Table of Links Abstract and 1. Introduction 2 Related Work and 2.1 Technology Convergence Approaches 2.2 Technology Convergence Measurements 2.3 Technology Convergence Models 3 Data 4 Method and 4.1 Proximity Indices 4.2 Interpolation and Fitting Data 4.3 Clustering 4.4 Forecasting 5 Results and Discussion and 5.1 Overall Results 5.2 Case Study 5.3 Limitations and Future Works 6 Conclusion and References Appendix 4 Method This section outlines the specifics of our proposed method, introducing novel mechanisms for calculating proximity indices using keywords, collaborations, and citations. Additionally, we delve into the details of how we fitted the data through interpolation to enhance the clustering and forecasting capabilities of the proposed method. 4.1 Proximity Indices We aim to understand if certain indices capture specific stages of convergence more effectively than others. Additionally, we explore whether these indicators consistently align or may contradict each other in certain cases. Our hypothesis is that regardless of the indicator used to model technological convergence, they will generally reveal convergence between two technologies if it exists. However, this convergence may manifest differently or at different times. To investigate this, we create multiple indicators of technological convergence based on common keywords, citations, and collaboration between technologies. We assign papers that are attributed to technologies with a score between 0 and 1. Similarly, computed keywords are assigned to papers with a ’cosine similarity’ score between 0 and 1. To capture the importance of each author in the field of encryption technologies, we compute an artificial h-index specific to the set of papers under consideration. For each indicator, our approach involves incorporating all relevant information for each month of each year, assigning appropriate weights to the variables, and constructing a time series. For example, in the case of keywords, we compute all common keywords between two technologies for each month of each year. We then calculate the average number of occurrences of each keyword in papers related to these two technologies during that month. This value is multiplied by the average cosine similarity and the average score of attribution to the pairs of technologies of the papers where each keyword occurred. These weights reflect the importance of specific keywords and the pair of technologies at a given moment in time. Similar computations are performed for other indices. It’s important to note that the indices presented in this paper draw inspiration from the literature, where common keywords, collaboration, and citations are commonly used. However, the explicit form of the indices is novel, as they directly leverage a unique database attributing every paper to concepts with a score between 0 and 1. Furthermore, we opt not to normalize the indices. Common normalization factors are either volatile, introducing bias, or yield very small indices, resulting in flat curves that hinder the identification of trends in technological convergence. The non-normalized time series of all indicators are presented on a single plot for all pairs of technologies. Each index discussed below is computed for a specific month from 2002 to 2022. However, for simplicity, we denote the variables used in the computation of the indices without explicitly specifying their dependence on a month, even though these variables are inherently tied to a specific month. Keywords Proximity Index Citations Proximity Index Collaboration Proximity Index The h-indices evaluate the influence of scientists by considering citations. In our case, h-indices enable a precise assessment of the significance of each author in establishing collaboration between two technologies. In fact, if an author has a high h-index, his impact on the collaboration between two technologies is assumed to be significant. For a given month m, we define the two indices of proximity based on the collaboration of authors between two technologies, as follows. These indices aim to measure how significant a collaboration is for the proximity between a technology t1 and t2. The approach we utilize involves the aggregation of coefficients assigned to each author, quantifying the significance and influence of their work within the connection between two technologies. This aggregation spans across all authors who have contributed to publications related to both technologies under consideration. As a result, this index evaluates the proximity of technologies based on collaboration. This paper is available on arxiv under CC BY 4.0 DEED license. 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. 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 4 Method This section outlines the specifics of our proposed method, introducing novel mechanisms for calculating proximity indices using keywords, collaborations, and citations. Additionally, we delve into the details of how we fitted the data through interpolation to enhance the clustering and forecasting capabilities of the proposed method. 4.1 Proximity Indices We aim to understand if certain indices capture specific stages of convergence more effectively than others. Additionally, we explore whether these indicators consistently align or may contradict each other in certain cases. Our hypothesis is that regardless of the indicator used to model technological convergence, they will generally reveal convergence between two technologies if it exists. However, this convergence may manifest differently or at different times. To investigate this, we create multiple indicators of technological convergence based on common keywords, citations, and collaboration between technologies. We assign papers that are attributed to technologies with a score between 0 and 1. Similarly, computed keywords are assigned to papers with a ’cosine similarity’ score between 0 and 1. To capture the importance of each author in the field of encryption technologies, we compute an artificial h-index specific to the set of papers under consideration. For each indicator, our approach involves incorporating all relevant information for each month of each year, assigning appropriate weights to the variables, and constructing a time series. For example, in the case of keywords, we compute all common keywords between two technologies for each month of each year. We then calculate the average number of occurrences of each keyword in papers related to these two technologies during that month. This value is multiplied by the average cosine similarity and the average score of attribution to the pairs of technologies of the papers where each keyword occurred. These weights reflect the importance of specific keywords and the pair of technologies at a given moment in time. Similar computations are performed for other indices. It’s important to note that the indices presented in this paper draw inspiration from the literature, where common keywords, collaboration, and citations are commonly used. However, the explicit form of the indices is novel, as they directly leverage a unique database attributing every paper to concepts with a score between 0 and 1. Furthermore, we opt not to normalize the indices. Common normalization factors are either volatile, introducing bias, or yield very small indices, resulting in flat curves that hinder the identification of trends in technological convergence. The non-normalized time series of all indicators are presented on a single plot for all pairs of technologies. Each index discussed below is computed for a specific month from 2002 to 2022. However, for simplicity, we denote the variables used in the computation of the indices without explicitly specifying their dependence on a month, even though these variables are inherently tied to a specific month. Keywords Proximity Index Keywords Proximity Index Citations Proximity Index Citations Proximity Index Collaboration Proximity Index Collaboration Proximity Index The h-indices evaluate the influence of scientists by considering citations. In our case, h-indices enable a precise assessment of the significance of each author in establishing collaboration between two technologies. In fact, if an author has a high h-index, his impact on the collaboration between two technologies is assumed to be significant. For a given month m , we define the two indices of proximity based on the collaboration of authors between two technologies, as follows. m These indices aim to measure how significant a collaboration is for the proximity between a technology t1 and t2. The approach we utilize involves the aggregation of coefficients assigned to each author, quantifying the significance and influence of their work within the connection between two technologies. This aggregation spans across all authors who have contributed to publications related to both technologies under consideration. As a result, this index evaluates the proximity of technologies based on collaboration. 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.
