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Investigating Weak Supervision in Deep Ranking

International Conference on Machine learning (ICML-05) 89-96. doi:10.1145/1102351.1102363 Burges C. Shaked T. Renshaw E. Lazier A. Deeds M. Hamilton N. & Hullender G. N. 2005 Learning to rank using gradient descent Proceedings of the 22nd International Conference on Machine learning (ICML-05) 89 96 10.1145/1102351.1102363 Chapelle, O., & Zhang, Y. (2009). A dynamic bayesian network click model for web search ranking. Proceedings of the 18th International Conference on World Wide Web 1-10. doi:10.1145/1526709.1526711 Chapelle

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Understanding and Evaluating Research and Scholarly Publishing in the Social Sciences and Humanities (SSH)

R. 2018 The Flemish performance-based research funding system: A unique variant of the Norwegian model Journal of Data and Information Science 3 4 45 60 Engels, T. C., Starcic, A. I., Kulczycki, E., Pölönen, J., & Sivertsen, G. (2018). Are book publications disappearing from scholarly communication in the social sciences and humanities? Aslib Journal of Information Management,70 (6), 592–607. 10.1108/AJIM-05-2018-0127 Engels T. C. Starcic A. I. Kulczycki E. Pölönen J. Sivertsen G. 2018 Are book publications disappearing

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An Influence Prediction Model for Microblog Entries on Public Health Emergencies

, and other environmental concerns. Natural Hazards 83(1), 729-760. 10.1007/s11069-016-2327-8 Finch K. C. Snook K. R. Duke C. H. Fu K. W. Tse Z. T. H. Adhikari A. & Fung I. C. H. 2016 Public health implications of social media use during natural disasters, environmental disasters, and other environmental concerns Natural Hazards 83 1 729 – 760 Fu, Y., & Chen, Y. (2014). Relationship analysis of microblogging user with link prediction. Computer Science 41(2), 201-205. Fu Y. & Chen Y. 2014 Relationship analysis

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Providing Research Data Management (RDM) Services in Libraries: Preparedness, Roles, Challenges, and Training for RDM Practice

. L., Bakker, T. A., Svirsky, M. A., Neuman, A. C., & Rambo, N. (2013). Informationist role: clinical data management in auditory research. Journal of eScience Librarianship 2(1), 25-29. 10.7191/jeslib.2013.1030 Hanson K. L. Bakker T. A. Svirsky M. A. Neuman A. C. & Rambo N. 2013 Informationist role: clinical data management in auditory research Journal of eScience Librarianship 2 1 25 29 Hasman, L., Berryman, D., & Mcintosh, S. (2013). NLM Informationist Grant – web assisted tobacco intervention for community college students

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Comparison of emissions depending on the type of vehicle engine

biodiesel from Citrullus lanatus seeds oil and diesel blends“ Industrial Crops And Products, vol. 122, pp. 702-708. Doi: 10.1016/j.indcrop.2018.06.002 [15] Li, Z., Liu, G., Cui, X., Sun, X., Li, S., & Qian, Y. et al. (2018). „Effects of the variation in diesel fuel components on the particulate matter and unregulated gaseous emissions from a common rail diesel engine“ Fuel, vol. 232, pp. 279-289. Doi: 10.1016/j.fuel.2018.05.170 [16] Shim, E., Park, H., & Bae, C. (2018). Intake air strategy for low HC and CO emissions in dual-fuel (CNG-diesel) premixed charge

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A framework for use in modelling the modal choice decision making process in North West England’s Atlantic Gateway

: Decision factors and attitudes. Maritime Policy and Management. 19(2), 115-126. DOI: 10.1080/03088839200000019. de Jong, G.C., Gommers, M.A. & Klooster, J.P.G. (2000). Time Valuation in Freight Transport: methods and results. In: de Ortuzar, J.D. (Ed.) Stated Preferences Modelling (pp.231-242). London, PTRC Education and Research Services. de Oses, F. X. M. & Castells, M. (2008). Selection of Short Sea Shipping transport alternatives in SW Europe. Polytechnic University of Catalonia, Barcelona, Spain. Dial, R.B (1979). A

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Systematic Approach to Sustainability of Novel Internet-Based System for Food Logistics

, 30(4), 211-220. DOI: 10.1108/09590550210423681 4. San-Martín, S., Prodanova J. & Jiménez, N. (2015). The impact of age in the generation of satisfaction and WOM in mobile shopping. Journal of Retailing and Consumer Services, 23, 1-8. DOI: 10.1016/j.jretconser.2014.11.001 5. Björklund, M., Forslund, H. & Isaksson, M. (2016). Exploring logistics-related environmental sustainability in large retailers. International Journal of Retail & Distribution Management, 44(1), 38-57. DOI: 10.1108/IJRDM-05-2015-0071 6. Kolk, A

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Integration of Travel Agencies with other Supply Chain Members: Impact on Efficiency

R eferences Alamdari, Fariba. (2002). Regional development in airlines and travel agents relationship. Journal of Air Transport Management, 8 (5), 339-348. doi: 10.1016/s0969-6997(02)00014-5 Assaf, A., Barros, C. P., & Josiassen, A. (2010). Hotel efficiency: A bootstrapped metafrontier approach. International Journal of Hospitality Management, 29 (3), 468-475. doi: 10.1016/j.ijhm.2009.10.020 Assaf, A. George, Barros, Carlos Pestana, & Dieke, Peter U. C. (2011). Portuguese tour operators: A fight for survival. Journal of Air Transport

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Attitude of Motorists towards Road Ethics: Empirical Study

-504. 8. Atubi, A. (2015). Modelling deaths from road traffic accidents. American International Journal of Social Science, 4(5), 199-213. 9. Beck, A.T., Rush, A.J., Brian, F.E. & Emery, G. (1979). Cognitive therapy of depression. New York: The Guilford Press, 11. 10. Bliss, T. & Breen, J. (2009). Implementing the recommendations of the world report on road traffic injury prevention, The World Bank Global Road Safety Facility . 11. Chien-Ming, T., Hsin-Li, C.T. & Hugh. (2013). Modeling motivation and habit in driving behavior under life time driver

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A Three-way Interaction Model of Information Withholding: Investigating the Role of Information Sensitivity, Prevention Focus, and Interdependent Self-Construal

variables; (b) information sensitivity, prevention focus, and interdependent self-construal; (c) the two-way interaction; (d) the three-way interaction. Information withholding was regarded as the dependent variable. The results of the regression analyses are indicated in Table 5 . In Model 1, not all of the control variables significantly predicted information withholding. In Model 2, information sensitivity (β=0.116, p<0.05) and prevention focus (β=.306, p<.01) were significant, accounting for an additional 13.3% of the variance (ΔF=24.699, p<0.01) in information

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