TY  - CONF
AU  - Singer, P.
AU  - Helic, D.
AU  - Hotho, A.
AU  - Strohmaier, M.
A2  - 
T1  - Hyptrails: A bayesian approach for comparing hypotheses about human trails
T2  - 24th International World Wide Web Conference (WWW2015)
PB  - ACM
C1  - Firenze, Italy
PY  - 2015/may 18 - may 22
CY  -  
VL  - 
IS  - 
SP  - 
EP  - 
UR  - http://www.www2015.it/documents/proceedings/proceedings/p1003.pdf
DO  - 
KW  - 2015
KW  - bibsonomy
KW  - compare
KW  - human
KW  - hypotheses
KW  - hyptrails
KW  - myown
KW  - trails
KW  - web
L1  - 
SN  - 
N1  - 
N1  - 
AB  - 
ER  -

TY  - JOUR
AU  - Panisson, André
AU  - Barrat, Alain
AU  - Cattuto, Ciro
AU  - den Broeck, Wouter Van
AU  - Ruffo, Giancarlo
AU  - Schifanella, Rossano
T1  - On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks
JO  - Ad Hoc Networks
PY  - 2011/
VL  - In Press, Accepted Manuscript
IS  - 
SP  -  
EP  -  
UR  - http://www.sciencedirect.com/science/article/pii/S1570870511001272
DO  - 10.1016/j.adhoc.2011.06.003
KW  - dynamics
KW  - human
KW  - model
KW  - networks
KW  - proximity
KW  - rfid
KW  - toread
L1  - 
SN  - 
N1  - ScienceDirect - Ad Hoc Networks : On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks
N1  - 
AB  - We report on a data-driven investigation aimed at understanding the dynamics of message spreading in a real-world dynamical network of human proximity. We use data collected by means of a proximity-sensing network of wearable sensors that we deployed at three different social gatherings, simultaneously involving several hundred individuals. We simulate a message spreading process over the recorded proximity network, focusing on both the topological and the temporal properties. We show that by using an appropriate technique to deal with the temporal heterogeneity of proximity events, a universal statistical pattern emerges for the delivery times of messages, robust across all the data sets. Our results are useful to set constraints for generic processes of data dissemination, as well as to validate established models of human mobility and proximity that are frequently used to simulate realistic behaviors.
ER  -

TY  - JOUR
AU  - Kulkarni, S S
AU  - Reddy, N P
AU  - Hariharan, S I
T1  - Facial expression (mood) recognition from facial images using committee neural networks
JO  - Biomed Eng Online
PY  - 2009/
VL  - 8
IS  - 
SP  - 16
EP  - 16
UR  - http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2731770/
DO  - 10.1186/1475-925X-8-16
KW  - automatic
KW  - everyaware
KW  - facial
KW  - human
KW  - image
KW  - mood
KW  - person
KW  - recognition
KW  - subjective
L1  - 
SN  - 
N1  - Facial expression (mood) recognition from facial images using committee neural networks
N1  - 
AB  - Facial expressions are important in facilitating human communication and interactions. Also, they are used as an important tool in behavioural studies and in medical rehabilitation. Facial image based mood detection techniques may provide a fast and practical approach for non-invasive mood detection. The purpose of the present study was to develop an intelligent system for facial image based expression classification using committee neural networks.Several facial parameters were extracted from a facial image and were used to train several generalized and specialized neural networks. Based on initial testing, the best performing generalized and specialized neural networks were recruited into decision making committees which formed an integrated committee neural network system. The integrated committee neural network system was then evaluated using data obtained from subjects not used in training or in initial testing.The system correctly identified the correct facial expression in 255 of the 282 images (90.43% of the cases), from 62 subjects not used in training or in initial testing. Committee neural networks offer a potential tool for image based mood detection.
ER  -