This paper investigates the application of self-organizing networks in solving the interference problem in underwater acoustic communication networks (UACNs) with the coexistence of multi-node. In this network, each node autonomously adjusts its power based on locally observed information without central controller intervention. Considering the non-convexity of the optimization problem with quality-of-service constraints and the dynamic nature of the underwater environment, we propose a reinforcement learning (RL)-based approach coupled with a distributed coordination mechanism, namely the multi-agent-based transfer Dyna-Q algorithm (MA-TDQ). This algorithm combines Q-learning with Dyna structure and transfer learning, and can quickly obtain optimal intelligent resource management strategies. Furthermore, we rigorously demonstrate the convergence of the MA-TDQ algorithm to Nash equilibrium. Simulation results indicate that the proposed distributed coordination learning algorithm outperforms other existing learning algorithms in terms of learning efficiency, network transmission rate, and communication service quality.

The test oracle problem exists widely in modern complex software testing, and metamorphic testing (MT) has become a promising testing technique to alleviate this problem. The inference of efficient metamorphic relations (MRs) is the core problem of metamorphic testing. Studies have proven that the combination of simple metamorphic relations can construct more efficient metamorphic relations. In most previous studies, metamorphic relations have been mainly manually inferred by experts with professional knowledge, which is an inefficient technique and hinders the application. In this paper, a genetic algorithm-based approach is proposed to construct composite metamorphic relations automatically for the program to be tested. We use a set of relation sequences to represent a particular class of MRs and turn the problem of inferring composite MRs into a problem of searching for suitable sequences. We then dynamically implement multiple executions of the program and use a genetic algorithm to search for the optimal set of relation sequences. We conducted empirical studies to evaluate our approach using scientific functions in the GNU scientific library (abbreviated as GSL). From the empirical results, our approach can automatically infer high-quality composite MRs, on average, five times more than basic MRs. More importantly, the inferred composite MRs can increase the fault detection capabilities by at least $30\%$ more than the original metamorphic relations.

This article studies the distributed power management problem in underwater acoustic communication networks (UACNs) with the coexistence of multiple transmitter–receiver pairs, where each transmitter selects its transmit power based only on local observations without the involvement of any central controller. This article aims to maximize the network transmission rate based on maintaining Nash equilibrium (NE) among multiple transmitter–receiver pairs. We model the selfish behavior of each emitter as a noncooperative game. In this game, on the one hand, the distance between the transmitter and the receiver is introduced as an interference weight factor to modify the effective interference model; on the other hand, the utility function of the game is constructed by combining the information transmission rate of the transmitter and its remaining energy. Moreover, it is successfully proved that the utility function has an NE solution. Subsequently, a multiagent dual $Q$ network algorithm (MA-DDQN-PR) based on priority replay is proposed to achieve the optimal transmission strategy in dynamically changing channel and interference environments. Each transmitter in this algorithm acts as an agent, interacting with the communication environment and receiving different observations. But at the same time, they have a common reward function and centrally train the $Q$ network by summarizing the actions of other agents, thereby improving the power control selected by each agent. Finally, simulation results show that the proposed algorithm outperforms other existing learning algorithms in terms of network transmission rate, network energy efficiency, and transmitter lifetime.

Most of the existing research papers study the emotion recognition of Minnan songs from the perspectives of music analysis theory and music appreciation. However, these investigations do not explore any possibility of carrying out an automatic emotion recognition of Minnan songs. In this paper, we propose a model that consists of four main modules to classify the emotion of Minnan songs by using the bimodal data—song lyrics and audio. In the proposed model, an attention-based Long Short-Term Memory (LSTM) neural network is applied to extract lyrical features, and a Convolutional Neural Network (CNN) is used to extract the audio features from the spectrum. Then, two kinds of extracted features are concatenated by multimodal compact bilinear pooling, and finally, the concatenated features are input to the classifying module to determine the song emotion. We designed three experiment groups to investigate the classifying performance of combinations of the four main parts, the comparisons of proposed model with the current approaches and the influence of a few key parameters on the performance of emotion recognition. The results show that the proposed model exhibits better performance over all other experimental groups. The accuracy, precision and recall of the proposed model exceed 0.80 in a combination of appropriate parameters.