作者机构：
[Wu, Libing; Cao, Shuqin] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.;[Wu, Libing; Cao, Shuqin] Guangdong Lab Artificial Intelligence & Digital Ec, Wuhan 518132, Peoples R China.;[Chen, Yanjiao] Zhejiang Univ, Coll Elect Engn, Hangzhou 310007, Peoples R China.;[Li, Jianxin] Deakin Univ, Sch Informat Technol, Burwood, Vic 3217, Australia.;[Cui, Jianqun; Chang, Yanan] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Wu, L.] S;[Chen, Y.] C;School of Computer Science, Wuhan University, Wuhan, 430072, China<&wdkj&>Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Wuhan, 518132, China
摘要：
A mobility model is a basis of constructing the simulation environment for vehicular ad hoc network (VANET) research. Most existing models mainly focus on the geographical movement of individual mobile communication devices. However, few works focus on the cooperative movement of multiple autonomous vehicles. In this paper, we propose a cooperative mobility model for multiple autonomous vehicles, making vehicles run as a swarm in an orderly manner. Specifically, inspired by artificial fish swarm algorithms, we draw on the cooperative behaviors of the fish swarm to model the collaboration and self-organization in multi-vehicle formation. Then we design several force functions to express the interactions between vehicles and the influence of the driving environment based on the artificial potential field. Under Newtonian dynamics, the proposed mobility model determines the coordinated movement of multiple autonomous vehicles by force functions. Furthermore, we introduce a parallel orientation area in the interaction area division to improve vehicle stability. Following existing works, we assume that the road is straight and of infinite length. This is, the considered environment is suitable for intersection-free double-lane roads. To comprehensively verify the effectiveness of our proposed approach, we conduct extensive simulations under different traffic scenarios. Simulation results confirm that using our mobility model, multiple vehicles are able to keep driving in the center of the lane at the allowed speed limit, form an ordered collision-free motorcade, and collaboratively avoid collisions with obstacles. Particularly, our proposed mobility model has better stability.
作者机构：
[Yi Chen; Jin Zhou; Jing Gao; Sai Wang] School of Computer Science, Central China Normal University, Wuhan, P.R.China;[Wei Zhang] National Engineering Laboratory of Educational Big Data Application Technology, Central China Normal University, Wuhan, P.R.China;[Ge Gao] School of Computer Science, Wuhan University, Wuhan, P.R.China
会议名称：
2021 IEEE International Conference on Engineering, Technology & Education (TALE)
会议时间：
05 December 2021
会议地点：
Wuhan, Hubei Province, China
会议论文集名称：
2021 IEEE International Conference on Engineering, Technology & Education (TALE)
摘要：
Estimating and improving students' engagement in a collaborative learning environment is an important component in the field of learning research. Collaborative learning is a strategy of learning activities employed by small groups in which cooperative learning behaviors are closely related to other members or objects in the group. Researchers showed that students who are actively involved in class learn more. Therefore, gaze behavior and facial expression are important nonverbal indicators in cooperative learning environments. In this paper, we proposed a multimodal deep neural network (MDNN) to solve the engagement prediction problem in collaborative learning. We combined facial expression and gaze direction as individual streams of MDNN to predict engagement levels in collaborative learning environments. Our multi-modal solution was evaluated in a real collaborative environment with a significant accuracy of 74%. The results show that the model can accurately predict students' performance in the collaborative learning environment.
作者机构：
[Wu, Libing; Cao, Shuqin; Chen, Yanjiao] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.;[Wu, Libing] Wuhan Univ, Sch Cyber Sci & Engn, Wuhan, Peoples R China.;[Wu, Libing] Wuhan Univ, Shenzhen Res Inst, Wuhan, Peoples R China.;[Cui, Jianqun; Chang, Yanan] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.
通讯机构：
[Libing Wu] S;School of Computer Science, Wuhan University, Wuhan, 430072, China<&wdkj&>School of Cyber Science and Engineering, Wuhan University, China<&wdkj&>Shenzhen Research Institute of Wuhan University, China
作者机构：
[陈怡; 赵尔敦; 杨青; 郑世珏] School of Computer Science, Huazhong Normal University, Wuhan;430070, China;[高戈] School of Computer Science, Wuhan University, Wuhan;[陈怡; 赵尔敦; 高戈; 杨青; 郑世珏] 430070, China
关键词：
Spatial keyword query;Internet of industrial vehicles;Wireless data broadcast;Air index
摘要：
With the development of the Internet of Things (IoT), the industrial vehicle ad hoc networks are revolving into the Internet of Industrial Vehicles (IoIV). Due to the popularity of the geographical devices used on the Industrial vehicle, location-based information is extensively available in IoIV. This development calls for spatial keyword queries (SKQ), which takes into account both the locations and textual descriptions of objects. This paper addresses the issue of processing SKQ in IoIV environment, which focuses on two types of SKQ queries, namely Boolean kNN Queries and Top-k Queries. A general air index called Extended Spatial Keyword query index in IoIV environment (ESKIV) is proposed, which supports both network space pruning and textual pruning simultaneously. Based on ESKIV, efficient algorithms are designed to deal with these two types of SKQ respectively. The proposed ESKIV also can be used to deal with other kinds of queries, such as range SKQ. Finally, extensive simulations are conducted to demonstrate the efficiency of our ESKIV index and the corresponding query processing algorithms.
摘要：
The microbiota living in the human body plays a very important role in our health and disease, so the identification of microbes associated with diseases will contribute to improving medical care and to better understanding of microbe functions, interactions. However, the known associations between the diseases and microbes are very less. We proposed a new method for prioritization of candidate microbes to predict disease-microbe relationships that based on the random walking on the heterogeneous network. Here, we first constructed a heterogeneous network by connecting the disease network and microbe network using the disease-microbe relationship information, then extended the random walk to the heterogeneous network, finally we used leave-one-out cross-validation to evaluate the method and ranked the candidate disease-causing microbes. We used the algorithm to disclose some potential association between disease and microbe that cannot be found by microbe network or disease network alone. Furthermore, we studied three representative diseases, Type 2 diabetes, Asthma and Psoriasis, and presented the potential microbes associated with these diseases, respectively. We confirmed that the discovery of the associations will be a good clinical solution for disease mechanism understanding, diagnosis and therapy.
期刊：
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering,2017年201:462-467 ISSN：1867-8211
通讯作者：
Chen, Yi
作者机构：
[Chen, Yi] Huazhong Normal Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China.;[Gao, Ge] Wuhan Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China.
通讯机构：
[Chen, Yi] H;Huazhong Normal Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China.
会议名称：
12th International Conference on Collaborate Computing - Networking, Applications and Worksharing (CollaborateCom)
会议时间：
NOV 10-11, 2016
会议地点：
Beijing, PEOPLES R CHINA
会议主办单位：
[Chen, Yi] Huazhong Normal Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China.^[Gao, Ge] Wuhan Univ, Sch Comp Sci, Wuhan, Hubei, Peoples R China.
会议论文集名称：
Collaborate Computing: Networking, Applications and Worksharing
关键词：
Correlation methods;Global optimization;Optimization;Quality of service;Access point (APs);Delay;Global optimization algorithm;Multi-views;Multiple viewpoints;Packet scheduling;Spatial and temporal correlation;Transmission rates;Cameras
摘要：
Emerging multimedia Multiview video systems consist of a dense deployment of multiple partial-overlapped wireless cameras, as well as some access points (Aps) and many wireless distributed relay nodes. Correlated views are captured by cameras followed being transmitted to destination by different Aps and networks links. Packet expiration of one camera flow may harm the whole task. To effectively integrate multiple viewpoints into a whole image, the correlated data rate and deadline of flows from multiple cameras are meaningful. There is a trade-off between data redundancy and time deadline among correlated multi-views subjecting to the constraints of limited buffer length. However, most researches in this field have not considered packet expiration suffering from varieties of delays after multipath. In this paper, we conduct this problem to optimally adjust multiple flows of viewpoints by exploiting spatial and temporal correlations among cameras to reduce delay variances. A global optimization algorithm based on joint rate-distortion and delay-distortion model is proposed. Simulation results show that quality of service for Multiview streaming can be improved by allocating suitable transmission rates among correlated cameras as well as appropriate playout deadline. The PSNR quality shows that better performance can be achieved compared with baseline policies.
摘要：
As we all know, the microbiota show remarkable variability within individuals. At the same time, those microorganisms living in the human body play a very important role in our health and disease, so the identification of the relationships between microbes and diseases will contribute to better understanding of microbes interactions, mechanism of functions. However, the microbial data which are obtained through the related technical sequencing is too much, but the known associations between the diseases and microbes are very less. In bioinformatics, many researchers choose the network topology analysis to solve these problems. Inspired by this idea, we proposed a new method for prioritization of candidate microbes to predict potential disease-microbe association. First of all, we connected the disease network and microbe network based on the known disease-microbe relationships information to construct a heterogeneous network, then we extended the random walk to the heterogeneous network, and used leave-one-out cross-validation and ROC curve to evaluate the method. In conclusion, the algorithm could be effective to disclose some potential associations between diseases and microbes that cannot be found by microbe network or disease network only. Furthermore, we studied three representative diseases, Type 2 diabetes, Asthma and Psoriasis, and finally presented the potential microbes associated with these diseases by ranking candidate disease-causing microbes, respectively. We confirmed that the discovery of the new associations will be a good clinical solution for disease mechanism understanding, diagnosis and therapy.
摘要：
This paper proposes a technique to improve the robustness of spread spectrum (SS) audio watermarking for acoustic propagation. The appropriate embedding areas should be selected to achieve good perceptibility and high robustness. In order to improve the redundancy and robustness, the cross spread spectrum (CSS) scheme based on highly correlated cross frames is proposed which could decrease the variance of decision statistic and lead to a small error probability. Then we propose an improved cross spread spectrum (ICSS) scheme by incorporating CSS and improved spread spectrum (ISS). Our theoretical analysis shows that ICSS significantly reduces the host interference and improve the watermarking decoding performance. Simulation results demonstrated that the proposed ICSS algorithm is more robust not only to room reverberation and environmental noises, but also to MP3 compression and a variety of other distortions in Stirmark Benchmark for Audio.
期刊：
International Journal of Wireless and Mobile Computing,2016年11(1):18-23 ISSN：1741-1084
通讯作者：
Shen, Xianjun(xjshen@mail.ccnu.edu.cn)
作者机构：
[Wenjie Hu] College of Information Engineering, Xianning Vocational Technical College, Xianning, Hubei, China;[Jincai Yang; Yao Chen; Xianjun Shen] School of Computer, Central China Normal University, Wuhan, Hubei, China;Collaborative & Innovative Center for Educational Technology, Central China Normal University, Wuhan, Hubei, China;[Xianjun Shen] Collaborative and Innovative Center for Educational Technology, Central China Normal University, Wuhan, Hubei, China
通讯机构：
School of Computer, Central China Normal University, Wuhan, Hubei, China
关键词：
Monte Carlo methods;Sensor nodes;Wireless sensor networks;Hill climbing;Localisation;MCL algorithm;Mobile nodes;Optimisations;Particle swarm optimisation;Positioning accuracy;Sampling rates;Particle swarm optimization (PSO)
