Chinese Organization Name Recognition Using Chunk Analysis

PACLIC 20 / Wuhan, China / 1-3 November, 200

The Effects of Gamification Rewards in E-Learning: A Longitudinal Field Study on Motivation and Mental Fatigue

E-Learning, as a prevalent instructional approach in the midst of the COVID-19 pandemic, is often criticized for reducing motivation and increasing mental fatigue among learners. Despite the attractiveness of various gamification designs to resolve these issues, there still exists a lack of comprehensive and integrated understanding of the pedagogic effectiveness of gamification rewards. Motivated thus, this study assesses and compares four different types of gamification rewards: unexpected-hedonic rewards, expected-hedonic rewards, unexpected-utilitarian rewards, and expected-utilitarian rewards. Drawing from self-determination theory and opportunity cost model of subjective effort and task performance, this study evaluates the effect of gamification reward type on learning motivation and mental fatigue. The effect of gamification reward type will be examined in a longitudinal field experiment in an introductory undergraduate computer science course

AI-Generated Voice in Short Videos: A Digital Consumer Engagement Perspective

The AI-generated voice (AIGV) has been widely applied in the short video industry to facilitate video creation. However, the impact of AIGV on digital consumer engagement (DCE) remains unclear. In light of this, the study investigates the effect of AIGV on DCE by analyzing a panel dataset with observations of 21,541 videos for 3,647 content creators on TikTok. Preliminary results of a series of fixed-effect panel regressions reveal that using AIGV has a significantly negative effect on DCE (5.4% reduction in the number of likes, 5.2% reduction in the number of comments, and 7.4% reduction in the number of shares). Our further analyses show that this negative effect is particularly significant at the rising action stage of short videos. With these findings, this study is expected to have theoretical contributions to the literature on short videos and practical implications about the appropriate usage of AIGV

Representation Disparity-aware Distillation for 3D Object Detection

In this paper, we focus on developing knowledge distillation (KD) for compact 3D detectors. We observe that off-the-shelf KD methods manifest their efficacy only when the teacher model and student counterpart share similar intermediate feature representations. This might explain why they are less effective in building extreme-compact 3D detectors where significant representation disparity arises due primarily to the intrinsic sparsity and irregularity in 3D point clouds. This paper presents a novel representation disparity-aware distillation (RDD) method to address the representation disparity issue and reduce performance gap between compact students and over-parameterized teachers. This is accomplished by building our RDD from an innovative perspective of information bottleneck (IB), which can effectively minimize the disparity of proposal region pairs from student and teacher in features and logits. Extensive experiments are performed to demonstrate the superiority of our RDD over existing KD methods. For example, our RDD increases mAP of CP-Voxel-S to 57.1% on nuScenes dataset, which even surpasses teacher performance while taking up only 42% FLOPs.Comment: Accepted by ICCV2023. arXiv admin note: text overlap with arXiv:2205.15156 by other author

Deep-Learning-Enabled Fast Optical Identification and Characterization of Two-Dimensional Materials

Advanced microscopy and/or spectroscopy tools play indispensable role in nanoscience and nanotechnology research, as it provides rich information about the growth mechanism, chemical compositions, crystallography, and other important physical and chemical properties. However, the interpretation of imaging data heavily relies on the "intuition" of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, we use the optical characterization of two-dimensional (2D) materials as a case study, and demonstrate a neural-network-based algorithm for the material and thickness identification of exfoliated 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, segment sizes and their distributions, based on which we develop an ensemble approach topredict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other applications such as identifying layer numbers of a new 2D material, or materials produced by a different synthetic approach. Our artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials and potentially accelerate new material discoveries

SVCV: segmentation volume combined with cost volume for stereo matching

Stereo matching between binocular stereo images is fundamental to many computer vision tasks, such as three‐dimensional (3D) reconstruction and robot navigation. Various structures of real 3D scenes lead stereo matching to be an old yet still challenging problem. In this study, the authors proposed a novel adaptive support weights technique which exploits the hierarchical information provided by multilevel segmentation to preserve the robustness to imaging conditions and spatial proximity in cost aggregation. Besides, a generalisable cost refinement strategy is designed to remove the matching ambiguity in large weakly textured regions. The proposed strategy utilises both the fluctuation of the filtered cost volume and the colour information to further improve the matching accuracy. Experimental results of 50 stereo images demonstrate the effectiveness and efficiency of the proposed method. Furthermore, a systematic evaluation is developed to assess the conventional steps in local stereo methods and then reliable suggestions are given to the beginners and researchers outside the stereo matching field

A Novel Adversarial Based Hyperspectral and Multispectral Image Fusion

In order to reconstruct a high spatial and high spectral resolution image (H2SI), one of the most common methods is to fuse a hyperspectral image (HSI) with a corresponding multispectral image (MSI). To effectively obtain both the spectral correlation of bands in HSI and the spatial correlation of pixels in MSI, this paper proposes an adversarial selection fusion (ASF) method for the HSI⁻MSI fusion problem. Firstly, the unmixing based fusion (UF) method is adopted to dig out the spatial correlation in MSI. Then, to acquire the spectral correlation in HSI, a band reconstruction-based fusion (BRF) method is proposed, regarding H2SI as the product of the optimized band image dictionary and reconstruction coefficients. Finally, spectral spatial quality (SSQ) index is designed to guide the adversarial selection process of UF and BRF. Experimental results on four real-world images demonstrate that the proposed strategy achieves smaller errors and better reconstruction results than other comparison methods