Current Technologies in Fairness, Transparency, Security and Safety: Methods, Applications and Challenges

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 2361

Special Issue Editors

Department of Computer, Electronic and Communication Technologies, University of Deusto, 24, 48007 Bilbo, Bizkaia, Spain
Interests: machine learning; security; big data

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Guest Editor
Department of Computer, Electronic and Communication Technologies, University of Deusto, 24, 48007 Bilbo, Bizkaia, Spain
Interests: machine learning

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Guest Editor
Deusto Social Values research group, University of Deusto, 24, 48007 Bilbao, Bizkaia, Spain
Interests: human rights; new technologies; ICT law

Special Issue Information

Dear Colleagues,

The use of smartphones and mobile devices has become a core part of our everyday life and work. In the same line, artificial intelligence and IoT have recently emerged as critical elements. These technologies have an increased presence in our lives, offering almost the same functionality as a personal computer, though also allowing access to critical information, like medical or financial information, location data, or if you ride a bike or drive a car. These technologies are critical holders and managers of our data, which is why it is so essential to ensure the fairness, transparency, security, and privacy of such devices, even more so in conflict situations (for example, regarding human rights violations and violence against women). The development and advancement of these technologies are of critical importance, and there is a great need for interdisciplinary research to cover all aspects of these challenges.

In this Special Issue, we welcome manuscripts of various types, such as empirical research articles (whatever the methodology used), literature reviews, reasoned comments, and justified argumentative essays, that contribute to advancing our understanding of the capacity of these technologies as well as the impact that they have. We want to evaluate the implications of the exponential increase in the use of technologies in society and the best practices to develop technology that has positive impacts on the community.

Dr. Borja Sanz
Dr. Iker Pastor López
Dr. María López Belloso
Guest Editors

Manuscript Submission Information

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Keywords

  • Android, iOS, and Windows mobile security and privacy
  • Secure mobile application development
  • Smartphone device/hardware security/privacy
  • Mobile and cloud application security
  • Mobile and cloud user privacy
  • Modeling, design, and testing secure mobile clouds
  • Secure algorithms and methodologies for mobile cloud
  • Secure and efficient private mobile cloud
  • Mobile and cloud devices and application management
  • Applications of mobile devices computing for smart cities, smart grids, and sensors
  • Applications of mobile devices computing for engineering in general
  • Applications of mobile devices computing for detecting human trafficking
  • Applications of mobile devices computing for preventing violence against women
  • Applications of mobile devices computing for human rights violations
  • mobile and cloud
  • security
  • privacy
  • interdisciplinary

Published Papers (1 paper)

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Research

16 pages, 5733 KiB  
Article
Analysis of Harassment Complaints to Detect Witness Intervention by Machine Learning and Soft Computing Techniques
by Marina Alonso-Parra, Cristina Puente, Ana Laguna and Rafael Palacios
Appl. Sci. 2021, 11(17), 8007; https://doi.org/10.3390/app11178007 - 29 Aug 2021
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Abstract
This research is aimed to analyze textual descriptions of harassment situations collected anonymously by the Hollaback! project. Hollaback! is an international movement created to end harassment in all of its forms. Its goal is to collect stories of harassment through the web and [...] Read more.
This research is aimed to analyze textual descriptions of harassment situations collected anonymously by the Hollaback! project. Hollaback! is an international movement created to end harassment in all of its forms. Its goal is to collect stories of harassment through the web and a free app all around the world to elevate victims’ individual voices to find a societal solution. Hollaback! pretends to analyze the impact of a bystander during a harassment in order to launch a public awareness-raising campaign to equip everyday people with tools to undo harassment. Thus, the analysis presented in this paper is a first step in Hollaback!’s purpose: the automatic detection of a witness intervention inferred from the victim’s own report. In a first step, natural language processing techniques were used to analyze the victim’s free-text descriptions. For this part, we used the whole dataset with all its countries and locations. In addition, classification models, based on machine learning and soft computing techniques, were developed in the second part of this study to classify the descriptions into those that have bystander presence and those that do not. For this machine learning part, we selected the city of Madrid as an example, in order to establish a criterion of the witness behavior procedure. Full article
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