896 research outputs found

    Revisiting Language Support for Generic Programming: When Genericity Is a Core Design Goal

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    ContextGeneric programming, as defined by Stepanov, is a methodology for writing efficient and reusable algorithms by considering only the required properties of their underlying data types and operations. Generic programming has proven to be an effective means of constructing libraries of reusable software components in languages that support it. Generics-related language design choices play a major role in how conducive generic programming is in practice.InquirySeveral mainstream programming languages (e.g. Java and C++) were first created without generics; features to support generic programming were added later, gradually. Much of the existing literature on supporting generic programming focuses thus on retrofitting generic programming into existing languages and identifying related implementation challenges. Is the programming experience significantly better, or different when programming with a language designed for generic programming without limitations from prior language design choices?ApproachWe examine Magnolia, a language designed to embody generic programming. Magnolia is representative of an approach to language design rooted in algebraic specifications. We repeat a well-known experiment, where we put Magnolia’s generic programming facilities under scrutiny by implementing a subset of the Boost Graph Library, and reflect on our development experience.KnowledgeWe discover that the idioms identified as key features for supporting Stepanov-style generic programming in the previous studies and work on the topic do not tell a full story. We clarify which of them are more of a means to an end, rather than fundamental features for supporting generic programming. Based on the development experience with Magnolia, we identify variadics as an additional key feature for generic programming and point out limitations and challenges of genericity by property.GroundingOur work uses a well-known framework for evaluating the generic programming facilities of a language from the literature to evaluate the algebraic approach through Magnolia, and we draw comparisons with well-known programming languages.ImportanceThis work gives a fresh perspective on generic programming, and clarifies what are fundamental language properties and their trade-offs when considering supporting Stepanov-style generic programming. The understanding of how to set the ground for generic programming will inform future language design.</p

    Blending the Material and Digital World for Hybrid Interfaces

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    The development of digital technologies in the 21st century is progressing continuously and new device classes such as tablets, smartphones or smartwatches are finding their way into our everyday lives. However, this development also poses problems, as these prevailing touch and gestural interfaces often lack tangibility, take little account of haptic qualities and therefore require full attention from their users. Compared to traditional tools and analog interfaces, the human skills to experience and manipulate material in its natural environment and context remain unexploited. To combine the best of both, a key question is how it is possible to blend the material world and digital world to design and realize novel hybrid interfaces in a meaningful way. Research on Tangible User Interfaces (TUIs) investigates the coupling between physical objects and virtual data. In contrast, hybrid interfaces, which specifically aim to digitally enrich analog artifacts of everyday work, have not yet been sufficiently researched and systematically discussed. Therefore, this doctoral thesis rethinks how user interfaces can provide useful digital functionality while maintaining their physical properties and familiar patterns of use in the real world. However, the development of such hybrid interfaces raises overarching research questions about the design: Which kind of physical interfaces are worth exploring? What type of digital enhancement will improve existing interfaces? How can hybrid interfaces retain their physical properties while enabling new digital functions? What are suitable methods to explore different design? And how to support technology-enthusiast users in prototyping? For a systematic investigation, the thesis builds on a design-oriented, exploratory and iterative development process using digital fabrication methods and novel materials. As a main contribution, four specific research projects are presented that apply and discuss different visual and interactive augmentation principles along real-world applications. The applications range from digitally-enhanced paper, interactive cords over visual watch strap extensions to novel prototyping tools for smart garments. While almost all of them integrate visual feedback and haptic input, none of them are built on rigid, rectangular pixel screens or use standard input modalities, as they all aim to reveal new design approaches. The dissertation shows how valuable it can be to rethink familiar, analog applications while thoughtfully extending them digitally. Finally, this thesis’ extensive work of engineering versatile research platforms is accompanied by overarching conceptual work, user evaluations and technical experiments, as well as literature reviews.Die Durchdringung digitaler Technologien im 21. Jahrhundert schreitet stetig voran und neue Geräteklassen wie Tablets, Smartphones oder Smartwatches erobern unseren Alltag. Diese Entwicklung birgt aber auch Probleme, denn die vorherrschenden berührungsempfindlichen Oberflächen berücksichtigen kaum haptische Qualitäten und erfordern daher die volle Aufmerksamkeit ihrer Nutzer:innen. Im Vergleich zu traditionellen Werkzeugen und analogen Schnittstellen bleiben die menschlichen Fähigkeiten ungenutzt, die Umwelt mit allen Sinnen zu begreifen und wahrzunehmen. Um das Beste aus beiden Welten zu vereinen, stellt sich daher die Frage, wie neuartige hybride Schnittstellen sinnvoll gestaltet und realisiert werden können, um die materielle und die digitale Welt zu verschmelzen. In der Forschung zu Tangible User Interfaces (TUIs) wird die Verbindung zwischen physischen Objekten und virtuellen Daten untersucht. Noch nicht ausreichend erforscht wurden hingegen hybride Schnittstellen, die speziell darauf abzielen, physische Gegenstände des Alltags digital zu erweitern und anhand geeigneter Designparameter und Entwurfsräume systematisch zu untersuchen. In dieser Dissertation wird daher untersucht, wie Materialität und Digitalität nahtlos ineinander übergehen können. Es soll erforscht werden, wie künftige Benutzungsschnittstellen nützliche digitale Funktionen bereitstellen können, ohne ihre physischen Eigenschaften und vertrauten Nutzungsmuster in der realen Welt zu verlieren. Die Entwicklung solcher hybriden Ansätze wirft jedoch übergreifende Forschungsfragen zum Design auf: Welche Arten von physischen Schnittstellen sind es wert, betrachtet zu werden? Welche Art von digitaler Erweiterung verbessert das Bestehende? Wie können hybride Konzepte ihre physischen Eigenschaften beibehalten und gleichzeitig neue digitale Funktionen ermöglichen? Was sind geeignete Methoden, um verschiedene Designs zu erforschen? Wie kann man Technologiebegeisterte bei der Erstellung von Prototypen unterstützen? Für eine systematische Untersuchung stützt sich die Arbeit auf einen designorientierten, explorativen und iterativen Entwicklungsprozess unter Verwendung digitaler Fabrikationsmethoden und neuartiger Materialien. Im Hauptteil werden vier Forschungsprojekte vorgestellt, die verschiedene visuelle und interaktive Prinzipien entlang realer Anwendungen diskutieren. Die Szenarien reichen von digital angereichertem Papier, interaktiven Kordeln über visuelle Erweiterungen von Uhrarmbändern bis hin zu neuartigen Prototyping-Tools für intelligente Kleidungsstücke. Um neue Designansätze aufzuzeigen, integrieren nahezu alle visuelles Feedback und haptische Eingaben, um Alternativen zu Standard-Eingabemodalitäten auf starren Pixelbildschirmen zu schaffen. Die Dissertation hat gezeigt, wie wertvoll es sein kann, bekannte, analoge Anwendungen zu überdenken und sie dabei gleichzeitig mit Bedacht digital zu erweitern. Dabei umfasst die vorliegende Arbeit sowohl realisierte technische Forschungsplattformen als auch übergreifende konzeptionelle Arbeiten, Nutzerstudien und technische Experimente sowie die Analyse existierender Forschungsarbeiten

    Artificial Intelligence and International Conflict in Cyberspace

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    This edited volume explores how artificial intelligence (AI) is transforming international conflict in cyberspace. Over the past three decades, cyberspace developed into a crucial frontier and issue of international conflict. However, scholarly work on the relationship between AI and conflict in cyberspace has been produced along somewhat rigid disciplinary boundaries and an even more rigid sociotechnical divide – wherein technical and social scholarship are seldomly brought into a conversation. This is the first volume to address these themes through a comprehensive and cross-disciplinary approach. With the intent of exploring the question ‘what is at stake with the use of automation in international conflict in cyberspace through AI?’, the chapters in the volume focus on three broad themes, namely: (1) technical and operational, (2) strategic and geopolitical and (3) normative and legal. These also constitute the three parts in which the chapters of this volume are organised, although these thematic sections should not be considered as an analytical or a disciplinary demarcation

    Digital agriculture: research, development and innovation in production chains.

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    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

    Machine learning for the sustainable energy transition: a data-driven perspective along the value chain from manufacturing to energy conversion

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    According to the special report Global Warming of 1.5 °C of the IPCC, climate action is not only necessary but more than ever urgent. The world is witnessing rising sea levels, heat waves, events of flooding, droughts, and desertification resulting in the loss of lives and damage to livelihoods, especially in countries of the Global South. To mitigate climate change and commit to the Paris agreement, it is of the uttermost importance to reduce greenhouse gas emissions coming from the most emitting sector, namely the energy sector. To this end, large-scale penetration of renewable energy systems into the energy market is crucial for the energy transition toward a sustainable future by replacing fossil fuels and improving access to energy with socio-economic benefits. With the advent of Industry 4.0, Internet of Things technologies have been increasingly applied to the energy sector introducing the concept of smart grid or, more in general, Internet of Energy. These paradigms are steering the energy sector towards more efficient, reliable, flexible, resilient, safe, and sustainable solutions with huge environmental and social potential benefits. To realize these concepts, new information technologies are required, and among the most promising possibilities are Artificial Intelligence and Machine Learning which in many countries have already revolutionized the energy industry. This thesis presents different Machine Learning algorithms and methods for the implementation of new strategies to make renewable energy systems more efficient and reliable. It presents various learning algorithms, highlighting their advantages and limits, and evaluating their application for different tasks in the energy context. In addition, different techniques are presented for the preprocessing and cleaning of time series, nowadays collected by sensor networks mounted on every renewable energy system. With the possibility to install large numbers of sensors that collect vast amounts of time series, it is vital to detect and remove irrelevant, redundant, or noisy features, and alleviate the curse of dimensionality, thus improving the interpretability of predictive models, speeding up their learning process, and enhancing their generalization properties. Therefore, this thesis discussed the importance of dimensionality reduction in sensor networks mounted on renewable energy systems and, to this end, presents two novel unsupervised algorithms. The first approach maps time series in the network domain through visibility graphs and uses a community detection algorithm to identify clusters of similar time series and select representative parameters. This method can group both homogeneous and heterogeneous physical parameters, even when related to different functional areas of a system. The second approach proposes the Combined Predictive Power Score, a method for feature selection with a multivariate formulation that explores multiple sub-sets of expanding variables and identifies the combination of features with the highest predictive power over specified target variables. This method proposes a selection algorithm for the optimal combination of variables that converges to the smallest set of predictors with the highest predictive power. Once the combination of variables is identified, the most relevant parameters in a sensor network can be selected to perform dimensionality reduction. Data-driven methods open the possibility to support strategic decision-making, resulting in a reduction of Operation &amp; Maintenance costs, machine faults, repair stops, and spare parts inventory size. Therefore, this thesis presents two approaches in the context of predictive maintenance to improve the lifetime and efficiency of the equipment, based on anomaly detection algorithms. The first approach proposes an anomaly detection model based on Principal Component Analysis that is robust to false alarms, can isolate anomalous conditions, and can anticipate equipment failures. The second approach has at its core a neural architecture, namely a Graph Convolutional Autoencoder, which models the sensor network as a dynamical functional graph by simultaneously considering the information content of individual sensor measurements (graph node features) and the nonlinear correlations existing between all pairs of sensors (graph edges). The proposed neural architecture can capture hidden anomalies even when the turbine continues to deliver the power requested by the grid and can anticipate equipment failures. Since the model is unsupervised and completely data-driven, this approach can be applied to any wind turbine equipped with a SCADA system. When it comes to renewable energies, the unschedulable uncertainty due to their intermittent nature represents an obstacle to the reliability and stability of energy grids, especially when dealing with large-scale integration. Nevertheless, these challenges can be alleviated if the natural sources or the power output of renewable energy systems can be forecasted accurately, allowing power system operators to plan optimal power management strategies to balance the dispatch between intermittent power generations and the load demand. To this end, this thesis proposes a multi-modal spatio-temporal neural network for multi-horizon wind power forecasting. In particular, the model combines high-resolution Numerical Weather Prediction forecast maps with turbine-level SCADA data and explores how meteorological variables on different spatial scales together with the turbines' internal operating conditions impact wind power forecasts. The world is undergoing a third energy transition with the main goal to tackle global climate change through decarbonization of the energy supply and consumption patterns. This is not only possible thanks to global cooperation and agreements between parties, power generation systems advancements, and Internet of Things and Artificial Intelligence technologies but also necessary to prevent the severe and irreversible consequences of climate change that are threatening life on the planet as we know it. This thesis is intended as a reference for researchers that want to contribute to the sustainable energy transition and are approaching the field of Artificial Intelligence in the context of renewable energy systems

    eLuna : A Co-Design Framework for Mixed Reality Narrative Game- Based Learning

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    De siste tiårs utvidede fokus på læring utenfor skolen har bidratt til økt anvendelse av vitensentre som læringsarena for barn i grunnskole og videregående utdanning. En læringsløype er en type integrert læringsmiljø der de lærende, fysiske installasjoner, og digitale hjelpemidler bidrar til å fremme læringsinnhold og mål. På vitensentre brukes læringsløyper som pedagogisk støtte innen et bredt spekter av pensumplaner og programmer, gjennom å kombinere forskjellige sett av installasjoner og ved å vektlegge forskjellige aspekter av installasjonenes innhold. Siden de er sammensatt av både fysiske installasjoner og digitale hjelpemidler, er læringsløyper blandet virkelighet systemer, der de lærende interagerer med elementer i både den fysiske og virtuelle virkeligheten. Forskning har vist at både narrativ og spillmekanikker er blant de mest effektive komponentene som kan ligge til grunn for at læringsløyper skal kunne oppnå økt fokus på læringsinnhold, og for å engasjere de lærende ved å sette dem i en tilstand av flyt (av engelsk flow). Forskningen som presenteres i denne avhandlingen har som hovedmål å forbedre læring på vitensentre, gjennom å bidra med et co-design-rammeverk for blandet virkelighet narrative spillbaserte læringsløyper som underbygger positive effekter på engasjement, motivasjon, og læring. Narrativ har vært brukt til læring og instruksjon siden forhistorisk tid, og spill for læring har vært teoretisert og anvendt i mennesker i århundrer, i enda større grad etter oppfinnelsen av datamaskiner, og mulighetene bragt på banen gjennom digitale spill. Selv om bade narrative og spill har vært vist å kunne ha positive effekter når anvendt for læring, har forskning på effekter fra narrative spillbasert læring vist variable og motstridende resultater. Mangelen av en felles modell for kategorisering av narrative spill medfører manglende kunnskap relatert til hvordan og under hvilke forutsetninger narrative spill har effekt på læring. På tross av at de fleste studier av narrativ spillbasert læring unnlater å nevne narratologiske modeller, og de som gjør det primært refererer til modeller lånt fra andre media som mangler de nødvendige egenskapene til å kategorisere hendelsesflyten som benyttes i mange spill, finnes det en ludo narrativ variabel modell (LNVM), som er en narratologisk modell som kategorisere alle spill som narrativ. Denne forskningen videreutvikler LNVM, og presenterer en felles modell for kategorisering av narrativ spillbasert læring; eLNVM (fra engelsk: The extended LNVM). Narrative spillbaserte læringsløyper består av interaktive installasjoner og digitale hjelpemidler som belyser læringsmål innenfor pensumprogrammer. Det er derfor nødvendig med deltakelse både fra pedagoger og utviklere når slike læringsløyper skal designes og presenteres til lærende. Forskning viser at det er mangel av modeller, metoder, og rammeverk som myndiggjør pedagoger og utvikleres felles design av spillbasert læring, noe som enten resulterer i tapt fokus på læringsinnhold til fordel for engasjerende spillmekanikk, eller i at underholdningspotensialet i spill blir underordnet læringsmålene. Slike rammeverk må videre kunne skille mellom fysiske og virtuelle elementer for å være anvendbare i blandet virkelighet omgivelser. Forskningen presentert i denne avhandlingen benytter et rammeverk for informasjonssystemer som vitenskapelig metode til å utvikle eLuna co-design-rammeverket for blandet virkelighet narrative spillbaserte læringsløyper som underbygger positive effekter på engasjement, motivasjon, og læring. En systematisk litteraturstudie identifiserte 15 studier som rapporterte effekter fra digitale spillbaserte læringssystemer på engasjement, motivasjon, og læring. Disse systemene ble kategorisert med bruk av eLNVM og sortert basert på deres rapportering for å identifisere karakteristikker av narrative digital spillbasert læring som har positive effekter på engasjement, motivasjon, og læring. Denne forskningen benytter en iterativ design-basert forskningsprosess der karakteristikkene assosiert med de positive effektene legges til grunn for et co-design-rammeverk bestående av en metode og et visuelt språk. Co-design-rammeverket blir deretter utvidet med kapasitet til å separere mellom fysiske og virtuelle elementer i blandet virkelighet omgivelser. Rammeverket blir gjennom prosessen testet i deltakende co-design workshops og evaluert med bruk av varierte metoder, inkludert fokus grupper, intervjuer, spørreskjemaer, tematisk analyse, og heuristisk evaluering. Forskningen som blir presentert i denne doktoravhandlingen resulterer i eLuna co-design-rammeverket for narrative spillbasert læring, som kan bli brukt av pedagoger og utviklere til å lage både narrative digitale spillbaserte læringssystemer, og blandet virkelighet narrative spillbaserte læringsløyper som optimaliserer potensiale for positive effekter på engasjement, motivasjon, og læring.Increased focus on out of school learning over the last decades has led to extended use of science centres as learning arenas for pupils in primary and secondary education. A learning trail is a form of embedded learning environment in which the learners themselves, physical exhibits, and digital companions are elements that promote learning content and goals. When used in science centres, learning trails can combine different sets of exhibits and emphasize various aspects of their content to support learning goals inside a broad range of curricular plans and programs. Being comprised of physical exhibits and digital companions, science centre learning trails are mixed reality systems in which learner interaction occurs in both the physical and virtual domains. Research has shown that narratives and game mechanics are among the most effective components for science centre learning trails to achieve increased focus on the learning content, and to induce flow and engagement in learners. With an aim to contribute to improving science centre learning, the main objective of this research is to develop a co-design framework for mixed reality narrative game-based learning trails that enforce positive effects on engagement, motivation, and learning. Narratives have been used in learning and instruction since prehistoric times, and games for learning have been theorized and applied in human culture for centuries, increasingly so with the advent of the computer, and opportunities provided by digital games. While both narratives and games are shown to have the ability to positively affect learning, research on the effects from narrative game-based learning has shown mixed and contradictory results. The lack of a common model to categorize narrative games has led to a knowledge gap regarding how and under which conditions narrative games have effects on learning. Whereas most studies of narrative game-based learning neglect mentioning a narratological model at all, the ones that do mainly refer to models adapted from different media that lack the capabilities to properly categorize the event flow of many digital games. An exception is the ludo narrative variable model (LNVM), a narratological model that can properly categorize all games as narratives. Building on the LNVM, this research fills this gap with the development of the extended LNVM (eLNVM), a common model to categorize and isolate narratives in digital game-based learning. Narrative game-based learning trails comprise interactive exhibits and digital companions and promote learning goals inside curricular programs. Therefore, they require participation from educator and developer stakeholders to be properly designed and brought to learners. Research has shown that there is a lack of models, methods, or frameworks that empower educators and developers to co-design game-based learning, something which results in either the learning content being lost in the engaging mechanics of the game, or the fun of the games becoming inferior to the learning goals. Furthermore, to be applicable in science centres, such a co-design framework must also distinguish between physical and digital elements in mixed reality environments. Applying an information system research framework as a design science methodology, the eLuna co-design framework for mixed reality narrative game-based learning trails that enforce positive effects on engagement, motivation, and learning was developed. A systematic literature review identified 15 studies that self-reported effects of digital game-based learning systems on engagement, motivation, and learning. These were categorized on the eLNVM and sorted by their self-reported effects to identify what characterizes narrative digital game-based learning systems that positively affect engagement, motivation, and learning. Using an iterative design-based research process these characteristics associated with positive effects were then applied in a co-design framework comprising a method and a visual language, which was later extended with the capabilities to distinguish between physical and virtual elements in mixed reality learning trails. Throughout the process the framework was tested in co-design workshops with stakeholders and evaluated through mixed methods, including focus groups, semi-structured interviews, questionnaires, thematic analysis, and heuristic usability inspection. The research presented in this PhD dissertation contributes the eLuna co-design framework for narrative game-based learning, which empowers educators and developers in the creation of both narrative digital game-based learning and mixed reality narrative game-based learning trails that optimize the potential to induce positive effects on engagement, motivation, and learning.Doktorgradsavhandlin

    Digital agriculture: research, development and innovation in production chains.

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    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.Translated by Beverly Victoria Young and Karl Stephan Mokross

    Computer-supported movement guidance: investigating visual/visuotactile guidance and informing the design of vibrotactile body-worn interfaces

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    This dissertation explores the use of interactive systems to support movement guidance, with applications in various fields such as sports, dance, physiotherapy, and immersive sketching. The research focuses on visual, haptic, and visuohaptic approaches and aims to overcome the limitations of traditional guidance methods, such as dependence on an expert and high costs for the novice. The main contributions of the thesis are (1) an evaluation of the suitability of various types of displays and visualizations of the human body for posture guidance, (2) an investigation into the influence of different viewpoints/perspectives, the addition of haptic feedback, and various movement properties on movement guidance in virtual environments, (3) an investigation into the effectiveness of visuotactile guidance for hand movements in a virtual environment, (4) two in-depth studies of haptic perception on the body to inform the design of wearable and handheld interfaces that leverage tactile output technologies, and (5) an investigation into new interaction techniques for tactile guidance of arm movements. The results of this research advance the state of the art in the field, provide design and implementation insights, and pave the way for new investigations in computer-supported movement guidance
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