摘要:
A dense point cloud with rich and realistic texture is generated from multiview images using dense reconstruction algorithms such as Multi View Stereo (MVS). However, its spatial precision depends on the performance of the matching and dense reconstruction algorithms used. Moreover, outliers are usually unavoidable as mismatching of image features. The lidar point cloud lacks texture but performs better spatial precision because it avoids computational errors. This paper proposes a multiresolution patch-based 3D dense reconstruction method based on integrating multiview images and the laser point cloud. A sparse point cloud is firstly generated with multiview images by Structure from Motion (SfM), and then registered with the laser point cloud to establish the mapping relationship between the laser point cloud and multiview images. The laser point cloud is reprojected to multiview images. The corresponding optimal level of the image pyramid is predicted by the distance distribution of projected pixels, which is used as the starting level for patch optimization during dense reconstruction. The laser point cloud is used as stable seed points for patch growth and expansion, and stored by the dynamic octree structure. Subsequently, the corresponding patches are optimized and expanded with the pyramid image to achieve multiscale and multiresolution dense reconstruction. In addition, the octree's spatial index structure facilitates parallel computing with highly efficiency. The experimental results show that the proposed method is superior to the traditional MVS technology in terms of model accuracy and completeness, and have broad application prospects in high-precision 3D modeling of large scenes.
作者机构:
[Deng, Yongjian] Beijing Univ Technol, Coll Comp Sci, Beijing, Peoples R China.;[Chen, Hao] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.;[Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.;[Li, Youfu] City Univ Hong Kong, Dept Mech Engn, Hong Kong, Peoples R China.
会议名称:
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
会议时间:
JUN 18-24, 2022
会议地点:
New Orleans, LA
会议主办单位:
[Deng, Yongjian] Beijing Univ Technol, Coll Comp Sci, Beijing, Peoples R China.^[Chen, Hao] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China.^[Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.^[Li, Youfu] City Univ Hong Kong, Dept Mech Engn, Hong Kong, Peoples R China.
会议论文集名称:
IEEE Conference on Computer Vision and Pattern Recognition
摘要:
Event cameras attract researchers' attention due to their low power consumption, high dynamic range, and extremely high temporal resolution. Learning models on event-based object classification have recently achieved massive success by accumulating sparse events into dense frames to apply traditional 2D learning methods. Yet, these approaches necessitate heavy-weight models and are with high computational complexity due to the redundant information introduced by the sparse-to-dense conversion, limiting the potential of event cameras on real-life applications. This study aims to address the core problem of balancing accuracy and model complexity for event-based classification models. To this end, we introduce a novel graph representation for event data to exploit their sparsity better and customize a lightweight voxel graph convolutional neural network (EV-VGCNN) for event-based classification. Specifically, (1) using voxel-wise vertices rather than previous point-wise inputs to explicitly exploit regional 2D semantics of event streams while keeping the sparsity; (2) proposing a multi-scale feature relational layer (MFRL) to extract spatial and motion cues from each vertex discriminatively concerning its distances to neighbors. Comprehensive experiments show that our model can advance state-of-the-art classification accuracy with extremely low model complexity (merely 0.84M parameters).
作者机构:
[Wang, Zihao; Zhong, Dantong; Song, Dan-Xia] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan 430079, Hubei, Peoples R China.;[Wang, Zihao; Zhong, Dantong; Song, Dan-Xia] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Hubei, Peoples R China.
会议名称:
IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
会议时间:
JUL 17-22, 2022
会议地点:
Kuala Lumpur, MALAYSIA
会议主办单位:
[Wang, Zihao;Song, Dan-Xia;Zhong, Dantong] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan 430079, Hubei, Peoples R China.^[Wang, Zihao;Song, Dan-Xia;Zhong, Dantong] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Hubei, Peoples R China.
会议论文集名称:
IEEE International Symposium on Geoscience and Remote Sensing IGARSS
关键词:
FVC;Landsat;Sentinel;data fusion
摘要:
Fractional vegetation cover (FVC) is an important indicator reflecting changes in the Earth's ecosystem, as it is essential for simulating growth processes and modeling land surfaces. At present, there have been several FVC products available on the global scale using a variety of satellite data with different retrieval methods. Compared with other FVC products, the Landsat-based FVC is able to describe global vegetation cover at a resolution of 30 m with long time series, which can better represent vegetation cover information with more spatial details. However, the revisit cycle of Landsat satellite is 16 days, resulting in poor performance of Landsat in temporal continuity. In this study, the Landsat-based FVC was enhanced by combining normalized the Sentinel-2 data and filling in some of the missing dates. By applying different temporal reconstruction methods, the temporal continuity of 30 m FVC was significantly improved, and the strengths and weaknesses of each method were also discussed.
作者机构:
[Chi, Maomao; Ma, Haiyan] China Univ Geosci, Sch Econ & Management, Wuhan, Peoples R China.;[Wang, Yunran] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
会议名称:
3rd International Conference on Design, Operation and Evaluation of Mobile Communications (MOBILE) Held as Part of 24th International Conference on Human-Computer Interaction (HCII)
会议时间:
JUN 26-JUL 01, 2022
会议地点:
ELECTR NETWORK
会议主办单位:
[Chi, Maomao;Ma, Haiyan] China Univ Geosci, Sch Econ & Management, Wuhan, Peoples R China.^[Wang, Yunran] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
会议论文集名称:
Lecture Notes in Computer Science
关键词:
Mobile games;Uses and gratifications theory;Stickiness
摘要:
Despite the huge growth potential that has been predicted for mobile game continuous usage intention, little is known about what motives users to be sticky under the mobile game context. Drawing on the Uses and Gratifications theory (UGT), this study aims to investigate the influencing effects of players' characteristics and the mobile game structures on players' mobile game behavior (e.g. stickiness). After surveying 439 samples, the research model is tested with Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that both individual gratifications and mobile game presence positively affect users' stickiness. Furthermore, we find that leisure boredom of individual situations and integration of mobile game governance positively affect users' stickiness. The results provide further insights into the design and governance strategies of mobile games.
作者:
Wang, Tong;Cui, Jianqun;Chang, Yanan;Huang, Feng;Yang, Yi
作者机构:
[Huang, Feng; Wang, Tong] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan, Peoples R China.;[Cui, Jianqun; Chang, Yanan] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.;[Yang, Yi] NE Illinois Univ, Dept Comp Sci, Chicago, IL USA.
会议名称:
18th IEEE International Conference on Mobility, Sensing and Networking (MSN)
会议时间:
DEC 14-16, 2022
会议地点:
ELECTR NETWORK
会议主办单位:
[Wang, Tong;Huang, Feng] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan, Peoples R China.^[Cui, Jianqun;Chang, Yanan] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.^[Yang, Yi] NE Illinois Univ, Dept Comp Sci, Chicago, IL USA.
关键词:
DTNs;traffic light;probability of encountering;node state
摘要:
Delay-Tolerant Networks (DTNs), a supplementary means of communication network in extreme situations, have aroused wide attention from scholars. However, it is challenging to efficiently utilize DTNs since they have intermittent and high-latency characteristics. In the design of DTNs routing scheme, the selection of relay nodes takes on a great significance in efficient communication. However, existing research has either considered only one of the node features, or simply fused node attributes without fully using their potential correlations. If the above problems are not effectively solved, the propagation of messages between nodes will become blind, and a considerable number of caches will be occupied and wasted by invalid copies. To solve the above challenges, a novel routing, "Traffic Light Routing Based on Node State Awareness (TLRNSA)", is proposed for efficient communication. To be specific, the node's own state, the environmental state, and the historical encounter state are synthesized. The traffic value of the node is obtained based on the adaptive weight adjustment mechanism. The node is divided into three traffic light states, including red, green, and yellow, in accordance with the traffic value. Different routing strategies are developed for the above three states to enhance their performance. The results of the comprehensive experiments suggested that TLRNSA outperforms other state-of-theart algorithms in delivery rate and latency. Compared with the two classic algorithms and the two optimized algorithms, the proposed method increases the delivery rate by 109.1%, 84.12%, 5.09%, and 1.09%, respectively, it reduces the delay by 32.16%, 36.46%, 32.77%, and 6.77%, respectively.
作者机构:
[Jiang, Hui] Shandong Inst Business & Technol, Sch Management Sci & Engn, Yantai, Peoples R China.;[Zhu, Yongdi; Jiang, Hui; Duan, Yaoqing] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
会议名称:
10th International Conference on Big Data (BigData) Held as Part of the 19th Services Conference Federation (SCF)
会议时间:
DEC 10-14, 2021
会议地点:
ELECTR NETWORK
会议主办单位:
[Jiang, Hui] Shandong Inst Business & Technol, Sch Management Sci & Engn, Yantai, Peoples R China.^[Jiang, Hui;Duan, Yaoqing;Zhu, Yongdi] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
会议论文集名称:
Lecture Notes in Computer Science
关键词:
Continuous-use intention;Open data;Open government data
摘要:
An improved understanding of the factors that influence citizens' continuance use intention will help to promote and improve the practice of open government data. This paper constructs an integrated model that provides insight into factors that influence citizens' continued intention to use open government data. The model contains 296 effective samples from questionnaires, which are then tested by the Structural Equation Model. It is found that perceived usefulness and satisfaction significantly affect the public's continuous adoption of OGD; expectation confirmation significantly affects satisfaction and perceived usefulness, and thus indirectly affects the public's continuous adoption of OGD; perceived ease of use significantly affects the satisfaction, trust in government and trust in the Internet significantly affects the expectation confirmation. But perceived usefulness, trust in government and trust in the Internet had no significant effect on public satisfaction.
作者机构:
[Zhang, H; Tong, Hang; Liu, Sanya; Li, Yaopeng; Zhang, Hao; Min, Yuandong] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr Learning, Wuhan 430079, Peoples R China.;[Zhang, H; Tong, Hang; Liu, Sanya; Li, Yaopeng; Zhang, Hao; Min, Yuandong] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
会议名称:
IEEE International Performance, Computing, and Communications Conference (IPCCC)
会议时间:
NOV 11-13, 2022
会议地点:
Austin, TX
会议主办单位:
[Zhang, Hao;Min, Yuandong;Liu, Sanya;Tong, Hang;Li, Yaopeng] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr Learning, Wuhan 430079, Peoples R China.^[Zhang, Hao;Min, Yuandong;Liu, Sanya;Tong, Hang;Li, Yaopeng] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
会议论文集名称:
IEEE International Performance Computing and Communications Conference (IPCCC)
作者机构:
[Zhou, Jie; Liu, Xuan; Cui, Yilin; Xiong, Xuqian] Cent China Normal Univ, Sch Urban & Environm Sci, Wuhan, Peoples R China.;[Zhou, Jie; Liu, Xuan; Cui, Yilin; Xiong, Xuqian] Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan, Peoples R China.;[Jia, Li; Lu, Jing] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China.;[Zhou, Jie] Delft Univ Technol, Delft, Netherlands.
会议名称:
IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
会议时间:
JUL 17-22, 2022
会议地点:
Kuala Lumpur, MALAYSIA
会议主办单位:
[Zhou, Jie;Liu, Xuan;Xiong, Xuqian;Cui, Yilin] Cent China Normal Univ, Sch Urban & Environm Sci, Wuhan, Peoples R China.^[Zhou, Jie;Liu, Xuan;Xiong, Xuqian;Cui, Yilin] Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan, Peoples R China.^[Jia, Li;Lu, Jing] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China.^[Zhou, Jie] Delft Univ Technol, Delft, Netherlands.
会议论文集名称:
IEEE International Symposium on Geoscience and Remote Sensing IGARSS
关键词:
vegetation anomalies;uncertainty;EO-based vegetation products;Vegetation Condition Index
摘要:
Satellite-based Earth Observation systems archived a variety of vegetation products during the last 50 years, which can reveal regional to global ecosystem dynamics across diverse spatiotemporal scales. The anomaly metrics such as Vegetation Condition Index (VCI) defined by comparing the current vegetation growth condition to historical average status based on long-term EO-based vegetation products were widely used to delineate abnormal vegetation variation exerted by either climatic or anthropogenic factors (e.g., droughts, wildfires). However, currently available long-term vegetation products may differ from each other in terms of sensors (observational platform or spectral bands), biophysical definitions (e.g., NDVI, EVI, LAI, and VOD), spatiotemporal resolution, as well as the time-spans, which results in inconsistency across these vegetation products. Taking the VCI as an example, this study evaluated the uncertainty of vegetation anomalies detected based on different vegetation products over the middle reach of the Yangtze River by explicitly considering the effect of sensors, biophysical definitions, and time-spans. The preliminary results showed that VCI derived from NDVI products from different sensors (AVHRR vs. MODIS) induced significant inconsistent anomalies over most landscapes. The differences resulting from products with different biophysical definitions (NDVI vs. EVI, LAI, and VOD) are much lower than those from different sensors but still significant over specific areas. As for the time-spans, the 20-year NDVI based VCI presented a considerable reduction in variance over the study area on average compared to VCI calculated based on 5-year NDVI. In summary, caution should be taken when applying EO-based vegetation products for vegetation anomalies mapping, especially for quantitative assessment.
摘要:
How to automatically obtain cross-features with different weight values is a significant issue in the research of recommendation models. Traditional recommendation models cannot automatically learn the deep-level features of users and items to obtain cross-features. The mixed processing of dense numerical features and sparse categorical features will result in more information loss during dimensionality reduction. Cross features occupy the same weight in the recommendation process, which will lead to the non-prominence of critical features and reduce the accuracy of model recommendations. This paper proposes a personalized recommendation model (MSRN) for self-attention perceptron with automatic feature correlation. The model first processes the numerical features and category features in double towers to reduce the loss of feature information. Numerical cross-feature matrix and category cross-feature matrix use multilayer perceptrons to automatically mine the hidden knowledge and relationships between features. The model uses the Hadamard product to process it to obtain the cross feature matrix and uses the self-attention mechanism to assign different weights to the extracted cross-features. The experimental results on the public data set show that the recommended evaluation indicators of this model, MAE, and RMSE, are better than the current advanced recommendation models and have better accuracy and stability.
期刊:
2022 2ND IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND ARTIFICIAL INTELLIGENCE (SEAI 2022),2022年:199-203
作者机构:
[Qiao, Xiaobin; Yongmin, Shuai] Wuhan Maritime Commun Res Inst, Wuhan, Peoples R China.;[Zhang, Yu; Liu, Fuhao] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan, Peoples R China.;[Zeng, Yong; Xiong, Yunfei] Wuhan Fiberhome Tech Serv Co Ltd, Wuhan, Peoples R China.
会议名称:
2nd IEEE International Conference on Software Engineering and Artificial Intelligence (SEAI) / 7th International Workshop on Pattern Recognition (IWPR)
会议时间:
JUN 10-12, 2022
会议地点:
Huaqiao Univ, Coll Comp Sci & Technol, Xiamen, PEOPLES R CHINA
会议主办单位:
Huaqiao Univ, Coll Comp Sci & Technol
关键词:
power grid;information network;transportation network;co-simulation system
摘要:
The three-network integration of power grid, information network and transportation network has become a global issue and trend. However, the current research on triple play is still in its infancy. Most of the researches define the concept of triple play, and lack of simulation research on the power grid-information network-transportation network coupling system. Therefore, this paper studies the key technologies of power grid-information network-transportation network co-simulation. The key technologies of simulation are data interaction method and time synchronization method. By building a simulation prototype system, it provides simulation support for the theoretical study of the power grid-information network-transportation network coupling system.
期刊:
IEEE/ACM Transactions on Computational Biology and Bioinformatics,2022年19(3):1322-1333 ISSN:1545-5963
通讯作者:
Jiang, X.
作者机构:
[He, Tingting; Jiang, Xingpeng; Ma, Yingjun] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Ma, Yingjun] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei, Peoples R China.;[He, Tingting; Jiang, Xingpeng] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Hubei, Peoples R China.;[Tan, Yuting] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.;[Tan, Yuting] Hubei Key Lab Math Sci, Wuhan 430079, Hubei, Peoples R China.
通讯机构:
Central China Normal University, School of Computer, Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Hubei, Wuhan, China
会议名称:
18th Asia Pacific Bioinformatics Conference (APBC)
会议时间:
AUG 18-20, 2020
会议地点:
ELECTR NETWORK
会议主办单位:
[Ma, Yingjun;He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.^[Ma, Yingjun] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei, Peoples R China.^[He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Hubei, Peoples R China.^[Tan, Yuting] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.^[Tan, Yuting] Hubei Key Lab Math Sci, Wuhan 430079, Hubei, Peoples R China.
摘要:
Infectious diseases are currently the most important and widespread health problem, and identifying viral infection mechanisms is critical for controlling diseases caused by highly infectious viruses. Because of the lack of non-interactive protein pairs and serious imbalance between positive and negative sample ratios, the supervised learning algorithm is not suitable for prediction. At the same time, due to the lack of information on viral proteins and significant dissimilarity in sequence, some ensemble learning models have poor generalization ability. In this paper, we propose a Sequence-Based Ensemble Learning (Seq-BEL) method to predict the potential virus-human PPIs. Specifically, based on the amino acid sequence of proteins and the currently known virus-human PPI network, Seq-BEL calculates various features and similarities of human proteins and viral proteins, and then combines these similarities and features to score the potential of virus-human PPIs. The computational results show that Seq-BEL achieves success in predicting potential virus-human PPIs and outperforms other state-of-the-art methods. More importantly, Seq-BEL also has good predictive performance for new human proteins and new viral proteins. In addition, the model has the advantages of strong robustness and good generalization ability, and can be used as an effective tool for virus-human PPI prediction.
作者机构:
[Zhang, Lei] Cent China Normal Univ, Sch Chinese Language & Literature, Wuhan, Peoples R China.;[Yang, Shanshan] Cent China Normal Univ, Sch Foreign Languages, Wuhan, Peoples R China.;[Dong, Sicong] Harbin Inst Technol, Sch Humanities & Social Sci, Shenzhen, Peoples R China.
会议名称:
22nd Chinese Lexical Semantics Workshop (CLSW)
会议时间:
MAY 15-16, 2021
会议地点:
Nanjing Normal Univ, Nanjing, PEOPLES R CHINA
会议主办单位:
Nanjing Normal Univ
会议论文集名称:
Lecture Notes in Artificial Intelligence
关键词:
Constructional innovation;"(sic)NP" [ni bixu zhidao de NP] (NP that you must know);Limitations
摘要:
The present study addresses the limitations of constructional innovation by looking into the case of "(sic)NP" [ni bixu zhidao de NP] (NP that you must know), an emerging construction popular in Chinese titles. Upon linguistic data collection and case analysis, it is found that, constructional innovation, while fulfilling the expected functions, is at the same time subject to the context of linguistic innovation. On one hand, new constructions with certain functional attributes have a better chance of rising from a specific context. The rise of the "(sic)NP" [ni bixu zhidao de NP] (NP that you must know) as a popular title option in the Chinese new media is believed to benefit from its functional attributes that "highlight high-value information", "express the author's stance", and "recruit readers' empathy". On the other hand, newly emerging constructions are restricted by the linguistic context as well. Therefore, the construction "(sic)NP" [ni bixu zhidao de NP] (NP that you must know) is distinctive in both its form and semantics.
摘要:
Event cameras asynchronously capture pixel-level intensity changes in scenes and output a stream of events. Compared with traditional frame-based cameras, they can offer competitive imaging characteristics: low latency, high dynamic range, and low power consumption. It means that event cameras are ideal for vision tasks in dynamic scenarios, such as human action recognition. The best-performing event-based algorithms convert events into frame-based representations and feed them into existing learning models. However, generating informative frames for long-duration event streams is still a challenge since event cameras work asynchronously without a fixed frame rate. In this work, we propose a novel frame-based representation named Compact Event Image (CEI) for action recognition. This representation is generated by a self-attention based module named Event Tubelet Compressor (EVTC) in a learnable way. The EVTC module adaptively summarizes the long-term dynamics and temporal patterns of events into a CEI frame set. We can combine EVTC with conventional video backbones for end-to-end event-based action recognition. We evaluate our approach on three benchmark datasets, and experimental results show it outperforms state-of-the-art methods by a large margin.
摘要:
The neuro-transfer function (neuro-TF) methods have been widely used in electromagnetic (EM) parametric modeling. This paper reviews the advanced neuro-TE techniques for EM parametric modeling in recent years, which includes neuro-TF using pole/residue as coefficients, neuro-TF using hybrid coefficients, and decomposition technique. An example of a fourth-order bandpass filter is given to verify the accuracy of the decomposition method.
作者机构:
[Ren, Xiaotong] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Hubei, Peoples R China.
会议名称:
13th International Conference on Graphics and Image Processing (ICGIP)
会议时间:
AUG 18-20, 2021
会议地点:
Yunnan Univ, Kunming, PEOPLES R CHINA
会议主办单位:
Yunnan Univ
会议论文集名称:
Proceedings of SPIE
关键词:
Virtual Reality Technology;Virtual Research Travel;Suzhou Shantang Street Research Travel
摘要:
Research travel have gradually been applied and studied by many schools in teaching practice, but under many interference factors, the vigorous implementation of research travel has not been carried out successfully. And virtual reality technology can effectively solve this problem, help students conduct practical learning, and improve students' inquiry ability. This research developed a virtual research program which is taking Suzhou Shantang Street as an example to explore the application of virtual reality technology in the middle school research travel course.
作者机构:
[Li, Wanxin; Wang, Wei; Jin, Lianghao; Xie, Wei] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.;[Li, Wanxin; Wang, Wei; Jin, Lianghao; Xie, Wei] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Tu, Zhigang] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Peoples R China.
会议名称:
IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)
会议时间:
JUL 18-23, 2022
会议地点:
Padua, ITALY
会议主办单位:
[Li, Wanxin;Xie, Wei;Wang, Wei;Jin, Lianghao] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.^[Li, Wanxin;Xie, Wei;Wang, Wei;Jin, Lianghao] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.^[Tu, Zhigang] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Peoples R China.
会议论文集名称:
IEEE International Joint Conference on Neural Networks (IJCNN)
摘要:
Group activity recognition aims to identify group activities from the videos. Most of the previous methods focus on modeling between individuals (one-to- one), which ignores the fact that a single individual's behavior may be jointly determined by multiple individual behaviors (many-to-one). For this reason, we propose a Multi-Hyperedge Hypergraph (MHH) to capture high-order relationships between multiple people. Specifically, we build three different types of hyperedges on the hypergraph structure. Each hyperedge can accommodate the characteristics of multiple nodes to capture different types of high-order relationships between nodes. Then, we use the late fusion method to fuse the three features to further enhance the overall behavioral representation. Finally, we perform a series of experiments on two of the most widely used benchmarks in group activity recognition, which have proved the effectiveness of MHH. More importantly, as far as we know, this is the first case of using a hypergraph structure for group activity recognition.
摘要:
The scientific and reasonable evaluation indicators that can fully reflect the various dimensions of students' computational thinking (CT) skills is the basis and premise of accurately evaluating students' CT skills, which is of great significance to the cultivation of students' CT skills. However, the current research on how to construct the evaluation indicators is still inadequate, and most of the research is put forward by the subjective experience of researchers, lacking objectivity and the universality of ability. In the paper, we comprehensively reviewed the concepts of CT in the theoretical literature of CT, aiming to construct the comprehensive and effective evaluation indicators of CT for students by clustering the keywords of CT concepts and extracting indicators. The validity of indicators is verified by qualitative analysis, quantitative analysis and expert evaluation. The results show that the evaluation indicators of CT constructed by spectral clustering technology are a more scientific, more comprehensive reflection of the ability dimensions of CT. It has unique advantages in constructing objective and comprehensive evaluation indicators and provides an evaluation basis for the evaluation practice of CT skills.
作者机构:
[Zhang, Kui; Dai, Zhicheng; Wang, Chunran; Chen, Rongjin; Zhu, Fuming] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
会议名称:
10th International Conference on Information and Education Technology (ICIET)
会议时间:
APR 09-11, 2022
会议地点:
Matsue, JAPAN
会议主办单位:
[Dai, Zhicheng;Zhang, Kui;Wang, Chunran;Chen, Rongjin;Zhu, Fuming] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
摘要:
It is an important issue to effectively perceive learners' emotional state in the field of smart education, which helps to enhance the interaction between teaching groups and stimulate learners' enthusiasm for learning. Taking "emotion perception" as the theme, this paper retrieved 533 relevant core literature from the CNKI (China National Knowledge Infrastructure) database and used econometric methods and visualization software CiteSpace to analyze the number of literature, authors, institutions, and keywords. The results show that the number of literature published on learners' emotion perception has increased year by year in the past 30 years and is in the mature stage of development; The authors and institutions are relatively scattered, and the core research system of emotion perception has not been formed. Building a multimodal perception model using techniques such as expression recognition, posture recognition, and physiological parameter detection is research hotspots in the field of emotion perception. The research trends in this field are to collect and fuse multimodal emotional data, and deeply analyze the change rule of learners' emotions based on deep learning and data mining.