This paper introduces Technology Redux, a new methodological approach in HCI that seeks to reenact past technological experiences in today’s everyday life, critically reflect on the roles and impacts of present technologies, and provoke new perspectives on future directions of technological development. Building on reflections from, BeeperRedux, a case study that recreated the 1990s beeper as a smartphone application, we present four practical strategies for enacting Technology Redux: (1) reproducing experiences beyond replicating past technologies, (2) balancing friction for provocation and everyday integration, (3) integrating personal narratives as research resources, and (4) surfacing the sociocultural and infrastructural contexts. We argue that this methodology uniquely integrates the perspectives and strengths of the historicist approach, speculative design, and research products, introducing a critical and actionable research practice for HCI.

Creating a cast of characters by attending to their relational dynamics is a critical aspect of most long-form storywriting. However, our formative study (N=14) reveals that writers struggle to envision new characters that could influence existing ones, balance similarities and differences among characters, and intricately flesh out their relationships. Based on these observations, we designed Constella , an LLM-based multi-agent tool that supports storywriters’ interconnected character creation process. Constella suggests related characters (FRIENDS DISCOVERY feature), reveals the inner mindscapes of several characters simultaneously (JOURNALS feature), and manifests relationships through inter-character responses (COMMENTS feature). Our 7–8 day deployment study with storywriters (N=11) shows that Constella enabled the creation of expansive communities composed of related characters, facilitated the comparison of characters’ thoughts and emotions, and deepened writers’ understanding of character relationships. We conclude by discussing how multi-agent interactions can help distribute writers’ attention and effort across the character cast.

Score distillation sampling (SDS) demonstrates a powerful capability for text-conditioned 2D image and 3D object generation by distilling the knowledge from learned score functions. However, SDS often suffers from blurriness caused by noisy gradients. When SDS meets the image editing, such degradations can be reduced by adjusting bias shifts using reference pairs, but the de-biasing techniques are still corrupted by erroneous gradients. To this end, we introduce Identity-preserving Distillation Sampling (IDS), which compensates for the gradient leading to undesired changes in the results. Based on the analysis that these errors come from the text-conditioned scores, a new regularization technique, called fixed-point iterative regularization (FPR), is proposed to modify the score itself, driving the preservation of the identity even including poses and structures. Thanks to a self-correction by FPR, the proposed method provides clear and unambiguous representations corresponding to the given prompts in image-to-image editing and editable neural radiance field (NeRF). The structural consistency between the source and the edited data is obviously maintained compared to other state-of-the-art methods. Our code is https://github.com/shhh0620/IDS

Designing user experiences for group recommendation systems (GRS) is challenging, requiring a nuanced understanding of the influence of social interactions between users. Using Spotify Blend as a real-world case of music GRS, we conducted empirical studies to investigate intricate social interactions among South Korean users in GRS. Through a preliminary survey about Blend experiences in general, we narrowed the focus for the main study to relationships between two users who are acquainted or close. Building on this, we conducted a 21-day diary study and interviews with 30 participants (15 pairs) to probe more in-depth interpersonal dynamics within Blend. Our findings reveal that users engaged in implicit social interactions, including tacit understanding of their companions and indirect communication. We conclude by discussing the newly discovered value of GRS as a social catalyst, along with design attributes and challenges for the social experiences it mediates.

With the increased popularity of social media, editing and sharing selfies using augmented reality filters, face editors, and sticker features have become popular social trends. However, it can be challenging for people with visual impairments to edit and add fun elements to their selfies, although they actively participate in social media. We conducted an online survey in which 47 participants with visual impairments identified their experience with and demands for using such features. Based on the results, we designed and developed a selfie editing application with sticker features based on voice command and voice feedback for people with visual impairments. We then conducted a design probe study with four participants who were visually impaired to provide design guidelines to increase the accessibility of selfie editing apps with sticker features. Voice command and feedback were both highly appreciated by participants, and we also investigated their requirements for selfie editing features.

Over the past decade, crowdsourcing has been actively studied for indoor localization since surveying sites (e.g. wardriving) is a costly process. However, the existing localization systems based on crowdsourcing usually achieve lower location accuracy than the site survey based systems. We note that the magnetic field is robust to environmental changes like pedestrian activities and door/window movements, particularly compared with radio signals such as WiFi. To overcome the low performance of the crowdsourcing based approaches, we design an indoor positioning system using the crowdsourced data of the magnetic field. We substantiate a novel HMM-based learning model to construct a database of magnetic field fingerprints from smartphone users. Experiments in an indoor space consisting of aisles show that the proposed system achieves the learning accuracy of 96.47% and median positioning accuracy of 0.25m.