4,346 research outputs found

    Architecture Smells vs. Concurrency Bugs: an Exploratory Study and Negative Results

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    Technical debt occurs in many different forms across software artifacts. One such form is connected to software architectures where debt emerges in the form of structural anti-patterns across architecture elements, namely, architecture smells. As defined in the literature, ``Architecture smells are recurrent architectural decisions that negatively impact internal system quality", thus increasing technical debt. In this paper, we aim at exploring whether there exist manifestations of architectural technical debt beyond decreased code or architectural quality, namely, whether there is a relation between architecture smells (which primarily reflect structural characteristics) and the occurrence of concurrency bugs (which primarily manifest at runtime). We study 125 releases of 5 large data-intensive software systems to reveal that (1) several architecture smells may in fact indicate the presence of concurrency problems likely to manifest at runtime but (2) smells are not correlated with concurrency in general -- rather, for specific concurrency bugs they must be combined with an accompanying articulation of specific project characteristics such as project distribution. As an example, a cyclic dependency could be present in the code, but the specific execution-flow could be never executed at runtime

    General government fiscal plan for 2024–2027

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    The purpose of the General Government Fiscal Plan is to support decision-making related to general government finances as well as compliance with the Medium-Term Objective set for the structural budgetary position of general government finances. The plan contains sections related to central government finances, wellbeing services county finances, local government finances, statutory earnings-related pension funds and other social security funds. The Government prepares the General Government Fiscal Plan for the parliamentary term and revises it annually for the following four years by the end of April. The General Government Fiscal Plan also includes Finland’s Stability Programme, and it meets the EU’s requirement for a medium-term fiscal plan. The General Government Fiscal Plan for 2024–2027 does not propose any new policy definitions. It is based on current legislation and takes into account the impact of the decisions previously made by Prime Minister Marin’s Government on the expenditure and revenue levels in the coming years. This General Government Fiscal Plan does not set any budgetary position targets. The first General Government Fiscal Plan of the Government to be appointed after the parliamentary election in spring 2023 will be drawn up in autumn 2023, and this will include a Stability Programme. The General Government Fiscal Plan also includes the central government spending limits decision, but it does not specify a parliamentary term expenditure ceiling

    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

    The Disputation: The Enduring Representations in William Holman Hunt's “The Finding of the Saviour in the Temple,” 1860

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    This interdisciplinary thesis problematizes the Jewish presence in the painting The Finding of the Saviour in the Temple (1860) by William Holman Hunt. This “Jewish presence” refers to characters within the painting, Jews who posed for the picture and the painting’s portrayal of Judaism. The thesis takes a phenomenological and hermeneutical approach to The Finding providing careful description and interpretation of what appears in the painting. It situates the painting within a newly configured genre of disputation paintings depicting the Temple scene from the Gospel of Luke (2:47 – 52). It asks two questions. Why does The Finding look the way it does? And how did Holman Hunt know how to create the picture? Under the rubric of the first question, it explores and challenges customary accounts of the painting, explicitly challenging the over reliance upon F.G. Stephens’s pamphlet. Additionally, it examines Pre-Raphaelite and Victorian religious contexts and bringing hitherto unacknowledged artistic contexts to the fore. The second question examines less apparent influences through an analysis of the originary Lukan narrative in conjunction with the under-examined genre of Temple “disputation” paintings, and a legacy of scholarly and religious disputation. This demonstrates a discourse of disputation informing The Finding over and above the biblical narrative. In showing that this discourse strongly correlates with the painting’s objectifying and spectacular properties, this thesis provides a new way to understand The Finding’s orientalism which is further revealed in its typological critical reworking of two Christian medieval and renaissance paintings. As a demonstration of the discourse, the thesis includes an examination of Jewish artists who addressed the theme of disputation overtly or obliquely thereby engaging with and challenging the assumptions upon which the disputation rests

    'Exarcheia doesn't exist': Authenticity, Resistance and Archival Politics in Athens

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    My thesis investigates the ways people, materialities and urban spaces interact to form affective ecologies and produce historicity. It focuses on the neighbourhood of Exarcheia, Athens’ contested political topography par excellence, known for its production of radical politics of discontent and resistance to state oppression and eoliberal capitalism. Embracing Exarcheia’s controversial status within Greek vernacular, media and state discourses, this thesis aims to unpick the neighbourhoods’ socio-spatial assemblage imbued with affect and formed through the numerous (mis)understandings and (mis)interpretations rooted in its turbulent political history. Drawing on theory on urban spaces, affect, hauntology and archival politics, I argue for Exarcheia as an unwavering archival space composed of affective chronotopes – (in)tangible loci that defy space and temporality. I posit that the interwoven narratives and materialities emerging in my fieldwork are persistently – and perhaps obsessively – reiterating themselves and remaining imprinted on the neighbourhood’s landscape as an incessant reminder of violent histories that the state often seeks to erase and forget. Through this analysis, I contribute to understandings of place as a primary ethnographic ‘object’ and the ways in which place forms complex interactions and relationships with social actors, shapes their subjectivities, retains and bestows their memories and senses of historicity

    Proactive Interference-aware Resource Management in Deep Learning Training Cluster

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    Deep Learning (DL) applications are growing at an unprecedented rate across many domains, ranging from weather prediction, map navigation to medical imaging. However, training these deep learning models in large-scale compute clusters face substantial challenges in terms of low cluster resource utilisation and high job waiting time. State-of-the-art DL cluster resource managers are needed to increase GPU utilisation and maximise throughput. While co-locating DL jobs within the same GPU has been shown to be an effective means towards achieving this, co-location subsequently incurs performance interference resulting in job slowdown. We argue that effective workload placement can minimise DL cluster interference at scheduling runtime by understanding the DL workload characteristics and their respective hardware resource consumption. However, existing DL cluster resource managers reserve isolated GPUs to perform online profiling to directly measure GPU utilisation and kernel patterns for each unique submitted job. Such a feedback-based reactive approach results in additional waiting times as well as reduced cluster resource efficiency and availability. In this thesis, we propose Horus: an interference-aware and prediction-based DL cluster resource manager. Through empirically studying a series of microbenchmarks and DL workload co-location combinations across heterogeneous GPU hardware, we demonstrate the negative effects of performance interference when colocating DL workload, and identify GPU utilisation as a general proxy metric to determine good placement decisions. From these findings, we design Horus, which in contrast to existing approaches, proactively predicts GPU utilisation of heterogeneous DL workload extrapolated from the DL model computation graph features when performing placement decisions, removing the need for online profiling and isolated reserved GPUs. By conducting empirical experimentation within a medium-scale DL cluster as well as a large-scale trace-driven simulation of a production system, we demonstrate Horus improves cluster GPU utilisation, reduces cluster makespan and waiting time, and can scale to operate within hundreds of machines

    Great expectations: unsupervised inference of suspense, surprise and salience in storytelling

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    Stories interest us not because they are a sequence of mundane and predictable events but because they have drama and tension. Crucial to creating dramatic and exciting stories are surprise and suspense. Likewise, certain events are key to the plot and more important than others. Importance is referred to as salience. Inferring suspense, surprise and salience are highly challenging for computational systems. It is difficult because all these elements require a strong comprehension of the characters and their motivations, places, changes over time, and the cause/effect of complex interactions. Recently advances in machine learning (often called deep learning) have substantially improved in many language-related tasks, including story comprehension and story writing. Most of these systems rely on supervision; that is, huge numbers of people need to tag large quantities of data to tell the system what to teach these systems. An example would be tagging which events are suspenseful. It is highly inflexible and costly. Instead, the thesis trains a series of deep learning models via only reading stories, a self-supervised (or unsupervised) system. Narrative theory methods (rules and procedures) are applied to the knowledge built into the deep learning models to directly infer salience, surprise, and salience in stories. Extensions add memory and external knowledge from story plots and from Wikipedia to infer salience on novels such as Great Expectations and plays such as Macbeth. Other work adapts the models as a planning system for generating new stories. The thesis finds that applying the narrative theory to deep learning models can align with the typical reader. In follow up work, the insights could help improve computer models for tasks such as automatic story writing, assistance for writing, summarising or editing stories. Moreover, the approach of applying narrative theory to the inherent qualities built in a system that learns itself (self-supervised) from reading from books, watching videos, listening to audio is much cheaper and more adaptable to other domains and tasks. Progress is swift in improving self-supervised systems. As such, the thesis's relevance is that applying domain expertise with these systems may be a more productive approach in many areas of interest for applying machine learning

    Petro-Modernity and Urban Visual Culture since the Mid-Twentieth Century

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    Petro-modernity is a local phenomenon essential to the history of Kuwait, while also a global experience and one of the prime sources of climate change. The book investigates petroleum’s role in the visual culture of Kuwait to understand the intersecting ideologies of modernization, political representation, and oil. The notion of iridescence, the ambiguous yet mesmerizing effect of a rainbowlike color play, serves as analytical-aesthetic concept to discuss petroleum’s ambiguous contribution to modernity: both promise of prosperity and destructive force of socio-cultural and ecological environments. Covering a broad spectrum of historical material from aerial and color photography, visual arts, postage stamps, and master plans to architecture and also contemporary art from the Gulf, it dismantles petro- modernity’s visual legacy
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