[DMANET] *** CALL FOR PAPERS *** (CloudAM 2025) 14th International Workshop on Cloud and Edge Computing, and Applications Management

*** CALL FOR PAPERS ***
14th International Workshop on Cloud and Edge Computing, and Applications Management (CloudAM 2025)
In conjunction with the 18th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2025)
01-04 December, 2025
Nantes, France

https://sites.google.com/unizar.es/cloudam2025

### Background ###
Cloud computing, virtualization and virtualized (eScience) applications have been generating substantial interest in the community for more than a decade, and this interest enabled the development of several architectures, techniques, and mechanisms for cloud and edge computing. With the actual deployment of edge computing infrastructures, exemplified with Fog Computing and Mobile Cloud Computing, the interest in these developments is still prominent. Cloud and edge infrastructures can work together to fulfill requirements from a variety of applications, composing the so-called Cloud Continuum to the edge. Clouds must provide appropriate levels of performance to large groups of diverse users, and those clouds are accessed through virtualized wide area networks, where edge/fog devices can act as a first layer of computing capacity closer to the user. Management systems are essential for that and thereby for the future success of the fog-cloud hierarchy. New systems, methods, and approaches for cloud and edge computing, virtualization and (eScience) applications management are to be discussed at this workshop.

Overall topics of interest include but are not limited to:

- Autonomic cloud computing
- Big data / complex event processing in the cloud-edge hierarchy
- Business models for cloud, fog, and edge computing
- Cloud, fog, and edge APIs
- Cloud, fog, and edge computing environments
- Cloud, fog, and edge data management
- Cloud, fog, and edge load balancing
- Cloud, fog, and edge scalable monitoring
- Cloud, fog, and edge (micro)services orchestration
- Cloud, fog, and edge scheduling
- Computing Continuum - IoT/Smart Cities integration
- Data center management
- Green cloud computing and energy consumption optimization
- Environmental impacts of Clouds
- Hybrid clouds
- Management of virtual slices and virtualized hardware resources
- Mobile clouds and mobile edge computing
- Multi-cloud/Inter-clouds
- Social clouds
- Network-specific mechanisms for optimized cloud access
- Performance modeling & evaluation
- Policy-driven service/resource life-cycle management
- QoS/QoE management
- Security and privacy in the cloud-edge hierarchy
- Self-distributed systems in the cloud-edge hierarchy
- Scientific workflows management and optimization

Important Dates
Paper submission due: 15 September, 2025 (Extended)
Notification to authors: 07 October, 2025
Camera-ready papers due: 20 October, 2025
Workshop date: 01-04 December 2025

Submission Guidelines
The CloudAM workshop invites authors to submit original and unpublished work. Manuscripts are submitted in PDF format and should not exceed 6 pages single-spaced double-column, using the ACM format. Additional pages might be purchased upon the approval of the proceedings chair.

Submission Link on Easychair: https://easychair.org/conferences/?conf=cloudam2025

Submission is double-blind. This means that author information shouldn't be indicated in any part of the submission including acknowledgements, citations, discussion of related work, etc. that would make the authorship apparent. Submissions containing author identifying information may be subject to rejection without review.

Submission requires the willingness of at least one of the authors to register as author, non-student rate and present the paper in person.

At least one author of each paper must be registered for the conference for the paper to be published in the proceedings. The conference proceedings will be published by ACM and made available online via the IEEE Xplore Digital Library and ACM Digital Library.

Please check the CloudAM website for updated information: https://sites.google.com/unizar.es/cloudam2025

WORKSHOP TECHNICAL COMMITTEE
Abderrahmane Maaradji, UDST, Qatar
Amel Benna, CERIST, Algeria
Agustin Caminero, Universidad Nacional de Educacion a Distancia, Spain
Christophe Cerin, Univ. Paris13, France
Claudio Geyer, Federal Univeristy of Rio Grande do Sul, Brazil
Daniel Balouek, Inria, France
Eddy Caron, Univ. Lyon1, France
Felix Freitag, Universitat Politècnica de Catalunya, Spain
Fabio Costa, Federal University of Goias, Brazil
Gleb Radchenko, South Ural State University, Russia
Helio Guardia, Federal University of São Carlo, Brazil
Ioan Petri, Cardiff University, UK
Jose Angel Bañares, University of Zaragoza, Spain
Jeremy Mechouche, Devoteam, France
Luciano Baresi, Politecnico di Milano, Italy
Michael Bauer, University of Western Ontario, Canada
Pedro Valderas, UPV, Spain
Vladimir Vlassov, KTH Royal Institute of Technology, Sweden
Wilson Akio Higashino, Google, USA

WORKSHOP CHAIRS
Carlo Puliafito - University of Pisa, Italy
Laurent Lefevre - Inria, France
Luiz Bittencourt - Universidade Estadual de Campinas (UNICAMP), Brazil
Zakaria Benomar - Thales cortAIx Labs, France

WORKSHOP HONORARY CHAIRS
Bruno Schulze - LNCC, BR
Rafael Tolosana-Calasanz - UNIZAR, ES

**********************************************************
*
* Contributions to be spread via DMANET are submitted to
*
* DMANET@zpr.uni-koeln.de
*
* Replies to a message carried on DMANET should NOT be
* addressed to DMANET but to the original sender. The
* original sender, however, is invited to prepare an
* update of the replies received and to communicate it
* via DMANET.
*
* DISCRETE MATHEMATICS AND ALGORITHMS NETWORK (DMANET)
* http://www.zaik.uni-koeln.de/AFS/publications/dmanet/
*
**********************************************************

[DMANET] [JAIR Special Track - 9th CFP] Integration of Logical Constraints in Deep Learning - deadline approaching

(apologies for cross-posting)

======================================================================
Special Track on Integration of Logical Constraints in Deep Learning
----------------------------------------------------------------------
Journal of Artificial Intelligence Research (JAIR)
Deadline: September 30, 2025
Info: https://www.jair.org/index.php/jair/SpecialTrack-LogicDL
======================================================================
Track Editors:
----------------------------------------------------------------------
Alessandro Abate, University of Oxford, U.K.
Eleonora Giunchiglia, Imperial College London, U.K.
Bettina Könighofer, Graz University of Technology, Austria
Luca Pasa, University of Padova, Italy
Matteo Zavatteri, University of Padova, Italy
======================================================================

Overview:

Over the last few years, the integration of logical constraints in Deep Learning models has gained significant attention from research communities for its potential to enhance the interpretability, robustness, safety, and generalization capabilities of these models. This integration opens the possibility of incorporating prior knowledge, handling incomplete data, and combining symbolic and subsymbolic reasoning. Moreover, the use of logical constraints improves generalization, formal verification, and ethical decision-making. The versatility of logical constraint integration spans diverse domains, presenting both research challenges and opportunities. In recent times, there has been a growing trend in incorporating logical constraints into deep learning models, especially in safety-critical applications. Looking ahead, challenges in this field extend to the development of Machine Learning models that not only incorporate logical constraints but also provide robust assurances. This involves ensuring that AI systems adhere to specific (temporal) logical or ethical constraints, offering a level of guarantees in their behavior.

Thus, this special track seeks submissions on the integration of logical constraints into deep learning approaches. We are particularly interested in the following broad content areas.

- Formal verification of neural networks is an active area of research that has been proposing methods, tools, specification languages (e.g., VNNLIB), and annual competitions (e.g., VNN-COMP) devoted to verify that a neural network satisfies a certain property typically given in (a fragment) of first order logic.

- Synthesis aims at synthesizing neural networks that are compliant with some given constraint. Approaches to achieve this aim range from modifying the loss function in the training phase (i.e., soft constraint injection) to exploit counterexample guided inductive synthesis (CEGIS).

- Monitoring: Logical constraints can be used to mitigate and/or neutralize constraint violations of machine learning systems when formal verification and synthesis are not possible. Shielding techniques intervene by changing the output of the network when a constraint is being violated. Runtime monitoring can be used to anticipate failures of AI systems without modifying them.

- Explainability: Automated learning of formulae and logical constraints from past executions of the system provides natural explanations for neural network predictions and poses another avenue for future research. Formulae and constraints offer a high degree of explainability since they carry a precise syntax and semantics, and thus they can be "read" by humans more easily than other explainability methods.

This special track aims to explore and showcase recent advancements in the integration of logical constraints within deep learning models, spanning the spectrum of verification, synthesis, monitoring and explainability, by considering exact and approximate solutions, online and offline approaches. The focus will also extend to encompass innovative approaches that address the challenges associated with handling logical constraints in neural networks.

Submissions:

This special track seeks contributions that delve into various aspects of logic constraint integration in deep learning, including, but not limited to:

- Learning with logical constraints
- Enhancing neural network expressiveness for logical constraints
- Formal verification (certification) of neural networks
- Automated synthesis of certified neural networks, or of AI systems with neural nets
- Decision making: Strategy/policy synthesis for AI systems with neural networks
- Runtime monitoring of AI systems
- Learning of (temporal) logic formulae for explainable and interpretable AI
- Scalability challenges in neural networks with logical constraints
- Real-world applications of neural networks with logical constraints
- Enhancing model explainability via logical constraints
- Design of neural networks under temporal logical requirements

Pertinent review papers of exceptional quality may also be considered.
**********************************************************
*
* Contributions to be spread via DMANET are submitted to
*
* DMANET@zpr.uni-koeln.de
*
* Replies to a message carried on DMANET should NOT be
* addressed to DMANET but to the original sender. The
* original sender, however, is invited to prepare an
* update of the replies received and to communicate it
* via DMANET.
*
* DISCRETE MATHEMATICS AND ALGORITHMS NETWORK (DMANET)
* http://www.zaik.uni-koeln.de/AFS/publications/dmanet/
*
**********************************************************