Related papers: Fine-grained CDN Delegation
Deploying Convolutional Neural Networks (CNNs) on resource-constrained devices necessitates efficient management of computational resources, often via distributed environments susceptible to latency from straggler nodes. This paper…
Deconvolutional layers have been widely used in a variety of deep models for up-sampling, including encoder-decoder networks for semantic segmentation and deep generative models for unsupervised learning. One of the key limitations of…
Recently, FCNs have attracted widespread attention in the CD field. In pursuit of better CD performance, it has become a tendency to design deeper and more complicated FCNs, which inevitably brings about huge numbers of parameters and an…
Delamination assessment of the bridge deck plays a vital role for bridge health monitoring. Thermography as one of the nondestructive technologies for delamination detection has the advantage of efficient data acquisition. But there are…
Content Delivery Networks (CDNs) offer a protection layer for enhancing the security of websites. However, a significant security flaw named Absence of Domain Verification (DVA) has become emerging recently. Although this threat is…
V2X (Vehicle-to-everything) communication relies on short messages for short-range transmissions over a fading wireless channel, yet requires high reliability and low latency. Hard-decision decoding sacrifices the preservation of diversity…
Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement…
Decentralized computation outsourcing should allow anyone to access the large amounts of computational power that exists in the Internet of Things. Unfortunately, when trusted third parties are removed to achieve this decentralization,…
We develop a mechanism for unsecured lending among pseudonymous users that does not rely on collateral, legal identity, or centralized underwriting. New borrowers enter only through sponsors who delegate part of their own credit capacity,…
Content distribution networks (CDNs) which serve to deliver web objects (e.g., documents, applications, music and video, etc.) have seen tremendous growth since its emergence. To minimize the retrieving delay experienced by a user with a…
DNS manipulation is an increasingly common technique used by censors and other network adversaries to prevent users from accessing restricted Internet resources and hijack their connections. Prior work in detecting DNS manipulation relies…
Recent advancements in Large Language Models (LLMs) have led to their widespread adoption in daily applications. Despite their impressive capabilities, they remain vulnerable to adversarial attacks, as even minor meaning-preserving changes…
Software development in the aerospace domain requires adhering to strict, high-quality standards. While there exist regulatory guidelines for commercial software in this domain (e.g., ARP-4754 and DO-178), these do not apply to software…
Past few years have witnessed exponential growth of interest in deep learning methodologies with rapidly improving accuracies and reduced computational complexity. In particular, architectures using Convolutional Neural Networks (CNNs) have…
Content-Centric Networking (CCN) is a concept being considered as a potential future alternative to, or replacement for, today's Internet IP-style packet-switched host-centric networking. One factor making CCN attractive is its focus on…
In recent years deep learning algorithms have shown extremely high performance on machine learning tasks such as image classification and speech recognition. In support of such applications, various FPGA accelerator architectures have been…
Existing DNS configuration verification tools face significant issues (e.g., inefficient and lacking support for incremental verification). Inspired by the advancements in recent work of distributed data plane verification and the…
Deep image denoisers achieve state-of-the-art results but with a hidden cost. As witnessed in recent literature, these deep networks are capable of overfitting their training distributions, causing inaccurate hallucinations to be added to…
Diffusion language models (DLMs) have recently emerged as a strong alternative to autoregressive models by enabling parallel text generation. To improve inference efficiency and KV-cache compatibility, prior work commonly adopts block-based…
The importance of cloud computing has grown over the last years, which resulted in a significant increase of Data Center (DC) network requirements. Virtualisation is one of the key drivers of that transformation and enables a massive…