K-Space at TRECVid 2007

In this paper we describe K-Space participation in TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance. The first of the two systems was a ‘shot’ based interface, where the results from a query were presented as a ranked list of shots. The second interface was ‘broadcast’ based, where results were presented as a ranked list of broadcasts. Both systems made use of the outputs of our high-level feature submission as well as low-level visual features

Effectiveness of a Faith-placed Cardiovascular Health Promotion Intervention for Rural Adults

Introduction: Cardiovascular disease (CVD) is the leading cause of mortality in the US. Further, rural US adults experience disproportionately high CVD prevalence and mortality compared to non-rural. Cardiovascular risk-reduction interventions for rural adults have shown short-term effectiveness, but long-term maintenance of outcomes remains a challenge. Faith organizations offer promise as collaborative partners for translating evidence-based interventions to reduce CVD. Methods: We adapted and implemented a collaborative, faith-placed, CVD risk-reduction intervention in rural Illinois. We used a quasi-experimental, pre-post design to compare changes in dietary and physical activity among participants. Intervention components included Heart Smart for Women (HSFW), an evidence-based program implemented weekly for 12 weeks followed by Heart Smart Maintenance (HSM), implemented monthly for two years. Participants engaged in HSFW only, HSM only, or both. We used regression and generalized estimating equations models to examine changes in outcomes after one year. Results: Among participants who completed both baseline and one-year surveys (n = 131), HSFW+HSM participants had significantly higher vegetable consumption (p = .007) and combined fruit/vegetable consumption (p = .01) compared to the HSM-only group at one year. We found no differences in physical activity. Conclusion: Improving and maintaining CVD-risk behaviors is a persistent challenge in rural populations. Advancing research to improve our understanding of effective translation of CVD risk-reduction interventions in rural populations is critical