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Calbimonte Jean-Paul

Professeur-e HES Associé-e

Main skills

Professeur-e HES Associé-e

Desktop: TP106

HES-SO Valais-Wallis - Haute Ecole de Gestion
Route de la Plaine 2, Case postale 80, 3960 Sierre, CH

Faculty
Economie et services

Main Degree Programme
Informatique de gestion

BSc HES-SO en Informatique de gestion - HES-SO Valais-Wallis - Haute Ecole de Gestion

Python
Systèmes d'information Géographiques
Project Team
Emerging Technologies
Advanced Data Management

MSc HES-SO en Business Administration - HES-SO Master

Valorisation de données

MSc HES-SO en Engineering - HES-SO Master

Functional Programming

Ongoing

HEU - HORIZON-CL4-2022-DATA-01 - Semantic Low-code Programming Tools for Edge Intelligence - SEFRI - 22.00539

AGP

Role: Main Applicant

Description du projet : FUNDING BY SEFRI, CHF Abstract: The objective of the SMARTEDGE project is to enable the dynamic integration of decentralised edge intelligence at runtime while ensuring reliability, security, privacy and scalability. We will achieve this by enabling a semantic-based interplay of the edge devices of such systems via a cross-layer toolchain that facilitates the seamless and real-time discoverability and composability of autonomous intelligence swarm. Hence, an application can be freely built by distributing the processing, data fusion and control across heterogeneous sensors, devices and edges with ubiquitous low-latency connectivity. The goal of this project is to develop a SMARTEDGE solution with a low-code tool programming environment with various tools: (1) Continuous Semantic Integration (CSI); (2) Dynamic Swarm Network (DSW); and (3) Low-code Toolchain for Edge Intelligence. CSI allows the SMARTEDGE solution to interact with devices according to a (i) standardized semantic interface, via a (ii) continuous conversion process based on declarative mappings and scalable from edge to cloud, and (iii) providing a declarative approach for the creation and orchestration of apps based on swarm intelligence. DSW provides (i) automatic discovery and dynamic network swarm formation in near real time, (ii) hardware-accelerated in-network operations for context-aware swarm networking, and (iii) embedded network security. The low-code tool chain provides (i) semantic-driven multimodal stream fusion for Edge devices; (ii) swarm elasticity via Edge-Cloud Interplay; (iii) adaptive coordination and optimization; (iv) cross-layer toolchain for Device-Edge-Cloud Continuum. The SMARTEDGE solution will be comprehensively demonstrated over four application areas: automotive, city, factory and heath via the strong collaboration of eight industrial partners, Dell, Siemens, Bosch, IMC, Conveq, Cefiel and NVIDIA with eight research institutes.

Research team within HES-SO: Buzcu Berk , Calbimonte Jean-Paul , Calvaresi Davide , Anuraj Banani

Partenaires académiques: VS - Institut Informatique; SEFRI; Calbimonte Jean-Paul, VS - Institut Informatique

Durée du projet: 01.01.2023 - 31.12.2025

Montant global du projet: 511'580 CHF

Statut: Ongoing

MedRED

Role: Co-applicant

Financement: swissuniversities

Description du projet :

MedRED@HES-SO | Swissuniversities | 2016-2018 Platform for Health data acquisition.

Research team within HES-SO: Calbimonte Jean-Paul , Cotting Alexandre

Statut: Ongoing

PERSIST: Big data / AI enabled care plan for cancer survivors

Role: Collaborator

Financement: H2020

Description du projet :

PERSIST. Patients-centered care plan after Cancer treatments based on AI. Patient trajectory analytics based on Machine Learnig techniques.

Research team within HES-SO: Calbimonte Jean-Paul , Schumacher Michael Ignaz

Partenaires académiques: -, University of Maribor

Partenaires professionnels: Gradiant

Durée du projet: - 17.02.2023

Statut: Ongoing

Completed

Sciences écocitoyennes : aborder les pollutions industrielles au-delà de la dichotomie savoirs experts/ savoirs profanes. Cas d'étude dans le Chablais

AGP

Role: Collaborator

Financement: HES-SO Rectorat; VS - Institut Informatique; VS - Institut Santé; VS - Institut Travail social

Description du projet : Ce projet de sciences écocitoyennes vise à concilier savoirs experts et profanes à travers un cas d'étude touchant aux pollutions aux substances per- et polyfluoroalkylées (PFAS) dans le Chablais valaisan. Par une démarche transdisciplinaire, son objectif principal est d'associer citoyens et experts dans des processus de recherche et dans la diffusion des résultats, dans un but de sensibilisation de la population. Les activités sont développées en partenariat avec des associations, des collectivités et des scientifiques en vue de co-construire un diagnostic socio-environnemental, développer des outils digitaux et tester la présence de PFAS dans différents milieux. In fine, le projet prétend amorcer la création d'une plateforme de sciences écocitoyennes en Valais.

Research team within HES-SO: Martin Simon , Portela Dos Santos Omar , Fournier Claude-Alexandre , Calbimonte Jean-Paul , Kurt Stefanie Tamara , Borrelli Lisa Marie , Loloum Tristan , Savioz Alexandre , Cotting Alexandre

Durée du projet: 01.01.2023 - 31.01.2024

Montant global du projet: 43'750 CHF

Url of the project site: https://pfas.iigweb.hevs.ch/

Statut: Completed

Recommandation personnalisée : de la polarisation à l'ouverture - IMI

AGP

Role: Collaborator

Requérant(e)s: VS - Institut Informatique

Financement: Initiative Media Innovation

Description du projet : à compléter

Research team within HES-SO: Reichenbach Julien , Manzo Gaetano , Calbimonte Jean-Paul , Pannatier Yvan , Gay Cathy , Piguet Jean-Gabriel

Partenaires académiques: VS - Institut Informatique

Durée du projet: 01.11.2020 - 31.10.2022

Statut: Completed

SemPryv: Automatic Semantization for Personalized Health Data

Role: Co-applicant

Financement: Innosuisse

Description du projet :

SemPryv: Automatic Semantization for Personalized Health Data. Machine Learning based methods for semantic annotation of personal data streams.

Research team within HES-SO: Calbimonte Jean-Paul , Schumacher Michael Ignaz

Partenaires professionnels: Pryv SA

Durée du projet: 01.04.2018 - 31.03.2020

Statut: Completed

2023

Autonomy in the age of knowledge graphs :

Scientific paper ArODES

vision and challenges

Jean-Paul Calbimonte, Andrei Ciortea, Timotheus Kampik, Simon Mayer, Terry R. Payne, Valentina Tamma, Antoine Zimmermann

Transactions on Graph Data and Knowledge (TGDK), 1, 1, pp. 13:1-13:22

Link to the publication

Summary:

In this position paper, we propose that Knowledge Graphs (KGs) are one of the prime approaches to support the programming of autonomous software systems at the knowledge level. From this viewpoint, we survey how KGs can support different dimensions of autonomy in such systems: For example, the autonomy of systems with respect to their environment, or with respect to organisations; and we discuss related practical and research challenges. We emphasise that KGs need to be able to support systems of autonomous software agents that are themselves highly heterogeneous, which limits how these systems may use KGs. Furthermore, these heterogeneous software agents may populate highly dynamic environments, which implies that they require adaptive KGs. The scale of the envisioned systems - possibly stretching to the size of the Internet - highlights the maintainability of the underlying KGs that need to contain large-scale knowledge, which requires that KGs are maintained jointly by humans and machines. Furthermore, autonomous agents require procedural knowledge, and KGs should hence be explored more towards the provisioning of such knowledge to augment autonomous behaviour. Finally, we highlight the importance of modelling choices, including with respect to the selected abstraction level when modelling and with respect to the provisioning of more expressive constraint languages.

Exploring agent-based chatbots :

Scientific paper ArODES

a systematic literature review

Davide Calvaresi, Stefan Eggenschwiler, Yazan Mualla, Michael Schumacher, Jean-Paul Calbimonte

Journal of ambient intelligence and humanized computing, 2023, vol. 14, pp. 11207–11226

Link to the publication

Summary:

In the last decade, conversational agents have been developed and adopted in several application domains, including education, healthcare, finance, and tourism. Nevertheless, chatbots still need to address several limitations and challenges, especially regarding personalization, limited knowledge-sharing capabilities, multi-domain campaign support, real-time monitoring, or integration of chatbot communities. To cope with these limitations, many approaches based on multi-agent systems models and technologies have been proposed in the literature, opening new research directions in this context. To better understand the current panorama of the different chatbot technology solutions employing agent-based methods, this Systematic Literature Review investigates the different application domains, end-users, requirements, objectives, technology readiness levels, designs, strengths, limitations, and future challenges of the solutions found in this scope. The results of this review are intended to provide researchers, software engineers, and innovators with a complete overview of the current state of the art and a discussion of the open challenges.

Breast cancer survival analysis agents for clinical decision support

Scientific paper 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

Link to the publication

Summary:

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.

Decentralized semantic provision of personal health streams

Scientific paper ArODES

Jean-Paul Calbimonte, Orfeas Aidonopoulos, Fabien Dubosson, Benjamin Pocklington, Ilia Kebets, Pierre-Mikael Legris, Michael Schumacher

Journal of web semantics, Avril 2023, vol. 76

Link to the publication

Summary:

Personalized healthcare is nowadays driven by the increasing volumes of patient data, observed and produced continuously thanks to medical devices, mobile sensors, patient-reported outcomes, among other data sources. This data is made available as streams, due to their dynamic nature, which represents an important challenge for processing, querying and interpreting the incoming information. In addition, the sensitive nature of healthcare data poses significant restrictions regarding privacy, which has led to the emergence of decentralized personal data management systems. Data semantics play a key role in order to enable both decentralization and integration of personal health data, as they introduce the capability to represent knowledge and information using ontologies and semantic vocabularies. In this paper we describe the SemPryv system, which provides the means to manage personal health data streams enriched with semantic information. SemPryv is designed as a decentralized system, so that users have the possibility of hosting their personal data at different sites, while keeping control of access rights. The semantization of data in SemPryv is implemented through different strategies, ranging from rule-based annotation to machine learning-based suggestions, fed from third-party specialized healthcare metadata providers. The system has been made available as Open Source, and is integrated as part of the Pryv.io platform used and commercialized in the healthcare and personal data management industry

2022

A DEXiRE for extracting propositional rules from neural networks via binarization

Scientific paper 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

Link to the publication

Summary:

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.

Ethical and legal considerations for nutrition virtual coaches

Scientific paper ArODES

Davide Calvaresi, Rachele Carli, Jean-Gabriel Piguet, Victor H. Contreras, Gloria Luzzani, Amro Najjar, Jean-Paul Calbimonte, Michael Schumacher

AI and Ethics, 2022

Link to the publication

Summary:

Choices and preferences of individuals are nowadays increasingly infuenced by countless inputs and recommendations provided by artifcial intelligence-based systems. The accuracy of recommender systems (RS) has achieved remarkable results in several domains, from infotainment to marketing and lifestyle. However, in sensitive use-cases, such as nutrition, there is a need for more complex dynamics and responsibilities beyond conventional RS frameworks. On one hand, virtual coaching systems (VCS) are intended to support and educate the users about food, integrating additional dimensions w.r.t. the conventional RS (i.e., leveraging persuasion techniques, argumentation, informative systems, and recommendation paradigms) and show promising results. On the other hand, as of today, VCS raise unexplored ethical and legal concerns. This paper discusses the need for a clear understanding of the ethical/legal-technological entanglements, formalizing 21 ethical and ten legal challenges and the related mitigation strategies. Moreover, it elaborates on nutrition sustainability as a further nutrition virtual coaches dimension for a better society.

2021

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

Scientific paper 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

Link to the publication

OpenDataSyris = Open Data pour Syris :

Report ArODES

système de recommandation pour itinéraires pédestres de santé

Alexandre Cotting, Jean-Paul Calbimonte

2021, Sierre : HES-SO Valais-Wallis, 19 p.

Link to the publication

Summary:

The popularity of hiking has steadily increased in the latest years, across different segments of the population. Although there is considerable evidence of the benefits for hikers regarding physical and mental health, the inherent risks of these outdoor activities cannot be underestimated. Accident prevention and an increase of awareness about possible risks are necessary to minimize hiking and pedestrian tourism’s negative consequences. In most hiking information maps and interactive applications, there is usually not enough information about difficulty points or the granularity level required to provide tailored recommendations to hikers with physical or psychological limitations. This report documents the process of preparing and opening the data of the Syris project, a geo-information system for hiking itineraries that incorporates Points-Of-Difficulty to assess the level of effort, technique, and risk of hiking trails. This dataset published in Zenodo1, is a pioneering effort to offer comprehensive information about detailed difficulty and effort levels in hiking paths at Val d’Anniviers in Switzerland, with the long term goal of expanding the database to the rest of the country.

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

Scientific paper 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

Link to the publication

Summary:

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.

Leveraging inter-tourists interactions via chatbots to bridge academia, tourism industries and future societies

Scientific paper ArODES

Davide Calvaresi, Ahmed Ibrahim, Jean-Paul Calbimonte, Emmanuel Fragnière, Roland Schegg, Michael Schumacher

Journal of tourism futures, To be published

Link to the publication

Summary:

Purpose : The tourism and hospitality sectors are experiencing radical innovation boosted by the advancements in Information and Communication Technologies. Increasingly sophisticated chatbots are introducing novel approaches, re-shaping the dynamics among tourists and service providers, and fostering a remarkable behavioral change in the overall sector. Therefore, the objective of this paper is two-folded: (1) to highlight the academic and industrial standing points with respect to the current chatbots designed/deployed in the tourism sector and (2) to develop a proof-of-concept embodying the most prominent opportunities in the tourism sector. Design/methodology/approach : This work elaborates on the outcomes of a Systematic Literature Review (SLR) and a Focus Group (FG) composed of experts from the tourism industry. Moreover, it presents a proof-of-concept relying on the outcomes obtained from both SLR and FG. Eventually, the proof-of-concept has been tested with experts and practitioners of the tourism sector. Findings : Among the findings elicited by this paper, we can mention the quick evolution of chatbot-based solutions, the need for continuous investments, upskilling, system innovation to tackle the eTourism challenges and the shift toward new dimensions (i.e. tourist-to-tourist-to-chatbot and personalized multi-stakeholder systems). In particular, we focus on the need for chatbot-based activity and thematic aggregation for next-generation tourists and service providers. Originality/value : Both academic- and industrial-centered findings have been structured and discussed to foster the practitioners' future research. Moreover, the proof-of-concept presented in the paper is the first of its kind, which raised considerable interest from both technical and business-planning perspectives.

EREBOTS :

Scientific paper 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

Link to the publication

Summary:

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.

2020

Dynamic consent management for clinical trials via private blockchain technology

Scientific paper ArODES

Giuseppe Albanese, Jean-Paul Calbimonte, Michael Schumacher, Davide Calvaresi

Journal of ambient intelligence and humanized computing, 2020, vol. 11, no. 11, pp. 4909–4926

Link to the publication

Summary:

Clinical trials (CTs) are essential for the advancement of medical research, paving the way for the development and adoption of new treatments, and contributing to the evolution of healthcare. An essential factor for the success of a CT is the appropriate management of its participants and their personal data. According to the current regulations, collecting and using personal data from participants must comply with rigorous standards. Therefore, healthcare institutes need to obtain freely given, specific, informed, and unambiguous consent before being able to collect the data. Some of the major limitations of the current technological solutions are the lack of control over the granularity of consent grants, as well as the difficulty of handling dynamic changes of consent over time. In this paper, we present SCoDES, an approach for trusted and decentralized management of dynamic consent in clinical trials, based on blockchain technology (BCT). The usage of blockchain provides a set of features that allow maintaining consent information with trust guarantees while avoiding the need for a dedicated or centralized third trusted party. We provide a full implementation of SCoDES, made available as a self-contained infrastructure, with the possibility to interact with external services, and using hyperledger as a blockchain framework.

Agent-based modeling for ontology-driven analysis of patient trajectories

Scientific paper ArODES

Davide Calvaresi, Michael Schumacher, Jean-Paul Calbimonte

Journal of medical systems, 2020, vol. 44, no. 9, article 158

Link to the publication

Summary:

Patients are often required to follow a medical treatment after discharge, e.g., for a chronic condition, rehabilitation after surgery, or for cancer survivor therapies. The need to adapt to new lifestyles, medication, and treatment routines, can produce an individual burden to the patient, who is often at home without the full support of healthcare professionals. Although technological solutions –in the form of mobile apps and wearables– have been proposed to mitigate these issues, it is essential to consider individual characteristics, preferences, and the context of a patient in order to offer personalized and effective support. The specific events and circumstances linked to an individual profile can be abstracted as a patient trajectory, which can contribute to a better understanding of the patient, her needs, and the most appropriate personalized support. Although patient trajectories have been studied for different illnesses and conditions, it remains challenging to effectively use them as the basis for data analytics methodologies in decentralized eHealth systems. In this work, we present a novel approach based on the multi-agent paradigm, considering patient trajectories as the cornerstone of a methodology for modelling eHealth support systems. In this design, semantic representations of individual treatment pathways are used in order to exchange patient-relevant information, potentially fed to AI systems for prediction and classification tasks. This paper describes the major challenges in this scope, as well as the design principles of the proposed agent-based architecture, including an example of its use through a case scenario for cancer survivors support.

Real-time compliant stream processing agents for physical rehabilitation

Scientific paper ArODES

Davide Calvaresi, Jean-Paul Calbimonte

Sensors, 2020, vol. 20, no. 3, pp. 1-34

Link to the publication

Summary:

Digital rehabilitation is a novel concept that integrates state-of-the-art technologies for motion sensing and monitoring, with personalized patient-centric methodologies emerging from the field of physiotherapy. Thanks to the advances in wearable and portable sensing technologies, it is possible to provide patients with accurate monitoring devices, which simplifies the tracking of performance and effectiveness of physical exercises and treatments. Employing these approaches in everyday practice has enormous potential. Besides facilitating and improving the quality of care provided by physiotherapists, the usage of these technologies also promotes the personalization of treatments, thanks to data analytics and patient profiling (e.g., performance and behavior). However, achieving such goals implies tackling both technical and methodological challenges. In particular, (i) the capability of undertaking autonomous behaviors must comply with strict real-time constraints (e.g., scheduling, communication, and negotiation), (ii) plug-and-play sensors must seamlessly manage data and functional heterogeneity, and finally (iii) multi-device coordination must enable flexible and scalable sensor interactions. Beyond traditional top-down and best-effort solutions, unsuitable for safety-critical scenarios, we propose a novel approach for decentralized real-time compliant semantic agents. In particular, these agents can autonomously coordinate with each other, schedule sensing and data delivery tasks (complying with strict real-time constraints), while relying on ontology-based models to cope with data heterogeneity. Moreover, we present a model that represents sensors as autonomous agents able to schedule tasks and ensure interactions and negotiations compliant with strict timing constraints. Furthermore, to show the feasibility of the proposal, we present a practical study on upper and lower-limb digital rehabilitation scenarios, simulated on the MAXIM-GPRT environment for real-time compliance. Finally, we conduct an extensive evaluation of the implementation of the stream processing multi-agent architecture, which relies on existing RDF stream processing engines.

2019

The good, the bad, and the ethical implications of bridging blockchain and multi-agent systems

Scientific paper ArODES

Davide Calvaresi, Jean-Paul Calbimonte, Alevtina Dubovitskaya, Valerio Mattioli, Jean-Gabriel Piguet, Michael Schumacher

Information, 2019, vol. 10, no. 12, article 363

Link to the publication

Summary:

The agent based approach is a well established methodology to model distributed intelligent systems. Multi-Agent Systems (MAS) are increasingly employed in applications dealing with safety and information critical tasks (e.g., in eHealth, financial, and energy domains). Therefore, transparency and the trustworthiness of the agents and their behaviors must be enforced. For example, employing reputation based mechanisms can promote the development of trust. Nevertheless, besides recent early stage studies, the existing methods and systems are still unable to guarantee the desired accountability and transparency adequately. In line with the recent trends, we advocate that combining blockchain technology (BCT) and MAS can achieve the distribution of the trust, removing the need for trusted third parties (TTP), potential single points of failure. This paper elaborates on the notions of trust, BCT, MAS, and their integration. Furthermore, to attain a trusted environment, this manuscript details the design and implementation of a system reconciling MAS (based on the Java Agent DEvelopment Framework (JADE)) and BTC (based on Hyperledger Fabric). In particular, the agents’ interactions, computation, tracking the reputation, and possible policies for disagreement-management are implemented via smart contracts and stored on an immutable distributed ledger. The results obtained by the presented system and similar solutions are also discussed. Finally, ethical implications (i.e., opportunities and challenges) are elaborated before concluding the paper.

Towards profile and domain modelling in agent-based applications for behavior change

Book chapter ArODES

Jean-Paul Calbimonte, Davide Calvaresi, Fabien Dubosson, Michael Schumacher

Dans Demazeau, Yves, Advances in practical applications of survivable agents and multi-agent systems : the PAAMS collection (pp. 16-28). 2019, Cham : Springer

Link to the publication

Summary:

Health support programs play a vital role in public health and prevention strategies at local and national levels, for issues such as smoking cessation, physical rehabilitation, nutrition, or to regain mobility. A key success factor in these topics is related to the appropriate use of behavior change techniques, as well as tailored recommendations for users/patients, adapted to their goals and the continuous monitoring of their progress. Social networks interactions and the use of multi-agent technologies can further improve the effectiveness of these programs, especially through personalization and profiling of users and patients. In this paper we propose an agent-based model for supporting behavior change in eHealth programs. Moreover, we identify the main challenges in this area, especially regarding profile and domain modeling profiles for healthcare behavioral programs, where the definition of goals, expectations and argumentation play a key role in the success of a intervention.

A startup assessment approach based on multi-agent and blockchain technologies

Book chapter ArODES

Davide Calvaresi, Ekaterina Voronova, Jean-Paul Calbimonte, Valerio Mattioli, Michael Schumacher

Dans De La Prieta. Fernando, et al., Highlights of practical applications of survivable agents and multi-agent systems. The PAAMS collection: International Workshops of PAAMS 2019, Ávila, Spain, June 26–28, 2019, Proceedings (12 p.). 2019, Cham : Springer

Link to the publication

Summary:

The dynamic nature of startups is linked to both high risks in investments as well as potentially important financial benefits. A key aspect to manage interactions among investors, experts, and startups, is the establishment of trust guarantees. This paper presents the formalization and implementation of a system enforcing trust in the startup assessment domain. To do so, an existing architecture has been extended, incorporating a multi-agent community and related interactions via private blockchain technology. The developed system enables a trust-based community, immutably storing, tracking, and monitoring the agents’ interactions and reputations.

2018

Multi-agent systems and blockchain :

Book chapter ArODES

results from a systematic literature review

Davide Calvaresi, Alevtina Dubovitskaya, Jean-Paul Calbimonte, Kuldar Taveter, Michael Schumacher

Dans An, Bo, Bajo, Javier, Demazeau, Yves, Fernández-Caballero, Antonio, Advances in practical applications of agents, multi-agent systems, and complexity : the PAAMS collection : 16th International Conference, PAAMS 2018, Toledo, Spain, June 20–22, 2018, Proceedings (Pp. 110-126). 2018, Cham : Springer

Link to the publication

Summary:

Multi-Agent Systems (MAS) technology is widely used for the development of intelligent distributed systems that manage sensitive data (e.g., ambient assisted living, healthcare, energy trading). To foster accountability and trusted interactions, recent trends advocate the use of blockchain technologies (BCT) for MAS. Although most of these approaches have only started exploring the topic, there is an impending need for establishing a research road-map, as well as identifying scientific and technological challenges in this scope. As a first necessary step towards this goal, this paper presents a systematic literature review of studies involving MAS and BCT as reconciling solutions. Aiming at providing a comprehensive overview of their application domains, we analyze motivations, assumptions, requirements, strengths, and limitations presented in the current state of the art. Moreover, discussing the future challenges, we introduce our vision on how MAS and BCT could be combined in different application scenarios.

2017

The MedRed ontology for representing clinical data acquisition metadata

Book chapter ArODES

Jean-Paul Calbimonte, Fabien Dubosson, Roger Hilfiker, Alexandre Cotting, Michael Schumacher

The Semantic Web – ISWC 2017 : 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part II (pp. 38-47). 2017, Cham : Springer

Link to the publication

Summary:

Electronic Data Capture (EDC) software solutions are progressively being adopted for conducting clinical trials and studies, carried out by biomedi-cal, pharmaceutical and health-care research teams. In this paper we present the MedRed Ontology, whose goal is to represent the metadata of these studies, using well-established standards, and reusing related vocabularies to describe essential aspects, such as validation rules, composability, or provenance. The paper de-scribes the design principles behind the ontology and how it relates to existing models and formats used in the industry. We also reuse well-known vocabularies and W3C recommendations. Furthermore, we have validated the ontology with ex-isting clinical studies in the context of the MedRed project, as well as a collection of metadata of well-known studies. Finally, we have made the ontology available publicly following best practices and vocabulary sharing guidelines.

Toward self-monitoring smart cities :

Scientific paper ArODES

the OpenSense2

Jean-Paul Calbimonte, Julien Eberle, Karl Aberer

Informatik-Spektrum, February 2017, vol. 40, issue 1, pp. 75–87

Link to the publication

Summary:

The sustained growth of urban settlements in the last years has had an inherent impact on the environment and the quality of life of their inhabitants. In order to support sustainability and improve quality of life in this context, we advocate the fostering of ICT-empowered initiatives that allow citizens to self-monitor their environment and assess the quality of the resources in their surroundings. More concretely, we present the case of such a self-monitoring Smart City platform for estimating the air quality in urban environments at high resolution and large scale. Our approach is a combination of mobile and human sensing that exploits both dedicated and participatory monitoring. We identify the main challenges in such a crowdsensing scenario for Smart Cities, and in particular we analyze issues related to scalability, accuracy, accessibility, privacy, and discoverability, among others. Moreover, we show that our approach has the potential to empower citizens to diagnose their environment using mobile and portable sensing devices, combining their personal data with a public higher accuracy air quality network.

MedRed :

Scientific paper ArODES

a health-care data acquisition service for research purposes

Jean-Paul Calbimonte, Fabien Dubosson, Roger Hil?ker, Alexandre Cotting, Michael Schumacher

Swiss Medical Informatics, 2017, vol. 33, pp. 1-7

Link to the publication

Summary:

Research in the health-care domain requires the collection of important and exhaustive datasets, in order to validate a scientific hypothesis, or to assess the effectiveness of a treatment, technology, medicine, or procedure. The data acquisition phase for this type of work requires an often under-estimated amount of time and effort, while needing to keep high quality standards for the entire process. Many of the tasks associated with data acquisition are often carried out manually, resulting in error-prone procedures, hand-transcription, inaccuracy, and time delays to produce a resulting usable dataset. This paper presents MedRed (Medical Research Data Acquisition Platform ), a platform and a service designed to facilitate the data acquisition process for researchers in the health-care do-main, using the REDCap software for data capture. This service is available in a first stage, for all scientists of the HES-SO (University of Applied Sciences and Arts Western Switzerland) schools in Switzerland, and partially supported by the SwissUniversities CUS-P2 program.

Semantic representation and processing of hypoglycemic events derived from wearable sensor data

Scientific paper ArODES

Jean-Paul Calbimonte, Jean-Eudes Ranvier, Fabien Dubosson, Karl Aberer

Journal of ambient intelligence and smart environments, 2017, vol. 9, no. 1, pp. 97-109

Link to the publication

Summary:

Diabetes Type 1 is a metabolic disease which results in a lack of insulin production, causing high glucose levels in the blood. It is crucial for diabetic patients to balance this glucose level, and they depend on external substances to do so. In order to keep this level under control, they usually need to resort to invasive glucose control methods, such as taking a sample drop of blood from their finger and have it analyzed. Recently, other directions emerged to offer alternative ways to estimate glucose level, using indirect sensor measurements including ECG monitoring and other physiological parameters. This paper showcases a framework for inferring semantically annotated glycemic events on the patient, which leverages data from mobile wearable sensors deployed on a sport-belt. This work is part of the D1namo project for non-invasive diabetes monitoring, and focuses on the representation and query processing of the data produced by the wearable sensors, using semantic technologies and vocabularies that extend existing Web standards. Furthermore, this work shows how different layers of data, from raw measurements to complex events can be represented and linked in this framework, and experimental evidence is provided of how these layers can be efficiently exploited using an RDF Stream Processing engine.

2016

A query model to capture event pattern matching in RDF stream processing query languages

Book chapter ArODES

Daniele Dell’Aglio, Minh Dao-Tran, Jean-Paul Calbimonte, Danh Le Phuoc, Emanuele Della Valle

Knowledge engineering and knowledge management : 20th International Conference, EKAW 2016, Bologna, Italy, November 19-23, 2016, Proceedings (pp. 145-162). 2016, Cham, Springer : Cham, Springer

Link to the publication

Summary:

The current state of the art in RDF Stream Processing (RSP) proposes several models and implementations to combine Semantic Web technologies with Data Stream Management System (DSMS) operators like windows. Meanwhile, only a few solutions combine Semantic Web and Complex Event Processing (CEP), which includes relevant features, such as identifying sequences of events in streams. Current RSP query languages that support CEP features have several limitations: EP-SPARQL can identify sequences, but its selection and consumption policies are not all formally defined, while C-SPARQL offers only a naive support to pattern detection through a timestamp function. In this work, we introduce an RSP query language, called RSEP-QL, which supports both DSMS and CEP operators, with a special interest in formalizing CEP selection and consumption policies. We show that RSEP-QL captures EP-SPARQL and C-SPARQL, and offers features going beyond the ones provided by current RSP query languages.

2023

Study-Buddy :

Conference ArODES

a knowledge graph-powered learning companion for school students

Fernanda Martinez, Diego Collarana, Davide Calvaresi, Martin Arispe, Carla Florida, Jean-Paul Calbimonte

The Semantic Web: ESWC 2023 Satellite Events

PEOPLE@HES-SO Directory and Skills inventory

Calbimonte Jean-Paul

Professeur-e HES Associé-e

Main skills

Medical Informatics

Semantic Web

Ontology Engineering

Web Technologies

Stream Processing

eHealth

Knowledge Management

Professeur-e HES Associé-e

PEOPLE@HES-SO
Directory and Skills inventory