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BACKGROUND Timely, accurate, and precise demographic estimates at various levels of geography are crucial for planning, policymaking, and analysis. In the United States, data from the decennial census and annual American Community Survey (ACS) serve as the main sources for subnational demographic estimates. While estimates derived from these sources are widely regarded as accurate, their timeliness is limited and variability sizable for small geographic units like towns and neighborhoods. OBJECTIVE This paper investigates the potential for using nonrepresentative consumer trace data assembled by commercial vendors to produce valid and timely estimates. We focus on data purchased from Data Axle, which contains the names and addresses of over 150 million Americans annually. METHODS We identify the predictors of over- and undercounts of households as measured with consumer trace data and compare a range of calibration approaches to assess the extent to which systematic errors in the data can be adjusted for over time. We also demonstrate the utility of the data for predicting contemporaneous (nowcasting) tract-level household counts in the 2020 Decennial Census. RESULTS We find that adjusted counts at the county, ZIP Code Tabulation Areas (ZCTA), and tract levels deviate from ACS survey-based estimates by an amount roughly equivalent to the ACS margins of error. Machine-learning methods perform best for calibration of countyand tract-level data. The estimates are stable over time and across regions of the country. We also find that when doing nowcasts, incorporating Data Axle estimates improved prediction bias relative to using the most recent ACS five-year estimates alone. CONTRIBUTION Despite its affordability and timeliness compared to survey-based measures, consumer trace data remains underexplored by demographers. This paper examines one consumer trace data source and demonstrates that challenges with representativeness can be overcome to produce household estimates that align with survey-based estimates and improve demographic forecasts. At the same time, the analysis also underscores the need for researchers to examine the limits of the data carefully before using them for specific applications. [ABSTRACT FROM AUTHOR]

Recent progress in machine learning has been driven by the development of new algorithms and low-cost computation. The adoption of machine learning methods can be found in science, technology, and commerce including health care, manufacturing, education, finance, and marketing. In this epoch of data revolution, it has become important to examine machine learning and its influence in higher education. This paper examines initiatives that American universities have taken to implement machine learning as part of their data science educational curriculum. This paper also explores popular categories of courses offered by universities as part of their machine learning curriculum. Previous studies have shown that there is currently a shortage of data scientists. This study finds that many American universities are now offering machine learning courses to train students with the necessary skills to meet this shortage. [ABSTRACT FROM AUTHOR]

This paper proposes a novel methodology for recovering missing time series data, a crucial task for subsequent Machine Learning (ML) analyses. The methodology is specifically applied to Significant Wave Height (SWH) time series in the field of marine engineering. The proposed approach involves two phases. Firstly, the SWH time series for each buoy is independently reconstructed using three transfer function models: regression-based, correlation-based, and distance-based. The distance-based transfer function exhibits the best overall performance. Secondly, Evolutionary Artificial Neural Networks (EANNs) are utilised for the final recovery of each time series, using as inputs highly correlated buoys that have been intermediately recovered. The EANNs are evolved considering two metrics, the novel squared error relevance area, which balances the importance of extreme and around-mean values, and the well-known mean squared error. The study considers SWH time series data from 15 buoys in two coastal zones in the United States. The results demonstrate that the distance-based transfer function is generally the best transfer function, and that EANNs outperform a range of state-of-the-art ML techniques in 12 out of the 15 buoys, with a number of connections comparable to linear models. Furthermore, the proposed methodology outperforms the two most popular approaches for time series reconstruction, BRITS and SAITS, for all buoys except one. Therefore, the proposed methodology provides a promising approach, which may be applied to time series from other fields, such as wind or solar energy farms in the field of green energy. • Massive time series reconstruction is carried out by a novel two-phased approach. • Three transfer functions are used in the 1st phase: the distance-based stands out. • Evolutionary Artificial Neural Networks obtain the best performance in the 2nd phase. • New SERA metric optimises 2nd phase models to pay more attention to extreme values. • This method is applied to accurately recover time series of renewable energy sources. [ABSTRACT FROM AUTHOR]

Abstract Rainfall derivatives are in their infancy since starting trading on the Chicago Mercantile Exchange (CME) in 2011. Being a relatively new class of financial instruments there is no generally recognised pricing framework used within the literature. In this paper, we propose a novel Genetic Programming (GP) algorithm for pricing contracts. Our novel algorithm, which is called Stochastic Model GP (SMGP), is able to generate and evolve stochastic equations of rainfall, which allows us to probabilistically transform rainfall predictions from the risky world to the risk-neutral world. In order to achieve this, SMGP's representation allows its individuals to comprise of two weighted parts, namely a seasonal component and an autoregressive component. To create the stochastic nature of an equation for each SMGP individual, we estimate the weights by using a probabilistic approach. We evaluate the models produced by SMGP in terms of rainfall predictive accuracy and in terms of pricing performance on 42 cities from Europe and the USA. We compare SMGP to 8 methods: its predecessor DGP, 5 well-known machine learning methods (M5 Rules, M5 Model trees, k-Nearest Neighbors, Support Vector Regression, Radial Basis Function), and two statistical methods, namely AutoRegressive Integrated Moving Average (ARIMA) and Monte Carlo Rainfall Prediction (MCRP). Results show that the proposed algorithm is able to statistically outperform all other algorithms. [ABSTRACT FROM AUTHOR]

The war on terrorism, radicalism and violent extremism is no longer confined to the battlefield; it has become omnipresent in the recent years with militant, terrorists and insurgent groups actively recruiting new technologies as platform to impel their ideologies worldwide. Nonetheless, one thing remains constant in the fight against radicalism equation, that is, the information about radical individuals, whether personal, demographical, social or economic information. The ability to identify, classify and profile potential radicals based on such information has an appealing trait for security agencies around the world. The Intelligence and Security Informatics research field emphasizes the importance of engaging multidiscipline technologies to provide security-related applications that serve as aiding tools in combating terrorism and other forms of violent extremism. In this paper, the authors focus on radical Islamism and hypothesize that Islamist radicals have identifiable information and behavioral traits that could be utilized to identify their ideological motive uniquely amongst other radicals. Four different supervised machine learning algorithms are applied to validate this hypothesis using the “profiles of individual radicalization in the United States” dataset and their performance is compared and discussed. The evaluation results support the authors’ hypothesis and show that profiling religious extremists can be achieved with high recall and precision using machine learning models. [ABSTRACT FROM AUTHOR]

Abstract: The increasing prevalence of multimedia and research data generated by scientific work affords an opportunity to reformulate the idea of a scientific article from the traditional static document, or even one with links to supplemental material in remote databases, to a self-contained, multimedia-rich interactive publication. This paper describes our concept of such a document, and the design of tools for authoring (Forge) and visualization/analysis (Panorama). They are platform-independent applications written in Java, and developed in Eclipse 1 [1] http://www.eclipse.org. using its Rich Client Platform (RCP) framework. Both applications operate on PDF files with links to XML files that define the media type, location, and action to be performed. We also briefly cite the challenges posed by the potentially large size of interactive publications, the need for evaluating their value to improved comprehension and learning, and the need for their long-term preservation by the National Library of Medicine and other libraries. [Copyright &y& Elsevier]

• Built-environment and socioeconomic variables are impactful to predict shipment statuses. • Sensitivity of bid is the highest for larger shipments, C2C market, and out-of-state requests. • Smartphone app gives senders realistic estimates. • Smartphone app empowers senders to influence the probability of delivery. This paper empirically studies the matching and delivery process in a major crowd-sourced delivery platform. The aim is to develop models to understand and predict crowd-shipping delivery performance and using the findings to design incentives to improve user experiences as well as system performance. We apply the random forest machine learning algorithm to predict the shipment status of 14,858 crowd-shipping requests recorded between January 2015 and December 2016 throughout the U.S. The models are used to predict three phases of the crowd-shipping performance, namely bidding, acceptance, and delivery, using shipping request, built-environment, and socioeconomic features as explanatory variables. The results demonstrate that the context of the shipment provides strong predictive performance even when shipping request and package information is unknown. Calculating the sensitivity of bid probability, we show that offering a higher reward and posting a shipping request in the morning has the largest effect on the probability to secure a bid. We also find that larger shipments, out-of-state destinations, and peer-to-peer shipments lead to higher sensitivity, likely reflecting the higher perceived risks of such transactions. In practice, the models presented in this study show promise in their ability to effectively predict shipment status in real time. We illustrate a valuable application of the sensitivity analysis derived from the random forest models to develop customer-tailored crowd-shipping smartphone applications. Based on the data mined from past deliveries, customers are given empirically based delivery forecasts for their specific package request and can modify delivery requests to increase their odds of delivery. We find that pricing is the variable with the highest potential to increase delivery probability followed by the timing of the request. [ABSTRACT FROM AUTHOR]