Introduction to the Special Issue on Recent Developments in Multimedia Watermarking Using Machine Learning

2018 ◽  
Vol 27 (1) ◽  
pp. 1-3
Author(s):  
Amit Kumar Singh
Keyword(s):  
Machine Learning ◽  
Special Issue ◽  
Recent Developments ◽  
Multimedia Watermarking

scholarly journals Governing artificial intelligence: ethical, legal and technical opportunities and challenges

2018 ◽  
Vol 376 (2133) ◽  
pp. 20180080 ◽  
Author(s):  
Corinne Cath
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Law Enforcement ◽  
Urban Infrastructure ◽  
Humanitarian Aid ◽  
Special Issue ◽  
Theme Issue ◽  
Ethical Frameworks ◽  
Recent Developments ◽  
Risk Areas

This paper is the introduction to the special issue entitled: ‘Governing artificial intelligence: ethical, legal and technical opportunities and challenges'. Artificial intelligence (AI) increasingly permeates every aspect of our society, from the critical, like urban infrastructure, law enforcement, banking, healthcare and humanitarian aid, to the mundane like dating. AI, including embodied AI in robotics and techniques like machine learning, can improve economic, social welfare and the exercise of human rights. Owing to the proliferation of AI in high-risk areas, the pressure is mounting to design and govern AI to be accountable, fair and transparent. How can this be achieved and through which frameworks? This is one of the central questions addressed in this special issue, in which eight authors present in-depth analyses of the ethical, legal-regulatory and technical challenges posed by developing governance regimes for AI systems. It also gives a brief overview of recent developments in AI governance, how much of the agenda for defining AI regulation, ethical frameworks and technical approaches is set, as well as providing some concrete suggestions to further the debate on AI governance. This article is part of the theme issue ‘Governing artificial intelligence: ethical, legal, and technical opportunities and challenges’.

scholarly journals Special Issue on Selected Papers from IVAPP 2018

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 171
Author(s):  
Alexandru Telea ◽  
Andreas Kerren
Keyword(s):  
Machine Learning ◽  
Information Visualization ◽  
Visual Analytics ◽  
Data Science ◽  
Time Dependent ◽  
Dependent Data ◽  
Special Issue ◽  
Making Sense ◽  
Recent Developments

Recent developments at the crossroads of data science, datamining,machine learning, and graphics and imaging sciences have further established information visualization and visual analytics as central disciplines that deliver methods, techniques, and tools for making sense of and extracting actionable insights and results fromlarge amounts of complex,multidimensional, hybrid, and time-dependent data.[...]

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

scholarly journals Special Issue “Interplay between Financial and Actuarial Mathematics”

Risks ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 139
Author(s):  
Corina Constantinescu ◽  
Julia Eisenberg
Keyword(s):  
Machine Learning ◽  
Control Theory ◽  
Option Pricing ◽  
Interest Rates ◽  
Financial Mathematics ◽  
Special Issue ◽  
Actuarial Mathematics ◽  
Mortality Models ◽  
Low Interest Rates

The Special Issue aims to highlight the interaction between actuarial and financial mathematics, which, due to the recent low interest rates and implications of COVID-19, requires an interlace between actuarial and financial methods, along with control theory, machine learning, mortality models, option pricing, hedging, unit-linked contracts and drawdown analysis, among others [...]

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