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arxiv: 2406.16173 · v3 · submitted 2024-06-23 · 💻 cs.HC

Crepe: A Mobile Screen Data Collector Using Graph Query

Yuwen Lu , Meng Chen , Qi Zhao , Victor Cox , Yang Yang , Meng Jiang , Jay Brockman , Tamara Kay
show 1 more author
Toby Jia-Jun Li
This is my paper

Pith reviewed 2026-05-23 23:48 UTC · model grok-4.3

classification 💻 cs.HC
keywords mobile data collectionscreen content extractiongraph queryAndroid toolno-code research appUI data gatheringparticipant privacy
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The pith

Crepe lets researchers collect specific data from Android screens by demonstrating the target once, then uses graph queries to find it on other screens.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Crepe is a no-code Android app built to let academic researchers gather information shown on mobile app screens without writing code. Users demonstrate the desired data on example screens, after which the system builds graph queries that locate and extract matching content from new screens. The approach targets the gap where screen content is needed for studies but remains hard for academics to access compared with commercial data holders. Privacy is built in through full visibility of what is collected and simple opt-out options, with the tool limited to consented research use. The code will be released openly to aid further work.

Core claim

The paper claims that augmenting mobile UI screen structures with a Graph Query technique enables flexible identification, location, and collection of specific data pieces after a user demonstrates the target data on example screens, thereby providing a practical way for researchers to obtain screen content while preserving participant privacy and agency.

What carries the argument

Graph Query technique that augments the structures of mobile UI screens to support flexible identification, location, and collection of specific data pieces

If this is right

  • Researchers without programming skills can still gather screen-displayed data for their studies.
  • Data collection remains under participant control through transparency and easy opt-out.
  • Academic work can proceed independently of commercial data monopolies on mobile content.
  • The open-sourced tool can be reused or adapted for additional research projects that need screen information.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same demonstration-plus-query pattern might transfer to iOS or web interfaces if the underlying screen structures can be represented as graphs.
  • If the queries prove stable, the method could combine with existing mobile sensing frameworks to create richer consented datasets.
  • Longer-term use might reveal whether certain app categories produce persistently higher mismatch rates, pointing to needed refinements in the graph augmentation step.

Load-bearing premise

Demonstrating target data on example screens produces graph queries that reliably identify and extract the intended content across varied apps, layouts, and dynamic screen states without manual tuning or high error rates.

What would settle it

Deploy Crepe on a broad sample of apps and screen states, then measure extraction accuracy and failure rates when queries are generated solely from the initial demonstrations with no further adjustments.

Figures

Figures reproduced from arXiv: 2406.16173 by Jay Brockman, Meng Chen, Meng Jiang, Qi Zhao, Tamara Kay, Toby Jia-Jun Li, Victor Cox, Yang Yang, Yuwen Lu.

Figure 1
Figure 1. Figure 1: The Crepe app provides a low-code solution for academic researchers to collect data displayed on mobile screens. Through a programming by demonstraion paradigm, a researcher taps on the target data to collect on the screen (A). Crepe will automatically generate a novel Graph Query we designed (B), to accurately identify and locate the UI element containing the target data on the screen’s UI Snapshot (C). T… view at source ↗
Figure 2
Figure 2. Figure 2: The workflow of Crepe for our main user groups: data collection researchers and participants. Researchers create a new data collector by demonstration (A) and share the collector ID with participants (B). Participants add the collector to their own devices (C), which runs in the background to collect the specified target data (D). The collected data is transmitted to a database (E) for the researcher to an… view at source ↗
Figure 3
Figure 3. Figure 3: Details regarding the process of query generation and execution in [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Number of unique Instagram Story Ads collected by participants using the Crepe data collector. The top subplot [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
read the original abstract

Collecting mobile datasets remains challenging for academic researchers due to limited data access and technical barriers. Commercial organizations often possess exclusive access to mobile data, leading to a "data monopoly" that restricts the independence of academic research. Existing open-source mobile data collection frameworks primarily focus on mobile sensing data rather than screen content, which is crucial for various research studies. We present Crepe, a no-code Android app that enables researchers to collect information displayed on screen through simple demonstrations of target data. Crepe utilizes a novel Graph Query technique which augments the structures of mobile UI screens to support flexible identification, location, and collection of specific data pieces. The tool emphasizes participants' privacy and agency by providing full transparency over collected data and allowing easy opt-out. We designed and built Crepe for research purposes only and in scenarios where researchers obtain explicit consent from participants. Code for Crepe will be open-sourced to support future academic research data collection.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper presents Crepe, a no-code Android app that enables researchers to collect screen-displayed data through simple demonstrations of target elements. It introduces a novel Graph Query technique that augments mobile UI screen structures to support flexible identification, location, and extraction of specific data pieces, while emphasizing participant privacy, transparency, consent, and opt-out. The tool is positioned as a response to data access barriers and commercial monopolies, with plans to open-source the code for academic use.

Significance. If the Graph Query approach delivers the claimed robustness, Crepe could meaningfully expand independent academic access to mobile screen content data, supporting HCI and related studies that currently rely on limited sensing frameworks. The explicit focus on consent and open-sourcing represents a constructive contribution to research tooling.

major comments (2)
  1. [Abstract] Abstract and system description: the central claim that a single demonstration produces graph queries that reliably identify and extract target data across varied apps, layouts, and dynamic screen states lacks any supporting implementation details, error rates, robustness metrics, or user studies. This assumption is load-bearing for the contribution.
  2. [Full manuscript (system description)] No comparison or baseline is provided against existing accessibility tree selectors or tree-query methods, despite the manuscript noting that mobile UI hierarchies are trees; without this, the novelty and necessity of the graph augmentation cannot be assessed.
minor comments (1)
  1. [Abstract] The abstract would benefit from a concise statement of the query language syntax or augmentation invariants to allow readers to evaluate the claimed flexibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback. We address each major comment below and commit to revisions that strengthen the manuscript's empirical grounding and positioning of the Graph Query contribution.

read point-by-point responses

  1. Referee: [Abstract] Abstract and system description: the central claim that a single demonstration produces graph queries that reliably identify and extract target data across varied apps, layouts, and dynamic screen states lacks any supporting implementation details, error rates, robustness metrics, or user studies. This assumption is load-bearing for the contribution.

    Authors: We agree that the current manuscript, which centers on system design and the privacy-focused no-code workflow, does not yet provide quantitative robustness metrics or user studies. In the revision we will expand the system description with concrete implementation details on graph construction from UI hierarchies, the query matching algorithm, and preliminary cross-app robustness tests. We will also moderate the abstract's claims to reflect the scope of a system paper while noting planned evaluations. revision: yes

  2. Referee: [Full manuscript (system description)] No comparison or baseline is provided against existing accessibility tree selectors or tree-query methods, despite the manuscript noting that mobile UI hierarchies are trees; without this, the novelty and necessity of the graph augmentation cannot be assessed.

    Authors: We accept this observation. Although the manuscript explains that graph augmentation enables relations and dynamic matching beyond strict tree traversal, a side-by-side comparison is absent. We will add a dedicated subsection (or table) in Related Work that contrasts Crepe's Graph Query with standard accessibility-tree selectors and existing tree-query techniques, explicitly articulating the added flexibility for cross-layout and dynamic-screen scenarios. revision: yes

Circularity Check

0 steps flagged

No circularity: system description with no derivations

full rationale

The paper presents Crepe as a no-code Android app for collecting screen data via demonstrations and a graph query technique on UI structures. No equations, parameters, predictions, or derivation chains appear anywhere in the manuscript. The contribution is a practical tool description emphasizing privacy and open-sourcing, with no self-referential logic, fitted inputs renamed as outputs, or load-bearing self-citations that reduce claims to their own inputs. The work is self-contained against external benchmarks as an engineering artifact.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on standard domain assumptions about Android UI accessibility and user consent rather than new axioms or fitted parameters.

axioms (1)
  • domain assumption Mobile UI screens can be reliably represented and queried as augmented graphs for data extraction
    Invoked in the description of the Graph Query technique as the basis for flexible identification.

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