期刊：
Security and Communication Networks,2022年2022 ISSN：1939-0114
作者机构：
[Cao, Yangzhou] Cent China Normal Univ Wuhan, Cent China Normal Univ, Wollongong Joint Inst, Wuhan, Peoples R China.;[Chen, Jiageng] Cent China Normal Univ Wuhan, Sch Comp, Wuhan, Peoples R China.;[Cao, Yajun] Yichang Ctr Dis Control, Prevent Yichang, Yichang, Peoples R China.
摘要：
In this study, we propose a blockchain-based privacy-preserving vaccine passport system for the global prevention and control of infectious diseases. The system operates a double-chain framework which consists of a public blockchain and a consortium blockchain. Among them, the combination of the immutability of the public blockchain and Internet of Things (IoT) technology in the supply chain ensures the openness and transparency of the cold chain logistics records of the vaccines covering the stages from auditing to the target vaccination hospitals. The system adopts the consortium blockchain to achieve the balance between the protection of users' vaccination privacy and auditing by the government departments. Specifically, a distributed system-based threshold signature is adopted in the vaccine qualification phase to resist collusion between the vaccine manufacturing company and vaccine approval institutions. The cryptographic tools such as the anonymous credentials, zero-knowledge protocols, and range proofs ensure that users do not disclose any private information other than proving that they have a legally valid vaccine passport when users display the vaccine passports to customs. At the same time, customs can apply various vaccine prevention policies based on the conditions on the specific vaccine passports. Regarding the security properties of the system, a formal security model is given along with the corresponding security proofs.
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.
期刊：
IEEE Transactions on Circuits and Systems for Video Technology,2022年32(12):8646-8659 ISSN：1051-8215
通讯作者：
Tu, Z.
作者机构：
[Tu, Zhigang; Zhang, Jiaxu] Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan, 430079, China;[Jia, Yifan] Renmin Hospital of Wuhan University, Department of Pain, Wuhan, 430060, China;[Xie, Wei] Central China Normal University, School of Computer, Wuhan, 430079, China
通讯机构：
[Tu, Z.] W;Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan, China
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
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：
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：
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.
作者机构：
[Zeng, Peng] Hunan Inst Land & Resources Planning, Changsha 410007, Peoples R China.;[Zhou, Dongbo; Lin, Shixuan] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.;[Sun, Hao] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
通讯机构：
[Shixuan Lin] F;Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
Today, cloud storage services increased the popular for data storage in the cloud and retrieve from any location without any time limitations. One of the most important demands required in cloud is secured data transmission in un-trusted cloud applications. Particularly, secure and efficient multiparty communications in Untrusted Cloud Environments (UCE) attract widespread attentions. The equipment used in UCE have the particularity of being heterogeneous and UCE communication environment are asynchronous networks in which multiple users cannot transmit their messages simultaneously. How to ensure secure communication between these heterogeneous intelligent devices is a major challenge for multiparty communication applied in UCE. In such an asynchronous environment, the asynchronous transmission can cause security problems in cryptographic functions. Therefore, how to implement rational secret sharing (RSS) in an asynchronous model of the UCE networks has become a burning research topic. The RSS refers to finding a solution composed of strategies to encourage players in the secret reconstruction to act honestly even players are rational to act for their own interest. If each player plays the game for the best response to the best response of other players, the game is in Nash equilibrium. The objective of an RSS is to achieve the Nash equilibrium state corresponding to the global optima. In this paper, we propose an information-theoretic secure RSS in asynchronous model for UCE. Our design uses Petersen's VSS to allow every player to divide his share into multiple pieces for other players. Then, shares can be revealed asynchronously. If any player acts maliciously, his share can be recovered by other players. This feature can encourage players to act honestly since any malicious action (i.e., either revealing a fake share or refusing to release one) is useless. Our scheme is practically valuable for secure group-oriented applications in UCE.
摘要：
Context: Modern software systems (e.g., Apache Spark) are usually written in multiple programming languages (PLs). There is little understanding on the phenomenon of multi-programming-language commits (MPLCs), which involve modified source files written in multiple PLs.Objective: This work aims to explore MPLCs and their impacts on development difficulty and software quality.Methods: We performed an empirical study on eighteen non-trivial Apache projects with 197,566 commits.Results : (1) the most commonly used PL combination consists of all the four PLs, i.e., C/C++, Java, JavaScript, and Python; (2) 9% of the commits from all the projects are MPLCs, and the proportion of MPLCs in 83% of the projects goes to a relatively stable level; (3) more than 90% of the MPLCs from all the projects involve source files in two PLs; (4) the change complexity of MPLCs is significantly higher than that of non-MPLCs; (5) issues fixed in MPLCs take significantly longer to be resolved than issues fixed in non-MPLCs in 89% of the projects; (6) MPLCs do not show significant effects on issue reopen; (7) source files undergoing MPLCs tend to be more bug-prone; and (8) MPLCs introduce more bugs than non-MPLCs.Conclusions: MPLCs are related to increased development difficulty and decreased software quality.(c) 2022 Elsevier Inc. All rights reserved.
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.
