2025
Other
Restricted
InfraScience research activity report 2024
Angioni S., Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim Ahmed, La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Oliviero A., Pagano P., Panichi G., Teresa M. T., Pavone G., Peccerillo B., Piccioli T., Procaccini M., Straccia U., Vannini G. L., Versienti L.
InfraScience is a research group within the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR), based in Pisa. This activity report outlines the group's research achievements and initiatives throughout 2024. InfraScience focused its efforts on key challenges in the areas of Data Infrastructures, e-Science, and Intelligent Systems, maintaining a strong synergy between research and development and a firm commitment to open science principles. In 2024, the group played a leading role in the development and evolution of two major Open Science infrastructures: D4Science and OpenAIRE. InfraScience researchers contributed significantly to the scientific community through the publication of peer-reviewed papers, active participation in EU-funded research projects, organization of international conferences and training activities, and engagement in various working groups and task forces. This report highlights these contributions and underscores the group's ongoing dedication to advancing open, collaborative, and impactful science.DOI: 10.32079/isti-ar-2025/001
Metrics:
Angioni S., Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim Ahmed, La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Oliviero A., Pagano P., Panichi G., Teresa M. T., Pavone G., Peccerillo B., Piccioli T., Procaccini M., Straccia U., Vannini G. L., Versienti L.
InfraScience is a research group within the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR), based in Pisa. This activity report outlines the group's research achievements and initiatives throughout 2024. InfraScience focused its efforts on key challenges in the areas of Data Infrastructures, e-Science, and Intelligent Systems, maintaining a strong synergy between research and development and a firm commitment to open science principles. In 2024, the group played a leading role in the development and evolution of two major Open Science infrastructures: D4Science and OpenAIRE. InfraScience researchers contributed significantly to the scientific community through the publication of peer-reviewed papers, active participation in EU-funded research projects, organization of international conferences and training activities, and engagement in various working groups and task forces. This report highlights these contributions and underscores the group's ongoing dedication to advancing open, collaborative, and impactful science.DOI: 10.32079/isti-ar-2025/001
Metrics:
2025
Conference article
Open Access
Novel benchmark for NER in the wastewater and stormwater domain
Cardillo F. A., Debole F., Frontini F., Aelami M., Chahinian N., Conrad S.
Efficient wastewater and stormwater management is mandatory for sustainable cities. Extracting structured knowledge from reports and regulations is challenging due to domain-specific terminology and multilingual contexts. This work focuses on domain-specific Named Entity Recognition (NER) as a first step towards effective relation and information extraction to support decision making. A multilingual benchmark is crucial for evaluating these methods. This study develops a French-Italian domain-specific text corpus for wastewater management. It evaluates state-of-the-art NER methods, including LLM-based approaches, to provide a reliable baseline for future strategies and explores automated annotation projection in view of an extension of the corpus to new languages.Source: COLLOQUIUM IN INFORMATION SCIENCE AND TECHNOLOGY, pp. 226-231. Marrakech, Morocco, 2025
DOI: 10.1109/cist65886.2025.11224095
DOI: 10.48550/arxiv.2506.01938
Metrics:
Cardillo F. A., Debole F., Frontini F., Aelami M., Chahinian N., Conrad S.
Efficient wastewater and stormwater management is mandatory for sustainable cities. Extracting structured knowledge from reports and regulations is challenging due to domain-specific terminology and multilingual contexts. This work focuses on domain-specific Named Entity Recognition (NER) as a first step towards effective relation and information extraction to support decision making. A multilingual benchmark is crucial for evaluating these methods. This study develops a French-Italian domain-specific text corpus for wastewater management. It evaluates state-of-the-art NER methods, including LLM-based approaches, to provide a reliable baseline for future strategies and explores automated annotation projection in view of an extension of the corpus to new languages.Source: COLLOQUIUM IN INFORMATION SCIENCE AND TECHNOLOGY, pp. 226-231. Marrakech, Morocco, 2025
DOI: 10.1109/cist65886.2025.11224095
DOI: 10.48550/arxiv.2506.01938
Metrics:
See at:
arXiv.org e-Print Archive 
| doi.org 
| doi.org | SPIRE - Sciences Po Institutional REpository | CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2024
Conference article
Open Access
Italian word embeddings for the medical domain
Cardillo F. A., Debole F.
Neural word embeddings have proven valuable in the development of medical applications. However, for the Italian language, there are no publicly available corpora, embeddings, or evaluation resources tailored to this domain. In this paper, we introduce an Italian corpus for the medical domain, that includes texts from Wikipedia, medical journals, drug leaflets, and specialized websites. Using this corpus, we generate neural word embeddings from scratch. These embeddings are then evaluated using standard evaluation resources, that we translated into Italian exploiting the concept graph in the UMLS Metathesaurus. Despite the relatively small size of the corpus, our experimental results indicate that the new embeddings correlate well with human judgments regarding the similarity and the relatedness of medical concepts. Moreover, these medical-specific embeddings outperform a baseline model trained on the full Wikipedia corpus, which includes the medical pages we used. We believe that our embeddings and the newly introduced textual resources will foster further advancements in the field of Italian medical Natural Language Processing.Project(s): DeepHealth
, TAILOR , STARWARS
Cardillo F. A., Debole F.
Neural word embeddings have proven valuable in the development of medical applications. However, for the Italian language, there are no publicly available corpora, embeddings, or evaluation resources tailored to this domain. In this paper, we introduce an Italian corpus for the medical domain, that includes texts from Wikipedia, medical journals, drug leaflets, and specialized websites. Using this corpus, we generate neural word embeddings from scratch. These embeddings are then evaluated using standard evaluation resources, that we translated into Italian exploiting the concept graph in the UMLS Metathesaurus. Despite the relatively small size of the corpus, our experimental results indicate that the new embeddings correlate well with human judgments regarding the similarity and the relatedness of medical concepts. Moreover, these medical-specific embeddings outperform a baseline model trained on the full Wikipedia corpus, which includes the medical pages we used. We believe that our embeddings and the newly introduced textual resources will foster further advancements in the field of Italian medical Natural Language Processing.Project(s): DeepHealth
, TAILOR , STARWARS
See at:
aclanthology.org | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
PN-OWL: a two-stage algorithm to learn fuzzy concept inclusions from OWL 2 ontologies
Cardillo F. A., Debole F., Straccia U.
Given a target class T of an OWL 2 ontology, positive (and possibly negative) examples of T, we address the problem of learning, viz. inducing, from the examples, fuzzy class inclusion rules that aim to describe conditions for being an individual classified as an instance of the class T. To do so, we present PN-OWL which is a two-stage learning algorithm consisting of a P-stage and an N-stage. In the P-stage, the algorithm learns fuzzy class inclusion rules (the P-rules). These rules aim to cover as many positive examples as possible, increasing recall, without compromising too much precision. In the N-stage, the algorithm learns fuzzy class inclusion rules (the N-rules), that try to rule out as many false positives, covered by the rules learnt at the P-stage, as possible. Roughly, the P-rules tell why an individual should be classified as an instance of T, while the N-rules tell why it should not. PN-OWL then aggregates the P-rules and the N-rules by combining them via an aggregation function to allow for a final decision on whether an individual is an instance of T or not. We also illustrate the effectiveness of PN-OWL through extensive experimentation.Source: FUZZY SETS AND SYSTEMS, vol. 490 (issue 109048)
DOI: 10.1016/j.fss.2024.109048
Project(s): TAILOR, Future Artificial Intelligence Research, STARWARS
Metrics:
Cardillo F. A., Debole F., Straccia U.
Given a target class T of an OWL 2 ontology, positive (and possibly negative) examples of T, we address the problem of learning, viz. inducing, from the examples, fuzzy class inclusion rules that aim to describe conditions for being an individual classified as an instance of the class T. To do so, we present PN-OWL which is a two-stage learning algorithm consisting of a P-stage and an N-stage. In the P-stage, the algorithm learns fuzzy class inclusion rules (the P-rules). These rules aim to cover as many positive examples as possible, increasing recall, without compromising too much precision. In the N-stage, the algorithm learns fuzzy class inclusion rules (the N-rules), that try to rule out as many false positives, covered by the rules learnt at the P-stage, as possible. Roughly, the P-rules tell why an individual should be classified as an instance of T, while the N-rules tell why it should not. PN-OWL then aggregates the P-rules and the N-rules by combining them via an aggregation function to allow for a final decision on whether an individual is an instance of T or not. We also illustrate the effectiveness of PN-OWL through extensive experimentation.Source: FUZZY SETS AND SYSTEMS, vol. 490 (issue 109048)
DOI: 10.1016/j.fss.2024.109048
Project(s): TAILOR
, Future Artificial Intelligence Research, STARWARS Metrics:
See at:
arXiv.org e-Print Archive | IRIS Cnr | doi.org | Hal | Hal | Hal | HAL Descartes | HAL Descartes | IRIS Cnr | IRIS Cnr | Fuzzy Sets and Systems | CNR IRIS | CNR IRIS | CNR IRIS
2023
Other
Open Access
InfraScience research activity report 2023
Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim A. S. T., La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Pagano P., Panichi G., Paratore M. T., Pavone G., Piccioli T., Sinibaldi F., Straccia U., Vannini G. L.
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2023 to highlight the major results. In particular, the InfraScience group engaged in research challenges characterising Data Infrastructures, e-Science, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2023 InfraScience members contributed to the publishing of several papers, to the research and development activities of several research projects (primarily funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2023/002
Project(s): Blue Cloud, EOSC Future , TAILOR
Metrics:
Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim A. S. T., La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Pagano P., Panichi G., Paratore M. T., Pavone G., Piccioli T., Sinibaldi F., Straccia U., Vannini G. L.
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2023 to highlight the major results. In particular, the InfraScience group engaged in research challenges characterising Data Infrastructures, e-Science, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2023 InfraScience members contributed to the publishing of several papers, to the research and development activities of several research projects (primarily funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2023/002
Project(s): Blue Cloud
, EOSC Future , TAILOR Metrics:
2022
Other
Open Access
InfraScience research activity report 2021
Artini M, Assante M, Atzori C, Baglioni M, Bardi A, Bove P, Candela L, Casini G, Castelli D, Cirillo R, Coro G, De Bonis M, Debole F, Dell'Amico A, Frosini L, La Bruzzo S, Lazzeri E, Lelii L, Manghi P, Mangiacrapa F, Mangione D, Mannocci A, Ottonello E, Pagano P, Panichi G, Pavone G, Piccioli T, Sinibaldi F, Straccia U
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2021 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, eScience, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2021 InfraScience members contributed to the publishing of 25 papers, to the research and development activities of 18 research projects (15 funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2022/001
Project(s): ARIADNEplus, Blue Cloud , PerformFISH , EOSC-Pillar , DESIRA , EOSC Future , EOSCsecretariat.eu , EcoScope , RISIS 2 , OpenAIRE-Advance , OpenAIRE Nexus , SoBigData-PlusPlus
Metrics:
Artini M, Assante M, Atzori C, Baglioni M, Bardi A, Bove P, Candela L, Casini G, Castelli D, Cirillo R, Coro G, De Bonis M, Debole F, Dell'Amico A, Frosini L, La Bruzzo S, Lazzeri E, Lelii L, Manghi P, Mangiacrapa F, Mangione D, Mannocci A, Ottonello E, Pagano P, Panichi G, Pavone G, Piccioli T, Sinibaldi F, Straccia U
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2021 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, eScience, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2021 InfraScience members contributed to the publishing of 25 papers, to the research and development activities of 18 research projects (15 funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2022/001
Project(s): ARIADNEplus
, Blue Cloud , PerformFISH , EOSC-Pillar , DESIRA , EOSC Future , EOSCsecretariat.eu , EcoScope , RISIS 2 , OpenAIRE-Advance , OpenAIRE Nexus , SoBigData-PlusPlus Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Other
Open Access
InfraScience research activity report 2022
Artini M, Assante M, Atzori C, Baglioni M, Bardi A, Bove P, Candela L, Casini G, Castelli D, Cirillo R, Coro G, De Bonis M, Debole F, Dell'Amico A, Frosini L, La Bruzzo S, Lelii L, Manghi P, Mangiacrapa F, Mangione D, Mannocci A, Ottonello E, Pagano P, Panichi G, Pavone G, Piccioli T, Sinibaldi F, Straccia U, Zoppi F
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2022 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, e-Science, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2022 InfraScience members contributed to the publishing of several papers, to the research and development activities of 18 research projects (15 funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2022/004
Project(s): ARIADNEplus, Blue Cloud , EOSC-Pillar , DESIRA , EOSC Future , RISIS 2 , TAILOR , SoBigData-PlusPlus
Metrics:
Artini M, Assante M, Atzori C, Baglioni M, Bardi A, Bove P, Candela L, Casini G, Castelli D, Cirillo R, Coro G, De Bonis M, Debole F, Dell'Amico A, Frosini L, La Bruzzo S, Lelii L, Manghi P, Mangiacrapa F, Mangione D, Mannocci A, Ottonello E, Pagano P, Panichi G, Pavone G, Piccioli T, Sinibaldi F, Straccia U, Zoppi F
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2022 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, e-Science, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2022 InfraScience members contributed to the publishing of several papers, to the research and development activities of 18 research projects (15 funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2022/004
Project(s): ARIADNEplus
, Blue Cloud , EOSC-Pillar , DESIRA , EOSC Future , RISIS 2 , TAILOR , SoBigData-PlusPlus Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2021
Journal article
Open Access
The VISIONE video search system: exploiting off-the-shelf text search engines for large-scale video retrieval
Amato G, Bolettieri P, Carrara F, Debole F, Falchi F, Gennaro C, Vadicamo L, Vairo C
This paper describes in detail VISIONE, a video search system that allows users to search for videos using textual keywords, the occurrence of objects and their spatial relationships, the occurrence of colors and their spatial relationships, and image similarity. These modalities can be combined together to express complex queries and meet users' needs. The peculiarity of our approach is that we encode all information extracted from the keyframes, such as visual deep features, tags, color and object locations, using a convenient textual encoding that is indexed in a single text retrieval engine. This offers great flexibility when results corresponding to various parts of the query (visual, text and locations) need to be merged. In addition, we report an extensive analysis of the retrieval performance of the system, using the query logs generated during the Video Browser Showdown (VBS) 2019 competition. This allowed us to fine-tune the system by choosing the optimal parameters and strategies from those we tested.Source: JOURNAL OF IMAGING, vol. 7 (issue 5)
DOI: 10.3390/jimaging7050076
DOI: 10.48550/arxiv.2008.02749
Project(s): AI4Media
Metrics:
Amato G, Bolettieri P, Carrara F, Debole F, Falchi F, Gennaro C, Vadicamo L, Vairo C
This paper describes in detail VISIONE, a video search system that allows users to search for videos using textual keywords, the occurrence of objects and their spatial relationships, the occurrence of colors and their spatial relationships, and image similarity. These modalities can be combined together to express complex queries and meet users' needs. The peculiarity of our approach is that we encode all information extracted from the keyframes, such as visual deep features, tags, color and object locations, using a convenient textual encoding that is indexed in a single text retrieval engine. This offers great flexibility when results corresponding to various parts of the query (visual, text and locations) need to be merged. In addition, we report an extensive analysis of the retrieval performance of the system, using the query logs generated during the Video Browser Showdown (VBS) 2019 competition. This allowed us to fine-tune the system by choosing the optimal parameters and strategies from those we tested.Source: JOURNAL OF IMAGING, vol. 7 (issue 5)
DOI: 10.3390/jimaging7050076
DOI: 10.48550/arxiv.2008.02749
Project(s): AI4Media
Metrics:
See at:
arXiv.org e-Print Archive | Journal of Imaging | Journal of Imaging | CNR IRIS | ISTI Repository | ISTI Repository | DOAJ-Articles | www.mdpi.com | Journal of Imaging | ZENODO | doi.org | CNR IRIS | CNR IRIS
2021
Other
Open Access
InfraScience Research Activity Report 2020
Artini M, Assante M, Atzori C, Baglioni M, Bardi A, Candela L, Casini G, Castelli D, Cirillo R, Coro G, Debole F, Dell'Amico A, Frosini L, La Bruzzo S, Lazzeri E, Lelii L, Manghi P, Mangiacrapa F, Mannocci A, Pagano P, Panichi G, Piccioli T, Sinibaldi F, Straccia U
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2020 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, e\-Sci\-ence, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, \ie D4Science and OpenAIRE. During 2020 InfraScience members contributed to the publishing of 30 papers, to the research and development activities of 12 research projects (11 funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2021/002
Project(s): ARIADNEplus, Blue Cloud , PerformFISH , EOSC-Pillar , DESIRA , EOSCsecretariat.eu , RISIS 2 , TAILOR , I-GENE , MOVING , OpenAIRE-Advance , SoBigData-PlusPlus
Metrics:
Artini M, Assante M, Atzori C, Baglioni M, Bardi A, Candela L, Casini G, Castelli D, Cirillo R, Coro G, Debole F, Dell'Amico A, Frosini L, La Bruzzo S, Lazzeri E, Lelii L, Manghi P, Mangiacrapa F, Mannocci A, Pagano P, Panichi G, Piccioli T, Sinibaldi F, Straccia U
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2020 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, e\-Sci\-ence, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, \ie D4Science and OpenAIRE. During 2020 InfraScience members contributed to the publishing of 30 papers, to the research and development activities of 12 research projects (11 funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2021/002
Project(s): ARIADNEplus
, Blue Cloud , PerformFISH , EOSC-Pillar , DESIRA , EOSCsecretariat.eu , RISIS 2 , TAILOR , I-GENE , MOVING , OpenAIRE-Advance , SoBigData-PlusPlus Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2021
Other
Restricted
SerGenCovid19 - Relazione analisi requisiti SerGenCovid19
Debole F, Dell'Amico A, Piccioli T, Testa A
Prima analisi dei requisiti della piattaforma ISTI da realizzare nel contesto del progetto di ricerca "SERGENCOVID-19 (serum genetic covid-19 study) indagine sierologica e genetica sull'immunità e la suscettibilità all'infezione da sars-cov-2 e creazione di una biobanca" riguardo al Work Package 6: Progettazione e implementazione della piattaforma informatica per la gestione di "Raccolta, conservazione e consultazione dei dati sanitari relativi ai prelievi ematici" . Prima bozza di disegno delle interfacce della piattaforma da realizzare.
Debole F, Dell'Amico A, Piccioli T, Testa A
Prima analisi dei requisiti della piattaforma ISTI da realizzare nel contesto del progetto di ricerca "SERGENCOVID-19 (serum genetic covid-19 study) indagine sierologica e genetica sull'immunità e la suscettibilità all'infezione da sars-cov-2 e creazione di una biobanca" riguardo al Work Package 6: Progettazione e implementazione della piattaforma informatica per la gestione di "Raccolta, conservazione e consultazione dei dati sanitari relativi ai prelievi ematici" . Prima bozza di disegno delle interfacce della piattaforma da realizzare.
2021
Other
Open Access
Progettazione piattaforma ISTI per il progetto SerGenCovid-19
Debole F., Dell'Amico A., Piccioli T., Fantini E., Lipari G., Volpini F.
Per portare a termine i propri obiettivi il progetto SerGenCovid-19 si articola in sei work package, l'ISTI e in particolare il gruppo S2i2S, è responsabile del Work Package 6: Progettazione e implementazione della piattaforma informatica per la gestione di "Raccolta, conservazione e consultazione dei dati sanitari relativi ai prelievi ematici". In questo rapporto tecnico vengono descritte le scelte di progettazione della suddetta piattaforma informatica ISTI.
Debole F., Dell'Amico A., Piccioli T., Fantini E., Lipari G., Volpini F.
Per portare a termine i propri obiettivi il progetto SerGenCovid-19 si articola in sei work package, l'ISTI e in particolare il gruppo S2i2S, è responsabile del Work Package 6: Progettazione e implementazione della piattaforma informatica per la gestione di "Raccolta, conservazione e consultazione dei dati sanitari relativi ai prelievi ematici". In questo rapporto tecnico vengono descritte le scelte di progettazione della suddetta piattaforma informatica ISTI.
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2021
Other
Open Access
Realizzazione piattaforma ISTI per il progetto SerGenCovid-19
Debole F, Dell'Amico A, Piccioli T, Volpini F, Lipari G, Luconi Trombacchi L, Martinelli M, Assante M
Nel contesto del progetto di ricerca denominato "SerGenCovid-19 (Serum Genetic Covid-19 study) Indagine sierologica e genetica sull'immunità e la suscettibilità all'infezione da SARS-CoV-2 e creazione di una biobanca", l'ISTI `e coinvolto come responsabile nel Work Package 6: Progettazione e implementazione della piattaforma informatica per la gestione di "Raccolta, conservazione e consultazione dei dati sanitari relativi ai prelievi ematici". In questo rapporto di progetto viene descritta la prima fase del progetto dove vengono realizzate la parte di backend e le due piattaforme, una per il partecipante e una per l'operatore.
Debole F, Dell'Amico A, Piccioli T, Volpini F, Lipari G, Luconi Trombacchi L, Martinelli M, Assante M
Nel contesto del progetto di ricerca denominato "SerGenCovid-19 (Serum Genetic Covid-19 study) Indagine sierologica e genetica sull'immunità e la suscettibilità all'infezione da SARS-CoV-2 e creazione di una biobanca", l'ISTI `e coinvolto come responsabile nel Work Package 6: Progettazione e implementazione della piattaforma informatica per la gestione di "Raccolta, conservazione e consultazione dei dati sanitari relativi ai prelievi ematici". In questo rapporto di progetto viene descritta la prima fase del progetto dove vengono realizzate la parte di backend e le due piattaforme, una per il partecipante e una per l'operatore.
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2020
Other
Metadata Only Access
Realizzazione di un Wiki del servizio S2I2S (Servizio Infrastruttura informatica ISTI e supporto ai servizi)
Dell'Amico A, Debole F, Piccioli T, D'Angelo C
La realizzazione del wiki in questione nasce dall'esigenza di rendere facilmente fruibili i servizi gestiti dal nuovo gruppo S2I2S che ha provveduto ad aggiornare e riorganizzare l'infrastruttura informatica dell'ISTI. Il sito è stato progettato e sviluppato utilizzando la piattaforma di collaborazione e documentazione open source MediaWiki (https://www.mediawiki.org ). Di seguito sono elencati i contributi forniti dagli singoli autori per la realizzazione del progetto : - Progettazione, installazione, configurazione e manutenzione sono state eseguiti dal gruppo S2I2S e specificatamente da Andrea Dell'Amico, Franca Debole e Tommaso Piccioli; - Struttura, organizzazione e creazione dei contenuti sono stati fatti da Caterina D'Angelo con il costante confronto e supporto degli altri autori del sito. I contenuti delle pagine web del wiki sono pubbliche: non è stato configurato nessun autenticazione per l'accesso.
Dell'Amico A, Debole F, Piccioli T, D'Angelo C
La realizzazione del wiki in questione nasce dall'esigenza di rendere facilmente fruibili i servizi gestiti dal nuovo gruppo S2I2S che ha provveduto ad aggiornare e riorganizzare l'infrastruttura informatica dell'ISTI. Il sito è stato progettato e sviluppato utilizzando la piattaforma di collaborazione e documentazione open source MediaWiki (https://www.mediawiki.org ). Di seguito sono elencati i contributi forniti dagli singoli autori per la realizzazione del progetto : - Progettazione, installazione, configurazione e manutenzione sono state eseguiti dal gruppo S2I2S e specificatamente da Andrea Dell'Amico, Franca Debole e Tommaso Piccioli; - Struttura, organizzazione e creazione dei contenuti sono stati fatti da Caterina D'Angelo con il costante confronto e supporto degli altri autori del sito. I contenuti delle pagine web del wiki sono pubbliche: non è stato configurato nessun autenticazione per l'accesso.
See at:
CNR IRIS | mediawiki-s2i2s.isti.cnr.it
2020
Other
Metadata Only Access
Realizzazione di un Wiki di supporto agli utenti interni per le procedure ed i servizi di Istituto
Dell'Amico A, Debole F, Piccioli T, D'Angelo C
Questo sito web è stato creato in una intranet riservata e presenta una serie di informazioni relative ad alcune normative e procedure CNR, di interesse per il personale ISTI. La tecnologia alla base di questa sito web è quella del wiki, un'applicazione che permette la creazione e la modifica di pagine web in maniera collaborativa e dinamica, in questo caso le pagine del wiki ISTI è via via composto ed editato con l'apporto congiunto del personale dei servizi ISTI. Il wiki attualmente è organizzato in 8 sezioni, ognuna gestita nella struttura e nei contenuti da referenti diversi con credenziali operative proprie: 1)Servizio Amministrazione (Giulio Galesi, Serena Paoletti) 2)Servizio Attività Generali (Roberto Scopigno) 3)Servizio Attività Logistiche (Caterina D'Angelo) 4)Servizio Biblioteca (Silvia Giannini, Anna Molino) 5)Segreteria Scientifica (Daniela Falconetti, Claudia Raviolo) 6)Sito e Social (Giuseppe Lipari) 7)Telefonia Mobile (Paolo Bolettieri) 8)Ufficio Gestione Privacy (Rosaria Deluca) Il sito è stato progettato e sviluppato utilizzando la piattaforma di collaborazione e documentazione open source MediaWiki (https://www.mediawiki.org/wiki/MediaWiki/it). Di seguito sono elencati i contributi forniti dai singoli autori per la realizzazione del progetto: o La progettazione, l'installazione e la configurazione sono state condotte dal gruppo S2i2S (Andrea Dell'Amico, Franca Debole e Tommaso Piccioli) con la collaborazione di Caterina D'Angelo; o I contenuti sono stati creati e organizzati/strutturati da ciascuno dei referenti sopra elencati. I contenuti delle pagine web del wiki ISTI sono riservate al personale ISTI, per questo l'accesso a queste è possibile solo tramite autenticazione. In particolare il processo di autenticazione per il wiki viene gestito dallo Identity Provider per il personale dell'istituto tramite l'uso del protocollo OpenID Connect (OIDC). Lo Identity provider è basato sul software open source Keycloak.
Dell'Amico A, Debole F, Piccioli T, D'Angelo C
Questo sito web è stato creato in una intranet riservata e presenta una serie di informazioni relative ad alcune normative e procedure CNR, di interesse per il personale ISTI. La tecnologia alla base di questa sito web è quella del wiki, un'applicazione che permette la creazione e la modifica di pagine web in maniera collaborativa e dinamica, in questo caso le pagine del wiki ISTI è via via composto ed editato con l'apporto congiunto del personale dei servizi ISTI. Il wiki attualmente è organizzato in 8 sezioni, ognuna gestita nella struttura e nei contenuti da referenti diversi con credenziali operative proprie: 1)Servizio Amministrazione (Giulio Galesi, Serena Paoletti) 2)Servizio Attività Generali (Roberto Scopigno) 3)Servizio Attività Logistiche (Caterina D'Angelo) 4)Servizio Biblioteca (Silvia Giannini, Anna Molino) 5)Segreteria Scientifica (Daniela Falconetti, Claudia Raviolo) 6)Sito e Social (Giuseppe Lipari) 7)Telefonia Mobile (Paolo Bolettieri) 8)Ufficio Gestione Privacy (Rosaria Deluca) Il sito è stato progettato e sviluppato utilizzando la piattaforma di collaborazione e documentazione open source MediaWiki (https://www.mediawiki.org/wiki/MediaWiki/it). Di seguito sono elencati i contributi forniti dai singoli autori per la realizzazione del progetto: o La progettazione, l'installazione e la configurazione sono state condotte dal gruppo S2i2S (Andrea Dell'Amico, Franca Debole e Tommaso Piccioli) con la collaborazione di Caterina D'Angelo; o I contenuti sono stati creati e organizzati/strutturati da ciascuno dei referenti sopra elencati. I contenuti delle pagine web del wiki ISTI sono riservate al personale ISTI, per questo l'accesso a queste è possibile solo tramite autenticazione. In particolare il processo di autenticazione per il wiki viene gestito dallo Identity Provider per il personale dell'istituto tramite l'uso del protocollo OpenID Connect (OIDC). Lo Identity provider è basato sul software open source Keycloak.
See at:
CNR IRIS | wiki-amministrazione.isti.cnr.it
2019
Conference article
Open Access
An Image Retrieval System for Video
Bolettieri P, Carrara F, Debole F, Falchi F, Gennaro C, Vadicamo L, Vairo C
Since the 1970's the Content-Based Image Indexing and Retrieval (CBIR) has been an active area. Nowadays, the rapid increase of video data has paved the way to the advancement of the technologies in many different communities for the creation of Content-Based Video Indexing and Retrieval (CBVIR). However, greater attention needs to be devoted to the development of effective tools for video search and browse. In this paper, we present Visione, a system for large-scale video retrieval. The system integrates several content-based analysis and retrieval modules, including a keywords search, a spatial object-based search, and a visual similarity search. From the tests carried out by users when they needed to find as many correct examples as possible, the similarity search proved to be the most promising option. Our implementation is based on state-of-the-art deep learning approaches for content analysis and leverages highly efficient indexing techniques to ensure scalability. Specifically, we encode all the visual and textual descriptors extracted from the videos into (surrogate) textual representations that are then efficiently indexed and searched using an off-the-shelf text search engine using similarity functions.DOI: 10.1007/978-3-030-32047-8_29
Metrics:
Bolettieri P, Carrara F, Debole F, Falchi F, Gennaro C, Vadicamo L, Vairo C
Since the 1970's the Content-Based Image Indexing and Retrieval (CBIR) has been an active area. Nowadays, the rapid increase of video data has paved the way to the advancement of the technologies in many different communities for the creation of Content-Based Video Indexing and Retrieval (CBVIR). However, greater attention needs to be devoted to the development of effective tools for video search and browse. In this paper, we present Visione, a system for large-scale video retrieval. The system integrates several content-based analysis and retrieval modules, including a keywords search, a spatial object-based search, and a visual similarity search. From the tests carried out by users when they needed to find as many correct examples as possible, the similarity search proved to be the most promising option. Our implementation is based on state-of-the-art deep learning approaches for content analysis and leverages highly efficient indexing techniques to ensure scalability. Specifically, we encode all the visual and textual descriptors extracted from the videos into (surrogate) textual representations that are then efficiently indexed and searched using an off-the-shelf text search engine using similarity functions.DOI: 10.1007/978-3-030-32047-8_29
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | doi.org | CNR IRIS | CNR IRIS
2019
Software
Open Access
VISIONE Content-Based Video Retrieval System, VBS 2019
Amato G, Bolettieri P, Carrara F, Debole F, Falchi F, Gennaro C, Vadicamo L, Vairo C
VISIONE is a content-based video retrieval system that participated to VBS for the very first time in 2019. It is mainly based on state-of-the-art deep learning approaches for visual content analysis and exploits highly efficient indexing techniques to ensure scalability. The system supports query by scene tag, query by object location, query by color sketch, and visual similarity search.
Amato G, Bolettieri P, Carrara F, Debole F, Falchi F, Gennaro C, Vadicamo L, Vairo C
VISIONE is a content-based video retrieval system that participated to VBS for the very first time in 2019. It is mainly based on state-of-the-art deep learning approaches for visual content analysis and exploits highly efficient indexing techniques to ensure scalability. The system supports query by scene tag, query by object location, query by color sketch, and visual similarity search.
See at:
bilioso.isti.cnr.it | CNR IRIS | CNR IRIS
2019
Conference article
Open Access
VISIONE at VBS2019
Amato G, Bolettieri P, Carrara F, Debole F, Falchi F, Gennaro C, Vadicamo L, Vairo C
This paper presents VISIONE, a tool for large-scale video search. The tool can be used for both known-item and ad-hoc video search tasks since it integrates several content-based analysis and re- trieval modules, including a keyword search, a spatial object-based search, and a visual similarity search. Our implementation is based on state-of- the-art deep learning approaches for the content analysis and leverages highly efficient indexing techniques to ensure scalability. Specifically, we encode all the visual and textual descriptors extracted from the videos into (surrogate) textual representations that are then efficiently indexed and searched using an off-the-shelf text search engine.DOI: 10.1007/978-3-030-05716-9_51
Metrics:
Amato G, Bolettieri P, Carrara F, Debole F, Falchi F, Gennaro C, Vadicamo L, Vairo C
This paper presents VISIONE, a tool for large-scale video search. The tool can be used for both known-item and ad-hoc video search tasks since it integrates several content-based analysis and re- trieval modules, including a keyword search, a spatial object-based search, and a visual similarity search. Our implementation is based on state-of- the-art deep learning approaches for the content analysis and leverages highly efficient indexing techniques to ensure scalability. Specifically, we encode all the visual and textual descriptors extracted from the videos into (surrogate) textual representations that are then efficiently indexed and searched using an off-the-shelf text search engine.DOI: 10.1007/978-3-030-05716-9_51
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2019
Journal article
Open Access
Virtual restoration and content analysis of ancient degraded manuscripts
Tonazzini A, Savino P, Salerno E, Hanif M, Debole F
In recent years, extensive campaigns of digitization of the documental heritage conserved in libraries and archives have been performed, with the primary goal to ensure the preservation and fruition of this important part of the human cultural and historical patrimony. Besides protecting conservation, the availability of high quality digital copies has increasingly stimulated the use of image processing techniques, to perform a number of operations on documents and manuscripts, without harming the often precious and fragile originals. Among those, virtual restoration tasks are crucial, as they facilitate the traditional work of philologists and paleographers, and constitute a first step towards an automatic analysis of the written contents. Here we report our experience in this field, referring, as a case study, to the problem of removing one of the most frequent and impairing degradations affecting ancient manuscripts, i.e., the bleed-through distortion.We show that techniques of blind source separation are versatile tools to either cancel these unwanted interferences or isolate specific features for content analysis goals. Specialized algorithms, based on recto-verso models and sparse image representation, are then shown to be able to perform a fine and selective removal of the degradation, while preserving the original appearance of the manuscript.Source: INTERNATIONAL JOURNAL OF INFORMATION SCIENCE AND TECHNOLOGY, vol. 3 (issue 5), pp. 16-25
DOI: 10.57675/imist.prsm/ijist-v3i5.133
Metrics:
Tonazzini A, Savino P, Salerno E, Hanif M, Debole F
In recent years, extensive campaigns of digitization of the documental heritage conserved in libraries and archives have been performed, with the primary goal to ensure the preservation and fruition of this important part of the human cultural and historical patrimony. Besides protecting conservation, the availability of high quality digital copies has increasingly stimulated the use of image processing techniques, to perform a number of operations on documents and manuscripts, without harming the often precious and fragile originals. Among those, virtual restoration tasks are crucial, as they facilitate the traditional work of philologists and paleographers, and constitute a first step towards an automatic analysis of the written contents. Here we report our experience in this field, referring, as a case study, to the problem of removing one of the most frequent and impairing degradations affecting ancient manuscripts, i.e., the bleed-through distortion.We show that techniques of blind source separation are versatile tools to either cancel these unwanted interferences or isolate specific features for content analysis goals. Specialized algorithms, based on recto-verso models and sparse image representation, are then shown to be able to perform a fine and selective removal of the degradation, while preserving the original appearance of the manuscript.Source: INTERNATIONAL JOURNAL OF INFORMATION SCIENCE AND TECHNOLOGY, vol. 3 (issue 5), pp. 16-25
DOI: 10.57675/imist.prsm/ijist-v3i5.133
Metrics:
See at:
CNR IRIS | ISTI Repository | International Journal of Information Science and Technology | www.innove.org | doi.org | CNR IRIS
2019
Conference article
Open Access
Intelligenza Artificiale per Ricerca in Big Multimedia Data
Carrara F, Amato G, Debole F, Di Benedetto M, Falchi F, Gennaro C, Messina N
La diffusa produzione di immagini e media digitali ha reso necessario l'utilizzo di metodi automatici di analisi e indicizzazione su larga scala per la loro fruzione. Il gruppo AIMIR dell'ISTI-CNR si è specializzato da anni in questo ambito ed ha abbracciato tecniche di Deep Learning basate su reti neurali artificiali per molteplici aspetti di questa disciplina, come l'analisi, l'annotazione e la descrizione automatica di contenuti visuali e il loro recupero su larga scala.
Carrara F, Amato G, Debole F, Di Benedetto M, Falchi F, Gennaro C, Messina N
La diffusa produzione di immagini e media digitali ha reso necessario l'utilizzo di metodi automatici di analisi e indicizzazione su larga scala per la loro fruzione. Il gruppo AIMIR dell'ISTI-CNR si è specializzato da anni in questo ambito ed ha abbracciato tecniche di Deep Learning basate su reti neurali artificiali per molteplici aspetti di questa disciplina, come l'analisi, l'annotazione e la descrizione automatica di contenuti visuali e il loro recupero su larga scala.
See at:
CNR IRIS | ISTI Repository | www.ital-ia.it | CNR IRIS
2019
Other
Open Access
AIMIR 2019 Research Activities
Amato G, Bolettieri P, Carrara F, Ciampi L, Di Benedetto M, Debole F, Falchi F, Gennaro C, Lagani G, Massoli Fv, Messina N, Rabitti F, Savino P, Vadicamo L, Vairo C
Multimedia Information Retrieval (AIMIR) research group is part of the NeMIS laboratory of the Information Science and Technologies Institute "A. Faedo" (ISTI) of the Italian National Research Council (CNR). The AIMIR group has a long experience in topics related to: Artificial Intelligence, Multimedia Information Retrieval, Computer Vision and Similarity search on a large scale. We aim at investigating the use of Artificial Intelligence and Deep Learning, for Multimedia Information Retrieval, addressing both effectiveness and efficiency. Multimedia information retrieval techniques should be able to provide users with pertinent results, fast, on huge amount of multimedia data. Application areas of our research results range from cultural heritage to smart tourism, from security to smart cities, from mobile visual search to augmented reality. This report summarize the 2019 activities of the research group.
Amato G, Bolettieri P, Carrara F, Ciampi L, Di Benedetto M, Debole F, Falchi F, Gennaro C, Lagani G, Massoli Fv, Messina N, Rabitti F, Savino P, Vadicamo L, Vairo C
Multimedia Information Retrieval (AIMIR) research group is part of the NeMIS laboratory of the Information Science and Technologies Institute "A. Faedo" (ISTI) of the Italian National Research Council (CNR). The AIMIR group has a long experience in topics related to: Artificial Intelligence, Multimedia Information Retrieval, Computer Vision and Similarity search on a large scale. We aim at investigating the use of Artificial Intelligence and Deep Learning, for Multimedia Information Retrieval, addressing both effectiveness and efficiency. Multimedia information retrieval techniques should be able to provide users with pertinent results, fast, on huge amount of multimedia data. Application areas of our research results range from cultural heritage to smart tourism, from security to smart cities, from mobile visual search to augmented reality. This report summarize the 2019 activities of the research group.
See at:
CNR IRIS | ISTI Repository | CNR IRIS