Evaluation of Models for Analyzing Unguided Search in Unstructured Networks

Because of the complexity and high dimensionality of the problem, speech recognition—perhaps more than any other problem of current interest in network research—will profit from human neurophysiology, psychoacoustics and psycholinguistics: approaches based exclusively on engineering principles will provide only limited benefits. Despite the great power of current learning algorithms in homogeneous or unstructured networks, a number of difficulties in speech recognition seem to indicate that homogeneous networks taken alone will be insufficient for the task, and that structure—representing constraints—will also be required. In the biological system, the sources of such structure include developmental and evolutionary effects. Recent considerations of the evolutionary sources of neural structure in the human speech and language systems, including models of the interrelationship between speech motor system and auditory system, are analyzed with special reference to neural network approaches.

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Adaptive Broadcast Cancellation Query Mechanism for Unstructured Networks

2015 9th International Conference on Next Generation Mobile Applications, Services and Technologies ◽

10.1109/ngmast.2015.41 ◽

2015 ◽

Author(s):

Rui Lima ◽

Carlos Baquero ◽

Hugo Miranda

Keyword(s):

Unstructured Networks

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A Generic Encapsulation to Unravel Social Spreading of a Pandemic: An Underlying Architecture

Computers ◽

10.3390/computers10010012 ◽

2021 ◽

Vol 10 (1) ◽

pp. 12

Author(s):

Saad Alqithami

Keyword(s):

Viral Infections ◽

Daily Basis ◽

Reference Network ◽

Community Settings ◽

Viral Spread ◽

Rate Of Spread ◽

Global Pandemic ◽

Candidate Network ◽

Transmission Pathways ◽

Unstructured Networks

Cases of a new emergent infectious disease caused by mutations in the coronavirus family, called “COVID-19,” have spiked recently, affecting millions of people, and this has been classified as a global pandemic due to the wide spread of the virus. Epidemiologically, humans are the targeted hosts of COVID-19, whereby indirect/direct transmission pathways are mitigated by social/spatial distancing. People naturally exist in dynamically cascading networks of social/spatial interactions. Their rational actions and interactions have huge uncertainties in regard to common social contagions with rapid network proliferations on a daily basis. Different parameters play big roles in minimizing such uncertainties by shaping the understanding of such contagions to include cultures, beliefs, norms, values, ethics, etc. Thus, this work is directed toward investigating and predicting the viral spread of the current wave of COVID-19 based on human socio-behavioral analyses in various community settings with unknown structural patterns. We examine the spreading and social contagions in unstructured networks by proposing a model that should be able to (1) reorganize and synthesize infected clusters of any networked agents, (2) clarify any noteworthy members of the population through a series of analyses of their behavioral and cognitive capabilities, (3) predict where the direction is heading with any possible outcomes, and (4) propose applicable intervention tactics that can be helpful in creating strategies to mitigate the spread. Such properties are essential in managing the rate of spread of viral infections. Furthermore, a novel spectra-based methodology that leverages configuration models as a reference network is proposed to quantify spreading in a given candidate network. We derive mathematical formulations to demonstrate the viral spread in the network structures.

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Requirements and design for neutral trust management framework in unstructured networks

The Journal of Supercomputing ◽

10.1007/s11227-010-0502-3 ◽

2010 ◽

Vol 64 (3) ◽

pp. 702-716 ◽

Cited By ~ 8

Author(s):

Ruidong Li ◽

Jie Li

Keyword(s):

Trust Management ◽

Management Framework ◽

Unstructured Networks

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