Understanding emotion for self-awareness requires recognizing not only its type and intensity but also the surrounding context and the insights it provides. While prior work has studied emotion recording, little attention has given to how such records might foster reflection on context. To address this gap, we developed Moodialogue, a system that enables users to personify emotion and engage in dialogue with it. In a six-week field study with nine participants, we found that personified emotion records supported dialogues that uncovered overlooked contexts and coexisting feelings beyond a single entry. Participants also reported savoring positive emotions more deeply and reframing negative ones from new perspectives. These findings point to design opportunities for systems that move beyond recording, enabling post-recording interactions that deepen reflection on emotional context and meaning.
AI has advanced radiology, yet variability across hospitals and devices undermines reliability and trust. We present a federated learning framework that combines frequency-domain harmonization and instruction-conditioned personalization to deliver consistent and interpretable diagnostic outcomes. Using FFT-based reconstructions informed by radiomics descriptors, the system reduces equipment dependency, while CLIP-based text conditioning enables clinicians to guide reconstructions to local practices and patient needs. We evaluated the framework across four hospitals with fifteen radiologists and fifty patients, spanning polyp detection, rotator cuff tear diagnosis, pneumothorax classification, and breast cancer classification/segmentation. Results show significant gains in accuracy, calibration, and robustness under cross-site transfer, without introducing prohibitive latency. Radiologists reported improved interpretability and preserved professional agency, while patients expressed greater trust, reduced anxiety, and stronger acceptance of AI involvement. This work advances a human-centered design for medical AI, aligning federated learning with transparency, equity, and trustworthy deployment.
Virtual Reality (VR) offers portable and flexible workspaces. However, enabling efficient and comfortable interactions without external input devices remains challenging. We propose leveraging redirected input to enable comfortable and touch-like interaction for quick and intuitive control. Our design study revealed that while touch interaction performs well with direct input, its performance degrades significantly under input redirection. In contrast, using pinch improves redirected input by providing self-haptic feedback and reducing input dimensionality, thereby compensating for spatial discrepancies. Based on these findings, we introduce Redirected Pinch, a bare-hand interaction technique that combines input redirection with pinch confirmation. It creates a virtual plane at waist height, remapping hand movements on the plane to a vertical window, with pinch gestures used for confirmation. A user study demonstrated that Redirected Pinch achieves a strong balance of accuracy, efficiency, comfort, and sense of agency across fundamental interactions.
Parametric modeling is a prevailing 3D modeling approach in design, architecture, and engineering. The emergence of multimodal large language models (LLMs) brings a new opportunity to lower the entry barriers to this powerful tool. However, describing 3D geometries through natural language can be fuzzy and challenging. We introduce co-speech gesture, a natural and expressive interaction modality to complement text prompts for LLM-empowered generative parametric modeling. We first conducted an elicitation study to explore and categorize co-speech gesture expressions. Based on the findings, we designed a multimodal fusion pipeline that parametrizes gestures and synthesizes them with speech. This approach reduces language ambiguity by translating implicit user intentions into explicit parametric attributes, thus lifting the model generation performance. We conducted a two-session user study testing and comparing it with traditional language and sketch inputs. This work streamlines the parametric modeling workflow and explores novel multimodal interaction paradigms for LLM-empowered design and creation. Project page: https://rl-duan.github.io/JustShape/
