Generating expressive and diverse human gestures from audio is crucial in fields like human-computer interaction, virtual reality, and animation. While existing methods have achieved remarkable performance, they often exhibit limitations due to constrained dataset diversity and the restricted amount of information derived from audio inputs. To address these challenges, we present VarGes, a novel variationdriven framework designed to enhance co-speech gesture generation by integrating visual stylistic cues while maintaining naturalness. Our approach begins with a variation-enhanced feature extraction module, which seamlessly incorporates style-reference video data into a 3D human pose estimation network to extract StyleCLIPS, thereby enriching the input with stylistic information. Subsequently, we employ a variation-compensation style encoder, a transformer-style encoder equipped with an additive attention mechanism pooling layer, to robustly encode diverse StyleCLIPS representations and effectively manage stylistic variations. Finally, a variation-driven gesture predictor module fuses MFCC audio features with StyleCLIPS encodings via cross-attention, injecting this fused data into a cross-conditional autoregressive model to modulate 3D human gesture generation based on audio input and stylistic clues. The efficacy of our approach is validated on benchmark datasets, on which it outperforms existing methods in terms of gesture diversity and naturalness. Our code and video results are publicly available at https://github.com/mookerr/VarGES/.

State sharding is a promising approach to address the scalability issue in the blockchain system. However, the previous sharding schemes using the fixed data partitioning mechanism bring high proportion of costly cross-shard transactions and cannot effectively handle the workload imbalance that occurs in practice, which slows down the performance. To address these issues, we propose EFSHARD, an efficient state sharding blockchain system that enables allocating states flexibly and timely across shards according to recent transactions. Firstly, we propose a hierarchical state partition to enable flexible mapping of states to shards. Second, we design a new state transfer protocol to efficiently migrate states across shards while guaranteeing consistency and liveness. Then, we provide a greedy-based state allocation algorithm to decide when and how to migrate states. The allocation mechanism groups highly correlated state data into the same shard to reduce the proportion of cross-shard transactions and distributes state data to shards with relatively low load to balance workload, thus improving the performance. In the end, we conduct extensive experiments to evaluate EFSHARD and the results demonstrate that EFSHARD outperforms state-of-the-art approaches in terms of transaction throughput, confirmation latency, workload balance, and queue size of transaction pool.

Generative adversarial network (GAN) has excellent performance for data generation and is widely used in image synthesis. Outsourcing GAN to cloud platform is a popular way to save local computation resources and improve the efficiency, but it still faces the privacy leakage concerns: (1) the sensitive information of the training dataset may be disclosed in the cloud; (2) the trained model may reveal the privacy of training samples since it extracts the characteristics from the data. In this paper, we propose a lightweight privacy-preserving GAN framework (LP-GAN) for model training and image synthesis based on secret sharing scheme. Specifically, we design a series of efficient secure interactive protocols for different layers (convolution, batch normalization, ReLU, Sigmoid) of neural network (NN) used in GAN. Our protocols are scalable to build secure training or inference tasks for NN-based applications. We utilize edge computing to reduce the latency and all the protocols are executed on two edge servers collaboratively. Compared with the existing schemes, the proposed solution greatly improves efficiency, reduces communication overhead, and guarantees the privacy. We prove the correctness and security of LP-GAN by theoretical analysis. Extensive experiments on different real-world datasets demonstrate the effectiveness, accuracy, and efficiency of our scheme.