With the Taliban having now taken over Afghanistan, there are growing concerns about how it might use the data from the huge biometrics programme that has been left behind. An extensive database of people in Afghanistan was built up during the previous regime, but the quick transition has meant much of it remains in tact. The US first established a programme to collect the fingerprints, iris scans and facial images of Afghan national security forces after testing prototypes of the system in 2002. The programme's initial goal was to keep criminals and Taliban insurgents from infiltrating the army and police force. To collect and store this data, the US Department of Defense launched its Automated Biometric Identification System (ABIS) in 2004.

An incredible £36,000 ($50,000) accessory, the'CyberLandr', has been unveiled for the Tesla Cybertruck that turns it into a camper complete with shower, cooker, toilet and seats that transform into a double bed. There's even a sink with a voice-controlled tap. Anti-Vaxxer says mandate is'psychological warfare' Karl holds his tongue on the Sussexes' statement about Afghanistan Taliban's special forces unit Badri-313 seen manning the streets NASA's Curiosity rover explores mountain in crater on Mars Japan's new crescent-shaped Island created by volcanic eruption Tesla CEO Elon Musk unveiled the Cybertruck for the first time in November 2019 and famously managed to shatter two of the vehicle's'bulletproof' windows during the launch event. The prototype hit the streets of Manhattan in May of this year, leading hundreds of New Yorkers to line up outside the Tesla showroom to catch a rare glimpse of it. In July, Musk revealed production is now being delayed to sometime in 2022, over battery shortages.

But the debates between Facebook and the White House throughout the spring and into summer also gave rise to a broader and still unresolved disagreement over what constitutes misinformation, according to the person familiar with Facebook's thinking. Facebook strongly believes people should have the right to broadly express themselves without censorship on social platforms, and had reviewed research that shows that friends and family can often be more effective at countering misinformation than official sources that people distrust. The Facebook executives thought the Biden camp was going too far, by identifying specific pieces of content as problematic and asking it to potentially suppress valuable conversations where people express fears and skepticism.

Health and human services (HHS) agencies often struggle to serve some of society's most needy populations. At many HHS agencies today, tight budgets limit the size of the workforce, even as the volume of caseloads continues to grow. That imbalance makes it hard to provide efficient and effective solutions to address the critical needs of individuals and families, and can leave employees feeling stressed and overworked. Those same employees may also see few opportunities for career development or advancement. High rates of turnover can put a steady stream of inexperienced staff into critical jobs with little training to prepare them.

The ongoing recalibration of AI research and development underscores a fundamental tenet of machine learning: We must learn to crawl before we can walk. Thus far, AI hype has mostly talked the talk rather than walking the walk. Returning to what appear to be engineering first principles, U.S. research efforts are attempting to move beyond current "brittle" AI models that excel at only specific tasks. The goal is developing more generalized models that can adapt much like humans do in new situations. Among those efforts is a Machine Common Sense program overseen by the Defense Advanced Research Projects Agency (DARPA) that seeks to imbue machine learning models with the kinds of commonplace reasoning displayed by among the fastest learners on the planet: infants.

The Afghan Girls Robotics Team works on their robot at a 2017 competition in Washington. The Afghan Girls Robotics Team works on their robot at a 2017 competition in Washington. The Afghan Girls Robotics Team made headlines in 2017 when they came to Washington for an international competition just a few blocks from the White House. Most members of the team were born after the Taliban were ousted from power in 2001, symbolizing a new Afghanistan where girls were free to go to school and women were getting at least some opportunities that had been long denied. But with the Taliban back, the future of these girls -- some of them now young women -- has turned precarious.

However, the evolution of mainstream transportation systems towards a connected cyber infrastructure, such as connected traffic signal controllers, is increasing system vulnerability to potential cyber attack, allowing malicious actors (individuals, criminals, or terrorist organizations) to exploit security vulnerabilities of such transportation infrastructure (1)-(3). In the U.S., the number of cyberattacks on smart mobility systems has jumped significantly in recent years (4). As vehicles are moving towards connected and automated driving, and cities are focusing on creating a transportation cyber infrastructure that will transform legacy transportation infrastructure to connected, adaptable, and automated systems, the security problems will only increase and further compromise public safety (5). Many studies show that a cyber attack on connected vehicle-based (CV-based) traffic signal control algorithms can break down a traffic network by creating severe congestion (6-10). An adaptive traffic signal controller (ATSC) combined with a connected vehicle (CV) concept uses real-time vehicle trajectory data to regulate green time; this combination also has the ability to reduce intersection waiting time significantly and improve travel time in a signalized corridor (11). A CV-based ATSC can be manipulated in two ways: (i) gain access through vulnerabilities and exploit the ATSC; and (ii) produce abnormal behavior through the manipulation of inputs of vehicle-related data (9).

A resilient and robust positioning, navigation, and timing (PNT) system is a necessity for the navigation of autonomous vehicles (AVs). Global Navigation Satelite System (GNSS) provides satellite-based PNT services. However, a spoofer can temper an authentic GNSS signal and could transmit wrong position information to an AV. Therefore, a GNSS must have the capability of real-time detection and feedback-correction of spoofing attacks related to PNT receivers, whereby it will help the end-user (autonomous vehicle in this case) to navigate safely if it falls into any compromises. This paper aims to develop a deep reinforcement learning (RL)-based turn-by-turn spoofing attack detection using low-cost in-vehicle sensor data. We have utilized Honda Driving Dataset to create attack and non-attack datasets, develop a deep RL model, and evaluate the performance of the RL-based attack detection model. We find that the accuracy of the RL model ranges from 99.99% to 100%, and the recall value is 100%. However, the precision ranges from 93.44% to 100%, and the f1 score ranges from 96.61% to 100%. Overall, the analyses reveal that the RL model is effective in turn-by-turn spoofing attack detection.

This paper presents a sensor fusion based Global Navigation Satellite System (GNSS) spoofing attack detection framework for autonomous vehicles (AV) that consists of two concurrent strategies: (i) detection of vehicle state using predicted location shift -- i.e., distance traveled between two consecutive timestamps -- and monitoring of vehicle motion state -- i.e., standstill/ in motion; and (ii) detection and classification of turns (i.e., left or right). Data from multiple low-cost in-vehicle sensors (i.e., accelerometer, steering angle sensor, speed sensor, and GNSS) are fused and fed into a recurrent neural network model, which is a long short-term memory (LSTM) network for predicting the location shift, i.e., the distance that an AV travels between two consecutive timestamps. This location shift is then compared with the GNSS-based location shift to detect an attack. We have then combined k-Nearest Neighbors (k-NN) and Dynamic Time Warping (DTW) algorithms to detect and classify left and right turns using data from the steering angle sensor. To prove the efficacy of the sensor fusion-based attack detection framework, attack datasets are created for four unique and sophisticated spoofing attacks-turn-by-turn, overshoot, wrong turn, and stop, using the publicly available real-world Honda Research Institute Driving Dataset (HDD). Our analysis reveals that the sensor fusion-based detection framework successfully detects all four types of spoofing attacks within the required computational latency threshold.

Autonomous spacecraft relative navigation technology has been planned for and applied to many famous space missions. The development of on-board electronics systems has enabled the use of vision-based and LiDAR-based methods to achieve better performances. Meanwhile, deep learning has reached great success in different areas, especially in computer vision, which has also attracted the attention of space researchers. However, spacecraft navigation differs from ground tasks due to high reliability requirements but lack of large datasets. This survey aims to systematically investigate the current deep learning-based autonomous spacecraft relative navigation methods, focusing on concrete orbital applications such as spacecraft rendezvous and landing on small bodies or the Moon. The fundamental characteristics, primary motivations, and contributions of deep learning-based relative navigation algorithms are first summarised from three perspectives of spacecraft rendezvous, asteroid exploration, and terrain navigation. Furthermore, popular visual tracking benchmarks and their respective properties are compared and summarised. Finally, potential applications are discussed, along with expected impediments.