Knowledge Tracing (KT) is pivotal in intelligent tutoring systems, as it models the dynamic evolution of student knowledge from their learning interactions. However, the cross-disciplinary generalization of existing KT models is subjected to a dual constraint: the heterogeneity in students' cognitive abilities and the divergent disciplinary-specific knowledge structures.To address this challenge, we propose DTransKT, a dual transferable knowledge tracing framework tailored for cross-disciplinary adaptability. DTransKT enhances existing knowledge tracing models by dynamically aligning student representations and integrating external knowledge semantics.Specifically, the framework incorporates a Cross-disciplinary Graph-matching (CG) module, which captures meta-skill representations based on students' learning trajectories. Through cross-disciplinary node matching, the CG module aligns student-specific features, thereby improving tracing accuracy. Additionally, the Cross-disciplinary Attention-assisting (CA) module leverages pre-trained language models to extract meta-semantic from textual content, enhancing transferability.Extensive experimental evaluations demonstrate that DTransKT consistently enhances the performance of seven prominent KT models under direct transfer settings, achieving average improvements of 14.2% in accuracy (ACC) and 4.5% in area under the curve (AUC) across diverse datasets. These findings affirm the efficacy of our approach in enabling cross-disciplinary transfer for knowledge tracing. Code and pre-trained models are available at: https://github.com/Dual-KT/DTransKT.

Unmanned Aerial Vehicle (UAV) video surveillance has become indispensable for intelligent transportation systems, autonomous driving and unmanned vehicle applications. However, traffic objects captured from high altitudes typically exhibit small sizes and weak feature representation, while being susceptible to interference from UAV motion and complex road environments, resulting in detection challenges. Additionally, multi-scale object detection requires a balance between accuracy and efficiency. To address these issues, we propose a real-time small traffic object detection method based on adaptive fine-grained attention and multi-scale fusion mechanism. Building upon the Real-Time Detection Transformer (RT-DETR), we construct a Small Object Enhanced Real-Time Detection Transformer (SOE-RTDETR) with an adaptive fine-grained channel attention block to enhance small object feature extraction. The SOE-RTDETR incorporates CSP-Dilated Reparam Residual Blocks (CSP-DRRB) that expand the convolutional receptive field while maintaining computational efficiency through depthwise convolution and reparameterization. We further optimize the attention-based intra-scale feature interaction module using deformable attention mechanism and propose a bidirectional feature pyramid network specifically designed for small traffic object detection to strengthen multi-scale feature fusion. Experimental results on the VisDrone2019 dataset demonstrate that the proposed SOE-RTDETR achieves improvements of 2.9% in mAP@50 and 2.3% in mAP@50:95 on the validation set, and 2.3% and 1.5% on the test set compared to the baseline RT-DETR-r18 model, while maintaining equivalent model size and computational cost.

Knowledge Tracing (KT) involves using deep neural networks (DNNs) to track students’ learning progress, but over-fitting can be an issue with small datasets. Adversarial examples have been introduced to improve generalization, but they may overlook individual student differences. To address this issue, we propose a new model called Customized Adversarial Training Knowledge Tracing (CATKT). The model generates unique adversarial perturbations for each sample based on the characteristics of knowledge tracking tasks, thereby better adapting to students’ learning traits and enhancing the effectiveness and accuracy of knowledge tracking tasks. Specifically, CATKT can dynamically adjust the level of perturbations according to the difficulty of the knowledge, adding non-uniform and effective perturbations to each interaction embedding, and replacing the original labels with adaptively smoothed labels to improve task accuracy. Experimental results show that CATKT outperforms previous knowledge tracking methods in terms of performance and provides new ideas and methods for teaching assessment and personalized learning in the education field.

In a smart grid, the identity authentication between a smart meter and an aggregator is a prerequisite for both parties to establish a secure channel. All existing authentication schemes have their own shortcomings in either security or efficiency to make them difficult to meet the security requirements of the smart grid. In this paper, we are motivated to propose an efficient and secure mutual authentication scheme based on the extended Chebyshev chaotic maps. The smart meters and aggregators are registered with a trusted third party to conduct a mutual authentication. The proposed scheme provides anonymity protection for smart meters to achieve perfect forward secrecy. Through security analysis, we conclude that the proposed scheme has the characteristics of high security. In addition, we compare the proposed scheme with other three schemes in terms of number of encryption operations, computation delay. The performance comparison demonstrates that the proposed scheme is efficient without sacrificing the desired security properties.

With the development of smart grid, the continuous updating of power grid data texts has increased the redundancy of the database, which has led to difficulties of filtering and obtaining information. Therefore, an intelligent recommendation method for power grid data is proposed, which combines deep learning to mark entities based on existing knowledge bases and knowledge graphs, and then uses intent prediction matching algorithms to deeply mine user search intentions to realize accurate prediction of user search intent. The experimental results show that the model has a good prediction effect, a wide range of applications, and effectively improves work efficiency.

Mobile edge computing (MEC) in cyber-physical systems (CPSs) with massive resource-constrained edge computing node (ECN) faces new challenges in security provisioning. The traditional centralized security authentication schemes with low performance are no longer applied for MEC in CPS. Due to the mobility of ECN, it is extraordinarily practical for ECN to establish a security association with another AP once leaving the service area of its current AP. In this paper, we represent the related research and propose a novel and efficient software-defined networking (SDN)-based handover authentication scheme for MEC in CPS (SHAS). An authentication handover module (AHM) in the SDN controller is applied for key distribution and authentication management. Before ECN handovers, the AHM distributes a key to the current serving AP for ECN further handover. Whenever a handover happens, target AP requests the AHM for the one-time session key (OSK) to authenticate the ECN. The target AP and ECN can proceed with the 3-way handshake protocol by the OSK to achieve mutual authentication and secret key confidentiality. Using the logical derivation of Burrows, Abadi, and Needham and formal verification by automated validation of Internet security protocols and applications (AVISPAs), proposed SHAS scheme can get mutual authentication and secret key confidentiality with a strong anti-attack ability. The simulation results show that the SHAS scheme has the characteristics of lower computational delay and less communication resources. Finally, the practical demonstration of our scheme is done using the widely accepted NS-3 simulation.