291 research outputs found

    A study of the inter-relationship of identity and urban heritage in Chiang Mai Old City, Thailand

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    The urban heritage identity of historical cities has received growing attention due to the weakening of their urban identity. For this reason, urban identity has been identified as a preliminary study of this research. Forty years ago, many researchers attempted to explain a broader understanding of urban heritage identity, which is relevant to human factors that affect urban, place, and built environment relationships. This involved the three interrelated concepts of identity: distinctiveness; urban heritage; and place attachment. These establish a balance between people and their identification with places. Urban heritage identity is associated a place's physicality and heritage attributes that reflect socio-cultural values. It can be concluded that urban heritage identity becomes significant through concepts of environmental psychology. Distinctiveness theory, as a part of identity theory, has been used in this study to describe the genuine perception of local participants and is a fundamental part of defining place identity. Furthermore, the definition of place attachment has been used to explain the relationship of distinct places on time of residence, frequency of use, emotional, physical, social, and activities. The study also explores Chiang Mai Old City’s built environment, which especially analyses the façade and streetscape characteristics that reflect the city's socio-cultural value. The research concludes with suggestions for preserving the city's urban heritage characteristics. Chiang Mai Old City has unprecedented diversity and cultural dynamics related to its intangible and tangible urban heritage. Moreover, the city is in the critical stage of being nominated as a new World Heritage Site by UNESCO, with the city's distinctiveness and place attachment being significant in supporting further heritage management strategies. The research mainly focuses on how local people interpret and understand the urban heritage identity of Chiang Mai Old City. This has been achieved through surveys of four hundred participants living in the Old City, two-way focus groups with five participants in each group, in-depth interviews with twenty-five participants, and ten architects drawing suggestions for further built environment management strategies. The results are described through seven aspects that explore the distinctiveness and place attachment theories of Chiang Mai Old City. The findings can be described in seven aspects: historical value; cultural activities; a particular character; landmark; identity; community; and everyday life. The results reveal that there are five distinct places in the city: Pra Singha Temple; Chedi Luang Temple; Three Kings monument square; Tha-Pare gate square; and Chiang Mai Old City's Moat. The results can also be used to develop an assessment indicator for defining the distinctiveness of other historic cities through the engagement of local people. The study repeatedly employs distinct places to describe in-place attachment theory. The results reveal positivity, emotion, and the spiritual anchor of place attached to local people in social engagement, explicitly divulging the rootedness of religion, culture, and community activities through the length of time. All five distinct places have an inseparable ability to display tangible heritage value and such a positive emotion to places is crucial in contributing to urban heritage characteristics. Moreover, the time or length of residency is a vital aspect to people’s perception of the city's distinctiveness; however, the value of the physical setting itself can increase the sense of belonging of newcomers.This research used a mixed methods approach in defining place identity process and socio-cultural values in distinctive streetscapes scenes in the city. This study strongly believes that the findings demonstrate that local people can help to develop the management of the city. The results presented suggest that the heritage value of streetscapes is related to historical attributes, natural objects, people, and cultural events in the scenes that explain the meanings ascribed to places associated with social and cultural values. The built environment characteristics and heritage value can be assumed from human experience. The study can be a new perspective for local authorities, urban designers, and heritage teams to determine whether projects will strengthen the existing urban heritage identity. Most importantly, this research has revealed new perspectives on urban heritage identity and practical study methods whilst also contributing to management strategies. In addition, continuing research into urban heritage identity will significantly improve knowledge development, practical support, and collaboration with local people and architects to establish and maintain cherished distinct places and living environments for urban residents

    Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

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    Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

    Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

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    Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/

    Improving Classification in Single and Multi-View Images

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    Image classification is a sub-field of computer vision that focuses on identifying objects within digital images. In order to improve image classification we must address the following areas of improvement: 1) Single and Multi-View data quality using data pre-processing techniques. 2) Enhancing deep feature learning to extract alternative representation of the data. 3) Improving decision or prediction of labels. This dissertation presents a series of four published papers that explore different improvements of image classification. In our first paper, we explore the Siamese network architecture to create a Convolution Neural Network based similarity metric. We learn the priority features that differentiate two given input images. The metric proposed achieves state-of-the-art Fβ measure. In our second paper, we explore multi-view data classification. We investigate the application of Generative Adversarial Networks GANs on Multi-view data image classification and few-shot learning. Experimental results show that our method outperforms state-of-the-art research. In our third paper, we take on the challenge of improving ResNet backbone model. For this task, we focus on improving channel attention mechanisms. We utilize Discrete Wavelet Transform compression to address the channel representation problem. Experimental results on ImageNet shows that our method outperforms baseline SENet-34 and SOTA FcaNet-34 at no extra computational cost. In our fourth paper, we investigate further the potential of orthogonalization of filters for extraction of diverse information for channel attention. We prove that using only random constant orthogonal filters is sufficient enough to achieve good channel attention. We test our proposed method using ImageNet, Places365, and Birds datasets for image classification, MS-COCO for object detection, and instance segmentation tasks. Our method outperforms FcaNet, and WaveNet and achieves the state-of-the-art results

    Machine Learning Algorithm for the Scansion of Old Saxon Poetry

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    Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input verses

    Before Blue Birds Became X-tinct: Understanding the Effect of Regime Change on Twitter's Advertising and Compliance of Advertising Policies

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    Social media platforms, including Twitter (now X), have policies in place to maintain a safe and trustworthy advertising environment. However, the extent to which these policies are adhered to and enforced remains a subject of interest and concern. We present the first large-scale audit of advertising on Twitter focusing on compliance with the platform's advertising policies, particularly those related to political and adult content. We investigate the compliance of advertisements on Twitter with the platform's stated policies and the impact of recent acquisition on the advertising activity of the platform. By analyzing 34K advertisements from ~6M tweets, collected over six months, we find evidence of widespread noncompliance with Twitter's political and adult content advertising policies suggesting a lack of effective ad content moderation. We also find that Elon Musk's acquisition of Twitter had a noticeable impact on the advertising landscape, with most existing advertisers either completely stopping their advertising activity or reducing it. Major brands decreased their advertising on Twitter, suggesting a negative immediate effect on the platform's advertising revenue. Our findings underscore the importance of external audits to monitor compliance and improve transparency in online advertising

    Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

    Get PDF
    Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/
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