Security News This Week: The Cybersecurity Apocalypse Is Coming in'Months,' AI Giants Warn Plus: Hackers target over 100 US water systems, ICE puts in an order for robot dogs, and you'll never guess what "MrChildPorn" was arrested for. You may have noticed that Flock Safety's automatic license plate reader cameras--and the cops who misuse them--are getting a lot of coverage lately. This week, WIRED found a particularly wild case: A cop in Alpharetta, Georgia, was accused of searching for the license plate of a coworker dozens of times after an affair between the two ended, according to internal documents obtained by WIRED. The same police department where the former lovers worked also shared the data captured from its Flock cameras with more than 2,000 police departments, colleges, and other organizations across the United States, and accessed data from more than 1,300 entities in exchange. Also at the intersection of love and surveillance, background-check company PeopleFinder is making use of its extensive dossiers on people to start a new dating site called Stud or Dud.

Alpharetta, Georgia, cops share data with thousands of Flock users, ranging from federal agencies to a fish and wildlife commission. The reasons why show how vast--and invasive--the network has become. Alpharetta, a prosperous suburb of Atlanta, Georgia, is home to about 67,000 people, served by about 120 local police officers. It also hosts several dozen cameras sold by Flock Safety, the increasingly controversial surveillance company that collects license plate data and other information and makes it searchable by police. WIRED has made this article free for all to read because it is primarily based on reporting from public records requests. Please consider subscribing to support our journalism.

After an affair with a fellow police officer ended, a Georgia cop used Flock to track her movements--and those of a man whose vehicle often showed up near hers, internal investigation records show. In recent months, there has been a surge in reports of police officers misusing automatic license plate readers (ALPRs) sold by Flock Safety to stalk former romantic partners--but details about these incidents are often scarce. Now, with documents obtained via a public records request, WIRED can reveal specific details about one extensive Flock-fueled surveillance campaign and the subsequent investigation and fallout. Dustin Bozzo, who until this month worked as a patrolman in the Atlanta-area suburb of Alpharetta, Georgia, allegedly used Flock 56 times between March and May to search for the license plate of a former romantic partner--a fellow member of the Alpharetta Police Department. When Bozzo noticed that his ex's car was often near that of another police officer, he began searching for that officer's vehicle as well.

Terahertz (THz) space communications (Tera-SpaceCom) is envisioned as a promising technology to enable various space science and communication applications. Mainly, the realm of Tera-SpaceCom consists of THz sensing for space exploration, data centers in space providing cloud services for space exploration tasks, and a low earth orbit (LEO) mega-constellation relaying these tasks to ground stations (GSs) or data centers via THz links. Moreover, to reduce the computational burden on data centers as well as resource consumption and latency in the relaying process, the LEO mega-constellation provides satellite edge computing (SEC) services to directly compute space exploration tasks without relaying these tasks to data centers. The LEO satellites that receive space exploration tasks offload (i.e., distribute) partial tasks to their neighboring LEO satellites, to further reduce their computational burden. However, efficient joint communication resource allocation and computing task offloading for the Tera-SpaceCom SEC network is an NP-hard mixed-integer nonlinear programming problem (MINLP), due to the discrete nature of space exploration tasks and sub-arrays as well as the continuous nature of transmit power. To tackle this challenge, a graph neural network (GNN)-deep reinforcement learning (DRL)-based joint resource allocation and task offloading (GRANT) algorithm is proposed with the target of long-term resource efficiency (RE). Particularly, GNNs learn relationships among different satellites from their connectivity information. Furthermore, multi-agent and multi-task mechanisms cooperatively train task offloading and resource allocation. Compared with benchmark solutions, GRANT not only achieves the highest RE with relatively low latency, but realizes the fewest trainable parameters and the shortest running time.

The market for domestic robots made to perform household chores is growing as these robots relieve people of everyday responsibilities. Domestic robots are generally welcomed for their role in easing human labor, in contrast to industrial robots, which are frequently criticized for displacing human workers. But before these robots can carry out domestic chores, they need to become proficient in several minor activities, such as recognizing their surroundings, making decisions, and picking up on human behaviors. Reinforcement learning, or RL, has emerged as a key robotics technology that enables robots to interact with their environment and learn how to optimize their actions to maximize rewards. However, the goal of Deep Reinforcement Learning is to address more complicated, continuous action-state spaces in real-world settings by combining RL with Neural Networks. The efficacy of DeepRL can be further augmented through interactive feedback, in which a trainer offers real-time guidance to expedite the robot's learning process. Nevertheless, the current methods have drawbacks, namely the transient application of guidance that results in repeated learning under identical conditions. Therefore, we present a novel method to preserve and reuse information and advice via Deep Interactive Reinforcement Learning, which utilizes a persistent rule-based system. This method not only expedites the training process but also lessens the number of repetitions that instructors will have to carry out. This study has the potential to advance the development of household robots and improve their effectiveness and efficiency as learners.

Expensive ultrasonic anemometers are usually required to measure wind speed accurately. The aim of this work is to overcome the loss of accuracy of a low cost hot-wire anemometer caused by the changes of air temperature, by means of a probabilistic calibration using Gaussian Process Regression. Gaussian Process Regression is a non-parametric, Bayesian, and supervised learning method designed to make predictions of an unknown target variable as a function of one or more known input variables. Our approach is validated against real datasets, obtaining a good performance in inferring the actual wind speed values. By performing, before its real use in the field, a calibration of the hot-wire anemometer taking into account air temperature, permits that the wind speed can be estimated for the typical range of ambient temperatures, including a grounded uncertainty estimation for each speed measure.

MotorDNA seeks to close the data gap between the Insurers, Consumers, Auto Lenders, Fleet Managers, Regulators, and OEMs with a platform to encourage the production and demand for safer vehicles. The US Automobile Insurance industry, valued at greater than $300B, is modernizing and transforming its products to mirror the transformation in the mobility space. Automobile technology is rapidly advancing, and vehicles are getting smarter. Safety features, sometimes referred to as ADAS (Advanced Driver Assistance Systems) and their effectiveness are progressing quickly on the journey to autonomous driving. However, vehicle build data is neither readily available in the market nor has it been organized to make it actionable for insurers to create products and pricing based on accurately assessing the risk of each vehicle.

MotorDNA seeks to close the data gap between the Insurers, Consumers, Auto Lenders, Fleet Managers, Regulators, and OEMs with a platform to encourage the production and demand for safer vehicles. The US Automobile Insurance industry, valued at greater than $300B, is modernizing and transforming its products to mirror the transformation in the mobility space. Automobile technology is rapidly advancing, and vehicles are getting smarter. Safety features, sometimes referred to as ADAS (Advanced Driver Assistance Systems) and their effectiveness are progressing quickly on the journey to autonomous driving. However, vehicle build data is neither readily available in the market nor has it been organized to make it actionable for insurers to create products and pricing based on accurately assessing the risk of each vehicle.

The use of artificial intelligence and machine learning in bank fraud analytics is continuing to move from reactively mitigating fraud that's already occurred to preventing fraudulent activities from actually happening--but in ways that try not to block legitimate customer transactions. As anti-fraud technology has become more advanced and scalable, some banks are now investing in a cross-product, omnichannel view of customer behavior, says Philippe Guiral, who leads Accenture's North America fraud and financial crime practice. This means leveraging customer data across domains within the organization to gain more insights of customer behavior to better assess whether any particular transaction is suspicious. A growing number of banks are now building cases to show these solutions can not only improve fraud prevention rates, but also enhance the customer experience and be applied across additional functions--including financial crime, 'Know Your Customer,' risk and customer intelligence--to uncover hidden risks and discover new opportunities, he says. Indeed, it's critical to have a strong fraud analytics solution that can give banks a comprehensive view of a customer's identity and real-time insights into application activity, says Kimberly White, senior director of fraud & identity at LexisNexis Risk Solutions in Alpharetta, Georgia.

As data accessibility and analysis capabilities have rapidly advanced in recent years, new digital platforms driven by artificial intelligence (AI) and machine learning (ML) are increasingly finding practical applications in industry. "Data are so readily available now. Several years ago, we didn't have the manipulation capability, the broad platform or cloud capacity to really work with large volumes of data. We've got that now, so that has been huge in making AI more practical," says Paige Morse, industry marketing director for chemicals at Aspen Technology, Inc. (Bedford, Mass.; www.aspentech.com). While AI and ML have been part of the digitalization discussion for many years, these technologies have not seen a great deal of practical application in the chemical process industries (CPI) until relatively recently, says Don Mack, global alliance manager at Siemens Industry, Inc. (Alpharetta, Ga.; www.industry.usa.siemens.com). "In order for AI to work correctly, it needs data. Control systems and historians in chemical plants have a lot of data available, but in many cases, those data have just been sitting dormant, not really being put to good use. However, new digitalization tools enable us to address some use cases for AI that until recently just weren't possible." This convergence of technologies, from smart sensors to high-performance computing and cloud storage, along with advances in data science, deep learning and access to free and open-source software, have enabled the field of industrial AI to move beyond pure research to practical applications with business benefits, says Samvith Rao, chemical and petroleum industry manager at MathWorks (Natick, Mass.; www.mathworks.com).