26 research outputs found
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
Intelligent Search Optimized Edge Potential Function (EPF) Approach to Synthetic Aperture Radar (SAR) Scene Matching
Research on synthetic aperture radar (SAR) scene matching in the aircraft end-guidance has a significant value for both research and real-world application. The conventional scene matching methods, however, suffer many disadvantages such as heavy computation burden and low convergence rate so that these methods cannot meet the requirement of end-guidance system in terms of fast and real-time data processing. Furthermore, there are complex noises in the SAR image, which also compromise the effectiveness of using the conventional scene matching methods. To address the above issues, in this paper, the intelligent optimization method, Free Search with Adaptive Differential Evolution Exploitation and Quantum-Inspired Exploration, has been introduced to tackle the SAR scene matching problem. We first establish the effective similarity measurement function for target edge feature matching through introducing the edge potential function (EPF) model. Then, a new method, ADEQFS-EPF, has been proposed for SAR scene matching. In ADEQFS-EPF, the previous studied theoretical model, ADEQFS, is combined with EPF model. We also employed three recent proposed evolutionary algorithms to compare against the proposed method on optical and SAR datasets. The experiments based on Matlab simulation have verified the effectiveness of the application of ADEQFS and EPF model to the field of SAR scene matching
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