VTechWorks
VTechWorks provides global access to Virginia Tech scholarship, including journal articles, books, theses, dissertations, conference papers, slide presentations, technical reports, working papers, administrative documents, videos, images, and more by faculty, students, and staff. Faculty can deposit items to VTechWorks from Elements, including journal articles covered by the University open access policy. Email vtechworks@vt.edu for help.
Open Access Policy
Virginia Tech's open access policy enables researchers to deposit the accepted version of scholarly articles with no embargo.
Theses and Dissertations
Virginia Tech was first in the world to require ETDs in 1997, and continues to add scans of older theses and dissertations.
Open Textbooks
More than 50 freely available and openly licensed textbooks are among our most downloaded items.
Communities in VTechWorks
Select a community to browse its collections.
Recent Submissions
Librarians Engineering Our Way to Good Peer Review Feedback: An Interactive Panel on How to Avoid Being Reviewer #2
Durkin Ruth, Kelly; Carpenter, Elizabeth; Over, Sarah (2025-06-24)
Peer review is a process many of us have engaged in, whether through ASEE or other professional publishing opportunities, both as author and reviewer. Though it is a common part of our academic lives, it is not something that is often taught - there is no Peer Reviewing 101. This interactive panel will give attendees a chance to think critically about the function of peer review, engage with examples of both positive and negative peer review experiences, and thoughtfully discuss ways to improve our own understanding of peer review and methods for conducting peer review well.
Our primary audience is for librarians who participate in peer review, either as authors or as reviewers. More broadly, anyone who is in a position to give feedback or constructive criticism in their jobs, whether formally or informally, and internally through work evaluations and externally through formal peer review processes may find this session relevant.
Applying the SARAH Model to Library Change Management
Kern, Sara; Barbrow, Sarah; Dooley, Sarah Jane; Lester, Sarah; Over, Sarah; Weiss, Sarah (2026-06-21)
Academic libraries are the heart of colleges and universities and, as such, are often centrally involved in and impacted by changes in the greater higher education landscape. For those working in these spaces, familiarity with strategies for managing change is essential to support
yourself and others during especially turbulent times. The SARAH model, described below in bullet points with our modifications in brackets, offers a memorable way to understand responses to change, be they our own or others.
S - Shock or Surprise
A – Anger [Anxiety, Alert, or Anticipation]
R – Resistance or Rejection [Re-engagement, or Rationalization]
A - Acceptance
H - Help or Hope
This is not an inevitable cycle and some people may not move through the stages in a linear manner. The existing model offers structure, but our addition adds flexibility, opportunities for positive reactions, and library-specific examples and context. This model, and our modifications to it, helps individuals identify their own or others reaction to change, recognize where they might be in the process, and respond with empathy and support. By understanding feelings and reactions of ourselves and others, we can provide thoughtful and intentional support during difficult times.
United by their shared name, the Sara(h)s of ELD bring together their diverse experiences and perspectives to present the SARAH model for use in academic libraries, and particularly for those working with Engineering or STEM departments. This paper and poster will present the SARAH model alongside strategies and examples for supporting both yourself and others when managing change in a library context.
Connecting Digital Scholarly Researcher Identifiers & University Systems: Assessment of Engineering Faculty at a Major Research Institution
Over, Sarah; Yin, Mengyu; Mazure, Emily S.; Stovall, Connie; Khan, M. Shehryar; Wang, Jiren; Craig, Brian; Miles, Rachel A. (American Society for Engineering Education, 2026-06-23)
At Virginia Tech, a unit within University Libraries is responsible for supporting institution-wide efforts to improve impact and visibility of campus research. Beyond providing consultations and workshops for growing research impact, the unit evaluated and improved the research data ecosystem to ensure more complete and accurate data capture. Part of this process involved assessment of how researchers use, if at all, researcher scholarly identifiers (ID). Digital scholarly researcher identifiers, such as the ORCID iD and Scopus Author ID, are important for disambiguating researchers’ works and helping to capture accurate global impact data. This is especially critical for our institution’s College of Engineering, which contributes a large share of campus research outputs.
The researcher IDs and systems involved in this project are Scopus Author IDs, ORCID iDs, Academic Analytics, Symplectic Elements profiles, and our institution’s Banner instance (human resources records). This paper will cover the process of finding, assessing, and integrating identifiers across systems for use in research impact reporting and benchmarking, including ways this can be applied to any research institution. The College of Engineering at Virginia Tech’s status of researcher IDs will be detailed along with related efforts by the University Libraries’ unit. Some surprising results included the number of duplicate IDs and incorrect institutions associated with IDs, leading to inaccurate reporting metrics. In the future, this work will be contained within an internal university data site for assessment by department heads and other leadership to improve Virginia Tech’s use of these key researcher IDs and their corresponding metrics.
Neurosymbolic Representation Learning with Holographic Reduced Representations
Velasquez Olivera, Jhonny J. (Virginia Tech, 2026-05-11)
Artificial intelligence has dramatically impacted our modern world, as rapid advancements in model capabilities have opened opportunities for AI to be used in various aspects of our everyday lives, finding applications in fields ranging from robotics and engineering, to creative endeavors such as writing and art. However, despite the big impact of these models, and our growing reliance on them, they still largely lack many of the key facets of human intelligence, such as the ability to reason under certainty, and to leverage novel combinations of concepts that were previously encountered. This has led to an interest in developing ways to augment the powerful feature extraction capabilities of deep neural networks (DNNs) with explicit rule-based symbolic methods, a combination which is often known as a neurosymbolic AI system. Neurosymbolic methods can provide DNNs with the missing tools they need to symbolically represent and manipulate ideas, potentially enabling more rigorous reasoning while also introducing more transparency into the reasoning process itself, through interpretable reasoning traces on discrete objects. Although neurosymbolic methods hold a lot of promise to enhance the capabilities of DNNs, many implementations are application-specific and cannot be broadly applied. Thus, the ability to represent arbitrary data in a neurosymbolic fashion could be an important step towards enabling these enhanced capabilities. This thesis provides a step towards addressing this problem of neurosymbolic representation learning through a novel integration of DNNs with holographic reduced representations (HRRs), a framework for implementing symbolic processing with continuous vectors. In particular, this thesis develops an HRR method that can be applied to the general problem of disentanglement, that focuses on separating the factors of variation in a dataset, something which comes naturally to humans. Whereas prior works approached the problem with fully neural implementations, it is shown that this neurosymbolic approach is able to create disentangled representations of data that produce qualitative and quantitative results that outperform many prior baselines. The empirical findings are complemented with an information-theoretic analysis of the proposed method. Additionally, it is shown that the process produces approximately independent symbol-value pairs (also called slots) and a per-slot capacity bound that quantifies how many distinct symbolic concepts the representation can reliably encode is also derived, providing a quantitative account of the inductive bias that leads to disentanglement. The disentangled representations produced in this process differ from other autoencoder based models in that the individual latent units are vectors themselves, which are summed together to form the whole representation, differing from the paradigm of latent units behaving as scalar dimensions of low dimensional vectors. It is shown that this distributed type of representation is more robust to noise than other disentangled representations and can maintain good reconstruction performance across a range of signal-to-noise rations (SNRs), while simultaneously gracefully degrading in the recoverable semantic content. These findings on robustness are also complemented with a quantification of how Gaussian noise affects the bit error rate of a separate family of symbolic vectors which use binary entries. In a nutshell, the results of this thesis show that the use of vector symbolic architectures (VSA), such as HRRs, may hold a promising potential for representation learning, paving the way toward exploring new ways in which the symbolic benefits of VSAs can be used to represent data with DNNs.
The Turk Is Mechanical, and Yet He Plays: Modeling AIs as Testifiers in Social Epistemology
Bell, Ian (Virginia Tech, 2026-07-07)
Advances in artificial intelligence (AI) have brought AI systems new salience in our epistemic lives: in many situations, AI systems can act much like human experts, advisors, and epistemic peers. Because AIs function like human beings in some epistemic contexts, there are AI analogs to traditional social epistemological questions in many domains. For instance, just as we face the problem of deciding which human experts to trust among many contenders, we must decide which AI pseudoexperts to rely on.
This situation poses a conundrum for social epistemology because most of the socioepistemological concepts and vocabulary we would use to articulate these questions are anthropocentric. But AI systems do not necessarily fit anthropocentric definitions. Testimony, for example, is usually defined as requiring communicative intentions on the part of the testifier, but present-day AIs probably lack such mental states. How, then, should we proceed with the social epistemology of AI when it implicates anthropocentric concepts?
In this thesis, I examine two possible stances on this question and their consequences for social epistemology. I first examine what I call the Revisionary Stance, the latent stance on this question in existing socio-epistemological work (Freiman 2024; Hauswald 2025b; Shin 2026) that revises existing concepts to suit AI or invents new, AI-specific ones. I argue that the Revisionary Stance does not do much to help us solve important questions in AI social epistemology, and propose the Modeling as a Testifier (MAT) stance as a superior alternative. MAT involves modeling, or treating AIs as if they were human testifiers, to answer important socio-epistemological questions. This solves the problem of anthropocentrism by confining anthropocentric terminology, which is inapplicable to AI, to model-talk.
After providing an account of the epistemology of MAT via Mary Hesse's account of analogies and analogical modeling in science (1966), I demonstrate MAT's practicality by showing how we can understand some recent work by Amber Ross on appropriate deference to opaque AI systems as an instance of employing MAT to reason about AI (2024). Finally, I respond to some objections and provide suggestions for the further development of MAT.