arXiv subjects

Gabe Fierro

Publications and source records attributed to Gabe Fierro.

5 recordsLinked to original sources

arXiv2026

Evidence-Carrying Validation for Knowledge Graphs

Programs that consume a knowledge graph they do not maintain, such as applications, authoring platforms, and LLM agents, need to know whether the graph contains the information their task requires. Validating the graph against a schema can answer this question, but existing validation interfaces usually return a conformance bit or failure-oriented report without identifying why checks pass or the partial matches behind failures. We present an evidence-carrying validation interface: every selected node-shape check returns either a satisfaction trace or failure witness. These are mutually recursive objects that retain constraints, cardinality decisions, paths, and supporting triples. We implement this interface in Shifty, an experimental SHACL validator. Against two real-world shape graph corpora, materializing all-pair evidence costs a median 1.54-2.07X conformance-only validation. A case study then shows how programs combine passing and failing evidence to diagnose missing information and guide repair.

cs.DB↗

arXiv2025

Systematic Evaluation of Knowledge Graph Repair with Large Language Models

We present a systematic approach for evaluating the quality of knowledge graph repairs with respect to constraint violations defined in shapes constraint language (SHACL). Current evaluation methods rely on \emph{ad hoc} datasets, which limits the rigorous analysis of repair systems in more general settings. Our method addresses this gap by systematically generating violations using a novel mechanism, termed violation-inducing operations (VIOs). We use the proposed evaluation framework to assess a range of repair systems which we build using large language models. We analyze the performance of these systems across different prompting strategies. Results indicate that concise prompts containing both the relevant violated SHACL constraints and key contextual information from the knowledge graph yield the best performance.

cs.DB↗

arXiv2025

Assessing Student Adoption of Generative Artificial Intelligence across Engineering Education from 2023 to 2024

Generative Artificial Intelligence (GenAI) tools and models have the potential to re-shape educational needs, norms, practices, and policies in all sectors of engineering education. Empirical data, rather than anecdata and assumptions, on how engineering students have adopted GenAI is essential to developing a foundational understanding of students' GenAI-related behaviors and needs during academic training. This data will also help formulate effective responses to GenAI by both academic institutions and industrial employers. We collected two representative survey samples at the Colorado School of Mines, a small engineering-focused R-1 university in the USA, in May 2023 ($n_1=601$) and September 2024 ($n_2=862$) to address research questions related to (RQ1) how GenAI has been adopted by engineering students, including motivational and demographic factors contributing to GenAI use, (RQ2) students' ethical concerns about GenAI, and (RQ3) students' perceived benefits v.s. harms for themselves, science, and society. Analysis revealed a statistically significant rise in GenAI adoption rates from 2023 to 2024. Students predominantly leverage GenAI tools to deepen understanding, enhance work quality, and stay informed about emerging technologies. Although most students assess their own usage of GenAI as ethical and beneficial, they nonetheless expressed significant concerns regarding GenAI and its impacts on society. We collected student estimates of ``P(doom)'' and discovered a bimodal distribution. Thus, we show that the student body at Mines is polarized with respect to future impacts of GenAI on the engineering workforce and society, despite being increasingly willing to explore GenAI over time. We discuss implications of these findings for future research and for integrating GenAI in engineering education.

cs.HC↗

arXiv2024

Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023

Because of the rapid development and increasing public availability of Generative Artificial Intelligence (GenAI) models and tools, educational institutions and educators must immediately reckon with the impact of students using GenAI. There is limited prior research on computing students' use and perceptions of GenAI. In anticipation of future advances and evolutions of GenAI, we capture a snapshot of student attitudes towards and uses of yet emerging GenAI, in a period of time before university policies had reacted to these technologies. We surveyed all computer science majors in a small engineering-focused R1 university in order to: (1) capture a baseline assessment of how GenAI has been immediately adopted by aspiring computer scientists; (2) describe computing students' GenAI-related needs and concerns for their education and careers; and (3) discuss GenAI influences on CS pedagogy, curriculum, culture, and policy. We present an exploratory qualitative analysis of this data and discuss the impact of our findings on the emerging conversation around GenAI and education.

cs.CY↗

arXiv2020

CoVista: A Unified View on Privacy Sensitive Mobile Contact Tracing Effort

Governments around the world have become increasingly frustrated with tech giants dictating public health policy. The software created by Apple and Google enables individuals to track their own potential exposure through collated exposure notifications. However, the same software prohibits location tracking, denying key information needed by public health officials for robust contract tracing. This information is needed to treat and isolate COVID-19 positive people, identify transmission hotspots, and protect against continued spread of infection. In this article, we present two simple ideas: the lighthouse and the covid-commons that address the needs of public health authorities while preserving the privacy-sensitive goals of the Apple and google exposure notification protocols.

cs.CR↗