Dynamic Graph Neural Networks (DGNNs) have become powerful tools for analyzing continuously evolving graph data, combining Graph Neural Network (GNN) models to extract structural information and Recurrent Neural Network (RNN) models to capture temporal semantics across snapshots. However, despite extensive research, existing DGNN solutions still face significant limitations, particularly low data parallelism caused by their snapshot-by-snapshot execution. This sequential paradigm exacerbates memory contention due to irregular, repeated vertex feature accesses and enforces strict temporal dependencies. In this paper, we propose TaGNN, an efficient topology-aware DGNN accelerator that addresses these performance bottlenecks. Specifically, we present a topology-aware concurrent execution approach into the accelerator design that calculates the final features of affected vertices while ensuring that unaffected vertices are loaded and computed only once per layer across multiple snapshots, maximizing data parallelism while minimizing memory usage. TaGNN employs a cache-friendly storage format that compactly organizes affected vertices across multiple snapshots by their timestamps and topological characteristics, reducing indexing overhead and enhancing data locality. In addition, TaGNN further proposes a similarity-aware cell skipping strategy to alleviate the stringent temporal data dependencies. It selectively reuses the RNN results from the previous snapshot to bypass RNN operations in the current snapshot when the output features of the GNN module across two consecutive snapshots are similar, achieving significant efficiency gains with minimal accuracy loss. We have implemented and assessed TaGNN on a Xilinx Alveo U280 FPGA card. Experimental results show that TaGNN achieves average speedups of 535.2x and 84.3x, and energy savings of 742.6x and 104.9x over state-of-the-art software DGNNs on Intel Xeon CPUs and NVIDIA A100 GPUs, respectively. Compared to leading DGNN accelerators (i.e., DGNN-Booster, E-DGCN, and Cambricon-DG), TaGNN delivers average speedups of 13.5x, 10.2x, and 6.5x, and energy savings of 15.9x, 11.7x, and 7.8x, respectively.

Shadow removal is an important yet challenging task in image processing and computer vision. Existing methods are limited in extracting good global features due to the interference of shadow. And also, most of them ignore a fact that features inside and outside the shaded area should be treated disparately because of different semantics or materials. In this letter, we propose a novel deep neural network Mask-ShadowNet for shadow removal. The core of our approach is a well-designed masked adaptive instance normalization (MAdaIN) mechanism with embedded aligners that serves two goals: 1) producing hidden features that considering an illumination consistency of different regions. 2) treating the feature statistics of shadow and non-shadow areas discriminately based on the shadow mask. Experimental results demonstrate that the proposed model outperforms the state-of-the-art on the ISTD benchmark. Our code is available inhttps://github.com/penguinbing/Mask-ShadowNet.

P2P Networks are self-organized and distributed. Efficient nodes risk assessment is one of the key factors for high quality resource exchanging. Most assessment methods based on trust or reputation have some remarkable drawbacks. For example, some methods impose too many restrictions to the samples, and many methods can’t identify the malicious recommendations, which result in that the final results are not convincible and credible. To solve these problems, we propose a novel risk assessment method based on grey theory. In our scheme, the communication nodes’ incomplete information state is described as several key attributes. Original data of these attributes is collected using taste concourse method to avoid malicious recommendation. The analysis and computing example shows this scheme is an efficient incomplete information nodes risk assessment method in P2P networks

We propose an identity-based proxy blind signature scheme from bilinear pairings which combines the advantages of proxy signature and of blind signature. Furthermore, our scheme can prevent the original signer from generating the proxy blind signature, thus the profits of the proxy signer are guaranteed. We introduce bilinear pairings to minimize computational overhead and to improve the related performance of our scheme. In addition, the proxy blind signature presented in This work is nonrepudiable and it fulfills perfectly the security requirements of a proxy blind signature.