Current research in Electrocardiogram (ECG) biometrics mainly emphasizes resting - state conditions, leaving the performance decline in rest - exercise scenarios largely unresolved. This paper introduces CrossStateECG, a robust ECG - based authentication model e xplicitly tailored for cross - state (rest - exercise) conditions. The proposed model creatively combines multi - scale d eep c onvolu-tional feature extraction with attention mechanisms to ensure strong identification across different physiological states. Experim ental results on the exercise - ECGID dataset validate the effectiveness of CrossStateECG, achieving an identification accuracy of 92.50% in the Rest - to - Exercise scenario (training on resting ECG and testing on post - exercis e ECG) and 94.72% in the Exercise - t o - Rest scenario (training on post - exercis e ECG and testing on rest ing ECG). Furthermore, CrossStateECG demonstrates exceptional performance across both state combinations, reaching an accuracy of 99.94% in Rest - to - Rest scenarios and 97.85% in Mixed - to - Mixed scenarios. Additional validations on the ECG - ID and MIT - BIH datasets further confirmed the generalization abilities of CrossStateECG, underscoring it s potential as a practical solution for post - exercise ECG - based authentication in dynamic real - world settings.

Almost any organization experiences one of the main problems with the personnel policy - personnel discipline. It is particularly relevant for large companies. Here are several unpleasant consequences that this problem leads to. First of all, these are quite tangible hidden costs due to insufficient production of goods and services, foregone turnover, loss of important customers, and other losses from the irrational use of working time by employees. Let's say an employee takes 15-20 extra minutes every day to be late, protract a lunch break, have smoke breaks, go home early, and other things. Shouldn't be too hard to calculate that eventually it all takes an entire working day from one working month. In reality, people spend much more working time on personal needs: up to 35%. 

This paper deals with the identification of machines in a smart city environment. The concept of machine biometrics is proposed in this work for the first time, as a way to authenticate machine identities interacting with humans in everyday life. This definition is imposed in modern years where autonomous vehicles, social robots, etc. are considered active members of contemporary societies. In this context, the case of car identification from the engine behavioral biometrics is examined. For this purpose, 22 sound features were extracted and their discrimination capabilities were tested in combination with 9 different machine learning classifiers, towards identifying 5 car manufacturers. The experimental results revealed the ability of the proposed biometrics to identify cars with high accuracy up to 98% for the case of the Multilayer Perceptron (MLP) neural network model.

AR/VR startup Magic Leap's new patent application links facial recognition, identity authentication, and document verification and could signal the stealth company's interest in cybersecurity applications. Magic Leap – a much-hyped "mixed reality" startup that has raised almost $2B to date – has yet to release a product. But with investors like Google, Kleiner Perkins, Alibaba, and others betting big on Magic Leap's success, the company's mysterious ambitions have captivated interest across the tech sector. A new patent application surfaced in the CB Insights database is the latest clue to the stealth startup's strategy: The technology outlined in the patent could turn an augmented reality system into a tool for hyper-rapid identity verification using facial recognition. Get this data driven report on the patent activity for Google, Facebook, Microsoft, Amazon and more top tech companies.