Spain

&quot;Sensors are multiplying on machines, networks and living things. Reasoning and extracting knowledge from this huge amount of data is among nowadays challenges. In data- based diagnostic applications, large amounts of data are often available but a key issue is that data remain unlabelled because labelling would require too much time and imply prohibitive costs. The different situations, e.g. normal or faulty, must hence be learned from the data. Clustering methods, also qualified as unsupervised classification methods, can then be used to create groups of samples according to some similarity criterion. The different groups can supposedly be associated to different situations.
Numerous algorithms have been developed in recent years for clustering numeric data but these methods are not applicable to qualitative (categorical) data. However, in many application domains, qualitative features are key to properly describe the different situations. This paper presents DyClee-C, an extension of the numeric feature based DyClee algorithm to qualitative data. DyClee-C is applied to two data sets: a soybean data set to diagnose the disease soybean plants and a breast cancer data set to assess the current diagnosis in terms of recurrence events and prognose possible relapse.&quot;

ECAI 2020

DyClee-C: a clustering algorithm for qualitative data based diagnosis

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

"Sensors are multiplying on machines, networks and living things. Reasoning and extracting knowledge from this huge amount of data is among nowadays challenges. In data- based diagnostic applications, large amounts of data are often available but a key issue is that data remain unlabelled because labelling would require too much time and imply prohibitive costs. The different situations, e.g. normal or faulty, must hence be learned from the data. Clustering methods, also qualified as unsupervised classification methods, can then be used to create groups of samples according to some similarity criterion. The different groups can supposedly be associated to different situations.
Numerous algorithms have been developed in recent years for clustering numeric data but these methods are not applicable to qualitative (categorical) data. However, in many application domains, qualitative features are key to properly describe the different situations. This paper presents DyClee-C, an extension of the numeric feature based DyClee algorithm to qualitative data. DyClee-C is applied to two data sets: a soybean data set to diagnose the disease soybean plants and a breast cancer data set to assess the current diagnosis in terms of recurrence events and prognose possible relapse."

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.

Abstract: Ethics is an ancient matter for human kind, from the origin of civilizations ethics have been related with the most relevant human concerns and determined cultures. Ethics was initially related to religion, politics and philosophy to then be fragmented into specific communities of practice. The undergoing digital revolution enabled by Artificial Intelligence and Data are bringing ethical wicked problems in the social application of these technologies. However, a broader perspective is also necessary. We now face global and highly dynamics challenges that affect groups and individuals, specially those that are most vulnerable. Individual-oriented ethics are not longer sufficient, the new ethic has to consider the several scales in which the current complex society is organized and the interconnections between different systems. Ethics should also give a response to the systemic changes in behavior produced by external factors and threats. Furthermore, AI and digital technologies are global and make us more connected and smart but also more homogeneous, predictable and ultimately controllable. Ethic must take a stand to preserve and keep promoting individuals rights and uniqueness and cultural heterogeneity. Digital technologies have to the foundation for new models of society and help ensure ethical individual and collective values. For these reasons science has to be at the core of the new ethic as it helps understand the complex world. Finally, AI has advanced through the ambition to humanize matter, so we should expect ethics to give a response to the future status of machines and their interactions with humans."

Digital Frameworks For A New Ethic

Combination of AI, semi-physical models, and in-situ data for Earth Observation applications

AIDA: Artificial Intelligence Data Analysis 

" Pre-training large-scale language models (LMs) requires huge amounts of text corpora. 
 LMs for English enjoy the ever-growing corpora of diverse language resources.
 However, less-resourced languages and their mono- and multilingual LMs often struggle to obtain bigger datasets.
  A typical approach in this case implies using machine translation of English corpora to a target language.
  In this work, we study the caveats of applying directly translated corpora for fine-tuning LMs for downstream natural language processing tasks and demonstrate that careful curation along with post-processing leads to improved performance and overall LMs robustness."

How Machine Translated Corpora Affects Language Models in MRC Tasks

Structural Onomatology for Username Generation: A Partial Account

Applying LSTM Networks to Predict Multi-drug Resistance Using Binary Multivariate Clinical Sequences

Modelling an Intelligent Tutoring System based on Supervised Behaviour Biometrics

On Multi-Agent Coordination of Agri-Robot Fleets

I will briefly survey recent and expected developments in AI and their implications. Some are enormously positive, while others, such as the development of autonomous weapons and the replacement of humans in economic roles, may be negative. Beyond these, one must expect that AI capabilities will eventually exceed those of humans across a range of real-world-decision making scenarios. Should this be a cause for concern, as Elon Musk, Stephen Hawking, and others have suggested? And, if so, what can we do about it? While some in the mainstream AI community dismiss the issue, I will argue that the problem is real and that the technical aspects of it are solvable if we replace current definitions of AI with a version based on provable benefit to humans.

How not to destroy the world with AI

Earth observation sensors deliver ever-expanding collections of geospatial data at multiple resolutions (spatial, temporal and thematic or spectral). Efficient tools to extract knowledge from these collections are currently missing. Here we present the first release of Clustering geo-Data Cubes (CDC), a Python package to cluster geospatial data cubes by explicitly considering their dimensionality. CDC has three main hallmarks: 1/ it is based on innovative co- and tri-clustering methods that identify groups of pixels with similar spatio-temporal and/or thematic information by simultaneously considering all the dimensions of the data. This overcomes a major limitations of traditional clustering approaches, which analyze each dimension separately; 2/ it provides refined clusters by re-grouping the results obtained from co- and/or tri-clustering. These refined clusters better capture the patterns present in the data and represent a more automatic approach to analyze geospatial data cubes because the number of clusters is automatically chosen via an optimization procedure; and 3/ it allows users to run tasks efficiently by either using NumPy’s threading capabilities or Dask’s parallel computing power. Hence, CDC is a scalable package that can analyze both small and big geospatial data cubes. These hallmarks are showcased through several case studies

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