MD-SOAR Maryland Shared Open Access Repository
Not a member yet
34521 research outputs found
Sort by
Modeling Inhabited Smart Spaces to Support Interoperable IoT-Based Applications
No full textThe 26th IEEE International Conference on Mobile Data Management, Jun 2025, Irvine (CA), United StatesIoT deployments in smart spaces can enable the development of useful services for their inhabitants. However, the diversity of smart spaces and their sensor infrastructures makes it challenging to develop space-agnostic applications. Moreover, existing schemas addressing interoperability challenges often lack the vocabulary needed to represent the integration of smart space systems and their inhabitants. We present a schema to annotate inhabited smart spaces in support of inhabitant-oriented applications. Our schema integrates well-known ontologies to represent inhabitants, events/activities, and the space itself, along with their interconnections. It also supports the representation of uncertain information from IoT and mobile sensors (e.g., a person's location or occupancy/attendance at an event). Additionally, we introduce an annotation tool that uses an easy-to-use GUI to describe a smart space based on our schema. We demonstrate the potential of our approach through a series of SPARQL queries and a system deployed at the UCI campus that annotates sensor data to support a space-agnostic occupancy monitoring application.This work is partially supported by the HPI Research Center in Machine Learning and Data Science at UC Irvine, NSF Grants No. 1952247, 2245372, 2420846, 2133391, 2008993, 2008993, 1952247, 2032525, and the Horizon Europe project PANDORA (grant agreement No. 101135775).https://hal.science/hal-0506639
BlockSchool – A Gamified Mobile App Educating Millennial-aged Individuals About Cryptocurrency
No full textMillennials, who account for 22.5% of global consumer spending, are not only significant economic contributors but also prolific smartphone users. The widespread adoption of smartphones has transformed how individuals access information, including mobile learning. Despite growing interest in cryptocurrency—particularly among millennials, who are the most likely demographic to invest in it—many remain confused by its complexity. Approximately 44% of millennials believe cryptocurrency to be risky or difficult to understand. Furthermore, 50% of millennials believe it is the future of finance. In response to this gap between interest and understanding, I created BlockSchool– a gamified mobile application designed to educate millennial-aged individuals on cryptocurrency concepts in an engaging, low-risk environment. The project began with a literature review to explore existing research in both cryptocurrency and gamification spaces. This was followed by participatory design sessions that guided the development of initial prototypes. Usability testing then informed further refinements, leading to the final iteration of the app design. BlockSchool aims to bridge the knowledge gap in cryptocurrency through accessible, mobile-first learning experiences that combine gamification concepts with educational value. The goal is to empower users with cryptocurrency knowledge while fostering confidence in navigating the cryptocurrency landscape.
Keywords: Smartphones, gamification, cryptocurrency, millennials, participatory design, usabilit
SmartShift: A Secure and Efficient Approach to Smart Contract Migration
No full textFoundations of Software Engineering (FSE) June 23–28, 2025, Trondheim, NorwayBlockchain and smart contracts have emerged as revolutionary technologies transforming distributed computing. While platform evolution and smart contracts' inherent immutability necessitate migrations both across and within chains, migrating the vast amounts of critical data in these contracts while maintaining data integrity and minimizing operational disruption presents a significant challenge. To address these challenges, we present SmartShift, a framework that enables secure and efficient smart contract migrations through intelligent state partitioning and progressive function activation, preserving operational continuity during transitions. Our comprehensive evaluation demonstrates that SmartShift significantly reduces migration downtime while ensuring robust security, establishing a foundation for efficient and secure smart contract migration systems.http://arxiv.org/abs/2504.0931
BL(u)E CRAB: A User-Centric Framework for Identifying Suspicious Bluetooth Trackers
No full text2025 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 17-21 March 2025, Washington DC, DC, USAGiven the pervasiveness of Bluetooth Low Energy (BLE)-based devices, detecting unwanted or suspicious trackers is challenging, especially due to their heterogeneity, cross-platform compatibility issues, and inconsistent detection methods. BL(u)E CRAB identifies suspicious BLE trackers based on various risk factors within minutes. It does so by collecting information including the number of encounters, time with the user, distance traveled with the user, number of areas each device appeared in, and device proximity to user. After collecting this information, BL(u)E CRAB performs an outlier detection analysis to flag suspicious devices. BL(u)E CRAB presents this information in a simple, intuitive, and customizable way for the user to determine which devices pose the biggest threat to them based on their context.https://ieeexplore.ieee.org/document/1103854
BiasLab: Toward Explainable Political Bias Detection with Dual-Axis Annotations and Rationale Indicators
No full textWe present BiasLab, a dataset of 300 political news articles annotated for perceived ideological bias. These articles were selected from a curated 900-document pool covering diverse political events and source biases. Each article is labeled by crowdworkers along two independent scales, assessing sentiment toward the Democratic and Republican parties, and enriched with rationale indicators. The annotation pipeline incorporates targeted worker qualification and was refined through pilot-phase analysis. We quantify inter-annotator agreement, analyze misalignment with source-level outlet bias, and organize the resulting labels into interpretable subsets. Additionally, we simulate annotation using schema-constrained GPT-4o, enabling direct comparison to human labels and revealing mirrored asymmetries, especially in misclassifying subtly right-leaning content. We define two modeling tasks: perception drift prediction and rationale type classification, and report baseline performance to illustrate the challenge of explainable bias detection. BiasLab's rich rationale annotations provide actionable interpretations that facilitate explainable modeling of political bias, supporting the development of transparent, socially aware NLP systems. We release the dataset, annotation schema, and modeling code to encourage research on human-in-the-loop interpretability and the evaluation of explanation effectiveness in real-world settings.Dr. Goldwasser provided guidance and funding for the original MTurk pilot phasehttp://arxiv.org/abs/2505.1608
The 157-month Swift-BAT All-Sky Hard X-Ray Survey
No full textThe Burst Alert Telescope (BAT) onboard the Neil Gehrels Swift observatory has been serving as a survey instrument for the hard X-ray sky, and has detected thousands of X-ray sources (e.g., AGNs, X-ray binaries, etc). BAT monitors these X-ray sources and follows their light curves on time scales from minutes to years. In addition, BAT discovers hundreds of new X-ray sources in survey images stacked throughout the mission lifetime. We present the updated BAT survey catalog since the last published BAT 105 month survey catalog (Oh et al. 2018) with additional of 4.5 years of data until December 2017. Data since 2007 are reprocessed to include updated instrumental calibration. Analysis in this study shows that additional systematic noise can be seen in the 157-month mosaic images, resulting in decreases in the expected improvement in sensitivity and the number of new detections. The BAT 157-month survey reaches a sensitivity of 8.83 × 10⁻¹² erg s⁻¹ cm⁻² for 90% of the sky and 6.44 × 10⁻¹² erg s⁻¹ cm⁻² for 10% of the sky. This catalog includes spectra, monthly and snapshot light curves in eight energy bands (14-20, 20-24, 24-35, 35-50, 50-75, 75-100, 100-150, and 150-195 keV) for 1888 sources, including 256 new detections above the detection threshold of 4.8σ. The light curves, spectra, and tables that summarize the information of the detected-sources are available in the online journal and in the catalog web page https://swift.gsfc.nasa.gov/results/bs157mon/.http://arxiv.org/abs/2506.0410
Be Brief, Be Consistent, Be Neutral: Comments on US Draft Circular A-4, “Regulatory Analysis”
No full textEx ante, my primary concerns were about implementation across the wide expanse of federal applications, supporting the supplemental use of distributional weighting, trying to find a supportable middle ground on discounting using the expected value of bounds and a more consistent scope of analysis. Ex post, I felt heard if not followed, perhaps not uncommon for reviewers.https://www.cambridge.org/core/product/identifier/S2194588825000120/type/journal_articl
On Sierpiński and Riesel Repdigits and Repintegers
No full textFor positive integers b≥2 , k<b, and t we say that an integer k₆⁽ᵗ⁾ is a b-repdigit if k₆⁽ᵗ⁾ can be expressed as the digit k repeated t times in base-b representation, i.e., k₆⁽ᵗ⁾ =k(bᵗ-1)/(b-1). In the case of k=1, we say that 1₆⁽ᵗ⁾ is a b-repunit. In this article, we investigate the existsence of b-repdigits and b-repunits among the sets of Sierpiński numbers and Riesel numbers. A Sierpiński number is defined as an odd integer k for which k⋅2ⁿ+1 is composite for all positive integers and Riesel numbers are similarly defined for the expression k⋅2ⁿ-1.http://arxiv.org/abs/2505.0077
2025 Deporting International Students Risks Making The US A Less Attractive Destination, Putting Its Economic Engine At Risk
No full textThe U.S. Department of Homeland Security recently announced that it would reverse the terminations after courts across the country determined they did not have merit. David Di Maria, vice provost of global engagement at the University of Maryland Baltimore County, explains how these moves come as the White House seeks to enhance vetting and screening of all foreign nationals. As an administrator and scholar who specializes in international higher education, Di Maria knows that inserting additional bureaucracy into current processes could make the U.S. a less attractive study destination and hamper the Trump administration’s ability to achieve its “America First” priorities related to the economy, science and technology, and national security.https://umbc.edu/stories/deporting-international-students-risks-us-economy
Multi-Contextual Learning in Spatio-temporal Neighborhoods
No full textThis dissertation presents a multi-contextual learning framework within spatiotemporalneighborhoods to tackle analytical challenges in Earth observation data.
The rapid rise in the amount of environmental data —as demonstrated by the European
Space Agency’s Copernicus program expanding from 2 to 20 petabytes per year
and NASA’s Earth Observing System producing 16 terabytes per day—poses difficulties
to conventional analytical methods for managing multi-source data fusion,
cross-scale pattern recognition, and spatial autocorrelation. The work develops a context-aware spatio-temporal data analysis approachwith neighborhood-based spatio-temporal framework at its foundation. The framework
employs Voronoi tessellation for micro-neighborhood generation and attributebased
grouping for macro-neighborhood generation. By incorporating contextual
information from both spatial proximity and attribute similarity, the approach captures
nuanced patterns that traditional methods tend to ignore. This multi-contextual learning framework is validated through two complementaryapplication domains that serve as case studies. The Greenland Ice Sheet
case demonstrates how the application of neighborhood analysis successfully encapsulates
intricate melt behavior, accounting for local variability, seasonality, and
couplings between temperature, albedo, and other variables. Importantly, the framework
aids in comparing surface and subsurface processes influencing change in the
ice mass in marine-terminating glaciers in Southeast Greenland. Digital twin simulations
form a second test case by demonstrating that the same neighborhood-based
approach is capable of defining areas with analogous variance structures within
high-resolution atmospheric data. The methodological contributions of the dissertation are: (1) multi-contextuallearning for spatio-temporal neighborhood formation; (2) Graph Deviation Networks
for multivariate anomaly detection in such neighborhoods; (3) a technique for differentiating
surface versus subsurface process dominance in analyzing ice mass change
through hotspot analysis; and (4) spatial clustering for variance analysis. Each
of these components tackles intrinsic challenges in spatio-temporal data analysis
while offering practical solutions for environmental monitoring application scenarios.
Results indicate that multi-contextual learning in spatio-temporal neighborhoods
substantially enhances detection and interpretation capability for complex
Earth observation data, with immediate implications for environmental monitoring,
modeling, and satellite-based observational systems