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Complex networks describe a wide range of systems in nature and society. Frequently cited examples include the cell, a network of chemicals linked by chemical reactions, and the Internet, a network of routers and compute...

Materias / palabras clave: Computer science; The Internet; Vision; Telecommunications; Identification (biology); Paradigm shift; Data science; Wireless sensor network; Internet of Things; World Wide Web; Computer security; Computer network

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, expo...

The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within local re...

An ad-hoc network is the cooperative engagement of a collection of mobile nodes without the required intervention of any centralized access point or existing infrastructure. We present Ad-hoc On Demand Distance Vector Ro...

The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools fo...

The IEEE has standardized the 802.11 protocol for wireless local area networks. The primary medium access control (MAC) technique of 802.11 is called the distributed coordination function (DCF). The DCF is a carrier sens...

Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a n...

We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational appro...

We derive a new self-organizing learning algorithm that maximizes the information transferred in a network of nonlinear units. The algorithm does not assume any knowledge of the input distributions, and is defined here f...

The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through ...

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVR...

Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning alg...

BACKGROUND: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protei...

Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessaril...

We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution f...

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describe...

List of Boxes List of Tables and Figures Foreword Acknowledgments 1. Defining Content Analysis Is Content Analysis Easy? Is It Something That Anyone Can Do? A Six-Part Definition of Content Analysis 2. Milestones in the ...

"Penetration Testing (short pentesting) is a methodology for assessing network security, by generating and executing possible attacks exploiting known vulnerabilities of operating systems and applications. Doing so autom...

Materias / palabras clave: parallel and distributed processing, computer networking, machine learning, software engineering internet of thing (iot), pattern recognition, image processing; Science: Mathematics: Instruments and machines: Electronic ...

Materias / palabras clave: physical layer techniques, network management, network signal processing, neural networks and machine learning, bayesian networks and statistical inference, tensor networks; Technology: Electrical engineering. Electronic...

Materias / palabras clave: information technology, networking technologies, data access, communication technologies; Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware; Scienc...

Materias / palabras clave: signal processing, parallel and distributed processing, computer-aided education, software engineering, computer networking and operative systems; Technology: Electrical engineering. Electronics. Nuclear engineering: Ele...

Materias / palabras clave: ai and green ai, computer applications, software engineering, computer security and cyber security, computer networks and cloud computing, multimedia; Science: Mathematics: Instruments and machines: Electronic computers....