Sherri Weitl-Harms

@creighton.edu

Associate Professor, Computer Science, Design, & Journalism
Creighton University

Sherri Weitl-Harms


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https://researchid.co/sherriweitlharms
Sherri Weitl-Harms is an Associate Professor of Computer Science at Creighton University. In addition to several years of industry experience, she has been in academia for more than twenty-five years and served as chair of the Cyber Systems department at the University of Nebraska at Kearney for 12 years. Her research areas include machine learning/artificial intelligence, natural language processing, CS education, gamification, and spatial-temporal data mining. She has numerous peer reviewed publications and has been involved with over $12 million in research grants. Dr. Weitl-Harms is heavily involved in undergraduate research, serves as a councilor of Math/CS for the Council for Undergraduate Research, and is actively committed to service learning, with student projects that have aided hundreds of local, regional, and national organizations. She is a program committee member and reviewer for several professional conferences/journals.

EDUCATION

• Ph.D., Computer Engineering & Computer Science, University of Missouri – Columbia, 2002
Cumulative GPA: 4.00/4.00. Dissertation: Associating and Predicting Episodes in Multiple Time Series for Supporting Policy Decision Making.
• MS, Iowa State University, 1990. Major: Computer Science
Thesis: Comparison of Nested Query Performance of Four Relational Database Management Systems.
• BS, Buena Vista University, 1987. Major: Computer Science/Math/Education; Summa Cum Laude
Iowa Teacher’s Certificate 234534, Endorsement in Secondary Education, Mathematics, 1987-1997.

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Information Systems and Management
19

Scopus Publications

922

Scholar Citations

14

Scholar h-index

15

Scholar i10-index

Scopus Publications

  • Toward Automated Knowledge Discovery in Case-Based Reasoning
    Proceedings of the International Florida Artificial Intelligence Research Society Conference Flairs, 2024
  • Iterative Service-Learning: A Computing-Based Case-study Applied to Small Rural Organizations
    Sherri Weitl-Harms
    Proceedings Frontiers in Education Conference Fie, 2024
    This innovative practice full paper describes the iterative use of service learning to develop, review, and improve computing-based artifacts for small rural organizations, over an extended period. It is well- known that computing students benefit from service-learning experiences as do the community partners. It is also well-known that computing artifacts rarely function well long-term without versioning and updates. Service-learning projects are often one-time engagements, completed by single teams of students over the course of a semester or year long course. This limits the benefit for the community partners, such as small rural organizations, that do not have the expertise or resources to review and update a project on their own. Over the course of several years, teams of undergraduate students in a computing capstone social media development course created tailored social media plans for numerous small rural organizations. The projects were required to meet the client's specific needs, with identified audiences, measurable goals, and a minimum of three recommended social media strategies and tactics to reach the identified goals. This paper builds on previously reported initial results for 60 projects conducted over several years. Nine clients were selected to participate in the iterative follow-up process, where new student teams conducted client interviews, reviewed the initial plans, and analyzed metrics from the social media strategies and tactics already in place to provide updated, improved artifacts. Using ABET computing learning objectives as a basis, clients reviewed the student teams and the artifacts created. Students also reflected on their experiences. This research provides a longitudinal study of the impact of the interventions in increasing implementation and sustained use rates of computing artifacts developed through service learning, along with lessons learned. Both students and clients reported high satisfaction levels, and clients were particularly satisfied with the iterative improvement process. This research demonstrates an innovative practice for creating and maintaining computing artifacts through iterative service learning, while addressing the resource constraints of small rural organizations.
  • Using LLMs to Establish Implicit User Sentiment of Software Desirability
    Sherri Weitl-Harms, John D. Hastings, Jonah Lum
    Proceedings 2024 International Conference on Machine Learning and Applications Icmla 2024, 2024
    This study explores the use of LLMs for providing quantitative zero-shot sentiment analysis of implicit software desirability, addressing a critical challenge in product evaluation where traditional review scores, though convenient, fail to capture the richness of qualitative user feedback. Innovations include establishing a method that 1) works with qualitative user experience data without the need for explicit review scores, 2) focuses on implicit user satisfaction, and 3) provides scaled numerical sentiment analysis, offering a more nuanced understanding of user sentiment, instead of simply classifying sentiment as positive, neutral, or negative. Data is collected using the Microsoft Product Desirability Toolkit (PDT), a well-known qualitative user experience analysis tool. For initial exploration, the PDT metric was given to users of two software systems. PDT data was fed through several LLMs (Claude Sonnet 3 and 3.5, GPT4, and GPT4o) and through a leading transfer learning technique, Twitter-Roberta-Base-Sentiment, and Vader, a leading sentiment analysis tool. Each system was asked to evaluate the data in two ways, by looking at the sentiment expressed in the PDT word/explanation pairs; and by looking at the sentiment expressed by the users in their grouped selection of five words and explanations, as a whole. Numerical analysis is used to provide insights into the magnitude of sentiment to drive high quality decisions regarding product desirability. Each LLM is asked to provide its confidence (low, medium, high) in its sentiment score, along with an explanation of its score. All LLMs tested were able to statistically detect user sentiment from the users' grouped data, whereas TRBS and Vader were not. The confidence and explanation of confidence provided by the LLMs assisted in understanding user sentiment. This study adds deeper understanding of evaluating user experiences, toward the goal of creating a universal tool that quantifies implicit sentiment.
  • Utilizing Large Language Models to Synthesize Product Desirability Datasets
    John D. Hastings, Sherri Weitl-Harms, Joseph Doty, Zachary J. Myers, Warren Thompson
    Proceedings 2024 IEEE International Conference on Big Data Bigdata 2024, 2024
    This research explores the application of large language models (LLMs) to generate synthetic datasets for Product Desirability Toolkit (PDT) testing, a key component in evaluating user sentiment and product experience. Utilizing gpt-4o-mini, a cost-effective alternative to larger commercial LLMs, three methods, Word+Review, Review+Word, and Supply-Word, were each used to synthesize 1000 product reviews. The generated datasets were assessed for sentiment alignment, textual diversity, and data generation cost. Results demonstrated high sentiment alignment across all methods, with Pearson correlations ranging from 0.93 to 0.97. Supply-Word exhibited the highest diversity and coverage of PDT terms, although with increased generation costs. Despite minor biases toward positive sentiments, in situations with limited test data, LLM-generated synthetic data offers significant advantages, including scalability, cost savings, and flexibility in dataset production.
  • Assessing User Experiences with ZORQ: A Gamification Framework for Computer Science Education
    Proceedings of the Annual Hawaii International Conference on System Sciences, 2023
  • A Framework for an Intelligent Adaptive Education Platform for Quantum Cybersecurity
    Ruchitha Mallipeddi, Chris Schaaf, Mahadevan Subramaniam, Abhishek Parakh, Sherri Weitl-Harms
    Proceedings Frontiers in Education Conference Fie, 2023
    This Work in Progress outlines a framework of a new intelligent e-learning platform for quantum cybersecurity education. The platform uses intelligent notebooks to support multiple modes of learning through a rich set of media that includes text, videos, interactive widgets, simulations and can even incorporate serious games. The new platform, named Quark, is designed for undergraduate and graduate students, professionals and self-learners wanting to learn the basics of emerging areas in cybersecurity. The Quark platform consists of a learning bank data repository based on FAIR (Findable, Accessible, Inter-operable, and Reusable) principles storing learning objects designed using object oriented (OO) methods and incorporated using contemporary pedagogical methods to support a multiple, varied learning experiences for each topic. The overarching vision of this work is the development of a novel e-learning platform that synthesizes engaging learning experiences so that learners can achieve targeted proficiency in quantum cybersecurity education. In contrast to e-books and other elearning repositories, Quark offers a dynamic way to create subject content satisfying a given set of student-learning objectives for achieving the desired student learning outcomes with high levels of engagement and proficiency. Domain experts drive the content creation enriched with metadata that allows automatic processing of content through algorithms in Quark to synthesize a family of Python-based Jupyter Notebooks and lesson plans. Students select their learning objectives and outcomes, time to completion and student learning choices. The system then dynamically builds the lesson plan based on the dependencies in the metadata defined by the domain experts. This work in progress describes the Quark framework using sample skeleton content. Quark is the first intelligent notebook platform of its kind designed for quantum cybersecurity. In the future, Quark will be able to customize content continuously based on student interactions and informed learning choices and can be set-up for use with any topic.
  • Database Service-learning Projects: Addressing Community Needs while Measuring and Meeting Computer Science Learning Outcomes
    Sherri Weitl-Harms
    2022 10th International Conference on Information and Education Technology Iciet 2022, 2022
    This paper describes efforts and experiences from integrating a service-learning project into an upper-level/graduate-level database systems course that is taught both on-campus and online. Each student analyzes, designs, and implements a small database project for a self-selected client. The final product must match the client specification and needs and include the database design and the final working database system with embedded user documentation. The project design and implementation from a curricular perspective are also presented. Client evaluations were used to measure Computer Science (CS) student learning outcomes, which follow the five core ABET CS student outcomes. Client assessment of the 152 projects over the past seven years indicate that the clients agree or strongly agree that the students effectively met each of the objectives measured (91%-99%). Additionally, student reflections indicate that students gained confidence and felt pride in helping meet a community need by completing a computing for social good project.
  • ZORQ: A Gamification Framework for Computer Science Education
    John Hastings, Sherri Weitl-Harms, Adam Spanier, Matthew Rokusek, Ryan Henszey
    Proceedings Frontiers in Education Conference Fie, 2022
    This research paper introduces a unique system called ZORQ that is a combination of a game development framework and a gamification framework (GDGF). The ZORQ GDGF acts as a catalyst to help motivate students by increasing student engagement and success within undergraduate Computer Science (CS) education, regardless of student experience and background. The dynamic gamification elements utilized within the GDGF make it an attractive learning method for students. After collaborative game space customization, ZORQ gameplay sees each student tasked with designing a ship movement philosophy and then implementing their own code to autonomously control the ship in an interstellar game space filled with supplies, obstacles, and enemy ships. The particulars of engagements between ships can vary greatly by semester, along with the resources/objects present in the game, depending on the collaborative customization and the independent ship strategies implemented.A preliminary ZORQ trial was conducted over five years in an undergraduate Data Structures and Algorithms (DSA) course. The ZORQ trial is designed to fulfill the following objectives: 1) implement DSA concepts discussed within the course, 2) identify appropriate problem-solving approaches, 3) apply one or more solutions, 4) build depth with a coding language, 5) bridge the gap between limited concept assignments and large, multi-developer software systems by allowing students to build code within a larger architecture, 6) introduce students to version control, 7) illustrate the use of prior mathematics coursework in practical applications, and 8) introduce unit testing in software systems. In exit surveys, students expressed overwhelming satisfaction with this approach. More than 84% of the students surveyed found the system useful in their educational experience and saw benefit to inspecting a completed software project. 82% of the students found that ZORQ increased software development comprehension. 80% enjoyed using their own personal creativity in designing a ship controller, 76% found ZORQ helped them learn how to implement and use DSAs. 71% found the system engaging and found the system interaction to be clear and understandable. Observations of student performance in later courses suggest better student maturity and comprehension in preparation for proposing and implementing their own independent projects.
  • A Classification Scheme for Gamification in Computer Science Education: Discovery of Foundational Gamification Genres in Data Structures Courses
    Adam Spanier, Sherri Weitl Harms, John Hastings
    Proceedings Frontiers in Education Conference Fie, 2021
    This research full paper presents two main outcomes: 1) a novel classification system for gamification implementations including proposed genres, and 2) a comprehensive study and categorization of existing DSA gamification applications and a discussion of genres absent existing applications. Gamification presents a great potential to improve user engagement, motivation, and learning in nearly all fields of study including computer science (CS) education. However, it lacks formalized study and comprehensive analysis in CS education, and thus what makes for effective gamification is still a key question. Rather than initially trying to examine and catalog existing gamification applications and studies across the breadth of CS education as a whole, this paper instead focuses on Data Structures and Algorithms (DSA) courses. In general, DSA courses tend to be difficult due to the inherent complexity and abstraction exhibited by the fundamental concepts. As such, gamification presents a potential opportunity to convey these complex ideas in meaningful and unique ways. To carry out this work, a literature review of current DSA gamification applications is presented, the applications are categorized, and the pros and cons analyzed. Based on this analysis, a classification system is created and two new abstract genres are identified: dynamic gamification and collaborative gamification development. Potential uses, benefits and detriments are suggested for these newly identified genres. With this analysis and classification of gamification along with the identification of new abstract genres, the practice of gamification in DSA coursework can be made more efficient and effective. Upon a more thorough understanding of DSA gamification, pedagogical considerations can be made to better aid teachers and instructors in the integration of gamification into existing curriculum. The paper also touches on the applicability of the classification system to CS gamification examples outside of DSA.
  • A cross-curricular approach to fostering innovation such as virtual reality development through student-led projects
    Sherri Harms, John Hastings
    Proceedings Frontiers in Education Conference Fie, 2016
    As the Computer Science (CS) Curricula 2013 report states, CS programs should prepare students “for the workforce in a more holistic way than simply conveying technical facts. Indeed, soft skills (such as teamwork, verbal and written communication, time management, problem solving, and flexibility) and personal attributes (such as risk tolerance, collegiality, patience, work ethic, identification of opportunity ...) play a critical role in the workplace.” It also states that CS programs must “expose students to multiple programming languages, tools, paradigms, and technologies as well as the fundamental underlying principles”. Meeting all of these curricular goals is a challenge, especially for small CS programs, where resources are limited. This paper describes a model for enabling student innovation through the use of student-led projects, across the CS curriculum, within several foundational CS courses and as part of the senior design course. To illustrate how this model incorporates emerging technologies, sample student-led Virtual Reality (VR) projects are described. Results show student-led projects promote learning and help students express creativity and innovation while developing their soft skills and personal attributes. Additionally, this approach instills a culture of creativity and innovation embraced by the CS student body, where advanced students assist newer students as they embark on their journey; and it has had a significant impact on retaining CS students.
  • Algorithm and feature selection for VegOut: A vegetation condition prediction tool
    Sherri Harms, Tsegaye Tadesse, Brian Wardlow
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009
  • Discovering associations between climatic and oceanic parameters to monitor drought in Nebraska using data-mining techniques
    Tsegaye Tadesse, Donald A. Wilhite, Michael J. Hayes, Sherri K. Harms, Steve Goddard
    Journal of Climate, 2005
  • Drought monitoring using data mining techniques: A case study for Nebraska, USA
    Tsegaye Tadesse, Donald A. Wilhite, Sherri K. Harms, Michael J. Hayes, Steve Goddard
    Natural Hazards, 2004
  • Sequential Association Rule Mining with Time Lags
    Sherri K. Harms, Jitender S. Deogun
    Journal of Intelligent Information Systems, 2004
  • Interpolation techniques for geo-spatial association rule mining
    Dan Li, Jitender Deogun, Sherri Harms
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2003
  • Building knowledge discovery into a geo-spatial decision support system
    Sherri K. Harms, Jitender Deogun, Steve Goddard
    Proceedings of the ACM Symposium on Applied Computing, 2003
  • Geospatial decision support for drought risk management
    Steve Goddard, Sherri K. Harms, Stephen E. Reichenbach, Tsegaye Tadesse, William J. Waltman
    Communications of the ACM, 2003
  • Discovering sequential association rules with constraints and time lags in multiple sequences
    Sherri K. Harms, Jitender Deogun, Tsegaye Tadesse
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2002
  • Discovering representative episodal association rules from event sequences using frequent closed episode sets and event constraints
    Proceedings IEEE International Conference on Data Mining Icdm, 2001

RECENT SCHOLAR PUBLICATIONS

  • The Human-Centered Design With Iterative Service-Learning Framework: Applied to Small Rural Organizations
    S Weitl-Harms
    Journal of Organizational Psychology 25 (1), 79-105 , 2025
    2025
  • Using LLMs to Synthesize Product Desirability Datasets
    J Hastings, S Weitl-Harms, J Doty, ZJ Myers, W Thompson
    2025
  • Using LLMs to establish implicit user sentiment of software desirability
    S Weitl-Harms, JD Hastings, J Lum
    2024 International Conference on Machine Learning and Applications (ICMLA … , 2024
    2024
    Citations: 6
  • Synthetic Product Desirability Datasets for Sentiment Analysis Testing
    JD Hastings, S Weitl-Harms, J Doty, ZJ Myers, W Thompson
    https://zenodo.org/records/14188456 , 2024
    2024
  • Utilizing Large Language Models to Synthesize Product Desirability Datasets
    JD Hastings, S Weitl-Harms, J Doty, ZL Myers, W Thompson
    https://arxiv.org/abs/2411.13485 , 2024
    2024
    Citations: 9
  • Tackling CS education in K-12: Implementing a Google CS4HS Grant Program in a Rural Underserved Area
    S Harms
    arXiv preprint arXiv:2407.17483 , 2024
    2024
  • Toward automated knowledge discovery in case-based reasoning
    S Weitl-Harms, J Hastings, J Powell
    The International FLAIRS Conference Proceedings 37 , 2024
    2024
    Citations: 5
  • Cyber Security Operations Educational Gamification Application Listing
    S Weitl-Harms, A Spanier, JD Hastings
    https://arxiv.org/abs/2406.17882 , 2024
    2024
    Citations: 2
  • Iterative Service-Learning: A Computing-Based Case-study Applied to Small Rural Organizations
    S Weitl-Harms
    arXiv preprint https://arxiv.org/abs/2406.15679 , 2024
    2024
    Citations: 5
  • Using service learning projects to introduce social media marketing to small rural organizations
    S Weitl-Harms
    INTED2024 Proceedings, 5907-5913 , 2024
    2024
    Citations: 2
  • A framework for an intelligent adaptive education platform for quantum cybersecurity
    R Mallipeddi, C Schaaf, M Subramaniam, A Parakh, S Weitl-Harms
    2023 IEEE Frontiers in Education Conference (FIE), 1-5 , 2023
    2023
    Citations: 7
  • Framing Gamification in Undergraduate Cybersecurity Education
    MR Sherri Weitl-Harms, Adam Spanier, John Hastings
    Journal of The Colloquium for Information Systems Security Education 10 (1), 7 , 2023
    2023
    Citations: 3
  • A Systematic Mapping Study on Gamification Applications for Undergraduate Cybersecurity Education.
    S Weitl-Harms, A Spanier, J Hastings, M Rokusek
    Journal of Cybersecurity Education, Research and Practice 2023 (1) , 2023
    2023
    Citations: 26
  • Assessing User Experiences with ZORQ: A Gamification Framework for Computer Science Education
    SK Weitl-Harms, A Spanier, JD Hastings, M Rokusek
    HICSS 2023 , 2023
    2023
    Citations: 4
  • ZORQ: A Gamification Framework for Computer Science Education
    JD Hastings, SK Weitl-Harms, A Spanier, M Rokusek, R Hensey
    IEEE Frontiers in Education (FIE) , 2022
    2022
    Citations: 9
  • Framing Gamification in Undergraduate Cybersecurity Education
    SW Harms
    Colloquium for Information Systems Security Education (CISSE) , 2022
    2022
  • Database Service-Learning Projects: Addressing Community Needs While Measuring and Meeting Computer Science Learning Outcomes
    S Weitl-Harms
    2022 10th IEEE International Conference on Information and Education … , 2022
    2022
    Citations: 6
  • A Classification Scheme for Gamification in Computer Science Education: Discovery of Foundational Gamification Genres in Data Structures Courses
    A Spanier, S Weitl Harms, J Hastings
    IEEE Frontiers in Education (FIE) 2021, 1-9 , 2021
    2021
    Citations: 14
  • TweetDrought: A Deep-Learning Drought Impacts Recognizer based on Twitter Data
    B Zhang, F Schilder, K Smith, M Hayes, S Harms, Tadesse, Tsegaye
    Tackling Climate Change with Machine Learning workshop at ICML … , 2021
    2021
    Citations: 15
  • Current and Future Contributing Factors and Trends in the Usage of IT Cloud Computing in Manufacturing and Service Sectors
    VK Agrawal, VK Agrawal, NN Chau, MJ Miller, SK Harms
    Information Technology and Management Science 23, 10 , 2020
    2020
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Sequential association rule mining with time lags
    SK Harms, JS Deogun
    Journal of Intelligent Information Systems 22 (1), 7-22 , 2004
    2004
    Citations: 169
  • Drought monitoring using data mining techniques: A case study for Nebraska, USA
    T Tadesse, DA Wilhite, SK Harms, MJ Hayes, S Goddard
    Natural Hazards 33 (1), 137-159 , 2004
    2004
    Citations: 134
  • Discovering sequential association rules with constraints and time lags in multiple sequences
    SK Harms, J Deogun, T Tadesse
    International symposium on methodologies for intelligent systems, 432-441 , 2002
    2002
    Citations: 122
  • Geospatial decision support for drought risk management
    S Goddard, SK Harms, SE Reichenbach, T Tadesse, WJ Waltman
    Communications of the ACM 46 (1), 35-37 , 2003
    2003
    Citations: 101
  • Discovering representative episodal association rules from event sequences using frequent closed episode sets and event constraints
    SK Harms, J Deogun, J Saquer, T Tadesse
    Proceedings 2001 IEEE International Conference on Data Mining, 603-606 , 2001
    2001
    Citations: 84
  • Discovering associations between climatic and oceanic parameters to monitor drought in Nebraska using data-mining techniques
    T Tadesse, DA Wilhite, MJ Hayes, SK Harms, S Goddard
    Journal of Climate 18 (10), 1541-1550 , 2005
    2005
    Citations: 42
  • Data mining in a geospatial decision support system for drought risk management
    SK Harms, S Goddard, SE Reichenbach, WJ Waltman, T Tadesse
    Proceedings of the 1st national conference on digital government, 9-16 , 2001
    2001
    Citations: 31
  • Building knowledge discovery into a geo-spatial decision support system
    SK Harms, J Deogun, S Goddard
    Proceedings of the 2003 ACM symposium on Applied computing, 445-449 , 2003
    2003
    Citations: 28
  • A Systematic Mapping Study on Gamification Applications for Undergraduate Cybersecurity Education.
    S Weitl-Harms, A Spanier, J Hastings, M Rokusek
    Journal of Cybersecurity Education, Research and Practice 2023 (1) , 2023
    2023
    Citations: 26
  • Time-series data mining in a geospatial decision support system
    D Li, S Harms, S Goddard, W Waltman, J Deogun
    The 3rd National Conference on Digital Government , 2003
    2003
    Citations: 18
  • A cross-curricular approach to fostering innovation such as virtual reality development through student-led projects
    SK Harms, J Hastings
    2016 IEEE Frontiers in Education Conference (FIE), 1-9 , 2016
    2016
    Citations: 17
  • Efficient rule discovery in a geo-spatial decision support system
    S Harms, D Li, J Deogun, T Tadesse
    Proceedings of the 2002 annual national conference on Digital government … , 2002
    2002
    Citations: 17
  • TweetDrought: A Deep-Learning Drought Impacts Recognizer based on Twitter Data
    B Zhang, F Schilder, K Smith, M Hayes, S Harms, Tadesse, Tsegaye
    Tackling Climate Change with Machine Learning workshop at ICML … , 2021
    2021
    Citations: 15
  • A Classification Scheme for Gamification in Computer Science Education: Discovery of Foundational Gamification Genres in Data Structures Courses
    A Spanier, S Weitl Harms, J Hastings
    IEEE Frontiers in Education (FIE) 2021, 1-9 , 2021
    2021
    Citations: 14
  • Interpolation techniques for geo-spatial association rule mining
    D Li, J Deogun, S Harms
    International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular … , 2003
    2003
    Citations: 13
  • Utilizing Large Language Models to Synthesize Product Desirability Datasets
    JD Hastings, S Weitl-Harms, J Doty, ZL Myers, W Thompson
    https://arxiv.org/abs/2411.13485 , 2024
    2024
    Citations: 9
  • ZORQ: A Gamification Framework for Computer Science Education
    JD Hastings, SK Weitl-Harms, A Spanier, M Rokusek, R Hensey
    IEEE Frontiers in Education (FIE) , 2022
    2022
    Citations: 9
  • A framework for an intelligent adaptive education platform for quantum cybersecurity
    R Mallipeddi, C Schaaf, M Subramaniam, A Parakh, S Weitl-Harms
    2023 IEEE Frontiers in Education Conference (FIE), 1-5 , 2023
    2023
    Citations: 7
  • Digital government: reviving the newhall simulation model to understand the patterns and trends of soil climate regimes and drought events
    WJ Waltman, S Goddard, SE Reichenbach, G Gu, IJ Cottingham, ...
    Proceedings of the 2004 annual national conference on Digital government … , 2004
    2004
    Citations: 7
  • Using LLMs to establish implicit user sentiment of software desirability
    S Weitl-Harms, JD Hastings, J Lum
    2024 International Conference on Machine Learning and Applications (ICMLA … , 2024
    2024
    Citations: 6