Industrial-scale linear assignment problems (LAPs) are frequently encountered in various industrial scenarios, e.g., asset allocation within the domain of credit management. However, optimization algorithms for such problems (e.g., PJ-ADMM) are highly sensitive to hyper-parameters. Existing solving systems rely on empirical parameter selection, which is challenging to achieve convergence and extremely time-consuming. Additionally, the resulting parameter rules are often inefficient. To alleviate this issue, we propose RL-ISLAP, an efficient and lightweight Reinforcement Learning framework for Industrial-Scale Linear Assignment Problems. We formulate the hyper-parameter selection for PJ-ADMM as a sequential decision problem and leverage reinforcement learning to enhance its convergence. Addressing the sparse reward challenge inherent in learning policies for such problems, we devise auxiliary rewards to provide dense signals for policy optimization, and present a rollback mechanism to prevent divergence in the solving process. Experiments on OR-Library benchmark demonstrate that our method is competitive to SOTA stand-alone solvers. Furthermore, the scale-independent design of observations enables us to transfer the acquired hyper-parameter policy to a scenario of LAPs in varying scales. On two real-world industrial-scale LAPs with up to 10 millions of decision variables, our proposed RL-ISLAP achieves solutions of comparable quality in 2/3 of the time when compared to the SOTA distributed solving system employing fine-tuned empirical parameter rules.

Trajectory prediction of moving traffic agents is crucial for the safety of autonomous vehicles, whereas previous approaches usually rely on sufficiently long-observed trajectory (e.g., 2 seconds) to predict the future trajectory of the agents. However, in many real-world scenarios, it is not realistic to collect adequate observed locations for moving agents, leading to the collapse of most prediction models. For instance, when a moving car suddenly appears and is very close to an autonomous vehicle because of the obstruction, it is quite necessary for the autonomous vehicle to quickly and accurately predict the future trajectories of the car with limited observed trajectory locations. In light of this, we focus on investigating the task of instantaneous trajectory prediction, i.e., two observed locations are available during inference. To this end, we put forward a general and plug-and-play instantaneous trajectory prediction approach, called ITPNet. Specifically, we propose a backward forecasting mechanism to reversely predict the latent feature representations of unobserved historical trajectories of the agent based on its two observed locations and then leverage them as complementary information for future trajectory prediction. Meanwhile, due to the inevitable existence of noise and redundancy in the predicted latent feature representations, we further devise a Noise Redundancy Reduction Former (NRRFormer) module, which aims to filter out noise and redundancy from unobserved trajectories and integrate the filtered features and observed features into a compact query representation for future trajectory predictions. In essence, ITPNet can be naturally compatible with existing trajectory prediction models, enabling them to gracefully handle the case of instantaneous trajectory prediction. Extensive experiments on the Argoverse and nuScenes datasets demonstrate ITPNet outperforms the baselines by a large margin and shows its efficacy with different trajectory prediction models.

Vehicular social network (VSN) has been emerged in recent years with the prosperity of Internet of Vehicles (IoV). VSN service providers can exploit the social relationships of vehicles to improve the performance of vehicular networks, such as improving the order-taking efficiency of taxis. However, when social relationships are captured by attackers, other contact information is passively disclosed without their knowledge, seriously threatening user privacy. This article proposes a$(\theta ,\epsilon)$privacy technique for protecting the social relationships of vehicles that can be used to shorten the average order-taking distance of taxis during order allocation. Two variants are included in the proposed technique, which combines graph theory and graph differential privacy (GDP) mechanisms. 1)$(\theta ,\epsilon)$-GDP–a projection-based GDP algorithm is proposed to protect social relationships among the vehicles. 2) Furthermore, an improved order allocation scheme named$(\theta ,\epsilon)$-PrivOT is proposed to reduce the average order-taking distance. Real data evaluations are provided to verify the outperformance of the proposed technique. The privacy level of the proposed algorithm is improved by up to 8.77% while its data utility is improved by up to 22.94% compared with the edge removal scheme. The proposed dispatching scheme can shorten the average order-taking distance by up to 6.89% compared with the scheme of original data with 100 vehicles.

Graph structured data often possess dynamic characters in nature, such as the addition of links and nodes, in many real-world applications. Recent years have witnessed the increasing attentions paid to dynamic graph neural networks for modelling graph data. However, almost all existing approaches operate under the assumption that, upon the establishment of a new link, the embeddings of the neighboring nodes should undergo updates to learn temporal dynamics. Nevertheless, these approaches face the following limitation: If the node introduced by a new connection contains noisy information, propagating its knowledge to other nodes becomes unreliable and may even lead to the collapse of the model. In this paper, we proposeAda-DyGNN: a robust knowledgeAdaptation framework via reinforcement learning forDynamicGraphNeuralNetworks. In contrast to previous approaches, which update the embeddings of the neighbor nodes immediately after adding a new link, Ada-DyGNN adaptively determines which nodes should be updated. Considering that the decision to update the embedding of one neighbor node can significantly impact other neighbor nodes, we conceptualize the node update selection as a sequence decision problem and employ reinforcement learning to address it effectively. By this means, we can adaptively propagate knowledge to other nodes for learning robust node embedding representations. To the best of our knowledge, our approach constitutes the first attempt to explore robust knowledge adaptation via reinforcement learning specifically tailored for dynamic graph neural networks. Extensive experiments on three benchmark datasets demonstrate that Ada-DyGNN achieves the state-of-the-art performance. In addition, we conduct experiments by introducing different degrees of noise into the dataset, quantitatively and qualitatively illustrating the robustness of Ada-DyGNN. The source code of this work is available athttps://github.com/BitLhj/Ada-DyGNN/