In the last years, Cyber Ranges have become a widespread solution to train professionals for responding to cyber threats and attacks. Cloud computing plays a key role in this context since it enables the creation of virtual infrastructures on which Cyber Ranges are based. However, the setup and management of Cyber Ranges are expensive and time-consuming activities. In this paper, we highlight the novel features for the next-generation Cyber Range platforms. In particular, these features include the creation of a virtual clone for an actual corporate infrastructure, relieving the security managers from the setup of the training scenarios and sessions, the automatic monitoring of the participants' activities, and the emulation of their behavior.

The Morrison Government is investing $12 million to strengthen partnerships between the technology sector and regional Australians to solve uniquely Australian challenges. The new Catalysing the Artificial Intelligence Opportunity in Our Regions program will provide competitive grant funding over three rounds to regional organisations for artificial intelligence (AI) projects that deliver benefits to regional industries, businesses and communities. The program is part of the Government's $124.1 million investment under the AI Action Plan, which sets out a vision for Australia to become a global leader in developing and adopting trusted, secure and responsible artificial intelligence. Under round one, grants of between $250,000 and $500,000 are available. Minister for Science and Technology Melissa Price said the program would showcase the opportunities for AI to boost regional capabilities, grow trust in this technology and create a pathway for high-skilled jobs in regional Australia.

What AI and data analytics trends are taking the industry by storm this year? This comprehensive review highlights upcoming directions in AI to carefully watch and consider implementing in your personal work or organization.

China's spaceplanes are so advanced that they may not need launch sites and can take off and land at airports. The test flight of Tengyun was carried out in July by its developer, state-owned China Aerospace Science and Technology Corporation (CASC). So advanced is its technology of Horizontal Take-off and Horizontal Landing (HTHL) that it edges out its U.S. equivalent of X-37B Orbital Test Vehicle (OTV), which is rocket-launched, reported South China Morning Post. The report, quoting the Chinese military magazine Naval and Merchant Ships, said this cost-saving development has added to concerns over the weaponization of space in the era of hypersonic missiles. "Chinese spaceplane technology was inspired by the US X-37B, but the American OTV still needs to be launched by rocket, while China has now overcome this limitation," magazine editor-in-chief Su Ming was quoted by the news outlet.

Sergey Kiryushin, special representative for the digital transformation, told Moscow daily RBK that the ministry would be partnering with the Ivannikov Institute for System Programming to develop the concept next year. "Based on the results of the conceptual design, we will determine the financing of the program, but for now, this issue is under discussion," Kiryushin said. The official declined to disclose what kind of data the machine would be used to analyze, simply noting that there are many various tasks in foreign policy that such artificial intelligence could help with. This latest development is part of the Ministry of Foreign Affairs' wide-ranging digital transformation program. From 2021-2023, the department plans to spend 2.3 billion rubles ($31 million) to improve its information systems and become independent of foreign software.

For centuries, cities in the United States used an inexpensive, malleable, and leak-resistant material for constructing their water pipes: lead. Today, the health risks posed by lead pipes are well-known. Drinking lead-contaminated water can stunt children's development and cause heart and kidney problems among adults.¹ The Environmental Protection Agency (EPA) banned the use of lead pipes for new construction in 1986. Yet, today, lead services lines (the pipes that take water from city lines into individual homes) are still prevalent across the country.

GENEVA – Japan, the United States and other countries have blocked any advancement in U.N. talks toward legally binding measures to ban and regulate the development and use of lethal autonomous weapon systems. The Sixth Review Conference of the Convention on Certain Conventional Weapons ended Friday in Geneva without progress, failing to reflect eight years of work and leaving countries and nongovernmental organizations that have called for legally binding rules expressing disappointment. Also referred to as "killer robots," autonomous weapons are artificial intelligence-powered weapons using facial recognition and algorithms. Once activated, the weapons can select and attack targets without the assistance of a human operator. They pose ethical, legal and security risks.

In video reviews of the latest drone models to his 80,000 YouTube subscribers, Indiana college student Carson Miller doesn't seem like an unwitting tool of Chinese spies. Yet that's how the U.S. is increasingly viewing him and thousands of other Americans who purchase drones built by Shenzhen-based SZ DJI Technology Co., the world's top producer of unmanned aerial vehicles. Miller, who bought his first DJI model in 2016 for $500 and now owns six of them, shows why the company controls more than half of the U.S. drone market. "If tomorrow DJI were completely banned," the 21-year-old said, "I would be pretty frightened." Critics of DJI warn the dronemaker may be channeling reams of sensitive data to Chinese intelligence agencies on everything from critical infrastructure like bridges and dams to personal information such as heart rates and facial recognition.

Machine learning has recently emerged as a promising approach for studying complex phenomena characterized by rich datasets. In particular, data-centric approaches lend to the possibility of automatically discovering structures in experimental datasets that manual inspection may miss. Here, we introduce an interpretable unsupervised-supervised hybrid machine learning approach, the hybrid-correlation convolutional neural network (Hybrid-CCNN), and apply it to experimental data generated using a programmable quantum simulator based on Rydberg atom arrays. Specifically, we apply Hybrid-CCNN to analyze new quantum phases on square lattices with programmable interactions. The initial unsupervised dimensionality reduction and clustering stage first reveals five distinct quantum phase regions. In a second supervised stage, we refine these phase boundaries and characterize each phase by training fully interpretable CCNNs and extracting the relevant correlations for each phase. The characteristic spatial weightings and snippets of correlations specifically recognized in each phase capture quantum fluctuations in the striated phase and identify two previously undetected phases, the rhombic and boundary-ordered phases. These observations demonstrate that a combination of programmable quantum simulators with machine learning can be used as a powerful tool for detailed exploration of correlated quantum states of matter.

In today's era of digital misinformation, we are increasingly faced with new threats posed by video falsification techniques. Such falsifications range from cheapfakes (e.g., lookalikes or audio dubbing) to deepfakes (e.g., sophisticated AI media synthesis methods), which are becoming perceptually indistinguishable from real videos. To tackle this challenge, we propose a multi-modal semantic forensic approach to discover clues that go beyond detecting discrepancies in visual quality, thereby handling both simpler cheapfakes and visually persuasive deepfakes. In this work, our goal is to verify that the purported person seen in the video is indeed themselves by detecting anomalous correspondences between their facial movements and the words they are saying. We leverage the idea of attribution to learn person-specific biometric patterns that distinguish a given speaker from others. We use interpretable Action Units (AUs) to capture a persons' face and head movement as opposed to deep CNN visual features, and we are the first to use word-conditioned facial motion analysis. Unlike existing person-specific approaches, our method is also effective against attacks that focus on lip manipulation. We further demonstrate our method's effectiveness on a range of fakes not seen in training including those without video manipulation, that were not addressed in prior work.