arXiv subjects

Edward A. Fox

Publications and source records attributed to Edward A. Fox.

At least 19 recordsLinked to original sources

arXiv2026

URL Extraction from Scholarly Documents: A Cross-Format Comparative Analysis

URLs in scholarly documents link to rich external resources such as datasets, software, publications, and websites. Extracting these URLs is crucial in the data preparation stage of many downstream tasks, such as link rot analysis, web crawling, and building knowledge graphs. However, existing studies often downplay this phase, simply extracting URLs from a single format, usually text directly converted from PDFs. We present a systematic study evaluating URL extraction across six input formats (text with annotation layer, LaTeX, HTML, XML, Markdown, and PNG converted from PDF). To support the evaluation, we compiled a benchmark dataset consisting of 2,338 manually annotated URLs from 200 arXiv papers spanning a wide range of domains over a 33-year period. In addition to evaluating individual file formats, we also compared 63 composite input-format combinations. Our extensive evaluations indicate that TEXTWAL achieves the best performance among single-format inputs, while TEXTWAL+LaTeX achieves the best overall URL extraction performance. The same trend is observed for URLs linking to open-access datasets and software. To further validate these findings, we apply our format-specific URL extraction pipelines to a longitudinal random sample of 364,744 arXiv papers spanning 33 years. We observe a sharp increase in URL density after 2015, along with remarkable differences in URL extraction across file formats over time. Overall, our study highlights the importance of selecting an appropriate format for URL extraction from scholarly documents. The dataset and code are publicly available at: https://github.com/lamps-lab/arxiv-url-bench .

cs.DL↗

arXiv2025

Toward Robust URL Extraction for Open Science: A Study of arXiv File Formats and Temporal Trends

In this work, we study how URL extraction results depend on input format. We compiled a pilot dataset by extracting URLs from 10 arXiv papers and used the same heuristic method to extract URLs from four formats derived from the PDF files or the source LaTeX files. We found that accurate and complete URL extraction from any single format or a combination of multiple formats is challenging, with the best F1-score of 0.71. Using the pilot dataset, we evaluate extraction performance across formats and show that structured formats like HTML and XML produce more accurate results than PDFs or Text. Combining multiple formats improves coverage, especially when targeting research-critical resources. We further apply URL extraction on two tasks, namely classifying URLs into open-access datasets and software and the others, and analyzing the trend of URLs usage in arXiv papers from 1992 to 2024. These results suggest that using a combination of multiple formats achieves better performance on URL extraction than a single format, and the number of URLs in arXiv papers has been steadily increasing since 1992 to 2014 and has been drastically increasing from 2014 to 2024. The dataset and the Jupyter notebooks used for the preliminary analysis are publicly available at https://github.com/lamps-lab/arxiv-urls

cs.DL↗

arXiv2025

When LLMs Disagree: Diagnosing Relevance Filtering Bias and Retrieval Divergence in SDG Search

Large language models (LLMs) are increasingly used to assign document relevance labels in information retrieval pipelines, especially in domains lacking human-labeled data. However, different models often disagree on borderline cases, raising concerns about how such disagreement affects downstream retrieval. This study examines labeling disagreement between two open-weight LLMs, LLaMA and Qwen, on a corpus of scholarly abstracts related to Sustainable Development Goals (SDGs) 1, 3, and 7. We isolate disagreement subsets and examine their lexical properties, rank-order behavior, and classification predictability. Our results show that model disagreement is systematic, not random: disagreement cases exhibit consistent lexical patterns, produce divergent top-ranked outputs under shared scoring functions, and are distinguishable with AUCs above 0.74 using simple classifiers. These findings suggest that LLM-based filtering introduces structured variability in document retrieval, even under controlled prompting and shared ranking logic. We propose using classification disagreement as an object of analysis in retrieval evaluation, particularly in policy-relevant or thematic search tasks.

cs.IR↗

arXiv2025

AI-Facilitated Episodic Future Thinking For Adults with Obesity

Episodic Future Thinking (EFT) involves vividly imagining personal future events and experiences in detail. It has shown promise as an intervention to reduce delay discounting-the tendency to devalue delayed rewards in favor of immediate gratification- and to promote behavior change in a range of maladaptive health behaviors. We present EFTeacher, an AI chatbot powered by the GPT-4-Turbo large language model, designed to generate EFT cues for users with lifestyle-related conditions. To evaluate the feasibility and usability of EFTeacher, we conducted a mixed-methods study that included usability assessments, user evaluations based on content characteristics questionnaires, and semi-structured interviews. Qualitative findings indicate that participants perceived EFTeacher as communicative and supportive through an engaging dialogue. The chatbot facilitated imaginative thinking and reflection on future goals. Participants appreciated its adaptability and personalization features, though some noted challenges such as repetitive dialogue and verbose responses. Our findings underscore the potential of large language model-based chatbots in EFT interventions targeting maladaptive health behaviors.

cs.HC↗

arXiv2024

Agentic AI for Improving Precision in Identifying Contributions to Sustainable Development Goals

As research institutions increasingly commit to supporting the United Nations' Sustainable Development Goals (SDGs), there is a pressing need to accurately assess their research output against these goals. Current approaches, primarily reliant on keyword-based Boolean search queries, conflate incidental keyword matches with genuine contributions, reducing retrieval precision and complicating benchmarking efforts. This study investigates the application of autoregressive Large Language Models (LLMs) as evaluation agents to identify relevant scholarly contributions to SDG targets in scholarly publications. Using a dataset of academic abstracts retrieved via SDG-specific keyword queries, we demonstrate that small, locally-hosted LLMs can differentiate semantically relevant contributions to SDG targets from documents retrieved due to incidental keyword matches, addressing the limitations of traditional methods. By leveraging the contextual understanding of LLMs, this approach provides a scalable framework for improving SDG-related research metrics and informing institutional reporting.

cs.DL↗

arXiv2024

Automating Chapter-Level Classification for Electronic Theses and Dissertations

Traditional archival practices for describing electronic theses and dissertations (ETDs) rely on broad, high-level metadata schemes that fail to capture the depth, complexity, and interdisciplinary nature of these long scholarly works. The lack of detailed, chapter-level content descriptions impedes researchers' ability to locate specific sections or themes, thereby reducing discoverability and overall accessibility. By providing chapter-level metadata information, we improve the effectiveness of ETDs as research resources. This makes it easier for scholars to navigate them efficiently and extract valuable insights. The absence of such metadata further obstructs interdisciplinary research by obscuring connections across fields, hindering new academic discoveries and collaboration. In this paper, we propose a machine learning and AI-driven solution to automatically categorize ETD chapters. This solution is intended to improve discoverability and promote understanding of chapters. Our approach enriches traditional archival practices by providing context-rich descriptions that facilitate targeted navigation and improved access. We aim to support interdisciplinary research and make ETDs more accessible. By providing chapter-level classification labels and using them to index in our developed prototype system, we make content in ETD chapters more discoverable and usable for a diverse range of scholarly needs. Implementing this AI-enhanced approach allows archives to serve researchers better, enabling efficient access to relevant information and supporting deeper engagement with ETDs. This will increase the impact of ETDs as research tools, foster interdisciplinary exploration, and reinforce the role of archives in scholarly communication within the data-intensive academic landscape.

cs.DL↗

arXiv2024

Analyzing Quality, Bias, and Performance in Text-to-Image Generative Models

Advances in generative models have led to significant interest in image synthesis, demonstrating the ability to generate high-quality images for a diverse range of text prompts. Despite this progress, most studies ignore the presence of bias. In this paper, we examine several text-to-image models not only by qualitatively assessing their performance in generating accurate images of human faces, groups, and specified numbers of objects but also by presenting a social bias analysis. As expected, models with larger capacity generate higher-quality images. However, we also document the inherent gender or social biases these models possess, offering a more complete understanding of their impact and limitations.

cs.AI↗

arXiv2023

ETDPC: A Multimodality Framework for Classifying Pages in Electronic Theses and Dissertations

Electronic theses and dissertations (ETDs) have been proposed, advocated, and generated for more than 25 years. Although ETDs are hosted by commercial or institutional digital library repositories, they are still an understudied type of scholarly big data, partially because they are usually longer than conference proceedings and journals. Segmenting ETDs will allow researchers to study sectional content. Readers can navigate to particular pages of interest, discover, and explore the content buried in these long documents. Most existing frameworks on document page classification are designed for classifying general documents and perform poorly on ETDs. In this paper, we propose ETDPC. Its backbone is a two-stream multimodal model with a cross-attention network to classify ETD pages into 13 categories. To overcome the challenge of imbalanced labeled samples, we augmented data for minority categories and employed a hierarchical classifier. ETDPC outperforms the state-of-the-art models in all categories, achieving an F1 of 0.84 -- 0.96 for 9 out of 13 categories. We also demonstrated its data efficiency. The code and data can be found on GitHub (https://github.com/lamps-lab/ETDMiner/tree/master/etd_segmentation).

cs.CV↗

arXiv2023

AI Chatbot for Generating Episodic Future Thinking (EFT) Cue Texts for Health

We describe an AI-powered chatbot to aid with health improvement by generating Episodic Future Thinking (EFT) cue texts that should reduce delay discounting. In prior studies, EFT has been shown to address maladaptive health behaviors. Those studies involved participants, working with researchers, vividly imagining future events, and writing a description that they subsequently will frequently review, to ensure a shift from an inclination towards immediate rewards. That should promote behavior change, aiding in health tasks such as treatment adherence and lifestyle modifications. The AI chatbot is designed to guide users in generating personalized EFTs, automating the current labor-intensive interview-based process. This can enhance the efficiency of EFT interventions and make them more accessible, targeting specifically those with limited educational backgrounds or communication challenges. By leveraging AI for EFT intervention, we anticipate broadened access and improved health outcomes across diverse populations

cs.HC↗

arXiv2023

Maximizing Equitable Reach and Accessibility of ETDs

This poster addresses accessibility issues of electronic theses and dissertations (ETDs) in digital libraries (DLs). ETDs are available primarily as PDF files, which present barriers to equitable access, especially for users with visual impairments, cognitive or learning disabilities, or for anyone needing more efficient and effective ways of finding relevant information within these long documents. We propose using AI techniques, including natural language processing (NLP), computer vision, and text analysis, to convert PDFs into machine-readable HTML documents with semantic tags and structure, extracting figures and tables, and generating summaries and keywords. Our goal is to increase the accessibility of ETDs and to make this important scholarship available to a wider audience.

cs.DL↗

arXiv2023

MetaEnhance: Metadata Quality Improvement for Electronic Theses and Dissertations of University Libraries

Metadata quality is crucial for digital objects to be discovered through digital library interfaces. However, due to various reasons, the metadata of digital objects often exhibits incomplete, inconsistent, and incorrect values. We investigate methods to automatically detect, correct, and canonicalize scholarly metadata, using seven key fields of electronic theses and dissertations (ETDs) as a case study. We propose MetaEnhance, a framework that utilizes state-of-the-art artificial intelligence methods to improve the quality of these fields. To evaluate MetaEnhance, we compiled a metadata quality evaluation benchmark containing 500 ETDs, by combining subsets sampled using multiple criteria. We tested MetaEnhance on this benchmark and found that the proposed methods achieved nearly perfect F1-scores in detecting errors and F1-scores in correcting errors ranging from 0.85 to 1.00 for five of seven fields.

cs.DL↗

arXiv2021

Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and Dissertations

Electronic Theses and Dissertations (ETDs) contain domain knowledge that can be used for many digital library tasks, such as analyzing citation networks and predicting research trends. Automatic metadata extraction is important to build scalable digital library search engines. Most existing methods are designed for born-digital documents, so they often fail to extract metadata from scanned documents such as for ETDs. Traditional sequence tagging methods mainly rely on text-based features. In this paper, we propose a conditional random field (CRF) model that combines text-based and visual features. To verify the robustness of our model, we extended an existing corpus and created a new ground truth corpus consisting of 500 ETD cover pages with human validated metadata. Our experiments show that CRF with visual features outperformed both a heuristic and a CRF model with only text-based features. The proposed model achieved 81.3%-96% F1 measure on seven metadata fields. The data and source code are publicly available on Google Drive (https://tinyurl.com/y8kxzwrp) and a GitHub repository (https://github.com/lamps-lab/ETDMiner/tree/master/etd_crf), respectively.

cs.DL↗

arXiv2021

ScanBank: A Benchmark Dataset for Figure Extraction from Scanned Electronic Theses and Dissertations

We focus on electronic theses and dissertations (ETDs), aiming to improve access and expand their utility, since more than 6 million are publicly available, and they constitute an important corpus to aid research and education across disciplines. The corpus is growing as new born-digital documents are included, and since millions of older theses and dissertations have been converted to digital form to be disseminated electronically in institutional repositories. In ETDs, as with other scholarly works, figures and tables can communicate a large amount of information in a concise way. Although methods have been proposed for extracting figures and tables from born-digital PDFs, they do not work well with scanned ETDs. Considering this problem, our assessment of state-of-the-art figure extraction systems is that the reason they do not function well on scanned PDFs is that they have only been trained on born-digital documents. To address this limitation, we present ScanBank, a new dataset containing 10 thousand scanned page images, manually labeled by humans as to the presence of the 3.3 thousand figures or tables found therein. We use this dataset to train a deep neural network model based on YOLOv5 to accurately extract figures and tables from scanned ETDs. We pose and answer important research questions aimed at finding better methods for figure extraction from scanned documents. One of those concerns the value for training, of data augmentation techniques applied to born-digital documents which are used to train models better suited for figure extraction from scanned documents. To the best of our knowledge, ScanBank is the first manually annotated dataset for figure and table extraction for scanned ETDs. A YOLOv5-based model, trained on ScanBank, outperforms existing comparable open-source and freely available baseline methods by a considerable margin.

cs.CV↗

arXiv2020

Differentially Private Synthetic Medical Data Generation using Convolutional GANs

Deep learning models have demonstrated superior performance in several application problems, such as image classification and speech processing. However, creating a deep learning model using health record data requires addressing certain privacy challenges that bring unique concerns to researchers working in this domain. One effective way to handle such private data issues is to generate realistic synthetic data that can provide practically acceptable data quality and correspondingly the model performance. To tackle this challenge, we develop a differentially private framework for synthetic data generation using R\'enyi differential privacy. Our approach builds on convolutional autoencoders and convolutional generative adversarial networks to preserve some of the critical characteristics of the generated synthetic data. In addition, our model can also capture the temporal information and feature correlations that might be present in the original data. We demonstrate that our model outperforms existing state-of-the-art models under the same privacy budget using several publicly available benchmark medical datasets in both supervised and unsupervised settings.

cs.LG↗

arXiv2020

On the Evaluation of Generative Adversarial Networks By Discriminative Models

Generative Adversarial Networks (GANs) can accurately model complex multi-dimensional data and generate realistic samples. However, due to their implicit estimation of data distributions, their evaluation is a challenging task. The majority of research efforts associated with tackling this issue were validated by qualitative visual evaluation. Such approaches do not generalize well beyond the image domain. Since many of those evaluation metrics are proposed and bound to the vision domain, they are difficult to apply to other domains. Quantitative measures are necessary to better guide the training and comparison of different GANs models. In this work, we leverage Siamese neural networks to propose a domain-agnostic evaluation metric: (1) with a qualitative evaluation that is consistent with human evaluation, (2) that is robust relative to common GAN issues such as mode dropping and invention, and (3) does not require any pretrained classifier. The empirical results in this paper demonstrate the superiority of this method compared to the popular Inception Score and are competitive with the FID score.

cs.CV↗

arXiv2020

Aspect Classification for Legal Depositions

Attorneys and others have a strong interest in having a digital library with suitable services (e.g., summarizing, searching, and browsing) to help them work with large corpora of legal depositions. Their needs often involve understanding the semantics of such documents. That depends in part on the role of the deponent, e.g., plaintiff, defendant, law enforcement personnel, expert, etc. In the case of tort litigation associated with property and casualty insurance claims, such as relating to an injury, it is important to know not only about liability, but also about events, accidents, physical conditions, and treatments. We hypothesize that a legal deposition consists of various aspects that are discussed as part of the deponent testimony. Accordingly, we developed an ontology of aspects in a legal deposition for accident and injury cases. Using that, we have developed a classifier that can identify portions of text for each of the aspects of interest. Doing so was complicated by the peculiarities of this genre, e.g., that deposition transcripts generally consist of data in the form of question-answer (QA) pairs. Accordingly, our automated system starts with pre-processing, and then transforms the QA pairs into a canonical form made up of declarative sentences. Classifying the declarative sentences that are generated, according to the aspect, can then help with downstream tasks such as summarization, segmentation, question-answering, and information retrieval. Our methods have achieved a classification F1 score of 0.83. Having the aspects classified with a good accuracy will help in choosing QA pairs that can be used as candidate summary sentences, and to generate an informative summary for legal professionals or insurance claim agents. Our methodology could be extended to legal depositions of other kinds, and to aid services like searching.

cs.CL↗

arXiv2020

Natural Language Processing Advancements By Deep Learning: A Survey

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a better understanding of the human language for linguistic-based human-computer communication. Recent developments in computational power and the advent of large amounts of linguistic data have heightened the need and demand for automating semantic analysis using data-driven approaches. The utilization of data-driven strategies is pervasive now due to the significant improvements demonstrated through the usage of deep learning methods in areas such as Computer Vision, Automatic Speech Recognition, and in particular, NLP. This survey categorizes and addresses the different aspects and applications of NLP that have benefited from deep learning. It covers core NLP tasks and applications and describes how deep learning methods and models advance these areas. We further analyze and compare different approaches and state-of-the-art models.

cs.CL↗

arXiv2020

CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Records

Deep learning models have demonstrated high-quality performance in areas such as image classification and speech processing. However, creating a deep learning model using electronic health record (EHR) data, requires addressing particular privacy challenges that are unique to researchers in this domain. This matter focuses attention on generating realistic synthetic data while ensuring privacy. In this paper, we propose a novel framework called correlation-capturing Generative Adversarial Network (CorGAN), to generate synthetic healthcare records. In CorGAN we utilize Convolutional Neural Networks to capture the correlations between adjacent medical features in the data representation space by combining Convolutional Generative Adversarial Networks and Convolutional Autoencoders. To demonstrate the model fidelity, we show that CorGAN generates synthetic data with performance similar to that of real data in various Machine Learning settings such as classification and prediction. We also give a privacy assessment and report on statistical analysis regarding realistic characteristics of the synthetic data. The software of this work is open-source and is available at: https://github.com/astorfi/cor-gan.

cs.LG↗