scholarly journals Masquerader Detection Using OCLEP: One-Class Classification Using Length Statistics of Emerging Patterns

2006 ◽  
Author(s):  
Lijun Chen ◽  
Guozhu Dong
Keyword(s):  
Emerging Patterns ◽  
One Class Classification

Emerging Patterns of Practice in the Era of Managed Care: Reply.

2004 ◽  
Vol 59 (2) ◽  
pp. 129-129
Author(s):  
Lisa M. Sanchez ◽  
Samuel M. Turner
Keyword(s):  
Managed Care ◽  
Emerging Patterns ◽  
Patterns Of Practice

Emerging patterns of artistic organizations in Portugal: A three case studies analysis

2020 ◽  
pp. 84-107
Author(s):  
Vera Borges ◽  
Luísa Veloso
Keyword(s):  
Labour Market ◽  
Economic Crisis ◽  
Performing Arts ◽  
Work Organization ◽  
The Arts ◽  
Emerging Patterns ◽  
New Models ◽  
Organizational Patterns ◽  
New Forms Of Work ◽  
Anal Yses

In the wake of the 2008 global financial and economic crisis, new forms of work organization emerged in Europe. Following this trend, Portugal has undergone a reconfiguration of its artistic organizations. In the performing arts, some organiza-tions seem to have crystalized and others are reinventing their artistic mission. They follow a plurality of organizational patterns and resilient profiles framed by cyclical, structural and occupational changes. Artistic organizations have had to adopt new models of work and seek new opportunities to try out alternatives in order to deal, namely, with the constraints of the labour market. The article anal-yses some of the restructuring processes taking place in three Portuguese artistic organizations, focusing on their contexts, individual trajectories and collective missions for adapting to contemporary challenges of work in the arts. We conclude that organizations are a key domain for understanding the changes taking place.

Users behaviors: The Heritage of Digital World

2014 ◽  
Vol 13 (7) ◽  
pp. 4675-4682
Author(s):  
Atefeh Danesh Moghadam ◽  
Alireza Alagha
Keyword(s):  
Technology Development ◽  
Second Step ◽  
Information Communication ◽  
The Third ◽  
Digital World ◽  
New Concepts ◽  
Emerging Patterns ◽  
History Of ◽  
Fourth Step ◽  
Communication And Technology

In the advent of information era, not only digital world is going to expand its territories, it is going to penetrate into the traditional notions about the meaning of the words and also valorize new concepts. According to Oxford Dictionary, the word heritage is defined: The history, tradition and qualities that a country or society has had for many years and that are considered an important part of its character. In order to present how emerging patterns, as the consequences of technology development, are going to be considered as the new concept of heritage, we follow four steps. In the first step, we present the convergence of Information, Communication and Technology (ICT) and a concise history of its convergence. In the second step, we argue how convergence has culminated in emerging patterns and also has made changes in digital world. In the third step, the importance of users behaviors and its mining is surveyed. Finally, in the fourth step; we illustrate User Generated Contents (UGC) as the most prominent users behaviors in digital world.

scholarly journals Rust Detection of Steel Structure via One-Class Classification and L2 Sparse Representation with Decision Fusion

2020 ◽  
Vol E103.D (2) ◽  
pp. 450-453
Author(s):  
Guizhong ZHANG ◽  
Baoxian WANG ◽  
Zhaobo YAN ◽  
Yiqiang LI ◽  
Huaizhi YANG
Keyword(s):  
Sparse Representation ◽  
Steel Structure ◽  
Decision Fusion ◽  
One Class Classification

scholarly journals Meta Learning for Few-Shot One-Class Classification

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 195-208
Author(s):  
Gabriel Dahia ◽  
Maurício Pamplona Segundo
Keyword(s):  
Feature Representation ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Target Class ◽  
Feature Representations ◽  
Shot Classification ◽  
Training Stage ◽  
Comparable Performance ◽  
Meta Learning ◽  
One Class Classification

We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem in which the meta-training stage repeatedly simulates one-class classification, using the classification loss of the chosen algorithm to learn a feature representation. To learn these representations, we require only multiclass data from similar tasks. We show how the Support Vector Data Description method can be used with our method, and also propose a simpler variant based on Prototypical Networks that obtains comparable performance, indicating that learning feature representations directly from data may be more important than which one-class algorithm we choose. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario, obtaining similar results to the state-of-the-art of traditional one-class classification, and that improves upon that of one-class classification baselines employed in the few-shot setting.

Sign in / Sign up

Export Citation Format

Share Document