Results 31 to 40 of about 10,137 (199)

Android Malware Detection Using Kullback-Leibler Divergence

open access: yesAdvances in Distributed Computing and Artificial Intelligence Journal, 2015
Many recent reports suggest that mareware applications cause high billing to victims by sending and receiving hidden SMS messages. Given that, there is a need to develop necessary technique to identify malicious SMS operations as well as differentiate ...
Vanessa N. COOPER   +2 more
doaj   +1 more source

Zipf–Mandelbrot law, f-divergences and the Jensen-type interpolating inequalities

open access: yesJournal of Inequalities and Applications, 2018
Motivated by the method of interpolating inequalities that makes use of the improved Jensen-type inequalities, in this paper we integrate this approach with the well known Zipf–Mandelbrot law applied to various types of f-divergences and distances, such ...
Neda Lovričević   +2 more
doaj   +1 more source

Unifying Computational Entropies via Kullback–Leibler Divergence [PDF]

open access: yes, 2019
We introduce hardness in relative entropy, a new notion of hardness for search problems which on the one hand is satisfied by all one-way functions and on the other hand implies both next-block pseudoentropy and inaccessible entropy, two forms of computational entropy used in recent constructions of pseudorandom generators and statistically hiding ...
Agrawal, Rohit   +3 more
openaire   +2 more sources

Divergence Measure of Belief Function and Its Application in Data Fusion

open access: yesIEEE Access, 2019
Divergence measure is widely used in many applications. To efficiently deal with uncertainty in real applications, basic probability assignment (BPA) in Dempster-Shafer evidence theory, instead of probability distribution, is adopted.
Yutong Song, Yong Deng
doaj   +1 more source

Kullback–Leibler Divergence Measure for Multivariate Skew-Normal Distributions

open access: yesEntropy, 2012
The aim of this work is to provide the tools to compute the well-known Kullback–Leibler divergence measure for the flexible family of multivariate skew-normal distributions.
Reinaldo B. Arellano-Valle   +1 more
doaj   +1 more source

Generative Models for Crystalline Materials

open access: yesAdvanced Materials, EarlyView.
Generative machine learning models are increasingly used in crystalline materials design. This review outlines major generative approaches and assesses their strengths and limitations. It also examines how generative models can be adapted to practical applications, discusses key experimental considerations for evaluating generated structures, and ...
Houssam Metni   +15 more
wiley   +1 more source

Association of Jensen’s inequality for s-convex function with Csiszár divergence

open access: yesJournal of Inequalities and Applications, 2019
In the article, we establish an inequality for Csiszár divergence associated with s-convex functions, present several inequalities for Kullback–Leibler, Renyi, Hellinger, Chi-square, Jeffery’s, and variational distance divergences by using particular s ...
Muhammad Adil Khan   +4 more
doaj   +1 more source

Hard‐Magnetic Soft Millirobots in Underactuated Systems

open access: yesAdvanced Robotics Research, EarlyView.
This review provides a comprehensive overview of hard‐magnetic soft millirobots in underactuated systems. It examines key advances in structural design, physics‐informed modeling, and control strategies, while highlighting the interplay among these domains.
Qiong Wang   +4 more
wiley   +1 more source

A Deep Representation Learning Method for Quantitative Immune Defense Function Evaluation and Its Clinical Applications

open access: yesAdvanced Science, EarlyView.
ImmuDef, a novel algorithm to quantitatively evaluate the anti‐infection immune defense function of an individual based on RNA‐seq data via a variational autoencoder (VAE) model. It is validated on 3200+ samples across four immune states with high accuracy. It can serve as a metric for disease severity and prognosis across pathogenic cohorts.
Zhen‐Lin Tan   +7 more
wiley   +1 more source

An Introduction to Predictive Processing Models of Perception and Decision‐Making

open access: yesTopics in Cognitive Science, EarlyView., 2023
Abstract The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision‐making, and motor control.
Mark Sprevak, Ryan Smith
wiley   +1 more source

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