The Internet of Vehicles (IoV), as a critical component of future intelligent transportation systems, enables vehicles to generate and exchange massive volumes of sensing data in real time. To facilitate efficient data sharing and alleviate the burden of local storage, vehicle data is typically encrypted and outsourced to the cloud, with data availability ensured through searchable encryption. However, existing IoV data sharing frameworks offer limited support for secure multi-keyword search and dynamic user revocation. Moreover, most current solutions rely on public-key searchable encryption, which compromises the efficiency required for data processing in IoV environments. To address these challenges, we propose an efficient multi-user searchable and revocable data sharing scheme (EMUSR-SSE) tailored for IoV. EMUSR-SSE extends symmetric searchable encryption to support efficient multi-user access control and trustworthy multi-keyword search by designing a proxy matrix transformation mechanism integrated with Intel SGX. To enable flexible and scalable user revocation, EMUSR-SSE introduces a revocable dynamic sparse Merkle tree structure to manage multi-user permissions effectively. Furthermore, by leveraging the trusted execution environment within SGX and encrypting each data item individually, EMUSR-SSE significantly mitigates the risk of key leakage. Security analysis and experimental evaluations demonstrate that EMUSR-SSE achieves high efficiency and strong practical performance in dynamic IoV environments.

Despite the growing interest in Behavioural Cloning for robots, few existing research has explicitly explored the impact of user interfaces on the effectiveness of expert demonstrations. We investigate the importance of user interface design in Behavioural Cloning, highlighting the critical role that interfaces play in conveying human demonstrations and robotics capabilities. This study compares the effectiveness of first and third-person perspective interfaces for robot shoe-lacing, a highly dexterous, bi-manual manipulation task that involves deformable objects and requires high precision. Our study highlights the importance of considering the impact of interface design on expert demonstration quality in Behavioural Cloning applications. By providing a first-person perspective, we observed significant differences in demonstration execution time and consistency compared to the third-person perspective. These findings suggest that the choice of interface can influence the quality of expert demonstrations, which in turn affects the performance of learning algorithms.

In cloud-assisted electronic health (eHealth) systems, the exponential growth of electronic health records (EHRs) has prompted healthcare organizations to move it to the cloud. However, EHRs are encrypted before being outsourced for privacy. Although searchable encryption schemes for EHRs have been proposed, their search efficiency and functionality for massive EHRs with high-dimensional are still insufficient. In this paper, we adopt an attribute hierarchy structure for medical datasets, enabling efficient multi-dimensional range search and reducing high-dimensional EHRs to low-dimensional vectors. To further improve search efficiency, we design an index tree that require no additional storage and computational overhead, significantly improving efficiency in search, trapdoor generation, and index building. Our scheme is well-suited for large-scale medical data scenarios, especially in dealing with high-dimensional and massive datasets. Extensive experiments demonstrate the superiority of our scheme over existing solutions, particularly in large-scale medical data scenarios. Compared to the classic EDMRS scheme, our scheme has a computational overhead in index building and search that is only about 1/500 and 1/10 of EDMRS when the number of keywords and electronic health records is 3,000 and 6,000, respectively. Moreover, as medical data and keywords increase, our scheme shows slower computational overhead growth compared to EDMRS.

Shoe lacing (SL) is a challenging sensorimotor task in daily life and a complex engineering problem in the shoe-making industry. In this paper, we propose a system for autonomous SL. It contains a mathematical definition of the SL task and searches for the best lacing pattern corresponding to the shoe configuration and the user preferences. We propose a set of action primitives and generate plans of action sequences according to the designed pattern. Our system plans the trajectories based on the perceived position of the eyelets and aglets with an active perception strategy, and deploys the trajectories on a bi-manual robot. Experiments demonstrate that the proposed system can successfully lace 3 different shoes in different configurations, with a completion rate of 92.0%, 91.6% and 77.5% for 6, 8 and 10-eyelet patterns respectively. To the best of our knowledge, this is the first demonstration of autonomous SL using a bi-manual robot.

User profiling plays an important role in online news recommendation systems. In this paper, we analyze the relationship between users' clicking behaviors and the category of the news story to model user's interests by mining web log data of an adaptive news system. We train a Memory-based User Profile (MUP), which imitates human being's learning, remembering and forgetting mechanisms, to predict users' potential interests dynamically. We mainly focus on experimental analysis to refine the MUP scheme. Firstly, we materialize the meanings of all parameters of MUP by important factors (i.e., absorbing factor, forgetting factor, timescale and learning strength) in human being's learning and forgetting process. Secondly, we demonstrate how to determine the values of parameters for different users to reflect their distinct learning and forgetting abilities. Thirdly, we derive a threshold from MUP's recursion formula, which can be used to simply distinguish long-term and short-term interests. Our evaluations are carried out on IdoIcan's web log data, results show MUP can model user's profile effectively.