The incorporation of elements of artificial intelligence into the logic of goal-oriented applicat... more The incorporation of elements of artificial intelligence into the logic of goal-oriented applications is a common objective. In particular, machine learning has been used to enhance applications so that otherwise brittle rules can be broadened to dynamically leverage the traits of a specific problem set. The difficulty posed by such systems is that they require the application developer to have significant machine learning knowledge. We present ongoing work that incorporates machine learning into a middleware infrastructure so that application developers can leverage machine learning facilities without requiring the developers to have any knowledge of machine learning algorithms and formal reasoning.
ABSTRACT Why do some terrorist organizations deploy women on the front lines and in violent attac... more ABSTRACT Why do some terrorist organizations deploy women on the front lines and in violent attacks? This study explores the social conditions, economic factors, and organizational characteristics that might explain women's participation in violent terrorist activity. With a new data set of 395 terrorist organizations, women's participation in terrorist attacks was quantified and coded. The logistic regression analysis results suggest that women's educational attainment, social rights, terrorist organization's age and size, and the level of a country's economic development are important predictors of the deployment of women in terrorist violence while a terrorist group's ideological or religious orientation and the level of democracy do not significantly influence the likelihood of women's participation.
Although there is a substantial literature examining the mental health consequences of parental d... more Although there is a substantial literature examining the mental health consequences of parental divorce, less attention has been paid to possible long-term physical health outcomes. The aim of this study was to examine the gender-specific association between childhood parental divorce and later incidence of stroke, while controlling for age, race ethnicity, socioeconomic status, health behaviors, diabetes, social support, marital status, mental health, and health care utilization. Secondary analysis of the population-based Behavioral Risk Factor Surveillance System survey; logistic regression analyses were conducted. The final sample included 4074 males and 5886 females. Respondents were excluded if they had experienced parental addictions to drugs or alcohol, any form of childhood abuse (physical, sexual, or emotional), or witnessed domestic violence. A threefold risk of stroke was found for males who had experienced parental divorce before the age of 18 in comparison with males whose parents had not divorced [age- and race ethnicity-adjusted model odds ratio (OR) = 2·99, 95% confidence interval (CI) = 1·79, 4·98; fully adjusted model OR = 3·01, 95% CI = 1·68, 5·39]. Parental divorce was not significantly associated with stroke among women (fully adjusted OR = 1·64, 95% CI = 0·89, 3·02). There is a robust association between parental divorce and stroke among males, even after adjustment for many known risk factors and the exclusion of respondents who had experienced parental addictions or family violence. Further research is needed to investigate plausible pathways linking parental divorce and stroke in males.
The incorporation of elements of artificial intelligence into the logic of goal-oriented applicat... more The incorporation of elements of artificial intelligence into the logic of goal-oriented applications is a common objective. In particular, machine learning has been used to enhance applications so that otherwise brittle rules can be broadened to dynamically leverage the traits of a specific problem set. The difficulty posed by such systems is that they require the application developer to have significant machine learning knowledge. We present ongoing work that incorporates machine learning into a middleware infrastructure so that application developers can leverage machine learning facilities without requiring the developers to have any knowledge of machine learning algorithms and formal reasoning.
ABSTRACT Why do some terrorist organizations deploy women on the front lines and in violent attac... more ABSTRACT Why do some terrorist organizations deploy women on the front lines and in violent attacks? This study explores the social conditions, economic factors, and organizational characteristics that might explain women's participation in violent terrorist activity. With a new data set of 395 terrorist organizations, women's participation in terrorist attacks was quantified and coded. The logistic regression analysis results suggest that women's educational attainment, social rights, terrorist organization's age and size, and the level of a country's economic development are important predictors of the deployment of women in terrorist violence while a terrorist group's ideological or religious orientation and the level of democracy do not significantly influence the likelihood of women's participation.
Although there is a substantial literature examining the mental health consequences of parental d... more Although there is a substantial literature examining the mental health consequences of parental divorce, less attention has been paid to possible long-term physical health outcomes. The aim of this study was to examine the gender-specific association between childhood parental divorce and later incidence of stroke, while controlling for age, race ethnicity, socioeconomic status, health behaviors, diabetes, social support, marital status, mental health, and health care utilization. Secondary analysis of the population-based Behavioral Risk Factor Surveillance System survey; logistic regression analyses were conducted. The final sample included 4074 males and 5886 females. Respondents were excluded if they had experienced parental addictions to drugs or alcohol, any form of childhood abuse (physical, sexual, or emotional), or witnessed domestic violence. A threefold risk of stroke was found for males who had experienced parental divorce before the age of 18 in comparison with males whose parents had not divorced [age- and race ethnicity-adjusted model odds ratio (OR) = 2·99, 95% confidence interval (CI) = 1·79, 4·98; fully adjusted model OR = 3·01, 95% CI = 1·68, 5·39]. Parental divorce was not significantly associated with stroke among women (fully adjusted OR = 1·64, 95% CI = 0·89, 3·02). There is a robust association between parental divorce and stroke among males, even after adjustment for many known risk factors and the exclusion of respondents who had experienced parental addictions or family violence. Further research is needed to investigate plausible pathways linking parental divorce and stroke in males.
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Papers by Angela Dalton