UMBC Information Systems Department

Permanent URI for this collectionhttp://hdl.handle.net/11603/51

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Carry-on for Consciousness: An Embodied Travel Writer Trainer
(ACM, 2025-03-04) Ables, Brandon
The Carry-on for Consciousness is a hard shell wheeled carry-on luggage bag with embedded electronics to allow for embodied travel writing during the most mundane aspects of a flying vacation. A traveler can wheel the carry-on around the airport and look down at the luggage to see eight motorized faders moving left and right toggling through each letter of the alphabet. The faders? movements are powered by sensors mounted on the glasses, chin, wrist, and ankle of the traveler. As the traveler blinks, chews gum, swings their arm by their belt in a natural motion, or walks and moves their ankle past their opposite ankle the accompanying faders will move. The traveler can type an up to eight letter word as an intentionality or goal statement to help them remain centered at the airport and reflect on their journey once they return from their destination.
Delivery of Tempol from Polyurethane Nanocapsules to Address Oxidative Stress Post-Injury
(ACS, 2025-02-19) Ale, Temitope; Ale, Tolulope; Baker, Kimberly J.; Zuniga, Kameel M.; Hutcheson, Jack; Lavik, Erin
Traumatic brain injuries (TBIs) result in significant morbidity and mortality due to the cascade of secondary injuries involving oxidative stress and neuroinflammation. The development of effective therapeutic strategies to mitigate these effects is critical. This study explores the fabrication and characterization of polyurethane nanocapsules for the sustained delivery of Tempol, a potent antioxidant. The nanocapsules were designed to extend the release of Tempol over a 30-day period, addressing the prolonged oxidative stress observed post-TBI. Tempol-loaded polyurethane nanocapsules were synthesized using interfacial polymerization and nanoemulsion techniques. Two generations of nanocapsules were produced, differing in Tempol loading and PEGylation levels. The first generation, with lower Tempol loading, exhibited an average size of 159.8 � 12.61 nm and a Z-average diameter of 771.9 � 87.95 nm. The second generation, with higher Tempol loading, showed an average size of 141.4 � 6.13 nm and a Z-average diameter of 560.7 � 171.1 nm. The zeta potentials were ?18.9 � 5.02 mV and ?11.9 � 3.54 mV for the first and second generations, respectively. Both generations demonstrated the presence of urethane linkages, confirmed by Fourier Transform Infrared Spectroscopy (FTIR). Loading studies revealed Tempol concentrations of 61.94 � 3.04 ?g/mg for the first generation and 77.61 � 3.04 ?g/mg for the second generation nanocapsules. Release profiles indicated an initial burst followed by a sustained, nearly linear release over 30 days. The higher PEGylation in the second generation nanocapsules is advantageous for intravenous administration, potentially enhancing their therapeutic efficacy in TBI treatment. This study demonstrates the feasibility of using polyurethane nanocapsules for the prolonged delivery of Tempol, offering a promising approach to manage oxidative stress and improve outcomes in TBI patients. Future work will include testing these nanocapsules in vivo to determine their potential at modulating recovery from TBI.
Exploring the Relationship Between Nursing Staff and Family Members? Appraisal of Resident Care in Nursing Homes: The Role of Facility Ownership
(MDPI, 45699) Millar, Roberto; Diehl, Christin; Kusmaul, Nancy; Stockwell, Ian
Background/Objectives: To address long-standing staffing challenges and elevating care standards in the United States, new legislation will require a minimum of 0.55 h per resident day (HPRD) of registered nurse (RN) care, 2.45 HPRD of certified nursing aide (CNA) care, and a combined total of 3.48 HPRD across any combination of nursing staff. We examine differences in family members? views of care quality between facilities meeting the minimum staffing requirements and those that do not and whether there is any difference in those associations by facility ownership. Methods: This cross-sectional study utilized public data from 218 Medicare and Medicaid-certified nursing facilities in Maryland, collected in 2023. We used regression analyses to examine the association between staffing requirements and quality of care ratings, considering facility ownership status as a potential moderator. Results: Compared to facilities with CNA staffing levels below the cut off, facilities that met the CNA staffing requirement were rated more favorably by family members in overall quality and across the subdomains of staffing, care, activities, and security. In contrast, meeting the RN 0.55 cut off was not associated with family ratings across any quality domain. A facility for-profit status did not moderate the relationship between staffing and family ratings. Conclusions: These results suggest that CNA staff time is a significant driver of care quality and that non-profit facilities may already be closer to meeting new federal requirements. These findings highlight the need for regulations that support the minimum nursing staffing requirements to enhance care quality. Future research should identify the specific factors contributing to higher quality care in non-profit facilities and explore ways to implement these practices in for-profit settings.
Supporting Campus Activism through Creating DIY-AT in a Social Justice Aligned Makerspace
(ACM, 2025-01-31) Higgins, Erin; Oliver, Zaria; Hamidi, Foad
Utilizing digital fabrication methods (e.g., 3D printing) has exciting implications for the design and production of customized assistive technology (AT). However, utilizing these tools currently requires a high level of technical expertise as well as time and money investments. Furthermore, facilitating collaboration between end users and makers needs effective and inclusive approaches with shared language and support for asynchronous, dispersed communication of design requirements. While these Do-It-Yourself (DIY) approaches are shown to support end-user agency and furthering technology democratization, research has to yet explore how they can further align with social justice values and practices. We explored these possibilities by facilitating DIY-AT design with students with disabilities, activist staff members, and community members within a university makerspace. By explicitly encouraging participants to consider social justice issues important to them as they engaged in DIY-AT design, we studied the considerations and supports needed for facilitating flexible co-design activities and broader conversations about accessibility barriers at the university. Adopting a transdisciplinary approach, we offer lessons learned about the potential of co-designing DIY-ATs as a way to investigate questions of social justice, inclusion, and access in academic contexts. We show how these created DIY-ATs can be leveraged by students and staff as tangible artifacts to encourage more funding and support from university administration for accessibility initiatives.
Co-designing a 3D-Printed Tactile Campus Map With Blind and Low-Vision University Students
(ACM, 2024-10-27) Crawford, Kirk Andrew; Posada, Jennifer; Okueso, Yetunde Esther; Higgins, Erin; Lachin, Laura; Hamidi, Foad
Blind and low-vision (BLV) university students often encounter campus accessibility challenges that impede their ability to navigate campus environments effectively. The lack of customization offered by some navigational-focused assistive technologies (ATs) often falls short in addressing their diverse and specific navigational needs. 3D printing, a promising tool for creating affordable and personalized aids, has been explored as a method to create customized tactile maps to aid BLV individuals with general navigation. However, the use of 3D-printed tactile maps by BLV university students and the impact of their direct involvement in the design process remain largely unexplored. We employed a participatory design (PD) approach to engage BLV students from a university in the United States (U.S.) through semi-structured interviews and a co-design session to create a prototype 3D-printed tactile map. Additionally, we consulted with a blind rehabilitation and independence expert for insight into their perspective on AT and, more specifically, tactile maps and showed the prototype to a group of visually impaired youth and instructors visiting our university for feedback. We present and discuss our findings, provide an overview of the prototype design process, and outline future work.
An Ecosystem of Support: A U.S. State Government-Supported DIY-AT Program for Residents with Disabilities
(ACM, 2024-10-27) Higgins, Erin; Sakowicz, Marie E.; Hamidi, Foad
While Do-It-Yourself (DIY) approaches to producing customized assistive technology (AT) have been shown to support end-user agency and further technology democratization, research has shown that the utilization of digital fabrication tools requires a high level of technical expertise as well as financial investment. Facilitation of collaborations between end users and makers is a possible solution to these issues, however, previous efforts have uncovered issues around shared language, lack of consistent communication, and liability concerns. A promising direction for addressing these issues is to conceive of new types of multi-organizational collaborations that draw on complementary strengths. We explored these possibilities through an Action Research study in which we collaborated with the Maryland department of disability to launch a state level DIY-AT program. Through developing and supporting this program, we studied motivations for participation, relationships to creating and customizing AT, how individuals participated in and grew the program, and how the program allowed for individuals to reflect on their disabilities and AT use. Our findings generated an ecosystem model describing the interdependent relationships between and roles held by each stakeholder in the state DIY-AT program as well as a description of how this ecosystem encouraged expanding and transcending the understandings and definitions of AT and disability. We offer lessons learned for the design of future government-supported DIY-AT programs and reflections on the role of HCI researchers within these ecosystems.
Decoding the Privacy Policies of Assistive Technologies
(ACM, 2024-10-22) Crawford, Kirk; Khoo, Yi Xuan; Kumar, Asha; Mentis, Helena; Hamidi, Foad
As assistive technologies (ATs) have evolved, they have become increasingly connected. However, these increasing connections pose significant privacy challenges, especially when user privacy is described using complex privacy policies. Our study decodes the privacy policies of 18 ATs to understand how data collection and processing are communicated with users. We find that (1) AT privacy policies are structured to offer legal protections to their companies and not always to protect user privacy, (2) AT privacy policies are absent protections for individuals with disabilities, (3) AT policies are inconsistent when describing data storage, handling, and security methods, (4) AT policies often do not differentiate between essential and non-essential data collection, and (5) there is often a lack of transparency in AT policies around third-party data sharing. These findings reveal that AT privacy policies overlook and underestimate a user?s acceptable privacy risks. We conclude our study by discussing AT design implications.
Advancing climate model interpretability: Feature attribution for Arctic melt anomalies
(2025-02-11) Ale, Tolulope; Schlegel, Nicole-Jeanne; Janeja, Vandana
The focus of our work is improving the interpretability of anomalies in climate models and advancing our understanding of Arctic melt dynamics. The Arctic and Antarctic ice sheets are experiencing rapid surface melting and increased freshwater runoff, contributing significantly to global sea level rise. Understanding the mechanisms driving snowmelt in these regions is crucial. ERA5, a widely used reanalysis dataset in polar climate studies, offers extensive climate variables and global data assimilation. However, its snowmelt model employs an energy imbalance approach that may oversimplify the complexity of surface melt. In contrast, the Glacier Energy and Mass Balance (GEMB) model incorporates additional physical processes, such as snow accumulation, firn densification, and meltwater percolation/refreezing, providing a more detailed representation of surface melt dynamics. In this research, we focus on analyzing surface snowmelt dynamics of the Greenland Ice Sheet using feature attribution for anomalous melt events in ERA5 and GEMB models. We present a novel unsupervised attribution method leveraging counterfactual explanation method to analyze detected anomalies in ERA5 and GEMB. Our anomaly detection results are validated using MEaSUREs ground-truth data, and the attributions are evaluated against established feature ranking methods, including XGBoost, Shapley values, and Random Forest. Our attribution framework identifies the physics behind each model and the climate features driving melt anomalies. These findings demonstrate the utility of our attribution method in enhancing the interpretability of anomalies in climate models and advancing our understanding of Arctic melt dynamics.
Identifying Flaky Tests in Quantum Code: A Machine Learning Approach
(2025-02-06) Kaur, Khushdeep; Kim, Dongchan; Jamshidi, Ainaz; Zhang, Lei
Testing and debugging quantum software pose significant challenges due to the inherent complexities of quantum mechanics, such as superposition and entanglement. One challenge is indeterminacy, a fundamental characteristic of quantum systems, which increases the likelihood of flaky tests in quantum programs. To the best of our knowledge, there is a lack of comprehensive studies on quantum flakiness in the existing literature. In this paper, we present a novel machine learning platform that leverages multiple machine learning models to automatically detect flaky tests in quantum programs. Our evaluation shows that the extreme gradient boosting and decision tree-based models outperform other models (i.e., random forest, k-nearest neighbors, and support vector machine), achieving the highest F1 score and Matthews Correlation Coefficient in a balanced dataset and an imbalanced dataset, respectively. Furthermore, we expand the currently limited dataset for researchers interested in quantum flaky tests. In the future, we plan to explore the development of unsupervised learning techniques to detect and classify quantum flaky tests more effectively. These advancements aim to improve the reliability and robustness of quantum software testing.
Gender Dynamics in Software Engineering: Insights from Research on Concurrency Bug Reproduction
(2025-02-27) Zaman, Tarannum Shaila; Kishan, Macharla Hemanth; Lota, Lutfun Nahar
Reproducing concurrency bugs is a complex task due to their unpredictable behavior. Researchers, regardless of gender, are contributing to automating this complex task to aid software developers. While some studies have investigated gender roles in the broader software industry, limited research exists on gender representation specifically among researchers working in concurrent bug reproduction. To address this gap, in this paper, we present a literature review to assess the gender ratio in this field. We also explore potential variations in technique selection and bug-type focus across genders. Our findings indicate that female researchers are underrepresented compared to their male counterparts in this area, with a current male-to-female author ratio of 29:6. Through this study, we emphasize the importance of fostering gender equity in software engineering research, ensuring a diversity of perspectives in the development of automated bug reproduction tools.
LLM-ProS: Analyzing Large Language Models' Performance in Competitive Problem Solving
(2025-02-04) Hossain, Md Sifat; Tabassum, Anika; Arefin, Md Fahim; Zaman, Tarannum Shaila
The rapid advancement of large language models has opened new avenues for automating complex problem-solving tasks such as algorithmic coding and competitive programming. This paper introduces a novel evaluation technique, LLM-ProS, to assess the performance of state-of-the-art LLMs on International Collegiate Programming Contest (ICPC) problems. Using a curated dataset of 166 World Finals problems from 2011 to 2024, we benchmark the models' reasoning, accuracy, and efficiency. We evaluate the five models-GPT-4o, Mistral Large, Llama-3.1-405B, and the o1 family, consisting of o1-mini and o1-preview, across critical metrics like correctness, resource utilization, and response calibration. Our results reveal significant differences in the models' abilities to generalize, adapt, and solve novel problems. We also investigated the impact of training methodologies, dataset contamination, and chain-of-thought reasoning on model performance. The findings provide new insights into optimizing LLMs for algorithmic tasks, highlighting both strengths and limitations of current models.
A Decentralized, Secure, and Reliable Vehicle Platoon Formation With Privacy Protection for Autonomous Vehicles
(IEEE, 2025-02-20) Khan, Rabia; Mehmood, Amjad; Song, Houbing; Maple, Carsten
Vehicle platooning enables vehicles to drive cooperatively on highways and motorways. This is to weigh numerous benefits such as lesser fuel consumption, safe drive and better road utilization. The main research focus of vehicle platoons is secure and dynamic platoon formation and ensuring privacy of vehicular data. Platoons are not safe from cyber-attacks and key is to safeguard the platoons from different known/unknown cyber threats. This paper introduces a dynamic and secure platoon formation technique targeting three different vehicle conditions on road. While the private credentials of CAVs is protected using zk-SNARK encryption protocol through permissioned Blockchain. The proposed system has been evaluated for its performance against DDoS and impersonation attack. Platoon formation time has been compared with other benchmarks and shows better results. While the performance against DDoS and impersonation attacks in comparison to the benchmarks is also improved. Since the limitation of zk-SNARK is it takes time to generate a proof and this has been also the focus of this study. The protocol has been fine tuned to adjust its parameters so that it could generate proof in less possible time.
Predicting Students' Interest from Small Group Conversational Characteristics: Insights from an AI Literacy Education with High School Students
(ACM, 2025-02-18) Zha, Shenghua; Chen, Lujie Karen; Hung, Woei; Gong, Na; Moore, Pamela; Klemetsrud, Bethany
Recent years have seen developments in AI instructional practices for K-12 students. In literature, students' interest in AI is shown to correlate with gaining AI knowledge; however, little is known about how AI interest manifests in classroom discourses during AI literacy lessons. This study examined students' participation in an integrated AI curriculum delivered to a cognitive science class in a high school in the southern US. Students worked in small groups and built a supervised machine learning model to recognize kids' drawings at different stages of artistic development. Our analysis showed that semantic features extracted from students' small group conversations significantly predicted their interest in learning AI. However, we found no significant relationship between students' social construction of knowledge and their interests. This study sheds light on the relationship between the learning process and interest; when further developed, this analysis may be developed into a classroom activity analytics tool that may provide real-time feedback to teachers engaged in AI literacy education to enhance teaching effectiveness in this nascent content area.
Securing the Future: Mitigating Cyber Threats in Personal Financial Planning: Planning firm owners must be proactive to secure their firm, protect their clients, and build trust
(FPA, 2025-01-01) Casas, C. Augusto
Cybersecurity Importance: Financial planning firms must prioritize cybersecurity toprotect sensitive client data and maintain regulatory compliance in an era of escalatingcyber threats. The increasing sophistication of cyberattacks, such as ransomware andphishing, demands a proactive approach to security.? NIST Cybersecurity Framework (CSF): The NIST CSF provides a structured,comprehensive approach for financial planning firms to manage cybersecurity risks. Itencompasses five core functions: Identify, Protect, Detect, Respond, and Recover,ensuring that all aspects of cybersecurity are systematically addressed.? Governance Role: Effective governance is critical in aligning cybersecurity efforts withthe firm's business objectives. It involves setting policies, defining roles andresponsibilities, and establishing accountability. Regular audits, risk assessments, andcontinuous improvement initiatives are essential to adapt to the evolving threatlandscape.? Technological Solutions: Implementing advanced security measures like multi-factorauthentication (MFA), encryption, and continuous monitoring can significantly reducevulnerabilities. These technologies help protect against unauthorized access, securedata, and detect anomalies in real-time.? Human Factors: Cybersecurity training and awareness are crucial in minimizing risksassociated with human error. Regular training programs and phishing simulations helpbuild a culture of security within the firm.? Lessons from the Industry: By learning from past cybersecurity incidents in thefinancial services sector, firms can strengthen their defenses, improve incidentresponse, and build resilience against future threats.
Engaging K-12 Learners in Data Annotation for AI Climate Models
(ACM, 2025-02-18) MacFerrin, Michael; Boyda, Edward; Young, Kimberly; Namayanja, Josephine; Subramanian, Aneesh; Mokbel, Mohamed F.; Chen, Lujie Karen; Janeja, Vandana
Due to the climate crisis, summers in Greenland have been rapidly getting warmer, causing increasing rates of ice melt on the Greenland ice sheet and speeding up sea-level rise. Evidence of this change can be measured by the number and location (elevation) of water pools and lakes that form on the surface of the ice sheet. In addition, crevasses can cause lakes to drain extremely rapidly causing the ice to flow faster, contributing to sea-level rise. However, the lack of annotated data makes it difficult to automatically detect and track these behavioral changes in the polar ice sheet lakes. This study describes how a team of polar and data scientists actively engaged middle and high school students in their classrooms in a data annotation process through an engaging curriculum unit to identify multiple ice sheet phenomena observed in satellite imagery. The findings describe the learning outcomes from both student and teacher perspectives. It also projects learners' understanding and sentiments about climate change and the role of artificial intelligence (AI) models coupled as an extension of citizen science in addressing climate change.
Evaluating Causal AI Techniques for Health Misinformation Detection
(IEEE, 2025-03-17) Clark, Ommo; Joshi, Karuna
The proliferation of health misinformation on social media, particularly regarding chronic conditions such as diabetes, hypertension, and obesity, poses significant public health risks. This study evaluates the feasibility of leveraging Natural Language Processing (NLP) techniques for real-time misinformation detection and classification, focusing on Reddit discussions. Using logistic regression as a baseline model, supplemented by Latent Dirichlet Allocation (LDA) for topic modeling and K-Means clustering, we identify clusters prone to misinformation. While the model achieved a 73% accuracy rate, its recall for misinformation was limited to 12%, reflecting challenges such as class imbalance and linguistic nuances. The findings underscore the importance of advanced NLP models, such as transformer based architectures like BERT, and propose the integration of causal reasoning to enhance the interpretability and robustness of AI systems for public health interventions.
An Anonymous, Trust and Fairness Based Privacy Preserving Service Construction Framework in Mobile Crowdsourcing
(IEEE, 2025-01-30) Chen, Xuechi; Yang, Bochang; He, Qian; Zhang, Shaobo; Wang, Tian; Song, Houbing; Liu, Anfeng
The proliferation of mobile smart devices with ever-improving sensing capacities means that Mobile Crowd Sensing (MCS) can economically provide a large-scale and flexible solution. However, existing MCSs face threats to privacy and fairness when recruiting workers due to information sensitivity, uncertainty about worker behavior, and budget constraints. To address the above issues, we propose an Anonymity, Trust, and Fairness in Privacy Protection (ATFPP) service construction framework to cost-effectively improve the quality of data at MCS. The main innovations are as follows: Firstly, on anonymity, in order to protect the privacy of workers, we propose a Privacy-Preserving (PP) framework based on an anonymous three-party platform, which realizes a full-process privacy-preserving scheme for workers. Second, on trust, we design more efficient Truth Discovery (TD) algorithm and adopt multifactor trust assessment method to identify more trustworthy workers. In addition, in terms of fairness, the fair distribution of compensation is realized through reasonable budget and approximate Shapley method. Finally, the proposed ATFPP scheme is theoretically proven to be correct and effective. Simulations based on real-world datasets illustrate that our ATFPP service construction scheme outperforms the state-of-the-art method in terms of both privacy protection and data quality.
Strengthening Workforce Education: Excellence in Programming Securely (SWEEPS)
(ACM, 2025-02-18) Kariuki, Deborah; Ngambeki, Ida; Dai, Jun; Bishop, Matt; Sun, Xiaoyan; Dark, Melissa; Daugherty, Jenny; Lowrie, Alex; Geissler, Markus; Nico, Phillip; Noor, Arshad
This paper presents and advocates for an initiative to expand access to secure programming education. The Strengthening Workforce Education: Excellence in Programming Securely (SWEEPS) initiative, funded by the National Centers of Academic Excellence in Cybersecurity (NCAE-C) program, seeks to advance secure programming and help achieve security aims. SWEEPS establishes a secure programming curriculum and workforce development coalition of seven institutions across two CAE (Center of Academic Excellence) regions (Northeast and Southwest) and five states (California, Massachusetts, Maryland, Indiana, and North Carolina). This coalition includes industry-based stakeholders collaborating with the US Army and government agencies on various projects. SWEEPS draws on prior work establishing critical concepts in secure programming, assessment tools, learning aids, and system infrastructure. The initiative offers a series of interconnected, stackable learning experiences tailored for early to mid-career professionals looking to enhance their cybersecurity skills. These experiences, which include practical one-day workshops and comprehensive year-long graduate certificates, provide a reassuring path for upskilling in secure programming. This paper recommends the efficacy of stackable training approaches in secure programming by exploring the practices of targeting and training individuals with diverse proficiency levels of programming experience who would benefit from increased knowledge and training.
Global Relevance of Online Health Information Sources: A Case Study of Experiences and Perceptions of Nigerians
(2024-11-13) Clark, Ommo; Joshi, Karuna; Reynolds, Tera L.
Online health information sources (OHIS) offer potential for improving access to health information especially in areas with limited healthcare infrastructure. However, OHIS predominantly originates from Western societies potentially ignoring the specific needs and cultural contexts of diverse populations. There is limited research on the global suitability of OHIS content. This study explores the global relevance of OHIS for diverse populations through a case study examining user experiences of Nigerians living in multiple countries. Findings reveal OHIS usage patterns are influenced by the country of residence and local health services availability. The study highlights the need for culturally inclusive OHIS content to ensure equitable health information access globally. Ultimately, for OHIS to serve a global audience effectively, there needs to be reliable information sources that acknowledge and cater to different users' cultural backgrounds, including prevalent health issues, medical practices, beliefs, languages, and healthcare expectations.
A Beam-Search Based Method to Select Classification and Imputation Methods for Fair and Accurate Data Analysis
(IEEE, 2024-12) Mowoh, Dodavah; Chen, Zhiyuan
Members from disadvantaged or minority groups are often more likely to have missing values in their record. Imputation is a common approach to deal with missing values before the data is being analyzed. Several studies have found interplay of imputation methods and classification methods with respect to accuracy and fairness: different combinations of imputation and classification methods will lead to different accuracy and fairness results. However, it is unclear how to choose the combination of imputation method and classification method to optimize the tradeoff between accuracy and fairness. An exhaustive search approach will be too expensive because it needs to check all combinations and measure both accuracy and fairness for every combination. This paper proposes a beam-search based method to select the optimal combination of imputation methods and classification methods. An empirical study was also conducted to compare the performance of the proposed method to exhaustive search. The proposed solution achieves the same result as the exhaustive search method but with much lower search cost.

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