Using Consensus Methods to User Classification in Interactive Systems

2002 ◽  
pp. 346-354 ◽  
Author(s):  
Janusz Sobecki ◽  
Ngoc Thanh Nguyen
Keyword(s):  
Interactive Systems ◽  
Consensus Methods ◽  
User Classification

Consensus methods in prescribing research

2001 ◽  
Vol 26 (1) ◽  
pp. 5-14 ◽  
Author(s):  
S. M. Campbell ◽  
J. A. Cantrill
Keyword(s):  
Consensus Methods

Necessity of incorporating human factors in the design of interactive systems

2014 ◽  
Vol 48 (4-6) ◽  
pp. 251-260
Author(s):  
Régis Mollard ◽  
Marion Wolff
Keyword(s):  
Human Factors ◽  
Interactive Systems

scholarly journals Tangibly understanding intangible complexities: Designing for distributed autonomous organizations

2019 ◽  
Vol 6 (1) ◽  
pp. 47-63 ◽  
Author(s):  
Bettina Nissen ◽  
Ella Tallyn ◽  
Kate Symons
Keyword(s):  
Distributed Systems ◽  
Interaction Design ◽  
Digital Technologies ◽  
Interactive Systems ◽  
Collaborative Practice ◽  
The Internet ◽  
The Face ◽  
Future Technologies ◽  
The Internet Of Things

Abstract New digital technologies such as Blockchain and smart contracting are rapidly changing the face of value exchange, and present new opportunities and challenges for designers. Designers and data specialists are at the forefront of exploring new ways of exchanging value, using Blockchain, cryptocurrencies, smart contracting and the direct exchanges between things made possible by the Internet of Things (Tallyn et al. 2018; Pschetz et al. 2019). For researchers and designers in areas of Human Computer Interaction (HCI) and Interaction Design to better understand and explore the implications of these emerging and future technologies as Distributed Autonomous Organisations (DAOs) we delivered a workshop at the ACM conference Designing Interactive Systems (DIS) in Edinburgh in 2017 (Nissen et al. 2017). The workshop aimed to use the lens of DAOs to introduce the principle that products and services may soon be owned and managed collectively and not by one person or authority, thus challenging traditional concepts of ownership and power. This workshop builds on established HCI research exploring the role of technology in financial interactions and designing for the rapidly changing world of technology and value exchange (Kaye et al. 2014; Malmborg et al. 2015; Millen et al. 2015; Vines et al. 2014). Beyond this, the HCI community has started to explore these technologies beyond issues of finance, money and collaborative practice, focusing on the implications of these emerging but rapidly ascending distributed systems in more applied contexts (Elsden et al. 2018a). By bringing together designers and researchers with different experiences and knowledge of distributed systems, the aim of this workshop was two-fold. First, to further understand, develop and critique these new forms of distributed power and ownership and second, to practically explore how to design interactive products and services that enable, challenge or disrupt existing and emerging models.

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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