For the past decades we have experienced an enormous expansion of the accumulated data that humanity produces. Daily a numerous number of smart devices, usually interconnected over internet, produce vast, real-values datasets. Time series representing datasets from completely irrelevant domains such as finance, weather, medical applications, traffic control etc. become more and more crucial in human day life. Analyzing and clustering these time series, or in general any kind of curves, could be critical for several human activities. In the current paper, the new Curves Clustering Using Common Patterns (3CP) methodology is introduced, which applies a repeated pattern detection algorithm in order to cluster sequences according to their shape and the similarities of common patterns between time series, data curves and eventually any kind of discrete sequences. For this purpose, the Longest Expected Repeated Pattern Reduced Suffix Array (LERP-RSA) data structure has been used in combination with the All Repeated Patterns Detection (ARPaD) algorithm in order to perform highly accurate and efficient detection of similarities among data curves that can be used for clustering purposes and which also provides additional flexibility and features.

Many of the biggest tech companies like Google, Facebook and Amazon have realized the value of creating their own AI platforms for both internal and customer-facing services. Facebook's FBLearner Flow helps the social media site filter out offensive posts, while Uber's Michelangelo gives users time predictions for food deliveries. To keep up with the competition, eBay has unveiled its AI platform, Krylov, which has given the company a wide range of new capabilities from improved language translation services to searching with images. In a blog post, eBay's Sanjeev Katariya, vice president and chief architect of the eBay AI and platforms, and Ashok Ramani, director of product management, computer vision, natural and language processing, discussed the creation of Krylov and how it has changed things both inside eBay and for users of the site. "With computer vision powered by eBay's modern AI platform, the technology helps you find items based on the click of your camera or an image. Users can go onto the eBay app and take a photo of what they are looking for and within milliseconds, the platform surfaces items that match the image," Katariya and Ramani wrote in December.

The global image recognition market was valued at USD 22,429.7 million in 2018 and is expected to reach USD xx million by 2026, growing at a CAGR of 18.4% during the forecast period. Image recognition is a method for collecting, processing, and scrutinizing images. Image recognition gathers a huge amount of data from the real world to generate symbolic or numerical information. The growth of the image recognition market is primarily driven by the increasing application of facial recognition in the financial industry and growing demand for security applications integrated with image recognition functions. Moreover, an upsurge in the usage of big data analytics across every industry vertical where image recognition plays a vital role is expected to create opportunities for the global image recognition market over the forecast period.

Google Cloud Vision API is a machine learning tool that can classify details from an image provided as an input into thousands of different categories with pre-trained API models. It offers these pre-trained models through an API and the categories are detected as individual objects within the image. In this tutorial, you are going to learn how to integrate Google Cloud Vision API in a React Native application and make use of real-time APIs. You can find the complete code inside this GitHub repo. If you are not familiar with Expo, this tutorial can be a good start.

The organisation of tomorrow will be built around data, and it will require artificial intelligence to make sense of all that data. Artificial intelligence is a broad discipline with the objective to develop intelligent machines. AI consists of several subfields: Machine learning (ML), a subset of AI that enables machines to learn from data. Reinforcement learning, which is a subset of ML and focuses on artificial agents that use trial and error to improve itself. And deep learning, also a subset of ML that aims to mimic the human brain to detect patterns in large datasets and benefit from those patterns.

Optical character recognition (OCR), or the conversion of images of handwritten or printed text into machine-readable text, is a science that dates back to the early '70s. But algorithms have long struggled to make out characters that aren't parallel with horizontal planes, which is why researchers at Amazon developed what they call TextTubes. They're detectors for curved text in natural images that model said text as tubes around their medial (middle) axes. In a paper describing their work, the coauthors claim that their approach achieves state-of-the-art results on a popular OCR benchmark. As the researchers explain, scene text is typically broken down into two successive tasks: text detection and text recognition.

A fundamental concept in Chinese philosophy and culture is Yin and Yang -- the belief that harmony is achieved when opposites coexist and share elements of the other. This can be interpreted to suggest that purpose and goodness can be found even in stuff like floodwaters, mosquitoes, and -- in the world of artificial intelligence -- adversarial examples. Adversarial examples are perturbations added to an image that are invisible to the human eye but can trick a computer vision system into misclassifying objects -- potentially causing for example an autonomous vehicle to drive through a stop sign. Adversarial examples are a bane to the researchers who build the neural networks that deliver much of today's advanced AI. Now, a team from Google and Johns Hopkins University says it has found a silver lining to adversarial examples.

Patterns, within the context of computational imaging, can be defined as "a more or less repeatable, discernible regularity in the spatial arrangement of a type of theme of more or less recurring objects, with possibly both radiometrical and geometrical features, sometimes referred to as elements of a collection of objects."[i] The term "radiometrical features" refers to what can be called "tone." Machines discern this portion of machine "sight" by measuring how an object responds to electromagnetic radiation. A machine blasts an object with either ultraviolet, visible, or infrared light, or all three and uses the resulting radiation to recognize patterns and compare them against known patterns.

This paper presents a review of deep learning (DL) based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potentials. We provided a comprehensive comparison among DL-based methods for lung and brain deformable registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of development in medical image registration using deep learning.

In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms. Although the learning based approach is inexact, we are able to generalize to count large patterns and data graphs in polynomial time compared to the exponential time of the original NP-complete problem. Different from other traditional graph learning problems such as node classification and link prediction, subgraph isomorphism counting requires more global inference to oversee the whole graph. To tackle this problem, we propose a dynamic intermedium attention memory network (DIAMNet) which augments different representation learning architectures and iteratively attends pattern and target data graphs to memorize different subgraph isomorphisms for the global counting. We develop both small graphs (<= 1,024 subgraph isomorphisms in each) and large graphs (<= 4,096 subgraph isomorphisms in each) sets to evaluate different models. Experimental results show that learning based subgraph isomorphism counting can help reduce the time complexity with acceptable accuracy. Our DIAMNet can further improve existing representation learning models for this more global problem.