Pub Date : 2022-10-01DOI: 10.1109/MBITS.2022.3187947
D. K. Crkvenjakov
Technical studies of paintings are based on many analytical techniques that complement art historical studies. Understanding the processes that happen with the aging of the paint is very important for conservators. The paint layer cracks and lifts, the materials discolor and new layers are added over time, slowly changing the appearance of the artwork. Crack patterns in the paint layer can be used in the study of the composition of the paint layer as well as its conservation history, even for more in-depth studies of the artwork creation. Particularly interesting examples are coming from the transition periods in art history, in which the changes in the painting style and painting technique were happening. Image analysis powered by machine learning was applied in the study of the two Serbian Baroque icons. The resulting crack pattern, besides being valuable conservation documentation, opened new research questions regarding the origin of the degradation processes in the paint layer.
{"title":"Image Processing and the Analysis of Paintings—The Case of Serbian Baroque Icons","authors":"D. K. Crkvenjakov","doi":"10.1109/MBITS.2022.3187947","DOIUrl":"https://doi.org/10.1109/MBITS.2022.3187947","url":null,"abstract":"Technical studies of paintings are based on many analytical techniques that complement art historical studies. Understanding the processes that happen with the aging of the paint is very important for conservators. The paint layer cracks and lifts, the materials discolor and new layers are added over time, slowly changing the appearance of the artwork. Crack patterns in the paint layer can be used in the study of the composition of the paint layer as well as its conservation history, even for more in-depth studies of the artwork creation. Particularly interesting examples are coming from the transition periods in art history, in which the changes in the painting style and painting technique were happening. Image analysis powered by machine learning was applied in the study of the two Serbian Baroque icons. The resulting crack pattern, besides being valuable conservation documentation, opened new research questions regarding the origin of the degradation processes in the paint layer.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115748871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1109/MBITS.2022.3197102
Zhuomin Zhang, Jia Li, David G. Stork, Elizabeth C. Mansfield, John Russell, Catherine Adams, James Ze Wang
Empirical research in science and the humanities is vulnerable to bias which, by definition, implies incorrect or misleading findings. Artificial intelligence-based analysis of visual artworks is vulnerable to bias in ways specific to the domain. Works of art belong to a distinct cultural category that often prioritizes such characteristics as hand-craftsmanship, uniqueness, originality, and imaginative content; works of art are also responsive to diverse social and cultural contexts. Ascertaining which features of an artwork can be rightly ascribed to an objective “truth,” without which the concept of bias is not even relevant, is itself challenging. Incorporating expert knowledge into machine learning applications can help reduce bias in final estimates. We review several sources of bias that can occur across different stages of AI-based analysis, protocols, and best practices for reducing bias, and approaches to measuring these biases. This systematic investigation of various types of bias can help researchers better understand bias, become aware of practical solutions, and ultimately cultivate the prudent adoption of AI-based approaches to artwork analysis.
{"title":"Reducing Bias in AI-Based Analysis of Visual Artworks","authors":"Zhuomin Zhang, Jia Li, David G. Stork, Elizabeth C. Mansfield, John Russell, Catherine Adams, James Ze Wang","doi":"10.1109/MBITS.2022.3197102","DOIUrl":"https://doi.org/10.1109/MBITS.2022.3197102","url":null,"abstract":"Empirical research in science and the humanities is vulnerable to bias which, by definition, implies incorrect or misleading findings. Artificial intelligence-based analysis of visual artworks is vulnerable to bias in ways specific to the domain. Works of art belong to a distinct cultural category that often prioritizes such characteristics as hand-craftsmanship, uniqueness, originality, and imaginative content; works of art are also responsive to diverse social and cultural contexts. Ascertaining which features of an artwork can be rightly ascribed to an objective “truth,” without which the concept of bias is not even relevant, is itself challenging. Incorporating expert knowledge into machine learning applications can help reduce bias in final estimates. We review several sources of bias that can occur across different stages of AI-based analysis, protocols, and best practices for reducing bias, and approaches to measuring these biases. This systematic investigation of various types of bias can help researchers better understand bias, become aware of practical solutions, and ultimately cultivate the prudent adoption of AI-based approaches to artwork analysis.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126149461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1109/MBITS.2022.3207125
B. Sober, S. Bucklow, Nathan Daly, I. Daubechies, P. Dragotti, C. Higgitt, Jun-Jie Huang, A. Pižurica, Wei Pu, Suzanne Reynolds, Miguel R. D. Rodrigues, C. Schönlieb, Su Yan
In recent decades, cultural heritage research—and in particular art investigation—has been undergoing a digital revolution. This is due both to improvements in the digitization and the acquisition of artifact’s images generated using traditional 2-D imaging methods as well as the growing adoption of a range of more recently introduced spectroscopic imaging techniques. A number of these imaging modalities use wavelengths of electromagnetic radiation that can penetrate surface layers thus yielding information from hidden features noninvasively. Different techniques are often used in combination to provide evidence of construction, condition, and past treatment. These can also be used to characterize the materials used, how they were combined, and map their distribution, giving insight into an artist’s working method and the means to understand changes that have occurred over time. This wealth of data calls for the development of algorithmic approaches in order to handle and fully explore and interpret it. The questions one seeks to answer are in some cases sufficiently different from those addressed in other fields that no existing off-the-shelf approaches can be applied. In this article, we discuss a few of the algorithmic challenges that arise in art investigation and conservation using modern imaging techniques.
{"title":"Revealing and Reconstructing Hidden or Lost Features in Art Investigation","authors":"B. Sober, S. Bucklow, Nathan Daly, I. Daubechies, P. Dragotti, C. Higgitt, Jun-Jie Huang, A. Pižurica, Wei Pu, Suzanne Reynolds, Miguel R. D. Rodrigues, C. Schönlieb, Su Yan","doi":"10.1109/MBITS.2022.3207125","DOIUrl":"https://doi.org/10.1109/MBITS.2022.3207125","url":null,"abstract":"In recent decades, cultural heritage research—and in particular art investigation—has been undergoing a digital revolution. This is due both to improvements in the digitization and the acquisition of artifact’s images generated using traditional 2-D imaging methods as well as the growing adoption of a range of more recently introduced spectroscopic imaging techniques. A number of these imaging modalities use wavelengths of electromagnetic radiation that can penetrate surface layers thus yielding information from hidden features noninvasively. Different techniques are often used in combination to provide evidence of construction, condition, and past treatment. These can also be used to characterize the materials used, how they were combined, and map their distribution, giving insight into an artist’s working method and the means to understand changes that have occurred over time. This wealth of data calls for the development of algorithmic approaches in order to handle and fully explore and interpret it. The questions one seeks to answer are in some cases sufficiently different from those addressed in other fields that no existing off-the-shelf approaches can be applied. In this article, we discuss a few of the algorithmic challenges that arise in art investigation and conservation using modern imaging techniques.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129957596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1109/MBITS.2022.3205288
Haizi Yu, L. Varshney, Heinrich Taube, James A. Evans
Information lattice learning (ILL) is a novel framework for knowledge discovery based on group-theoretic and information-theoretic foundations, which can rediscover the rules of music as known in the canon of music theory and also discover new rules that have remained unexamined. Such probabilistic rules are further demonstrated to be human-interpretable. ILL itself is a rediscovery and generalization of Shannon’s lattice theory of information, where probability measures are not given but are learned from training data. This article explains the basics of the ILL framework, including both how to construct a lattice-structured abstraction universe that specifies the structural possibilities of rules, and how to find the most informative rules by performing statistical learning through an iterative student–teacher algorithmic architecture that optimizes information functionals. The ILL framework is finally shown to support both pedagogy and novel patterns of music co-creativity.
{"title":"(Re)discovering Laws of Music Theory Using Information Lattice Learning","authors":"Haizi Yu, L. Varshney, Heinrich Taube, James A. Evans","doi":"10.1109/MBITS.2022.3205288","DOIUrl":"https://doi.org/10.1109/MBITS.2022.3205288","url":null,"abstract":"Information lattice learning (ILL) is a novel framework for knowledge discovery based on group-theoretic and information-theoretic foundations, which can rediscover the rules of music as known in the canon of music theory and also discover new rules that have remained unexamined. Such probabilistic rules are further demonstrated to be human-interpretable. ILL itself is a rediscovery and generalization of Shannon’s lattice theory of information, where probability measures are not given but are learned from training data. This article explains the basics of the ILL framework, including both how to construct a lattice-structured abstraction universe that specifies the structural possibilities of rules, and how to find the most informative rules by performing statistical learning through an iterative student–teacher algorithmic architecture that optimizes information functionals. The ILL framework is finally shown to support both pedagogy and novel patterns of music co-creativity.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116411540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1109/MBITS.2022.3202508
R. van Liere, K. Batenburg, I. Garachon, Ching-Ling Wang, J. Dorscheid
An important question in cultural heritage concerns the make process of an artifact. Understanding the make process provides insight related to the origin, techniques, and craftsmanship used to make the artifact. Searching for tool marks or traces left by the artist’s hand is one way of retrieving clues related to the make process. X-ray computed tomography is a nondestructive tool that produces volumetric images of structures inside an artifact. However, interactively searching in large volumetric images for tool marks is a difficult, tedious, and time-consuming task. In this article, we introduce the concept of a dual space. The governing idea is that the dual space represents the air in the interior of an object. In the context of cultural heritage, the dual space represents those materials that first belonged to the object but have been removed during the make process. Our main goal of creating the dual space is to facilitate searching, inspection, and interpretation of tool marks. We provide two examples of how the dual space can be used to study the make process.
{"title":"The Dual Space: Concept and Applications in Cultural Heritage","authors":"R. van Liere, K. Batenburg, I. Garachon, Ching-Ling Wang, J. Dorscheid","doi":"10.1109/MBITS.2022.3202508","DOIUrl":"https://doi.org/10.1109/MBITS.2022.3202508","url":null,"abstract":"An important question in cultural heritage concerns the make process of an artifact. Understanding the make process provides insight related to the origin, techniques, and craftsmanship used to make the artifact. Searching for tool marks or traces left by the artist’s hand is one way of retrieving clues related to the make process. X-ray computed tomography is a nondestructive tool that produces volumetric images of structures inside an artifact. However, interactively searching in large volumetric images for tool marks is a difficult, tedious, and time-consuming task. In this article, we introduce the concept of a dual space. The governing idea is that the dual space represents the air in the interior of an object. In the context of cultural heritage, the dual space represents those materials that first belonged to the object but have been removed during the make process. Our main goal of creating the dual space is to facilitate searching, inspection, and interpretation of tool marks. We provide two examples of how the dual space can be used to study the make process.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129080156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1109/mbits.2022.3217879
{"title":"IEEE BITS Editorial Board","authors":"","doi":"10.1109/mbits.2022.3217879","DOIUrl":"https://doi.org/10.1109/mbits.2022.3217879","url":null,"abstract":"","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132351817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-06DOI: 10.1109/MBITS.2022.3205143
J. Calder, Reed Coil, J. A. Melton, P. Olver, G. Tostevin, K. Yezzi-Woodley
Machine learning (ML), being now widely accessible to the research community at large, has fostered a proliferation of new and striking applications of these emergent mathematical techniques across a wide range of disciplines. In this article, we will focus on a particular case study: the field of paleoanthropology, which seeks to understand the evolution of the human species based on biological (e.g., bones, genetics) and cultural (e.g., stone tools) evidence. As we will show, the easy availability of ML algorithms and lack of expertise on their proper use among the anthropological research community has led to the foundational misapplications that have appeared throughout the literature. The resulting unreliable results not only undermine efforts to legitimately incorporate ML into anthropological research, but produce potentially faulty understandings about our human evolutionary and behavioral past. The aim of this article is to provide a brief introduction to some of the ways in which ML has been applied within paleoanthropology; we also include a survey of some basic ML algorithms for those who are not fully conversant with the field, which remains under active development. We discuss a series of missteps, errors, and violations of correct protocols of ML methods that appear disconcertingly often within the accumulating body of anthropological literature. These mistakes include the use of outdated algorithms and practices; inappropriate testing/training splits, sample composition, and textual explanations; as well as an absence of transparency due to the lack of data/code sharing, and the subsequent limitations imposed on independent replication. We assert that expanding samples, sharing data and code, re-evaluating approaches to peer review, and, most importantly, developing interdisciplinary teams that include experts in ML are all necessary for the progress in future research incorporating ML within anthropology and beyond.
{"title":"Use and Misuse of Machine Learning in Anthropology","authors":"J. Calder, Reed Coil, J. A. Melton, P. Olver, G. Tostevin, K. Yezzi-Woodley","doi":"10.1109/MBITS.2022.3205143","DOIUrl":"https://doi.org/10.1109/MBITS.2022.3205143","url":null,"abstract":"Machine learning (ML), being now widely accessible to the research community at large, has fostered a proliferation of new and striking applications of these emergent mathematical techniques across a wide range of disciplines. In this article, we will focus on a particular case study: the field of paleoanthropology, which seeks to understand the evolution of the human species based on biological (e.g., bones, genetics) and cultural (e.g., stone tools) evidence. As we will show, the easy availability of ML algorithms and lack of expertise on their proper use among the anthropological research community has led to the foundational misapplications that have appeared throughout the literature. The resulting unreliable results not only undermine efforts to legitimately incorporate ML into anthropological research, but produce potentially faulty understandings about our human evolutionary and behavioral past. The aim of this article is to provide a brief introduction to some of the ways in which ML has been applied within paleoanthropology; we also include a survey of some basic ML algorithms for those who are not fully conversant with the field, which remains under active development. We discuss a series of missteps, errors, and violations of correct protocols of ML methods that appear disconcertingly often within the accumulating body of anthropological literature. These mistakes include the use of outdated algorithms and practices; inappropriate testing/training splits, sample composition, and textual explanations; as well as an absence of transparency due to the lack of data/code sharing, and the subsequent limitations imposed on independent replication. We assert that expanding samples, sharing data and code, re-evaluating approaches to peer review, and, most importantly, developing interdisciplinary teams that include experts in ML are all necessary for the progress in future research incorporating ML within anthropology and beyond.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"229 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131832253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-25DOI: 10.1109/MBITS.2023.3262219
E. Dallas, Faidon Andreadakis, Daniel A. Lidar
It is well-known that nondegenerate quantum error correcting codes (QECCs) are constrained by a quantum version of the Hamming bound. Whether degenerate codes also obey such a bound, however, remains a long-standing question with practical implications for the efficacy of QECCs. We employ a combination of previously derived bounds on QECCs to demonstrate that a subset of all codes must obey the quantum Hamming bound. Specifically, we combine an analytical bound due to Rains with a numerical bound due to Li and Xing to show that no $((n,K,d))$((n,K,d)) code with $d< 127$d<127 can violate the quantum Hamming bound.
{"title":"No $((n,K,d< 127))$ Code Can Violate the Quantum Hamming Bound","authors":"E. Dallas, Faidon Andreadakis, Daniel A. Lidar","doi":"10.1109/MBITS.2023.3262219","DOIUrl":"https://doi.org/10.1109/MBITS.2023.3262219","url":null,"abstract":"It is well-known that nondegenerate quantum error correcting codes (QECCs) are constrained by a quantum version of the Hamming bound. Whether degenerate codes also obey such a bound, however, remains a long-standing question with practical implications for the efficacy of QECCs. We employ a combination of previously derived bounds on QECCs to demonstrate that a subset of all codes must obey the quantum Hamming bound. Specifically, we combine an analytical bound due to Rains with a numerical bound due to Li and Xing to show that no $((n,K,d))$((n,K,d)) code with $d< 127$d<127 can violate the quantum Hamming bound.","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121933733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.1109/mbits.2021.3134881
R. Calderbank
{"title":"Welcome to the First Issue of IEEE BITS","authors":"R. Calderbank","doi":"10.1109/mbits.2021.3134881","DOIUrl":"https://doi.org/10.1109/mbits.2021.3134881","url":null,"abstract":"","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125832123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.1109/mbits.2021.3132470
M. Medard
{"title":"Friends in Comment—A Conversation With Regina Barzilay","authors":"M. Medard","doi":"10.1109/mbits.2021.3132470","DOIUrl":"https://doi.org/10.1109/mbits.2021.3132470","url":null,"abstract":"","PeriodicalId":448036,"journal":{"name":"IEEE BITS the Information Theory Magazine","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130534365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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