Multimodal knowledge graph completion (MKGC) seeks to enrich knowledge graphs by integrating information from diverse modalities, facilitating more comprehensive knowledge representation and enhancing reasoning accuracy. However, existing models lack the flexibility to adapt to different tasks, and their performance still requires further improvement. To tackle these challenges, we propose an MKGC model based on Dynamic prompt learning and Multi-granularity cross-modal Aggregation, namely DM-MKGC. To be specific, a novel dynamic prompt template is proposed, which employs an adaptive task-guided mechanism to dynamically adjust the structure of entities, relations, and textual information. This approach enables the generation of prompts tailored to diverse tasks, ensuring both functional flexibility and structural consistency. Furthermore, a multi-granularity cross-modal aggregation method, which facilitates the aggregation of cross-modal information by facilitating the interaction between coarse-grained and fine-grained image features with textual features, is designed to enhance the model’s performance. Extensive experiments conducted on four datasets (FB15k-237-IMG, WN18-IMG, MNRE, and Twitter-2017) demonstrate our model outperforms other SOTA methods in knowledge completion, achieving an average improvement of 9.8%, 1.6%, and 1.23% in MR, Hits@n, and F1 respectively. Our model not only offers a novel method for multimodal knowledge graph completion but also contributes valuable insights for the advancement of knowledge graph technologies.

Traditional cloud Service Level Agreement (SLA) suffers from lacking a trustworthy platform for automatic enforcement. The emerging blockchain technique brings in an immutable solution for tracking transactions among business partners. However, it is still very challenging to prove the credibility of possible violations in the SLA before recording them onto the blockchain. To tackle this challenge, we propose a witness model using game theory and the smart contract techniques. The proposed model extends the existing service model with a new role called “witness” for detecting and reporting service violations. Witnesses gain revenue as an incentive for performing these duties, and the payoff function is carefully designed in a way that trustworthiness is guaranteed: in order to get the maximum profit, the witness has to always tell the truth. This is analyzed and proved through game theory using the Nash equilibrium principle. In addition, an unbiased sortition algorithm is proposed to ensure the randomness of the independent witnesses selection from the decentralized witness pool, to avoid possible unfairness or collusion. An auditing mechanism is also introduced in the paper to detect potential irrational or malicious witnesses. We have prototyped the system leveraging the smart contracts of Ethereum blockchain. Experimental results demonstrate the feasibility of the proposed model and indicate good performance in accordance with the design expectations.

In this paper, we consider communication on a two-hop channel in which a source wants to send information reliably and securely to the destination via a relay. We consider both the untrusted relay case and the external eavesdropper case. In the untrusted relay case, the relay behaves as an eavesdropper, and there is a cooperative node, which sends a jamming signal to confuse the relay when it is receiving from the source. In the external eavesdropper case, the relay is trusted, and there is an external node eavesdropping the communication. We propose two secure transmission schemes using the scaled compute-and-forward technique. One of the schemes is based on a random binning code, and the other one is based on a lattice chain code. It is proved that in the high signal-to-noise-ratio (SNR) scenario and/or the limited relay power scenario, if the destination is used as the jammer, both schemes outperform all existing schemes and achieve the upper bound. In particular, if the SNR is large and the source, the relay, and the cooperative jammer have identical power and channels, both schemes achieve the upper bound for secrecy rate, which is merely 1/2 bit per channel use lower than the channel capacity without secrecy constraints. We also prove that one of our schemes achieves a positive secrecy rate in the external eavesdropper case in which the relay is trusted and there exists an external eavesdropper.

Compute-and-forward (CF) is a technique which exploits broadcast and superposition in wireless networks. In this paper, the CF energy benefit is studied for networks with unicast sessions and modeled by connected graphs. This benefit is defined as the ratio of the minimum energy consumption by traditional routing techniques, not using broadcast and superposition features, and the corresponding CF consumption. It is shown to be upper bounded by min(d̅, K, 12√K), where d̅ and K are the average hop-count distance and the number of sessions, respectively. Also, it can be concluded that the energy benefit of network coding (NC) is also upper bounded by the same value, which is a new scaling law of the energy benefit for NC as a function of K.

A two-hop channel is considered, in which the source wants to send information to the destination while keeping the information confidential from the relay. A novel lattice chain and compute-and-forward based scheme is proposed in which the destination provides cooperative jamming. Channel state information is used at the source and the destination to scale the encoding lattices for the message and the jamming signal according to the channel gains. We compare the achievable secrecy rate of our scheme with an upper bound and with the achievable secrecy rate of other schemes. It follows that our scheme outperforms all existing schemes except in the low power region.

Signal superposition and broadcast are important features of the wireless medium. Compute-and-Forward, also known as Physical Layer Network Coding, is a technique exploiting these features in order to improve performance of wireless networks. In this paper, the possible benefits for the line network with multiple bi-directional sessions and local interference are investigated. Four different modes, indicating whether or not broadcast and/or superposition are exploited, are considered. In particular, expressions for the maximum achievable common rate are derived for each of the four different modes. Scheduling and coding schemes achieving these rates are presented. From the results it follows that, in most cases, the common rate is improved by a factor close to two by using Compute-and-Forward. However, it is also found that the benefit may be smaller for particular session configurations.