Various works have utilized deep reinforcement learning (DRL) to address the query optimization problem in database system. They either learn to construct plans from scratch in a bottom-up manner or guide the plan generation behavior of traditional optimizer using hints. While these methods have achieved some success, they face challenges in either low training efficiency or limited plan search space. To address these challenges, we introduce FOSS, a novel DRL-based framework for query optimization. FOSS initiates optimization from the original plan generated by a traditional optimizer and incrementally refines suboptimal nodes of the plan through a sequence of actions. Additionally, we devise an asymmetric advantage model to evaluate the advantage between two plans. We integrate it with a traditional optimizer to form a simulated environment. Leveraging this simulated environment, FOSS can bootstrap itself to rapidly generate a large amount of high-quality simulated experiences. FOSS then learns and improves its optimization capability from these simulated experiences. We evaluate the performance of FOSS on Join Order Benchmark, TPC-DS, and Stack Overflow. The experimental results demonstrate that FOSS outperforms the state-of-the-art methods in terms of latency performance and optimization time. Compared to PostgreSQL, FOSS achieves savings ranging from 15% to 83% in total latency across different benchmarks.

The Universal Health Services attack this past month has brought renewed attention to the threat of ransomware faced by health systems – and what hospitals can do to protect themselves against a similar incident. Security experts say that the attack, beyond being one of the most significant ransomware incidents in healthcare history, may also be emblematic of the ways machine learning and artificial intelligence are being leveraged by bad actors. With some kinds of "early worms," said Greg Foss, senior cybersecurity strategist at VMware Carbon Black, "we saw [cybercriminals] performing these automated actions, and taking information from their environment and using it to spread and pivot automatically; identifying information of value; and using that to exfiltrate." The complexity of performing these actions in a new environment relies on "using AI and ML at its core," said Foss. Once access is gained to a system, he continued, much malware doesn't require much user interference.

Three cybersecurity experts explained how artificial intelligence and machine learning can be used to evade cybersecurity defenses and make breaches faster and more efficient during a NCSA and Nasdaq cybersecurity summit. Kevin Coleman, the executive director of the National Cyber Security Alliance, hosted the conversation as part of Usable Security: Effecting and Measuring Change in Human Behavior on Tuesday, Oct. 6. Elham Tabassi, chief of staff information technology laboratory, National Institute of Standards and Technology, was one of the panelists in the "Artificial Intelligence and Machine Learning for Cybersecurity: The Good, the Bad, and the Ugly" session.text "Attackers can use AI to evade detections, to hide where they can't be found, and automatically adapt to counter measures," Tabassi said. Tim Bandos, chief information security officer at Digital Guardian, said that cybersecurity will always need human minds to build strong defenses and stop attacks.

FOSS is an acronym for Free and Open Source Software. The FOSS 2013 survey primarily targets FOSS contributors and relevant anonymized dataset is publicly available under CC by SA license. In this study, the dataset is analyzed from a critical perspective using statistical and clustering techniques (especially multiple correspondence analysis) with a strong focus on women contributors towards discovering hidden trends and facts. Important inferences are drawn about development practices and other facets of the free software and OSS worlds.