Evaluation of an eBook for Oral Health Literacy© to Promote Child Health: Readability, Suitability, Understandability, Actionability, and Gist-Based Message

The purpose of the study was to evaluate an oral health curriculum called an eBook for Oral Health Literacy© to determine its effectiveness for promoting child health. A secondary purpose was to describe and explain the design characteristics of readability, suitability, understandability, and actionability of the 17 chapters of the eBook. A third purpose was to conduct evaluations on verbatim representations (or literal facts) that are presented in the eBook chapters, including the gist representations that are not explicitly presented but inferred by the reader from the chapter information. Results found that the eBook for Oral Health Literacy© had acceptable, and in many cases, favorable scores, for the five design elements of readability, suitability, understandability, actionability, and gist comprehension. Ongoing dissemination of the eBook for Oral Health Literacy© curriculum has the potential to boost children who are “learning to read” and “reading to learn” about oral health hygiene and nutrition. Future studies should use one or more chapters from the curriculum as an intervention to test this educational premise as an explanatory basis for functional health literacy

INCMap: A Journey towards ontology-based data integration

Ontology-based data integration (OBDI) allows users to federate over heterogeneous data sources using a semantic rich conceptual data model. An important challenge in ODBI is the curation of mappings between the data sources and the global ontology. In the last years, we have built IncMap, a system to semi-automatically create mappings between relational data sources and a global ontology. IncMap has since been put into practice, both for academic and in industrial applications. Based on the experience of the last years, we have extended the original version of IncMap in several dimensions to enhance the mapping quality: (1) IncMap can detect and leverage semantic-rich patterns in the relational data sources such as inheritance for the mapping creation. (2) IncMap is able to leverage reasoning rules in the ontology to overcome structural differences from the relational data sources. (3) IncMap now includes a fully automatic mode that is often necessary to bootstrap mappings for a new data source. Our experimental evaluation shows that the new version of IncMap outperforms its previous version as well as other state-of-the-art systems

Estimation of the Thermodynamic Limit of Overheating for Bulk Water from Interfacial Properties

The limit of overheating or expanding is an important property of liquids, which is relevant for the design and safety assessment of processes involving pressurized liquids. In this work, the thermodynamic stability limit – the so-called spinodal – of water is calculated by molecular dynamics computer simulation, using the molecular potential model of Baranyai and Kiss. The spinodal pressure is obtained from the maximal tangential pressure within a liquid‒vapor interface layer. The results are compared to predictions of various equations of states. Based on these comparisons, a set of equations of state is identified which gives reliable results in the metastable (overheated or expanded) liquid region of water down to −55 MPa

MISIM: A Novel Code Similarity System

Code similarity systems are integral to a range of applications from code recommendation to automated software defect correction. We argue that code similarity is now a first-order problem that must be solved. To begin to address this, we present machine Inferred Code Similarity (MISIM), a novel end-to-end code similarity system that consists of two core components. First, MISIM uses a novel context-aware semantic structure, which is designed to aid in lifting semantic meaning from code syntax. Second, MISIM provides a neural-based code similarity scoring algorithm, which can be implemented with various neural network architectures with learned parameters. We compare MISIM to three state-of-the-art code similarity systems: (i) code2vec, (ii) Neural Code Comprehension, and (iii) Aroma. In our experimental evaluation across 328,155 programs (over 18 million lines of code), MISIM has 1.5x to 43.4x better accuracy than all three systems

QuickSel: Quick Selectivity Learning with Mixture Models

Estimating the selectivity of a query is a key step in almost any cost-based query optimizer. Most of today's databases rely on histograms or samples that are periodically refreshed by re-scanning the data as the underlying data changes. Since frequent scans are costly, these statistics are often stale and lead to poor selectivity estimates. As an alternative to scans, query-driven histograms have been proposed, which refine the histograms based on the actual selectivities of the observed queries. Unfortunately, these approaches are either too costly to use in practice---i.e., require an exponential number of buckets---or quickly lose their advantage as they observe more queries. In this paper, we propose a selectivity learning framework, called QuickSel, which falls into the query-driven paradigm but does not use histograms. Instead, it builds an internal model of the underlying data, which can be refined significantly faster (e.g., only 1.9 milliseconds for 300 queries). This fast refinement allows QuickSel to continuously learn from each query and yield increasingly more accurate selectivity estimates over time. Unlike query-driven histograms, QuickSel relies on a mixture model and a new optimization algorithm for training its model. Our extensive experiments on two real-world datasets confirm that, given the same target accuracy, QuickSel is 34.0x-179.4x faster than state-of-the-art query-driven histograms, including ISOMER and STHoles. Further, given the same space budget, QuickSel is 26.8%-91.8% more accurate than periodically-updated histograms and samples, respectively

Singing the same tune? International continuities and discontinuities in how police talk about using force

This article focuses on a research project conducted in six jurisdictions: England, The Netherlands, Germany, Australia, Venezuela, and Brazil. These societies are very different ethnically, socially, politically, economically, historically and have wildly different levels of crime. Their policing arrangements also differ significantly: how they are organised; how their officers are equipped and trained; what routine operating procedures they employ; whether they are armed; and much else besides. Most relevant for this research, they represent policing systems with wildly different levels of police shootings, Police in the two Latin American countries represented here have a justified reputation for the frequency with which they shoot people, whereas at the other extreme the police in England do not routinely carry firearms and rarely shoot anyone. To probe whether these differences are reflected in the way that officers talk about the use of force, police officers in these different jurisdictions were invited to discuss in focus groups a scenario in which police are thwarted in their attempt to arrest two youths (one of whom is a known local criminal) by the youths driving off with the police in pursuit, and concludes with the youths crashing their car and escaping in apparent possession of a gun, It might be expected that focus groups would prove starkly different, and indeed they were, but not in the way that might be expected. There was little difference in affirmation of normative and legal standards regarding the use of force. It was in how officers in different jurisdictions envisaged the circumstances in which the scenario took place that led Latin American officers to anticipate that they would shoot the suspects, whereas officers in the other jurisdictions had little expectation that they would open fire in the conditions as they imagined them to be