A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things.

Recent advances in hardware and information technology have accelerated the proliferation of smart and interconnected devices facilitating the rapid development of the Internet of Things (IoT). IoT applications and services are widely adopted in environments such as smart cities, smart industry, autonomous vehicles, and eHealth. As such, IoT devices are ubiquitously connected, transferring sensitive and personal data without requiring human interaction.

Transfer-Learning-Based Intrusion Detection Framework in IoT Networks.

Cyberattacks in the Internet of Things (IoT) are growing exponentially, especially zero-day attacks mostly driven by security weaknesses on IoT networks. Traditional intrusion detection systems (IDSs) adopted machine learning (ML), especially deep Learning (DL), to improve the detection of cyberattacks. DL-based IDSs require balanced datasets with large amounts of labeled data; however, there is a lack of such large collections in IoT networks.

Characterizing the spectrum of right ventricular remodelling in response to chronic training.

The significance and spectrum of reduced right ventricular (RV) deformation, reported in endurance athletes, is unclear. To comprehensively analyze the cardiac performance at rest of athletes, especially focusing on integrating RV size and deformation to unravel the underlying triggers of this ventricular remodelling. Hundred professional male athletes and 50 sedentary healthy males of similar age were prospectively studied.

Differentiating hypertrophic cardiomyopathy from athlete's heart: An electrocardiographic and echocardiographic approach.

Differential diagnosis of hypertrophic cardiomyopathy (HCM) vs athlete's heart is challenging in individuals with mild-moderate left-ventricular hypertrophy. This study aimed to assess ECG and echocardiographic parameters proposed for the differential diagnosis of HCM. The study included 75 men in three groups: control (n=30), "gray zone" athletes with interventricular septum (IVS) measuring 13-15mm (n=25) and HCM patients with IVS of 13-18mm (n=20).

No surviving evolved companions of the progenitor of SN 1006.

Type Ia supernovae are thought to occur when a white dwarf made of carbon and oxygen accretes sufficient mass to trigger a thermonuclear explosion. The accretion could be slow, from an unevolved (main-sequence) or evolved (subgiant or giant) star (the single-degenerate channel), or rapid, as the primary star breaks up a smaller orbiting white dwarf (the double-degenerate channel). A companion star will survive the explosion only in the single-degenerate channel.

Normative data for the Boston Naming Test and the Pyramids and Palm Trees Test in the elderly Spanish population.

The objective of this study was to obtain normative data for naming and semantic memory (SM) tests in the elderly Spanish population. A total of 121 Spanish-speaking senior citizens were assessed with the Boston Naming Test (BNT). Of these, 79 were assessed with the Semantic Fluency Test (Sem-Flu) and 72 with the Pyramids and Palm Trees Test (PPT).

The binary progenitor of Tycho Brahe's 1572 supernova.

The brightness of type Ia supernovae, and their homogeneity as a class, makes them powerful tools in cosmology, yet little is known about the progenitor systems of these explosions. They are thought to arise when a white dwarf accretes matter from a companion star, is compressed and undergoes a thermonuclear explosion. Unless the companion star is another white dwarf (in which case it should be destroyed by the mass-transfer process itself), it should survive and show distinguishing properties.