Titlebook: Network and Parallel Computing; 17th IFIP WG 10.3 In Xin He,En Shao,Guangming Tan Conference proceedings 2021 IFIP International Federation

事與愿違

A Dynamic Protection Mechanism for?GPU Memory Overflowd healthcare. However, most existing researches just focus on the performance but ignore the security issues of GPUs. In this paper, we design an efficient mechanism to dynamically monitor GPU heap buffer overflow by using the CPU. Concretely, we first analyze the specific requirements of GPU memory

旁觀者

MTLAT: A Multi-Task Learning Framework Based on Adversarial Training for Chinese Cybersecurity NER Chinese cybersecurity texts contain not only a large number of professional security domain entities but also many English person and organization entities, as well as a large number of Chinese-English mixed entities. Chinese Cybersecurity NER is a domain-specific task, current models rarely focus

巧思

Learning-Based Evaluation of Routing Protocol in Vehicular Network Using WEKAlogy and communication links frequently change due to the high mobility of vehicles. So, the key challenge of our work is to choose the best routing protocol using machine learning algorithms. When choosing routing protocol, most research focuses on the improvement of the performance of specific rou

芳香一點(diǎn)

MOAN

裙帶關(guān)系

貨物

FIR

Deep Visible and Thermal Image Fusion with Cross-Modality Feature Selection for?Pedestrian DetectionGB and thermal images respectively, and these features are fused with a cross-modality feature selection module for detection. It includes the following stages. First, we learn features from paired RGB and thermal images through a backbone network with a residual structure, and add a feature squeeze

Corral

補(bǔ)充

Security Situation Prediction of Network Based on Lstm Neural Network threats. In view of the single source of information and the lack of time attributes of the existing methods, we propose an optimal network security situation prediction model based on lstm neural network. We employ the stochastic gradient descent method as the minimum training loss to establish a