With the popularity of social media, users are able to express their opinions in multiple forms, such as text, audio, and video. Traditional unimodal sentiment analysis methods can no longer meet the processing requirements of such multisource heterogeneous data, which makes multimodal sentiment analysis a research hotspot. However, most existing methods rely on simple feature splicing or weighted fusion, neglecting the differences in the reliability of different modalities and failing to fully explore the intermodality consistency and difference information. In this article, we propose a multimodal competitive fusion mechanism and construct multimodal competitive fusion model (MCFM). The model first dynamically evaluates the reliability of each modality through the competition mechanism and adaptively assigns weights accordingly. Then it decomposed the modality representations into similar and dissimilar features through modality feature decomposition, supplemented by the overlap of orthogonal traffic channel attention constraints, to achieve the collaborative learning of consistency and dissimilarity features. We evaluate the proposed model on several datasets. In our experiments, we used textual modality data from the dataset with audio modality data for the experiments. The experimental results show that MCFM has 2%–3% higher binary accuracy (ACC2) than the baseline model on the sentiment classification task (with 2% higher binary accuracy under the negative/nonnegative metrics and 3% higher binary accuracy under the positive/negative metrics), and that on the regression task, MCFM’s mean absolute error on the test dataset is 3% lower than that of the baseline model.

Rumors on social media platforms have a significant negative impact on society, making rumor detection increasingly critical. However, most existing methods focus on identifying rumors only after they have already spread widely and negative impacts have occurred. Therefore, identifying rumors in the early stages is necessary. Early rumor detection is typically characterized by limited spread and small sample sizes, making it impractical to rely on large datasets or rumor propagation structures. To overcome these characteristics, a multimodal meta learning method based on few-shot learning (FSL) for early rumor detection is proposed. A multimodal feature extraction layer is designed to extract data features of various modalities, while a multimodal hidden information extraction layer is constructed to uncover deep information from these features. Furthermore, a multimodal fusion output layer is developed to combine and process the multimodal information, leading to rumor classification. A meta-learning algorithm is introduced to address the challenge of small sample sizes, utilizing fast and multistep update methods to enhance the adaptability and stability of model. Comparative experiments conducted on two publicly available datasets confirm that our proposed method demonstrates strong performance in early rumor detection.

This article investigates the finite-horizon $H_{\infty } $ tracking control problem for discrete-time (DT) linear systems with partial observation and unknown dynamics from a game-theoretic perspective. Unlike existing reinforcement learning (RL) approaches that primarily address infinite-horizon, time-invariant systems with full state information, our setting requires solving time-varying Riccati equations and developing model-free methods that rely solely on input-output data. To tackle these challenges, we reconstruct the system state from historical input-output trajectories, driving to a data-driven system representation, and we define an input-output-based time-varying $Q$ -function. We then propose two minimax $Q$ -learning algorithms that do not require an initially admissible policy and avoid the use of a discount factor, thereby removing a long-standing obstacle to stability guarantees. Moreover, the framework readily extends to both infinite-horizon and time-varying systems without structural modifications. Convergence is proved theoretically, and the effectiveness of the algorithms is validated through simulations.

Concerns regarding the potential risks associated with learners’ misusing ChatGPT necessitate an extensive investigation into learner attitudes towards ChatGPT-assisted language learning. This study adopts a mixed-method approach, combining structural equation modeling techniques and interviews. It aims to examine the influencing factors of learner attitudes regarding ChatGPT-assisted language learning under the extended three-tier technology use model from an interdisciplinary perspective, including the technology acceptance model, etc. The study finds that information system quality and hedonic motivation are more significant in contributing to performance expectancy and perceived satisfaction compared to self-regulation in ChatGPT-assisted language learning. Behavioral intention is a better predictor of learning effectiveness in ChatGPT-assisted language learning than perceived satisfaction and performance expectancy. This research also examines the partial or full mediating effects of behavioral intention and performance expectancy between other variables. Although this study is limited by some aspects (e.g., the outdated version of ChatGPT-3 or ChatGPT-3.5), it holds substantial implications for future practice and research. It appeals to more attention from future developers on hedonic motivation and information services of ChatGPT and from future researchers on a more comprehensive insight into influencing factors of learner attitudes towards ChatGPT-assisted language learning.

This article studies the stability issue of networked switched systems (NSSs) under denial-of-service (DoS) attacks. To address this issue, the derived limitations imposed on both the frequency of DoS attacks on each subsystem and the upper limit of attack duration that each subsystem can tolerate are mode-dependent, which is more efficient and flexible than the current results for NSSs. Moreover, we reveal the relationship between the upper bound of the average maximum tolerable attack duration associated with the corresponding subsystem and the actual mode-dependent average dwell time. Furthermore, we identify that the total tolerable DoS attack duration as a percentage of the system runtime in this article can be higher than existing results. Finally, an example is given to demonstrate the effectiveness of our work.

This paper investigates consensus of networked fractional-order systems over directed graph, with the networked double-integrator systems as its special case. An intermittent sampled position measurement distributed algorithm is proposed, which reduces the operation time and the update rates of controllers, and effectively responds to the circumstances if the information of agents’ velocity and current position cannot be measured. In order to reach consensus, some necessary and sufficient conditions with respect to the fractional order, communication width, coupling gains, and networked structure, are derived by applying the fractional Laplace transform and stability theory. Note that consensus can be reached only if some inequalities are fulfilled, which serves as a guide for selecting the appropriate communication width and sampling period to reach consensus. Finally, some simulation examples are illustrated to verify the theoretical results.

A new asymptotic Nash equilibrium for a class of linear quadratic mean-field game problem with a finite number of agents is found by using the cost function decomposition method. The cost function value corresponding with this equilibrium is also computed. This value turns out to be smaller than the one obtained by the state average approximation method. The difference is prominent when the number of agents is small, but vanishes as the agents' number tends to infinity.

This paper proposes fast convergent distributed algorithms for weighted average consensus of input data. For acyclic graphs, we give an algorithm that converges to the exact weighted average consensus in a finite number of iterations, equal to the graph diameter. For loopy (cyclic) graphs, we offer two remedies. In the first one, we give another distributed algorithm to enable our average consensus algorithm applicable to a loopy graph by converting it into a spanning tree. In the second one, we consider a slightly modified average consensus problem whose optimal solution approximates the consensus solution with arbitrary precision, and give a modified average consensus algorithm with guaranteed exponential convergence to the optimal solution. The proposed average consensus algorithms enjoy low complexities, robustness to transmission adversaries, and asynchronous implementation. Our algorithms are conceptually different from the popular graph Laplacian approach, and converge much faster than the latter approach.