Sîrbu, A.; Becker, M.; Caminiti, S.; De Baets, B.; Elen, B.; Francis, L.; Gravino, P.; Hotho, A.; Ingarra, S.; Loreto, V.; Molino, A.; Mueller, J.; Peters, J.; Ricchiuti, F.; Saracino, F.; Servedio, V. D. P.; Stumme, G.; Theunis, J.; Tria, F. & Van den Bossche, J. (2015), 'Participatory Patterns in an International Air Quality Monitoring Initiative', PLoS ONE 10 (8) , e0136763 .
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<p>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.</p>

Becker, M.; Hotho, A.; Mueller, J.; Kibanov, M.; Atzmueller, M. & Stumme, G. (2014), 'Subjective vs. Objective Data: Bridging the Gap' , CSSWS 2014, Poster .
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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.

Hasenfratz, D.; Saukh, O.; Walser, C.; Hueglin, C.; Fierz, M. & Thiele, L. (2014), Pushing the spatio-temporal resolution limit of urban air pollution maps, in 'Proceedings of the 12th International Conference on Pervasive Computing and Communications (PerCom 2014)' , pp. 69--77 .
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2013

Atzmueller, M. & Mueller, J. (2013), Subgroup Analytics and Interactive Assessment on Ubiquitous Data, in 'Proceedings of the International Workshop on Mining Ubiquitous and Social Environments (MUSE2013)' .
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Atzmueller, M. & Mueller, J. (2013), Subgroup Analytics and Interactive Assessment on Ubiquitous Data, in 'Mining Ubiquitous and Social Environments Workshop at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECMLPKDD 2013, Prague, Czech Republic -- September 23-27, 2013. Proceedings' , pp. 19--27 .
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This paper applies subgroup discovery for obtaining interesting descriptive patterns in ubiquitous data. Furthermore, we provide a novel graph-based analysis approach for assessing the relations between the obtained subgroup set, and for ranking subgroups according to their relationships to other subgroups. We present and discuss first results utilizing real-world data, given by noise measurements with associated subjective perceptions and a set of tags describing the semantic context.

Becker, M.; Mueller, J.; Hotho, A. & Stumme, G. (2013), A Generic Platform for Ubiquitous and Subjective Data, in '1st International Workshop on Pervasive Urban Crowdsensing Architecture and Applications, PUCAA 2013, Zurich, Switzerland -- September 9, 2013. Proceedings' , ACM, New York, NY, USA , pp. 1175--1182 .
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An increasing number of platforms like Xively or ThingSpeak are available to manage ubiquitous sensor data enabling the Internet of Things. Strict data formats allow interoperability and informative visualizations, supporting the development of custom user applications. Yet, these strict data formats as well as the common feed-centric approach limit the flexibility of these platforms. We aim at providing a concept that supports data ranging from text-based formats like JSON to images and video footage. Furthermore, we introduce the concept of extensions, which allows to enrich existing data points with additional information, thus, taking a data point centric approach. This enables us to gain semantic and user specific context by attaching subjective data to objective values. This paper provides an overview of our architecture including concept, implementation details and present applications. We distinguish our approach from several other systems and describe two sensing applications namely AirProbe and WideNoise that were implemented for our platform.

Becker, M.; Caminiti, S.; Fiorella, D.; Francis, L.; Gravino, P.; Haklay, M. (M.; Hotho, A.; Loreto, V.; Mueller, J.; Ricchiuti, F.; Servedio, V. D. P.; Sîrbu, A. & Tria, F. (2013), 'Awareness and Learning in Participatory Noise Sensing', PLoS ONE 8 (12) , e81638 .
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<p>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.</p>

Becker, M.; Caminiti, S.; Fiorella, D.; Francis, L.; Gravino, P.; Haklay, M. (M.; Hotho, A.; Loreto, V.; Mueller, J.; Ricchiuti, F.; Servedio, V. D. P.; Sîrbu, A. & Tria, F. (2013), 'Awareness and Learning in Participatory Noise Sensing', PLOS ONE 8 (12) , e81638 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

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.

Mueller, J.; Doerfel, S.; Becker, M.; Hotho, A. & Stumme, G. (2013), Tag Recommendations for SensorFolkSonomies, in 'Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings' , CEUR-WS, Aachen, Germany .
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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.

Mueller, J.; Doerfel, S.; Becker, M.; Hotho, A. & Stumme, G. (2013), Tag Recommendations for SensorFolkSonomies, in 'Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings' , ACM, , pp. New York, NY, USA .
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Piatkowski, N.; Lee, S. & Morik, K. (2013), 'Spatio-temporal random fields: compressible representation and distributed estimation', Machine Learning 93 (1) , 115-139 .
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2011

Barber, J. R.; Burdett, C. L.; Reed, S. E.; Warner, K. A.; Formichella, C.; Crooks, K. R.; Theobald, D. M. & Fristrup, K. M. (2011), 'Anthropogenic noise exposure in protected natural areas: estimating the scale of ecological consequences', Landscape Ecology 26 (9) , 1281-1295 .
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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.

Christin, D.; Reinhardt, A.; Kanhere, S. S. & Hollick, M. (2011), 'A survey on privacy in mobile participatory sensing applications', Journal of Systems and Software 84 (11) , 1928--1946 .
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Stenneth, L.; Wolfson, O.; Yu, P. S. & Xu, B. (2011), Transportation Mode Detection Using Mobile Phones and GIS Information, in 'Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems' , ACM, New York, NY, USA , pp. 54--63 .
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The transportation mode such as walking, cycling or on a train denotes an important characteristic of the mobile user's context. In this paper, we propose an approach to inferring a user's mode of transportation based on the GPS sensor on her mobile device and knowledge of the underlying transportation network. The transportation network information considered includes real time bus locations, spatial rail and spatial bus stop information. We identify and derive the relevant features related to transportation network information to improve classification effectiveness. This approach can achieve over 93.5% accuracy for inferring various transportation modes including: car, bus, aboveground train, walking, bike, and stationary. Our approach improves the accuracy of detection by 17% in comparison with the GPS only approach, and 9% in comparison with GPS with GIS models. The proposed approach is the first to distinguish between motorized transportation modes such as bus, car and aboveground train with such high accuracy. Additionally, if a user is travelling by bus, we provide further information about which particular bus the user is riding. Five different inference models including Bayesian Net, Decision Tree, Random Forest, Naïve Bayesian and Multilayer Perceptron, are tested in the experiments. The final classification system is deployed and available to the public.

2010

Lane, N.; Miluzzo, E.; Lu, H.; Peebles, D.; Choudhury, T. & Campbell, A. (2010), 'A survey of mobile phone sensing', Communications Magazine, IEEE 48 (9) , 140--150 .
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2009

Kulkarni, S. S.; Reddy, N. P. & Hariharan, S. I. (2009), 'Facial expression (mood) recognition from facial images using committee neural networks', Biomed Eng Online 8 , 16-16 .
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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.

2006

Hanjalic, A. (2006), 'Extracting moods from pictures and sounds: towards truly personalized TV', Signal Processing Magazine, IEEE 23 (2) , 90-100 .
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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