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Topic Review
Network Function Virtualization and Service Function Chaining Frameworks
Network slicing has become a fundamental property for next-generation networks, especially because an inherent part of 5G standardisation is the ability for service providers to migrate some or all of their network services to a virtual network infrastructure, thereby reducing both capital and operational costs. With network function virtualisation (NFV), network functions (NFs) such as firewalls, traffic load balancers, content filters, and intrusion detection systems (IDS) are either instantiated on virtual machines (VMs) or lightweight containers, often chained together to create a service function chain (SFC). 
  • 985
  • 01 Mar 2022
Topic Review
Single-Image Super-Resolution Neural Network
Single-image super-resolution (SISR) seeks to reconstruct a high-resolution image with the high-frequency information (meaning the details) restored from its low-resolution counterpart.
  • 405
  • 01 Mar 2022
Topic Review
Data-Driven Learning Methods for Network Intrusion Detection Systems
An effective anomaly-based intelligent IDS (AN-Intel-IDS) must detect both known and unknown attacks. Hence, there is a need to train AN-Intel-IDS using dynamically generated, real-time data in an adversarial setting. Furthermore, the lack of real-time data produces potentially biased models that are less effective in predicting unknown attacks. Therefore, training AN-Intel-IDS using imbalanced and adversarial learning is instrumental to their efficacy and high performance. Unfortunately, the public datasets available to train AN-Intel-IDS are ineluctably static, unrealistic, and prone to obsolescence. Furthermore, the lack of real-time data produces potentially biased models that are less effective in predicting unknown attacks. Therefore, training AN-Intel-IDS using imbalanced and adversarial learning is instrumental to their efficacy and high performance. 
  • 565
  • 28 Feb 2022
Topic Review
Background on Computing Paradigms
General overview of the different mentioned paradigms needs to be provided in order to be oriented in the computing field. For clarity and consistency, each paradigm is carefully discussed concisely in the oncoming text. The reason for discussing each of these paradigms is to have an overview that will guide the understanding of the research goal for this entry, which is primarily the information security and privacy aspects for each paradigm.
  • 3.3K
  • 25 Feb 2022
Topic Review
Deep Learning-Based Crack Detection Approaches
The application of deep architectures inspired by the fields of artificial intelligence and computer vision has made a significant impact on the task of crack detection. As the number of studies being published in this field is growing fast, it is important to categorize the studies at deeper levels.
  • 1.6K
  • 24 Feb 2022
Topic Review
Bayes Factor and Prior Elicitation
The Bayes factor is a ratio of the marginal likelihood of two competing models. The marginal likelihood for a model class is a weighted average of the likelihood over all the parameter values represented by the prior distribution. Therefore, carefully choosing priors and conducting a prior sensitivity analysis play an essential role when using Bayes factors as a model selection tool. This section briefly discusses the prior distributions, prior elicitation, and prior sensitivity analysis.
  • 520
  • 24 Feb 2022
Topic Review
Re-Identification in Urban Scenarios
Multi-object Re-Identification (ReID), based on a wide range of surveillance cameras, is nowadays a vital aspect in modern cities, to better understand city movement patterns among the different infrastructures, with the primary intention of rapidly mitigate abnormal situations, such as tracking car thieves, wanted persons, or even lost children. Given an image or video of an object-of-interest (query), object identification aims to identify the object from images or video feed taken from different cameras. 
  • 898
  • 24 Feb 2022
Topic Review
Computer Vision in Self-Steering Tractors
Agricultural machinery, such as tractors, is meant to operate for many hours in large areas and perform repetitive tasks. The automatic navigation of agricultural vehicles can ensure the high intensity of automation of cultivation tasks, the enhanced precision of navigation between crop structures, an increase in operation safety and a decrease in human labor and operation costs.
  • 687
  • 24 Feb 2022
Topic Review
Smart Distribution Network Situation Awareness
Due to the rapid development of emerging information and communication technologies (ICT) and advanced metering infrastructure (AMI), distribution networks are in an evolvement from passive to active distribution networks (ADN), also called smart distribution networks (SDN). Operation and maintenance (O&M) cost is an economic factor that the SDN management must consider. Among multiple O&M technologies, situation awareness (SA) emerges and is gradually integrated into the SDN. Facing a high proportion of RES, adequate monitoring, analysis, and prediction of the SDN operating status are urgent. Therefore, comprehensive SA, which contains detection, comprehension, and projection, becomes a significant guarantee for the optimal operation of SDN.
  • 736
  • 24 Feb 2022
Topic Review
Convolutional Neural Network + Recurrent Neural Network
A convolutional neural network and recurrent neural network (CNN + RNN) combination is an effective approach for many modern image recognition tasks that need to identify the behaviour of objects through a sequence of frames. For example, in a security CCTV camera footage, want to identify what abnormal actions a character is doing in the scene (e.g. fighting with someone, breaking into a store, etc.). A deep convolutional neural network (e.g. ResNet50) has many layers of abstraction and is good for extracting essential features in each frame of the input stream. These extracted features, which may represent low-level image features or even high-level objects, can be monitored over a sequence of frames by a recurrent neural network (e.g. ConvLSTM) so as to detect whether a certain action or event has happened.
  • 1.9K
  • 23 Feb 2022
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