scholarly journals Partial Adversarial Behavior Deception in Security Games

2020 ◽  
Author(s):  
Thanh H. Nguyen ◽  
Arunesh Sinha ◽  
He He
Keyword(s):  
Gradient Descent ◽  
Important Research ◽  
Behavioral Models ◽  
Scalable Algorithm ◽  
Defense Strategies ◽  
Important Research Topic ◽  
Adversarial Behavior ◽  
Security Games ◽  
Attack Data ◽  
Projected Gradient Descent

Learning attacker behavior is an important research topic in security games as security agencies are often uncertain about attackers' decision making. Previous work has focused on developing various behavioral models of attackers based on historical attack data. However, a clever attacker can manipulate its attacks to fail such attack-driven learning, leading to ineffective defense strategies. We study attacker behavior deception with three main contributions. First, we propose a new model, named partial behavior deception model, in which there is a deceptive attacker (among multiple attackers) who controls a portion of attacks. Our model captures real-world security scenarios such as wildlife protection in which multiple poachers are present. Second, we introduce a new scalable algorithm, GAMBO, to compute an optimal deception strategy of the deceptive attacker. Our algorithm employs the projected gradient descent and uses the implicit function theorem for the computation of gradient. Third, we conduct a comprehensive set of experiments, showing a significant benefit for the attacker and loss for the defender due to attacker deception.

scholarly journals An All-Batch Loss for Constructing Prediction Intervals

2021 ◽  
Vol 11 (4) ◽  
pp. 1728
Author(s):  
Hua Zhong ◽  
Li Xu
Keyword(s):  
Gradient Descent ◽  
State Of The Art ◽  
Prediction Interval ◽  
Feedforward Neural Networks ◽  
Important Research ◽  
Likelihood Principle ◽  
High Quality ◽  
Construction Methods ◽  
Important Research Topic ◽  
Benchmark Datasets

The prediction interval (PI) is an important research topic in reliability analyses and decision support systems. Data size and computation costs are two of the issues which may hamper the construction of PIs. This paper proposes an all-batch (AB) loss function for constructing high quality PIs. Taking the full advantage of the likelihood principle, the proposed loss makes it possible to train PI generation models using the gradient descent (GD) method for both small and large batches of samples. With the structure of dual feedforward neural networks (FNNs), a high-quality PI generation framework is introduced, which can be adapted to a variety of problems including regression analysis. Numerical experiments were conducted on the benchmark datasets; the results show that higher-quality PIs were achieved using the proposed scheme. Its reliability and stability were also verified in comparison with various state-of-the-art PI construction methods.

A Bibliometric Review on Outcome-Based Learning for Graduate Employability: Mapping the Research Front

2021 ◽  
pp. 002205742110164
Author(s):  
Mohammad Zahir Raihan ◽  
Md. Abul Kalam Azad
Keyword(s):  
Research Trend ◽  
Future Research ◽  
Research Front ◽  
Important Research ◽  
R Software ◽  
Future Research Agenda ◽  
Graduate Employability ◽  
Important Research Topic ◽  
Bibliometric Review ◽  
First Time

The outcome-based learning for graduate employability in higher education has been an important research topic among the policymakers, academicians, and researchers over the years. Yet, no bibliometric review on this topic has been published. This study, for the first time, examines bibliometric analysis on this topic examining current research trend and future research agenda. The bibliometrix package in R software and VOSviewer software are used for visualization and interpretation of results. A content analysis is performed to manually examine the bibliometric results.

Conformal Optimization Algorithm for Undevelopable Surfaces and its Application

2014 ◽  
Vol 602-605 ◽  
pp. 3570-3574
Author(s):  
Zhen Hua Luo ◽  
Fen Jiang
Keyword(s):  
Approximation Algorithms ◽  
Optimization Algorithm ◽  
Manufacturing Process ◽  
Research Topic ◽  
Important Research ◽  
Computer Aided Geometric Design ◽  
Industrial Manufacturing ◽  
Important Research Topic ◽  
Computer Aided ◽  
Planar Materials

In the industrial manufacturing process, most kinds of surfaces are processed by planar materials, but undevelopable surfaces are difficult develop to the plane. The approximation algorithms to develop a undevelopable surface is an important research topic in Computer Aided Geometric Design (CAGD). In this paper, we propose a new approximation algorithms based optimization algorithm. We guarantee the deformation vector make the minimum changes during the developing process. In the paper, some numerical example are given and the can illustrate the our method is effective.

Global coronal and heliospheric magnetic field modelling for Solar Orbiter

2021 ◽  
Author(s):  
Thomas Wiegelmann ◽  
Thomas Neukirch ◽  
Iulia Chifu ◽  
Bernd Inhester
Keyword(s):  
Magnetic Field ◽  
Solar Rotation ◽  
Coronal Magnetic Field ◽  
Important Research ◽  
Parker Spiral ◽  
Important Research Topic ◽  
Mhd Equilibria ◽  
Solar Wind Flow ◽  
Nonlinear Force

<p>Computing the solar coronal magnetic field and plasma<br>environment is an important research topic on it's own right<br>and also important for space missions like Solar Orbiter to<br>guide the analysis of remote sensing and in-situ instruments.<br>In the inner solar corona plasma forces can be neglected and<br>the field is modelled under the assumption of a vanishing<br>Lorentz-force. Further outwards (above about two solar radii)<br>plasma forces and the solar wind flow has to be considered.<br>Finally in the heliosphere one has to consider that the Sun<br>is rotating and the well known Parker-spiral forms.<br>We have developed codes based on optimization principles<br>to solve nonlinear force-free, magneto-hydro-static and<br>stationary MHD-equilibria. In the present work we want to<br>extend these methods by taking the solar rotation into account.</p>

Projected-Gradient-Descent in Rakeness-Based Compressed Sensing with Disturbance Rejection

2018 ◽  
Author(s):  
Mauro Mangia ◽  
Letizia Magenta ◽  
Alex Marchioni ◽  
Fabio Pareschi ◽  
Riccardo Rovatti ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Disturbance Rejection ◽  
Gradient Descent ◽  
Projected Gradient ◽  
Projected Gradient Descent

scholarly journals The Rise of Political Influencers— Perspectives on a Trend Towards Meaningful Content

2021 ◽  
Vol 6 ◽  
Author(s):  
Magdalena Riedl ◽  
Carsten Schwemmer ◽  
Sandra Ziewiecki ◽  
Lisa M. Ross
Keyword(s):  
Social Media ◽  
Political Communication ◽  
Political Ideology ◽  
Empirical Studies ◽  
Political Engagement ◽  
Opinion Leaders ◽  
Important Research ◽  
Conspiracy Theories ◽  
The Past ◽  
Important Research Topic

Despite an increasing information overflow in the era of digital communication, influencers manage to draw the attention of their followers with an authentic and casual appearance. Reaching large audiences on social media, they can be considered as digital opinion leaders. In the past, they predominantly appeared as experts for topics like fashion, sports, or gaming and used their status to cooperate with brands for marketing purposes. However, since recently influencers also turn towards more meaningful and political content. In this article, we share our perspective on the rise of political influencers using examples of sustainability and related topics covered on Instagram. By applying a qualitative observational approach, we illustrate how influencers make political communication look easy, while at the same time seamlessly integrating product promotions in their social media feeds. In this context, we discuss positive aspects of political influencers like contributions to education and political engagement, but also negative aspects such as the potential amplification of radical political ideology or conspiracy theories. We conclude by highlighting political influencers as an important research topic for conceptual and empirical studies in the future.

scholarly journals The happy victimizer phenomenon: Not found here

2014 ◽  
Vol 46 (1) ◽  
pp. 145-161
Author(s):  
Ana Jevtic ◽  
Jovan Miric
Keyword(s):  
Emotional Development ◽  
Positive Emotions ◽  
Negative Emotions ◽  
Age Groups ◽  
Research Topic ◽  
Important Research ◽  
Important Research Topic ◽  
Significant Difference ◽  
Moral Explanations

Children?s attribution of emotions to a moral transgressor is an important research topic in the psychology of moral and emotional development. This is especially because of the so-called Happy Victimizer Phenomenon (HVP) where younger children attribute positive emotions to a moral transgressor described in a story. In the two studies that we have conducted (children aged 5, 7 and 9, 20 of each age; 10 of each age in the second study) we have tested the possible influence of the fear of sanctions and the type of transgression (stealing and inflicting body injuries) on the attribution of emotions. Children were presented with stories that described transgressions and they were asked to answer how the transgressor felt. The fear of sanctions did not make a significant difference in attribution but the type of transgression did - more negative emotions were attributed for inflicting body injuries than for stealing. Positive emotions were explained with situational-instrumental explanations in 84% of cases while negative emotions were explained with moral explanations in 63,5%. Girls attributed more positive emotions (61%) than boys (39%). However, our main finding was that, for the aforementioned age groups, we did not find the HVP effect although it has regularly been registered in foreign studies. This finding denies the generalizability of the phenomenon and points to the significance of disciplining styles and, even more so, culture for children?s attribution of emotions to moral transgressors.

scholarly journals Carbon Nanotubes based Nanocomposites as Photocatalysts in Water Treatment

2021 ◽  
pp. 77-112
Author(s):  
T.L. Tan
Keyword(s):  
Carbon Nanotubes ◽  
Water Treatment ◽  
Photocatalytic Degradation ◽  
Water Demand ◽  
Metal Ion ◽  
Important Research ◽  
Important Research Topic ◽  
Persistent Organic Compounds ◽  
Tunable Morphology ◽  
Degradation Of Pollutants

The shortage of worldwide clean water and the increasing water demand are now ubiquitous problems around the world. Thus, efficient water treatment is an important research topic, of which phocatalysis is known as simplest and efficient technique utilized in the photocatalytic degradation of all major water pollutants, including heavy metal ion, organic and inorganic pollutants. In this context, the use of one- dimensional carbon nanotubes-based nanocomposites in water treatment have been widely demonstrated to be capable of removing persistent organic compounds due to their unique physical and electronic properties, large surface area, tunable morphology, biocompatible and chemical-environmental-thermal stability. This chapter begins with the discussion of the importance and properties of carbon nanotubes, and then briefs about the types and methods of preparation of carbon nanotubes-based nanocomposites in detail. The next section emphasizes the fundamentals of photocatalysis phenomenon and its proposed mechanism for the photocatalytic degradation of pollutants. The last section highlights the recent development in the carbon-based nanocomposites as photocatalyst in water treatment systems, supported by comprehensive literature account. Finally, the remaining challenges and perspectives for using carbon nanotubes-based nanocomposites are discussed.

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