5th International Conference. Business Meets Technology
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The theme of the conference is “Business Meets Technology”. By suggesting such a broad topic, we aim to invite researchers with a variety of interests in theory and research in various areas of science, commerce and arts related to business and technology. By providing a general motto, we emphasize that contributions from all areas of science are welcome. The objective of the event from its multidisciplinary approach is to allow generating and contributing valuable knowledge to face the great social challenges established as political priorities by the programs European science, research and innovation framework The international focus of the event, with the participation of leading experts from European universities, both in the scientific committee and in the scientific program as invited speakers, enriches the exchange of knowledge for all attendees
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- PublicationReal Estate meets technology. The impact of new technologies on the real estate sector in Spain(Editorial Universitat Politècnica de València, 2024-03-12) Llorca-Ponce, Alicia; Rius Sorolla, Gregorio; Departamento de Organización de Empresas; Escuela Técnica Superior de Arquitectura[EN] The real estate industry has traditionally been a low-tech industry. The arrival of new, more technological companies in the sector has brought about a revolution in an industry that is very conservative and not very innovative. The term Proptech, short for property technology, groups together new activities that include applications, platforms and, in general, technologies related to the digitalization of the real estate sector. It includes core technologies of the Industrial Revolution 4.0. such as Big Data, Artificial Intelligence, Blockchain, visualization technologies, the Internet of Things and cloud computing; technologies that are rapidly changing how the agents involved, buyers, sellers, investors, managers or tenants, operate. Due to the large number of activities and technologies involved, there needs to be some clarification about the scope of activities and technologies within Proptech. This paper explores the different classifications and maps of Proptech in Spain. The objective is to provide knowledge regarding the composition and delimitation of the Proptech sector in Spain and its comparison with the classifications made in other countries. To do this, an adaptation of the Proptech map of Spain will be made with the categorization created by Baum (2017, 2020) in which four large clusters or groups of activities are identified: Real Estate Fintech, Shared Economy, Smart Real Estate and Data Digitalization and Analytics.
- PublicationA bibliometric analysis on reciprocal human-machine-interactions(Editorial Universitat Politècnica de València, 2024-03-12) Erdmann, Matthias; Sauer, Sebastian; Suárez Ruz, María Esperanza; Perelló Marín, María Rosario; Departamento de Organización de Empresas; Facultad de Administración y Dirección de Empresas; Escuela Técnica Superior de Ingeniería Industrial[EN] Research into artificial intelligence is not a very young field; its precursors can be traced back as far as the 16th century. Today's technical development, however, is virtually leaping forward, with new intelligent chat systems and social robots playing no small part in this. This is revolutionizing a wide range of scientific and social fields. The very large publication numbers in this field illustrate this as well. In order for academics and scientists to keep track of the discourse in the field, the representatives of the field, the publications as well as the topics and their future development, it is indispensable to prepare these in a bibliometric analysis. Only in this way is it possible to uncover thematic gaps as well as further points of contact and to drive research forward in a targeted and stringent manner. It is precisely this sorting and processing of the research discourse, the topics, and the authors, which is necessary for further research, that is carried out in this paper. For this purpose, using bibliometric analysis tools, an overview of the past, present and future of the research field is created, and the general relevant topics are uncovered. The analysis includes as performance analysis a) the total number of publications and b) the total number of citations, and for science mapping c) a co-citation analysis (past), d) a bibliographic coupling (present) and e) a co-word analysis (future). The data needed for the analysis are identified and extracted from the SCOPUS or Web of Science (ISI) databases.
- PublicationUsing LSTM-Predicted Stock Prices and Risk-Adjusted Performance Metrics to Optimize Portfolios in the European Market(Editorial Universitat Politècnica de València, 2024-03-12) Martinez-Barbero, Xavier; Cervelló Royo, Roberto Elías; Ribal Sanchis, Francisco Javier; Centro de Investigación en Gestión de Empresas (CEGEA); Facultad de Administración y Dirección de Empresas; Departamento de Economía y Ciencias Sociales; Centro de Investigación de Ingeniería Económica[EN] Long short-term memory (LSTM) neural networks allow to capture long-range dependencies and non-linearities in sequential data and can learn complex patterns and relationships in the data improving the accuracy of future stock price predictions. Since classical portfolio optimization is highly sensitive to the estimated parameters used to construct an optimal portfolio, the purpose of our research is to leverage LSTM abilities to predict the parameters accurately and create portfolios that generate superior results.We predict the prices of the 50 components of the EURO STOXX 50® Index using LSTM and create prediction-based optimal portfolios for ten different investment time horizons. We define the risk as a combination of the standard deviation and the performance of the evaluation metrics obtained testing our model, allowing us to use a measure for the risk based on the level of confidence the model has in the prediction.Our portfolios consistently beat the market over the analyzed investment scenarios from 2021 until the first half of 2022 and are robust for both growing and bear markets. The proposed model achieves an average MAE of 0.01634, an average MSE of 0.00047, and an average accuracy of 95.8% in predicting the direction of the stock movements over the ten proposed periods.Our research contributes to the field of finance by providing an innovative framework for portfolio optimization that leverages the power of LSTM-based stock price prediction and risk-adjusted performance metrics.
- PublicationInfluence of storage conditions of PVC rooftop sheets on the hot air welding process(Editorial Universitat Politècnica de València, 2024-03-12) Michalak, Martin; Sover, Alexandru[EN] One of the common welding methods for polymer rooftop sheets is the overlap welding by hot air. For this method two rooftop sheets are put over each other with a certain overlapping part and then a hot air fan, followed by a pressure role is lead through the overlap. The surfaces of the sheets are melted and the pressure of the role bonds them together. This welding method depends on some different parameters to achieve a good welding quality. The main parameters are the welding temperature, the welding speed and the pressure applied to the rooftop sheets. However, as the sheets are used to cover roofs outside, there are a lot of other influences on the weldability of the sheets and the quality of the weld itself. For example, the ambient temperature during the welding process, the intensity of the sun or the humidity of the air. Another influence on which this work is focused, is the effect of the storage temperatures of rooftop sheets. There is no common guideline how storing temperatures effect the welding as producers of rooftop sheets usually only recommend a dry storage. To analyze and characterize this effect, a common PVC rooftop sheet is stored under different temperatures and welded directly after the temperature treatment. The welded samples are tested according to the DIN EN 12317-2 to determine the shear resistance of the weld as it is an important mechanical property and a good indicator for the quality of the weld.
- PublicationThe Social Media Hate Speech Barometer: Making of(Editorial Universitat Politècnica de València, 2024-03-12) Sauer, Sebastian; Piazza, Alexander; Schacht, Sigurd[EN] Hate speech, particularly on social media channels, is a pressing cybersecurity concern and can even threaten the very foundations of societal stability. While there is a growing body of literature on how to detect and mitigate hate speech, applied researchers lack a state-of-the-art yet easily accessible infrastructure to build their own hate speech detection pipelines. We aim to provide an example of such an infrastructure that can serve as a template for other researchers. The infrastructure we present is based on the latest machine learning technologies available in the R environment: The Tidymodels framework and its extension Tidytext, plus the Targets project management approach, are the building blocks of our proposed infrastructure. In short, our data pipeline starts with downloading and preprocessing tweets, using various methods to convert text into numerical information. We then apply state-of-the-art supervised machine learning pipelines, drawing on a range of learning algorithms and incorporating new tuning capabilities. The focus of this paper is to explain the setup and rationale of the infrastructure. Our infrastructure is freely available on Github at https://github.com/sebastiansauer/hate-speech-barometer.