Papers

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2018
Lopez, J. Amador Dia, Molina-Solana, M., & Kennedy, M. T.. (2018). foo.castr: visualising the future AI workforce. Big Data Analytics, 3(1). https://doi.org/10.1186/s41044-018-0034-z
Moody, K. (2018). High tech, low growth: Robots and the future of work abstract. Historical Materialism, 26(4), 3 - 34. https://doi.org/10.1163/1569206X-00001745
Mendling, J., Decker, G., Hull, R., Reijers, H. A., & Weber, I.. (2018). How do machine learning, robotic process automation, and blockchains affect the human factor in business process management?. Communications Of The Association For Information Systems, 297 - 320. https://doi.org/10.17705/1CAIS.04319
Mukhalipi, A. (2018). Human capital management and future of work; job creation and unemployment: a literature review. Oalib, 05(09), 1 - 17. https://doi.org/10.4236/oalib.1104859
J. Munoz, P., Boger, R., Dexter, S., Low, R., & Li, J.. (2018). Image Recognition of Disease-Carrying Insects: A System for Combating Infectious Diseases Using Image Classification Techniques and Citizen Science (T. Bui, Tran.). In Hawaii International Conference on System SciencesProceedings of the 51st Hawaii International Conference on System Sciences. Presented at the Hawaii International Conference on System SciencesProceedings of the 51st Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2018.00010.24251/HICSS.2018.359
Mehic, A. (2018). Industrial employment and income inequality: Evidence from panel data. Structural Change And Economic Dynamics, 45, 84 - 93. https://doi.org/10.1016/j.strueco.2018.02.006
Mlynar, J., Alavi, H. S., Verma, H., & Cantoni, L.. (2018). Lecture Notes in Computer ScienceArtificial General IntelligenceTowards a Sociological Conception of Artificial Intelligence (Vol. 10999, pp. 130 - 139; M. Iklé, Franz, A., Rzepka, R., & Goertzel, B., Eds.). In (Vol. 10999, pp. 130 - 139). https://doi.org/10.1007/978-3-319-97676-1
Humphries, G., & Huettmann, F.. (2018). Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective. In G. Humphries, Magness, D. R., & Huettmann, F. (Eds.), Machine Learning for Ecology and Sustainable Natural Resource Management (pp. 3 - 26). https://doi.org/10.1007/978-3-319-96978-7_1

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