230 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

    Get PDF

    Undergraduate Catalog of Studies, 2022-2023

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    Applying machine learning: a multi-role perspective

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    Machine (and deep) learning technologies are more and more present in several fields. It is undeniable that many aspects of our society are empowered by such technologies: web searches, content filtering on social networks, recommendations on e-commerce websites, mobile applications, etc., in addition to academic research. Moreover, mobile devices and internet sites, e.g., social networks, support the collection and sharing of information in real time. The pervasive deployment of the aforementioned technological instruments, both hardware and software, has led to the production of huge amounts of data. Such data has become more and more unmanageable, posing challenges to conventional computing platforms, and paving the way to the development and widespread use of the machine and deep learning. Nevertheless, machine learning is not only a technology. Given a task, machine learning is a way of proceeding (a way of thinking), and as such can be approached from different perspectives (points of view). This, in particular, will be the focus of this research. The entire work concentrates on machine learning, starting from different sources of data, e.g., signals and images, applied to different domains, e.g., Sport Science and Social History, and analyzed from different perspectives: from a non-data scientist point of view through tools and platforms; setting a problem stage from scratch; implementing an effective application for classification tasks; improving user interface experience through Data Visualization and eXtended Reality. In essence, not only in a quantitative task, not only in a scientific environment, and not only from a data-scientist perspective, machine (and deep) learning can do the difference

    Multimodal machine learning in medical screenings

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    The healthcare industry, with its high demand and standards, has long been considered a crucial area for technology-based innovation. However, the medical field often relies on experience-based evaluation. Limited resources, overloading capacity, and a lack of accessibility can hinder timely medical care and diagnosis delivery. In light of these challenges, automated medical screening as a decision-making aid is highly recommended. With the increasing availability of data and the need to explore the complementary effect among modalities, multimodal machine learning has emerged as a potential area of technology. Its impact has been witnessed across a wide range of domains, prompting the question of how far machine learning can be leveraged to automate processes in even more complex and high-risk sectors. This paper delves into the realm of multimodal machine learning in the field of automated medical screening and evaluates the potential of this area of study in mental disorder detection, a highly important area of healthcare. First, we conduct a scoping review targeted at high-impact papers to highlight the trends and directions of multimodal machine learning in screening prevalent mental disorders such as depression, stress, and bipolar disorder. The review provides a comprehensive list of popular datasets and extensively studied modalities. The review also proposes an end-to-end pipeline for multimodal machine learning applications, covering essential steps from preprocessing, representation, and fusion, to modelling and evaluation. While cross-modality interaction has been considered a promising factor to leverage fusion among multimodalities, the number of existing multimodal fusion methods employing this mechanism is rather limited. This study investigates multimodal fusion in more detail through the proposal of Autofusion, an autoencoder-infused fusion technique that harnesses the cross-modality interaction among different modalities. The technique is evaluated on DementiaBank’s Pitt corpus to detect Alzheimer’s disease, leveraging the power of cross-modality interaction. Autofusion achieves a promising performance of 79.89% in accuracy, 83.85% in recall, 81.72% in precision, and 82.47% in F1. The technique consistently outperforms all unimodal methods by an average of 5.24% across all metrics. Our method consistently outperforms early fusion and late fusion. Especially against the late fusion hard-voting technique, our method outperforms by an average of 20% across all metrics. Further, empirical results show that the cross-modality interaction term enhances the model performance by 2-3% across metrics. This research highlights the promising impact of cross-modality interaction in multimodal machine learning and calls for further research to unlock its full potential

    GPT-4V(ision) as A Social Media Analysis Engine

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    Recent research has offered insights into the extraordinary capabilities of Large Multimodal Models (LMMs) in various general vision and language tasks. There is growing interest in how LMMs perform in more specialized domains. Social media content, inherently multimodal, blends text, images, videos, and sometimes audio. Understanding social multimedia content remains a challenging problem for contemporary machine learning frameworks. In this paper, we explore GPT-4V(ision)'s capabilities for social multimedia analysis. We select five representative tasks, including sentiment analysis, hate speech detection, fake news identification, demographic inference, and political ideology detection, to evaluate GPT-4V. Our investigation begins with a preliminary quantitative analysis for each task using existing benchmark datasets, followed by a careful review of the results and a selection of qualitative samples that illustrate GPT-4V's potential in understanding multimodal social media content. GPT-4V demonstrates remarkable efficacy in these tasks, showcasing strengths such as joint understanding of image-text pairs, contextual and cultural awareness, and extensive commonsense knowledge. Despite the overall impressive capacity of GPT-4V in the social media domain, there remain notable challenges. GPT-4V struggles with tasks involving multilingual social multimedia comprehension and has difficulties in generalizing to the latest trends in social media. Additionally, it exhibits a tendency to generate erroneous information in the context of evolving celebrity and politician knowledge, reflecting the known hallucination problem. The insights gleaned from our findings underscore a promising future for LMMs in enhancing our comprehension of social media content and its users through the analysis of multimodal information

    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
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