195 research outputs found
Facilitating prosociality through technology: Design to promote digital volunteerism
Volunteerism covers many activities involving no financial rewards for volunteers but which contribute
to the common good. There is existing work in designing technology for volunteerism in HumanComputer Interaction (HCI) and related disciplines that focuses on motivation to improve
performance, but it does not account for volunteer wellbeing. Here, I investigate digital volunteerism
in three case studies with a focus on volunteer motivation, engagement, and wellbeing. My research
involved volunteers and others in the volunteering context to generate recommendations for a
volunteer-centric design for digital volunteerism. The thesis has three aims:
1. To investigate motivational aspects critical for enhancing digital volunteers’ experiences
2. To identify digital platform attributes linked to volunteer wellbeing
3. To create guidelines for effectively supporting volunteer engagement in digital volunteering
platforms
In the first case study I investigate the design of a chat widget for volunteers working in an
organisation with a view to develop a design that improves their workflow and wellbeing. The second
case study investigates the needs, motivations, and wellbeing of volunteers who help medical
students improve their medical communication skills. An initial mixed-methods study was followed by
an experiment comparing two design strategies to improve volunteer relatedness; an important
indicator of wellbeing. The third case study looks into volunteer needs, experiences, motivations, and
wellbeing with a focus on volunteer identity and meaning-making on a science-based research
platform. I then analyse my findings from these case studies using the lens of care ethics to derive
critical insights for design.
The key contributions of this thesis are design strategies and critical insights, and a volunteer-centric
design framework to enhance the motivation, wellbeing and engagement of digital volunteers
Cybersecurity applications of Blockchain technologies
With the increase in connectivity, the popularization of cloud services, and the rise
of the Internet of Things (IoT), decentralized approaches for trust management
are gaining momentum. Since blockchain technologies provide a distributed ledger,
they are receiving massive attention from the research community in different application
fields. However, this technology does not provide cybersecurity by itself.
Thus, this thesis first aims to provide a comprehensive review of techniques and
elements that have been proposed to achieve cybersecurity in blockchain-based systems.
The analysis is intended to target area researchers, cybersecurity specialists
and blockchain developers. We present a series of lessons learned as well. One of
them is the rise of Ethereum as one of the most used technologies.
Furthermore, some intrinsic characteristics of the blockchain, like permanent
availability and immutability made it interesting for other ends, namely as covert
channels and malicious purposes.
On the one hand, the use of blockchains by malwares has not been characterized
yet. Therefore, this thesis also analyzes the current state of the art in this area. One
of the lessons learned is that covert communications have received little attention.
On the other hand, although previous works have analyzed the feasibility of
covert channels in a particular blockchain technology called Bitcoin, no previous
work has explored the use of Ethereum to establish a covert channel considering all
transaction fields and smart contracts.
To foster further defence-oriented research, two novel mechanisms are presented
on this thesis. First, Zephyrus takes advantage of all Ethereum fields and smartcontract
bytecode. Second, Smart-Zephyrus is built to complement Zephyrus by
leveraging smart contracts written in Solidity. We also assess the mechanisms feasibility
and cost. Our experiments show that Zephyrus, in the best case, can embed
40 Kbits in 0.57 s. for US 1.82 per bit), the provided stealthiness might be worth the price for attackers. Furthermore,
these two mechanisms can be combined to increase capacity and reduce
costs.Debido al aumento de la conectividad, la popularización de los servicios en la nube
y el auge del Internet de las cosas (IoT), los enfoques descentralizados para la
gestión de la confianza están cobrando impulso. Dado que las tecnologías de cadena
de bloques (blockchain) proporcionan un archivo distribuido, están recibiendo
una atención masiva por parte de la comunidad investigadora en diferentes campos
de aplicación. Sin embargo, esta tecnología no proporciona ciberseguridad por sí
misma. Por lo tanto, esta tesis tiene como primer objetivo proporcionar una revisión
exhaustiva de las técnicas y elementos que se han propuesto para lograr la ciberseguridad
en los sistemas basados en blockchain. Este análisis está dirigido a investigadores
del área, especialistas en ciberseguridad y desarrolladores de blockchain. A
su vez, se presentan una serie de lecciones aprendidas, siendo una de ellas el auge
de Ethereum como una de las tecnologías más utilizadas.
Asimismo, algunas características intrínsecas de la blockchain, como la disponibilidad
permanente y la inmutabilidad, la hacen interesante para otros fines, concretamente
como canal encubierto y con fines maliciosos.
Por una parte, aún no se ha caracterizado el uso de la blockchain por parte
de malwares. Por ello, esta tesis también analiza el actual estado del arte en este
ámbito. Una de las lecciones aprendidas al analizar los datos es que las comunicaciones
encubiertas han recibido poca atención.
Por otro lado, aunque trabajos anteriores han analizado la viabilidad de los
canales encubiertos en una tecnología blockchain concreta llamada Bitcoin, ningún
trabajo anterior ha explorado el uso de Ethereum para establecer un canal encubierto
considerando todos los campos de transacción y contratos inteligentes.
Con el objetivo de fomentar una mayor investigación orientada a la defensa,
en esta tesis se presentan dos mecanismos novedosos. En primer lugar, Zephyrus
aprovecha todos los campos de Ethereum y el bytecode de los contratos inteligentes.
En segundo lugar, Smart-Zephyrus complementa Zephyrus aprovechando los contratos inteligentes escritos en Solidity. Se evalúa, también, la viabilidad y el coste
de ambos mecanismos. Los resultados muestran que Zephyrus, en el mejor de los
casos, puede ocultar 40 Kbits en 0,57 s. por 1,64 US$, y recuperarlos en 2,8 s.
Smart-Zephyrus, por su parte, es capaz de ocultar un secreto de 4 Kb en 41 s. Si
bien es cierto que es caro (alrededor de 1,82 dólares por bit), el sigilo proporcionado
podría valer la pena para los atacantes. Además, estos dos mecanismos pueden
combinarse para aumentar la capacidad y reducir los costesPrograma de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José Manuel Estévez Tapiador.- Secretario: Jorge Blasco Alís.- Vocal: Luis Hernández Encina
From labs to real-world: developing smartphone-based methodologies for enhanced phenotyping of human decision-making in clinical settings
The application of computational approaches in behavioral and cognitive science has advanced our understanding of the biological mechanisms involved in learning and decision-making. However, these advances have not been effectively translated into clinical applications, partly due to the limitations of small-scale, lab-based experiments in capturing the complexity of the brain and its interaction with the environment. The widespread use of smartphones presents an opportunity to overcome the limitations of these lab-based experiments. This thesis aims to develop and validate smartphone-based methodologies that can provide richer data sets and larger samples for studying human decision-making and facilitating clinical translation. Several case studies were conducted to demonstrate the feasibility of these methodologies.
The first step involved moving experimental settings from laboratories to naturalistic settings using lab-in-the-field methodology. Two case studies were conducted: one involv- ing individuals with chronic pain and another involving patients with Parkinson’s Disease. These studies revealed correspondences between altered decision-making performance and clinical variables of interest, showcasing the potential of testing decision-making pheno- types outside the laboratory. To capture momentary phenotypes in different contexts and time points, the Ecological Momentary Assessment (EMA) methodology was employed. A proof-of-concept EMA study assessed momentary tinnitus experience and its impact on decision-making using a mobile app, demonstrating the possibility of dense sampling of human decision-making in the daily contexts. Ethical and legal constraints on large- scale human phenotyping were also addressed. The feasibility of training computational models of decision-making using distributed learning strategies was examined using a "many-labs" data pool. Federated Learning offered an alternative when large-scale private data collection using smartphones is restricted.
In summary, this thesis explored smartphone-based methodologies to develop robust neurocognitive models of mental health conditions, implemented through various be- havioral and cognitive studies. These methodologies have the potential to complement traditional lab-based experiments, significantly enhancing our understanding of cognitive mechanisms and expediting clinical translation
Peer-to-Peer Energy Trading in Smart Residential Environment with User Behavioral Modeling
Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid.
Trading energy among users in a decentralized fashion has been referred to as Peer- to-Peer (P2P) Energy Trading, which has attracted significant attention from the research and industry communities in recent times. However, previous research has mostly focused on engineering aspects of P2P energy trading systems, often neglecting the central role of users in such systems. P2P trading mechanisms require active participation from users to decide factors such as selling prices, storing versus trading energy, and selection of energy sources among others. The complexity of these tasks, paired with the limited cognitive and time capabilities of human users, can result sub-optimal decisions or even abandonment of such systems if performance is not satisfactory. Therefore, it is of paramount importance for P2P energy trading systems to incorporate user behavioral modeling that captures users’ individual trading behaviors, preferences, and perceived utility in a realistic and accurate manner. Often, such user behavioral models are not known a priori in real-world settings, and therefore need to be learned online as the P2P system is operating.
In this thesis, we design novel algorithms for P2P energy trading. By exploiting a variety of statistical, algorithmic, machine learning, and behavioral economics tools, we propose solutions that are able to jointly optimize the system performance while taking into account and learning realistic model of user behavior. The results in this dissertation has been published in IEEE Transactions on Green Communications and Networking 2021, Proceedings of IEEE Global Communication Conference 2022, Proceedings of IEEE Conference on Pervasive Computing and Communications 2023 and ACM Transactions on Evolutionary Learning and Optimization 2023
Untangle sustainable development goal 8 through data visualization and HCI methods
Following the approval of the 2030 Agenda for Sustainable Development in 2015, sustainability became a hotly debated topic. In order to build a better and more sustainable future by 2030, this agenda addressed several global issues, including inequality, climate change, peace, and justice, in the form of 17 Sustainable Development Goals (SDGs), that should be understood and pursued by nations, corporations, institutions, and individuals.
In this thesis, we researched how to exploit and integrate Human-Computer Interaction (HCI) and Data Visualization to promote knowledge and awareness about SDG 8, which wants to encourage lasting, inclusive, and sustainable economic growth, full and productive employment, and decent work for all. In particular, we focused on three targets: green economy, sustainable tourism, employment, decent work for all, and social protection. The primary goal of this research is to determine whether HCI approaches may be used to create and validate interactive data visualization that can serve as helpful decision-making aids for specific groups and raise their knowledge of public-interest issues. To accomplish this goal, we analyzed four case studies. In the first two, we wanted to promote knowledge and awareness about green economy issues: we investigated the Human-Building Interaction inside a Smart Campus and the dematerialization process inside a University. In the third, we focused on smart tourism, investigating the relationship between locals and tourists to create meaningful connections and promote more sustainable tourism. In the fourth, we explored the industry context to highlight sustainability policies inside well-known companies.
This research focuses on the hypothesis that interactive data visualization tools can make communities aware of sustainability aspects related to SDG8 and its targets. The research questions addressed are two: "how to promote awareness about SDG8 and its targets through interactive data visualizations?" and "to what extent are these interactive data visualizations effective?"
Empirical Analysis of Privacy Preservation Models for Cyber Physical Deployments from a Pragmatic Perspective
The difficulty of privacy protection in cyber-physical installations encompasses several sectors and calls for methods like encryption, hashing, secure routing, obfuscation, and data exchange, among others. To create a privacy preservation model for cyber physical deployments, it is advised that data privacy, location privacy, temporal privacy, node privacy, route privacy, and other types of privacy be taken into account. Consideration must also be given to other types of privacy, such as temporal privacy. The computationally challenging process of incorporating these models into any wireless network also affects quality of service (QoS) variables including end-to-end latency, throughput, energy use, and packet delivery ratio. The best privacy models must be used by network designers and should have the least negative influence on these quality-of-service characteristics. The designers used common privacy models for the goal of protecting cyber-physical infrastructure in order to achieve this. The limitations of these installations' interconnection and interface-ability are not taken into account in this. As a result, even while network security has increased, the network's overall quality of service has dropped. The many state-of-the-art methods for preserving privacy in cyber-physical deployments without compromising their performance in terms of quality of service are examined and analyzed in this research. Lowering the likelihood that such circumstances might arise is the aim of this investigation and review. These models are rated according to how much privacy they provide, how long it takes from start to finish to transfer data, how much energy they use, and how fast their networks are. In order to maximize privacy while maintaining a high degree of service performance, the comparison will assist network designers and researchers in selecting the optimal models for their particular deployments. Additionally, the author of this book offers a variety of tactics that, when used together, might improve each reader's performance. This study also provides a range of tried-and-true machine learning approaches that networks may take into account and examine in order to enhance their privacy performance
Trustworthy Federated Learning: A Survey
Federated Learning (FL) has emerged as a significant advancement in the field
of Artificial Intelligence (AI), enabling collaborative model training across
distributed devices while maintaining data privacy. As the importance of FL
increases, addressing trustworthiness issues in its various aspects becomes
crucial. In this survey, we provide an extensive overview of the current state
of Trustworthy FL, exploring existing solutions and well-defined pillars
relevant to Trustworthy . Despite the growth in literature on trustworthy
centralized Machine Learning (ML)/Deep Learning (DL), further efforts are
necessary to identify trustworthiness pillars and evaluation metrics specific
to FL models, as well as to develop solutions for computing trustworthiness
levels. We propose a taxonomy that encompasses three main pillars:
Interpretability, Fairness, and Security & Privacy. Each pillar represents a
dimension of trust, further broken down into different notions. Our survey
covers trustworthiness challenges at every level in FL settings. We present a
comprehensive architecture of Trustworthy FL, addressing the fundamental
principles underlying the concept, and offer an in-depth analysis of trust
assessment mechanisms. In conclusion, we identify key research challenges
related to every aspect of Trustworthy FL and suggest future research
directions. This comprehensive survey serves as a valuable resource for
researchers and practitioners working on the development and implementation of
Trustworthy FL systems, contributing to a more secure and reliable AI
landscape.Comment: 45 Pages, 8 Figures, 9 Table
Software Architecture Design for Federated Learning Systems
The advancements in deep learning and machine learning as the subdomain of AI have been demonstrated in multiple industries. However, the requirement for data by deep machine learning models has raised data privacy concerns. For instance, the EU's General Data Protection Regulation (GDPR) stipulates a range of data protection measures, causing data hungriness issues. Furthermore, trustworthy and responsible AI have emerged as hot topics recently thanks to the new ethical, legal, social, and technological challenges brought on by the technology. All of that led to the need for decentralised machine learning approaches.
Federated learning is an emerging privacy-preserving AI technique that trains models locally and formulates a global model without transferring local data externally. Being widely distributed with different components and stakeholders, federated learning requires software system design thinking and software engineering considerations. Nonetheless, the different software engineering challenges and the software architectural approaches of federated learning have not previously been conceptualised systematically in the software architecture literature. This thesis aims to address the software engineering research gap of federated learning systems and to provide system-level solutions to achieve trustworthy and responsible federated learning by design.
We first report the findings of a systematic literature review on federated learning from its software engineering perspective. Based on the study, the software architecture design concerns in building federated learning systems have been largely ignored. Thus, we present a collection of architectural patterns for the design challenges of federated learning systems and a set of decision models to assist software architects in pattern selection and perform architecture validations. The evaluation results show that the approaches are feasible and useful in serving as a guideline for federated learning software architecture design. We propose FLRA, a reference architecture for federated learning systems, and adopt the FLRA as the design basis to enhance trust for federated learning software architecture. Finally, we evaluated the designed federated learning architecture. The evaluation results show that the approach is feasible to enable accountability and improve fairness. Ultimately, the proposed system-level solution can achieve trustworthy and responsible federated learning
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
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