Are you interested in the latest trending topics in the area of AI? Do you want to get answers from the AI experts for your pending questions? This autumn you can look forward to the following two resonating topics: AI Ethics and AI Curriculum. The events will be live streamed via the CLAIRE YouTube channel. Everyone is welcome to actively participate and ask any burning questions through the chat. The CLAIRE AQuA – AI Ethics will take place on 25 October 2023 14:00 CEST and you can look forward to the following speakers: Frederic Heymans, Kevin Baum, Alex Moltzau, and others.

By combining the undecimated wavelet transform within a Word Embedded Semantic Marginal Autoencoder (WESMA), this research study provides a novel strategy for improving security measures and denoising multiple languages. The incorporation of these strategies is intended to address the issues of robustness, privacy, and multilingualism in data processing applications. The undecimated wavelet transform is used as a feature extraction tool to identify prominent language patterns and structural qualities in the input data. The proposed system may successfully capture significant information while preserving the temporal and geographical links within the data by employing this transform. This improves security measures by increasing the system's ability to detect abnormalities, discover hidden patterns, and distinguish between legitimate content and dangerous threats. The Word Embedded Semantic Marginal Autoencoder also functions as an intelligent framework for dimensionality and noise reduction. The autoencoder effectively learns the underlying semantics of the data and reduces noise components by exploiting word embeddings and semantic context. As a result, data quality and accuracy are increased in following processing stages. The suggested methodology is tested using a diversified dataset that includes several languages and security scenarios. The experimental results show that the proposed approach is effective in attaining security enhancement and denoising capabilities across multiple languages. The system is strong in dealing with linguistic variances, producing consistent outcomes regardless of the language used. Furthermore, incorporating the undecimated wavelet transform considerably improves the system's ability to efficiently address complex security concerns

The new 19,200 square meter site brings together three areas of expertise (energy, high-performance computing (HPC) and artificial intelligence) and 1,000 Atos employees who were previously based at Grenoble and the historic site in Echirolles. With a capacity of up to 1,320 people, the new site will be able to accommodate the 250 new hires planned for 2023. With this new campus, Atos reinforces its innovation strategy as the new European R&D center will promote local excellence on a worldwide scale. Funded by the Auvergne-Rhône-Alpes Region, through the European Regional Development Fund (ERDF), and by Grenoble-Alpes Métropole, this center and its 300 employees are mainly focused on Artificial Intelligence. Atos' teams have already partnered with the MIAI@Grenoble Alpes program from the Grenoble Interdisciplinary Institute of Artificial Intelligence (3IA), which has received government support.

Information and communication technologies (ICTs) are revolutionizing the world, and the health sector is no exception. ICTs are considered a strategic factor in improving individuals' health and guaranteeing a high-quality, modern and sustainable healthcare system. One of the most promising technologies is artificial intelligence (AI), which is capable of creating and training computer systems to make automatic data-based decisions. Two recent studies in which researchers from the Universitat Oberta de Catalunya (UOC) and the August Pi i Sunyer Biomedical Research Institute (IDIBAPS) have been involved highlight the potential of applying AI to the fields of image processing, and bioinformatics and genetics, respectively. "There are a wide range of applications for big data and artificial intelligence in CT scans, X-rays, ultrasound and magnetic resonance imaging," said Jordi Casas Roma, a researcher in the ADaS Lab research group at the eHealth Center, and a member of the Faculty of Computer Science, Multimedia and Telecommunications and director of the Master's Degree in Data Science at the UOC.

On March 8, International Women's Day (IWD) we celebrate the political, socioeconomic and cultural achievements of women and the women right's movement towards gender equality. "Whilst the social and political rights of women are greater in some places than others, there is no country where gender equality has been achieved" says Mary Evans, professor at the London School of Economics and Political Science in her book "The persistence of gender inequality" (Polity Press 2017). In 2022 this situation has not changed either globally or at the European level as indicated in the EU Gender Equality index for 2020 where the average of the EU is 67.4% and the maximum is Sweden with 83.8%. Although there has been a clear commitment from the European Union on gender equality (specially in innovation and science), there are still structural forms of inequality that must be challenged and changed. It is not the aim of this article to analyse or comment on those, but to show what is being done and is available, especially in the European Union, for us to contribute as individuals and as a community towards gender equality in the field of robotics.

Stock Market can be easily seen as one of the most attractive places for investors, but it is also very complex in terms of making trading decisions. Predicting the market is a risky venture because of the uncertainties and nonlinear nature of the market. Deciding on the right time to trade is key to every successful trader as it can lead to either a huge gain of money or totally a loss in investment that will be recorded as a careless trade. The aim of this research is to develop a prediction system for stock market using Fuzzy Logic Type2 which will handle these uncertainties and complexities of human behaviour in general when it comes to buy, hold or sell decision making in stock trading. The proposed system was developed using VB.NET programming language as frontend and Microsoft SQL Server as backend. A total of four different technical indicators were selected for this research. The selected indicators are the Relative Strength Index, William Average, Moving Average Convergence and Divergence, and Stochastic Oscillator. These indicators serve as input variable to the Fuzzy System. The MACD and SO are deployed as primary indicators, while the RSI and WA are used as secondary indicators. Fibonacci retracement ratio was adopted for the secondary indicators to determine their support and resistance level in terms of making trading decisions. The input variables to the Fuzzy System is fuzzified to Low, Medium, and High using the Triangular and Gaussian Membership Function. The Mamdani Type Fuzzy Inference rules were used for combining the trading rules for each input variable to the fuzzy system. The developed system was tested using sample data collected from ten different companies listed on the Nigerian Stock Exchange for a total of fifty two periods. The dataset collected are Opening, High, Low, and Closing prices of each security.

Despite pandemic-driven restrictions on movement, there were over 12,000 accidents in Madrid in 2020, leading to 31 fatalities. In Barcelona, there were more than 5,700 collisions, causing 14 deaths. Pedestrian and vehicle safety is a priority, which is why a research project at the Universitat Oberta de Catalunya (UOC) is harnessing artificial intelligence (AI) to make decisions that will make cities safer. The researchers have looked into the correlation between the complexity of certain urban areas and the likelihood of an accident occurring there. According to the researchers, the data they have gathered can be used to train neural networks to detect probable hazards in an area and work out patterns associated with this high risk potential.

At this turbulent time for the economy, the viability of many companies depends on their commercial efficiency. The huge volume of data they generate is an opportunity to turn this information into knowledge that will help them come up with more efficient and competitive commercial solutions. Decision Making project is being led by UOC researcher and the Falset Marçà Agricultural Cooperative in conjunction with Centre Vinícola del Penedès and the Federation of Agricultural Cooperatives of Catalonia, and is funded by the European Agricultural Fund for Rural Development and the Ministry of Agriculture, Livestock, Fisheries and Food of the Government of Catalonia. According to Xavi Domènech, manager of the Falset Marçà Cooperative and a graduate of Computer Engineering from the UOC, "Companies today amass enough data to be able to take much more calculated commercial decisions than they are generally taking. Personalizing their commercial structure and equipping it with rigorous methods and processes is key to guaranteeing their survival. We believe that supporting this process by introducing the intelligence of analytics can be a differentiating factor."

CLAIRE, the Confederation of Laboratories for AI Research in Europe, launched its COVID-19 Initiative in March 2020 as the first wave of the pandemic hit the continent. Its objective was to coordinate volunteer efforts from its members to contribute to tackling the effects of the disease. The taskforce was able to quickly gather a group of about 150 researchers, scientists and experts in AI organized into seven topic groups: epidemiological data analysis, mobility data analysis, bioinformatics, medical imaging, social dynamics monitoring, robotics, and scheduling and resource management. We brought you a comprehensive article about the activities of this initiative in one of last month's AI for Good series posts. You can read more about the outcomes and experience of this bottom-up approach in the article: The CLAIRE COVID-19 Initiative: a bottom-up effort from the European AI community.

CLAIRE, the Confederation of Laboratories for AI Research in Europe, launched its COVID-19 initiative in March 2020 as the first wave of the pandemic hit the continent. Its objective is to coordinate volunteer efforts of its members to contribute to tackling the effects of the disease. The taskforce was able to quickly gather a group of about 150 researchers, scientists and experts in AI organized in seven topic groups: epidemiological data analysis, mobility data analysis, bioinformatics, medical imaging, social dynamics monitoring, robotics, and scheduling and resource management. Activities of these groups yielded multiple outcomes including a publicly released resource on COVID-19 related data for drug-repurposing; the development the COVID-19 Infodemic Observatory to track spread of misinformation in social media and tools for the diagnosis based on CT scans using High Performance Computing (HPC) platforms. The latter was the catalyst for establishing a partnership between CLAIRE, the Italian National Inter-University Consortium for Informatics (CINI) and the Associazione Big Data (ABD) to provide HPC-enabled AI technologies to our network members.