scholarly journals A Distributed E-Cross Learning Algorithm for Intelligent Multiple Network Slice Selection

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Guomin Wu ◽  
Guoping Tan ◽  
Defu Jiang
Keyword(s):  
User Experience ◽  
Learning Algorithm ◽  
Dynamic Game ◽  
Research Field ◽  
Distributed Network ◽  
Intelligent Algorithm ◽  
Network Slicing ◽  
Two Factors ◽  
Multiple Network ◽  
Dynamic Game Model

Recently, some technological issues in network slicing have been explored. However, most works focus on the physical resource management in this research field and less on slice selection. Different from the existing studies, we explore the problem of intelligent multiple slice selection, which makes some effort to dynamically obtain better user experience in a changeable state. Herein, we consider two factors about user experience: its throughput and energy consumption. Accordingly, a distributed E-cross learning algorithm is developed in the multiagent system where each terminal is regarded as an agent in the distributed network. Furthermore, its convergence is theoretically proven for the dynamic game model. In addition, the complexity of the proposed algorithm is discussed. A mass of simulation results are presented for the convergence and effectiveness of the proposed distributed learning algorithm. Compared with greedy algorithm, the proposed intelligent algorithm has a faster convergence speed. Besides, better user experience is attained effectively with multiple slice access.

scholarly journals To isolate or not to isolate: The impact of changing behavior on COVID-19 transmission

2020 ◽  
Author(s):  
Folashade B. Agusto ◽  
Igor V. Erovenko ◽  
Alexander Fulk ◽  
Qays Abu-Saymeh ◽  
Daniel Daniel Romero-Alvarez ◽  
...  
Keyword(s):  
Social Learning ◽  
Learning Algorithm ◽  
Dynamic Game ◽  
Behavioral Model ◽  
Altruistic Behavior ◽  
Behavioral Strategies ◽  
Game Theoretic ◽  
Dynamic Game Model ◽  
Novel Coronavirus ◽  
The Impact

The COVID-19 pandemic has caused more than 25 million cases and 800 thousand deaths worldwide to date. Neither vaccines nor therapeutic drugs are currently available for this novel coronavirus. All measures to prevent the spread of COVID-19 are thus based on reducing contact between infected and susceptible individuals. Most of these measures such as quarantine and self-isolation require voluntary compliance by the population. However, humans may act in their (perceived) self-interest only. We construct a mathematical model of COVID-19 transmission with quarantine and hospitalization coupled with a dynamic game model of adaptive human behavior. Susceptible and infected individuals adopt various behavioral strategies based on perceived prevalence and burden of the disease and sensitivity to isolation measures, and they evolve their strategies using a social learning algorithm (imitation dynamics). This results in complex interplay between the epidemiological model, which affects success of different strategies, and the game-theoretic behavioral model, which in turn affects the spread of the disease. We found that the second wave of the pandemic, which has been observed in the US, can be attributed to rational behavior of susceptible individuals, and that multiple waves of the pandemic are possible if the rate of social learning of infected individuals is sufficiently high. To reduce the burden of the disease on the society, it is necessary to incentivize such altruistic behavior by infected individuals as voluntary self-isolation.

scholarly journals To Isolate or Not To Isolate: The Impact of Changing Behavior on COVID-19 Transmission

2020 ◽  
Author(s):  
Folashade B. Agusto ◽  
Igor V. Erovenko ◽  
Alexander Fulk ◽  
Qays Abu-Saymeh ◽  
Daniel Romero-Alvarez ◽  
...  
Keyword(s):  
Social Learning ◽  
Learning Algorithm ◽  
Dynamic Game ◽  
Behavioral Model ◽  
Altruistic Behavior ◽  
Behavioral Strategies ◽  
Game Theoretic ◽  
Dynamic Game Model ◽  
Novel Coronavirus ◽  
The Impact

Abstract The COVID-19 pandemic has caused more than 25 million cases and 800 thousand deaths worldwide to date. Neither vaccines nor therapeutic drugs are currently available for this novel coronavirus. All measures to prevent the spread of COVID-19 are thus based on reducing contact between infected and susceptible individuals. Most of these measures such as quarantine and self-isolation require voluntary compliance by the population. However, humans may act in their (perceived) self-interest only. We construct a mathematical model of COVID-19 transmission with quarantine and hospitalization coupled with a dynamic game model of adaptive human behavior. Susceptible and infected individuals adopt various behavioral strategies based on perceived prevalence and burden of the disease and sensitivity to isolation measures, and they evolve their strategies using a social learning algorithm (imitation dynamics). This results in complex interplay between the epidemiological model, which affects success of different strategies, and the game-theoretic behavioral model, which in turn affects the spread of the disease. We found that the second wave of the pandemic, which has been observed in the US, can be attributed to rational behavior of susceptible individuals, and that multiple waves of the pandemic are possible if the rate of social learning of infected individuals is sufficiently high. To reduce the burden of the disease on the society, it is necessary to incentivize such altruistic behavior by infected individuals as voluntary self-isolation.

scholarly journals Potential and effects of personalizing gameful fitness applications using behavior change intentions and Hexad user types

2021 ◽  
Author(s):  
Maximilian Altmeyer ◽  
Pascal Lessel ◽  
Subhashini Jantwal ◽  
Linda Muller ◽  
Florian Daiber ◽  
...  
Keyword(s):  
Physical Activity ◽  
Behavior Change ◽  
User Experience ◽  
Design Elements ◽  
Online Study ◽  
Positive Effects ◽  
Two Factors ◽  
Study Support ◽  
Affective Experiences ◽  
Relevant Domain

AbstractPersonalizing gameful applications is essential to account for interpersonal differences in the perception of gameful design elements. Considering that an increasing number of people lead sedentary lifestyles, using personalized gameful applications to encourage physical activity is a particularly relevant domain. In this article, we investigate behavior change intentions and Hexad user types as factors to personalize gameful fitness applications. We first explored the potential of these two factors by analyzing differences in the perceived persuasiveness of gameful design elements using a storyboards-based online study ($$N=178$$ N = 178 ). Our results show several significant effects regarding both factors and thus support the usefulness of them in explaining perceptual differences. Based on these findings, we implemented “Endless Universe,” a personalized gameful application encouraging physical activity on a treadmill. We used the system in a laboratory study ($$N=20$$ N = 20 ) to study actual effects of personalization on the users’ performance, enjoyment and affective experiences. While we did not find effects on the immediate performance of users, positive effects on user experience-related measures were found. The results of this study support the relevance of behavior change intentions and Hexad user types for personalizing gameful fitness systems further.

scholarly journals On the Unfounded Enthusiasm for Soft Selective Sweeps III: The Supervised Machine Learning Algorithm That Isn’t

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 527
Author(s):  
Eran Elhaik ◽  
Dan Graur
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
A Priori ◽  
Neutral Theory ◽  
Dominant Mode ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Selective Sweeps ◽  
Two Factors ◽  
Negative Controls

In the last 15 years or so, soft selective sweep mechanisms have been catapulted from a curiosity of little evolutionary importance to a ubiquitous mechanism claimed to explain most adaptive evolution and, in some cases, most evolution. This transformation was aided by a series of articles by Daniel Schrider and Andrew Kern. Within this series, a paper entitled “Soft sweeps are the dominant mode of adaptation in the human genome” (Schrider and Kern, Mol. Biol. Evolut. 2017, 34(8), 1863–1877) attracted a great deal of attention, in particular in conjunction with another paper (Kern and Hahn, Mol. Biol. Evolut. 2018, 35(6), 1366–1371), for purporting to discredit the Neutral Theory of Molecular Evolution (Kimura 1968). Here, we address an alleged novelty in Schrider and Kern’s paper, i.e., the claim that their study involved an artificial intelligence technique called supervised machine learning (SML). SML is predicated upon the existence of a training dataset in which the correspondence between the input and output is known empirically to be true. Curiously, Schrider and Kern did not possess a training dataset of genomic segments known a priori to have evolved either neutrally or through soft or hard selective sweeps. Thus, their claim of using SML is thoroughly and utterly misleading. In the absence of legitimate training datasets, Schrider and Kern used: (1) simulations that employ many manipulatable variables and (2) a system of data cherry-picking rivaling the worst excesses in the literature. These two factors, in addition to the lack of negative controls and the irreproducibility of their results due to incomplete methodological detail, lead us to conclude that all evolutionary inferences derived from so-called SML algorithms (e.g., S/HIC) should be taken with a huge shovel of salt.

scholarly journals Distributed Network Slicing Management Using Blockchains in E-Health Environments

2021 ◽  
Author(s):  
João Paulo de Brito Gonçalves ◽  
Henrique Carvalho de Resende ◽  
Rodolfo da Silva Villaca ◽  
Esteban Municio ◽  
Cristiano B. Both ◽  
...  
Keyword(s):  
Distributed Network ◽  
Network Slicing

scholarly journals A Novel Dynamic Method in Distributed Network Attack-Defense Game

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Liu Xiaojian ◽  
Yuan Yuyu
Keyword(s):  
Information Acquisition ◽  
Dynamic Method ◽  
Dynamic Game ◽  
Evolutionary Process ◽  
Target System ◽  
Distributed Network ◽  
Network Attack

We analyze the distributed network attack-defense game scenarios, and we find that attackers and defenders have different information acquisition abilities since the ownership of the target system. Correspondingly, they will have different initiative and reaction in the game. Based on that, we propose a novel dynamic game method for distributed network attack-defense game. The method takes advantage of defenders’ information superiority and attackers’ imitation behaviors and induces attackers’ reaction evolutionary process in the game to gain more defense payoffs. Experiments show that our method can achieve relatively more average defense payoffs than previous work.

scholarly journals A Dynamic Game Model of Collective Choice in Multiagent Systems

2018 ◽  
Vol 63 (3) ◽  
pp. 768-782 ◽  
Author(s):  
Rabih Salhab ◽  
Roland P. Malhame ◽  
Jerome Le Ny
Keyword(s):  
Multiagent Systems ◽  
Dynamic Game ◽  
Collective Choice ◽  
Game Model ◽  
Dynamic Game Model
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