percom-reproducibility

name	percom-reproducibility
description	Use when strengthening IEEE PerCom reproducibility and open-data evidence for human-subjects sensing, covering the dataset-availability statement, de-identified datasets with IRB/consent handling, sensing provenance (devices, sampling, labeling), cross-subject reproducibility, honest degrees of reproducibility, and consistency between what the paper says and what the dataset contains.

PerCom Reproducibility

Use this before submission and again before camera-ready. In pervasive computing, reproducibility turns on the sensing data: a PerCom result is only as trustworthy as the dataset behind it, how it was collected, and whether it generalizes across people. The goal is that a competent reader could rebuild your pipeline and reach your conclusions — and, where ethics permit, on your actual data.

Evidence map

• Map each recognition/system claim and reported number to a verifiable location — a paper section, a table generated from logged data, or a script in the artifact.
• For recognizers, give enough of the features, model, hyperparameters, and evaluation split (leave-one-subject-out / leave-one-session-out) that a reader could re-run it.
• For datasets, report subjects and their selection, sensors and placement, sampling rates, labeling protocol and inter-annotator agreement, and preprocessing (filtering, windowing, normalization).
• Keep the dataset-availability statement truthful and specific: what is shared, where it will live after acceptance, and — if something cannot be shared — exactly why (privacy, IRB, consent).
• Keep the paper and the dataset consistent: a number in the PDF that no script reproduces from the released data is the contradiction reviewers read as carelessness.

Dataset-availability statement audit

Claim in the paper	Weak availability answer	PerCom-ready answer
"We collected data from N participants"	"Dataset available on request"	De-identified dataset + datasheet, or a documented restricted-access path with the ethics reason
"Our recognizer generalizes across users"	"Code will be released"	Runnable pipeline with a LOSO reproduction script and a README demo
"We labeled activities"	Nothing about protocol	Labeling protocol, annotator agreement, and the label files
"We deployed in a smart space"	"Testbed is proprietary"	Sensor list, placement, and sampling; simulated/sample data if the raw cannot ship

"Available on request" reads as not available; convert every such line into a concrete, de-identified dataset or an explicit, justified exception with a request path.

Sensing provenance floor

[Devices]    device models + firmware, sensor types, sampling rates, placement on body/space
[Labels]     labeling protocol, who labeled, inter-annotator agreement, label schema
[Preprocess] filtering, windowing, normalization, resampling -- the exact pipeline, not prose
[Splits]     leave-one-subject-out / session-out defined so a reader reproduces the same folds
[Ethics]     IRB/approval status, consent scope, and the de-identification performed
[Compute]    hardware, training time, number of runs so a reader can size a reproduction
[Randomness] seeds for any stochastic step; say what is and is not deterministic

Degrees of reproducibility (state the one you achieved)

• Turnkey: one documented command regenerates each table/figure (including the LOSO result) from released data.
• Scripted: scripts exist but require documented manual steps or restricted-data access.
• Descriptive: prose detailed enough that a competent reader could rebuild the pipeline.

For PerCom, aim turnkey for anything a reviewer might rerun quickly (inference on a bundled sample, a plot from logged features); full raw human-subjects data may stay scripted with restricted access when consent/IRB forbids public release — but say so honestly rather than promising turnkey behavior that cannot legally run.

Vignette: a wearable HAR study

Consider a study collecting wrist-IMU data from participants doing daily activities. Its reproducibility spine: the collection protocol and device/sampling details; the de-identified extracted dataset with a datasheet; the labeling protocol with annotator agreement; the feature and model code; the leave-one-subject-out evaluation scripts that regenerate the F1 table; and one honest sentence about the parts (raw video used for labeling, re-identifiable timestamps) that cannot be shared and why.

Consistency and camera-ready pass

• Before submission: every scored number traces to the artifact; the availability statement matches reality; the review package is anonymized (no testbed, lab, or owner strings).
• Before camera-ready: swap anonymized links for a permanent, DOI-issuing, de-identified deposit (IEEE DataPort / Zenodo), and align the statement with what you actually release (percom-artifact-evaluation).

Output format

[Claim inventory] <claim -> evidence location>
[Dataset availability] concrete / vague / restricted-with-reason / missing
[Provenance gaps] <devices / labels / preprocessing / splits / ethics / seeds / compute>
[Cross-subject reproduction] LOSO script present and matching the paper? yes/no
[Reproducibility level] turnkey / scripted / descriptive, stated honestly
[Paper fixes] <must appear in the PDF>
[Dataset fixes] <additions before release>