Endorsing domestic energy saving behavior using micro-moment classification

With the ever-growing rise of energy consumption and its devastating financial and environmental repercussions, it is of utmost significance to moderate energy usage with proper energy efficiency tools. This is particularly applicable to domestic energy end-users, where an accurate profile is a prerequisite for motivating energy saving behavior. This article presents an innovative method for accurately understanding domestic energy usage patterns through a classification system. It capitalizes on the emerging concept of micro-moments, short energy-related events, and builds a comprehensive profile of end-user's energy activities with unprecedented accuracy. Micro-moments are classified based on a set of criteria per the given appliance. Five classifiers with different parameter settings were trained and tested on 10-fold cross-validated simulated data, with ensemble bagged trees topping with an accuracy of 88.0%. We also observed that linear classifiers lack in accuracy due to their inability to capture the dataset's specific structure and patterns. Fused with the other components of our framework, the proposed classification system is a novel contribution to domestic energy profiling in an effort to step energy efficiency up to the next level. - 2019 Elsevier LtdThis paper was made possible by National Priorities Research Program (NPRP) Grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving

This paper presents a proof-of-concept implementation of the AI-as-a-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving personalization system according to the output of an automatic driver’s stress recognition algorithm, both of them realizing a Cyber-Physical System of Systems. In addition, we implemented a data-gathering subsystem to collect data from different sensors, i.e., wearables and cameras, to automatize stress recognition. The system was attached for testing to a driving emulation software, CARLA, which allows testing the approach’s feasibility with minimum cost and without putting at risk drivers and passengers. At the core of the relative subsystems, different learning algorithms were implemented using Deep Neural Networks, Recurrent Neural Networks, and Reinforcement Learning

Dependable Integration Concepts for Human-Centric AI-Based Systems

The rising demand for adaptive, cloud-based and AI-based systems is calling for an upgrade of the associated dependability concepts. That demands instantiation of dependability-orientated processes and methods to cover the whole life cycle. However, a common solution is not in sight yet That is especially evident for continuously learning AI and/or dynamic runtime-based approaches. This work focuses on engineering methods and design patterns that support the development of dependable AI-based autonomous systems. The emphasis on the human-centric aspect leverages users’ physiological, emotional, and cognitive state for the adaptation and optimisation of autonomous applications. We present the related body of knowledge of the TEACHING project and several automotive domain regulation activities and industrial working groups. We also consider the dependable architectural concepts and their applicability to different scenarios to ensure the dependability of evolving AI-based Cyber-Physical Systems of Systems (CPSoS) in the automotive domain. The paper shines the light on potential paths for dependable integration of AI-based systems into the automotive domain through identified analysis methods and targets

TEACHING-Trustworthy autonomous cyber-physical applications through human-centred intelligence

This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum. TEACHING puts forward a human-centred vision leveraging the physiological, emotional, and cognitive state of the users as a driver for the adaptation and optimization of the autonomous applications. It does so by building a distributed, embedded and federated learning system complemented by methods and tools to enforce its dependability, security and privacy preservation. The paper discusses the main concepts of the TEACHING approach and singles out the main AI-related research challenges associated with it. Further, we provide a discussion of the design choices for the TEACHING system to tackle the aforementioned challenge