The rapid development of social media provides diversified channels for the dissemination of rumors. How to accurately identify rumor information under different modalities becomes a significant challenge in the current field of information security. Limited by the heterogeneity of multimodal data itself, existing multimodal rumor detection methods often face the issue of modality laziness, that is, the model converges prematurely during the training process, resulting in some modality encoders failing to fully learn effective representations, ultimately weakening the model’s detection performance. To alleviate the problem of modality laziness, we propose an innovative alternating modality optimization network (AMONet), whose core innovations are embodied in three aspects: (1) An alternating modality optimization (AMO) parameter updating mechanism is proposed, to balance optimization opportunities for each modality encoder by cyclically training the branch networks of each modality; (2) To tackle the knowledge forgetting problem caused by alternating optimization, a fisher information-based regularization (FIR) method is introduced, which can impose elastic constraints on specific parameters to preserve crucial knowledge; (3) An innovative quantitative evaluation metric, modality inconsistency variance (MIV), is proposed to characterize modality laziness. Extensive experiments demonstrate that AMONet outperforms previous benchmarks.

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.

Efficient intersection planning is one of the most challenging tasks for an autonomous vehicle at present. Politeness to other traffic participants and reaction to surrounding information are the key aspects that determine the performance of planning algorithm for an autonomous vehicle. In order to capture these aspects, we propose a planning framework, based on the partially observable Markov decision process (POMDP), to ensure social compliance and optimize the motion response of autonomous vehicles. The framework designs a novel POMDP model to ensure effective prediction of behavioral intentions and decision-making at intersections with no traffic signs and low traffic volume. Then, a deterministic planner is employed as the baseline to realize planning. Specifically, we consider both the driving position and the facing angle of the vehicle in real-world situations. At the same time, we utilize scattering methods for probability updating and intent determination to improve the algorithm’s adaptability to real-world scenarios. The proposed framework is evaluated in different scenarios to demonstrate its capabilities in terms of the interactive planning for an autonomous vehicle.

This article copes with vibration suppression and angular position tracking problems of a robotic manipulator system comprised of a rotating hub and a length-varying manipulator. To obtain precise dynamic response, the manipulator system is modeled in infinite-dimension with partial differential equations. S-curve acceleration/deceleration (S-CA/D) scheme is employed for speed regulation of the length-varying manipulator. Two novel observers are developed to estimate both the unknown disturbances and their time-derivatives, and two auxiliary systems are put forward to tackle input constraints. With assistance of the auxiliary systems and observers, two boundary control laws are put forward to manage vibration suppression and angular position tracking of the proposed manipulator system. Through Lyapunov’s theory, the closed-loop system is proved to be bounded. Numerical simulations have displayed the effectiveness of the observers and boundary control laws.

In this paper, a novel control strategy for synchronization of heterogeneous network systems in industrial applications is proposed. Nonidentical nodes are adopted to describe the different industrial processes. The target trajectory is the output of an autonomous linear time-invariant system. The designed controller for each nonidentical node is distributed and calculated on the local information. Each distributed controller composes by the reference generator (RG) and the regulator (RE), where RG can copy the dynamics of the target trajectory and RE can ensure the synchronization. Limited to communication constraints, the time-varying sampled-data control strategy is utilized to reduce the updated frequency of the controller and communication burden of the network. The upper bound of the sampling instants is calculated by the theory of small gain theorem and the theory of integral quadratic constraints. Finally, a practical numerical example is presented to illustrate the effectiveness of the distributed controller design strategy.

This paper considers the sample-data-based consensus problem of multiagent systems with time-varying delay and packet losses. To distinguish the time delays caused by network-induced time-delay and packet losses, the switched system is utilized. A lower gain controller is designed based on the solution of a parametric algebraic Riccati equation. The Lyapunov stability theory is utilized to obtain the limitations on the frequency and the duration of packet losses which guarantees the consensus of multiagent systems. On the other hand, we theoretically prove that refined time-delay function can reduce the conservatism. A simulation example is given to illustrate the effectiveness of the proposed method.