A Case Study on Capturing and Visualising Face-to-Face Interactions in the Workplace

Face-to-face interactions have proven to accelerate team and larger organisation success. Many past research has explored the benefits of quantifying face-to-face interactions for informed workplace management, with little attention being paid to how this information is perceived by the employees. In this paper, we offer a reflection on the automated feedback of personal interactions in a workplace through a longitudinal study of capturing, modelling and visualisation of face-to-face interactions of 47 employees for 4 months in an industrial research lab in Europe. We conducted semi-structured interviews with 20 employees to understand their perception and experience with the system. Our findings suggest that the short-term feedback on personal face-to-face interactions was not perceived as an effective external cue to promote self-reflection by most, and that employees desire long- term feedback annotated with actionable attributes.

Recent Publications

August 09, 2017

A Cloud Native Approach to 5G Network Slicing

  • Francini A.
  • Miller R.
  • Sharma S.

5G networks will have to support a set of very diverse and often extreme requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements on a shared network infrastructure. This paper presents a cloud native approach to network slicing. The cloud ...

August 01, 2017

Modeling and simulation of RSOA with a dual-electrode configuration

  • De Valicourt G.
  • Liu Z.
  • Violas M.
  • Wang H.
  • Wu Q.

Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

July 12, 2017

PrivApprox: Privacy-Preserving Stream Analytics

  • Chen R.
  • Christof Fetzer
  • Le D.
  • Martin Beck
  • Pramod Bhatotia
  • Thorsten Strufe

How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...