The traditional formation control methods of multi-agent systems (MASs) often rely on restrictive linear matrix inequalities and strong assumptions. To overcome this limitation, this work proposes an adaptive optimal time-varying formation control protocol for uncertain MASs, integrating reinforcement learning(RL)-based neural network observer. First, a radial basis function neural network(RBFNN)with adaptive law is designed to approximate the unknown nonlinear dynamics. An RBFNN-based state estimator and a novel disturbance estimator are developed to reconstruct the unmeasurable system state and the disturbance generated by an exosystem, respectively. Second, using the state estimate, a distributed formation control error system is established, and a performance index based on a Hamilton–Jacobi–Bellman(HJB)equation is introduced to achieve the objective of optimal formation control. Third, leveraging the state and disturbance estimates, an actor-critic RL-based optimal time-varying formation control protocol with adaptive update laws is proposed. Actor and critic networks are constructed to approximate unknown nonlinear terms in HJB equation. It is proved that estimator error systems and RBFNN approximation errors are semi-globally uniformly ultimately bounded(SGUUB). Simulation examples are provided to verify the effectiveness of the proposed estimators and optimal formation control protocol. Moreover, the method is successfully applied to unmanned aerial vehicles (UAVs) formation control in the Isaac Sim simulation environment, demonstrating the method’s practical feasibility.

In the context of consensus tracking control for multiagent systems (MASs), the system performance may be significantly degraded by multiple potential factors, including actuator/sensor faults and external disturbances. However, the development of a distributed fault estimation (FE) mechanism integrated with a fault-tolerant consensus control protocol remains a critical research challenge. This work proposes a novel distributed extended simultaneous observer (DESO) and observer-based fault-tolerant consensus tracking control (OB-FTCT) protocol for nonlinear MASs. First, system state as well as actuator and sensor faults for each agent are integrated into a new augmented vector. The original system is transformed into a descriptor system, and a DESO, tailored to the augmented vector, is designed to simultaneously estimate the system state and faults. Second, a novel OB-FTCT protocol is designed to achieve theH∞consensus tracking and compensate the impact of the two faults. Through the establishment of Lyapunov functions and an algorithm, the stability of consensus tracking error system is proved and ensured. Third, the effectiveness of the proposed method is verified by simulation examples. Also, we apply the proposed method to single-link flexible joint robot, and compare it with existing method to prove the effectiveness.

Since the advent of ChatGPT, large language models (LLMs) have attracted widespread attention from academia and industry. They have also brought significant changes to software engineering. However, until now, there has been a lack of comprehensive studies comparing LLMs with previous smaller code pre-trained models. To address this gap, we conduct a study in this paper to illustrate the performance of LLMs in different software engineering tasks. Specifically, we select three open-source large language models, CodeGen, LLaMA, and StarCoder, for the research targets, and our study is conducted from four aspects, including code syntax understanding, code semantic reasoning, encoding representation quality, and adaptation performance for different software engineering tasks to compare LLMs with previous code pre-trained models. Four aspects build on each other, forming important components of AI for Software Engineering.We conclude that: (1) Compared with previous smaller pre-trained models like CodeBERT, LLMs exhibit distinct trends in how they learn code syntax or semantics as the number of layers increases. Additionally, mastering code semantics proves to be more challenging, with semantic information usually learned in the final layers; (2) Causal decoder architecture with left-to-right attention masking does not perform well in zero-shot tasks; (3) For classification tasks, the mean vector representation generated by LLMs over a sequence tends to outperform the last token representation in the sequence; (4) Incorporating parameter-efficient fine-tuning techniques into LLMs for downstream tasks can help LLMs achieve better performance than previous code pre-trained models on code generation tasks but may not be optimal in some code understanding tasks; (5) LoRA emerges as a more effective PEFT technique for LLMs in downstream code-related tasks. We hope these findings will better guide future researchers in designing more powerful code models.

Deep learning has introduced significant improvements in many software analysis tasks. Although the Large Language Models (LLMs) based neural code models demonstrate commendable performance when trained and tested within the intra-project independent and identically distributed (IID) setting, they often struggle to generalize effectively to real-world inter-project out-of-distribution (OOD) data. In this work, we show that this phenomenon is caused by the heavy reliance on project-specific shortcuts for prediction instead of ground-truth evidence. We propose a Cond-Idf measurement to interpret this behavior, which quantifies the relatedness of a token with a label and its project-specificness. The strong correlation between model behavior and the proposed measurement indicates that without proper regularization, models tend to leverage spurious statistical cues for prediction. Equipped with these observations, we propose a novel bias mitigation mechanism that regularizes the model’s learning behavior by leveraging latent logic relations among samples. Experimental results on two representative program analysis tasks indicate that our mitigation framework can improve both inter-project OOD generalization and adversarial robustness, while not sacrificing accuracy on intra-project IID data.

Mainstream backdoor attack methods typically demand substantial tuning data for poisoning, limiting their practicality and potentially degrading the overall performance when applied to Large Language Models (LLMs). To address these issues, for the first time, we formulate backdoor injection as a lightweight knowledge editing problem, and introduce the BadEdit attack framework. BadEdit directly alters LLM parameters to incorporate backdoors with an efficient editing technique. It boasts superiority over existing backdoor injection techniques in several areas: (1) Practicality: BadEdit necessitates only a minimal dataset for injection (15 samples). (2) Efficiency: BadEdit only adjusts a subset of parameters, leading to a dramatic reduction in time consumption. (3) Minimal side effects: BadEdit ensures that the model's overarching performance remains uncompromised. (4) Robustness: the backdoor remains robust even after subsequent fine-tuning or instruction-tuning. Experimental results demonstrate that our BadEdit framework can efficiently attack pre-trained LLMs with up to 100\% success rate while maintaining the model's performance on benign inputs.

Recently, deep learning models have been widely applied in program understanding tasks, and these models achieve state-of-the-art results on many benchmark datasets. A major challenge of deep learning for program understanding is that the effectiveness of these approaches depends on the quality of their datasets, and these datasets often contain noisy data samples. A typical kind of noise in program understanding datasets is label noise, which means that the target outputs for some inputs are incorrect.

GitHub commits, which record the code changes with natural language messages for description, play a critical role in software developers’ comprehension of software evolution. Due to their importance in software development, several learning-based works are conducted for GitHub commits, such as commit message generation and security patch identification. However, most existing works focus on customizing specialized neural networks for different tasks. Inspired by the superiority of code pre-trained models, which has confirmed their effectiveness across different downstream tasks, to promote the development of open-source software community, we first collect a large-scale commit benchmark including over 7.99 million commits across 7 programming languages. Based on this benchmark, we present CommitBART, a pre-trained encoder-decoder Transformer model for GitHub commits. The model is pre-trained by three categories (i.e., denoising objectives, cross-modal generation, and contrastive learning) for six pre-training tasks to learn commit fragment representations. Our model is evaluated on one understanding task and three generation tasks for commits. The comprehensive experiments on these tasks demonstrate that CommitBART significantly outperforms previous pre-trained works for code. Further analysis also reveals that each pre-training task enhances the model performance.

Backdoor attacks for neural code models have gained considerable attention due to the advancement of code intelligence.However, most existing works insert triggers into task-specific data for code-related downstream tasks, thereby limiting the scope of attacks.Moreover, the majority of attacks for pre-trained models are designed for understanding tasks.In this paper, we propose task-agnostic backdoor attacks for code pre-trained models.Our backdoored model is pre-trained with two learning strategies (i.e., Poisoned Seq2Seq learning and token representation learning) to support the multitarget attack of downstream code understanding and generation tasks.During the deployment phase, the implanted backdoors in the victim models can be activated by the designed triggers to achieve the targeted attack.We evaluate our approach on two code understanding tasks and three code generation tasks over seven datasets.Extensive experiments demonstrate that our approach can effectively and stealthily attack code-related downstream tasks.

Recommender systems across many Internet services have become a critical part of online businesses, as consumers would refer to them before making decisions. However, the lack of explicit ratings for items on many services makes it challenging to capture user preferences and item characteristics. Both academia and the industry have drawn attention to rating predications as a fundamental problem in recommendation systems. With the emergence of social networks, social recommender systems have been proposed to utilize the relationship between users and items to alleviate the data sparsity problem for rating predictions. However, they either concentrate on the opinion mining for each user and item, or consider the connections between users only. In this paper, we present an effective framework, Triple-hierarchical Attention Graph-based social rating prediction (TAG), to exploit the social relationships between users, the user-item interest relationships, the correlation relationships between items, and reviews for rating predictions. In order to consider opinions from reviews and these complex relationships, we first employ two triple-hierarchical attention to extract user and item features from reviews. We then design an inductive GNN, which generates effective embedding for users and items. Experiments over Yelp show that TAG outperforms state-of-the-art methods across RMSE, MAE, and NDCG metrics.

This article proposes a robust and precise localization scheme for unmanned ground vehicle (UGV) in global positioning system (GPS)-denied and GPS-challenged environments via multisensor fusion approach. The localization scheme is proposed to be under an available point-cloud map. First, initialization in localization module is designed to calculate the initial position of UGV in map using the Gaussian projection approach and obtain the frame transformation between the 3-D lidar and the inertial measurement unit (IMU). Second, the best alignment between each scan frame and the available submap is obtained, and the pose of vehicle relative to the origin of map is calculated. Third, the precise pose of UGV is well predicted by integrating the data from 3-D lidar and IMU. Fourth, in order to visually verify the proposed localization scheme, the motion of vehicle is visualized by designing the visualization module. Note that the preprocessing module aims to process the raw scan data. The availability of our proposed localization scheme is verified by conducting experiments in our campus.

This paper proposes a novel control strategy for the synchronization of network systems. The designed distributed controllers adopt the communication channels to exchange information. The designed controller for each heterogeneous node includes two parts: 1) the reference generator (RG) to copy the dynamics of the leader and 2) adaptive regulator (AR) to achieve synchronization purpose. Under the action of sampled-data control law, outputs of all RGs converge to the output of the leader. The closed-loop system of the leader and all RGs are be equivalently written as the interaction of an operator and a linear time-invariant system. The small gain theorem is utilized to calculate the upper bound of the sampling intervals. Furthermore, the integral quadratic constraints can provide the passivity-type property of the operator and give the less conservative results. Meanwhile, the AR can ensure that nonidentical node tracks its exosystem. Thus, all nonidentical nodes and the leader achieve output synchronization. The proposed control strategy is similar to the separation principle, which includes two steps. Finally, a numerical example is given to demonstrated the effectiveness of the proposed control strategy.

In the era of big data, information on relationships changes along with time, and the graphs of relationships captured at consecutive timestamps form the multiple temporal relational (MTR) network. To identify the relations in the network while preserving the network structure, a common solution is to learn the network representations through network embedding methods, and then build the relations upon the similarity among these representations. However, the existing network embedding methods either focus on a single relation or ignore the correlation between the heterogeneous and homogeneous relations, and thus it is difficult to investigate the multiple temporal features in the network. In this paper, we propose a novel network embedding method, named Homo- Hetero Network Embedding (HHNE), for the MTR networks. The HHNE utilizes the Graph Convolutional Network (GCN) to extract homogeneous features from each temporal relational network and then generates the homo-hetero network embeddings by fusing the single temporal relational features through a Multi- Layer Perceptron (MLP). Therefore, HHNE could capture the multi- dimensional characteristics in the network, including both intra-relation information and inter-relation information. To show the efficiency of HHNE, we conduct experiments in a real-world dataset on predicting new relations in the MTR networks. The result reveals that our method could outperform several legacy network embedding methods and state- of-the-art multi-relational network embedding methods in the task of relation prediction, demonstrating that the proposed embedding method is more suitable for the MTR network.