期刊：
Journal of Information Security and Applications,2022年65:103087 ISSN：2214-2126
通讯作者：
Je Sen Teh
作者机构：
[Yeoh, Wei-Zhu] CISPA Helmholtz Ctr Informat Secur, Saarbrucken, Germany.;[Teh, Je Sen] Univ Sains Malaysia, Sch Comp Sci, Gelugor, Penang, Malaysia.;[Teh, Je Sen] Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust SnT, Luxembourg, Luxembourg.;[Chen, Jiageng] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
通讯机构：
[Je Sen Teh] S;School of Computer Sciences, Universiti Sains Malaysia, Malaysia<&wdkj&>Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg
摘要：
In the literature, most previous studies on English implicit inter-sentence relation recognition only focused on semantic interactions, which could not exploit the syntactic interactive information in Chinese due to its complicated syntactic structure characteristics. In this paper, we propose a novel and effective model DSGCN-RoBERTa to learn the interaction features implied in sentences from both syntactic and semantic perspectives. To generate a rich contextual sentence embedding, we exploit RoBERTa, a large-scale pre-trained language model based on the transformer unit. DSGCN-RoBERTa consists of two key modules, the syntactic interaction and the semantic interaction modules. Specifically, the syntactic interaction module helps capture the depth-level structure information, including non-consecutive words and their relations, while the semantic interaction module enables the model to understand the context from the whole sentence to the local words. Furthermore, on top of such multi-perspective feature representations, we design a strength-dependent matching strategy that is able to adaptively capture the strong relevant interactive information in a fine-grained level. Extensive experiments demonstrate that the proposed method achieved state-of-the-art results on benchmarks Chinese compound sentence corpus CCCS and Chinese discourse corpus CDTB datasets. We also achieve comparable performance on the English corpus PDTB that demonstrates the superiority of our method.
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：
[Guo, Yimin; Guo, YM] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan, Peoples R China.;[Zhang, Zhenfeng] Chinese Acad Sci, Inst Software, Trusted Comp & Informat Assurance Lab, Beijing, Peoples R China.;[Guo, Yajun] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.
通讯机构：
[Guo, YM ] Z;Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan, Peoples R China.
关键词：
Authentication;Fog computing;Lightweight;Security;Smart home
摘要：
Fog computing is the best solution for IoT applications with low latency and real-time interaction. Fog can endow smart home with many smart functions and services. One of the most important services is that users can remotely access and control smart devices. Since remote users and smart homes communicate through insecure channels, it is necessary to design a secure and effective remote authentication scheme to guarantee secure communications. The existing authentication schemes designed for smart homes have some security issues and are not suitable for fog-enabled smart home environments. Therefore, this paper designs a secure remote user authentication scheme, SecFHome. It supports secure communication at the edge of the network and remote authentication in fog-enabled smart home systems. Specifically, We present an efficient authentication mode in the fog-enabled environment, which includes the edge negotiation phase and the authentication phase. SecFHome adds updated information to the authenticator, which can verify the message synchronization simultaneously with the authentication, thus improving the authentication efficiency. In addition, SecFHome does not store sensitive information of users and smart devices in the memory of the smart gateway, which can avoid various attacks caused by the compromised gateway. The formal security proof and informal security analysis show that the SecFHome is secure and can resist known attacks. Compared with the related authentication schemes, SecFHome only needs fewer communication costs and computation costs, and achieves more security features.
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.
期刊：
2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA),2022年:1-6
作者机构：
[Jiawen Luo] College of Global Talents, Beijing Institute of Technology Zhuhai, Zhuhai, China;[Jinghao Wen] School of Computer, Central China Normal University, Wuhan, China;[Yangjia Zhang] School of Artificial Intelligence, Jianghan University, Wuhan, China;[Yuyan Li] Weatherhead School of Management, Case Western Reserve University, Cleveland, OH, United States
摘要：
With the development of science and technology, more and more people use mobile phones outdoors, which will cause danger to pedestrians using mobile phones and drivers on the road. This paper uses OpenPose method to detect whether pedestrians on the road are using mobile phones. We describe an approach to recognize and classify pedestrian posture in an individual context, more precisely in open door environment. The posture belongs into two main groups: phone usage and not. This paper uses 15 points OpenPose model to estimate human key points. Then select and calculate features using the estimated coordinates. Four neural network models are trained and tested using different feature combinations to classify the posture. The best model achieves 89.66% accuracy in classification.
摘要：
Over the past decades, Chemical-induced Disease (CID) relations have attracted extensive attention in biomedical community, reflecting wide applications in biomedical research and healthcare field. However, prior efforts fail to make full use of the interaction between local and global contexts in biomedical document, and the derived performance needs to be improved accordingly. In this paper, we propose a novel framework for document-level CID relation extraction. More specifically, a stacked Hypergraph Aggregation Neural Network (HANN) layers are introduced to model the complicated interaction between local and global contexts, based on which better contextualized representations are obtained for CID relation extraction. In addition, the CID Relation Heterogeneous Graph is constructed to capture the information with different granularities and improve further the performance of CID relation classification. Experiments on a real-world dataset demonstrate the effectiveness of the proposed framework.
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：
[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：
[Wang, Wei;Xie, Wei;Li, Wanxin;Jin, Lianghao] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.^[Wang, Wei;Xie, Wei;Li, Wanxin;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：
In skeleton-based action recognition task, graph convolutional network has attracted widespread attention and achieved remarkable results. However, most of the current methods are performing graph convolution on the entire skeleton graph, ignoring the fact that people are composed of different body parts. In addition, previous work ignores the temporal and spatial independence and relevance of different parts. Thus, to solve these issues, we optimize the representation of the skeleton graph, graph convolution and temporal convolution respectively. In this work, we propose multi-part adaptive graph convolution (MPA-GC) to adaptively learn the topology of each part of the body and dynamically aggregate the relevance between them. Meanwhile, we add a multi-scale temporal convolution module to better obtain temporal dimension features. Ultimately, we develop a powerful graph convolutional network named MPA-GCN, and extensive experiments on two public large-scale datasets NTU-RGB+D and NTU-RGB+D120 demonstrate the effectiveness of our module, which outperforms state-of-the-art methods.
期刊：
Wireless Communications and Mobile Computing,2022年2022 ISSN：1530-8669
通讯作者：
Cui, J.
作者机构：
[Wu, Jike; Cui, Jianqun; Zhang, Ruijie; Chang, Yanan; Wan, Qiyun] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Zhou, Hao] Wuhan Polytech Univ, Network & Informatizat Ctr, Wuhan, Peoples R China.
通讯机构：
[Yanan Chang; Qiyun Wan; Jike Wu; Jianqun Cui; Ruijie Zhang] S;[Hao Zhou] N;School of Computer,Central China Normal University,Wuhan,China<&wdkj&>Network and Informatization Center,Wuhan Polytechnic University,Wuhan,China
关键词：
Introduction;Materials and Methods;Results;Discussion;Conclusion;Abstract;Data Availability;Additional Points;Ethical Approval;Consent;Disclosure;Conflicts of Interests;Authors’ Contributions;Funding Statement;Acknowledgements;Acknowledgments;Supplementary Materials;Reference;Dataset Description;Dataset Files;Abstract;Introduction;Introduction and Materials;Introduction and Methods;Materials;Materials and Methods;Methods;Results;Discussion;Results and Discussion;Discussion and Conclusion;Results and Conclusion;Conclusion;Conclusions;Data Availability;Additional Points;Ethical Approval;Consent;Disclosure;Conflicts of Interest;Authors’ Contributions;Funding Statement;Acknowledgements;Supplementary Materials;References;Appendix;Abbreviations;Preliminaries;Introduction and Preliminaries;Notation;Proof of Theorem;Proofs;Analysis of Results;Examples;Numerical Example;Applications;Numerical Simulation;Model;Model Formulation;Systematic Palaeontology;Nomenclatural Acts;Taxonomic Implications;Experimental;Synthesis;Overview;Characterization;Background;Experimental;Theories;Calculations;Model Verification;Model Implementation;Geographic location;Study Area;Geological setting;Data Collection;Field Testing;Data and Sampling;Dataset;Literature Review;Related Works;Related Work;System Model;Methods and Data;Experimental Results;Results and Analysis;Evaluation;Implementation;Case Presentation;Case Report;Search Terms;Case Description;Case Series;Background;Limitations;Additional Points;Case;Case 1;Case 2 etc.;Concern Details;Retraction Details;Copyright;Related Articles
摘要：
How to select proper relay nodes to ensure the successful delivery of messages is still a hot topic in delay tolerant networks (DTN). In this paper, we propose a probabilistic Spray-and-Wait routing algorithm based on node interest preference (called NIP-PSW). Firstly, considering the influence of the social attributes of nodes, we define a metric called node interest preference (NIP) to measure the probability of nodes becoming friends. Secondly, in view of the influence of node quality and connection time between nodes on message forwarding, we define the delivery probability (DP). Finally, according to the historical information of nodes, the node interest similarity (NIS) is proposed. In spray phase, NIP and DP are used to select the relay node and allocate the number of message copies adaptively. In wait phase, it is judged whether to forward the message to the encountering nodes again according to the NIS and the DP. In addition, the concept of message storage value (MSV) and the acknowledgment (ACK) mechanism are introduced to manage the buffer of nodes. The simulation results show that the NIP-PSW not only can significantly improve the delivery rate and reduce the average delay but also shows good performance in network overhead and average number of hops.
