In order to address challenges related to the absence of visual modality and coarse-grained semantic inconsistency in multi-modal entity alignment, we present a novel framework named Context-based Image Generation and Fine-Grained Semantic Structure Embedding (CIGFSE). First, CIGFSE leverages the textual context of entities along with large language models to generate prompts for entities lacking images, which are then input into image generation models to produce auxiliary images enriching the multi-modal knowledge graph. Next, it captures structural information via semantic-augmented structure embedding and applies a feedforward neural network to the other modalities. Furthermore, CIGFSE adopts attention-guided modality fusion and contrastive learning to optimize the model. Extensive experiments on both monolingual and bilingual datasets demonstrate that CIGFSE achieves state-of-the-art performance in multi-modal entity alignment.

Aiming at the problem of network performance degradation caused by the characteristics of mobile ad hoc network, this paper proposes a cooperative packet forwarding algorithm based on game combination and third-party altruism. First of all, we analyze the static game among nodes, and calculate the forwarding probability of nodes in the case of static game through the theorem of Nash equilibrium. Then, for the sake of solving the shortcomings of static game, we introduced the self-game model. Next, this paper puts forward the definition of node's benefit, which is vague in many studies, and proposes the concept of incentive factor based on third-party altruism to improve node collaboration. Finally, using the static game and self-game, combined with the incentive factor to calculate the node forwarding probability, the data packet cooperative forwarding algorithm is designed. The simulation results demonstrate that the proposed algorithm improves the performance indicators of MANET such as packet delivery ratio, average transmission delay, throughput and routing overhead.

In recent years, sensor multimodal time series data fusion has attracted widespread attention. One of the key challenges is to extract features from multimodal data to obtain shared representations, which combines the characteristics of time series data to further improve prediction performance. To solve this problem, based on Stacked Sparse Auto-Encoder (SSAE) and Long Short-Term Memory (LSTM), we propose a multimodal time series data fusion model called SSAE-LSTM. SSAE mines the inherent correlation features of multimodal data to extract a good shared representation, which is used as the input of the LSTM neural network to perform data fusion processing. Experiments on real time series datasets demonstrate that SSAE-LSTM can obtain a good shared representation of multimodal data to predict the future development trend. Compared with other neural networks, SSAE-LSTM has better performance in Precision, Accuracy, Recall, F-score and so on.