Agent Skillsbrycewang-stanford/Awesome-Journal-Skills › percom-artifact-evaluation

percom-artifact-evaluation

GitHub

指导IEEE PerCom感知类论文的可复现性打包,涵盖匿名评审包与公开数据部署、去标识化要求及评估者检查重点。

PerCom-Skills/skills/percom-artifact-evaluation/SKILL.md brycewang-stanford/Awesome-Journal-Skills

Trigger Scenarios

准备PerCom论文的可复现性材料 打包感知数据集和代码以符合IEEE可复现性标准 处理人类受试者数据的去标识化和伦理合规

Install

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill percom-artifact-evaluation -g -y
More Options

Non-standard path

npx skills add https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PerCom-Skills/skills/percom-artifact-evaluation -g -y

Use without installing

npx skills use brycewang-stanford/Awesome-Journal-Skills@percom-artifact-evaluation

指定 Agent (Claude Code)

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill percom-artifact-evaluation -a claude-code -g -y

安装 repo 全部 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --all -g -y

预览 repo 内 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --list

SKILL.md

Frontmatter
{
    "name": "percom-artifact-evaluation",
    "description": "Use when packaging an IEEE PerCom sensing artifact and dataset for reproducibility and any badging (IEEE Open Research Objects \/ Results Reproduced, IEEE DataPort or Zenodo deposit), covering what a ubicomp evaluator checks first for human-subjects sensing data, cross-subject reproduction, de-identification, and honest degrees of reproducibility."
}

PerCom Artifact Evaluation

Use this for reproducibility packaging. First, a cycle caveat: unlike SIGSOFT venues, PerCom has not historically run a mandatory formal artifact-evaluation track with a fixed badge set, and whether a given edition offers a reproducibility/badging track (e.g., IEEE Open Research Objects / Results Reproduced) is 待核实 — confirm on the current call. Regardless of whether a badge is offered, a well-packaged, de-identified sensing dataset and reproducible pipeline is a scored strength in the double-blind review and a lasting community contribution.

What "reproducible" means for a ubicomp sensing paper

Two deliverables, kept distinct:

  • The anonymized review package (at submission): dataset link and code scrubbed of owner, testbed, and lab identity, for the double-blind reviewers.
  • The public deposit (after acceptance): a de-identified dataset and code in a DOI-issuing archive under an open license — the version others cite and reuse, and the version any badge program evaluates.

What a ubicomp evaluator opens first

Claim type First thing inspected Common failure caught
An activity/context recognizer The script that regenerates cross-subject (LOSO) results Only a pooled-accuracy script; no leave-one-subject-out path
A sensing dataset The data itself + a datasheet (subjects, sensors, labels) Link present, data missing; no de-identification described
A deployed system A demo on bundled sample data Only-runs-on-authors'-testbed; hardware not documented
A model result Trained weights + inference on sample input Requires the full raw dataset or private compute to run

Assume an evaluator gives your package a bounded time budget on a clean machine with no access to your sensors or subjects. Design for the first ten minutes — a demo on bundled, de-identified sample data — to succeed.

Packaging plan

[Container]   ship a Dockerfile or a pinned environment (requirements/lockfile); avoid
              "install these 40 things by hand"
[README]      one-screen orientation: what it is, install, run the demo, reproduce each claim,
              expected runtime and outputs
[Datasheet]   a dataset datasheet: subjects (count, relevant demographics), sensors (device,
              firmware, sampling rate, placement), labels + protocol, and known biases
[Mapping]     an explicit table: paper claim -> script -> expected result (with the LOSO split)
[Data]        the de-identified dataset itself (or documented restricted-access), not just a query
[Ethics]      IRB/consent status and the de-identification performed before release
[License]     an open, DOI-issuing deposit (IEEE DataPort, Zenodo) so others can reuse and cite

De-identification is the ubicomp-specific bar

Human-subjects sensing data leaks identity in ways code does not: raw audio/video, GPS traces, timestamps that pinpoint a home, and even accelerometer gait can re-identify. Before any release:

  • Remove or transform direct identifiers and re-identifying traces; document exactly what you did.
  • Confirm your consent and IRB approval permit public release of the de-identified data — some approvals do not, and then the honest move is restricted access with a documented request path.
  • Never ship a dataset that a subject did not consent to have published.

Worked vignette: a wearable-HAR dataset + recognizer

To make a HAR paper reproducible: ship a Docker image with the recognizer pre-built; a run_demo.sh that classifies on a small bundled, de-identified sample in under a minute; a reproduce/ directory whose scripts regenerate the leave-one-subject-out F1 table (not just a pooled number) from logged features; a datasheet listing subjects, sensor placement, and sampling rate; the de-identified dataset with a documented consent/IRB basis; and an open license with a DOI. State honestly which results are turnkey and which need the full (slow) training run.

Calibration

  • Whether a badge/reproducibility track runs, and which badges, is 待核实 per cycle — confirm on the current call rather than assuming a SIGSOFT-style scheme.
  • The DOI-issuing deposit and datasheet are portable value even when no badge is offered.
  • Anonymize the review package; de-identify (and confirm consent for) the public dataset — these are different obligations.

Output format

[Track status] formal reproducibility/badge track this cycle? yes/no/待核实
[Artifact role] anonymized review package / public de-identified deposit
[Contents] <recognizer/dataset/datasheet/scripts/ethics/license>
[Ten-minute test] does install + demo on bundled sample data succeed on a clean machine? yes/no
[Cross-subject reproduction] does a script regenerate the LOSO result? yes/no
[De-identification] documented + consent/IRB permits release? yes/no
[Fixes before deposit] <ordered list>

Version History

  • 9f86f09 Current 2026-07-19 17:18

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