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Now showing items 11-20 of 34
Reasoning under uncertainty: variations of subjective logic deduction
(IEEE, 2013)
This work develops alternatives to the classical subjective logic deduction operator. Given antecedent and consequent propositions, the new operators form opinions of the consequent that match the variance of the consequent ...
TRIBE: Trust revision for information based on evidence
(IEEE, 2013)
In recent years, the number of information sources available to support decision-making has increased dramatically. However, more information sources do not always mean higher precision in the fused information. This is ...
Trust estimation and fusion of uncertain information by exploiting consistency
(IEEE, 2014)
Agents may cooperate by communicating their opinions about various phenomena. These opinions are then fused by agents and used for informed decision-making. However, fusing opinions from diverse sources is not trivial - ...
Interpretability of deep learning models: a survey of results
(IEEE, 2018-06-26)
Deep neural networks have achieved near-human accuracy levels in various types of classification and prediction tasks including images, text, speech, and video data. However, the networks continue to be treated mostly as ...
TIDY: A trust-based approach to information fusion through diversity
(IEEE, 2013)
Trust and reputation are significant components in open dynamic systems for making informed and reliable decisions. State-of-the-art information fusion models that exploit these mechanisms generally rely on reports from ...
Reasoning with uncertain information and trust
(SPIE, 2013)
A limitation of standard Description Logics is its inability to reason with uncertain and vague knowledge. Although probabilistic and fuzzy extensions of DLs exist, which provide an explicit representation of uncertainty, ...
A generalized stereotype learning approach and its instantiation in trust modeling
(Elsevier, 2018-08)
Owing to the lack of historical data regarding an entity in online communities, a user may rely on stereotyping to estimate its behavior based on historical data about others. However, these stereotypes cannot accurately ...
A 2020 perspective on “A generalized stereotype learning approach and its instantiation in trust modeling”
(Elsevier, 2020-03)
Owing to the rapid increase of user data and development of machine learning techniques, user modeling has been explored in depth and exploited by both academia and industry. It has prominent impacts in e-commercerelated ...
On context-aware DDoS attacks using deep generative networks
(IEEE, 2018-10)
Distributed Denial of Service (DDoS) attacks continue to be one of the most severe threats in the Internet. The intrinsic challenge in preventing DDoS attacks is to distinguish them from legitimate flash crowds since two ...
Trust estimation of sources over correlated propositions
(IEEE, 2018-09-05)
This work analyzes the impact of correlated propositions when estimating the reporting behavior of information sources. These behavior estimates are critical for fusion, and traditional methods assume the propositions are ...
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