Human instance matting aims to estimate an alpha matte for each human instance in an image, which is challenging as it easily fails in complex cases requiring disentangling mingled pixels belonging to multiple instances along hairy and thin boundary structures. In this work, we address this by introducing MP-Mat, a novel 3D-and-instance-aware matting framework with multiplane representation, where the multiplane concept is designed from two different perspectives: scene geometry level and instance level. Specifically, we first build feature-level multiplane representations to split the scene into multiple planes based on depth differences. This approach makes the scene representation 3D-aware, and can serve as an effective clue for splitting instances in different 3D positions, thereby improving interpretability and boundary handling ability especially in occlusion areas. Then, we introduce another multiplane representation that splits the scene in an instance-level perspective, and represents each instance with both matte and color. We also treat background as a special instance, which is often overlooked by existing methods. Such an instance-level representation facilitates both foreground and background content awareness, and is useful for other down-stream tasks like image editing. Once built, the representation can be reused to realize controllable instance-level image editing with high efficiency. Extensive experiments validate the clear advantage of MP-Mat in matting task. We also demonstrate its superiority in image editing tasks, an area under-explored by existing matting-focused methods, where our approach under zero-shot inference even outperforms trained specialized image editing techniques by large margins. Code is open-sourced at https://github.com/JiaoSiyi/MPMat.git.

Emotional support is a crucial aspect of communication between community members and police dispatchers during incident reporting. However, there is a lack of understanding about how emotional support is delivered through text-based systems, especially in various non-emergency contexts. In this study, we analyzed two years of chat logs comprising 57,114 messages across 8,239 incidents from 130 higher education institutions. Our empirical findings revealed significant variations in emotional support provided by dispatchers, influenced by the type of incident, service time, and a noticeable decline in support over time across multiple organizations. To improve the consistency and quality of emotional support, we developed and implemented a fine-tuned Large Language Model (LLM), named dispatcherLLM, designed to suggest replies through simulating human dispatchers' languages with appropriate emotional support. We evaluated dispatcherLLM by comparing its generated responses to those of human dispatchers and other off-the-shelf models using real chat messages. Additionally, we conducted a human evaluation to assess the perceived effectiveness of the support provided by dispatcherLLM. This study not only contributes new empirical understandings of emotional support in text-based dispatch systems but also demonstrates the significant potential of generative AI in improving service delivery.

Federated learning (FL) is widely used in neural network-based deep learning, which allows multiple users to jointly train a model without disclosing their data. However, the data quality of the users is not uniform, and some users with poor computing ability and outdated equipments called irregular ones may collect low-quality data and thus reduce the accuracy of the global model. In addition, the untrusted server may return wrong aggregation results to cheat the users. To solve these problems, we propose a verifiable privacy-preserving FL protocol with irregular users (VPFLI) based on single server. The protocol is privacy-preserving for the untrusted server and it is proved secure based on drop-tolerant homomorphic encryption. For low-quality datasets, their proportion would be decreased in the aggregation results in order to ensure the accuracy of the global model. Also, the aggregation results can be effectively verified by the users based on linear homomorphic hash. Moreover, VPFLI is proposed based on single server, which is more applicable in reality compare with the previous ones based on two non-colluding servers. The experiments show that VPFLI improves the accuracy of the model from 83.5% to 91.5% based on MNIST dataset compared to the traditional FL protocols.

Federated learning (FL) is a distributed machine learning framework, which allows multiple users to collaboratively train and obtain a global model with high accuracy. Currently, FL is paid more attention by researchers and a growing number of protocols are proposed. This article first analyzes the security vulnerabilities of the VerifyNet and VeriFL protocols, and proposes a new aggregation protocol for FL. We use additive homomorphic encryption and double masking to simultaneously protect the user’s local model and the aggregated global model while most of the existing protocols only consider the privacy of the local model. Also, linear homomorphic hash and digital signature are used to achieve traceable verification, which means the users can not only verify the aggregation results, but also be able to identify the wrong epoch if the results are wrong. In summary, our protocol can realize the privacy of the local model and global model and achieve verification traceability even if the cloud server colludes with malicious users. The experimental results show that the proposed protocol improves the security of FL without decreasing the efficiency of the users and classification accuracy of the training model.