Shawn M. Jones

Shawn M. Jones, Diane Oyen

Much computer vision research has focused on natural images, but technical documents typically consist of abstract images, such as charts, drawings, diagrams, and schematics. How well do general web search engines discover abstract images? Recent advancements in computer vision and machine learning have led to the rise of reverse image search engines. Where conventional search engines accept a text query and return a set of document results, including images, a reverse image search accepts an image as a query and returns a set of images as results. This paper evaluates how well common reverse image search engines discover abstract images. We conducted an experiment leveraging images from Wikimedia Commons, a website known to be well indexed by Baidu, Bing, Google, and Yandex. We measure how difficult an image is to find again (retrievability), what percentage of images returned are relevant (precision), and the average number of results a visitor must review before finding the submitted image (mean reciprocal rank). When trying to discover the same image again among similar images, Yandex performs best. When searching for pages containing a specific image, Google and Yandex outperform the others when discovering photographs with precision scores ranging from 0.8191 to 0.8297, respectively. In both of these cases, Google and Yandex perform better with natural images than with abstract ones achieving a difference in retrievability as high as 54\% between images in these categories. These results affect anyone applying common web search engines to search for technical documents that use abstract images.

Kehinde Ajayi, Xin Wei, Martin Gryder et al.

Recent advances in computer vision (CV) and natural language processing have been driven by exploiting big data on practical applications. However, these research fields are still limited by the sheer volume, versatility, and diversity of the available datasets. CV tasks, such as image captioning, which has primarily been carried out on natural images, still struggle to produce accurate and meaningful captions on sketched images often included in scientific and technical documents. The advancement of other tasks such as 3D reconstruction from 2D images requires larger datasets with multiple viewpoints. We introduce DeepPatent2, a large-scale dataset, providing more than 2.7 million technical drawings with 132,890 object names and 22,394 viewpoints extracted from 14 years of US design patent documents. We demonstrate the usefulness of DeepPatent2 with conceptual captioning. We further provide the potential usefulness of our dataset to facilitate other research areas such as 3D image reconstruction and image retrieval.

Shawn M. Jones, Diane Oyen

Understanding the spread of images across the web helps us understand the reuse of scientific visualizations and their relationship with the public. The "Flatten the Curve" graphic was heavily used during the COVID-19 pandemic to convey a complex concept in a simple form. It displays two curves comparing the impact on case loads for medical facilities if the populace either adopts or fails to adopt protective measures during a pandemic. We use five variants of the "Flatten the Curve" image as a case study for viewing the spread of an image online. To evaluate its spread, we leverage three information channels: reverse image search engines, social media, and web archives. Reverse image searches give us a current view into image reuse. Social media helps us understand a variant's popularity over time. Web archives help us see when it was preserved, highlighting a view of popularity for future researchers. Our case study leverages document URLs can be used as a proxy for images when studying the spread of images online.

Nishath Rajiv Ranasinghe, Shawn M. Jones, Michal Kucer et al.

Large Language Models (LLMs) are increasingly being leveraged for generating and translating scientific computer codes by both domain-experts and non-domain experts. Fortran has served as one of the go to programming languages in legacy high-performance computing (HPC) for scientific discoveries. Despite growing adoption, LLM-based code translation of legacy code-bases has not been thoroughly assessed or quantified for its usability. Here, we studied the applicability of LLM-based translation of Fortran to C++ as a step towards building an agentic-workflow using open-weight LLMs on two different computational platforms. We statistically quantified the compilation accuracy of the translated C++ codes, measured the similarity of the LLM translated code to the human translated C++ code, and statistically quantified the output similarity of the Fortran to C++ translation.

Shawn M. Jones, Michele C. Weigle, Martin Klein et al.

To allow previewing a web page, social media platforms have developed social cards: visualizations consisting of vital information about the underlying resource. At a minimum, social cards often include features such as the web resource's title, text summary, striking image, and domain name. News and scholarly articles on the web are frequently subject to social card creation when being shared on social media. However, we noticed that not all web resources offer sufficient metadata elements to enable appealing social cards. For example, the COVID-19 emergency has made it clear that scholarly articles, in particular, are at an aesthetic disadvantage in social media platforms when compared to their often more flashy disinformation rivals. Also, social cards are often not generated correctly for archived web resources, including pages that lack or predate standards for specifying striking images. With these observations, we are motivated to quantify the levels of inclusion of required metadata in web resources, its evolution over time for archived resources, and create and evaluate an algorithm to automatically select a striking image for social cards. We find that more than 40% of archived news articles sampled from the NEWSROOM dataset and 22% of scholarly articles sampled from the PubMed Central dataset fail to supply striking images. We demonstrate that we can automatically predict the striking image with a Precision@1 of 0.83 for news articles from NEWSROOM and 0.78 for scholarly articles from the open access journal PLOS ONE.

Shawn M. Jones, Alexander C. Nwala, Martin Klein et al.

Tools such as Google News and Flipboard exist to convey daily news, but what about the past? In this paper, we describe how to combine several existing tools with web archive holdings to perform news analysis and visualization of the "biggest story" for a given date. StoryGraph clusters news articles together to identify a common news story. Hypercane leverages ArchiveNow to store URLs produced by StoryGraph in web archives. Hypercane analyzes these URLs to identify the most common terms, entities, and highest quality images for social media storytelling. Raintale then uses the output of these tools to produce a visualization of the news story for a given day. We name this process SHARI (StoryGraph Hypercane ArchiveNow Raintale Integration).

Shawn M. Jones, Martin Klein, Michele C. Weigle et al.

For traditional library collections, archivists can select a representative sample from a collection and display it in a featured physical or digital library space. Web archive collections may consist of thousands of archived pages, or mementos. How should an archivist display this sample to drive visitors to their collection? Search engines and social media platforms often represent web pages as cards consisting of text snippets, titles, and images. Web storytelling is a popular method for grouping these cards in order to summarize a topic. Unfortunately, social media platforms are not archive-aware and fail to consistently create a good experience for mementos. They also allow no UI alterations for their cards. Thus, we created MementoEmbed to generate cards for individual mementos and Raintale for creating entire stories that archivists can export to a variety of formats.

Shawn M. Jones, Michele C. Weigle, Michael L. Nelson

Used by a variety of researchers, web archive collections have become invaluable sources of evidence. If a researcher is presented with a web archive collection that they did not create, how do they know what is inside so that they can use it for their own research? Search engine results and social media links are represented as surrogates, small easily digestible summaries of the underlying page. Search engines and social media have a different focus, and hence produce different surrogates than web archives. Search engine surrogates help a user answer the question "Will this link meet my information need?" Social media surrogates help a user decide "Should I click on this?" Our use case is subtly different. We hypothesize that groups of surrogates together are useful for summarizing a collection. We want to help users answer the question of "What does the underlying collection contain?" But which surrogate should we use? With Mechanical Turk participants, we evaluate six different surrogate types against each other. We find that the type of surrogate does not influence the time to complete the task we presented the participants. Of particular interest are social cards, surrogates typically found on social media, and browser thumbnails, screen captures of web pages rendered in a browser. At $p=0.0569$, and $p=0.0770$, respectively, we find that social cards and social cards paired side-by-side with browser thumbnails probably provide better collection understanding than the surrogates currently used by the popular Archive-It web archiving platform. We measure user interactions with each surrogate and find that users interact with social cards less than other types. The results of this study have implications for our web archive summarization work, live web curation platforms, social media, and more.

Shawn M. Jones, Michele C. Weigle, Michael L. Nelson

Web archive collections are created with a particular purpose in mind. A curator selects seeds, or original resources, which are then captured by an archiving system and stored as archived web pages, or mementos. The systems that build web archive collections are often configured to revisit the same original resource multiple times. This is incredibly useful for understanding an unfolding news story or the evolution of an organization. Unfortunately, over time, some of these original resources can go off-topic and no longer suit the purpose for which the collection was originally created. They can go off-topic due to web site redesigns, changes in domain ownership, financial issues, hacking, technical problems, or because their content has moved on from the original topic. Even though they are off-topic, the archiving system will still capture them, thus it becomes imperative to anyone performing research on these collections to identify these off-topic mementos. Hence, we present the Off-Topic Memento Toolkit, which allows users to detect off-topic mementos within web archive collections. The mementos identified by this toolkit can then be separately removed from a collection or merely excluded from downstream analysis. The following similarity measures are available: byte count, word count, cosine similarity, Jaccard distance, Sørensen-Dice distance, Simhash using raw text content, Simhash using term frequency, and Latent Semantic Indexing via the gensim library. We document the implementation of each of these similarity measures. We possess a gold standard dataset generated by manual analysis, which contains both off-topic and on-topic mementos. Using this gold standard dataset, we establish a default threshold corresponding to the best F1 score for each measure. We also provide an overview of potential future directions that the toolkit may take.