ABSTRACT
Assessment is the process of comparing the actual to the expected behavior of a business phenomenon and judging the outcome of the comparison. The ${{\sf assess}}$assess querying operator has been recently proposed to support assessment based on the results of a query on a data cube. This operator requires (i) the specification of an OLAP query to determine a target cube;(ii) the specification of a reference cube of comparison (benchmark), which represents the expected performance;(iii) the specification of how to perform the comparison, and (iv) a labeling function that classifies the result of this comparison. Despite the adoption of a SQL-like syntax that hides the complexity of the assessment process, writing a complete assess statement is not easy. In this paper we focus on making the user experience more comfortable by letting the system suggest suitable completions for partially-specified statements. To this end we propose two interaction modes: progressive refinement and auto-completion, both starting from an assess statement partially declared by the user. These two modes are evaluated both in terms of scalability and user experience, with the support of two experiments made with real users.
ABSTRACT
Estimates of excess deaths have been widely used to measure the overall impact of the COVID-19 pandemic on mortality. We investigate the validity of a method-the later/earlier method- developed for forecasting the number of deaths one would expect if no shock occurred. We apply this method to estimate excess mortality during the first COVID-19 wave in France and Spain (February-June 2020), stratified by age, sex, and region. Although both countries recorded similar numbers of COVID-19 deaths, Spain had higher excess mortality. The results are informative about differences in COVID-19 vulnerability for population subgroups and spatial areas: adults aged 75-85 were the hardest hit;Ile-de-France (Paris region) in France and Comunidad de Madrid in Spain had the highest excess mortality. Applicable to other demographic phenomena, the later/earlier method is simple, requires fewer assumptions than other forecasting methods, and is less biased and more accurate than the 5-year-average method.
ABSTRACT
NASA will remotely administer a psychoacoustic test in late summer of 2022 as the first of two phases of a cooperative Urban Air Mobility (UAM) vehicle noise human response study. This study relies on the cooperation of multiple government agencies, academia, and industry to assemble a wide range of UAM vehicle sounds. This database of sounds will be used to create a rich database of human response to UAM noise that would be challenging for a single organization to acquire. The development of the remote test method to study human response to aviation noise was prompted by the novel coronavirus pandemic. The feasibility portion of the study described in this work will demonstrate and refine the remote test method for use in the implementation phase.This paper details the method for remotely administering the psychoacoustic test and the sound stimuli to be used in the Feasibility Test. Comparisons of annoyance response data from previous in-person tests will be used to demonstrate the viability of the remote test method. The paper also describes an effort to determine if providing a contextual cue to test subjects influences the annoyance response.
ABSTRACT
Assessment is the process of comparing the actual to the expected behavior of a business phenomenon and judging the outcome of the comparison. The assess querying operator has been recently proposed to support assessment based on the results of a query on a data cube. This operator requires (i) the specification of an OLAP query to determine a target cube;(ii) the specification of a reference cube of comparison (benchmark), which represents the expected performance;(iii) the specification of how to perform the comparison, and (iv) a labeling function that classifies the result of this comparison. Despite the adoption of a SQL-like syntax that hides the complexity of the assessment process, writing a complete assess statement is not easy. In this paper we focus on making the user experience more comfortable by letting the system suggest suitable completions for partially-specified statements. To this end we propose two interaction modes: progressive refinement and auto-completion, both starting from an assess statement partially declared by the user. These two modes are evaluated both in terms of scalability and user experience, with the support of two experiments made with real users. IEEE