Información de la conferencia
SASO 2019: International Conference on Self-Adaptive and Self-Organizing Systems
http://saso2019.cs.umu.se/
Día de Entrega:
2019-03-10
Fecha de Notificación:
2019-04-10
Fecha de Conferencia:
2019-06-16
Ubicación:
Umea, Sweden
Años:
13
QUALIS: b3   Vistas: 16987   Seguidores: 4   Asistentes: 2

Solicitud de Artículos
Aims and Motivation

The aim SASO 2019 is to provide a forum for the presentation and discussion of research on the foundations of engineered systems that self-adapt and self-organize. The complexity of current and emerging networks, software, and services can be characterized by issues such as scale, heterogeneity, openness, and dynamics in the environment. These issues have led the software engineering, distributed systems, and management communities to look for inspiration in diverse fields (e.g., complex systems, control theory, artificial intelligence, chemistry, psychology, sociology, and biology) to find new ways of designing and managing such computing systems in a principled way. In this endeavor, self-organization and self-adaptation have emerged as two promising, interrelated approaches. They form the basis of many other so-called self-* properties, such as self-configuration, self-healing, or self-optimization. SASO 2019 aims to be an interdisciplinary meeting, where contributions from participants with different backgrounds lead to the fostering of a cross-pollination of ideas, and where innovative theories, frameworks, methodologies, tools, and applications can emerge.

Scope

We invite novel contributions related to the  fundamental understanding of self-adaptive and self-organizing systems, along with principles and practices of their engineering and application. The topics of interest include, but are not limited to:

    Self-* systems theory: nature-inspired and socially-inspired paradigms and heuristics; theoretical frameworks and models; formal languages; control theory; requirement and goal expression techniques; uncertainty as a 1st class entity
    Self-* system properties: robustness; resilience; stability; anti-fragility; diversity; self-reference and reflection; emergent behavior; computational awareness and self-awareness;
    Self-* systems engineering: reusable mechanisms and algorithms; design patterns; programming languages; architectures; testing and validation methodologies; runtime models; techniques for assurance; platforms and toolkits; multi-agent systems;
    Principles and practices of self-organization: self-governance; security; change management; distributed consensus, knowledge management, and the general use of rules, norms and policies
    Mechanisms for self-adaptation: inter-operation of self-* mechanisms; evolution, logic, and learning; addressing large-scale and decentralized systems;
    Socio-technical self-* systems: human and social factors; visualization; crowdsourcing and collective awareness;
    Data-driven approaches to self-* systems: data mining; machine learning; data science and other statistical techniques to analyze, understand, and manage the behavior of complex systems;
    Self-adaptive and self-organizing hardware: self-* materials; self-construction; reconfigurable hardware;
    Self-* systems education: experience reports; curricula; innovative course concepts;
    Applications and experiences with self-* systems: smart grid, smart cities, smart homes, manufacturing and industrial plants, cyber-physical systems; autonomous vehicles and robotics; traffic management; self-adaptive cybersecurity; Internet of Things; fog/edge computing.

Submission Instructions

Submissions can have up to 10 pages formatted according to the standard IEEE Computer Society Press proceedings style guide. Please submit your papers electronically in PDF format using the SASO 2019 conference management system:

https://easychair.org/conferences/?conf=saso2019.

The proceedings will be published by IEEE Computer Society Press and made available as a part of the IEEE Digital Library. Note that a separate Call for Poster and Demo Submissions will also be issued. As per the standard IEEE policies, all submissions should be original, i.e., they should not have been previously published in any conference proceedings, book, or journal and should not currently be under review for another archival conference. We would like to also highlight IEEE’s policies regarding plagiarism and self-plagiarism:

(https://www.ieee.org/publications/rights/plagiarism/id-plagiarism.html).

Where relevant and appropriate, accepted papers will also be encouraged to participate in the Demo or Poster Sessions.
Review Criteria

Papers should present novel ideas in the cross-disciplinary research context described in this call, motivated by problems from current practice or applied research. Both theoretical and empirical contributions should be highlighted, substantiated by formal analysis, simulation, experimental evaluations, or comparative studies, etc. Appropriate references must be made to related work. Due to the cross-disciplinary nature of the SASO conference, we encourage papers to be intelligible and relevant to researchers who are not members of the same specialized sub-field. Authors are also encouraged to submit papers describing applications. Application papers should provide an indication of the real-world relevance of the problem that is solved, including a description of the domain, and an evaluation of performance, usability, or comparison to alternative approaches. Experience papers are also welcome, especially if they highlight insights into any aspect of design, implementation or management of self-* systems that would be of benefit to practitioners and the SASO community. All submissions will be rigorously peer reviewed and evaluated based on the quality of their technical contribution, originality, soundness, significance, presentation, understanding of the state of the art, and overall quality.
Última Actualización Por Dou Sun en 2018-12-01
Coeficiente de Aceptación
AñoEnviadosAceptadosAceptados(%)
2011972121.6%
2010892528.1%
2009742736.5%
20081474228.6%
20071202520.8%
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