M
Mario G. C. A. Cimino
Researcher at University of Pisa
Publications - 100
Citations - 1502
Mario G. C. A. Cimino is an academic researcher from University of Pisa. The author has contributed to research in topics: Stigmergy & Swarm behaviour. The author has an hindex of 19, co-authored 91 publications receiving 1191 citations.
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Patterns and technologies for enabling supply chain traceability through collaborative e-business
TL;DR: A data model for traceability and a set of suitable patterns to encode generic traceability semantics are introduced and a practical implementation of a traceability system is shown through a real world experience on food supply chains.
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Monitoring elderly behavior via indoor position-based stigmergy
Paolo Barsocchi,Mario G. C. A. Cimino,Erina Ferro,Alessandro Lazzeri,Filippo Palumbo,Gigliola Vaglini +5 more
TL;DR: A novel approach for monitoring elderly people living alone and independently in their own homes that is able to detect behavioral deviations of the routine indoor activities on the basis of a generic indoor localization system and a swarm intelligence method.
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A framework for detecting unfolding emergencies using humans as sensors
Marco Avvenuti,Mario G. C. A. Cimino,Stefano Cresci,Stefano Cresci,Andrea Marchetti,Maurizio Tesconi +5 more
TL;DR: A conceptual and architectural framework for the design of emergency detection systems based on the “human as a sensor” (HaaS) paradigm is proposed and a modular architecture, independent of a specific emergency type, is designed.
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Autonomic tracing of production processes with mobile and agent-based computing
TL;DR: A supply chain traceability system with a high level of automation that adopts an agent-based approach, in which cooperative software agents find solutions to back-end tracing problems by self-organization is discussed in this paper.
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Using an autoencoder in the design of an anomaly detector for smart manufacturing
TL;DR: An anomaly detection approach based on deep learning and aimed at providing a manageable machine learning pipeline and easy to interpret outcome is proposed, which combines an autoencoder, a deep neural network, and a discriminator based on a general heuristics to automatically discern anomalies from regular instances.