In this paper, we consider the analysis and modelling of trust in distributed information systems. We review the relations of trust relationships in our previous work. We discuss trust layers and hierarchy based on formal definition of trust relationship. We provide a set of definitions to describe the properties of trust direction and trust symmetry under our taxonomy framework. In order to an...

Modeling and naming general entity-entity relationships is challenging in construction of social networks. Given a seed denoting a person name, we utilize Google search engine, NER (Named Entity Recognizer) parser, and CODC (Co-Occurrence Double Check) formula to construct an evolving social network. For each entity pair in the network, we try to label their categories and relationships. Firstl...

Traditional Symbolic Regression applications are a form of supervised learning, where a label y is provided for every ~x and an explicit symbolic relationship of the form y = f(~x) is sought. This chapter explores the use of symbolic regression to perform unsupervised learning by searching for implicit relationships of the form f(~x, y) = 0. Implicit relationships are more general and more expr...

People’s interests and people’s social relationships are intuitively connected, but understanding their interplay and whether they can help predict each other has remained an open question. We examine the interface of two decisive structures forming the backbone of online social media: the graph structure of social networks — who connects with whom — and the set structure of topical affiliation...

This paper introduces a novel distance metric learning framework. Working with inequality constraints involving quadruplets of images, our approach aims at efficiently modeling similarity for rich or complex semantic label relationships. We study how our metric learning scheme can be exploited in contexts such as detection of important regions in Webpages or recognition with relative attributes...

Protein modifications are often required to study structure and function relationships. Instead of the random labeling of lysine residues, methods have been developed to (sequence) specific label proteins. Next to chemical modifications, tools to integrate new chemical groups for bioorthogonal reactions have been applied. Alternatively, proteins can also be selectively modified by enzymes. Here...

Multi-label classification (MC) is a standard machine learning problem in which data point can be associated with set of classes. A more challenging scenario given by hierarchical multi-label (HMC) problems, every prediction must satisfy hard constraints expressing subclass relationships between In this paper, we propose C-HMCNN(h), novel approach for solving HMC which, network h the underlying...

INTRODUCTION Relationships between people with health problems and their partners, families and friends are usually described as 'informal care'. Using a qualitative examination of older people's descriptions of their relationships with partners or other significant friends or relatives at times of change in health and mobility (walking), we questioned whether 'informal care' is an appropriate ...

Disclosure: This article discusses the off-label use of computed tomography devices (manufactured by General Electric and others) for screening. The content of this article has been reviewed by independent peer reviewers to ensure that it is balanced, objective, and free from commercial bias. No financial relationships relevant to the content of this article have been disclosed by the author, p...

This paper presents a novel method of foreground and shadow segmentation in monocular indoor image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian network is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. A maximum a posteriori—Markov random field estimation is used...