Various virtual locomotion techniques and visual transition methods are used in VR-based navigation research, yet few studies have systematically examined their effects on spatial learning, cognitive map formation, and navigational performance in complex indoor environments. We conducted a between-subjects study (N=142) in two high-fidelity VR hospital contexts, including free exploration and task-based wayfinding, while treating locomotion and viewpoint transitions as experimental factors. Spatial learning was measured through pointing, distance estimation, and sketch-map accuracy; performance was measured through completion time and distance traveled; and experience was measured through cybersickness, perceived presence, and usability. Locomotion techniques affected task completion time, with teleportation associated with faster performance in the task-based context. Spatial learning effects were mixed, with patterns indicating that techniques without viewpoint transitions may better support cognitive mapping. Empirical insights and guidelines are provided to improve the reliability and real-world applicability of VR-based wayfinding research.

Smart contracts on the open network (TON) have become vital in Internet of Things (IoT) applications due to their low latency and high scalability. However, the unique architectural features of TON introduce specialized vulnerabilities that existing tools fail to address comprehensively. In this letter, we propose a novel defect detection framework that combines large language models (LLMs) for automated defect discovery with a locatable call graph for precise and efficient code analysis. Our method identifies four new types of TON-specific defects: 1) Ignore Errors Mode Usage; 2) Premature Acceptance; 3) Pseudo Deletion; and 4) Improper Jetton Refund. Evaluated on 1640 real-world smart contracts written in FunC and Tact, the framework uncovers 669 defects, with an average of one defect every 2.45 code segments. The detection achieves an average F1 score of 99.75% for FunC and 100% for Tact contracts. Additionally, our approach demonstrates lightweight computational overhead, consuming only 12.6 MB of memory and achieving a mean response time of 0.05 s. These results highlight the accuracy, efficiency, and practicality of our framework for securing TON-based smart contracts in IoT ecosystems.

In this paper, we focus on diversifying the Video Moment Retrieval (VMR) model into more scenes. Most existing video moment retrieval methods focus on aligning video moments and queries by capturing the cross-modal relationship, which largely ignores the cross-instance relationship behind the representation learning. Thus, they may easily get trouble into the inaccurate cross-instance contrastive relationship in the training process: 1) Existing approaches can hardly identify similar semantic content across different scenes. They incorrectly treat such instances as negative samples (termed faulty negatives), which forces the model to learn the features from query-irrelevant scenes. 2) Existing methods perform unsatisfactorily in locating the queries with subtle differences. They neglect to mine the hard negative samples that belong to similar scenes but have different semantic content. In this paper, we propose a novel robust video moment retrieval method that prevents the model from overfitting the query-irrelevant scene features by accurately capturing both the cross-modal and cross-instance relationships. Specifically, we first develop a scene-independent cross-modal reasoning module that filters out the redundant scene contents and infers the video semantics under the guidance of query information. Then, the faulty and hard negative samples are mined from the negative ones and calibrated for their contribution to the overall loss in contrastive learning. We validate our contributions through extensive experiments on cross-scene video moment retrieval settings, where the training and test data are from different scenes. Experimental results show that the proposed robust video moment retrieval model can effectively retrieve target videos by capturing the real cross-modal and cross-instance relationships.