9,780 research outputs found

    Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective

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    This paper introduces a comprehensive, multi-stage machine learning methodology that effectively integrates information systems and artificial intelligence to enhance decision-making processes within the domain of operations research. The proposed framework adeptly addresses common limitations of existing solutions, such as the neglect of data-driven estimation for vital production parameters, exclusive generation of point forecasts without considering model uncertainty, and lacking explanations regarding the sources of such uncertainty. Our approach employs Quantile Regression Forests for generating interval predictions, alongside both local and global variants of SHapley Additive Explanations for the examined predictive process monitoring problem. The practical applicability of the proposed methodology is substantiated through a real-world production planning case study, emphasizing the potential of prescriptive analytics in refining decision-making procedures. This paper accentuates the imperative of addressing these challenges to fully harness the extensive and rich data resources accessible for well-informed decision-making

    Countermeasures for the majority attack in blockchain distributed systems

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    La tecnología Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus características únicas que la hacen ideal para registrar, verificar y administrar información de diferentes transacciones. A pesar de esto, Blockchain se enfrenta a diferentes problemas de seguridad, siendo el ataque del 51% o ataque mayoritario uno de los más importantes. Este consiste en que uno o más mineros tomen el control de al menos el 51% del Hash extraído o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnología. Este trabajo se enfocó en diseñar e implementar estrategias de detección y mitigación de ataques mayoritarios (51% de ataque) en un sistema distribuido Blockchain, a partir de la caracterización del comportamiento de los mineros. Para lograr esto, se analizó y evaluó el Hash Rate / Share de los mineros de Bitcoin y Crypto Ethereum, seguido del diseño e implementación de un protocolo de consenso para controlar el poder de cómputo de los mineros. Posteriormente, se realizó la exploración y evaluación de modelos de Machine Learning para detectar software malicioso de tipo Cryptojacking.DoctoradoDoctor en Ingeniería de Sistemas y Computació

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

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    Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data. A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability. To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity. A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case. The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change. The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence

    Visualisation of Fundamental Movement Skills (FMS): An Iterative Process Using an Overarm Throw

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    Fundamental Movement Skills (FMS) are precursor gross motor skills to more complex or specialised skills and are recognised as important indicators of physical competence, a key component of physical literacy. FMS are predominantly assessed using pre-defined manual methodologies, most commonly the various iterations of the Test of Gross Motor Development. However, such assessments are time-consuming and often require a minimum basic level of training to conduct. Therefore, the overall aim of this thesis was to utilise accelerometry to develop a visualisation concept as part of a feasibility study to support the learning and assessment of FMS, by reducing subjectivity and the overall time taken to conduct a gross motor skill assessment. The overarm throw, an important fundamental movement skill, was specifically selected for the visualisation development as it is an acyclic movement with a distinct initiation and conclusion. Thirteen children (14.8 ± 0.3 years; 9 boys) wore an ActiGraph GT9X Link Inertial Measurement Unit device on the dominant wrist whilst performing a series of overarm throws. This thesis illustrates how the visualisation concept was developed using raw accelerometer data, which was processed and manipulated using MATLAB 2019b software to obtain and depict key throw performance data, including the trajectory and velocity of the wrist during the throw. Overall, this thesis found that the developed visualisation concept can provide strong indicators of throw competency based on the shape of the throw trajectory. Future research should seek to utilise a larger, more diverse, population, and incorporate machine learning. Finally, further work is required to translate this concept to other gross motor skills

    TOWARDS AN UNDERSTANDING OF EFFORTFUL FUNDRAISING EXPERIENCES: USING INTERPRETATIVE PHENOMENOLOGICAL ANALYSIS IN FUNDRAISING RESEARCH

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    Physical-activity oriented community fundraising has experienced an exponential growth in popularity over the past 15 years. The aim of this study was to explore the value of effortful fundraising experiences, from the point of view of participants, and explore the impact that these experiences have on people’s lives. This study used an IPA approach to interview 23 individuals, recognising the role of participants as proxy (nonprofessional) fundraisers for charitable organisations, and the unique organisation donor dynamic that this creates. It also bought together relevant psychological theory related to physical activity fundraising experiences (through a narrative literature review) and used primary interview data to substantiate these. Effortful fundraising experiences are examined in detail to understand their significance to participants, and how such experiences influence their connection with a charity or cause. This was done with an idiographic focus at first, before examining convergences and divergences across the sample. This study found that effortful fundraising experiences can have a profound positive impact upon community fundraisers in both the short and the long term. Additionally, it found that these experiences can be opportunities for charitable organisations to create lasting meaningful relationships with participants, and foster mutually beneficial lifetime relationships with them. Further research is needed to test specific psychological theory in this context, including self-esteem theory, self determination theory, and the martyrdom effect (among others)

    Assessing Consistency in Single-Case Data Features Using Modified Brinley Plots

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    The current text deals with the assessment of consistency of data features from experimentally similar phases and consistency of effects in single-case experimental designs. Although consistency is frequently mentioned as a critical feature, few quantifications have been proposed so far: namely, under the acronyms CONDAP (consistency of data patterns in similar phases) and CONEFF (consistency of effects). Whereas CONDAP allows assessing the consistency of data patterns, the proposals made here focus on the consistency of data features such as level, trend, and variability, as represented by summary measures (mean, ordinary least squares slope, and standard deviation, respectively). The assessment of consistency of effect is also made in terms of these three data features, while also including the study of the consistency of an immediate effect (if expected). The summary measures are represented as points on a modified Brinley plot and their similarity is assessed via quantifications of distance. Both absolute and relative measures of consistency are proposed: the former expressed in the same measurement units as the outcome variable and the latter as a percentage. Illustrations with real data sets (multiple baseline, ABAB, and alternating treatments designs) show the wide applicability of the proposals. We developed a user-friendly website to offer both the graphical representations and the quantifications

    Synthesis and Characterisation of Low-cost Biopolymeric/mineral Composite Systems and Evaluation of their Potential Application for Heavy Metal Removal

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    Heavy metal pollution and waste management are two major environmental problems faced in the world today. Anthropogenic sources of heavy metals, especially effluent from industries, are serious environmental and health concerns by polluting surface and ground waters. Similarly, on a global scale, thousands of tonnes of industrial and agricultural waste are discarded into the environment annually. There are several conventional methods to treat industrial effluents, including reverse osmosis, oxidation, filtration, flotation, chemical precipitation, ion exchange resins and adsorption. Among them, adsorption and ion exchange are known to be effective mechanisms for removing heavy metal pollution, especially if low-cost materials can be used. This thesis was a study into materials that can be used to remove heavy metals from water using low-cost feedstock materials. The synthesis of low-cost composite matrices from agricultural and industrial by-products and low-cost organic and mineral sources was carried out. The feedstock materials being considered include chitosan (generated from industrial seafood waste), coir fibre (an agricultural by-product), spent coffee grounds (a by-product from coffee machines), hydroxyapatite (from bovine bone), and naturally sourced aluminosilicate minerals such as zeolite. The novel composite adsorbents were prepared using commercially sourced HAp and bovine sourced HAp, with two types of adsorbents being synthesized, including two- and three-component composites. Standard synthetic methods such as precipitation were developed to synthesize these materials, followed by characterization of their structural, physical, and chemical properties (by using FTIR, TGA, SEM, EDX and XRD). The synthesized materials were then evaluated for their ability to remove metal ions from solutions of heavy metals using single-metal ion type and two-metal ion type solution systems, using the model ion solutions, with quantification of their removal efficiency. It was followed by experimentation using the synthesized adsorbents for metal ion removal in complex systems such as an industrial input stream solution system obtained from a local timber treatment company. Two-component composites were considered as control composites to compare the removal efficiency of the three-component composites against. The heavy metal removal experiments were conducted under a range of experimental conditions (e.g., pH, sorbent dose, initial metal ion concentration, time of contact). Of the four metal ion systems considered in this study (Cd2+, Pb2+, Cu2+ and Cr as chromate ions), Pb2+ ion removal by the composites was found to be the highest in single-metal and two-metal ion type solution systems, while chromate ion removal was found to be the lowest. The bovine bone-based hydroxyapatite (bHAp) composites were more efficient at removing the metal cations than composites formed from a commercially sourced hydroxyapatite (cHAp). In industrial input stream solution systems (containing Cu, Cr and As), the Cu2+ ion removal was the highest, which aligned with the observations recorded in the single and two-metal ion type solution systems. Arsenate ion was removed to a higher extent than chromate ion using the three-component composites, while the removal of chromate ion was found to be higher than arsenate ion when using the two-component composites (i.e., the control system). The project also aimed to elucidate the removal mechanisms of these synthesized composite materials by using appropriate adsorption and kinetic models. The adsorption of metal ions exhibited a range of adsorption behaviours as both the models (Langmuir and Freundlich) were found to fit most of the data recorded in different adsorption systems studied. The pseudo-second-order model was found to be the best fitted to describe the kinetics of heavy metal ion adsorption in all the composite adsorbent systems studied, in single-metal ion type and two-metal ion type solution systems. The ion-exchange mechanism was considered as one of the dominant mechanisms for the removal of cations (in single-metal and two-metal ion type solution systems) and arsenate ions (in industrial input stream solution systems) along with other adsorption mechanisms. In contrast, electrostatic attractions were considered to be the dominant mechanism of removal for chromate ions

    AIUCD 2022 - Proceedings

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    L’undicesima edizione del Convegno Nazionale dell’AIUCD-Associazione di Informatica Umanistica ha per titolo Culture digitali. Intersezioni: filosofia, arti, media. Nel titolo è presente, in maniera esplicita, la richiesta di una riflessione, metodologica e teorica, sull’interrelazione tra tecnologie digitali, scienze dell’informazione, discipline filosofiche, mondo delle arti e cultural studies

    Graphical scaffolding for the learning of data wrangling APIs

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    In order for students across the sciences to avail themselves of modern data streams, they must first know how to wrangle data: how to reshape ill-organised, tabular data into another format, and how to do this programmatically, in languages such as Python and R. Despite the cross-departmental demand and the ubiquity of data wrangling in analytical workflows, the research on how to optimise the instruction of it has been minimal. Although data wrangling as a programming domain presents distinctive challenges - characterised by on-the-fly syntax lookup and code example integration - it also presents opportunities. One such opportunity is how tabular data structures are easily visualised. To leverage the inherent visualisability of data wrangling, this dissertation evaluates three types of graphics that could be employed as scaffolding for novices: subgoal graphics, thumbnail graphics, and parameter graphics. Using a specially built e-learning platform, this dissertation documents a multi-institutional, randomised, and controlled experiment that investigates the pedagogical effects of these. Our results indicate that the graphics are well-received, that subgoal graphics boost the completion rate, and that thumbnail graphics improve navigability within a command menu. We also obtained several non-significant results, and indications that parameter graphics are counter-productive. We will discuss these findings in the context of general scaffolding dilemmas, and how they fit into a wider research programme on data wrangling instruction

    Strung pieces: on the aesthetics of television fiction series

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    As layered and long works, television fiction series have aesthetic properties that are built over time, bit by bit. This thesis develops a group of concepts that enable the study of these properties, It argues that a series is made of strung pieces, a system of related elements. The text begins by considering this sequential form within the fields of film and television. This opening chapter defines the object and methodology of research, arguing for a non-essentialist distinction between cinema and television and against the adequacy of textual and contextual analyses as approaches to the aesthetics of these shows. It proposes instead that these programmes should be described as televisual works that can be scrutinised through aesthetic analysis. The next chapters propose a sequence of interrelated concepts. The second chapter contends that series are composed of building blocks that can be either units into which series are divided or motifs that unify series and are dispersed across their pans. These blocks are patterned according to four kinds of relations or principles of composition. Repetition and variation are treated in tandem in the third chapter because of their close connection, given that variation emerges from established repetition. Exception and progression are also discussed together in the fourth chapter since they both require a long view of these serial works. The former, in order to be recognised as a deviation from the patterns of repetition and variation. The latter, In order to be understood in Its many dimensions as the series advances. Each of these concepts is further detailed with additional distinctions between types of units, motifs, repetitions, variations, and exceptions, using illustrative examples from numerous shows. In contrast, the section on progression uses a single series as case study, Carnivàle (2003-05), because this is the overarching principle that encompasses all the others. The conclusion considers the findings of the research and suggests avenues for their application
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