作者:
Li, Huan;Zhu, Sha*;Wu, Di;Yang, Harrison Hao;Guo, Qing
期刊:
Education and Information Technologies,2023年28(10):13485-13504 ISSN:1360-2357
通讯作者:
Zhu, Sha;Yang, HH;Zhu, S
作者机构:
[Guo, Qing; Yang, HH; Zhu, Sha; Yang, Harrison Hao; Li, Huan; Zhu, S; Wu, Di] Cent China Normal Univ, Natl Engn Res Ctr Elearning, 152 Luoyu Rd, Wuhan, Hubei, Peoples R China.;[Zhu, Sha; Zhu, S; Wu, Di] Cent China Normal Univ, Res Ctr Sci & Technol Promoting Educ Innovat & Dev, Strateg Res Base, Minist Educ, Wuhan, Peoples R China.;[Yang, HH; Yang, Harrison Hao] SUNY Coll Oswego, Sch Educ, 232 Wilber Hall, Oswego, NY 12306 USA.
通讯机构:
[Yang, HH ; Zhu, S ; Zhu, S] C;Cent China Normal Univ, Natl Engn Res Ctr Elearning, 152 Luoyu Rd, Wuhan, Hubei, Peoples R China.;Cent China Normal Univ, Res Ctr Sci & Technol Promoting Educ Innovat & Dev, Strateg Res Base, Minist Educ, Wuhan, Peoples R China.;SUNY Coll Oswego, Sch Educ, 232 Wilber Hall, Oswego, NY 12306 USA.
摘要:
The adoption of online learning for adolescent students accelerated with the outbreak of the COVID-19 pandemic. However, few studies have investigated the mechanisms influencing adolescent students’ online learning engagement systematically and comprehensively. This study applied the Presage-Process-Product (3P) model of learning to investigate the direct effects of presage factors (i.e., information literacy and self-directed learning skills) and process factors (i.e., academic emotions) on high school students’ online learning engagement; and the mediating role of process factors. Data from 1993 high school students in China (49.3% males and 50.7% females) were analyzed using structural equation modeling. The result showed that students’ information literacy, self-directed learning skills, and positive academic emotions positively predicted their online learning engagement. Moreover, the positive impact of self-directed learning skills on students’ online learning engagement was significantly and largely enhanced through the mediation effects of positive academic emotions (
$${\upbeta }$$
= 0.606, 95% CI = [0.544, 0.674]). Based on these results, to enhance adolescent students’ online learning engagement, it is important for school administrators, teachers, and parents to improve students’ information literacy, self-directed learning skills, and positive academic emotions.
作者:
Hai Liu;Cheng Zhang;Yongjian Deng;Bochen Xie;Tingting Liu;...
期刊:
IEEE Transactions on Multimedia,2023年:1-14 ISSN:1520-9210
作者机构:
[Hai Liu; Cheng Zhang; Zhaoli Zhang] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China;[Yongjian Deng] College of Computer Science, Beijing University of Technology, Beijing, China;Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong;City University of Hong Kong Shenzhen Research Institute, Shenzhen, China;School of Education, Hubei University, Wuhan, Hubei, China
摘要:
Fine-grained bird image classification (FBIC) is not only meaningful for endangered bird observation and protection but also a prevalent task for image classification in multimedia processing and computer vision. However, FBIC suffers from several challenges, such as bird molting, complex background, and arbitrary bird posture. To effectively tackle these challenges, we present a novel invariant cues-aware feature concentration Transformer (TransIFC), which learns invariant and core information in bird images. To this end, two novel modules are proposed to leverage the characteristics of bird images, namely, the hierarchy stage feature aggregation (HSFA) module and the feature in feature abstraction (FFA) module. The HSFA module aggregates the multiscale information of bird images by concatenating multilayer features. The FFA module extracts the invariant cues of birds through feature selection based on discrimination scores. Transformer is employed as the backbone to reveal the long-dependent semantic relationships in bird images. Moreover, abundant visualizations are provided to prove the interpretability of the HSFA and FFA modules in TransIFC. Comprehensive experiments demonstrate that TransIFC can achieve state-of-the-art performance on the CUB-200-2011 dataset (91.0%) and the NABirds dataset (90.9%). Finally, extended experiments have been conducted on the Stanford Cars dataset to suggest the potential of generalizing our method on other fine-grained visual classification tasks.
作者:
Zhou, Chi;Wu, Di;Li, Yating;Yang, Harrison Hao;Man, Shuo;...
期刊:
Education and Information Technologies,2023年28(2):2207-2227 ISSN:1360-2357
通讯作者:
Min Chen
作者机构:
[Li, Yating; Zhou, Chi; Man, Shuo; Chen, Min] Cent China Normal Univ, Natl Engn Res Ctr Learning, Wuhan 430079, Hubei, Peoples R China.;[Zhou, Chi; Wu, Di] Cent China Normal Univ, Res Ctr Sci & Technol Promoting Educ Innovat & De, Strateg Res Base, Minist Educ, Wuhan 430079, Hubei, Peoples R China.;[Zhou, Chi] Cent China Normal Univ, Educ Informatizat Strategy Res Base Minist Educ, Wuhan 430079, Hubei, Peoples R China.;[Wu, Di] Cent China Normal Univ, Hubei Res Ctr Educ Informatizat Dev, Wuhan 430079, Hubei, Peoples R China.;[Yang, Harrison Hao] SUNY Coll Oswego, Sch Educ, Oswego, NY 60543 USA.
通讯机构:
[Min Chen] N;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
关键词:
Teacher learning;Technological pedagogical content knowledge;Student engagement;Stimulus-organism-response framework;Integrative model of behavior prediction
作者:
Miao, Tian-Chang;Gu, Chuan-Hua;Liu, Shengyingjie;Zhou, Z. K.*
期刊:
Behaviour & Information Technology,2023年42(11) ISSN:0144-929X
通讯作者:
Zhou, Z. K.
作者机构:
[Miao, Tian-Chang; Gu, Chuan-Hua; Zhou, Z. K.] Cent China Normal Univ, Sch Psychol, Wuhan 430079, Hubei, Peoples R China.;[Miao, Tian-Chang; Gu, Chuan-Hua; Zhou, Z. K.] Cent China Normal Univ, Minist Educ, Key Lab Adolescent Cyberpsychol & Behav, Wuhan, Peoples R China.;[Liu, Shengyingjie] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Zhou, Z. K.] Minist Educ, Key Lab Adolescent Cyberpsychol & Behav CCNU, Wuhan 430079, Hubei, Peoples R China.
通讯机构:
[Zhou, Z. K.] C;[Zhou, Z. K.] M;Cent China Normal Univ, Sch Psychol, Wuhan 430079, Hubei, Peoples R China.;Minist Educ, Key Lab Adolescent Cyberpsychol & Behav CCNU, Wuhan 430079, Hubei, Peoples R China.
摘要:
Tian-Chang Miao, Chuan-Hua Gu, Shengyingjie Liu & Z. K. Zhou (2020) Internet literacy and academic achievement among Chinese adolescent: a moderated mediation model, Behaviour & Information Technology, DOI: 10.1080/0144929X.2020.1831074
摘要:
Traditional Generative Adversarial Network (GAN) based Generalized Zero Shot Learning (GZSL) methods usually suffer from a problem that these methods ignore the differences between classes when using the standard normal distribution to fit the true distribution of each category, and the incompleteness of a single adversarial training makes the model unable to capture all the characteristics of the samples. To address this problem, a data-driven recurrent adversarial generative network is proposed in this paper. We first synthe-size visual prototypes for unseen classes using the transformation from semantic attributes to visual prototypes learned on seen classes. Then, some noise is generated from these pro-totypes to synthesize the unseen samples according to the corresponding semantic attri-butes. During the sample generation process, a recurrent generative adversarial network is designed to facilitate the generated visual features to be more representative. Extensive experiments on five popular datasets as well as detailed ablation studies demon-strate the effectiveness and superiority of the proposed method.(c) 2023 Elsevier Inc. All rights reserved.
期刊:
AI COMMUNICATIONS,2023年36(3):219-233 ISSN:0921-7126
通讯作者:
Liao, SB
作者机构:
[Liao, Shengbin; Liao, SB] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Wang, Xiaofeng; Yang, ZongKai] Cent China Normal Univ, Natl Engn Lab Educ Big Data Technol, Wuhan, Peoples R China.
通讯机构:
[Liao, SB ] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
关键词:
Human action recognition;mixed convolution;BN-Inception;two-stream network architecture
摘要:
The most widely used two-stream architectures and building blocks for human action recognition in videos generally consist of 2D or 3D convolution neural networks. 3D convolution can abstract motion messages between video frames, which is essential for video classification. 3D convolution neural networks usually obtain good performance compared with 2D cases, however it also increases computational cost. In this paper, we propose a heterogeneous two-stream architecture which incorporates two convolutional networks. One uses a mixed convolution network (MCN), which combines some 3D convolutions in the middle of 2D convolutions to train RGB frames, another one adopts BN-Inception network to train Optical Flow frames. Considering the redundancy of neighborhood video frames, we adopt a sparse sampling strategy to decrease the computational cost. Our architecture is trained and evaluated on the standard video actions benchmarks of HMDB51 and UCF101. Experimental results show our approach obtains the state-of-the-art performance on the datasets of HMDB51 (73.04%) and UCF101 (95.27%).
摘要:
In recent years, cross-modal hashing has attracted an increasing attention due to its fast retrieval speed and low storage requirements. However, labeled datasets are limited in real application, and existing unsupervised cross-modal hashing algorithms usually employ heuristic geometric prior as semantics, which introduces serious deviations as the similarity score from original features cannot reasonably represent the relationships among instances. In this paper, we study the unsupervised deep cross-modal hash retrieval method and propose a novel Semantic Graph Evolutionary Hashing (SGEH) to solve the above problem. The key novelty of SGEH is its evolutionary affinity graph construction method. To be concrete, we explore the sparse similarity graph with clustering results, which evolve from fusing the affinity information from code-driven graph on intrinsic data and subsequently extends to dense hybrid semantic graph which restricts the process of hash code learning to learn more discriminative results. Moreover, the batch-inputs are chosen from edge set rather than vertexes for better exploring the original spatial information in the sparse graph. Experiments on four benchmark datasets demonstrate the superiority of our framework over the state-of-the-art unsupervised cross-modal retrieval methods. Code is available at: https://github.com/theusernamealreadyexists/SGEH.
摘要:
We present a keyphrase extraction algorithm named TopicLPRank in this paper, which is an improved TopicRank algorithm. Different from the TopicRank which only uses the relative distance information of the text, we think that the length and absolute position of the text candidate keyphrases also have a certain influence on the results of the model for extraction keyphrases. Therefore, the proposed TopicLPRank incorporates these two factors on the basis of the TopicRank. The experimental results show that adding the location information and length information of candidate keyphrases can, respectively, increase the F-Score of the model by around 2.7
$$\%$$
points and 1.7
$$\%$$
points, which is equivalent to an increase of 19.6 and 12.3
$$\%$$
compared with the TopicRank. At the same time, the fusion of the length and location information of the candidate keyphrase can increase the F-Score by around 3.5 percentage points, which is equivalent to an increase of 25.21
$$\%$$
compared with the TopicRank in the dataset NUS.
期刊:
Measurement Science And Technology,2023年34(1):015108 ISSN:0957-0233
通讯作者:
Zhicheng Dai
作者机构:
[He, Lamei; Li, Xiaonian] Longdong Univ, Sch Informat Engn, Qingyang, Gansu, Peoples R China.;[Dai, Zhicheng] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
通讯机构:
[Zhicheng Dai] N;National Engineering Research Center for E-learning, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, People's Republic of China
摘要:
There are two problems with traditional indoor fingerprint location methods. First, irrelevant fingerprints in a fingerprint database interfere with the matching phase, which leads to poor positioning accuracy and stability of positioning results, and second, there is a large amount of computational overhead in the matching phase. Therefore, this paper proposes a K-nearest neighbor indoor fingerprint location method based on coarse positioning circular domain and the highest similarity threshold. In this method, a circular domain is formed in a coarse positioning process to narrow the positioning range. It solves the problem of the interference of irrelevant fingerprints. At the same time, a fault-tolerant mechanism is introduced to adjust the circular domain dynamically to ensure that the coarse positioning circular domain contains high similarity reference points and improve the fault tolerance of the coarse positioning. This method consists of offline and online phases. In the offline phase, the values of the received signal strength from Bluetooth low energy are preprocessed using a Gaussian filter to construct a fingerprint database. In the online phase, irrelevant fingerprints are filtered out by using the coarse positioning method. The filtered fingerprints are then matched with a testing point by the K-nearest neighbor algorithm, and the weighted centroids of the nearest reference points are solved. Finally, the coordinate of the testing point is obtained. The experimental results show that this method can effectively improve indoor positioning accuracy when compared with the traditional K-nearest neighbor. The average positioning error of the proposed method is 0.844 m.
期刊:
IEEE Transactions on Medical Imaging,2023年42(3):762-773 ISSN:0278-0062
通讯作者:
Cai, C;Wu, W
作者机构:
[Cai, Chang; Cai, C] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Kang, Huicong] Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Dept Neurol, Wuhan 430079, Hubei, Peoples R China.;[Hashemi, Ali] Tech Univ Berlin, Uncertainty Inverse Modeling & Machine Learning Gr, D-10587 Berlin, Germany.;[Hashemi, Ali] Tech Univ Berlin, Inst Software Engn & Theoret Comp Sci, Fac Elect Engn & Comp Sci 4, Machine Learning Grp, D-10587 Berlin, Germany.;[Chen, Dan] Wuhan Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
通讯机构:
[Wu, W ] A;[Cai, C ] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;Alto Neurosci Inc, Los Altos, CA 94022 USA.
摘要:
Simultaneously estimating brain source activity and noise has long been a challenging task in electromagnetic brain imaging using magneto- and electroencephalography. The problem is challenging not only in terms of solving the NP-hard inverse problem of reconstructing unknown brain activity across thousands of voxels from a limited number of sensors, but also for the need to simultaneously estimate the noise and interference. We present a generative model with an augmented leadfield matrix to simultaneously estimate brain source activity and sensor noise statistics in electromagnetic brain imaging (EBI). We then derive three Bayesian inference algorithms for this generative model (expectation-maximization (EBI-EM), convex bounding (EBI-Convex) and fixed-point (EBI-Mackay)) to simultaneously estimate the hyperparameters of the prior distribution for brain source activity and sensor noise. A comprehensive performance evaluation for these three algorithms is performed. Simulations consistently show that the performance of EBI-Convex and EBI-Mackay updates is superior to that of EBI-EM. In contrast to the EBI-EM algorithm, both EBI-Convex and EBI-Mackay updates are quite robust to initialization, and are computationally efficient with fast convergence in the presence of both Gaussian and real brain noise. We also demonstrate that EBI-Convex and EBI-Mackay update algorithms can reconstruct complex brain activity with only a few trials of sensor data, and for resting-state data, achieving significant improvement in source reconstruction and noise learning for electromagnetic brain imaging.
摘要:
Computer-supported collaborative concept mapping (CSCCM) integrates technology and concept mapping to support students’ knowledge understanding, and much research on the behavioral patterns involved in CSCCM activities has been conducted. However, there is limited understanding of the differences in knowledge understanding and behavioral patterns between students with different levels of collaboration perception. This study examined the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns in the CSCCM activity. A total of 36 individuals from the same university participated in this study. The findings suggested that compared with students with a low level of collaborative perception, students with a high level of collaborative perception could obtain better conceptual knowledge understanding. However, there was no significant difference in factual knowledge understanding between students with different levels of collaboration perception. For behavioral patterns, students with a high level of collaboration perception demonstrated more diverse behavioral transition sequences, students with a middle level of collaboration perception demonstrated more repetitive behavioral sequences, and students with a low level of collaboration perception demonstrated less behavioral transition sequences. The findings of this research can provide a reference for teachers to design CSCCM activities in the classroom.
期刊:
User Modeling and User-Adapted Interaction,2023年:1-33 ISSN:0924-1868
通讯作者:
Liang, RX
作者机构:
[Shen, Xiaoxuan; Yang, Zongkai; Liu, Sannyuya; Li, Qing; Liang, Ruxia; Du, Shangheng; Sun, Jianwen] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.;[Shen, Xiaoxuan; Yang, Zongkai; Liu, Sannyuya; Li, Qing; Liang, Ruxia; Du, Shangheng; Sun, Jianwen] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.;[Yang, Zongkai; Liu, Sannyuya] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.
通讯机构:
[Liang, RX ] C;Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
关键词:
Recommender systems;Group recommender systems;Adversarial learning;Knowledge transfer
摘要:
Many online services allow users to participate in various group activities such as online meeting or group buying and thus need to provide user groups with services that they are interested. The group recommender systems emerge as required and provide personalized services for various online user groups. Data sparsity is an important issue in group recommender systems, since even fewer group-item interactions are observed. Transfer learning has been one efficient tool to alleviate the data sparsity issue in recommender systems for individual users, but have not been utilized for group recommendation. Moreover, the group and the group members have complex and mutual relationships with each other, which exacerbates the difficulty in modelling the preferences of both a group and its members for recommendation. Therefore, group recommender systems face three main challenges that may significantly impact its quality and accuracy: (1) taking consideration of group member relationship and their interactions in modelling user and group preferences; (2) ensuring latent feature spaces between the users and groups are maximally matched; and (3) constructing a deep group recommendation method that both the individual user and group domains can benefit from a knowledge exchange. Hence, in this paper, we propose a deep adversarial group recommendation method, called DA-GR. User feature are separated into two subspaces to ensure only consistent group members’ feature knowledge can be extracted and shared with group preference modelling. Adversarial learning is used to effectively transfer consistent knowledge from individual user interactions to the group interaction domain through the bridge of group-user relationships. Extensive experiments, which demonstrate the effectiveness and superiority of our proposal, providing accurate recommendation for both individual users and groups, are conducted on public datasets. The source code of DA-GR is in
https://github.com/ccnu-mathits/DA-GR
.