Do you remember watching crime shows where investigating teams used to hire sketch artists to draw the image/face of criminal described by witnesses? And they would then hunt for the person to lock him up. But one might wonder today, are these tactics still common in detecting crime or criminals? With the rise in Artificial Intelligence enabled Face and Image Recognition technologies, the days of sketching criminal are long gone. The process of identifying or verifying the identity of a person using their face has made investigations a lot easier today.

Agriculture finance represents an important element of eradicating extreme poverty and boosting shared prosperity. According to the International Fund for Agricultural Development, smallholders manage over 80% of the world's estimated 500 million small farms and provide over 80% of the food consumed in a significant part of the developing world, making a major contribution to poverty reduction and food security. Most smallholder farms are in Asia and sub-Saharan Africa, and in both regions over 80% of farmland is managed by smallholders. Even though these farmers are generally characterized by limited resources--particularly in terms of land--and dependence on household members for farm labor, they represent a critical part of food systems in developing countries. In light of the size and importance of the smallholder farming sector, the development community has a growing focus on providing these farmers with the funding they need to thrive.

The algorithmic revolution is here, and nations are losing control of not only their understanding of the potential impact of artificial intelligence but also the governance model that enforced accountability on the advances in science and technology over the years at all levels. While each new technology innovation claims its territory for the economic advances in the human ecosystem with significant ramifications across cyberspace, geospace and/or space (CGS), the rise of artificial intelligence (AI) has not only undermined governance, management, and growth models, but it has also broken all barriers to boundaries defined by human decision makers. In addition, it is both blurring the boundaries between human intelligence and machine intelligence, and the boundaries between man and machine and real and fake. As a result, the power dynamics are shifting away from the select few across nations (and is moving away from humans entirely to algorithms)--re-defining the criteria upon which geopolitics was framed--and thereby threatening the foundations of global peace and security. Since the beginning of the technological age, each new idea, innovation, and invention has helped humans across nations usher in a new era of economic growth, changing the fundamentals of respective nations and their security.

Citation parsing, particularly with deep neural networks, suffers from a lack of training data as available datasets typically contain only a few thousand training instances. Manually labelling citation strings is very time-consuming, hence synthetically created training data could be a solution. However, as of now, it is unknown if synthetically created reference-strings are suitable to train machine learning algorithms for citation parsing. To find out, we train Grobid, which uses Conditional Random Fields, with a) human-labelled reference strings from 'real' bibliographies and b) synthetically created reference strings from the GIANT dataset. We find that both synthetic and organic reference strings are equally suited for training Grobid (F1 = 0.74). We additionally find that retraining Grobid has a notable impact on its performance, for both synthetic and real data (+30% in F1). Having as many types of labelled fields as possible during training also improves effectiveness, even if these fields are not available in the evaluation data (+13.5% F1). We conclude that synthetic data is suitable for training (deep) citation parsing models. We further suggest that in future evaluations of reference parsers both evaluation data similar and dissimilar to the training data should be used for more meaningful evaluations.

High-resolution images taken by satellites in orbit around Earth can detect swathes of plastic pollution in the world's oceans, a study has found for the first time. The European Space Agency's Sentinel-2 satellites are able to spot floating plastics and tell them apart from other materials such as seaweed and driftwood. Astronomers say the imaging technique can automatically spot the difference with 86 per cent accuracy. In one location where the method was tested, Canada's Gulf Islands, the method was 100 per cent accurate. Conservationists are calling for similar technology to be used in the fight to clean up the world of humanity's litter.

In recent years, several research articles have been published in the field of corona-virus caused diseases like severe acute respiratory syndrome (SARS), middle east respiratory syndrome (MERS) and COVID-19. In the presence of numerous research articles, extracting best-suited articles is time-consuming and manually impractical. The objective of this paper is to extract the activity and trends of corona-virus related research articles using machine learning approaches. The COVID-19 open research dataset (CORD-19) is used for experiments, whereas several target-tasks along with explanations are defined for classification, based on domain knowledge. Clustering techniques are used to create the different clusters of available articles, and later the task assignment is performed using parallel one-class support vector machines (OCSVMs). Experiments with original and reduced features validate the performance of the approach. It is evident that the k-means clustering algorithm, followed by parallel OCSVMs, outperforms other methods for both original and reduced feature space.

Apple orchards in the U.S. are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease detection. Incorrect and delayed diagnosis can result in either excessive or inadequate use of chemicals, with increased production costs, environmental, and health impacts. We have manually captured 3,651 high-quality, real-life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. A subset, expert-annotated to create a pilot dataset for apple scab, cedar apple rust, and healthy leaves, was made available to the Kaggle community for 'Plant Pathology Challenge'; part of the Fine-Grained Visual Categorization (FGVC) workshop at CVPR 2020 (Computer Vision and Pattern Recognition). We also trained an off-the-shelf convolutional neural network (CNN) on this data for disease classification and achieved 97% accuracy on a held-out test set. This dataset will contribute towards development and deployment of machine learning-based automated plant disease classification algorithms to ultimately realize fast and accurate disease detection. We will continue to add images to the pilot dataset for a larger, more comprehensive expert-annotated dataset for future Kaggle competitions and to explore more advanced methods for disease classification and quantification.

COVID-19 and its grave impact worldwide has emphasised just how critical it is for African countries to develop their healthcare systems. For the most part, these systems are woefully underfunded and understaffed. It will take many different approaches to repair these systems. Given my area of expertise and my research focus, I am interested in the role that Artificial Intelligence (AI) might play in bolstering the continent's health systems. AI embodies the field of knowledge that seeks to create machines (computers) that can emulate human cognitive functions such as learning, reasoning, understanding, vision, perception, recognition, and problem solving to a reasonable level.

The amount and size of spatiotemporal data sets from different domains have been rapidly increasing in the last years, which demands the development of robust and fast methods to analyze and extract information from them. In this paper, we propose a network-based model for spatiotemporal data analysis called chronnet. It consists of dividing a geometrical space into grid cells represented by nodes connected chronologically. The main goal of this model is to represent consecutive recurrent events between cells with strong links in the network. This representation permits the use of network science and graphing mining tools to extract information from spatiotemporal data. The chronnet construction process is fast, which makes it suitable for large data sets. In this paper, we describe how to use our model considering artificial and real data. For this purpose, we propose an artificial spatiotemporal data set generator to show how chronnets capture not just simple statistics, but also frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Additionally, we analyze a real-world data set composed of global fire detections, in which we describe the frequency of fire events, outlier fire detections, and the seasonal activity, using a single chronnet.

Most topic models are constructed under the assumption that documents follow a multinomial distribution. The Poisson distribution is an alternative distribution to describe the probability of count data. For topic modelling, the Poisson distribution describes the number of occurrences of a word in documents of fixed length. The Poisson distribution has been successfully applied in text classification, but its application to topic modelling is not well documented, specifically in the context of a generative probabilistic model. Furthermore, the few Poisson topic models in literature are admixture models, making the assumption that a document is generated from a mixture of topics. In this study, we focus on short text. Many studies have shown that the simpler assumption of a mixture model fits short text better. With mixture models, as opposed to admixture models, the generative assumption is that a document is generated from a single topic. One topic model, which makes this one-topic-per-document assumption, is the Dirichlet-multinomial mixture model. The main contributions of this work are a new Gamma-Poisson mixture model, as well as a collapsed Gibbs sampler for the model. The benefit of the collapsed Gibbs sampler derivation is that the model is able to automatically select the number of topics contained in the corpus. The results show that the Gamma-Poisson mixture model performs better than the Dirichlet-multinomial mixture model at selecting the number of topics in labelled corpora. Furthermore, the Gamma-Poisson mixture produces better topic coherence scores than the Dirichlet-multinomial mixture model, thus making it a viable option for the challenging task of topic modelling of short text.