Exploring emotion prediction from biometric-based keystroke dynamics data using multiagent systems

2015 ◽  
Author(s):  
M. Fairhurst ◽  
Cheng Li ◽  
M. Da Costa-Abreu
Keyword(s):  
Multiagent Systems ◽  
Keystroke Dynamics

Co-evolution of Hetero Multiagent Systems using Genetic Network Programming.

2003 ◽  
Vol 123 (3) ◽  
pp. 544-551 ◽  
Author(s):  
Kotaro Hirasawa ◽  
Masafumi Okubo ◽  
Jinglu Hu ◽  
Junichi Murata ◽  
Yuko Matsuya
Keyword(s):  
Multiagent Systems ◽  
Genetic Network ◽  
Network Programming ◽  
Genetic Network Programming

scholarly journals Partial Component Consensus of Discrete-Time Multiagent Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-5
Author(s):  
Wenjun Hu ◽  
Gang Zhang ◽  
Zhongjun Ma ◽  
Binbin Wu
Keyword(s):  
Multiagent Systems ◽  
Discrete Time ◽  
Matrix Theory ◽  
Pinning Control ◽  
Directed Network ◽  
Strong Function ◽  
Partial Stability ◽  
Partial Component ◽  
The Matrix ◽  
Error System

The multiagent system has the advantages of simple structure, strong function, and cost saving, which has received wide attention from different fields. Consensus is the most basic problem in multiagent systems. In this paper, firstly, the problem of partial component consensus in the first-order linear discrete-time multiagent systems with the directed network topology is discussed. Via designing an appropriate pinning control protocol, the corresponding error system is analyzed by using the matrix theory and the partial stability theory. Secondly, a sufficient condition is given to realize partial component consensus in multiagent systems. Finally, the numerical simulations are given to illustrate the theoretical results.

Energy-Constraint Formation for Multiagent Systems With Switching Interaction Topologies

2020 ◽  
Vol 67 (7) ◽  
pp. 2442-2454 ◽  
Author(s):  
Jianxiang Xi ◽  
Le Wang ◽  
Jianfei Zheng ◽  
Xiaojun Yang
Keyword(s):  
Multiagent Systems ◽  
Energy Constraint

scholarly journals Age and Gender as Cyber Attribution Features in Keystroke Dynamic-Based User Classification Processes

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 835
Author(s):  
Ioannis Tsimperidis ◽  
Cagatay Yucel ◽  
Vasilios Katos
Keyword(s):  
High Accuracy ◽  
Keystroke Dynamics ◽  
Time Intervals ◽  
Age And Gender ◽  
Common Features ◽  
User Classification ◽  
Gender And Age ◽  
And Gender ◽  
Keystroke Dynamic

Keystroke dynamics are used to authenticate users, to reveal some of their inherent or acquired characteristics and to assess their mental and physical states. The most common features utilized are the time intervals that the keys remain pressed and the time intervals that are required to use two consecutive keys. This paper examines which of these features are the most important and how utilization of these features can lead to better classification results. To achieve this, an existing dataset consisting of 387 logfiles is used, five classifiers are exploited and users are classified by gender and age. The results, while demonstrating the application of these two characteristics jointly on classifiers with high accuracy, answer the question of which keystroke dynamics features are more appropriate for classification with common classifiers.

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