activity
20182021
most citedChaotic Phase Synchronization and Desynchronization in an Oscillator Network for Object Selection

50 citations · 88 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CR20216 cited

FamDroid: Learning-Based Android Malware Family Classification Using Static Analysis

Wenhao fan, Liang Zhao, Jiayang Wang +3

Android is currently the most extensively used smartphone platform in the world. Due to its popularity and open source nature, Android malware has been rapidly growing in recent ye…

cs.LG20201 cited

A new network-base high-level data classification methodology (Quipus) by modeling attribute-attribute interactions

Esteban Wilfredo Vilca Zuñiga, Liang Zhao

High-level classification algorithms focus on the interactions between instances. These produce a new form to evaluate and classify data. In this process, the core is a complex net…

cs.LG2020

A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality

Esteban Vilca, Liang Zhao

Data classification is a major machine learning paradigm, which has been widely applied to solve a large number of real-world problems. Traditional data classification techniques c…

cs.SI2020

Spatiotemporal data analysis with chronological networks

Leonardo N. Ferreira, Didier A. Vega-Oliveros, Moshe Cotacallapa +4

The amount and size of spatiotemporal data sets from different domains have been rapidly increasing in the last years, which demands the development of robust and fast methods to a…

cs.CV202050 cited

Chaotic Phase Synchronization and Desynchronization in an Oscillator Network for Object Selection

Fabricio A Breve, Marcos G Quiles, Liang Zhao +1

Object selection refers to the mechanism of extracting objects of interest while ignoring other objects and background in a given visual scene. It is a fundamental issue for many c…

cs.LG202019 cited

Particle Competition and Cooperation for Semi-Supervised Learning with Label Noise

Fabricio Aparecido Breve, Liang Zhao, Marcos Gonçalves Quiles

Semi-supervised learning methods are usually employed in the classification of data sets where only a small subset of the data items is labeled. In these scenarios, label noise is…