This paper performs a classification task on data obtained from the Autism Brain Imaging Data Exchange (ABIDE) repository. In real-world case analysis, the number of autism spectrum disorder (ASD) patients is much sma...
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Since their initial inception, large language models have undergone many innovations. One of these innovations concerns multimodality. Several adaptation strategies have been developed to expand LLMs to process multim...
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Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains. However, adapting them to narrative content remains challenging. This paper explores the opportunities in adapting open-so...
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This paper presents an integrated solution for 3D object detection, recognition, and presentation to increase accessibility for various user groups in indoor areas through a mobile application. The system has three ma...
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This article focuses on the research, design and implementation of a prediction tool for air quality to estimate pollutant concentrations, contributing to environmental engineering. It addresses prediction of fine par...
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This paper proposes a computer vision-based workflow that analyses Google 360-degree street views to understand the quality of urban spaces regarding vegetation coverage and accessibility of urban amenities such as be...
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This paper presents a mobile-based solution that integrates 3D vision and voice interaction to assist people who are blind or have low vision to explore and interact with their surroundings. The key components of the ...
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Understanding adoption of new technology by its consumers helps us deal with the challenges it faces in any new market. Moreover, the impact it creates on society in terms of the environment, health, and justice is si...
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Artificial intelligence (AI) is increasingly being applied to disciplines beyond computerscience (CS). Engineers, statisticians, business analysts, biologists, physicists, physicians, and pharmacists, are among the m...
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ISBN:
(纸本)9781450398428
Artificial intelligence (AI) is increasingly being applied to disciplines beyond computerscience (CS). Engineers, statisticians, business analysts, biologists, physicists, physicians, and pharmacists, are among the many non-CS professionals who leverage the power of AI algorithms and systems for solving their domain-specific problems. Although AI has been found useful for solving a wide range of previously unsolvable problems, there are important limitations associated with contemporary AI. It is therefore important to inform current and future AI users regarding both strengths and weaknesses of AI in its current form, as well as what AI will be like in the foreseeable future. In this paper, the authors describe a pedagogical approach toward educating AI users from a range of STEM disciplines so that they can best exploit what AI has to offer. Specifically, a balanced approach is taken to ensure that learners gain knowledge and skills in what AI can or cannot do for them. A growing suite of experiential learning modules, which complement existing educational resources, serve as a vehicle for getting STEM learners ready for a future workplace characterized by significant use of AI technologies. These learning modules promote active learning and can be applied in a traditional classroom setting, self-directed online study, or a mix of the two modes. The paper ends with a presentation of encouraging results of actual use of the experiential learning modules in a mixed mode setting across multiple quantitative disciplines. All project artifacts, including the developed experiential learning modules, recommended uses, and best practices, are freely available on the project website. Interested educators and researchers are welcome to use the available resources and/or contribute to the on-going research.
Participating local community in cultural heritage management has always been a concern since the Venice Charter so far (1964). In addition, the Faro Convention (2005) shifted focus from cultural heritage values to th...
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