Conservationists hoping to save one of the world's most endangered animals have a new high-tech tool in their arsenal: Social media. Grevy's zebras once roamed across five countries in Africa, but their numbers have dwindled to barely 3,000 due to habitat loss and hunting. Now an online platform known as Wildbook is keeping tabs on these precious equines, by enabling volunteers take and upload photos that are then matched against zebras already in the site's database. The zebra's distinct stripes, which are as unique as fingerprints, allow them to be easily identified from among hundreds of thousands of submitted photos. Grevy's zebras once roamed across five countries in Africa.

Recent work has shown that deep learning models can be used to classify land-use data from geospatial satellite imagery. We show that when these deep learning models are trained on data from specific continents/seasons, there is a high degree of variability in model performance on out-of-sample continents/seasons. This suggests that just because a model accurately predicts land-use classes in one continent or season does not mean that the model will accurately predict land-use classes in a different continent or season. We then use clustering techniques on satellite imagery from different continents to visualize the differences in landscapes that make geospatial generalization particularly difficult, and summarize our takeaways for future satellite imagery-related applications.

This paper describes the approach proposed by the D2KLab team for the 2020 RecSys Challenge on the task of predicting user engagement facing tweets. This approach relies on two distinct stages. First, relevant features are learned from the challenge dataset. These features are heterogeneous and are the results of different learning modules such as handcrafted features, knowledge graph embeddings, sentiment analysis features and BERT word embeddings. Second, these features are provided in input to an ensemble system based on XGBoost. This approach, only trained on a subset of the entire challenge dataset, ranked 22 in the final leaderboard.

Global Artificial Intelligence (AI) in Cybersecurity Market reports provide in-depth analysis of Top Players, Geography, End users, Applications, Competitor analysis, SWOT Analysis, Revenue, Price, Gross Margin, Market Share, Import-Export data, Trends and Forecast 2025. Global Artificial Intelligence (AI) in Cybersecurity Market in-depth insights which includes the competitiveness of the trending players. Analysts have carefully evaluated the milestones achieved by the Artificial Intelligence (AI) in Cybersecurity Market and the current trends that are likely to shape its future. Primary and secondary research methodologies have been used to put together an exhaustive report on the subject. The latest report added by Market Info Reports demonstrates that the global Artificial Intelligence (AI) in Cybersecurity market will showcase a steady CAGR in the coming years.

Many businesses and organizations are turning to machine learning for solutions to challenging business goals and problems. Providing machine learning solutions to meet these needs requires that one follows a systematic process from problem to solution. The stages of a machine learning project constitute the machine learning pipeline. The machine learning pipeline is a systematic progression of a machine learning task from data to intelligence. During our training as ML engineers, a lot of focus is invested in learning about algorithms, techniques, and machine learning tools but often, less attention is given to how to approach industry and business problems from the problem to a usable solution.

Understanding data and reaching valid conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have widespread application for this purpose in different fields. One critically important yet less explored aspect is how data and model uncertainties are captured and analyzed. Proper quantification of uncertainty provides valuable information for optimal decision making. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, application of novel deep learning techniques to deal such uncertainties have significantly increased.

Knowledge Transfer has been applied in solving a wide variety of problems. For example, knowledge can be transferred between tasks (e.g., learning to handle novel situations by leveraging prior knowledge) or between agents (e.g., learning from others without direct experience). Without loss of generality, we relate knowledge transfer to KL-divergence minimization, i.e., matching the (belief) distributions of learners and teachers. The equivalence gives us a new perspective in understanding variants of the KL-divergence by looking at how learners structure their interaction with teachers in order to acquire knowledge. In this paper, we provide an in-depth analysis of KL-divergence minimization in Forward and Backward orders, which shows that learners are reinforced via on-policy learning in Backward. In contrast, learners are supervised in Forward. Moreover, our analysis is gradient-based, so it can be generalized to arbitrary tasks and help to decide which order to minimize given the property of the task. By replacing Forward with Backward in Knowledge Distillation, we observed +0.7-1.1 BLEU gains on the WMT'17 De-En and IWSLT'15 Th-En machine translation tasks.

The development of graph neural networks (GCN) makes it possible to learn structural features from evolving complex networks. Even though a wide range of realistic networks are directed ones, few existing works investigated the properties of directed and temporal networks. In this paper, we address the problem of temporal link prediction in directed networks and propose a deep learning model based on GCN and self-attention mechanism, namely TSAM. The proposed model adopts an autoencoder architecture, which utilizes graph attentional layers to capture the structural feature of neighborhood nodes, as well as a set of graph convolutional layers to capture motif features. A graph recurrent unit layer with self-attention is utilized to learn temporal variations in the snapshot sequence. We run comparative experiments on four realistic networks to validate the effectiveness of TSAM. Experimental results show that TSAM outperforms most benchmarks under two evaluation metrics.

If you happen to fall outside that specified range, navigating the internet, your community, even your own home, can become exponentially more difficult. But it doesn't have to be this way, argues artist, writer and design researcher Sara Hendren. In her new book, What Can a Body Do, Hendren examines the challenges that people with disabilities face on a daily basis in a world that often doesn't take their needs into account and shows that more inclusive design -- from cybernetic prosthetic arms and more accessible city streets to tactile doorbells for the deaf -- isn't just possible, it's already practical. In the excerpt below, Hendren looks at the Jaipur Foot, an unpowered, low-cost prosthetic that has helped nearly two million lower leg amputees in India and other countries regain their ability to walk. From WHAT CAN A BODY DO: How We Meet the Built World by Sara Hendren published on August 18, 2020 by Riverhead, an imprint of Penguin Publishing Group, a division of Penguin Random House LLC.

Global Artificial Intelligence-based Security Market reports provide in-depth analysis of Top Players, Geography, End users, Applications, Competitor analysis, Revenue, Price, Gross Margin, Market Share, Import-Export data, Trends and Forecast. Firstly, the Artificial Intelligence-based Security Market Report provides a basic overview of the industry including definitions, classifications, applications and chain structure. The Artificial Intelligence-based Security market analysis is provided for the international markets including development trends, competitive landscape analysis, and key regions development status. Key Players covered in this report are Nvidia Corporation, Intel Corporation, Xilinx Inc, Samsung Electronics Co., Ltd, Micron Technology, IBM Corporation, Cylance Inc, Threatmetrix, Securonix, Inc, Amazon, Sift Science, Acalvio Technologies, Skycure Inc,. Our industry professionals are working reluctantly to understand, assemble and timely deliver assessment on impact of COVID-19 disaster on many corporations and their clients to help them in taking excellent business decisions.