795 research outputs found

    Southern Adventist University Undergraduate Catalog 2023-2024

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    Southern Adventist University\u27s undergraduate catalog for the academic year 2023-2024.https://knowledge.e.southern.edu/undergrad_catalog/1123/thumbnail.jp

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    2023-2024 Catalog

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    The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

    Southern Adventist University Undergraduate Catalog 2022-2023

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    Southern Adventist University\u27s undergraduate catalog for the academic year 2022-2023.https://knowledge.e.southern.edu/undergrad_catalog/1121/thumbnail.jp

    University of Windsor Graduate Calendar 2023 Spring

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    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1027/thumbnail.jp

    Northeastern Illinois University, Academic Catalog 2023-2024

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    https://neiudc.neiu.edu/catalogs/1064/thumbnail.jp

    FlaKat: A Machine Learning-Based Categorization Framework for Flaky Tests

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    Flaky tests can pass or fail non-deterministically, without alterations to a software system. Such tests are frequently encountered by developers and hinder the credibility of test suites. Thus, flaky tests have caught the attention of researchers in recent years. Numerous approaches have been published on defining, locating, and categorizing flaky tests, along with auto-repairing strategies for specific types of flakiness. Practitioners have developed several techniques to detect flaky tests automatically. The most traditional approaches adopt repeated execution of test suites accompanied by techniques such as shuffled execution order, and random distortion of environment. State-of-the-art research also incorporates machine learning solutions into flaky test detection and achieves reasonably good accuracy. Moreover, strategies for repairing flaky tests have also been published for specific flaky test categories and the process has been automated as well. However, there is a research gap between flaky test detection and category-specific flakiness repair. To address the aforementioned gap, this thesis proposes a novel categorization framework, called FlaKat, which uses machine-learning classifiers for fast and accurate categorization of a given flaky test case. FlaKat first parses and converts raw flaky tests into vector embeddings. The dimensionality of embeddings is reduced and then used for training machine learning classifiers. Sampling techniques are applied to address the imbalance between flaky test categories in the dataset. The evaluation of FlaKat was conducted to determine its performance with different combinations of configurations using known flaky tests from 108 open-source Java projects. Notably, Implementation-Dependent and Order-Dependent flaky tests, which represent almost 75% of the total dataset, achieved F1 scores (harmonic mean of precision and recall) of 0.94 and 0.90 respectively while the overall macro average (no weight difference between categories) is at 0.67. This research work also proposes a new evaluation metric, called Flakiness Detection Capacity (FDC), for measuring the accuracy of classifiers from the perspective of information theory and provides proof for its effectiveness. The final obtained results for FDC also aligns with F1 score regarding which classifier yields the best flakiness classification

    University of Windsor Graduate Calendar 2023 Winter

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    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1026/thumbnail.jp

    Machine learning as a service for high energy physics (MLaaS4HEP): a service for ML-based data analyses

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    With the CERN LHC program underway, there has been an acceleration of data growth in the High Energy Physics (HEP) field and the usage of Machine Learning (ML) in HEP will be critical during the HL-LHC program when the data that will be produced will reach the exascale. ML techniques have been successfully used in many areas of HEP nevertheless, the development of a ML project and its implementation for production use is a highly time-consuming task and requires specific skills. Complicating this scenario is the fact that HEP data is stored in ROOT data format, which is mostly unknown outside of the HEP community. The work presented in this thesis is focused on the development of a ML as a Service (MLaaS) solution for HEP, aiming to provide a cloud service that allows HEP users to run ML pipelines via HTTP calls. These pipelines are executed by using the MLaaS4HEP framework, which allows reading data, processing data, and training ML models directly using ROOT files of arbitrary size from local or distributed data sources. Such a solution provides HEP users non-expert in ML with a tool that allows them to apply ML techniques in their analyses in a streamlined manner. Over the years the MLaaS4HEP framework has been developed, validated, and tested and new features have been added. A first MLaaS solution has been developed by automatizing the deployment of a platform equipped with the MLaaS4HEP framework. Then, a service with APIs has been developed, so that a user after being authenticated and authorized can submit MLaaS4HEP workflows producing trained ML models ready for the inference phase. A working prototype of this service is currently running on a virtual machine of INFN-Cloud and is compliant to be added to the INFN Cloud portfolio of services

    Simulating the nonlinear QED vacuum

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