9,451 research outputs found

    Influence of context on users’ views about explanations for decision-tree predictions

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    This research was supported in part by grant DP190100006 from the Australian Research Council. Ethics approval for the user studies was obtained from Monash University Human Research Ethics Committee (ID-24208). We thank Marko Bohanec, one of the creators of the Nursery dataset, for helping us understand the features and their values. We are also grateful to the anonymous reviewers for their helpful comments.Peer reviewedPostprin

    Measuring socioeconomic position in studies of health inequalities

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    There is a consistent finding that the higher the socioeconomic position (SEP), the better the health. The choice of SEP indicator is crucial in explaining these socioeconomic inequalities. However, a poorly motivated use of SEP indicators prevails in the literature on social health inequalities, hampering the transparency and comparability across studies. Its primary aim is to explore different ways of measuring SEP to identify social inequalities in health. The thesis focuses on the most common, objective SEP indicators (education, occupation, and income); subjective SEP; and childhood circumstances. This thesis consists of three papers. Papers I and III apply data from the Tromsø Study, and Paper II is based on an online survey investigating people's views on SEP, conducted in Norway and Australia. Paper I investigates the potential to combine education and income into a composite score for SEP and how it predicts inequalities in health-related quality of life (HRQoL). Paper II assesses the relative importance of objective SEP indicators and childhood circumstances in estimating subjective SEP. Paper III explores the role of circumstances and lifestyle factors in estimating inequalities in HRQoL and self-rated health. While we found that the combination of education and income demonstrated a non-linear relationship with overall SEP, the composite SEP score was not superior as a predictor of HRQoL compared to including education and income separately. Furthermore, we found that childhood circumstances demonstrated a lasting, independent impact on subjective SEP. Paper III revealed that there were inequalities arising from circumstances, with substantial contributions from financial circumstances in childhood and education. This thesis demonstrates the need to motivate the choice of SEP indicator in studies of health inequalities. It also stresses the importance of early-life factors as determinants of adult health, advocating for policies targeting childhood circumstances in equalising early life chances.Et svært vanlig funn på tvers av land, studiepopulasjoner og helseutfall er at desto høyere sosioøkonomisk posisjon (SEP), desto bedre helse. Valg av SEP-indikator som skal reflektere de sosioøkonomiske dimensjonene i helse er avgjørende for å forklare disse helseulikhetene. Likevel er det slik at bruken av SEP-indikatorer i studier om sosial ulikhet i helse ofte preges av svak eller ingen begrunnelse med utgangspunkt i teori og hypoteser, noe som begrenser muligheten til sammenligning mellom studier. Denne avhandlingen bruker ulike tilnærminger for å måle SEP i studier av helseulikhet. Et overordnet formål er å utforske ulike måter å måle sosial posisjon for å identifisere sosiale ulikhet i helse, og hvordan livsstilsfaktorer i tillegg påvirker dette forholdet. Fokuset vil være på de tre vanligste objektive SEP-indikatorene (utdanning, yrke og inntekt); subjektiv SEP; og indikatorer for barndomsforhold. Avhandlingen består av tre artikler. Artikkel I og III er basert på data fra Tromsøundersøkelsen, mens Artikkel II benytter data fra på en nettbasert spørreundersøkelse om folks betraktninger omkring SEP, som har blitt gjennomført i Norge og Australia. Alle de tre artiklene utforsker bruken av ulike SEP-indikatorer i en helseulikhetssammenheng. Artikkel I undersøker potensialet for å kombinere utdanning og inntekt til en samleindikator for SEP, samt hvordan denne samleindikatoren predikerer helse-relatert livskvalitet (HRQoL). Artikkel II måler objektive SEP-indikatorer (utdanning, yrke og inntekt) og barndomsforholds relative betydning i å estimere subjektiv SEP. Artikkel III utforsker hvordan variabler om barndomsforhold på den ene siden og livsstilsfaktorer på den andre estimerer HRQoL og selvrapportert helse, både på et bestemt tidspunkt og over tid. Vi fant at kombinasjonen av utdanning og inntekt viste en sterk ikke-lineær sammenheng med total SEP, mens samleindikatoren for SEP viste seg å ikke være bedre i å predikere HRQoL sammenlignet med å inkludere utdanning og inntekt separat. Videre fant vi at barndomsforhold så ut til å ha en vedvarende påvirkning på subjektiv SEP, som var uavhengig av objektiv SEP. Artikkel III viste at det var ulikheter i helse med røtter i barndomsforhold, med særlig påvirkning fra økonomiske forhold i barndommen og egen utdanning. Denne avhandlingen viser behovet for å gjøre et faglig motivert valg av SEP-indikator i studier av helseulikhet. Den understreker også viktigheten av barndomsforhold som bestemmende faktorer for helseutfall senere i livet, og etterlyser dermed politikk rettet mot tidlige barndomsforhold for å utjevne ulikheter og sikre gode livssjanser

    Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms

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    We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies of depth functions in linear and metric spaces, there is very little discussion on depth functions for non-standard data types such as partial orders. We introduce an adaptation of the well-known simplicial depth to the set of all partial orders, the union-free generic (ufg) depth. Moreover, we utilize our ufg depth for a comparison of machine learning algorithms based on multidimensional performance measures. Concretely, we analyze the distribution of different classifier performances over a sample of standard benchmark data sets. Our results promisingly demonstrate that our approach differs substantially from existing benchmarking approaches and, therefore, adds a new perspective to the vivid debate on the comparison of classifiers.Comment: Accepted to ISIPTA 2023; Forthcoming in: Proceedings of Machine Learning Researc

    An Investigation of Preference Judging Consistency

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    Preference judging has been proposed as an effective method to identify the most relevant documents for a given search query. In this thesis, we investigate the degree to which assessors using a preference judging system are able to consistently find the same top documents and how consistent they are in their own preferences. We also examine to what extent variability in assessor preferences affect the evaluation of information retrieval systems. We designed and conducted a user study where 40 participants were recruited to preference judge 30 topics taken from the 2021 TREC Health Misinformation track. The research study found that the number of judgments needed to find the top-10 preferred documents using preference judging is about twice the number of documents in that topic. It also suggests that relying on just one non-professional assessor to do preference judging is not sufficient for evaluating information retrieval systems. Additionally, the study showed that preference judging to find the top-10 documents does significantly change the rankings of runs as compared to the rankings reported in the TREC 2021 Health Misinformation track, with most changes happening among the lower-ranked runs rather than the top-ranked runs. Overall, this thesis provides insights into assessor behaviour and assessor agreement when using preference judgments for evaluating information retrieval systems

    Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models

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    This paper considers the specification of covariance structures with tail estimates. We focus on two aspects: (i) the estimation of the VaR-CoVaR risk matrix in the case of larger number of time series observations than assets in a portfolio using quantile predictive regression models without assuming the presence of nonstationary regressors and; (ii) the construction of a novel variable selection algorithm, so-called, Feature Ordering by Centrality Exclusion (FOCE), which is based on an assumption-lean regression framework, has no tuning parameters and is proved to be consistent under general sparsity assumptions. We illustrate the usefulness of our proposed methodology with numerical studies of real and simulated datasets when modelling systemic risk in a network

    Machine Learning Approaches for the Prioritisation of Cardiovascular Disease Genes Following Genome- wide Association Study

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    Genome-wide association studies (GWAS) have revealed thousands of genetic loci, establishing itself as a valuable method for unravelling the complex biology of many diseases. As GWAS has grown in size and improved in study design to detect effects, identifying real causal signals, disentangling from other highly correlated markers associated by linkage disequilibrium (LD) remains challenging. This has severely limited GWAS findings and brought the method’s value into question. Although thousands of disease susceptibility loci have been reported, causal variants and genes at these loci remain elusive. Post-GWAS analysis aims to dissect the heterogeneity of variant and gene signals. In recent years, machine learning (ML) models have been developed for post-GWAS prioritisation. ML models have ranged from using logistic regression to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models (i.e., neural networks). When combined with functional validation, these methods have shown important translational insights, providing a strong evidence-based approach to direct post-GWAS research. However, ML approaches are in their infancy across biological applications, and as they continue to evolve an evaluation of their robustness for GWAS prioritisation is needed. Here, I investigate the landscape of ML across: selected models, input features, bias risk, and output model performance, with a focus on building a prioritisation framework that is applied to blood pressure GWAS results and tested on re-application to blood lipid traits

    The food quality schemes of the European Union and their implications on the Hungarian market [védés előtt]

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    It is becoming increasingly important for consumers to know exactly what kind of foods they consume, while it is increasingly vital for food producers to excel from the competition in the global market. This requires a great deal of information exchange between these two market players. The most common way to do this is through various food labels. I have focused on one group of such labels in my research, trying to find out as much useful information as possible about the geographical indications (GI) of the European Union. I was looking for answers to the following questions: 1, How well-known are the labels of EU GI products among Hungarian consumers, and how well do they know their meaning? The awareness of the GI labels in Hungary is definitely low (in the best case, it was 31%), even if this number is not much lower than the average in the EU. This number is probably too low for these labels to be effective marketing tools for producers. It also includes the fact that only 50% of those who said they know the label know at least approximately the meaning of the label. Can you build a marketing campaign currently on these markings? Probably not an effective one, but what gives hope is that awareness of the labels compared to previous surveys is constantly increasing. The EU focuses on GI products, so this growth is expected to be continued. So far, the EU has completed more than 30 international agreements, which allow the recognition of many EU GI outside the boundaries of the EU and the recognition of non-EU GI inside the territories of the EU. GIs represent an increasingly important aspect of trade negotiations between the EU and other countries. The Commission separates around €50 million year after year to support quality products in the EU and all over the world. Taking this into account, these labels can play an important role in the food markets in the near future. 2, What is the level of trust in the labelling of EU GI products, and what influences this trust? About half of the respondents said that they trust the logo. When we analysed the possible variables, which can influence trust, we concluded that knowledge of logos is important because if someone knows the label, they have more than three times the chance to trust them, while in terms of meaning, the chance is almost double. Based on the research, we can say that gender, education, and age do not affect trust in EU GI labels. In the case of place of residence, it can be said that someone who lives in a more urban environment trusts less in GI labels. All in all, consumer education is most needed to build confidence in GI, as those who recognize the labels on food packaging or are aware of what those labels mean will treat these products with much greater confidence. 3, How often do consumers buy EU GI certified products, and what affects it? More than 35% of those surveyed are regular customers of GI-labelled products. The frequency of purchases is mostly determined by consumer confidence (the result is not significant for the PDO). Women become much fewer regular customers (not significant for the PGI). In terms of age, the older a consumer is, the less likely it is to become a regular buyer (not significant for PDO), while residents of rural, smaller settlements are more loyal buyers of PGI products. The highest level of education has no detectable effect here either. So, in this topic also, we have to repeat that the most important thing is to inform consumers as widely as possible. 4, In the Hungarian market, what is the market size of products with geographical indication, examining the example of discount stores? The number of GI products available in Hungarian discount stores is limited, with an average of 11 products per store. The supply is fairly constant; however, even though there are only a limited number of GI products on the shelves, they are at least always available to consumers and are part of the chains ’core product portfolio. However, the number of GI products usually increases during the thematic days (e.g., Greek days). We can see that the supply is very limited for GI products, so buyers rarely meet face to face with the label, they are even less likely to find out about the meaning of the markings on their own. Targeted information on GI labels is needed for consumers, and for that they start to appreciate them. 5, In Hungarian discount stores, what is the price premium of products with a geographical indication compared to their direct substitutes, estimated from below? The average price premium for GI products is 29% in the Aldi, 46% in the Penny Market and the highest was in Lidl with 54%. Overall, the average premium was around 43%. It is also important to mention that in addition to supply, prices did not really change during the observations. Although the price of some products may also change during promotional periods, consumers can plan to purchase GI products in advance. On the other hand, discount stores provide a continuous market for producers as well. 6, Are geographical indications positively related to comparative advantages in the beer market? Our results show that the number of GI-registered beers is positively related to comparative advantages. Countries with traditional beer products closely linked to their place of origin are usually with a higher level of comparative advantages as the number of GI beers positively determines SRCA indices. From this, it can be concluded that it is not pointless to promote the increase in the number of GI products and devote resources to GI labels

    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

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Enhancing Requirements Change Request Categorization and Prioritization in Agile Software Development Using Analytic Hierarchy Process (AHP)

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    Software development now relies heavily on agile methods, which call for the efficient administration and prioritization of change requests. In order to improve requirement prioritization using the Analytic Hierarchy Process (AHP) in Agile methods, this study article presents a new framework for classifying software requirements into Small Change Requests (SCRs) and Large Change Requests (LCRs). The paper examines the difficulties associated with requirement prioritization and categorization in Agile settings and offers a methodical system for dividing change requests into categories based on complexity, impact, and timeline. In order to provide a thorough grasp of the project scope and objectives, the framework considers both functional and non-functional needs. A case study containing several Agile software development projects is used to evaluate the performance of the suggested categorization and prioritization model. According to the findings, the combination of SCR and LCR categorization with AHP enables more effective teamwork and greater matching of development goals with partner objectives. The research also shows that the suggested framework's integration into the Agile development process results in a more efficient decision-making process, less time wasted on talks, and improved resource distribution. The model aids in risk mitigation by allowing a methodical and quantifiable approach to requirement prioritization. These risks are related to quick changes in project scope and changing client requirements. By presenting a fresh framework for requirement categorization and prioritization, this study adds to the current discussion on successful requirement management in Agile methods. Agile software development projects become more effective and adaptable overall thanks to the incorporation of AHP, which guarantees a more methodical and objective prioritization process. This study has the potential to greatly improve the administration of shifting needs and user expectations in Agile settings by offering a structured method to classify and rank change requests
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