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

De, Valerio; Bano, Saira; Machumilane, Achilles; Gotta, Alberto; Cassarà, Pietro; Carta, Antonio; Rudy, Semola,; Sardianos, Christos; Chronis, Christos; Varlamis, Iraklis; Tserpes, Konstantinos; Lomonaco, Vincenzo; Gallicchio, Claudio; Bacciu, Davide

doi:10.1109/percomworkshops53856.2022.9767501

Cited by 6 publications

(14 citation statements)

References 6 publications

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“…It means that these systems' components interact with each other by reading from and writing to data mediums. We found that components of real-time systems [22,23,36,59,60,66,83,106,116] act as subscribers and publishers of data to streams that represent the state of the data at different stages in a workflow, again in accordance with principles of dataflow architecture. For example, the components of Pytheas [60] apply operations on video streams to group them in sessions and optimise their users' QoE.…”

Section: Data As a First Class Citizenmentioning

confidence: 80%

“…Systems use streams when data is generated and needs to be analysed in real-time. We observed that the use of streams is particularly common for systems that are built with dataflow architecture [32,91], where data inputs are transformed by different components while they flow through the system [14,23,31,36,56,59,60,72,75,83,99,102,112,116,125]. A good example of system architected in that way is described by Sultana et al [116].…”

Section: Data As a First Class Citizenmentioning

confidence: 99%

“…Data that is produced by different sources in a continuous fashion fits the streams data model according to our review. That is the case of works that process video streams [43,49,56,60], sensors data [14,23,31,36,59,75,83,106,112,126], social media data [22,81,109,123,125], and network metrics [102,116]. Databases schemes are more appropriate as shared data models when system components require to store historical records [9,29,57] or process large amounts of data [105].…”

Section: Data As a First Class Citizenmentioning

confidence: 99%

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Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

Cabrera¹,

Paleyes²,

Thodoroff³

et al. 2023

Preprint

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With the upsurge of interest in artificial intelligence machine learning (ML) algorithms, originally developed in academic environments, are now being deployed as parts of real-life systems that deal with large amounts of heterogeneous, dynamic, and high-dimensional data. Deployment of ML methods in real life is prone to challenges across the whole system life-cycle from data management to systems deployment, monitoring, and maintenance. Data-Oriented Architecture (DOA) is an emerging software engineering paradigm that has the potential to mitigate these challenges by proposing a set of principles to create data-driven, loosely coupled, decentralised, and open systems. However DOA as a concept is not widespread yet, and there is no common understanding of how it can be realised in practice. This review addresses that problem by contextualising the principles that underpin the DOA paradigm through the ML system challenges. We explore the extent to which current architectures of ML-based real-world systems have implemented the DOA principles. We also formulate open research challenges and directions for further development of the DOA paradigm.

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Section: Data As a First Class Citizenmentioning

confidence: 80%

Section: Data As a First Class Citizenmentioning

confidence: 99%

Section: Data As a First Class Citizenmentioning

confidence: 99%

See 1 more Smart Citation

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

Cabrera¹,

Paleyes²,

Thodoroff³

et al. 2023

Preprint

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“…On-the-edge applications are requiring an increasingly frequent use of machine learning (ML) systems, a key component for the success of human-centric cyberphysical systems [1,2]. This type of application involves large numbers of participating users, each of whom is a source of inherently sequential data.…”

Section: Introductionmentioning

confidence: 99%

“…This type of application involves large numbers of participating users, each of whom is a source of inherently sequential data. This setting involves two main challenges: (1) the devices involved in this domain are often low-powered, and achieving a good trade-off between performance and efficiency in tasks on temporal data may be difficult; (2) the data may be subject to privacy constraints, which do not allow the use of common distributed learning techniques. The former is approached through the use of Echo State Networks [3], a recurrent neural network characterized by excellent accuracyefficiency trade-off.…”

Section: Introductionmentioning

confidence: 99%

Federated Adaptation of Reservoirs via Intrinsic Plasticity

De¹,

Gallicchio²,

Bacciu³

2022

ESANN 2022 Proceedings

Self Cite

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We propose a novel algorithm for performing federated learning with Echo State Networks (ESNs) in a client-server scenario. In particular, our proposal focuses on the adaptation of reservoirs by combining Intrinsic Plasticity with Federated Averaging. The former is a gradientbased method for adapting the reservoir's non-linearity in a local and unsupervised manner, while the latter provides the framework for learning in the federated scenario. We evaluate our approach on real-world datasets from human monitoring, in comparison with the previous approach for federated ESNs existing in literature. Results show that adapting the reservoir with our algorithm provides a significant improvement on the performance of the global model.

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