While photovoltaic (PV) systems are installed at an unprecedented rate, reliable information on an installation level remains scarce. As a result, automatically created PV registries are a timely contribution to optimize grid planning and operations. This paper demonstrates how aerial imagery and three-dimensional building data can be combined to create an address-level PV registry, specifying area, tilt, and orientation angles. We demonstrate the benefits of this approach for PV capacity estimation. In addition, this work presents, for the first time, a comparison between automated and officially-created PV registries. Our results indicate that our enriched automated registry proves to be useful to validate, update, and complement official registries.

This article is part 11 (and the final part) of a series reviewing selected papers from Altmetric's list of the top 100 most-discussed scholarly works of 2019. Deep neural networks (DNNs) are a key pattern recognition technology used in artificial intelligence (AI). A DNN finds the correct mathematical manipulation to turn the input into the output, whether it be a linear relationship or a non-linear relationship1. For example, in the context of facial recognition, a DNN creates a range of outputs correctly corresponding to the range of different facial inputs. However, research shows that DNNs can be easily fooled2.

Pattern mining is well established in data mining research, especially for mining binary datasets. Surprisingly, there is much less work about numerical pattern mining and this research area remains under-explored. In this paper, we propose Mint, an efficient MDL-based algorithm for mining numerical datasets. The MDL principle is a robust and reliable framework widely used in pattern mining, and as well in subgroup discovery. In Mint we reuse MDL for discovering useful patterns and returning a set of non-redundant overlapping patterns with well-defined boundaries and covering meaningful groups of objects. Mint is not alone in the category of numerical pattern miners based on MDL. In the experiments presented in the paper we show that Mint outperforms competitors among which Slim and RealKrimp.

I find them incredibly irritating. Those images you have to click on to prove that you are not a robot. If you are just one click away from a nice weekend away, you first have to figure out where you can see the traffic lights on 16 tiny fuzzy squares. Google makes grateful use of these puzzling attempts. For one thing, the company uses artificial intelligence to train its image recognition software.

Have you ever seen the promo art or box cover for a video game and thought "what the hell is this even about?" A pair of researchers have combined cutting-edge image recognition and natural language processing to create an AI system for video game genre classification. Yuhang Jiang and Lukun Zheng, in their recently published pre-print research paper "Deep learning for video game genre classification," describe the creation of a large training database and its use in developing a novel classification system. We created a large dataset of 50,000 video games including game cover images, description text, title text, and genre information. This dataset can be used for a variety of studies such as text recognition from images, automatic topic mining, and so on and will be made available to the public in the future.

Handwritten Digit Recognition is an interesting machine learning problem in which we have to identify the handwritten digits through various classification algorithms. There are a number of ways and algorithms to recognize handwritten digits, including Deep Learning/CNN, SVM, Gaussian Naive Bayes, KNN, Decision Trees, Random Forests, etc. In this article, we will deploy a variety of machine learning algorithms from the Sklearn's library on our dataset to classify the digits into their categories. We will use Sklearn's load_digits dataset, which is a collection of 8x8 images (64 features)of digits. The dataset contains a total of 1797 sample points.

On November 13, 2015, Google had open-sourced TensorFlow, an end-to-end machine learning platform. Apart from marking five years of being one of the most popular machine learning frameworks, last week was even more significant as TensorFlow crossed the 160 million downloads. This article lists some interesting TensorFlow projects, in no particular order, which enthusiasts can try their hands on. This Handwritten Text Recognition can be implemented using TensorFlow. In this project, the system is trained on the IAM off-line dataset.

In the Internet of Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of artificial intelligence of things (AIoT). This paper presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.

Prior to 2017, most renditions of neural network models were coded in a batch scripting style. As AI researchers and experienced software engineers became increasingly involved in research and design, we started to see a shift in the coding of models that reflected software engineering principles for reuse and design patterns. A design pattern implies that there is a "best practice" for constructing and coding a model that can be reapplied across a wide range of cases, such as image classification, object detection and tracking, facial recognition, image segmentation, super resolution and style transfer. The introduction of design patterns also helped advance convolutional neural networks (as well as other network architectures) by aiding other researchers in understanding and reproducing a model's architecture. A procedural style for reuse was one of the earliest versions of using design patterns for neural network models.

Digital fraud is a widespread problem, with fraud losses totaling more than $1.45 trillion annually around the globe. A fraud attack against a bank or business occurred every two minutes on average in 2019, for a total of 59,627 attacks that year. What's more, these figures do not take into account the innumerable schemes launched against individuals. The pandemic has only exacerbated the fraud threat as bad actors exploit economic uncertainties to scam individuals from all walks of life. One especially popular method of digital attack is authorized push payment (APP) fraud, which sees bad actors impersonating trusted merchants or officials and demanding payment from victims for goods or services.