The Effect of Nonlinear Medium on the Behavior of a Quantum Kerr-Cavity: Nondemolition Measurement and Filtering Approach

2021 ◽  
Vol 32 (1) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Shahram Aghaei
Keyword(s):  
Nonlinear Medium ◽  
Filtering Approach

Fock states generation in a kicked cavity with a nonlinear medium

1997 ◽  
Vol 44 (11-12) ◽  
pp. 2105-2123 ◽  
Author(s):  
W. Leoński ◽  
S. Dyrting ◽  
R. Tanaś
Keyword(s):  
Nonlinear Medium ◽  
Fock States

Towards Gene Recognition from Rare and Ambiguous Abbreviations using a Filtering Approach

2014 ◽  
Author(s):  
Matthias Hartung ◽  
Roman Klinger ◽  
Matthias Zwick ◽  
Philipp Cimiano
Keyword(s):  
Gene Recognition ◽  
Filtering Approach

A Post-Filtering Approach Based on Locally Linear Embedding Difference Compensation for Speech Enhancement

2017 ◽  
Author(s):  
Yi-Chiao Wu ◽  
Hsin-Te Hwang ◽  
Syu-Siang Wang ◽  
Chin-Cheng Hsu ◽  
Yu Tsao ◽  
...  
Keyword(s):  
Speech Enhancement ◽  
Locally Linear Embedding ◽  
Linear Embedding ◽  
Filtering Approach ◽  
Locally Linear

GEOMETRIC PHASE AND DISENTANGLEMENT OF A MOVING FOUR-LEVEL ATOM IN THE PRESENCE OF NONLINEAR MEDIUM

2012 ◽  
Vol 10 (01) ◽  
pp. 1250007 ◽  
Author(s):  
NOUR ZIDAN ◽  
S. ABDEL-KHALEK ◽  
M. ABDEL-ATY
Keyword(s):  
System Dynamics ◽  
Geometric Phase ◽  
Nonlinear Medium ◽  
Atomic Motion ◽  
Kerr Medium ◽  
Mode Structure ◽  
Field Mode ◽  
Level Atom

In this paper, we investigate the geometric phase of the field interacting with a moving four-level atom in the presence of Kerr medium. The results show that the atomic motion, the field-mode structure and Kerr medium play important roles in the evolution of the system dynamics. As illustration, we examine the behavior of the geometric phase and entanglement with experimentally accessible parameters. Some new aspects are observed and discussed.

Trust and fuzzy inference based cross domain serendipitous item recommendations (TFCDSRS)

2021 ◽  
pp. 1-13
Author(s):  
Richa ◽  
Punam Bedi
Keyword(s):  
Fuzzy Inference ◽  
Mean Absolute Error ◽  
Information Filtering ◽  
Absolute Error ◽  
User Acceptance ◽  
Decision Making Process ◽  
End User ◽  
Cross Domain ◽  
F Measure ◽  
Filtering Approach

Recommender System (RS) is an information filtering approach that helps the overburdened user with information in his decision making process and suggests items which might be interesting to him. While presenting recommendation to the user, accuracy of the presented list is always a concern for the researchers. However, in recent years, the focus has now shifted to include the unexpectedness and novel items in the list along with accuracy of the recommended items. To increase the user acceptance, it is important to provide potentially interesting items which are not so obvious and different from the items that the end user has rated. In this work, we have proposed a model that generates serendipitous item recommendation and also takes care of accuracy as well as the sparsity issues. Literature suggests that there are various components that help to achieve the objective of serendipitous recommendations. In this paper, fuzzy inference based approach is used for the serendipity computation because the definitions of the components overlap. Moreover, to improve the accuracy and sparsity issues in the recommendation process, cross domain and trust based approaches are incorporated. A prototype of the system is developed for the tourism domain and the performance is measured using mean absolute error (MAE), root mean square error (RMSE), unexpectedness, precision, recall and F-measure.

scholarly journals Contextualized Filtering for Shared Cyber Threat Information

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4890
Author(s):  
Athanasios Dimitriadis ◽  
Christos Prassas ◽  
Jose Luis Flores ◽  
Boonserm Kulvatunyou ◽  
Nenad Ivezic ◽  
...  
Keyword(s):  
Information Sharing ◽  
Business Processes ◽  
State Of The Art ◽  
Contextual Information ◽  
Coarse Grained ◽  
Business Information ◽  
Cyber Threat ◽  
Domain Expertise ◽  
Multi Level ◽  
Filtering Approach

Cyber threat information sharing is an imperative process towards achieving collaborative security, but it poses several challenges. One crucial challenge is the plethora of shared threat information. Therefore, there is a need to advance filtering of such information. While the state-of-the-art in filtering relies primarily on keyword- and domain-based searching, these approaches require sizable human involvement and rarely available domain expertise. Recent research revealed the need for harvesting of business information to fill the gap in filtering, albeit it resulted in providing coarse-grained filtering based on the utilization of such information. This paper presents a novel contextualized filtering approach that exploits standardized and multi-level contextual information of business processes. The contextual information describes the conditions under which a given threat information is actionable from an organization perspective. Therefore, it can automate filtering by measuring the equivalence between the context of the shared threat information and the context of the consuming organization. The paper directly contributes to filtering challenge and indirectly to automated customized threat information sharing. Moreover, the paper proposes the architecture of a cyber threat information sharing ecosystem that operates according to the proposed filtering approach and defines the characteristics that are advantageous to filtering approaches. Implementation of the proposed approach can support compliance with the Special Publication 800-150 of the National Institute of Standards and Technology.

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