Ursinus College

About: Ursinus College is a education organization based out in Collegeville, Pennsylvania, United States. It is known for research contribution in the topics: Population & Politics. The organization has 608 authors who have published 715 publications receiving 13475 citations.

The Macroeconomic Consequences of Terrorism

Abstract: We perform an empirical investigation of the macroeconomic consequences of international terrorism and interactions with alternative forms of collective violence. Our analysis is based on a rich unbalanced panel data set with annual observations on 177 countries from 1968 to 2000, which brings together information from the Penn World Table dataset, the ITERATE dataset for terrorist events, and datasets of external and internal conflict. We explore these data with cross-sectional and panel growth regression analysis and a structural VAR model. We find that, on average, the incidence of terrorism may have an economically significant negative effect on growth, albeit one that is considerably smaller and less persistent than that associated with either external wars or internal conflict. As well, terrorism is associated with a redirection of economic activity away from investment spending and towards government spending. However, our investigation also suggests important differences both regarding the incidence and the economic consequences of terrorism among different sets of countries. In OECD economies, in particular, terrorist incidents are considerably more frequent than in other nations, but the negative influence of these incidents on growth is smaller.

CREMA-D: Crowd-Sourced Emotional Multimodal Actors Dataset

Abstract: People convey their emotional state in their face and voice. We present an audio-visual dataset uniquely suited for the study of multi-modal emotion expression and perception. The dataset consists of facial and vocal emotional expressions in sentences spoken in a range of basic emotional states (happy, sad, anger, fear, disgust, and neutral). 7,442 clips of 91 actors with diverse ethnicbackgrounds were rated by multiple raters in three modalities: audio, visual, and audio-visual. Categorical emotion labels andreal-value intensity values for the perceived emotion were collected using crowd-sourcing from 2,443 raters. The human recognition of intended emotion for the audio-only, visual-only, and audio-visual data are 40.9, 58.2 and 63.6 percent respectively. Recognition rates are highest for neutral, followed by happy, anger, disgust, fear, and sad. Average intensity levels of emotion are rated highest forvisual-only perception. The accurate recognition of disgust and fear requires simultaneous audio-visual cues, while anger andhappiness can be well recognized based on evidence from a single modality. The large dataset we introduce can be used to probe other questions concerning the audio-visual perception of emotion.

Using Machine Learning to Detect Cyberbullying

Abstract: Cyber bullying is the use of technology as a medium to bully someone. Although it has been an issue for many years, the recognition of its impact on young people has recently increased. Social networking sites provide a fertile medium for bullies, and teens and young adults who use these sites are vulnerable to attacks. Through machine learning, we can detect language patterns used by bullies and their victims, and develop rules to automatically detect cyber bullying content. The data we used for our project was collected from the website Formspring.me, a question-and-answer formatted website that contains a high percentage of bullying content. The data was labeled using a web service, Amazon's Mechanical Turk. We used the labeled data, in conjunction with machine learning techniques provided by the Weka tool kit, to train a computer to recognize bullying content. Both a C4.5 decision tree learner and an instance-based learner were able to identify the true positives with 78.5% accuracy.

How Much Does Violence Tax Trade

Abstract: We investigate the empirical impact of violence as compared to other trade impediments on trade flows. Our analysis is based on a panel data set with annual observations on 177 countries from 1968 to 1999, which brings together information from the Rose [2004] dataset, the ITERATE dataset for terrorist events, and datasets of external and internal conflict. We explore these data with traditional and theoretical gravity models. We calculate that, for a given country year, the presence of terrorism, as well as internal and external conflict is equivalent to as much as a 30 percent tariff on trade. This is larger than estimated tariff-equivalent costs of border and language barriers and tariff-equivalent reduction through GSPs and WTO participation.