Skill UI

A detailed guide on the complex, hybrid review process for ACM SIGMETRICS submissions. It outlines the implications of the three distinct first-round outcomes (Accept, One-Shot Revision, Reject), the highly rigorous evaluation criteria (proofs, models, assumptions), and crucial procedural rules, such as the single-shot revision window and the 12-month resubmission bar. Essential reading for authors navigating academic publication.

This guide helps researchers determine the most appropriate venue (e.g., SIGMETRICS, NSDI, IMC, NeurIPS) for their computer systems performance evaluation research. It emphasizes that contributions must be rigorous performance-evaluation results—such as proven models, principled measurements, or guaranteed learning algorithms—rather than merely built systems or pure network measurements.

A comprehensive guide for authors revising performance-intensive academic papers, such as those for SIGMETRICS. It emphasizes the need to state a precise performance contribution on the first page, explicitly detail all modeling assumptions, and pair every quantitative claim with proportional evidence (proofs, simulations, and measurements).

This guide provides comprehensive best practices for designing and auditing cloud system evaluations, particularly for academic submissions. It mandates that claims must be supported by measured evidence from real or realistic deployments, moving beyond simple simulations. Key metrics emphasized include tail latency (p99), operational cost modeling, and verifiable behavior under scale and multi-tenancy.

This resource models the complex, multi-stage review process for top-tier academic conferences, such as SOSP. It details the journey of a paper from initial submission through staged reviews, author rebuttal, and the final live committee decision. It helps users understand the structural dynamics of scholarly evaluation and how to strategically respond to reviewers' critiques.

A comprehensive model detailing the rigorous evaluation process for submissions to STOC (Symposium on Theory of Computing). It covers the committee structure, the roles of program committee members and external subreviewers, the key evaluation axes (Correctness, Significance, Breadth, Novelty), the typical timeline, and strategic advice for authors on maximizing their leverage throughout the double-blind review process.

A comprehensive guide detailing the complex submission and evaluation pipeline for TACAS (Tools and Algorithms for Scientific Applications). It models key features such as category-specific blind review, the mandatory parallel artifact evaluation for tool papers, and the single-round conference decision-making process, helping authors strategically prepare their submissions.

A comprehensive guide detailing the rigorous, journal-style review process for IEEE VIS full-paper submissions. It explains the two-phase evaluation structure, the significance of a 'Conditional Accept' (treating it like a major R&R), and how the process differs fundamentally from single-decision conference models (e.g., CVPR, CHI). Essential reading for authors navigating academic publication.

Provides comprehensive guidelines for packaging and submitting research artifacts (datasets, models, code) for major conferences. It details the difference between mere 'existence' (badge verification) and true academic 'reproducibility,' covering archival strategies, web data licensing, takedown resilience, and minimal viable package structure for long-term scholarly impact.

This comprehensive guide outlines best practices for packaging research artifacts, including code, proprietary data logs, and models, for submission to top-tier academic venues. It addresses the crucial need for verifiability and reproducibility, providing strategies for handling limited space, proprietary datasets, and the absence of formal artifact grading systems, ensuring high academic credibility.

Scikit-learn is the industry-standard Python library for classical machine learning. It provides comprehensive tools for supervised and unsupervised learning tasks, including classification, regression, clustering, and dimensionality reduction. Use it to build robust, production-ready ML pipelines, perform rigorous model evaluation, and preprocess complex datasets for various data science applications.

This skill automates the creation of comprehensive Model Cards for OpenMed clinical NER or de-identification models. It gathers data from various OpenMed evaluation reports (e.g., `GateReport`, `fairness_report`, `error_report`) to structure critical governance information, including intended use, quantitative performance metrics, subgroup analysis, limitations, and required medical-device disclaimers. It ensures documentation is reproducible and compliant for clinical AI deployment.