Search results for "Hierarchical"

A secure intersection-based routing protocol for data collection in urban vehicular networks

2014

Data routing has gained great intention since the appearance of Vehicular Networks (VANETs). However, in the presence of attackers, reliable and trustworthy operations in such networks become impossible without securing routing protocols. In this paper, we target to study and design a secure routing protocol S-GyTAR for vehicular environments. Several kinds of routing techniques are proposed in the literature and could be classified into topology-based or position-based strategies. Position-based is the most investigated strategy in vehicular networks due to the unique characteristics of such networks. For this reason, this work is based on the well-known intersection-based routing protocol…

SUBOPTIMAL-OPTIMAL ROUTING FOR LAN INTERNETWORKING USING TRANSPARENT BRIDGES

1998

The current standard transparent bridge protocol IEEE-802.1D is based on the Spanning Tree (ST) algorithm. It has a very important restriction: it cannot work when the topology has active loops. Therefore, a tree is the only possible interconnection topology that can be used. The ST algorithm guarantees that the active topology is a tree discarding lines that form loops. However, because of this, network bandwidth cannot be fully utilized. Moreover, trees have a very serious bottleneck near the root. This paper proposes a new transparent bridge protocol for LAN interconnection that allows active loops. Therefore, strongly connected regular topologies like tori, hypercubes, meshes, etc., as…

How do we understand other's intentions? - An implementation of mindreading in artificial systems -

“Anti-Bayesian” flat and hierarchical clustering using symmetric quantiloids

2017

A myriad of works has been published for achieving data clustering based on the Bayesian paradigm, where the clustering sometimes resorts to Naive-Bayes decisions. Within the domain of clustering, the Bayesian principle corresponds to assigning the unlabelled samples to the cluster whose mean (or centroid) is the closest. Recently, Oommen and his co-authors have proposed a novel, counter-intuitive and pioneering PR scheme that is radically opposed to the Bayesian principle. The rational for this paradigm, referred to as the “Anti-Bayesian” (AB) paradigm, involves classification based on the non-central quantiles of the distributions. The first-reported work to achieve clustering using the A…

Action Recognition based on Hierarchical Self-Organizing Maps

2014

We propose a hierarchical neural architecture able to recognise observed human actions. Each layer in the architecture represents increasingly complex human activity features. The first layer consists of a SOM which performs dimensionality reduction and clustering of the feature space. It represents the dynamics of the stream of posture frames in action sequences as activity trajectories over time. The second layer in the hierarchy consists of another SOM which clusters the activity trajectories of the first-layer SOM and thus it learns to represent action prototypes independent of how long the activity trajectories last. The third layer of the hierarchy consists of a neural network that le…

Hierarchies of Self-Organizing Maps for action recognition

2016

We propose a hierarchical neural architecture able to recognise observed human actions. Each layer in the architecture represents increasingly complex human activity features. The first layer consists of a SOM which performs dimensionality reduction and clustering of the feature space. It represents the dynamics of the stream of posture frames in action sequences as activity trajectories over time. The second layer in the hierarchy consists of another SOM which clusters the activity trajectories of the first-layer SOM and learns to represent action prototypes. The third - and last - layer of the hierarchy consists of a neural network that learns to label action prototypes of the second-laye…

DINESERV along with fuzzy hierarchical TOPSIS to support the best practices observation and service quality improvement in the restaurant context

2019

Abstract The present work proposes a new Multi-Criteria-Decision-Analysis (MCDA)-based approach integrating the DINESERV model along with the hierarchical TOPSIS method as measurement tool for evaluating quality in the restaurant services context. More in detail, on the basis of the DINESERV theoretical framework of the restaurant service quality, hierarchical TOPSIS is applied to compare quality of restaurant services. Actually, due to the prioritization process of hierarchical TOPSIS, investigated services providers are consistently and effectively assessed against criteria and sub-criteria of DINESERV, so allowing the identification of both best practices and weaknesses of delivered serv…

A hierarchical clustering strategy and its application to proteomic interaction data

2003

We describe a novel strategy of hierarchical clustering analysis, particularly useful to analyze proteomic interaction data. The logic behind this method is to use the information for all interactions among the elements of a set to evaluate the strength of the interaction of each pair of elements. Our procedure allows the characterization of protein complexes starting with partial data and the detection of "promiscuous" proteins that bias the results, generating false positive data. We demonstrate the usefulness of our strategy by analyzing a real case that involves 137 Saccharomyces cerevisiae proteins. Because most functional studies require the evaluation of similar data sets, our method…

Correlation based hierarchical clustering in financial time series

2005

We review a correlation based clustering procedure applied to a portfolio of assets synchronously traded in a financial market. The portfolio considered consists of the set of 500 highly capitalized stocks traded at the New York Stock Exchange during the time period 1987-1998. We show that meaningful economic information can be extracted from correlation matrices.

A Comparative Study and an Evaluation Framework of Multi/Hyperspectral Image Compression

2009

In this paper, we investigate different approaches for multi/hyperspectral image compression. In particular, we compare the classic multi-2D compression approach and two different implementations of 3D approach (full 3D and hybrid) with regards to variations in spatial and spectral dimensions. All approaches are combined with a weighted Principal Component Analysis (PCA) decorrelation stage to optimize performance. For consistent evaluation, we propose a larger comparison framework than the conventionally used PSNR, including eight metrics divided into three families. The results show the weaknesses and strengths of each approach.