Existing research indicates that original federated learning is not absolutely secure; attackers can infer the original training data based on reconstructed gradient information. Therefore, we will further investigate methods to protect data privacy and prevent adversaries from reconstructing sensitive training samples from shared gradients. To achieve this, we propose a defense strategy called SLGD, which enhances model robustness by combining sparse learning and gradient perturbation techniques. The core idea of this approach consists of two parts. First, before processing training data at the RSU, we preprocess the data using sparse techniques to reduce data transmission and compress data size. Second, the strategy extracts feature representations from the model and performs gradient filtering based on the l 2 norm of this layer. Selected gradient values are then perturbed using Von Mises–Fisher (VMF) distribution to obfuscate gradient information, thereby defending against gradient reconstruction attacks and ensuring model security. Finally, we validate the effectiveness and superiority of the proposed method across different datasets and attack scenarios.

Intelligent traffic signal systems, crucial for intelligent transportation systems, have been widely studied and deployed to enhance vehicle traffic efficiency and reduce air pollution. Unfortunately, intelligent traffic signal systems are at risk of data spoofing attack, causing traffic delays, congestion, and even paralysis. In this paper, we reveal a multivehicle collaborative data spoofing attack to intelligent traffic signal systems and propose a collaborative attack sequence generation model based on multiagent reinforcement learning (RL), aiming to explore efficient and stealthy attacks. Specifically, we first model the spoofing attack based on Partially Observable Markov Decision Process (POMDP) at single and multiple intersections. This involves constructing the state space, action space, and defining a reward function for the attack. Then, based on the attack modeling, we propose an automated approach for generating collaborative attack sequences using the Multi‐Actor‐Attention‐Critic (MAAC) algorithm, a mainstream multiagent RL algorithm. Experiments conducted on the multimodal traffic simulation (VISSIM) platform demonstrate a 15% increase in delay time (DT) and a 40% reduction in attack ratio (AR) compared to the single‐vehicle attack, confirming the effectiveness and stealthiness of our collaborative attack.

Pathfinding becomes an important component in many real-world scenarios, such as popular warehouse systems and autonomous aircraft towing vehicles. With the development of reinforcement learning (RL) especially in the context of asynchronous advantage actor-critic (A3C), pathfinding is undergoing a revolution in terms of efficient parallel learning. Similar to other artificial intelligence-based applications, A3C-based pathfinding is also threatened by the adversarial attack. In this paper, we are the first to study the adversarial attack to A3C, that can unexpectedly wake up longtime retraining mechanism until successful pathfinding. We also discover an attack example generation to launch the attack based on gradient band, in which only one baffle of extremely few unit lengths can successfully perform the attack. Experiments with detailed analysis are conducted to show a high attack success rate of 95% with an average baffle length of 2.95. We also discuss defense suggestions leveraging the insights from our analysis.

The cross-media retrieval problem has received much attention in recent years due to the rapid increasing of multimedia data on the Internet. A new approach to the problem has been raised which intends to match features of different modalities directly. In this research, there are two critical issues: how to get rid of the heterogeneity between different modalities and how to match the cross-modal features of different dimensions. Recently metric learning methods show a good capability in learning a distance metric to explore the relationship between data points. However, the traditional metric learning algorithms only focus on single-modal features, which suffer difficulties in addressing the cross-modal features of different dimensions. In this paper, we propose a cross-modal similarity learning algorithm for the cross-modal feature matching. The proposed method takes a bilinear formulation, and with the nuclear-norm penalization, it achieves low-rank representation. Accordingly, the accelerated proximal gradient algorithm is successfully imported to find the optimal solution with a fast convergence rate O(1/t2). Experiments on three well known image-text cross-media retrieval databases show that the proposed method achieves the best performance compared to the state-of-the-art algorithms.

As a popular heuristic to the matrix rank minimization problem, nuclear norm minimization attracts intensive research attentions. Matrix factorization based algorithms can reduce the expensive computation cost of SVD for nuclear norm minimization. However, most matrix factorization based algorithms fail to provide the theoretical guarantee for convergence caused by their non-unique factorizations. This paper proposes an efficient and accurate Linearized Grassmannian Optimization (Lingo) algorithm, which adopts matrix factorization and Grassmann manifold structure to alternatively minimize the subproblems. More specially, linearization strategy makes the auxiliary variables unnecessary and guarantees the close-form solution for low per-iteration complexity. Lingo then converts linearized objective function into a nuclear norm minimization over Grassmannian manifold, which could remedy the non-unique of solution for the low-rank matrix factorization. Extensive comparison experiments demonstrate the accuracy and efficiency of Lingo algorithm. The global convergence of Lingo is guaranteed with theoretical proof, which also verifies the effectiveness of Lingo.

Event causality is indispensable for knowledge-driven intelligent systems. In this paper, we propose a supervised method of extracting event causalities such as forest is cut down $$\rightarrow $$ forest is destroyed from web text. While relation identification using lexico-syntactic patterns (LSPs) is not novel, it is still challenging to extract the event expressions with necessary arguments from identified causality mentions. To address this issue, our method divides event-pair extraction into two phases: event boundary identification and missing argument identification. In the first phase, we propose a Naive Baysian probability method to identify the boundary of causal events, and extract the corresponding text fragments as event expressions. Secondly, we learn a multi-class decision tree (LADTree) to identify the missing argument for each incomplete event. Experimental results showed the good effectiveness of our approach on a large-scale open corpus.

Max-margin matrix factorization (M3F) has been popularly applied to collaborative filtering for personalized recommendations. The nonparametric M3F model represents the latest progress of the M3F methods, which can auto-select the number of factors by using nonparametric techniques. However, existing non-parametric M3F methods assume a collection of user rating data can be fully obtained before training, and they are inapplicable for on-the-fly recommender systems where user rating data arrive continuously. In this paper, we present a new efficient online nonparametric 3F model for flexible recommendation. Specifically, we design an online nonparametric M3F model (OnM3F) based on the online Passive-Aggressive learning and solve the corresponding optimization problem by using the online stochastic gradient descent. Empirical studies on two large real-world data sets verify the effectiveness of the proposed method.

With the rapid development of XML language which has good flexibility and interoperability, more and more log files of software running information are represented in XML format, especially for Web services. Fault diagnosis by analyzing semi-structured and XML like log files is becoming an important issue in this area. For most related learning methods, there is a basic assumption that training data should be in identical structure, which does not hold in many situations in practice. In order to learn from training data in different structures, we propose a similarity-based Bayesian learning approach for fault diagnosis in this paper. Our method is to first estimate similarity degrees of structural elements from different log files. Then the basic structure of combined Bayesian network (CBN) is constructed, and the similarity-based learning algorithm is used to compute probabilities in CBN. Finally, test log data can be classified into possible fault categories based on the generated CBN. Experimental results show our approach outperforms other learning approaches on those training datasets which have different structures.