Skill UI

A comprehensive guide for authors submitting papers to mobile and wireless networking conferences (e.g., MobiCom). It emphasizes structuring the paper around measured evidence, ensuring every claim is paired with a precise operating regime and empirical proof. Key sections cover the 'first-page arc,' reporting distributions instead of superlatives, and managing the manuscript's page budget for maximum impact.

Provides a comprehensive methodology for evaluating mobile computing systems. It mandates rigorous, real-device testing, focusing on instrumenting energy consumption, thermal behavior, latency distribution (tails), and memory limits. This guide ensures academic submissions are credible, repeatable, and compare performance against robust, tuned baselines.

A comprehensive guide for structuring academic system evaluation papers (e.g., OSDI). It dictates how to frame experiments around core research questions, select robust baselines, utilize realistic workloads (including adversarial tests), and apply rigorous measurement disciplines (e.g., reporting distributions, p99s) while maintaining conciseness within strict page limits.

This guide provides a comprehensive audit checklist for designing and evaluating sensor-based systems (SenSys). It mandates rigorous, real-world measurements across five core axes: energy efficiency, on-device latency distribution, ground truth accuracy, deployment realism, and embedded AI footprint. It ensures that research results are based on measured hardware performance, not simulations or idealized baselines.

A comprehensive guide for designing and auditing high-stakes networking experiments, particularly for top-tier academic submissions like SIGCOMM. It outlines the 'evidence ladder' from microbenchmarks to live deployment, emphasizing the need to select robust baselines, report full performance distributions (percentiles and variance), and accurately map the mechanism's operational break points.

A comprehensive guide for authors in data systems and related fields on achieving rigorous, verifiable reproducibility for academic papers. It mandates detailing the provenance of every claimed result by pinning five critical layers: code version, configuration parameters, dataset source, workload characteristics, and hardware specifications. Best practices include reporting distributions (e.g., percentiles, variance) and maintaining a detailed 'repro debt ledger' to withstand intense peer review.

Provides comprehensive guidelines for designing rigorous technical evaluations for interactive systems, specifically tailored for top-tier venues like UIST. It emphasizes matching evaluation methods to specific artifact claims, reporting distributions over means, closing the interaction loop, and performing thorough ablations against real-world baselines. This framework ensures the scientific rigor and credibility of submitted research.

A comprehensive guide for building responsive UIs in Flutter. It promotes using fundamental layout widgets like Column, Row, Expanded, and Flexible for flexible composition, advising developers to build layouts from the constraint system outward. Learn best practices for space distribution, content flow management, and avoiding rigid, hardcoded coordinates for robust, adaptive designs.

Seaborn is a high-level Python visualization library designed for creating publication-quality statistical graphics. It facilitates dataset-oriented plotting, multivariate analysis, and complex statistical visualizations (like KDEs, distributions, and relationships) directly from Pandas DataFrames with minimal code. It builds upon Matplotlib to provide semantic mapping and statistical awareness.