In process mining, process models are extracted from event logs using process discovery algorithms and are commonly assessed using multiple quality dimensions. While the metrics that measure the relationship of an extracted process model to its event log are well-studied, quantifying the level by which a process model can describe the unobserved behavior of its underlying system falls short in the literature. In this paper, a novel deep learning-based methodology called Adversarial System Variant Approximation (AVATAR) is proposed to overcome this issue. Sequence Generative Adversarial Networks are trained on the variants contained in an event log with the intention to approximate the underlying variant distribution of the system behavior. Unobserved realistic variants are sampled either directly from the Sequence Generative Adversarial Network or by leveraging the Metropolis-Hastings algorithm. The degree by which a process model relates to its underlying unknown system behavior is then quantified based on the realistic observed and estimated unobserved variants using established process model quality metrics. Significant performance improvements in revealing realistic unobserved variants are demonstrated in a controlled experiment on 15 ground truth systems. Additionally, the proposed methodology is experimentally tested and evaluated to quantify the generalization of 60 discovered process models with respect to their systems.

Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub-tasks to work on in parallel. Underlying the human ability to collaborate is theory-of-mind, the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi-agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by inverse planning. These inferences enable agents to flexibly decide in the absence of communication when to cooperate on the same sub-task and when to work on different sub-tasks in parallel. We test this model in a suite of multi-agent Markov decision processes inspired by cooking problems. To succeed, agents must coordinate both their high-level plans (e.g., what sub-task they should work on) and their low-level actions (e.g., avoiding collisions). Bayesian Delegation bridges these two levels and rapidly aligns agents' beliefs about who should work on what without any communication. When agents cooperate on the same sub-task, coordinated plans emerge that enable the group of agents to achieve tasks no agent can complete on their own. Our model outperforms lesioned agents without Bayesian Delegation or without the ability to cooperate on the same sub-task.

UPS has partnered with the German tech company Wingcopter to build a fleet of rugged, high speed delivery drones. The drones will be based on a model designed by Wingcopter, which can travel at speeds of up to 150mph and has a range of 75 miles. The drones can also endure a variety of difficult weather conditions, including wind speeds of up to 45mph. The agreements marks the first external partnership for UPS's Flight Forward program, which is focused on developing a range of drone delivery options, according to a report in TechCrunch. 'Drone delivery is not a one-size-fits-all operation,' UPS's Bala Ganesh said.

Is there a video game you've been meaning to play but not yet found the time? If so, now might be the perfect opportunity to complete a few. From first person shooters to open world RPGs, as well as family friendly games, there is a wide offering of video games to entertain young and adult gamers. Ahead we bring you ten games to keep you busy if you're bored at home... Set in America 1899 at the end of the wild west era, Red Dead Redemption 2 sees you play as Van der Linde gang leader Arthur Morgan, who is forced to flee from lawmen after a robbery goes badly wrong. Explore the rugged heartland of America on horseback as you evade federal agents and bounty hunters who are hot on your tail.

Roundup Let's get cracking with some machine-learning news. Starksy Robotics is no more: Self-driving truck startup Starsky Robotics has shut down after running out of money and failing to raise more funds. CEO Stefan Seltz-Axmacher bid a touching farewell to his upstart, founded in 2016, in a Medium post this month. He was upfront and honest about why Starsky failed: "Supervised machine learning doesn't live up to the hype," he declared. Neural networks only learn to pick up on certain patterns after they are faced with millions of training examples.

With all the hype building around Machine Learning and Artificial Intelligence, sometimes students find it difficult to decide whether to perceive machine learning as a career choice, whether to obtain Machine Learning certificate or not. This article is supposed to be your guide to machine learning certification. At first, we'll tell you what Machine Learning is and then explain why you should obtain a certification in the field. Machine learning is a technique of data analysis that strives to automate the whole process of analytics as well as model creation. In other words, it allows your device to search for more info rather than program it to read and interpret fixed data.

Hisham Elamir is a data scientist with expertise in machine learning, deep learning, and statistics. He currently lives and works in Cairo, Egypt. In his work projects, he faces challenges ranging from natural language processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.

A little over a year ago we reported on a little yellow robot that was making deliveries around China. The two-foot-tall autonomous robot, called the RoboPony, comes from Zhen Robotics Corp. It has been seen in places like malls, hospitals, offices and retail spaces. Now with the recent coronavirus outbreak and many finding themselves under quarantine, the demand for the RoboPony has surged. Cities around the world are attempting to cut down on human to human interaction but there is still a demand for things like groceries and medication.

Governments around the world are using high-tech surveillance measures to combat the coronavirus outbreak. But are they worth it? Edward Snowden doesn't think so. The former CIA contractor, whose leaks exposed the scale of spying programs in the US, warns that once this tech is taken out the box, it will be hard to put it back. "When we see emergency measures passed, particularly today, they tend to be sticky," Snowden said in an interview with the Copenhagen International Documentary Film Festival.

In 2018, the New York Foundling, a charity that offers child welfare, adoption, and mental health services, was stuck in cut-and-paste hell. Clinicians and admin staff were spending hours transferring text between different documents and databases to meet varied legal requirements. Arik Hill, the charity's chief information officer, blames the data entry drudgery for an annual staff turnover of 42 percent at the time. "We are not a very glamorous industry," says Hill. "We are really only just moving on from paper clinical records." Since then, the New York Foundling has automated much of this grunt work using what are known as software robots--simple programs hand-crafted to perform dull tasks.