IEEE Access special section editorial: Artificial intelligence enabled networking

With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS)

A new security architecture for SIP based P2P computer networks

Many applications are transferred from C/S (Client/Server) mode to P2P (Peer-to-Peer) mode such as VoIP (Voice over IP). This paper presents a new security architecture, i.e. a trustworthy authentication algorithm of peers, for Session Initialize Protocol (SIP) based P2P computer networks. A mechanism for node authentication using a cryptographic primitive called one-way accumulator is proposed to secure the P2P SIP computer networks. It leverages the distributed nature of P2P to allow for distributed resource discovery and rendezvous in a SIP network, thus eliminating (or at least reducing) the need for centralized servers. The distributed node authentication algorithm is established for the P2P SIP computer networks. The corresponding protocol has been implemented in our P2P SIP experiment platform successfully. The performance study has verified the proposed distributed node authentication algorithm for SIP based P2P computer networks

Distinguishing humans from computers in the game of go: a complex network approach

We compare complex networks built from the game of go and obtained from databases of human-played games with those obtained from computer-played games. Our investigations show that statistical features of the human-based networks and the computer-based networks differ, and that these differences can be statistically significant on a relatively small number of games using specific estimators. We show that the deterministic or stochastic nature of the computer algorithm playing the game can also be distinguished from these quantities. This can be seen as tool to implement a Turing-like test for go simulators.Comment: 7 pages, 6 figure

Optimized usage of network resources based on context information

Today an efficient (cost-effective) design and usage of networks is of particular importance. As more and more computer systems become context-aware the question of how context information can be used to improve computer networks arises. In this poster we describe how context information can be used to optimize the usage of resources in a computer network. By means of a mobile payment system we show how these optimization method can be applied

A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e. deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision - convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.Comment: Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm

Delayed commutation in quantum computer networks

In the same way that classical computer networks connect and enhance the capabilities of classical computers, quantum networks can combine the advantages of quantum information and communications. We propose a non-classical network element, a delayed commutation switch, that can solve the problem of switching time in packet switching networks. With the help of some local ancillary qubits and superdense codes we can route the information after part of it has left the network node.Comment: 4 pages. 4 figures. Preliminar versio