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Smart City pp 193-219 | Cite as

Smart Security: Integrated Systems for Security Policies in Urban Environments

  • Enrico di BellaEmail author
  • Francesca Odone
  • Matteo Corsi
  • Alberto Sillitti
  • Ruth Breu
Chapter
Part of the Progress in IS book series (PROIS)

Abstract

Smart Security systems are applications of the Smart City paradigm for local crime prevention. Like most Smart City tools, they consist of informational and technological components that support decision-making processes. A pre-requisite for such tools is that they are supposed to be means of ongoing management and policy innovations: we therefore review some of the crucial components of a Smart Security system from the viewpoint of a local government or a local branch of the public administration, in order to analyze the high-level requisites, characteristics and potentials of such a system. The objective is to help Public officials in identifying both what defines a useful technical tool but also what is required on the part of the public administration to actually make it useful. We therefore discuss the following problems. First, we address the issue of indicators, data and the use of statistical analysis to infer the likely determinants of crime and to define risk parameters for urban spaces. In doing that, we suggest innovative tools to introduce spatial information in crime count models. Second, we discuss sensors and sensor output analysis, trying to define the circumstances that make it useful and the new possibilities offered by current technology. Then we discuss about integration of different information both from a conceptual and a technical point of view, stressing the importance of closing the gap between cold and hot data in order to realize an integrated early warning system. Finally, we discuss the problem of creating a scalable Smart Security system in a local government , indicating a list of significant international experiences.

Keywords

Crime mapping Urban security policies Security dashboard Smart security Intelligent video surveillance 

References

  1. 1.
    Nam, T., & Pardo, T. A. (2011). Smart city as urban innovation: Focusing on management, policy, and context. In Proceedings of the 5th International Conference on Theory and Practice of Electronic Governance (pp. 185–194).Google Scholar
  2. 2.
    Harrison, C., & Donnelly, I. (2011). A theory of smart cities. In: Proceedings of the 55th Annual Meeting of the International Society for the Systems Sciences (ISSS), Hull (Vol. 55, pp. 1–15).Google Scholar
  3. 3.
    Van den Berg, L., Pol, P. M. J, Mingardo, G., & Speller, C. J. M. (Eds.) (2006). The safe city: Safety and urban development in European cities. Farnham: Ashgate Publishing.Google Scholar
  4. 4.
    Weisburd, D., & McEwen, T. (1998). Crime mapping and crime prevention. New York: Criminal Justice Press.Google Scholar
  5. 5.
    Weisburd, D., Mastrofski, S. D., Greenspan, R., & Willis, J. J. (2004). Growth of Compstat in American policing. US Department of Justice: National Institute of Justice.Google Scholar
  6. 6.
    Steden, R., Boutellier, H., Scholte, R. D., & Heijnen, M. (2012). Beyond crime statistics: The construction and application of a criminogenity monitor in Amsterdam. European Journal on Criminal Policy and Research, 19, 47–62.CrossRefGoogle Scholar
  7. 7.
    Bonatsos, A., Middleton, L., Melas, P., & Sabeur, Z. (2013). Crime open data aggregation and management for the design of safer spaces in urban environments. In Environmental Software Systems. Fotering information Sharing (pp. 311–320). Berlin, Heidelberg: Springer.Google Scholar
  8. 8.
    Bettencourt, L. (2013). The uses of big data in cities. Santa Fe Institute Working Paper 29.Google Scholar
  9. 9.
    Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., et al. (2012). Smart cities of the future. European Physical Journal: Special Topics, 214(1), 481–518.Google Scholar
  10. 10.
    Paskaleva, K. A. (2011). The smart city: A nexus for open innovation? Intelligent Buildings International, 3(3), 153–171.CrossRefGoogle Scholar
  11. 11.
    De Haan, J., Vrancken, J. L. M., & Lukszo, Z. (2011). Why is intelligent technology alone not an intelligent solution? Futures, 43(9), 970–978.CrossRefGoogle Scholar
  12. 12.
    Denzin, N. K. (2006). Sociological methods: A sourcebook. Piscataway: Aldine Transactions.Google Scholar
  13. 13.
    Paulsen, D. J., & Robinson, M. B. (2009). Crime mapping and spatial aspects of crime. Boston: Allyn & Bacon.Google Scholar
  14. 14.
    Chamlin, M. B., & Cochran, J. K. (2004). An excursus on the population size-crime relationship. Western Criminology Review, 5(2), 119–130.Google Scholar
  15. 15.
    Andresen, M. A. (2007). Location quotients, ambient populations, and the spatial analysis of crime in Vancouver, Canada. Environment and Planning A, 39(10), 2423–2444.CrossRefGoogle Scholar
  16. 16.
    Andresen, M. A. (2006). Crime measures and the spatial analysis of criminal activity. British Journal of Criminology, 46(2), 258–285.CrossRefGoogle Scholar
  17. 17.
    Boggs, S. L. (1965). Urban crime patterns. American Sociological Review, 30(6), 899–908.CrossRefGoogle Scholar
  18. 18.
    Cohen, L. E., Kaufman, R. L., & Gottfredson, M. R. (1985). Risk-based crime statistics: A forecasting comparison for burglary and auto theft. Journal of Criminal Justice, 13(5), 445–457.CrossRefGoogle Scholar
  19. 19.
    Harries, K. D. (1991). Alternative denominators in conventional crime rates. In: P. Brantingham & P. Brantingham (Eds.), Environmental criminology (2nd ed., pp. 147–165). USA: Waveland Press.Google Scholar
  20. 20.
    Harries, K. D. (2006). Property crimes and violence in United States: an analysis of the influence of population density. International Journal of Criminal Justice Sciences, 1(2), 24–34.Google Scholar
  21. 21.
    Sparks, R. F. (1981). Measuring crime rates and opportunities for crime. In: R.G. Lehnen & W. G. Skogan (Eds.), The national crime survey: Working papers volume I: current and historical perspectives (Vol. 1, pp. 52–58). Washington, DC: U.S. Department of Justice.Google Scholar
  22. 22.
    Stipak, B. (1988). Alternatives to population-based crime rates. International Journal of Comparative and Applied Criminal Justice, 12(2), 247–260.CrossRefGoogle Scholar
  23. 23.
    Zhang, H., & Peterson, M. (2007). A spatial analysis of neighborhood crime in Omaha, Nebraska using alternative measures of crime rates. Internet Journal of Criminology. http://www.internetjournalofcriminology.com/Zhang%20Peterson%20-%20A%20SPATIAL%20ANALYSIS%20OF%20NEIGHBOURHOOD%20CRIME.pdf.
  24. 24.
    Thomas, W. I. (1966). Social disorganization and social reorganization. In M. Janovitz (Ed.), On social organization and social personality: Selected papers (pp. 3–11). Chicago: The University of Chicago Press.Google Scholar
  25. 25.
    Guerry, A. M. (1833). Essai sur la statistique morale de la France. Cochard.Google Scholar
  26. 26.
    Jacobs, J. (1961). The death and life of great american cities. New York: Vintage.Google Scholar
  27. 27.
    Newman, O. (1972). Defensible space: Crime prevention trough urban design. New York: MacMillan.Google Scholar
  28. 28.
    Jeffery, C. (1971). Crime prevention through environmental design. Thousand Oaks: Sage Publishing.Google Scholar
  29. 29.
    Clarke, R. V. G. (1997). Situational crime prevention. New York: Criminal Justice Press.Google Scholar
  30. 30.
    Clarke, R. V. (1983). Situational crime prevention: Its theoretical basis and practical scope. In M. Tonry & N. Morris (Eds.), Crime and justice: an annual review of research (pp. Vol. 14, pp. 225–256). Chicago: The Chicago University Press.Google Scholar
  31. 31.
    Clarke, R. V. G. (1992). Situational crime prevention: Successful case studies. New York: Harrow and Heston.Google Scholar
  32. 32.
    Clarke, R. V. (1995). Situational crime prevention. In M. Tonry & D. Farrington (Eds.), Building a safer society: Strategic approaches to crime prevention. Crime and justice: A review of research (Vol. 19, pp. 91–150). Chicago: The Chicago University Press.Google Scholar
  33. 33.
    Felson, M., & Clarke, R. V. (1998). Opportunity makes the thief: Practical theory for crime prevention. London: Police and Reducing Crime Unit; Research, Development and Statistics Directorate. Police Research Series Paper 98.Google Scholar
  34. 34.
    Hillier, B. (1988). Against enclosure. In N. Teymur, T. A. Markus, & T. Woolley (Eds.), Rehumanizing housing (pp. 63–88). London: Butterworths.Google Scholar
  35. 35.
    Hillier, B., & Hanson, J. (1984). The social logic of space. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  36. 36.
    Hillier, B., & Sahbaz, O. (2008). An evidence based approach to crime and urban design. In: R. Cooper, C. Boyko, G. Evans, & M. Adams (Eds.), Designing sustainable cities: Decision-making tools and resources for design (pp. 163–186). London: Wiley-Blackwell.Google Scholar
  37. 37.
    Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  38. 38.
    Beavon, D. J. K., Brantingham, P. L., & Brantingham, P. J. (1994). The influence of street networks on the patterning of property offences. In R. V. Clarke (Ed.), Crime prevention studies (Vol. 2). New York: Willow Tree Press.Google Scholar
  39. 39.
    Cohen, L., & Felson, M. (1979). Social change and crime rates. American Sociological Review, 44, 588–608.CrossRefGoogle Scholar
  40. 40.
    Eck, J. E., & Weisburd, D. (1995). Crime places in crime theory. In J. E. Eck & D. Weisburd (Eds.), Crime and place. Monsey.Google Scholar
  41. 41.
    Roncek, D. W. (1981). Dangerous places: Crime and residential environment. Social Forces, 60, 74–96.CrossRefGoogle Scholar
  42. 42.
    Rice, K. J., & Smith, W. R. (2002). Socioecological models of automotive theft: Integrating routine activity and social disorganization approaches. Journal of Research in Crime and Delinquency, 39, 304–336.CrossRefGoogle Scholar
  43. 43.
    Block, R., & Davis, S. (1996). The environs of rapid transit stations: A focus for street crime or just another risky place? In R. Clarke (Ed.), Preventing mass transit crime. New York: Criminal Justice Press.Google Scholar
  44. 44.
    Chih-Feng Shu, S. (2000). Housing layout and crime vulnerability. Urban Design International, 5(3–4), 177–188.CrossRefGoogle Scholar
  45. 45.
    Hillier, B. (2004). Can streets be made safe? Urban Design International, 9(1), 31–45.CrossRefGoogle Scholar
  46. 46.
    Sinkiene, J., Stankeviče, I., & Navickaite, K. (2012). Creating safer cities through urban planning and development. Public Policy and Administration, 11(3), 390–403.CrossRefGoogle Scholar
  47. 47.
    di Bella, E., Persico, L., & Corsi, M. (2011). A multivariate analysis of the space syntax out-put for the definition of strata in street security surveys. In DISEFIN Series of Economic Working Papers 5.Google Scholar
  48. 48.
    Piquero, A. R., & Weisburd, D. (Eds.). (2010). Handbook of quantitative criminology. Berlin: Springer.Google Scholar
  49. 49.
    Schaffers, H., Komninos, N., & Pallot, M. (2012). Smart cities as innovation ecosystems sustained by the future internet. In FIREBALL Project White Paper. EU (pp. 1–65).Google Scholar
  50. 50.
    Yovanof, G. S., & Hazapis, G. N. (2009). An architectural framework and enabling wireless technologies for digital cities & intelligent urban environments. Wireless Personal Communications, 49, 445–463.CrossRefGoogle Scholar
  51. 51.
    Farrington, D. P., Gill, M., Waples, S. J., & Argomaniz, J. (2007). The effects of closed-circuit television on crime: Meta-analysis of an English national quasi-experimental multi-site evaluation. Journal of Experimental Criminology, 3(1), 21–38.CrossRefGoogle Scholar
  52. 52.
    Ratcliffe, J. H., Taniguchi, T., & Taylor, R. B. (2009). The crime reduction effects of public CCTV cameras: A multi-method spatial approach. Justice Quarterly, 26(4), 746–770.CrossRefGoogle Scholar
  53. 53.
    Welsh, B. C., & Farrington, D. P. (2003). Effects of closed-circuit television on crime. Annals of the American Academy of Political and Social Science, 587, 110–135.CrossRefGoogle Scholar
  54. 54.
    Welsh, B. C., & Farrington, D. P. (2005). Evidence-based crime prevention: Conclusions and directions for a safer society. Canadian Journal of Criminology and Criminal Justice, 47(2), 337–354.CrossRefGoogle Scholar
  55. 55.
    Welsh, B. C., & Farrington, D. P. (2009). Public area CCTV and crime prevention: An updated systematic review and meta-analysis. Justice Quarterly, 26(4), 716–745.CrossRefGoogle Scholar
  56. 56.
    Welsh, B. C., Mudge, M. E., & Farrington, D. P. (2010). Reconceptualizing public area surveillance and crime prevention: Security guards, place managers and defensible space. Security Journal, 23(4), 299–319.CrossRefGoogle Scholar
  57. 57.
    Remagnino, P., Monekosso, D. N., & Jain, L. C. (2011). Innovations in defence support systems—3: Intelligent paradigms in security. Berlin: Springer.Google Scholar
  58. 58.
    van den Hengel, A., Hill, R., Wart, B., Cichowski, A., Detmold, H., Madden, C., Dick, A., & Bastian, J. (2009). Automatic camera placement for large scale surveillance networks. In Workshop on Applications of Computer Vision.Google Scholar
  59. 59.
    Zini, L., Cavallaro, A., & Odone, F. (2013). Action-based multi-camera synchronization. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 3(2), 165–174.CrossRefGoogle Scholar
  60. 60.
    Haritaoglu, I., Harwood, D., & Davis, L. (2000). W4: real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 809–830.CrossRefGoogle Scholar
  61. 61.
    Khan, B. S., & Shah, M. (2003). Consistent labeling of tracked objects in multiple cameras with overlapping fields of view. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10), 1355–1360.CrossRefGoogle Scholar
  62. 62.
    Taj, M., & Cavallaro, A. (2011). Distributed and decentralized multi-camera tracking: A survey. IEEE Signal Processing Magazine, 28(3), 46–58.CrossRefGoogle Scholar
  63. 63.
    Gheissari, N., Sebastian, T., & Hartley, R. (2006). Person re-identification using spatiotemporal appearance. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 1528–1535).Google Scholar
  64. 64.
    Zheng, W., Gong, S., & Xiang, T. (2011). Person re-identification by probabilistic relative distance comparison. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 649–656).Google Scholar
  65. 65.
    Stan, L., & Jain, A. (2011). Handbook of face recognition. Berlin: Springer.Google Scholar
  66. 66.
    Shachtman, N. (2006, January 25). The new security: cameras that never forget your face. The New York Times, Published.Google Scholar
  67. 67.
    Viola, P., & Jones, M. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154.CrossRefGoogle Scholar
  68. 68.
    Poppe, R. (2010). A survey on vision-based action recognition. Image and Vision Computing, 28(6), 976–990.CrossRefGoogle Scholar
  69. 69.
    Bird, N., Masoud, O., Papanikolopoulos, N., & Isaacs, A. (2005). Detection of loitering individuals in public transport areas. IEEE Transactions on Intelligent Transportation Systems, 6(2), 167–177.CrossRefGoogle Scholar
  70. 70.
    Krausz, B., & Bauckhage, C. (2011). Automatic detection of dangerous motion behavior in human crowds. In IEEE International Conference on Advanced Video and Signal-based Surveillance, AVSS.Google Scholar
  71. 71.
    Stauffer, C., Eric, W., & Grimson, L. (2000). Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 747–757.CrossRefGoogle Scholar
  72. 72.
    Atev, S., Masoud, O., & Papanikolopoulos, N. (2006). Learning traffic patterns at intersections by spectral clustering of motion trajectories. In IROS Intelligent Robots and Systems (pp. 4851–4856).Google Scholar
  73. 73.
    Morris, B. T., & Trivedi, M. M. (2008). A survey of vision-based trajectory learning and analysis for surveillance. IEEE Transactions on Circuits and Systems for Video Technology, 18(8), 1114–1127.CrossRefGoogle Scholar
  74. 74.
    Noceti, N., & Odone, F. (2012). Learning common behaviors from large sets of unlabeled temporal series. Image and Vision Computing, 30(11), 875–895.CrossRefGoogle Scholar
  75. 75.
    Estellés-Arolas, E., & González-Ladrón-De-Guevara, F. (2012). Towards an integrated crowdsourcing definition. Journal of Information Science, 38, 189–200.CrossRefGoogle Scholar
  76. 76.
    Cuff, D., Hansen, M., & Kang, J. (2008). Urban sensing: Out of the woods. In Communications of the ACM, Vol. 51.Google Scholar
  77. 77.
    Ganti, R. K., Ye, F., & Lei, H. (2011). Mobile crowdsensing: Current state and future challenges. In IEEE Communications Magazine (pp. 32–39).Google Scholar
  78. 78.
    Cardone, G., & Foschini, L. (2013). Fostering participation in smart cities: A geo-social crowdsensing platform. IEEE Communications Magazine, 51(6), 112–119.CrossRefGoogle Scholar
  79. 79.
    Coric, V. & Gruteser, M. (2013). Crowdsensing maps of on-street parking spaces. In 2013 IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 115–122).Google Scholar
  80. 80.
    Ghose, A., Bhaumik, C., & Chakravarty, T. (2013). BlueEye: A system for proximity detection using bluetooth on mobile phones. In Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication (pp. 1135–1142).Google Scholar
  81. 81.
    Heipke, C. (2010). Crowdsourcing geospatial data. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 550–557.CrossRefGoogle Scholar
  82. 82.
    Kumagai, J., & Cherry, S. (2004). Sensors and sensibility. IEEE Spectrum, 41(7), 22–28.CrossRefGoogle Scholar
  83. 83.
    The Constitution Project. (2006). Guidelines for public video surveillance, a guide to protecting communities and preserving civil liberties.Google Scholar
  84. 84.
    Newton, E. M., Sweeney, L., & Malin, B. (2005). Preserving privacy by de-identifying face images. IEEE Transactions on Knowledge and Data Engineering, 17(2), 232–243.CrossRefGoogle Scholar
  85. 85.
    Cavallaro, A. (2004). Adding privacy constraints to video-based applications. In European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology.Google Scholar
  86. 86.
    Dufour, J. Y. (Ed.) (2012). Intelligent video surveillance systems. London: Wiley.Google Scholar
  87. 87.
    Block, C. (1998). The geoArchive: An information foundation for community policing. In Crime Mapping and Crime Prevention (pp. 27–81).Google Scholar
  88. 88.
    Buslik, M., & Maltz, M. (1998). Power to the people: Mapping and information sharing in the Chicago Police Department. In Crime Mapping and Crime Prevention, Crime Prevention Studies (Vol. 8).Google Scholar
  89. 89.
    Cohen, J., Gorr, W., & Olligschlaeger, A. (2007). Leading indicators and spatial interactions: A crime forecasting model for proactive police deployment. Geographical Analysis, 39(1), 105–127.CrossRefGoogle Scholar
  90. 90.
    Cohen, J., & Gorr, W. (2005). Development of crime forecasting and mapping systems for use by police. Pittsburgh: H. John Heinz III School of Public Policy and Management, Carnegie Mellon University.Google Scholar
  91. 91.
    Cocx, T. K., Kosters, W. A., & Laros, J. F. J. (2008). An Early Warning System for the Prediction of Criminal Careers. MICAI 2008: Advances in Artificial Intelligence (pp. 77–89). Berlin, Heidelberg: Springer.Google Scholar
  92. 92.
    Andersen, J. J. (2013). Assess the urban surveillance infrastructure: Develop a framework. In IBM Development (pp. 1–11).Google Scholar
  93. 93.
    Rezaei, A., Rossi, B., Sillitti, A., & Succi, G. (2012). Knowledge extraction from events flows. In G. Anastasi, E. Bellini, E. Di Nitto, C. Ghezzi, L. Tanca, & E. Zimeo (Eds.), Methodologies and technologies for networked enterprises. Berlin: Springer.Google Scholar
  94. 94.
    Kimball, R., & Ross, M. (2013). The data warehouse toolkit: The definitive guide to dimensional modeling. London: Wiley.Google Scholar
  95. 95.
  96. 96.
    Predonzani, P., Sillitti, A., & Vernazza, T. (2001). Components and data-flow applied to the integration of web services. In IEEE Conference on Industrial Electronics Society (Vol. 3, pp. 2204–2207).Google Scholar
  97. 97.
  98. 98.
    Scotto, M., Sillitti, A., Vernazza, T., & Succi, G. (2001). Managing web-based information. In 5th International Conference on Enterprise Information Systems (Vol. 1, pp. 575–578).Google Scholar
  99. 99.
    Sillitti, A., Scotto, M., Succi, G., & Vernazza, T. (2003). News miner: A tool for information retrieval. In 7th International Conference on Intelligent Engineering Systems.Google Scholar
  100. 100.
  101. 101.
    Moore, M. H., & Aa, Braga. (2003). Measuring and improving police performance: The lessons of Compstat and its progeny. Policing: An International Journal of Police Strategies & Management, 26, 439–453.Google Scholar
  102. 102.
    Weisburd, D., Mastrofski, S. D., Greenspan, R., & Willis, J. J. (2004). The growth of compstat in American policing. Washington, DC: Police Foundation.Google Scholar
  103. 103.
    Chainey, S., & Ratcliffe, J. (2008). GIS and crime mapping. London: Wiley.Google Scholar
  104. 104.
    Harris, K. D. (1999). Mapping crime: principles and practice. Washington, DC: U.S. Department of Justice.Google Scholar
  105. 105.
    Kennedy, L. W., Caplan, J. M., & Piza, E. (2010). Risk clusters, hotspots, and spatial intelligence: Risk terrain modeling as an algorithm for police resource allocation strategies. Journal of Quantitate Criminology, 27, 339–362.CrossRefGoogle Scholar
  106. 106.
    Groff, E., Fleury, J., & Stoe, D. (2001). Strategic approaches to community safety initiative (SACSI): Enhancing the analytic capacity of a local problem-solving effort. Washington, DC: National Institute of Justice.Google Scholar
  107. 107.
    Pattavina, A., Pierce, G., & Saiz, A. (2002). Urban neighborhood information systems: crime prevention and control applications. Journal of Urban Technology, 9, 37–41.CrossRefGoogle Scholar
  108. 108.
    Roehl, J., Rosenbaum, D. P., Costello, S. K., Coldren, J. R, Jr, Schuck, A. M., Kunard, L., et al. (2008). Brief paving the way for project safe neighborhoods: SACSI in 10 US cities. Washington, DC: U.S Department of Justice.Google Scholar
  109. 109.
    Boba, R. (2009). Crime analysis with crime mapping. Beverley Hills: Sage Publications.Google Scholar
  110. 110.
    Monahan, T., & Mokos, T. J. (2013). Crowdsourcing urban surveillance: The development of homeland security markets for environmental sensor networks. Geoforum, 49, 279–288.CrossRefGoogle Scholar
  111. 111.
    Lee, J., & Yu, C. (2010). The development of Urban Crime Simulator. In Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & ApplicationCOM.Geo’10 (p. 1). New York, USA: ACM Press.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Enrico di Bella
    • 1
    Email author
  • Francesca Odone
    • 2
  • Matteo Corsi
    • 1
  • Alberto Sillitti
    • 3
  • Ruth Breu
    • 4
  1. 1.Department of Economics and Business StudiesUniversity of GenoaGenoaItaly
  2. 2.Department of Informatics Bioengineering Robotics and Systems EngineeringUniversity of GenoaGenoaItaly
  3. 3.Center for Applied Software EngineeringFree University of BozenBolzanoItaly
  4. 4.Institut für InformatikUniversity of InnsbruckInnsbruckAustria

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