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 challenges

[1]  Andrew Y. Ng,et al.  Zero-Shot Learning Through Cross-Modal Transfer , 2013, NIPS.

[2]  Fabio Roli,et al.  Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning , 2018, CCS.

[3]  Toshiyuki Yamane,et al.  Recent Advances in Physical Reservoir Computing: A Review , 2018, Neural Networks.

[4]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[5]  Claudio Gallicchio,et al.  Deep reservoir computing: A critical experimental analysis , 2017, Neurocomputing.

[6]  Herbert Jaeger,et al.  Reservoir computing approaches to recurrent neural network training , 2009, Comput. Sci. Rev..

[7]  Insup Lee,et al.  Cyber-physical systems: The next computing revolution , 2010, Design Automation Conference.

[8]  Davide Bacciu,et al.  Dependable Integration Concepts for Human-Centric AI-Based Systems , 2021, SAFECOMP Workshops.

[9]  Davide Bacciu,et al.  Continual Learning for Recurrent Neural Networks: an Empirical Evaluation , 2021, Neural Networks.

[10]  Stefano Chessa,et al.  On the need of machine learning as a service for the internet of things , 2017, IML.

[11]  Tianjian Chen,et al.  Federated Machine Learning: Concept and Applications , 2019 .

[12]  Michael P. Wellman,et al.  SoK: Security and Privacy in Machine Learning , 2018, 2018 IEEE European Symposium on Security and Privacy (EuroS&P).

[13]  Continual Learning with Gated Incremental Memories for sequential data processing , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

[14]  Marvin Minsky,et al.  The society of intelligent veillance , 2013, 2013 IEEE International Symposium on Technology and Society (ISTAS): Social Implications of Wearable Computing and Augmediated Reality in Everyday Life.

[15]  Gregory Ditzler,et al.  Learning in Nonstationary Environments: A Survey , 2015, IEEE Computational Intelligence Magazine.

[16]  Michael McDonald,et al.  Fundamentals of Modern Manufacturing: Materials, Processes and Systems , 2016 .

[17]  Davide Bacciu,et al.  Continual Learning with Echo State Networks , 2021, ESANN.

[18]  Stefano Chessa,et al.  Internet of Robotic Things-Converging Sensing / Actuating , Hypoconnectivity , Artificial Intelligence and IoT Platforms , 2017 .

[19]  Davide Bacciu,et al.  An experimental characterization of reservoir computing in ambient assisted living applications , 2013, Neural Computing and Applications.

[20]  Lanlan Chen,et al.  Detecting driving stress in physiological signals based on multimodal feature analysis and kernel classifiers , 2017, Expert Syst. Appl..

[21]  Davide Bacciu,et al.  Federated Reservoir Computing Neural Networks , 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

[22]  Davide Morelli,et al.  Randomized neural networks for preference learning with physiological data , 2018, Neurocomputing.