Considerations about quality in model-driven engineering

The final publication is available at Springer via http://dx.doi.org/10.1007/s11219-016-9350-6The virtue of quality is not itself a subject; it depends on a subject. In the software engineering field, quality means good software products that meet customer expectations, constraints, and requirements. Despite the numerous approaches, methods, descriptive models, and tools, that have been developed, a level of consensus has been reached by software practitioners. However, in the model-driven engineering (MDE) field, which has emerged from software engineering paradigms, quality continues to be a great challenge since the subject is not fully defined. The use of models alone is not enough to manage all of the quality issues at the modeling language level. In this work, we present the current state and some relevant considerations regarding quality in MDE, by identifying current categories in quality conception and by highlighting quality issues in real applications of the model-driven initiatives. We identified 16 categories in the definition of quality in MDE. From this identification, by applying an adaptive sampling approach, we discovered the five most influential authors for the works that propose definitions of quality. These include (in order): the OMG standards (e.g., MDA, UML, MOF, OCL, SysML), the ISO standards for software quality models (e.g., 9126 and 25,000), Krogstie, Lindland, and Moody. We also discovered families of works about quality, i.e., works that belong to the same author or topic. Seventy-three works were found with evidence of the mismatch between the academic/research field of quality evaluation of modeling languages and actual MDE practice in industry. We demonstrate that this field does not currently solve quality issues reported in industrial scenarios. The evidence of the mismatch was grouped in eight categories, four for academic/research evidence and four for industrial reports. These categories were detected based on the scope proposed in each one of the academic/research works and from the questions and issues raised by real practitioners. We then proposed a scenario to illustrate quality issues in a real information system project in which multiple modeling languages were used. For the evaluation of the quality of this MDE scenario, we chose one of the most cited and influential quality frameworks; it was detected from the information obtained in the identification of the categories about quality definition for MDE. We demonstrated that the selected framework falls short in addressing the quality issues. Finally, based on the findings, we derive eight challenges for quality evaluation in MDE projects that current quality initiatives do not address sufficiently.F.G, would like to thank COLCIENCIAS (Colombia) for funding this work through the Colciencias Grant call 512-2010. This work has been supported by the Gene-ralitat Valenciana Project IDEO (PROMETEOII/2014/039), the European Commission FP7 Project CaaS (611351), and ERDF structural funds.Giraldo-Velásquez, FD.; España Cubillo, S.; Pastor López, O.; Giraldo, WJ. (2016). Considerations about quality in model-driven engineering. Software Quality Journal. 1-66. https://doi.org/10.1007/s11219-016-9350-6S166(1985). Iso information processing—documentation symbols and conventions for data, program and system flowcharts, program network charts and system resources charts. ISO 5807:1985(E) (pp. 1–25).(2011). Iso/iec/ieee systems and software engineering – architecture description. ISO/IEC/IEEE 42010:2011(E) (Revision of ISO/IEC 42010:2007 and IEEE Std 1471-2000) (pp. 1–46).Abran, A., Moore, J.W., Bourque, P., Dupuis, R., & Tripp, L.L. (2013). Guide to the Software Engineering Body of Knowledge (SWEBOK) version 3 public review. IEEE. ISO Technical Report ISO/IEC TR 19759.Agner, L.T.W., Soares, I.W., Stadzisz, P.C., & Simão, J.M. (2013). A brazilian survey on {UML} and model-driven practices for embedded software development. Journal of Systems and Software, 86(4), 997–1005. {SI} : Software Engineering in Brazil: Retrospective and Prospective Views.Amstel, M.F.V. (2010). The right tool for the right job: assessing model transformation quality. pages 69–74. Affiliation: Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, Netherlands. Cited By (since 1996):1.Aranda, J., Damian, D., & Borici, A. (2012). Transition to model-driven engineering: what is revolutionary, what remains the same?. In Proceedings of the 15th international conference on model driven engineering languages and systems, MODELS’12 (pp. 692–708). Berlin, Heidelberg: Springer.Arendt, T., & Taentzer, G. (2013). A tool environment for quality assurance based on the eclipse modeling framework. Automated Software Engineering, 20(2), 141–184.Atkinson, C., Bunse, C., & Wüst, J. (2003). Driving component-based software development through quality modelling, volume 2693. Cited By (since 1996):3.Baker, P., Loh, S., & Weil, F. (2005). Model-driven engineering in a large industrial context—motorola case study. In Briand, L., & Williams, C. (Eds.) Model Driven Engineering Languages and Systems, volume 3713 of Lecture Notes in Computer Science (pp. 476–491). Berlin, Heidelberg: Springer.Barišić, A., Amaral, V., Goulão, M., & Barroca, B. (2011). Quality in use of domain-specific languages: a case study. In Proceedings of the 3rd ACM SIGPLAN workshop on evaluation and usability of programming languages and tools, PLATEAU ’11 (pp. 65–72). New York: ACM.Becker, J., Bergener, P., Breuker, D., & Rackers, M. (2010). Evaluating the expressiveness of domain specific modeling languages using the bunge-wand-weber ontology. In 2010 43rd Hawaii international conference on system sciences (HICSS) (pp. 1–10).Bertrand Portier, L.A. (2009). Model driven development misperceptions and challenges.Bézivin, J., & Kurtev, I. (2005). Model-based technology integration with the technical space concept. In Proceedings of the Metainformatics Symposium: Springer.Brambilla, M. (2016). How mature is of model-driven engineering as an engineering discipline @ONLINE.Brambilla, M., & Fraternali, P. (2014). Large-scale model-driven engineering of web user interaction: The webml and webratio experience. Science of Computer Programming, 89 Part B(0), 71 – 87. Special issue on Success Stories in Model Driven Engineering.Brown, A. (2009). Simple and practical model driven architecture (mda) @ONLINE.Bruel, J.-M., Combemale, B., Ober, I., & Raynal, H. (2015). Mde in practice for computational science. Procedia Computer Science, 51, 660–669.Budgen, D., Burn, A.J., Brereton, O.P., Kitchenham, B.A., & Pretorius, R. (2011). Empirical evidence about the uml: a systematic literature review. Software: Practice and Experience, 41(4), 363–392.Burden, H., Heldal, R., & Whittle, J. (2014). Comparing and contrasting model-driven engineering at three large companies. In Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM ’14 (pp. 14:1–14:10). New York: ACM.Cabot, J. Has mda been abandoned (by the omg)?Cabot, J. (2009). Modeling will be commonplace in three years time @ONLINE.Cachero, C., Poels, G., Calero, C., & Marhuenda, Y. (2007). Towards a Quality-Aware Engineering Process for the Development of Web Applications. Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/462, Ghent University, Faculty of Economics and Business Administration.Challenger, M., Kardas, G., & Tekinerdogan, B. (2015). A systematic approach to evaluating domain-specific modeling language environments for multi-agent systems. Software Quality Journal, 1–41.Chaudron, M.V., Heijstek, W., & Nugroho, A. (2012). How effective is uml modeling? Software & Systems Modeling, 11(4), 571–580. J2: Softw Syst Model.Chenouard, R., Granvilliers, L., & Soto, R. (2008). Model-driven constraint programming. pages 236–246. Affiliation: CNRS, LINA, Universit de Nantes, France; Affiliation: Pontificia Universidad Catlica de, Valparaiso, Chile. Cited By (since 1996):8.Clark, T., & Muller, P.-A. (2012). Exploiting model driven technology: a tale of two startups. Software and Systems Modeling, 11(4), 481–493.Corneliussen, L. (2008). What do you think of model-driven software development?Costal, D., Gómez, C., & Guizzardi, G. (2011). Formal semantics and ontological analysis for understanding subsetting, specialization and redefinition of associations in uml. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6998 LNCS:189–203. cited By (since 1996)3.Cruz-Lemus, J.A., Maes, A., Género, M., Poels, G., & Piattini, M. (2010). The impact of structural complexity on the understandability of uml statechart diagrams. Information Sciences, 180(11), 2209–2220. Cited By (since 1996):14.Cuadrado, J.S., Izquierdo, J.L.C., & Molina, J.G. (2014). Applying model-driven engineering in small software enterprises. Science of Computer Programming, 89 Part B(0), 176 – 198. Special issue on Success Stories in Model Driven Engineering.Da Silva, A.R. (2015). Model-driven engineering: a survey supported by the unified conceptual model. Computer Languages Systems and Structures, 43, 139–155.Da Silva Teixeira, D.G.M., Quirino, G.K., Gailly, F., De Almeida Falbo, R., Guizzardi, G., & Perini Barcellos, M. (2016). PoN-S: a Systematic Approach for Applying the Physics of Notation (PoN), (pp. 432–447). Cham: Springer International Publishing.Davies, I., Green, P., Rosemann, M., Indulska, M., & Gallo, S. (2006). How do practitioners use conceptual modeling in practice? Data and Knowledge Engineering, 58(3), 358 – 380. Including the special issue : {ER} 2004ER 2004.Davies, J., Milward, D., Wang, C.-W., & Welch, J. (2015). Formal model-driven engineering of critical information systems. Science of Computer Programming, 103(0), 88 – 113. Selected papers from the First International Workshop on Formal Techniques for Safety-Critical Systems (FTSCS 2012).De Oca, I.M.-M., Snoeck, M., Reijers, H.A., & Rodríguez-Morffi, A. (2015). A systematic literature review of studies on business process modeling quality. Information and Software Technology, 58, 187–205.DenHaan, J. (2009). 8 reasons why model driven development is dangerous @ONLINE.DenHaan, J. (2010). Model driven engineering vs the commando pattern @ONLINE.DenHaan, J. (2011a). Why aren’t we all doing model driven development yet @ONLINE.DenHaan, J. (2011b). Why there is no future model driven development @ONLINE.Di Ruscio, D., Iovino, L., & Pierantonio, A. (2013). Managing the coupled evolution of metamodels and textual concrete syntax specifications. cited By (since 1996)0.Dijkman, R.M., Dumas, M., & Ouyang, C. (2008). Semantics and analysis of business process models in {BPMN}. Information and Software Technology, 50(12), 1281–1294.Domínguez-Mayo, F.J., Escalona, M.J., Mejías, M., Ramos, I., & Fernández, L. (2011). A framework for the quality evaluation of mdwe methodologies and information technology infrastructures. International Journal of Human Capital and Information Technology Professionals, 2(4), 11–22.Domínguez-Mayo, F.J., Escalona, M.J., Mejías, M., & Torres, A.H. (2010). A quality model in a quality evaluation framework for mdwe methodologies. pages 495–506. Affiliation: Departamento de Lenguajes y Sistemas Informíticos, University of Seville, Seville, Spain., Cited By (since 1996):1.Dubray, J.-J. (2011). Why did mde miss the boat?.Escalona, M.J., Gutiérrez, J.J., Pérez-Pérez, M., Molina, A., Domínguez-Mayo, E., & Domínguez-Mayo, F.J. (2011). Measuring the Quality of Model-Driven Projects with NDT-Quality, (pp. 307–317). New York: Springer.Espinilla, M., Domínguez-Mayo, F.J., Escalona, M.J., Mejías, M., Ross, M., & Staples, G. (2011). A Method Based on AHP to Define the Quality Model of QuEF (Vol. 123, pp. 685–694). Berlin, Heidelberg: Springer.Fabra, J., Castro, V.D., Álvarez, P., & Marcos, E. (2012). Automatic execution of business process models: exploiting the benefits of model-driven engineering approaches. Journal of Systems and Software, 85(3), 607–625. Novel approaches in the design and implementation of systems/software architecture.Falkenberg, E.D., Hesse, W., Lindgreen, P., Nilsson, B.E., Oei, J.L.H., Rolland, C., Stamper, R.K., Assche, F.J.M.V., Verrijn-Stuart, A.A., & Voss, K. (1996). Frisco: a framework of information system concepts. Technical report, The IFIP WG 8. 1 Task Group FRISCO.Fettke, P., Houy, C., Vella, A.-L., & Loos, P. (2012). Towards the Reconstruction and Evaluation of Conceptual Model Quality Discourses – Methodical Framework and Application in the Context of Model Understandability, volume 113 of Lecture Notes in Business Information Processing, chapter 28, pages 406–421, Springer, Berlin, Heidelberg.Finnie, S. (2015). Modeling community: Are we missing something?Fournier, C. (2008). Is uml [email protected], R., & Rumpe, B. (2007). Model-driven development of complex software: a research roadmap. In Future of Software Engineering, 2007, FOSE ’07 (pp. 37–54).Gallego, M., Giraldo, F.D., & Hitpass, B. (2015). Adapting the pbec-otss software selection approach for bpm suites: an application case. In 2015 34th International Conference of the Chilean Computer Science Society (SCCC) (pp. 1–10).Galvão, I., & Goknil, A. (2007). Survey of traceability approaches in model-driven engineering. cited By (since 1996)22.Giraldo, F., España, S., Giraldo, W., & Pastor, O. (2015). Modelling language quality evaluation in model-driven information systems engineering: a roadmap. In 2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS) (pp. 64–69).Giraldo, F., España, S., & Pastor, O. (2014). Analysing the concept of quality in model-driven engineering literature: a systematic review. In 2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS) (pp. 1–12).Giraldo, F.D., España, S., & Pastor, O. (2016). Evidences of the mismatch between industry and academy on modelling language quality evaluation. arXiv: 1606.02025 .González, C., & Cabot, J. (2014). Formal verification of static software models in mde: a systematic review. Information and Software Technology, 56(8), 821–838. cited By (since 1996)0.González, C.A., Büttner, F., Clarisó, R., & Cabot, J. (2012). Emftocsp: a tool for the lightweight verification of emf models. pages 44–50. Affiliation: cole des Mines de Nantes, INRIA, LINA, Nantes, France; Affiliation: Universitat Oberta de Catalunya, Barcelona, Spain. Cited By (since 1996):1.Gorschek, T., Tempero, E., & Angelis, L. (2014). On the use of software design models in software development practice: an empirical investigation. Journal of Systems and Software, 95(0), 176– 193.Goulão, M., Amaral, V., & Mernik, M. (2016). Quality in model-driven engineering: a tertiary study. Software Quality Journal, 1–33.Grobshtein, Y., & Dori, D. (2011). Generating sysml views from an opm model: design and evaluation. Systems Engineering, 14(3), 327–340.Haan, J.d. (2008). 8 reasons why model-driven approaches (will) fail.Harel, D., & Rumpe, B. (2000). Modeling languages: Syntax, semantics and all that stuff, part i: The basic stuff, Israel. Technical report Jerusalem Israel.Harel, D., & Rumpe, B. (2004). Meaningful modeling: what’s the semantics of semantics? Computer, 37(10), 64–72.Hebig, R., & Bendraou, R. (2014). On the need to study the impact of model driven engineering on software processes. In Proceedings of the 2014 International Conference on Software and System Process, ICSSP 2014 (pp. 164–168). New York: ACM.Heidari, F., & Loucopoulos, P. (2014). Quality evaluation framework (qef): modeling and evaluating quality of business processes. International Journal of Accounting Information Systems, 15(3), 193–223. Business Process Modeling.Heymans, P., Schobbens, P.Y., Trigaux, J.C., Bontemps, Y., Matulevicius, R., & Classen, A. (2008). Evaluating formal properties of feature diagram languages. Software, IET, 2(3), 281–302. ID 2.Hindawi, M., Morel, L., Aubry, R., & Sourrouille, J.-L. (2009). Description and Implementation of a UML Style Guide (Vol. 5421, pp. 291–302). Berlin: Springer.Hoang, D. (2012). Current limitations of mdd and its implications @ONLINE.Hodges, W. (2013). Model theory Zalta, E.N. (Ed.) The Stanford Encyclopedia of Philosophy. Fall 2013 edition.Hutchinson, J., Rouncefield, M., & Whittle, J. (2011a). Model-driven engineering practices in industry. In Proceedings of the 33rd International Conference on Software Engineering, ICSE’11 (pp. 633–642). New York: ACM.Hutchinson, J., Whittle, J., & Rouncefield, M. (2014). Model-driven engineering practices in industry: social, organizational and managerial factors that lead to success or failure. Science of Computer Programming, 89 Part B(0), 144–161. Special issue on Success Stories in Model Driven Engineering.Hutchinson, J., Whittle, J., Rouncefield, M., & Kristoffersen, S. (2011b). Empirical assessment of mde in industry. In Proceedings of the 33rd International Conference on Software Engineering, ICSE’11 (pp. 471–480). New York: ACM.Igarza, I.M.H., Boada, D.H.G., & Valdés, A.P. (2012). Una introducción al desarrollo de software dirigido por modelos. Serie Científica, 5(3).ISO/IEC (2001). ISO/IEC 9126. Software engineering—Product quality. ISO/IEC.Izurieta, C., Rojas, G., & Griffith, I. (2015). Preemptive management of model driven technical debt for improving software quality. In Proceedings of the 11th International ACM SIGSOFT Conference on Quality of Software Architectures, QoSA’15 (pp. 31–36). New York: ACM.Jalali, S., & Wohlin, C. (2012). Systematic literature studies: Database searches vs. backward snowballing. In Proceedings of the ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM’12 (pp. 29–38). New York: ACM.Kahraman, G., & Bilgen, S. (2013). A framework for qualitative assessment of domain-specific languages. Software & Systems Modeling, 1–22.Kessentini, M., Langer, P., & Wimmer, M. (2013). Searching models, modeling search: On the synergies of sbse and mde (pp. 51–54).Kitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. Technical Report EBSE 2007-001, Keele University and Durham University Joint Report.Kitchenham, B., Pfleeger, S., Pickard, L., Jones, P., Hoaglin, D., El Emam, K., & Rosenberg, J. (2002). Preliminary guidelines for empirical research in software engineering. IEEE Transactions on Software Engineering, 28(8), 721–734.Klinke, M. (2008). Do you use mda/mdd/mdsd, any kind of model-driven approach? Will it be the future?Köhnlein, J. (2013). Eclipse diagram editors from a user’s perspective.Kolovos, D.S., Paige, R.F., & Polack, F.A. (2008). The grand challenge of scalability for model driven engineering. In Models in Software Engineering (pp. 48–53): Springer.Kolovos, D.S., Rose, L.M., Matragkas, N., Paige, R.F., Guerra, E., Cuadrado, J.S., De Lara, J., Ráth, I., Varró, D., Tisi, M., & Cabot, J. (2013). A research roadmap towards achieving scalability in model driven engineering. In Proceedings of the Workshop on Scalability in Model Driven Engineering, BigMDE’13 (pp. 2:1–2:10). New York: ACM.Krill, P. (2016). Uml to be ejected from microsoft visual studio (infoworld).Krogstie, J. (2012a). Model-based development and evolution of information systems: a quality approach, Springer Publishing Company, Incorporated.Krogstie, J. (2012b). Quality of modelling languages, (pp. 249–280). London: Springer.Krogstie, J. (2012c). Quality of models, (pp. 205–247). London: Springer.Krogstie, J. (2012d). Specialisations of SEQUAL, (pp. 281–326). London: Springer.Krogstie, J., Lindland, O.I., & Sindre, G. (1995). Defining quality aspects for conceptual models. In Proceedings of the IFIP International Working Conference on Information System Concepts: Towards a Consolidation of Views (pp. 216–231). London: Chapman & Hall, Ltd.Kruchten, P. (2000). The rational unified process: an introduction, 2nd edn. Boston: Addison-Wesley Longman Publishing Co., Inc.Kruchten, P., Nord, R., & Ozkaya, I. (2012). Technical debt: from metaphor to theory and practice. Software, IEEE, 29(6), 18–21.Kulkarni, V., Reddy, S., & Rajbhoj, A. (2010). Scaling up model driven engineering – experience and lessons learnt. In Petriu, D., Rouquette, N., & Haugen, y. (Eds.) Model Driven Engineering Languages and Systems, volume 6395 of Lecture Notes in Computer Science (pp. 331–345). Berlin, Heidelberg: Springer.Laguna, M.A., & Marqués, J.M. (2010). Uml support for designing software product lines: the package merge mechanism, 16(17), 2313–2332.Lange, C. (2007a). Model size matters. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4364 LNCS:211–216. cited By (since 1996)1.Lange, C., & Chaudron, M. (2005). Managing Model Quality in UML-Based Software Development. In 13th IEEE International Workshop on Technology and Engineering Practice, 2005 (pp. 7–16).Lange, C., Chaudron, M.R.V., Muskens, J., Somers, L.J., & Dortmans, H.M. (2003). An empirical investigation in quantifying inconsistency and incompleteness of uml designs. In Incompleteness of UML Designs, Proceedings Workshop on Consistency Problems in UML-based Software Development, 6th International Conference on Unified Modeling Language, UML, 2003.Lange, C., DuBois, B., Chaudron, M., & Demeyer, S. (2006). An experimental investigation of uml modeling conventions. In Nierstrasz, O., Whittle, J., Harel, D., & Reggio, G. (Eds.) Model Driven Engineering Languages and Systems, volume 4199 of Lecture Notes in Computer Science (pp. 27–41). Berlin, Heidelberg: Springer.Lange, C.F.J., & Chaudron, M.R.V. (2006). Effe

Semi-automatic assessment of unrestrained Java code: a Library, a DSL, and a workbench to assess exams and exercises

© ACM 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in http://dx.doi.org/10.1145/2729094.2742615Automated marking of multiple-choice exams is of great interest in university courses with a large number of students. For this reason, it has been systematically implanted in almost all universities. Automatic assessment of source code is however less extended. There are several reasons for that. One reason is that almost all existing systems are based on output comparison with a gold standard. If the output is the expected, the code is correct. Otherwise, it is reported as wrong, even if there is only one typo in the code. Moreover, why it is wrong remains a mystery. In general, assessment tools treat the code as a black box, and they only assess the externally observable behavior. In this work we introduce a new code assessment method that also verifies properties of the code, thus allowing to mark the code even if it is only partially correct. We also report about the use of this system in a real university context, showing that the system automatically assesses around 50% of the work.This work has been partially supported by the EU (FEDER) and the Spanish Ministerio de Economíay Competitividad (Secretaría de Estado de Investigación, Desarrollo e Innovación) under grant TIN2013-44742-C4-1-R and by the Generalitat Valenciana under grant PROMETEOII2015/013. David Insa was partially supported by the Spanish Ministerio de Educación under FPU grant AP2010-4415.Insa Cabrera, D.; Silva, J. (2015). Semi-automatic assessment of unrestrained Java code: a Library, a DSL, and a workbench to assess exams and exercises. ACM. https://doi.org/10.1145/2729094.2742615SK. A Rahman and M. Jan Nordin. A review on the static analysis approach in the automated programming assessment systems. In National Conference on Programming 07, 2007.K. Ala-Mutka. A survey of automated assessment approaches for programming assignments. In Computer Science Education, volume 15, pages 83--102, 2005.C. Beierle, M. Kula, and M. Widera. Automatic analysis of programming assignments. In Proc. der 1. E-Learning Fachtagung Informatik (DeLFI '03), volume P-37, pages 144--153, 2003.J. Biggs and C. Tang. Teaching for Quality Learning at University : What the Student Does (3rd Edition). In Open University Press, 2007.P. Denny, A. Luxton-Reilly, E. Tempero, and J. Hendrickx. CodeWrite: Supporting student-driven practice of java. In Proceedings of the 42nd ACM technical symposium on Computer science education, pages 09--12, 2011.R. Hendriks. Automatic exam correction. 2012.P. Ihantola, T. Ahoniemi, V. Karavirta, and O. Seppala. Review of recent systems for automatic assessment of programming assignments. In Proceedings of the 10th Koli Calling International Conference on Computing Education Research, pages 86--93, 2010.H. Kitaya and U. Inoue. An online automated scoring system for Java programming assignments. In International Journal of Information and Education Technology, volume 6, pages 275--279, 2014.M.-J. Laakso, T. Salakoski, A. Korhonen, and L. Malmi. Automatic assessment of exercises for algorithms and data structures - a case study with TRAKLA2. In Proceedings of Kolin Kolistelut/Koli Calling - Fourth Finnish/Baltic Sea Conference on Computer Science Education, pages 28--36, 2004.Y. Liang, Q. Liu, J. Xu, and D. Wang. The recent development of automated programming assessment. In Computational Intelligence and Software Engineering, pages 1--5, 2009.K. A. Naudé, J. H. Greyling, and D. Vogts. Marking student programs using graph similarity. In Computers & Education, volume 54, pages 545--561, 2010.A. Pears, S. Seidman, C. Eney, P. Kinnunen, and L. Malmi. Constructing a core literature for computing education research. In SIGCSE Bulletin, volume 37, pages 152--161, 2005.F. Prados, I. Boada, J. Soler, and J. Poch. Automatic generation and correction of technical exercices. In International Conference on Engineering and Computer Education (ICECE 2005), 2005.M. Supic, K. Brkic, T. Hrkac, Z. Mihajlovic, and Z. Kalafatic. Automatic recognition of handwritten corrections for multiple-choice exam answer sheets. In Information and Communication Technology, Electronics and Microelectronics (MIPRO), pages 1136--1141, 2014.S. Tung, T. Lin, and Y. Lin. An exercise management system for teaching programming. In Journal of Software, 2013.T. Wang, X. Su, Y. Wang, and P. Ma. Semantic similarity-based grading of student programs. In Information and Software Technology, volume 49, pages 99--107, 2007

A Practical Procedure to Integrate the First 1:500 Urban Map of Valencia into a Tile-Based Geospatial Information System

[EN] The use of geographic data from early maps is a common approach to understanding urban geography as well as to study the evolution of cities over time. The specific goal of this paper is to provide a means for the integration of the first 1:500 urban map of the city of Valencia (Spain) on a tile-based geospatial system. We developed a workflow consisting of three stages: the digitization of the original 421 map sheets, the transformation to the European Terrestrial Reference System of 1989 (ETRS89), and the conversion to a tile-based file format, where the second stage is clearly the most mathematically involved. The second stage actually consists of two steps, one transformation from the pixel reference system to the 1929 local reference system followed by a second transformation from the 1929 local to the ETRS89 system. The last stage comprises a map reprojection to adapt to tile-based geospatial standards. The paper describes a pilot study of one map sheet and results showed that the affine and bilinear transformations performed well in both transformations with average residuals under 6 and 3 cm respectively. The online viewer developed in this study shows that the derived tile-based map conforms to common standards and lines up well with other raster and vector datasets.Villar-Cano, M.; Jiménez-Martínez, MJ.; Marqués-Mateu, Á. (2019). A Practical Procedure to Integrate the First 1:500 Urban Map of Valencia into a Tile-Based Geospatial Information System. ISPRS International Journal of Geo-Information. 8(9). https://doi.org/10.3390/ijgi809037837889Bitelli, G., & Gatta, G. (2011). Digital Processing and 3D Modelling of an 18th Century Scenographic Map of Bologna. Advances in Cartography and GIScience. Volume 2, 129-146. doi:10.1007/978-3-642-19214-2_9Brovelli, M. A., Minghini, M., Giori, G., & Beretta, M. (2012). Web Geoservices and Ancient Cadastral Maps: The Web C.A.R.T.E. Project. Transactions in GIS, 16(2), 125-142. doi:10.1111/j.1467-9671.2012.01311.xBitelli, G., Cremonini, S., & Gatta, G. (2014). Cartographic heritage: Toward unconventional methods for quantitative analysis of pre-geodetic maps. Journal of Cultural Heritage, 15(2), 183-195. doi:10.1016/j.culher.2013.04.003Cardesín Díaz, J. M., & Araujo, J. M. (2016). Historic Urbanization Process in Spain (1746–2013). Journal of Urban History, 43(1), 33-52. doi:10.1177/0096144215583481Villar-Cano, M., Marqués-Mateu, Á., & Jiménez-Martínez, M. J. (2019). Triangulation network of 1929–1944 of the first 1:500 urban map of València. Survey Review, 52(373), 317-329. doi:10.1080/00396265.2018.1564599Chen, W., & Hill, C. (2005). Evaluation Procedure for Coordinate Transformation. Journal of Surveying Engineering, 131(2), 43-49. doi:10.1061/(asce)0733-9453(2005)131:2(43)ISO 19157:2013: Geographic Information—Data Qualityhttps://www.iso.org/standard/32575.htmlASPRS Positional Accuracy Standards for Digital Geospatial Datahttps://www.asprs.org/news-resources/asprs-positional-accuracy-standards-for-digital-geospatial-dataEven-Tzur, G. (2018). Coordinate transformation with variable number of parameters. Survey Review, 52(370), 62-68. doi:10.1080/00396265.2018.1517477Yuanxi, Y., & Tianhe, X. (2002). Combined method of datum transformation between different coordinate systems. Geo-spatial Information Science, 5(4), 5-9. doi:10.1007/bf02826467Lehmann, R. (2014). Transformation model selection by multiple hypotheses testing. Journal of Geodesy, 88(12), 1117-1130. doi:10.1007/s00190-014-0747-

On potential cognitive abilities in the machine kingdom

The final publication is available at Springer via http://dx.doi.org/10.1007/s11023-012-9299-6Animals, including humans, are usually judged on what they could become, rather than what they are. Many physical and cognitive abilities in the ‘animal kingdom’ are only acquired (to a given degree) when the subject reaches a certain stage of development, which can be accelerated or spoilt depending on how the environment, training or education is. The term ‘potential ability’ usually refers to how quick and likely the process of attaining the ability is. In principle, things should not be different for the ‘machine kingdom’. While machines can be characterised by a set of cognitive abilities, and measuring them is already a big challenge, known as ‘universal psychometrics’, a more informative, and yet more challenging, goal would be to also determine the potential cognitive abilities of a machine. In this paper we investigate the notion of potential cognitive ability for machines, focussing especially on universality and intelligence. We consider several machine characterisations (non-interactive and interactive) and give definitions for each case, considering permanent and temporal potentials. From these definitions, we analyse the relation between some potential abilities, we bring out the dependency on the environment distribution and we suggest some ideas about how potential abilities can be measured. Finally, we also analyse the potential of environments at different levels and briefly discuss whether machines should be designed to be intelligent or potentially intelligent.We thank the anonymous reviewers for their comments, which have helped to significantly improve this paper. This work was supported by the MEC-MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, the COST - European Cooperation in the field of Scientific and Technical Research IC0801 AT. Finally, we thank three pioneers ahead of their time(s). We thank Ray Solomonoff (1926-2009) and Chris Wallace (1933-2004) for all that they taught us, directly and indirectly. And, in his centenary year, we thank Alan Turing (1912-1954), with whom it perhaps all began.Hernández-Orallo, J.; Dowe, DL. (2013). On potential cognitive abilities in the machine kingdom. Minds and Machines. 23(2):179-210. https://doi.org/10.1007/s11023-012-9299-6S179210232Amari, S., Fujita, N., Shinomoto, S. (1992). Four types of learning curves. Neural Computation 4(4), 605–618.Aristotle (Translation, Introduction, and Commentary by Ross, W.D.) (1924). Aristotle’s Metaphysics. Oxford: Clarendon Press.Barmpalias, G. & Dowe, D. L. (2012). Universality probability of a prefix-free machine. Philosophical transactions of the Royal Society A [Mathematical, Physical and Engineering Sciences] (Phil Trans A), Theme Issue ‘The foundations of computation, physics and mentality: The Turing legacy’ compiled and edited by Barry Cooper and Samson Abramsky, 370, pp 3488–3511.Chaitin, G. J. (1966). On the length of programs for computing finite sequences. Journal of the Association for Computing Machinery, 13, 547–569.Chaitin, G. J. (1975). A theory of program size formally identical to information theory. Journal of the ACM (JACM), 22(3), 329–340.Dowe, D. L. (2008, September). Foreword re C. S. Wallace. Computer Journal, 51(5):523–560, Christopher Stewart WALLACE (1933–2004) memorial special issue.Dowe, D. L. (2011). MML, hybrid Bayesian network graphical models, statistical consistency, invariance and uniqueness. In: P. S. Bandyopadhyay, M. R. Forster (Eds), Handbook of the philosophy of science—Volume 7: Philosophy of statistics (pp. 901–982). Amsterdam: Elsevier.Dowe, D. L. & Hajek, A. R. (1997a). A computational extension to the turing test. Technical report #97/322, Dept Computer Science, Monash University, Melbourne, Australia, 9 pp, http://www.csse.monash.edu.au/publications/1997/tr-cs97-322-abs.html .Dowe, D. L. & Hajek, A. R. (1997b, September). A computational extension to the Turing Test. in Proceedings of the 4th conference of the Australasian Cognitive Science Society, University of Newcastle, NSW, Australia, 9 pp.Dowe, D. L. & Hajek, A. R. (1998, February). A non-behavioural, computational extension to the Turing Test. In: International conference on computational intelligence and multimedia applications (ICCIMA’98), Gippsland, Australia, pp 101–106.Dowe, D. L., Hernández-Orallo, J. (2012). IQ tests are not for machines, yet. Intelligence, 40(2), 77–81.Gallistel, C. R., Fairhurst, S., & Balsam, P. (2004). The learning curve: Implications of a quantitative analysis. In Proceedings of the National Academy of Sciences of the United States of America, 101(36), 13124–13131.Gardner, M. (1970). Mathematical games: The fantastic combinations of John Conway’s new solitaire game “life”. Scientific American, 223(4), 120–123.Goertzel, B. & Bugaj, S. V. (2009). AGI preschool: A framework for evaluating early-stage human-like AGIs. In Proceedings of the second international conference on artificial general intelligence (AGI-09), pp 31–36.Hernández-Orallo, J. (2000a). Beyond the Turing Test. Journal of Logic, Language & Information, 9(4), 447–466.Hernández-Orallo, J. (2000b). On the computational measurement of intelligence factors. In A. Meystel (Ed), Performance metrics for intelligent systems workshop (pp 1–8). Gaithersburg, MD: National Institute of Standards and Technology.Hernández-Orallo, J. (2010). On evaluating agent performance in a fixed period of time. In M. Hutter et al. (Eds.), Proceedings of 3rd international conference on artificial general intelligence (pp. 25–30). New York: Atlantis Press.Hernández-Orallo, J., & Dowe, D. L. (2010). Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence, 174(18), 1508–1539.Hernández-Orallo, J. & Dowe, D. L. (2011, April). Mammals, machines and mind games. Who’s the smartest?. The conversation, http://theconversation.edu.au/mammals-machines-and-mind-games-whos-the-smartest-566 .Hernández-Orallo J., Dowe D. L., España-Cubillo S., Hernández-Lloreda M. V., & Insa-Cabrera J. (2011). On more realistic environment distributions for defining, evaluating and developing intelligence. In: J. Schmidhuber, K. R. Thórisson, & M. Looks (Eds.), Artificial general intelligence 2011, volume 6830, LNAI series, pp. 82–91. New York: Springer.Hernández-Orallo, J., Dowe, D. L., & Hernández-Lloreda, M. V. (2012a, March). Measuring cognitive abilities of machines, humans and non-human animals in a unified way: towards universal psychometrics. Technical report 2012/267, Faculty of Information Technology, Clayton School of I.T., Monash University, Australia.Hernández-Orallo, J., Insa, J., Dowe, D. L., & Hibbard, B. (2012b). Turing tests with Turing machines. In A. Voronkov (Ed.), The Alan Turing centenary conference, Turing-100, Manchester, volume 10 of EPiC Series, pp 140–156.Hernández-Orallo, J., & Minaya-Collado, N. (1998). A formal definition of intelligence based on an intensional variant of Kolmogorov complexity. In Proceedings of the international symposium of engineering of intelligent systems (EIS’98) (pp 146–163). Switzerland: ICSC Press.Herrmann, E., Call, J., Hernández-Lloreda, M. V., Hare, B., & Tomasello, M. (2007). Humans have evolved specialized skills of social cognition: The cultural intelligence hypothesis. Science, 317(5843), 1360–1366.Herrmann, E., Hernández-Lloreda, M. V., Call, J., Hare, B., & Tomasello, M. (2010). The structure of individual differences in the cognitive abilities of children and chimpanzees. Psychological Science, 21(1), 102–110.Horn, J. L., & Cattell, R. B. (1966). Refinement and test of the theory of fluid and crystallized general intelligences. Journal of educational psychology, 57(5), 253.Hutter, M. (2005). Universal artificial intelligence: Sequential decisions based on algorithmic probability. New York: Springer.Insa-Cabrera, J., Dowe, D. L., España, S., Hernández-Lloreda, M. V., & Hernández-Orallo, J. (2011a). Comparing humans and AI agents. In AGI: 4th conference on artificial general intelligence—Lecture Notes in Artificial Intelligence (LNAI), volume 6830, pp 122–132. Springer, New York.Insa-Cabrera, J., Dowe, D. L., & Hernández-Orallo, J. (2011b). Evaluating a reinforcement learning algorithm with a general intelligence test. In CAEPIA—Lecture Notes in Artificial Intelligence (LNAI), volume 7023, pages 1–11. Springer, New York.Kearns, M. & Singh, S. (2002). Near-optimal reinforcement learning in polynomial time. Machine Learning, 49(2), 209–232.Kolmogorov, A. N. (1965). Three approaches to the quantitative definition of information. Problems of Information Transmission, 1, 4–7.Legg, S. (2008, June). Machine super intelligence. Department of Informatics, University of Lugano.Legg, S. & Hutter, M. (2007). Universal intelligence: A definition of machine intelligence. Minds and Machines, 17(4), 391–444.Legg, S., & Veness, J. (2012). An approximation of the universal intelligence measure. In Proceedings of Solomonoff 85th memorial conference. New York: Springer.Levin, L. A. (1973). Universal sequential search problems. Problems of Information Transmission, 9(3), 265–266.Li, M., Vitányi, P. (2008). An introduction to Kolmogorov complexity and its applications (3rd ed). New York: Springer.Little, V. L., & Bailey, K. G. (1972). Potential intelligence or intelligence test potential? A question of empirical validity. Journal of Consulting and Clinical Psychology, 39(1), 168.Mahoney, M. V. (1999). Text compression as a test for artificial intelligence. In Proceedings of the national conference on artificial intelligence, AAAI (pp. 486–502). New Jersey: Wiley.Mahrer, A. R. (1958). Potential intelligence: A learning theory approach to description and clinical implication. The Journal of General Psychology, 59(1), 59–71.Oppy, G., & Dowe, D. L. (2011). The Turing Test. In E. N. Zalta (Ed.), Stanford encyclopedia of philosophy. Stanford University. http://plato.stanford.edu/entries/turing-test/ .Orseau, L. & Ring, M. (2011). Self-modification and mortality in artificial agents. In AGI: 4th conference on artificial general intelligence—Lecture Notes in Artificial Intelligence (LNAI), volume 6830, pages 1–10. Springer, New York.Ring, M. & Orseau, L. (2011). Delusion, survival, and intelligent agents. In AGI: 4th conference on artificial general intelligence—Lecture Notes in Artificial Intelligence (LNAI), volume 6830, pp. 11–20. Springer, New York.Schaeffer, J., Burch, N., Bjornsson, Y., Kishimoto, A., Muller, M., Lake, R., et al. (2007). Checkers is solved. Science, 317(5844), 1518.Solomonoff, R. J. (1962). Training sequences for mechanized induction. In M. Yovits, G. Jacobi, & G. Goldsteins (Eds.), Self-Organizing Systems, 7, 425–434.Solomonoff, R. J. (1964). A formal theory of inductive inference. Information and Control, 7(1–22), 224–254.Solomonoff, R. J. (1967). Inductive inference research: Status, Spring 1967. RTB 154, Rockford Research, Inc., 140 1/2 Mt. Auburn St., Cambridge, Mass. 02138, July 1967.Solomonoff, R. J. (1978). Complexity-based induction systems: comparisons and convergence theorems. IEEE Transactions on Information Theory, 24(4), 422–432.Solomonoff, R. J. (1984). Perfect training sequences and the costs of corruption—A progress report on induction inference research. Oxbridge research.Solomonoff, R. J. (1985). The time scale of artificial intelligence: Reflections on social effects. Human Systems Management, 5, 149–153.Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge: The MIT press.Thorp, T. R., & Mahrer, A. R. (1959). Predicting potential intelligence. Journal of Clinical Psychology, 15(3), 286–288.Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433–460.Veness, J., Ng, K. S., Hutter, M., & Silver, D. (2011). A Monte Carlo AIXI approximation. Journal of Artificial Intelligence Research, JAIR, 40, 95–142.Wallace, C. S. (2005). Statistical and inductive inference by minimum message length. New York: Springer.Wallace, C. S., & Boulton, D. M. (1968). An information measure for classification. Computer Journal, 11, 185–194.Wallace, C. S., & Dowe, D. L. (1999a). Minimum message length and Kolmogorov complexity. Computer Journal 42(4), 270–283.Wallace, C. S., & Dowe, D. L. (1999b). Refinements of MDL and MML coding. Computer Journal, 42(4), 330–337.Woergoetter, F., & Porr, B. (2008). Reinforcement learning. Scholarpedia, 3(3), 1448.Zvonkin, A. K., & Levin, L. A. (1970). The complexity of finite objects and the development of the concepts of information and randomness by means of the theory of algorithms. Russian Mathematical Surveys, 25, 83–124

Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location

[EN] A large volume of the water produced for public supply is lost in the systems between sources and consumers. An important-in many cases the greatest-fraction of these losses are physical losses, mainly related to leaks and bursts in pipes and in consumer connections. Fast detection and location of bursts plays an important role in the design of operation strategies for water loss control, since this helps reduce the volume lost from the instant the event occurs until its effective repair (run time). The transient pressure signals caused by bursts contain important information about their location and magnitude, and stamp on any of these events a specific "hydraulic signature". The present work proposes and evaluates three methods to disaggregate transient signals, which are used afterwards to train artificial neural networks (ANNs) to identify burst locations and calculate the leaked flow. In addition, a clustering process is also used to group similar signals, and then train specific ANNs for each group, thus improving both the computational efficiency and the location accuracy. The proposed methods are applied to two real distribution networks, and the results show good accuracy in burst location and characterization.Manzi, D.; Brentan, BM.; Meirelles, G.; Izquierdo Sebastián, J.; Luvizotto Jr., E. (2019). Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location. Water. 11(11):1-13. https://doi.org/10.3390/w11112279S1131111Creaco, E., & Walski, T. (2017). Economic Analysis of Pressure Control for Leakage and Pipe Burst Reduction. Journal of Water Resources Planning and Management, 143(12), 04017074. doi:10.1061/(asce)wr.1943-5452.0000846Campisano, A., Creaco, E., & Modica, C. (2010). RTC of Valves for Leakage Reduction in Water Supply Networks. Journal of Water Resources Planning and Management, 136(1), 138-141. doi:10.1061/(asce)0733-9496(2010)136:1(138)Campisano, A., Modica, C., Reitano, S., Ugarelli, R., & Bagherian, S. (2016). Field-Oriented Methodology for Real-Time Pressure Control to Reduce Leakage in Water Distribution Networks. Journal of Water Resources Planning and Management, 142(12), 04016057. doi:10.1061/(asce)wr.1943-5452.0000697Vítkovský, J. P., Simpson, A. R., & Lambert, M. F. (2000). Leak Detection and Calibration Using Transients and Genetic Algorithms. Journal of Water Resources Planning and Management, 126(4), 262-265. doi:10.1061/(asce)0733-9496(2000)126:4(262)Pérez, R., Puig, V., Pascual, J., Quevedo, J., Landeros, E., & Peralta, A. (2011). Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks. Control Engineering Practice, 19(10), 1157-1167. doi:10.1016/j.conengprac.2011.06.004Jung, D., & Kim, J. (2017). Robust Meter Network for Water Distribution Pipe Burst Detection. Water, 9(11), 820. doi:10.3390/w9110820Colombo, A. F., Lee, P., & Karney, B. W. (2009). A selective literature review of transient-based leak detection methods. Journal of Hydro-environment Research, 2(4), 212-227. doi:10.1016/j.jher.2009.02.003Choi, D., Kim, S.-W., Choi, M.-A., & Geem, Z. (2016). Adaptive Kalman Filter Based on Adjustable Sampling Interval in Burst Detection for Water Distribution System. Water, 8(4), 142. doi:10.3390/w8040142Christodoulou, S. E., Kourti, E., & Agathokleous, A. (2016). Waterloss Detection in Water Distribution Networks using Wavelet Change-Point Detection. Water Resources Management, 31(3), 979-994. doi:10.1007/s11269-016-1558-5Guo, X., Yang, K., & Guo, Y. (2012). Leak detection in pipelines by exclusively frequency domain method. Science China Technological Sciences, 55(3), 743-752. doi:10.1007/s11431-011-4707-3Holloway, M. B., & Hanif Chaudhry, M. (1985). Stability and accuracy of waterhammer analysis. Advances in Water Resources, 8(3), 121-128. doi:10.1016/0309-1708(85)90052-1Sanz, G., Pérez, R., Kapelan, Z., & Savic, D. (2016). Leak Detection and Localization through Demand Components Calibration. Journal of Water Resources Planning and Management, 142(2), 04015057. doi:10.1061/(asce)wr.1943-5452.0000592Zhang, Q., Wu, Z. Y., Zhao, M., Qi, J., Huang, Y., & Zhao, H. (2016). Leakage Zone Identification in Large-Scale Water Distribution Systems Using Multiclass Support Vector Machines. Journal of Water Resources Planning and Management, 142(11), 04016042. doi:10.1061/(asce)wr.1943-5452.0000661Mounce, S. R., & Machell, J. (2006). Burst detection using hydraulic data from water distribution systems with artificial neural networks. Urban Water Journal, 3(1), 21-31. doi:10.1080/15730620600578538Covas, D., Ramos, H., & de Almeida, A. B. (2005). Standing Wave Difference Method for Leak Detection in Pipeline Systems. Journal of Hydraulic Engineering, 131(12), 1106-1116. doi:10.1061/(asce)0733-9429(2005)131:12(1106)Liggett, J. A., & Chen, L. (1994). Inverse Transient Analysis in Pipe Networks. Journal of Hydraulic Engineering, 120(8), 934-955. doi:10.1061/(asce)0733-9429(1994)120:8(934)Caputo, A. C., & Pelagagge, P. M. (2002). An inverse approach for piping networks monitoring. Journal of Loss Prevention in the Process Industries, 15(6), 497-505. doi:10.1016/s0950-4230(02)00036-0Van Zyl, J. E. (2014). Theoretical Modeling of Pressure and Leakage in Water Distribution Systems. Procedia Engineering, 89, 273-277. doi:10.1016/j.proeng.2014.11.187Izquierdo, J., & Iglesias, P. . (2004). Mathematical modelling of hydraulic transients in complex systems. Mathematical and Computer Modelling, 39(4-5), 529-540. doi:10.1016/s0895-7177(04)90524-9Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), 107-144. doi:10.1007/s10618-007-0064-zNavarrete-López, C., Herrera, M., Brentan, B., Luvizotto, E., & Izquierdo, J. (2019). Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework. Water, 11(2), 246. doi:10.3390/w11020246Meirelles, G., Manzi, D., Brentan, B., Goulart, T., & Luvizotto, E. (2017). Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks. Water Resources Management, 31(13), 4339-4351. doi:10.1007/s11269-017-1750-2Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1-4), 28-40. doi:10.1016/j.jhydrol.2011.06.013Brentan, B., Meirelles, G., Luvizotto, E., & Izquierdo, J. (2018). Hybrid SOM+ k -Means clustering to improve planning, operation and management in water distribution systems. Environmental Modelling & Software, 106, 77-88. doi:10.1016/j.envsoft.2018.02.013Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics - Theory and Methods, 3(1), 1-27. doi:10.1080/0361092740882710

Identifying and classifying attributes of packaging for customer satisfaction-A Kano Model Approach

[EN] The packaging industry in India is predicted to grow at 18% annually. In recent years Packaging becomes a potential marketing tool. The marketer should design the packaging of high quality from customer perspective.  As the research in the area of packaging is very few, study of quality attributes of Packaging is the need of the hour and inevitable. An empirical research was conducted by applying Kano Model. The researcher is interested to find out the perception of the customers on 22 quality attributes of packaging. 500 respondents which were selected randomly were asked about their experience of packing on everyday commodities through a well-structured questionnaire.  The classification of attribute as must-be quality, one-dimensional quality, attractive quality, indifferent quality and reverse quality was done by three methods. Marketer should make a note of it and prioritise the attributes for customer satisfaction.Dash, SK. (2021). Identifying and classifying attributes of packaging for customer satisfaction-A Kano Model Approach. International Journal of Production Management and Engineering. 9(1):57-64. https://doi.org/10.4995/ijpme.2021.13683OJS576491Bakhitar, A.,Hannan, A., Basit, A., Ahmad, J.(2015). Prioritization of value based services of software by using AHP and fuzzy KANO model. International Conference on Computational and Social Sciences, 8, 25- 27.Basfirinci, C., Mitra, A. (2015). A cross cultural investigation of airlines service quality through integration of Servqual and the Kano model. Journal of Air Transport Management, 42(1), 239-48. https://doi.org/10.1016/j.jairtraman.2014.11.005Berger, C., Blauth, R., Boger, D., Bolster, C., Burchill, G., DuMouchel, W., Pouliot, F., Richter, R., Rubinoff, A., Shen, D., Timko, M., Walden, D. (1993). Kano's methods for understanding customer-defined quality. The Center for Quality of Management Journal, 2(4), 2-36.Brown, G.H. (1950). Measuring consumer attitudes towards products. Journal of Marketing, 14(5), 691-98. https://doi.org/10.1177/002224295001400505Chaudha, A., Jain, R., Singh, A.R., Mishra, P.K. (2011). Integration of Kano's Model into Quality Function Deployment (QFD). Journal Advice Manufacture Technology, 53, 689-698. https://doi.org/10.1007/s00170-010-2867-0Cole, R.E. (2001). From continuous improvement to continuous innovation. Quality Management Journal, 8(4), 7-21. https://doi.org/10.1080/10686967.2001.11918977Dash, S.K. (2019). Application of Kano Model in Identifying Attributes. A Case Study on School Bus Services. International Journal of Management Studies, 6(1), 31-37. https://doi.org/10.18843/ijms/v6i1(3)/03Dziuba, S.T., Śron, B. (2014). FAM-FMC system as an alternative element of the software used in a grain and flour milling enterprise. Production Engineering Archives, 4(3),29-31. https://doi.org/10.30657/pea.2014.04.08Ernzer, M., Kopp, K.(2003). Application of KANO method to life cycle design. IEEE Proceedings of Eco Design: Third International Symposium on Environmentally Conscious De-sign and Inverse Manufacturing, Tokyo Japan, December 8-11, 383-389. https://doi.org/10.1109/ECODIM.2003.1322697Feigenbaum, A.V. (1991).Total Quality Control. McGraw-Hill. Fundin, A., Nilsson, L. (2003). Using Kano's theory of attractive quality to better understand customer satisfaction with e-services. Asian Journal on Quality, 4(2), 32-49. https://doi.org/10.1108/15982688200300018Friman, M., Edvardsson, B. (2003). A content analysis of complaints and compliments. Managing Service Quality, 13(1), 20-26. https://doi.org/10.1108/09604520310456681Garvin, D.A. (1987). Competing on the eight dimensions of quality. Harvard Business Review, 65(6), 101-109.Hanan, M., Karp, P. (1989). Customer satisfaction, how to maximise, measure and market your company's "ultimate product". AMACOM.Herzberg, F., Bernard, M., Snyderman, B.B. (1959). The Motivation to Work. John Wiley and Sons.Hoch, S.J., Ha, Y.W. (1986). Consumer learning: advertising and the ambiguity of product experience. Journal of Consumer Research, 13, 221-33.https://doi.org/10.1086/209062Johnson, M.D., Nilsson, L. (2003). The Importance of Reliability and Customization from Goods to Services. Quality Management Journal, 10(1), 8-19. https://doi.org/10.1080/10686967.2003.11919049Kano, N., Seraku, N., Takahashi, F., Tsuji, S. (1984). Attractive Quality and Must-Be Quality. Journal of the Japanese Society for Quality Control, 41, 39-48.Kapalle, P.K, Lehmann, D.R. (1995). The effects of advertised and observed quality on expectations about new product quality. Journal of Marketing Research, 32(8), 280-90. https://doi.org/10.1177/002224379503200304Lee, M.C., Newcomb, J.F. (1997). Applying the Kano methodology to meet customer requirements: NASA's microgravity science program. Quality Management Journal, 4(3), 95-110. https://doi.org/10.1080/10686967.1997.11918805Löfgren, M. (2005). Winning at the first and second moments of truth: An exploratory study. Journal of Service Theory and Practice, 15(1), 102-15. https://doi.org/10.1108/09604520510575290Löfgren, M., Witell, L. (2005). Kano's Theory of Attractive Quality and Packaging. Quality Management Journal, 12(3), 7-20. https://doi.org/10.1080/10686967.2005.11919257Matzler, K., Hinterhuber, H.H., Bailom, F., Sauerwein, E. (1996). How to delight your customers. Journal of Product & Brand Management, 5(2), 6-18. https://doi.org/10.1108/10610429610119469Miarka, D., Żukowska, J., Siwek, A., Nowacka,A., Nowak, D. (2015). Microbial hazards reduction during creamy cream cheese production. Production Engineering Archives, 6(1), 39-44. https://doi.org/10.30657/pea.2015.06.10Nelson, P. (1970), Information and consumer behaviour. Journal of Political Economy, 78, 311-29. https://doi.org/10.1086/259630Nilsson-Witell, L, Fundin, A. (2005). Dynamics of service attributes: a test of Kano's theory of attractive quality. International Journal of Service Industry Management, 16(2), 152-168. https://doi.org/10.1108/09564230510592289Parasuraman, A. (1997). Reflections on gaining competitive advantage through customer value. Academy of Marketing Science Journal, 25(2), 154-61. https://doi.org/10.1007/BF02894351Parasuraman, A., Colby, C.L. (2001). Techno-Ready Marketing. Free Press.Qiting, P., Uno, N., Kubota, Y. (2013). Kano Model Analysis of Customer Needs and Satisfaction at the Shanghai Disneyland. In Proceedings of the 5th Intl Congress of the Intl Association of Societies of Design Research, Tokyo, Japan. http://design-cu.jp/iasdr2013/papers/1835-1b.pdf Accessed on January 2021.Sauerwein, E., Bailom, F., Matzler, K., Hinterhuber, H.H. (1996). The Kano Model: How to delight your Customers. Volume I of the IX. International Working Seminar on Production Economics, Innsbruck/Igls/Austria, February 19-23 1996, pp. 313-327. https://is.muni. cz/el/econ/podzim2009/MPH_MAR2/um/9899067/THE_KANO_MODEL_-_HOW_TO_DELIGHT_YOUR_CUSTOMERS.pdfShewhart, W.A. (1931). Economic Control of Quality of Manufactured Product. D. Van Nostrand Company, Inc.Underwood, R.L., Klein, N.M. (2002). Packaging as Brand Communication: Effects of Product Pictures on Consumer Responses to the Package and Brand. Journal of Marketing Theory and Practice, 10(4), 58-68. https://doi.org/10.1080/10696679.2002.11501926Underwood, R.L. Klein, N.M., Burke, R.R. (2001). Packaging communication: attentional effects of product imagery. Journal of Product & Brand Management, 10(7), 403-22. https://doi.org/10.1108/10610420110410531Watson, G.H. (2003), "Customer focus and competitiveness", in Stephens, K.S. (Ed.), Six Sigma and Related Studies in the Quality Disciplines, ASQ Quality Press, Milwaukee, WI.Williams, D. (2020). The future of the packaging industry in India. Packaging Gateway. https://packaging-gateway.com/features/futurepackaging-industry-in-india Accessed on January 2021.Williams,H., Wikström,F., Löfgren.M. (2008). A life cycle perspective on environmental effects of customer focused packaging development." Journal of Cleaner Production, 16(7), 853-859. https://doi.org/10.1016/j.jclepro.2007.05.006Woodruff, R.B. (1997). Customer value: the next source for competitive advantage. Journal of Academy of Marketing Science, 25(2), 139- 53. https://doi.org/10.1007/BF02894350Zeithaml, V.A. (1988). Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. Journal of Marketing, 52, 2-22. https://doi.org/10.1177/00222429880520030

Development of Pisa 2015 Based Chemical Literacy Assessment Instrument For High School Students

This study aims to develop valid and reliable chemical literacy assessment instruments based on PISA 2015. The development procedures carried out were 1) research and information collecting, 2) planning, 3) development preliminary form of product, 4) preliminary field testing, and 5) main product revision. Instrument of development result was validated(content validity and empirical validity). Content validity assessment data was obtained from the validity test results from two chemistry lecturers. Empirical validity test data were acquired from68 grade XI students as test subjects who came from five high schools in Malang. An empirical validity test was used to obtain the level of validity, reliability, discrimination index, difficulty level, and effectiveness of distractors of the items developed in the instrument. The instrument of development results consisted of 20 multiple choice items and 4 attitude questionnaires. The results of the content validity test indicated a valid instrument (the average score for the aspects of substance, construction, and language was 83.9). The results of the empirical validity test showed that multiple-choice items had a correlation value of 0.37-0.77, categorized as valid, and the reliability value was 0.86, classified as highly reliable. The discrimination index obtained was five items ranked as sufficiently good and 15 items categorized as good, while five items classified as easy item, 14 moderate items, and one difficult item, all distractors were functioning. The empirical validity test results in the form of an attitude questionnaire showed a correlation value of 0.65-0.69, so they were valid, and the reliability value was 0.59, classified as quite high criteria. Instrument development results proved to be valid and reliable, so it is feasible to be used to measure students' chemical literacy skills.ReferencesAmerican Association for the Advancement of Science (AAAS). (1993). Benchmarks for science literacy: a project 2061 report. New York: Oxford University Press.Arikunto, S. (1993). Dasar-Dasar Evaluasi Pendidikan. Jakarta: Bumi Aksara.Bond, D. (1989). In Pursuit of Chemical Literacy: A Place for Chemical Reactions. Journal of Chemical Education, 66(2), 157.Celik, S. (2014).Chemical Literacy Levels of Science And Mathematics Teacher Candidates. Australian Journal of Teacher Education, 39(1), 1 – 15Cigdemoglu, C., & Geban, O. (2015). Improving Students' Chemical Literacy Level on Thermochemical And Thermodynamics Concepts through Context-Based Approach. Chemistry Education Research And Practice, 16, 302 – 317.Cigdemoglu, C., Arslan, H. O., & Cam, A. (2017).Argumentation to Foster Pre-Service Science Teachers' Knowledge, Competency, And Attitude on The Domains of Chemical Literacy of Acids And Bases. Chemistry Education Research And Practice, 18(2), 288 – 303.Direktorat Pembinaan SMA. (2017). Panduan Penilaian oleh Pendidik dan Satuan Pendidikan Sekolah Menengah Atas. Jakarta: Kementerian Pendidikan dan Kebudayaan RI.Kohen, Z., Herscovitz, O., & Dori, Y. J. (2020). How to Promote Chemical Literacy? Online Question Posing And Communicating With Scientists. Chemistry Education Research And Practice, 21(1), 250 – 266Mudiono, A. (2016). Keprofesionalan Guru dalam Menghadapi Pendidikan di Era Global. Makalah disajikan dalam Seminar Nasional, Jurusan KSDP FIP UM, Malang 25 September.Mumba, F., & Hunter, W. J. F. (2009). Representative Nature of Scientific Literacy Themes in A High School Chemistry Course: The Case of Zambia. Chemistry Education Research And Practice, 10(3), 219 – 226.Naganuma, S. (2017). An Assessment of Civic Scientific Literacy in Japan: Development of A More Authentic Assessment Task And Scoring Rubric. International Journal of Science Education, Part B, 7(4), 301 – 322Norris, S. P., & Philip, L. M. (2003). How literacy in its fundamental sense in central to scientific literacy. Science Education, 87(2), 224 – 240.Organisation for Economic Co-operation and Development (OECD). (2016). PISA 2015 Assessment And Analytical Framework: Science, Reading, Mathematic And Financial Literacy. Paris: OECD PublishingOrganisation for Economic Co-operation and Development (OECD). (2018). PISA 2018 Result Combined Executive Summaries Volume I, II, & III. Paris: Organisation for Economic Co-operation and Development.Osborne, J. F. (2010). Arguing to Learn in Science: The Role of Collaborative, Critical Discourse. Science, 328(5977), 463 – 466Rahayu, S. (2014). Menuju Masyarakat Berliterasi Sains: Harapan dan Tantangan Kurikulum 2013. Makalah disajikan dalam Seminar Nasional Kimia dan Pembelajarannya, Jurusan Kimia FMIPA UM, Malang 6 September.Rahayu, S. (2017). Mengoptimalkan Aspek Literasi dalam Pembelajaran Kimia Abad 21. Makalah disajikan dalam Seminar Nasional Kimia, Jurusan Pendidikan Kimia FMIPA UNY, Yogyakarta, 14 Oktober.Riduwan. (2011). Belajar Mudah Penelitian: untuk Guru-Karyawan, dan Peneliti Pemula. Bandung: AlfabetaRiduwan. (2013). Dasar-Dasar Statistika. Bandung: AlfabetaShe, H. C., Stacey, K., & Schmidt, W. H. (2018).Science And Mathematics Literacy: PISA for Better School Education. International Journal of Science And Mathematics Education, 16(1), 1 – 5Shwartz, Y., Ben-Zvi, R., & Hofstein, A. (2005). The Importance of Involving High-School Chemistry Teachers in The Process of Defining the Operational Meaning of Chemical Literacy. International Journal of ScienceEducation, 27(3), 323 – 344.Thummathong, R., & Thathong, K. (2016). Construction of A Chemical Literacy Test for Engineering Students. Journal of Turkish Science Education, 13(3), 185 – 198.United Nations Environment Programme (UNEP). (2012). 21 Issues for the 21st Century: Result of the UNEP Foresight Process on Emerging Environmental Issues. Nairobi, Kenya: United Nations Environment Programme.Vogelzang, J., Admiraal, W. F., & van Driel, J. H. (2020). Effects of Scrum Methodology on Students' Critical Scientific Literacy: The Case of Green Chemistry. Chemistry Education Research And Practice, 21(3), 940 – 952.World Economic Forum (WEF). (2016). New Vision for Education: Fostering Social And Emotional Learning through Technology

On the detection of SOurce COde re-use

© {Owner/Author | ACM} {2014}. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in FIRE '14 Proceedings of the Forum for Information Retrieval Evaluation, http://dx.doi.org/10.1145/2824864.2824878"This paper summarizes the goals, organization and results of the first SOCO competitive evaluation campaign for systems that automatically detect the source code re-use phenomenon. The detection of source code re-use is an important research field for both software industry and academia fields. Accordingly, PAN@FIRE track, named SOurce COde Re-use (SOCO) focused on the detection of re-used source codes in C/C++ and Java programming languages. Participant systems were asked to annotate several source codes whether or not they represent cases of source code re-use. In total five teams submitted 17 runs. The training set consisted of annotations made by several experts, a feature which turns the SOCO 2014 collection in a useful data set for future evaluations and, at the same time, it establishes a standard evaluation framework for future research works on the posed shared task.PAN@FIRE (SOCO) has been organised in the framework of WIQ-EI (EC IRSES grantn. 269180) and DIANA-APPLICATIONS (TIN2012-38603-C02- 01) research projects. The work of the last author was supported by CONACyT Mexico Project Grant CB-2010/153315, and SEP-PROMEP UAM-PTC-380/48510349.Flores Sáez, E.; Rosso, P.; Moreno Boronat, LA.; Villatoro-Tello, E. (2014). On the detection of SOurce COde re-use. En FIRE '14 Proceedings of the Forum for Information Retrieval Evaluation. ACM. 21-30. https://doi.org/10.1145/2824864.2824878S2130C. Arwin and S. Tahaghoghi. Plagiarism detection across programming languages. Proceedings of the 29th Australian Computer Science Conference, Australian Computer Society, 48:277--286, 2006.N. Baer and R. Zeidman. Measuring whitespace pattern sequence as an indication of plagiarism. Journal of Software Engineering and Applications, 5(4):249--254, 2012.M. Chilowicz, E. Duris, and G. Roussel. Syntax tree fingerprinting for source code similarity detection. In Program Comprehension, 2009. ICPC '09. IEEE 17th International Conference on, pages 243--247, 2009.D. Chuda, P. Navrat, B. Kovacova, and P. Humay. The issue of (software) plagiarism: A student view. Education, IEEE Transactions on, 55(1):22--28, 2012.G. Cosma and M. Joy. Evaluating the performance of lsa for source-code plagiarism detection. Informatica, 36(4):409--424, 2013.B. Cui, J. Li, T. Guo, J. Wang, and D. Ma. Code comparison system based on abstract syntax tree. In Broadband Network and Multimedia Technology (IC-BNMT), 3rd IEEE International Conference on, pages 668--673, Oct 2010.J. A. W. Faidhi and S. K. Robinson. An empirical approach for detecting program similarity and plagiarism within a university programming environment. Comput. Educ., 11(1):11--19, Jan. 1987.Fire, editor. FIRE 2014 Working Notes. Sixth International Workshop of the Forum for Information Retrieval Evaluation, Bangalore, India, 5--7 December, 2014.J. L. Fleiss. Measuring nominal scale agreement among many raters. Psychological bulletin, 76(5):378, 1971.E. Flores, A. Barrón-Cedeño, L. Moreno, and P. Rosso. Uncovering source code reuse in large-scale academic environments. Computer Applications in Engineering Education, pages n/a--n/a, 2014.E. Flores, A. Barrón-Cedeño, P. Rosso, and L. Moreno. DeSoCoRe: Detecting source code re-use across programming languages. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstration Session, NAACL-HLT, pages 1--4. Association for Computational Linguistics, 2012.E. Flores, A. Barrón-Cedeño, P. Rosso, and L. Moreno. Towards the Detection of Cross-Language Source Code Reuse. Proceedings of 16th International Conference on Applications of Natural Language to Information Systems, NLDB-2011, Springer-Verlag, LNCS(6716), pages 250--253, 2011.E. Flores, M. Ibarra-Romero, L. Moreno, G. Sidorov, and P. Rosso. Modelos de recuperación de información basados en n-gramas aplicados a la reutilización de código fuente. In Proc. 3rd Spanish Conf. on Information Retrieval, pages 185--188, 2014.D. Ganguly and G. J. Jones. Dcu@ fire-2014: an information retrieval approach for source code plagiarism detection. In Fire [8].R. García-Hernández and Y. Lendeneva. Identification of similar source codes based on longest common substrings. In Fire [8].M. Joy and M. Luck. Plagiarism in programming assignments. Education, IEEE Transactions on, 42(2):129--133, May 1999.A. Marcus, A. Sergeyev, V. Rajlich, and J. Maletic. An information retrieval approach to concept location in source code. In Reverse Engineering, 2004. Proceedings. 11th Working Conference on, pages 214--223, Nov 2004.S. Narayanan and S. Simi. Source code plagiarism detection and performance analysis using fingerprint based distance measure method. In Proc. of 7th International Conference on Computer Science Education, ICCSE '12, pages 1065--1068, July 2012.M. Potthast, M. Hagen, A. Beyer, M. Busse, M. Tippmann, P. Rosso, and B. Stein. Overview of the 6th international competition on plagiarism detection. In L. Cappellato, N. Ferro, M. Halvey, and W. Kraaij, editors, Working Notes for CLEF 2014 Conference, Sheffield, UK, September 15-18, 2014., volume 1180 of CEUR Workshop Proceedings, pages 845--876. CEUR-WS.org, 2014.L. Prechelt, G. Malpohl, and M. Philippsen. Finding plagiarisms among a set of programs with JPlag. Journal of Universal Computer Science, 8(11):1016--1038, 2002.I. Rahal and C. Wielga. Source code plagiarism detection using biological string similarity algorithms. Journal of Information & Knowledge Management, 13(3), 2014.A. Ramírez-de-la Cruz, G. Ramírez-de-la Rosa, C. Sánchez-Sánchez, W. A. Luna-Ramírez, H. Jiménez-Salazar, and C. Rodríguez-Lucatero. Uam@soco 2014: Detection of source code reuse by means of combining different types of representations. In Fire [8].F. Rosales, A. García, S. Rodríguez, J. L. Pedraza, R. Méndez, and M. M. Nieto. Detection of plagiarism in programming assignments. IEEE Transactions on Education, 51(2):174--183, 2008.K. Sparck and C. van Rijsbergen. Report on the need for and provision of an "ideal" information retrieval test collection. British Library Research and Development Report, 5266, University of Cambridge, 1975.G. Whale. Software metrics and plagiarism detection. Journal of Systems and Software, 13(2):131--138, 1990

A systematic literature review of Total Quality Management (TQM) implementation in the organization

[EN] In today’s market situation and complex business environment, organization must be able to deliver the customer’s requirement and the expectations which are critical to the satisfaction such as high product quality, faster delivery and competitive cost. Organization need to apply a comprehensive concept and method on managing those requirements. The concept of Total Quality Management (TQM) is considered as one of a popular concept used to manage the quality of product and services comprehensively. This research is to observe is this concept and method still relevant to be use and effectively improved the business performance as well as customer satisfaction. It is a systematic literature review to the literatures from many industry sectors that were collected and reviewed in detail. The result show that this concept is still being used by many organizations around the world and its successfully help the organization to improve their competitiveness, business growth and the sustainability as well as increase employee’s morale.This article was completed thanks to the financial support from the university of Mercu Buana, Jakarta-Indonesia. It also completed with the purpose and motivation of the authors to have an innovate research thinking as well as the contribution to the future researcher.Permana, A.; Purba, H.; Rizkiyah, N. (2021). A systematic literature review of Total Quality Management (TQM) implementation in the organization. International Journal of Production Management and Engineering. 9(1):25-36. https://doi.org/10.4995/ijpme.2021.13765OJS253691Alanazi, M.H. (2020). The mediating role of primary TQM factors and strategy in the relationship between supportive TQM factors and organisational results: An empirical assessment using the MBNQA model. Cogent Business and Management, 7(1). https://doi.org/10.1080/23311975.2020.1771074Antunes, M.G., Mucharreira, P.R., Justino, M. do R.T., & Quirós, J.T. (2018). Total Quality Management Implementation in Portuguese Higher Education Institutions. Proceedings MDPI, 2(21), 1342. https://doi.org/10.3390/proceedings2211342Arifin, J. (2016). Penguatan Manajemen Syariah Melalui Total Quality Managementbagi Pelaku Lembaga Keuangan Syariah Di Kota Semarang. Jurnal At-Taqaddum, Volume 8, Nomor 2, November 2016, 8(2), 180. https://doi.org/10.21580/at.v8i2.1170Balasubramanian, M. (2016). Total Quality Management [TQM] in the Healthcare Industry - Challenges, Barriers and Implementation Developing a Framework for TQM Implementation in a Healthcare Setup. Science Journal of Public Health, 4(4), 271. https://doi.org/10.11648/j.sjph.20160404.11Benzaquen, J., Carlos, M., Norero, G., Armas, H., & Pacheco, H. (2019). Quality in private health companies in Peru: The relation of QMS & ISO 9000 principles on TQM factor. International Journal of Healthcare Management, 0(0), 1-9. https://doi.org/10.1080/20479700.2019.1644472Bigliardi, B., & Galati, F. (2014). The implementation of TQM in R&D environments. Journal of Technology Management and Innovation, 9(2), 157-171. https://doi.org/10.4067/S0718-27242014000200012Bunglowala, A., & Asthana, N. (2016). A Total Quality Management Approach in Teaching and Learning Process. International Journal of Management (IJM), 7(5), 223-227. http://www.iaeme.com/MasterAdmin/uploadfolder/IJM_07_05_021/IJM_07_05_021.pdfBusu, M. (2019). Applications of TQM Processes to Increase the Management Performance of Enterprises in the Romanian Renewable Energy Sector. Processes MDPI. https://doi.org/10.3390/pr7100685Dahlgaard, J.J., Kristensen, K., & Kanji, G.K. (2002). Fundamentals of Total Quality Management: Process analysis and improvement Jens. Original illustrations © Taylor & Francis 2002. https://doi.org/10.4324/9780203930021Dewi, H.P., Lumbanraja, P., & Matondang, R. (2015). Implementation of Total Quality Management and Interpersonal Communication in Achieving Student Satisfaction through Service Quality at Yayasan Pendidikan Islam, Miftahussalam, Medan. International Journal of Research and Review, 2(6), 343-347. http://www.gkpublication.in/IJRR_Vol.2_Issue6_June2015/IJRR0066.pdfEltawy, N., & Gallear, D. (2017). Leanness and agility: A comparative theoretical view. Industrial Management and Data Systems, 117(1), 149-165. https://doi.org/10.1108/IMDS-01-2016-0032Fitriani, F. (2019). Persiapan Total Quality Management (Tqm). Adaara: Jurnal Manajemen Pendidikan Islam, 9(2), 908-919. https://doi.org/10.35673/ajmpi.v9i2.426Garcia-Alcaraz, J.L., Flor-Montalvo, F.J., Avelar-Sosa, L., Sánchez-Ramírez, C., & Jiménez-Macías, E. (2019). Human resource abilities and skills in TQM for sustainable enterprises. Sustainability MDPI, 11(22), 6488. https://doi.org/10.3390/su11226488George, S., & Weimerskirch, A. (1998). Total quality management: Strategies and techniques proven at todays' most successful companies (Second ed.). John Wiley & Sons, Inc.Green, F.B. (2006). Six-sigma and the revival of TQM. Total Quality Management and Business Excellence, 17(10), 1281-1286. https://doi.org/10.1080/14783360600753711Gómez-López, R., Serrano-Bedia, A.M., & López-Fernández, M.C. (2016). Motivations for implementing TQM through the EFQM model in Spain: an empirical investigation. Total Quality Management and Business Excellence, 27(11-12), 1224-1245. https://doi.org/10.1080/14783363.2015.1068688Haffar, M., Al-Karaghouli, W., & Ghoneim, A. (2013). An analysis of the influence of organisational culture on TQM implementation in an era of global marketing: The case of Syrian manufacturing organisations. International Journal of Productivity and Quality Management, 11(1), 96-115. https://doi.org/10.1504/IJPQM.2013.050570Hasan, K., Islam, M.S., Shams, A.T., & Gupta, H. (2018). Total Quality Management (TQM): Implementation in Primary Education System of Bangladesh. International Journal of Research in Industrial Engineering, 7(3), 370-380. https://doi.org/10.22105/riej.2018.128170.1041Houston, D. (2007). TQM and higher education: A critical systems perspective on fitness for purpose. Quality in Higher Education, 13(1), 3-17. https://doi.org/10.1080/13538320701272672Kaname, O. (2003). Handbook for TQM and QCC Vol 1. In Handbook (Vol. 1). Kantardjieva, M. (2015). The Relationship between Total Quality Management (TQM) and Strategic Management. Journal of Economics, Business and Management, 3(5), 537-541. https://doi.org/10.7763/JOEBM.2015.V3.242Kim, G.-S. (2016). Effect of Total Quality Management on Customer Satisfaction. International Journal of Engineering Sciences & Research Technology, 5(6), 507-514. https://doi.org/10.5281/zenodo.55618Kiruthiga, K. (2016). Major factors affecting the execution of total quality management in the construction industry in India. Journal of Chemical and Pharmaceutical Sciences, 9(2), E135-E140.Kumar, S., & Shanmuganathan, J. (2019). A structural relationship between TQM practices and organizational performance with reference to selected auto component manufacturing companies. International Journal of Management, 10(5). https://doi.org/10.34218/IJM.10.5.2019/009Kumar, U., Kumar, V., de Grosbois, D., & Choisne, F. (2009). Continuous improvement of performance measurement by TQM adopters. Total Quality Management & Business Excellence, 20(6), 603-616. https://doi.org/10.1080/14783360902924242Kuo, C. (2016). Effects of Total Quality Management Implementation and Supply Chain Management Capability on Customer Capital. The Journal of Global Business Management, 12(2), 47-60.Lawrence, J.J., & McCollough, M.A. (2004). Implementing Total Quality Management in the Classroom by Means of Student Satisfaction Guarantees. Total Quality Management and Business Excellence, 15(2), 235-254. https://doi.org/10.1080/1478336032000149063Mensah, J.O., Copuroglu, G., & Fening, F.A. (2012). Total Quality Management in Ghana: Critical Success Factors and Model for Implementation of a Quality Revolution. Journal of African Business, 13(2), 123-133. https://doi.org/10.1080/15228916.2012.693444Mercy, O., & Taiye, T.B. (2015). Strategic Imperatives of Total Quality Management and Customer Satisfaction in Organizational Sustainability. International Journal of Academic Research in Business and Social Sciences, 5(4), 1-22. https://doi.org/10.6007/IJARBSS/v5-i4/1538Mitreva, E., Cvetkovik, D., Filiposki, O., Taskov, N., & Gjorshevski, H. (2016). The Effects of Total Quality Management Practices on Performance within a Company for Frozen Food in the Republic of Macedonia. TEM Journal, 5(3), 339-346. https://doi.org/10.18421/TEM53-14Morath, C., & Doluschitz, R. (2009). Total Quality Management in the food industry - Current situation and potential in Germany. Applied Studies In Agribusiness And Commerce, 3(3-4), 83-87. https://doi.org/10.19041/APSTRACT/2009/3-4/18Musenze, I.A., & Thomas, M.S. (2020). Development and validation of a total quality management model for Uganda's local governments. Cogent Business and Management, 7(1), 1-22. https://doi.org/10.1080/23311975.2020.1767996Neyestani, B., & Juanzon, J.B.P. (2016). Developing an Appropriate Performance Measurement Framework for Total Quality Management (TQM) in Construction and Other Industries. IRA-International Journal of Technology & Engineering (ISSN 2455-4480), 5(2), 32. https://doi.org/10.21013/jte.v5.n2.p2Ngambi, M.T., & Nkemkiafu, A.G. (2015). The Impact of Total Quality Management on Firm's Organizational Performance Marcel. American Journal of Management, 15(4), 57-76.Nicolaou, N., & Kentas, G. (2017). Total Quality Management Implementation Failure Reasons in Healthcare Sector. Journal of Health Science 5 (2017) 110-113, 5(2), 110-113. https://doi.org/10.17265/2328-7136/2017.02.007Nugroho, T.W., & Nurcahyo, R. (2018). Analysis of Total Quality Management (TQM) implementation in small medium industries. Proceedings of the International Conference on Industrial Engineering and Operations Management, 2018(Jul), 607-618.Oakland, J.S. (2003). Total quality management - Text with cases. In Butterworth-Heinemann (Third Edit). Butterworth-Heinemann.Phan, A.C., Nguyen, H.T., Nguyen, H.A., & Matsui, Y. (2019). Effect of total quality management practices and jit production practices on flexibility performance: Empirical evidence from international manufacturing plants. MDPI Sustainability (Switzerland), 11(11). https://doi.org/10.3390/su11113093Prajogo, D.I., & Brown, A. (2004). The Relationship between TQM Practices and Quality Performance and the Role of Formal TQM Programs: An Australian Empirical Study. Quality Management Journal, 11(4), 31-42. https://doi.org/10.1080/10686967.2004.11919131Ramlawati, & Putra, A.H.P.K. (2018). Total Quality Management as the Key of the Company to Gain the Competitiveness, Performance Achievement and Consumer Satisfaction. International Review of Management and Marketing, 8(5), 60-69.Rogers, R.E. (2013). Implementation of Total Quality Management A Comprehensive Training Program. 1996 by The Haworth Press, Inc. All rights reserved.Sabet, E., Adams, E., & Yazdani, B. (2014). Quality management in heavy duty manufacturing industry: TQM vs. Six Sigma. Total Quality Management and Business Excellence, 27(1-2), 215-225. https://doi.org/10.1080/14783363.2014.972626Sader, S., Husti, I., & Daróczi, M. (2017). Suggested Indicators To Measure the Impact of Industry 4.0 on Total Quality Management. International Scientific Journal: Industry 4.0, 2(6), 298-301. https://stumejournals.com/journals/i4/2017/6/298/pdfSadikoglu, E., & Olcay, H. (2014). The Effects of Total Quality Management Practices on Performance and the Reasons of and the Barriers to TQM Practices in Turkey. Laboratory Management Information Systems: Current Requirements and Future Perspectives, 2014, 996-1027. https://doi.org/10.1155/2014/537605Sainis, G., Haritos, G., Kriemadis, T., & Fowler, M. (2017). The quality journey for Greek SMEs and their financial performance. Production and Manufacturing Research, 5(1), 306-327. https://doi.org/10.1080/21693277.2017.1374891Santos, A.C. de S.G. dos, Carvalho, L.M., Souza, C.F. de, Reis, A. da C., & Freitag, A.E.B. (2019). Total Quality Management: the case of an electricity distribution company. Brazilian Journal of Operations & Production Management, 16(1), 53-65. https://doi.org/10.14488/BJOPM.2019.v16.n1.a5Sari, & Firdaus, A. (2018). The Impact of Total Quality Management Implementation on Small and Medium Manufacturing Companies. Esensi: Jurnal Bisnis Dan Manajemen, 8(1), 67-78. https://doi.org/10.15408/ess.v8i1.5852Sila, I., & Walczak, S. (2017). Universal versus contextual effects on TQM: a triangulation study using neural networks. Production Planning and Control, 28(5), 367-386. https://doi.org/10.1080/09537287.2017.1296598Sivalai, T., & Rojniruttikul, N. (2018). Determinants of the state railway of Thailand's (SRT) total quality management process: SEM analysis. Journal of International Studies, 11(2). https://doi.org/10.14254/2071-8330.2018/11-2/9Small, E.P., Ayyash, L., & Hamouri, K. Al. (2017). Benchmarking Performance of TQM Principals in Electrical Subcontracting in Dubai: A Case Study. Procedia Engineering, 196(June), 622-629. https://doi.org/10.1016/j.proeng.2017.08.050Sousa-Mendes, G.H. de, Gomes-Salgado, E., & Moro-Ferrari, B.E. (2016). Prioritization of TQM practices in Brazilian medical device SMEs using Analytical Hierarchy Process (AHP) Glauco. DYNA (Colombia), 83(197), 195-203. https://doi.org/10.15446/dyna.v83n197.52205Steiber, A., & Alänge, S. (2013). Do TQM principles need to change? Learning from a comparison to Google Inc. Total Quality Management and Business Excellence, 24(1-2), 48-61. https://doi.org/10.1080/14783363.2012.733256Suarez-Barraza, M.F., & Ablanedo-Rosas, J.H. (2014). Total quality management principles: Implementation experience from Mexican organisations. Total Quality Management and Business Excellence, 25(5-6), 546-560. https://doi.org/10.1080/14783363.2013.867606Sukardi, R.A. (2016). Pengaruh Total Quality Management (TQM) Terhadap Kepuasan Pelanggan Pada Matahari Department Store di Plaza Mulia Samarinda. EJournal Administrasi Bisnis, 4(3), 758-772.Sukdeo, N., Pretorius, J.H., & Vermeulen, A. (2017). The role of Total Quality Management (TQM) practices on improving organisational performance in manufacturing and service organisations. Proceedings of the International Conference on Industrial Engineering and Operations Management, 2017(OCT), 1133-1152.Sutrisno, T.F.C.W. (2019). Relationship between Total Quality Management element, operational performance and organizational performance in food production SMEs. Jurnal Aplikasi Manajemen, 17(2), 285-294. https://doi.org/10.21776/ub.jam.2019.017.02.11Sweis, R., Ismaeil, A., Obeidat, B., & Kanaan, R.K. (2019). Reviewing the Literature on Total Quality Management and Organizational Performance. Journal of Business & Management (COES&RJ-JBM), 7(3), 192-215. https://doi.org/10.25255/jbm.2019.7.3.192.215Talib, F., & Rahman, Z. (2015). Identification and prioritization of barriers to total quality management implementation in service industry: An analytic hierarchy process approach. TQM Journal, 27(5), 591-615. https://doi.org/10.1108/TQM-11-2013-0122Tervonen, P., Pahkala, N., & Haapasalo, H. (2009). Development of TQM in steel manufacturers' production. Ibima Business Review, 1-3, 52-59.Tesfaye, G., & Kitaw, D. (2017). A TQM and JIT Integrated Continuous Improvement Model for Organizational Success: An Innovative Framework. Journal of Optimization in Industrial Engineering, 22, 15-23. https://doi.org/10.22094/joie.2017.265Vukomanovic, M., Radujkovic, M., & Nahod, M.M. (2014). EFQM excellence model as the TQM model of the construction industry of southeastern Europe. Journal of Civil Engineering and Management, 20(1), 70-81. https://doi.org/10.3846/13923730.2013.843582Yang, C.O., & Tsai, M.C. (2014). Improving operations performance through TQM in the post-financial crisis era: An exploratory case study of a multinational IM firm in the Greater China region. Total Quality Management and Business Excellence, 25(5-6), 561-581. https://doi.org/10.1080/14783363.2013.839167Yeng, S.K., Jusoh, M.S., & Ishak, N.A. (2018). The impact of Total Quality Management (TQM) On competitive advantage: A conceptual mixed method study in the Malaysia Luxury hotel industries. Academy of Strategic Management Journal, 17(2), 1-9.Zairi, M. (1991). Total Quality Management for Engineers. In Ccc (Vol. 1). Woodhead Publishing Limited. https://doi.org/10.1533/9781845698911.1Žitkienė, R., & Deksnys, M. (2018). Organizational agility conceptual model. Montenegrin Journal of Economics, 14(2), 115-129. https://doi.org/10.14254/1800-5845/2018.14-2.

Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest - A review

[EN] Computer vision systems are becoming a scientific but also a commercial tool for food quality assessment. In the field, these systems can be used to predict yield, as well as for robotic harvesting or the early detection of potentially dangerous diseases. In postharvest handling, it is mostly used for the automated inspection of the external quality of the fruits and for sorting them into commercial categories at very high speed. More recently, the use of hyperspectral imaging is allowing not only the detection of defects in the skin of the fruits but also their association to certain diseases of particular importance. In the research works that use this technology, wavelengths that play a significant role in detecting some of these dangerous diseases are found, leading to the development of multispectral imaging systems that can be used in industry. This article reviews recent works that use colour and non-standard computer vision systems for the automated inspection of citrus. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of internal and external features of these fruits. Particular attention is paid to inspection for the early detection of some dangerous diseases like citrus canker, black spot, decay or citrus Huanglongbing.This work was supported by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA) through projects RTA2012-00062-C04-01 and RTA2012-00062-C04-03 with the support of European FEDER funds. The authors would like to thank and acknowledge the contributions that were made by all the students, postdocs, technicians and visiting scholars in the Precision Agriculture Laboratory at the University of Florida and the Computer Vision Laboratory at the Agricultural Engineering Centre of IVIA.Cubero García, S.; Lee, WS.; Aleixos Borrás, MN.; Albert Gil, FE.; Blasco Ivars, J. (2016). Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest - A review. Food and Bioprocess Technology. 9(10):1623-1639. https://doi.org/10.1007/s11947-016-1767-1S16231639910Adebayo, S. E., Hashim, N., Abdan, K., & Hanafi, M. (2016). Application and potential of backscattering imaging techniques in agricultural and food processing—a review. Journal of Food Engineering, 169, 155–164.Aleixos, N., Blasco, J., Navarrón, F., & Moltó, E. (2002). Multispectral inspection of citrus in real time using machine vision and digital signal processors. Computers and Electronics in Agriculture, 33(2), 121–137.Annamalai, P., & Lee, W. S. (2003). Citrus yield mapping system using machine vision. ASAE Paper No. 031002. St. Joseph: ASAE.Annamalai, P., & Lee, W. S. (2004). Identification of green citrus fruits using spectral characteristics. ASAE Paper No. FL04–1001. St. Joseph: ASAE.Balasundaram, D., Burks, T. F., Bulanon, D. M., Schubert, T., & Lee, W. S. (2009). Spectral reflectance characteristics of citrus canker and other peel conditions of grapefruit. Postharvest Biology and Technology, 51, 220–226.Bansal, R., Lee, W. S., & Satish, S. (2013). Green citrus detection using fast Fourier transform (FFT) leakage. Precision Agriculture, 14(1), 59–70.Barreiro, P., Zheng, C., Sun, D.-W., Hernández-Sánchez, N., Pérez-Sánchez, J. M., & Ruiz-Cabello, J. (2008). Non-destructive seed detection in mandarins: comparison of automatic threshold methods in FLASH and COMSPIRA MRIs. Postharvest Biology and Technology, 47, 189–198.Basavaprasad, B., & Ravi, M. (2014). A comparative study on classification of image segmentation methods with a focus on graph based techniques. International Journal of Research in Engineering and Technology, 3, 310–315.Birth, G. S. (1976). How light interacts with foods. In: Gafney J.Jr.(Ed.), Quality detection in foods (pp. 6–11). St. Joseph: ASAE.Blanc, P.G.R., Blasco, J., Moltó, E., Gómez-Sanchis, J., & Cubero, S. (2010) System for the automatic selective separation of rotten citrus fruits. Patent number EP2133157 A1 CN101678405A, EP2133157A4, EP2133157B1, US20100121484Blasco, J., Aleixos, N., & Moltó, E. (2007a). Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of Food Engineering, 81(3), 535–543.Blasco, J., Aleixos, N., Gómez, J., & Moltó, E. (2007b). Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of Food Engineering, 83(3), 384–393.Blasco, J., Aleixos, N., Gómez-Sanchis, J., & Moltó, E. (2009). Recognition and classification of external skin damages in citrus fruits using multispectral data and morphological features. Biosystems Engineering, 103(2), 137–145.Blasco, J., Cubero, S., & Moltó, E. (2016). Quality evaluation of citrus fruits. In D.-W. Sun (Ed.), Computer vision technology for food quality evaluation (2nd ed.). San Diego: Academic Press.Bulanon, D. M., Burks, T. F., & Alchanatis, V. (2009). Image fusion of visible and thermal images for fruit detection. Biosystems Engineering, 103, 12–22.Bulanon, D.M., Burks, T.F., Kim, D.G., & Ritenour, M.A. (2013). Citrus black spot detection using hyperspectral image analysis. Agricultural Engineering International: CIGR Journal, 15,(3)171.Burks, T. F., Villegas, F., Hannan, M. W., & Flood, S. (2003). Engineering and horticultural aspects of robotic fruit harvesting: opportunities and constraints. HortTechnology, 15(1), 79–87.Campbell, B. L., Nelson, R. G., Ebel, R. C., Dozier, W. A., Adrian, J. L., & Hockema, B. R. (2004). Fruit quality characteristics that affect consumer preferences for Satsuma mandarins. Hortscience, 39(7), 1664–1669.Chinchuluun, R., Lee, W. S., & Ehsani, R. (2009). Machine vision system for determining citrus count and size on a canopy shake and catch harvester. Applied Engineering in Agriculture, 25(4), 451–458.Choi, D., Lee, W. S., Ehsani, R., & Roka, F. M. (2015). A machine vision system for quantification of citrus fruit dropped on the ground under the canopy. Transactions of the ASABE, 58(4), 933–946.Codex Alimentarius, (2011). Codex standard for oranges. Available at: http://www.codexalimentarius.org/download/standards/10372/CXS_245e.pdf . Accessed March 2016Cubero, S., Aleixos, N., Albert, A., Torregrosa, A., Ortiz, C., García-Navarrete, O., & Blasco, J. (2014a). Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform. Precision Agriculture, 15(1), 80–94.Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., & Blasco, J. (2011). Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food and Bioprocess Technology, 4(4), 487–504.Cubero, S., Diago, M. P., Blasco, J., Tardáguila, J., Millán, B., & Aleixos, N. (2014b). A new method for pedicel/peduncle detection and size assessment of grapevine berries and other fruits by image analysis. Biosystems Engineering, 117, 62–72.Dong, C.-W., Ye, Y., Zhang, J.-Q., Zhu, H.-K., & Liu, F. (2014). Detection of thrips defect on green-peel citrus using hyperspectral imaging technology combining PCA and B-Spline lighting correction method. Journal of Integrative Agriculture, 13(10), 2229–2235.FAOSTAT (2012). URL: http://faostat.fao.org http://www.fao.org/fileadmin/templates/est/COMM_MARKETS_MONITORING/Citrus/Documents/CITRUS_BULLETIN_2012.pdf . Accessed March 2016.Farrell, T. J., Patterson, M. S., & Wilson, B. (1992). A diffusion-theory model of spatially resolved steady-state diffuse reflectance for the noninvasive determination of tissue optical-properties in vivo. Medical Physics, 19, 879–888.Flood, S. J., Burks, T. F., & Teixeira, A. A. (2006). Physical properties of oranges in response to applied gripping forces for robotic harvesting. Transactions of ASAE, 49(2), 341–346.Gaffney, J. J. (1973). Reflectance properties of citrus fruit. Transactions of ASAE, 16(2), 310–314.Garcia-Ruiz, F., Sankaran, S., Maja, J. M., Lee, W. S., Rasmussen, J., & Ehsani, R. (2013). Comparison of two aerial imaging platforms for identification of Huanglongbing infected citrus trees. Computers and Electronics in Agriculture, 91, 106–115.Gómez, J., Blasco, J., Moltó, E., & Camps-Valls, G. (2007). Hyperspectral detection of citrus damage with a Mahalanobis kernel classifier. Electronics Letters, 43(20), 1082–1084.Gómez-Sanchis, J., Blasco, J., Soria-Olivas, E., Lorente, D., Escandell-Montero, P., Martínez-Martínez, J. M., Martínez-Sober, M., & Aleixos, N. (2013). Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most relevant bands and non-linear classifiers. Postharvest Biology and Technology, 82, 76–86.Gómez-Sanchis, J., Gómez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., Moltó, E., & Blasco, J. (2008). Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering, 89(1), 80–86.Gómez-Sanchis, J., Lorente, D., Soria-Olivas, E., Aleixos, N., Cubero, S., & Blasco, J. (2014). Development of a hyperspectral computer vision system based on two liquid crystal tuneable filters for fruit inspection. Application to detect citrus fruits decay. Food and Bioprocess Technology, 7, 1047–1056.Gómez-Sanchis, J., Martín-Guerrero, J. D., Soria-Olivas, E., Martínez-Sober, M., Magdalena-Benedito, R., & Blasco, J. (2012). Detecting rottenness caused by Penicillium in citrus fruits using machine learning techniques. Expert Systems with Applications, 39(1), 780–785.Gong, A., Yu, J., He, Y., & Qiu, Z. (2013). Citrus yield estimation based on images processed by an android mobile phone. Biosystems Engineering, 115, 162–170.Gottwald, T. R., Graham, J. H., & Schubert, T. S. (2002). Citrus canker: the pathogen and its impact. Plant Health Progress. doi: 10.1094/PHP-2002-0812-01-RV.Hannan, M., Burks, T. F., & Bulanon, D.M. (2009). A machine vision algorithm for orange fruit detection. The CIGR Ejournal. Manuscript 1281. Vol XI. December 2009.Harrell, R. C., Adsit, P. D., & Slaughter, D. C. (1985). Real-time vision-servoing of a robotic tree-fruit harvester. ASAE Paper No (pp. 85–3550). St. Joseph: ASAE.Hernández-Sánchez, N., Barreiro, P., & Ruiz-Cabello, J. (2006). On-line identification of seeds in mandarins with magnetic resonance imaging. Biosystems Engineering, 95, 529–536.Holmes, G. J., & Eckert, J. W. (1999). Sensitivity of Penicillium digitatum and P. italicum to postharvest citrus fungicides in California. Phytopathology, 89(9), 716–721.Iqbal, S. M., Gopal, A., Sankaranarayanan, P. E., & Nair, A. B. (2016). Classification of selected citrus fruits based on color using machine vision system. International Journal of Food Properties, 19, 272–288.Jackson, J. E. (1991). A user’s guide to principal components. New York: Wiley.Jafari, A., Fazayeli, A., & Zarezadeh, M. R. (2014). Estimation of orange skin thickness based on visual texture coarseness. Biosystems Engineering, 117, 73–82.Jiménez-Cuesta, M. J., Cuquerella, J., & Martínez-Jávega, J. M. (1981). Determination of a color index for citrus fruit degreening. In Proceedings of the International Society of Citriculture, 2, 750–753.Kim, D. G., Burks, T. F., Qin, J., & Bulanon, D. M. (2009). Classification of grapefruit peel diseases using color texture feature analysis. International Journal of Agricultural and Biological Engineering, 2, 41–50.Kim, D. G., Burks, T. F., Ritenour, M. A., & Qin, J. (2014). Citrus black spot detection using hyperspectral imaging. International Journal of Agricultural and Biological Engineering, 7, 20–27.Kohno, Y., Kondo, N., Iida, M., Kurita, M., Shiigi, T., Ogawa, Y., Kaichi, T., & Okamoto, S. (2011). Development of a mobile grading machine for citrus fruit. Engineering in Agriculture, Environment and Food, 4, 7–11.Kondo, N., Kuramoto, M., Shimizu, H., Ogawa, Y., Kurita, M., Nishizu, T., Chong, V. K., & Yamamoto, K. (2009). Identification of fluorescent substance in mandarin orange skin for machine vision system to detect rotten citrus fruits. Engineering in Agriculture, Environment and Food, 2, 54–59.Kurita, M., Kondo, N., Shimizu, H., Ling, P. P., Falzea, P. D., Shiigi, T., Ninomiya, K., Nishizu, T., & Yamamoto, K. (2009). A double image acquisition system with visible and UV LEDs for citrus fruit. Journal of Robotics and Mechatronics, 21, 533–540.Kurtulmus, F., Lee, W. S., & Vardar, A. (2011). Green citrus detection using eigenfruit, color and circular Gabor texture features under natural outdoor conditions. Computers and Electronics in Agriculture, 78(2), 140–149.Ladaniya, M. S. (2010). Citrus fruit: biology, technology and evaluation. San Diego: Academic Press.Li, H., Lee, W. S., & Wang, K. (2016). Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor colour images. Precision Agriculture. doi: 10.1007/s11119-016-9443-z.Li, H., Lee, W. S., Wang, K., Ehsani, R., & Yang, C. (2014). Extended spectral angle mapping (ESAM) for citrus greening disease detection using airborne hyperspectral imaging. Precision Agriculture, 15, 162–183.Li, J., Rao, X., & Ying, Y. (2011). Detection of common defects on oranges using hyperspectral reflectance imaging. Computers and Electronics in Agriculture, 78, 38–48.Li, J., Rao, X., & Ying, Y. (2012a). Development of algorithms for detecting citrus canker based on hyperspectral reflectance imaging. Journal of the Science of Food and Agriculture, 92, 125–134.Li, J., Rao, X., Wang, F., Wu, W., & Ying, Y. (2013). Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods. Postharvest Biology and Technology, 82, 59–69.Li, J., Rao, X., Ying, Y., & Wang, D. (2010). Detection of navel oranges canker based on hyperspectral imaging technology. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 26, 222–228.Li, X., Lee, W. S., Li, M., Ehsani, R., Mishra, A., Yang, C., & Mangan, R. (2012b). Spectral difference analysis and airborne imaging classification for citrus greening infected trees. Computers and Electronics in Agriculture, 83, 32–46.Li, X., Lee, W. S., Li, M., Ehsani, R., Mishra, A. R., Yang, C., & Mangan, R. L. (2015). Feasibility study on Huanglongbing (citrus greening) detection based on WorldView-2 satellite imagery. Biosystems Engineering, 132, 28–38.Lopes, L. B., VanDeWall, H., Li, H. T., Venugopal, V., Li, H. K., Naydin, S., Hosmer, J., Levendusky, M., Zheng, H., Bentley, M. V., Levin, R., & Hass, M. A. (2010). Topical delivery of lycopene using microemulsions: enhanced skin penetration and tissue antioxidant activity. Journal of Pharmaceutical Sciences, 99, 1346–1357.López, J. J., Cobos, M., & Aguilera, E. (2011). Computer-based detection and classification of flaws in citrus fruits. Neural Computing and Applications, 20, 975–981.López-García, F., Andreu, G., Blasco, J., Aleixos, N., & Valiente, J. M. (2010). Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Computers and Electronics in Agriculture, 71, 189–197.Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., & Blasco, J. (2013a). Selection of optimal wavelength features for decay detection in citrus fruit using the ROC curve and neural networks. Food and Bioprocess Technology, 6(2), 530–541.Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., & Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology, 5(4), 1121–1142.Lorente, D., Blasco, J., Serrano, A. J., Soria-Olivas, E., Aleixos, N., & Gómez-Sanchis, J. (2013b). Comparison of ROC feature selection method for the detection of decay in citrus fruit using hyperspectral images. Food and Bioprocess Technology, 6(12), 3613–3619.Lorente, D., Zude, M., Regen, C., Palou, L., Gómez-Sanchis, J., & Blasco, J. (2013c). Early decay detection in citrus fruit using laser-light backscattering imaging. Postharvest Biology and Technology, 86, 424–430.Lorente, D., Zude, M., Idler, C., Gómez-Sanchis, J., & Blasco, J. (2015). Laser-light backscattering imaging for early decay detection in citrus fruit using both a statistical and a physical model. Journal of Food Engineering, 154, 76–85.Maf Industries. (2016). VIOTEC brochure. http://mafindustries.com/wp-content/uploads/2015/02/viotec3.pdf . Accessed March 2016.Magwaza, L. S., Opara, U. L., Nieuwoudt, H., Cronje, P. J. R., Saeys, W., & Nicolaï, B. (2012). NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Food and Bioprocess Technology, 5(2), 425–444.Mehta, S. S., & Burks, T. F. (2014). Vision-based control of robotic manipulator for citrus harvesting. Computers and Electronics in Agriculture, 102, 146–158.Moltó, E., Blasco, J., & Gómez-Sanchis, J. (2010). Analysis of hyperspectral images of citrus fruits. In D.-W. Sun (Ed.), Hyperspectral imaging for food quality analysis and control (pp. 321–348). California: Academic Press.Moltó, E., Plá, F., & Juste, F. (1992). Vision systems for the location of citrus fruit in a tree canopy. Journal of Agricultural Engineering Research, 52, 101–110.Momin, A., Kondo, N., Kuramoto, M., Ogawa, Y., Yamamoto, K., & Shiigi, T. (2012). Investigation of excitation wavelength for fluorescence emission of citrus peels based on UV-VIS spectra. Engineering in Agriculture, Environment and Food, 5, 126–132.Momin, A., Kondo, N., Ogawa, Y., Ido, K., & Ninomiya, K. (2013b). Patterns of fluorescence associated with citrus peel defects. Engineering in Agriculture, Environment and Food, 6, 54–60.Momin, A., Kuramoto, M., Kondo, N., Ido, K., Ogawa, Y., Shiigi, T., & Ahmad, U. (2013a). Identification of UV-fluorescence components for detecting peel defects of lemon and yuzu using machine vision. Engineering in Agriculture, Environment and Food, 6, 165–171.Morgan, S. P., & Stockford, I. M. (2003). Surface-reflection elimination in polarization imaging of superficial tissue. Optics Letters, 28, 114–116.Niphadkar, N. P., Burks, T. F., Qin, J., & Ritenour, M. (2013b). Edge effect compensation for citrus canker lesion detection due to light source variation—a hyperspectral imaging application. Agricultural Engineering International: CIGR Journal, 15, 314–327.Niphadkar, N. P., Burks, T. F., Qin, J. W., & Ritenour, M. A. (2013a). Estimation of citrus canker lesion size using hyperspectral reflectance imaging. International Journal of Agricultural and Biological Engineering, 6, 41–51.Obenland, D., Margosan, D., Smilanick, J. L., & Mackey, B. (2010). Ultraviolet fluorescence to identify navel oranges with poor peel quality and decay. HortTechnology, 20, 991–995.Ogawa, Y., Abdul, M. M., Kuramoto, M., Kohno, Y., Shiigi, T., Yamamoto, K., & Kondo, K. (2011). Rotten part detection on citrus fruit surfaces by use of fluorescent images. The Review of Laser Engineering, 394, 255–261.Okamoto, H., & Lee, W. S. (2009). Green citrus detection using hyperspectral imaging. Computers and Electronics in Agriculture, 66(2), 201–208.Omid, M., Khojastehnazhand, M., & Tabatabaeefar, A. (2010). Estimating volume and mass of citrus fruits by image processing technique. Journal of Food Engineering, 100, 315–321.Ottavian, M., Barolo, M., & García-Muñoz, S. (2013). Maintenance of machine vision systems for product quality assessment. Part I. Addressing changes in lighting conditions. Industrial & Engineering Chemistry Research, 52, 12309–12318.Ottavian, M., Barolo, M., & García-Muñoz, S. (2014). Maintenance of machine vision systems for product quality assessment. Part II. Addressing camera replacement. Industrial & Engineering Chemistry Research, 53, 1529–1536.Palou, L. (2014). Penicillium digitatum, Penicillium italicum (green mold, blue mold). In S. Bautista-Baños (Ed.), Postharvest decay. Control strategies. London: Elsevier.Palou, L., Smilanick, J. L., Montesinos-Herrero, C., Valencia-Chamorro, S., & Pérez-Gago, M. B. (2011). Novel approaches for postharvest preservation of fresh citrus fruits. In Slaker (Ed.), Citrus fruits: properties, consumption and nutrition. New York: Nova Science Publishers, Inc..Pongnumkul, S., Chaovalit, P., & Surasvadi, N. (2015). Applications of smartphone-based sensors in agriculture: a systematic review of research. Journal of Sensors, Open Access Article ID 195308.Pourreza, A., Lee, W. S., Ehsani, R., Schueller, J. K., & Raveh, E. (2015a). An optimum method for real-time in-field detection of Huanglongbing disease using a vision sensor. Computers and Electronics in Agriculture, 110, 221–232.Pourreza, A., Lee, W. S., Etxeberria, E., & Banerjee, A. (2015b). An evaluation of a vision based sensor performance in Huanglongbing disease identification. Biosystems Engineering, 130, 13–22.Qin, J., Burks, T. F., Kim, M. S., Chao, K., & Ritenour, M. A. (2008). Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method. Sensing and Instrumentation for Food Quality and Safety, 2(3), 168–177.Qin, J., Burks, T. F., Ritenour, M. A., & Gordon Bonn, W. (2009). Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. Journal of Food Engineering, 93, 183–191.Qin, J., Burks, T. F., Zhao, X., Niphadkar, N., & Ritenour, M. A. (2011). Multispectral detection of citrus canker using hyperspectral band selection. Transactions of the ASABE, 54, 2331–2341.Qin, J., Burks, T. F., Zhao, X., Niphadkar, N., & Ritenour, M. A. (2012). Development of a two-band spectral imaging system for real-time citrus canker detection. Journal of Food Engineering, 108, 87–93.Sengupta, S., & Lee, W. S. (2014). Identification and determination of the number of immature green citrus fruit under different ambient light conditions.