A facility to Search for Hidden Particles (SHiP) at the CERN SPS

A new general purpose fixed target facility is proposed at the CERN SPS accelerator which is aimed at exploring the domain of hidden particles and make measurements with tau neutrinos. Hidden particles are predicted by a large number of models beyond the Standard Model. The high intensity of the SPS 400~GeV beam allows probing a wide variety of models containing light long-lived exotic particles with masses below ${\cal O}$(10)~GeV/c$^2$, including very weakly interacting low-energy SUSY states. The experimental programme of the proposed facility is capable of being extended in the future, e.g. to include direct searches for Dark Matter and Lepton Flavour Violation

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

A novel approach to provenance management for privacy preservation

Can, Ozgu/0000-0002-8064-2905WOS: 000513714100001Provenance determines the origin of the data by tracing and recording the actions that are performed on the data. Therefore, provenance is used in many fields to ensure the reliability and quality of data. in this work, provenance information is used to meet the security needs in information systems. For this purpose, a domain-independent provenance model is proposed. the proposed provenance model is based on the Open Provenance Model and Semantic Web technologies. the goal of the proposed provenance model is to integrate the provenance and security concepts in order to detect privacy violations by querying the provenance data. in order to evaluate the proposed provenance model, we illustrated our domain-independent model by integrating it with an infectious disease domain and implemented the Healthcare Provenance Information System

ProvVacT: A Provenance Based mHealth Application for Tracking Vaccine History

45th Annual International IEEE-Computer-Society Computers, Software, and Applications Conference (COMPSAC) -- JUL 12-16, 2021 -- ELECTR NETWORKmHealth is the use of mobile devices and communication technologies for healthcare researches and practices. The widespread use of smartphones, the growth of mobile application development and the increasing demands of users have promoted the development, and usage of mHealth applications. In this work, a mHealth application called as ProvVacT is developed for tracking vaccination history. The main contribution of ProvVacT is that the application uses provenance information in order to trace the vaccination history. Provenance is used to refer to the origin of the information. Thus, it provides the trustworthiness of data. In this work, the ProvVacT application is based on an ontology-based provenance management approach in order to improve the quality, reliability, and reusability of vaccination data. Moreover, the vaccine information is also represented with an ontology-based approach.IEEE,IEEE Comp So

Web Based Anomaly Detection Using Zero-Shot Learning With CNN

In recent years, attacks targeting websites have become a persistent threat. Therefore, web application security has become a significant issue. Dealing with unbalanced data is the biggest obstacle to providing security for web applications since there are fewer malicious requests despite a large number of benign requests. This paper suggests a novel Zero-Shot Learning method employing a Convolutional Neural Network (ZSL-CNN) to address unbalanced data problem and high false positive rates. This approach uses only benign data during training while predicting unseen malicious requests. Five web request datasets are used for validation on a diverse set of samples. The first dataset is a novel dataset containing Internet banking web request logs provided by Yapı Kredi Teknoloji. Other datasets are (i) an open-source WAF dataset, (ii) CSIC 2010 HTTP dataset, (iii) HTTP Params 2015 dataset, and (iv) a hybrid dataset. URIs are extracted from these datasets and fed to the ZSL-CNN model after code embedding. The same datasets are tested using other well-known models such as Isolation Forest, Autoencoder, Denoising Autoencoder with Dropout, and One-Class SVM. As per the comparison of the outcomes, it is seen that true positive rate of ZSL-CNN model is the greatest, reaching 99.29%