Switching Handedness of One-Handed Typing Operation of an On-Screen Keyboard

This publication describes techniques for triggering changes to the display of an on-screen keyboard on a display screen of a mobile device, for example, a foldable mobile device or tablet. The foldable mobile device can be operated via the main display screen with two hands and the mobile device open flat. A user may desire to quickly switch handedness of an on-screen keyboard displayed on the display screen between left side and right side of the display screen. The techniques allow a user to provide input that triggers the mobile device to reposition the on-screen keyboard for one-handed typing operation on either side of the display screen of the mobile device. The user may utilize a summon command that corresponds to a gesture detected on the opposite side of the display screen to trigger repositioning of the on-screen keyboard. The user may utilize a pass command that corresponds to a gesture detected on the same side of the display screen to trigger repositioning of the on-screen keyboard. The user may adjust the height of the on-screen keyboard, adjust the width of the on-screen keyboard, or choose to duplicate the on-screen keyboard on both the left-hand side and the right-hand side of the mobile device

ACTS in Need: Automatic Configuration Tuning with Scalability Guarantees

To support the variety of Big Data use cases, many Big Data related systems expose a large number of user-specifiable configuration parameters. Highlighted in our experiments, a MySQL deployment with well-tuned configuration parameters achieves a peak throughput as 12 times much as one with the default setting. However, finding the best setting for the tens or hundreds of configuration parameters is mission impossible for ordinary users. Worse still, many Big Data applications require the support of multiple systems co-deployed in the same cluster. As these co-deployed systems can interact to affect the overall performance, they must be tuned together. Automatic configuration tuning with scalability guarantees (ACTS) is in need to help system users. Solutions to ACTS must scale to various systems, workloads, deployments, parameters and resource limits. Proposing and implementing an ACTS solution, we demonstrate that ACTS can benefit users not only in improving system performance and resource utilization, but also in saving costs and enabling fairer benchmarking

The influence of aging on technological innovation：Empirical evidence from China

Using panel data from 30 Chinese provinces for the period between 2003 and 2017, this paper explores the relationship between the aging and technological innovation. The global principal components analysis is used to construct the index system of technology innovation from three aspects: innovation input, innovation output and innovation diffusion. Then, the inverted U-shape relationship between population aging and technological innovation is obtained from the dynamic perspective by using two-step system generalized method of moments（SYS-GMM）.The influence of population aging on technological innovation is firstly promoted and then suppressed. In addition, the degree of opening up, the level of economic development, the construction of network infrastructure and human capital all have positive impact on technological innovation

The Influence of Aging on Technological Innovation： Empirical Evidence from China

1110-1115Using panel data from 30 Chinese provinces for the period between 2003 and 2017, this paper explores the relationship between the aging and technological innovation. The global principal components analysis is used to construct the index system of technological innovation from three aspects: innovation input, innovation output and innovation diffusion. Then, the inverted U-shape relationship between population aging and technological innovation is obtained from the dynamic perspective by using two-step system generalized method of moments (SYS-GMM). The influence of population aging on technological innovation is firstly promoted and then suppressed. In addition, the degree of opening up, the level of economic development, the construction of network infrastructure and human capital all have positive impact on technological innovation

Nonlinear system identification using a genetic algorithm and recurrent artificial neural networks

In this study, the application of Recurrent Artificial Neural Network (RANN) in nonlinear system identification has been extensively explored. Three RANN-based identification models have been presented to describe the behavior of the nonlinear systems. The approximation accuracy of RANN-based models relies on two key factors: architecture and weights. Due to its inherent property of parallelism and evolutionary mechanism, a Genetic Algorithm (GA) becomes a promising technique to obtain good neural network architecture. A GA is developed to approach the optimal architecture of a RANN with multiple hidden layers in this study. In order to approach the optimal architecture of Neural Networks in the sense of minimizing the identification error, an effective encoding scheme is in demand. A new Direct Matrix Mapping Encoding (DMME) method is proposed to represent the architecture of a neural network. A modified Back-propagation (BP) algorithm, in the sense of not only tuning NN weights but tuning other adjustable parameters as well, is utilized to tune the weights of RANNs and other parameters. The RANN with optimized or approximately optimized architecture and trained weights have been applied to the identification of nonlinear dynamic systems with unknown nonlinearities, which is a challenge in the control community. The effectiveness of these models and identification algorithms are extensively verified in the identification of several complex nonlinear systems such as a "smart" actuator preceded by hysteresis and friction-plague harmonic drive