817 research outputs found

    Systematic mapping of software engineering management with an agile approach

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    El enfoque ágil ha generado una amplia variedad de estrategias para administrar con éxito diversos proyectos de software en todo el mundo. Además, podemos asegurar que los proyectos de software se han beneficiado de los métodos ágiles ya conocidos. En este sentido, este artículo busca demostrar cómo se aplica el enfoque ágil en las áreas de la gestión en la ingeniería del Software. Para ello, este estudio realiza un mapeo sistemático para identificar las principales tendencias en la gestión de la ingeniería de software con un enfoque ágil. Se han identificado un total de 1137 artículos, de los cuales 165 son relevantes para los fines de este estudio, estos indican que la entrega temprana de valor, un principio clave de la agilidad, sigue siendo la principal tendencia para el uso de métodos ágiles. Sin embargo, también existen fuertes tendencias enfocadas en puntos clave de la gestión en ingeniería de software, como optimizar la gestión de calidad, optimizar la especificación de requisitos, optimizar la gestión de riesgos y mejorar la comunicación y coordinación del equipo, estos resultados permitirán generar nuevas líneas de investigación para cada punto clave de la gestión en la ingeniería del software impactado por el enfoque ágil.The agile approach has generated a wide variety of strategies to successfully manage various software projects worldwide. In addition, we can ensure that software projects have benefited from the already known agile methods. In this sense, this article seeks to demonstrate how the agile approach is applied in Software engineering management areas. To do this, this study performs a systematic mapping to identify the main trends in software engineering management with an agile approach. A total of 1137 articles have identified, of which 165 are relevant for the purposes of this study, these indicate that early value delivery, a key principle of agility, continues to be the main trend for the use of agile methods. However, there are also strong trends focused on key points of management in software engineering, such as optimize quality management, optimize requirements specification, optimize risk management, and improve team communication and coordination, these results will allow generating new lines of research for each key point of management in software engineering impacted by the agile approach

    Challenging the gold standard: a methodological study of the quality and errors of web tracking data

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    Measuring what people consume and do online is crucial across the social sciences. In the last few years, web tracking data has gained popularity, being considered by most as the gold standard for measuring online behaviours. This thesis studies whether this prevailing notion holds true. Specifically, through a combination of traditional survey and computational methods, I assess the quality of web tracking data, its associated errors, and the consequences of these. The thesis is comprised of three distinct papers. In the first paper, inspired by the Total Survey Error, I present a Total Error framework for digital traces collected with Meters (TEM). The TEM framework describes the data generation and the analysis process for web tracking data and documents the sources of bias and variance that may arise in each step of this process. The framework suggests that metered data might indeed be affected by the error sources identified in our framework and, to some extent, biased. The second paper adopts an empirical approach to address a key error identified in the TEM framework: researchers’ failure to capture data from all the devices and browsers that individuals utilize to go online. The paper shows that tracking undercoverage is highly prevalent when using commercial panels. Additionally, through a simulation study, it demonstrates that web tracking estimates, both univariate and multivariate, are often substantially biased due to tracking undercoverage. The third paper explores the validity and reliability of web tracking data when used to measure media exposure. Merging traditional psychometric and computational techniques, I conduct a multiverse analysis to assess the predictive validity and true-score reliability of thousands of web tracking measures of media exposure. The findings show that web tracking measures have an overall low validity but remarkably high reliability. Additionally, results suggest that the design decisions made by researchers when designing web tracking measurements can have a substantial impact on their measurement properties. Collectively, this thesis challenges the prevailing belief in web tracking data as the gold standard to measure online behaviours. Methodologically, it illustrates how computational methods can be used to adapt survey methodology techniques to assess the quality of digital trace data

    University of Windsor Graduate Calendar 2023 Spring

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

    THE DEVELOPMENT OF TYPE 2 IMMUNE AGONISTS AS PRO-REGENERATIVE IMMUNOTHERAPIES

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    Adult mammals have large deficits in their ability to heal traumatic soft-tissue injuries without subsequent scarring and long-term fibrosis. Type 2 innate and adaptive immunity has been strongly connected to wound healing and has been shown to be a critical factor for the healing of musculoskeletal injuries. Helminth parasites are a well characterized class of pathosymbionts that have co-evolved to boost host type 2 immune responses, which ultimately enhance the mutual survival of the parasite and host through the promotion of anti-inflammatory and wound healing mechanisms. While many aspects of the immune response to helminth infection and egg products have been well-characterized, the mechanistic link of type 2 immunity to tissue regeneration, and the potential for safely using helminth products as immunotherapies in wound repair remains unclear. Here, we develop and validate a pro-regenerative immunotherapy from Schistosoma mansoni (S. mansoni) helminth egg products. In agreement with previous literature, we found that soluble egg antigens (SEA) boosted type 2 immune signatures, but also increased inflammatory signatures we have previously associated with diminished tissue repair. Seeking to develop an immunotherapy to boost short-term type 2 immune responses, decrease inflammation, and incorporate pro-resolving aspects of helminth egg products, we modify the isolation process to establish a new formulation of SEA. We apply this ‘regenerative’ SEA (RSEA) to murine skeletal muscle injuries and find that the short-term accumulation of T helper type 2 cells, regulatory T cells, and eosinophils correlated with decreased expression of pro-inflammatory signatures. Further, we find that this led to a decrease in tissue fibrosis and improved functional repair when assessed at later timepoints post-injury. Seeking to determine the regenerative potential of RSEA in semi-immune privileged tissues, we apply RSEA in articular joint and corneal injury models and find crucial myeloid-adipose-lymphocyte interactions may be driving the type 2 immunity repair process. Lastly, we compare other type 2 agonists in the wound to RSEA at early stages and propose mechanistic pathways that may be crucial to the pro-regenerative responses found among some type 2 agonists

    University of Windsor Graduate Calendar 2023 Winter

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

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)

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    This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023

    Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

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    Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table
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