September 12, 2018

Automated iBeacon-Based Community Detection: Data-Driven Approach to Recover Face-to-Face Interaction from Noisy & Incomplete Sensor Data

  • Akyamac A.
  • Lee J.
  • Phadke C.
  • Uzunalioglu H.

In this paper, we address the detection of face-to-face (f2f, i.e., physical) interactions between people from noisy sensor data and determination of groups and communities that individuals form in the physical world. The sensor data may include, for example, received signal strength indicator (RSSI) information based on iBeacon, Wi-Fi, Zigbee or similar technologies. This is a challenging problem since this type of sensor data is very noisy, is often incomplete with a lot of missing values, and is easily perturbed by the mobility of people and nearby obstacles (for example, people and/or objects between or around receivers and transmitters). These effects are especially visible in very dynamic indoor environments. Furthermore, detection of interaction needs to be updated in real-time and be quickly adaptable to indoor mobility of people. The key idea of the paper is to transform the original noisy sensor data into specific feature vectors, to enable measurement of proximity between the sensors. To obtain reliable features that well reflect interaction between people, the feature engineering to generate the feature vectors is done by a series of statistical methods such as time series smoothing, change point detection, and exploratory data analysis (EDA). Clustering similar feature vectors allows correlation of people with similar patterns depending on their co-varying information, without the need to rely on localization information. Therefore, this solely data-driven approach provides robust community detection that is not sensitive to noise and missing/dropped signals, but that automatically captures the dynamic interaction between people.

Recent Publications

January 01, 2019

Friendly, appealing or both? Characterising user experience in sponsored search landing pages

  • Bron M.
  • Chute M.
  • Evans H.
  • Lalmas M.
  • Redi M.
  • Silvestri F.

© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Many of today's websites have recognised the importance of mobile friendly pages to keep users engaged and to provide a satisfying user experience. However, next to the experience provided by the sites themselves, ...

January 01, 2019

Analyzing uber's ride-sharing economy

  • Aiello L.
  • Djuric N.
  • Grbovic M.
  • Kooti F.
  • Lerman K.
  • Radosavljevic V.

© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Uber is a popular ride-sharing application that matches people who need a ride (or riders) with drivers who are willing to provide it using their personal vehicles. Despite its growing popularity, there exist ...

January 01, 2019

The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race

  • Cresci S.
  • Petrocchi M.
  • Pietro R.
  • Spognardi A.
  • Tesconi M.

© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots. Here, for the first time, we extensively study this novel ...