| El Faro: An Advanced Frugal AI Stack for Context-Driven Resilience | Jose Ruiz | University of Europe for Applied Sciences, Potsdam Campus | The rapid advancement of Artificial Intelligence (AI) promises significant socio-economic gains, yet over 2 billion people in "digital silence zones" remain excluded due to infrastructure gaps. Current AI ecosystems rely on energy-intensive centralized clouds or edge models requiring periodic synchronization, both of which consume massive water and energy resources for cooling and operation, failing in crisis zones where such resources are absent. This paper introduces El Faro, a self-sustaining "frugal AI stack" designed for permanent-offline operation through the strategic repurposing of e-waste and local materials. The framework integrates three synergistic layers: (1) an intelligence layer utilizing 4-bit quantized models on legacy silicon; (2) a resilient hybrid mesh communication backbone; and (3) a biomimetic, clay-based passive cooling enclosure that eliminates mechanical energy overhead. Preliminary validation demonstrates that high-utility AI services can be sustained with zero-energy thermal management, transforming environmental liabilities into a decentralized "Data Commons." El Faro provides an architectural blueprint for inclusive innovation, proving that technological sovereignty and digital agency can be maintained through ecological integration, even in total infrastructure collapse. |
| Spondon: A Low-Cost, Multi-Sensor Wearable for Understanding Stress in Resource-Constrained Countries | Shahariar Ifti, Rahat Jahangir Rony, Shuvashish Chakraborty, Asif Mahbub, Nova Ahmed | Design Inclusion and Access Lab (DIAL), North South University; School of Computer Science and Informatics, Cardiff University | Stress remains a significant health concern, yet commercial monitoring devices are often too expensive and complex for populations in developing countries. This research presents Spondon, a low-cost, multi-sensor wearable designed to detect stress in resource-constrained environments. Utilizing an ESP32 microcontroller with MAX30102, GSR, and temperature sensors, the device collects physiological data. We evaluated the system with 15 participants using the Stroop test to induce stress across baseline, task, and recovery phases. Statistical analysis confirmed a distinct 7.95% drop in HRV (RMSSD) during stress. Machine learning models trained on normalized data achieved a 75.0% accuracy in binary stress classification using a Random Forest classifier. This work contributes a validated, accessible hardware platform and methodology, democratizing stress research for low-income communities and highlighting the importance of personalized, context-aware design in HCI. |
| HUTlogger: Co-Designing a Community-Controlled Environmental Data Logger for Heat Justice in Little Haiti, Miami | Oluwafemi Oladosu, Carlos Aguilera, Lillian Agosto Maldonado, Keith Maull, Agbeli Ameko, Curtis Walker, Amy Quarkume | Howard University | Environmental monitoring infrastructure is not neutral. The communities bearing the highest urban heat burden, fence-line neighborhoods like Little Haiti, Miami, remain structurally absent from the data systems that could compel intervention on their behalf. This paper presents HUTlogger, an open-hardware environmental data logger co-designed with Little Haiti residents and community partners through a two-year participatory process. Five students from Little Haiti and Allapattah co-designed the device in Fall 2025, conducting community interviews, testing enclosure prototypes, and identifying deployment sites alongside community partners. Students visited in Winter 2025 to advance the hardware design. The physical device was fabricated in February 2026 and the complete data pipeline validated in March 2026. Full community deployment in Little Haiti is planned for Summer 2026. HUTlogger addresses four structural gaps in available heat monitoring tools—WiFi and power dependency, researcher-mediated data access, proprietary data custody, and cost barriers to dense deployment—through an ESP32-S3 microcontroller with a BME688 multi-parameter sensor and PA1010D GPS module, logging temperature, humidity, barometric pressure, and gas resistance every 30 seconds with GPS-verified coordinates, syncing via WiFi or BLE to a real-time community dashboard with one-click CSV download. The 3D printed enclosure, decorated by student co-designers, is community-designed and community-named. |
| Environmental Constraints in UAV-Based Litter Detection: Deployment Insights from a Forest Park Case Study | Jason Zhao | Obra D. Tompkins High School, Katy, TX, USA | Unmanned Aerial Vehicles (UAVs) enable scalable and low-cost environmental monitoring. However, their deployment is still challenged by complex natural environmental conditions in the real world. This study employs a controlled experiment to investigate the impact of litter type, ground type, and illumination on litter detection performance in a forest park. A dataset of 375 aerial images was collected by a UAV, and a YOLO-based object detection model was trained and evaluated. ANOVA was used to test the impact of litter type, ground type, and illumination conditions on litter detection accuracy. The results show that litter type is a statistically significant factor, with significant interaction effects between litter type and ground type. Ground type and illumination alone do not exhibit significant effects on detection accuracy. Additional subgroup analysis revealed that composite environmental conditions influence detection accuracy for certain litter types. The findings indicate that detection performance is influenced by interactions between litter types and certain environmental conditions, rather than by individual factors alone. Based on the findings, three deployment-oriented strategies are proposed, including litter-type-aware monitoring, interaction-aware environmental planning, and composite condition-aware model training. |
| Toward Faith-Aligned Mental Health Chatbots: Understanding Muslim Users' Expectations and Design Needs | Mohammad Rakin Uddin, Afsana Hossain Anima, S M Rhydh Arnab, Susmita Biswas, Jannatun Noor | C2SG Research Group, Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh | Chatbots are being widely used to support emotional well-being, yet many are designed from secular perspectives that overlook spiritual and religious values. This paper examines how Muslim users perceive spiritually grounded emotional support through AI. Through qualitative interviews with 17 participants, we find that users seek systems that are not only empathetic but also theologically grounded, emotionally responsive, and sensitive to privacy and credibility. Chatbots are envisioned as spiritually aligned companions embedded in everyday practices. We frame this role as a mediated religious agent, whose credibility comes from the religious authority it represents. These findings contribute to design considerations for faith-aligned mental health technologies that move beyond generic support toward contextually meaningful care. |
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