1,180 research outputs found

    Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

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    While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain joint feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.Comment: CVPR 2018 Spotligh

    EXPLORING E-LEARNING BEHAVIOR THROUGH LEARNING DISCOURSES

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    As many studies predict e-learning behaviors through intention, few of them investigate user’s learning behaviors directly. In addition to intention, individual’s e-learning behaviors may be influenced by technology readiness and group influences, such as social identity and social bond. This research-in-progress study explores how e-learning behaviors vary with intention, technology readiness, social identity and social bond. Our investigation was based on analyzing the speech acts embedded in fourteen learners’ online discourses in an eighteen-week e-learning course. We then compared how speech acts varied among groups with different degree of intention, technology readiness, social identity, and social bond. Our findings contribute e-learning research by clarifying how intention, technology readiness, social identity, and social bond influence learning behaviors in e-learning context

    Bridge scour evaluation based on ambient vibration

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    The vulnerability of bridges to hazards such as earthquakes, wind and floods necessitates special structural characteristics. To guarantee the stability of bridge structures, the precise evaluation of the scour depth of bridge foundation has recently become an important issue, as most of the unexpected damage to or collapse of bridges has been attributed to hydraulic issues. In this paper, a vibration-based bridge health monitoring system that utilizes only the response of superstructure to rapidly evaluate the embedded depth of a bridge column is proposed. To clarify the complex fluid-solid coupling phenomenon, the effects of embedded depth and water level were first verified through a series of static experiments. A confined finite element model simulated by soil spring effects was then established to illustrate the relationship between the fundamental frequency and the embedded depth. Using the proposed algorithm, the health of the bridge is able to be inferred by processing the ambient vibration response of the superstructure. To implement the proposed algorithm, a SHM prototype system monitoring environmental factors such as temperature, water level, and inclination was developed to support on-line processing. The performance of the proposed system was verified by a series of dynamic bridge scour experiments conducted in a laboratory flume and compared with readings from a water-proof camera. The results showed that using the proposed vibration-based bridge health monitoring system, the embedded depth of bridge column during complex scour processes is able to be reliably calculated

    THE EFFECT OF COUNTER MOVEMENT JUMP PERFORMANCE IN MIDDLEAGED ELDERLY PRACTICING TAI-CHI EXERCISE

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    The purpose of this study was to investigate biomechanical effects of Tal-Chi exercise on the lower-extremity in middle-aged elders during counter-movement jump. Twelve middle-aged elders with regular Tai Chi exercise experience and twelve healthy middle-aged elders participated in this study. Ten Vicon Motion System cameras, two Kistler force plates were used simultaneously to capture the kinematic and dynamlc parameters of standing vertical jumps. Independent samples &test was performed for statistical analysis ( u = .05 ). Since the jump height of Tai Chi group was significantly higher ( p c .05 ). It showed that practicing Tai Chi exercise could effectively slow down the degeneration of the moment and power at the hip Joint. Therefore, middle-aged elders were recommended to engage in long-term Tai Chi exercise
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