280 research outputs found

    Data quality processing for photovoltaic system measurements

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    The operation and maintenance activities in photovoltaic systems use meteorological and electrical measurements that must be reliable to check system performance. The International Electrotechnical Commission (IEC) standards have established general criteria to filter erroneous information; however, there is no standardized process for the evaluation of measurements. In the present work we developed 3 procedures to detect and correct measurements of a photovoltaic system based on the single diode model. The performance evaluation of each criterion was tested with 6 groups of experimental measurements from a 3 kWp installation. Based on the error of the 3 procedures performed, the most unfavorable case has been prioritized. Then, the reduction of errors between the estimated and measured value has been achieved, reducing the number of measurements to be corrected. For the clear sky categories, the coefficient of determination is 0.9975 and 0.9961 for the high irradiance profile. Although an increase of 2.5% for coefficient of determination has been achieved, the overcast sky categories should be analyzed in more detail. Finally, the different causes of measurement error should be analyzed, associated with calibration errors and sensor quality

    Automation for network security configuration: state of the art and research trends

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    The size and complexity of modern computer networks are progressively increasing, as a consequence of novel architectural paradigms such as the Internet of Things and network virtualization. Consequently, a manual orchestration and configuration of network security functions is no more feasible, in an environment where cyber attacks can dramatically exploit breaches related to any minimum configuration error. A new frontier is then the introduction of automation in network security configuration, i.e., automatically designing the architecture of security services and the configurations of network security functions, such as firewalls, VPN gateways, etc. This opportunity has been enabled by modern computer networks technologies, such as virtualization. In view of these considerations, the motivations for the introduction of automation in network security configuration are first introduced, alongside with the key automation enablers. Then, the current state of the art in this context is surveyed, focusing on both the achieved improvements and the current limitations. Finally, possible future trends in the field are illustrated

    Chatbots for Modelling, Modelling of Chatbots

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    Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 28-03-202

    Investigating the role of circulating cell-free dna as a mechanistic biomarker in inflammatory bowel disease: development of an integrated precision-medicine enabled platform in Scotland

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    BACKGROUND: Circulating cell-free DNA (cfDNA) represents a class of biological molecules whose role in inflammation remains poorly understood. Inflammatory bowel disease (IBD), from ulcerative colitis to Crohn’s disease, comprises a spectrum of chronic immune-mediated conditions with complex pathogenic mechanisms that manifest primarily as gut-mucosal inflammation. There remains an unmet need that requires a greater understanding of disease mechanisms to find better treatments for patients. HYPOTHESIS/METHODS: cfDNA is a biomarker in IBD that captures a dimension of disease activity not covered by current clinical biomarkers. Mitochondrial cfDNA may identify a subset of patients whose disease is driven by immune-mediated recognition of mitochondrial cfDNA. cfDNA metagenomics may provide new insights into disease biology. Two multi-centre translational cohort studies were set up – GI-DAMPs (cross-sectional) and MUSIC (longitudinal) and the execution of which is discussed. Clinical sampling was performed, and subsequent analysis was carried out using Qubit for total quantification, digital polymerase chain reaction (dPCR) for COX3, ND2 and GAPDH genes, fragment analysis with the Agilent BioAnalyzer, and cfDNA sequencing using both Nanopore and Illumina platforms. RESULTS: Patients with highly active IBD (requiring admission to hospital) had significantly higher total cfDNA (median 0.52 ng/uL, Kruskal-Wallis p<0.001), mitochondrial ND2 (median 359 copies/uL, Kruskal-Wallis p<0.05) and genomic GAPDH levels (median 8.7 copies/uL, Kruskal-Wallis p<0.01) compared to patients with active disease or remission. Digital PCR techniques provide better resolution compared to Qubit. cfDNA fragment analysis shows an increase in the 160bp peak and the release of longer fragments in highly active disease, suggesting increased apoptosis and necrosis, compared to patients in remission or healthy controls. cfDNA sequencing and bioinformatic analysis were feasible. cfDNA metagenomics reveals that patients with active disease have reduced alpha diversity (median Chao1 2612, p=0.07 and median Shannon 0.06, p=0.43) and significantly different beta diversity profiles (permanova R2 0.766, p<0.01) compared to patients in remission or healthy controls. CONCLUSION: The analysis of cfDNA with modern advances in technology is an unexplored dimension of inflammation biology. cfDNA correlates with IBD activity and further study is required to validate its use as a clinical and mechanistic biomarker. Further scientific work in cfDNA could unlock new insights into both cfDNA and IBD biology, potentially allowing the development of better mechanistic and predictive biomarkers, new therapeutics, and general insights into other inflammatory diseases

    On the assessment of generative AI in modeling tasks: an experience report with ChatGPT and UML

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    Most experts agree that large language models (LLMs), such as those used by Copilot and ChatGPT, are expected to revo- lutionize the way in which software is developed. Many papers are currently devoted to analyzing the potential advantages and limitations of these generative AI models for writing code. However, the analysis of the current state of LLMs with respect to software modeling has received little attention. In this paper, we investigate the current capabilities of ChatGPT to perform modeling tasks and to assist modelers, while also trying to identify its main shortcomings. Our findings show that, in contrast to code generation, the performance of the current version of ChatGPT for software modeling is limited, with various syntactic and semantic deficiencies, lack of consistency in responses and scalability issues. We also outline our views on how we perceive the role that LLMs can play in the software modeling discipline in the short term, and how the modeling community can help to improve the current capabilities of ChatGPT and the coming LLMs for software modeling.Funding for open access publishing: Universidad de Málaga/ CBU

    Deep learning for Alzheimer’s disease: towards the development of an assistive diagnostic tool

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    The past decade has witnessed rapid advances at the intersection of machine learning and medicine. Owing to the tremendous amount of digitized hospital data, machine learning is poised to bring innovation to the traditional healthcare workflow. Though machine learning models have strong predictive power, it is challenging to translate a research project into a clinical tool partly due to the lack of a rigorous validation framework. In this dissertation, I presented a range of machine learning models that were trained to classify Alzheimer’s disease - a condition with an insidious onset - using routinely collected clinical data. In addition to reporting the model performance, I discussed several considerations, including feature selection, data harmonization, effect of confounding variables, diagnostic scope, model interpretability and validation, which are critical to the design, development, and validation of machine learning models. From the methodological standpoint, I presented a multidisciplinary collaboration in which medical domain knowledge which was obtained from experts and tissue examinations was tightly integrated with the interpretable outcomes derived from our machine learning frameworks. I demonstrated that the model, which generalized well on multiple independent cohorts, achieved diagnostic performance on par with a group of medical professionals. The interpretable analysis of our model showed that its underlying decision logic corresponds with expert ratings and neuropathological findings. Taken together, this work presented a machine learning system for classification of Alzheimer’s disease, marking an important milestone towards a translatable clinical application in the future

    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

    Data-driven and production-oriented tendering design using artificial intelligence

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    Construction projects are facing an increase in requirements since the projects are getting larger, more technology is integrated into the buildings, and new sustainability and CO2 equivalent emissions requirements are introduced. As a result, requirement management quickly gets overwhelming, and instead of having systematic requirement management, the construction industry tends to trust craftsmanship. One method for a more systematic requirement management approach successful in other industries is the systems engineering approach, focusing on requirement decomposition and linking proper verifications and validations. This research project explores if a systems engineering approach, supported by natural language processing techniques, can enable more systematic requirement management in construction projects and facilitate knowledge transfer from completed projects to new tendering projects.The first part of the project explores how project requirements can be extracted, digitised, and analysed in an automated way and how this can benefit the tendering specialists. The study is conducted by first developing a work support tool targeting tendering specialists and then evaluating the challenges and benefits of such a tool through a workshop and surveys. The second part of the project explores inspection data generated in production software as a requirement and quality verification method. First, a dataset containing over 95000 production issues is examined to understand the data quality level of standardisation. Second, a survey addressing production specialists evaluates the current benefits of digital inspection reporting. Third, future benefits of using inspection data for knowledge transfers are explored by applying the Knowledge Discovery in Databases method and clustering techniques. The results show that applying natural language processing techniques can be a helpful tool for analysing construction project requirements, facilitating the identification of essential requirements, and enabling benchmarking between projects. The results from the clustering process suggested in this thesis show that inspection data can be used as a knowledge base for future projects and quality improvement within a project-based organisation. However, higher data quality and standardisation would benefit the knowledge-generation process.This research project provides insights into how artificial intelligence can facilitate knowledge transfer, enable data-informed design choices in tendering projects, and automate the requirements analysis in construction projects as a possible step towards more systematic requirements management

    The Impact of Online Real Estate Listing Data on the Transparency of the Real Estate Market - Using the Example of Vacancy Rates

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    Despite the increasing digitization of the real estate market and the accompanying greater availability of data, as evidenced, for example, by the proliferation of online real estate listing platforms, there are still deficiencies in market transparency associated with a variety of negative aspects. This study aimed to investigate the impact of online real estate listing data on market transparency by examining the suitability of these data for scientific use in general and for the example of estimating vacancy rates in particular. Therefore, a comprehensive data set consisting of more than seven million listings was collected over one and a half years and analyzed with regard to all available features in terms of their quality and quantity. Furthermore, their explanatory power for estimating vacancy rates was tested by their application in different regression models. The features specified in online real estate listings showed an average completeness of 85.97 % and, most widely, plausible feature specifications. Exceptions were information regarding energy demand, which were only available in 20.79 % of listings, and the specification of the building quality and condition, which showed indications of being positively biased. The estimation of vacancy rates on the district level, based on online real estate listing data, showed promising results, being able to explain vacancy rates with a goodness of fit of a pseudo R² of 0.81 and a mean absolute error of 0.84 percentage points. These results suggest that information contained in online real estate listing data are a good basis for scientific evaluation and are specifically well suited for estimating vacancy rates. The findings imply the utilization of online real estate listing data for a diverse range of purposes, extending beyond the current focus of price-related research

    Large Language Models of Code Fail at Completing Code with Potential Bugs

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    Large language models of code (Code-LLMs) have recently brought tremendous advances to code completion, a fundamental feature of programming assistance and code intelligence. However, most existing works ignore the possible presence of bugs in the code context for generation, which are inevitable in software development. Therefore, we introduce and study the buggy-code completion problem, inspired by the realistic scenario of real-time code suggestion where the code context contains potential bugs -- anti-patterns that can become bugs in the completed program. To systematically study the task, we introduce two datasets: one with synthetic bugs derived from semantics-altering operator changes (buggy-HumanEval) and one with realistic bugs derived from user submissions to coding problems (buggy-FixEval). We find that the presence of potential bugs significantly degrades the generation performance of the high-performing Code-LLMs. For instance, the passing rates of CodeGen-2B-mono on test cases of buggy-HumanEval drop more than 50% given a single potential bug in the context. Finally, we investigate several post-hoc methods for mitigating the adverse effect of potential bugs and find that there remains a large gap in post-mitigation performance.Comment: 25 page
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