Current vulnerability detection methods encounter challenges, such as inadequate feature representation, constrained feature extraction capabilities, and coarse-grained detection. To address these issues, we propose a fine-grained hybrid semantic vulnerability detection framework based on Transformer, named TFHSVul. Initially, the source code is transformed into sequential and graph-based representations to capture multilevel features, thereby solving the problem of insufficient information caused by a single intermediate representation. To enhance feature extraction capabilities, TFHSVul integrates multiscale fusion convolutional neural network, residual graph convolutional network, and pretrained language model into the core architecture, significantly boosting performance. We design a fine-grained detection method based on a self-attention mechanism, achieving statement-level detection to address the issue of coarse detection granularity. In comparison to existing baseline methods on public data sets, TFHSVul achieves a 0.58 improvement in F1 score at the function level compared to the best performing model. Moreover, it demonstrates a 10% enhancement in Top-10 accuracy at the statement-level detection compared to the best performing method.

This article considers the time-varying formation (TVF) tracking issue of heterogeneous multiagent systems (HMASs) with the dynamic event-triggered control. The HMASs contain heterogeneous multiple leaders, all of which have the input signals to generate flexible reference, and only the output information can be measured. All leaders do not have access to the same followers, that is, the well-informed follower assumption is removed in this article. In this setting, the adaptive multileader state compensator is designed for each follower to estimate the integrated state information of all leaders, which can equip with two kinds of dynamic event-triggered mechanisms, that is, node-based event-triggered mechanism and edge-based event-triggered mechanism, to save communication bandwidth. Then, the TVF controllers are built by some estimation values to regulate the followers to achieve and maintain the geometric shape while tracking the reference which is the convex combination of outputs of leaders. The event-triggered compensator and TVF controller constitute the control protocol of HMASs, which are independent of global information with the fully distributed manner. The stability analysis and numerical simulations are given to verify the presented control protocol.

In this article, the time-varying output formation issue of heterogeneous multiagent systems is investigated by the event-triggered control scheme. Only the outputs of all agents, including leader agent and follower agents, are measurable. The leader agent contains an unknown input signal to generate flexible reference trajectory. Also, only a subset of follower agents have the direct access to the leader agent. First, for each follower, the leader-state compensator is designed to estimate the state of leader. Two kinds of dynamic event-triggered (DET) mechanisms, i.e., node- and edge-based event-triggered schemes, can be equipped on the compensator to save the communication bandwidth of leader-follower and follower-follower interactions, respectively. Then, the distributed formation controller is built to drive each follower achieving formation tracking. The presented control protocol consisting of the DET state compensator and formation controller is fully distributed, which is independent of the global information of communication topology, such as the eigenvalues of Laplacian matrix of communication topology and amount of whole agents. Finally, the numerical experiments and comparison experiments are exhibited to verify the effectiveness of the presented control protocol.

This article focuses on the problem of adaptive bipartite output tracking for a class of heterogeneous linear multiagent systems (MASs) by asynchronous edge-event-triggered communications under jointly connected signed topologies. By designing the observers to estimate the states of followers and the dynamic compensators to estimate the states of zero input and nonzero input leader, respectively, the fully distributed edge-event-triggered control protocol is presented. Moreover, it is proven that the bipartite output tracking problem is implemented, and the systems do not exhibit Zeno behavior under a fully distributed control strategy with edge-event-triggered mechanisms. Compared with the existing works, one of the highlights of this article is the design of triggering mechanisms, under which the leader avoids continuous information transmission and any pair of followers that make up the edge asynchronously transmit information through the edge. The methods greatly avoid unnecessary information transmission in the systems. Finally, several simulation examples are introduced to demonstrate the theoretical results obtained in this article.

In the study, a MIMO model-free-adaptive-iterative-learning-control-based (MFAILC-based) consensus tracking scheme for multiagent systems (MASs) has been proposed. The dynamics of agents are heterogeneous and unknown. And the compact form dynamic linearization (CFDL) technique is utilized to describe the unknown nonlinear dynamics of agents along iteration axis. Then, the MIMO MFAILC-based consensus tracking algorithm is proposed for MASs under fixed topology. From the proof, we can obtain that the consensus tracking error can converge to zero along iteration axis asymptotically. Next, the MFAILC-based consensus tracking algorithm is extended to controlling the MASs under iteration-switching topologies and the MASs with external disturbances, respectively. Compared with prior work, the main features of this article are the MFAILC-based consensus strategy can be utilized for MIMO MASs, and the framework of robust MFAILC is built for MIMO MASs with external disturbances. Finally, three simulations are given to verify the effectiveness of the consensus strategy for MASs under fixed and switching topology and MASs with external disturbances, respectively.