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.

%B PLoS One %V 10 %P e0136763 %8 2015 %G eng %R 10.1371/journal.pone.0136763 %0 Journal Article %J Theoretical Computer Science %D 2014 %T On the complexity of quantified linear systems %A Salvatore Ruggieri %A Eirinakis, Pavlos %A Subramani, K %A Wojciechowski, Piotr %X In this paper, we explore the computational complexity of the conjunctive fragment of the first-order theory of linear arithmetic. Quantified propositional formulas of linear inequalities with (k−1) quantifier alternations are log-space complete in ΣkP or ΠkP depending on the initial quantifier. We show that when we restrict ourselves to quantified conjunctions of linear inequalities, i.e., quantified linear systems, the complexity classes collapse to polynomial time. In other words, the presence of universal quantifiers does not alter the complexity of the linear programming problem, which is known to be in P. Our result reinforces the importance of sentence formats from the perspective of computational complexity. %B Theoretical Computer Science %V 518 %P 128–134 %G eng %R 10.1016/j.tcs.2013.08.001 %0 Journal Article %J Annals of Mathematics and Artificial Intelligence %D 2014 %T On quantified linear implications %A Eirinakis, Pavlos %A Salvatore Ruggieri %A Subramani, K %A Wojciechowski, Piotr %X A Quantified Linear Implication (QLI) is an inclusion query over two polyhedral sets, with a quantifier string that specifies which variables are existentially quantified and which are universally quantified. Equivalently, it can be viewed as a quantified implication of two systems of linear inequalities. In this paper, we provide a 2-person game semantics for the QLI problem, which allows us to explore the computational complexities of several of its classes. More specifically, we prove that the decision problem for QLIs with an arbitrary number of quantifier alternations is PSPACE-hard. Furthermore, we explore the computational complexities of several classes of 0, 1, and 2-quantifier alternation QLIs. We observed that some classes are decidable in polynomial time, some are NP-complete, some are coNP-hard and some are ΠP2Π2P -hard. We also establish the hardness of QLIs with 2 or more quantifier alternations with respect to the first quantifier in the quantifier string and the number of quantifier alternations. All the proofs that we provide for polynomially solvable problems are constructive, i.e., polynomial-time decision algorithms are devised that utilize well-known procedures. QLIs can be utilized as powerful modelling tools for real-life applications. Such applications include reactive systems, real-time schedulers, and static program analyzers. %B Annals of Mathematics and Artificial Intelligence %V 71 %P 301–325 %G eng %R 10.1007/s10472-013-9332-3 %0 Journal Article %J Nature Methods %D 2012 %T Wisdom of crowds for robust gene network inference %A Daniel Marbach %A J.C. Costello %A Robert Küffner %A N.M. Vega %A R.J. Prill %A D.M. Camacho %A K.R. Allison %A Manolis Kellis %A J.J. Collins %A Aderhold, A. %A Gustavo Stolovitzky %A Bonneau, R. %A Chen, Y. %A Cordero, F. %A Martin Crane %A Dondelinger, F. %A Drton, M. %A Esposito, R. %A Foygel, R. %A De La Fuente, A. %A Gertheiss, J. %A Geurts, P. %A Greenfield, A. %A Grzegorczyk, M. %A Haury, A.-C. %A Holmes, B. %A Hothorn, T. %A Husmeier, D. %A Huynh-Thu, V.A. %A Irrthum, A. %A Karlebach, G. %A Lebre, S. %A De Leo, V. %A Madar, A. %A Mani, S. %A Mordelet, F. %A Ostrer, H. %A Ouyang, Z. %A Pandya, R. %A Petri, T. %A Pinna, A. %A Poultney, C.S. %A Rezny, S. %A Heather J Ruskin %A Saeys, Y. %A Shamir, R. %A Alina Sirbu %A Song, M. %A Soranzo, N. %A Statnikov, A. %A N.M. Vega %A Vera-Licona, P. %A Vert, J.-P. %A Visconti, A. %A Haizhou Wang %A Wehenkel, L. %A Windhager, L. %A Zhang, Y. %A Zimmer, R. %B Nature Methods %V 9 %P 796-804 %G eng %U http://www.scopus.com/inward/record.url?eid=2-s2.0-84870305264&partnerID=40&md5=04a686572bdefff60157bf68c95df7ea %R 10.1038/nmeth.2016 %0 Conference Proceedings %B Lecture Notes in Computer Science %D 2004 %T Knowledge Discovery in Databases: PKDD 2004, 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, Pisa, Italy, September 20-24, 2004, Proceedings %A Jean-François Boulicaut %A Floriana Esposito %A Fosca Giannotti %A Dino Pedreschi %B Lecture Notes in Computer Science %I Springer %V 3202 %@ 3-540-23108-0 %G eng %0 Conference Proceedings %B Lecture Notes in Computer Science %D 2004 %T Machine Learning: ECML 2004, 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, Proceedings %A Jean-François Boulicaut %A Floriana Esposito %A Fosca Giannotti %A Dino Pedreschi %B Lecture Notes in Computer Science %I Springer %V 3201 %@ 3-540-23105-6 %G eng