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Article ; Online: Strangely mined bitcoins: Empirical analysis of anomalies in the bitcoin blockchain transaction network.

Óskarsdóttir, María / Mallett, Jacky

PloS one

2021 Volume 16, Issue 9, Page(s) e0258001

Abstract: The blockchain technology introduced by bitcoin, with its decentralised peer-to-peer network and cryptographic protocols, provides a public and accessible database of bitcoin transactions that have attracted interest from both economics and network ... ...

Abstract	The blockchain technology introduced by bitcoin, with its decentralised peer-to-peer network and cryptographic protocols, provides a public and accessible database of bitcoin transactions that have attracted interest from both economics and network science as an example of a complex evolving monetary network. Despite the known cryptographic guarantees present in the blockchain, there exists significant evidence of inconsistencies and suspicious behavior in the chain. In this paper, we examine the prevalence and evolution of two types of anomalies occurring in coinbase transactions in blockchain mining, which we reported on in earlier research. We further develop our techniques for investigating the impact of these anomalies on the blockchain transaction network, by building networks induced by anomalous coinbase transactions at regular intervals and calculating a range of network measures, including degree correlation and assortativity, as well as inequality in terms of wealth and anomaly ratio using the Gini coefficient. We obtain time series of network measures calculated over the full transaction network and three sub-networks. Inspecting trends in these time series allows us to identify a period in time with particularly strange transaction behavior. We then perform a frequency analysis of this time period to reveal several blocks of highly anomalous transactions. Our technique represents a novel way of using network science to detect and investigate cryptographic anomalies.
MeSH term(s)	Blockchain ; Commerce/trends ; Technology/trends
Language	English
Publishing date	2021-09-30
Publishing country	United States
Document type	Journal Article
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0258001
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Article ; Online: Interaction with Medical Venues in Megacities.

Óskarsdóttir, María / Íslind, Anna Sigríður

Studies in health technology and informatics

2020 Volume 270, Page(s) 996–1000

Abstract: With the vast amounts of data that is being generated today, come new possibilities of understanding patient mobility. In this study, of urban mobility in ten mega cities worldwide, we try to understand the relationship between patients' environment and ... ...

Abstract	With the vast amounts of data that is being generated today, come new possibilities of understanding patient mobility. In this study, of urban mobility in ten mega cities worldwide, we try to understand the relationship between patients' environment and behaviour with regards to venues that provide some kind of medical care. We analyze longitudinal mobility data set from ten of the world's megacities and investigate urban dynamics and travel patterns in terms of interaction with medical centers. Our goal is to investigate universal patterns and gain an understanding of where people are coming from when they visit such venues and where they go afterwards as well how travel patterns progress throughout the day.
MeSH term(s)	Cities ; Humans ; Travel
Language	English
Publishing date	2020-05-29
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI200311
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Book ; Online: Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction

Zandi, Sahab / Korangi, Kamesh / Óskarsdóttir, María / Mues, Christophe / Bravo, Cristián

2024

Abstract: Whereas traditional credit scoring tends to employ only individual borrower- or loan-level predictors, it has been acknowledged for some time that connections between borrowers may result in default risk propagating over a network. In this paper, we ... ...

Abstract	Whereas traditional credit scoring tends to employ only individual borrower- or loan-level predictors, it has been acknowledged for some time that connections between borrowers may result in default risk propagating over a network. In this paper, we present a model for credit risk assessment leveraging a dynamic multilayer network built from a Graph Neural Network and a Recurrent Neural Network, each layer reflecting a different source of network connection. We test our methodology in a behavioural credit scoring context using a dataset provided by U.S. mortgage financier Freddie Mac, in which different types of connections arise from the geographical location of the borrower and their choice of mortgage provider. The proposed model considers both types of connections and the evolution of these connections over time. We enhance the model by using a custom attention mechanism that weights the different time snapshots according to their importance. After testing multiple configurations, a model with GAT, LSTM, and the attention mechanism provides the best results. Empirical results demonstrate that, when it comes to predicting probability of default for the borrowers, our proposed model brings both better results and novel insights for the analysis of the importance of connections and timestamps, compared to traditional methods.
Keywords	Quantitative Finance - General Finance ; Computer Science - Machine Learning
Subject code	006
Publishing date	2024-01-31
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The Future of Sleep Measurements: A Review and Perspective.

Arnardottir, Erna Sif / Islind, Anna Sigridur / Óskarsdóttir, María

Sleep medicine clinics

2021 Volume 16, Issue 3, Page(s) 447–464

Abstract: This article provides an overview of the current use, limitations, and future directions of the variety of subjective and objective sleep assessments available. This article argues for various ways and sources of collecting, combining, and using data to ... ...

Abstract	This article provides an overview of the current use, limitations, and future directions of the variety of subjective and objective sleep assessments available. This article argues for various ways and sources of collecting, combining, and using data to enlighten clinical practice and the sleep research of the future. It highlights the prospects of digital management platforms to store and present the data, and the importance of codesign when developing such platforms and other new instruments. It also discusses the abundance of opportunities that data science and machine learning open for the analysis of data.
MeSH term(s)	Forecasting ; Humans ; Sleep/physiology ; Sleep Wake Disorders/diagnosis ; Sleep Wake Disorders/therapy
Language	English
Publishing date	2021-07-06
Publishing country	United States
Document type	Journal Article ; Review
ISSN	1556-4088
ISSN (online)	1556-4088
DOI	10.1016/j.jsmc.2021.05.004
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Book ; Online: Evolution of Credit Risk Using a Personalized Pagerank Algorithm for Multilayer Networks

Bravo, Cristián / Óskarsdóttir, María

2020

Abstract: In this paper we present a novel algorithm to study the evolution of credit risk across complex multilayer networks. Pagerank-like algorithms allow for the propagation of an influence variable across single networks, and allow quantifying the risk single ...

Abstract	In this paper we present a novel algorithm to study the evolution of credit risk across complex multilayer networks. Pagerank-like algorithms allow for the propagation of an influence variable across single networks, and allow quantifying the risk single entities (nodes) are subject to given the connection they have to other nodes in the network. Multilayer networks, on the other hand, are networks where subset of nodes can be associated to a unique set (layer), and where edges connect elements either intra or inter networks. Our personalized PageRank algorithm for multilayer networks allows for quantifying how credit risk evolves across time and propagates through these networks. By using bipartite networks in each layer, we can quantify the risk of various components, not only the loans. We test our method in an agricultural lending dataset, and our results show how default risk is a challenging phenomenon that propagates and evolves through the network across time. Comment: Conference camera-ready paper - accepted at KDD MLF 2020. 15 pages, 10 figures
Keywords	Computer Science - Social and Information Networks ; Computer Science - Machine Learning ; Physics - Physics and Society ; Statistics - Machine Learning
Subject code	000
Publishing date	2020-05-25
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Multilayer Network Analysis for Improved Credit Risk Prediction

Óskarsdóttir, María / Bravo, Cristián

2020

Abstract: We present a multilayer network model for credit risk assessment. Our model accounts for multiple connections between borrowers (such as their geographic location and their economic activity) and allows for explicitly modelling the interaction between ... ...

Abstract	We present a multilayer network model for credit risk assessment. Our model accounts for multiple connections between borrowers (such as their geographic location and their economic activity) and allows for explicitly modelling the interaction between connected borrowers. We develop a multilayer personalized PageRank algorithm that allows quantifying the strength of the default exposure of any borrower in the network. We test our methodology in an agricultural lending framework, where it has been suspected for a long time default correlates between borrowers when they are subject to the same structural risks. Our results show there are significant predictive gains just by including centrality multilayer network information in the model, and these gains are increased by more complex information such as the multilayer PageRank variables. The results suggest default risk is highest when an individual is connected to many defaulters, but this risk is mitigated by the size of the neighbourhood of the individual, showing both default risk and financial stability propagate throughout the network. Comment: 24 pages, 15 figures. v4 - accepted
Keywords	Computer Science - Social and Information Networks ; Computer Science - Machine Learning
Subject code	332
Publishing date	2020-10-19
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Importance of Getting Enough Sleep and Daily Activity Data to Assess Variability: Longitudinal Observational Study.

Óskarsdóttir, María / Islind, Anna Sigridur / August, Elias / Arnardóttir, Erna Sif / Patou, François / Maier, Anja M

JMIR formative research

2022 Volume 6, Issue 2, Page(s) e31807

Abstract: Background: The gold standard measurement for recording sleep is polysomnography performed in a hospital environment for 1 night. This requires individuals to sleep with a device and several sensors attached to their face, scalp, and body, which is both ...

Abstract	Background: The gold standard measurement for recording sleep is polysomnography performed in a hospital environment for 1 night. This requires individuals to sleep with a device and several sensors attached to their face, scalp, and body, which is both cumbersome and expensive. Self-trackers, such as wearable sensors (eg, smartwatch) and nearable sensors (eg, sleep mattress), can measure a broad range of physiological parameters related to free-living sleep conditions; however, the optimal duration of such a self-tracker measurement is not known. For such free-living sleep studies with actigraphy, 3 to 14 days of data collection are typically used. Objective: The primary goal of this study is to investigate if 3 to 14 days of sleep data collection is sufficient while using self-trackers. The secondary goal is to investigate whether there is a relationship among sleep quality, physical activity, and heart rate. Specifically, we study whether individuals who exhibit similar activity can be clustered together and to what extent the sleep patterns of individuals in relation to seasonality vary. Methods: Data on sleep, physical activity, and heart rate were collected over 6 months from 54 individuals aged 52 to 86 years. The Withings Aura sleep mattress (nearable; Withings Inc) and Withings Steel HR smartwatch (wearable; Withings Inc) were used. At the individual level, we investigated the consistency of various physical activities and sleep metrics over different time spans to illustrate how sensor data from self-trackers can be used to illuminate trends. We used exploratory data analysis and unsupervised machine learning at both the cohort and individual levels. Results: Significant variability in standard metrics of sleep quality was found between different periods throughout the study. We showed specifically that to obtain more robust individual assessments of sleep and physical activity patterns through self-trackers, an evaluation period of >3 to 14 days is necessary. In addition, we found seasonal patterns in sleep data related to the changing of the clock for daylight saving time. Conclusions: We demonstrate that >2 months' worth of self-tracking data are needed to provide a representative summary of daily activity and sleep patterns. By doing so, we challenge the current standard of 3 to 14 days for sleep quality assessment and call for the rethinking of standards when collecting data for research purposes. Seasonal patterns and daylight saving time clock change are also important aspects that need to be taken into consideration when choosing a period for collecting data and designing studies on sleep. Furthermore, we suggest using self-trackers (wearable and nearable ones) to support longer-term evaluations of sleep and physical activity for research purposes and, possibly, clinical purposes in the future.
Language	English
Publishing date	2022-02-22
Publishing country	Canada
Document type	Journal Article
ISSN	2561-326X
ISSN (online)	2561-326X
DOI	10.2196/31807
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Article ; Online: The quantification of vaccine uptake in the Nordic countries and impact on key indicators of COVID-19 severity and healthcare stress level via age range comparative analysis.

Islind, Anna Sigridur / Óskarsdóttir, María / Cot, Corentin / Cacciapaglia, Giacomo / Sannino, Francesco

Scientific reports

2022 Volume 12, Issue 1, Page(s) 16891

Abstract: In this paper we analyze the impact of vaccinations on spread of the COVID-19 virus for different age groups. More specifically, we examine the deployment of vaccines in the Nordic countries in a comparative analysis where we focus on factors such as ... ...

Abstract	In this paper we analyze the impact of vaccinations on spread of the COVID-19 virus for different age groups. More specifically, we examine the deployment of vaccines in the Nordic countries in a comparative analysis where we focus on factors such as healthcare stress level and severity of disease through new infections, hospitalizations, intensive care unit (ICU) occupancy and deaths. Moreover, we analyze the impact of the various vaccine types, vaccination rate on the spread of the virus in each age group for Denmark, Finland, Iceland, Norway and Sweden from the start of the vaccination period in December 2020 until the end of September 2021. We perform a threefold analysis: (i) frequency analysis of infections and vaccine rates by age groups; (ii) rolling correlations between vaccination strategies, severity of COVID-19 and healthcare stress level and; (iii) we also employ the epidemic Renormalization Group (eRG) framework. The eRG is used to mathematically model wave structures, as well as the impact of vaccinations on wave dynamics. We further compare the Nordic countries with England. Our main results outline the quantification of the impact of the vaccination campaigns on age groups epidemiological data, across countries with high vaccine uptake. The data clearly shows that vaccines markedly reduce the number of new cases and the risk of serious illness.
MeSH term(s)	COVID-19/epidemiology ; COVID-19/prevention & control ; Delivery of Health Care ; Humans ; Scandinavian and Nordic Countries/epidemiology ; Vaccination ; Vaccines
Chemical Substances	Vaccines
Language	English
Publishing date	2022-10-07
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-21055-0
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Book ; Online: Effects of the COVID-19 Pandemic on Learning and Teaching

Flores, Nidia Guadalupe López / Islind, Anna Sigridur / Óskarsdóttir, María

a Case Study from Higher Education

2021

Abstract: In December 2019, the first case of SARS-CoV-2 infection was identified in Wuhan, China. Since that day, COVID-19 has spread worldwide, affecting 153 million people. Education, as many other sectors, has managed to adapt to the requirements and barriers ... ...

Abstract	In December 2019, the first case of SARS-CoV-2 infection was identified in Wuhan, China. Since that day, COVID-19 has spread worldwide, affecting 153 million people. Education, as many other sectors, has managed to adapt to the requirements and barriers implied by the impossibility to teach students face-to-face as it was done before. Yet, little is known about the implications of emergency remote teaching (ERT) during the pandemic. This study describes and analyzes the impact of the pandemic on the study patterns of higher education students. The analysis was performed by the integration of three main components: (1) interaction with the learning management system (LMS), (2) Assignment submission rate, and (3) Teachers' perspective. Several variables were created to analyze the study patterns, clicks on different LMS components, usage during the day, week and part of the term, the time span of interaction with the LMS, and grade categories. The results showed significant differences in study patterns depending on the year of study, and the variables reflecting the effect of teachers' changes in the course structure are identified. This study outlines the first insights of higher education's new normality, providing important implications for supporting teachers in creating academic material that adequately addresses students' particular needs depending on their year of study, changes in study pattern, and distribution of time and activity through the term. Comment: 18 pages, 13 figures
Keywords	Computer Science - Computers and Society
Subject code	370
Publishing date	2021-05-04
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: INFLECT-DGNN

Tiukhova, Elena / Penaloza, Emiliano / Óskarsdóttir, María / Baesens, Bart / Snoeck, Monique / Bravo, Cristián

Influencer Prediction with Dynamic Graph Neural Networks

2023

Abstract: Leveraging network information for predictive modeling has become widespread in many domains. Within the realm of referral and targeted marketing, influencer detection stands out as an area that could greatly benefit from the incorporation of dynamic ... ...

Abstract	Leveraging network information for predictive modeling has become widespread in many domains. Within the realm of referral and targeted marketing, influencer detection stands out as an area that could greatly benefit from the incorporation of dynamic network representation due to the ongoing development of customer-brand relationships. To elaborate this idea, we introduce INFLECT-DGNN, a new framework for INFLuencer prEdiCTion with Dynamic Graph Neural Networks that combines Graph Neural Networks (GNN) and Recurrent Neural Networks (RNN) with weighted loss functions, the Synthetic Minority Oversampling TEchnique (SMOTE) adapted for graph data, and a carefully crafted rolling-window strategy. To evaluate predictive performance, we utilize a unique corporate data set with networks of three cities and derive a profit-driven evaluation methodology for influencer prediction. Our results show how using RNN to encode temporal attributes alongside GNNs significantly improves predictive performance. We compare the results of various models to demonstrate the importance of capturing graph representation, temporal dependencies, and using a profit-driven methodology for evaluation. Comment: 26 pages, 10 figures
Keywords	Computer Science - Social and Information Networks ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-07-16
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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