Related papers: ICANN as Regulator
We conduct longitudinal and temporal analyses on active DNS measurement data to investigate how the Russia-Ukraine conflict impacted the network infrastructures supporting domain names under ICANN's CZDS new gTLDs. Our findings revealed…
Information dissemination applications (video, news, social media, etc.) with large number of receivers need to be efficient but also have limited loss tolerance. The new Information-Centric Networks (ICN) paradigm offers an alternative…
Learning accurate users and news representations is critical for news recommendation. Despite great progress, existing methods seem to have a strong bias towards content representation or just capture collaborative filtering relationship.…
Domain lists are a key ingredient for representative censuses of the Web. Unfortunately, such censuses typically lack a view on domains under country-code top-level domains (ccTLDs). This introduces unwanted bias: many countries have a rich…
The multi-criteria (MC) recommender system, which leverages MC rating information in a wide range of e-commerce areas, is ubiquitous nowadays. Surprisingly, although graph neural networks (GNNs) have been widely applied to develop various…
Filtering packet traffic and rules of permit/denial of data packets into network nodes are granted by facilitating Access Control Lists (ACL). This paper proposes a procedure of adding a link load threshold value to the access control list…
The deep Convolutional Neural Network (CNN) is the state-of-the-art solution for large-scale visual recognition. Following basic principles such as increasing the depth and constructing highway connections, researchers have manually…
We propose the fully explainable Decision Tree Graph Neural Network (DT+GNN) architecture. In contrast to existing black-box GNNs and post-hoc explanation methods, the reasoning of DT+GNN can be inspected at every step. To achieve this, we…
These years much effort has been devoted to improving the accuracy or relevance of the recommendation system. Diversity, a crucial factor which measures the dissimilarity among the recommended items, received rather little scrutiny.…
The goal of Content Delivery Networks (CDNs) is to serve content to end-users with high performance. In order to do that, a CDN measures the latency on the paths from its servers to users and then selects a best available server for each…
The concept of agile domain name system (DNS) refers to dynamic and rapidly changing mappings between domain names and their Internet protocol (IP) addresses. This empirical paper evaluates the bias from this kind of agility for DNS-based…
Deep neural networks (DNNs) may outperform human brains in complex tasks, but the lack of transparency in their decision-making processes makes us question whether we could fully trust DNNs with high stakes problems. As DNNs' operations…
Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing…
The Internet's TCP/IP architecture was designed for resilient packet delivery between hosts identified by IP addresses. Over time, however, the consolidation of applications and services into large-scale platforms built on that universal…
This paper asks how much we can gain in terms of bandwidth and user satisfaction, if recommender systems became bandwidth aware and took into account not only the user preferences, but also the fact that they may need to serve these users…
It has been observed that message-passing graph neural networks (GNN) sometimes struggle to maintain a healthy balance between the efficient/scalable modeling of long-range dependencies across nodes while avoiding unintended consequences…
A frequent problem in settings where a unique resource must be shared among users is how to resolve the contention that arises when all of them must use it, but the resource allows only for one user each time. The application of efficient…
The growing need to manage and exploit the proliferation of online data sources is opening up new opportunities for bringing people closer to the resources they need. For instance, consider a recommendation service through which researchers…
Deep Neural Nets (DNNs) learn latent representations induced by their downstream task, objective function, and other parameters. The quality of the learned representations impacts the DNN's generalization ability and the coherence of the…
Networking companies, especially the ones with the biggest market shares, tend to offer end to end solutions for their customers, with large discounts, that it would sound irrational to decline such offers, helping contractors make larger…