6,519 research outputs found

    Towards A Practical High-Assurance Systems Programming Language

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    Writing correct and performant low-level systems code is a notoriously demanding job, even for experienced developers. To make the matter worse, formally reasoning about their correctness properties introduces yet another level of complexity to the task. It requires considerable expertise in both systems programming and formal verification. The development can be extremely costly due to the sheer complexity of the systems and the nuances in them, if not assisted with appropriate tools that provide abstraction and automation. Cogent is designed to alleviate the burden on developers when writing and verifying systems code. It is a high-level functional language with a certifying compiler, which automatically proves the correctness of the compiled code and also provides a purely functional abstraction of the low-level program to the developer. Equational reasoning techniques can then be used to prove functional correctness properties of the program on top of this abstract semantics, which is notably less laborious than directly verifying the C code. To make Cogent a more approachable and effective tool for developing real-world systems, we further strengthen the framework by extending the core language and its ecosystem. Specifically, we enrich the language to allow users to control the memory representation of algebraic data types, while retaining the automatic proof with a data layout refinement calculus. We repurpose existing tools in a novel way and develop an intuitive foreign function interface, which provides users a seamless experience when using Cogent in conjunction with native C. We augment the Cogent ecosystem with a property-based testing framework, which helps developers better understand the impact formal verification has on their programs and enables a progressive approach to producing high-assurance systems. Finally we explore refinement type systems, which we plan to incorporate into Cogent for more expressiveness and better integration of systems programmers with the verification process

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Ab Initio Language Teaching in British Higher Education

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    Drawing extensively on the expertise of teachers of German in universities across the UK, this volume offers an overview of recent trends, new pedagogical approaches and practical guidance for teaching at beginners level in the higher education classroom. At a time when entries for UK school exams in modern foreign languages are decreasing, this book serves the urgent need for research and guidance on ab initio learning and teaching in HE. Using the example of teaching German, it offers theoretical reflections on teaching ab initio and practice-oriented approaches that will be useful for teachers of both German and other languages in higher education. The first chapters assess the role of ab initio provision within the wider context of modern languages departments and language centres. They are followed by sections on teaching methods and innovative approaches in the ab initio classroom that include chapters on the use of music, textbook evaluation, the effective use of a flipped classroom and the contribution of language apps. Finally, the book focuses on the learner in the ab initio context and explores issues around autonomy and learner strengths. The whole builds into a theoretically grounded guide that sketches out perspectives for teaching and learning ab initio languages that will benefit current and future generations of students

    Edge-resolved non-line-of-sight imaging

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    Over the past decade, the possibility of forming images of objects hidden from line-of-sight (LOS) view has emerged as an intriguing and potentially important expansion of computational imaging and computer vision technology. This capability could help soldiers anticipate danger in a tunnel system, autonomous vehicles avoid collision, and first responders safely traverse a building. In many scenarios where non-line-of-sight (NLOS) vision is desired, the LOS view is obstructed by a wall with a vertical edge. In this thesis we show that through modeling and computation, the impediment to LOS itself can be exploited for enhanced resolution of the hidden scene. NLOS methods may be active, where controlled illumination of the hidden scene is used, or passive, relying only on already present light sources. In both active and passive NLOS imaging, measured light returns to the sensor after multiple diffuse bounces. Each bounce scatters light in all directions, eliminating directional information. When the scene is hidden behind a wall with a vertical edge, that edge occludes light as a function of its incident azimuthal angle around the edge. Measurements acquired on the floor adjacent to the occluding edge thus contain rich azimuthal information about the hidden scene. In this thesis, we explore several edge-resolved NLOS imaging systems that exploit the occlusion provided by a vertical edge. In addition to demonstrating novel edge-resolved NLOS imaging systems with real experimental data, this thesis includes modeling, performance bound analyses, and inversion algorithms for the proposed systems. We first explore the use of a single vertical edge to form a 1D (in azimuthal angle) reconstruction of the hidden scene. Prior work demonstrated that temporal variation in a video of the floor may be used to image moving components of the hidden scene. In contrast, our algorithm reconstructs both moving and stationary hidden scenery from a single photograph, without assuming uniform floor albedo. We derive a forward model that describes the measured photograph as a nonlinear combination of the unknown floor albedo and the light from behind the wall. The inverse problem, which is the joint estimation of floor albedo and a 1D reconstruction of the hidden scene, is solved via optimization, where we introduce regularizers that help separate light variations in the measured photograph due to floor pattern and hidden scene, respectively. Next, we combine the resolving power of a vertical edge with information from the relationship between intensity and radial distance to form 2D reconstructions from a single passive photograph. We derive a new forward model, accounting for radial falloff, and propose two inversion algorithms to form 2D reconstructions from a single photograph of the penumbra. The performances of both algorithms are demonstrated on experimental data corresponding to several different hidden scene configurations. A Cramer-Rao bound analysis further demonstrates the feasibility and limitations of this 2D corner camera. Our doorway camera exploits the occlusion provided by the two vertical edges of a doorway for more robust 2D reconstruction of the hidden scene. This work provides and demonstrates a novel inversion algorithm to jointly estimate two views of change in the hidden scene, using the temporal difference between photographs acquired on the visible side of the doorway. A Cramer-Rao bound analysis is used to demonstrate the 2D resolving power of the doorway camera over other passive acquisition strategies and to motivate the novel biangular reconstruction grid. Lastly, we present the active corner camera. Most existing active NLOS methods illuminate the hidden scene using a pulsed laser directed at a relay surface and collect time-resolved measurements of returning light. The prevailing approaches are inherently limited by the need for laser scanning, a process that is generally too slow to image hidden objects in motion. Methods that avoid laser scanning track the moving parts of the hidden scene as one or two point targets. In this work, based on more complete optical response modeling yet still without multiple illumination positions, we demonstrate accurate reconstructions of objects in motion and a `map’ of the stationary scenery behind them. This new ability to count, localize, and characterize the sizes of hidden objects in motion, combined with mapping of the stationary hidden scene could greatly improve indoor situational awareness in a variety of applications

    Discourses of sexual violence: A critical analysis of the representation of victims and perpetrators on Twitter

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    Esta tesis investiga críticamente los recursos y patrones lingüísticos utilizados por los tuiteros para debatir sobre discursos de género y opresión patriarcal en relación con la violencia sexual. Este estudio tiene dos objetivos principales: 1) examinar los discursos e ideologías de los tuiteros respecto a la violencia sexual y cómo estos contribuyen a la negociación de las identidades de víctima-perpetrador, y 2) identificar el papel del lenguaje evaluativo en la (re)producción y resistencia de discursos e ideologías de género. Para ello, esta tesis toma como caso de estudio la controvertida nominación del juez asociado Brett Kavanaugh al Tribunal Supremo de los Estados Unidos. Tras hacerse pública su nominación, fue acusado de intento de violación por la Dra. Christine Blasey Ford. Sus alegaciones fueron seguidas por otras similares de otras dos mujeres. La nominación se convirtió en un tema de conversación importante en la red social Twitter, ya que los tuiteros utilizaron diferentes hashtags para expresar su apoyo u oposición a la nominación. La Dra. Ford también se convirtió en el blanco de agresiones verbales por parte de quienes apoyaban la candidatura. Sin embargo, sus alegaciones también fueron apoyadas por aquellos tuiteros que validaron su testimonio y, a su vez, provocaron el resurgimiento del hashtivismo feminista. Se compilaron dos corpus de tuits que contenían los hashtags #KavanaughConfirmation (N = 1.753.370 palabras) y #NoKavanaughConfirmation (N = 612.416 palabras) para analizarlos y compararlos en relación con los objetivos de este estudio. Los corpus se analizaron desde un enfoque de análisis del discurso asistido por corpus (ADAC) (Partington et al., 2013) que combinaba herramientas de lingüística de corpus con el Análisis Crítico del Discurso Feminista (ACFD) (Lazar, 2005, 2018) y la Teoría de la Valoración (2005). Los resultados sugieren que los tuiteros verbalizaron discursos relacionados con la violencia de género tanto para denunciar como para perpetuar la cultura de la violación y la opresión patriarcal en la sociedad estadounidense. Estos discursos contribuyeron a la negociación de las identidades de víctima y agresor, que eran inestables y fluidas según los grupos sociopolíticos de los tuiteros. Se mostró que los discursos antifeministas y patriarcales contribuyeron a la representación de AsJ Kavanaugh como una víctima política, retratando así a la Dra. Ford como una agresora política. Por el contrario, los discursos de veracidad y feminismo dieron credibilidad al testimonio de la Dra. Ford y se opusieron a la confirmación. A su vez, estos discursos retrataron a AsJ Kavanaugh como un mentiroso y un agresor sexual. Por otro lado, el análisis del lenguaje evaluativo reveló que en ambos corpus predominaban recursos valorativos negativos para transmitir evaluaciones inmorales y poco éticas y angustia emocional colectiva, lo que contribuyó aún más a la construcción inestable de las identidades de víctima y perpetrador. En definitiva, esta tesis proporciona información sobre las prácticas digitales de los tuiteros para debatir dinámicas de género y resistir/reproducir discursos patriarcales derivados de la cultura de la violación. Además, también demuestra la fructífera combinación de los métodos de la lingüística de corpus, el FCDA y la teoría de la valoración para el análisis de la violencia de género y los datos de los redes sociales.This thesis critically traces the linguistic resources and patterns deployed by tweeters to discuss gendered discourses and patriarchal oppression concerning sexual violence. There are two primary aims of this study: 1) to examine tweeters’ discourses and ideologies regarding sexual violence and how they contribute to the negotiation of victim-perpetrator identities, and 2) to identify the role of evaluative language in the (re)production and resistance of gendered discourses and ideologies. To do so, this thesis takes AsJ Brett Kavanaugh’s controversial nomination to the Supreme Court of the United States as a case study. After his nomination was made public, he was accused of attempted rape by Dr. Christine Blasey Ford. Her allegations were followed by similar claims from two more women. The nomination became a major topic on Twitter as tweeters used different hashtags to express (dis)affiliation. Dr. Ford also became the target of verbal aggression by those who supported his nomination. However, her claims were also supported by tweeters who validated her testimony and, in turn, sparked the re-emergence of hashtag feminism. Two corpora of tweets containing the hashtags #KavanaughConfirmation (N = 1,753,370 words) and #NoKavanaughConfirmation (N = 612,416 words) were compiled to analyze and compare each dataset in relation to the objectives of this study. The corpora were investigated from a corpus-assisted discourse analysis approach (Partington et al., 2013) which combined corpus linguistic tools with Feminist Critical Discourse Analysis (FCDA) (Lazar, 2005, 2018) and Appraisal Theory (2005). The findings suggest that tweeters invoked discourses relating to gender-based violence to both denounce and perpetuate rape culture and patriarchal oppression in American society. Such discourses contributed to the negotiation of the identities of victims and perpetrators, which were unstable and fluid depending on tweeters’ socio-political groups. Antifeminist and patriarchal discourses were found to contribute to the portrayal of AsJ Kavanaugh as a political victim, thus portraying Dr. Ford as a political aggressor. In contrast, discourses of veracity and feminism gave credibility to Dr. Ford’s testimony and opposed the confirmation. These discourses depicted AsJ Kavanaugh as a liar and a sexual aggressor. On the other hand, the analysis of evaluative language revealed that negative Appraisal resources predominated in both corpora to convey immoral and unethical evaluations and collective emotional distress, which further contributed to the unstable construction of victim-perpetrator identities. All in all, this thesis provides insights into tweeters’ digital practices to discuss gendered dynamics and resist/reproduce patriarchal discourses derived from rape culture. In addition, it also shows the fruitful combination of corpus linguistics methods, FCDA, and Appraisal Theory in the analysis of gender-based violence and social media data

    The Viability and Potential Consequences of IoT-Based Ransomware

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    With the increased threat of ransomware and the substantial growth of the Internet of Things (IoT) market, there is significant motivation for attackers to carry out IoT-based ransomware campaigns. In this thesis, the viability of such malware is tested. As part of this work, various techniques that could be used by ransomware developers to attack commercial IoT devices were explored. First, methods that attackers could use to communicate with the victim were examined, such that a ransom note was able to be reliably sent to a victim. Next, the viability of using "bricking" as a method of ransom was evaluated, such that devices could be remotely disabled unless the victim makes a payment to the attacker. Research was then performed to ascertain whether it was possible to remotely gain persistence on IoT devices, which would improve the efficacy of existing ransomware methods, and provide opportunities for more advanced ransomware to be created. Finally, after successfully identifying a number of persistence techniques, the viability of privacy-invasion based ransomware was analysed. For each assessed technique, proofs of concept were developed. A range of devices -- with various intended purposes, such as routers, cameras and phones -- were used to test the viability of these proofs of concept. To test communication hijacking, devices' "channels of communication" -- such as web services and embedded screens -- were identified, then hijacked to display custom ransom notes. During the analysis of bricking-based ransomware, a working proof of concept was created, which was then able to remotely brick five IoT devices. After analysing the storage design of an assortment of IoT devices, six different persistence techniques were identified, which were then successfully tested on four devices, such that malicious filesystem modifications would be retained after the device was rebooted. When researching privacy-invasion based ransomware, several methods were created to extract information from data sources that can be commonly found on IoT devices, such as nearby WiFi signals, images from cameras, or audio from microphones. These were successfully implemented in a test environment such that ransomable data could be extracted, processed, and stored for later use to blackmail the victim. Overall, IoT-based ransomware has not only been shown to be viable but also highly damaging to both IoT devices and their users. While the use of IoT-ransomware is still very uncommon "in the wild", the techniques demonstrated within this work highlight an urgent need to improve the security of IoT devices to avoid the risk of IoT-based ransomware causing havoc in our society. Finally, during the development of these proofs of concept, a number of potential countermeasures were identified, which can be used to limit the effectiveness of the attacking techniques discovered in this PhD research

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    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
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