Graph Federated Learning (GFL) leverages distributed collaborative training for global graph neural networks and it is widely applied in many fields, such as social network analysis, recommendation systems and so on. However, GFL often suffers from heterogeneous label noise interferences across clients, and existing studies mainly handle label noise issues for images, where the data is independent and the labeled samples are sufficient. However, these methods are not suitable for GFL scenarios and they fail to cope with the challenges of label noise from graph-structured data, in which nodes are dependent and label information is highly sparse. To address the above problems, we propose a Two-stage Dual-branch Robust Graph Federated Learning Framework (TD-RGFL). Specifically, based on the established neighborhood conflict estimation scheme, we first design an available noisy clients identification algorithm, to effectively utilize the limited label information in graph data and capture the characteristics of noisy information. In addition, we propose a feasible dual-branch collaborative training method by means of debiased knowledge distillation, so as to fully exploit the useful data information of noisy clients and enhance the overall robustness of federated learning. Moreover, we devise a flexible federated aggregation and local model update strategy based on the proximal regularization principle, to further reduce the negative impact of noisy clients and improve the generalization ability of the global model across different clients. Finally, we implement a series of experiments, and the corresponding results indicate that our algorithm significantly outperforms existing methods under different conditions.

Ship detection plays a critical role in intelligent maritime applications including port management, marine monitoring and so on. Most existing ship detectors are trained on high-quality conventional-sized ship images under normal lighting conditions. However,low-quality images with poor lighting conditions often exist, where the features are difficult to be distinguished. Additionally, small ships with fewer pixels exhibit minimal appearance information and weak contour characteristics in night scenes, which are harmful to the multi-scale feature fusion. To address the above challenges, we propose an effective multi-scale feature fusion network for small ship detection in night scenes named MFF-YOLO. Specifically, we first design a night-friendly enhanced channel attention module, to better represent channel-dimensional features of small ships. In addition, we construct a multi-scale feature fusion architecture based on space and channel, to obtain richer semantic information of small ships in poor lighting conditions and further enhance the feature distinguishability. Finally, a series of experiments are implemented and corresponding results demonstrate the effectiveness and feasibility of our proposed method.

Disk and memory faults are the leading causes of server breakdown. A proactive solution is to predict such hardware failure at the runtime and then isolate the hardware at risk and backup the data. However, the current model-based predictors are incapable of using the discrete time-series data, such as the values of device attributes, which conveys high-level information of the device behavior. In this paper, we propose a novel deep-learning based prediction scheme for system-level hardware failure prediction. We normalize the distribution of samples' attributes from different vendors to make use of diverse training sets. We propose a temporal Convolution Neural Network based model that is insensitive to the noise in the time dimension. Finally, we design a loss function to train the model with extremely imbalanced samples effectively. Experimental results from an open S.M.A.R.T data set and an industrial data set show the effectiveness of the proposed scheme.

Saliency detection plays an important role in image segmentation, content-aware resizing and object recognition. Most approaches obtain promising performance recently, which is useful for the postprocessing. We propose a clustering-based method to detect refined regions with comparative performance. For coarse-grained classification with unknown clusters number, an adaptive algorithm called f-means is developed in this paper. Pixels are clustered by f-means based on color and spatial features, and then the centroids are used to compute their saliency values. Experiments show that our algorithm generates more fine maps, which outperform the state-of-the-art approaches on MSRA dataset. Relying on the saliency map, we also get superior results in foreground extracting, image resizing and thumbnails generation.

To reduce the risk of intentional destruction in CSCW systems, an intelligent agent based intrusion detection and protection system solution is presented. The system uses rules to check intrusion and adopts machine learning algorithms to recognize unknown actions and to update rules or create new ones automatically. Cooperating with other security products is another useful characteristic for more integrated protection, and the final test shows its feasibility