Mobility on demand (MoD) systems show great promise in realizing flexible and efficient urban transportation. However, significant technical challenges arise from operational decision making associated with MoD vehicle dispatch and fleet rebalancing. For this reason, operators tend to employ simplified algorithms that have been demonstrated to work well in a particular setting. To help bridge the gap between novel and existing methods, we propose a modular framework for fleet rebalancing based on model-free reinforcement learning (RL) that can leverage an existing dispatch method to minimize system cost. In particular, by treating dispatch as part of the environment dynamics, a centralized agent can learn to intermittently direct the dispatcher to reposition free vehicles and mitigate against fleet imbalance. We formulate RL state and action spaces as distributions over a grid partitioning of the operating area, making the framework scalable and avoiding the complexities associated with multiagent RL. Numerical experiments, using real-world trip and network data, demonstrate that this approach has several distinct advantages over baseline methods including: improved system cost; high degree of adaptability to the selected dispatch method; and the ability to perform scale-invariant transfer learning between problem instances with similar vehicle and request distributions.

Sound event detection is an important facet of audio tagging that aims to identify sounds of interest and define both the sound category and time boundaries for each sound event in a continuous recording. With advances in deep neural networks, there has been tremendous improvement in the performance of sound event detection systems, although at the expense of costly data collection and labeling efforts. In fact, current state-of-the-art methods employ supervised training methods that leverage large amounts of data samples and corresponding labels in order to facilitate identification of sound category and time stamps of events. As an alternative, the current study proposes a semi-supervised method for generating pseudo-labels from unsupervised data using a student-teacher scheme that balances self-training and cross-training. Additionally, this paper explores post-processing which extracts sound intervals from network prediction, for further improvement in sound event detection performance. The proposed approach is evaluated on sound event detection task for the DCASE2020 challenge. The results of these methods on both "validation" and "public evaluation" sets of DESED database show significant improvement compared to the state-of-the art systems in semi-supervised learning.

Russia is developing an array of autonomous weapons platforms utilizing artificial intelligence as part of an ambitious push supported by high-tech cooperation with neighboring China. The extent to which Russia has prioritized AI in modernizing its military was featured in a report entitled "Artificial Intelligence and Autonomy in Russia," which was published Monday by the CNA nonprofit research and analysis group located in Arlington, Virginia. The report's authors worked closely with the Pentagon's Joint Artificial Intelligence Center to produce what the organization called "the first major piece of US research that articulates contemporary Russia's main initiatives, achievements, and accomplishments in AI and autonomy efforts and places those initiatives within the broader technological landscape in Russia." "Russian military strategists have placed a premium on establishing what they refer to as'information dominance on the battlefield,'" the report stated, "and AI-enhanced technologies promise to take advantage of the data available on the modern battlefield to protect Russia's own forces and deny that advantage to the adversary." While there are significant challenges and some reservations toward ceding critical decision-making capabilities to artificial intelligence and away from human minds, trends clearly signal that Russian efforts to introduce these advanced capabilities are well underway.

Federal agencies are getting better at buying advanced technologies like artificial intelligence but still lag in deploying those tools due to security concerns, according to a Homeland Security Department procurement official. "We're doing a really good job at procuring things," Jessica Clark, an official on the Acquisition Systems Team in DHS's Office of the Chief Procurement Officer, said Tuesday during the Professional Services Council's annual Tech Trends conference. "But getting it up and running safely is always going to be an issue for our program managers." Clark said DHS takes a different procurement strategy when looking at new and innovative technologies, preferring a phased approach where a relatively large pool of vendors is whittled down over the course of multiple prototypes and demonstrations, with each subsequent phase using larger datasets that are more and more relevant to the program. She cited the department's work integrating AI into the Contractor Performance Assessment Reporting System, or CPARS, which contracting officers use to gauge a vendor's past performance on government contracts. For the CPARS AI effort, DHS started with nine vendors, which was then down-selected to six and then four.

As NASA attempts to return to the moon in 2024, the U.S. space agency has tasked Lockheed Martin and General Motors to create a new electric, autonomous lunar rover. The rover will use GM's autonomous driving technology and allow it to go'significantly farther' than the ones the auto maker worked on during the Apollo program, some 50 years ago. Though the rover is still in the planning stages, both companies highlighted that it is imperative it allows astronauts to traverse difficult terrains of the lunar south pole, which could hold a number of interesting discoveries, including water. A concept of what the Lockheed Martin-GM rover might look like on the moon's south pole The lunar south pole is a site of interest for scientists and agencies planning crewed missions to the Moon. This is because water ice has been found in shadowed areas in that region with craters that never get sunlight.

SpaceX successfully launches NASA astronauts from Kennedy Space Center into space. NASA's ambitious lunar program Artemis will send the agency's first mobile robot to the moon in late 2023. The Volatiles Investigating Polar Exploration Rover, also known as VIPER, would search the planet for ice and other resources on and below its surface that could potentially be harvested for long-term exploration in the future. WHO GETS TO BE AN ASTRONAUT DURING THE PRIVATE SPACEFLIGHT BOOM? Using the first-ever headlights on a lunar rover, VIPER will explore the lunar South Pole and "permanently" dark regions of the moon – some of the coldest areas in the solar system.

Previously, computing power was centralized in the cloud or an on-premises data center. But many enterprise tasks require a decentralized model, where capabilities are brought closer to the devices and users that need these resources. This need for low latency, data-rich digital capabilities is moving compute to the edge of the network. Computing power is distributed at the edge, fueling growth in data-driven intelligence among burgeoning numbers of Internet of Things (IoT) devices. One downside to the rising popularity of edge computing is increasing infrastructure complexity.

During the past few months, Microsoft Exchange servers have been like chum in a shark-feeding frenzy. Threat actors have attacked critical zero-day flaws in the email software: an unrelenting cyber campaign that the US government has described as "widespread domestic and international exploitation" that could affect hundreds of thousands of people worldwide. Gaining visibility into an issue like this requires a full understanding of all assets connected to a company's network. This type of continuous tracking of inventory doesn't scale with how humans work, but machines can handle it easily. For business executives with multiple, post-pandemic priorities, the time is now to start prioritizing security. "It's pretty much impossible these days to run almost any size company where if your IT goes down, your company is still able to run," observes Matt Kraning, chief technology officer and co-founder of Cortex Xpanse, an attack surface management software vendor recently acquired by Palo Alto Networks. You might ask why companies don't simply patch their systems and make these problems disappear. If only it were that simple. Unless businesses have implemented a way to find and keep track of their assets, that supposedly simple question is a head-scratcher. But businesses have a tough time answering what seems like a straightforward question: namely, how many routers, servers, or assets do they have? If cybersecurity executives don't know the answer, it's impossible to then convey an accurate level of vulnerability to the board of directors. And if the board doesn't understand the risk--and is blindsided by something even worse than the Exchange Server and 2020 SolarWinds attacks--well, the story almost writes itself. That's why Kraning thinks it's so important to create a minimum set of standards.

Stopping ransomware has become a priority for many organizations. So, they are turning to artificial intelligence (AI) and machine learning (ML) as their defenses of choice. However, threat actors are also turning to AI and ML to launch their attacks. One specific type of attack, data poisoning, takes advantage of this. Like any other tech, AI is a two-sided coin.

ACUS issues a statement to help agencies make more informed decisions about artificial intelligence. Federal agencies increasingly rely on artificial intelligence (AI) tools to do their work and carry out their missions. Nearly half the federal agencies surveyed for a recent report commissioned by the Administrative Conference of the United States (ACUS) employ or have experimented with AI tools. The agencies used AI tools across an array of governance tasks, including adjudication, enforcement, data collection and analysis, internal management, and public communications. Agencies' interest in AI tools is not surprising.