Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work and 2.1 Technology Convergence Approaches 2 Related Work and 2.1 Technology Convergence Approaches 2.2 Technology Convergence Measurements 2.2 Technology Convergence Measurements 2.3 Technology Convergence Models 2.3 Technology Convergence Models 3 Data 3 Data 4 Method and 4.1 Proximity Indices 4 Method and 4.1 Proximity Indices 4.2 Interpolation and Fitting Data 4.2 Interpolation and Fitting Data 4.3 Clustering 4.3 Clustering 4.4 Forecasting 4.4 Forecasting 5 Results and Discussion and 5.1 Overall Results 5 Results and Discussion and 5.1 Overall Results 5.2 Case Study 5.2 Case Study 5.3 Limitations and Future Works 5.3 Limitations and Future Works 6 Conclusion and References 6 Conclusion and References Appendix Appendix 6 Conclusion In this study, we introduce a method that uses text mining and bibliometrics techniques, utilizing the ”OpenAlex” catalog, to create and predict technological proximity indices specific to encryption technologies. A case study applying our method highlights the convergence between blockchain and public-key cryptography. The insights gained from our method can offer valuable guidance for stakeholders and enthusiasts navigating the impacts of encryption technology. We recognize certain constraints in our study. Our literature corpus was limited to ”OpenAlex,” which may not comprehensively represent the core interests and activities in the research community working on encryption technologies. Additionally, the non-normalized nature of our indices hinders direct comparisons among various data sources. These limitations also suggest potential paths for future research to expand our findings, enriching the field of scientific monitoring within the encryption technology community. Supplementary information. The code of this project is on https://github.com/technometrics-lab/Proximity-indices-applied-to-OpenAlex. Supplementary information. https://github.com/technometrics-lab/Proximity-indices-applied-to-OpenAlex Acknowledgments. We thank Jacques Roitel, whose work was the original base of this paper. Additionally, we would like to thank the other interns working at armasuisse at the time this research was led for their helpful coding advice: L´eo Meynent, Marc Egli, Francesco Intoci, Lucas Crijns, and Alexander Glavackij. Acknowledgments References [1] Zhou, Y., Dong, F., Kong, D., Liu, Y.: Unfolding the convergence process of scientific knowledge for the early identification of emerging technologies. Technological Forecasting and Social Change 144, 205–220 (2019). https: //doi.org/10.1016/j.techfore.2019.03.014. Accessed 2022-09-30 [2] Wang, Z., Porter, A.L., Wang, X., Carley, S.: An approach to identify emergent topics of technological convergence: A case study for 3D printing. Technological Forecasting and Social Change 147, 723–732 (2019). https://doi.org/10.1016/j.techfore.2018.12.015. Accessed 2022-11-24 [3] Carley, S.F., Newman, N.C., Porter, A.L., Garner, J.G.: An indicator of technical emergence. Scientometrics 115(1), 35–49 (2018). https://doi. org/10.1007/s11192-018-2654-5. Accessed 2022-09-30 [4] Halaweh, M.: Emerging Technology: What is it. Journal of technology management & innovation 8(3), 108–115 (2013). https://doi. org/10.4067/S0718-27242013000400010. Publisher: Universidad Alberto Hurtado. Facultad de Econom´ıa y Negocios. Accessed 2022-09-12 [5] Porter, A.L., Garner, J., Carley, S.F., Newman, N.C.: Emergence scoring to identify frontier R&D topics and key players. Technological Forecasting and Social Change 146, 628–643 (2019). https://doi.org/10.1016/j. techfore.2018.04.016. Accessed 2022-09-13 [6] Curran, C.-S., Broring, S., Leker, J.: Anticipating converging industries using publicly available data. Technological Forecasting and Social Change 77(3), 385–395 (2010). https://doi.org/10.1016/j.techfore.2009. 10.002. Accessed 2022-10-21 [7] Song, C.H., Elvers, D., Leker, J.: Anticipation of converging technology areas — A refined approach for the identification of attractive fields of innovation. Technological Forecasting and Social Change 116, 98–115 (2017). https://doi.org/10.1016/j.techfore.2016.11.001. Accessed 2022-09-30 [8] Huang, L., Cao, X., Cai, Y., Ren, H., Xing, T., Ye, P.: Detecting Technological Recombination using Semantic Analysis and Dynamic Network Analysis, 11 [9] Song, K., Kim, K.S., Lee, S.: Discovering new technology opportunities based on patents: Text-mining and F-term analysis. Technovation 60-61, 1–14 (2017). https://doi.org/10.1016/j.technovation.2017.03.001. Accessed 2022-09-30 [10] Blouin, M., Sery, N., Cluzeau, D., Brun, J.-J., Bedecarrats, A.: Balkanized Research in Ecological Engineering Revealed by a Bibliometric Analysis of Earthworms and Ecosystem Services. Environmental Management 52(2), 309–320 (2013). https://doi.org/10.1007/s00267-013-0079-8. Accessed 2022-10-19 [11] Chand Bhatt, P., Kumar, V., Lu, T.-C., Daim, T.: Technology convergence assessment: Case of blockchain within the IR 4.0 platform. Technology in Society 67, 101709 (2021). https://doi.org/10.1016/j.techsoc.2021. 101709. Accessed 2022-10-20 [12] Kim, T.S., Sohn, S.Y.: Machine-learning-based deep semantic analysis approach for forecasting new technology convergence. Technological Forecasting and Social Change 157, 120095 (2020). https://doi.org/10.1016/ j.techfore.2020.120095. Accessed 2022-10-20 [13] Li, H., An, H., Wang, Y., Huang, J., Gao, X.: Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network. Physica A: Statistical Mechanics and its Applications 450, 657–669 (2016). https://doi.org/10.1016/j. physa.2016.01.017. Accessed 2022-09-09 [14] Chen, H., Zhang, Y., Zhu, D.: Identifying Technological Topic Changes in Patent Claims Using Topic Modeling. In: Daim, T.U., Chiavetta, D., Porter, A.L., Saritas, O. (eds.) Anticipating Future Innovation Pathways Through Large Data Analysis. Innovation, Technology, and Knowledge Management, pp. 187–209. Springer, Cham (2016). https://doi.org/10. 1007/978-3-319-39056-7 11 [15] Cojocaru, S., Bragaru, C., Ciuchi, O.: The role of language in constructing social realities. The Appreciative Inquiry and the reconstruction of organisational ideology. Revista de Cercetare si Interventie Sociala 36, 31–43 (2012) [16] He, C., Shi, F., Tan, R.: A synthetical analysis method of measuring technology convergence. Expert Systems with Applications 209, 118262 (2022). https://doi.org/10.1016/j.eswa.2022.118262. Accessed 2022-10-21 [17] Klarin, A., Suseno, Y., Lajom, J.: Systematic Literature Review of Convergence: A Systems Perspective and Re-evaluation of the Convergence Process. IEEE Transactions on Engineering Management, 1–13 (2021). https://doi.org/10.1109/TEM.2021.3126055 [18] MacRoberts, M.H., MacRoberts, B.R.: Problems of citation analysis. Scientometrics 36(3), 435–444 (1996). https://doi.org/10.1007/BF02129604. Accessed 2022-11-23 [19] Liu, P., Xia, H.: Structure and evolution of co-authorship network in an interdisciplinary research field. Scientometrics 103(1), 101–134 (2015). https://doi.org/10.1007/s11192-014-1525-y. Accessed 2022-11-30 [20] Lu, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications 390(6), 1150–1170 (2011). [https://doi.org/10.1016/j.physa.2010.11.027. Accessed 2022-10-17 ](https://doi.org/10.1016/j.physa.2010.11.027. Accessed 2022-10-17 ) [21] Kim, J., Kim, S., Lee, C.: Anticipating technological convergence: Link prediction using Wikipedia hyperlinks. Technovation 79, 25–34 (2019). https://doi.org/10.1016/j.technovation.2018.06.008. Accessed 2022-10-21 [22] Breitzman, A., Thomas, P.: The Emerging Clusters Model: A tool for identifying emerging technologies across multiple patent systems. Research Policy 44(1), 195–205 (2015). https://doi.org/10.1016/j.respol.2014.06. 006. Accessed 2022-10-21 [23] Kumar, T., Vaidyanathan, S., Ananthapadmanabhan, H., Parthasarathy, S., Ravindran, B.: Hypergraph clustering by iteratively reweighted modularity maximization. Applied Network Science 5(1), 1–22 (2020). https://doi.org/10.1007/s41109-020-00300-3. Number: 1 Publisher: SpringerOpen. Accessed 2022-10-20 [24] Schoen, A., Villard, L., Laurens, P., Cointet, J.-P., Heimeriks, G., Alkemade, F.: The Network Structure of Technological Developments; Technological Distance as a Walk on the Technology Map (2012) [25] Kim, J., Lee, S.: Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020. Scientometrics 111(1), 47–65 (2017). https://doi.org/10.1007/ s11192-017-2275-4. Accessed 2022-10-21 [26] Lee, C., Kwon, O., Kim, M., Kwon, D.: Early identification of emerging technologies: A machine learning approach using multiple patent indicators. Technological Forecasting and Social Change 127, 291–303 (2018). https://doi.org/10.1016/j.techfore.2017.10.002. Accessed 2022-10-17 [27] Kajikawa, Y., Takeda, Y.: Structure of research on biomass and bio-fuels: A citation-based approach. Technological Forecasting and Social Change 75(9), 1349–1359 (2008). https://doi.org/10.1016/j.techfore.2008.04.007. Accessed 2022-10-18 [28] Mitrea, C., Lee, C., Wu, Z.: A Comparison between Neural Networks and Traditional Forecasting Methods: A Case Study. International Journal of Engineering Business Management 1 (2009). https://doi.org/10.5772/ 6777 [29] Priem, J., Piwowar, H., Orr, R.: OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts. arXiv. arXiv:2205.01833 [cs] (2022). https://doi.org/10.48550/arXiv.2205.01833. http://arxiv.org/abs/2205.01833 Accessed 2022-10-03 [30] Brown, B.B., et al.: Delphi process: a methodology used for the elicitation of opinions of experts. Rand Corporation Santa Monica, CA (1968) [31] Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics 20, 53–65 (1987) This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Alessandro Tavazz, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland, Institute of Mathematics, EPFL, 1015, Lausanne, Switzerland and a Corresponding author (tavazale@gmail.com); (2) Dimitri Percia David, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland and Institute of Entrepreneurship & Management, University of Applied Sciences of Western Switzerland (HES-SO Valais-Wallis), Techno-Pole 1, Le Foyer, 3960, Sierre, Switzerland; (3) Julian Jang-Jaccard, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland; (4) Alain Mermoud, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland. Authors: Authors: (1) Alessandro Tavazz, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland, Institute of Mathematics, EPFL, 1015, Lausanne, Switzerland and a Corresponding author (tavazale@gmail.com); (2) Dimitri Percia David, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland and Institute of Entrepreneurship & Management, University of Applied Sciences of Western Switzerland (HES-SO Valais-Wallis), Techno-Pole 1, Le Foyer, 3960, Sierre, Switzerland; (3) Julian Jang-Jaccard, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland; (4) Alain Mermoud, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland.
