Creating games together is both a playful and effective way to develop skills in computational thinking, collaboration, and more.However, game development can be challenging for younger developers who lack formal training.While teenage developers frequently turn to online communities for peer support, their experiences may vary.To better understand the benefits and challenges teens face within online developer communities, we conducted interviews with 18 teenagers who created games or elements in Roblox and received peer support from one or more online Roblox developer communities.Our findings show that developer communities provide teens with valuable resources for technical, social, and career growth.However, teenagers also struggle with inter-user conflicts and a lack of community structure, leading to difficulties in handling complex issues that may arise, such as financial scams.Based on these insights, we propose takeaways for creating positive and safe online spaces for teenage game creators.

Interest in Direct Current Power Line Communication (DC-PLC) is growing as industries seek to reduce wiring complexity and cost by combining power and data over a single medium. A programmable DC-PLC testbed is developed to enable power transfer and bidirectional communication over a shared DC power line bus. The system employs a master–slave scheme in half-duplex mode under a flexible MAC protocol, using Voltage Polarity Modulation (VPM) for downlink and Current Amplitude Modulation (CAM) for uplink. Implemented with low-cost microcontrollers, the master and slave are designed as modular units for easy connection and expansion. The platform achieves approximately 100 kbps throughput in both directions and demonstrates reliable operation with a simple polling-based MAC protocol, highlighting its potential for future DC-PLC research and applications.

International aid is a critical mechanism for promoting economic growth and well-being in developing nations, supporting progress toward the Sustainable Development Goals (SDGs). However, tracking aid contributions remains challenging due to labor-intensive data management, incomplete records, and the heterogeneous nature of aid data. Recognizing the urgency of this challenge, we partnered with government agencies to develop an AI model that complements manual classification and mitigates human bias in subjective interpretation. By integrating SDG-specific semantics and leveraging prior knowledge from language models, our approach enhances classification accuracy and accommodates the diversity of aid projects. When applied to a comprehensive dataset spanning multiple years, our model can reveal hidden trends in the temporal evolution of international development cooperation. Expert interviews further suggest how these insights can empower policymakers with data-driven decision-making tools, ultimately improving aid effectiveness and supporting progress toward SDGs.

Recent advances in electric vehicles, robots, and renewable energy have renewed interest in direct current (DC) power line communication (PLC) technology. However, existing DC-PLC systems face from several limitations, including low data rates, lack of support for duplex communication, and the absence of an effective medium access control (MAC) mechanism. To overcome these challenges, we propose D2-PLC, a novel DCPLC system that features a redesigned physical layer, enabling high-speed duplex communication over a single pair of wires supporting simultaneous power and data transmission. D2-PLC introduces voltage polarity modulation (VPM) and current amplitude modulation (CAM) for downlink and uplink communication, respectively. In addition, we develop a custom data link layer and MAC protocols to coordinate communication in a bus topology where multiple slave nodes interact with a single master node (the power source), minimizing the risk of collisions. We implement a fully functional prototype and evaluate on a 5-node testbed as well as via 256-node simulations. Results demonstrate that D2-PLC achieves a maximum data rate of ~100 kbps–260% improvement over existing solutions(Cwhile maintaining 99+% reliability. These findings highlight D2-PLC’s potential to reduce the cost and weight of battery-powered systems such as electric vehicles

While detecting offensive language in online spaces remains an important societal issue, there is still a significant gap in existing research and practial datasets specific to chatbots. Furthermore, many of the current efforts by service providers to automatically filter offensive language are vulnerable to users’ deliberate text manipulation tactics, such as misspelling words. In this study, we analyze offensive language patterns in real logs of 6,254,261 chat utterance pairs from the commercial chat service Simsimi, which cover a variety of conversation topics. Based on the observed patterns, we introduce a novel offensive language detection method—a contrastive learning model that embeds chat content with a random masking strategy. We show that this model outperforms existing models in detecting offensive language in open-domain chat conversations while also demonstrating robustness against users’ deliberate text manipulation tactics when using offensive language. We release our curated chatbot dataset to foster research on offensive language detection in open-domain conversations and share lessons learned from mitigating offensive language on a live platform.

While measuring socioeconomic indicators is critical for local governments to make informed policy decisions, such measurements are often unavailable at fine-grained levels like municipality. This study employs deep learning-based predictions from satellite images to close the gap. We propose a method that assigns a socioeconomic score to each satellite image by capturing the distributional behavior observed in larger areas based on the ground truth. We train an ordinal regression scoring model and adjust the scores to follow the common power law within and across regions. Evaluation based on official statistics in South Korea shows that our method outperforms previous models in predicting population and employment size at both the municipality and grid levels. Our method also demonstrates robust performance in districts with uneven development, suggesting its potential use in developing countries where reliable, fine-grained data is scarce.

Mobile video-based learning attracts many learners with its mobility and ease of access. However, most lectures are designed for desktops. Our formative study reveals mobile learners’ two major needs: more readable content and customizable video design. To support mobile-optimized learning, we present FitVid, a system that provides responsive and customizable video content. Our system consists of (1) an adaptation pipeline that reverse-engineers pixels to retrieve design elements (e.g., text, images) from videos, leveraging deep learning with a custom dataset, which powers (2) a UI that enables resizing, repositioning, and toggling in-video elements. The content adaptation improves the guideline compliance rate by 24% and 8% for word count and font size. The content evaluation study (n=198) shows that the adaptation significantly increases readability and user satisfaction. The user study (n=31) indicates that FitVid significantly improves learning experience, interactivity, and concentration. We discuss design implications for responsive and customizable video adaptation.

Most video-based learning content is designed for desktops without considering mobile environments. We (1) investigate the gap between mobile learners’ challenges and video engineers’ considerations using mixed methods and (2) provide design guidelines for creating mobile-friendly MOOC videos. To uncover learners’ challenges, we conducted a survey (n=134) and interviews (n=21), and evaluated the mobile adequacy of current MOOCs by analyzing 41,722 video frames from 101 video lectures. Interview results revealed low readability and situationally-induced impairments as major challenges. The content analysis showed a low guideline compliance rate for key design factors. We then interviewed 11 video production engineers to investigate design factors they mainly consider. The engineers mainly focus on the size and amount of content while lacking consideration for color, complex images, and situationally-induced impairments. Finally, we present and validate guidelines for designing mobile-friendly MOOCs, such as providing adaptive and customizable visual design and context-aware accessibility support.