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2023
Report
Open Access
AIMH Research Activities 2023
Aloia N., Amato G., Bartalesi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Molinari A., Moreo A., Nardi A., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is dedicated to exploring and pushing the boundaries in the field of Artificial Intelligence, with a particular focus on its application in digital media and humanities. This lab's objective is to enhance the current state of AI technology particularly on deep learning, text analysis, computer vision, multimedia information retrieval, multimedia content analysis, recognition, and retrieval. This report encapsulates the laboratory's progress and activities throughout the year 2023.Source: ISTI Annual Reports, 2023
DOI: 10.32079/isti-ar-2023/001
Metrics:
Aloia N., Amato G., Bartalesi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Molinari A., Moreo A., Nardi A., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is dedicated to exploring and pushing the boundaries in the field of Artificial Intelligence, with a particular focus on its application in digital media and humanities. This lab's objective is to enhance the current state of AI technology particularly on deep learning, text analysis, computer vision, multimedia information retrieval, multimedia content analysis, recognition, and retrieval. This report encapsulates the laboratory's progress and activities throughout the year 2023.Source: ISTI Annual Reports, 2023
DOI: 10.32079/isti-ar-2023/001
Metrics:
See at:
ISTI Repository 
| CNR ExploRA
2022
Conference article
Open Access
A detailed overview of LeQua 2022: learning to quantify
Esuli A., Moreo A., Sebastiani F., Sperduti G.
LeQua 2022 is a new lab for the evaluation of methods for "learning to quantify" in textual datasets, i.e., for training predictors of the relative frequencies of the classes of interest Y = {y1 , ..., yn } in sets of unlabelled textual documents. While these predictions could be easily achieved by first classifying all documents via a text classifier and then counting the numbers of documents assigned to the classes, a growing body of literature has shown this approach to be suboptimal, and has proposed better methods. The goal of this lab is to provide a setting for the comparative evaluation of methods for learning to quantify, both in the binary setting and in the single-label multiclass setting; this is the first time that an evaluation exercise solely dedicated to quantification is organized. For both the binary setting and the single-label multiclass setting, data were provided to participants both in ready-made vector form and in raw document form. In this overview article we describe the structure of the lab, we report the results obtained by the participants in the four proposed tasks and subtasks, and we comment on the lessons that can be learned from these results.Source: CLEF 2022 - 13th Conference and Labs of the Evaluation Forum, pp. 1849–1868, Bologna, Italy, 5-8/9/2022
Project(s): AI4Media
, SoBigData-PlusPlus
Esuli A., Moreo A., Sebastiani F., Sperduti G.
LeQua 2022 is a new lab for the evaluation of methods for "learning to quantify" in textual datasets, i.e., for training predictors of the relative frequencies of the classes of interest Y = {y1 , ..., yn } in sets of unlabelled textual documents. While these predictions could be easily achieved by first classifying all documents via a text classifier and then counting the numbers of documents assigned to the classes, a growing body of literature has shown this approach to be suboptimal, and has proposed better methods. The goal of this lab is to provide a setting for the comparative evaluation of methods for learning to quantify, both in the binary setting and in the single-label multiclass setting; this is the first time that an evaluation exercise solely dedicated to quantification is organized. For both the binary setting and the single-label multiclass setting, data were provided to participants both in ready-made vector form and in raw document form. In this overview article we describe the structure of the lab, we report the results obtained by the participants in the four proposed tasks and subtasks, and we comment on the lessons that can be learned from these results.Source: CLEF 2022 - 13th Conference and Labs of the Evaluation Forum, pp. 1849–1868, Bologna, Italy, 5-8/9/2022
Project(s): AI4Media
, SoBigData-PlusPlus
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2022
Conference article
Open Access
A concise overview of LeQua@CLEF 2022: Learning to Quantify
Esuli A., Moreo A., Sebastiani F., Sperduti G.
LeQua 2022 is a new lab for the evaluation of methods for "learning to quantify" in textual datasets, i.e., for training predictors of the relative frequencies of the classes of interest Y={y1,...,yn} in sets of unlabelled textual documents. While these predictions could be easily achieved by first classifying all documents via a text classifier and then counting the numbers of documents assigned to the classes, a growing body of literature has shown this approach to be suboptimal, and has proposed better methods. The goal of this lab is to provide a setting for the comparative evaluation of methods for learning to quantify, both in the binary setting and in the single-label multiclass setting; this is the first time that an evaluation exercise solely dedicated to quantification is organized. For both the binary setting and the single-label multiclass setting, data were provided to participants both in ready-made vector form and in raw document form. In this overview article we describe the structure of the lab, we report the results obtained by the participants in the four proposed tasks and subtasks, and we comment on the lessons that can be learned from these results.Source: CLEF 2022 - 13th Conference and Labs of the Evaluation Forum, pp. 362–381, Bologna, Italy, 5-8/9/2022
DOI: 10.1007/978-3-031-13643-6_23
Project(s): AI4Media, SoBigData-PlusPlus
Metrics:
Esuli A., Moreo A., Sebastiani F., Sperduti G.
LeQua 2022 is a new lab for the evaluation of methods for "learning to quantify" in textual datasets, i.e., for training predictors of the relative frequencies of the classes of interest Y={y1,...,yn} in sets of unlabelled textual documents. While these predictions could be easily achieved by first classifying all documents via a text classifier and then counting the numbers of documents assigned to the classes, a growing body of literature has shown this approach to be suboptimal, and has proposed better methods. The goal of this lab is to provide a setting for the comparative evaluation of methods for learning to quantify, both in the binary setting and in the single-label multiclass setting; this is the first time that an evaluation exercise solely dedicated to quantification is organized. For both the binary setting and the single-label multiclass setting, data were provided to participants both in ready-made vector form and in raw document form. In this overview article we describe the structure of the lab, we report the results obtained by the participants in the four proposed tasks and subtasks, and we comment on the lessons that can be learned from these results.Source: CLEF 2022 - 13th Conference and Labs of the Evaluation Forum, pp. 362–381, Bologna, Italy, 5-8/9/2022
DOI: 10.1007/978-3-031-13643-6_23
Project(s): AI4Media
, SoBigData-PlusPlus Metrics:
See at:
ISTI Repository | link.springer.com 
| CNR ExploRA
2022
Report
Open Access
AIMH research activities 2022
Aloia N., Amato G., Bartalesi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Metilli D., Molinari A., Moreo A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2022 activities of the research group.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/002
Metrics:
Aloia N., Amato G., Bartalesi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Metilli D., Molinari A., Moreo A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2022 activities of the research group.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/002
Metrics:
See at:
ISTI Repository | CNR ExploRA
2021
Conference article
Open Access
Garbled-word embeddings for jumbled text
Sperduti G., Moreo A., Sebastiani F.
"Aoccdrnig to a reasrech at Cmabrigde Uinervtisy, it deosn't mttaer in waht oredr the ltteers in a wrod are, the olny itmopnrat tihng is taht the frist and lsat ltteer be at the rghit pclae. The rset can be a toatl mses and you can sitll raed it wouthit porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe". We investigate the extent to which this phenomenon applies to computers as well. Our hypothesis is that computers are able to learn distributed word representations that are resilient to character reshuffling, without incurring a significant loss in performance in tasks that use these representations. If our hypothesis is confirmed, this may form the basis for a new and more efficient way of encoding character-based representations of text in deep learning, and one that may prove especially robust to misspellings, or to corruption of text due to OCR. This paper discusses some fundamental psycho-linguistic aspects that lie at the basis of the phenomenon we investigate, and reports on a preliminary proof of concept of the above idea.Source: IIR 2021 - 11th Italian Information Retrieval Workshop, Bari, Italy, 13-15/09/21
Sperduti G., Moreo A., Sebastiani F.
"Aoccdrnig to a reasrech at Cmabrigde Uinervtisy, it deosn't mttaer in waht oredr the ltteers in a wrod are, the olny itmopnrat tihng is taht the frist and lsat ltteer be at the rghit pclae. The rset can be a toatl mses and you can sitll raed it wouthit porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe". We investigate the extent to which this phenomenon applies to computers as well. Our hypothesis is that computers are able to learn distributed word representations that are resilient to character reshuffling, without incurring a significant loss in performance in tasks that use these representations. If our hypothesis is confirmed, this may form the basis for a new and more efficient way of encoding character-based representations of text in deep learning, and one that may prove especially robust to misspellings, or to corruption of text due to OCR. This paper discusses some fundamental psycho-linguistic aspects that lie at the basis of the phenomenon we investigate, and reports on a preliminary proof of concept of the above idea.Source: IIR 2021 - 11th Italian Information Retrieval Workshop, Bari, Italy, 13-15/09/21
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2021
Report
Open Access
AIMH research activities 2021
Aloia N., Amato G., Bartalesi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Coccomini D., Ciampi L., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2021 activities of the research group.Source: ISTI Annual Report, ISTI-2021-AR/003, pp.1–34, 2021
DOI: 10.32079/isti-ar-2021/003
Metrics:
Aloia N., Amato G., Bartalesi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Coccomini D., Ciampi L., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Massoli F. V., Meghini C., Messina N., Metilli D., Molinari A., Moreo A., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2021 activities of the research group.Source: ISTI Annual Report, ISTI-2021-AR/003, pp.1–34, 2021
DOI: 10.32079/isti-ar-2021/003
Metrics:
See at:
ISTI Repository | CNR ExploRA