Machine Learning (ML) has been integrated into various software and systems. Two main components are essential for training an ML model: the training data and the ML algorithm. Given the critical role of data in ML system development, it has become increasingly important to assess the quality of data attributes and ensure that the data meets specific requirements before its utilization. This work proposes an approach to guide non-experts in identifying data requirements for ML systems using goal modeling. In this approach, we first develop the Data Requirement Goal Model (DRGM) by surveying the white literature to identify and categorize the issues and challenges faced by data scientists and requirement engineers working on ML-related projects. An initial DRGM was built to accommodate common tasks that would generalize across projects. Then, based on insights from both white and gray literature, a customization mechanism is built to help adjust the tasks, KPIs, and goals' importance of different elements within the DRGM. The generated model can aid its users in evaluating different datasets using GRL evaluation strategies. We then validate the approach through two illustrative examples based on real-world projects. The results from the illustrative examples demonstrate that the data requirements identified by the proposed approach align with the requirements of real-world projects, demonstrating the practicality and effectiveness of the proposed framework. The proposed dataset selection customization mechanism and the proposed DRGM are helpful in guiding non-experts in identifying the data requirements for machine learning systems tailored to a specific ML problem. This approach also aids in evaluating different dataset alternatives to choose the optimum dataset for the problem. For future work, we recommend implementing tool support to generate the DRGM based on a chatbot interface.

High spatial resolution wind data are essential for a wide range of applications in climate, oceanographic and meteorological studies. Large-scale spatial interpolation or downscaling of bivariate wind fields having velocity in two dimensions is a challenging task because wind data tend to be non-Gaussian with high spatial variability and heterogeneity. In spatial statistics, cokriging is commonly used for predicting bivariate spatial fields. However, the cokriging predictor is not optimal except for Gaussian processes. Additionally, cokriging is computationally prohibitive for large datasets. In this paper, we propose a method, called bivariate DeepKriging, which is a spatially dependent deep neural network (DNN) with an embedding layer constructed by spatial radial basis functions for bivariate spatial data prediction. We then develop a distribution-free uncertainty quantification method based on bootstrap and ensemble DNN. Our proposed approach outperforms the traditional cokriging predictor with commonly used covariance functions, such as the linear model of co-regionalization and flexible bivariate Mat\'ern covariance. We demonstrate the computational efficiency and scalability of the proposed DNN model, with computations that are, on average, 20 times faster than those of conventional techniques. We apply the bivariate DeepKriging method to the wind data over the Middle East region at 506,771 locations. The prediction performance of the proposed method is superior over the cokriging predictors and dramatically reduces computation time.

He overcame surrender and did not despair despite his projects having been rejected, at the beginning of his career, but eventually became an entrepreneur in technology across Middle East-wide through his company "WideBot," which specializes in artificial intelligence "AI" and its influential role in customer relationship management (CRM) and digital business management. He is "Mohamed Nabil," a 35-year-old, from Alexandria who graduated from the Faculty of Computer and Information Science, Mansoura University in 2007. Immediately after graduation, he began to think seriously about how to start his own business, he had already set up companies, some companies have failed miserably and some have succeeded, but it was not a great and overwhelming success. During Mohamed's struggle, he supported him and stood by his side, Ahmed was his college friend, and he is also with his technical co-founder. And their work on that idea took about two years, they presented their idea to more than one large supermarket in Egypt and abroad, but unfortunately, it was not successful enough and the idea of their project was very new to the market.

The humanoid robot'Sophia' made quite an impression back in 2017 when it was unveiled for the first time at the innovation conference in Riyadh, Saudi Arabia. Built by a Hong Kong-based company Hanson Robotics in 2015, the humanoid robot can mimic 62 human facial expressions. However, the highlight of the conference was not the unveiling of the AI-enabled entity or its onstage interview but the Kingdom's decision to grant citizenship to Sophia. Ironic for a country where women were not allowed to drive till last year! However, the intention was clear -- to adopt technology in modernizing the heavily Oil-dependent economy.