Real-time content-aware video retargeting on the Android platform for tunnel vision assistance

As mobile devices continue to rise in popularity, advances in overall mobile device processing power lead to further expansion of their capabilities. This, coupled with the fact that many people suffer from low vision, leaves substantial room for advancing mobile development for low vision assistance. Computer vision is capable of assisting and accommodating individuals with blind spots or tunnel vision by extracting the necessary information and presenting it to the user in a manner they are able to visualize. Such a system would enable individuals with low vision to function with greater ease. Additionally, offering assistance on a mobile platform allows greater access. The objective of this thesis is to develop a computer vision application for low vision assistance on the Android mobile device platform. Specifically, the goal of the application is to reduce the effects tunnel vision inflicts on individuals. This is accomplished by providing an in-depth real-time video retargeting model that builds upon previous works and applications. Seam carving is a content-aware retargeting operator which defines 8-connected paths, or seams, of pixels. The optimality of these seams is based on a specific energy function. Discrete removal of these seams permits changes in the aspect ratio while simultaneously preserving important regions. The video retargeting model incorporates spatial and temporal considerations to provide effective image and video retargeting. Data reduction techniques are utilized in order to generate an efficient model. Additionally, a minimalistic multi-operator approach is constructed to diminish the disadvantages experienced by individual operators. In the event automated techniques fail, interactive options are provided that allow for user intervention. Evaluation of the application and its video retargeting model is based on its comparison to existing standard algorithms and its ability to extend itself to real-time. Performance metrics are obtained for both PC environments and mobile device platforms for comparison

Building a STEM Mentoring Program in an Economically Disadvantaged Rural Community

Rural, economically disadvantaged communities face a bigger challenge than urban communities in recruiting and retaining high school (HS) students in science, technology, engineering, and mathematics (STEM) because many of these students do not have access to high-quality STEM opportunities. In this article, we describe a mentoring program we developed as part of a larger New York State education grant. This program was implemented in a rural community to connect undergraduate STEM students with HS students to increase HS students’ interest in these fields. In this program, HS students visited colleges, explored their interests in STEM, and learned about opportunities available to them in college and beyond. Here, we share the challenges and the successful strategies in implementing a mentoring program in a rural, economically disadvantaged region. The ideas described in the article were designed so other educators can gain insight on how to set up successful mentoring programs to attract and retain students in the STEM pipeline

Short- and long-term growth effects of special interest groups in the U.S. states: A dynamic panel error-correction approach

The perception of special interest groups as a serious threat to economic growth has strengthened over the years; however, the vast empirical literature surrounding this claim has produced mixed and inconclusive results. This study re-examines the issue incorporating a potentially important aspect that has generally been ignored by previous studies, namely, the implicit suggestion by some of the theoretical works that the extent and the intensity of the growth effects of special interest groups may differ significantly over different time frames. Specifically, this study uses dynamic panel error-correction methods (Pesaran, Shin, and Smith (1999)) to properly determine whether these effects, if they exist, occur mostly in the short run or the long run based on data from a panel of 48 U.S. states for the years 1975 – 2004. The joint Hausman-type test selected the preferred model, which controls for human capital achievement, initial income, income inequality, and the tax burden. This model produced results which are in sharp contrast to the simple linearly negative or positive findings reported in much of the literature by indicating that special interest groups have significant non-linearly inverted U-shaped long-run effects on growth, and that it takes time (about 8 years) for the full effects to become evident. The results provide evidence that U.S. states face a threshold point below which special interest groups’ lobbying and rent-seeking activities boost long-run growth performance but above which they have damaging effects on long-run growth effort. This is confirmed by the Lind and Mehlum (2010) u-test which also suggests that the threshold point is reached when the activities and strength of special interest groups (measured by the percentage of each state’s public and private non-agricultural wage and salary employees who are union members, and which varies from 3.8% to 38.7%)) is at the 15.8% level