TY - JOUR AU - Sîrbu, Alina AU - Becker, Martin AU - Caminiti, Saverio AU - De Baets, Bernard AU - Elen, Bart AU - Francis, Louise AU - Gravino, Pietro AU - Hotho, Andreas AU - Ingarra, Stefano AU - Loreto, Vittorio AU - Molino, Andrea AU - Mueller, Juergen AU - Peters, Jan AU - Ricchiuti, Ferdinando AU - Saracino, Fabio AU - Servedio, Vito D. P. AU - Stumme, Gerd AU - Theunis, Jan AU - Tria, Francesca AU - Van den Bossche, Joris T1 - Participatory Patterns in an International Air Quality Monitoring Initiative JO - PLoS ONE PY - 2015/08 VL - 10 IS - 8 SP - EP - UR - http://dx.doi.org/10.1371%2Fjournal.pone.0136763 M3 - 10.1371/journal.pone.0136763 KW - air KW - airprobe KW - everyaware KW - game KW - myown KW - pollution KW - quality L1 - SN - N1 - PLOS ONE: Participatory Patterns in an International Air Quality Monitoring Initiative N1 - AB -

The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.

ER - TY - GEN AU - Becker, Martin AU - Hotho, Andreas AU - Mueller, Juergen AU - Kibanov, Mark AU - Atzmueller, Martin AU - Stumme, Gerd A2 - T1 - Subjective vs. Objective Data: Bridging the Gap JO - PB - AD - PY - 2014/ VL - IS - SP - EP - UR - http://www.gesis.org/en/events/css-wintersymposium/poster-presentation/ M3 - KW - 2015 KW - everyaware KW - myown L1 - N1 - N1 - AB - Sensor data is objective. But when measuring our environment, measured values are contrasted with our perception, which is always subjective. This makes interpreting sensor measurements difficult for a single person in her personal environment. In this context, the EveryAware projects directly connects the concepts of objective sensor data with subjective impressions and perceptions by providing a collective sensing platform with several client applications allowing to explicitly associate those two data types. The goal is to provide the user with personalized feedback, a characterization of the global as well as her personal environment, and enable her to position her perceptions in this global context.

In this poster we summarize the collected data of two EveryAware applications, namely WideNoise for noise measurements and AirProbe for participatory air quality sensing. Basic insights are presented including user activity, learning processes and sensor data to perception correlations. These results provide an outlook on how this data can further be used to understand the connection between sensor data and perceptions. ER - TY - CONF AU - Hasenfratz, David AU - Saukh, Olga AU - Walser, Christoph AU - Hueglin, Christoph AU - Fierz, Martin AU - Thiele, Lothar A2 - T1 - Pushing the spatio-temporal resolution limit of urban air pollution maps T2 - Proceedings of the 12th International Conference on Pervasive Computing and Communications (PerCom 2014) PB - CY - Budapest, Hungary PY - 2014/03 M2 - VL - IS - SP - 69 EP - 77 UR - M3 - KW - air KW - everyaware KW - map KW - pollution L1 - SN - N1 - DB - Publications (List Selected Publications) N1 - AB - ER - TY - JOUR AU - Becker, Martin AU - Caminiti, Saverio AU - Fiorella, Donato AU - Francis, Louise AU - Gravino, Pietro AU - Haklay, Mordechai (Muki) AU - Hotho, Andreas AU - Loreto, Vittorio AU - Mueller, Juergen AU - Ricchiuti, Ferdinando AU - Servedio, Vito D. P. AU - Sîrbu, Alina AU - Tria, Francesca T1 - Awareness and Learning in Participatory Noise Sensing JO - PLoS ONE PY - 2013/12 VL - 8 IS - 12 SP - EP - UR - http://dx.doi.org/10.1371%2Fjournal.pone.0081638 M3 - 10.1371/journal.pone.0081638 KW - 2013 KW - everyaware KW - iteg KW - itegpub KW - l3s KW - noise KW - sensing KW - sitc KW - widenoise L1 - SN - N1 - PLOS ONE: Awareness and Learning in Participatory Noise Sensing N1 - AB -

The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.

ER - TY - JOUR AU - Becker, Martin AU - Caminiti, Saverio AU - Fiorella, Donato AU - Francis, Louise AU - Gravino, Pietro AU - Haklay, Mordechai (Muki) AU - Hotho, Andreas AU - Loreto, Vittorio AU - Mueller, Juergen AU - Ricchiuti, Ferdinando AU - Servedio, Vito D. P. AU - Sîrbu, Alina AU - Tria, Francesca T1 - Awareness and Learning in Participatory Noise Sensing JO - PLoS ONE PY - 2013/12 VL - 8 IS - 12 SP - EP - UR - http://dx.doi.org/10.1371%2Fjournal.pone.0081638 M3 - 10.1371/journal.pone.0081638 KW - 2013 KW - analysis KW - everyaware KW - myown KW - noise KW - sensing KW - widenoise L1 - SN - N1 - PLOS ONE: Awareness and Learning in Participatory Noise Sensing N1 - AB -

The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.

ER - TY - CONF AU - Mueller, Juergen AU - Doerfel, Stephan AU - Becker, Martin AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Tag Recommendations for SensorFolkSonomies T2 - Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings PB - CEUR-WS CY - Aachen, Germany PY - 2013/ M2 - VL - 1066 IS - SP - EP - UR - http://ceur-ws.org/Vol-1066/ M3 - KW - 2013 KW - everyaware KW - folksonomy KW - myown KW - recommender KW - sensor KW - tag L1 - SN - N1 - N1 - AB - With the rising popularity of smart mobile devices, sensor data-based

applications have become more and more popular. Their users record

data during their daily routine or specifically for certain events.

The application WideNoise Plus allows users to record sound samples

and to annotate them with perceptions and tags. The app is being

used to document and map the soundscape all over the world. The procedure

of recording, including the assignment of tags, has to be as easy-to-use

as possible. We therefore discuss the application of tag recommender

algorithms in this particular scenario. We show, that this task is

fundamentally different from the well-known tag recommendation problem

in folksonomies as users do no longer tag fix resources but rather

sensory data and impressions. The scenario requires efficient recommender

algorithms that are able to run on the mobile device, since Internet

connectivity cannot be assumed to be available. Therefore, we evaluate

the performance of several tag recommendation algorithms and discuss

their applicability in the mobile sensing use-case. ER - TY - JOUR AU - Barber, Jesse R. AU - Burdett, Chris L. AU - Reed, Sarah E. AU - Warner, Katy A. AU - Formichella, Charlotte AU - Crooks, Kevin R. AU - Theobald, Dave M. AU - Fristrup, Kurt M. T1 - Anthropogenic noise exposure in protected natural areas: estimating the scale of ecological consequences JO - Landscape Ecology PY - 2011/ VL - 26 IS - 9 SP - 1281 EP - 1295 UR - http://dx.doi.org/10.1007/s10980-011-9646-7 M3 - 10.1007/s10980-011-9646-7 KW - everyaware KW - noise KW - toread L1 - SN - N1 - Anthropogenic noise exposure in protected natural areas: estimating the scale of ecological consequences - Springer N1 - AB - The extensive literature documenting the ecological effects of roads has repeatedly implicated noise as one of the causal factors. Recent studies of wildlife responses to noise have decisively identified changes in animal behaviors and spatial distributions that are caused by noise. Collectively, this research suggests that spatial extent and intensity of potential noise impacts to wildlife can be studied by mapping noise sources and modeling the propagation of noise across landscapes. Here we present models of energy extraction, aircraft overflight and roadway noise as examples of spatially extensive sources and to present tools available for landscape scale investigations. We focus these efforts in US National Parks (Mesa Verde, Grand Teton and Glacier) to highlight that ecological noise pollution is not a threat restricted to developed areas and that many protected natural areas experience significant noise loads. As a heuristic tool for understanding past and future noise pollution we forecast community noise utilizing a spatially-explicit land-use change model that depicts the intensity of human development at sub-county resolution. For road noise, we transform effect distances from two studies into sound levels to begin a discussion of noise thresholds for wildlife. The spatial scale of noise exposure is far larger than any protected area, and no site in the continental US is free form noise. The design of observational and experimental studies of noise effects should be informed by knowledge of regional noise exposure patterns. ER - TY - JOUR AU - Christin, Delphine AU - Reinhardt, Andreas AU - Kanhere, Salil S AU - Hollick, Matthias T1 - A survey on privacy in mobile participatory sensing applications JO - Journal of Systems and Software PY - 2011/ VL - 84 IS - 11 SP - 1928 EP - 1946 UR - http://scholar.google.de/scholar.bib?q=info:qpMZngbCBHYJ:scholar.google.com/&output=citation&scisig=AAGBfm0AAAAAVJLgOK6PYLcv_X2uOph4-evGd2AOVDax&scisf=4&hl=en&scfhb=1 M3 - KW - everyaware KW - mobile KW - participatory KW - plattform KW - sensing KW - survey L1 - SN - N1 - N1 - AB - 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/ M3 - 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 - TY - JOUR AU - Hanjalic, A. T1 - Extracting moods from pictures and sounds: towards truly personalized TV JO - Signal Processing Magazine, IEEE PY - 2006/03 VL - 23 IS - 2 SP - 90 EP - 100 UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1621452 M3 - 10.1109/MSP.2006.1621452 KW - automatic KW - everyaware KW - images KW - mood KW - perception KW - sound L1 - SN - N1 - IEEE Xplore Abstract - Extracting moods from pictures and sounds: towards truly personalized TV N1 - AB - This paper considers how we feel about the content we see or hear. As opposed to the cognitive content information composed of the facts about the genre, temporal content structures and spatiotemporal content elements, we are interested in obtaining the information about the feelings, emotions, and moods evoked by a speech, audio, or video clip. We refer to the latter as the affective content, and to the terms such as happy or exciting as the affective labels of an audiovisual signal. In the first part of the paper, we explore the possibilities for representing and modeling the affective content of an audiovisual signal to effectively bridge the affective gap. Without loosing generality, we refer to this signal simply as video, which we see as an image sequence with an accompanying soundtrack. Then, we show the high potential of the affective video content analysis for enhancing the content recommendation functionalities of the future PVRs and VOD systems. We conclude this paper by outlining some interesting research challenges in the field ER -