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  1. Ikonographie und Interaktion. Computergestützte Analyse von Posen in Bildern der Heilsgeschichte
    Autor*in: Bell, Peter; Impett, Leonardo

    Abstract: The last few years have seen an explosion of medieval images in digital form, chiefly as a result of photo-library and manuscript digitisation projects. An entire corpus of images, even selected solely by scene or iconography, becomes an... mehr

     

    Abstract: The last few years have seen an explosion of medieval images in digital form, chiefly as a result of photo-library and manuscript digitisation projects. An entire corpus of images, even selected solely by scene or iconography, becomes an unwieldy object of study by traditional art-historical means. This is even more the case for medieval images, where authorship and dating are often cloudy and unclear, and the image itself is in many cases the first resource for scholarly inquiry.We take the digital image – in particular, the digital image of the body – as our object of study in a wide-ranging computationally-augmented reading of an image-corpus; ours is made up of thousands of depictions of the ‘Annunciation’ and ‘Baptism’, selected not only for their primacy in Christian art but for their dialogical interaction. Our corpus of 6,564 ‘Annunciations’ and 883 ‘Baptisms’, whilst not necessarily representative in density, includes a wide range of stylistic, theological and historical tendencies.We computationally extract not only body images but poses, gestures and interactions. Such a range of gestures allows for a morphological treatment of bodily motifs, whose multi-dimensional, quantitative nature allows us to complicate and problematise iconographic taxonomies, populating the spaces between categories. Finally, our gestural manifolds provide a morphological pointer to dissecting the microtemporalities of the scenes, and their relative dynamics and inconsistencies.

     
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    Quelle: Verbundkataloge
    Sprache: Deutsch
    Medientyp: Aufsatz aus einer Zeitschrift
    Format: Online
    Weitere Identifier:
    Übergeordneter Titel:
    Enthalten in: Das Mittelalter; Heidelberg : Heidelberg University Publishing, 1996-; 24, Heft 1 (2019), 31-53 (gesamt 23); Online-Ressource
    Weitere Schlagworte: computer vision; pose recognition; Christian iconography; art history; digital humanities
    Umfang: Online-Ressource
  2. Visual representation and stereotypes in news media
    Autor*in: Ash, Elliott; Durante, Ruben; Grebenshchikova, Maria; Schwarz, Carlo
    Erschienen: April 2022
    Verlag:  CESifo, Center for Economic Studies & Ifo Institute, Munich, Germany

    We propose a new method for measuring gender and ethnic stereotypes in news reports. By combining computer vision and natural language processing tools, the method allows us to analyze both images and text as well as the interaction between the two.... mehr

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    DS 63
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    We propose a new method for measuring gender and ethnic stereotypes in news reports. By combining computer vision and natural language processing tools, the method allows us to analyze both images and text as well as the interaction between the two. We apply this approach to over 2 million web articles published in the New York Times and Fox News between 2000 and 2020. We find that in both outlets, men and whites are generally over-represented relative to their population share, while women and Hispanics are under-represented. We also document that news content perpetuates common stereotypes such as associating Blacks and Hispanics with low-skill jobs, crime, and poverty, and Asians with high-skill jobs and science. For jobs, we show that the relationship between visual representation and racial stereotypes holds even after controlling for the actual share of a group in a given occupation. Finally, we find that group representation in the news is influenced by the gender and ethnic identity of authors and editors.

     
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/260816
    Schriftenreihe: CESifo working paper ; no. 9686 (2022)
    Schlagworte: stereotypes; gender; race; media; computer vision; text analysis
    Umfang: 1 Online-Ressource (circa 46 Seiten), Illustrationen
  3. Towards accountability in machine learning applications
    a system-testing approach
    Autor*in: Wan, Wayne Xinwei; Lindenthal, Thies
    Erschienen: [2022]
    Verlag:  [Department of Land Economy, Environment, Law & Economics, University of Cambridge, Real Estate Research Centre], [Cambridge]

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Schriftenreihe: Working paper series / Department of Land Economy, Environment, Law & Economics, University of Cambridge, Real Estate Research Centre ; no. 2022, 03
    Schlagworte: explainable machine learning; accountability gap; computer vision; real estate; urban studies
    Umfang: 1 Online-Ressource (circa 62 Seiten), Illustrationen
  4. Biased auctioneers
    Autor*in: Aubry, Mathieu; Kräussl, Roman; Manso, Gustavo; Spaenjers, Christophe
    Erschienen: [2022]
    Verlag:  Center for Financial Studies, Goethe University, Frankfurt am Main, Germany

    We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and non-visual object characteristics. We find that higher automated valuations relative to auction house pre-sale estimates are... mehr

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    Standort:
    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
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    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 108
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    keine Fernleihe

     

    We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and non-visual object characteristics. We find that higher automated valuations relative to auction house pre-sale estimates are associated with substantially higher price-to-estimate ratios and lower buy-in rates, pointing to estimates' informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers' prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.

     
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/268894
    Auflage/Ausgabe: This version: January 6, 2022
    Schriftenreihe: CFS working paper series ; no. 692
    Schlagworte: art; auctions; experts; asset valuation; biases; machine learning; computer vision
    Weitere Schlagworte: Array
    Umfang: 1 Online-Ressource (circa 46 Seiten), Illustrationen
  5. Towards accountability in machine learning applications
    a system-testing approach
    Autor*in: Wan, Wayne Xinwei; Lindenthal, Thies
    Erschienen: [2022]
    Verlag:  ZEW - Leibniz Centre for European Economic Research, Mannheim, Germany

    A rapidly expanding universe of technology-focused startups is trying to change and improve the way real estate markets operate. The undisputed predictive power of machine learning (ML) models often plays a crucial role in the ‘disruption’ of... mehr

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    DS 15
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    Standort:
    Universitätsbibliothek Mannheim
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    A rapidly expanding universe of technology-focused startups is trying to change and improve the way real estate markets operate. The undisputed predictive power of machine learning (ML) models often plays a crucial role in the ‘disruption’ of traditional processes. However, an accountability gap prevails: How do the models arrive at their predictions? Do they do what we hope they do – or are corners cut? Training ML models is a software development process at heart. We suggest to follow a dedicated software testing framework and to verify that the ML model performs as intended. Illustratively, we augment two ML image classifiers with a system testing procedure based on local interpretable model-agnostic explanation (LIME) techniques. Analyzing the classifications sheds light on some of the factors that determine the behavior of the systems.

     
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    Sprache: Englisch
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    Format: Online
    Weitere Identifier:
    hdl: 10419/250385
    Schriftenreihe: Discussion paper / ZEW ; no. 22, 001 (01/2022)
    Schlagworte: machine learning; accountability gap; computer vision; real estate; urban studies
    Umfang: 1 Online-Ressource (63 Seiten), Illustrationen
  6. Towards accountability in machine learning applications
    a system-testing approach
    Autor*in: Wan, Wayne Xinwei; Lindenthal, Thies
    Erschienen: 01/2022
    Verlag:  ZEW, Mannheim

    A rapidly expanding universe of technology-focused startups is trying to change and improve the way real estate markets operate. The undisputed predictive power of machine learning (ML) models often plays a crucial role in the ‘disruption’ of... mehr

    Standort:
    Niedersächsische Staats- und Universitätsbibliothek Göttingen
    Signatur:
    2 : Z 2027:2022,001
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    keine Fernleihe

    Standort:
    Badische Landesbibliothek
    Fernleihe:
    uneingeschränkte Fernleihe, Kopie und Ausleihe

     

    A rapidly expanding universe of technology-focused startups is trying to change and improve the way real estate markets operate. The undisputed predictive power of machine learning (ML) models often plays a crucial role in the ‘disruption’ of traditional processes. However, an accountability gap prevails: How do the models arrive at their predictions? Do they do what we hope they do – or are corners cut? Training ML models is a software development process at heart. We suggest to follow a dedicated software testing framework and to verify that the ML model performs as intended. Illustratively, we augment two ML image classifiers with a system testing procedure based on local interpretable model-agnostic explanation (LIME) techniques. Analyzing the classifications sheds light on some of the factors that determine the behavior of the systems.

     
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    Sprache: Englisch
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    Format: Druck
    Schriftenreihe: Discussion paper / ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung GmbH ; No. 22-001
    Schlagworte: machine learning; accountability gap; computer vision; real estate; urban studies
    Umfang: 63 Seiten, Illustrationen, Diagramme
  7. Using machine learning to create a property tax roll
    evidence from the city of Kananga, DR Congo
    Autor*in: Bergeron, Augustin; Fournier, Arnaud; Kabeya Kabeya, John; Tourek, Gabriel
    Erschienen: November 2023
    Verlag:  The International Centre for Tax and Development at the Institute of Development Studies, Brighton, UK

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    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9781804701539
    Weitere Identifier:
    Schriftenreihe: ICTD working paper ; 176
    Schlagworte: property tax; machine learning; Democratic Republic of Congo; computer vision; property valuation; state capacity
    Umfang: 1 Online-Ressource (circa 33 Seiten), Illustrationen
  8. Demand estimation with text and image data
    Autor*in: Compiani, Giovanni; Morozov, Ilya; Seiler, Stephan
    Erschienen: October 2023
    Verlag:  CESifo, Munich, Germany

    We propose a demand estimation method that allows researchers to estimate substitution patterns from unstructured image and text data. We first employ a series of machine learning models to measure product similarity from products' images and textual... mehr

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    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 63
    Fernleihe:
    keine Fernleihe

     

    We propose a demand estimation method that allows researchers to estimate substitution patterns from unstructured image and text data. We first employ a series of machine learning models to measure product similarity from products' images and textual descriptions. We then estimate a nested logit model with product-pair specific nesting parameters that depend on the image and text similarities between products. Our framework does not require collecting product attributes for each category and can capture product similarity along dimensions that are hard to account for with observed attributes. We apply our method to a dataset describing the behavior of Amazon shoppers across several categories and show that incorporating texts and images in demand estimation helps us recover a flexible cross-price elasticity matrix.

     
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    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/282383
    Schriftenreihe: CESifo working papers ; 10695 (2023)
    Schlagworte: demand estimation; unstructured data; computer vision; text models
    Umfang: 1 Online-Ressource (circa 30 Seiten), Illustrationen