A burst of technology in the 1960s--the Green Revolution--raised agricultural output significantly across developing economies. Since then, rising incomes have boosted protein consumption worldwide, and elevated new challenges: greenhouse-gas emissions from agriculture are increasing (more than a fifth of all emissions worldwide), while a host of practices, from waste to overfishing, threaten the sustainability of food supplies. The COVID-19 pandemic has brought these concerns to the fore: the disease has disrupted supply chains and demand, perversely increasing the amount of food waste in farms and fields while threatening food security for many. As agriculture gradually regains its footing, participants and stakeholders should be casting an eye ahead, to safeguarding food supplies against the potentially greater and more disruptive effects of climate change. Once again, innovation and advanced technologies could make a powerful contribution to secure and sustainable food production. For example, digital and biotechnologies could improve the health of ruminant livestock, requiring fewer methane-producing animals to meet the world's protein needs. Genetic technologies could play a supporting role by enabling the breeding of animals that produce less methane. Meanwhile, AI and sensors could help food processors sort better and slash waste, and other smart technologies could identify inedible by-products for reprocessing. Data and advanced analytics also could help authorities better monitor and manage the seas to limit overfishing--while enabling boat crews to target and find fish with less effort and waste.

Abstract-- In highway scenarios, an alert human driver will typically anticipate early cutin and cutout maneuvers of surrounding vehicles using only visual cues. Different sizes of the regions around the vehicles are analyzed, evaluating the importance of the interaction between vehicles and the context information in the performance. I. INTRODUCTION One of the closest and most plausible scenarios in the To deal with lane-change prediction of surrounding vehicles, adoption of the autonomous vehicles is autonomous navigation in this paper we pose the problem as an action at SAE L3 (chauffeur) or L4 (autopilot) on highways, recognition problem using visual information from cameras. The most advanced The idea behind our proposal is to use the same source of information automation systems to date are the Highway Chauffeur (visual cues) and the same type of approach (action (HC) and the Highway Autopilot (HA), which includes the recognition) that drivers use to anticipate these maneuvers. HC is mostly considered as L3 and HA as L4[1].

Formal Concept Analysis (FCA) has seen application in different knowledge areas, including Social Network Analysis (SNA). In turn, research has also shown the applicability of SNA in assessing team sports. In this project, to uncover frequent passing sequences of a soccer team, an FCA-based approach is introduced. The approach relies on a minimum cover of implications, the Duquenne-Guigues (DG) basis and the notion that a soccer team's passes describe a social network.

The importance of algorithmic fairness grows with the increasing impact machine learning has on people's lives. Recent work on fairness metrics shows the need for causal reasoning in fairness constraints. In this work, a practical method named FairTrade is proposed for creating flexible prediction models which integrate fairness constraints on sensitive causal paths. The method uses recent advances in variational inference in order to account for unobserved confounders. Further, a method outline is proposed which uses the causal mechanism estimates to audit black box models. Experiments are conducted on simulated data and on a real dataset in the context of detecting unlawful social welfare. This research aims to contribute to machine learning techniques which honour our ethical and legal boundaries.

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.