A Bibliometric Diagnosis and Analysis about Smart Cities

[EN] This article aims to present a bibliometric analysis of Smart Cities. The study analyzes the most important journals during the period between 1991 and 2019. It provides helpful insights into the document types, the distribution of countries/territories, the distribution of institutions, the authors' geographical distribution, the most active authors and their research interests or fields, the relationships between principal authors and more relevant publications, and the most cited articles. This paper also provides important information about the core and historical references and the most cited papers. The analysis used the keywords and thematic noun-phrases in the titles and abstracts of the sample papers to explore the hot research topics in the top journals (e.g., 'Smart Cities', 'Intelligent Cities', 'Sustainable Cities', 'e-Government', 'Digital Transformation', 'Knowledge-Based City', etc.). The main objective is to have a quantitative description of the published literature about Smart Cities; this description will be the basis for the development of a methodology for the diagnosis of the maturity of a Smart City. The results presented here help to define the scientific concept of Smart Cities and to measure the importance that the term has gained through the years. The study has allowed us to know the main indicators of the published literature in depth, from the date of publication of the first articles and the evolution of these indicators to the present day. From the main indicators in the literature, some were selected to be applied: The most influential journals on Smart Cities according to the general citation structure in Smart Cities, Global Impact Factor of Smart Cities, number of publications, publications on Smart Cities around the world, and their correlation.Pérez, LM.; Oltra Badenes, RF.; Oltra Gutiérrez, JV.; Gil Gómez, H. (2020). A Bibliometric Diagnosis and Analysis about Smart Cities. Sustainability. 12(16):1-43. https://doi.org/10.3390/su12166357S1431216Guo, Y.-M., Huang, Z.-L., Guo, J., Li, H., Guo, X.-R., & Nkeli, M. J. (2019). Bibliometric Analysis on Smart Cities Research. Sustainability, 11(13), 3606. doi:10.3390/su11133606Mora, L., Bolici, R., & Deakin, M. (2017). The First Two Decades of Smart-City Research: A Bibliometric Analysis. Journal of Urban Technology, 24(1), 3-27. doi:10.1080/10630732.2017.1285123Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart Cities: Definitions, Dimensions, Performance, and Initiatives. Journal of Urban Technology, 22(1), 3-21. doi:10.1080/10630732.2014.942092Li, C., Liu, X., Dai, Z., & Zhao, Z. (2019). Smart City: A Shareable Framework and Its Applications in China. Sustainability, 11(16), 4346. doi:10.3390/su11164346Merigó, J. M., & Yang, J.-B. (2016). Accounting Research: A Bibliometric Analysis. Australian Accounting Review, 27(1), 71-100. doi:10.1111/auar.12109Garg, K. C., & Sharma, C. (2017). Bibliometrics of Library and Information Science research in India during 2004-2015. DESIDOC Journal of Library & Information Technology, 37(3), 221-227. doi:10.14429/djlit.37.3.11188Metse, A. P., Wiggers, J. H., Wye, P. M., Wolfenden, L., Prochaska, J. J., Stockings, E. A., … Bowman, J. A. (2016). Smoking and Mental Illness: A Bibliometric Analysis of Research Output Over Time. Nicotine & Tobacco Research, 19(1), 24-31. doi:10.1093/ntr/ntw249Broadus, R. N. (1987). Toward a definition of «bibliometrics». Scientometrics, 12(5-6), 373-379. doi:10.1007/bf02016680Hood, W. W., & Wilson, C. S. (2001). Scientometrics, 52(2), 291-314. doi:10.1023/a:1017919924342Thelwall, M. (2008). Bibliometrics to webometrics. Journal of Information Science, 34(4), 605-621. doi:10.1177/0165551507087238Bar-Ilan, J. (2008). Informetrics at the beginning of the 21st century—A review. Journal of Informetrics, 2(1), 1-52. doi:10.1016/j.joi.2007.11.001Narin, F., Olivastro, D., & Stevens, K. A. (1994). Bibliometrics/Theory, Practice and Problems. Evaluation Review, 18(1), 65-76. doi:10.1177/0193841x9401800107Zupic, I., & Čater, T. (2014). Bibliometric Methods in Management and Organization. Organizational Research Methods, 18(3), 429-472. doi:10.1177/1094428114562629OSAREH, F. (1996). Bibliometrics, Citation Analysis and Co-Citation Analysis: A Review of Literature I. Libri, 46(3). doi:10.1515/libr.1996.46.3.149Merigó, J. M., Gil-Lafuente, A. M., & Yager, R. R. (2015). An overview of fuzzy research with bibliometric indicators. Applied Soft Computing, 27, 420-433. doi:10.1016/j.asoc.2014.10.035Blanco-Mesa, F., Merigó, J. M., & Gil-Lafuente, A. M. (2017). Fuzzy decision making: A bibliometric-based review. Journal of Intelligent & Fuzzy Systems, 32(3), 2033-2050. doi:10.3233/jifs-161640Björneborn, L., & Ingwersen, P. (2004). Toward a basic framework for webometrics. Journal of the American Society for Information Science and Technology, 55(14), 1216-1227. doi:10.1002/asi.20077Gupta, B. . M., & Dhawan, S. (2019). Electronic books A scientometric assessment of global literature during 1993 2018. DESIDOC Journal of Library & Information Technology, 39(5), 251-258. doi:10.14429/djlit.39.5.14573Kokol, P., Blažun Vošner, H., & Završnik, J. (2020). Application of bibliometrics in medicine: a historical bibliometrics analysis. Health Information & Libraries Journal, 38(2), 125-138. doi:10.1111/hir.12295Michalopoulos, A., & Falagas, M. E. (2005). A Bibliometric Analysis of Global Research Production in Respiratory Medicine. Chest, 128(6), 3993-3998. doi:10.1378/chest.128.6.3993Lefaivre, K. A., Shadgan, B., & O’Brien, P. J. (2011). 100 Most Cited Articles in Orthopaedic Surgery. Clinical Orthopaedics & Related Research, 469(5), 1487-1497. doi:10.1007/s11999-010-1604-1Kelly, J. C., Glynn, R. W., O’Briain, D. E., Felle, P., & McCabe, J. P. (2010). The 100 classic papers of orthopaedic surgery. The Journal of Bone and Joint Surgery. British volume, 92-B(10), 1338-1343. doi:10.1302/0301-620x.92b10.24867Zhang, M., Zhou, Y., Lu, Y., He, S., & Liu, M. (2019). The 100 most-cited articles on prenatal diagnosis. Medicine, 98(38), e17236. doi:10.1097/md.0000000000017236Zou, Y., Luo, Y., Zhang, J., Xia, N., Tan, G., & Huang, C. (2019). Bibliometric analysis of oncolytic virus research, 2000 to 2018. Medicine, 98(35), e16817. doi:10.1097/md.0000000000016817Svider, P. F., Choudhry, Z. A., Choudhry, O. J., Baredes, S., Liu, J. K., & Eloy, J. A. (2012). The use of theh-indexin academic otolaryngology. The Laryngoscope, 123(1), 103-106. doi:10.1002/lary.23569Poskevicius, L., De la Flor-Martínez, M., Galindo-Moreno, P., & Juodzbalys, G. (2019). Scientific Publications in Dentistry in Lithuania, Latvia, and Estonia Between 1996 and 2018: A Bibliometric Analysis. Medical Science Monitor, 25, 4414-4422. doi:10.12659/msm.914223Ahmad, P., Asif, J. A., Alam, M. K., & Slots, J. (2019). A bibliometric analysis of Periodontology 2000. Periodontology 2000, 82(1), 286-297. doi:10.1111/prd.12328Kostoff, R. N., Toothman, D. R., Eberhart, H. J., & Humenik, J. A. (2001). Text mining using database tomography and bibliometrics: A review. Technological Forecasting and Social Change, 68(3), 223-253. doi:10.1016/s0040-1625(01)00133-0Grant, J. (2000). Evaluating «payback» on biomedical research from papers cited in clinical guidelines: applied bibliometric study. BMJ, 320(7242), 1107-1111. doi:10.1136/bmj.320.7242.1107Vergidis, P. I., Karavasiou, A. I., Paraschakis, K., Bliziotis, I. A., & Falagas, M. E. (2005). Bibliometric analysis of global trends for research productivity in microbiology. European Journal of Clinical Microbiology & Infectious Diseases, 24(5), 342-346. doi:10.1007/s10096-005-1306-xSuárez Roldan, C., Chaparro, N., & Rojas-Galeano, S. (2019). Análisis Bibliométrico de la Revista Ingeniería (2010-2017). Ingeniería, 24(2). doi:10.14483/23448393.14678Ratten, V., Pellegrini, M. M., Fakhar Manesh, M., & Dabić, M. (2020). Trends and changes in Thunderbird International Business Review journal: A bibliometric review. Thunderbird International Business Review, 62(6), 721-732. doi:10.1002/tie.22124Baker, H. K., Kumar, S., & Pattnaik, D. (2020). Fifty years of The Financial Review  : A bibliometric overview. Financial Review, 55(1), 7-24. doi:10.1111/fire.12228Charlesworth, M., Klein, A. A., & White, S. M. (2019). A bibliometric analysis of the conversion and reporting of pilot studies published in six anaesthesia journals. Anaesthesia, 75(2), 247-253. doi:10.1111/anae.14817Van Noorden, R., Maher, B., & Nuzzo, R. (2014). The top 100 papers. Nature, 514(7524), 550-553. doi:10.1038/514550aNicoll, L. H., Oermann, M. H., Carter‐Templeton, H., Owens, J. K., & Edie, A. H. (2020). A bibliometric analysis of articles identified by editors as representing excellence in nursing publication: Replication and extension. Journal of Advanced Nursing, 76(5), 1247-1254. doi:10.1111/jan.14316Liu, W., Wang, Z., & Zhao, H. (2020). Comparative study of customer relationship management research from East Asia, North America and Europe: A bibliometric overview. Electronic Markets, 30(4), 735-757. doi:10.1007/s12525-020-00395-7Cronin, B. (2001). Bibliometrics and beyond: some thoughts on web-based citation analysis. Journal of Information Science, 27(1), 1-7. doi:10.1177/016555150102700101Durieux, V., & Gevenois, P. A. (2010). Bibliometric Indicators: Quality Measurements of Scientific Publication. Radiology, 255(2), 342-351. doi:10.1148/radiol.09090626Guerola Navarro, V., Oltra Badenes, R. F., Gil Gomez, H., & Gil Gomez, J. A. (2020). Customer Relationship Management (CRM): A Bibliometric Analysis. International Journal of Services Operations and Informatics, 10(3), 1. doi:10.1504/ijsoi.2020.10030517Vicedo, P., Gil-Gómez, H., Oltra-Badenes, R., & Guerola-Navarro, V. (2020). A bibliometric overview of how critical success factors influence on enterprise resource planning implementations. Journal of Intelligent & Fuzzy Systems, 38(5), 5475-5487. doi:10.3233/jifs-179639Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8), 981-1012. doi:10.1016/j.techfore.2006.04.004Fersht, A. (2009). The most influential journals: Impact Factor and Eigenfactor. Proceedings of the National Academy of Sciences, 106(17), 6883-6884. doi:10.1073/pnas.0903307106Fu, H.-Z., Wang, M.-H., & Ho, Y.-S. (2013). Mapping of drinking water research: A bibliometric analysis of research output during 1992–2011. Science of The Total Environment, 443, 757-765. doi:10.1016/j.scitotenv.2012.11.061Fu, H., Ho, Y., Sui, Y., & Li, Z. (2010). A bibliometric analysis of solid waste research during the period 1993–2008. Waste Management, 30(12), 2410-2417. doi:10.1016/j.wasman.2010.06.008Wang, H., He, Q., Liu, X., Zhuang, Y., & Hong, S. (2012). Global urbanization research from 1991 to 2009: A systematic research review. Landscape and Urban Planning, 104(3-4), 299-309. doi:10.1016/j.landurbplan.2011.11.006Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809-1831. doi:10.1007/s11192-015-1645-

The role of sex differences in detecting deception in computer-mediated communication in English

[EN] While deception seems to be a common approach in interpersonal communication, most examination on interpersonal deception sees the sex of the interlocutor as unconnected with the capability to notice deceptive messages. This research studies the truth and deception detection capability of both male and female receivers when replying to both true and deceptive messages from both male and female speakers. The outcomes indicate that sex may be a significant variable in comprehending the interpersonal detection probabilities of truth and of lies. An interaction of variables including the speakers’ sex, receivers’ sex, and whether the message appears to be truthful or deceptive is created to relate to detection capability.Kuzio, A. (2018). The role of sex differences in detecting deception in computer-mediated communication in English. Journal of Computer-Assisted Linguistic Research. 2(1):39-53. doi:10.4995/jclr.2018.10521SWORD395321Aamodt, M. G., & Custer, H. (2006). Who can best catch a liar? A meta-analysis of individual differences in detecting deception. The Forensic Examiner, 15(1), 6-11.Blalock, H. M. (1972). Social Statistics. New York: McGraw Hill.Bond, C. F., & DePaulo, B. M. (2006). Accuracy of deception judgments. Personality and Social Psychology Review, 10(3), 214-234. https://doi.org/10.1207/s15327957pspr1003_2Boush, D. M., Friestad, M., & Wright, P. (2009). Deception in the marketplace : The psychology of deceptive persuasion and consumer self-protection. New York: Routledge.Camden, C., Motley, M. T., & Wilson, A. (1984). White lies in interpersonal communication: A taxonomy and preliminary investigation of social motivations. Western Journal of Speech Communication, 48(4), 309-325. https://doi.org/10.1080/10570318409374167Carlson, J., George, J., Burgoon, J., Adkins, M., & White, C. (2004). Deception in computer mediated communication. Group Decision and Negotiation, 13, 5-28. https://doi.org/10.1023/B:GRUP.0000011942.31158.d8Daft, R.L. & Lengel, R.H. (1986). Information richness: A new approach to managerial behavior and organizational design. In Cummings, L. L. & Staw, B.M. (Eds.), Research in organizational behavior 6 (pp. 191-233). Homewood, IL: JAI Press.DePaulo, B. M., Epstein, J. A., & Wyer, M. M. (1993). Sex differences in lying: How women and men deal with the dilemma of deceit. In M. Lewis, & C. Saarni (Eds.), Lying and deception in everyday life (pp. 126-147). New York: Guilford Press.DePaulo, B. M., Kashy, D. A., Kirkendol, S. E., Wyer, M. M., & Epstein, J. A. (1996). Lying in everyday life. Journal of Personality and Social Psychology, 70(5), 979- 995. https://doi.org/10.1037/0022-3514.70.5.979DePaulo, B. M., Kirkendol, S. E., Tang, J., & O'Brien, T. P. (1988). The motivational impairment effect in the communication of deception: Replications and extensions. Journal of Nonverbal Behavior, 12(3), 177-202. https://doi.org/10.1007/BF00987487DePaulo, B. M., Lassiter, G. D., & Stone, J. L. (1982). Attention all determinants of success at detecting deception and truth. Personality and Social Psychology Bulletin, 8(2), 273-279. https://doi.org/10.1177/0146167282082014DePaulo, B. M., & Rosenthal, R. (1981). Telling lies. Journal of Personality and Social Psychology, 37(10), 1713-1722. https://doi.org/10.1037/0022-3514.37.10.1713Dreber, A., & Johannesson, M. (2008). Gender differences in deception. Economics Letters, 99(1), 197-199. https://doi.org/10.1016/j.econlet.2007.06.027Ekman, P., & O'Sullivan, M. (1991). Who can catch a liar? American Psychologist, 46(9), 913-920. https://doi.org/10.1037/0003-066X.46.9.913Ekman, P., O'Sullivan, M., & Frank, M. G. (1999). A few can catch a liar. Psychological Science, 10(3), 263-266. https://doi.org/10.1111/1467-9280.00147Feldman, R. S., Forrest, J. A., & Happ, B. R. (2002). Self-presentation and verbal deception: Do self-presenters lie more? Basic and Applied Social Psychology, 24(2), 163-170. https://doi.org/10.1207/153248302753674848George, J. F., & Robb, A. (2008). Deception and computer-mediated communication in daily life. Communication Reports, 21(2), 92-103. https://doi.org/10.1080/08934210802298108Hample, D. (1980). Purposes and effects of lying. Southern Speech Communication Journal, 46(1), 33-47. https://doi.org/10.1080/10417948009372474Hancock, J., Thom-Santelli, J., & Ritchie, T. (2004). Deception and design: The impact of communication technology on lying behavior. In E. Dykstra-Erickson, & M. Tscheligi (Eds.), Proceedings of the 2004 conference on human factors in computing systems (pp. 129-134). New York: Association for Computing Machinery.https://doi.org/10.1145/985692.985709Haselton, M. G., Buss, D. M., Oubaid, V., & Angleitner, A. (2005). Sex, lies, and strategic interference: The psychology of deception between the sexes. Personality and Social Psychology Bulletin, 31(1), 3-23. https://doi.org/10.1177/0146167204271303Inglehart, R., Basa-ez, M., & Moreno, A. (1998). Human values and beliefs: A crosscultural sourcebook. Ann Arbor, MI: University of Michigan Press. https://doi.org/10.3998/mpub.14858Knapp, L. M., Hart, R. P., & Dennis, H. S. (1974). An exploration of deception as a communication construct. Human Communication Research, 1(1), 15-29. https://doi.org/10.1111/j.1468-2958.1974.tb00250.xKraut, R. E. (1980). Behavioral roots of person perception: The deception judgments of customs inspectors and laymen. Journal of Personality and Social Psychology, 39(5), 784-798. https://doi.org/10.1037/0022-3514.39.5.784Kuzio, A. (2018). Cross-cultural Deception in Polish and American English in Computer-Mediated Communication. New Castle upon Tyne: Cambridge Scholars Publishing.Levine, T. R., & Kim, R. K. (2010). Some considerations for a new theory of deceptive communication. In M. S. McGlone, & M. L. Knapp (Eds.), The interplay of truth and deception: New agendas in theory and research (pp. 16-34). New York: Routledge.Levine, T. R., Park, H. S., & McCornack, S. A. (2006). Accuracy in detecting truths and lies: Documenting the "Veracity Effect". Communication Monographs, 66(2), 125- 144. https://doi.org/10.1080/03637759909376468Manstead, A., Wagner, H. L., & McDonald, C. J. (1986). Deceptive and non-deceptive communications: Sending experience, modality, and individual abilities. Journal of Nonverbal Behavior, 10(3), 147-167. https://doi.org/10.1007/BF00987612McCornack, S. A., & Parks, M. R. (1990). What women know that men don't: Sex differences in determining the truth behind deceptive messages. Journal of Social and Personal Relationships, 7(1), 107-118. https://doi.org/10.1177/0265407590071006Park, H. S., Levine, T. R., McCornack, S. A., Morrison, K., & Ferrara, M. (2002). How people really detect lies. Communication Monographs, 69(2), 144-157. https://doi.org/10.1080/714041710Prater, T., & Kiser, S. B. (2002). Lies, lies, and more lies. SAM Advanced Management Journal,67(2), 9-36.Sanchez-Pages, S., & Vorsatz, M. (2008). Enjoy the silence: An experiment on truthtelling. Experimental Economics, 12(2), 220-241. https://doi.org/10.1007/s10683-008-9211-7Seiter, J. S., Bruschke, J., & Bai, C. (2002). The acceptability of deception as a function of perceivers' culture, deceiver's intention, and deceiver-deceived relationship. Western Journal of Communication, 66(2), 158-180. https://doi.org/10.1080/10570310209374731Serota, K. B., Levine, T. R., & Boster, F. J. (2010). The prevalence of lying in America: Three studies of self-reported lies. Human Communication Research, 36(1), 2-25. https://doi.org/10.1111/j.1468-2958.2009.01366.xTurner, R. E., Edgley, C., & Olmstead, G. (1975). Information control in conversations: Honesty is not always the best policy. Kansas Journal of Sociology, 11(1), 69-89.https://doi.org/10.17161/STR.1808.6098Zuckerman, M., DePaulo, B. M., & Rosenthal, R. (1981). Verbal and nonverbal communication of deception. In L. Berkowitz (Ed.), Advances in experimental social psychology (volume 11, pp. 1-59). New York: Academic Press.https://doi.org/10.1016/S0065-2601(08)60369-

San Juan Bautista (Burguillos del Cerro, Badajoz), un ejemplo de documentación del Patrimonio con nuevas tecnologías

[ES] La presente comunicación pretende acercar la aplicación de las últimas técnicas en Levantamiento con Láser Escaner y textura fotorrealista en la Iglesia de San Juan Bautista (Burguillos del Cerro, Badajoz), dentro de un proyecto de puesta en valor de un yacimiento de notable interés para la arqueología extremeña y peninsular, donde se aunaron las técnicas más actuales en documentación del patrimonio con los resultados obtenidos de la intervención arqueológica, consiguiendo una completa y exhaustiva documentación del yacimiento arqueológico.[EN] The present communication explains the application of the latest techniques in Laser Scanner and photorealistic texture in the Church of San Juan Bautista (Burguillos del Cerro, Badajoz), in a project which objetive is increase the value of a site for peninsular archeology, where joined the lates techniques in cultural heritage documentation and the results of the archaeological dig. We obtain in this practical work a complete documentation of the archaeological site.Menéndez Menéndez, A.; Gibello Bravo, VM.; Ortiz Coder, P. (2011). San Juan Bautista (Burguillos del Cerro, Badajoz), un ejemplo de documentación del Patrimonio con nuevas tecnologías. Virtual Archaeology Review. 2(3):71-74. https://doi.org/10.4995/var.2011.4608OJS717423BÖHN, J., (2004): "Multi-image fusion for occlusion-free façade texturing. The International Archives of the Photogrammetry", Remote Sensing and Spatial Information Science, Volume XXXV-5, pp. 867-872.DAL PIAZ, V., GUARNIERI, A., PIROTTI, F. , A. VETTORE A. (2007): "International Archives of Photogrammetry", Remote Sensing and Spatial Information Sciences. Volume XXXVI-5/W47. Stability control of an historical structure with TLS survey. ETH Zurich, Switzerland, 12-13 July 2007.FRASER, C.S. (1997): "Digital camera self-calibration", ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 52, No. 4, pp. 149-159. http://dx.doi.org/10.1016/S0924-2716(97)00005-1GIBELLO BRAVO, V. M. (2008): El poblamiento islámico en Extremadura. Territorio, asentamientos e Itinerarios, Mérida.GIBELLO BRAVO, V.M. y AMIGO MARCOS, R. (2001): "San Juan Bautista: una rabita hispanomusulmana inédita en la antigua iglesia parroquial de Burguillos del Cerro (Badajoz)", Mérida ciudad y patrimonio. Revista de Arqueología, Arte y Urbanismo, nº5, pp. 173-189.GRÜN, A. and D. AKCA D. (2006): "Least Squares 3D Surface Matching". IAPRSSIS, Vol. 34(5/WG16), Dresden, Germany, on CDROM.HABIB, A., and MORGAN, M. (2003): "Automatic Calibration of Low-Cost Digital Cameras", Journal of Optical Engineering, Vol. 42, No. 4, pp.948-955. http://dx.doi.org/10.1117/1.1555732MENÉNDEZ MENÉNDEZ, A. (2010): Memoria de la intervención arqueológica para el Proyecto del Centro de Investigación Turismo y Cultura de San Juan Bautista (Burguillos del Cerro, Badajoz), Mérida (inédit

Improving Distributed Decision Making in Inventory Management: A Combined ABC-AHP Approach Supported by Teamwork

[EN] The need of organizations to ensure service levels that impact on customer satisfaction has required the design of collaborative processes among stakeholders involved in inventory decision making. The increase of quantity and variety of items, on the one hand, and demand and customer expectations, on the other hand, are transformed into a greater complexity in inventory management, requiring effective communication and agreements between the leaders of the logistics processes. Traditionally, decision making in inventory management was based on approaches conditioned only by cost or sales volume. These approaches must be overcome by others that consider multiple criteria, involving several areas of the companies and taking into account the opinions of the stakeholders involved in these decisions. Inventory management becomes part of a complex system that involves stakeholders from different areas of the company, where each agent has limited information and where the cooperation between such agents is key for the system's performance. In this paper, a distributed inventory control approach was used with the decisions allowing communication between the stakeholders and with a multicriteria group decision-making perspective. This work proposes a methodology that combines the analysis of the value chain and the AHP technique, in order to improve communication and the performance of the areas related to inventory management decision making. This methodology uses the areas of the value chain as a theoretical framework to identify the criteria necessary for the application of the AHP multicriteria group decision-making technique. These criteria were defined as indicators that measure the performance of the areas of the value chain related to inventory management and were used to classify ABC inventory of the products according to these selected criteria. Therefore, the methodology allows us to solve inventory management DDM based on multicriteria ABC classification and was validated in a Colombian company belonging to the graphic arts sector.Pérez Vergara, IG.; Arias Sánchez, JA.; Poveda Bautista, R.; Diego-Mas, JA. (2020). Improving Distributed Decision Making in Inventory Management: A Combined ABC-AHP Approach Supported by Teamwork. Complexity. 2020:1-13. https://doi.org/10.1155/2020/6758108S1132020Poveda-Bautista, R., Baptista, D. C., & García-Melón, M. (2012). Setting competitiveness indicators using BSC and ANP. International Journal of Production Research, 50(17), 4738-4752. doi:10.1080/00207543.2012.657964Castro Zuluaga, C. A., Velez Gallego, M. C., & Catro Urrego, J. A. (2011). Clasificación ABC Multicriterio: Tipos de Criterios y efectos en la asignación de pesos. ITECKNE, 8(2). doi:10.15332/iteckne.v8i2.35Morash, E. A., & Clinton, S. R. (1998). Supply Chain Integration: Customer Value through Collaborative Closeness versus Operational Excellence. Journal of Marketing Theory and Practice, 6(4), 104-120. doi:10.1080/10696679.1998.11501814Fabbe-Costes, N. (2015). Évaluer la création de valeurdu Supply Chain Management. Logistique & Management, 23(4), 41-50. doi:10.1080/12507970.2015.11758621Flores, B. E., & Clay Whybark, D. (1986). Multiple Criteria ABC Analysis. International Journal of Operations & Production Management, 6(3), 38-46. doi:10.1108/eb054765Partovi, F. Y., & Burton, J. (1993). Using the Analytic Hierarchy Process for ABC Analysis. International Journal of Operations & Production Management, 13(9), 29-44. doi:10.1108/01443579310043619Balaji, K., & Kumar, V. S. S. (2014). Multicriteria Inventory ABC Classification in an Automobile Rubber Components Manufacturing Industry. Procedia CIRP, 17, 463-468. doi:10.1016/j.procir.2014.02.044Ramanathan, R. (2006). ABC inventory classification with multiple-criteria using weighted linear optimization. Computers & Operations Research, 33(3), 695-700. doi:10.1016/j.cor.2004.07.014Van Kampen, T. J., Akkerman, R., & Pieter van Donk, D. (2012). SKU classification: a literature review and conceptual framework. International Journal of Operations & Production Management, 32(7), 850-876. doi:10.1108/01443571211250112Flores, B. E., Olson, D. L., & Dorai, V. K. (1992). Management of multicriteria inventory classification. Mathematical and Computer Modelling, 16(12), 71-82. doi:10.1016/0895-7177(92)90021-cGajpal, P. P., Ganesh, L. S., & Rajendran, C. (1994). Criticality analysis of spare parts using the analytic hierarchy process. International Journal of Production Economics, 35(1-3), 293-297. doi:10.1016/0925-5273(94)90095-7Scala, N. M., Rajgopal, J., & Needy, K. L. (2014). Managing Nuclear Spare Parts Inventories: A Data Driven Methodology. IEEE Transactions on Engineering Management, 61(1), 28-37. doi:10.1109/tem.2013.2283170Hadad, Y., & Keren, B. (2013). ABC inventory classification via linear discriminant analysis and ranking methods. International Journal of Logistics Systems and Management, 14(4), 387. doi:10.1504/ijlsm.2013.052744Altay Guvenir, H., & Erel, E. (1998). Multicriteria inventory classification using a genetic algorithm. European Journal of Operational Research, 105(1), 29-37. doi:10.1016/s0377-2217(97)00039-8Rezaei, J., & Dowlatshahi, S. (2010). A rule-based multi-criteria approach to inventory classification. International Journal of Production Research, 48(23), 7107-7126. doi:10.1080/00207540903348361Hatefi, S. M., Torabi, S. A., & Bagheri, P. (2013). Multi-criteria ABC inventory classification with mixed quantitative and qualitative criteria. International Journal of Production Research, 52(3), 776-786. doi:10.1080/00207543.2013.838328Ishizaka, A., Pearman, C., & Nemery, P. (2012). AHPSort: an AHP-based method for sorting problems. International Journal of Production Research, 50(17), 4767-4784. doi:10.1080/00207543.2012.657966Yu, M.-C. (2011). Multi-criteria ABC analysis using artificial-intelligence-based classification techniques. Expert Systems with Applications, 38(4), 3416-3421. doi:10.1016/j.eswa.2010.08.127Tsai, C.-Y., & Yeh, S.-W. (2008). A multiple objective particle swarm optimization approach for inventory classification. International Journal of Production Economics, 114(2), 656-666. doi:10.1016/j.ijpe.2008.02.017Aydin Keskin, G., & Ozkan, C. (2013). Multiple Criteria ABC Analysis with FCM Clustering. Journal of Industrial Engineering, 2013, 1-7. doi:10.1155/2013/827274Lolli, F., Ishizaka, A., & Gamberini, R. (2014). New AHP-based approaches for multi-criteria inventory classification. International Journal of Production Economics, 156, 62-74. doi:10.1016/j.ijpe.2014.05.015Raja, A. M. L., Ai, T. J., & Astanti, R. D. (2016). A Clustering Classification of Spare Parts for Improving Inventory Policies. IOP Conference Series: Materials Science and Engineering, 114, 012075. doi:10.1088/1757-899x/114/1/012075Zowid, F. M., Babai, M. Z., Douissa, M. R., & Ducq, Y. (2019). Multi-criteria inventory ABC classification using Gaussian Mixture Model. IFAC-PapersOnLine, 52(13), 1925-1930. doi:10.1016/j.ifacol.2019.11.484Babai, M. Z., Ladhari, T., & Lajili, I. (2014). On the inventory performance of multi-criteria classification methods: empirical investigation. International Journal of Production Research, 53(1), 279-290. doi:10.1080/00207543.2014.952791Schneeweiss, C. (2003). Distributed decision making––a unified approach. European Journal of Operational Research, 150(2), 237-252. doi:10.1016/s0377-2217(02)00501-5Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83. doi:10.1504/ijssci.2008.017590Cakir, O., & Canbolat, M. S. (2008). A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology. Expert Systems with Applications, 35(3), 1367-1378. doi:10.1016/j.eswa.2007.08.041Liu, J., Liao, X., Zhao, W., & Yang, N. (2016). A classification approach based on the outranking model for multiple criteria ABC analysis. Omega, 61, 19-34. doi:10.1016/j.omega.2015.07.004Douissa, M. R., & Jabeur, K. (2016). A New Model for Multi-criteria ABC Inventory Classification: PROAFTN Method. Procedia Computer Science, 96, 550-559. doi:10.1016/j.procs.2016.08.233Lolli, F., Balugani, E., Ishizaka, A., Gamberini, R., Rimini, B., & Regattieri, A. (2018). Machine learning for multi-criteria inventory classification applied to intermittent demand. Production Planning & Control, 30(1), 76-89. doi:10.1080/09537287.2018.1525506Kartal, H., Oztekin, A., Gunasekaran, A., & Cebi, F. (2016). An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification. Computers & Industrial Engineering, 101, 599-613. doi:10.1016/j.cie.2016.06.004López-Soto, D., Angel-Bello, F., Yacout, S., & Alvarez, A. (2017). A multi-start algorithm to design a multi-class classifier for a multi-criteria ABC inventory classification problem. Expert Systems with Applications, 81, 12-21. doi:10.1016/j.eswa.2017.02.048Dweiri, F., Kumar, S., Khan, S. A., & Jain, V. (2016). Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Systems with Applications, 62, 273-283. doi:10.1016/j.eswa.2016.06.030Bruno, G., Esposito, E., Genovese, A., & Simpson, M. (2016). Applying supplier selection methodologies in a multi-stakeholder environment: A case study and a critical assessment. Expert Systems with Applications, 43, 271-285. doi:10.1016/j.eswa.2015.07.016Poza, C. (2020). A Conceptual Model to Measure Football Player’s Market Value. A Proposal by means of an Analytic Hierarchy Process. [Un modelo conceptual para medir el valor de mercado de los futbolistas. Una propuesta a través de un proceso analítico jerárquico]. RICYDE. Revista internacional de ciencias del deporte, 16(59), 24-42. doi:10.5232/ricyde2020.05903Guarnieri, P., Sobreiro, V. A., Nagano, M. S., & Marques Serrano, A. L. (2015). The challenge of selecting and evaluating third-party reverse logistics providers in a multicriteria perspective: a Brazilian case. Journal of Cleaner Production, 96, 209-219. doi:10.1016/j.jclepro.2014.05.040Ishizaka, A., & Labib, A. (2011). Selection of new production facilities with the Group Analytic Hierarchy Process Ordering method. Expert Systems with Applications, 38(6), 7317-7325. doi:10.1016/j.eswa.2010.12.004Partovi, F. Y., & Anandarajan, M. (2002). Classifying inventory using an artificial neural network approach. Computers & Industrial Engineering, 41(4), 389-404. doi:10.1016/s0360-8352(01)00064-xAlonso-Manzanedo, M., De-la -Fuente-Aragon, M. V., & Ros-McDonnell, L. (2013). A Proposed Collaborative Network Enterprise Model in the Fruit-and-Vegetable Sector Using Maturity Models. Annals of Industrial Engineering 2012, 359-366. doi:10.1007/978-1-4471-5349-8_42Augusto, M., Lisboa, J., Yasin, M., & Figueira, J. R. (2008). Benchmarking in a multiple criteria performance context: An application and a conceptual framework. European Journal of Operational Research, 184(1), 244-254. doi:10.1016/j.ejor.2006.10.05

Solution Approaches for the Management of the Water Resources in Irrigation Water Systems with Fuzzy Costs

[EN] Currently, the management of water networks is key to increase their sustainability. This fact implies that water managers have to develop tools that ease the decision-making process in order to improve the efficiency of irrigation networks, as well as their exploitation costs. The present research proposes a mathematical programming model to optimize the selection of the water sources and the volume over time in water networks, minimizing the operation costs as a function of the water demand and the reservoir capacity. The model, which is based on fuzzy methods, improves the evaluation performed by water managers when they have to decide about the acquisition of the water resources under uncertain costs. Different fuzzy solution approaches have been applied and assessed in terms of model complexity and computational efficiency, showing the solution accomplished for each one. A comparison between different methods was applied in a real water network, reaching a 20% total cost reduction for the best solution.Sanchis, R.; Díaz-Madroñero Boluda, FM.; López Jiménez, PA.; Pérez-Sánchez, M. (2019). Solution Approaches for the Management of the Water Resources in Irrigation Water Systems with Fuzzy Costs. Water. 11(12):1-22. https://doi.org/10.3390/w11122432S1221112Biswas, A. K. (2004). Integrated Water Resources Management: A Reassessment. Water International, 29(2), 248-256. doi:10.1080/02508060408691775Pahl-Wostl, C. (2006). Transitions towards adaptive management of water facing climate and global change. Water Resources Management, 21(1), 49-62. doi:10.1007/s11269-006-9040-4Wu, K., & Zhang, L. (2014). Progress in the Development of Environmental Risk Assessment as a Tool for the Decision-Making Process. Journal of Service Science and Management, 07(02), 131-143. doi:10.4236/jssm.2014.72011Hernández-Bedolla, J., Solera, A., Paredes-Arquiola, J., Pedro-Monzonís, M., Andreu, J., & Sánchez-Quispe, S. (2017). The Assessment of Sustainability Indexes and Climate Change Impacts on Integrated Water Resource Management. Water, 9(3), 213. doi:10.3390/w9030213Hunink, J., Simons, G., Suárez-Almiñana, S., Solera, A., Andreu, J., Giuliani, M., … Bastiaanssen, W. (2019). A Simplified Water Accounting Procedure to Assess Climate Change Impact on Water Resources for Agriculture across Different European River Basins. Water, 11(10), 1976. doi:10.3390/w11101976Pérez-Sánchez, M., Sánchez-Romero, F., Ramos, H., & López-Jiménez, P. (2016). Modeling Irrigation Networks for the Quantification of Potential Energy Recovering: A Case Study. Water, 8(6), 234. doi:10.3390/w8060234Corominas, J. (2010). Agua y energía en el riego, en la época de la sostenibilidad. Ingeniería del agua, 17(3). doi:10.4995/ia.2010.2977Romero, L., Pérez-Sánchez, M., & Amparo López-Jiménez, P. (2017). Improvement of sustainability indicators when traditional water management changes: a case study in Alicante (Spain). AIMS Environmental Science, 4(3), 502-522. doi:10.3934/environsci.2017.3.502Davies, E. G. R., & Simonovic, S. P. (2011). Global water resources modeling with an integrated model of the social–economic–environmental system. Advances in Water Resources, 34(6), 684-700. doi:10.1016/j.advwatres.2011.02.010ALCAMO, J., DÖLL, P., HENRICHS, T., KASPAR, F., LEHNER, B., RÖSCH, T., & SIEBERT, S. (2003). Development and testing of the WaterGAP 2 global model of water use and availability. Hydrological Sciences Journal, 48(3), 317-337. doi:10.1623/hysj.48.3.317.45290Sanchis, R., & Poler, R. (2019). Enterprise Resilience Assessment—A Quantitative Approach. Sustainability, 11(16), 4327. doi:10.3390/su11164327Rahaman, M. M., & Varis, O. (2005). Integrated water resources management: evolution, prospects and future challenges. Sustainability: Science, Practice and Policy, 1(1), 15-21. doi:10.1080/15487733.2005.11907961Markantonis, V., Reynaud, A., Karabulut, A., El Hajj, R., Altinbilek, D., Awad, I. M., … Bidoglio, G. (2019). Can the Implementation of the Water-Energy-Food Nexus Support Economic Growth in the Mediterranean Region? The Current Status and the Way Forward. Frontiers in Environmental Science, 7. doi:10.3389/fenvs.2019.00084Food and Agriculture Organization (FAO)www.fao.orgDirective 2000/60/EC of the European Parliament and of the Councilhttps://eur-lex.europa.eu/eli/dir/2000/60/ojNamany, S., Al-Ansari, T., & Govindan, R. (2019). Sustainable energy, water and food nexus systems: A focused review of decision-making tools for efficient resource management and governance. Journal of Cleaner Production, 225, 610-626. doi:10.1016/j.jclepro.2019.03.304Archibald, T. W., & Marshall, S. E. (2018). Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty. Environmental Modeling & Assessment, 23(6), 753-777. doi:10.1007/s10666-018-9628-0Chen, S., Shao, D., Gu, W., Xu, B., Li, H., & Fang, L. (2017). An interval multistage water allocation model for crop different growth stages under inputs uncertainty. Agricultural Water Management, 186, 86-97. doi:10.1016/j.agwat.2017.03.001Xie, Y. L., Xia, D. H., Huang, G. H., Li, W., & Xu, Y. (2015). A multistage stochastic robust optimization model with fuzzy probability distribution for water supply management under uncertainty. Stochastic Environmental Research and Risk Assessment, 31(1), 125-143. doi:10.1007/s00477-015-1164-8Heumesser, C., Fuss, S., Szolgayová, J., Strauss, F., & Schmid, E. (2012). Investment in Irrigation Systems under Precipitation Uncertainty. Water Resources Management, 26(11), 3113-3137. doi:10.1007/s11269-012-0053-xPereira-Cardenal, S. J., Mo, B., Riegels, N. D., Arnbjerg-Nielsen, K., & Bauer-Gottwein, P. (2015). Optimization of Multipurpose Reservoir Systems Using Power Market Models. Journal of Water Resources Planning and Management, 141(8), 04014100. doi:10.1061/(asce)wr.1943-5452.0000500Kumari, S., & Mujumdar, P. P. (2017). Fuzzy Set–Based System Performance Evaluation of an Irrigation Reservoir System. Journal of Irrigation and Drainage Engineering, 143(5), 04017002. doi:10.1061/(asce)ir.1943-4774.0001155Jairaj, P. G., & Vedula, S. (2000). Water Resources Management, 14(6), 457-472. doi:10.1023/a:1011117918943Li, M., Guo, P., Singh, V. P., & Zhao, J. (2016). Irrigation Water Allocation Using an Inexact Two-Stage Quadratic Programming with Fuzzy Input under Climate Change. JAWRA Journal of the American Water Resources Association, 52(3), 667-684. doi:10.1111/1752-1688.12415Bozorg-Haddad, O., Malmir, M., Mohammad-Azari, S., & Loáiciga, H. A. (2016). Estimation of farmers’ willingness to pay for water in the agricultural sector. Agricultural Water Management, 177, 284-290. doi:10.1016/j.agwat.2016.08.011Raju, K. S., & Duckstein, L. (2003). Multiobjective fuzzy linear programming for sustainable irrigation planning: an Indian case study. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 7(6), 412-418. doi:10.1007/s00500-002-0230-6Regulwar, D. G., & Gurav, J. B. (2012). Sustainable Irrigation Planning with Imprecise Parameters under Fuzzy Environment. Water Resources Management, 26(13), 3871-3892. doi:10.1007/s11269-012-0109-yMula, J., Poler, R., & Garcia-Sabater, J. P. (2008). Capacity and material requirement planning modelling by comparing deterministic and fuzzy models. International Journal of Production Research, 46(20), 5589-5606. doi:10.1080/00207540701413912Díaz-Madroñero, M., Mula, J., Jiménez, M., & Peidro, D. (2016). A rolling horizon approach for material requirement planning under fuzzy lead times. International Journal of Production Research, 55(8), 2197-2211. doi:10.1080/00207543.2016.1223382Mula, J., Poler, R., & Garcia, J. P. (2006). MRP with flexible constraints: A fuzzy mathematical programming approach. Fuzzy Sets and Systems, 157(1), 74-97. doi:10.1016/j.fss.2005.05.045Mula, J., Poler, R., & Garcia-Sabater, J. P. (2007). Material Requirement Planning with fuzzy constraints and fuzzy coefficients. Fuzzy Sets and Systems, 158(7), 783-793. doi:10.1016/j.fss.2006.11.003Díaz-Madroñero, M., Mula, J., & Jiménez, M. (2014). Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions. International Journal of Production Research, 52(23), 6971-6988. doi:10.1080/00207543.2014.920115Pérez-Sánchez, M., Díaz-Madroñero, M., Díaz-Madroñero, D.-M., … Josefa, J. (2017). Mathematical Programming Model for Procurement Selection in Water Irrigation Systems. A Case Study. Journal of Engineering Science and Technology Review, 10(6), 154-162. doi:10.25103/jestr.106.19Herrera, F., & Verdegay, J. L. (1995). Three models of fuzzy integer linear programming. European Journal of Operational Research, 83(3), 581-593. doi:10.1016/0377-2217(93)e0338-xHerrera, F., & Verdegay, J. L. (1996). Fuzzy boolean programming problems with fuzzy costs: A general study. Fuzzy Sets and Systems, 81(1), 57-76. doi:10.1016/0165-0114(94)00324-6Alavidoost, M. H., Babazadeh, H., & Sayyari, S. T. (2016). An interactive fuzzy programming approach for bi-objective straight and U-shaped assembly line balancing problem. Applied Soft Computing, 40, 221-235. doi:10.1016/j.asoc.2015.11.025Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193-214. doi:10.1016/j.fss.2007.08.010Yager, R. R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information Sciences, 24(2), 143-161. doi:10.1016/0020-0255(81)90017-7Lai, Y.-J., & Hwang, C.-L. (1992). A new approach to some possibilistic linear programming problems. Fuzzy Sets and Systems, 49(2), 121-133. doi:10.1016/0165-0114(92)90318-xZimmermann, H.-J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45-55. doi:10.1016/0165-0114(78)90031-3Selim, H., & Ozkarahan, I. (2006). A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach. The International Journal of Advanced Manufacturing Technology, 36(3-4), 401-418. doi:10.1007/s00170-006-0842-6Bellman, R. E., & Zadeh, L. A. (1970). Decision-Making in a Fuzzy Environment. Management Science, 17(4), B-141-B-164. doi:10.1287/mnsc.17.4.b14

Multimodal Sentiment Analysis of Instagram Using Cross-media Bag-of-words Model

Instagram, one of social media sharing services has increasing growth of use and popularity during recent years. Photos or videos shared by Instagram users are challenging to be mined and analyzed for some purposes. One type of studies can be applied to Instagram data is sentiment analysis, a field of study that learn and analyze people opinion, sentiment, and (or) evaluation about something. Sentiment analysis applied to Instagram can be used as analytics tool for some business purposes such as user behavior, market intelligence and user evaluation. This research aimed to analyze sentiment contained on Instagrams post by considering two modalities: images and English text on its caption. The Cross-media Bag-of-Words Model (CBM) was applied for analyzing the sentiment contained on Instagrams post. CBM treated text and image features as a unit of vector representation. These cross-media features then classified using logistic regression to predict sentiment values which categorized into three classes: positive, negative and neutral. Simulation results showed that the combination of unigram text features and 56-length images features achieves the highest accuracy. The accuracy achieved is 87.2%. Keywords : Instagram, sentiment analysis, Cross-media Bag-of-Words Model (CBM), logistic regression, classification Bibliography [1] D. Borth, R. Ji, T. Chen, T. Breuel, and S.-F. Chang, “Large-scale visual sentiment ontology and detectors using adjective noun pairs,” in Proceedings of the 21st ACM International Conference on Multimedia, ser. MM '13. New York, NY, USA: ACM, 2013, pp. 223–232. [2] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “Liblinear: A library for large linear classification,” J. Mach. Learn. Res., vol. 9, pp. 1871– 1874, Jun. 2008. [3] E. Ferrara, R. Interdonato, and A. Tagarelli, “Online popularity and topical interests through the lens of instagram,” in Proceedings of the 25th ACM Conference on Hypertext and Social Media, ser. HT '14. New York, NY, USA: ACM, 2014, pp. 24–34. [4] N. Gunawardena, J. Plumb, N. Xiao, and H. Zhang, “Instagram hashtag sentiment analysis,” in University of Utah CS530/CS630 Conference of Machine Learning 2013. Universiti of Utah CS530/CS630 Conference of Machine Learning 2013, 2013. [5] J. Han, Data Mining: Concepts and Techniques. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2005. [6] M. Hu and B. Liu, “Mining and summarizing customer reviews,” inProceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2004, pp. 168–177.[7] Y. Hu, L. Manikonda, and S. Kambhampati, “What we instagram: A first analysis of instagram photo content and user types,” International AAAI Conference on Weblogs and Social Media, 2014. [8] L. S. Huey and R. Yazdanifard, “How instagram can be used as a tool in social networking marketing,” Help College of Art and Technology Malaysia, Tech. Rep., 2014. [9] D. Jurafsky and J. H. Martin, Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition(2nd Edition). Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 2009. [10] S. G. K and S. Joseph, “Text classification by augmenting bag of words ( bow ) representation with co-occurrence feature,” IOSR Journal of Computer Engineering (IOSR-JCE), vol. 16, pp. 34–38, 1 2014. [11] B. B. Kachru, The Alchemy of English: The Spread, Functions and Models of Non-native Englishes. Champaign: University of Illinois Press, 1990. [12] S. S. Keerthi and C.-J. Lin, “Asymptotic behaviors of support vector machines with gaussian kernel,” Neural Comput., vol. 15, no. 7, pp. 1667–1689, Jul. 2003. [13] M. Koppel and J. Schler, “The importance of neutral examples for learning sentiment,” in In Workshop on the Analysis of Informal and Formal Information Exchange during Negotiations, 2005.[14] A. Kowcika, A. Gupta, K. Sondhi, N. Shivhre, and R. Kumar, “Sentiment analysis for social media,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 7, 7 2013. [15] F.-F. Li and P. Perona, “A bayesian hierarchical model for learning natural scene categories,” in Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02, ser. CVPR '05. Washington, DC, USA: IEEE Computer Society, 2005, pp. 524–531. [16] B. Liu, Sentiment Analysis and Opinion Mining. Morgan and Claypool Publisher, 2012. [17] W.-Y. Ma and K.-J. Chen, “A bottom-up merging algorithm for chinese unknown word extraction,” in Proceedings of the Second SIGHAN Workshop on Chinese Language Processing - Volume 17, ser. SIGHAN '03. Stroudsburg, PA, USA: Association for Computational Linguistics, 2003, pp. 31–38. [18] Z. McCune, “Consumer production in social media networks: A case study of the instagram iphone app,” Ph.D. dissertation, Dr. John Thompson, 2011. [19] W. Medhata, A. Hassanb, and H. Korashyb, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, 2014. [20] L.-P. Morency, R. Mihalcea, and P. Doshi, “Toward multimodal sentiment analysis: Harvesting opinion from the web,” in International Conference on Multimodal Interface, 2011. [21] A. Pak and P. Paroubek, “Twitter as a corpus for sentiment analysis and opinion mining,” in Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), N. C. C. Chair), K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, and D. Tapias, Eds. Valletta, Malta: European Language Resources Association (ELRA), may 2010.[22] B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Foundation and Trends in Information Retrieval, vol. 2, no. 1-2, p. 4, 2008. [23] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: Sentiment classification using machine learning techniques,” in Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10, ser. EMNLP '02. Stroudsburg, PA, USA: Association for Computational Linguistics, 2002, pp. 79–86. [24] C.-Y. J. Peng, K. L. Lee, and G. M. Ingersol, “An introduction to logistic regression analysis and reporting,” The Journal of Educational Research, vol. 96, no. 1, September/October 2002. [25] V. V. Piyush Bansal, Romil Bansal, “Towards deep semantic analysis of hashtags,” 37th European Conference on Information Retrieval, 2015. [26] R. Plutchik, Emotion: A Psycho-evolutionary Synthesis. Harper and Row, 1980. [27] S. Poria, A. Hussain, and E. Cambria, “Beyond text based sentiment analysis: Towards multi-modal systems,” University of Stirling, Stirling FK9 4LA, UK, Tech. Rep., 2013. [Online]. Available: http://www.cs.stir.ac.uk/~spo/publication/resources/cogcomp.pdf [28] E. Praseyto, Data Mining Konsep dan Aplikasi Menggunakan Matlab. Yogyakarta: Andi, 2012.[29] A. Qazi, R. G. Raj, M. Tahir, E. Cambria, and K. B. S. Syed, “Enhancing business intelligence by means of suggestive reviews,” The Scientific World Journal, vol. 2014, June 2014. [30] R. Schapire, “Machine learning algorithms for classification,” Princeton University, Tech. Rep. [31] S. Siersdorfer, E. Minack, F. Deng, and J. Hare, “Analyzing and predicting sentiment of images on the social web,” in Proceedings of the International Conference on Multimedia, ser. MM '10. New York, NY, USA: ACM, 2010, pp. 715–718. [32] T. H. Silva, P. O. S. V. de Melo, J. M. Almeida, J. Salles, and A. A. F. Loureiro, “A comparison of foursquare and instagram to the study of city dynamics and urban social behavior,” in Proceedings of the 2Nd ACM SIGKDD International Workshop on Urban Computing, ser. UrbComp '13. New York, NY, USA: ACM, 2013, pp. 4:1–4:8. [33] P. N. Stuart Russell, Artificial Intelligence A Modern Approach, M. Hirsch, Ed. New Jersey: Pearson Education, 2010. [34] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, “Lexicon-based methods for sentiment analysis,” Comput. Linguist., vol. 37, no. 2, pp. 267– 307, Jun. 2011. [35] C.-F. Tsai, “Bag-of-words representation in image annotation: A review,” ISRN Artificial Intelligence, p. 19, 2012. [36] A. J. Viera and J. M. Garrett, “Understanding interobserver agreement: The kappa statistic,” Family Medicine, vol. 37, no. 5, pp. 360–363, May 2005. [37] M. Wang, D. Cao, L. Li, S. Li, and R. Ji, “Microblog sentiment analysis based on cross-media bag-of-words model,” in Proceedings of International Conference on Internet Multimedia Computing and Service, ser. ICIMCS '14. New York, NY, USA: ACM, 2014, pp. 76:76–76:80. [38] A. Westerski, “Sentiment analysis: Introduction and the state of the art overview,” Universidad Politecnica de Madrid, Spain, Tech. Rep., 2009. [39] F. Yu, L. Cao, R. Feris, J. Smith, and S.-F. Chang, “Designing category-level attributes for discriminative visual recognition,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, June 2013. [40] L. Yu and H. Liu, “Efficiently handling feature redundancy in high-dimensional data,” in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD '03. New York, NY, USA: ACM, 2003, pp. 685–690. [41] J. Yuan, S. Mcdonough, Q. You, and J. Luo, “Sentribute: Image sentiment analysis from a mid-level perspective,” in Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining, ser. WISDOM '13. New York, NY, USA: ACM, 2013, pp. 10:1– 10:8

Bibliometric studies on single journals: a review

This paper covers a total of 82 bibliometric studies on single journals (62 studies cover unique titles) published between 1998 and 2008 grouped into the following fields; Arts, Humanities and Social Sciences (12 items); Medical and Health Sciences (19 items); Sciences and Technology (30 items) and Library and Information Sciences (21 items). Under each field the studies are described in accordance to their geographical location in the following order, United Kingdom, United States and Americana, Europe, Asia (India, Africa and Malaysia). For each study, elements described are (a) the journal’s publication characteristics and indexation information; (b) the objectives; (c) the sampling and bibliometric measures used; and (d) the results observed. A list of journal titles studied is appended. The results show that (a)bibliometric studies cover journals in various fields; (b) there are several revisits of some journals which are considered important; (c) Asian and African contributions is high (41.4 of total studies; 43.5 covering unique titles), United States (30.4 of total; 31.0 on unique titles), Europe (18.2 of total and 14.5 on unique titles) and the United Kingdom (10 of total and 11 on unique titles); (d) a high number of bibliometrists are Indians and as such coverage of Indian journals is high (28 of total studies; 30.6 of unique titles); and (e) the quality of the journals and their importance either nationally or internationally are inferred from their indexation status

Development and Evaluation of an Undergraduate Science Communication Module

This paper describes the design and evaluation of an undergraduate final year science communication module for the Science Faculty at the University of East Anglia. The module focuses specifically on science communication and aims to bring an understanding of how science is disseminated to the public. Students on the module are made aware of the models surrounding science communication and investigate how the science culture interfaces with the public. During the module they learn how to adapt science concepts for different audiences and how to talk confidently about science to a lay-audience. Student motivation for module choice centres on the acquisition of transferable skills and students develop these skills through designing, running and evaluating a public outreach event at a school or in a public area. These transferable skills acquired include communication, interaction with different organisations such as museums and science centres, developing understanding of both the needs of different audiences and the importance of time management. They also develop skills relating to self-reflection and how to use this as a tool for future self development. The majority of students completing the module go on to further study, either a PhD, MSc or teacher training. The module can be sustained in its present formed if capped at 40 students, however it is recognised that to increase cohort size, further investment of faculty time and resources would be required