1,721,370 research outputs found
Medicolegal aspects of donor safety evaluation.
No full textCurrent Italian statutes concern the safety of organ donors. The law of April 1, 1999 (no. 91), established the fundamental principles for a new structure for organ transplants in Italy and, therefore, for the quality of transplanted organs. The Ministry of Health decree of February 8, 2002, defined organ conditions that preclude the utilization of an organ and identified conditions for suitability of non-optimal organs for some types of transplants or for some recipients. Guidelines from the National Transplant Center establish risk levels, criteria for absolute exclusion, and standard procedures for the evaluation of donor risk and organ suitability for transplant. This article also examines the levels of responsibility of various professionals involved in the harvesting-transplant process
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Mel Spectrogram-Based CNN Framework for Explainable Audio Deepfake Detection
No full textThe rise of audio deepfakes is becoming a growing concern for media credibility, particularly on social platforms. This study explores an approach to detecting audio deepfakes using Convolutional Neural Networks (CNNs) applied to Mel spectrograms, which serve as visual representations of audio signals. Six CNN architectures (VGG16, VGG19, ResNet50, DenseNet121, MobileNetV2, and EfficientNetB0) were evaluated using the FakeAVCelebV2 dataset, considering metrics such as precision, recall, F1-score, and accuracy. To provide better insight into model decisions, Grad-CAM, an Explainable Artificial Intelligence (XAI) technique, was employed to highlight the most relevant regions of the spectrogram for distinguishing between real and fake audio. The study also tested the model’s performance under conditions with added Gaussian and white noise to assess its robustness. The results confirm that CNN-based Mel spectrogram analysis is an effective method for audio deepfake detection, and they underline the importance of interpretability to ensure trustworthy media detection systems
Efficient k-NN query over encrypted data in cloud with limited key-disclosure and offline data owner
No full textSeveral schemes for k-nearest neighbors (k-NN) query over encrypted data in cloud have been proposed recently. Nevertheless, existing schemes either suppose each query user is fully-trusted, or need data owner to be online for each query. A fully-trusted query user is assumed to obtain the decryption key of data owner's outsourced dataset, thus, cloud server could entirely break the outsourced dataset upon gaining the decryption key from some untrustworthy query user. Because of the online requirement, data owner still needs to burden too many computational tasks during the k-NN queries, which thus is impractical. In this paper, we propose a new scheme to perform k-NN query over encrypted data in cloud while protecting the privacy of both data owner and query users from cloud. Our new method just reveals limited information about data owner's key to query users, and has no need of an online data owner. For gaining the properties, we present a new scalar product protocol, then the new protocol and some other transformation approaches are merged into our secure k-NN query system. Additionally, we confirm our security and efficiency through theoretical analysis and extensive simulation experiments
Modeling Security Requirements for Cloud-based System Development
No full texthe Cloud Computing paradigm provides a new model for the more flexible utilization of computing and storage services. However, such enhanced flexibility, which implies outsourcing the data and business applications to a third party, may introduce critical security issues. Therefore, there is a clear necessity of new security paradigms able to face all the problems introduced by the cloud approach. Although, in the last years, several solutions have been proposed, the implementation of secure cloud applications and services is still a complex and far from consolidated task. Starting from these considerations, this work fosters the development of a methodology that considers security concerns as an integral part of cloud-based applications design and implementation. Accordingly, we present a set of stereotypes that defines a vocabulary for annotating Unified Modeling Language based models with information relevant for integrating the specification of security requirements into cloud architectures. This approach can be used to significantly improve productivity and overall success in the development of secure distributed cloud applications and systems
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