Spain

ECAI 2020

Onboard and ground-based automated scheduling for the Mars 2020 Rover Mission

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.

technical paper

"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."

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

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.

Onboard and ground-based automated scheduling for the Mars 2020 Rover Mission

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Onboard and ground-based automated scheduling for the Mars 2020 Rover Mission

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

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A Deep Learning Approach to Space Weather Forecasting for Orbital Prediction

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