Smart materials have garnered significant attention in both academia and industry, yet identifying pragmatically impactful applications still requires contributions from multiple stakeholders, including researchers, designers, and industry professionals.Although previous research has explored novel technical approaches or user-centered applications of smart materials, this study focuses on how to stimulate effective dialogue among stakeholders to explore impactful smart material applications.

This paper presents AccessiBot, a tactile paving detection robot developed to address urban accessibility challenges. Beyond collecting real-time data for evidence-based governance, this study explores AccessiBot's potential to encourage citizen engagement. Using the Wizard-of-Oz methodology, we conducted an in-the-wild study to simulate real-world interactions and observe how people respond to the robot. Our observation findings show that people do help robots, and their engagement with the robot varied significantly, ranging from passive acknowledgement to active assistance. Besides, post-hoc interviews indicated that participants recognized the robot's social value.

Model checking has achieved remarkable success in the hardware domain, largely due to the accumulation of intricate optimizations and finely tuned implementation details. As tools evolve, diagnosing performance differences to better understand the interplay of these factors has become increasingly important. Yet existing problems that reveal such differences are often too large for meaningful inspection, limiting their diagnostic value.To address the problem, this paper proposes AIGROW, a framework for generating hardware model checking problems, and introduces our experience on diagnosing performance differences in model checkers with the generated problems. AIGROW uses a feedback-guided process that evolves problems based on runtime information, selectively retaining those that become more difficult for a target checker. Performance differences are then revealed by evaluating these problems across hardware model checkers that have similar algorithms.Our evaluation demonstrates that AIGROW generates problems that are more than 100 times smaller than those produced by existing generators, while still revealing substantial performance differences. Diagnosing the performance differences has led to concrete improvements in CAR-based checkers: (1) uncovering structural inefficiencies in their exploration strategies, (2) solving 18 previously unsolvable HWMCC’24 problems, and (3) reducing runtime from hours to minutes in several cases.

Spatial Database Management Systems (SDBMSs) aim to store, manipulate, and retrieve spatial data . SDBMSs are employed in various modern applications, such as geographic information systems, computer-aided design tools, and location-based services. However, the presence of logic bugs in SDBMSs can lead to incorrect results, substantially undermining the reliability of these applications. Detecting logic bugs in SDBMSs is challenging due to the lack of ground truth for identifying incorrect results. In this paper, we propose an automated geometry-aware generator to generate high-quality SQL statements for SDBMSs and a novel concept named Affine Equivalent Inputs (AEI) to validate the results of SDBMSs. We implemented them as a tool named Spatter ( Spat ial DBMS Tes ter ) for finding logic bugs in four popular SDBMSs: PostGIS, DuckDB Spatial, MySQL, and SQL Server. Our testing campaign detected 34 previously unknown and unique bugs in these SDBMSs, of which 30 have been confirmed, and 18 have already been fixed. Our testing efforts have been well appreciated by the developers. Experimental results demonstrate that the geometry-aware generator significantly outperforms a naive random-shape generator in detecting unique bugs, and AEI can identify 14 logic bugs in SDBMSs that were totally overlooked by previous methodologies.

Existing mainstream MOT methods can be categorised into two frameworks including two-stage and one-stage ones. Two-stage ones divide MOT task into object detection and association tasks which usually achieve high accuracy. One-stage ones train a joint model to achieve both detection and tracking. So their advantage usually lies in the high tracking efficiency. In this paper, we inherit the advantages of the two types frameworks and propose the object knowledge distilled joint detection and tracking framework (OKD-JDT) to achieve accurate as well as efficient tracking. Firstly, the performance of two-stage methods largely depends on the highly performed detection network. So, we treat the detection network as the teacher network to guide the discriminative object feature learning in one-stage methods by using knowledge distillation. Then, in distillation learning, we design the adaptive attention learning to learn the discriminative features from teacher network to student network. In addition, with the similar appearance and uniform moving behaviour of objects in satellite videos, we propose to use joint center point distance and intersection-over-onion (IOU) to generate tracklets. Experiments on JiLin-1 satellite videos with different objects demonstrate the effectiveness and the state-of-the-art performance of the proposed method.