2,748 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    VeriFx: Correct Replicated Data Types for the Masses

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    Distributed systems adopt weak consistency to ensure high availability and low latency, but state convergence is hard to guarantee due to conflicts. Experts carefully design replicated data types (RDTs) that resemble sequential data types and embed conflict resolution mechanisms that ensure convergence. Designing RDTs is challenging as their correctness depends on subtleties such as the ordering of concurrent operations. Currently, researchers manually verify RDTs, either by paper proofs or using proof assistants. Unfortunately, paper proofs are subject to reasoning flaws and mechanized proofs verify a formalization instead of a real-world implementation. Furthermore, writing mechanized proofs is reserved for verification experts and is extremely time-consuming. To simplify the design, implementation, and verification of RDTs, we propose VeriFx, a specialized programming language for RDTs with automated proof capabilities. VeriFx lets programmers implement RDTs atop functional collections and express correctness properties that are verified automatically. Verified RDTs can be transpiled to mainstream languages (currently Scala and JavaScript). VeriFx provides libraries for implementing and verifying Conflict-free Replicated Data Types (CRDTs) and Operational Transformation (OT) functions. These libraries implement the general execution model of those approaches and define their correctness properties. We use the libraries to implement and verify an extensive portfolio of 51 CRDTs, 16 of which are used in industrial databases, and reproduce a study on the correctness of OT functions

    Undergraduate Catalog of Studies, 2022-2023

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    Digital Traces of the Mind::Using Smartphones to Capture Signals of Well-Being in Individuals

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    General context and questions Adolescents and young adults typically use their smartphone several hours a day. Although there are concerns about how such behaviour might affect their well-being, the popularity of these powerful devices also opens novel opportunities for monitoring well-being in daily life. If successful, monitoring well-being in daily life provides novel opportunities to develop future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). Taking an interdisciplinary approach with insights from communication, computational, and psychological science, this dissertation investigated the relation between smartphone app use and well-being and developed machine learning models to estimate an individual’s well-being based on how they interact with their smartphone. To elucidate the relation between smartphone trace data and well-being and to contribute to the development of technologies for monitoring well-being in future clinical practice, this dissertation addressed two overarching questions:RQ1: Can we find empirical support for theoretically motivated relations between smartphone trace data and well-being in individuals? RQ2: Can we use smartphone trace data to monitor well-being in individuals?Aims The first aim of this dissertation was to quantify the relation between the collected smartphone trace data and momentary well-being at the sample level, but also for each individual, following recent conceptual insights and empirical findings in psychological, communication, and computational science. A strength of this personalized (or idiographic) approach is that it allows us to capture how individuals might differ in how smartphone app use is related to their well-being. Considering such interindividual differences is important to determine if some individuals might potentially benefit from spending more time on their smartphone apps whereas others do not or even experience adverse effects. The second aim of this dissertation was to develop models for monitoring well-being in daily life. The present work pursued this transdisciplinary aim by taking a machine learning approach and evaluating to what extent we might estimate an individual’s well-being based on their smartphone trace data. If such traces can be used for this purpose by helping to pinpoint when individuals are unwell, they might be a useful data source for developing future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). With this aim, the dissertation follows current developments in psychoinformatics and psychiatry, where much research resources are invested in using smartphone traces and similar data (obtained with smartphone sensors and wearables) to develop technologies for detecting whether an individual is currently unwell or will be in the future. Data collection and analysis This work combined novel data collection techniques (digital phenotyping and experience sampling methodology) for measuring smartphone use and well-being in the daily lives of 247 student participants. For a period up to four months, a dedicated application installed on participants’ smartphones collected smartphone trace data. In the same time period, participants completed a brief smartphone-based well-being survey five times a day (for 30 days in the first month and 30 days in the fourth month; up to 300 assessments in total). At each measurement, this survey comprised questions about the participants’ momentary level of procrastination, stress, and fatigue, while sleep duration was measured in the morning. Taking a time-series and machine learning approach to analysing these data, I provide the following contributions: Chapter 2 investigates the person-specific relation between passively logged usage of different application types and momentary subjective procrastination, Chapter 3 develops machine learning methodology to estimate sleep duration using smartphone trace data, Chapter 4 combines machine learning and explainable artificial intelligence to discover smartphone-tracked digital markers of momentary subjective stress, Chapter 5 uses a personalized machine learning approach to evaluate if smartphone trace data contains behavioral signs of fatigue. Collectively, these empirical studies provide preliminary answers to the overarching questions of this dissertation.Summary of results With respect to the theoretically motivated relations between smartphone trace data and wellbeing (RQ1), we found that different patterns in smartphone trace data, from time spent on social network, messenger, video, and game applications to smartphone-tracked sleep proxies, are related to well-being in individuals. The strength and nature of this relation depends on the individual and app usage pattern under consideration. The relation between smartphone app use patterns and well-being is limited in most individuals, but relatively strong in a minority. Whereas some individuals might benefit from using specific app types, others might experience decreases in well-being when spending more time on these apps. With respect to the question whether we might use smartphone trace data to monitor well-being in individuals (RQ2), we found that smartphone trace data might be useful for this purpose in some individuals and to some extent. They appear most relevant in the context of sleep monitoring (Chapter 3) and have the potential to be included as one of several data sources for monitoring momentary procrastination (Chapter 2), stress (Chapter 4), and fatigue (Chapter 5) in daily life. Outlook Future interdisciplinary research is needed to investigate whether the relationship between smartphone use and well-being depends on the nature of the activities performed on these devices, the content they present, and the context in which they are used. Answering these questions is essential to unravel the complex puzzle of developing technologies for monitoring well-being in daily life.<br/

    Technical Training to Nonprofit Managers Influences Using Big Data Technology in Business Operations

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    This nonexperimental, survey-based online quantitative study on nonprofit managers’ technical training measures the extent of the influence on big data technology use. The unified theory of acceptance and use of technology is a theoretical framework to determine whether business managers are trained to have know-how in using big data technology. This study followed a quantitative methodology to help narrow the gap in research between what is not known in relation to the nonprofit manager’s technical training on the use of big data technology. Today’s data is the most critical asset, but progress toward big data technology-oriented usage needs to be accessed by the nonprofit. Nonprofits need to use big data technology to gain insights into identifying the program activities and monitor them to make better decisions that maximize societal impact. Big data technology allows nonprofit managers to be effective by getting insights into the problem-solving of the social programs where they operate to reduce unemployment, poverty, social exclusion, and low education levels. This study seeks to answer how nonprofit managers differ in technical training (facilitating conditions) using big data technology compared to managers who have not used big data technology to manage business operations. The study may contribute to bridging existing research gaps in managers’ technical training and using big data technology

    Implementation design of energy trading monitoring application for blockchain technology-based wheeling cases

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    One obstacle to the energy industry’s tendency toward adopting renewable energy is the requirement for a monitoring system for energy transactions based on microgrids in the wheeling scheme (shared use of utility networks). The quantity of transaction expenses for each operational generator is not monitored in any case. In this project, a mobile phone application is developed and maintained to track the total amount of fees paid and received by all wheeling parties and the amount of electricity produced by the microgrid. In the wheeling case system research, the number of transaction costs, such as network rental fees, loss costs, and profit margins, must be pretty calculated for all wheeling participants. The approach created in this study uses a blockchain system to execute transactions, and transactions can only take place if the wheeling actor and the generator have an existing contract. The application of energy trading is the main contribution of this research. The created application may track energy transfers and track how many fees each wheeling actor is required to receive or pay. Using a security system to monitor wheeling transactions will make energy trades transparent

    Fast Deterministic Gathering with Detection on Arbitrary Graphs: The Power of Many Robots

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    Over the years, much research involving mobile computational entities has been performed. From modeling actual microscopic (and smaller) robots, to modeling software processes on a network, many important problems have been studied in this context. Gathering is one such fundamental problem in this area. The problem of gathering k robots, initially arbitrarily placed on the nodes of an n-node graph, asks that these robots coordinate and communicate in a local manner, as opposed to global, to move around the graph, find each other, and settle down on a single node as fast as possible. A more difficult problem to solve is gathering with detection, where once the robots gather, they must subsequently realize that gathering has occurred and then terminate. In this paper, we propose a deterministic approach to solve gathering with detection for any arbitrary connected graph that is faster than existing deterministic solutions for even just gathering (without the requirement of detection) for arbitrary graphs. In contrast to earlier work on gathering, it leverages the fact that there are more robots present in the system to achieve gathering with detection faster than those previous papers that focused on just gathering. The state of the art solution for deterministic gathering [Ta-Shma and Zwick, TALG, 2014] takes O˜(n 5 log ℓ) rounds, where ℓ is the smallest label among robots and O˜ hides a polylog factor. We design a deterministic algorithm for gathering with detection with the following trade-offs depending on how many robots are present: (i) when k ≥ ⌊n/2⌋ + 1, the algorithm takes O(n 3 ) rounds, (ii) when k ≥ ⌊n/3⌋+1, the algorithm takes O(n 4 log n) rounds, and (iii) otherwise, the algorithm takes O˜(n 5 ) rounds. The algorithm is not required to know k, but only

    Essays on Innovations in Public Sector Auditing

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    The current antecedents of innovation in the public sector, that is, the adoption of SDGs and the unprecedented technological advancements exert pressures on the Supreme audit institutions’(SAIs) current socio-technical system. This has led SAIs to adopt different strategies to maintain their relevance and improve the quality of their work and operations. This thesis investigated the different types of innovations currently happening in the SAIs environment and how SAIs are reacting to the demands of these changes. This exploratory work captured public sector audit innovation through the following three essays: The first essay focused on Digital Transformation (DT), investigated how SAIs approach, and interpret DT. In this regard, DT was investigated from a SAIs perspective. Due to it being a novel topic in public sector auditing research, a qualitative research method was adopted, this method was supported with expert interviews and archival and or document data. Key findings revealed that the definition of DT varies from SAI to SAI, and this variation resulted from the differences in the level of digital development in each country. SAIs applied reactive and, in some situations proactive change strategies were applied. In the reactive strategy, SAIs reacted to change induced by a situational demand while in the proactive strategy, they experiment with technologies in advance. Most of the SAIs applying proactive change strategy operates an innovation lab or an experimentation space(see Bojovic, Sabatier, and Coblence 2020; Bucher and Langley 2016; Cartel, Boxenbaum, and Aggeri 2019; Wulf 2000). As an impact on public sector auditing profession, the research addresses the popular narrative of SAI’s equating digitization or the use of digital technologies to Digital transformation. It reiterated the holistic nature of DT, by pointing at the risk involved when DT is tied solely to technology adoption strategy ignoring other aspects such as people, organizational structure, strategy, culture, etc.La trasformazione in corso dell'ambiente esterno delle Istituzioni Superiori di Controllo (ISC, Corte dei conti) sta modificando le esigenze di controllo e le aspettative dei vari stakeholders coinvolti. Infatti, questa trasformazione, innescato dai progressi tecnologici, dall'adozione degli Obiettivi di Sviluppo Sostenibile (OSS) e dalla trasparenza sta modificando il modo e gli strumenti con cui viene esercitata l’attività di controllo. Ciò ha portato le ISC a adottare diverse strategie ed a introdurre diverse innovazioni per mantenere la loro rilevanza e migliorare la qualità del loro servizio. Vari autori hanno evidenziato la necessità di indagare circa le implicazioni del cambio della strategia di controllo e dell’adozione delle varie innovazioni tecnologiche nelle ISC. Il lavoro di tesi contribuisce in questa direzione e indaga sulle varie innovazioni tecnologiche adottate dalle ISC e come questi Istituzioni hanno reagito alle pressioni esterne di cambiamento. La tesi adotta un approccio esplorativo e sviluppa tre diverse ricerche per rispondere alla domanda principale di ricerca. La prima ricerca si concentra sulla trasformazione digitale (TD), e indaga su come le ISC hanno affrontato e interpretato la TD. La metodologia utilizzata è di tipo qualitativo. Sono state effettuate varie interviste a esperti del settore a livello internazionale oltre all’analisi documentale degli archivi delle varie istituzioni analizzate. I risultati hanno mostrato una diversa interpretazione e percezione, tra le istituzioni oggetto dello studio, del concetto della TD, dovuta alle differenze di sviluppo digitale nei vari paesi analizzati. Inoltre, i risultati mostrano che le ISC hanno adottato strategie reattive di cambiamento e, in alcune situazioni, hanno adottato strategie proattive. Nel primo caso, che rappresenta la maggioranza dei casi analizzati, le ISC hanno reagito al bisogno ovvero quando si presenta una necessità di cambiamento. Mentre nel secondo caso, ovvero di strategia di cambiamento proattivo, le ISC hanno sperimentato le tecnologie in anticipo. La maggior parte delle Istituzioni che ha adottato strategie proattive di cambiamento gestisce un laboratorio di innovazione o uno spazio di sperimentazione (vedi Bojovic, Sabatier e Coblence 2020; Bucher e Langley 2016; Cartel, Boxenbaum e Aggeri 2019; Wulf 2000). Inoltre, la ricerca mostra come la digitalizzazione o l'uso delle tecnologie digitali vengono equiparati alla TD nelle ISC. Questo rischio di interpretazione del concetto si concretizza soprattutto, come mostrano i risultati, quando la TD viene legata esclusivamente alla strategia di adozione della tecnologia ignorando altri aspetti come le persone, la struttura organizzativa, la strategia, la cultura, ecc

    A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data

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    The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines. One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials. In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications. Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains
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