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.

VL - 10 ER - TY - JOUR T1 - On the complexity of quantified linear systems JF - Theoretical Computer Science Y1 - 2014 A1 - Salvatore Ruggieri A1 - Eirinakis, Pavlos A1 - Subramani, K A1 - Wojciechowski, Piotr AB - 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. VL - 518 ER - TY - JOUR T1 - On quantified linear implications JF - Annals of Mathematics and Artificial Intelligence Y1 - 2014 A1 - Eirinakis, Pavlos A1 - Salvatore Ruggieri A1 - Subramani, K A1 - Wojciechowski, Piotr AB - 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. VL - 71 ER - TY - JOUR T1 - Wisdom of crowds for robust gene network inference JF - Nature Methods Y1 - 2012 A1 - Daniel Marbach A1 - J.C. Costello A1 - Robert Küffner A1 - N.M. Vega A1 - R.J. Prill A1 - D.M. Camacho A1 - K.R. Allison A1 - Manolis Kellis A1 - J.J. Collins A1 - Aderhold, A. A1 - Gustavo Stolovitzky A1 - Bonneau, R. A1 - Chen, Y. A1 - Cordero, F. A1 - Martin Crane A1 - Dondelinger, F. A1 - Drton, M. A1 - Esposito, R. A1 - Foygel, R. A1 - De La Fuente, A. A1 - Gertheiss, J. A1 - Geurts, P. A1 - Greenfield, A. A1 - Grzegorczyk, M. A1 - Haury, A.-C. A1 - Holmes, B. A1 - Hothorn, T. A1 - Husmeier, D. A1 - Huynh-Thu, V.A. A1 - Irrthum, A. A1 - Karlebach, G. A1 - Lebre, S. A1 - De Leo, V. A1 - Madar, A. A1 - Mani, S. A1 - Mordelet, F. A1 - Ostrer, H. A1 - Ouyang, Z. A1 - Pandya, R. A1 - Petri, T. A1 - Pinna, A. A1 - Poultney, C.S. A1 - Rezny, S. A1 - Heather J Ruskin A1 - Saeys, Y. A1 - Shamir, R. A1 - Alina Sirbu A1 - Song, M. A1 - Soranzo, N. A1 - Statnikov, A. A1 - N.M. Vega A1 - Vera-Licona, P. A1 - Vert, J.-P. A1 - Visconti, A. A1 - Haizhou Wang A1 - Wehenkel, L. A1 - Windhager, L. A1 - Zhang, Y. A1 - Zimmer, R. VL - 9 UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-84870305264&partnerID=40&md5=04a686572bdefff60157bf68c95df7ea ER - TY - Generic T1 - 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 T2 - Lecture Notes in Computer Science Y1 - 2004 A1 - Jean-François Boulicaut A1 - Floriana Esposito A1 - Fosca Giannotti A1 - Dino Pedreschi JF - Lecture Notes in Computer Science PB - Springer VL - 3202 SN - 3-540-23108-0 ER - TY - Generic T1 - Machine Learning: ECML 2004, 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, Proceedings T2 - Lecture Notes in Computer Science Y1 - 2004 A1 - Jean-François Boulicaut A1 - Floriana Esposito A1 - Fosca Giannotti A1 - Dino Pedreschi JF - Lecture Notes in Computer Science PB - Springer VL - 3201 SN - 3-540-23105-6 ER -