This study introduces a refined Flooding Injection Rate-adjustable Denial-of-Service (DoS) model for Network-on-Chips (NoCs) and more importantly presents DL2Fence, a novel framework utilizing Deep Learning (DL) and Frame Fusion (2F) for DoS detection and localization. Two Convolutional Neural Networks models for classification and segmentation were developed to detect and localize DoS respectively. It achieves detection and localization accuracies of 95.8% and 91.7%, and precision rates of 98.5% and 99.3% in a 16x16 mesh NoC. The framework's hardware overhead notably decreases by 76.3% when scaling from 8x8 to 16x16 NoCs, and it requires 42.4% less hardware compared to state-of-the-arts. This advancement demonstrates DL2Fence's effectiveness in balancing outstanding detection performance in large-scale NoCs with extremely low hardware overhead.

Network slicing is a key technique in 5G and beyond for efficiently supporting diverse services. Many network slicing solutions rely on deep learning to manage complex and high-dimensional resource allocation problems. However, deep learning models suffer limited generalization and adaptability to dynamic slicing configurations. In this paper, we propose a novel framework that integrates constrained optimization methods and deep learning models, resulting in strong generalization and superior approximation capability. Based on the proposed framework, we design a new neural-assisted algorithm to allocate radio resources to slices to maximize the network utility under inter-slice resource constraints. The algorithm exhibits high scalability, accommodating varying numbers of slices and slice configurations with ease. We implement the proposed solution in a system-level network simulator and evaluate its performance extensively by comparing it to state-of-the-art solutions including deep reinforcement learning approaches. The numerical results show that our solution obtains near-optimal quality-of-service satisfaction and promising generalization performance under different network slicing scenarios.

It also provides a generative aspect that allows for robust testing as well as an additional way to understand your data through manual inspection. The dual nature of validation and generation is a natural fit for deep learning models that consist of paired discriminator/generator models. TLDR: In this post we show that you can leverage the dual nature of clojure.spec's A common use of clojure.spec is at the boundaries to validate that incoming data is indeed in the expected form. Again, this is boundary is a fitting place to integrate models for the deep learning paradigm and our traditional software code.

The field of Artificial Intelligence has made rapid progress in recent years, matching or in some cases, even surpassing human accuracy at tasks such as computer vision, natural language processing and machine translation. The intersection of artificial intelligence (AI) and GIS is creating massive opportunities that weren't possible before. AI, machine learning and deep learning are helping us make a better world by helping increase crop yield through precision agriculture, to fighting crime by deploying predictive policing models, to predicting when the next big storm will hit and being better equipped to handle it. Broadly speaking, AI is the ability of computers to perform a task that typically requires some level of human intelligence. Machine learning is one type of engine that makes this possible.

Last week Rohit Singh and Alberto Nieto hosted a GeoDev Webinar on "Integrating Deep Learning with ArcGIS Using Python". We had a lot of great questions come out of the webinar and will be updating this post to add the Q&A that were not addressed during the webinar. Many viewers requested to have access to the PowerPoint presentation, and the presenters were more than happy to share. In their presentation, they offered access to their demos and resources that may be helpful for those developing in Python. Did you miss the webinar?