Hidden nodes in a wireless network refer to nodes that are out of range of other nodes or a collection of nodes. We will discuss a few problems introduced by the RTS/CTS mechanism of collision avoidance and focus on the virtual jamming problem, which allows a malicious node to effectively jam a large fragment of a wireless network at minimum expense of power. We have also discussed WiCCP (Wirel...

In August 2013, the Tor network experienced a sudden, drastic reduction in performance due to the Mevade/Sefnit botnet. This botnet ran its command and control server as a Tor hidden service, so that all infected nodes contacted the command and control through Tor. In this paper, we consider several protocol changes to protect Tor against future incidents of this nature, describing the research...

We introduce a novel, greatly simplified classifier for binarized data. The model contains a sparse, “digital” hidden layer of Parity interactions, followed by a sigmoidal output node. We propose priors for the cases: a) input space obeys a metrics, b) inputs encode discrete attributes. Stochastic search for the hidden layer allows capacity and smoothness of the approximation to be controlled b...

Abstract The use of network analysis to investigate social structures has recently seen a rise due the high availability data and numerous insights it can provide into different fields. Most analyses focus on topological characteristics networks estimation relationships between nodes. We adopt perspective by considering whole as random variable conveying effect an exposure response. This point ...

Evolving Cascade Neural Networks (ECNNs) and a new training algorithm capable of selecting informative features are described. The ECNN initially learns with one input node and then evolves by adding new inputs as well as new hidden neurons. The resultant ECNN has a near minimal number of hidden neurons and inputs. The algorithm is successfully used for training ECNN to recognise artefacts in s...

The problem of determining the architecture of a multilayer perceptron together with the disadvantages of the standard backpropagation algorithm, directed the research towards algorithms that determine not only the weights but also the structure of the network necessary for learning the data. We propose a Constructive Algorithm with Multiple Operators using Statistical Test (MOST) for determini...

We present a novel approach to the unsupervised learning of morphology. In particular, we use a Multiple Cause Mixture Model (MCMM), a type of autoencoder network consisting of two node layers—hidden and surface—and a matrix of weights connecting hidden nodes to surface nodes. We show that an MCMM shares crucial graphical properties with autosegmental morphology. We argue on the basis of this g...

In mobile ad hoc networks (MANETs), the network topology changes frequently and unpredictably due to the arbitrary mobility of nodes. This feature leads to frequent path failures and route reconstructions, which causes an increase in the routing control overhead. Effective routing protocol needed to developed or modifying existing one. Protocols can be divided into topologyand position-based pr...

Graph contrastive learning (GCL) has been subject to more attention and widely applied numerous graph tasks such as node classification link prediction. Although it achieved great success even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained topology, especially for social networks. Howev...