Computer Science

How do we know the correctness of an evolved circuit? While Evolutionary Hardware is exhibiting its effectiveness, we argue that it is very difficult to design a large-scale digital circuit by conventional evolutionary techniques alone, if we are using a subset of the entire truth table for fitness evaluation. The test vector generation problem for testing VLSI (Very Large Scale Integration) suggests that there is no efficient way to determine a training set which assures full correctness of an evolved circuit

We assess and compare computer science skills among final-year computer science undergraduates (seniors) in four major economic and political powers that produce approximately half of the science, technology, engineering, and mathematics graduates in the world. We find that seniors in the United States substantially outperform seniors in China, India, and Russia by 0.76–0.88 SDs and score comparably with seniors in elite institutions in these countries. Seniors in elite institutions in the United States further outperform seniors in elite institutions in China, India, and Russia by ∼0.85 SDs. The skills advantage of the United States is not because it has a large proportion of high-scoring international students. Finally, males score consistently but only moderately higher (0.16–0.41 SDs) than females within all four countries.

Computer vision approaches have been widely used to automatically recognize the activities of workers from videos. While considerable advancements have been made to capture complementary information from still frames, it remains a challenge to obtain motion between them. As a result, this has hindered the ability to conduct real-time monitoring. Considering this challenge, an improved convolutional neural network (CNN) that integrates Red-Green-Blue (RGB), optical flow, and gray stream CNNs, is proposed to accurately monitor and automatically assess workers' activities associated with installing reinforcement during construction. A database containing photographs of workers installing reinforcement is created from activities undertaken on several construction projects in Wuhan, China. The database is then used to train and test the developed CNN network. Results demonstrate that the developed method can accurately detect the activities of workers. The developed computer vision-b...