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Supporting Campus Activism through Creating DIY-AT in a Social Justice Aligned Makerspace
No full textUtilizing 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.This work is supported by the National Science Foundation under Grants DRL-2005502 and DRL-2005484. We would like to thank everyone who participated in our study.https://dl.acm.org/doi/10.1145/371596
Your smart home exchanged 3M messages: defining and analyzing smart device passive mode
No full text2025 IEEE International Conference on Pervasive Computing and Communications (PerCom) 17-21 March 2025, Washington DCThe constant connectedness of smart home devices and their sensing capabilities pose a unique threat to individuals’ privacy. While users may expect devices to exhibit minimal activity while they are not performing their intended functions, this is not necessarily the case, and traditional idle mode designations are insufficient to address the current landscape of smart home devices. To address this we propose a passive mode designation based on a comprehensive categorization of smart home devices. We then measure the network traffic of thirty-two devices in their respective passive modes. We find that 97% of the devices exhibit near-constant network activity in these modes (exchanging over 3M messages in 24 hours), with many of the devices initiating and responding to LAN communications with other devices, which potentially exposes users to privacy leakages.This work is partially supported by the Horizon Europe projects DI-Hydro (grant agreement No. 101122311) and MEDIATE (grant agreement No. 101168465). We also thank Krish Chatterjie, Hannah Stasik, and Ben Hawkins for their help running experiments.https://ieeexplore.ieee.org/document/1101870
Is Bigger Safer? Analyzing Factors Related to Data Breaches Using Publicly Available Information
No full textData breaches have affected hundreds of millions of people. As consumers are exposed to constant risks of data breaches, it makes sense to ask what are the factors that contribute to data breaches such that consumers can make more conscious decisions to reduce risks. For example, suppose a consumer want to open a bank account, shall she use a bigger international bank or a smaller community bank considering risks of data breaches? Existing work on risk or vulnerability analysis typically requires detail internal information of an information system, which is not available to the public. Furthermore organizations typically do not want results of such analysis of their IT systems to be made public. This paper proposes a novel approach that analyzes publicly available information to identify factors contributing to higher data breach risks. This paper valso presents an initial study that correlates data breaches in the US from 2005 to 2017 with publicly available information about affected organizations. We find that size and name recognition of these organizations are two factors contributing to higher data breach risks. This calls for further study in this direction.https://userpages.umbc.edu/~zhchen/papers/icissp-data-breach.pd
FRIDA to the Rescue! Analyzing Synthetic Data Effectiveness in Object-Based Common Sense Reasoning for Disaster Response
No full textLarge Language Models (LLMs) have the potential for substantial common sense reasoning. However, these capabilities are often emergent in larger models. This means smaller models that can be run locally are less helpful and capable with respect to certain reasoning tasks. To meet our problem space requirements, we fine-tune smaller LLMs to disaster domains, as these domains involve complex and low-frequency physical common sense knowledge. We introduce a pipeline to create Field Ready Instruction Decoding Agent (FRIDA) models, where domain experts and linguists combine their knowledge to make high-quality seed data that is used to generate synthetic data for fine-tuning. We create a set of 130 seed instructions for synthetic generation, a synthetic dataset of 25000 instructions, and 119 evaluation instructions relating to both general and earthquake-specific object affordances. We fine-tune several LLaMa and Mistral instruction-tuned models and find that FRIDA models outperform their base models at a variety of sizes. We then run an ablation study to understand which kinds of synthetic data most affect performance and find that training physical state and object function common sense knowledge alone improves over FRIDA models trained on all data. We conclude that the FRIDA pipeline is capable of instilling general common sense, but needs to be augmented with information retrieval for specific domain knowledge.http://arxiv.org/abs/2502.1845
The Impact of Mental Health Campaigns on Self-Identification with Mental Illness, Perceived Control over Problems, and Perceived Need for Professional Treatment
No full textIt has been speculated that increased mental health awareness, with a focus on mental wellbeing, is inadvertently contributing to the reported rise in mental health problems through the overinterpretation of minor distress as symptoms of mental illness. This study aimed at testing this hypothesis by examining the impact of a common mental health disorder (anxiety) campaign and a more severe disorder (schizophrenia) campaign on self-identification with mental illness. Additionally, this study examined the impact of self-identifying with mental illness on perceived control over problems and perceived need for professional treatment. As hypothesized, individuals exposed to the anxiety campaign reported higher self-identification with mental illness than individuals exposed to the schizophrenia campaign. Additionally, higher self-identification with mental illness was associated with lower perceived control over problems and higher perceived need for professional treatment. These findings increase current understanding on the impact of mental health campaigns and implications of self-identifying with mental illness.Psi Chi, The International Honors Society in Psycholog
Neurosymbolic Approach for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks
No full text2025 International Wireless Communications and Mobile Computing (IWCMC) Abu Dhabi, United Arab Emirates, 12-16 May 2025Accurate travel demand prediction is essential for effective transportation planning, infrastructure development, and resource optimization. Traditional models often lack both interpretability and the ability to capture complex, nonlinear relationships across geospatial and socioeconomic variables. To address this, we propose a Neurosymbolic AI framework that integrates decision tree (DT) based symbolic reasoning with neural network (NN) learning for travel demand forecasting. The model leverages multisource data including geospatial, economic, and mobility datasets and embeds interpretable if-then rules extracted from DTs as additional features in the NN. Our experiments demonstrate that this hybrid approach improves predictive accuracy across key metrics, achieving up to 24.19% reduction in Mean Absolute Error (MAE), 1.79% improvement in R², and 6.49% in Common Part of Commuters (CPC) compared to NN models. Rules extracted at finer variance thresholds (e.g., 0.0001) capture nuanced patterns and enhance model alignment with observed commuter behavior. By combining symbolic interpretability with neural generalization, the proposed method advances both the transparency and performance of travel demand modeling. The data and code can be accessed on GitHub.This material is based upon work supported by the NASA Aeronautics Research Mission Directorate (ARMD) University Leadership Initiative (ULI) under cooperative agreement number 80NSSC23M0059. This research was also partially supported by the U.S. National Science Foundation through Grant No. 2317117 and Grant No. 2309760.https://ieeexplore.ieee.org/document/1105946
Comments on Alexander Field: The Economic Consequences of Mobilization for the Second World War
No full textField’s excellent book provides an impressive blend of careful quantification through total factor productivity analysis with a well developed and documented narrative account of key episodes of U.S. mobilization during the second world war. Field’s basic thrust of the book challenging the common view that U.S. mobilization for war constituted an economic miracle or at least a major economic policy success and arguing instead that the U.S. faced major production challenges and inefficiencies is quite convincing. One important issue to consider regards the relevant counterfactuals. Mobilization as such is not a very clearly defined treatment effect. Field’s assessment is appropriately on actual mobilization efforts as conducted. But that assessment poses the question of whether there were better alternatives and if so what were the margins of choice? One issue of interpretation and context concerns the relationship between mobilization for the "hot" second world war and the subsequent deterrence and containment effort of the “cold” war. A more general counterfactual issue concerns alternative forms for mobilization. U.S mobilization involved some mix of private enterprise, civilian government and military officials. Here comparative analysis with how mobilization worked elsewhere both for allied and axis powers would be informative. A final topic raised by Field’s book that warrants further consideration is is the implications of the war effort for profile of both corporate R&D and Federal government R&D. Field’s book deserves a wide readership well beyond specialists in the history of the second world war.https://www.cambridge.org/core/journals/social-science-history/article/abs/comments-on-alexander-field-the-economic-consequences-of-mobilization-for-the-second-world-war/0E6CD6EBA130D5727D8A4EA2D5E1B94
Can Climate Action and Conservation Unite?
No full textTwo new books by local authors Paula Whyman’s Bad Naturalist and Mike Tidwell’s The Lost Trees of Willow Avenue grapple with the challenges and hopes of conservation and climate action. Sunil Dasgupta talks with climate activist and Takoma Park resident Tidwell and fiction-author-turned conservationist Whyman, a longtime Bethesda resident, about their approaches to saving the world. Books at Music by Washington art-pop rock band Catscan!https://open.spotify.com/episode/2wjaT48cLcxSUHS92ZSnZ
LLM-based Corroborating and Refuting Evidence Retrieval for Scientific Claim Verification
No full textIn this paper, we introduce CIBER (Claim Investigation Based on Evidence Retrieval), an extension of the Retrieval-Augmented Generation (RAG) framework designed to identify corroborating and refuting documents as evidence for scientific claim verification. CIBER addresses the inherent uncertainty in Large Language Models (LLMs) by evaluating response consistency across diverse interrogation probes. By focusing on the behavioral analysis of LLMs without requiring access to their internal information, CIBER is applicable to both white-box and black-box models. Furthermore, CIBER operates in an unsupervised manner, enabling easy generalization across various scientific domains. Comprehensive evaluations conducted using LLMs with varying levels of linguistic proficiency reveal CIBER's superior performance compared to conventional RAG approaches. These findings not only highlight the effectiveness of CIBER but also provide valuable insights for future advancements in LLM-based scientific claim verification.https://arxiv.org/abs/2503.0793
Determinants of Self-Care Practices among Black Women in Helping Professions: An Empirical Study
No full textThe field of human resource management is paying greater attention to self-care as a strategy to address employee stress and burnout. Women in helping professions, such as social work, nursing, and education, have received scholarly attention about their health and wellness. However, there is a paucity of empirical research about the self-care practices of Black women in helping professions. This quantitative study aimed to address this research gap. More specifically, this study examined the impact of mentoring, social support, role overload, and satisfaction with compensation policies on the self-care practices of Black women in the helping professions. Data were collected using a survey of 224 Black women from organizations such as the North American Association of Christians in Social Work, Black Nurses Network, and Black Women Education Leaders. After validating the self-care scale using exploratory factor analysis, a multiple regression analysis was performed to examine the determinants of self-care practices. Results showed that social support and satisfaction with compensation and benefit policies were positive predictors of self-care practices, and role overload was a negative predictor. A thematic analysis of the open-ended responses reinforced the quantitative findings. Compensation policies that provide work-life balance, flexibility, competitive wages, and various types of insurance are essential in promoting self-care practices. The lived experiences of Black women in this study indicated that their intersectional lives require not only the absence of structural and systemic barriers but also the necessity of time and the presence of equitable, safe, and inclusive spaces for them to thrive in all aspects of life