Misinformation spans various domains, but detection methods trained on specific domains often perform poorly when applied to others. With the rapid development of Large Language Models (LLMs), researchers have begun to utilize LLMs for cross-domain misinformation detection. However, existing LLM-based methods often fail to adequately analyze news in the target domain, limiting their detection capabilities. More importantly, these methods typically rely on manually designed decision rules, which are limited by domain knowledge and expert experience, thus limiting the generalizability of decision rules to different domains. To address these issues, we propose a MultiAgent Framework for cross-domain misinformation detection with Automated Decision Rule Optimization (MARO). Under this framework, we first employs multiple expert agents to analyze target-domain news. Subsequently, we introduce a question-reflection mechanism that guides expert agents to facilitate higherquality analysis. Furthermore, we propose a decision rule optimization approach based on carefully-designed cross-domain validation tasks to iteratively enhance the effectiveness of decision rules in different domains. Experimental results and in-depth analysis on commonlyused datasets demonstrate that MARO achieves significant improvements over existing methods.

The concern, as Edward Teller saw it, was quite literally the end of the world. He had run the calculations, and there was a real possibility, he told his Manhattan Project colleagues in 1942, that when they detonated the world's first nuclear bomb, the blast would set off a chain reaction. All life on Earth would be incinerated. Some of Teller's colleagues dismissed the idea, but others didn't. If there were even a slight possibility of atmospheric ignition, said Arthur Compton, the director of a Manhattan Project lab in Chicago, all work on the bomb should halt.

Talent acquisition and management has evolved significantly in recent years and it will continue to change in the future. Digital solutions are empowering businesses with new and more effective ways to find and retain the best talent. Modern tools allow companies to fill open positions in record time and with minimal unnecessary fuss. A major contributor to this evolution is the introduction and adoption of AI-based tools. Used properly, automation can help businesses and employees work in a way that is more efficient and can ultimately make working more productive and fulfilling.

"Will AI take over jobs?" is a very controversial and interesting question that has been around for many years, and yet it will be questioned even more in the upcoming years, as artificial intelligence rapidly develops. Some people believe that AI will create more jobs than it destroys. They argue that as AI automates certain tasks, it will free up workers to do other, more creative or complex tasks. For instance, a bank teller whose job is automated by AI may be able to use their freed-up time to provide financial planning services to customers. Similarly, a manufacturing worker whose job is taken over by a robot may be able to move into maintenance or quality control. Others believe that AI will destroy more jobs than it creates.

Sertaç Taşdelen, a Turkish entrepreneur and creator of the fortune-telling app Faladdin, does his best to resemble Aladdin's genie. When I recently visited his coworking office in downtown Istanbul, Taşdelen was wearing an electric blue jacket, white trousers, and a fluffy button-down shirt, and his bearded, square-jawed face carried the mischievous smile of the fictional jinn. "Faladdin is my alter ego," Taşdelen said of the psychic he plays in the app. "If I quit business today," he whispered, leaning in, "I'd be a gypsy fortune-teller living in a caravan." In Apple's App Store, Faladdin describes itself as "far beyond a fortune telling app." The description states that it can predict one's destiny "by evaluating a person's past." It does this by bringing the Turkish tradition of coffee fortune-telling into the Internet age.

We tend to consider emotions a manifestation of our innermost nature of human beings. Emotions characterize our lives in many ways and they chaperon every rational activity we carry out. Despite their pervasiveness, there are still many things we ignore about emotions. Among them, our understanding of how living beings transfer emotions is limited. In particular, there are highly sophisticated interactions between human beings that we would like to comprehend. For instance, think of a movie director who knows in advance the strong emotional impact that a certain scene will have on the spectators. Although many artists rely on some emotional devices, their talent and vision are still the key factors. In this work we analyze high-level protocols for transferring emotions between two intelligent agents. To the best of our knowledge, this is the first attempt to use communication protocols for modeling the exchange of human emotions. By means of a number of examples, we show that our protocols adequately model the engagement of the two parties. Beyond the theoretical interest, our proposal can provide a stepping stone for several applications that we also discuss in this paper.

Mankind wants to be creative. We want to dream, debate, play, have fun and experience magic in everyday life. Instead, we are stuck with the mundane. We are doing dishes, mopping floors, making utility and credit card bill payments, and buying groceries. Naturally, given we are also inventive, we have automated these dull and boring tasks in the form of washing machines, dishwashers, autonomous vacuum cleaners, standing instructions to our bank accounts to make automated payments, and even have systems to order groceries and essentials without lifting a finger ("Alexa, add an N95 mask to my Amazon cart").

As technologies become increasingly capable of taking on a wide variety of repeatable tasks, many workers may find themselves increasingly nervous about their place in the workforce. Anxieties about technology in the workplace are nothing new -- in fact, they go back centuries. The good news is that the fear of humans being replaced en masse by machines has never been borne out by reality. Rather, history has repeatedly shown that as machines transform whole industries, they also create new opportunities for human workers. Indeed, the US is one of the most developed economies, and therefore one of the most automated, but it also currently has record-low unemployment.

The design and implementation of an e-voting system is a challenging task. Formal analysis can be of great help here. In particular, it can lead to a better understanding of how the voting system works, and what requirements on the system are relevant. In this paper, we propose that the state-of-art model checker Uppaal provides a good environment for modelling and preliminary verification of voting protocols. To illustrate this, we present an Uppaal model of Pr\^et \`a Voter, together with some natural extensions. We also show how to verify a variant of receipt-freeness, despite the severe limitations of the property specification language in the model checker.

The world of artificial intelligence has exploded in recent years. Computers armed with AI do everything from drive cars to pick movies you'll probably like. Some have warned we're putting too much trust in computers that appear to do wondrous things. But what exactly do people mean when they talk about artificial intelligence? It's hard to find a universally accepted definition of artificial intelligence.