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DTSTART;TZID=Europe/Copenhagen:20230601T154500
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DTSTAMP:20260421T164010
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SUMMARY:Luca Aiello at FRCCS 2023
DESCRIPTION:Coloring Social Relationships\nSocial relationships are the key determinant of crucial societal outcomes\, including diffusion of innovation\, productivity\, happiness\, and life expectancy. To better attain such outcomes at scale\, it is therefore paramount to have technologies that can effectively capture the type of social relationships from digital data. NLP researchers have tried to do so from conversational text but mostly focusing on sentiment or topic mining\, techniques that fall short on either conciseness or exhaustiveness. We propose a theoretical model of 10 dimensions (colors) of social relationships that is backed by decades of research in social sciences and that captures most of the common relationship types. We trained a deep-learning model to accurately classify text along these ten dimensions. By applying this tool on large-scale conversational data\, we show that the combination of the predicted dimensions suggests both the types of relationships people entertain and the types of real-world communities they shape. We believe that the ability of capturing interpretable social dimensions from language using AI will help closing the gap between the oversimplified social constructs that existing social network analysis methods can measure and the multifaceted understanding of social dynamics that has been developed by decades of theoretical research.
URL:https://nerds.itu.dk/event/luca-aiello-at-frccs-2023/
CATEGORIES:NERDS away
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DTSTART;TZID=Europe/Copenhagen:20230612T120000
DTEND;TZID=Europe/Copenhagen:20230612T130000
DTSTAMP:20260421T164010
CREATED:20230425T131833Z
LAST-MODIFIED:20230425T131833Z
UID:1588-1686571200-1686574800@nerds.itu.dk
SUMMARY:Roberta at Reykjavik University
DESCRIPTION:Quantitative understanding of success and inequality through network science\nThe unprecedented availability of large-scale datasets on human activities and interactions has enabled us to quantitatively understand how networks shape success and drive inequalities in various fields. In this talk\, I will highlight the crucial role of network science in comprehending social phenomena\, particularly in the realms of art and science. Specifically\, I will present a series of findings on the evolution of careers in the arts\, utilizing a random walk model that successfully predicts artists’ success even twenty years into the future. I will then shift my focus to the COVID-19 pandemic and the advent of Large Language models\, discussing how they have amplified network inequalities in science. Through these examples\, I will demonstrate the importance of network science in measuring\, predicting\, and designing algorithms for social phenomena.
URL:https://nerds.itu.dk/event/roberta-at-reykjavik-university/
CATEGORIES:NERDS away
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BEGIN:VEVENT
DTSTART;TZID=Europe/Copenhagen:20230613T120000
DTEND;TZID=Europe/Copenhagen:20230613T130000
DTSTAMP:20260421T164010
CREATED:20230425T132133Z
LAST-MODIFIED:20230425T132133Z
UID:1590-1686657600-1686661200@nerds.itu.dk
SUMMARY:Michael at Reykjavik University
DESCRIPTION:Computational\, evidence-based approaches to bicycle network planning\n  \nIn this talk\, I explore the opportunities and limitations of network and data science for bicycle infrastructure planning. With the increasing popularity of cycling as a mode of transportation\, and the necessity to invest into sustainable transport\, more accurate and reliable data and computational methods on bicycle infrastructure analysis are becoming a crucial support for planners\, engineers\, and policymakers. In particular\, I will introduce our new bicycle infrastructure data and network quality assessment tool BikeDNA\, and discuss various graph-based algorithms for assisting in bicycle network analysis and planning.
URL:https://nerds.itu.dk/event/michael-at-reykjavik-university/
CATEGORIES:NERDS away
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DTSTART;TZID=America/Chicago:20230626T130000
DTEND;TZID=America/Chicago:20230626T140000
DTSTAMP:20260421T164010
CREATED:20230625T204820Z
LAST-MODIFIED:20230625T204820Z
UID:1622-1687784400-1687788000@nerds.itu.dk
SUMMARY:Roberta at ICSSI
DESCRIPTION:Exploring Gender Bias and Collaborative Dynamics in Science: Lessons from natural and controlled experiments
URL:https://nerds.itu.dk/event/roberta-at-icssi/
CATEGORIES:NERDS away
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DTSTART;TZID=Europe/Copenhagen:20230628T160000
DTEND;TZID=Europe/Copenhagen:20230628T163000
DTSTAMP:20260421T164010
CREATED:20230609T095935Z
LAST-MODIFIED:20230609T095935Z
UID:1610-1687968000-1687969800@nerds.itu.dk
SUMMARY:Anastassia at FOSS4G
DESCRIPTION:https://talks.osgeo.org/foss4g-2023/talk/RPVTPU/ \nBikeDNA: A tool for Bicycle Infrastructure Data & Network Assessment\n2023-06-28\, 16:00–16:30 (Europe/Tirane)\, UBT C / N110 – Second Floor\n\n\nAccess to high-quality data on existing bicycle infrastructure is a requirement for evidence-based bicycle network planning\, which can support a green transition of human mobility. However\, this requirement is rarely met: Data from governmental agencies or crowdsourced projects like OpenStreetMap often suffer from unknown\, heterogeneous\, or low quality. Currently available tools for road network data quality assessment often fail to account for network topology\, spatial heterogeneity\, and bicycle-specific data characteristics. \nTo fill these gaps\, we introduce BikeDNA\, an open-source tool for reproducible quality assessment tailored to bicycle infrastructure data. BikeDNA performs either a standalone analysis of one data set or a comparative analysis between OpenStreetMap and a reference data set\, including feature matching. Data quality metrics are considered both globally for the entire study area and locally on grid cell\, thus exposing spatial variation in data quality with a focus on network structure and connectivity. Interactive maps and HTML/PDF reports are generated to facilitate the visual exploration and communication of results. \nBikeDNA is based on open-source python libraries and Jupyter notebooks\, requires minimal programming knowledge\, and supports data quality assessments for a wide range of applications – from urban planning to OpenStreetMap data improvement or transportation network research. In this talk we will introduce how to use BikeDNA to evaluate and improve local data sets on bicycle infrastructure\, examine what BikeDNA can teach us on the current state of data for active mobility\, and discuss the importance of local quality assessments to support increased uptake of open and crowd-sourced data.
URL:https://nerds.itu.dk/event/anastassia-at-foss4g/
CATEGORIES:NERDS away
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