Spain

Agent-Based Models (ABMs) can be used to numerically simulate highly non-linear phenomena that emerge from local interactions of multiple, independent entities. ABMs are often used to understand dynamic processes such as tissue regeneration in biomedicine, in which group-level patterns and space-time constraints are of interest. High fidelity ABMs, especially those in biomedical applications, usually have a large number of parameters, creating substantial uncertainty and high dimensionality at both local and global levels. Uncertainty
 analysis (output variance estimation) and sensitivity analysis are essential steps in investigating model robustness. In addition to allocating uncertainty to each parameter, sensitivity analysis can also be used to reduce ABM dimensionality for effective model calibration and optimization. I discuss common sensitivity analysis methods that have been used to decrease the number of parameters in complex ABMs, highlighting Garg et al. (2019)’s paper, where random forests – a non-parametric ensemble algorithm – are used as a sensitivity analysis method for ranking parameters in a biomedical ABM.

ECAI 2020

Sensitivity Analysis for Dimensionality Reduction in Agent-Based Modeling

dimensionality reduction

sensitivity analysis

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
The opportunity to present at NAACL should not depend on a researcher’s travel budget, or their family status. This is why it is so important to make virtual participation at NAACL as good an experience as possible – but we want to also provide a good experience for in-person participants. As a community, we are still working out the best way to do that. This year at NAACL, we are trying out a big virtual poster session ahead of the conference, with the hope that this will make make for a lively and interactive experience. At the same time, we are reducing virtual oral presentations, which seem to be particularly tricky to make to work well. A big thanks to the NAACL program chairs and to Luciana Benotti for all their ideas and work to improve the virtual experience. And participants, virtual as well as in-person: Please let us know what worked for you and what didn’t, so we can continue to improve hybrid conferences. 
 
I have been lucky to work with many amazing people. Without their insight, dedication and patience, and without the many hours of work they put in, NAACL would not have been possible. A huge thank you to the program chairs Helena Gomez, Kevin Duh, and Steve Bethard – you are the best! 
 
Finally, I would like to thank all authors, invited speakers and panelists, area chairs and reviewers, the volunteers organizing and chairing sessions, and all attendees, in-person and virtual. Thank you for helping us make NAACL 2024 come to life. 
 
Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
*You can read the full Welcome message in the [Conference Handbook (downloadable)](https://drive.google.com/file/d/1H1NvW0VASQjkSCYgw3mr-3l4yYDSxz6B/view?usp=sharing)*

To access the event page you need to register [**here.**](http://acl.swoogo.com/naacl2024) 
Your access to the event page is limited based on your registration type. If you registered for workshops only, you will gain access to full workshops content on the day of the workshops program.

Please register!

NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

poster

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

A picture is worth a thousand words”, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed, reveal different and complementary information that, if combined, result in more information than the
 sum of that contained in the single media. The combination of visual and textual information can be obtained by linking the entities mentioned in the text with those shown in the pictures. To further integrate this with agent background knowledge, an additional step is necessary. That is, either finding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the
 picture or, extending the knowledge base with the newly discovered entities. We call this complex task Visual-Textual-Knowledge Entity Linking (VTKEL). In this paper, we precisely define the VTKEL task and present two datasets composed of 1k and 30k pictures, annotated with visual and textual entities, and linked to the YAGO ontology. Successively, we develop the first unsupervised algorithm for the solution of the VTKEL task. The evaluation of the algorithm shows promising results on both 1k and 30k VTKEL datasets.

VT-LINKER: Visual-Textual-Knowledge Entity Linker

In this paper, we present an approach for generating dialogues for characters within the context of computational narratives using personality-based features for deep neural networks. The approach integrates the requirements of both narrative genres and personality traits for the definition of character-based stylistic models. The modelling of characters’ features from existing datasets of complete stories permits the generation of personality-rich character dialogues. We present early results from an evaluation based on a sample of characters’ personality traits across different narrative genres, demonstrating variability in the resulting dialogues.

Influence of Personality-based Features for Dialogue Generation in Computational Narratives

Inter-Slice image augmentation based on fame interpolation for boosting medical image segmentation accuracy

Strong Statistical Correlation Revealed by Quantum Entanglement for Supervised Learning 

Black-box Adversarial Attacks Against Deep Learning Based Malware Binaries Detection with GAN

Explainable Inference in the FRANK Query Answering System

Algorithms are here to stay. They are transforming the way we live and work and increasingly replace cognitively human ways of making decisions. The so called “algorithmocracy” or the ecosystem that we all now inhabit, where algorithms govern many aspects of our behavior, has the potential to bias and be deployed at large scales. Because the automation of decisions by algorithms promise efficiency and resource maximization, AI technologies can be used to meet the 17 Sustainable Development Goals and its 169 targets. However, we must ensure that its application is humane and ethical and within a normative framework guided by basic ethical principles. Here, we try to clarify what people really mean by “ethics” in AI ethics and elucidate a roadmap to implement “ethics by design” standards to establish satisfactory measures of fairness, transparency and explainability of algorithms when use for social good as for example the promotion of Sustainable Development Goals

AI ethics for Sustainable Development Goals

The Artificial Intelligence (AI) revolution brings unique opportunities to help improve the life of every citizen, but also new threats. AI is transforming not only business and every aspect of our lives but also the social fabric we inhabit, including the governments and public organisations. In this Digital Transformation process, we find that many decision-makers, politicians and public servants feel lost on how to apply AI, how to benefit from data analytics and how to transform to improve not only the existing services but the administration too. In this Digital Transformation process is crucial that administrations take into consideration the ethical dimension of the use of AI in data analytics. Public administrations need a vision of how to integrate AI ethically to guarantee fair and unbiased services for the citizens. In this paper, we explore the AI Digital transformation in public services, the fAIr initiative and introduce a current application to a real case in Jalisco, Mexico. The explosion of AI technologies with connected benefits and risks are at the centre of a global discussion.

To be fAIr or not be. Using AI for the good of citizens.

The alarming trend of increasing mental health problems and the global inability to find effective ways to address them is hampering both individual and societal prosperity. Barriers to access mental health care are many and high, ranging from socio-economic inequalities to personal stigmas. This gives technology, especially technology based in artificial intelligence, the opportunity to help alleviate the situation and offer unique solutions. The multi- and interdisciplinary research on persuasive technology, which attempts to change behavior or attitudes without deception or coercion, shows promise in improving well-being, which results in increased equality. This paper presents such systems with a brief overview of the field, and offers general, technical and critical thoughts on the implementation as well as impact. We believe that such technology can complement existing mental health care solutions to reduce inequalities in access as well as inequalities resulting from the lack of it.

Advancing Towards (Mental Health Care) Equality with Persuasive Technology for Mental Health

Digital discrimination is a form of discrimination whereby users are automatically treated unfairly, unethically or just differently based on their personal data by a machine learning (ML) system. Examples of digital discrimination include low-income neighbourhood’s targeted with high-interest loans or low credit scores, and women being undervalued by 21% in online marketing. Recently, different techniques and tools have been proposed to detect biases that may lead to digital discrimination. These tools often require technical expertise to be executed and for their results to be interpreted.  To allow non-technical users to benefit from ML, simpler notions and concepts to represent and reason about digital discrimination are needed. In this paper, we use norms as an abstraction to represent different situations that may lead to digital discrimination. In particular, we formalise non-discrimination norms in the context of ML systems and propose an algorithm to check whether ML systems violate these norms.

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Next from ECAI 2020

VT-LINKER: Visual-Textual-Knowledge Entity Linker

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