Existing adaptive modulation and coding schemes suffer from performance degradation under time-varying underwater acoustic channels and jamming attacks, resulting in increased video jitter and energy consumption, as well as reduced peak signal-to-noise ratio (PSNR). In this paper, we first propose a reinforcement learning (RL)-based underwater video transmission scheme, which jointly optimizes the channel coding methods, subcarrier modulation order, quantization parameter, and transmit power against jamming. The direct sequence spread spectrum mechanism is used to reduce the jamming power in each subcarrier frequency band, which ensures that the transmitter obtains a reliable feedback message. In addition, a multi-frame statistical scheme is designed to support transmission policy selection, which mitigates the impact of channel instability by averaging the observed transmission performance and channel gain. To further enhance communication performance and robustness under large state-action spaces and dynamic channel conditions, we propose a deep RL (DRL)-based anti-jamming video transmission scheme to compress the excessive state-action space, mitigate quantization errors, and improve transmission stability. Two target networks are employed in DRL to ensure learning stability by mitigating fluctuations in Q-value and R-value updates caused by the time-varying channel. In addition, the performance bounds of transmission delay and energy consumption related to modulation order and channel coding rate are derived. Simulation and experimental results demonstrate that our schemes improve the video transmission performance by reducing transmission delay, energy consumption, and video jitter while increasing PSNR and video quality compared with the benchmarks.

The widespread application of industrial control systems has driven the development of industrial control protocols. However, traditional industrial control protocols suffer from issues such as a lack of security mechanisms, resulting in the existence of many dangerous vulnerabilities in industrial control systems . Fuzzing, as a commonly used technique for vulnerability discovery, has its own set of issues, including low testing efficiency, lack of adaptive capability, and high repetition rate of generated test cases . To solve the existing problems, we propose a low-repetition rate Modbus TCP fuzzing method based on Transformer and Mutation Target Adaptation. Firstly, the syntactic features of the industrial control protocol Modbus TCP are learned by using a simplified Transformer model. The model effectively reduces the training and generation time without decreasing the acceptance rate of test cases; Secondly, in the test case generation phase, in order to improve the mutation efficiency of test cases, the byte mutation probability adaptive strategy is introduced to replace the greedy strategy of Transformer. This strategy can dynamically adjust the mutation probability of each byte in the newly generated test cases , so as to improve the abnormal rate and reduce the repetition rate of test cases; Finally, the mutation results are selected by the mutation byte adaptive selection strategy, which not only improves the mutation adaptivity, but also maintains the diversity of mutations. The experimental results indicate that, compared to traditional methods, our approach has improved acceptance rates and abnormal rates by at least 10%. In comparison to AI-based fuzzing methods, our approach maintains a similar acceptance rate while increasing the abnormal rate by 3% to 25%.

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.

As language is intrinsic to the expression of culture, the rise and fall of a language directly affect the culture associated with it. Therefore, constructing a computational model to study the mechanisms of language competition and explore policies of language preservation is very important. We address the language system’s macroscopic aspects, such as the prestige of languages, the difficulty level of learning languages, and natives’ tolerance toward nonnative languages, as well as individual interactions at the microscopic level, and then propose an agent network computation-based evolutionary game model (ANC-EGM), including two major components—the definition of language attractiveness and the language competition game, to model a more realistic dynamic evolving language system. The replicator equation is adopted to solve the evolutionary equilibrium, and the stability of the equilibrium points is analyzed by the local stability analysis of the Jacobian matrix. The theoretical analysis and simulations illustrate that the ANC-EGM can comprehensively model the competition between two languages and estimate how individual interactions lead to the demise or coexistence of languages. We further validate the conclusions of the ANC-EGM on the empirical data of the Minnan dialect and Mandarin, which show that the ANC-EGM can provide an experimental computing platform for the in-depth study of language policy regulation and language evolution rules.

Summary Uncertain data is widely used in many practical applications, such as data cleaning, location‐based services, privacy protection, and so on. With the development of technology, data has a tendency to high‐dimensionality. The most common indexes for nearest neighbor search on uncertain data are the R‐Tree and the KD‐Tree. These indexes will inevitably bring about “curse of dimension.” Focus on this problem, article proposes a new hash algorithm, called the LSR‐forest, which based on locality sensitive hashing and R‐Tree, to solve the high‐dimensional uncertain data approximate neighbor search problem. The LSR‐forest can hash similar high‐dimensional uncertain data into a same bucket with a high probability, and then constructs multiple R‐Tree‐based indexes for hashed buckets. When querying, it is possible to judge neighbors by checking the data in the hypercube which the query point is in. One can also adjust the query range automatically by different parameter of k. Many experiments on different datasets are presented in this article. The results show that LSR‐forest has better effectiveness and efficiency than R‐Tree on high‐dimensional datasets.

Since underwater acoustic channels are shared by multiple heterogeneous entities and can suffer from severe interference, underwater acoustic communication networks (UACNs) are faced with the challenge of mitigating interference and improving communication quality by implementing distributed resource allocation approaches. In this article, we introduce the concept of reinforced learning in intelligent control to the UACNs by treating the nodes as intelligent agents and the node networks as multiagent networks. By partitioning the state space and the action space, we formulate a reward function and a search strategy and propose a distributed resource allocation algorithm based on cooperative Q -Learning. In addition, we verify the convergence of the proposed algorithm. Finally, simulation results in two different underwater application scenarios show that the proposed algorithm outperforms the existing algorithms in improving the network transmission capacity, and can reduce the overhead of resource allocation by using cooperative Q -Learning.

The following topics are dealt with: cellular radio; probability; wireless channels; radiofrequency interference; mobile radio; MIMO communication; error statistics; telecommunication traffic; Long Term Evolution; quality of service.