The rapid development of Artificial Intelligence Generated Content (AIGC) techniques has enabled the creation of high-quality synthetic content, but it also raises significant security concerns. Current detection methods face two major limitations: (1) the lack of multidimensional explainable datasets for generated images and videos. Existing open-source datasets (e.g., WildFake, GenVideo) rely on oversimplified binary annotations, which restrict the explainability and trustworthiness of trained detectors. (2) Prior MLLM-based forgery detectors (e.g., FakeVLM) exhibit insufficiently fine-grained interpretability in their step-by-step reasoning, which hinders reliable localization and explanation. To address these challenges, we introduce Ivy-Fake, the first large-scale multimodal benchmark for fake image and video detection. It consists of over 106K richly annotated training samples (images and videos) and 5,000 manually verified evaluation examples, sourced from multiple generative models and real-world datasets through a carefully designed pipeline to ensure both diversity and quality. Furthermore, we propose Ivy-xDetector, a multimodel large language model (MLLM) based on reinforcement fine-tuning (RFT), capable of producing explainable reasoning chains and achieving robust performance across multiple fake image and video detection benchmarks.

We present a novel 360° panoramic video conferencing system that dynamically synchronizes virtual backgrounds with real-time camera motion, addressing the limitations of static backgrounds in conventional systems. By integrating robust human segmentation, monocular visual odometry (VO), and virtual environment rendering, our method achieves seamless alignment between foreground participants and immersive 3D virtual scenes. Unlike prior approaches that suffer from foreground-background desynchronization during camera rotations or user movements, our framework estimates camera rotation in 3-DoF using a hybrid pipeline combining feature-based patch tracking and pose smoothing, while ignoring translation artifacts to maintain stability. This work bridges the gap between computational efficiency and MR-driven telepresence, offering a practical solution for next-generation virtual collaboration.

Graph federated learning (GFL) is increasingly utilized in domains such as social network analysis and recommendation systems, where non-IID data exist extensively and necessitate a strong emphasis on personalized learning. However, existing methods focus only on the personality among different clients instead of the personality within a client which widely exists in the real social networks, where intra-client personality addresses the heterogeneity of known data, while inter-client personality always tackle client heterogeneity under privacy constraint. In this paper, we propose a novel automatic personalized graph federated learning (PGFL) scheme named FedCCH to capture both inter-client and intra-client heterogeneity. For intra-client heterogeneity, we innovatively propose the learnable Personalized Factor (PF) to automatically normalize each graph representation within clients by learnable parameters, which weakens the impact of non-IID data distribution. For inter-client heterogeneity, we propose a novel hash-based similarity clustering method to generate the hash signature for each client, and then group similar clients for joint training among different clients. Ultimately, we collaboratively train intra-client and inter-client modules to improve the effectiveness of capturing the heterogeneity of the graph data of clients. Experiment results demonstrate that FedCCH outperforms other state-of-the-art baseline methods.

The rise of Virtual Reality (VR) sports has been driven by evolving work patterns, limited access to physical exercise spaces, and a growing focus on health and wellness. Beyond the enjoyment and convenience offered by VR exercise, we aim to enhance users' athletic performance through this medium. Prior research on the Proteus Effect in VR sports has demonstrated the potential of customized, stronger, or younger avatars to improve exercise outcomes. However, such effects are limited for users who already perceive themselves as strong and youthful. To address this, we propose a more general approach: representing perceived avatar capability through facial expressions and vocal cues to influence user performance. In this study, we examined how manipulating the perceived capability level of virtual avatars (high, neutral, low) during dumbbell lateral raises in a VR gym affected exercise performance. Results indicated that avatars with high-capability expressions significantly enhanced performance and motivation compared to low-capability avatars. These findings underscore the promise of using facial and auditory cues to represent perceived capability, offering new directions for designing emotionally intelligent fitness applications.

Federated Learning (FL) empowers multiple clients to collaboratively learn a global generalization model without the need to share their local data, thus reducing privacy risks and expanding the scope of AI applications. However, current works focus less on data in a highly nonidentically distributed manner such as graph data which are common in reality, and ignore the problem of model personalization between clients for graph data training in federated learning. In this paper, we propose a novel personality graph federated learning framework based on variational graph autoencoders that incorporates model contrastive learning and local fine-tuning to achieve personalized federated training on graph data for each client, which is called FedVGAE. Then we introduce an encoder-sharing strategy to the proposed framework that shares the parameters of the encoder layer to further improve personality performance. The node classification and link prediction experiments demonstrate that our method achieves better performance than other federated learning methods on most graph datasets in the non-iid setting. Finally, we conduct ablation experiments, the result demonstrates the effectiveness of our proposed method.