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.

To reduce the repetitive and complex work of instructors, exam paper generation (EPG) technique has become a salient topic in the intelligent education field, which targets at generating high-quality exam paper automatically according to instructor-specified assessment criteria. The current advances utilize the ability of heuristic algorithms to optimize several well-known objective constraints, such as difficulty degree, number of questions, etc., for producing optimal solutions. However, in real scenarios, considering other equally relevant objectives (e.g., distribution of exam scores, skill coverage) is extremely important. Besides, how to develop an automatic multi-objective solution that finds an optimal subset of questions from a huge search space of large- sized question datasets and thus composes a high-quality exam paper is urgent but non-trivial. To this end, we skillfully design a reinforcement learning guided Multi-Objective Exam Paper Generation framework, termed MOEPG, to simultaneously optimize three exam domain-specific objectives including difficulty degree, distribution of exam scores, and skill coverage. Specifically, to accurately measure the skill proficiency of the examinee group, we first employ deep knowledge tracing to model the interaction information between examinees and response logs. We then design the flexible Exam Q-Network, a function approximator, which automatically selects the appropriate question to update the exam paper composition process. Later, MOEPG divides the decision space into multiple subspaces to better guide the updated direction of the exam paper. Through extensive experiments on two real-world datasets, we demonstrate that MOEPG is feasible in addressing the multiple dilemmas of exam paper generation scenario1. 1https://github.com/researcher-tiger/MOEPG

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.