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CAIN 2024 : 3rd International Conference on AI Engineering – Software Engineering for AI

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Link: https://conf.researchr.org/track/cain-2024/cain-2024-call-for-papers
 
When Apr 14, 2024 - Apr 15, 2024
Where Lisbonne, Portugal
Abstract Registration Due Nov 10, 2023
Submission Deadline Nov 17, 2023
Notification Due Jan 10, 2024
Final Version Due Jan 28, 2024
Categories    ai engineering   artificial intelligence
 

Call For Papers

Call for Papers
We invite submissions of research and experience papers in two categories:

Long paper: Long papers are research or experience papers describing research results, case studies, or insights from industry experience. A research or experience full paper is up to 10 pages plus a maximum of 2 pages for references.

Short paper: Papers describing new challenges, new research results, visionary ideas, or experiences from, or in cooperation with, practitioners are welcome as short papers. In progress research with interim results is also appropriate as a short paper. A short paper is up to 5 pages plus a maximum of 1 page for references. The paper submissions will undergo a review process with three independent reviews and a virtual PC discussion. Acceptance criteria include contribution to the field of software engineering for AI, novelty, research and industrial relevance, soundness, and results. The accepted full and short papers will be published in ACM Proceedings.

Scope and Topics of Interest
The area of interest for CAIN is Software Engineering for AI — improving the development of AI-based systems throughout the full life cycle. Topics include but are not limited to:

* System and software requirements and their relationship to AI/ML modeling.
* Data management ensuring relevance and efficiency related to business goals.
* System and software architecture for AI-enabled systems.
* Integration of AI and software development processes into the AI system development life cycle, including continuous integration and deployment, and system and software evolution.
* Ensuring and managing system and software nonfunctional properties and their relationship to AI/ML properties, including runtime properties such as performance, safety, security, and reliability; and life-cycle properties including reusability, maintainability and evolution.
* Collaboration, organizational, and management practices for a successful development of AI-enabled systems.
* Building effective infrastructures to support development of AI systems and components.

Note: Submissions that report strictly on data science or model development without any connection to software engineering and AI-enabled systems will be desk-rejected. As stated earlier, there are many venues for those papers where authors would get much more valuable and relevant feedback.

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