作者：
Tingting Liu;Minghong Wang;Bing Yang*;Hai Liu;Shaoxin Yi
期刊：
Neurocomputing,2025年612:128711 ISSN：0925-2312
通讯作者：
Bing Yang
作者机构：
[Minghong Wang] Faculty of Education, The University of Hong Kong, 999077, Hong Kong;[Bing Yang] School of Education, Hubei University, Wuhan 430062, China;[Shaoxin Yi] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China;[Tingting Liu] Faculty of Education, The University of Hong Kong, 999077, Hong Kong<&wdkj&>School of Education, Hubei University, Wuhan 430062, China;[Hai Liu] School of Education, Hubei University, Wuhan 430062, China<&wdkj&>National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China
通讯机构：
[Bing Yang] S;School of Education, Hubei University, Wuhan 430062, China
作者机构：
[Qiao, Yufei; Shang, Yingying; Zhu, Min; Sun, Wen; Shang, YY] Peking Union Med Coll Hosp, Dept Otorhinolaryngol, Beijing 100730, Peoples R China.;[Sun, Yang] Shenyang Normal Univ, Sch Educ Sci, Shenyang, Peoples R China.;[Cai, Chang; Long, Yuanshun] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan, Peoples R China.;[Guo, Hua] Tsinghua Univ, Sch Med, Dept Biomed Engn, Beijing, Peoples R China.;[Shen, Hang] Peking Union Med Coll Hosp, Dept Neurol, Beijing, Peoples R China.
通讯机构：
[Shang, YY ; Shen, H ] P;Peking Union Med Coll Hosp, Dept Otorhinolaryngol, Beijing 100730, Peoples R China.;Peking Union Med Coll Hosp, Dept Neurol, Beijing, Peoples R China.
摘要：
Evidence from epidemiological studies suggests that hearing loss is associated with an accelerated decline in cognitive function, but the underlying pathophysiological mechanism remains poorly understood. Studies using auditory tasks have suggested that degraded auditory input increases the cognitive load for auditory perceptual processing and thereby reduces the resources available for other cognitive tasks. Attention-related networks are among the systems overrecruited to support degraded auditory perception, but it is unclear how they function when no excessive recruitment of cognitive resources for auditory processing is needed. Here, we implemented an EEG study using a nonauditory visual attentional selection task in 30 individuals with age-related hearing loss (ARHLs, 60-73 years) and compared them with aged (N = 30, 60-70 years) and young (N = 35, 22-29 years) normal-hearing controls. Compared with their normal-hearing peers, ARHLs demonstrated a significant amplitude reduction for the posterior contralateral N2 component, which is a well-validated index of the allocation of selective visual attention, despite the comparable behavioral performance. Furthermore, the amplitudes were observed to correlate significantly with hearing acuities (pure tone audiometry thresholds) and higher-order hearing abilities (speech-in-noise thresholds) in aged individuals. The target-elicited alpha lateralization, another mechanism of visuospatial attention, demonstrated in control groups was not observed in ARHLs. Although behavioral performance is comparable, the significant decrease in N2pc amplitude in ARHLs provides neurophysiologic evidence that may suggest a visual attentional deficit in ARHLs even without extra-recruitment of cognitive resources by auditory processing. It supports the hypothesis that constant degraded auditory input in ARHLs has an adverse impact on the function of cognitive control systems, which is a possible mechanism mediating the relationship between hearing loss and cognitive decline.
作者机构：
[Yang, Zongkai; Liu, Sannyuya; Duan, Huimin; Liu, Zhi; Liu, Shiqi] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr Educ Big Data, Wuhan, Peoples R China.;[Yang, Zongkai; Liu, Sannyuya; Liu, Zhi] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Mu, Rui] Shenzhen Senior High Sch, Yantian Sch, Shenzhen, Peoples R China.
通讯机构：
[Liu, SQ ] C;Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr Educ Big Data, Wuhan, Peoples R China.
关键词：
Conversational agents;Online learning;Scaffolding;Zone of Proximal Development;Quasi- experiment
摘要：
Conversational agents (CAs) primarily adopt knowledge scaffolding (KS) or emotional scaffolding (ES) to intervene in learners' knowledge gain and emotional experience in online learning. However, the ill-defined design for KS and ES, as well as insufficient understanding of their interactive effects on learning outcomes, have hindered the advancement of CAs in theory and practice. This study proposed systematic KS and ES design principles based on Zone of Proximal Development and growth mindset theories. We investigated their individual and combined impacts on knowledge gain and emotional experience. A quasi -experiment was conducted with 128 undergraduate students divided into four groups, corresponding to four distinct CAs: a nonscaffolding control group (CG), ES, KS, and Knowledge and Emotional Scaffolding (K&ES) CA. The results showed that K&ES-based CA had a significant impact on knowledge gain and emotional experience, with both being slightly improved compared to CG. Besides, KS -based CA had a positive effect on knowledge gain and emotional experience, while ES -based CA only slightly improved emotional experience compared to CG. The results validated the effectiveness of the proposed ES and KS design principles. The fine-grained analysis revealed a significant correlation between the achievement positive emotion and knowledge transfer, highlighting the importance of integrating KS and ES. In conclusion, this study offers valuable theoretical, methodological, and empirical insights for utilizing CAs to optimize online learning experiences.
摘要：
Interictal epileptiform discharges (IED) as large intermittent electrophysiological events are associated with various severe brain disorders. Automated IED detection has long been a challenging task, and mainstream methods largely focus on singling out IEDs from backgrounds from the perspective of waveform, leaving normal sharp transients/artifacts with similar waveforms almost unattended. An open issue still remains to accurately detect IED events that directly reflect the abnormalities in brain electrophysiological activities, minimizing the interference from irrelevant sharp transients with similar waveforms only. This study then proposes a dual-view learning framework (namely V2IED) to detect IED events from multi-channel EEG via aggregating features from the two phases: (1) Morphological Feature Learning: directly treating the EEG as a sequence with multiple channels, a 1D-CNN (Convolutional Neural Network) is applied to explicitly learning the deep morphological features; and (2) Spatial Feature Learning: viewing the EEG as a 3D tensor embedding channel topology, a CNN captures the spatial features at each sampling point followed by an LSTM (Long Short-Term Memories) to learn the evolution of these features. Experimental results from a public EEG dataset against the state-of-the-art counterparts indicate that: (1) compared with the existing optimal models, V2IED achieves a larger area under the receiver operating characteristic (ROC) curve in detecting IEDs from normal sharp transients with a 5.25% improvement in accuracy; (2) the introduction of spatial features improves performance by 2.4% in accuracy; and (3) V2IED also performs excellently in distinguishing IEDs from background signals especially benign variants.
期刊：
IEEE Transactions on Dielectrics and Electrical Insulation,2024年31(4):2198-2207 ISSN：1070-9878
通讯作者：
Zhu, KH
作者机构：
[Liu, Jiazheng; Deng, Yi; Zhu, Kuihu] Wuhan Text Univ, Sch Elect & Elect Engn, Wuhan 430200, Peoples R China.;[Deng, Yi] Wuhan Text Univ, State Key Lab New Text Mat & Adv Proc Technol, Wuhan 430200, Peoples R China.;[Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
通讯机构：
[Zhu, KH ] W;Wuhan Text Univ, Sch Elect & Elect Engn, Wuhan 430200, Peoples R China.
摘要：
Partial discharge pattern recognition (PDPR) is the fundamental cornerstone for fault diagnosis. It has emerged as a pivotal focal point in the field of power systems. However, PDPR faces several challenges, such as low signal quality and complex discharge patterns. We propose a multiscale residual aggregation transformer network (MRATNet) to address these challenges effectively. MRATNet learns long-dependent semantic relationships and discriminative features in partial discharge (PD) signals. Moreover, it integrates convolutional and transformer architectures as the feature extraction backbone. Thus, multiscale residual convolution (MSRC) blocks are incorporated to aggregate diverse information, and the transformer is leveraged to capture long-dependent semantic relationships. Meanwhile, the cross-attention mechanism is introduced to capture the spatial and channel feature distributions. The composite embedded feature selection (CEFS) module is proposed to extract discriminative features. Comprehensive experiments demonstrate the effectiveness of MRATNet, yielding exceptional performance on DEPD dataset (91.47%) and PDMDB dataset (86.05%). Finally, extended experiments have been conducted using the Technical University of Berlin's German emotional language library, suggesting the potential for generalizing our method to other recognition tasks.
作者机构：
[Xu, Ruyi; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;[Chen, Jingying; Han, Jiaxu] Cent China Normal Univ, Natl Engn Res Ctr Elearning, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;[Chen, Jingying] Ningbo Yuxing Educ Technol Co Ltd, Ningbo 315200, Zhejiang, Peoples R China.
通讯机构：
[Jingying Chen] N;National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, China<&wdkj&>National Engineering Research Center for E-learning, Central China Normal University, Wuhan, China<&wdkj&>Ningbo Yuxing Educational Technology Co., Ltd, Ningbo, China
作者机构：
[Yang, JiuMei; Wu, LongKai; Chen, ShengQing; Wu, LK] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.;[Wu, LongKai; Fan, ZhangQi; Wu, LK] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.;[Wu, LongKai; Wu, LK] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
会议名称：
17th International Conference on Blended Learning. Intelligent Computing in Education (ICBL)
会议时间：
JUL 29-AUG 01, 2024
会议地点：
Macao SAR, PEOPLES R CHINA
会议主办单位：
[Yang, JiuMei;Chen, ShengQing;Wu, LongKai] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.^[Fan, ZhangQi;Wu, LongKai] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.^[Wu, LongKai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
会议论文集名称：
Lecture Notes in Computer Science
关键词：
AIGC;Blended learning;Deep learning ability;Teaching Practice
摘要：
Artificial Intelligence Generated Content (AIGC) is an important issue in the field of higher education. Although many studies on AIGC have appeared, they are mainly theoretical rather than empirical studies, and the study of how to effectively integrate AIGC into teaching is insufficient. Considering the compatibility between artificial intelligence and blended learning, based on constructivist learning theory and deep learning theory, and using AIGC, we designed a blended learning involving teacher, student, and AIGC, and three segments of pre-class, in-class, and post-class, and conducted teaching practice. A total of 79 students majoring in computer science and technology were selected as the research participants, and a single-group pre- and post-test method was employed to verify the effectiveness of the experiment. The results show that the blended learning design based on AIGC is feasible and effective, and students can adapt to and deeply participate in the teaching process. It also significantly improves students' deep learning ability and ensures that students master the knowledge of the course.
作者机构：
[Liu, Sannyuya; Yuan, Xin; Yue, Jieyu; Li, Zhen; Li, Qing; Liu, SNYY; Hu, Tianhui; Chen, Sijing; Sun, Jianwen] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.;[Liu, Sannyuya; Liu, SNYY] Cent China Normal Univ, Natl Engn Res Ctr E Elearning, Wuhan 430079, Peoples R China.
通讯机构：
[Liu, SNYY ; Chen, SJ] C;Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Natl Engn Res Ctr E Elearning, Wuhan 430079, Peoples R China.
摘要：
The purpose of this study was to investigate the frontier, science, and public engagement of educational science research. This paper conducted a systematic literature review of 101 educational science research articles published in Nature and Science in 1982-2021 based on the Web of Science database and analyzed the current status of research in terms of basic publication characteristics, research themes, and research processes. Five research topics were recognized, namely, education policy evaluation and reform, learning mechanisms and learning interventions, science education, educational technology, and education equity. Content of each topic had a distinctive emphasis. Findings revealed that most studies were dominated by empirical research, involving causal relationships between various educational phenomena, diverse range of research subjects, rigorous scientific randomized experiments, and quantitative analysis. We encourage more research on educational science in the future from four feasible directions, namely, developing active learning approaches to promoting effective learning, extending the research subjects and objectives of science education, conducting long-term, large-scale and practice-oriented research, and introducing new research methods into educational research.
作者：
Li, Jiayuan;Bai, Jie;Zhu, Sha;Yang, Harrison Hao
期刊：
Electronics,2024年13(2):385- ISSN：2079-9292
通讯作者：
Zhu, S;Yang, HH
作者机构：
[Zhu, Sha; Zhu, S; Bai, Jie; Li, Jiayuan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Yang, HH; Yang, Harrison Hao] SUNY Coll Oswego, Sch Educ, Oswego, NY 13126 USA.
通讯机构：
[Yang, HH ] S;[Zhu, S ] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;SUNY Coll Oswego, Sch Educ, Oswego, NY 13126 USA.
关键词：
digital literacy;digital game-based assessment;ECGD;AHP;assessment model
摘要：
<jats:p>This study measured secondary students’ digital literacy using a digital game-based assessment system that was designed and developed based on the Evidence-Centered Game Design (ECGD) approach. A total of 188 secondary students constituted the valid cases in this study. Fine-grained behavioral data generated from students’ gameplay processes were collected and recorded with the assessment system. The Delphi method was used to extract feature variables related to digital literacy from the process data, and the Analytic Hierarchy Process (AHP) method was used to construct the measurement model. The assessment results of the ECGD-based assessment had a high correlation with standardized test scores, which have been shown to be reliable and valid in prior large-scale assessment studies.</jats:p>
期刊：
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2024年PP:1-12 ISSN：2168-2194
作者机构：
[Xueli Pan; Frank van Harmelen] Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China;National Language Resources Monitor Research Center for Network Media, Central China Normal University, Wuhan, China;School of Computer Science, Central China Normal University, Wuhan, China
摘要：
It is commonly known that food nutrition is closely related to human health. The complex interactions between food nutrients and diseases, influenced by gut microbial metabolism, present challenges in systematizing and practically applying knowledge. To address this, we propose a method for extracting triples from a vast amount of literature, which is used to construct a comprehensive knowledge graph on nutrition and human health. Concurrently, we develop a query-based question answering system over our knowledge graph, proficiently addressing three types of questions. The results show that our proposed model outperforms other state-of-art methods, achieving a precision of 0.92, a recall of 0.81, and an F1 score of 0.86in the nutrition and disease relation extraction task. Meanwhile, our question answering system achieves an accuracy of 0.68 and an F1 score of 0.61 on our benchmark dataset, showcasing competitiveness in practical scenarios. Furthermore, we design five independent experiments to assess the quality of the data structure in the knowledge graph, ensuring results characterized by high accuracy and interpretability. In conclusion, the construction of our knowledge graph shows significant promise in facilitating diet recommendations, enhancing patient care applications, and informing decision-making in clinical research.
摘要：
Dialogue state tracking (DST) is a core component of task-oriented dialogue systems. Recent works focus mainly on end-to-end DST models that omit the spoken language understanding (SLU) module to directly obtain the dialogue state based on a user’s dialogue. However, the slot information detected by slot filling in SLU is closely tied to the slot–value pair that needs to be updated in DST. Efficient use of the key slot semantic knowledge obtained by slot filling contributes to improving the performance of DST. Based on this idea, we introduce slot filling as a subtask and build an end-to-end joint model to explicitly integrate the slot information detected by slot filling, which further guides DST. In this article, a novel stack-propagation framework with slot filling for multidomain DST is proposed. The stack-propagation framework is introduced to jointly model slot filling and DST. The framework directly feeds the key slot semantic knowledge detected by slot filling into the DST module. In addition, a slot-masked attention mechanism is designed to enable DST to focus on the key slot information obtained by slot filling. When the slot value is updated, a slot–value softcopy mechanism is designed to enhance the influence of the words marked by key slots. Experiments show that our approach outperforms previous methods and performs outstandingly on two benchmark datasets. IEEE
期刊：
International Journal of Human-Computer Interaction,2024年 ISSN：1044-7318
通讯作者：
Zhu, S
作者机构：
[Guo, Qing] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Hubei, Peoples R China.;[Zhu, Sha; Zhu, S; Yang, Harrison Hao; Wu, Di] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Yang, Harrison Hao] SUNY Coll Oswego, Sch Educ, Oswego, NY USA.
通讯机构：
[Zhu, S ] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
摘要：
The assessment of information literacy (IL) is challenging. Traditional methods often fail to capture students' true performance and their behavioral processes. Evidence-centered game design (ECgD) offers an alternative assessment framework. However, IL assessment based on ECgD often overlooks further analysis of behavioral processes, which could reveal detailed performance differences beyond scores. In this study, a general ECgD paradigm is presented and this paradigm was used to develop assessment rules. Game log data were collected from 132 middle school students, who also completed an external IL test. Game logs recorded the behavioral processes, from which evidence required for assessment was extracted. Six supervised machine learning models were employed to predict students' IL levels. The assessment results indicate that students have lower levels of information knowledge and skills, as well as information thinking and behavior. Further analysis of in-game behaviors revealed that students with higher IL levels were able to complete tasks correctly and spent more time in the game, earning more coins, while seeking help less frequently. These findings demonstrate the feasibility of using ECgD for IL assessment and uncover deeper differences in student performance underlying IL levels. Finally, the theoretical and practical implications of the study are discussed, and future research directions are proposed.
摘要：
<jats:p>We propose an end-to-end generative adversarial network that allows for controllable ink wash painting generation from sketches by specifying the colors via color hints. To the best of our knowledge, this is the first study for interactive Chinese ink wash painting colorization from sketches. To help our network understand the ink style and artistic conception, we introduced an ink style prediction mechanism for our discriminator, which enables the discriminator to accurately predict the style with the help of a pre-trained style encoder. We also designed our generator to receive multi-scale feature information from the feature pyramid network for detail reconstruction of ink wash painting. Experimental results and user study show that ink wash paintings generated by our network have higher realism and richer artistic conception than existing image generation methods.</jats:p>
摘要：
To monitor and assess social dynamics and risks at large gatherings, we propose "SocialVis," a comprehensive monitoring system based on multi-object tracking and graph analysis techniques. Our SocialVis includes a camera detection system that operates in two modes: a real-time mode, which enables participants to track and identify close contacts instantly, and an offline mode that allows for more comprehensive post-event analysis. The dual functionality not only aids in preventing mass gatherings or overcrowding by enabling the issuance of alerts and recommendations to organizers, but also allows for the generation of proximity-based graphs that map participant interactions, thereby enhancing the understanding of social dynamics and identifying potential high-risk areas. It also provides tools for analyzing pedestrian flow statistics and visualizing paths, offering valuable insights into crowd density and interaction patterns. To enhance system performance, we designed the SocialDetect algorithm in conjunction with the BYTE tracking algorithm. This combination is specifically engineered to improve detection accuracy and minimize ID switches among tracked objects, leveraging the strengths of both algorithms. Experiments on both public and real-world datasets validate that our SocialVis outperforms existing methods, showing 1.2%$$ 1.2\% $$ improvement in detection accuracy and 45.4%$$ 45.4\% $$ reduction in ID switches in dense pedestrian scenarios. Our SocialVis is a framework for visualizing social dynamics, providing real-time monitoring of individual behavior and social distancing in crowds. Its SocialDetect algorithm, using LSKA attention and Wise-IOUv3 loss, improves accuracy in dense settings with occlusion and scale changes. To simulate interactions among individuals, we employ a graph theory-based approach that utilizes multi-object tracking, combined with SPGA, PFV, and PPV techniques, to provide comprehensive insights into crowd behavior. image
