Pump it Up workshop report

Workshop held 28-29 September 2017, Cape Cod, MAA two-day workshop was conducted to trade ideas and brainstorm about how to advance our understanding of the ocean’s biological pump. The goal was to identify the most important scientific issues that are unresolved but might be addressed with new and future technological advances

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Automated Video-Based Fall Detection

Automatically detecting falls is a desired part of caring for a live-alone senior. Researchers have developed various video-based fall detection methods, including moving-region-based 3D-projection-based methods. We introduce a video-based fall detection method that is simpler and more efficient than previous methods, while being equally or more accurate. The method is based on the moving-regions, represented as a minimum bounding rectangle (MBR) around the person in video. The method uses fall detectors that use a particular feature of the MBR, such as height or width, to contribute a fall likelihood score. Many fall likelihood scores can be combined to produce a single-camera fall score. Multiple cameras can be combined to produce a multi-camera fall score. We evaluated our method on a commonly used video data set featuring a middle-aged, male actor performing falls and in-home activities. We report accuracy as sensitivity and specificity, and efficiency as frames per second (FPS). The method for a single-camera achieved 0.960 sensitivity and 0.995 specificity, and for 2 or more cameras achieved at least 0.990 sensitivity and at least 0.990 specificity. The method runs at 32.1 FPS while single-threaded on a 3.30 GHz Xeon processor. Our method was more accurate than the state-of-the-art MBR-based methods, while being equally efficient. Also, our method was about 10x more efficient than the state-of-the-art projection-based algorithms, while being more accurate with 3 cameras and equally accurate with 4+ cameras

Automated Video-Based Fall Detection

Automatically detecting falls is a desired part of caring for a live-alone senior. Researchers have developed various video-based fall detection methods, including moving-region-based 3D-projection-based methods. We introduce a video-based fall detection method that is simpler and more efficient than previous methods, while being equally or more accurate. The method is based on the moving-regions, represented as a minimum bounding rectangle (MBR) around the person in video. The method uses fall detectors that use a particular feature of the MBR, such as height or width, to contribute a fall likelihood score. Many fall likelihood scores can be combined to produce a single-camera fall score. Multiple cameras can be combined to produce a multi-camera fall score. We evaluated our method on a commonly used video data set featuring a middle-aged, male actor performing falls and in-home activities. We report accuracy as sensitivity and specificity, and efficiency as frames per second (FPS). The method for a single-camera achieved 0.960 sensitivity and 0.995 specificity, and for 2 or more cameras achieved at least 0.990 sensitivity and at least 0.990 specificity. The method runs at 32.1 FPS while single-threaded on a 3.30 GHz Xeon processor. Our method was more accurate than the state-of-the-art MBR-based methods, while being equally efficient. Also, our method was about 10x more efficient than the state-of-the-art projection-based algorithms, while being more accurate with 3 cameras and equally accurate with 4+ cameras