Issue
Dagstuhl Manifestos, Volume 3, Issue 1
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Volume:
Dagstuhl Manifestos, Volume 3
Journal: Dagstuhl Manifestos (DagMan)
Publication Details
- published at: 2013-11-29
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- DBLP: db/journals/dagstuhl-manifestos/dagstuhl-manifestos3
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Complete Issue
Dagstuhl Manifestos, Volume 3, Issue 1, January - December 2013, Complete Issue
Abstract
Dagstuhl Manifestos, Volume 3, Issue 1, January - December 2013, Complete Issue
Cite as
Dagstuhl Manifestos, Volume 3, Issue 1, pp. 1-52, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)
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@Article{DagMan.3.1,
title = {{Dagstuhl Manifestos, Volume 3, Issue 1, January - December 2013, Complete Issue}},
pages = {1--52},
journal = {Dagstuhl Manifestos},
ISSN = {2193-2433},
year = {2014},
volume = {3},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagMan.3.1},
URN = {urn:nbn:de:0030-drops-47909},
doi = {10.4230/DagMan.3.1},
annote = {Keywords: Dagstuhl Manifestos, Volume 3, Issue 1, January - December 2013, Complete Issue}
}
Document
Front Matter
Dagstuhl Manifestos, Table of Contents, Volume 3, Issue 1, 2013
Abstract
Dagstuhl Manifestos, Table of Contents, Volume 3, Issue 1, 2013
Cite as
Dagstuhl Manifestos, Volume 3, Issue 1, pp. i-ii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)
Copy BibTex To Clipboard
@Article{DagMan.3.1.i,
title = {{Dagstuhl Manifestos, Table of Contents, Volume 3, Issue 1, 2013}},
pages = {i--ii},
journal = {Dagstuhl Manifestos},
ISSN = {2193-2433},
year = {2014},
volume = {3},
number = {1},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagMan.3.1.i},
URN = {urn:nbn:de:0030-drops-47896},
doi = {10.4230/DagMan.3.1.i},
annote = {Keywords: Dagstuhl Manifestos, Table of Contents, Volume 3, Issue 1, 2013}
}
Document
Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371)
Abstract
The study of learning in adversarial environments is an emerging discipline at the juncture between machine learning and computer security. The interest in learning-based methods for security- and system-design applications comes from the high degree of complexity of phenomena underlying the security and reliability of computer systems. As it becomes increasingly difficult to reach the desired properties solely using statically designed mechanisms, learning methods are being used more and more to obtain a better understanding of various data collected from these complex systems. However, learning approaches can be evaded by adversaries, who change their behavior in response to the learning methods. To-date, there has been limited research into learning techniques that are resilient to attacks with provable robustness guarantees
The Perspectives Workshop, "Machine Learning Methods for Computer Security" was convened to bring together interested researchers from both the computer security and machine learning communities to discuss techniques, challenges, and future research directions for secure learning and learning-based security applications. As a result of the twenty-two invited presentations, workgroup sessions and informal discussion, several priority areas of research were
identified. The open problems identified in the field ranged from traditional applications of machine learning in security, such as attack detection and analysis of malicious software, to methodological issues related to secure learning, especially the development of new formal approaches with provable security guarantees. Finally a number of other potential applications were
pinpointed outside of the traditional scope of computer security in which
security issues may also arise in connection with data-driven methods. Examples of such applications are social media spam, plagiarism detection, authorship identification, copyright enforcement, computer vision (particularly in the context of biometrics), and sentiment analysis.
Cite as
Anthony D. Joseph, Pavel Laskov, Fabio Roli, J. Doug Tygar, and Blaine Nelson. Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371). In Dagstuhl Manifestos, Volume 3, Issue 1, pp. 1-30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)
Copy BibTex To Clipboard
@Article{joseph_et_al:DagMan.3.1.1,
author = {Joseph, Anthony D. and Laskov, Pavel and Roli, Fabio and Tygar, J. Doug and Nelson, Blaine},
title = {{Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371)}},
pages = {1--30},
journal = {Dagstuhl Manifestos},
ISSN = {2193-2433},
year = {2013},
volume = {3},
number = {1},
editor = {Joseph, Anthony D. and Laskov, Pavel and Roli, Fabio and Tygar, J. Doug and Nelson, Blaine},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagMan.3.1.1},
URN = {urn:nbn:de:0030-drops-43569},
doi = {10.4230/DagMan.3.1.1},
annote = {Keywords: Adversarial Learning, Computer Security, Robust Statistical Learning, Online Learning with Experts, Game Theory, Learning Theory}
}
Document
ICT for Bridging Biology and Medicine (Dagstuhl Perspectives Workshop 13342)
Abstract
The systems paradigm of modern medicine presents both, an opportunity and a challenge, for current Information and Communication Technology (ICT). The opportunity is to understand the spatio-temporal organisation and dynamics of the human body as an integrated whole, incorporating the biochemical, physiological, and environmental interactions that sustain life. Yet, to accomplish this, one has to meet the challenge of integrating, visualising, interpreting, and utilising an unprecedented amount of in-silico, in-vitro and in-vivo data related to health care in a systematic, transparent, comprehensible, and reproducible fashion. This challenge is substantially compounded by the critical need to align technical solutions with the increasingly social dimension of modern ICT and the wide range of stakeholders in modern health-care systems.
Unquestionably, advancing health-care related ICT has the potential of fundamentally revolutionising care-delivery systems, affecting all our lives both, personally and -- in view of the enormous costs of health--care systems in modern societies -- also financially.
Accordingly, to ponder the options of ICT for delivering the promise of systems approaches to medical care, medical researchers and physicians, biologists and mathematicians, computer scientists and information--systems experts
from three continents and from both, industry and academia, met in Dagstuhl for a Dagstuhl Perspectives Workshop on ICT Strategies for Bridging Biology and Medicine from August 18 to 23, 2013, to thoroughly discuss this multidisciplinary topic and to derive and compile a comprehensive list of pertinent recommendations -- rather than just to deliver a set package of sanitised powerpoint presentations on medical ICT. The recommendations in this manifesto reflect points of convergence that emerged during the intense discussions and analyses taking place the workshop. They also reflect a particular attention given to the identification of challenges for improving the effectiveness of ICT approaches to Systems Biomedicine.
Cite as
Jonas S. Almeida, Andreas Dress, Titus Kühne, and Laxmi Parida. ICT for Bridging Biology and Medicine (Dagstuhl Perspectives Workshop 13342). In Dagstuhl Manifestos, Volume 3, Issue 1, pp. 31-50, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)
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@Article{almeida_et_al:DagMan.3.1.31,
author = {Almeida, Jonas S. and Dress, Andreas and K\"{u}hne, Titus and Parida, Laxmi},
title = {{ICT for Bridging Biology and Medicine (Dagstuhl Perspectives Workshop 13342)}},
pages = {31--50},
journal = {Dagstuhl Manifestos},
ISSN = {2193-2433},
year = {2014},
volume = {3},
number = {1},
editor = {Almeida, Jonas S. and Dress, Andreas and K\"{u}hne, Titus and Parida, Laxmi},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops-dev.dagstuhl.de/entities/document/10.4230/DagMan.3.1.31},
URN = {urn:nbn:de:0030-drops-44292},
doi = {10.4230/DagMan.3.1.31},
annote = {Keywords: Systems medicine, health-care related information systems, biomedical workflow engines, medical cloud, patient participation, ICT literacy}
}