The interaction between autonomous vehicles (AVs) and pedestrians has gained significant attention, leading to the exploration of external human-machine interfaces (eHMIs) equipped on AVs to facilitate effective communication. While existing research suggests that perceptions of vehicle appearances may influence interactions between pedestrians and AVs, a comprehensive study on the eHMIs related to AV appearance remains lacking. Therefore, we conducted a virtual reality (VR) experiment to investigate how AV appearances affect pedestrians’ acceptance regarding Awareness, Intent, and Harmony during interactions with various eHMIs. Leveraging the fuzzy set qualitative comparative analysis (fsQCA) method, we identified specific combinations of AV appearances and eHMIs that yield either high or low-performance interactions. For example, our findings reveal that text displays exhibit high performance in terms of awareness on AVs that are aggressive and ordinary. Furthermore, we distilled design guidelines to provide actionable suggestions for the design of eHMIs, fostering the acceptance of AVs among pedestrians.

Touch fastening structures are widely used to quickly assemble and disassemble an object with multiple parts. However, such structures are under-explored in the context of additive manufacturing for personal fabrication. We proposed Touch-n-Go, a method for designing touch-fastening structures with customizable mechanical properties such as holding capacities or shearing strength. Additionally, the customization of fastener patterns enables both static and dynamic connections, and the dynamic connections grant the freedom of rotation and translation. To facilitate the customization process, we developed a design tool that allows the integration of fastening structures on the surface of a 3D-printed object. Furthermore, we validated the fastening properties of Touch-n-Go through a series of experiments, and the result exhibits performances that match or even surpass off-the-shelf fasteners. Finally, we demonstrated the implementation of Touch-n-Go through a collection of applications.

Continuously orchestrating in-network model aggregations for federated learning faces fundamental challenges such as the combinatorial nature of traffic reduction, the dynamic trade-offs between system overhead and model convergence, and the unpredictable inputs from uncertain system environments. In this work, we model a nonlinear mixed-integer program to optimize the long-term total cost of federated learning computation overhead, traffic reduction, network delay, and programmable switch reconfigurations over time. To attack the lexicographic minimax, submodular, and online nature of this problem, we propose a polynomial-time algorithmic framework to judiciously designate the timing of reconfigurations, while designing and invoking a linearized transformation for selecting routing paths, a greedy sub-algorithm for selecting aggregation locations, and an online learning sub-algorithm for controlling federated learning convergence. We demonstrate our rigorous mathematical insights behind our algorithms, and prove the competitive ratio as the performance guarantee. Using trace-driven evaluations, we have validated our approach's superiority over existing methods.

This paper presents MagneDot, a novel method for making interactive magnetic shape displays through an integrated fabrication process. Magnetic soft materials can potentially create fast, responsive morphing structures for interactions. However, novice users and designers typically do not have access to sophisticated equipment and materials or cannot afford heavy labor to create interactive objects based on this material. Modified from an open-source 3D printer, the fabrication system of MagneDot integrates the processes of mold-making, pneumatic extrusion, magnetization, and actuation, using cost-effective materials only. By providing a design tool, MagneDot allows users to generate G-code for fabricating and actuating displays of various morphing effects. Finally, a series of design examples demonstrate the possibilities of shape displays enabled by MagneDot.

The field of Human-Computer-Interaction (HCI) has been consistently utilizing kinematic mechanisms to create tangible dynamic interfaces and objects. However, the design and fabrication of these mechanisms are challenging due to complex spatial structures, step-by-step assembly processes, and unstable joint connections resulting from the inevitable matching errors within separated parts. In this paper, we propose an integrated fabrication method for one-step FDM 3D printing (FDM3DP) kinematic mechanisms to create dynamic objects without additional post-processing. We describe the Arch-printing and Support-bridges method, which we call All-in-One Print, that compiles given arbitrary solid 3D models into printable kinematic models as G-Code for FDM3DP. To expand the design space, we investigate a series of motion structures (e.g., rotate, slide, and screw) with multi-stabilities and develop a design tool to help users quickly design such dynamic objects. We also demonstrate various application cases, including physical interfaces, toys with interactive aesthetics and daily items with internalized functions.

4D printing encodes transformability over time, which empowers users to create artifacts by on-demand deformation. The creative process of 4D printing shape-changing artifacts can be challenging because of its discontinuous fabrication steps, such as digital designing, specific path planning, automatic printing and manual triggering. We hypothesize that switching from typical 4D printing reliant on 3D printers to a more “handcrafted” method can allow users to understand and continuously reflect upon the artifact and its transformability. Towards this vision, we introduce 4Doodle, a hybrid craft approach that integrates unique deformation controllability and five techniques for freehand 4D printing, using a 3D pen. To tackle the shape-changing challenges of uncertain hands-on fabrication, we develop a mixed reality system to help novices master the manual skills of 4D printing. We also demonstrate a series of 4D printed artifacts with fully human intervention. Finally, our user study shows that 4Doodle lowers the skill-acquisition barrier associated with handcrafting 4D printed artifacts, and it has great potential for creative production and spatial ability.

Orthoses with electronic functions have emerged as a promising medical product in response to the increasing demand for rehabilitation training, therapy assistance, and health monitoring. However, fabricating this “smart orthosis” often requires long development cycles and exorbitant prices. We introduce E-Orthosis, an integrated fabrication approach with construction toolkits for healthcare professionals to quickly embed electronics in off-the-shelf orthoses with customized functions cost-effectively and time-efficiently. Specifically, we develop components with magnets and pogo pins to support rapid attachment and sustainable use, and textile-based electrodes with snap installation to improve the wearing experience. We also provide a circuit iron tool to apply circuit traces on complex surfaces of orthoses directly and a hot punch tool to embed magnet ports and electrodes. Three application examples, technical evaluations, and expert reviews demonstrate the functionality of E-Orthosis and the potential for democratizing rapid-developed and low-cost smart orthoses for patients.

Delayed Gradient Averaging (DGA) has gained massive attention for improving the training efficiency of Federated Learning (FL) at edge networks, by allowing local computation in parallel to communication. However, it faces multiple challenges due to data distribution across heterogeneous edge devices and dynamic network environments. To address these challenges, we present A-DGA, a novel communication learning parallel federated learning algorithm designed for fluctuating, heterogeneous, and high-latency network conditions. Our proposed A-DGA dynamically sets extra local updates based on network status, avoiding inefficient training upon the outdated gradients. Theoretical analysis demonstrates that A-DGA outperforms DGA in terms of convergence rates given the fixed time slot length. We further conduct massive evaluations under various conditions, including different datasets, models, and data distribution. The experimental results show that A-DGA performs the best, compared to the state-of-the-art methods, achieving an acceleration factor of approximately x2 ∼ x4 and x1.2 ∼ x2, compared to FedAvg and DGA, respectively. Besides, our A-DGA also reduces energy consumption by about 40% ∼ 50% compared to DGA.

Bridges are unique structures appeared in fused deposition modeling (FDM) that make rigid prints flexible but not fully explored. This paper presents X-Bridges, an end-to-end workflow that allows novice users to design tunable bridges that can enrich 3D printed objects' deformable and physical properties. Specifically, we firstly provide a series of deformation primitives (e.g. bend, twist, coil, compress and stretch) with three versions of stiffness (loose, elastic, stable) based on parametrized bridging experiments. Embedding the printing parameters, a design tool is developed to modify the imported 3D model, evaluate optimized printing parameters for bridges, preview shape-changing process, and generate the G-code file for 3D printing. Finally, we demonstrate the design space of X-Bridges through a set of applications that enable foldable, resilient, and interactive shape-changing objects.