Papers
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. (2020). The benefits of eHRM and AI for talent acquisition. Journal Of Tourism Futures. https://doi.org/10.1108/JTF-02-2020-0013
. (2019). Beyond Dyadic Interactions: Considering Chatbots as Community Members (). In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI '19 (1-13). https://doi.org/10.1145/3290605
. (2017). Blending machine and human learning processes. In Hawai'i International Conference on System Sciences. https://doi.org/10.24251/HICSS.2017.009
training v3 to share.pdf (245.57 KB). (2018). Boundary spanning at the science–policy interface: the practitioners’ perspectives. Sustainability Science, 13(4), 1175 - 1183. https://doi.org/10.1007/s11625-018-0550-9
. (2019). Can nurses remain relevant in a technologically advanced future?. International Journal Of Nursing Sciences, 6(1), 106 - 110. https://doi.org/10.1016/j.ijnss.2018.09.013
. (2017). Chatbots as assistants, an architectural framework. In CASCON '17 Proceedings of the 27th Annual International Conference on Computer Science and Software Engineering. Retrieved de https://dl.acm.org/citation.cfm?id=3172805
. (2016). Citizen science on a smartphone: Participants’ motivations and learning. Public Understanding Of Science, 25(1), 45 - 60. https://doi.org/10.1177/0963662515602406
. (2020). Competing with Robots: Firm-Level Evidence from France. Aea Papers And Proceedings, 110, 383–388. https://doi.org/10.1257/pandp.20201003
. (2008). Context-linked virtual assistants for distributed teams (). In the ACM 2008 conferenceProceedings of the ACM 2008 conference on Computer supported cooperative work - CSCW '08 (361). https://doi.org/10.1145/1460563.1460623
. (2008). Context-linked virtual assistants for distributed teams: an astrophysics case study. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (361–370).
. (2013). The convergence of engineering and the life sciences. National Academy Of Engineering, 43(3). Retrieved de https://www.nae.edu/88364/The-Convergence-of-Engineering-and-the-Life-Sciences
. (2019). A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal Of Cancer, 111, 148 - 154. https://doi.org/10.1016/j.ejca.2019.02.005
. (2019). A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal Of Cancer, 111, 148 - 154. https://doi.org/10.1016/j.ejca.2019.02.005
. (2019). A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal Of Cancer, 111, 148 - 154. https://doi.org/10.1016/j.ejca.2019.02.005
. (2019). A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal Of Cancer, 111, 148 - 154. https://doi.org/10.1016/j.ejca.2019.02.005
. (2018). A critical approach to human helping in information systems: Heteromation in the Brazilian correspondent banking system. Information And Organization, 28(3), 111 - 128. https://doi.org/10.1016/j.infoandorg.2018.08.002
. (2018). Cryptocurrency, Artificial Intelligence and basic income as innovative technological system. Path Of Science, 4(8), 2024 - 2030. https://doi.org/10.22178/pos10.22178/pos.37-6
. (2018). Cryptocurrency, Artificial Intelligence and basic income as innovative technological system. Path Of Science, 4(8), 2024 - 2030. https://doi.org/10.22178/pos10.22178/pos.37-6
. (2017). Data tracking in search of workflows (). In the 2017 ACM ConferenceProceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing - CSCW '17 (2153 - 2165). https://doi.org/10.1145/2998181.2998296
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001