@inproceedings{singer2015hyptrails, author = {Singer, P. and Helic, D. and Hotho, A. and Strohmaier, M.}, title = {Hyptrails: A bayesian approach for comparing hypotheses about human trails}, booktitle = {24th International World Wide Web Conference (WWW2015)}, publisher = {ACM}, address = {Firenze, Italy}, year = {2015}, url = {http://www.www2015.it/documents/proceedings/proceedings/p1003.pdf}, keywords = {2015, bibsonomy, compare, human, hypotheses, hyptrails, myown, trails, web} }

%0 = inproceedings %A = Singer, P. and Helic, D. and Hotho, A. and Strohmaier, M. %B = 24th International World Wide Web Conference (WWW2015) %C = Firenze, Italy %D = 2015 %I = ACM %T = Hyptrails: A bayesian approach for comparing hypotheses about human trails %U = http://www.www2015.it/documents/proceedings/proceedings/p1003.pdf

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.

@article{Panisson2011, author = {Panisson, André and Barrat, Alain and Cattuto, Ciro and den Broeck, Wouter Van and Ruffo, Giancarlo and Schifanella, Rossano}, title = {On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks}, journal = {Ad Hoc Networks}, year = {2011}, volume = {In Press, Accepted Manuscript}, pages = { - }, url = {http://www.sciencedirect.com/science/article/pii/S1570870511001272}, doi = {10.1016/j.adhoc.2011.06.003}, issn = {1570-8705}, keywords = {dynamics, human, model, networks, proximity, rfid, toread}, abstract = {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.} }

%0 = article %A = Panisson, André and Barrat, Alain and Cattuto, Ciro and den Broeck, Wouter Van and Ruffo, Giancarlo and Schifanella, Rossano %D = 2011 %T = On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks %U = http://www.sciencedirect.com/science/article/pii/S1570870511001272

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.

@article{Kulkarni:2009:Biomed-Eng-Online:19656402, author = {Kulkarni, S S and Reddy, N P and Hariharan, S I}, title = {Facial expression (mood) recognition from facial images using committee neural networks}, journal = {Biomed Eng Online}, year = {2009}, volume = {8}, pages = {16-16}, url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2731770/}, doi = {10.1186/1475-925X-8-16}, keywords = {automatic, everyaware, facial, human, image, mood, person, recognition, subjective}, abstract = {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.} }

%0 = article %A = Kulkarni, S S and Reddy, N P and Hariharan, S I %D = 2009 %T = Facial expression (mood) recognition from facial images using committee neural networks %U = http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2731770/