Generating content for scenario-based seriousgames using CrowdSourcing

Scenario-based serious-games have become an important tool for teaching new skills and capabilities. An important factor in the development of such systems is reducing the time and cost overheads in manually creating content for these scenarios. To address this challenge, we present Scenario-Gen, an automatic method for generating content about everyday activities through combining computer science techniques with the crowd. ScenarioGen uses the crowd in three different ways: to capture a database of scenarios of everyday activities, to generate a database of likely replacements for specific events within that scenario, and to evaluate the resulting scenarios. We evaluated ScenarioGen in 6 different content domains and found that it was consistently rated as coherent and consistent as the originally captured content. We also compared ScenarioGen’s content to that created by traditional planning techniques. We found that both methods were equally effective in generating coherent and consistent scenarios, yet ScenarioGen’s content was found to be more varied and easier to create

Crowdsourcing complex workflows under budget constraints

We consider the problem of task allocation in crowdsourcing systems with multiple complex workflows, each of which consists of a set of interdependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a budget constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then determines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the corresponding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper

Divisors in a Dedekind domain

5 páginas.-- 1991 Mathematics Subject Classification: 11R04, 11A05.Peer reviewe

Calcium antagonists and mortality in patients with coronary artery disease: A Cohort study of 11,575 patients

AbstractObjectives. This study sought to establish the risk ratio for mortality associated with calcium antagonists in a large population of patients with chronic coronary artery disease.Background. Recent reports have suggested that the use of short-acting nifedipine may cause an increase in overall mortality in patients with coronary artery disease and that a similar effect may be produced by other calcium antagonists, in particular those of the dihydropyridine type.Methods. Mortality data were obtained for 11,575 patients screened for the Bezafibrate Infarction Prevention study (5,843 with and 5,732 without calcium antagonists) after a mean follow-up period of 3.2 years.Results. There were 495 deaths (8.5%) in the calcium antagonist group compared with 410 in the control group (7.2%). The age-adjusted risk ratio for mortality was 1.08 (95% confidence interval [CI] 0.95 to 1.24). After adjustment for the differences between the groups in age and gender and the prevalence of previous myocardial infarction, angina pectoris, hypertension, New York Heart Association functional class, peripheral vascular disease, chronic obstructive pulmonary disease, diabetes and current smoking, the adjusted risk ratio declined to 0.97 (95% CI 0.84 to 1.11). After further adjustment for concomintant medication, the risk ratio was estimated at 0.94 (95% CI 0.82 to 1.08).Conclusions. The current analysis does not support the claim that calcium antagonist therapy in patients with chronic coronary artery disease, whether myocardial infarction survivors or others, harbors an increased risk of mortality

Development and validation of a multivariable risk factor questionnaire to detect oesophageal cancer in 2-week wait patients

INTRODUCTION: Oesophageal cancer is associated with poor health outcomes. Upper GI (UGI) endoscopy is the gold standard for diagnosis but is associated with patient discomfort and low yield for cancer. We used a machine learning approach to create a model which predicted oesophageal cancer based on questionnaire responses. METHODS: We used data from 2 separate prospective cross-sectional studies: the Saliva to Predict rIsk of disease using Transcriptomics and epigenetics (SPIT) study and predicting RIsk of diSease using detailed Questionnaires (RISQ) study. We recruited patients from National Health Service (NHS) suspected cancer pathways as well as patients with known cancer. We identified patient characteristics and questionnaire responses which were most associated with the development of oesophageal cancer. Using the SPIT dataset, we trained seven different machine learning models, selecting the best area under the receiver operator curve (AUC) to create our final model. We further applied a cost function to maximise cancer detection. We then independently validated the model using the RISQ dataset. RESULTS: 807 patients were included in model training and testing, split in a 70:30 ratio. 294 patients were included in model validation. The best model during training was regularised logistic regression using 17 features (median AUC: 0.81, interquartile range (IQR): 0.69-0.85). For testing and validation datasets, the model achieved an AUC of 0.71 (95% CI: 0.61-0.81) and 0.92 (95% CI: 0.88-0.96) respectively. At a set cut off, our model achieved a sensitivity of 97.6% and specificity of 59.1%. We additionally piloted the model in 12 patients with gastric cancer; 9/12 (75%) of patients were correctly classified. CONCLUSIONS: We have developed and validated a risk stratification tool using a questionnaire approach. This could aid prioritising patients at high risk of having oesophageal cancer for endoscopy. Our tool could help address endoscopic backlogs caused by the COVID-19 pandemic