[DMANET] Next-Generation Models for Generative AI- DDP 2025- IEEE

CALL FOR PAPERS

Second Workshop on "Next-Generation Models for Generative AI"
(In conjunction with the Fifth International Conference on Digital Data Processing 2025-IEEE)
University of Bedfordshire, Luton. UK
August 18-20, 2025
(IEEE CPS will publish the proceedings, and papers should follow the IEEE template)

AI models use billions of parameters to detect and retrieve text and images. The currently available LL models are being tested for efficiency, while newer models are also being developed. There are many challenges associated with generative AI. The pre-training volume and efficiency are a focus. To leverage the training set with comprehensiveness and accuracy, billions of parameters are injected into the LLM. Current GPTs face criticism, and governments are cautious about their use.
The agenda for the future of Generative AI needs to be clarified. Improved generative tools should be capable of characterising extremist narratives in corpora to reveal different contexts, which may lead to building semantic-rich content for end-users. Evaluating the current models and their outcomes may inform future research. Considering these issues, we organised a workshop to address the theme of next-generation models. The workshop themes include, but are not limited to, the following.

Text, Image, Code, Video, 3D models
Domain-specific models
Impact of Generative AI on Teaching and Learning
Knowledge and Semantic Issues in Generative AI
Future LLM
AI Ethical Issues
AI and NLP
Embedding in AI
Reinforcement learning
Data Support and Datasets in AI
Standards and Benchmarks
Compositional generative models

Workshop Chairs

Gloria Tengyue Li
North China University of Technology
Beijing
China

Simon Fong
University of Macau
Macau

Hathairat Ketmaneechairat
King Mongkut's University of Technology
North Bangkok, Thailand

Important Dates

Submission of Papers: July 15, 2025
Notification of Acceptance/Rejection: August 10, 2025
Camera-ready: August 31, 2025
Registration: August 31, 2025
Conference Dates: August 18-20, 2025
Post-Conference Proceedings Release: November 30, 2025

Paper Submission: http://socio.org.uk/ddp/paper-submission

Contact: ddp@socio.org.uk

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[DMANET] [JAIR Special Track - 7th CFP] Integration of Logical Constraints in Deep Learning

(apologies for cross-posting)

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Special Track on Integration of Logical Constraints in Deep Learning
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Journal of Artificial Intelligence Research (JAIR)
Deadline extension: September 30, 2025
Info: https://www.jair.org/index.php/jair/SpecialTrack-LogicDL
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Track Editors:
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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.
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[DMANET] BRAINS 2025 (Extended Deadline) - Call for Papers, Posters, and Demos - Zurich - November 2025

_Apologies for cross-posting._

*** CALL FOR PAPERS ***

7th Conference on Blockchain Research & Applications for Innovative
Networks and Services

BRAINS 2025

November 18 - 21, 2025

Zurich, Switzerland

(In-person conference)

https://brains.dnac.org/2025/ [1]

- Full and Short Paper submission deadline: July 7, 2025 (Firm)

- Demo/Poster Submission deadline: September 15, 2025

Submissions Link: https://edas.info/N33559 [2]

BRAINS 2025 is technically co-sponsored by IEEE and IEEE ComSoc, all
accepted and presented papers will be submitted for publication in IEEE
Xplore.

The best technical papers presented at the conference will be invited to
submit an extended version for fast-track review in the ACM DLT journal
(Distributed Ledger Technologies: Research and Practice

Decentralized technologies (Web3, Blockchain, Distributed Ledger
Technologies, IPFS) have started to disrupt multiple domains, including
finance and payments but also networks, computing, supply chain,
identity management or Artificial Intelligence with decentralized
learning.

BRAINS conference is dedicated to these advances that could make the
world of networks and services more secure while enabling new
distributed business models.

Areas of interest include, but are not limited to:

Effective challenges for decentralized systems

*

Theoretical contributions to Blockchain, DLT and decentralized storage

*

Distributed consensus and fault tolerance solutions, including
domain-specific consensus

*

Protocols and algorithms

*

Distributed ledger analytics

*

Trade-offs between decentralization, scalability, performance, and
security

*

Zero-Knowledge proofs

*

Layer 2 solutions for scalability and privacy

*

Blockchain interoperability and cross-chain mechanisms

*

Storage solutions and data availability
*

Obstacles to achieve effective decentralization
*

Adversarial attacks and participant's strategic behaviour

Fundamentals of Decentralized Apps, Smart contracts, and chain code:

*

Languages and tooling for dApp development

*

Security, privacy, and forensics

*

Transaction monitoring and analysis

*

Collaboration between on-chain and off-chain code

*

Token Economy, incentives, and protocols

*

Tokenization of Intangible, Real-world Assets

*

Blockchain-defined networking

*

Web3 and distributed storage and computation

Application and service cases of DLT and Smart contracts:

*

Blockchain and AI, federated and decentralized learning

*

Identity management

*

Finance, payments, and fraud detection and prevention

*

Decentralized Finance (DeFi) and payments

*

IoT and cyber-physical systems

*

Smart grids and Industry 4.0, including dataspaces

*

V2X, connected and autonomous vehicles

*

Networking, Edge, and Cloud technologies

*

Blockchain for Beyond 5G and 6G technologies

*

Service or resource marketplaces

*

Public sector Blockchain solutions and infrastructures

*

Results from large collaborative projects on these topics

*

Blockchain for education, public administration, health

*

Blockchain for Business Process and Supply Chain Management

*

Regulations and policies

Submission Guidelines

Submitted papers must represent original material that is not currently
under review in any other conference or journal and has not been
previously published. All submissions should be written in English
following the Two-Column IEEE Conference Format, with a maximum of eight
(8) pages (Full Papers), four (4) pages (Short Papers and work in
progress), or two (2) pages (Poster Papers), including text, figures,
and references. Papers should be submitted through EDAS at:
https://edas.info/N33559 [2]

Double-Blind Review

All submissions must be anonymous. We follow a relaxed double-blind peer
review process: authors are allowed to share their work on platforms
such as arXiv and present it publicly. However, authors should not
mention their own name or affiliation in the submission, or include
obvious references that reveal their identity. A reviewer who has not
previously encountered the work should be able to read the submission
without learning the authors' identities.

If your work is not yet available online (e.g., on arXiv), we recommend
waiting until after the notification of acceptance before posting it
publicly.

For any questions regarding the double-blind policy, please contact the
general co-chairs of BRAINS 2025

Best paper awards

The two best paper awards will be delivered:

*

Best Full Paper Award
*

Best Student Paper Award

Important Dates:

*

Paper Submission deadline: July 7, 2025 (Firm)
*

Notification of Acceptance: September 11, 2025
*

Camera-Ready: September 22, 2025

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#Call for Posters and Demos

BRAINS 2025 will also hold demo and poster sessions in the areas
specified above. Hence, prospective authors are invited to submit their
demo and poster proposals in the form of a 3-page paper in IEEE
conference double-column format.

All submissions must be done electronically through EDAS using the
following link: https://edas.info/N33559 [2]

IMPORTANT DATES

---------------

Demo/Poster Submission deadline: September 15, 2025

Notification of Acceptance: September 30, 2025

Camera-Ready: October 15, 2025

For more details, visit :

https://brains.dnac.org/2025/call-for-demos-posters/ [3]

****************************************************************

TPC Chairs

Quentin Bramas (University of Strasbourg, France)

Sheng-Nan Li (UZH BCC, Switzerland)

Philip Raschke (TU Berlin, Germany )

Marcus Völp (CritiX Lab, Interdisciplinary Centre for Security,
Reliability and Trust, University of Luxembourg, Luxembourg)

General Chairs:

Emmanuel Bertin (Orange Innovation, France)

Claudio J. Tessone (UZH BCC, Switzerland)

Details: https://brains.dnac.org/ [1]

_Looking forward to your submissions! _

Links:
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[1] https://brains.dnac.org/2024/
[2] https://edas.info/N33559
[3] https://brains.dnac.org/2025/call-for-demos-posters/
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[DMANET] Dutch Day on Optimization - save the date!

[SAVE THE DATE] Dutch Day on Optimization 2025 (Tilburg University)

On November 13, 2025 the Dutch Day on Optimization will take place at Tilburg University in the Netherlands. This is the yearly meeting of the Dutch optimization community across the areas operations research, computer science and discrete mathematics. The event highlights recent research of (inter)national speakers.

We are happy to confirm the following keynote speakers:
- Karen Aardal (Delft University of Technology)
- Ahmadreza Marandi (Eindhoven University of Technology)
- Ward Romeijnders (University of Groningen)

A call for early-career researchers (PhD students, postdocs and assistant professors) to submit a contributed talk will follow early September.

Keep an eye on the website of this event for more info: https://www.tilburguniversity.edu/current/events/dutch-day-optimization2025

We hope to see many of you in Tilburg,
Marleen Balvert, Sander Gribling, Pieter Kleer, Sven Polak & Martijn Schoot Uiterkamp

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