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Incorrect or corrupted labels in tabular datasets can significantly degrade supervised learning performance, particularly when mislabeling is subtle and not easily detectable from feature space alone. In the context of automated or AI-augmented data science workflows, robust detection of such label noise is critical fo...
Differences in GenAI use and perceived learning experiences are examined and perceptions of GenAI-enhanced learning appear to reflect cognitive, performance, and self-efficacy benefits, while concerns about stress and diminished critical thinking are associated with lower perceived learning benefits.
It is shown that the trade-off between locality and balance holds only where each silo can hold several relations, weakening as the number of silos increases, and the study provides practical guidance for deployments constrained by cross-silo reasoning or by silo load.
The empirical findings of FedV-KGQA on multi-hop question answering over vertically partitioned graphs are presented, and an interactive prototype that runs real inference and traces the full pipeline, per question, on released checkpoints is contributed.
Real-world data for knowledge graph question answering is often distributed across different organizations due to governance and data sovereignty constraints. While centralized systems exist, they cannot answer multi-hop questions when the required facts are split across vertically partitioned silos. In this paper, we...
A seven-stage graph-grounded pipeline that converts domain documents into a complete, auditable Web Ontology Language (OWL) Terminological Box (TBox) without any unconstrained generation step is presented, demonstrating that the pipeline produces stable, reusable domain representations from large document corpora.
SNAP-KG (Streaming Node Assignment via Projection for Knowledge Graph Entity Integration), a framework supporting graph-structural multi-view relational clustering and inductive inference for streaming entities, is proposed.
PFN-Syn achieves the highest average downstream utility among evaluated synthetic generators, with higher generation throughput than training-based baselines, while maintaining competitive distributional similarity and distance-to-closest-record privacy behavior.
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