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‘Doesn’t anyone care anymore?’ - Bystander intervention to hate crime.
No full textWhile previous studies have focused on bystander intervention, current understanding specifically in the area of bystander intervention to hate crime is limited. This study seeks to focus on bystander intervention to hate crime in the United Kingdom. This study utilised 10 semi-structure interviews with participants who had personally witnessed a hate crime incident, exploring reasons for intervention, or lack thereof. Results revealed that for some who intervened, the decision to do so often stemmed from an instinctive, impulsive place, whereas for others it was a calculative decision-making process. The findings also reveal that there are various factors which influence participants in deciding whether to intervene. Critically, while all factors were described as pivotal to influencing participants in choosing whether to intervene or remain bystanders, there was no hierarchy of factors which can be generalised. The study concludes that the decision to intervene is a complex multi-faceted process and promotes awareness-raising about the various options available when witnessing a hate crime
Advanced hybrid malware identification framework for the Internet of Medical Things, driven by deep learning
No full textThe Internet of Things (IoT) effortlessly enables communication between items on the World Wide Web and other systems. This extensive use of IoTs has created new services and automation in numerous industries, enhancing the standard of living, especially in healthcare. Internet of Medical Things (IoMT) adoption has been beneficial during pandemic conditions by enabling remote patient monitoring and therapy. Nevertheless, the excessive use of IoMT has raised security concerns as it can compromise critical data. This breach in security can result in an inaccurate diagnosis or violate privacy. This research presents a novel approach to hybrid deep learning‐based detection of malware solutions for the IoT. This study uses RNN‐Bi‐LSTM to detect and extract significant features related to an already existing dataset. The proposed model exhibits a detection accuracy of 98.38% when evaluated using these existing datasets. Statistical tests like Mathew co‐relation and Log Loss also indicated reliability of proposed framework. The distinguished feature of our framework is its ability to combine complex deep learning models for IoMT security, which is of economic and scientific importance. It certainly offers a reliable solution for healthcare applications that rely on real‐time functionality and dependency on IoMT systems
Melanoma identification and classification model based on fine-tuned convolutional neural network
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Book Review: A Badge of Injury: The Pink Triangle as Global Symbol of Memory by Sébastien Tremblay
No full textBeyond Synodal Listening: Theological Action Research and Cultures of Conversation
No full textIn the contemporary Catholic Church’s engagement with synodality listening has become the dominant emphasis; methods for “spiritual conversation” are designed to give priority to listening—to one another and, fundamentally, the Holy Spirit. This paper offers some critiques of this emphasis and seeks to explore conversation as the synodal fundamental. Conversation overly controlled by listening is, I argue, susceptible to subtle and unhelpful power dynamics. “Ordinary conversation”—interruptive, informal, spontaneous, and not always polite—has its own theological significance and must also find its place in a truly synodal church. Drawing on extensive experience of theological action research and insights from systems theory, the paper sets out why such “ordinary conversation,” distinct from what might be seen as “formal listening,” is ecclesiologically essential for a full realisation of church. By way of conclusion, key themes are identified whose development is needed for the further enrichment of synodal processes through attention to ordinary conversation
