cross-domain
cross-domain-recsys-interactions
Cross-Domain RecSys Interactions — 15 sources, integer-indexed
One unified, integer-indexed interaction corpus glued from 15 recommendation sources for
foundation / sequential recommenders. Built in 3 additive versions that share one schema and
one global index space, so reading all of them together = the full corpus, collision-free.
Interactions only — no model, no embeddings. Item text is provided separately as title+description
metadata, joinable 1:1 by item_idx.
Full corpus: 882… See the full description on the dataset page: https://huggingface.co/datasets/TOPAPEC/cross-domain-recsys-interactions.partial-spoof-cross-domain-audit-data
Partial-Spoof Cross-Domain Audit: detector score outputs
Per-utterance and per-frame score outputs of three detectors (MRM, BAM, CFPRF) on
PartialSpoof, LlamaPartialSpoof, PartialEdit, and HQ-MPSD. Together with the analysis code they reproduce the reported
results of the cross-domain operational audit.
An earlier version of this study was submitted to IJCB 2026; that submission was
withdrawn and was never published. These arrays support one manuscript, currently in
preparation… See the full description on the dataset page: https://huggingface.co/datasets/sukhdeveyash/partial-spoof-cross-domain-audit-data.CrossDomainTransferArtifactscrossdomainfoundationmodeladaption-craterThis dataset is part of the work by Guo Zhixiang et al.https://github.com/ProgrammerZXG/Cross-Domain-Foundation-Model-Adaptation?tab=readme-ov-file
The dataset is originally available on Zenodo https://zenodo.org/records/12798750
And licensed under Creative Commons Attribution 4.0 International
Please cite the following article if you use this dataset:
@misc{guo2024crossdomainfoundationmodeladaptation,
title={Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for… See the full description on the dataset page: https://huggingface.co/datasets/porestar/crossdomainfoundationmodeladaption-crater.crossdomain-atomic-skills
AutoMark — Cross-Domain Atomic-Skill Annotations
Self-induced atomic-skill annotations across several public segment-annotated video
datasets, all in one folder-dataset format. Every annotation is a parameterized primitive
verb(object) (e.g. pour(water), open(door), season(steak)) with a coarse functional
core, localized in seconds — produced by the AutoMark
skillgen pipeline (multi-model ensemble +
consensus), the domain-general generalization of the cooking-specific atomiclm… See the full description on the dataset page: https://huggingface.co/datasets/Chenwei1999/crossdomain-atomic-skills.crossdomainfoundationmodeladaption-deepfaultThis dataset is part of the work by Guo Zhixiang et al.https://github.com/ProgrammerZXG/Cross-Domain-Foundation-Model-Adaptation?tab=readme-ov-file
The dataset is originally available on Zenodo https://zenodo.org/records/12798750
And licensed under Creative Commons Attribution 4.0 International
Please cite the following article if you use this dataset:
@misc{guo2024crossdomainfoundationmodeladaptation,
title={Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for… See the full description on the dataset page: https://huggingface.co/datasets/porestar/crossdomainfoundationmodeladaption-deepfault.