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PEOPLE@HES-SO – Annuaire et Répertoire des compétences
PEOPLE@HES-SO – Annuaire et Répertoire des compétences

PEOPLE@HES-SO
Annuaire et Répertoire des compétences

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Manzo Gaetano

Manzo Gaetano

Assistant-e post-doc

Compétences principales

Machine learning / Deep learning

Data Science

Software Engineering

Computer networking

eHealth

Recommender Systems

Data visualization

  • Contact

  • Enseignement

  • Recherche

  • Publications

  • Conférences

Contrat principal

Assistant-e post-doc

Téléphone: +41 58 606 90 99

Bureau: A distance

HES-SO Valais-Wallis - Haute Ecole de Gestion
Route de la Plaine 2, Case postale 80, 3960 Sierre, CH
HEG - VS
BSc HES-SO en Informatique de gestion - HES-SO Valais-Wallis - Haute Ecole de Gestion
  • Programmation Python
  • Algorithms and Data Structures
  • Développement Mobile Android

Terminés

Projet The Ark n°519-04 : REPS-CITI Real Estate PaaS - The Ark
AGP

Rôle: Collaborateur/trice

Requérant(e)s: VS - Institut Informatique, Russo David, VS - Institut Informatique

Financement: The Ark

Description du projet : L'objectif de ce projet est d'augmenter l'offre et la valeur ajoutée de la plateforme CITI Software, une solution logicielle de gestion immobilière , en permettant l'intégration de services de tiers (ex: tarification dynamique, service de nettoyage) et en proposant un modèle d'affaires " Pay per Use" adapté à l'usage de ces services par les clients de la solution (principalement des agences de location de vacances).

Equipe de recherche au sein de la HES-SO: Duc Alain , Manzo Gaetano , Pitteloud Pascal , Russo David

Partenaires académiques: VS - Institut Informatique; Russo David, VS - Institut Informatique

Durée du projet: 01.04.2019 - 30.06.2020

Montant global du projet: 41'750 CHF

Statut: Terminé

Axe Développement régional 2019 - Chèques DR
AGP

Rôle: Collaborateur/trice

Financement: VS - Direction / Ra&D; VS - Direction / Ra&D

Description du projet : Projet spécifique de l'Axe développement régional dédié à l'attribution des chèques de développement régional. 14 chèques financés en 2019 suite à l'appel à projets lancé.

Equipe de recherche au sein de la HES-SO: Genoud Stéphane , Ellert Christoph , Pignat Loïse , Strumans Aurélie , Schumann René , Loubier Jean-Christophe , Larpin Blaise , Grèzes Vincent , Grèzes Sandra , Papilloud Lucien , Knobel Meret , Bocchi Yann , Crelier Simon , Bétrisey Karine , Rizzo Gianluca , Simon Maya , Cachelin Christian Pierre , Page Jessen , Gabioud Dominique , Vermot-Petit-Outhenin Nicolas , Hofstädter Martine , Martinet David , Brück Wolfram Manuel , Schegg Roland , Nanchen Benjamin , Doctor Marut , Udry Julien , Bocquel Dimitri , Fournier Thibault , Jordan Nicolas , Widmer Antoine , Margot-Cattin Pierre , Mathieu Nicolas , Hilfiker Roger , Mittaz Hager Anne-Gabrielle , Antonin-Tattini Véronique , Manzo Gaetano , Bonvin Mittaz Annick , Seppey Sherine , Fioretto Anne-Sophie , Pitteloud Mélanie , Piana Valentino , Mastelic Joëlle , Ramseyer Randolf , Richter Marina , Emery Mabillard Martine , Ireland Robert , Antille Alain , Lehner Bruno , Baudin Martine

Durée du projet: 01.07.2019 - 31.12.2019

Montant global du projet: 90'000 CHF

Statut: Terminé

2023

Breast cancer survival analysis agents for clinical decision support
Article scientifique ArODES

Gaetano Manzo, Yves Pannatier, Patrick Duflot, Philippe Kolh, Marcela Chavez, Valérie Bleret, Davide Calvaresi, Oscar Jimenez-del-Toro, Michael Schumacher, Jean-Paul Calbimonte

Computer methods and programs in biomedicine,  Avril 2023, vol. 231

Lien vers la publication

Résumé:

Personalized support and assistance are essential for cancer survivors, given the physical and psychological consequences they have to suffer after all the treatments and conditions associated with this illness. Digital assistive technologies have proved to be effective in enhancing the quality of life of cancer survivors, for instance, through physical exercise monitoring and recommendation or emotional support and prediction. To maximize the efficacy of these techniques, it is challenging to develop accurate models of patient trajectories, which are typically fed with information acquired from retrospective datasets. This paper presents a Machine Learning-based survival model embedded in a clinical decision system architecture for predicting cancer survivors’ trajectories. The proposed architecture of the system, named PERSIST, integrates the enrichment and pre-processing of clinical datasets coming from different sources and the development of clinical decision support modules. Moreover, the model includes detecting high-risk markers, which have been evaluated in terms of performance using both a third-party dataset of breast cancer patients and a retrospective dataset collected in the context of the PERSIST clinical study.

2022

A DEXiRE for extracting propositional rules from neural networks via binarization
Article scientifique ArODES

Victor Contreras, Niccolo Marini, Lora Fanda, Gaetano Manzo, Yazan Mualla, Jean-Paul Calbimonte, Michael Schumacher, Davide Calvaresi

Electronics,  2022, vol. 11, no 24, p. 4171

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Résumé:

Background: Despite the advancement in eXplainable Artificial Intelligence, the expla nations provided by model-agnostic predictors still call for improvements (i.e., lack of accurate descriptions of predictors’ behaviors). Contribution: We present a tool for Deep Explanations and Rule Extraction (DEXiRE) to approximate rules for Deep Learning models with any number of hid den layers. Methodology: DEXiRE proposes the binarization of neural networks to induce Boolean functions in the hidden layers, generating as many intermediate rule sets. A rule set is inducted between the first hidden layer and the input layer. Finally, the complete rule set is obtained using inverse substitution on intermediate rule sets and first-layer rules. Statistical tests and satisfiability algorithms reduce the final rule set’s size and complexity (filtering redundant, inconsistent, and non-frequent rules). DEXiRE has been tested in binary and multiclass classifications with six datasets having different structures and models. Results: The performance is consistent (in terms of accuracy, fidelity, and rule length) with respect to the state-of-the-art rule extractors (i.e., ECLAIRE). Moreover, compared with ECLAIRE, DEXiRE has generated shorter rules (i.e., up to 74% fewer terms) and has shortened the execution time (improving up to 197% in the best-case scenario). Conclusions: DEXiRE can be applied for binary and multiclass classification of deep learning predictors with any number of hidden layers. Moreover, DEXiRE can identify the activation pattern per class and use it to reduce the search space for rule extractors (pruning irrelevant/redundant neurons)—shorter rules and execution times with respect to ECLAIRE.

2021

Exploiter les données du dossier médical informatisé pour améliorer la qualité des soins en ambulatoire
Article scientifique ArODES

Arnaud Chiolero, Jean-Paul Calbimonte, Gaetano Manzo, Bruno Alves, Michael Schumacher, Samuel Gaillard, Philippe Schaller, Valérie Santschi

Revue médicale suisse,  2021, vol. 17, no. 760, pp. 2056-2059

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Cohort and trajectory analysis in multi-agent support systems for cancer survivors
Article scientifique ArODES

Gaetano Manzo, Davide Calvaresi, Oscar Alfonso Jiménez del Toro, Jean-Paul Calbimonte, Michael Schumacher

Journal of medical systems,  2021, vol. 45, article no. 109, pp. 1-10

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Résumé:

In the past decades, the incidence rate of cancer has steadily risen. Although advances in early and accurate detection have increased cancer survival chances, these patients must cope with physical and psychological sequelae. The lack of personalized support and assistance after discharge may lead to a rapid diminution of their physical abilities, cognitive impairment, and reduced quality of life. This paper proposes a personalized support system for cancer survivors based on a cohort and trajectory analysis (CTA) module integrated within an agent-based personalized chatbot named EREBOTS. The CTA module relies on survival estimation models, machine learning, and deep learning techniques. It provides clinicians with supporting evidence for choosing a personalized treatment, while allowing patients to benefit from tailored suggestions adapted to their conditions and trajectories. The development of the CTA within the EREBOTS framework enables to effectively evaluate the significance of prognostic variables, detect patient’s high-risk markers, and support treatment decisions.

EREBOTS :
Article scientifique ArODES
privacy-compliant agent-based platform for multi-scenario personalized health-assistant chatbots

Davide Calvaresi, Jean-Paul Calbimonte, Enrico Siboni, Stefan Eggenschwiler, Gaetano Manzo, Roger Hilfiker, Michael Schumacher

Electronics,  2021, vol. 10, no. 6, pp. article no. 666, pp. 1-30

Lien vers la publication

Résumé:

Context. Asynchronous messaging is increasingly used to support human–machine interactions, generally implemented through chatbots. Such virtual entities assist the users in activities of different kinds (e.g., work, leisure, and health-related) and are becoming ingrained into humans’ habits due to factors including (i) the availability of mobile devices such as smartphones and tablets, (ii) the increasingly engaging nature of chatbot interactions, (iii) the release of dedicated APIs from messaging platforms, and (iv) increasingly complex AI-based mechanisms to power the bots’ behaviors. Nevertheless, most of the modern chatbots rely on state machines (implementing conversational rules) and one-fits-all approaches, neglecting personalization, data-stream privacy management, multi-topic management/interconnection, and multimodal interactions. Objective. This work addresses the challenges above through an agent-based framework for chatbot development named EREBOTS. Methods. The foundations of the framework are based on the implementation of (i) multi-front-end connectors and interfaces (i.e., Telegram, dedicated App, and web interface), (ii) enabling the configuration of multi-scenario behaviors (i.e., preventive physical conditioning, smoking cessation, and support for breast-cancer survivors), (iii) online learning, (iv) personalized conversations and recommendations (i.e., mood boost, anti-craving persuasion, and balance-preserving physical exercises), and (v) responsive multi-device monitoring interface (i.e., doctor and admin). Results. EREBOTS has been tested in the context of physical balance preservation in social confinement times (due to the ongoing pandemic). Thirteen individuals characterized by diverse age, gender, and country distribution have actively participated in the experimentation, reporting advancements in the physical balance and overall satisfaction of the interaction and exercises’ variety they have been proposed.

Optimal strategies for floating anchored information with partial infrastructure support
Article scientifique ArODES

Giancarlo Rizzo, Marco Ajmone Marsan, Torsten Braun, Gaetano Manzo

Vehicular communications,  2021, vol. 27, article 100287, pp. 1-15

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Résumé:

Floating Content (FC) is a communication paradigm to locally share ephemeral content without direct support from infrastructure. It is based on constraining the opportunistic replication of content in a way that strikes a balance between minimizing resource usage and maximizing content availability among the intended recipients. However, existing approaches to management of FC schemes are unfit for realistic scenarios with non-uniform user distributions, resulting in heavy overdimensioning of resources allocated to FC. In this work, we propose a new version of FC, called Cellular Floating Content (CFC), which optimizes the use of bandwidth and memory by adapting the content replication and storage strategies to the spatial distribution of users, and to their mobility patterns. The main idea underlying our approach is to partition users into small “local communities”, and to optimally weight their contributions to the FC paradigm according to their specific mobility features, and to the resources required to achieve a target performance level. We characterize numerically the properties of the optimal strategies in a variety of mobility patterns and traffic conditions, showing the accuracy of our approach, and the significant savings it enables in the amount of resources necessary to run FC, which in a realistic setup can be as high as 27% with respect to traditional FC dimensioning strategies.

Cohort and Trajectory Analysis
Article scientifique

Manzo Gaetano

A2HC - AAMAS, 2021

2025

PICO to PICOS :
Conférence ArODES
weak supervision to extend datasets with new labels

Anjani Dhrangadhariya, Gaetano Manzo, Henning Müller

Digital Health and Informatics Innovations for Sustainable Health Care Systems : proceedings of MIE 2024

Lien vers la conférence

Résumé:

Hand-labelling clinical corpora can be costly and inflexible, requiring re-annotation every time new classes need to be extracted. PICO (Participant, Intervention, Comparator, Outcome) information extraction can expedite conducting systematic reviews to answer clinical questions. However, PICO frequently extends to other entities such as Study type and design, trial context, and timeframe, requiring manual re-annotation of existing corpora. In this paper, we adapt Snorkel’s weak supervision methodology to extend clinical corpora to new entities without extensive hand labelling. Specifically, we enrich the EBM-PICO corpus with new entities through an example of “Study type and design” extraction. Using weak supervision, we obtain programmatic labels on 4,081 EBM-PICO documents, achieving an F1-score of 85.02% on the test set.

2023

Serendipity and diversity boosting for personalized streaming media recommendation
Conférence ArODES

Gaetano Manzo, Yvan Pannatier, Gabriel Autès, Michaël De Lucia, Jean-Gabriel Piguet, Jean-Paul Calbimonte

Proceedings of the 13th Italian Information Retrieval Workshop (IIR 2023)

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Résumé:

Streaming media platforms constitute a significant source of information and entertainment for different population segments. Although major corporations have taken the lead in market share, public media companies have also started to produce and broadcast films, series, and documentaries centered on locally-created content. Moreover, beyond the purely commercial goals of major corporations, these public streaming platforms have the mission of expanding the cultural landscape of the viewers, for instance, through the exploration of content produced in other regions and other languages, especially in multicultural societies such as Switzerland. In such a context, this paper proposes a novel approach for personalized recommendations of streaming media content, focusing on serendipity and multicultural diversity, while minimizing the need for personal data sharing. The approach is based on the feature extraction from user media consumption and a combination of data-driven recommendation algorithms. The approach has been tested with real data from the public PlaySuisse streaming platform.

Towards semantic modeling of patient trajectories for rehabilitation of osteoarthritis
Conférence ArODES

Gaetano Manzo, Benjamin Pocklington, Yvan Pannatier, Cathy Gay, Anjani Dhrangadhariya, Sophie Carrard, Roger Hilfiker, Jean-Paul Calbimonte

Proceedings of the 14th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4HCLS 2023)

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Résumé:

This poster paper describes the challenges and opportunities of modeling patient trajectories for osteoarthritis rehabilitation using semantically rich abstractions.

2022

A personalized agent-based chatbot for nutritional coaching
Conférence ArODES

Davide Calvaresi, Stefan Eggenschwiler, Jean-Paul Calbimonte, Gaetano Manzo, Michael Schumacher

Proceedings of WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

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Résumé:

Intelligent systems increasingly support users’ behavior change, including exercise adherence, smoking cessation, and healthy diet adoption. Their effectiveness is affected by the personalization degree of advice/coaching and HMI mechanisms. This paper proposes a personalized agent-based chatbot platform assisting the user in healthy nutrition via pervasive technologies leveraging dynamical, multi-modal, and personalized interactions. The system provides diet recommendations and tracks the user’s food intake and nutritional behaviors to promote a healthy lifestyle. The study concludes with a user study and performance evaluation.

2021

Study of context-based personalized recommendations for points of interest
Conférence ArODES

Gaetano Manzo, Davide Calvaresi, Jean-Paul Calbimonte, Okoro Esteem, Michael Schumacher

Proceedings of the 2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2021)

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Résumé:

Location-based services are essential to delivering information for users in the context of travel, leisure, and sports application. Nevertheless, these services are often implemented as recommendations and suggestions that may overwhelm users, or fail to adapt to their goals, behavior, and context. To address these limitations, this paper presents NearMe, an application that provides tailored recommendations of Points of Interest surrounding the user. Beyond existing approaches, NearMeallows the generation of dynamic recommendations from heterogeneous service providers, and the definition of regions to which notifications are related. Moreover, it allows to fine-tune notifications, thus preventing over-information and noise. A preliminary study has been conducted involving a heterogeneous group of potential users and service providers that elaborates on their vision, expectations, features desiderata, and possible interfaces.

2020

DeepNDN :
Conférence ArODES
opportunistic data replication and caching in support of vehicular named data

Gaetano Manzo, Eirini Kalogeiton, Antonio Di Maio, Torsten Braun, Maria Rita Palattella, Ion Turcanu, Ridha Soua, Gianluca Rizzo

Proceedings of the 21st International Symposium on a World of Wireless, Mobile and Multimedia Networks (IEEE WoWMoM 2020)

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Résumé:

Although many target applications in VANETs are information-centric, the performance of Named Data Networking (NDN) in vehicular ad-hoc networks is severely hampered by persistent network partitioning, typical of many vehicular scenarios. Existing approaches try to address this issue by relying on opportunistic communications. However, they leave open the crucial issue of how to guarantee content persistence and tight QoS levels while optimizing the resource utilization in the vehicular environment. In this work we propose DeepNDN, a communication scheme based on the joint application of NDN and of probabilistic spatial content caching, which enables content retrieval in fragmented and dynamic network topologies with tight delay constraints. We present a data-based approach to DeepNDN management, based on locally modulating content replication and delivery in order to achieve a target hit ratio in a resource-efficient manner. Our management algorithm employs a Convolutional Neural Network (CNN) architecture for effectively capturing the complex relations between spatio-temporal patterns of mobility and content requests and DeepNDN performance. Its numerical assessment in realistic, measurement-based scenarios suggest that our management approach achieves its target set goals while outperforming a set of reference schemes.

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