Quadratic Surveys (QSs) elicit more accurate preferences than traditional methods like Likert-scale surveys. However, the cognitive load associated with QSs has hindered their adoption in digital surveys for collective decision-making. We introduce a two-phase "organize-then-vote" QS to reduce cognitive load. As interface design significantly impacts survey results and accuracy, our design scaffolds survey takers' decision-making while managing the cognitive load imposed by QS. In a 2x2 between-subject in-lab study on public resource allotment, we compared our interface with a traditional text interface across a QS with 6 (short) and 24 (long) options. Two-phase interface participants spent more time per option and exhibited shorter voting edit distances. We qualitatively observed shifts in cognitive effort from mechanical operations to constructing more comprehensive preferences. We conclude that this interface promoted deeper engagement, potentially reducing satisficing behaviors caused by cognitive overload in longer QSs. This research clarifies how human-centered design improves preference elicitation tools for collective decision-making.

Positive social interactions can occur in groups of many shapes and sizes, spanning from small and private to large and open. However, social media tends to binarize our experiences into either isolated small groups or into large public squares. In this paper, we introduce Burst, a social media design that allows users to share and curate content between many spaces of varied size and composition. Users initially post content to small trusted groups, who can then ''burst'' that content, routing it to the groups that would be the best audience. We instantiate this approach into a mobile phone application, and demonstrate through a ten-day field study (N=36) that Burst enabled a participatory curation culture. With this work, we aim to articulate potential new design directions for social media sharing.

Image editing tools have a plethora of commercial and creative applications — content-creation, digital photography, advertisements, graphic design, and development of educational media. The shortcomings of image editing software include difficulty of use and, for AI-based software, reliance on single image editing models, which often poses the dilemma of a tradeoff between image editing quality and user-friendliness. While the performances of individual image editing models have improved with their evolution over time, these singular models are often specialized on specific image editing tasks. In this work, we introduce HILITE, an open-source interactive image editing platform with a human-in-the-loop design that combines six diffusion-based image editing models. For one, HILITE’s accessible and easily-understandable user interface provides a straightforward user workflow from image input and prompt entry to selection of desired output. Secondly, the combination of several models with diverse specializations in turn allows HILITE to generalize on a wide variety of image editing tasks, essentially creating a "one-stop shop" for image editing. Third, HILITE iteratively takes user feedback, which both enhances the user experience and enables collection of crowd-sourced data for image editing. HILITE outperforms two major image editing softwares, OpenAI’s DALL•E 3 and Google’s Imagen 3, across two widely-user quantitative metrics for image editing evaluation. Considering the growing demand for readily-available and high-performing image editing tools, HILITE provides a novel platform design with multifaceted use cases in both business and academia. The platform can be found at https://platform.opennlplabs.org/ or https://platform-deployment.vercel.app/.

This innovative practice full paper describes an approach to visualizing and analyzing collaborative learning. Collaborative learning is vital for enriching computer science education and developing key skills. However, educators often lack tools for effectively tracking and analyzing student engagement in group work. Our study introduces GA2Graph, a data-driven system that uses Ne04j to visualize and analyze online collaborative patterns in classroom activities. It graphically represents students and their interactions, helping educators understand group dynamics. We tested this system in a large-enrollment database course, revealing that despite emphasis on assigned roles like those in POGIL, students frequently disregarded them and adopted more individualistic or varied collaborative strategies.

Temporal graphs, depicting time-evolving node connections through temporal edges, are extensively utilized in domains where temporal connection patterns are essential, such as recommender systems, financial networks, healthcare, and sensor networks. Despite recent advancements in temporal graph representation learning, performance degradation occurs with periodic collections of new temporal edges, owing to their dynamic nature and newly emerging information. This paper investigates online representation learning on temporal graphs, aiming for efficient updates of temporal models to sustain predictive performance during deployment. Unlike costly retraining or exclusive fine-tuning susceptible to catastrophic forgetting, our approach aims to distill information from previous model parameters and adapt it to newly gathered data. To this end, we propose TGOnline, an adaptive online meta-learning framework, tackling two key challenges. First, to distill valuable knowledge from complex temporal parameters, we establish an optimization objective that determines new parameters, either by leveraging global ones or by placing greater reliance on new data, where global parameters are meta-trained across various data collection periods to enhance temporal generalization. Second, to accelerate the online distillation process, we introduce an edge reduction mechanism that skips new edges lacking additional information and a node deduplication mechanism to prevent redundant computation within training batches on new data. Extensive experiments on four real-world temporal graphs demonstrate the effectiveness and efficiency of TGOnline for online representation learning, outperforming 18 state-of-the-art baselines. Notably, TGOnline not only outperforms the commonly utilized retraining strategy but also achieves a significant speedup of ~30x.

This paper investigates the few-shot temporal reasoning capability within the hyperbolic space. The goal is to forecast future events for newly emerging entities within temporal knowledge graphs (TKGs), leveraging only a limited set of initial observations. Hyperbolic space is advantageous for modeling emerging graph entities for two reasons: First, its geometric property of exponential expansion aligns with the rapid growth of new entities in real-world graphs; Second, it excels in capturing power-law patterns and hierarchical structures, well-suitable for new entities distributed at the peripheries of graph hierarchies and loosely connected with others through few links. We therefore propose a meta-learning framework, MetaHKG, to enable few-shot temporal reasoning within a hyperbolic space. Unlike prior hyperbolic learning works, MetaHKG addresses the challenges of effectively representing new entities in TKGs and adapting model parameters by incorporating novel hyperbolic time encodings and temporal attention networks that achieve translational invariance. We also introduce a meta hyperbolic optimization algorithm to enhance model adaptation by learning both global and entity-specific parameters through bi-level optimization. Comprehensive experiments conducted on three real-world temporal knowledge graphs demonstrate the superiority of MetaHKG over a diverse range of baselines, which achieves average 5.2% relative improvements. Compared to its Euclidean counterpart, MetaHKG operates in a lower-dimensional space but yields a more stable and efficient adaptability towards new entities.

In spite of efforts to increase participation, many online groups struggle to survive past the initial days, as members leave and activity atrophies. We argue that a main assumption of online group design---that groups ask nothing of their members beyond lurking---may be preventing many of these groups from sustaining a critical mass of participation. In this paper, we explore an alternative commitment design for online groups, which requires that all members commit at regular intervals to participating, as a condition of remaining in the group. We instantiate this approach in a mobile group chat platform called Commit, and perform a field study comparing commitment against a control condition of social psychological nudges with N=57 participants over three weeks. Commitment doubled the number of contributions versus the control condition, and resulted in 87% (vs. 19%) of participants remaining active by the third week. Participants reported that commitment provided safe cover for them to post even when they were nervous. Through this work, we argue that more effortful, not less effortful, membership may support many online groups.