Inductive reasoning aims to infer missing knowledge for unseen entities and relations. Existing methods exhibit limited generalization capabilities due to their dependence on localized structural patterns and inadequate handling of graph imbalance. To address these challenges, we propose a novel Hi erarchical Mod eling with Graph Perturbation-Enhanced Network (HiMod), which effectively integrates hierarchical relation modeling with a dynamic perturbation mechanism to enhance the generalization ability of inductive reasoning models. HiMod leverages a hierarchical relation modeling mechanism that maps specific relations to higher-level general concepts within a global semantic framework. This allows for capturing semantic commonalities across relations, enabling robust reasoning for unseen queries. Simultaneously, a dynamic perturbation mechanism is introduced to adjust perturbation strength based on node importance and graph sparsity, facilitating deeper exploration of the latent semantic space and mitigating the effects of graph imbalance. Extensive experiments on three benchmark inductive knowledge graph reasoning datasets demonstrate that HiMod achieves the most significant MRR improvements among the four split versions, with 11.17% on WN18RR, 4.61% on FB15K-237, and 5.47% on NELL-995. Our code is available at https://github.com/HubuKG/HiMod .

For the Internet of Vehicles (IoV), driving safety applications require reliable and up-to-date knowledge of the state of vehicles and traffic. A single vehicle cannot meet all the reliability requirements because of the limited capability of information acquisition. Thus, cooperation among vehicles for information sharing is essential. However, due to the high dynamic network topology and harsh channel conditions, maintaining long-term cooperation is not feasible. Only the messages that most affect the driving state can obtain the transmission opportunity for avoiding network congestion. In this paper, we propose a cooperative safety-enhanced control framework (SCF). This framework concentrates on the construction of dynamic and adaptive cooperation among vehicles and evaluates the key feature parameters to achieve an optimal safety utility for feedback control over the driving state. We construct a general multi-layer solution framework for driving assistance in SCF. First, we construct multiple temporary cooperative platoons to coordinate adjacent vehicles and realize a relatively uniform driving state. The cooperative platoon maintains short-term stability for vehicle sensing and tracing. Second, we propose a utility evaluation model for extracting the key feature parameters related to the driving state, which is the basis of the optimization for message transmission and driving control. Third, we design a two-level joint optimization mechanism for the deep fusion of the multi-source heterogeneous data to maximize the total utility of driving safety. Finally, we propose an adaptive feedback control model for the cooperative platoon, which actively adjusts the driving control strategy and the message transmission strategy in a real-time manner. Then the optimal driving assistant decision can be made. Extensive simulation results show that SCF outperforms related communication mechanisms for safe driving in the IoV, demonstrating that SCF can effectively enhance driving assistance control.

Knowledge graphs (KGs) are frequently confronted with the challenge of incompleteness, a problem that extends to multimodal knowledge graphs (MKGs). The primary goal of multimodal knowledge graph completion (MKGC) is to predict missing entities within MKGs. However, current MKGC methods face difficulties in adequately addressing modal preferences and imbalances in modal information. To overcome these issues, we introduce AdaMKGC, an innovative hybrid model incorporating an adaptive modality interaction transformer. This model employs a dynamic attention interaction strategy and a self-enhancing sampling approach. AdaMKGC achieves a more precise utilization of multimodal information by integrating modal preference information into modal interactions. Additionally, it effectively mitigates the issue of modal imbalance through targeted sampling and adjustment for entities with deficient information. Experimental evaluations demonstrate AdaMKGC’s superior performance in overcoming these prevalent challenges. Compared to existing state-of-the-art MKGC models, AdaMKGC shows a notable enhancement of 28% in MR on the WN18-IMG dataset and an improvement of 2.7% in Hits@1 on the FB15k-237-IMG dataset. Our code is available at https://github.com/HubuKG/AdaMKGC .

Epilepsy, a widespread neurological disorder, creates substantial physical and psychological challenges for patients. Accurate seizure prediction allows for prompt symptom intervention and treatment guidance. To achieve this goal, we introduce a classification model for epilepsy using variational mode quadratic decomposition. It first processes EEG signals from epilepsy patients with variational mode decomposition. Then, a second variational mode decomposition filters and breaks down the residual signal further. After decomposition, signals are reconstructed into continuous wavelet transform feature images. A combination of convolutional neural network and temporal convolutional network then classifies epileptic seizure periods, including ictal, preictal, interictal, and non-ictal phases. The extensive experimental results show the model reaches 85% accuracy and 88.8% precision in classifying epileptic seizure, pre-ictal, inter-ictal, and non-ictal periods, evidencing the proposed method's effectiveness.