As the gap between network and CPU speeds rapidly increases, the CPU-centric network stack proves inadequate due to excessive CPU and memory overheads. Though hardware-offloaded network stacks alleviate these issues, they suffer from limited flexibility in both control and data planes. It seems promising to offload network stacks to Smart-NICs to provide high flexibility. However, naive offloading leads to low throughput due to the inherent architectural limitations of widespread off-path SmartNICs. Even simple operations on staged network traffic would overwhelm the limited SmartNIC memory bandwidth. To this end, we design SmartNS, a SmartNIC-centric network stack with software transport programmability and line-rate packet processing capabilities. To tackle the limitations of SmartNIC-induced challenges, we propose a header-only offloading TX path and an unlimited-working-set in-cache processing RX path to minimize memory traffic to fit the wimpy SmartNIC memory bandwidth. To fully utilize the SmartNIC computing resources, we propose a programmable offloading engine to enable cloud providers to offload customized tasks along with the network stack processing. We prototype SmartNS using the widespread Nvidia BlueField-3 SmartNIC, and implement RoCEv2 and Solar transport protocols by leveraging SmartNS's software programmability. SmartNS achieves 2.2× higher throughput than the microkernel-based baseline in block storage disaggregation and 1.3× higher throughput than the hardware-offloaded baseline in KVCache transfer.

Brain tumor localization and segmentation from MRI is a challenging task in the field of Medical AI. With recent advancements, various techniques have been developed to assist medical professionals in detecting brain tumors using Artificial Intelligence. Although machine learning algorithms have shown efficiency in tumor segmentation, they often lack interpretability, making it difficult to trust and validate their predictions. In this paper, we developed an interpre table UNet model for brain tumor segmentation, incorporating Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP) to enhance model transparency. The BraTS2020 benchmark dataset was used for training and evaluation. The model demonstrated strong performance, achieving an accuracy of 99.35%. Grad-CAM was applied to visualize the regions of interest identified by the model, enhancing interpretability. Additionally, the SHAP library was utilized to explain the predictions of multiple machine learning models (Random Forest, KNN, SVC, MLP) employed for estimating patient survival days, further contributing to the model’s transparency and clinical utility.

In this paper, the uniform embedding for JPEG steganography is generalized to the motion vector (MV) domain of H.264 video (UED_H.264). By taking into account the several key issues in video steganography, e.g., the MV correlations, the local optimality and the degradation of the reconstructed video frames, the comprehensive distortion function is proposed to incorporate the minimal distortion embedding framework. The proposed scheme uses MVs as cover elements, which includes both forward and backward MVs, and the embedding is deliberately carried out with ternary syndrome-trellis codes (STC) according to the proposed scheme. Experimental results show that the proposed scheme can improve the security performance on resisting the steganalytic attacks while preserving the coding efficiency within acceptable level.