The absence of the Kerr black hole in the Ho\v{r}ava-Lifshitz gravity

We show that the Kerr metric does not exist as a fully rotating black hole solution to the modified Ho\v{r}ava-Lifshitz (HL) gravity with $\Lambda_W=0$ and $\lambda=1$ case. We perform it by showing that the Kerr metric does not satisfy full equations derived from the modified HL gravity.Comment: 35 pages, no figure

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

A generative adversarial network (GAN) technique for internet of medical things data

The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the “Internet of Medical Things” (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. For this reason, data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models. In this work, we apply the concept of generative adversarial networks (GANs) to perform a data augmentation from patient data obtained through IoMT sensors for Chronic Obstructive Pulmonary Disease (COPD) monitoring. We also apply an explainable AI algorithm to demonstrate the accuracy of the synthetic data by comparing it to the real data recorded by the sensors. The results obtained demonstrate how synthetic datasets created through a well-structured GAN are comparable with a real dataset, as validated by a novel approach based on machine learning

Características del ajuste de la economía mexicana

Incluye BibliografíaAnaliza el programa de ajuste que se instrumentó a partir de la crisis de 1982, así como la evolución de la economía mexicana en su intento por corregir los graves desequilibrios que caracterizaron su modelo de crecimiento en los últimos 15 años

An on-line intrusion detection approach to identify low-rate DoS attacks

This paper addresses the problem of detection of \u201cSlow\u201d Denial of Service attacks. The problem is particularly challenging in virtue of the reduced amount of bandwidth generated by the attacks. A novel detection method is presented, which analyzes specific spectral features of traffic over small time horizons. No packet inspection is required. Extrapolated data refer to real traffic traces, elaborated over the Local Area Network of our Institute. Different kinds of attacks have been considered as well. The results show how the proposed method is reliable and applicable in many other contexts