The importance of visual context in object recognition has been intensively studied over the years. Along with the advent of deep convolutional neural networks (CNN), using contextual information with such systems starts to receive attention in the literature. Regardless of deep learning advances, aerial image analysis still poses many great challenges. Satellite images are often taken under poor lighting conditions and contain low resolution objects, many times occluded. For this particular task, visual context could be of great help, but there are still very few papers that consider context in aerial image understanding. Our work addresses the task of object segmentation in aerial images with a novel dual-stream deep convolutional neural network that integrates the local object appearance and global contextual information into a unified network. Our model learns to combine local object appearance and global semantic knowledge simultaneously and in a complementary way, so that together they form a powerful classifier. Experiments on the Massachusetts Buildings Dataset demonstrate the superiority of our model over state-of-the-art methods. We also introduce two new challenging datasets for the task of buildings and road segmentation. While our local-global model could also be useful in general recognition tasks, we clearly demonstrate the effectiveness of visual context in conjunction with deep nets in aerial image understanding.

I am a Machine Learning Engineer, Deep Learning Engineer and even an Indie Game Developer with a Major in Compilers and a Master's degree in Artificial Intelligence from University Politehnica of Bucharest. I am passionate about Games and Artificial Intelligence. I love to give life to A.I. agents in my project or my friend's projects and I want to teach you too.

A Deutsche Telekom official says artificial intelligence (AI) will be needed to break smart city data out of its silos. As reported by Mobile World Live, thoughts about smart city evolution appeared on the blog of Claudia Nemat, the Deutsche Telekom (DT) board member who oversees Technology & Innovation Europe. In her piece, Nemat described current smart city practices as trapping data from sensors in an isolated "landlocked lake." But as sensors proliferate at blinding speed in global smart cities, the rivers of data being produced will turn into oceans. This will be complicated by the multiple digital platforms running simultaneously in each city.

The next time you open the door to find the pizza you ordered (via an app of course), you may not find a delivery person standing on your doorstep. Expect to see a drone dropping off your dinner. And perhaps that dinner was by prepped, at least in part, by robots. Eventually, some of your pizza's toppings may be lab grown-- but in the meantime, even those fresh-off-the-farm ingredients have a good chance of reaching your fork via robotics. Today, automation is shaping up to be our generation's food revolution.

A Deutsche Telekom official says artificial intelligence (AI) will be needed to break smart city data out of its silos. As reported by Mobile World Live, thoughts about smart city evolution appeared on the blog of Claudia Nemat, the Deutsche Telekom (DT) board member who oversees Technology & Innovation Europe. In her piece, Nemat described current smart city practices as trapping data from sensors in an isolated "landlocked lake." But as sensors proliferate at blinding speed in global smart cities, the rivers of data being produced will turn into oceans. This will be complicated by the multiple digital platforms running simultaneously in each city.

Romanian startup Grobyk is organizing ready-to-use, trending information from your chosen sources, using Artificial Intelligence algorithms. Their 3 words pitch is: Instant Relevant Research. Content marketers keep up with a lot of sources, from social media, blogs, news site and so on. Search engine trends are misleading. What's relevant are social media shares.

Do you fancy a technical talk to kick off this week? The AI Machine Learning community gets back at TechHub Bucharest for an interesting presentation. Join them on Monday, June 6, to find out more about the AlphaGo model and the machine learning algorithms it employs. During this meetup you'll get an in-depth presentation of the AlphaGo model and the machine learning algorithms it employs. AlphaGo is the artificial Go player developed by DeepMind, the first one to win against a professional human Go player.

Textual complexity is widely used to assess the difficulty of reading materials and writing quality in student essays. At a lexical level, word complexity can represent a building block for creating a comprehensive model of lexical networks that adequately estimates learners’ understanding. In order to best capture how lexical associations are created between related concepts, we propose automated indices of word complexity based on Age of Exposure (AoE). AOE indices computationally model the lexical learning process as a function of a learner's experience with language. This study describes a proof of concept based on the on a large-scale learning corpus (i.e., TASA). The results indicate that AoE indices yield strong associations with human ratings of age of acquisition, word frequency, entropy, and human lexical response latencies providing evidence of convergent validity.

In this paper we present a real-world application (the first of its kind) of machine listening in the context of a live concert in a world-famous concert hall - the Concertgebouw in Amsterdam. A real-time music tracking algorithm listens to the Royal Concertgebouw Orchestra performing Richard Strauss' Alpensinfonie and follows the progress in the sheet music, i.e., continuously tracks the most likely position of the live music in the printed score. This information, in turn, is used to enrich the concert experience for members of the audience by streaming synchronised visual content (the sheet music, explanatory text and videos) onto tablet computers in the concert hall. The main focus of this paper is on the challenges involved in tracking live orchestral music, i.e., how to deal with heavily polyphonic music, how to prepare the data needed, and how to achieve the necessary robustness and precision.

In this article some formal, content-related and procedural considerations towards the sense of humor are articulated and the analysis of both everyday humor behavior and of comic styles leads to the initial proposal of a four factor-model of humor (4FMH). This model is tested in a new dataset and it is also examined whether two forms of comic styles (benevolent humor and moral mockery) do fit in. The model seems to be robust but further studies on the structure of the sense of humor as a personality trait are required.