作者：
Cai, Chang;Chen, Jessie;Findlay, Anne M.;Mizuiri, Danielle;Sekihara, Kensuke;...
期刊：
FRONTIERS IN HUMAN NEUROSCIENCE,2021年15 ISSN：1662-5161
通讯作者：
Kirsch, H.E.;Cai, C.;Nagarajan, S.S.
作者机构：
[Nagarajan, Srikantan S.; Cai, Chang] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Mizuiri, Danielle; Findlay, Anne M.; Cai, Chang; Chen, Jessie; Kirsch, Heidi E.] Univ Calif San Francisco, Dept Radiol & Biomed Imaging, San Francisco, CA 94143 USA.;[Sekihara, Kensuke] Tokyo Med & Dent Univ, Dept Adv Technol Med, Tokyo, Japan.;[Sekihara, Kensuke] Signal Anal Inc, Hachioji, Tokyo, Japan.;[Kirsch, Heidi E.] Univ Calif San Francisco, Dept Neurol, San Francisco, CA 02115 USA.
通讯机构：
[Cai, C.; Nagarajan, S.S.] N;[Kirsch, H.E.] D;Department of Radiology and Biomedical Imaging, San Francisco, San Francisco, United States;National Engineering Research Center for E-Learning, China
作者机构：
[Liu, Leyuan; Ke, Zeran; Huo, Jiao; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Liu, Leyuan; Chen, Jingying] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
通讯机构：
[Chen, Jingying] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
关键词：
computer vision;head pose estimation;3D face reconstruction;facial keypoints matching
摘要：
Mainstream methods treat head pose estimation as a supervised classification/regression problem, whose performance heavily depends on the accuracy of ground-truth labels of training data. However, it is rather difficult to obtain accurate head pose labels in practice, due to the lack of effective equipment and reasonable approaches for head pose labeling. In this paper, we propose a method which does not need to be trained with head pose labels, but matches the keypoints between a reconstructed 3D face model and the 2D input image, for head pose estimation. The proposed head pose estimation method consists of two components: the 3D face reconstruction and the 3D-2D matching keypoints. At the 3D face reconstruction phase, a personalized 3D face model is reconstructed from the input head image using convolutional neural networks, which are jointly optimized by an asymmetric Euclidean loss and a keypoint loss. At the 3D-2D keypoints matching phase, an iterative optimization algorithm is proposed to match the keypoints between the reconstructed 3D face model and the 2D input image efficiently under the constraint of perspective transformation. The proposed method is extensively evaluated on five widely used head pose estimation datasets, including Pointing'04, BIWI, AFLW2000, Multi-PIE, and Pandora. The experimental results demonstrate that the proposed method achieves excellent cross-dataset performance and surpasses most of the existing state-of-the-art approaches, with average MAEs of 4.78 degrees on Pointing'04, 6.83 degrees on BIWI, 7.05 degrees on AFLW2000, 5.47 degrees on Multi-PIE, and 5.06 degrees on Pandora, although the model of the proposed method is not trained on any of these five datasets.
作者机构：
[Zhang, Zhaoli; Nie, Hanwen; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Liu, Hai] UCL, UCL Interact Ctr, London, England.;[Li, You-Fu; Liu, Hai] City Univ Hong Kong, Dept Mech Engn, Kowloon, Hong Kong, Peoples R China.
通讯机构：
[Zhang, Zhaoli] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
摘要：
Head pose estimation is an important way to understand human attention in the human-computer interaction. In this paper, we propose a novel anisotropic angle distribution learning (AADL) network for head pose estimation task. Firstly, two key findings are revealed as following: 1) Head pose image variations are different at the yaw and pitch directions with the same pose angle increasing on a fixed central pose; 2) With the fixed angle interval increasing, the image variations increase firstly and then decrease in yaw angle direction. Then, the maximum a posterior technology is employed to construct the head pose estimation network, which includes three parts, such as convolutional layer, covariance pooling layer and output layer. In the output layer, the labels are constructed as the anisotropic angle distributions on the basis of two key findings. And the anisotropic angle distributions are fitted by the 2D Gaussian like distributions (groundtruth labels). Furthermore, the Kullback-Leibler divergence is selected to measure the predication label and the groundtruth one. The features of head pose images are perceived at the AADL-based convolutional neural network in an end-to-end manner. Experimental results demonstrate that the developed AADL-based labels have several advantages, such as robustness for head pose image missing, insensitivity for the motion blur. Moreover, the proposed method has achieved good performance compared to several state-of-the-art methods on the Pointing'04 and CAS_PEAL_R1 databases. (c) 2020 Elsevier B.V. All rights reserved.
摘要：
The automatical recognition of human facial expression has attracted attention in the field of computer vision and machine learning. Previous works on this topic set many constraints, such as the impact caused by restricted scenarios and low image quality. To address those problems, we propose a new infrared facial expression recognition method with multi-label distribution learning for understanding non-verbal behaviors in the classroom. Specifically, we first compute the feature similarities of seven basic facial expressions to describe the relationship among the adjacent expression images. Then, the similarity values are fitted by a Cauchy distribution function. Furthermore, we construct a new deep network with Cauchy distribution-based label learning (CDLLNet), instead of the conventional single expression labels. By these revised labels, one infrared facial expression can contribute to the learning of neighboring expression labels, as well as its real expression label. The performance of proposed network is evaluated on two facial expression datasets: Oulu-CASIA and CK+. Several qualitative and quantitative experimental results verify that the CDLLNet network can achieve robust results and significantly outperforms the existing state-of-the-art facial expression algorithms.
摘要：
Improving the time efficiency of fringe projection profilometry (FPP) is an attractive problem. For FPP using phase-shifting, it is desired to improve the efficiency by reducing the step number for phase retrieval. This paper proposes a two-step phase-shifting algorithm dedicated to FPP. Considering the physical process of FPP, the captured fringe image is formulated with two variables, i.e. surface reflectance and phase value. And a phase shift is introduced to get the two equations, which lead to the close-form solution for phase calculation. Then the phase error due to ambient light is analyzed via a line-circle model, and an algorithm of refining the phase calculation is proposed based on the estimation of the actual fringe contrast. The validity of the proposed approach is demonstrated with experiments.
期刊：
Arabian Journal of Geosciences,2021年14(24):1-1 ISSN：1866-7511
通讯作者：
Wang, Zhe
作者机构：
[Li, Jingjing] Huazhong Agr Univ, Coll Life Sci & Technol, Wuhan 430070, Hubei, Peoples R China.;[Li, Jingjing] Hubei Inst Water Resources Survey & Design, Wuhan 430070, Hubei, Peoples R China.;[Wang, Zhe] Cent China Normal Univ, Natl Engn Res Ctr Learning, Wuhan 430079, Hubei, Peoples R China.;[Wang, Zhe] Wuhan Univ Sci & Technol, Sch Literature Law & Econ, Wuhan 430065, Hubei, Peoples R China.
通讯机构：
[Wang, Zhe] C;[Wang, Zhe] W;Cent China Normal Univ, Natl Engn Res Ctr Learning, Wuhan 430079, Hubei, Peoples R China.;Wuhan Univ Sci & Technol, Sch Literature Law & Econ, Wuhan 430065, Hubei, Peoples R China.
期刊：
FRONTIERS IN HUMAN NEUROSCIENCE,2021年15:656578 ISSN：1662-5161
通讯作者：
Xu, R.;Liu, L.
作者机构：
[Liu, Leyuan; Peng, Shixin; Liu, Lili; Yi, Xin; Xu, Ruyi; Hu, Xin] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.;[Liu, Leyuan; Peng, Shixin; Liu, Lili; Yi, Xin; Xu, Ruyi; Hu, Xin] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
通讯机构：
[Xu, R.; Liu, L.] N;National Engineering Laboratory for Education Big Data, China
关键词：
Early Screening 1;Autism Spectrum Disorder (ASD) 2;Electroencephalogram (EEG) Signal 3;Feature Selection 4;Event-Related Potential (ERP) 5
摘要：
Early screening is vital and helpful for implementing intensive intervention and rehabilitation therapy for children with autism spectrum disorder (ASD). Research has shown that electroencephalogram (EEG) signals can reflect abnormal brain function of children with ASD, and screening with EEG signals has the characteristics of good real-time performance and high sensitivity. However, the existing EEG screening algorithms mostly focus on the data analysis in the resting state, and the extracted EEG features have some disadvantages such as weak representation capacity and information redundancy. In this study, we utilized the event-related potential (ERP) technique to acquire the EEG data of the subjects under positive and negative emotional stimulation and proposed an EEG Feature Selection Algorithm based on L1-norm regularization to perform screening of autism. The proposed EEG Feature Selection Algorithm includes the following steps: (1) extracting 20 EEG features from the raw data, (2) classification with support vector machine, (3) selecting appropriate EEG feature with L1-norm regularization according to the classification performance. The experimental results show that the accuracy for screening of children with ASD can reach 93.8% and 87.5% under positive and negative emotional stimulation and the proposed algorithm can effectively eliminate redundant features and improve screening accuracy.
作者机构：
[Yan, Xiaoyan] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Sun, Bo; Jian, Fanghong] Cent China Normal Univ, Natl Engn Res Ctr ELearning, Wuhan 430079, Peoples R China.
通讯机构：
[Yan, X.] S;School of Information Management, China
摘要：
Sentiment analysis of online reviews is an important task in natural language processing. It has received much attention not only in academia but also in industry. Data have become an important source of competitive intelligence. Various pretraining models such as BERT and ERNIE have made great achievements in the task of natural language processing, but lack domain-specific knowledge. Knowledge graphs can enhance language representation. Furthermore, knowledge graphs have high entity / concept coverage and strong semantic expression ability. We propose a sentiment analysis knowledge graph (SAKG)-BERT model that combines sentiment analysis knowledge and the language representation model BERT. To improve the interpretability of the deep learning algorithm, we construct an SAKG in which triples are injected into sentences as domain knowledge. Our investigation reveals promising results in sentence completion and sentiment analysis tasks.
期刊：
JOURNAL OF COMMUNICATIONS AND NETWORKS,2021年23(4):271-280 ISSN：1229-2370
通讯作者：
Liao, Shengbin
作者机构：
[Liao, Shengbin] Cent China Normal Univ, Natl Engn Ctr E Learning, Wuhan, Peoples R China.
通讯机构：
[Liao, Shengbin] C;Cent China Normal Univ, Natl Engn Ctr E Learning, Wuhan, Peoples R China.
关键词：
Index Terms;Consensus algorithm;network utility maximization;power control;primary-dual algorithm;wireless sensor networks
摘要：
This paper investigates a method to distributively solve a Network Utility Maximization (NUM) problem with coupled variables and applies it to study power control in wireless sensor networks (WSNs). We present a dual decomposition-based consistency price algorithm to solve the coupled problem. However, the consistency price algorithm suffers from slow convergence. We then propose a two-step method to address the given issue. The first step is to build up a global consensus problem by introducing slack variables to transform the NUM problem with globally coupled variables into a NUM problem with coupled constraints. The second step is to design a distributed algorithm that combines the first-order gradient/subgradient method and a local consensus algorithm to solve the global consensus problem. The proposed algorithm is a primary algorithm which has faster convergence speed than the consistency price algorithm which is a primary-dual algorithm. Experimental results have demonstrated the effectiveness of our proposed approach.
期刊：
IEEE Transactions on Knowledge and Data Engineering,2021年33(5):1906-1918 ISSN：1041-4347
通讯作者：
Liu, H.;Yi, B.
作者机构：
[Shen, Xiaoxuan; Zhang, Wei; Zhang, Zhaoli; Yi, Baolin; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Liu, Sannyuya] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Xiong, Naixue] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
通讯机构：
[Yi, B.; Liu, H.] N;National Engineering Research Center for E-Learning, China
摘要：
Automatic recommendation has become an increasingly relevant problem to industries, which allows users to discover new items that match their tastes and enables the system to target items to the right users. In this article, we have proposed a deep learning based fully Bayesian treatment recommendation framework, DVMF, which has high-quality performance and ability to integrate any kinds of side information handily and efficiently. In DVMF, the variational inference technique and the reparameterization tricks are introduced to make DVMF possible to be optimized by the stochastic gradient-based methods, in addition, two novel deep neural networks have been constructed to infer the hyper-parameters of the distributions of latent factors from the knowledge of user and item, which are represented as low-dimensional real-valued vectors retaining primary features. Experimental results on five public databases indicate that the proposed method performs better than the state-of-the-art recommendation algorithms on prediction accuracy in terms of quantitative assessments.
摘要：
Knowledge graph embedding aims to learn the embedded representation of entities and relations in knowledge graphs which is very important for the subsequent link prediction task. However, two key issues are existed for learning knowledge graph embedding: 1) How to take full advantage of the deep learning algorithms to generate expressive embeddings? 2) How to solve the polysemy phenomenon caused by multi-relations knowledge graphs that entities and relations show different semantics after involving different predictions? In this article, to tackle the first problem, the multi-layer convolutional networks are adopted to generate features about entities and relations then used to predict candidate entity. Moreover, the representation power of the networks is strengthened by integrating an effective recalibration mechanism which can accentuate informative features selectively. To tackle the second problem, we propose to learn multiple specific interaction embeddings. Instead of directly learning one general embedding to preserve all information for each entity and relation, their interactions are captured to model the cross-semantic influence from relations to entities and from entities to relations. Compared to traditional embedding models, the proposed model can provide more generalization capabilities and effectively capture potential links between entities and relations. Experimental results have revealed that the proposed model achieves the state-of-the-art performance for general evaluation metrics on link prediction tasks. (c) 2020 Elsevier B.V. All rights reserved.
期刊：
Journal of Systems Science and Systems Engineering,2021年30(4):417-432 ISSN：1004-3756
通讯作者：
Lei Niu
作者机构：
[Huang, Litian; Yu, Xinguo; Niu, Lei] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan 430000, Peoples R China.;[Zhao, Jinhua] Wuhan Univ, Sch Econ & Management, Wuhan 430000, Peoples R China.;[Yu, Xinguo] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430000, Peoples R China.
通讯机构：
[Lei Niu] C;Central China Normal University Wollongong Joint Institute, Central China Normal University, Wuhan, China
摘要：
The research of multiple negotiations considering issue interdependence across negotiations is considered as a complex research topic in agent negotiation. In the multiple negotiations scenario, an agent conducts multiple negotiations with opponents for different negotiation goals, and issues in a single negotiation might be interdependent with issues in other negotiations. Moreover, the utility functions involved in multiple negotiations might be nonlinear, e.g., the issues involved in multiple negotiations are discrete. Considering this research problem, the current work may not well handle multiple interdependent negotiations with complex utility functions, where issues involved in utility functions are discrete. Regarding utility functions involving discrete issues, an agent may not find an offer exactly satisfying its expected utility during the negotiation process. Furthermore, as sub-offers on issues in every single negotiation might be restricted by the interdependence relationships with issues in other negotiations, it is even harder for the agent to find an offer satisfying the expected utility and all involved issue interdependence at the same time, leading to a high failure rate of processing multiple negotiations as a final outcome. To resolve this challenge, this paper presents a negotiation model for multiple negotiations, where interdependence exists between discrete issues across multiple negotiations. By introducing the formal definition of “interdependence between discrete issues across negotiations”, the proposed negotiation model applies the multiple alternating offers protocol, the clustered negotiation procedure and the proposed negotiation strategy to handle multiple interdependent negotiations with discrete issues. In the proposed strategy, the “tolerance value” is introduced as an agent’s consideration to balance between the overall negotiation goal and the negotiation outcomes. The experimental results show that, 1) the proposed model well handles the multiple negotiations with interdependence between discrete issues, 2) the proposed approach is able to help agents in the decision-making process of proposing acceptable offers, 3) an agent can choose a proper “tolerance value” to balance between the success rate of multiple negotiations and its expected utility.
通讯机构：
[Müller, Klaus-Robert] M;[Hashemi, Ali] I;[Haufe, Stefan] B;[Nagarajan, Srikantan S] D;Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA<&wdkj&>Machine Learning Group, Technische Universität Berlin, Germany<&wdkj&>BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany<&wdkj&>Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea<&wdkj&>Max Planck Institute for Informatics, Saarbrücken, Germany<&wdkj&>Uncertainty, Inverse Modeling and Machine Learning Group, Technische Universität Berlin, Germany<&wdkj&>Berlin Center for Advanced Neuroimaging (BCAN), Charité – Universitätsmedizin Berlin, Germany<&wdkj&>Mathematical Modelling and Data Analysis Department, Physikalisch-Technische Bundesanstalt Braunschweig und Berlin, Germany<&wdkj&>Bernstein Center for Computational Neuroscience, Berlin, Germany
摘要：
Methods for electro- or magnetoencephalography (EEG/MEG) based brain source imaging (BSI) using sparse Bayesian learning (SBL) have been demonstrated to achieve excellent performance in situations with low numbers of distinct active sources, such as event-related designs. This paper extends the theory and practice of SBL in three important ways. First, we reformulate three existing SBL algorithms under the majorization-minimization (MM) framework. This unification perspective not only provides a useful theoretical framework for comparing different algorithms in terms of their convergence behavior, but also provides a principled recipe for constructing novel algorithms with specific properties by designing appropriate bounds of the Bayesian marginal likelihood function. Second, building on the MM principle, we propose a novel method called LowSNR-BSI that achieves favorable source reconstruction performance in low signal-to-noise-ratio (SNR) settings. Third, precise knowledge of the noise level is a crucial requirement for accurate source reconstruction. Here we present a novel principled technique to accurately learn the noise variance from the data either jointly within the source reconstruction procedure or using one of two proposed cross-validation strategies. Empirically, we could show that the monotonous convergence behavior predicted from MM theory is confirmed in numerical experiments. Using simulations, we further demonstrate the advantage of LowSNR-BSI over conventional SBL in low-SNR regimes, and the advantage of learned noise levels over estimates derived from baseline data. To demonstrate the usefulness of our novel approach, we show neurophysiologically plausible source reconstructions on averaged auditory evoked potential data.
作者机构：
[Zhao, Liang; Zhu, Xiaoliang] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Ye, Shihao; Dai, Zhicheng] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
摘要：
As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, including uneven illumination, head deflection, and facial posture. In this paper, we propose a convenient facial expression recognition cascade network comprising spatial feature extraction, hybrid attention, and temporal feature extraction. First, in a video sequence, faces in each frame are detected, and the corresponding face ROI (range of interest) is extracted to obtain the face images. Then, the face images in each frame are aligned based on the position information of the facial feature points in the images. Second, the aligned face images are input to the residual neural network to extract the spatial features of facial expressions corresponding to the face images. The spatial features are input to the hybrid attention module to obtain the fusion features of facial expressions. Finally, the fusion features are input in the gate control loop unit to extract the temporal features of facial expressions. The temporal features are input to the fully connected layer to classify and recognize facial expressions. Experiments using the CK+ (the extended Cohn Kanade), Oulu-CASIA (Institute of Automation, Chinese Academy of Sciences) and AFEW datasets obtained recognition accuracy rates of 98.46%, 87.31%, and 53.44%, respectively. This demonstrated that the proposed method achieves not only competitive performance comparable to state-of-the-art methods but also greater than 2% performance improvement on the AFEW dataset, proving the significant outperformance of facial expression recognition in the natural environment.
期刊：
IEEE Transactions on Instrumentation and Measurement,2021年70:1-10 ISSN：0018-9456
通讯作者：
Li, Y.
作者机构：
[Li, Youfu; Liu, Hai; Liu, Meng] City Univ Hong Kong, Dept Mech Engn, Hong Kong, Peoples R China.;[Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr Learning, Wuhan 430079, Peoples R China.
通讯机构：
[Li, Y.] D;Department of Mechanical Engineering, Hong Kong
摘要：
In order to precisely predict 3-D gaze points, calibration is needed for each subject prior to first use the mobile gaze tracking system. However, traditional calibration methods normally expect the user to stare at predefined targets in the scene, which is troublesome and time-consuming. In this study, we proposed a novel method to remove the explicit user calibration and achieve robust 3-D gaze estimation in the room-scale area. Our proposed framework treats salient regions in the scene as possible 3-D locations of gaze points. To improve the efficiency of predicting 3-D gaze from visual saliency, the bag-of-word algorithm is adopted for eliminating redundant scene image data based on their similarities. After the elimination, saliency maps are generated from those scene images, and the geometrical relationship among the scene and eye cameras is obtained through aggregating 3-D salient targets with eye visual directions. Finally, we calculate the 3-D point of regard (PoR) by utilizing 3-D structures of the scene. The experimental results indicate that our method enhances the reliability of saliency maps and achieves promising performances on 3-D gaze estimation with different subjects.
摘要：
Malicious behavior detection is a key topic that has been a focus in the field of intrusion detection. Current intrusion detection systems are primarily based on single-point monitoring and detection and cannot detect attack modes with a hidden attack frequency. The idea presented in this paper is the incorporation of API call sequence software into the analysis and the construction of behavior chains to express the behavior patterns in software. This paper introduces related definitions of behavioral points and behaviors and proposes a depth-detection method for malware based on behavior chains (MALDC). The method monitors behavior points based on API calls and then uses the calling sequence of those behavior points at runtime to construct a behavior chain. Finally, we use depth detection method based on long short-term memory(LSTM) to detect malicious behavior from the behavior chains. To verify the performance of the proposed model, we conducted a large experiment on 54,324 malware and 53,361 benign samples collected from Windows systems and used those samples to train and test the model. Comparative verification by using various classifiers showed that the behavior points extracted based on the above method and the constructed behavior chains can be used to recognize malicious behavior at a high recognition rate. The method achieved an accuracy of 98.64% with a false positive rate of less than 2% in the best case, which is a satisfactory recognition rate for detecting malicious software behavior.
作者机构：
[Yan, Lijuan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Liu, Yanshen; Liu, Yi; Yan, Lijuan] Cent China Normal Univ, Hubei Res Ctr Educ Informationizat, Wuhan 430079, Peoples R China.
通讯机构：
[Yan, Lijuan] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Hubei Res Ctr Educ Informationizat, Wuhan 430079, Peoples R China.
关键词：
interval feature;transformation;time series classification;perceptually important points
摘要：
A novel feature reconstruction method, referred to as interval feature transformation (IFT), is proposed for time series classification. The IFT uses perceptually important points to segment the series dynamically into subsequences of unequal length, and then extract interval features from each time series subsequence as a feature vector. The IFT distinguishes the best top-k discriminative feature vectors from a data set by information gain. Utilizing these discriminative feature vectors, transformation is applied to generate new k-dimensional data which are lower-dimensional representations of the original data. In order to verify the effectiveness of this method, we use the transformed data in conjunction with some traditional classifiers to solve time series classification problems and make comparative experiments to several state-of-the-art algorithms. Experiment results verify the effectiveness, noise robustness and interpretability of the IFT.
期刊：
Computer Applications in Engineering Education,2020年28(4):892-907 ISSN：1061-3773
通讯作者：
Wang, Mengting
作者机构：
[Liu, Sannyuya; Tang, Liqiong; Dai, Zhicheng; Wang, Mengting] Cent China Normal Univ, Natl Engn Res Ctr E Learning, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Wang, Mengting] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
关键词：
classroom teaching;information and communication technology tool;instructional mode;mind mapping;technology acceptance model
摘要：
As a simple and effective tool of information integration, mind mapping can facilitate students' epistemic efficiency and improve teachers' pedagogical content knowledge, which can solve the problems being encountered in the reform of Chinese universities. To establish a systematic instructional method for mind mapping, this paper proposed a new instructional mode based on mind mapping and developed instructional mapping (IM), a classroom teaching software based on the starC system. The starC system is a cloud learning platform, which integrates learning resource, intelligent management, online community, and third-party service. The starC system has been in use in 16 smart classrooms of Central China Normal University since 2017. However, it seems that many teachers prefer traditional instructional modes than IM. To determine the factors that can encourage teachers to accept IM, we carried out an empirical study based on the technology acceptance model (TAM). The external variables were school factors and student factors. School factors included evaluation and incentive systems. Student factors included students' interest in IM and their evaluation of teachers' use of IM. The empirical study was validated using survey data from 110 teachers. The findings in the TAM analysis revealed significant evidence in favor of the adoption of IM and provided theoretical and practical implications for teachers, school administrators, and instructional mode researchers and developers.
期刊：
INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE,2020年30(3):435-448 ISSN：1641-876X
通讯作者：
Liao, Mengyi
作者机构：
[Wang, Guangshuai; Chen, Chang; Liao, Mengyi; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.;[Chen, Jingying] Cent China Normal Univ, Natl Engn Lab Technol Big Data Applicat Educ, Wuhan 430079, Hubei, Peoples R China.;[Liao, Mengyi] Pingdingshan Univ, Coll Comp Sci & Technol, Pingdingshan 467000, Henan, Peoples R China.
通讯机构：
[Liao, Mengyi] C;[Liao, Mengyi] P;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.;Pingdingshan Univ, Coll Comp Sci & Technol, Pingdingshan 467000, Henan, Peoples R China.
关键词：
autism spectrum disorder;eye fixation;facial expression;cognitive level;improved random forest
摘要：
Early identification can significantly improve the prognosis of children with autism spectrum disorder (ASD). Yet existing identification methods are costly, time consuming, and dependent on the manual judgment of specialists. In this study, we present a multimodal framework that fuses data on a child’s eye fixation, facial expression, and cognitive level to automatically identify children with ASD, to improve the identification efficiency and reduce costs. The proposed methodology uses an optimized random forest (RF) algorithm to improve classification accuracy and then applies a hybrid fusion method based on the data source and time synchronization to ensure the reliability of the classification results. The classification accuracy of the framework was 91%, which is higher than that of the RF, support vector machine, and discriminant analysis methods. The results suggest that data on a child’s eye fixation, facial expression, and cognitive level are useful for identifying children with ASD. Because the proposed framework can separate ASD children from typically developing (TD) children, it can facilitate the early identification of ASD and may improve intervention programs for children with ASD.
作者机构：
[Xu, Luhui; Xu, Ruyi; Tan, Lei; Chen, Jingying; Han, Jiaxu] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Xu, Luhui; Xu, Ruyi; Tan, Lei; Chen, Jingying; Han, Jiaxu] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.
通讯机构：
[Chen, Jingying] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.
