2

Adversarial decision strategies in multiple network phased oscillators: the Blue-Green-Red Kuramoto-Sakaguchi model

We model a three-networked system of frustrated phased oscillators, with each population labelled Blue, Green and Red.

Anomaly detection of aircraft lead-acid battery

The experimental results show that the latest isolation‐based anomaly detectors, iForest and iNNE, have outstanding performance on this task and have promising applicability as efficient methods for guaranteeing the lead‐acid battery quality and reliability in civil aviation aircraft.

A technical survey on statistical modelling and design methods for crowdsourcing quality control

Our survey provides technical details on how different frameworks systematically unify crowdsourcing aspects to determine the response quality.

Cloud-assisted privacy-conscious large-scale Markowitz portfolio

The theory of Markowitz portfolio has had enormous value and extensive applications in finance since it came into being. A Markowitz model (MM) is taken into consideration for outsourcing to a public cloud in a privacy-conscious way.

Lowest probabilitymass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms

We propose to use mass-based dissimilarity, which employs estimates of the probability mass to measure dissimilarity, to replace the distance metric.

Grouping Points by Shared Subspaces for Effective Subspace Clustering

We propose a new subspace clustering framework named CSSub (Clustering by Shared Subspaces).

Anomaly detection of aircraft lead-acid battery

We propose a new measure called Local Contrast, as an alternative to density, to find cluster centers and detect clusters.

Isolation‐based anomaly detection using nearest‐neighbor ensembles

With a different isolation mechanism, iNNE has been shown to outperform iForest in terms of detecting local anomalies and tolerance to irrelevant attributes, which becomes obvious in the high‐dimensional data sets.

Density-ratio based clustering for discovering clusters with varying densities

We propose a new measure called Local Contrast, as an alternative to density, to find cluster centers and detect clusters.