The Indian edition of dating app Tinder is trialing a new feature which gives women an additional level of scrutiny and security before they allow men to start messaging conversations, with a view to rolling the function out globally. The'My Move' feature allows women to choose in their settings that only they can start a conversation with a male match after both have approved each other with Tinder's swiping function. Normally, the app gives both parties to a successful match - where both have swiped yes on the other's photograph - the right to text each other immediately. The'My Move' feature allows women to choose in their settings that only they can start a conversation with a male match after both have approved each other with Tinder's swiping function. Tinder has been testing the function in India for several months and plans to spread it worldwide if the full rollout proves successful.

Two tiny robots have landed safely on an asteroid after a Japanese spacecraft dropped them there on Friday. The scientists behind the historic mission expressed their delight as the rovers sent back the first images from the surface of the space rock Ryugu. Dubbed MINERVA-II1, the robotic explorers are the first of their kind to be successfully landed on an asteroid. The Japanese space agency JAXA announced that both units were operational after a period of silence between the unmanned spacecraft Hayabusa-2 depositing them and connection being established with the team on Earth. "I cannot find words to express how happy I am that we were able to realise mobile exploration on the surface of an asteroid," said Hayabusa-2 project manager Dr Yuichi Tsuda.

Forza Horizon 4 is exactly as fun as you'd expect. And it's so beautiful that it will continue to shock you hours after you started playing it. This year, the game lands in Britain, which is also the home of developers Playground Games. It is rendered with loving care – sometimes a little too much, so that the rough edges are sanded away – and becomes a fitting location for one of the best driving games ever. The real Britain, of course, hasn't had it easy in recent years.

In a hospital's intensive care unit (ICU), the sickest patients receive round-the-clock care as they lie in beds with their bodies connected to a bevy of surrounding machines. This advanced medical equipment is designed to keep an ailing person alive. Intravenous fluids drip into the bloodstream, while mechanical ventilators push air into the lungs. Sensors attached to the body track heart rate, blood pressure, and other vital signs, while bedside monitors graph the data in undulating lines. When the machines record measurements that are outside of normal parameters, beeps and alarms ring out to alert the medical staff to potential problems. While this scene is laden with high tech, the technology isn't being used to best advantage.

A Mars rover roaming the red planet cannot whip out a smartphone to check its location based on GPS. Instead, the robotic explorer must take panoramic pictures of the surrounding landscape so that a human back on Earth can painstakingly compare the ground images with Mars satellite maps taken from above by orbiting spacecraft. Locating a Mars mission after it first touches down, using that manual process of scrutinizing landscape features and making image comparisons, can take up to 24 hours. What's more, it still requires at least thirty minutes to confirm a rover's updated location after it's on the move. But a new AI approach that trains deep learning algorithms to perform the necessary image comparisons could reduce the localization process to mere seconds.

Despite being a slow-moving disaster, famine is notoriously difficult to predict. The reason for this, experts say, is that severe food shortages are hardly ever about food supply alone. A famine might be triggered by drought or some other climatic interference in crop production, but other powerful forces usually bring the scourge to full bloom: food price inflation, political instability, military conflict and even too much rain. "The root cause of famine is extremely complex," said Franck Bousquet, senior director of the World Bank Fragility, Conflict, and Violence Group (FCV). "Usually, the poorest and most vulnerable are the most affected and the least able to cope with shocks that other populations can absorb. Out of the last 10 major famines, nine have resulted from conflict and war."

Despite deep reinforcement learning has recently achieved great successes, however in multiagent environments, a number of challenges still remain. Multiagent reinforcement learning (MARL) is commonly considered to suffer from the problem of non-stationary environments and exponentially increasing policy space. It would be even more challenging to learn effective policies in circumstances where the rewards are sparse and delayed over long trajectories. In this paper, we study Hierarchical Deep Multiagent Reinforcement Learning (hierarchical deep MARL) in cooperative multiagent problems with sparse and delayed rewards, where efficient multiagent learning methods are desperately needed. We decompose the original MARL problem into hierarchies and investigate how effective policies can be learned hierarchically in synchronous/asynchronous hierarchical MARL frameworks. Several hierarchical deep MARL architectures, i.e., Ind-hDQN, hCom and hQmix, are introduced for different learning paradigms. Moreover, to alleviate the issues of sparse experiences in high-level learning and non-stationarity in multiagent settings, we propose a new experience replay mechanism, named as Augmented Concurrent Experience Replay (ACER). We empirically demonstrate the effects and efficiency of our approaches in several classic Multiagent Trash Collection tasks, as well as in an extremely challenging team sports game, i.e., Fever Basketball Defense.

In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-the-art methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods.

The Knowledge graph (KG) uses the triples to describe the facts in the real world. It has been widely used in intelligent analysis and understanding of big data. In constructing a KG, especially in the process of automation building, some noises and errors are inevitably introduced or much knowledges is missed. However, learning tasks based on the KG and its underlying applications both assume that the knowledge in the KG is completely correct and inevitably bring about potential errors. Therefore, in this paper, we establish a unified knowledge graph triple trustworthiness measurement framework to calculate the confidence values for the triples that quantify its semantic correctness and the true degree of the facts expressed. It can be used not only to detect and eliminate errors in the KG but also to identify new triples to improve the KG. The framework is a crisscrossing neural network structure. It synthesizes the internal semantic information in the triples and the global inference information of the KG to achieve the trustworthiness measurement and fusion in the three levels of entity-level, relationship-level, and KG-global-level. We conducted experiments on the common dataset FB15K (from Freebase) and analyzed the validity of the model's output confidence values. We also tested the framework in the knowledge graph error detection or completion tasks. The experimental results showed that compared with other models, our model achieved significant and consistent improvements on the above tasks, further confirming the capabilities of our model.

New generations of distributed systems are opening novel perspectives for logic programming (LP): on the one hand, service-oriented architectures represent nowadays the standard approach for distributed systems engineering; on the other hand, pervasive systems mandate for situated intelligence. In this paper we introduce the notion of Logic Programming as a Service (LPaaS) as a means to address the needs of pervasive intelligent systems through logic engines exploited as a distributed service. First we define the abstract architectural model by re-interpreting classical LP notions in the new context; then we elaborate on the nature of LP interpreted as a service by describing the basic LPaaS interface. Finally, we show how LPaaS works in practice by discussing its implementation in terms of distributed tuProlog engines, accounting for basic issues such as interoperability and configurability.