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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

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: 115'330 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

2024

Towards representing processes and reasoning with process descriptions on the web

Scientific paper ArODES

Andreas Harth, Tobias Käfer, Anisa Rula, Jean-Paul Calbimonte, Eduard Kamburjan, Martin Giese

Transactions on Graph Data and Knowledge (TGDK), Vol. 2, Issue 1, 1:1–1:32

Link to the publication

Summary:

We work towards a vocabulary to represent processes and temporal logic specifications as graph-structured data. Different fields use incompatible terminologies for describing essentially the same process-related concepts. In addition, processes can be represented from different perspectives and levels of abstraction: both state-centric and event-centric perspectives offer distinct insights into the underlying processes. In this work, we strive to unify the representation of processes and related concepts by leveraging the power of knowledge graphs. We survey approaches to representing processes and reasoning with process descriptions from different fields and provide a selection of scenarios to help inform the scope of a unified representation of processes. We focus on processes that can be executed and observed via web interfaces. We propose to provide a representation designed to combine state-centric and event-centric perspectives while incorporating temporal querying and reasoning capabilities on temporal logic specifications. A standardised vocabulary and representation for processes and temporal specifications would contribute towards bridging the gap between the terminologies from different fields and fostering the broader application of methods involving temporal logics, such as formal verification and program synthesis.

Grounding stream reasoning research

Scientific paper ArODES

Pieter Bonte, Jean-Paul Calbimonte, Daniel de Leng, Daniele Dell’Aglio, Emanuele Della Valle, Thomas Eiter, Federico Giannini, Fredrik Heintz, Konstantin Schekotihin, Danh Le-Phuoc, Alessandra Mileo, Patrik Schneider, Riccardo Tommasini, Jacopo Urbani, Giacomo Ziffer

Transactions on Graph Data and Knowledge (TGDK), Vol. 2, Issue 1, 2:1-2:47

Link to the publication

Summary:

In the last decade, there has been a growing interest in applying AI technologies to implement complex data analytics over data streams. To this end, researchers in various fields have been organising a yearly event called the "Stream Reasoning Workshop" to share perspectives, challenges, and experiences around this topic. In this paper, the previous organisers of the workshops and other community members provide a summary of the main research results that have been discussed during the first six editions of the event. These results can be categorised into four main research areas: The first is concerned with the technological challenges related to handling large data streams. The second area aims at adapting and extending existing semantic technologies to data streams. The third and fourth areas focus on how to implement reasoning techniques, either considering deductive or inductive techniques, to extract new and valuable knowledge from the data in the stream. This summary is written not only to provide a crystallisation of the field, but also to point out distinctive traits of the stream reasoning community. Moreover, it also provides a foundation for future research by enumerating a list of use cases and open challenges, to stimulate others to join this exciting research area.

Knowledge engineering for wind energy

Scientific paper ArODES

Yuriy Marykovskiy, Thomas Clark, Justin Day, Marcus Wiens, Charles Henderson, Julian Quick, Imad Abdallah, Anna Maria Sempreviva, Jean-Paul Calbimonte, Eleni Chatzi, Sarah Barber

Wind Energy Science, 9, 4, 883-917

Link to the publication

Summary:

With the rapid evolution of the wind energy sector, there is an ever-increasing need to create value from the vast amounts of data made available both from within the domain and from other sectors. This article addresses the challenges faced by wind energy domain experts in converting data into domain knowledge, connecting and integrating them with other sources of knowledge, and making them available for use in next-generation artificial intelligence systems. To this end, this article highlights the role that knowledge engineering can play in the digital transformation of the wind energy sector. It presents the main concepts underpinning knowledge-based systems and summarises previous work in the areas of knowledge engineering and knowledge representation in a manner that is relevant and accessible to wind energy domain experts. A systematic analysis of the current state of the art on knowledge engineering in the wind energy domain is performed with available tools put into perspective by establishing the main domain actors and their needs, as well as identifying key problematic areas. Finally, recommendations for further development and improvement are provided.

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

Link to the conference

Summary:

Large Language Models (LLMs) have the potential to substantially improve educational tools for students. However, they face limitations, including factual accuracy, personalization, and the lack of control over the sources of information. This paper presents Study-Buddy, a prototype of a conversational AI assistant for school students to address the above-mentioned limitations. Study-Buddy embodies an AI assistant based on a knowledge graph, LLMs models, and computational persuasion. It is designed to support educational campaigns as a hybrid AI solution. The demonstrator showcases interactions with Study-Buddy and the crucial role of the Knowledge Graph for the bot to present the appropriate activities to the students. A video demonstrating the main features of Study-Buddy is available at: https://youtu.be/DHPTsN1RI9o.

Serendipity and diversity boosting for personalized streaming media recommendation

Conference 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)

Link to the conference

Summary:

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

Conference 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)

Link to the conference

Summary:

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

Conference 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

Link to the conference

Summary:

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

Conference 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)

Link to the conference

Summary:

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.

Towards explainable visionary agents :

Conference ArODES

license to dare and imagine

Giovanni Ciatto, Amro Najjar, Jean-Paul Calbimonte, Davide Calvaresi

Explainable and Transparent AI and Multi-Agent Systems : Third International Workshop, EXTRAAMAS 2021, Virtual Event, May 3–7, 2021, Revised Selected Papers

Link to the conference

Summary:

Since their appearance, computer programs have embodied discipline and structured approaches and methodologies. Yet, to this day, equipping machines with imaginative and creative capabilities remains one of the most challenging and fascinating goals we pursue. Intelligent software agents can behave intelligently in well-defined scenarios, relying on Machine Learning (ML), symbolic reasoning, and the ability of their developers for tailoring smart behaviors to specific application domains. However, to forecast the evolution of all possible scenarios is unfeasible. Thus, intelligent agents should autonomously/creatively adapt to the world’s mutability. This paper investigates the meaning of imagination in the context of cognitive agents. In particular, it addresses techniques and approaches to let agents autonomously imagine/simulate their course of action and generate explanations supporting it, and formalizes thematic challenges. Accordingly, we investigate research areas including: (i) reasoning and automatic theorem proving to synthesize novel knowledge via inference; (ii) automatic planning and simulation, used to speculate over alternative courses of action; (iii) machine learning and data mining, exploited to induce new knowledge from experience; and (iv) biochemical coordination, which keeps imagination dynamic by continuously reorganizing it.

A platform for difficulty assessment and recommendation of hiking trails

Conference ArODES

Jean-Paul Calbimonte, Simon Martin, Davide Calvaresi, Alexandre Cotting

Information and communication technologies in tourism 2021 : proceedings of the ENTER 2021 eTourism Conference

Link to the conference

Summary:

In recent years, the popularity of hiking has steadily increased 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. In this paper, we present Syris, a geo-information system for hiking itineraries that incorporates Points-Of-Difficulty to assess the level of effort, technique, and risk of hiking trails. The system allows users to filter itineraries and obtain recommendations based on the assessment of difficulty following a well-established methodology. The system has been implemented, deployed and tested with real data in the region of Val d’Anniviers in Switzerland, and is openly available to enable further developments and refinement.

The evolution of chatbots in tourism :

Conference ArODES

a systematic literature review

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

Information and Communication Technologies in Tourism 2021 : Proceedings of the ENTER 2021 eTourism Conference, January 19–22, 2021

Link to the conference

Summary:

In the last decade, Information and Communication Technologies have revolutionized the tourism and hospitality sector. One of the latest innovations shaping new dynamics and fostering a remarkable behavioral change in the interaction between the service provider and the tourist is the employment of increasingly sophisticated chatbots. This work analyzes the most recent systems presented in the literature (since 2016) investigated via 12 research questions. The often appreciated quick evolution of such solutions is the primary outcome. However, such technological and financial fast-pace requires continuous investments, upskilling, and system innovation to tackle the eTourism challenges, which are shifting towards new dimensions.

2020

Decentralized management of patient profilesand trajectories through semantic web agents

Conference ArODES

Jean-Paul Calbimonte, Davide Calvaresi, Michael Schumacher

Proceedings of the 3rd International Workshop on Semantic Web Meets Health Data Management (SWH 2020)

Link to the conference

Summary:

The usage of healthcare data for analytics and patient applications has increased in recent years opening a number of technical, ethical and scientific challenges. Among these, those related to the management of personal and sensitive health data have been addressed through decentralized solutions for patient data, often implemented and modelled using distributed agents and semantic technologies. In this paper, we present a technical summary of our previous works in this area, comprising efforts to: (i) use ontology models to represent patient trajectories,(ii) employ agent-based architectures to model and employ decentralized patient data exchanges, (iii) define agent cooperation and negotiation strategies for healthcare data interactions, (iv) adopt semantic data models for privacy-aware agents, and (v) implement multi-agent systems for real-time healthcare data processing

Ethical concerns and opportunities in binding intelligent systems and blockchain technology

Conference ArODES

Davide Calvaresi, Jean-Gabriel Piguet, Jean-Paul Calbimonte, Timotheus Kampik, Amro Najjar, Guillaume Gadek, Michael Schumacher

Proceedings of International Workshops of PAAMS 2020 : Highlights in Practical Applications of Agents, Multi-Agent Systems, and Trust-worthiness.

Link to the conference

Summary:

Intelligent systems are becoming increasingly complex and pervade a broad range of application domains, including safety-critical systems such as e-health, finance, and energy management. Traditional approaches are no longer capable of addressing the demand for trust and transparency in these applications. Hence, the current decade is demanding intelligent systems to be autonomous, and in particular explainable, transparent, and trustworthy. To satisfy such requirements, and therefore to comply with the recent EU regulations in the matter (e.g., GDPR), intelligent systems (e.g., Multi-Agent Systems - MAS) and technologies enabling tamper-proof and distributed consensus (e.g., Blockchain Technology - BCT) are conveying into reconciling solutions. Recently, the empowerment of MAS with BCT (and the use of BCT themselves) has gained considerable momentum, raising challenges, and unveiling opportunities. However, several ethical concerns have yet to be faced. This paper elaborates on the entanglement among ethical and technological challenges while proposing and discussing approaches that address these emerging research opportunities.

Personal data privacy semantics in multi-agent systems interactions

Conference ArODES

Davide Calvaresi, Michael Schumacher, Jean-Paul Calbimonte

Proceedings of the 18th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2020) : Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness

Link to the conference

Summary:

In recent years, we have witnessed the growth of applications relying on the use and processing of personal data, especially in the health and well-being domains. Users themselves produce these data (e.g., through self-reported data acquisition, or personal devices such as smartphones, smartwatches or other wearables). A key challenge in this context is to guarantee the protection of personal data privacy, respecting the rights of users for deciding about data reuse, consent to data processing and storage, anonymity conditions, or the right to withhold or delete personal data. With the enforcement of recent regulations in this domain, such as the GDPR, applications are required to guarantee compliance, challenging current practices for personal data management. In this paper, we address this problem in the context of decentralized personal data applications, which may need to interact and negotiate conditions of data processing and reuse. Following a distributed paradigm without a top-down organization, we propose an agent-based model in which personal data providers and data consumers are embedded into privacy-aware agents capable of negotiating and coordinating data reuse, consent, and policies, using semantic vocabularies for privacy and provenance.

SEAMLESS :

Conference ArODES

simulation and analysis for multi-agent system in time-constrained environments

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

Link to the conference

Summary:

The correctness of a system operating in time-constrained scenarios leverages on both precision and delivery time of its outcome. This paper presents SEAMLESS, a system enabling the design, simulation, and in-depth analysis of Multi-Agent Systems (MAS). In particular, SEAMLESS allows defining in detail the agents’ knowledge (set of tasks it might execute), needs (set of tasks to be negotiated), local scheduler (execution of the task-set), negotiation protocols, possible communication delays, and heuristics related to the parameters mentioned above. This tool is pivotal in the strive to study and realize real-time MAS.

Semantic data models for hiking trail difficulty assessment

Conference ArODES

Jean-Paul Calbimonte, Simon Martin, Davide Calvaresi, Nancy Zappelaz, Alexandre Cotting

Proceedings of Information and Communication Technologies in Tourism 2020 : proceedings of the International Conference in Surrey

Link to the conference

Summary:

Hiking is a popular outdoor activity that if practiced regularly, can bring significant health benefits. Moreover, considering that hikers range from expert mountaineers to older adults with limited physical capabilities, it touches a large target audience, and is strategically included in several tourism packages across the globe. Thus, a precise characterization of the tracks, especially regarding their points of difficulty, is crucial to effectively cope with the challenge of identifying the best-suited hiking trails for heterogeneous users. This paper introduces a semantic model for representing and integrating the main characteristics of a track, including their different types of difficulties, using Semantic Web ontologies. The construction of knowledge graphs that use such a model may constitute a first step towards a system for personalized recommendations of trails based on difficulty-classification criteria.

2019

Autonomous RDF stream processing for IoT edge devices

Conference ArODES

Manh Nguyen-Duc, Anh Le-Tuan, Jean-Paul Calbimonte, Danh Le-Phuoc, Manfred Hauswirth

Proceedings of the 9th Joint International Semantic Technology Conference (JIST2019)

Link to the conference

Summary:

The wide adoption of increasingly cheap and computationally powerful single-board computers, has triggered the emergence of new paradigms for collaborative data processing among IoT devices. Motivated by the billions of ARM chips having been shipped as IoT gateways so far, our paper proposes a novel continuous federation approach that uses RDF Stream Processing (RSP) engines as autonomous processing agents. These agents can coordinate their resources to distribute processing pipelines by delegating partial workloads to their peers via subscribing continuous queries. Our empirical study in “cooperative sensing” scenarios with resourceful experiments on a cluster of Raspberry Pi nodes shows that the scalability can be significantly improved by adding more autonomous agents to a network of edge devices on demand. The findings open several new interesting follow-up research challenges in enabling semantic interoperability for the edge computing paradigm.

Social network chatbots for smoking cessation :

Conference ArODES

agent and multi-agent frameworks

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

Proceedings of WI '19 : IEEE/WIC/ACM International Conference on Web Intelligence

Link to the conference

Summary:

Asynchronous messaging is leading human-machine interaction due to the boom of mobile devices and social networks. The recent release of dedicated APIs from messaging platforms boosted the development of computer programs able to conduct conversations, (i.e., chatbots), which have been adopted in several domain-specific contexts. This paper proposes SMAG: a chatbot framework supporting a smoking cessation program (JDF) deployed on a social network. In particular, it details the single-agent implementation, the campaign results, a multi-agent design for SMAG enabling the modelization of personalized behavior and user profiling, and highlighting of coupling chatbot technology with and multi-agent systems.

Towards semantic models for profiling and behavior change in eHealth applications

Conference ArODES

Jean-Paul Calbimonte, Fabien Dubosson, Michael Schumacher

Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems co-located with 15th International Conference on Semantic Systems (SEMANTiCS 2019)

Link to the conference

Summary:

Behavior change is a complex process in which people receive support in order to improve aspects of their behavior, for instance re- garding their health or lifestyle. Although there exist several theoretical approaches to model behavior change, including abstractions that can be applied to different use-cases, these are not easily translated into reusable components that can be integrated into implementable systems for per- suasion. This work discusses the need for an ontology-based approach to modelling interactions in eHealth systems, with the goal of achieving behavior change. This contribution includes an analysis of current mod- elling needs in behavior change, specially regarding: stages of change, motivation & ability factors, plans & actions, argumentation, and do- main modeling.

Towards semantic models for proceeding and behavior change in eHealth applications

Conference ArODES

Jean-Paul Calbimonte, Fabien Dubosson, Michael Schumacher

Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems co-located with 15th International Conference on Semantic Systems (SEMANTiCS 2019)

Link to the conference

Summary:

Behavior change is a complex process in which people receive support in order to improve aspects of their behavior, for instance regarding their health or lifestyle. Although there exist several theoretical approaches to model behavior change, including abstractions that can be applied to different use-cases, these are not easily translated into reusable components that can be integrated into implementable systems for persuasion. This work discusses the need for an ontology-based approach to modelling interactions in eHealth systems, with the goal of achieving behavior change. This contribution includes an analysis of current modelling needs in behavior change, specially regarding: stages of change, motivation & ability factors, plans & actions, argumentation, and domain modeling

Semi-automatic semantic enrichment of personal data streams

Conference ArODES

Jean-Paul Calbimonte, Fabien Dubosson, Ilia Kebets, Pierre-Mikael Legris, Michael Schumacher

Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems co-located with 15th International Conference on Semantic Systems (SEMANTiCS 2019)

Link to the conference

Summary:

Current information technologies allow people to acquire personal data related to their health, lifestyle, behavior, and activities, often using wearable and mobile devices. Personal data management technologies have emerged recently, in order to cope with the requirements of this type of data, ranging from personal clouds to self-storage solutions. Pryv.io is a comprehensive solution for managing this particularly sensible type of data streams, focusing both on data privacy and decentralization. In this paper, we describe SemPryv, a system aiming at providing a semantization mechanism for enriching personal data streams with standardized specialized vocabularies from third-party providers. It relies on third providers of semantic concepts, and includes rule-based mechanisms for facilitating the semantization process. A full implementation of SemPryv has been produced, pluggable to the existing Pryv.io platform, showing the feasibility of the approach.

Stream reasoning agents

Conference ArODES

Riccardo Tommasini, Davide Calvaresi, Jean-Paul Calbimonte

Proceedings of the International Conference on Autonomous Agent and Multi-Agent Systems (AAMAS)

Link to the conference

Summary:

Data streams are increasingly needed for different types of applications and domains, where dynamicity and data velocity are of foremost importance. In this context, research challenges raise regarding the generation, publication, processing, and discovery of these streams, especially in distributed, heterogeneous and collaborative environments such as the Web. Stream reasoning has addressed some of these challenges in the last decade, presenting a novel data processing paradigm that lays at the intersection among semantic data modeling, stream processing, and inference techniques. However, stream reasoning works have focused almost exclusively on architectures and approaches that assume an isolated processing environment. Therefore, they lack, in general, the means for discovering, collaborating, negotiating, sharing, or validating data streams on a highly heterogeneous ecosystem as the Web. Agents and multi-agent systems research has long developed principles and foundations for enabling some of these features, although usually under assumptions that require to be revised in order to comply with the characteristics of data streams. This paper presents a vision for a Web of stream reasoning agents, capable of sharing not only streaming data, but also processing duties, using collaboration and negotiation protocols, while relying on common vocabularies and protocols that take into account the high dynamicity of their knowledge, goals, and behavioral patterns.

2018

SanTour :

Conference ArODES

towards personalized hiking trails adapted to health profiles

Jean-Paul Calbimonte, Nancy Zappellaz, Emeline Hébert, Maya Simon, Nicolas Délétroz, Roger Hilfiker, Alexandre Cotting

Proceedings of the 1st International Workshop on Knowledge Graphs on Travel and Tourism at the 18th ICWE 2018

Link to the conference

Summary:

Health tourism represents a promising niche still insufficiently exploited in Europe and Switzerland. Hiking has been a popular tourist activity for years and staying healthy is an important motivation for hiking. However, physical and psychological limitations in potential hik- ers often represent an unsurmountable barrier to complete a particular path. This mismatch between trail and user results in a poor visitor ex- perience, affecting negatively both the user and the touristic destination. This paper presents SanTour, a novel concept in health tourism centered on the needs of visitors by considering their physical capacities and lim- its, as well as their expectations. SanTour exploits two main knowledge bases: one centered on the user, including a health profile, and another centered on the hiking trails. In a pilot phase, the concept has been prototyped and tested on a limited scale, with support from a tourist of- fice in Switzerland. We plan to further develop this application that will provide an innovative service to hikers by cross-referencing their physical abilities and the characteristics of the hiking trails.

2017

On a web of data streams

Conference ArODES

Daniele Dell'Aglio, Danh Le Phuoc, Anh Le-Tuan, Muhammad Intizar Ali, Jean-Paul Calbimonte

Proceedings of the workshop on decentralizing the semantic Web 2017 co-located with 16th International Semantic Web Conference (ISWC 2017)

Link to the conference

Summary:

With the growing adoption of IoT and sensor technologies, an enormous amount of data is being produced at a very rapid pace and in different application domains. This sensor data consists mostly of live data streams containing sensor observations, generated in a distributed fashion by multiple heterogeneous infrastructures with minimal or no interoperability. RDF streams emerged as a model to represent data streams, and RDF Stream Processing (RSP) refers to a set of technologies to process such data. RSP research has produced several successful results and scientific output, but it can be evidenced that in most of the cases the Web dimension is marginal or missing. It also noticeable the lack of proper infrastructures to enable the exchange of RDF streams over heterogeneous and different types of RSP systems, whose features may vary from data generation to querying, and from reasoning to visualisation. This article defines a set of requirements related to the creation of a web of RDF stream processors. These requirements are then used to analyse the current state of the art, and to build a novel proposal, WeSP, which addresses these concerns.

Linked data notifications for RDF streams

Conference ArODES

Jean-Paul Calbimonte

Proceedings of the Web Stream Processing workshop (WSP 2017) and the 2nd International Workshop on Ontology Modularity, Contextuality, and Evolution (WOMoCoE 2017) co-located with 16th International Semantic Web Conference (ISWC 2017)

Link to the conference

Summary:

RDF streams. We propose extensions to the protocol for this particular use case, and we show the feasibility with an initial implementation of an LDN-based RDF stream interface.Linked Data Notifications (LDN) is a W3C recommendation for interchanging notifications on the Web through a decentralized protocol. As LDN is not specific to any application domain, this paper analyzes how it can be used to enable a decentralized communication among senders, receivers and consumers of RDF streams. We propose extensions to the protocol for this particular use case, and we show the feasibility with an initial implementation of an LDN-based RDF stream.

Detection of hypoglycemic events through wearable sensors

Conference ArODES

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

Proceedings of the 1st Workshop on Semantic Web Technologies for Mobile and Pervasive Environments co-located with the 13th Extended Semantic Web Conference (ESWC 2016)

Link to the conference

Summary:

Diabetic patients are dependent on external substances to balance their blood glucose level. In order to control this level, they historically needed to sample a drop a blood from their hand and have it analyzed. Recently, other directions emerged to offer alternative ways to estimate glucose level. In this paper, we present our ongoing work on a framework for inferring semantically annotated glycemic events on the patient, which leverages mobile wearable sensors on a sport-belt.

2016

TripleWave :

Conference ArODES

spreading RDF streams on the web

Andrea Mauri, Jean-Paul Calbimonte, Daniele Dell’Aglio, Marco Balduini, Marco Brambilla, Emanuele Della Valle, Karl Aberer

Proceedings of the 15th International Semantic Web Conference (ISWC2016)

Link to the conference

Summary:

Processing data streams is increasingly gaining momentum, given the need to process these flows of information in real-time and at Web scale. In this context, RDF Stream Processing (RSP) and Stream Reasoning (SR) have emerged as solutions to combine semantic technologies with stream and event processing techniques. Research in these areas has proposed an ecosystem of solutions to query, reason and perform real-time processing over heterogeneous and distributed data streams on the Web. However, so far one basic building block has been missing: a mechanism to disseminate and exchange RDF streams on the Web. In this work we close this gap, proposing TripleWave, a reusable and generic tool that enables the publication of RDF streams on the Web. The features of TripleWave were selected based on requirements of real use-cases, and support a diverse set of scenarios, independent of any specific RSP implementation. TripleWave can be fed with existing Web streams (e.g. Twitter and Wikipedia streams) or time-annotated RDF datasets (e.g. the Linked Sensor Data dataset). It can be invoked through both pull- and push-based mechanisms, thus enabling RSP engines to automatically register and receive data from TripleWave.

Achievements

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

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