Collaborating Authors


Spacewell Acquires DEXMA, Provider of AI-Powered Energy Intelligence Software


MUNICH, Germany and ANTWERP, Belgium, Dec 14, 2020 – The Nemetschek Group, one of the world's leading software providers for the architecture, engineering, construction, and building operations (AECO) industry, announced that its subsidiary Spacewell – headquartered in Antwerp, Belgium – has acquired 100% of DEXMA. Based in Barcelona, Spain, DEXMA is a fast-growing provider of innovative SaaS solutions with artificial intelligence and machine learning capabilities for energy data management. The company enables over 4,000 customers in 30 countries worldwide to effectively measure, monitor, and manage their energy consumption and costs. "Buildings account for 30 percent of our total energy use and 28 percent of global carbon emissions. This acquisition is a huge benefit for our customers who are aiming to become more sustainable in their operations. Energy management is an important element in creating truly autonomous buildings that automatically adapt their behaviors to the occupants and stakeholders," says Koen Matthijs, Chief Division Officer, Operate & Manage Division at the Nemetschek Group.

Financial Document Causality Detection Shared Task (FinCausal 2020) Machine Learning

We present the FinCausal 2020 Shared Task on Causality Detection in Financial Documents and the associated FinCausal dataset, and discuss the participating systems and results. Two sub-tasks are proposed: a binary classification task (Task 1) and a relation extraction task (Task 2). A total of 16 teams submitted runs across the two Tasks and 13 of them contributed with a system description paper. This workshop is associated to the Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), held at The 28th International Conference on Computational Linguistics (COLING'2020), Barcelona, Spain on September 12, 2020.

Public road CAV testing by Catalonia Living Lab and Aurora Snowbox JV


Catalonia Living Lab, a public-private project for the development of CAVs, and Aurora Snowbox, the Finnish testing organization, have reached an agreement to collaborate on test drives of connected and automated vehicles on public roads. Noting that there is an imminent need for public road test drives in all weather conditions (including summer and winter), the organizations have agreed to team up for the next two years. The alliance is expected to be mutually beneficial in strengthening the services provided by both entities and expanding their activities in the field of connected and automated vehicles. As a first step in their collaboration, both companies have aligned their service portfolios and marketing strategies to ensure an identical user experience on both test beds. A strategic analysis performed within the framework of Catalonia Living Lab resulted in the identification of a series of objectives for testing of CAVs on public roads.

AI may not predict the next pandemic, but big data and machine learning can fight this one


In April, at the height of the lockdown, computer-science professor Àlex Arenas predicted that a second wave of coronavirus was highly possible this summer in Spain. At the time, many scientists were still confident that high temperature and humidity would slow the impact and spread of the virus over the summer months, as happens with seasonal flu. Unfortunately, Arenas' predictions have turned out to be accurate. Madrid, the Basque country, Aragon, Catalonia, and other Spanish regions are currently dealing with a surge in COVID-19 cases, despite the use of masks, hand-washing and social distancing. Admittedly, August is not as bad as March for Spain, but it's still not a situation many foresaw.

Elephants vs trains: This is how AI helps ensure they don't collide


Michel André has good listening skills. He's used them to study the sounds of the marine environment, why sperm whales collide with ferries in the Canary Islands, and the acoustic problems that pink dolphins face in the Amazon river. Last year, he and his team from the Laboratory of Applied Bioacoustics from Polytechnic University of Catalonia (UPC)-BarcelonaTech in Vilanova i la Geltrú were called in solve a problem in India. Records show that the Siliguri-Jalapaiguri railway line has the largest number of fatal collisions involving elephants in the country. Over the past 10 years, trains on this line have struck and killed more than 200 elephants.

Leveraging the Power of AI for European Cultural Heritage


BARCELONA, Spain, July 28, 2020 -- AI will now be well-versed in cultural heritage due to a new EU-funded project called Saint George on a Bike. Composed of researchers from the Barcelona Supercomputing Center (BSC) and Europeana Foundation, the project has begun training natural language processing and deep learning algorithms in culture, symbols, and historical context with the aim of automatically generating rich metadata for hundreds of thousands of images from various European cultural heritage repositories. Training AI to be aware of cultural heritage contexts is not as simple as teaching it to identify different objects in a picture. Saint George on a Bike is fine-tuning the algorithms so that it "thinks" in context and according to time parameters. "The AI we are developing will be able to tell whether a painting shows Saint George on a horse or a bike," said Maria Cristina Marinescu, senior researcher at BSC and coordinator of the Saint George on a Bike project. "This is not as easy as it sounds because the shapes are similar.

5G And Machine Learning: Taking Cellular Base Stations From Smart To Genius


An illuminated 5G sign hangs behind a weave of electronic cables on the opening day of the MWC ... [ ] Barcelona in Barcelona, Spain, on Monday, Feb. 25, 2019. At the wireless industry's biggest conference, over 100,000 people are set to see the latest innovations in smartphones, artificial intelligence devices and autonomous drones exhibited by more than 2,400 companies. At the core of this evolutionary step is the use of machine learning algorithms. The ability to be more dynamic with real-time network optimization capabilities such as resource loading, power budget balancing and interference detection is what made networks "smart" in the 4G era. While there are many uses of machine learning across all layers of a 5G network from the physical layer through to the application layer, the base station is emerging as a key application for machine learning.

Predictive Analytics for Water Asset Management: Machine Learning and Survival Analysis Machine Learning

Understanding performance and prioritizing resources for the maintenance of the drinking-water pipe network throughout its life-cycle is a key part of water asset management. Renovation of this vital network is generally hindered by the difficulty or impossibility to gain physical access to the pipes. We study a statistical and machine learning framework for the prediction of water pipe failures. We employ classical and modern classifiers for a short-term prediction and survival analysis to provide a broader perspective and long-term forecast, usually needed for the economic analysis of the renovation. To enrich these models, we introduce new predictors based on water distribution domain knowledge and employ a modern oversampling technique to remedy the high imbalance coming from the few failures observed each year. For our case study, we use a dataset containing the failure records of all pipes within the water distribution network in Barcelona, Spain. The results shed light on the effect of important risk factors, such as pipe geometry, age, material, and soil cover, among others, and can help utility managers conduct more informed predictive maintenance tasks.

Introduction to Data Science


This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses. Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain.

Artificial Intelligence – Idees


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