864 research outputs found
Applications of Deep Learning Models in Financial Forecasting
In financial markets, deep learning techniques sparked a revolution, reshaping conventional approaches and amplifying predictive capabilities. This thesis explored the applications of deep learning models to unravel insights and methodologies aimed at advancing financial forecasting.
The crux of the research problem lies in the applications of predictive models within financial domains, characterised by high volatility and uncertainty. This thesis investigated the application of advanced deep-learning methodologies in the context of financial forecasting, addressing the challenges posed by the dynamic nature of financial markets. These challenges were tackled by exploring a range of techniques, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), autoencoders (AEs), and variational autoencoders (VAEs), along with
approaches such as encoding financial time series into images. Through analysis, methodologies such as transfer learning, convolutional neural networks, long short-term memory networks, generative modelling, and image encoding of time series data were examined. These methodologies collectively offered a comprehensive toolkit for extracting meaningful insights from financial data.
The present work investigated the practicality of a deep learning CNN-LSTM model within the Directional Change framework to predict significant DC eventsâa task crucial for timely decisionmaking in financial markets. Furthermore, the potential of autoencoders and variational autoencoders to enhance financial forecasting accuracy and remove noise from financial time series data was explored. Leveraging their capacity within financial time series, these models offered promising avenues for improved data representation and subsequent forecasting. To further contribute to
financial prediction capabilities, a deep multi-model was developed that harnessed the power of pre-trained computer vision models. This innovative approach aimed to predict the VVIX, utilising the cross-disciplinary synergy between computer vision and financial forecasting. By integrating knowledge from these domains, novel insights into the prediction of market volatility were provided
La traduzione specializzata allâopera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.
Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The âLanguage Toolkit â Le lingue straniere al servizio dellâinternazionalizzazione dellâimpresaâ project, promoted by the Department of Interpreting and Translation (ForlĂŹ Campus) in collaboration with the Romagna Chamber of Commerce (ForlĂŹ-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices
Tensor-variate machine learning on graphs
Traditional machine learning algorithms are facing significant challenges as the world enters the era of big data, with a dramatic expansion in volume and range of applications and an increase in the variety of data sources. The large- and multi-dimensional nature of data often increases the computational costs associated with their processing and raises the risks of model over-fitting - a phenomenon known as the curse of dimensionality. To this end, tensors have become a subject of great interest in the data analytics community, owing to their remarkable ability to super-compress high-dimensional data into a low-rank format, while retaining the original data structure and interpretability. This leads to a significant reduction in computational costs, from an exponential complexity to a linear one in the data dimensions.
An additional challenge when processing modern big data is that they often reside on irregular domains and exhibit relational structures, which violates the regular grid assumptions of traditional machine learning models. To this end, there has been an increasing amount of research in generalizing traditional learning algorithms to graph data. This allows for the processing of graph signals while accounting for the underlying relational structure, such as user interactions in social networks, vehicle flows in traffic networks, transactions in supply chains, chemical bonds in proteins, and trading data in financial networks, to name a few.
Although promising results have been achieved in these fields, there is a void in literature when it comes to the conjoint treatment of tensors and graphs for data analytics. Solutions in this area are increasingly urgent, as modern big data is both large-dimensional and irregular in structure. To this end, the goal of this thesis is to explore machine learning methods that can fully exploit the advantages of both tensors and graphs. In particular, the following approaches are introduced: (i) Graph-regularized tensor regression framework for modelling high-dimensional data while accounting for the underlying graph structure; (ii) Tensor-algebraic approach for computing efficient convolution on graphs; (iii) Graph tensor network framework for designing neural learning systems which is both general enough to describe most existing neural network architectures and flexible enough to model large-dimensional data on any and many irregular domains. The considered frameworks were employed in several real-world applications, including air quality forecasting, protein classification, and financial modelling. Experimental results validate the advantages of the proposed methods, which achieved better or comparable performance against state-of-the-art models. Additionally, these methods benefit from increased interpretability and reduced computational costs, which are crucial for tackling the challenges posed by the era of big data.Open Acces
Digital agriculture: research, development and innovation in production chains.
Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil
Measuring the impact of COVID-19 on hospital care pathways
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospitalâs new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
KĂŒnstliche Intelligenz in der Hochschulbildung. Chancen und Grenzen des KI-gestĂŒtzten Lernens und Lehrens
Der Einsatz von KĂŒnstlicher Intelligenz erfĂ€hrt in hochschuldidaktischen Kontexten wachsendes Interesse. Durch individualisierte Lernwege ergeben sich fĂŒr Studierende und Dozierende neue Lern-Lehr-Möglichkeiten. Der Band befasst sich jedoch auch mit den daraus resultierenden Gefahren: Es sind Demarkationslinien zu ziehen, wenn es um Datenschutz oder ethische Bedenken geht. Die Autor*innen des Bandes nehmen verschiedene Sichtweisen ein, um eine multidisziplinĂ€re Betrachtung der neuen Problemstellungen, die die Anwendung von KI im Hochschulkontext mit sich bringt, zu ermöglichen. (DIPF/Orig.)Higher education teaching contexts are becoming increasingly interested in the use of artificial intelligence. Individualized learning paths give students and lecturers new opportunities for learning and teaching. The volume, however, also covers the risks that follow: There must be clear boundaries when it comes to privacy or ethical issues. To enable a multidisciplinary view of the new issues raised by the application of AI in the university context, the volume\u27s authors adopt various vantage points. (Editors
Towards a human-centric data economy
Spurred by widespread adoption of artificial intelligence and machine learning, âdataâ is becoming
a key production factor, comparable in importance to capital, land, or labour in an increasingly
digital economy. In spite of an ever-growing demand for third-party data in the B2B
market, firms are generally reluctant to share their information. This is due to the unique characteristics
of âdataâ as an economic good (a freely replicable, non-depletable asset holding a highly
combinatorial and context-specific value), which moves digital companies to hoard and protect
their âvaluableâ data assets, and to integrate across the whole value chain seeking to monopolise
the provision of innovative services built upon them. As a result, most of those valuable assets
still remain unexploited in corporate silos nowadays.
This situation is shaping the so-called data economy around a number of champions, and it is
hampering the benefits of a global data exchange on a large scale. Some analysts have estimated
the potential value of the data economy in US$2.5 trillion globally by 2025. Not surprisingly, unlocking
the value of data has become a central policy of the European Union, which also estimated
the size of the data economy in 827C billion for the EU27 in the same period. Within the scope of
the European Data Strategy, the European Commission is also steering relevant initiatives aimed
to identify relevant cross-industry use cases involving different verticals, and to enable sovereign
data exchanges to realise them.
Among individuals, the massive collection and exploitation of personal data by digital firms
in exchange of services, often with little or no consent, has raised a general concern about privacy
and data protection. Apart from spurring recent legislative developments in this direction,
this concern has raised some voices warning against the unsustainability of the existing digital
economics (few digital champions, potential negative impact on employment, growing inequality),
some of which propose that people are paid for their data in a sort of worldwide data labour
market as a potential solution to this dilemma [114, 115, 155].
From a technical perspective, we are far from having the required technology and algorithms
that will enable such a human-centric data economy. Even its scope is still blurry, and the question
about the value of data, at least, controversial. Research works from different disciplines have
studied the data value chain, different approaches to the value of data, how to price data assets,
and novel data marketplace designs. At the same time, complex legal and ethical issues with
respect to the data economy have risen around privacy, data protection, and ethical AI practices. In this dissertation, we start by exploring the data value chain and how entities trade data assets
over the Internet. We carry out what is, to the best of our understanding, the most thorough survey
of commercial data marketplaces. In this work, we have catalogued and characterised ten different
business models, including those of personal information management systems, companies born
in the wake of recent data protection regulations and aiming at empowering end users to take
control of their data. We have also identified the challenges faced by different types of entities,
and what kind of solutions and technology they are using to provide their services.
Then we present a first of its kind measurement study that sheds light on the prices of data
in the market using a novel methodology. We study how ten commercial data marketplaces categorise
and classify data assets, and which categories of data command higher prices. We also
develop classifiers for comparing data products across different marketplaces, and we study the
characteristics of the most valuable data assets and the features that specific vendors use to set
the price of their data products. Based on this information and adding data products offered by
other 33 data providers, we develop a regression analysis for revealing features that correlate with
prices of data products. As a result, we also implement the basic building blocks of a novel data
pricing tool capable of providing a hint of the market price of a new data product using as inputs
just its metadata. This tool would provide more transparency on the prices of data products in
the market, which will help in pricing data assets and in avoiding the inherent price fluctuation of
nascent markets.
Next we turn to topics related to data marketplace design. Particularly, we study how buyers
can select and purchase suitable data for their tasks without requiring a priori access to such
data in order to make a purchase decision, and how marketplaces can distribute payoffs for a
data transaction combining data of different sources among the corresponding providers, be they
individuals or firms. The difficulty of both problems is further exacerbated in a human-centric
data economy where buyers have to choose among data of thousands of individuals, and where
marketplaces have to distribute payoffs to thousands of people contributing personal data to a
specific transaction.
Regarding the selection process, we compare different purchase strategies depending on the
level of information available to data buyers at the time of making decisions. A first methodological
contribution of our work is proposing a data evaluation stage prior to datasets being selected
and purchased by buyers in a marketplace. We show that buyers can significantly improve the
performance of the purchasing process just by being provided with a measurement of the performance
of their models when trained by the marketplace with individual eligible datasets. We
design purchase strategies that exploit such functionality and we call the resulting algorithm Try
Before You Buy, and our work demonstrates over synthetic and real datasets that it can lead to
near-optimal data purchasing with only O(N) instead of the exponential execution time - O(2N)
- needed to calculate the optimal purchase. With regards to the payoff distribution problem, we focus on computing the relative value
of spatio-temporal datasets combined in marketplaces for predicting transportation demand and
travel time in metropolitan areas. Using large datasets of taxi rides from Chicago, Porto and
New York we show that the value of data is different for each individual, and cannot be approximated
by its volume. Our results reveal that even more complex approaches based on the
âleave-one-outâ value, are inaccurate. Instead, more complex and acknowledged notions of value
from economics and game theory, such as the Shapley value, need to be employed if one wishes
to capture the complex effects of mixing different datasets on the accuracy of forecasting algorithms.
However, the Shapley value entails serious computational challenges. Its exact calculation
requires repetitively training and evaluating every combination of data sources and hence O(N!)
or O(2N) computational time, which is unfeasible for complex models or thousands of individuals.
Moreover, our work paves the way to new methods of measuring the value of spatio-temporal
data. We identify heuristics such as entropy or similarity to the average that show a significant
correlation with the Shapley value and therefore can be used to overcome the significant computational
challenges posed by Shapley approximation algorithms in this specific context.
We conclude with a number of open issues and propose further research directions that leverage
the contributions and findings of this dissertation. These include monitoring data transactions
to better measure data markets, and complementing market data with actual transaction prices
to build a more accurate data pricing tool. A human-centric data economy would also require
that the contributions of thousands of individuals to machine learning tasks are calculated daily.
For that to be feasible, we need to further optimise the efficiency of data purchasing and payoff
calculation processes in data marketplaces. In that direction, we also point to some alternatives
to repetitively training and evaluating a model to select data based on Try Before You Buy and
approximate the Shapley value. Finally, we discuss the challenges and potential technologies that
help with building a federation of standardised data marketplaces.
The data economy will develop fast in the upcoming years, and researchers from different
disciplines will work together to unlock the value of data and make the most out of it. Maybe
the proposal of getting paid for our data and our contribution to the data economy finally flies,
or maybe it is other proposals such as the robot tax that are finally used to balance the power
between individuals and tech firms in the digital economy. Still, we hope our work sheds light on
the value of data, and contributes to making the price of data more transparent and, eventually, to
moving towards a human-centric data economy.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en IngenierĂa TelemĂĄtica por la Universidad Carlos III de MadridPresidente: Georgios Smaragdakis.- Secretario: Ăngel Cuevas RumĂn.- Vocal: Pablo RodrĂguez RodrĂgue
Getting Around to It: How Design Science Researchers Set Future Work Agendas
Background: There is a long tradition of writing about future work in research papers, and information systems design science research (IS DSR) is no exception. However, there is a lack of studies on (1) how IS DSR authors currently envision the next steps for their work and (2) guidelines to improve the communication of opportunities to accumulate knowledge.
Method: This paper contributes to this topic, building on a systematic literature review of 123 IS DSR papers published between 2018 and 2022.
Results: Design-oriented research requires the research team to decide which tasks to carry out immediately in building the future and which to postpone as research debt. The paper\u27s contribution is threefold. First, we propose a research debt lifecycle to support (1) project stakeholders, (2) IS DSR community, and (3) societies looking for better futures. Second, we discuss the anatomy of future work in recent IS DSR. Finally, we suggest guidelines to manage and report the next research steps.
Conclusion: This paper presents a pioneering assessment of future work suggestions in the IS field, focusing on the design science research paradigm. Future work directions emerge from researchers\u27 choices during the IS DSR process that must be continuously managed
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