Short-form video (SFV) platforms are increasingly popular, yet the rapid context switching and their potential effects on children’s cognitive functions are not well understood. In this work, we conducted a between-subjects experiment (N = 180) to examine how YouTube Shorts affects young children’s short-term memory (STM) and working memory (WM), measured using the forward and backward digit span tasks. The study focused on two core platform features of SFV: the easy-to-use swipe interface and the recommendation system. Using a 2 × 2 factorial design, we compared four SFV group conditions that varied by interaction mode and content source, complemented by two long-form video baseline conditions (one with constant context switching and one without). Our results show that the feature combinations and the baseline comparisons were not associated with changes in STM or WM. However, swipe interaction increased video switching, while recommendation-based content increased category switching. The higher combined levels of video and category switching across participants were associated with marginal effects on working memory performance, while STM remained unaffected.

The ubiquity of technology and the “zero-cost” nature of cloud services result in users overlooking the environmental impact of their online usage. While some cloud services (e.g., LLM inference) have significantly higher environmental impact, this work focuses on emails, a service widely used yet representing one of the smallest usages of cloud resources. Just as recycling a piece of paper may not have a great impact but can contribute to environmental awareness, deleting emails symbolizes a small yet meaningful behavior change - a ’gesture’ toward environmental responsibility. In this work, we propose E-litter, a mobile system combining a bulk-delete UI with eco-feedback to encourage email deletion. By representing email storage in tangible terms (e.g., sheets of paper), E-litter nudges users to become more aware of cloud usage. A user study with Gmail users shows that E-litter significantly increases email deletion, highlighting the potential of UI and eco-feedback in impacting cloud usage behavior.

The Myers-Briggs Type Indicator (MBTI) is a widely used personality assessment tool that groups individuals into 16 types (based on 4 dimensions). The popularity of MBTI and the availability of online MBTI assessments have led to the increasing usage of MBTI, including sharing and displaying their MBTI types as part of their online identity. This study investigates the trend of combining social media and personal assessment tools such as MBTI by exploring how people interpret their MBTI and how they form impressions and interact with others based on others' MBTI labels. Through a thematic analysis of posts from the subreddit r/mbti and a two-part online survey, people use MBTI for self-reflection, validation, and personal development, while often overestimating their type. The 4-letter MBTI also helps in understanding interpersonal relationships, but can reinforce stereotypes in first impressions. Based on these findings, we show how ''one MBTI (type) does not fit all'' and explore how modified MBTI stickers (with percentage information) can help reduce bias on social media profiles.

Graph Neural Networks (GNNs) have emerged as a promising class of Machine Learning algorithms to train on non-euclidean data. GNNs are widely used in recommender systems, drug discovery, text understanding, and traffic forecasting. Due to the energy efficiency and high-performance capabilities of GPUs, GPUs are a natural choice for accelerating the training of GNNs. Thus, we want to better understand the architectural and system-level implications of training GNNs on GPUs. Presently, there is no benchmark suite available designed to study GNN training workloads. In this work, we address this need by presenting GNNMark, a feature-rich benchmark suite that covers the diversity present in GNN training workloads, datasets, and GNN frameworks. Our benchmark suite consists of GNN workloads that utilize a variety of different graph-based data structures, including homogeneous graphs, dynamic graphs, and heterogeneous graphs commonly used in a number of application domains that we mentioned above. We use this benchmark suite to explore and characterize GNN training behavior on GPUs. We study a variety of aspects of GNN execution, including both compute and memory behavior, highlighting major bottlenecks observed during GNN training. At the system level, we study various aspects, including the scalability of training GNNs across a multi-GPU system, as well as the sparsity of data, encountered during training. The insights derived from our work can be leveraged by both hardware and software developers to improve both the hardware and software performance of GNN training on GPUs.