Observing and Modeling User Behavior onSocio-Spatial Interaction Networks

Conformance, Exceptions, and Anomalies

Martin Atzmueller, Çiçek Güven, Parisa Shayan, Spyroula Masiala, Rick Mackenbach, Werner Liebregts

Research output: Contribution to conferencePaperOther research output

Abstract

For modeling user behavior in AI systems, we canmake use of diverse data sources, that are heterogeneous, andcover different user facets. This paper investigates socio-spatialinteraction networks for modeling user interactions from threeperspectives: We analyze preferences and perceptions of face-to-face human interactions in relation to the interactions observedusing wearable sensors. For that, we investigate the correspon-dence of according networks, in order to identify conformance,exceptions, and anomalies. The analysis is performed on a real-world dataset capturing networks of face-to-face proximity (asa proxy for actual face-to-face communication between partici-pants) coupled with self-report questionnaires about preferencesand perception of those interactions.
Original languageEnglish
Publication statusAccepted/In press - 2019
Event2019 First International Conference on ​Transdisciplinary AI - Laguna Hills, California, United States
Duration: 25 Sep 201927 Sep 2019
https://www.transai.org/

Conference

Conference2019 First International Conference on ​Transdisciplinary AI
Abbreviated titleTransAI
CountryUnited States
CityCalifornia
Period25/09/1927/09/19
Internet address

Fingerprint

Communication
Wearable sensors

Cite this

Atzmueller, M., Güven, Ç., Shayan, P., Masiala, S., Mackenbach, R., & Liebregts, W. (Accepted/In press). Observing and Modeling User Behavior onSocio-Spatial Interaction Networks: Conformance, Exceptions, and Anomalies. Paper presented at 2019 First International Conference on ​Transdisciplinary AI , California, United States.
Atzmueller, Martin ; Güven, Çiçek ; Shayan, Parisa ; Masiala, Spyroula ; Mackenbach, Rick ; Liebregts, Werner. / Observing and Modeling User Behavior onSocio-Spatial Interaction Networks : Conformance, Exceptions, and Anomalies. Paper presented at 2019 First International Conference on ​Transdisciplinary AI , California, United States.
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title = "Observing and Modeling User Behavior onSocio-Spatial Interaction Networks: Conformance, Exceptions, and Anomalies",
abstract = "For modeling user behavior in AI systems, we canmake use of diverse data sources, that are heterogeneous, andcover different user facets. This paper investigates socio-spatialinteraction networks for modeling user interactions from threeperspectives: We analyze preferences and perceptions of face-to-face human interactions in relation to the interactions observedusing wearable sensors. For that, we investigate the correspon-dence of according networks, in order to identify conformance,exceptions, and anomalies. The analysis is performed on a real-world dataset capturing networks of face-to-face proximity (asa proxy for actual face-to-face communication between partici-pants) coupled with self-report questionnaires about preferencesand perception of those interactions.",
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year = "2019",
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note = "2019 First International Conference on ​Transdisciplinary AI , TransAI ; Conference date: 25-09-2019 Through 27-09-2019",
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Atzmueller, M, Güven, Ç, Shayan, P, Masiala, S, Mackenbach, R & Liebregts, W 2019, 'Observing and Modeling User Behavior onSocio-Spatial Interaction Networks: Conformance, Exceptions, and Anomalies' Paper presented at 2019 First International Conference on ​Transdisciplinary AI , California, United States, 25/09/19 - 27/09/19, .

Observing and Modeling User Behavior onSocio-Spatial Interaction Networks : Conformance, Exceptions, and Anomalies. / Atzmueller, Martin; Güven, Çiçek; Shayan, Parisa; Masiala, Spyroula; Mackenbach, Rick ; Liebregts, Werner.

2019. Paper presented at 2019 First International Conference on ​Transdisciplinary AI , California, United States.

Research output: Contribution to conferencePaperOther research output

TY - CONF

T1 - Observing and Modeling User Behavior onSocio-Spatial Interaction Networks

T2 - Conformance, Exceptions, and Anomalies

AU - Atzmueller, Martin

AU - Güven, Çiçek

AU - Shayan, Parisa

AU - Masiala, Spyroula

AU - Mackenbach, Rick

AU - Liebregts, Werner

PY - 2019

Y1 - 2019

N2 - For modeling user behavior in AI systems, we canmake use of diverse data sources, that are heterogeneous, andcover different user facets. This paper investigates socio-spatialinteraction networks for modeling user interactions from threeperspectives: We analyze preferences and perceptions of face-to-face human interactions in relation to the interactions observedusing wearable sensors. For that, we investigate the correspon-dence of according networks, in order to identify conformance,exceptions, and anomalies. The analysis is performed on a real-world dataset capturing networks of face-to-face proximity (asa proxy for actual face-to-face communication between partici-pants) coupled with self-report questionnaires about preferencesand perception of those interactions.

AB - For modeling user behavior in AI systems, we canmake use of diverse data sources, that are heterogeneous, andcover different user facets. This paper investigates socio-spatialinteraction networks for modeling user interactions from threeperspectives: We analyze preferences and perceptions of face-to-face human interactions in relation to the interactions observedusing wearable sensors. For that, we investigate the correspon-dence of according networks, in order to identify conformance,exceptions, and anomalies. The analysis is performed on a real-world dataset capturing networks of face-to-face proximity (asa proxy for actual face-to-face communication between partici-pants) coupled with self-report questionnaires about preferencesand perception of those interactions.

M3 - Paper

ER -

Atzmueller M, Güven Ç, Shayan P, Masiala S, Mackenbach R, Liebregts W. Observing and Modeling User Behavior onSocio-Spatial Interaction Networks: Conformance, Exceptions, and Anomalies. 2019. Paper presented at 2019 First International Conference on ​Transdisciplinary AI , California, United States.