Research Through Art is establishing artistic practice as a legitimate mode of inquiry, yet the operational work of exhibition-making remains under-articulated. While exhibitions are frequently used to present research-based artworks, they are often treated as outcomes rather than as structured research environments shaped by curatorial decisions. Building on the Research Through Art method, we combine first-person reflection and annotated portfolio to articulate a set of modular components that structure how such exhibitions are designed, staged, and encountered. Illustrated through a case study, Perversion of Interfaces—an exhibition of research based critical objects—we present basic operational modules for exhibition-based research output. These modules include shared curatorial prompts, interpretation layers, spatial and experiential planning, peripheral exhibition artifacts, public programming, and post-exhibition reflection. We hope this transferable framework can support educators and curators to present work as exhibitions and to develop modular toolkits for organizing art shows.

As artificial intelligence systems increasingly mediate everyday communication—assisting users with emails, tone, and social interactions—we observe a pattern in which the cognitive labor of social self-regulation is externalized and outsourced. Inter:Lace is a speculative wearable art project that explores the consequences of outsourcing social judgment to a large-language model-based assistive system. Designed to be worn as a conspicuous-looking choker, Inter:Lace monitors the conversational context of the wearer and performs “behavioral intervention” through a tightening mechanism designed to prompt politeness, composure, and social compliance. As a provocative object, Inter:Lace was designed and presented as a commercial product, accompanied by supporting materials, such as instructions and coupons. By positioning the piece as a marketable consumer good that induces bodily anxiety, both the object itself and its display aim to reveal a fundamental misalignment between social assistance and embodied experience in contemporary consumerism.

This full-day studio invites participants to explore Upstream Salvage as a design method—a mindset that transforms broken, discarded, or obsolete electronics into sites of creativity, experimentation, and care. Building on the “Vape Synth” project—a sound instrument created from recycled vape hardware—the session expands this approach into a collective workshop on re-use and unmaking. Participants disassemble vapes and other salvaged devices to build synthesizers and quick, tangible prototypes that reveal new interaction logics. Over the day, participants collectively experiment with transforming the fragments of salvaged electronics into quick, tangible prototypes that reveal new human-device interactions. Situated within global traditions of resourceful making, such as gambiarra (Brazil), jugaad(India), Shanzhai and Lajilao(China), and more, this studio explores salvaging as a process-oriented methodology for prototyping—working with materials that foreground improvisation, disassembly, and iterative discovery. Participants leave with a prototype, a documented process, and a collaborative zine of “salvage recipes.”

Representational forms are central to how we explore, communicate, and learn. Yet, they can be challenging to engage with as designers because they vary across disciplines, cultures, and communities. In this paper, we describe our analysis of a dance and computing learning environment through the lens of distributed cognition to examine how representations and processing of information across people and systems impacted the learning process. We analyzed video and audio data from three workshops of dance and STEM instructors learning about, creating with, and co-designing computing activities with danceON—a creative computing platform that supports coding animations over dance videos. We identified the ways that the instructors used their bodies as a shared point of negotiation while co-creating dance artifacts, the participatory role of danceON within the sensemaking process, the ways that the instructors interpreted and translated representations across physical and digital spaces, and the impact of the instructors’ collaborative interactions to their sensemaking.

Abstract Visible‐infrared person re‐identification (VI‐ReID) aims to recognize images of the same person captured in different modalities. Existing methods mainly focus on learning single‐granularity representations, which have limited discriminability and weak robustness. This paper proposes a novel dual‐granularity feature fusion network for VI‐ReID. Specifically, a dual‐branch module that extracts global and local features and then fuses them to enhance the representative ability is adopted. Furthermore, an identity‐aware modal discrepancy loss that promotes modality alignment by reducing the gap between features from visible and infrared modalities is proposed. Finally, considering the influence of non‐discriminative information in the modal‐shared features of RGB‐IR, a greyscale conversion is introduced to extract modality‐irrelevant discriminative features better. Extensive experiments on the SYSU‐MM01 and RegDB datasets demonstrate the effectiveness of the framework and superiority over state‐of‐the‐art methods.

Graph attention networks (GATs)-based method performs well in Human–Object Interaction (HOI) detection due to its ability to aggregate contextual information. However, the traditional GATs-based methods are computationally intensive, and the graph’s structure cannot effectively represent the HOI in an image. Meanwhile, the graph models are unable to predict the interactions that involve less contextual information correctly. In this paper, we design a method based on graph models called V-SGATs, which stands for Visual Branch and Star Graph Attention Networks. The human-centric star graph and object-centric star graph are adopted to reduce computational complexity, and the logical structure of the star graph can represent HOI more reasonably. Meanwhile, the visual branch that recognizes the interaction without utilizing the contextual information is designed to aid the graph model in predicting the interactions that involve less contextual information. Experiments are carried out on two large-scale HOI public benchmarks V-COCO and HICO-DET, and the results show that the proposed method performs better than most of the existing methods based on GATs.