An Empirical Study of Civil Servants’ Lifelong E-Learning Continuance Intention

E-learning is an effective way for job-training and continuous education. In recognizing the need for civil servants to embrace the lifelong learning to sustain competitiveness, many countries around the world have created policies to develop e-learning. This study is focus on civil servants’ e-learning continuance intention and through e-learning experience to achieve lifelong learning. Based on Information system (IS) success model proposed by Seddon (1997) and adding organizational factors (Incentive, Supervisor Support, and Technical Support) to survey civil servants’ e-learning behaviour. The sample for the study was taken from the civil servants in Taiwan who have the experience of using the lifelong e-learning websites. The results also support Seddon’s IS success model. Finally, the implications and limitations of the study are discussed

Quantum-trajectory analysis for charge transfer in solid materials induced by strong laser fields

We investigate the dependence of charge transfer on the intensity of driving laser field when SiO2 crystal is irradiated by an 800 nm laser. It is surprising that the direction of charge transfer undergoes a sudden reversal when the driving laser intensity exceeds critical values with different carrier envelope phases. By applying quantum-trajectory analysis, we find that the Bloch oscillation plays an important role in charge transfer in solid. Also, we study the interaction of strong laser with gallium nitride (GaN) that is widely used in optoelectronics. A pump-probe scheme is applied to control the quantum trajectories of the electrons in the conduction band. The signal of charge transfer is controlled successfully by means of theoretically proposed approach

Compressed Video Action Recognition

Training robust deep video representations has proven to be much more challenging than learning deep image representations. This is in part due to the enormous size of raw video streams and the high temporal redundancy; the true and interesting signal is often drowned in too much irrelevant data. Motivated by that the superfluous information can be reduced by up to two orders of magnitude by video compression (using H.264, HEVC, etc.), we propose to train a deep network directly on the compressed video. This representation has a higher information density, and we found the training to be easier. In addition, the signals in a compressed video provide free, albeit noisy, motion information. We propose novel techniques to use them effectively. Our approach is about 4.6 times faster than Res3D and 2.7 times faster than ResNet-152. On the task of action recognition, our approach outperforms all the other methods on the UCF-101, HMDB-51, and Charades dataset.Comment: CVPR 2018 (Selected for spotlight presentation