Spain

ECAI 2020

CIDetector: Semi-Supervised Method for Multi-Topic Confidential Information Detection

confidential information detection

semi-supervised learning

deep learning

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

technical paper

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.

Improving VAE generations of multimodal data through data-dependent conditional priors

Explanations for Ontology-Mediated Query Answering in Description Logics

A Fuzzy Inference System for a Visually Grounded Robot State of Mind

Neural Network Heuristics for Classical Planning: A Study of Hyperparameter Space

Maximal Information Propagation with Budgets

On the Complexity of Constructive Control under Nearly Single-Peaked Preferences

RPN: A Residual Pooling Network for Efficient Federated Learning

Logic is responsible for scientific progress in many disciplines.  In particular, computer science and AI would be impossible  without it. Classical logics have long been accepted as a normative
 framework for human reasoning to capture correct reasoning. However,  many psychological findings such as the Wason Selection Task  have demonstrated that classical logic cannot serve as a possible  descriptive language for the human inference process, which is the   aim of what we call a cognitive logic. Recently, some nonmonotonic  logics have been employed to explain human inferences for some  well-known examples. In this paper, we discuss possible features of
 cognitive logics, present first results, and highlight new challenges.

Cognitive Logics: Features, Formalisms, and Challenges

This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computation, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations

Adaptive learning in multiagent systems for automated energy contacts negotiation

In the past few years, several large companies have published ethical principles of Artificial Intelligence (AI). National governments, the European Commission, and inter-governmental organizations have come up with requirements to ensure the good use of AI. However, individual organizations that want to join this effort, are faced with many unsolved questions. This paper proposes guidelines for organizations committed to the responsible use of AI, but lack the required knowledge and experience. The guidelines consist of two parts: i) helping organizations to decide what principles to adopt, and ii) a methodology for implementing the principles in organizational processes. In case of future AI regulation, organizations following this approach will be well- prepared.

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Improving VAE generations of multimodal data through data-dependent conditional priors

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Progressive Class Semantic Matching for Semi-supervised Text Classification

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