This paper introduces Modular Gaussian Splatting (Modular-GS), a novel method that leverages 3D Gaussian Splatting and Mixture of Experts (MoEs) for decomposing and representing 3D scenes as a combination of Instance Gaussians. Modular-GS achieves scene decomposition by inputting multi-view data and automatically generated masks, facilitating fine-grained modeling of individual objects. Our approach enables controllable editing and dynamic rendering of the scene by selectively combining different experts, supporting instance insertion across datasets. Experimental results demonstrate that Modular-GS improves scene modeling quality and efficiency, offering new possibilities for Radiance Field Rendering. This work extends the boundaries of 3D scene representation and editing, advancing techniques for semantic understanding and real-time rendering. Our code and models will be at https://modular-gs.github.io/.

With the rise of mobile Internet and AI, social media integrating short messages, images, and videos has developed rapidly. As a guarantee for the stable operation of social media, information security, especially graph anomaly detection (GAD), has become a hot issue inspired by the extensive attention of researchers. Most GAD methods are mainly limited to enhancing the homophily or considering homophily and heterophilic connections. Nevertheless, due to the deceptive nature of homophily connections among anomalies, the discriminative information of the anomalies can be eliminated. To alleviate the issue, we explore a novel method TA-Detector in GAD by introducing the concept of trust into the classification of connections. In particular, the proposed approach adopts a designed trust classier to distinguish trust and distrust connections with the supervision of labeled nodes. Then, we capture the latent factors related to GAD by graph neural networks, which integrate node interaction type information and node representation. Finally, to identify anomalies in the graph, we use the residual network mechanism to extract the deep semantic embedding information related to GAD. Experimental results on two real benchmark datasets verify that our proposed approach boosts the overall GAD performance in comparison to benchmark baselines.

Implicit Neural Radiance Field (NeRF) techniques have been widely applied and shown promising results for scene decomposition learning and rendering. Existing methods typically require encoding spatial and semantic coordinates separately, followed by deep neural networks (MLP) to obtain representations of the entire scene and individual objects respectively. However, these implicit neural field methods mix scene data and differentiable rendering together, which results in issues with expensive computation, low interpretability and limited scalability. In this article, we propose NeRF-IS (Explicit Neural Radiance Fields in Semantic Space), a novel 4D neural radiance field model architecture, that integrates 3D space and semantic space modeling, which can perform both scene-level and object-level modeling. Specifically, we design a hybrid method of explicit spatial modeling and implicit feature representation, which enhances the model’s ability in scene semantic editing and realistic rendering. For efficient training of NeRF-IS, we apply low rank tensor decomposition to compress the model and speed up the training. We also introduce an importance sampling algorithm that uses a volume density prediction network to provide more accurate samples for the whole system with a coarse-to-fine strategy. Extensive experiments demonstrate that our system not only achieves competitive performance for scene-level representation and rendering of static scene, but also enables object-level rendering and editing.

Efficiently mining frequent itemsets and association rules on the encrypted outsourced data remains a great challenge for the time-consuming ciphertext computations. Nowadays, it has been not well addressed for privacy-preserving frequent itemsets and association rule mining schemes with mining efficiency, dataset, and query confidentiality simultaneously. In this paper, we investigate the study of privacy issues on frequent itemset mining and association rule mining on outsourced data in a two-cloud model, where the data are encrypted and outsourced by multiple owners holding different public keys. We develop several secure computation protocols based on additively homomorphic cryptosystem and additive secret sharing, which enable the clouds could securely mine the frequent itemsets and association rules. Furthermore, we also design two kinds of frequent itemset and association rule query service models, i.e., service customers query the cloud-mined results, and service customers query with their own decided threshold. The proposed scheme not only supports the mining process on the data encrypted by multiple public keys without compromising the security of the datasets, query data and query results, but also offline users. In addition, the experimental results show that our query scheme is much more efficient than the state-of-the-art work.

Graph Convolutional Networks (GCNs) and their variants have achieved impressive performance in a wide range of graph-based tasks. For graph data, both feature information and structural information play a crucial role. Most GCNs update the node representation by aggregating the information from neighbors. However, the structural information may contain noise, which may mislead the downstream tasks. Hence, a new graph topology optimization method for the semi-supervised node classification tasks, GTNACP is proposed to improve the quality of structural information. The core idea of our method is to filter the structural information to be optimized by comparing the difference between the clustering results of the input data and the pseudo-label values obtained from pre-training. Due to the introduction of pseudo-labels with noise, instead of fully confiding in the generated labeled set, we design new loss functions as measurements of their confidence. In this way, GTNACP can alleviate the impact of incorrect pseudo-labels. Moreover, we experimentally find that deleting or adding edges directly by error can irreversibly degrade the performance. In order to alleviate such negative impact, GTNACP adopts an edge modification method based on node similarity and clustering performance. Our experiments verify that GTNACP can be easily combined with traditional GCNs and outperform baseline models in various semi-supervised node classification tasks, and to some extent, can effectively mitigate over-smoothing.

Differentially Private Stochastic Gradient Descent (DP-SGD) is a prime method for training machine learning models with rigorous privacy guarantees. Since its birth, DP-SGD has gained popularity and has been widely adopted in both academic and industrial research. One well-known challenge when using DP-SGD is how to improve utility while maintaining privacy. To this end, recently we have seen several proposals that clip the gradients with adaptive thresholds rather than a fixed one. Although each proposal comes with some theoretical justification, the theories often rely on strong assumptions and are not compatible with each other. It is hard to know whether they are good in practice and how good they are. In this paper, we investigate adaptive clipping in DP-SGD from an empirical perspective. With extensive experiments, we were able to gain some fresh insights and proposed two new adaptive clipping strategies based on them. We cross-compared the existing methods and our new strategies experimentally. Results showed that our strategies did provide a substantial improvement in model accuracy, and outperformed the state-of-the-art adaptive clipping methods consistently.

While gradient aggregation playing a vital role in federated or collaborative learning, recent studies have revealed that gradient aggregation may suffer from some attacks, such as gradient inversion, where the private training data can be recovered from the shared gradients. However, the performance of the existing attack methods is limited because they usually require prior knowledge in Batch Normalization and could only reconstruct a single image or a small batch one. To make the attacks less restrictive and more applicable, we propose an effective and practical gradient inversion method in this paper. Specifically, we use cosine similarity to measure the difference of gradients between the synthesized and ground-truth images, and then construct an input regularization for the fully connected layer to ensure the fidelity of the image. Moreover, we apply the total variation denoising strategy to the convolution feature map for further improving the smoothness of the reconstructed image. Experimental results demonstrate that our method can reconstruct high fidelity training data on a large batch size for complex data sets, such as ImageNet.

Graph Convolutional Networks (GCNs) are powerful representation learning methods for non-Euclidean data. Compared with the Euclidean data, labeling the non-Euclidean data is more expensive. Meanwhile, most existing GCNs only utilize few labeled data but ignore most of the unlabeled data. To address this issue, we design a novel end-to-end Iterative Feature Clustering Graph Convolutional Networks (IFC-GCN) that enhances the standard GCN with an Iterative Feature Clustering (IFC) module. The proposed IFC module constrains node features iteratively based on the predicted pseudo labels and feature clustering. Further, we design an EM-like framework for IFC-GCN training, which improves the network performance by rectifying the pseudo labels and the node features alternately. Theoretical analysis and experimental results show that our proposed IFC module can effectively modify the node features. Experimental results on public datasets demonstrate that IFC-GCN outperforms state-of-the-art methods on the semi-supervised node classification task.