CS4624: Multimedia, Hypertext, and Information Access

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This collection contains the final projects of the students in in the course Computer Science 4624: Multimedia, Hypertext, and Information Access, at Virginia Tech. This course, taught by Professor Ed Fox, is part of the Human-Computer Interaction track, the Knowledge, Information, and Data track, and the Media/Creative Computing track. The curriculum introduces the architectures, concepts, data, hardware, methods, models, software, standards, structures, technologies, and issues involved with: networked multimedia (e.g., image, audio, video) information, access and systems; hypertext and hypermedia; electronic publishing; virtual reality. Coverage includes text processing, search, retrieval, browsing, time-based performance, synchronization, quality of service, video conferencing and authoring.


Recent Submissions

Now showing 1 - 20 of 269
  • Parking Spaces Occupancy Prediction
    Across Virginia Tech’s campus, finding parking is consistently a source of frustration for students and faculty. During peak hours, locating free parking spots becomes a challenging task; leading to significant delays and increased traffic around campus. Leveraging modern data-driven technologies such as Smart City infrastructure and Intelligent Transportation, we can alleviate some of the school’s congestion and enhance the parking experience for Virginia Tech residents. The proposed solution is a web app that users can integrate into their daily commute. With the help of live data, the app will give real-time parking recommendations as well various other helpful insights. It will analyze the live data at each of the garages, to predict the occupancy of the garages at a given time of arrival. Machine learning will allow us to estimate the occupancy of each of the garages a given time into the future, depending on the distance to each garage, and provide a recommendation for which garage to target. The application will also allow for more effective collection of data for parking services and could eventually take into account more factors such as schedules and live traffic.
  • Visualizing eTextbook Study Sessions
    Lily Chiang, Arjun Vellanki, Egor Lukiyanov, Tuan Chau, Kavya Polina (2023-11-30)
    OpenDSA is an online platform that allows professors to create e-Textbooks with fundamental CS courses. Our project seeks to enhance OpenDSA by providing instructors with a user-friendly web interface and visualization tool. This tool allows them to understand student interactions during study sessions, in the areas of: Reading, Visualizations, and Exercises. The tool could lead to improvements in a student’s learning process. OpenDSA is heavily used at Virginia Tech and other universities for CS courses. It records student interactions with learning materials but doesn’t have an efficient way for instructors to understand these interactions. Our project tackles this issue by developing a web interface that visualizes student interactions. We expand upon past research by sorting interactions into Reading, Visualizations, and Exercises, displaying detailed study session data. These visualizations will give insight into whether students are active learners or credit-seekers.
  • Mass Shooting Digital Library
    In light of the escalating prevalence of mass shootings in the U.S., there is an urgent need for a structured digital repository to centralize, categorize, and offer detailed analyses of these events. This project aims to develop a comprehensive website functioning as a digital library. This library will house mass shooting objects where each object symbolizes a specific mass shooting event, elaborating on who, what, when, where, why, and how. The website's central features will include the ability to visualize and compare various mass shooting incidents, facilitating a broader understanding of trends, patterns, and anomalies. Users will be able to explore the data via geographic visualizations, timelines, and more, providing an immersive and informative experience. Underpinning the platform, our backend system will utilize Python, Flask, and MongoDB, ensuring robust data collection and management. This data includes information fields, URL sources associated with each event, and more. On the front end, technologies like NextJS, React, and Javascript will drive the user interface, supported by essential libraries such as React Chrono and Leaflet.js for advanced visualization. Deployment will be executed via Firebase or AWS for the frontend and Heroku for the backend. Two primary user categories have been identified: general users, who can view the data, and administrators, who can modify the contents. Ensuring the integrity of the data input, admin access will be safeguarded by authentication processes. In summary, this digital library emerges as a timely and crucial initiative in response to the rising tide of mass shootings in the U.S. This project aims to provide comprehensive insights into the tragic events that have marked the nation. Beyond its functional capabilities, the digital library strives to improve understanding, awareness, and ultimately, change in the narrative surrounding mass shootings.
  • Automated Students' short answers assessment
    Padath, Mathew; Wang, Wenmiao; Jiang, Westin; McGovern, Ryan; Wan, Yifei (2023-11-26)
    The objective of this innovative project was to create an automated web application for the assessment and scoring of computer science-related short answers. This solution directly addresses the often labor-intensive and time-consuming process of manually grading written responses, a challenge that educators across various academic disciplines frequently encounter. The developed web application stands out not just for its efficiency but also for its versatility, being applicable to a wide range of subjects beyond computer science, provided that appropriate teacher answer files are supplied. At the heart of the application lies a user-friendly interface created using ReactJS. This frontend allows educators to seamlessly upload 'teacher' and 'student' files in .tsv format. Following the upload, the application's backend, developed using Flask, takes over. It processes these submissions by comparing student responses against predefined model answers. The scoring mechanism of the application is particularly noteworthy. It employs an advanced semantic analysis approach, utilizing a pre-existing deep learning model, RoBERTa Large. This model is integral to the AutoGrader class, which is responsible for the semantic evaluation of the text. The grading logic embedded within the AutoGrader class is both innovative and sophisticated. It assesses student responses by breaking them down into phrases and then computing the semantic similarity between each phrase and the concepts outlined in the model answers. The process employs SentenceTransformer to generate text embeddings, allowing for a nuanced evaluation based on cosine similarity between vector representations. This method ensures a grading system that transcends simple keyword matching, delving into the semantic content and understanding of the student answers. The application boasts several key features that enhance user experience and provide educators with comprehensive insights into student performance. These include the ability to display scores and grades directly on the web application, download detailed Grade Reports that include each question, student's response, the grade awarded, and the model answer. Additionally, the application allows for the viewing of previous submissions and the downloading of historical documents such as past versions of 'teacher file', 'student file', and grade reports. In terms of future development, the project team has outlined several ambitious goals. These include implementing a dataset-driven strategy for enhancing the training of deep learning models, thereby significantly advancing the current framework. Another focus will be on allowing for a variety of file types to be uploaded for both teacher and student files, thereby increasing the accessibility and usability of the system. Lastly, there are plans to update the functionality and appearance of the web application, incorporating features such as scrolling, standardized formatting, and improved design elements to enhance the overall user experience. The project was developed with the invaluable guidance and support of Dr. Mohamed Farag, a research associate at the Center for Sustainable Mobility at Virginia Tech. Dr. Farag's expertise in computer science and his commitment to educational innovation have been instrumental in steering the project towards success. In conclusion, this project marks a significant advancement in the field of educational technology, particularly in the realm of academic grading. By leveraging the power of artificial intelligence and modern web technologies, it provides an efficient, reliable, and versatile tool for educators, streamlining the grading process and offering a scalable solution adaptable to various academic contexts. The future developments outlined promise to further enhance the capabilities of this already impressive tool, pointing towards a new era in academic assessment.
  • Automated Crisis Collection Builder - Final Project Report
    Brian Hays; Alex Zhang; Mitchel Rifae; Trevor Kappauf; Parsa Nikpour (2023-11-30)
    In the contemporary digital landscape, access to timely and relevant information during crisis events is crucial for effective decision-making and response coordination. This project addresses the need for a specialized web application equipped with a sophisticated crawler system to streamline the process of collecting pertinent information related to a user-specified crisis event. The inherent challenge lies in the vast and dynamic nature of online content, where identifying and extracting valuable data from a multitude of sources can be overwhelming. This project aims to empower users by allowing them to input a list of newline-delimited URLs associated with the crisis at hand. The embedded crawler software then systematically traverses these URLs, extracting additional outgoing links for further exploration. Afterwards, the contents of each outgoing URL is then run through a predict function, which evaluates the relevance of each URL based on a scoring system ranging from 0 to 1. This scoring mechanism serves as a critical filter, ensuring that the collected web pages are not only related to the specified crisis event but also possess a significant degree of pertinence. We allow the user to set these thresholds, which enhances the efficiency of information retrieval by prioritizing content most likely to be valuable to the user's needs. Throughout the crawling process, our system tracks a range of statistics, including individual website domains, the origin of each child URL, and the average score assigned to each domain. To provide users with a comprehensive and visually intuitive experience, our user interface leverages React and D3 to display these statistics effectively. Moreover, to enhance user engagement and customization, our platform allows users to create individual accounts. This feature not only provides a personalized experience but also grants users access to a historical record of every crawl they have executed. Users are further empowered with the ability to effortlessly export or delete any of their previous crawls based on their preferences. In terms of deliverables, our project commits to providing fully developed code encompassing both frontend and backend components. Complementing this, we will furnish comprehensive user and developer manuals, facilitating seamless continuity for future students or developers who may build upon our work. Additionally, our final deliverables include a detailed report and a compelling presentation, serving the dual purpose of showcasing our team's progress across various project stages and providing insights into the functionalities and outcomes achieved.
  • Crisis Events One-Class Text Classification
    Caleb McIrvin (2023-11-30)
    Analyzing web articles related to crisis events can help social scientists gauge public sentiment and form public policy around how to react to such disasters. However, data collection for such tasks is difficult. Manual dataset curation is time-consuming and costly, as a user needs to use some sort of search engine to iterate through multiple web pages, painstakingly analyzing each document thoroughly to determine the crisis events it may be related to. Automated processes, however, such as web crawlers, operate primarily via rule-based methods, which may not accurately classify individual documents as being related to the crisis event of interest. In our work, we seek to use machine learning techniques to determine whether individual documents are related to a specific crisis event using natural language processing techniques. To accomplish this, we treat the area of interest as a single class, and consider all other topics as not being of interest. We hypothesize that natural language processing techniques can be used to to classify a particular webpage as being relevant to a certain crisis. A potential motivation for this approach is to guide efficient web crawling using techniques from semantic analysis.
  • Practice 10k: Music App
    Georgiev, Alexander; McKelway, Bailey; Aneja, Rahul; Holmes, Claire; Zemui, Mahlet (Virginia Tech, 2023-11-30)
    In the world of music education, inspiring students to maintain consistent and effective practice routines has long been a challenge. Recognizing this dilemma, our clients embarked on a journey to leverage technology and reimagine the practice experience for budding musicians. The result of their efforts was a music application that aims to revolutionize the way musicians approach their practice routines by addressing both convenience and motivation. The innovative concept offers users a number of features that enhance their practice sessions, monitor their progress, and make the entire experience more engaging. Among its many functionalities, the application allows users to plan practice sessions and initiate them with the aid of a built-in timer to track their practice duration. Moreover, the application presents users with visualization representations of their progress on a daily, weekly, monthly, and overall basis through diverse graphs, each highlighting distinct aspects of their practice habits. Additionally, users can delve into a journal-like feature in the application, allowing them to explore and reflect on their musical journey, drawing insights from past practice sessions. To address the core functionalities mentioned above, our project relies on the integration of Firebase for user authentication and backend data storage, coupled with React Native to ensure cross-platform compatibility in the frontend. This framework facilitates effective communication between the backend and frontend, enabling the exchange of user-related data in order to meet the clients’ requirements within the application. This is notably exemplified by our organization of user information in the backend, utilizing specific collections for swift reading and writing of data as users engage with the application. That being said, as we reflect on the culmination of this semester-long project, it is evident that overcoming challenges and seizing opportunities has been instrumental in our gaining invaluable experience in both client collaboration and implementing diverse solutions. However, acknowledging the iterative nature of application development, we understand the ongoing need for refining the existing features and incorporating new ones in future development.
  • Traffic Simulator Input/Output GUI
    This project aims to address weaknesses in the configuration of the INTEGRATION 2.40 microscopic traffic simulation software. The project is very powerful, capable of simulating hundreds of thousands of vehicles travelling across thousands of roads, while recording a wide variety of metrics. However, the simulation software is configured by a variety of plaintext input files. These files contain newline-delimited fields, with some fields being multipart and whitespace-delimited. These fields are documented in English, with the expected type of the field, as well as other constraints such as field length or numeric field range, expressed in a table format in the documentation. Additionally, some field constraints span multiple files. For example, links in the simulation, defined in the Link File reference start and end nodes that must be defined in a separate Node File. Since there is no validation program to ensure that these input files, which can be tens to hundreds of lines long, with multiple hundreds of fields, have correct and sane values in an interactive, easily runnable format, producing and validating these files is a tedious process. The project solves the aforementioned problems by implementing a web-based interface to create, edit, manage, and validate the input files for the simulation tool. Users can upload a set of six input files, which together are defined as an input package, to the interface, which keeps track of the configured values. The fields in the input files can be edited through the interface through intuitive controls, such as text fields for text content, and drop-down menus for selections. Through the interface, users can perform automatic validation of the files. Any errors in constraint validation will then be surfaced to the user through the web interface, directing them to the appropriate field in the appropriate file for correction. Cross-file validation is also performed to ensure the input files are in a suitable form to be run by the simulation software. After editing and validating fields in the managed Package, users can opt to save the package to the server hosting the web interface, as well as download all input files as a zip package. Additionally, the web interface allows users to template traffic demand values, which is a key component of the Demand File. Users are able to parameterize and create multiple Demand Files (as well as multiple Master Files, which reference all other input files) for combinations of vehicle classes provided by the user. For example, a user might want to create separate input files where Vehicle Class 1 has a traffic demand between 0 and 0.5 (to a maximum of 1), and Vehicle Class 2 has a traffic demand between 0 and 0.2, both in increments of 0.1. This results in 6 * 3 = 18 demand files in total for each combination of the Vehicle Class 1 demand and Vehicle Class 2 demand. The web interface saves time needed to create these files manually, which can be extreme in cases with many combinations of vehicle demand classes. Our project is intended to help users of INTEGRATION 2.40 in saving their time and effort when creating input files for the simulation tool. Apart from the project implementation details, we also ensured that project infrastructure reduces user effort and maintenance. The project can be deployed on multiple platforms and can also be packaged as an easy to deploy Docker image for maximum flexibility.
  • Crisis Event LLM
    Kunal Nakka, Srikaran Bachu, Bhargava Elavarthi (2023-11-30)
    Navigating through the intricate landscape of understanding and classifying crisis events, the "Crisis Events Language Model" project embarked on a comprehensive exploration leveraging Natural Language Processing (NLP) and machine learning. With a primary focus on utilizing BERT, a powerful PreTrained Language Model, our objective was to create an adept classification system for textual data related to crisis events sourced from the web. Our methodology involved the adept use of the BeautifulSoup library in Python for web scraping, enabling the extraction of textual data from URLs associated with crisis events. This rich dataset served as the backbone for training and evaluating our models. Post-data acquisition, we fine-tuned BERT to align with our specific use case, adapting its output layer to meet our unique classification goals. This strategic modification enhanced BERT's capabilities in recognizing, interpreting, and categorizing crisis event data with precision. Simultaneously, on the front-end development front, we constructed an intuitive interface using HTML and CSS. This user-friendly interface not only facilitates the visualization of the model's outputs but also simplifies user interaction and data input. The result is a practical tool poised for deployment in real-time crisis management situations. Anticipating multiple impacts, our project positions itself to simplify the comprehension and categorization of crisis events. This functionality, tailored for decision-makers and crisis management teams, promises to be a valuable asset in the face of urgent situations. Moreover, for the participating students, the project provides a dynamic learning experience, bridging theoretical knowledge with practical applications in NLP, text classification, and transfer learning. Throughout the project's duration, team members assumed diverse roles, from web scraping and model implementation to front-end development and meticulous documentation. This collaborative effort blended skills in programming, software engineering, Python, and machine learning, ensuring a holistic approach to project development. In conclusion, our project not only serves as a testament to the technical prowess and collaboration within our team but also makes substantive contributions to the realms of crisis management and NLP. It underscores the potential of integrating machine learning and language models in crisis management, offering valuable insights and avenues for future exploration and development in this critical area.
  • Behind Density Lines: An Interface to Manually Segment Scanning Electron Microscopy Images
    Nguyen, Anthony; DiGiovanna, Luke; Siegel, John; Lin, Alex (Virginia Tech, 2023-11-30)
    SEM (Scanning Electron Microscopy) is a strong imaging technique used in many scientific domains, including materials science, biology, and nanotechnology. Researchers can use SEM to obtain high-resolution images of specimen surface morphology and topography, providing a precise glimpse of structures at the nanoscale. SEM photos reveal intricate surface details, allowing scientists to investigate the texture, shape, and size of particles, cells, or materials with incredible accuracy. Currently, manual segmentation of SEM images is an important stage in the analysis process for researchers. Manual segmentation entails painstakingly drawing and naming sections of interest within photographs, such as specific structures or particles. Researchers often trace object boundaries using sophisticated software tools built for picture processing and analysis. We built a gameified multiplayer online application allowing individual contributors to manually segment a SEM picture in real time due to the time and effort required. One important goal was to involve the next generation of scientists and researchers with a demonstration at the Virginia Tech Science Festival in November 2023. We designed a comparison score technique for a given segmentation to a reference segmentation for a specific SEM picture to provide participants with fast feedback. This enabled individuals and groups to measure their performance while also incorporating a gaming element. We now have a comprehensive understanding of how to create a full-stack project thanks to this initiative. We discovered how to leverage Amazon Web Services, such as EC2, to scale the infrastructure of our website from the backend. Through the use of Javascript frameworks and packages such as NextJS, Socket.io, and ThreeJS, we have created an intuitive user interface for group manual segmentation.
  • Chorobesity: Modern Insight Into An Enduring Epidemic
    Nguyen, Van Ha; Burnett, Sarah; Freedman, Bradley; Ganesan, Bharathi; Ravindran, Roshan (Virginia Tech, 2023-11-30)
    Health researchers are looking for all possible relationships between two health conditions, obesity and diabetes. To investigate the issue robustly, create detailed experimentation, and develop lasting solutions, the Chorobesity project presents a visual tool of the geographical relationship between obesity and diabetes for our clients to utilize in their studies. Making use of different levels of maps, as well as different color “keys”, the user can study different regions’ health condition statuses. The Chorobesity project aims to be a visual and dynamic tool that researchers can use to further their understanding of the geographical correlation between obesity and diabetes. It provides relevant data and tools for the user to easily interpret and tweak this data for their best understanding. This interactive map, in providing a snapshot of the current health profile of the United States, seeks to be an indispensable tool for policymakers, health professionals, and the general public to understand how obesity and diabetes correlate as the clients see fit.
  • Topic Modeling Toolkit
    Lin, Jiayue; Pang, Mingkai; Liu, Yulong (Virginia Tech, 2023-05-08)
    The Topic Modeling Toolkit project began with an existing text mining toolkit and aimed to enhance its functionality by incorporating cutting-edge topic modeling techniques. Specifically, BERTopic, CTM, and LDA were used to extract pertinent topics from a corpus of text documents. The resulting web-based platform provides users with a search engine, a recommendation system, and a usable interface for browsing and exploring these topics. In addition to these enhancements, our team also implemented a text-filtering framework and redesigned the user interface using Tailwind CSS. The final deliverables of the project include a fully functional website, user documentation, and an open-source toolkit that can be used to train machine learning models and support browsing and searching for various text datasets. While the current version of the toolkit includes BERTopic, CTM, and LDA, there is potential for future work to incorporate additional topic modeling methods. It is important to note that while the project originally focused on electronic theses and dissertations (ETDs), the resulting platform can be used to explore and comprehend complex subjects within any corpus of text documents. The topic modeling toolkit is available as an open-source package that users can install and use on their own computers. It is available for use and can be used to support browsing and searching for various text datasets. The intended user group for the platform includes researchers, students, and other users interested in exploring and understanding complex topics within a given corpus of text documents. The resulting topic modeling toolkit offers features that facilitate the exploration and comprehension of intricate topics within text document collections. This tool has the potential to aid researchers, students, and other users in their respective fields.
  • Classifying ETDs
    Shah, Vedant; Ramesh, Vaishali; Daniel, Reema; Gathani, Mihir D. (Virginia Tech, 2023-05-17)
    Electronic Theses and Dissertations (ETDs) are academic documents that provide an in-depth insight into an account of the research work of a graduate student and are designed to be stored in machine archives and retrieved globally. These documents contain abundant information that may be utilized by various machine learning tasks such as classification, summarization, and question-answering. However, these documents often have incomplete, incorrect, or inconsistent metadata which makes it challenging to accurately categorize these documents without manual intervention since there is no one uniform format to develop the metadata. Therefore, through the Classifying ETDs capstone project, we aim to create a gold standard classification dataset, leverage machine learning and deep learning algorithms to automatically classify ETDs with missing metadata, and develop a website to allow a user to classify an ETD with missing metadata and view already classified ETDs. The expected impact of this project is to advance information availability from long documents and eventually aid in improving long document information accessibility through regular search engines.
  • Chapter Summarization
    Peta, Manasi; Simms, Aidan; Grilli, Joe; Chokkan, Nandha (Virginia Tech, 2023-05-12)
    A thesis is the amalgamation of research that serves as the final product of a graduate student’s knowledge about the information they learned throughout their graduate research. A dissertation is a graduate student’s opportunity to present their original research that they have worked on during a doctorate program to contribute new theories, practices, or knowledge to their field. Theses and dissertations represent the culmination of research of students and therefore can be extremely long. Electronic theses and dissertations (ETDs) are the digital versions of theses and dissertations so that the research and knowledge explored can be more accessible to the world. ETDs typically contain an abstract describing the work done in the document. However, these abstracts are simply too general, which means they often don’t help readers. There is no happy medium between getting essentially no information from generic abstracts and reading through a dense paper. This is an issue on a global scale. We created chapter summaries of ETDs which aim to help readers decompose and understand the documents faster. We make use of existing machine learning summarization models, specifically Python packages and language models, to help with the summarization. Part of this project is to create a dataset we can work with to create and test our summarization model on. This summarization dataset has been created by annotating the chapters from 100 ETDs (after chapter segmentation). We want to be as diverse as possible, while also being able to pick up on patterns, which is why our ETDs are from a plethora of fields. We have implemented a data extraction pipeline that builds on work done by the Object Retrieval Code from Aman Ahuja et al. Based on this we have created a summarization framework that accepts the chapter text as input and generates chapter summaries that are integrated into the given base front-end website application. We have completed 4 summarization scripts that utilize pre-trained models from Hugging Face which intake the data extracted from the chapter and output a summary of the input data. The four models we used were BART, BigBirdPegasus, T5, and Longformer Encoder Decoder (LED). We were able to use these scripts on all the chapters that we manually segmented to get summaries of all the chapters. We organized these summaries based on what model we used to obtain them in our GitLab repository. We used these summaries to populate a database which was intended to be used for the search functionality of our frontend application. There is more about the specifics of the backend and frontend in section 6.0 Implementation. We gained a holistic understanding of working on a full-stack project. On the backend portion, we learned how to use existing libraries and resources like pandas, PDFPlumber, and WordNinja to extract and format data from an input source. We also learned how to use resources like Hugging Face to understand natural language processing models and the pros and cons of various types of models. By creating scripts to utilize such models for text summarization, we were able to learn the nuances of working with pre-trained models and understand how that can affect our product. For example, if a model was pre-trained on a massive text repository, then it had better chances of recognizing more uncommon words in ETDs. On the frontend portion, we gained experience using React and JavaScript to create a functioning website. We also learned the process of understanding, dissecting, and updating a codebase we inherited from another team. We learned how to create and populate a database in PostgreSQL (commonly referred to as Postgres).
  • Summarization Evaluation
    Goel, Harshil; Choudhary, Varun; Dhondi, Anish; Desai, Parth (2023-04-13)
    Electronic Theses and Dissertations (ETDs) are digital versions of academic papers of graduate students. ETDs are highly complicated and lengthy texts: these include multimedia elements and other forms of information. A digital library with summaries for each ETD would enable more people to explore all these texts to learn about various domains. However, most chapters of ETDs don’t have summaries so the solution was to create AI generated summaries for users across disciplines to read. Before these summaries are accessible to the public, Bipasha will find researchers in various disciplines to evaluate the summaries. Incorporating human feedback into AI-generated summaries results in improved accuracy, relevance, originality, engagement and satisfaction. Quantitative measures for evaluating AI-generated content are great but qualitative feedback is important too. Subject matter experts can detect errors and inconsistencies in these summaries: this feedback provides guidance. Our team has developed a website that enables users to view and rank AI-generated summaries of texts against the ground truth (provided) chapter summaries. The users will not know beforehand which is which. The scholars (those with an education level of graduate school and beyond) should be able to accurately evaluate the summaries for the ETDs in their field. The purpose of this project is to allow human evaluation of these texts. The ranking feature serves as a form of feedback to perfect the AI generated summaries. The domain experts will use this website to determine the model that serves as a gold standard for summarization. For now, the intended users for this application are subject matter experts at Virginia Tech, so that they can evaluate the summaries. The evaluation will provide an idea of which model performs best to serve as a gold standard for AI generated summaries. Eventually, the final model will be used to serve users outside of Virginia Tech who want to know more about the domain that the ETD is associated with. Providing these summaries allows those outside the domain to grasp the basic concept of the ETD and its associated chapters without having to read the entire paper.
  • Object Detection and Document Accessibility
    Devera, Alan; Nader, Michael; Zhang, Zehua; Keegan, Elizabeth; Gunn, Theodore; Nguyen, Gabrielle; Wevley, Luke (Virginia Tech, 2023-05-10)
    Electronic Theses and Dissertations (ETDs) are the primary way that students and professors write down and report their degree research. They allow new minds to understand where that field of study was left off, and how to continue the work that has been left. However, since many of the ETDs uploaded onto the internet are presented via PDF, it's difficult for users to view these ETDs in an effective manner, especially when you consider potential students with disabilities such as visual impairments. The goal of this project was to extend upon the previous work that has been done to make a Flask-based web application so that we can transform these long documents into something much more readable, user-friendly, and accessible via HTML rather than PDF. Also, our goal was to apply an algorithm to the returned bounding boxes that come from the object detection model to make sure that separate paragraphs and references are placed into their own box for correct XML generation on the website. To make the application's UI usable, we have applied a few changes to improve the experience. We have created the option for users to download the paper via PDF or XML, have a side-bar on the left of the website that contains a dynamic table of contents to jump to whatever part of the paper you select, and have a side-bar view on the right of the website that contains the original PDF so that any errors in our application don't ruin the user's understanding. We plan for future contributors to add a dark mode and dyslexic-friendly font. Lots of accessibility features will be added via HTML/CSS/React through improving the UI, but what's also included is the option to use an on-screen reader. Our project focuses on using NVDA, a popular screen reader, to allow for users with potential visual impairments to be able to listen along to the ETD instead. This was studied thoroughly throughout the course of this project. Finally, for the algorithms side of the project, the focus has been to improve upon the returned bounding boxes from the object detection models to separate paragraph and reference bounding boxes to only include one paragraph or one reference per box. The object detection models do the best they can for the amount of training they've received, but errors are still possible. This side of the project focused on fixing those errors from the model to make sure that the XML generation works well and the text is readable on our final application. The algorithms team was able to get a good post-processing algorithm to work for around 90% of the paragraphs in the ETDs that were tested, but were unable to get to the references part of the deliverable. This is left for future collaborators.
  • ETD Recommendation System
    Blakemore, Talia; Phan, Long (Virginia Tech, 2023-05-11)
    Our project involves expanding upon a previous recommendation system built by CS 5604 students. Previous CS 5604 teams have created a chapter summarization model to generate summaries for over 5000 Electronic Theses and Dissertations (ETDs). We used these summaries to fuel our recommendation system. Using chapter summaries improved our ability to predict resources that a user may be interested in because we narrowed our focus to individual chapters rather than the abstract of the whole paper. Authors will benefit from this recommendation system because their work will be more accessible. We provide a web page for users to explore how different clustering algorithms impact the search results, giving the user the ability to modify parameters such as the number of clusters and minimum cluster size. This web page will appeal to niche users interested in experimenting with recommendation systems, allowing them to fine-tune the recommendation results. We recommend for future work to continue exploring different clustering algorithms, as well as using our chapter recommendations to fuel a recommendation list based on each chapter. During this project, we learned about clustering algorithms, working as a team, and starting a project from the ground up. A previous CS5604 team built a stand-alone website that supports search, a recommendation system, and the ability to experiment with different search methods. During this semester, we expanded upon the existing website, using clustering algorithms to experiment with the recommendation system. Users may specify different parameters to understand how different clustering algorithms may change the recommendations.
  • Marine Blender
    Shaffer, Zachary; Macht, Henry; Shirazi, Adrian; Campbell, Mitchell (Virginia Tech, 2023-05-17)
    A model of a realistic marine environment is needed for training a rugged, onboard optical sensor designed by Cell Matrix Corporation, a VTCRC COgro member (i.e., a small company in Virginia Tech's Corporate Research Center), a project led by Dr. Peter Athanas, an ECE professor at Virginia Tech. This will be accomplished within Blender, a free and open 3D modeling and rendering tool. The chosen environment is the intercoastal waters of the Palm Beach Inlet in Florida, between the Port of Palm Beach and the Inlet, approaching the Inlet from the south side of Peanut Island. This active inlet and port area gives the scene of the Blender model. To build an accurate representation of the specified area we will construct a terrain model for the Palm Beach Inlet water area from the Port of Palm Beach to the Inlet, including where the Intercoastal Waterway channel meets the Inlet channel, south of Peanut Island. This covers the surrounding islands and land masses, bridges, and large structures. There will also be roughly five types of boats to model (i.e., yachts, sailboats, mega-yachts, cargo ships, fishing boats, and other boats commonly found in the area), to represent different situations. Different looking classes of boats are needed to train the marine sensor to recognize them, so we choose different classes and create or find-and-customize a model for a boat from each class. The team will be provided with the trajectories of individual boats traveling this area from AIS ship tracking data published by the US Coast Guard. To simulate these realistic situations we have written a Blender script that allows boats to transit along these AIS tracks. The renders we created from our blender project are representations of the Palm Beach Inlet water area, and will hopefully serve as a useful resource for AI model training.
  • SharkPulseApp
    Pham, Khanh; Lemus, Catalina; Ansari, Mohammed Al (2023-05-15)
    The team was required to redesign, update, and implement changes to an existing website called SharkPulse for a project focused on monitoring global wild shark populations. Currently, the project is led by Dr. Francesco Feretti, an Assistant Professor for the Department of Fish and Wildlife Conservation. The website was built using WordPress with frontend CSS and HTML, and the backend support was provided by PHP, Javascript, and RShiny to implement the Validation Monitor Page. Our team’s primary objectives were to convert the static framework of the website to a dynamic, responsive one. Additionally, we aimed to convert the Instagram monitor from a PHP script to a R Shiny app and merge the Instagram and Flickr monitors into a single page, which would allow toggling between the two pipelines. The Validation Monitor is a user interface that validates records collected from Flickr and Instagram (from the data_mining and Instagram tables). Since shark photos are primarily collected from social media platforms like Instagram and Flickr, the accuracy of the shark information may not be completely reliable. To address this issue, a form is provided with each picture of the shark for users to fill in, in order to validate the originality, type, pieces, and location of the shark. However, the Monitor's user interface is currently not responsive, dynamic, or easily navigable by users. By the end of the semester, we successfully combined two versatile pipelines on one page and redesigned both the Validation Form for Instagram and the Flickr Pipeline. Additionally, we added a feature for common name and scientific species autocompletion, supporting users in filling out the Validation form more easily. Furthermore, the map in the Instagram validation form now functions properly, showing the current location of the shark marker and allowing users to search for another location by name or coordinates, helping validate the shark's accurate location.
  • CS 3604 Case Study Library III
    Shivaraman, Thrilok; Ouzhinski, Theo; Denman, William; Geibel, Katie (Virginia Tech, 2023-05-14)
    This submission describes the CS 3604: Professionalism in Computing Case Study Library, a Library coordinated by our client Dr. Dan Dunlap, that contains the recent case studies written by students in the class. The Case Study Library website provides a platform through which these case studies can be viewed. This was the third group to work on the Library, and the current Library allows for student case study upload, searching, and filtering by course topic. However, upload was through one admin account given to all students provided by the teacher. This meant once a student uploaded, they could not go back to edit their submission as there was no way to link users to uploads. Additionally, the interactivity of the website was limited. The first goal of this iteration was to implement login functionality in a manner so that students can log in using their Virginia Tech accounts. This enables us to link users with their uploads and thereby allows them to edit. In order to improve the interactivity of the site, metadata fields will be added for tags and liking. When uploading, students will be able to select various tags from a bank of options that pertain to the subject of their case study, which can later be used for sorting. When viewing case studies, website users will be able to like a submission, and the number of likes on each submission will be stored which can be later used for a recommended page. Our work will increase the opportunity for interaction with users of the website, allowing students to better search for case studies by topic, and to like the studies that others upload. Currently, all of the features that the group attempted to create are working and present, but upload is still not working due to the bucket pointing to the wrong place. The group worked together to build these features as requested by the client, and had to go through a few refactors of the goals in order to reach reasonable milestones over the course of the semester.