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  • Digital Humanities and Cultural Heritage
  • 2018-2022
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  • Digital Humanities and Cultural Her...

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  • Authors: Aires Horta, Martim; Piette, Elie; Woram, Kevin;
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  • Authors: Geenen, Kristien; De Nys-Keteles, Simon;

    This article deals with the way urban planning during colonial times affects the mobility of pedestrians today. In Kinshasa, a green belt cuts the eldest part of the city right in two, and this hinders a smooth traffic flow. The belt is what remains of the neutral zone which the colonial authorities implemented to separate the European from the African neighborhoods; it consisted of several large walled off facilities, such as a zoo, a park and a hospital. In this article, we explore how pedestrians in Kinshasa deal with these obstructions to their mobility. We show that they forge their pedestrian itineraries through walls designed to be impermeable, in particular by shortcutting a hospital. These alternative itineraries have solidified through time, laying bare the effectiveness of their persistent daily walks. As we argue, the pedestrians actively redefine the mobility patterns of their city. Urban landscapes of colonial/postcolonial health care. Towards a spatial mapping of the performance of hospital infrastructure in Kinshasa, Mbandaka and Kisangani (DR Congo) from past to present (1920-2014)

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    Authors: Mineur, Mattias;

    A lot of interaction between humans take place on social media. Groups and communities are sometimes formed both with and without intention. These interactions generate a large quantity of text data. This project aims to detect dissimilarity in discourse between communities on social media using a distributed approach. A data set of tweets was used to test and evaluate the method. Tweets produced from two communities were extracted from the data set. Two Natural Language Processing techniques were used to vectorise the tweets for each community. Namely LIWC, dictionary based on knowledge acquired from professionals in linguistics and psychology, and BERT, an embedding model which uses machine learning to present words and sentences as a vector of decimal numbers. These vectors were then used as representations of the text to measure the similarity of discourse between the communities. Both distance and similarity were measured. It was concluded that none of the combinations of measure or vectorisation method that was tried could detect a dissimilarity in discourse on social media for the sample data set. 

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    Authors: Ulasik, Malgorzata Anna; Hürlimann, Manuela; Dubel, Bogumila; Kaufmann, Yves; +5 Authors

    This paper presents the contribution of ZHAW-CAI to the Shared Task ”Swiss German Speech to Standard German Text” at the SwissText 2021 conference. Our approach combines three models based on the Fairseq, Jasper and Wav2vec architectures trained on multilingual, German and Swiss German data. We applied an ensembling algorithm on the predictions of the three models in order to retrieve the most reliable candidate out of the provided translations for each spoken utterance. With the ensembling output, we achieved a BLEU score of 39.39 on the private test set, which gave us the third place out of four contributors in the competition.

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    ZHAW digitalcollection
    Other ORP type . 2022
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  • Authors: Westin, Jonathan;

    The first Swedish Antarctic Expedition (1901-1903), led by Otto Nordenskjöld, sailed to Antarctica on the ship Antarctic captained by CA Larsen, and established a research station on Snow Hill Island. There six members overwintered and performed paleontological, meteorological, geomagnetic and geological studies, while the rest of the expedition set sail for South Orkney. After the winter, on the way back to Snow Hill Island, the Antarctic got stuck in the ice and sank. At this point, the expedition members were divided into three groups. One of these overwintered an extra year on Snow Hill Island, whereas the other two groups were forced to build stone huts in order to overwinter at Hope Bay and Paulet Island. An Argentinean vessel, the Corbeta Uruguay, rescued the expedition in November 1903. CHAQ 2020 is an Argentinean-Swedish project with fieldwork in the area around the Antarctic Peninsula aiming to investigating and documenting the historical remains of the first Swedish South Polar expedition under the leadership of Otto Nordenskjöld 1901-1903. The material was collected in January and February 2020. This catalogue entry collects the photographic material and the panoramas from Hope Bay. The set consist of panoramas obtained from three scanning sessions with the Faro Focus m70 on January 22 and 24, 2020 by Jonathan Westin. The resolution of the panoramas is 20288 x 10144 pixels. Corresponding raw data point clouds in FLS-format for each of the scanning positions can be downloaded from the repository (catalogue entry "CHAQ2020 - Hope Bay - Spatial data"). Syftet med forskningsprojektet var att dokumentera lämningarna efter Otto Nordenskjölds Antarktisexpeditionen 1901-1903. Materialet består av fotografier, planer, ritningar, 3d-modeller och drönarfilmer från Snow Hillön, Hoppets vik och Seymourön. Denna katalogpost samlar fotografier och panoraman från Hoppets Vik. Se den engelska beskrivningen för mer information. Datasetet består av panoramabilderna skapade under två skanningsessioner med en Faro Focus m70 den 22 och 24 januari, 2020 av Jonathan Westin. Upplösningen på bilderna är 20288 x 10144. Högupplösta punktmoln för varje panorama-position finns lagrade i katalogpost "CHAQ2020 - Hoppets vik - Spatiala data".

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    Other ORP type . 2021
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  • Authors: Stanković, Ranka; Kitanović, Olivera; Tomašević, Aleksandra;

    RudOnto - uzorak iz onlajn tezaurusa rudarskih termina. RudOnto - sample from an online thesaurus of mining terms, compiled by University of Belgrade, Faculty of Mining and Geology.

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    Authors: Rozas Krause, Valentina J.;

    Memorials and the Cult of Apology examines how contemporary memorials have come to embody more than memory. It begins with a simple observation of the growing demand for apologies across the globe and the related proliferation of memorials that aim to atone for past injustices. In effect, apologies are being materialized into memorials, a phenomenon of global importance, which presents a major shift in national self-representation. In the broadest terms, my research is an intervention into the cultural history of the built environment. As the first scholarly work to address memorials as apologies, my dissertation builds an empirical and theoretical understanding of multiple aspects of apology and memorialization, of their material forms, the actors involved, and the diverse effects built apologies produce. It uses five representative case studies located in Berlin, Buenos Aires, and San Francisco, to develop this argument. Since memorialization is an inherently interdisciplinary topic, my work incorporates methods, readings, and theories from a vast array of humanistic disciplines, particularly postcolonial theory, Holocaust and human rights scholarship, and debates about justice, recognition, reparation, and morality. My archival and field research combines methods drawn from architectural history and the humanities ¬–close reading, literary interpretation, and storytelling–, which I apply to the formal analysis of built memorials and their urban contexts. This formal analysis is complemented with ethnographic interviews with designers, experts and site visitors, as well as participant observation of both commemorative events and what has been called ‘apology activism.’

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    Authors: Mekala, Dheeraj;

    Text classification plays a fundamental role in transforming unstructured text data to structured knowledge. State-of-the-art text classification techniques rely on heavy domain-specific annotations to build massive machine(deep) learning models. Although these deep learning models exhibit superior performance, the lack of training data and expensive human effort in the manual annotation is a key bottleneck that forbids them from being adopted in many practical scenarios. To address this bottleneck, our research exploits the data and develops a family of data-driven text classification frameworks with minimal supervision, for e.g. class names, a few label-indicative seed words per class.The massive volume of text data and complexity of natural language pose significant challenges to categorizing the text corpus without human annotations. For instance, the user- provided seed words can have multiple interpretations depending on the context, and their respective user-intended interpretation has to be identified for accurate classification. Moreover, metadata information like author, year, and location is widely available in addition to the text data, and it could serve as a strong, complementary source of supervision. However, leveraging metadata is challenging because (1) metadata is multi-typed, therefore it requires systematic modeling of different types and their combinations, (2) metadata is noisy, some metadata entities (e.g., authors, venues) are more compelling label indicators than others. And also, the label set is typically assumed to be fixed in traditional text classification problems. However, in many real-world applications, new classes especially more fine-grained ones will be introduced as the data volume increases. The goal of our research is to create general data-driven methods that transform real-world text data into structured categories of human knowledge with minimal human effort.This thesis outlines a family of weakly supervised text classification approaches, which upon combining can automatically categorize huge text corpus into coarse and fine-grained classes, with just label hierarchy and a few label-indicative seed words as supervision. Specifically, it first leverages contextualized representations of word occurrences and seed word information to automatically differentiate multiple interpretations of a seed word, and thus result- ing in contextualized weak supervision. Then, to leverage metadata, it organizes the text data and metadata together into a text-rich network and adopt network motifs to capture appropriate combinations of metadata. Finally, we introduce a new problem called coarse-to-fine grained classification, which aims to perform fine-grained classification on coarsely annotated data. Instead of asking for new fine-grained human annotations, we opt to leverage label surface names as the only human guidance and weave in rich pre-trained generative language models into the iterative weak supervision strategy. We have performed extensive experiments on real-world datasets from different domains. The results demonstrate significant advantages of using contextualized weak supervision and leveraging metadata, and superior performance over baselines.

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    Authors: Jörg Lehmann (Eberhard Karls Universität Tübingen), author;

    This text elucidates how we make use of the FAIR data principles.

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  • Authors: Gouet-Brunet, Valérie; Khokhlova, Margarita; Kosti, Ronak; Chen, Liming; +1 Authors

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  • Authors: Aires Horta, Martim; Piette, Elie; Woram, Kevin;
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  • Authors: Geenen, Kristien; De Nys-Keteles, Simon;

    This article deals with the way urban planning during colonial times affects the mobility of pedestrians today. In Kinshasa, a green belt cuts the eldest part of the city right in two, and this hinders a smooth traffic flow. The belt is what remains of the neutral zone which the colonial authorities implemented to separate the European from the African neighborhoods; it consisted of several large walled off facilities, such as a zoo, a park and a hospital. In this article, we explore how pedestrians in Kinshasa deal with these obstructions to their mobility. We show that they forge their pedestrian itineraries through walls designed to be impermeable, in particular by shortcutting a hospital. These alternative itineraries have solidified through time, laying bare the effectiveness of their persistent daily walks. As we argue, the pedestrians actively redefine the mobility patterns of their city. Urban landscapes of colonial/postcolonial health care. Towards a spatial mapping of the performance of hospital infrastructure in Kinshasa, Mbandaka and Kisangani (DR Congo) from past to present (1920-2014)

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    Authors: Mineur, Mattias;

    A lot of interaction between humans take place on social media. Groups and communities are sometimes formed both with and without intention. These interactions generate a large quantity of text data. This project aims to detect dissimilarity in discourse between communities on social media using a distributed approach. A data set of tweets was used to test and evaluate the method. Tweets produced from two communities were extracted from the data set. Two Natural Language Processing techniques were used to vectorise the tweets for each community. Namely LIWC, dictionary based on knowledge acquired from professionals in linguistics and psychology, and BERT, an embedding model which uses machine learning to present words and sentences as a vector of decimal numbers. These vectors were then used as representations of the text to measure the similarity of discourse between the communities. Both distance and similarity were measured. It was concluded that none of the combinations of measure or vectorisation method that was tried could detect a dissimilarity in discourse on social media for the sample data set. 

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    Authors: Ulasik, Malgorzata Anna; Hürlimann, Manuela; Dubel, Bogumila; Kaufmann, Yves; +5 Authors

    This paper presents the contribution of ZHAW-CAI to the Shared Task ”Swiss German Speech to Standard German Text” at the SwissText 2021 conference. Our approach combines three models based on the Fairseq, Jasper and Wav2vec architectures trained on multilingual, German and Swiss German data. We applied an ensembling algorithm on the predictions of the three models in order to retrieve the most reliable candidate out of the provided translations for each spoken utterance. With the ensembling output, we achieved a BLEU score of 39.39 on the private test set, which gave us the third place out of four contributors in the competition.

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  • Authors: Westin, Jonathan;

    The first Swedish Antarctic Expedition (1901-1903), led by Otto Nordenskjöld, sailed to Antarctica on the ship Antarctic captained by CA Larsen, and established a research station on Snow Hill Island. There six members overwintered and performed paleontological, meteorological, geomagnetic and geological studies, while the rest of the expedition set sail for South Orkney. After the winter, on the way back to Snow Hill Island, the Antarctic got stuck in the ice and sank. At this point, the expedition members were divided into three groups. One of these overwintered an extra year on Snow Hill Island, whereas the other two groups were forced to build stone huts in order to overwinter at Hope Bay and Paulet Island. An Argentinean vessel, the Corbeta Uruguay, rescued the expedition in November 1903. CHAQ 2020 is an Argentinean-Swedish project with fieldwork in the area around the Antarctic Peninsula aiming to investigating and documenting the historical remains of the first Swedish South Polar expedition under the leadership of Otto Nordenskjöld 1901-1903. The material was collected in January and February 2020. This catalogue entry collects the photographic material and the panoramas from Hope Bay. The set consist of panoramas obtained from three scanning sessions with the Faro Focus m70 on January 22 and 24, 2020 by Jonathan Westin. The resolution of the panoramas is 20288 x 10144 pixels. Corresponding raw data point clouds in FLS-format for each of the scanning positions can be downloaded from the repository (catalogue entry "CHAQ2020 - Hope Bay - Spatial data"). Syftet med forskningsprojektet var att dokumentera lämningarna efter Otto Nordenskjölds Antarktisexpeditionen 1901-1903. Materialet består av fotografier, planer, ritningar, 3d-modeller och drönarfilmer från Snow Hillön, Hoppets vik och Seymourön. Denna katalogpost samlar fotografier och panoraman från Hoppets Vik. Se den engelska beskrivningen för mer information. Datasetet består av panoramabilderna skapade under två skanningsessioner med en Faro Focus m70 den 22 och 24 januari, 2020 av Jonathan Westin. Upplösningen på bilderna är 20288 x 10144. Högupplösta punktmoln för varje panorama-position finns lagrade i katalogpost "CHAQ2020 - Hoppets vik - Spatiala data".

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  • Authors: Stanković, Ranka; Kitanović, Olivera; Tomašević, Aleksandra;

    RudOnto - uzorak iz onlajn tezaurusa rudarskih termina. RudOnto - sample from an online thesaurus of mining terms, compiled by University of Belgrade, Faculty of Mining and Geology.

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    Authors: Rozas Krause, Valentina J.;

    Memorials and the Cult of Apology examines how contemporary memorials have come to embody more than memory. It begins with a simple observation of the growing demand for apologies across the globe and the related proliferation of memorials that aim to atone for past injustices. In effect, apologies are being materialized into memorials, a phenomenon of global importance, which presents a major shift in national self-representation. In the broadest terms, my research is an intervention into the cultural history of the built environment. As the first scholarly work to address memorials as apologies, my dissertation builds an empirical and theoretical understanding of multiple aspects of apology and memorialization, of their material forms, the actors involved, and the diverse effects built apologies produce. It uses five representative case studies located in Berlin, Buenos Aires, and San Francisco, to develop this argument. Since memorialization is an inherently interdisciplinary topic, my work incorporates methods, readings, and theories from a vast array of humanistic disciplines, particularly postcolonial theory, Holocaust and human rights scholarship, and debates about justice, recognition, reparation, and morality. My archival and field research combines methods drawn from architectural history and the humanities ¬–close reading, literary interpretation, and storytelling–, which I apply to the formal analysis of built memorials and their urban contexts. This formal analysis is complemented with ethnographic interviews with designers, experts and site visitors, as well as participant observation of both commemorative events and what has been called ‘apology activism.’

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    Authors: Mekala, Dheeraj;

    Text classification plays a fundamental role in transforming unstructured text data to structured knowledge. State-of-the-art text classification techniques rely on heavy domain-specific annotations to build massive machine(deep) learning models. Although these deep learning models exhibit superior performance, the lack of training data and expensive human effort in the manual annotation is a key bottleneck that forbids them from being adopted in many practical scenarios. To address this bottleneck, our research exploits the data and develops a family of data-driven text classification frameworks with minimal supervision, for e.g. class names, a few label-indicative seed words per class.The massive volume of text data and complexity of natural language pose significant challenges to categorizing the text corpus without human annotations. For instance, the user- provided seed words can have multiple interpretations depending on the context, and their respective user-intended interpretation has to be identified for accurate classification. Moreover, metadata information like author, year, and location is widely available in addition to the text data, and it could serve as a strong, complementary source of supervision. However, leveraging metadata is challenging because (1) metadata is multi-typed, therefore it requires systematic modeling of different types and their combinations, (2) metadata is noisy, some metadata entities (e.g., authors, venues) are more compelling label indicators than others. And also, the label set is typically assumed to be fixed in traditional text classification problems. However, in many real-world applications, new classes especially more fine-grained ones will be introduced as the data volume increases. The goal of our research is to create general data-driven methods that transform real-world text data into structured categories of human knowledge with minimal human effort.This thesis outlines a family of weakly supervised text classification approaches, which upon combining can automatically categorize huge text corpus into coarse and fine-grained classes, with just label hierarchy and a few label-indicative seed words as supervision. Specifically, it first leverages contextualized representations of word occurrences and seed word information to automatically differentiate multiple interpretations of a seed word, and thus result- ing in contextualized weak supervision. Then, to leverage metadata, it organizes the text data and metadata together into a text-rich network and adopt network motifs to capture appropriate combinations of metadata. Finally, we introduce a new problem called coarse-to-fine grained classification, which aims to perform fine-grained classification on coarsely annotated data. Instead of asking for new fine-grained human annotations, we opt to leverage label surface names as the only human guidance and weave in rich pre-trained generative language models into the iterative weak supervision strategy. We have performed extensive experiments on real-world datasets from different domains. The results demonstrate significant advantages of using contextualized weak supervision and leveraging metadata, and superior performance over baselines.

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    Authors: Jörg Lehmann (Eberhard Karls Universität Tübingen), author;

    This text elucidates how we make use of the FAIR data principles.

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  • Authors: Gouet-Brunet, Valérie; Khokhlova, Margarita; Kosti, Ronak; Chen, Liming; +1 Authors

    International audience