• Publications
  • Influence
The Mobile Data Challenge: Big Data for Mobile Computing Research
TLDR
This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of related mobile data analysis methodologies. Expand
  • 465
  • 51
  • PDF
Towards rich mobile phone datasets: Lausanne data collection campaign
Mobile phones have recently been used to collect large-scale continuous data about human behavior. In a paradigm known as people centric sensing, users are not only the carriers of sensing devices,Expand
  • 304
  • 37
  • PDF
Modeling scenes with local descriptors and latent aspects
TLDR
We present a new approach to model visual scenes in image collections, based on local invariant features and probabilistic latent space models. Expand
  • 444
  • 30
  • PDF
Mining large-scale smartphone data for personality studies
TLDR
In this paper, we investigate the relationship between automatically extracted behavioral characteristics derived from rich smartphone data and self-reported Big-Five personality traits (extraversion, agreeableness, conscientiousness, emotional stability and openness to experience). Expand
  • 298
  • 28
  • PDF
StressSense: detecting stress in unconstrained acoustic environments using smartphones
TLDR
We propose StressSense for unobtrusively recognizing stress from human voice using smartphones. Expand
  • 438
  • 27
  • PDF
Modeling Semantic Aspects for Cross-Media Image Indexing
TLDR
In this paper, we present three alternatives to learn a probabilistic latent semantic analysis (PLSA) model for annotated images and evaluate their respective performance for automatic image search. Expand
  • 207
  • 24
  • PDF
A Nonverbal Behavior Approach to Identify Emergent Leaders in Small Groups
TLDR
This paper presents an analysis on how an emergent leader is perceived in newly formed, small groups, and tackles the task of automatically inferring emergent leaders, using a variety of communicative nonverbal cues extracted from audio and video channels. Expand
  • 140
  • 19
  • PDF
Discovering routines from large-scale human locations using probabilistic topic models
TLDR
We develop an unsupervised methodology based on two differing probabilistic topic models and apply them to the daily life of 97 mobile phone users over a 16-month period to achieve the discovery and analysis of human routines that characterize both individual and group behaviors in terms of location patterns. Expand
  • 227
  • 18
  • PDF
PLSA-based image auto-annotation: constraining the latent space
TLDR
We address the problem of unsupervised image auto-annotation with probabilistic latent space models, constraining the definition of the latent space to ensure its consistency in semantic terms, while retaining the ability to jointly model visual information. Expand
  • 280
  • 17
  • PDF
Discovering places of interest in everyday life from smartphone data
TLDR
In this paper, a new framework to discover places-of-interest from multimodal mobile phone data is presented. Expand
  • 110
  • 16
  • PDF