Researchers use figures to communicate rich, complex information in scientific papers. The captions of these figures are critical to conveying effective messages. However, low-quality figure captions commonly occur in scientific articles and may decrease understanding. In this paper, we propose an end-to-end neural framework to automatically generate informative, high-quality captions for scientific figures. To this end, we introduce SCICAP, a large-scale figure-caption dataset based on computer science arXiv papers published between 2010 and 2020. After pre-processing - including figure-type classification, sub-figure identification, text normalization, and caption text selection - SCICAP contained more than two million figures extracted from over 290,000 papers. We then established baseline models that caption graph plots, the dominant (19.2%) figure type. The experimental results showed both opportunities and steep challenges of generating captions for scientific figures.

Synthesising the spatial and temporal dynamics of the human body skeleton remains a challenging task, not only in terms of the quality of the generated shapes, but also of their diversity, particularly to synthesise realistic body movements of a specific action (action conditioning). In this paper, we propose Kinetic-GAN, a novel architecture that leverages the benefits of Generative Adversarial Networks and Graph Convolutional Networks to synthesise the kinetics of the human body. The proposed adversarial architecture can condition up to 120 different actions over local and global body movements while improving sample quality and diversity through latent space disentanglement and stochastic variations. Our experiments were carried out in three well-known datasets, where Kinetic-GAN notably surpasses the state-of-the-art methods in terms of distribution quality metrics while having the Figure 1: Synthetic set of actions generated by our graph ability to synthesise more than one order of magnitude regarding convolutional generator trained on NTU RGB D [32] (first the number of different actions.

In this paper, we apply conformal prediction to time series data. Conformal prediction isa method that produces predictive regions given a confidence level. The regions outputs arealways valid under the exchangeability assumption. However, this assumption does not holdfor the time series data because there is a link among past, current, and future observations.Consequently, the challenge of applying conformal predictors to the problem of time seriesdata lies in the fact that observations of a time series are dependent and therefore do notmeet the exchangeability assumption. This paper aims to present a way of constructingreliable prediction intervals by using conformal predictors in the context of time series. Weuse the nearest neighbors method based on the fast parameters tuning technique in theweighted nearest neighbors (FPTO-WNN) approach as the underlying algorithm. Dataanalysis demonstrates the effectiveness of the proposed approach.

Python is one of the most widely used programming languages in the world, and for good reason. Because of its vast libraries and flexible structure, it's simple to learn, has consistent and easy-to-parse syntax, and is utilized for artificial intelligence applications. The platform's spectacular ascent has sparked a devoted community, fueled in no little part by its adoption by big companies such as DropBox, Reddit, and Instagram, to name a few. Check out this list of Python developers to follow if you're seeking Python programmers who are leading the charge. The people on this list have solid technical credentials, are constantly adding new and interesting features to the platform, and have a strong social media presence.

Mozambique is a country in southern Africa, a former Portuguese colony, where almost 40% of girls and adolescents become pregnant before the age of 18. An enormous challenge in developing countries to overcome poverty is to ensure that girls do not become pregnant at an early age. To this end, various international organizations and NGOs are actively working in Africa to help the governments advance this agenda. Currently, there are initiatives of support and sexual and reproductive education for girls, young women, and adolescents through digital media and SMS. In these media there is a dialogue between experts who guide and orient those who write, there are millions of conversations collected in recent years. All these conversations contain unstructured data that are of great value to understand over time the evolution of the concerns of those who use these channels and how to use this information to make better public policy decisions by the government with international support.

This paper presents a novel keypoints-based attention mechanism for visual recognition in still images. Deep Convolutional Neural Networks (CNNs) for recognizing images with distinctive classes have shown great success, but their performance in discriminating fine-grained changes is not at the same level. We address this by proposing an end-to-end CNN model, which learns meaningful features linking fine-grained changes using our novel attention mechanism. It captures the spatial structures in images by identifying semantic regions (SRs) and their spatial distributions, and is proved to be the key to modelling subtle changes in images. We automatically identify these SRs by grouping the detected keypoints in a given image. The ``usefulness'' of these SRs for image recognition is measured using our innovative attentional mechanism focusing on parts of the image that are most relevant to a given task. This framework applies to traditional and fine-grained image recognition tasks and does not require manually annotated regions (e.g. bounding-box of body parts, objects, etc.) for learning and prediction. Moreover, the proposed keypoints-driven attention mechanism can be easily integrated into the existing CNN models. The framework is evaluated on six diverse benchmark datasets. The model outperforms the state-of-the-art approaches by a considerable margin using Distracted Driver V1 (Acc: 3.39%), Distracted Driver V2 (Acc: 6.58%), Stanford-40 Actions (mAP: 2.15%), People Playing Musical Instruments (mAP: 16.05%), Food-101 (Acc: 6.30%) and Caltech-256 (Acc: 2.59%) datasets.

Unlike the courts in western countries, public records of Indian judiciary are completely unstructured and noisy. No large scale publicly available annotated datasets of Indian legal documents exist till date. This limits the scope for legal analytics research. In this work, we propose a new dataset consisting of over 10,000 judgements delivered by the supreme court of India and their corresponding hand written summaries. The proposed dataset is pre-processed by normalising common legal abbreviations, handling spelling variations in named entities, handling bad punctuations and accurate sentence tokenization. Each sentence is tagged with their rhetorical roles. We also annotate each judgement with several attributes like date, names of the plaintiffs, defendants and the people representing them, judges who delivered the judgement, acts/statutes that are cited and the most common citations used to refer the judgement. Further, we propose an automatic labelling technique for identifying sentences which have summary worthy information. We demonstrate that this auto labeled data can be used effectively to train a weakly supervised sentence extractor with high accuracy. Some possible applications of this dataset besides legal document summarization can be in retrieval, citation analysis and prediction of decisions by a particular judge.

She has been described as "a vision of the future" who is every bit as good as other abstract artists today, but Ai-Da – the world's first ultra-realistic robot artist – hit a temporary snag before her latest exhibition when Egyptian security forces detained her at customs. Ai-Da is due to open and present her work at the Great Pyramid of Giza on Thursday, the first time contemporary art has been allowed next to the pyramid in thousands of years. But because of "security issues" that may include concerns that she is part of a wider espionage plot, both Ai-Da and her sculpture were held in Egyptian customs for 10 days before being released on Wednesday, sparking a diplomatic fracas.

Currently, the development of computer games has shown a tremendous surge. The ease and speed of internet access today have also influenced the development of computer games, especially computer games that are played online. Internet technology has allowed computer games to be played in multiplayer mode. Interaction between players in a computer game can be built in several ways, one of which is by providing balanced opponents. Opponents can be developed using intelligent agents. On the other hand, research on developing intelligent agents is also growing rapidly. In computer game development, one of the easiest ways to measure the performance of an intelligent agent is to develop a virtual environment that allows the intelligent agent to interact with other players. In this research, we try to develop an intelligent agent and virtual environment for the board game. To be easily accessible, the intelligent agent and virtual environment are then developed into an Application Programming Interface (API) service called Gapoera API. The Gapoera API service that is built is expected to help game developers develop a game without having to think much about the artificial intelligence that will be embedded in the game. This service provides a basic multilevel intelligent agent that can provide users with playing board games commonly played in Indonesia. Although the Gapoera API can be used for various types of games, in this paper, we will focus on the discussion on a popular traditional board game in Indonesia, namely Mancala. The test results conclude that the multilevel agent concept developed has worked as expected. On the other hand, the development of the Gapoera API service has also been successfully accessed on several game platforms.

Missing data is a recurrent problem in remote sensing, mainly due to cloud coverage for multispectral images and acquisition problems. This can be a critical issue for crop monitoring, especially for applications relying on machine learning techniques, which generally assume that the feature matrix does not have missing values. This paper proposes a Gaussian Mixture Model (GMM) for the reconstruction of parcel-level features extracted from multispectral images. A robust version of the GMM is also investigated, since datasets can be contaminated by inaccurate samples or features (e.g., wrong crop type reported, inaccurate boundaries, undetected clouds, etc). Additional features extracted from Synthetic Aperture Radar (SAR) images using Sentinel-1 data are also used to provide complementary information and improve the imputations. The robust GMM investigated in this work assigns reduced weights to the outliers during the estimation of the GMM parameters, which improves the final reconstruction. These weights are computed at each step of an Expectation-Maximization (EM) algorithm by using outlier scores provided by the isolation forest algorithm. Experimental validation is conducted on rapeseed and wheat parcels located in the Beauce region (France). Overall, we show that the GMM imputation method outperforms other reconstruction strategies. A mean absolute error (MAE) of 0.013 (resp. 0.019) is obtained for the imputation of the median Normalized Difference Index (NDVI) of the rapeseed (resp. wheat) parcels. Other indicators (e.g., Normalized Difference Water Index) and statistics (for instance the interquartile range, which captures heterogeneity among the parcel indicator) are reconstructed at the same time with good accuracy. In a dataset contaminated by irrelevant samples, using the robust GMM is recommended since the standard GMM imputation can lead to inaccurate imputed values.