The Chinese University of Hong Kong

About: The Chinese University of Hong Kong is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Population & Computer science. The organization has 43411 authors who have published 93672 publications receiving 3066651 citations.

Robust Scene Text Detection with Convolution Neural Network Induced MSER Trees

Abstract: Maximally Stable Extremal Regions (MSERs) have achieved great success in scene text detection. However, this low-level pixel operation inherently limits its capability for handling complex text information efficiently (e. g. connections between text or background components), leading to the difficulty in distinguishing texts from background components. In this paper, we propose a novel framework to tackle this problem by leveraging the high capability of convolutional neural network (CNN). In contrast to recent methods using a set of low-level heuristic features, the CNN network is capable of learning high-level features to robustly identify text components from text-like outliers (e.g. bikes, windows, or leaves). Our approach takes advantages of both MSERs and sliding-window based methods. The MSERs operator dramatically reduces the number of windows scanned and enhances detection of the low-quality texts. While the sliding-window with CNN is applied to correctly separate the connections of multiple characters in components. The proposed system achieved strong robustness against a number of extreme text variations and serious real-world problems. It was evaluated on the ICDAR 2011 benchmark dataset, and achieved over 78% in F-measure, which is significantly higher than previous methods.

Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

Abstract: Wireless powered mobile-edge computing (MEC) has recently emerged as a promising paradigm to enhance the data processing capability of low-power networks, such as wireless sensor networks and internet of things (IoT). In this paper, we consider a wireless powered MEC network that adopts a binary offloading policy, so that each computation task of wireless devices (WDs) is either executed locally or fully offloaded to an MEC server. Our goal is to acquire an online algorithm that optimally adapts task offloading decisions and wireless resource allocations to the time-varying wireless channel conditions. This requires quickly solving hard combinatorial optimization problems within the channel coherence time, which is hardly achievable with conventional numerical optimization methods. To tackle this problem, we propose a Deep Reinforcement learning-based Online Offloading (DROO) framework that implements a deep neural network as a scalable solution that learns the binary offloading decisions from the experience. It eliminates the need of solving combinatorial optimization problems, and thus greatly reduces the computational complexity especially in large-size networks. To further reduce the complexity, we propose an adaptive procedure that automatically adjusts the parameters of the DROO algorithm on the fly. Numerical results show that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computation time by more than an order of magnitude compared with existing optimization methods. For example, the CPU execution latency of DROO is less than 0.1 second in a 30-user network, making real-time and optimal offloading truly viable even in a fast fading environment.

Cultural Intelligence Domain and Assessment

Abstract: The construct of cultural intelligence, recently introduced to the management literature, has enormous potential in helping to explain effectiveness in cross cultural interactions. However, at present, no generally accepted definition or operationalization of this nascent construct exists. In this article, we develop a conceptualization of cultural intelligence that addresses a number of important limitations of previous definitions. We present a concise definition of cultural intelligence as a system of interacting abilities, describe how these elements interact to produce culturally intelligent behavior, and then identify measurement implications.

Flexible Piezoelectric-Induced Pressure Sensors for Static Measurements Based on Nanowires/Graphene Heterostructures

Abstract: The piezoelectric effect is widely applied in pressure sensors for the detection of dynamic signals. However, these piezoelectric-induced pressure sensors have challenges in measuring static signals that are based on the transient flow of electrons in an external load as driven by the piezopotential arisen from dynamic stress. Here, we present a pressure sensor with nanowires/graphene heterostructures for static measurements based on the synergistic mechanisms between strain-induced polarization charges in piezoelectric nanowires and the caused change of carrier scattering in graphene. Compared to the conventional piezoelectric nanowire or graphene pressure sensors, this sensor is capable of measuring static pressures with a sensitivity of up to 9.4 × 10–3 kPa–1 and a fast response time down to 5–7 ms. This demonstration of pressure sensors shows great potential in the applications of electronic skin and wearable devices.