The human mind is still an unknown process of neuroscience in many aspects. Nevertheless, for decades the scientific community has proposed computational models that try to simulate their parts, specific applications, or their behavior in different situations. The most complete model in this line is undoubtedly the LIDA model, proposed by Stan Franklin with the aim of serving as a generic computational architecture for several applications. The present project is inspired by the LIDA model to apply it to the process of movie recommendation, the model called MIRA (Movie Intelligent Recommender Agent) presented percentages of precision similar to a traditional model when submitted to the same assay conditions. Moreover, the proposed model reinforced the precision indexes when submitted to tests with volunteers, proving once again its performance as a cognitive model, when executed with small data volumes. Considering that the proposed model achieved a similar behavior to the traditional models under conditions expected to be similar for natural systems, it can be said that MIRA reinforces the applicability of LIDA as a path to be followed for the study and generation of computational agents inspired by neural behaviors.

We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture. We specifically target discontinuity, an under-explored aspect that poses a significant challenge to computational treatment of MWEs. Two neural architectures are explored: Graph Convolutional Network (GCN) and multi-head self-attention. GCN leverages dependency parse information, and self-attention attends to long-range relations. We finally propose a combined model that integrates complementary information from both through a gating mechanism. The experiments on a standard multilingual dataset for verbal MWEs show that our model outperforms the baselines not only in the case of discontinuous MWEs but also in overall F-score.

Kalashnikov, which is most famous for its AK-47 assault rifle, is moving into new territory with a drone that can carry up to three kilograms of explosives and detonate on impact. The KUB-UAV can travel at up to 130 kilometers per hour (78 mph) and stay in the air for 30 minutes. As if there were any doubt as to Kalashnikov's intention for the device, a YouTube video shows the drone attacking some remote targets. The company wants the KUB-UAV to be inexpensive and easy to operate. Kalashnikov demonstrated the device at a defense exhibition in Abu Dhabi this week.

It is true in politics, business and investing. Right now, there is no question that the money is headed into artificial intelligence. Gartner, a global IT research and advisory company, surveyed 3,000 CIOs operating in 89 countries in January. The Stamford, Conn., firm found that AI implementations grew 37% during 2018, and 270% over the last four years. This is a trend investors should embrace.

On the seventh day the Kohen shall examine him, and if the affection has remained unchanged in color and the disease has not spread on the skin, the Kohen shall isolate him for another seven days. Melanoma – considered the most dangerous kind of skin cancer – occurs when the body is overexposed to ultraviolet (UV) radiation from the sun or from tanning beds. This exposure triggers genetic defects that cause skin cells to multiply rapidly and form malignant tumors. People who are genetically predisposed to the disease at are even higher risk. Nearly six decades ago, preserved mummies from Peru dating back 2,400 years were examined and found to have signs of melanoma on their skin and cancerous growths that spread from there to their bones.

Considering the interdependencies between water and electricity use is critical for ensuring conservation measures are successful in lowering the net water and electricity use in a city. This water-electricity demand nexus will become even more important as cities continue to grow, causing water and electricity utilities additional stress, especially given the likely impacts of future global climatic and socioeconomic changes. Here, we propose a modeling framework based in statistical learning theory for predicting the climate-sensitive portion of the coupled water-electricity demand nexus. The predictive models were built and tested on six Midwestern cities. The results showed that water use was better predicted than electricity use, indicating that water use is slightly more sensitive to climate than electricity use. Additionally, the results demonstrated the importance of the variability in the El Nino/Southern Oscillation index, which explained the majority of the covariance in the water-electricity nexus. Our modeling results suggest that stronger El Ninos lead to an overall increase in water and electricity use in these cities. The integrated modeling framework presented here can be used to characterize the climate-related sensitivity of the water-electricity demand nexus, accounting for the coupled water and electricity use rather than modeling them separately, as independent variables.

We are an international tech company with development hubs based in Belarus and Argentina. We landed in Argentina in 2013, with two offices located in Buenos Aires and Santa Fe city. Our mission is to provide technology solutions through software development, helping Startups in USA and Europe to build their innovative process and products. As we are passionate about technology our main projects are related to Blockchain, Machine Learning, Artificial Intelligence, and IoT. To make this happen the tools we use are: Javascript, Ruby and Ethereum technologies.

This paper focuses on how to take advantage of external knowledge bases (KBs) to improve recurrent neural networks for machine reading. Traditional methods that exploit knowledge from KBs encode knowledge as discrete indicator features. Not only do these features generalize poorly, but they require task-specific feature engineering to achieve good performance. We propose KBLSTM, a novel neural model that leverages continuous representations of KBs to enhance the learning of recurrent neural networks for machine reading. To effectively integrate background knowledge with information from the currently processed text, our model employs an attention mechanism with a sentinel to adaptively decide whether to attend to background knowledge and which information from KBs is useful. Experimental results show that our model achieves accuracies that surpass the previous state-of-the-art results for both entity extraction and event extraction on the widely used ACE2005 dataset.

When it comes to analytics tools, data scientists have a plethora of options available to them. Features that may appeal to one data scientist don't necessarily work for another. When it comes to offerings from H2O.ai, users expressed different reasons for their choices. Last week, Datanami was a guest at H2O.ai's annual user conference, called H2O World, and had a chance to talk with several customers, including Ruben Diaz, a data scientist with Vision Banco, and Bharath Sudharsan, director of data science and innovation at Armada Health. Vision Banco is one of Paraguay's largest banks, with consumer and micro-finance lines of business.

Global Artificial Intelligence In Transportation Market Research Report 2019 to 2025 provides a unique tool for evaluating the market, highlighting opportunities, and supporting strategic and tactical decision-making. This report recognizes that in this rapidly-evolving and competitive environment, up-to-date marketing information is essential to monitor performance and make critical decisions for growth and profitability. Global Artificial Intelligence In Transportation market size will increase to Million US$ by 2025, from Million US$ in 2018, at a CAGR of during the forecast period. In this study, 2018 has been considered as the base year and 2019 to 2025 as the forecast period to estimate the market size for Artificial Intelligence In Transportation. This report studies the global market size of Artificial Intelligence In Transportation in key regions like North America, Europe, Asia Pacific, Central & South America and Middle East & Africa, focuses on the consumption of Artificial Intelligence In Transportation in these regions.