When U.N. member states unanimously adopted the 2030 Agenda in 2015, the narrative around global development embraced a new paradigm of sustainability and inclusion--of planetary stewardship alongside economic progress, and inclusive distribution of income. This comprehensive agenda--merging social, economic and environmental dimensions of sustainability--is not supported by current modes of data collection and data analysis, so the report of the High-Level Panel on the post-2015 development agenda called for a "data revolution" to empower people through access to information.1 Today, a central development problem is that high-quality, timely, accessible data are absent in most poor countries, where development needs are greatest. In a world of unequal distributions of income and wealth across space, age and class, gender and ethnic pay gaps, and environmental risks, data that provide only national averages conceal more than they reveal. This paper argues that spatial disaggregation and timeliness could permit a process of evidence-based policy making that monitors outcomes and adjusts actions in a feedback loop that can accelerate development through learning. Big data and artificial intelligence are key elements in such a process. Emerging technologies could lead to the next quantum leap in (i) how data is collected; (ii) how data is analyzed; and (iii) how analysis is used for policymaking and the achievement of better results. Big data platforms expand the toolkit for acquiring real-time information at a granular level, while machine learning permits pattern recognition across multiple layers of input. Together, these advances could make data more accessible, scalable, and finely tuned. In turn, the availability of real-time information can shorten the feedback loop between results monitoring, learning, and policy formulation or investment, accelerating the speed and scale at which development actors can implement change.

Recent advances in employing neural networks on graph domains helped push the state of the art in link prediction tasks, particularly in recommendation services. However, the use of temporal contextual information, often modeled as dynamic graphs that encode the evolution of user-item relationships over time, has been overlooked in link prediction problems. In this paper, we consider the hypothesis that leveraging such information enables models to make better predictions, proposing a new neural network approach for this. Our experiments, performed on the widely used ML-100k and ML-1M datasets, show that our approach produces better predictions in scenarios where the pattern of user-item relationships change over time. In addition, they suggest that existing approaches are significantly impacted by those changes.

ABSTRACT The problem to deal with in this project is the problem of routing electric vehicles, which consists of finding the best routes for this type of vehicle, so that they reach their destination, without running out of power and optimizing to the maximum transportation costs. The importance of this problem is mainly in the sector of shipments in the recent future, when obsolete energy sources are replaced with renewable sources, where each vehicle contains a number of packages that must be delivered at specific points in the city, but, being electric, they do not have an optimal battery life, so having the ideal routes traced is a vital aspect for the proper functioning of these. Now days you can see applications of this problem in the cleaning sector, specifically with the trucks responsible for collecting garbage, which aims to travel the entire city in the most efficient way, without letting excessive garbage accumulate. PAGE SIZE All material on each page should fit within a rectangle of 18 23.5 cm (7" 9.25"), centered on the page, beginning 1.9 cm (0.75") from the top of the page and ending with 2.54 cm (1") from the bottom. The right and left margins should be 1.9 cm (.75"). The text should be in two 8.45 cm (3.33") columns with a .83

Homicide mortality is a worldwide concern and has occupied the agenda of researchers and public managers. In Brazil, homicide is the third leading cause of death in the general population and the first in the 15-39 age group. In South America, Brazil has the third highest homicide mortality, behind Venezuela and Colombia. To measure the impacts of violence it is important to assess health systems and criminal justice, as well as other areas. In this paper, we analyze the spatial distribution of homicide mortality in the state of Goias, Center-West of Brazil, since the homicide rate increased from 24.5 per 100,000 in 2002 to 42.6 per 100,000 in 2014 in this location. Moreover, this state had the fifth position of homicides in Brazil in 2014. We considered socio-demographic variables for the state, performed analysis about correlation and employed three clustering algorithms: K-means, Density-based and Hierarchical. The results indicate the homicide rates are higher in cities neighbors of large urban centers, although these cities have the best socioeconomic indicators.

Supervised dimensionality reduction strategies have been of great interest. However, current supervised dimensionality reduction approaches are difficult to scale for situations characterized by large datasets given the high computational complexities associated with such methods. While stochastic approximation strategies have been explored for unsupervised dimensionality reduction to tackle this challenge, such approaches are not well-suited for accelerating computational speed for supervised dimensionality reduction. Motivated to tackle this challenge, in this study we explore a novel direction of directly learning optimal class-aware embeddings in a supervised manner via the notion of supervised random projections (SRP). The key idea behind SRP is that, rather than performing spectral decomposition (or approximations thereof) which are computationally prohibitive for large-scale data, we instead perform a direct decomposition by leveraging kernel approximation theory and the symmetry of the Hilbert-Schmidt Independence Criterion (HSIC) measure of dependence between the embedded data and the labels. Experimental results on five different synthetic and real-world datasets demonstrate that the proposed SRP strategy for class-aware embedding learning can be very promising in producing embeddings that are highly competitive with existing supervised dimensionality reduction methods (e.g., SPCA and KSPCA) while achieving 1-2 orders of magnitude better computational performance. As such, such an efficient approach to learning embeddings for dimensionality reduction can be a powerful tool for large-scale data analysis and visualization.

China now has seminars to teach other countries how to censor free speech as its'techno-dystopia' spreads, a worrying report has found. Governments worldwide are stepping up use of online tools to suppress dissent and tighten their grip on power, a human rights watchdog study found. Chinese officials have held sessions on controlling information with 36 of the 65 countries assessed, and provided telecom and surveillance equipment to a number of foreign governments, researchers said. India led the world in the number of internet shutdowns, with over 100 reported incidents in 2018 so far, claiming that the moves were needed to halt the flow of disinformation and incitement to violence. Many governments, including Saudi Arabia, are employing'troll armies' to manipulate social media and in many cases drown out the voices of dissidents.

Personal electronic devices such as smartphones give access to a broad range of behavioral signals that can be used to learn about the characteristics and preferences of individuals. In this study we explore the connection between demographic and psychological attributes and digital records for a cohort of 7,633 people, closely representative of the US population with respect to gender, age, geographical distribution, education, and income. We collected self-reported assessments on validated psychometric questionnaires based on both the Moral Foundations and Basic Human Values theories, and combined this information with passively-collected multi-modal digital data from web browsing behavior, smartphone usage and demographic data. Then, we designed a machine learning framework to infer both the demographic and psychological attributes from the behavioral data. In a cross-validated setting, our model is found to predict demographic attributes with good accuracy (weighted AUC scores of 0.90 for gender, 0.71 for age, 0.74 for ethnicity). Our weighted AUC scores for Moral Foundation attributes (0.66) and Human Values attributes (0.60) suggest that accurate prediction of complex psychometric attributes is more challenging but feasible. This connection might prove useful for designing personalized services, communication strategies, and interventions, and can be used to sketch a portrait of people with similar worldviews.

There are various algorithms and methodologies used for automated screening of cervical cancer by segmenting and classifying cervical cancer cells into different categories. This study presents a critical review of different research papers published that integrated AI methods in screening cervical cancer via different approaches analyzed in terms of typical metrics like dataset size, drawbacks, accuracy etc. An attempt has been made to furnish the reader with an insight of Machine Learning algorithms like SVM (Support Vector Machines), GLCM (Gray Level Co-occurrence Matrix), k-NN (k-Nearest Neighbours), MARS (Multivariate Adaptive Regression Splines), CNNs (Convolutional Neural Networks), spatial fuzzy clustering algorithms, PNNs (Probabilistic Neural Networks), Genetic Algorithm, RFT (Random Forest Trees), C5.0, CART (Classification and Regression Trees) and Hierarchical clustering algorithm for feature extraction, cell segmentation and classification. This paper also covers the publicly available datasets related to cervical cancer. It presents a holistic review on the computational methods that have evolved over the period of time, in chronological order in detection of malignant cells.

An Indian student in the US has created a space-detecting algorithm that can help tackle the problem of finding a parking spot by using big data analytics and save a person's time and money. Sai Nikhil Reddy Mettupally, who is studying at The University of Alabama in Huntsville (UAH), has also won second prize at the 2018 Science and Technology Open House competition for his creation. According to a university press release, Sai's creation relies on big data analytics and deep-learning techniques to lead drivers directly to an empty parking spot. Big data analytics is a complex process of examining large and varied data sets to uncover information including hidden patterns, unknown correlations, market trends and customer preferences. Sai conceived the idea shortly after the university transitioned to zone parking last fall. "The data show that, on a typical day, there is a high chance that students or faculty members will have difficulty getting a parking spot between 11 am and 1 pm, leading to the wastage of time and fuel, and adding to the pollution," he says.

Abstract-- Dynamic classifier selection systems aim to select a group of classifiers that is most adequate for a specific query pattern. This is done by defining a region around the query pattern and analyzing the competence of the classifiers in this region. However, the regions are often surrounded by noise which can difficult the classifier selection. This fact makes the performance of most dynamic selection systems no better than static selections. In this paper we demonstrate that the performance of dynamic selection systems end up limited by the quality of the regions extracted. Thereafter, we propose a new dynamic classifier selection system that improves the regions of competence in order to achieve higher recognition rates. Results obtained from several classification databases show the proposed method not only significantly increase the recognition performance, but also decreases the computational cost. Multiple Classifier Systems/Ensemble of Classifiers have been widely studied in the past years as an alternative to increase efficiency and accuracy in pattern recognition problems [1], [2].