起 Acta Automatica Sinica 双盲外审的实证与理论证据链。

当你在为《自动化学报》(Acta Automatica Sinica, AAS) 撰写相关工作、文献综述或确立创新性(delta)时调用。给出以差异为先的文献组织法、控制/自动化/模式识别方向代表作的覆盖策略、与最接近工作的对比表、避免"重复前人/无新意"初审退稿的自检，以及综述栏目所需的分类框架与批判性评价方法，帮助在 Acta Automatica Sinica 的中英文双语与综述长文传统下清晰界定本文相对已有研究的增量。

当你在为《自动化学报》(Acta Automatica Sinica, AAS) 提升实验可复现性、组织复现包、防范数据污染时调用。覆盖环境与依赖固定、随机种子与多次运行、数据划分与防泄露、控制仿真的求解器/步长/系统参数记录、超参数与调参协议披露、以及配合双盲的匿名化复现材料，帮助控制/自动化/模式识别方向的中文稿在 Acta Automatica Sinica 外审中经得起"结果能否复现"的追问。

当你在需要理解《自动化学报》(Acta Automatica Sinica, AAS) 的双盲三审制审稿流程、各环节时间进度与作者可施加的杠杆时调用。建模编辑初审→双盲外审→退修/复审(多轮)→主编终审的完整链路，说明作者承诺前置、双盲对作者的约束、退修期限与重新送审、状态查询，帮助作者在 Acta Automatica Sinica 的评审各节点做出正确动作、管理预期并避免流程性失误。

当你在向《自动化学报》(Acta Automatica Sinica, AAS) 正式提交稿件前做投稿就绪审计时调用。覆盖官网 aas.net.cn 的 ScholarOne 投审稿系统(aas-cn)、双盲匿名化(删除页眉页脚/标题下方/尾注/文件名中的作者单位基金号)、全部作者签名加盖第一作者单位公章的《作者承诺》前置提交、只收原创非翻译稿与不接收 AI 署名、学术不端查重、栏目(综述/论文/短文)归属与退稿风险分诊。适用于把一份控制/自动化/模式识别方向的中文稿打磨到符合本刊双盲三审制门槛的场景。

当你在为《自动化学报》(Acta Automatica Sinica, AAS) 组织正文与附录/补充材料、决定哪些内容进正文哪些进附录时调用。给出按"决定性"划分正文与附录的原则、冗长证明与额外仿真的安置、补充材料/多媒体附件的组织与命名、以及配合双盲的匿名化，帮助控制/自动化/模式识别方向的中文长文在 Acta Automatica Sinica 的篇幅与体例约束下保持正文紧凑而证据完整。

当你在为《自动化学报》(Acta Automatica Sinica, AAS) 确定选题、判断稿件是否契合本刊控制与自动化学科定位、以及在综述/论文/短文三类栏目间定位时调用。帮助辨析本刊与兄弟中文刊(计算机学报/软件学报/电子学报/计算机研究与发展/计算机科学)的学科分工，评估创新性与贡献可数性，避免"主题超范围""无新意""重复前人"等初审退稿理由，并把选题打磨到匹配 Acta Automatica Sinica 的控制/模式识别/机器人/智能系统侧重与中英文双语综述长文传统。

当你在为《自动化学报》(Acta Automatica Sinica, AAS) 规划一次完整投稿的时间线、把 12 个技能编排成从选题到见刊的流水线时调用。给出选题定位→写作→实验→匿名投稿→双盲三审→多轮退修→录用定稿的阶段划分、每阶段的准入准出、里程碑与常见延误点，帮助作者在 Acta Automatica Sinica 的双盲三审制与中英文双语综述长文传统下合理排期，避免作者承诺漏交、双盲泄露、退修超期等踩坑，并在每个节点指明应调用哪个 aas- 技能。

当你在为《自动化学报》(Acta Automatica Sinica, AAS) 撰写或打磨中文稿件体例时调用。覆盖报道性中英文摘要(目的/方法/结果/结论四要素)、中英文关键词、中图分类号(自动化/计算机类 TP 打头)、参考文献 GB/T 7714 顺序编码制著录、量与单位法定计量、控制类公式与图表规范、以及综述/论文/短文三类栏目的体例差异，帮助把控制理论、模式识别、机器人、智能系统方向的中文长文写到符合 Acta Automatica Sinica 的双语传统与编排要求。

当你为《电子学报》(Acta Electronica Sinica, AES) 投稿准备代码、数据与硬件设计等研究制品(artifact)的可用性说明时使用。中文学报暂无独立的 artifact evaluation 徽章委员会，本技能说明本刊现状、如何自愿开放代码/数据以增强稿件可信度、如何在正文与附录中规范披露制品、如何在双盲阶段匿名托管仓库、以及电子/信号处理类工作(算法、电路、FPGA、数据集)的制品打包要点。帮助作者在没有强制徽章的前提下最大化《电子学报》稿件的可复现说服力。

在为《电子学报》(Acta Electronica Sinica, AES) 撰写审稿意见答复与修回说明时调用。适用于收到编辑部"修改后再审/退修"通知后，逐条回应外审专家意见、撰写"修改说明"对照表、在正文中标注修改痕迹、处理相互矛盾或要求补做实验的意见、把握修回时限与再审轮次。帮助电子/信息/通信/信号处理方向的中文稿件在《电子学报》多级审理中稳步走向录用，避免因答复不规范或情绪化措辞被再次退修甚至退稿。

当稿件被《电子学报》(Acta Electronica Sinica, AES) 录用后进入定稿、校样与清样阶段时使用。覆盖录用通知后的版权协议签署、按最终排版规范核对中英文题名/摘要/关键词/中图分类号、补全双盲阶段隐去的作者信息与基金编号、GB/T 7714 参考文献终校、图表与公式的印刷级质量、校样(清样)一校二校流程、版面费缴纳、DOI 与出版时序确认。帮助电子/信息/通信方向作者把已录用稿件零差错地送到《电子学报》正式出版。

当你为《电子学报》(Acta Electronica Sinica, AES) 稿件设计与呈现实验/仿真时使用。覆盖电子/信息/信号处理/通信类工作的实验目标与假设、数据集与仿真设置、基线与对比公平性、评价指标(SNR/BER/误差/准确率/资源占用)、消融与参数敏感性、统计显著性与多次运行、图表呈现规范、以及硬件/FPGA/测量实验的条件报告。帮助作者用严谨、可复现、对比公平的实验证据支撑创新点，抵御《电子学报》外审专家对"实验不充分、对比不公平、结果偶然"的质疑。