582 research outputs found

    Mapping the Focal Points of WordPress: A Software and Critical Code Analysis

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    Programming languages or code can be examined through numerous analytical lenses. This project is a critical analysis of WordPress, a prevalent web content management system, applying four modes of inquiry. The project draws on theoretical perspectives and areas of study in media, software, platforms, code, language, and power structures. The applied research is based on Critical Code Studies, an interdisciplinary field of study that holds the potential as a theoretical lens and methodological toolkit to understand computational code beyond its function. The project begins with a critical code analysis of WordPress, examining its origins and source code and mapping selected vulnerabilities. An examination of the influence of digital and computational thinking follows this. The work also explores the intersection of code patching and vulnerability management and how code shapes our sense of control, trust, and empathy, ultimately arguing that a rhetorical-cultural lens can be used to better understand code\u27s controlling influence. Recurring themes throughout these analyses and observations are the connections to power and vulnerability in WordPress\u27 code and how cultural, processual, rhetorical, and ethical implications can be expressed through its code, creating a particular worldview. Code\u27s emergent properties help illustrate how human values and practices (e.g., empathy, aesthetics, language, and trust) become encoded in software design and how people perceive the software through its worldview. These connected analyses reveal cultural, processual, and vulnerability focal points and the influence these entanglements have concerning WordPress as code, software, and platform. WordPress is a complex sociotechnical platform worthy of further study, as is the interdisciplinary merging of theoretical perspectives and disciplines to critically examine code. Ultimately, this project helps further enrich the field by introducing focal points in code, examining sociocultural phenomena within the code, and offering techniques to apply critical code methods

    An intelligent magnetic tape controller

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    This thesis describes a system to allow a mass storage device to be installed in a position remote from the computer system which controls it. This system is intended to allow undergraduate students in the Electrical Engineering department at UCT to make use of two nine channel tape drives installed in the undergraduate interfaced to accessed by laboratory for project work. The drives are the department's PDP-11/23 computer, and may be standard operating system directives, as the controller simulates a conventional computer peripheral. The system consists of an SA-Bus based tape transport controller which interfaces to the host computer system via a serial line. The following hardware was designed and built specifically for this system : 1. A CPU card based on the in Tel 80188 microprocessor, incorporating high speed DMA (direct memory access) channels and two interrupt driven serial lines. 2. A timing and control module for the tape transports. This consists of two SA-Bus cards. Two sets of software were written for the system. These are the following : 1. Software to operate the tape controller. This consists of six modules written in Pascal-86 and 8086 assemblers. 2. Software to allow the PDP-11/23 to control the tape drives. This is in the. form of an RSX-11 device driver written in PDP-11 assembler. To allow the particular to proposed local system allow area highly modular form. to be easily the system to network) , the upgraded in the future (in be incorporated into UCT's software was written in an addition to being controlled by a host system in remote mode the tape controller also has the ability to perform a variety of operations in local mode. These include the ability to copy and erase tapes, as well as a comprehensive set of diagnostic functions. When in local operations mode the controller is menu driven, making its use by persons who are not familiar with it quick and easy

    Doing Things with Words: The New Consequences of Writing in the Age of AI

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    Exploring the entanglement between artificial intelligence (AI) and writing, this thesis asks, what does writing with AI do? And, how can this doing be made visible, since the consequences of information and communication technologies (ICTs) are so often opaque? To propose one set of answers to the questions above, I begin by working with Google Smart Compose, the word-prediction AI Google launched to more than a billion global users in 2018, by way of a novel method I call AI interaction experiments. In these experiments, I transcribe texts into Gmail and Google Docs, carefully documenting Smart Compose’s interventions and output. Wedding these experiments to existing scholarship, I argue that writing with AI does three things: it engages writers in asymmetrical economic relations with Big Tech; it entangles unwitting writers in climate crisis by virtue of the vast resources, as Bender et al. (2021), Crawford (2021), and Strubell et al. (2019) have pointed out, required to train and sustain AI models; and it perpetuates linguistic racism, further embedding harmful politics of race and representation in everyday life. In making these arguments, my purpose is to intervene in normative discourses surrounding technology, exposing hard-to-see consequences so that we—people in the academy, critical media scholars, educators, and especially those of us in dominant groups— may envision better futures. Toward both exposure and reimagining, my dissertation’s primary contributions are research-creational work. Research-creational interventions accompany each of the three major chapters of this work, drawing attention to the economic, climate, and race relations that word-prediction AI conceals and to the otherwise opaque premises on which it rests. The broader wager of my dissertation is that what technologies do and what they are is inseparable: the relations a technology enacts must be exposed, and they must necessarily figure into how we understand the technology itself. Because writing with AI enacts particular economic, climate, and race relations, these relations must figure into our understanding of what it means to write with AI and, because of AI’s increasing entanglement with acts of writing, into our very understanding of what it means to write

    On Parallel Computation of Large Smooth-Degree Isogeny

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    The computation of large smooth-degree isogenies is considered to be the most time-consuming task in isogeny-based cryptosystems and, to this end, recently several proposals have been made to speed it up. For implementation in software using a single core, De Feo et al. presented an optimal way to compute such isogenies. The multi-core setting is however far more intricate but offers various ways to reduce the computation time and is an active area of research. This thesis presents a study of speeding-up large smooth-degree isogeny computation with various forms of parallelism and consists of three contributions. The first contribution of this thesis is two novel theoretical techniques for speeding-up the computation with parallelism. Our proposed technique, called precedence-constrained scheduling (PCS), transforms the isogeny computation into a task scheduling problem with precedence constraints and utilizes several task scheduling algorithms to tackle the problem. Another proposed technique of ours is to formulate the isogeny computation as an integer linear program. Combining both techniques, we are able to reduce the theoretical cost of the isogeny computation by up to 13.02% from the state-of-the-art. The second contribution of this thesis is two software implementations of the isogeny computation based on our PCS technique. We consider two execution environments for the implementations: one relies only on the parallelism provided by multi-core processors, and the other utilizes multi-core processors supporting the Intel's Advanced Vector eXtensions (AVX) technology. To our best knowledge, we are the first to utilize both parallelization technologies for the isogeny computation. Also, to achieve effective implementations, we modify PCS for each execution environments and equip both implementations with a synchronization handling technique. The implementation results show up to 14.36% speed-up for the first implementation and up to 34.05% speed-up for the second implementation. The third contribution of this thesis is two applications of using learning-based optimizations to speed-up the parallel isogeny computation. We consider the genetic algorithm and the reinforcement learning algorithm and detail our design rationale when instantiating both algorithms for our problem. From experimental results, the genetic algorithm is able to find a better approach for the isogeny computation. The approach found is nontrivial and is up to 9.95% faster than human's heuristic. On the other hand, the reinforcement learning lags PCS by as small as 2.73%. We use the experimental results of the reinforcement learning to argue that PCS may be nearly or even optimal for the computation

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

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    This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book

    Hands-on Science. Celebrating Science and Science Education

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    The book herein aims to contribute to the improvement of Science Education in our schools and to an effective implementation of a sound widespread scientific literacy at all levels of society

    Thermal and air quality modeling of an electric vehicle cabin with low-cost sensors and reinforcement learning

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    The design process of any electric vehicle system has to be oriented towards the best energy efficiency, together with the constraint of maintaining comfort in the vehicle cabin. Main aim of this study is to research the best thermal management solution in terms of HVAC efficiency without compromising occupant’s comfort and internal air quality. An Arduino controlled Low Cost System of Sensors was developed and compared against reference instrumentation (average R-squared of 0.92) and then used to characterise the vehicle cabin in real parking and driving conditions trials. Data on the energy use of the HVAC was retrieved from the car On-Board Diagnostic port. Energy savings using recirculation can reach 30 %, but pollutants concentration in the cabin builds up in this operating mode. Moreover, the temperature profile appeared strongly nonuniform with air temperature differences up to 10° C. Optimisation methods often require a high number of runs to find the optimal configuration of the system. Fast models proved to be beneficial for these task, while CFD-1D model are usually slower despite the higher level of detail provided. In this work, the collected dataset was used to train a fast ML model of both cabin and HVAC using linear regression. Average scaled RMSE over all trials is 0.4 %, while computation time is 0.0077 ms for each second of simulated time on a laptop computer. Finally, a reinforcement learning environment was built in OpenAI and Stable-Baselines3 using the built-in Proximal Policy Optimisation algorithm to update the policy and seek for the best compromise between comfort, air quality and energy reward terms. The learning curves show an oscillating behaviour overall, with only 2 experiments behaving as expected even if too slow. This result leaves large room for improvement, ranging from the reward function engineering to the expansion of the ML model

    Specialized IoT systems: Models, Structures, Algorithms, Hardware, Software Tools

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    Монография включает анализ проблем, модели, алгоритмы и программно- аппаратные средства специализированных сетей интернета вещей. Рассмотрены результаты проектирования и моделирования сети интернета вещей, мониторинга качества продукции, анализа звуковой информации окружающей среды, а также технология выявления заболеваний легких на базе нейронных сетей. Монография предназначена для специалистов в области инфокоммуникаций, может быть полезна студентам соответствующих специальностей, слушателям факультетов повышения квалификации, магистрантам и аспирантам
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