Insight Centre for Data Analytics - Journal Articles
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Item The role of FPGAs in Modern Option Pricing techniques: A survey(MDPI, 2024-08-12) O'Mahony, Aidan; Hanzon, Bernard; Popovici, Emanuel; Science Foundation Ireland; Intel Corporation; Dell TechnologiesIn financial computation, Field Programmable Gate Arrays (FPGAs) have emerged as a transformative technology, particularly in the domain of option pricing. This study presents the impact of Field Programmable Gate Arrays (FPGAs) on computational methods in finance, with an emphasis on option pricing. Our review examined 99 selected studies from an initial pool of 131, revealing how FPGAs substantially enhance both the speed and energy efficiency of various financial models, particularly Black–Scholes and Monte Carlo simulations. Notably, the performance gains—ranging from 270- to 5400-times faster than conventional CPU implementations—are highly dependent on the specific option pricing model employed. These findings illustrate FPGAs’ capability to efficiently process complex financial computations while consuming less energy. Despite these benefits, this paper highlights persistent challenges in FPGA design optimization and programming complexity. This study not only emphasises the potential of FPGAs to further innovate financial computing but also outlines the critical areas for future research to overcome existing barriers and fully leverage FPGA technology in future financial applications.Item Road pavement health monitoring system using smartphone sensing with a two-stage machine learning model(Elsevier, 2024-08-17) Zhao, Kai; Xu, Shuoshuo; Loney, James; Visentin, Andrea; Li, Zili; Science Foundation Ireland; European Regional Development FundDrive-by road pavement monitoring, using smartphone sensing, has faced longstanding challenges in adoption due to low accuracy and limited applicability. This stems from significant uncertainties during data collection in real-world scenarios, making it prohibitively difficult in applying conventional machine learning models to the detection of road pavement anomalies. This paper presents a two-stage machine learning approach that extracts potential anomalies from the dataset and classifies them into four typical road feature categories. Unlike time-series data analysis, this approach transforms time-series into geospatial series, allowing the analysis to be time-independent thereby capable of detecting road anomalies regardless of driving speeds. Additionally, a framework for a road pavement health monitoring system is proposed to collect data, integrate the machine learning engine, and visualise road anomalies. The developed system was tested on two shuttle buses with normal smartphones, which achieved 87% overall accuracy compared against manual inspection.Item Wave2Graph: Integrating spectral features and correlations for graph-based learning in sound waves(Elsevier, 2024-09-03) Van Truong, Hoang; Tran, Khanh-Tung; Vu, Xuan-Son; Nguyen, Duy-Khuong; Bhuyan, Monowar; Nguyen, Hoang D.; Science Foundation IrelandThis paper investigates a novel graph-based representation of sound waves inspired by the physical phenomenon of correlated vibrations. We propose a Wave2Graph framework for integrating multiple acoustic representations, including the spectrum of frequencies and correlations, into various neural computing architectures to achieve new state-of-the-art performances in sound classification. The capability and reliability of our end-to-end framework are evidently demonstrated in voice pathology for low-cost and non-invasive mass-screening of medical conditions, including respiratory illnesses and Alzheimer’s Dementia. We conduct extensive experiments on multiple public benchmark datasets (ICBHI and ADReSSo) and our real-world dataset (IJSound: Respiratory disease detection using coughs and breaths). Wave2Graph framework consistently outperforms previous state-of-the-art methods with a large magnitude, up to 7.65% improvement, promising the usefulness of graph-based representation in signal processing and machine learning.Item Market analysis of a data platform in the European data ecosystem(Springer, 2024-06-23) Castane, Gabriel G.; Martinez, Alejandro; Ramadan, Qusai; Gkika, Zaneta; Panagiotis, Mpempis; Vyhmeister, Eduardo; HORIZON EUROPE Digital, Industry and Space; Science Foundation IrelandIn the era of data analytics, industries can use data to enhance market analyses and seek better opportunities for current, or new, business opportunities. Furthermore, companies can consider their data as an intangible asset, allowing expansion, by a proper market analysis, and economic opportunities through data monetization. These strategies define the use of data for indirect and direct purposes. Despite the idea of adopting data monetization practices, a considerable gap exists between expectations and reality. For example, the definition of strategies, methods, and analyses to perform data valuation. This gap necessitates a comprehensive exploration considering the different nuisances of data monetization. To achieve a broader understanding of the data monetization landscape, a market evaluation, through an exploration of the market environment and the stakeholders involved, is necessary. The present work contributes to this understanding by facilitating a market analysis for data valuation within the European community. This analysis aims to shed light on critical aspects influencing the valuation of data in the European context, offering insights into the market dynamics, emerging trends, and the evolving landscape of data monetization strategies within this region.Item iSee: A case-based reasoning platform for the design of explanation experiences(Elsevier, 2024-08-08) Caro-Martínez; Recio-García, Juan A.; Díaz-Agudo, Belén; Darias, Jesus M.; Wiratunga, Nirmalie; Martin, Kyle; Wijekoon, Anjana; Nkisi-Orji, Ikechukwu; Corsar, David; Pradeep, Preeja; Bridge, Derek G.; Liret, Anne; CHIST-ERA; Engineering and Physical Sciences Research Council; Irish Research Council; Science Foundation Ireland; Agence Nationale de la Recherche; Ministerio de Ciencia e InnovaciónExplainable Artificial Intelligence (XAI) is an emerging field within Artificial Intelligence (AI) that has provided many methods that enable humans to understand and interpret the outcomes of AI systems. However, deciding on the best explanation approach for a given AI problem is currently a challenging decision-making task. This paper presents the iSee project, which aims to address some of the XAI challenges by providing a unifying platform where personalized explanation experiences are generated using Case-Based Reasoning. An explanation experience includes the proposed solution to a particular explainability problem and its corresponding evaluation, provided by the end user. The ultimate goal is to provide an open catalog of explanation experiences that can be transferred to other scenarios where trustworthy AI is required.