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Nowadays, the new network architectures with respect to the traditional network topologies must manage more data, which entails having increasingly robust and scalable network structures. As there is a process of growth, adaptability, and…

Information-centric networking (ICN) has gained attention from network research communities due to its capability of efficient content dissemination. In-network caching function in ICN plays an important role to achieve the design…

Emerging information-centric networking architectures seek to optimally utilize both bandwidth and storage for efficient content distribution. This highlights the need for joint design of traffic engineering and caching strategies, in order…

Software and hardware components are basic parts of modern networks. However the software compo- nent is typical sealed and function-oriented. Therefore it is very difficult to modify these components. This badly affected networking…

Recent literature suggests that the Internet of Things (IoT) scales much better in an Information-Centric Networking (ICN) model instead of the current host-centric Internet Protocol (IP) model. In particular, the Named Data Networking…

Information-centric networking (ICN) has long been advocating for radical changes to the IP-based Internet. However, the upgrade challenges that this entails have hindered ICN adoption. To break this loop, the POINT project proposed a…

The paper presents a FrameNet-based information extraction and knowledge representation framework, called FrameNet-CNL. The framework is used on natural language documents and represents the extracted knowledge in a tailor-made…

Recursive InterNetwork Architecture is a clean-slate approach to how to deal with the current issues of the Internet based on the traditional TCP/IP networking stack. Instead of using a fixed number of layers with dedicated functionality,…

Large machine learning models based on Convolutional Neural Networks (CNNs) with rapidly increasing number of parameters, trained with massive amounts of data, are being deployed in a wide array of computer vision tasks from self-driving…

Information-Centric Networking (ICN) has emerged as a paradigm to cope with the lack of built-in security primitives and efficient mechanisms for content distribution of today's Internet. However, deploying ICN in a wireless environment…

Convolutional Neural Networks (CNNs) have shown remarkable performance in general object recognition tasks. In this paper, we propose a new model called EnsNet which is composed of one base CNN and multiple Fully Connected SubNetworks…

Binarized neural networks, or BNNs, show great promise in edge-side applications with resource limited hardware, but raise the concerns of reduced accuracy. Motivated by the complex neural networks, in this paper we introduce complex…

Information-Centric Networking (ICN) has emerged as an interesting approach to overcome many of the limitations of legacy IP-based networks. However, the drastic changes to legacy infrastructure required to realise an ICN have significantly…

In recent years, using a deep convolutional neural network (CNN) as a feature encoder (or backbone) is the most commonly observed architectural pattern in several computer vision methods, and semantic segmentation is no exception. The two…

Convolutional neural network (CNN) is a class of artificial neural networks widely used in computer vision tasks. Most CNNs achieve excellent performance by stacking certain types of basic units. In addition to increasing the depth and…

The flux of social media and the convenience of mobile connectivity has created a mobile data phenomenon that is expected to overwhelm the mobile cellular networks in the foreseeable future. Despite the advent of 4G/LTE, the growth rate of…

Computer networks have evolved into an essential component of modern society, facilitating the seamless sharing and dissemination of digital information. This paper explores the fundamental concepts of networking, focusing on the…

Content dissemination networks are pervasive in todays Internet. Examples of content dissemination networks include peer-to-peer networks (P2P), content distribution networks (CDN) and information centric networks (ICN). In this paper, we…

MatConvNet is an implementation of Convolutional Neural Networks (CNNs) for MATLAB. The toolbox is designed with an emphasis on simplicity and flexibility. It exposes the building blocks of CNNs as easy-to-use MATLAB functions, providing…

While research on Information-Centric Networking (ICN) flourishes, its adoption seems to be an elusive goal. In this paper we propose Edge-ICN: a novel approach for deploying ICN in a single large network, such as the network of an Internet…