Indicators of Compromise (IOCs) are artifacts observed on a network or in an operating system that can be utilized to indicate a computer intrusion and detect cyber-attacks in an early stage. Thus, they exert an important role in the field of cybersecurity. However, state-of-the-art IOCs detection systems rely heavily on hand-crafted features with expert knowledge of cybersecurity, and require a large amount of supervised training corpora to train an IOC classifier. In this paper, we propose using a neural-based sequence labelling model to identify IOCs automatically from reports on cybersecurity without expert knowledge of cybersecurity. Our work is the first to apply an end-to-end sequence labelling to the task in IOCs identification. By using an attention mechanism and several token spelling features, we find that the proposed model is capable of identifying the low frequency IOCs from long sentences contained in cybersecurity reports. Experiments show that the proposed model outperforms other sequence labelling models, achieving over 88% average F1-score.

A dialogue act (DA) represents the meaning of an utterance at the illocutionary force level (Austin 1962) such as questions, requests, and greetings. Since DAs take charge of the most fundamental part of communication, we believe that the elucidation of DA learning mechanism is important for cognitive science and artificial intelligence. The purpose of this study is to verify that scaffolding takes place when a human teaches a robot, and to let a robot learn to estimate DAs and to make a response based on them step by step utilizing scaffolding provided by a human. To realize that, it is necessary for the robot to detect changes in utterance and rewards given by the partner and continue learning accordingly. Experimental results demonstrated that participants who continued interaction for a sufficiently long time often gave scaffolding for the robot. Although the number of experiments is still insufficient to obtain a definite conclusion, we observed that 1) the robot quickly learned to respond to DAs in most cases if the participants only spoke utterances that match the situation, 2) in the case of participants who builds scaffolding differently from what we assumed, learning did not proceed quickly, and 3) the robot could learn to estimate DAs almost exactly if the participants kept interaction for a sufficiently long time even if the scaffolding was unexpected.

Crowd simulators have been used to find appropriate regulations by simulating multiple scenarios with different regulations. However, this approach requires multiple simulation runs, which are time-consuming. In this paper, we propose a method to learn a function that outputs regulation effects given the current traffic situation as inputs. If the function is learned using the training data of many simulation runs in advance, we can obtain an appropriate regulation efficiently by bypassing simulations for the current situation. We use the graph convolutional networks for modeling the function, which enable us to find regulations even for unseen areas. With the proposed method, we construct a graph for each area, where a node represents a road, and an edge represents the road connection. By running crowd simulations with various regulations on various areas, we generate traffic situations and regulation effects. The graph convolutaional networks are trained to output the regulation effects given the graph with the traffic situation information as inputs. With experiments using real-world road networks and a crowd simulator, we demonstrate that the proposed method can find a road to close that reduces the average time needed to reach the destination.

The boss of Amazon's server business has joined Apple's Tim Cook in demanding Bloomberg retract a story claims Chinese spy chips were inside some of its servers. '@tim_cook is right,' Andy Jassy tweeted. 'Bloomberg story is wrong about Amazon, too. Andy Jassy said the'Reporters got played or took liberties'. Bloomberg has so far stood by its story, despite every firm involved denying it.

Futurists say in-store shopping experiences will be highly personalized before too long. Salespeople will know your likes and dislikes seemingly off the top of their heads, and they'll know to highlight the items you're most likely to find attractive. Artificial intelligence (AI) can help drive that kind of bespoke experience, and Mystore-E intends to establish an early foothold in the market with an adaptable, on-premise platform designed for retail outlets. It's well on its way: The two-year-old Tel Aviv startup recently raised $2.2 million in a seed round and secured an exclusive partnership with Signet Jewelers. "Mystore-E cares deeply about the future of … shopping," CEO Asaf Shapira said. "Our goal is to create a personalized shopping experience that benefits retail stores, while also catering to the customers' wants, needs, and style preferences."

A Hong Kong artificial intelligence lab backed by e-commerce giant Alibaba Group Holding and SenseTime, the world's most valuable AI start-up, has named seven companies that will receive funding under its accelerator programme. The Hong Kong AI and Data Laboratory announced in a ceremony on Thursday that it will provide US$100,000 in seed funding to each of the selected start-ups, who will also be given office space, access to AI resources from Alibaba and SenseTime, and a range of cloud computing services from Alibaba Cloud. "These start-ups not only have innovative technologies and business ideas, but they also offer valuable solutions for different industries and scenarios," Jeff Zhang, the chief technology officer of Alibaba, said at the ceremony. Zhang said it was important to work on the application of AI technologies in different industries. The lab was established in May by the Alibaba Hong Kong Entrepreneurs Fund and SenseTime, which became the city's first unicorn – a start-up valued at more than US$1 billion – in July last year.

These four new solution accelerators help financial services and insurance firms solve complex business challenges by discovering meaningful relationships between events that impact one another (correlation) and cause a future event to happen (causation). Following the success of Synechron's AI Automation Program – Neo, Synechron's AI Data Science experts have developed a powerful set of accelerators that allow financial firms to address business challenges related to investment research generation, predicting the next best action to take with a wealth management client, high-priority customer complaints, and better predicting credit risk related to mortgage lending. The Accelerators combine Natural Language Processing (NLP), Deep Learning algorithms and Data Science to solve the complex business challenges and rely on a powerful Spark and Hadoop platform to ingest and run correlations across massive amounts of data to test hypotheses and predict future outcomes. The Data Science Accelerators are the fifth Accelerator program Synechron has launched in the last two years through its Financial Innovation Labs (FinLabs), which are operating in 11 key global financial markets across North America, Europe, Middle East and APAC; including: New York, Charlotte, Fort Lauderdale, London, Paris, Amsterdam, Serbia, Dubai, Pune, Bangalore and Hyderabad. With this, Synechron's Global Accelerator programs now includes over 50 Accelerators for: Blockchain, AI Automation, InsurTech, RegTech, and AI Data Science and a dedicated team of over 300 employees globally.

OnePlus has been forced to change the release date of its new 6T phone after Apple announced it would hold a launch on the same day and in the same place. The 6T will mark a major launch for OnePlus, which is currently trying to push into the mainstream and take on phones like the iPhone. But Apple announced that it would be holding its latest event on 30 October, in New York City. That happened to be the same time and location as the OnePlus event, forcing it to change its schedule. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

Elon Musk is engaged in a bizarre fight with Fortnite fans after he appeared to call them "virgins". The SpaceX and Tesla boss – who has been repeatedly criticised over his tweets – posted a picture of a fake news article that seemed to suggest fans of the video game are "virgins". "Elon Musk buys Fortnite and deletes it," the hoax news story, shared on Mr Musk's Twitter, read. It claimed that the billionaire had said he had to remove the game to protect players from "eternal virginity". The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

Spektacom, a sports tech startup founded by Anil Kumble, one of the most accomplished cricketers in India, partnered with Microsoft to bring cutting-edge technology to the game of cricket. Spektacom built a platform that includes a 5-gram sticker that attaches itself to the cricket bat, a stump box that acts as an IoT gateway, and AI-powered analytics to deliver insights on the batting style of a batsman. The data collected in the cloud is instantly run through a machine learning model that assesses the quality of a shot. Anil officially calls the IoT-enabled bat as a power bat, which doesn't deviate from the specifications of a standard cricket bat. The technology behind the power bat is fascinating.