Average trapped surfaces on an arbitrary initial data set

1993 ◽  
Vol 47 (4) ◽  
pp. 1448-1453 ◽  
Author(s):  
T. Zannias
Keyword(s):  
Initial Data ◽  
Data Set ◽  
Trapped Surfaces

scholarly journals Automatic Benchmark Generation Framework for Malware Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Guanghui Liang ◽  
Jianmin Pang ◽  
Zheng Shan ◽  
Runqing Yang ◽  
Yihang Chen
Keyword(s):  
Initial Data ◽  
Malware Detection ◽  
Detection Methods ◽  
Small Data ◽  
Security Threats ◽  
Improved Genetic Algorithm ◽  
Data Set ◽  
Detection Model ◽  
Manual Selection ◽  
Selection Of

To address emerging security threats, various malware detection methods have been proposed every year. Therefore, a small but representative set of malware samples are usually needed for detection model, especially for machine-learning-based malware detection models. However, current manual selection of representative samples from large unknown file collection is labor intensive and not scalable. In this paper, we firstly propose a framework that can automatically generate a small data set for malware detection. With this framework, we extract behavior features from a large initial data set and then use a hierarchical clustering technique to identify different types of malware. An improved genetic algorithm based on roulette wheel sampling is implemented to generate final test data set. The final data set is only one-eighteenth the volume of the initial data set, and evaluations show that the data set selected by the proposed framework is much smaller than the original one but does not lose nearly any semantics.

scholarly journals Searching for binary central stars of planetary nebulae with Kepler

2011 ◽  
Vol 7 (S283) ◽  
pp. 344-345
Author(s):  
Dimitri Douchin ◽  
George H. Jacoby ◽  
Orsola De Marco ◽  
Steve B. Howell ◽  
Mattias Kronberger
Keyword(s):  
Initial Data ◽  
Light Curve ◽  
Planetary Nebulae ◽  
Central Star ◽  
Light Curves ◽  
Data Set ◽  
Lunar Cycles ◽  
Near Future ◽  
Central Stars

AbstractThe Kepler Observatory offers unprecedented photometric precision (<1 mmag) and cadence for monitoring the central stars of planetary nebulae, allowing the detection of tiny periodic light curve variations, a possible signature of binarity. With this precision free from the observational gaps dictated by weather and lunar cycles, we are able to detect companions at much larger separations and with much smaller radii than ever before. We have been awarded observing time to obtain light-curves of the central stars of the six confirmed and possible planetary nebulae in the Kepler field, including the newly discovered object Kn 61, at cadences of both 30 min and 1 min. Of these six objects, we could confirm for three a periodic variability consistent with binarity. Two others are variables, but the initial data set presents only weak periodicities. For the central star of Kn 61, Kepler data will be available in the near future.

scholarly journals A DYNAMICAL SYMMETRY OF THE QUASI-SPHERICAL (OR SPHERICAL) COLLAPSE

2005 ◽  
Vol 14 (10) ◽  
pp. 1761-1767 ◽  
Author(s):  
UJJAL DEBNATH ◽  
SUBENOY CHAKRABORTY ◽  
NARESH DADHICH
Keyword(s):  
Kinetic Energy ◽  
Initial Data ◽  
Dynamical Symmetry ◽  
Matter Field ◽  
Physical Parameters ◽  
Data Sets ◽  
Type I ◽  
Data Set ◽  
Spherical Collapse ◽  
Scaling Relationship

By linearly scaling the initial data set (mass and kinetic energy functions), it is found that the dynamics of quasi-spherical (or spherical) collapse remains invariant for dust or a general (Type I) matter field, provided the comoving radius is also appropriately scaled. This defines a symmetry of the quasi spherical (or spherical) collapse. That is, the linear transformation identifies an equivalence class of data sets which lead to the same end result as well as its evolution all through. In particular, it is shown that the physical parameters, density and shear remain invariant. What the transformation is exhibiting is an interesting scaling relationship between mass, kinetic energy and the size of the collapsing sphere which is respected not only by the initial data set but remarkably also by the dynamics of collapse.

scholarly journals DEFINITION OF SEDIMENTOLOGICAL MODEL IN LIMITED INITIAL DATA SET CONDITIONS

2018 ◽  
pp. 21-26
Author(s):  
A. V. Podnebesnykh
Keyword(s):  
Initial Data ◽  
Low Cost ◽  
Central Africa ◽  
Main Direction ◽  
Geological Exploration ◽  
Data Set ◽  
Definition Of

The article considers the main procedures which essentially increase the validity of sedimentological models in limited initial data set conditions. The Bentiu formation (Central Africa) is studied as an example of the research. Using simple and low-cost methods it is possible to identify not only the main direction of sedimentary material displacement but also the main characteristics of alluvial systems that will let to plan exploitation of reservoir formation of such type correctly even on geological exploration stage.

scholarly journals Decision Tree Based Interference Recognition for Fog Enabled IOT Architecture

2020 ◽  
Vol 2 (1) ◽  
pp. 15-25
Author(s):  
Dr. Mugunthan S. R.
Keyword(s):  
Internet Of Things ◽  
Initial Data ◽  
Decision Tree ◽  
False Alarm ◽  
Final Stage ◽  
Cyber Attacks ◽  
The Internet ◽  
Data Set ◽  
The Internet Of Things ◽  
Decision Forest

The cyber-attacks nowadays are becoming more and more erudite causing challenges in distinguishing them and confining. These attacks affect the sensitized information’s of the network by penetrating into the network and behaving normally. The paper devises a system for such interference recognition in the internet of things architecture that is aided by the FOG. The proposed system is a combination of variety of classifiers that are founded on the decision tree as well as the rule centered conceptions. The system put forth involves the JRip and the REP tree algorithm to utilize the features of the data set as input and distinguishes between the benign and the malicious traffic in the network and includes an decision forest that is improved with the penalizing attributes of the previous trees in the final stage to classify the traffic in the network utilizing the initial data set as well as the outputs of the classifiers that were engaged in the former stages. The proffered system was examined using the dataset such BOT-Internet of things and the CICIDS2017 to evince its competence in terms of rate of false alarm, detection, and accuracy. The attained results proved that the performance of the proposed system was better compared to the exiting methodologies to recognize the interference.

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