Skill UI

This module is designed to document, track, and manage quality nonconformance issues found during manufacturing or process audits. It is essential for root cause analysis, implementing corrective and preventive actions (CAPA), and ensuring compliance with industry quality standards. Use it when recording deviations from specified requirements.

A comprehensive guide for researchers in electronics and signal processing preparing submissions to Acta Electronica Sinica (AES). It outlines best practices for proactively structuring, documenting, and disclosing research artifacts—including code, datasets, and hardware designs—to maximize the paper's credibility and chances of acceptance, even without mandatory journal badges. Covers anonymization, reproducibility standards, and proper disclosure in the manuscript.

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 details the process of packaging and evaluating academic artifacts for ACM CCS conferences. It outlines the criteria for earning specific ACM badges (e.g., Functional, Reusable, Results Reproduced) by ensuring that supporting evidence—such as exploits, defenses, and measurement pipelines—is turnkey, reproducible, and rigorously documented for the review committee. It covers best practices for artifact planning, handling sensitive material, and justifying any withheld components.

Provides rigorous guidelines for conducting robust, verifiable, and reproducible evaluations in networking research. It emphasizes moving beyond simple simulations by demanding the use of real target platforms, documenting full system provenance (traces and configs), establishing honestly tuned baselines, and reporting detailed measurement statistics and uncertainty. Essential for authors aiming for top-tier conference submissions.

A comprehensive guide for establishing research reproducibility in Electronic Design Automation (EDA). It details best practices for pinning benchmark suites, disclosing specific tool versions (open/commercial), documenting technology nodes (PDKs), and handling stochastic data flows (seeds, variance). Essential for ensuring that semiconductor design claims and ML-for-EDA results are credible and verifiable by the academic community.

This comprehensive guide outlines best practices for achieving rigorous scientific reproducibility, especially for research involving storage systems and hardware dependencies. It mandates documenting not only the code but also the specific device models, firmware versions, operating states, and full measurement protocols to ensure verifiable results by peer readers.

Provides a comprehensive framework for generating structured, blameless post-mortem documents after a production outage or service degradation. It guides teams through reconstructing timelines, identifying root causes using methods like 5 Whys, quantifying impact, and generating concrete, actionable preventative measures to improve system resilience.

A comprehensive guideline for establishing and documenting the reproducibility of complex sensor-systems artifacts. It covers necessary components such as firmware sources, detailed hardware Bills of Materials (BOMs) and board files, raw sensor traces, ground truth data, and calibrated measurements. Use this guide to ensure that scientific claims can be verified by a competent reader, detailing the exact dependencies on physical equipment and software toolchains.

A comprehensive guide to ensuring reproducibility in radio frequency (RF) physical layer measurements. It details the necessity of maintaining a detailed provenance ledger—recording radio hardware, channel conditions, mobility patterns, and software versions at the time of the run. The goal is not bit-identical results, but documenting variance and methodology so that findings can be validated on different testbeds or later in time, while also addressing legal and ethical data shipping constraints.

This guide outlines the necessary steps to ensure that parallel computing performance papers are scientifically reproducible. Since parallel results depend heavily on machine architecture, topology, and runtime conditions, proper documentation must cover everything from CPU/GPU specifications and interconnects to thread pinning policies, random seeds, and compiler flags. The focus is on proving the scaling trend, not just the absolute numbers, allowing results to hold true across different hardware platforms.

This guide details the process of achieving ACM badges (Available, Functional, Reproduced) for academic artifacts at SenSys. It emphasizes creating a hardware-optional evaluation path, allowing reviewers to verify results and functionality without access to the original testbed. Key steps include implementing graded replay tiers, documenting data provenance (energy, traces), and performing smoke-runs.