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ItemIOQP: A simple Impact-Ordered Query Processor written in Rust(RWTH, Aachen University, 2023-01-01)Impact-ordered index organizations are suited to score-at-a-time query evaluation strategies. A key advantage of score-at-a-time processing is that query latency can be tightly controlled, leading to lower tail latency and less latency variance overall. While score-at-a-time evaluation strategies have been explored in the literature, there is currently only one notable system that promotes impact-ordered indexing and efficient score-at-a-time query processing. In this paper, we propose an alternative implementation of score-at-a-time retrieval over impact-ordered indexes in the Rust programming language. We detail the efficiency-effectiveness characteristics of our implementation through a range of experiments on two test collections. Our results demonstrate the efficiency of our proposed model in terms of both single-threaded latency, and multi-threaded throughput capability. We make our system publicly available to benefit the community and to promote further research in efficient impact-ordered query processing.
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ItemBarriers to implementation of sustainable construction in India(The University of Melbourne, 2023)Abstract: The Indian construction industry was estimated to be worth three trillion INR in 2022 and is expected to be the third largest construction market by 2025. The industry is responsible for a large amount of energy consumption, which not only contributes to the emission of greenhouse gases, but also adversely impacts resources like land, waterbodies, minerals, and other naturally sourced materials. Hence, implementing sustainable construction practices across the project life cycle is essential to reducing the detrimental impacts of the industry. Despite having 3 green building rating systems (GRIHA, IGBC, and LEED) and adopting certain national level initiatives, there is an absence of a systematic regulatory framework for the incorporation of sustainability principles in the Indian construction industry. It is critical to determine the existing issues that prevail in the industry to address the barriers in a timely manner. This paper determines the critical barriers to incorporating sustainable construction in India by reviewing the academic literature, Environmental Performance Index (EPI), and Sustainable Development Goals (SDG) 2022 reports. Unskilled workforce, low productivity, lack of monitoring schemes, inadequate technology, poor team integration and collaboration are the key barriers that are deduced from the systematic literature review. The ongoing national level initiatives and schemes promoting multiple goals of SDGs are also identified. The administrative framework of the Indian construction industry includes ministries, state departments, local authorities, and regulatory councils. Every state in India has building bye laws that differ from those of other states and this has also been identified as a barrier. One of the solutions determined by experts and researchers is for the Indian construction industry to comply with the 2030 Agenda for sustainable development. In order to accomplish that, policy makers, sustainable construction practitioners, and industry professionals must develop specific grassroot level mitigation factors to counter the key barriers.
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ItemOn the end of translation studies as we know it(Humanities Commons, 2024)Despite recent debates about the origins of translation studies, it is far more important and indeed urgent to consider how the discipline might fade away and eventually disappear. Here I consider a scenario of demise, some possible causes, and actions to take.
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ItemNo Preview AvailableAlgorithmic Decisions, Desire for Control, and the Preference for Human Review over Algorithmic Review(ASSOC COMPUTING MACHINERY, 2023)
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ItemNo Preview AvailableLossy Compression Options for Dense Index Retention(ASSOC COMPUTING MACHINERY, 2023)
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ItemNo Preview AvailableKey Considerations for The Design of Technology for Enrichment in Residential Aged Care: An Ethnographic Study(ASSOC COMPUTING MACHINERY, 2023)
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ItemNo Preview AvailableThe Future Can’t Help Fix The Past: Assessing Program Repair In The Wild(IEEE, 2023-10-01)
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ItemNo Preview AvailableThe Effect of Fetal Heart Rate Segment Selection on Deep Learning Models for Fetal Compromise Detection(IEEE, 2023)Monitoring the fetal heart rate (FHR) is common practice in obstetric care to assess the risk of fetal compromise. Unfortunately, human interpretation of FHR recordings is subject to inter-observer variability with high false positive rates. To improve the performance of fetal compromise detection, deep learning methods have been proposed to automatically interpret FHR recordings. However, existing deep learning methods typically analyse a fixed-length segment of the FHR recording after removing signal gaps, where the influence of this segment selection process has not been comprehensively assessed. In this work, we develop a novel input length invariant deep learning model to determine the effect of FHR segment selection for detecting fetal compromise. Using this model, we perform five times repeated five-fold cross-validation on an open-access database of 552 FHR recordings and assess model performance for FHR segment lengths between 15 and 60 minutes. We show that the performance after removing signal gaps improves with increasing segment length from 15 minutes (AUC = 0.50) to 60 minutes (AUC = 0.74). Additionally, we demonstrate that using FHR segments without removing signal gaps achieves superior performance across signal lengths from 15 minutes (AUC = 0.68) to 60 minutes (AUC = 0.76). These results show that future works should carefully consider FHR segment selection and that removing signal gaps might contribute to the loss of valuable information.