scholarly journals Programmable Potentials: Approximate N-body potentials from coarse-level logic

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Gunjan S. Thakur ◽  
Ryan Mohr ◽  
Igor Mezić
Keyword(s):  
Coarse Level

scholarly journals The behavior of hydrophobic-core/hydrophilic-shell structured microgels at an interface: from Mickering emulsion to colloidosomes with dual-level controlled permeability

RSC Advances ◽  
2016 ◽  
Vol 6 (97) ◽  
pp. 95067-95072 ◽  
Author(s):  
Yi Gong ◽  
Mao Wang ◽  
Jianying He
Keyword(s):  
Hydrophobic Core ◽  
Coarse Level ◽  
Controllable Release ◽  
Model Drug ◽  
Hydrophilic Shell

The release of model drug FITC-Dex from colloidosomes was examined in selected media and the controllable release was achieved by adjusting the pH (coarse level) and the ratio of the shell to core in the microgels (fine level).

Dual-Level Attack Detection, Characterization and Response for Networks Under DDoS Attacks

2011 ◽  
Vol 3 (1) ◽  
pp. 1-20
Author(s):  
Anjali Sardana ◽  
Ramesh C. Joshi
Keyword(s):  
Network Performance ◽  
Attack Detection ◽  
Microscopic Level ◽  
Collateral Damage ◽  
Ddos Attacks ◽  
Response Mechanism ◽  
Macroscopic Level ◽  
Coarse Level ◽  
Network Functionality ◽  
In Transit

DDoS attacks aim to deny legitimate users of the services. In this paper, the authors introduce dual - level attack detection (D-LAD) scheme for defending against the DDoS attacks. At higher and coarse level, the macroscopic level detectors (MaLAD) attempt to detect congestion inducing attacks which cause apparent slowdown in network functionality. At lower and fine level, the microscopic level detectors (MiLAD) detect sophisticated attacks that cause network performance to degrade gracefully and stealth attacks that remain undetected in transit domain and do not impact the victim. The response mechanism then redirects the suspicious traffic of anomalous flows to honeypot trap for further evaluation. It selectively drops the attack packets and minimizes collateral damage in addressing the DDoS problem. Results demonstrate that this scheme is very effective and provides the quite demanded solution to the DDoS problem.

scholarly journals Multigrid method with preconditioning on coarse level

1994 ◽  
Vol 39 (5) ◽  
pp. 365-373
Author(s):  
Jitka Křížková ◽  
Petr Vaněk
Keyword(s):  
Multigrid Method ◽  
Coarse Level

scholarly journals Multilabel Classification for Emotion Analysis of Multilingual Tweets

2019 ◽  
Vol 9 (1) ◽  
pp. 4453-4457
Keyword(s):  
Supervised Learning ◽  
English Language ◽  
Hybrid Approach ◽  
Indian Languages ◽  
Emotion Analysis ◽  
Multilabel Classification ◽  
Indian Politics ◽  
Fine Grain ◽  
Coarse Level ◽  
Level Analysis

Emotion Analysis of text targets to detect and recognize types of feelings expressed in text. Emotion analysis is successor of Sentiment analysis. The latter does coarse-level analysis and classify the text into positive and negative categories while former does fine-grain analysis and classify text in specific emotion categories like happy, surprise, angry. Analysis of text at fine-level provides deeper insight compared to coarse-level analysis. In this paper, tweets are classified in discrete eight basic emotions namely joy, trust, fear, surprise, sadness, anticipation, anger, disgust specified in Plutchik’s wheel of emotions [1]. Tweets for three languages collected out of which one is English language and rest two are Indian languages namely Gujarati and Hindi. The collected tweets are related to Indian politics and are annotated manually. Supervised Learning and Hybrid approach are used for classification of tweets. Supervised learning uses tf-idf as features while hybrid approach uses primary and secondary features. Primary features are generated using tf-idf weighting and two different algorithms of feature generation are proposed which generate secondary features using SenticNet resource. Multilabel classification is performed to classify tweets in emotion categories. Results of experiments show effectiveness of hybrid approach.

scholarly journals Can Defect Prediction Be Useful for Coarse-Level Tasks of Software Testing?

2020 ◽  
Vol 10 (15) ◽  
pp. 5372
Author(s):  
Can Cui ◽  
Bin Liu ◽  
Peng Xiao ◽  
Shihai Wang
Keyword(s):  
Resource Allocation ◽  
Software Testing ◽  
Performance Measure ◽  
Defect Prediction ◽  
Analytic Hierarchy ◽  
Software Defect ◽  
Coarse Level ◽  
Black Box Testing ◽  
Resource Allocation Strategy ◽  
White Box Testing

It is popular to use software defect prediction (SDP) techniques to predict bugs in software in the past 20 years. Before conducting software testing (ST), the result of SDP assists on resource allocation for ST. However, DP usually works on fine-level tasks (or white-box testing) instead of coarse-level tasks (or black-box testing). Before ST or without historical execution information, it is difficult to get resource allocated properly. Therefore, a SDP-based approach, named DPAHM, is proposed to assist on arranging resource for coarse-level tasks. The method combines analytic hierarchy process (AHP) and variant incidence matrix. Besides, we apply the proposed DPAHM into a proprietary software, named MC. Besides, we conduct an up-to-down structure, including three layers for MC. Additionally, the performance measure of each layer is calculated based on the SDP result. Therefore, the resource allocation strategy for coarse-level tasks is gained according to the prediction result. The experiment indicates our proposed method is effective for resource allocation of coarse-level tasks before executing ST.

Dual-Level Attack Detection, Characterization, and Response for Networks under DDoS Attacks

2012 ◽  
pp. 1-21
Author(s):  
Anjali Sardana ◽  
Ramesh C. Joshi
Keyword(s):  
Network Performance ◽  
Attack Detection ◽  
Microscopic Level ◽  
Collateral Damage ◽  
Ddos Attacks ◽  
Response Mechanism ◽  
Macroscopic Level ◽  
Coarse Level ◽  
Network Functionality ◽  
In Transit

DDoS attacks aim to deny legitimate users of the services. In this paper, the authors introduce dual - level attack detection (D-LAD) scheme for defending against the DDoS attacks. At higher and coarse level, the macroscopic level detectors (MaLAD) attempt to detect congestion inducing attacks which cause apparent slowdown in network functionality. At lower and fine level, the microscopic level detectors (MiLAD) detect sophisticated attacks that cause network performance to degrade gracefully and stealth attacks that remain undetected in transit domain and do not impact the victim. The response mechanism then redirects the suspicious traffic of anomalous flows to honeypot trap for further evaluation. It selectively drops the attack packets and minimizes collateral damage in addressing the DDoS problem. Results demonstrate that this scheme is very effective and provides the quite demanded solution to the DDoS problem.

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