Ball LJ, Ormerod TC, 1995. Structured and opportunistic processing in design: a critical discussion. Int J Hum-Comput Stud, 43(1):131–151. https://doi.org/10.1006/ijhc.1995.1038
Chan J, Dang S, Dow SP, 2016. Improving crowd innovation with expert facilitation. Proc 19th ACM Conf on Computer-Supported Cooperative Work & Social Computing, p.1223–1235. https://doi.org/10.1145/2818048.2820023
Chang DN, Chen CH, Lee KM, 2014. A crowdsourcing development approach based on a neuro-fuzzy network for creating innovative product concepts. Neurocomputing, 142:60–72. https://doi.org/10.1016/j.neucom.2014.03.044
Cross N, 2006. Designerly Ways of Knowing. Springer, London. https://doi.org/10.1007/1-84628-301-9
Dontcheva M, Morris RR, Brandt JR, et al., 2014. Combining crowdsourcing and learning to improve engagement and performance. Proc SIGCHI Conf on Human Factors in Computing Systems, p.3379–3388. https://doi.org/10.1145/2556288.2557217
Flores RL, Belaud JP, le Lann JM, et al., 2015. Using the collective intelligence for inventive problem solving: a contribution for open computer aided innovation. Expert Syst Appl, 42(23):9340–9352. https://doi.org/10.1016/j.eswa.2015.08.024
Gatys LA, Ecker AS, Bethge M, 2016. Image style transfer using convolutional neural networks. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.2414–2423. https://doi.org/10.1109/CVPR.2016.265
Glickman ME, 1999. Parameter estimation in large dynamic paired comparison experiments. J Roy Stat Soc Ser C, 48(3):377–394. https://doi.org/10.1111/1467-9876.00159
Goldschmidt G, 2015. Ubiquitous serendipity: potential visual design stimuli are everywhere. In: Gero JS (Ed.), Studying Visual and Spatial Reasoning for Design Creativity. Springer Dordrecht Netherlands, p.205–214. https://doi.org/10.1007/978-94-017-9297-4_12
Ikeda K, Morishima A, Rahman H, et al., 2016. Collaborative crowdsourcing with crowd4U. Proc VLDB Endowm, 9(13):1497–1500. https://doi.org/10.14778/3007263.3007293
Kim J, Dontcheva M, Li W, et al., 2015. Motif: supporting novice creativity through expert patterns. Proc 33rd Annual ACM Conf on Human Factors in Computing Systems, p.1211–1220. https://doi.org/10.1145/2702123.2702507
Lafreniere B, Grossman T, Anderson F, et al., 2016. Crowdsourced fabrication. Proc 29th Annual Symp on User Interface Software and Technology, p.15–28. https://doi.org/10.1145/2984511.2984553
Li W, Wu WJ, Wang HM, et al., 2017. Crowd intelligence in AI 2.0 era. Front Inform Technol Electron Eng, 18(1): 15–43. https://doi.org/10.1631/FITEE.1601859
Michelucci P, Dickinson JL, 2016. The power of crowds. Science, 351(6268):32–33. https://doi.org/10.1126/science.aad6499
O’Donovan P, Agarwala A, Hertzmann A, 2014. Learning layouts for single-pagegraphic designs. IEEE Trans Vis Comput Graph, 20(8):1200–1213. https://doi.org/10.1109/TVCG.2014.48
Pan YH, 2017. Special issue on artificial intelligence 2.0. Front Inform Technol Electron Eng, 18(1):1–2. https://doi.org/10.1631/FITEE.1710000
Park CH, Son KH, Lee JH, et al., 2013. Crowd vs. crowd: large-scale cooperative design through open team competition. Proc Conf on Computer Supported Cooperative Work, p.1275–1284. https://doi.org/10.1145/2441776.2441920
Pauwels P, de Meyer R, van Campenhout J, 2013. Design thinking support: information systems versus reasoning. Des Iss, 29(2):42–59. https://doi.org/10.1162/DESI_a_00209
Pinel F, Varshney LR, Bhattacharjya D, 2015. A culinary computational creativity system. In: Besold TR, Schorlemmer M, Smaill A (Eds.), Computational Creativity Research: Towards Creative Machines. Springer, Paris, p.327–346. https://doi.org/10.2991/978-94-6239-085-0_16
Prats M, Earl CF, 2006. Exploration through drawings in the conceptual stage of product design. In: Gero JS (Ed.), Design Computing and Cognition. Springer Dordrecht Netherlands, p.83–102. https://doi.org/10.1007/978-1-4020-5131-9_5
Ren J, Nickerson JV, Mason W, et al., 2014. Increasing the crowd’s capacity to create: how alternative generation affects the diversity, relevance and effectiveness of generated ads. Dec Supp Syst, 65:28–39. https://doi.org/10.1016/j.dss.2014.05.009
Schneider OS, Seifi H, Kashani S, et al., 2016. HapTurk: crowdsourcing affective ratings of vibrotactile icons. Proc CHI Conf on Human Factors in Computing Systems, p.3248–3260. https://doi.org/10.1145/2858036.2858279
Sun LY, Xiang W, Chai CL, et al., 2014a. Creative segment: a descriptive theory applied to computer-aided sketching. Des Stud, 35(1):54–79. https://doi.org/10.1016/j.destud.2013.10.003
Sun LY, Xiang W, Chai CL, et al., 2014b. Designers’ perception during sketching: an examination of creative segment theory using eye movements. Des Stud, 35(6): 593–613. https://doi.org/10.1016/j.destud.2014.04.004
Sun LY, Xiang W, Chen S, et al., 2015. Collaborative sketching in crowdsourcing design: a new method for idea generation. Int J Technol Des Educat, 25(3):409–427. https://doi.org/10.1007/s10798-014-9283-y
Suzuki R, Salehi N, Lam MS, et al., 2016. Atelier: repurposing expert crowdsourcing tasks as micro-internships. Proc CHI Conf on Human Factors in Computing Systems, p.2645–2656. https://doi.org/10.1145/2858036.2858121
van der Maaten L, Weinberger K, 2012. Stochastic triplet embedding. IEEE Int Workshop on Machine Learning for Signal Processing, p.1–6. https://doi.org/10.1109/MLSP.2012.6349720
Wah C, van Horn G, Branson S, et al., 2014. Similarity comparisons for interactive fine-grained categorization. IEEE Conf on Computer Vision and Pattern Recognition, p.859–866. https://doi.org/10.1109/CVPR.2014.115
Warby SC, Wendt SL, Welinder P, et al., 2014. Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods. Nat Methods, 11(4):385–392. https://doi.org/10.1038/nmeth.2855
Wiltschnig S, Christensen BT, Ball LJ, 2013. Collaborative problem–solution co-evolution in creative design. Des Stud, 34(5):515–542. https://doi.org/10.1016/j.destud.2013.01.002
Xiang W, Sun LY, Xia SC, et al., 2017. An evolutionary computation method of crowdsourcing ideation that integrates the balanced-exploration pattern. J Mech Eng, 53(15):73–80 (in Chinese). https://doi.org/10.3901/JME.2017.15.073
Xu AB, Rao HM, Dow SP, et al., 2015. A classroom study of using crowd feedback in the iterative design process. Proc 18th ACM Conf on Computer Supported Cooperative Work & Social Computing, p.1637–1648. https://doi.org/10.1145/2675133.2675140
Yu LX, Nickerson JV, 2011. Cooks or cobblers?: crowd creativity through combination. Proc SIGCHI Conf on Human Factors in Computing Systems, p.1393–1402. https://doi.org/10.1145/1978942.1979147
Yu LX, Kittur A, Kraut RE, 2014. Distributed analogical idea generation: inventing with crowds. Proc SIGCHI Conf on Human Factors in Computing Systems, p.1245–1254. https://doi.org/10.1145/2556288.2557371
Yu LX, Kraut RE, Kittur A, 2016. Distributed analogical idea generation with multiple constraints. Proc 19th ACM Conf on Computer-Supported Cooperative Work & Social Computing, p.1236–1245. https://doi.org/10.1145/2818048.2835201
Zhao Q, Huang ZH, Harper FM, et al., 2016. Precision crowdsourcing: closing the loop to turn information consumers into information contributors. Proc 19th ACM Conf on Computer-Supported Cooperative Work & Social Computing, p.1615–1625. https://doi.org/10.1145/2818048.2819957
Zhu JY, Krähenbühl P, Shechtman E, et al., 2016. Generative visual manipulation on the natural image manifold. Proc 14th European Conf on Computer Vision, p.597–613. https://doi.org/10.1007/978-3-319-46454-1_36