3,875 research outputs found

    Fairness-aware Machine Learning in Educational Data Mining

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    Fairness is an essential requirement of every educational system, which is reflected in a variety of educational activities. With the extensive use of Artificial Intelligence (AI) and Machine Learning (ML) techniques in education, researchers and educators can analyze educational (big) data and propose new (technical) methods in order to support teachers, students, or administrators of (online) learning systems in the organization of teaching and learning. Educational data mining (EDM) is the result of the application and development of data mining (DM), and ML techniques to deal with educational problems, such as student performance prediction and student grouping. However, ML-based decisions in education can be based on protected attributes, such as race or gender, leading to discrimination of individual students or subgroups of students. Therefore, ensuring fairness in ML models also contributes to equity in educational systems. On the other hand, bias can also appear in the data obtained from learning environments. Hence, bias-aware exploratory educational data analysis is important to support unbiased decision-making in EDM. In this thesis, we address the aforementioned issues and propose methods that mitigate discriminatory outcomes of ML algorithms in EDM tasks. Specifically, we make the following contributions: We perform bias-aware exploratory analysis of educational datasets using Bayesian networks to identify the relationships among attributes in order to understand bias in the datasets. We focus the exploratory data analysis on features having a direct or indirect relationship with the protected attributes w.r.t. prediction outcomes. We perform a comprehensive evaluation of the sufficiency of various group fairness measures in predictive models for student performance prediction problems. A variety of experiments on various educational datasets with different fairness measures are performed to provide users with a broad view of unfairness from diverse aspects. We deal with the student grouping problem in collaborative learning. We introduce the fair-capacitated clustering problem that takes into account cluster fairness and cluster cardinalities. We propose two approaches, namely hierarchical clustering and partitioning-based clustering, to obtain fair-capacitated clustering. We introduce the multi-fair capacitated (MFC) students-topics grouping problem that satisfies students' preferences while ensuring balanced group cardinalities and maximizing the diversity of members regarding the protected attribute. We propose three approaches: a greedy heuristic approach, a knapsack-based approach using vanilla maximal 0-1 knapsack formulation, and an MFC knapsack approach based on group fairness knapsack formulation. In short, the findings described in this thesis demonstrate the importance of fairness-aware ML in educational settings. We show that bias-aware data analysis, fairness measures, and fairness-aware ML models are essential aspects to ensure fairness in EDM and the educational environment.Ministry of Science and Culture of Lower Saxony/LernMINT/51410078/E

    Natural and Technological Hazards in Urban Areas

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    Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events

    3D Innovations in Personalized Surgery

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    Current practice involves the use of 3D surgical planning and patient-specific solutions in multiple surgical areas of expertise. Patient-specific solutions have been endorsed for several years in numerous publications due to their associated benefits around accuracy, safety, and predictability of surgical outcome. The basis of 3D surgical planning is the use of high-quality medical images (e.g., CT, MRI, or PET-scans). The translation from 3D digital planning toward surgical applications was developed hand in hand with a rise in 3D printing applications of multiple biocompatible materials. These technical aspects of medical care require engineers’ or technical physicians’ expertise for optimal safe and effective implementation in daily clinical routines.The aim and scope of this Special Issue is high-tech solutions in personalized surgery, based on 3D technology and, more specifically, bone-related surgery. Full-papers or highly innovative technical notes or (systematic) reviews that relate to innovative personalized surgery are invited. This can include optimization of imaging for 3D VSP, optimization of 3D VSP workflow and its translation toward the surgical procedure, or optimization of personalized implants or devices in relation to bone surgery

    Classifier Calibration: A survey on how to assess and improve predicted class probabilities

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    This paper provides both an introduction to and a detailed overview of the principles and practice of classifier calibration. A well-calibrated classifier correctly quantifies the level of uncertainty or confidence associated with its instance-wise predictions. This is essential for critical applications, optimal decision making, cost-sensitive classification, and for some types of context change. Calibration research has a rich history which predates the birth of machine learning as an academic field by decades. However, a recent increase in the interest on calibration has led to new methods and the extension from binary to the multiclass setting. The space of options and issues to consider is large, and navigating it requires the right set of concepts and tools. We provide both introductory material and up-to-date technical details of the main concepts and methods, including proper scoring rules and other evaluation metrics, visualisation approaches, a comprehensive account of post-hoc calibration methods for binary and multiclass classification, and several advanced topics

    Sensitivity of NEXT-100 detector to neutrinoless double beta decay

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    Nesta tese estúdiase a sensibilidade do detector NEXT-100 á desintegración dobre beta sen neutrinos. Existe un gran interese na busca desta desintegración xa que podería respostar preguntas fundamentais en física de neutrinos. O detector constitúe a terceira fase do experimento NEXT, colaboración na que se desenrolou esta tese. A continuación inclúese un resumo de cada un dos capítulos nos que se divide a tese. Comézase introducindo o marco teórico e experimental nas seccións Física de neutrinos, A busca da desintegración dobre beta sen neutrinos e O experimento NEXT. Posteriormente descríbense a parte principal do análise da tese en Simulación do detector, Procesamento de datos e Sensibilidade do detector NEXT-100

    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

    Robust Out-of-Distribution Detection in Deep Classifiers

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    Over the past decade, deep learning has gone from a fringe discipline of computer science to a major driver of innovation across a large number of industries. The deployment of such rapidly developing technology in safety-critical applications necessitates the careful study and mitigation of potential failure modes. Indeed, many deep learning models are overconfident in their predictions, are unable to flag out-of-distribution examples that are clearly unrelated to the task they were trained on and are vulnerable to adversarial vulnerabilities, where a small change in the input leads to a large change in the model’s prediction. In this dissertation, we study the relation between these issues in deep learning based vision classifiers. First, we benchmark various methods that have been proposed to enable deep learning meth- ods to detect out-of-distribution examples and we show that a classifier’s predictive confidence is well-suited for this task, if the classifier has had access to a large and diverse out-distribution at train time. We theoretically investigate how different out-of-distribution detection methods are related and show that several seemingly different approaches are actually modeling the same core quantities. In the second part we study the adversarial robustness of a classifier’s confidence on out- of-distribution data. Concretely, we show that several previous techniques for adversarial robustness can be combined to create a model that inherits each method’s strength while sig- nificantly reducing their respective drawbacks. In addition, we demonstrate that the enforce- ment of adversarially robust low confidence on out-of-distribution data enhances the inherent interpretability of the model by imbuing the classifier with certain generative properties that can be used to query the model for counterfactual explanations for its decisions. In the third part of this dissertation we will study the problem of issuing mathematically provable certificates for the adversarial robustness of a model’s confidence on out-of-distribution data. We develop two different approaches to this problem and show that they have comple- mentary strength and weaknesses. The first method is easy to train, puts no restrictions on the architecture that our classifier can use and provably ensures that the classifier will have low confidence on data very far away. However, it only provides guarantees for very specific types of adversarial perturbations and only for data that is very easy to distinguish from the in-distribution. The second approach works for more commonly studied sets of adversarial perturbations and on much more challenging out-distribution data, but puts heavy restrictions on the architecture that can be used and thus the achievable accuracy. It also does not guar- antee low confidence on asymptotically far away data. In the final chapter of this dissertation we show how ideas from both of these techniques can be combined in a way that preserves all of their strengths while inheriting none of their weaknesses. Thus, this thesis outlines how to develop high-performing classifiers that provably know when they do not know

    20th SC@RUG 2023 proceedings 2022-2023

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