Asking questions is one of the most crucial pedagogical techniques used by teachers in class. It not only offers open-ended discussions between teachers and students to exchange ideas but also provokes deeper student thought and critical analysis. Providing teachers with such pedagogical feedback will remarkably help teachers improve their overall teaching quality over time in classrooms. Therefore, in this work, we build an end-to-end neural framework that automatically detects questions from teachers' audio recordings. Compared with traditional methods, our approach not only avoids cumbersome feature engineering, but also adapts to the task of multi-class question detection in real education scenarios. By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions. We conducted extensive experiments on the question detection tasks in a real-world online classroom dataset and the results demonstrate the superiority of our model in terms of various evaluation metrics.

An approach for building mixed-initiative tools for the procedural generation of game levels using interactive evolutionary optimisation is introduced. A tool is created based on this approach which (a) is focused on supporting the designer to explore the design space and (b) only requires the designer to interact with it by designing levels. The tool identifies level design patterns in an initial hand-designed map and uses that information to drive an optimisation algorithm. This results in a number of suggestions which are presented to the designer, who can then edit them providing the system with valuable designer feedback. The effectiveness of this approach to create levels with similar level design patterns to a target is illustrated through a series of algorithm driven benchmark tests. To test the mixed-initiative aspect of the tool a triple-blind mixed-method, user study was conducted. When compared to a control group, provided with random level suggestions throughout the design process, the mixed-initiative approach increased engagement in the level design task and was effective in inspiring new ideas and design directions. This provides significant evidence that procedural content generation can be used as a powerful tool to support the human design process.

We are currently designing an object oriented model which describes static and dynamical knowledge in diff{\'e}rent domains. It provides a twin conceptual level. The internal level proposes: the object structure composed of sub-objects hierarchy, structure evolution with dynamical functions, same type objects comparison with evaluation functions. It uses multiple upward inheritance from sub-objects properties to the Object. The external level describes: object environment, it enforces object types and uses external simple inheritance from the type to the sub-types.

We present a novel framework for modeling traffic congestion events over road networks based on new mutually exciting spatio-temporal point process models with attention mechanisms and neural network embeddings. Using multi-modal data by combining count data from traffic sensors with police reports that report traffic incidents, we aim to capture two types of triggering effect for congestion events. Current traffic congestion at one location may cause future congestion over the road network, and traffic incidents may cause spread traffic congestion. To capture the non-homogeneous temporal dependence of the event on the past, we introduce a novel attention-based mechanism based on neural networks embedding for the point process model. To incorporate the directional spatial dependence induced by the road network, we adapt the "tail-up" model from the context of spatial statistics to the traffic network setting. We demonstrate the superior performance of our approach compared to the state-of-the-art methods for both synthetic and real data.

Whenever countries are threatened by a pandemic, as is the case with the COVID-19 virus, governments should take the right actions to safeguard public health as well as to mitigate the negative effects on the economy. In this regard, there are two completely different approaches governments can take: a restrictive one, in which drastic measures such as self-isolation can seriously damage the economy, and a more liberal one, where more relaxed restrictions may put at risk a high percentage of the population. The optimal approach could be somewhere in between, and, in order to make the right decisions, it is necessary to accurately estimate the future effects of taking one or other measures. In this paper, we use the SEIR epidemiological model (Susceptible - Exposed - Infected - Recovered) for infectious diseases to represent the evolution of the virus COVID-19 over time in the population. To optimize the best sequences of actions governments can take, we propose a methodology with two approaches, one based on Deep Q-Learning and another one based on Genetic Algorithms. The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system focused on meeting two objectives: firstly, getting few people infected so that hospitals are not overwhelmed with critical patients, and secondly, avoiding taking drastic measures for too long which can potentially cause serious damage to the economy. The conducted experiments prove that our methodology is a valid tool to discover actions governments can take to reduce the negative effects of a pandemic in both senses. We also prove that the approach based on Deep Q-Learning overcomes the one based on Genetic Algorithms for optimizing the sequences of actions.

This month, the cover of New Scientist ran the headline, "Is the Universe Conscious?" Mathematician and physicist Johannes Kleiner, at the Munich Center for Mathematical Philosophy in Germany, told author Michael Brooks that a mathematically precise definition of consciousness could mean that the cosmos is suffused with subjective experience. "This could be the beginning of a scientific revolution," Kleiner said, referring to research he and others have been conducting. Kleiner and his colleagues are focused on the Integrated Information Theory of consciousness, one of the more prominent theories of consciousness today. As Kleiner notes, IIT (as the theory is known) is thoroughly panpsychist because all integrated information has at least one bit of consciousness.

Last week, mayors representing over 750 million people, across the world's leading cities, published a statement of principles, warning against a return to "business as usual" as the world recovers from COVID-19. This advice is as relevant for enterprises as it is for society as a whole. COVID-19 has exposed a lack of resilience, severely impacting operational continuity. Indeed, Eurozone business activity fell to an all-time low in April. With the pandemic impacting every part of society, there are human considerations to every decision we make. While clearly the most important factor, public health professionals are already doing a great job of covering this issue.

IBM's and Google's platforms work in similar ways. They make it easy for clients to spin up chat or voice-based agents that act a lot like Alexa or Siri but are tailored to different applications. When users text or call in, they are free to speak in open-ended sentences. The system then uses natural-language processing to parse their "intent" and responds with the appropriate scripted answer or reroutes them to a human agent. For queries that can't be answered automatically, the algorithms group similar ones together to show the most commonly missed intents.

Internet speeds are still running largely as normal despite the increased pressure of lockdown, according to research from regulator Ofcom. Download speeds have only dropped by an average of 2 per cent, according to the research, even with the extra load. That is despite some high-profile outages, including Virgin Media problems that took the internet offline for users across the country. Networks have been under increased strain with more people across the country working from home, children using online platforms to carry on school work, and greater gaming and streaming as a source of entertainment. The communications regulator measured broadband performance for 3,481 users at the beginning and end of March, to compare results before and after lockdown started.

The Trump administration has extended the executive order banning American businesses from working with companies that pose a national security risk, extending the muddled relationship between US enterprise and Chinese conglomerates such as smartphone maker Huawei and telecom equipment manufacturer ZTE. The order, called the International Emergency Economic Powers Act which gives the president the authority to regulate commerce during a national emergency, was implemented in May 2019. The result of the ban, which has now been extended until May 2021, means that Google cannot provide Huawei with access to Google Mobile Services and therefore popular apps such as Maps and YouTube are not available on Huawei phones. Google Mobile Services are the commercial aspects to the Android platform that all major smartphones - apart from Apple's iPhones - use. It includes Google's apps and back-end services which powers other apps including Netflix and Citymapper.