期刊：
IEEE TRANSACTIONS ON SERVICES COMPUTING,2023年16(4):3000-3013 ISSN：1939-1374
通讯作者：
Hsu, CF
作者机构：
[Zhang, Ze; Cui, Janqun; Xu, Hang; Hsu, Chingfang; Zhao, Zhuo] Cent China Normal Univ, Comp Sch, Wuhan 430079, Hubei, Peoples R China.;[Harn, Lein] Univ Missouri, Dept Comp Sci Elect Engn, Kansas City, MO 64110 USA.
通讯机构：
[Hsu, CF ] C;Cent China Normal Univ, Comp Sch, Wuhan 430079, Hubei, Peoples R China.
关键词：
Authentication;Industrial Internet of Things;Security;Encryption;Protocols;Elliptic curves;Wireless sensor networks;Fuzzy biological extraction;industrial internet of things;key agreement;mutual authentication
摘要：
With the increasing popularity and wide application of the Internet, the users (such as managers and data consumers) in the Industrial Internet of Things (IIoT) can remotely analyze and control real-time data collected by various smart sensor devices. However, there are many security and privacy issues in the process of transmitting collected data through public channels in IIoT environment. In order to against the illegal access by opponents, a novel anonymous user authentication and key agreement scheme based on hash and elliptic curve encryption is proposed in this article, which not only uses a pseudonym tuple database in control nodes to realize the functions of user dynamic joining and anonymity protection, but also resists key loss and device capture attacks through fuzzy biometric extraction technology. In addition, the formal secure analysis of the proposed scheme is carried out using the BAN logic model and ROR model, which proves the security of the proposed scheme. Meanwhile, we also prove the scheme can against the described existing attacks and meet the design goals by a detailed informal security discussion. Compared with the latest similar IIoT authentication proposals, our solution has a very obvious advantage in communication efficiency and realizes more functions. Hence, our scheme is more suitable for the IIoT environment, and can also generate greater benefits.
通讯机构：
[Hao, S ] C;Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
关键词：
Reconfigurable intelligent surface (RIS);Uplink transmission;3-D space;Poisson point process (PPP);Phase shift control;Performance analysis
摘要：
Reconfigurable intelligent surface (RIS) has emerged as a crucial technology capable of improving the performance of future wireless communication (WC) systems. Although a significant body of studies has investigated into the performance analysis of RIS-aided WC systems, most of them fail to consider the impact of multiple RISs on WC systems. Particularly, the influence of RISs randomly distributed in 3-D (three dimension) space is still an open issue. Furthermore, how phase shift error and ARQ (automatic repeat request) scheme affect the transmission behavior of RIS-aided WC systems should also be taken into account. In light of the above limitations, we propose a novel theoretical model to analyze the uplink transmission performance of 3-D spatial RISs-aided WC systems. In the modeling process, we firstly provide an end-to-end (E2E) channel model using a single RIS, where the RIS enables optimal phase shift control. Next we construct a 3-D spatial uplink transmission model, where the multiple RISs are spatially distributed as a homogeneous 3-D PPP (Poisson point process). The impacts of multiple factors including the selection of RISs, buffer size of user, traffic rate and ARQ scheme are comprehensively considered. With this, we derive the closed-form expressions of uplink transmission metrics. Moreover, we further extend the proposed theoretical model under imperfect phase shift control. Finally, we evaluate the uplink transmission performance of 3-D spatial RIS-aided WC systems, and validate the proposed theoretical model.
期刊：
JOURNAL OF SUPERCOMPUTING,2023年79(6):6290-6308 ISSN：0920-8542
通讯作者：
Lisha Liu
作者机构：
[Liu, Lisha; Zhang, Maoyuan] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Peoples R China.;[Mi, Jiaxin; Liu, Lisha; Zhang, Maoyuan; Yuan, Xianqi] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Yuan, Xianqi] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netw, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Lisha Liu] H;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, China<&wdkj&>School of Computer, Central China Normal University, Wuhan, Hubei, China
摘要：
Aspect-Based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task, aiming at mining sentiment polarity towards specific aspects. Most existing work to address ABSA has focused on using Graph Neural Networks combined with syntactic dependency trees. However, existing models often fall into semantic confusion for sentiment analysis due to the information imbalance in the dependency tree. To solve the problem of semantic confusion, we propose a Local Enhanced Relational Graph Attention Network with Dual-level Dependency Parsing (DL-RGAT) model. The dual-level dependency parsing structure constructs a dependency grid for each aspect word, which contains only dependency relations that are related strongly to the aspect. It effectively isolates the negative impact from irrelevant words near the aspect on the aspect. Then the proposed Gaussian local context dynamic weighting structure adaptively adjusts the feature weights of the local context and filters the negative impact of local contexts far from the aspect on the aspect. In this way, the semantic confusion problem is effectively solved. Finally, the parsed dependency relations are encoded for sentiment analysis using a relational graph attention network. Extensive experiments on benchmark datasets have shown that DL-RGAT improves 1.44-5.24% and 1.64-6.7% in average accuracy and average Macro-F1 compared to the results of state-of-the-art studies over the past 3 years.
摘要：
Distributed optimization is a powerful paradigm to solve various problems in machine learning over networked systems. Existing first-order optimization methods perform cheap gradient descent by exchanging information per iteration only with single-hop neighbours in a network. However, in many agent networks such as sensor and robotic networks, it is prevalent that each agent can interact with other agents over multi-hop communication. Therefore, distributed optimization method over multi-hop networks is an important but overlooked topic that clearly needs to be developed. Motivated by this observation, in this paper, we apply multi-hop transmission to the outstanding distributed gradient descent (DGD) method and propose two typical versions (i.e., consensus and diffusion) of multi-hop DGD method, which we call CM-DGD and DM-DGD, respectively. Theoretically, we present the convergence guarantee of the proposed methods under mild assumptions. Moreover, we confirm that multi-hop strategy results in higher probability to improve the spectral gap of the underlying network, which has been shown to be a critical factor improving performance of distributed optimizations, thus achieves better convergence metrics. Experimentally, two distributed machine learning problems are picked to verify the theoretical analysis and show the effectiveness of CM-DGD and DM-DGD by using synthesized and real data sets.
期刊：
IEEE Transactions on Cognitive and Developmental Systems,2023年15(3):1183-1195 ISSN：2379-8920
通讯作者：
Liu, J.;Zhou, G.
作者机构：
[Liu, Jin; Xie, Zhiwen; Zhang, Geng] Wuhan University, School of Computer Science, Wuhan, 430072, China;[Zhou, Guangyou] Central China Normal University, School of Computer Science, Wuhan, 430079, China;[Yu, Xiao] Wuhan University of Technology, School of Computer Science and Technology, Wuhan, 430070, China;[Cui, Xiaohui] Wuhan University, School of Cyber Science and Engineering, Wuhan, 430072, China
通讯机构：
[Liu, J.] W;[Zhou, G.] C;Central China Normal University, China;Wuhan University, China
期刊：
IEEE/ACM Transactions on Computational Biology and Bioinformatics,2023年20(6):3635–3647 ISSN：1545-5963
作者机构：
[Xiaohua Hu] College of Computing & Informatics, Drexel University, Philadelphia, PA, USA;[Chuan Shi] School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China;[Wenjie Yao; Xingpeng Jiang; Tingting He] School of Computer, Central China Normal University, Wuhan, Hubei, China;[Weizhong Zhao] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, School of Computer, and National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan, Hubei, China
摘要：
Side effects of drugs have gained increasing attention in the biomedical field, and accurate identification of drug side effects is essential for drug development and drug safety surveillance. Although the traditional pharmacological experiments can accurately detect the side effects of drugs, the identifying process is time-consuming, costly, and may lead to incomplete identification of side effects. With the expanding of various biomedical databases, many computational methods have been developed for the task of drug-side effect associations (DSAs) prediction. However, existing methods have the following three drawbacks: 1). multiple drug-related databases are not fully used; 2). the complex semantics among drugs and side effects are not effectively captured; 3). the explainability of the predicted DSAs is missed for most existing methods. Therefore, there is an urgent need to find a more effective method for predicting DSAs. To address these issues, we propose a novel meta-path-based graph neural network model for drug-side effect associations prediction (MPGNN-DSA). In MPGNN-DSA, a heterogeneous information network is first constructed by combining multiple biological datasets. Then, a meta-path-based feature learning module is utilized for learning high-quality representations of drugs and side effects by capturing the semantics contained in meta-paths of the constructed HIN. With the learned features, the prediction module is conducted to derive the predicted side effects for drugs. In addition, the explainability of the predicted DSAs can be provided as well with the semantics contained in meta-paths. We conduct comprehensive experiments, and the results demonstrate the effectiveness of MPGNN-DSA, suggesting that the proposed method will be a feasible solution to the task of DSAs prediction. Side effects of drugs have gained increasing attention in the biomedical field, and accurate identification of drug side effects is essential for drug development and drug safety surveillance. Although the traditional pharmacological experiments can accurately detect the side effects of drugs, the identifying process is time-consuming, costly, and may lead to incomplete identification of side effects. With the expanding of various biomedical databases, many computational methods have been developed for the task of drug-side effect associations (DSAs) prediction. However, existing methods have the following three drawbacks: 1). multiple drug-related databases are not fully used; 2). the complex semantics among drugs and side effects are not effectively captured; 3). the explainability of the predicted DSAs is missed for most existing methods. Therefore, there is an urgent need to find a more effective method for predicting DSAs. To address these issues, we propose a novel meta-path-based graph neural network model for drug-side effect associations prediction (MPGNN-DSA). In MPGNN-DSA, a heterogeneous information network is first constructed by combining multiple biological datasets. Then, a meta-path-based feature learning module is utilized for learning high-quality representations of drugs and side effects by capturing the semantics contained in meta-paths of the constructed HIN. With the learned features, the prediction module is conducted to derive the predicted side effects for drugs. In addition, the explainability of the predicted DSAs can be provided as well with the semantics contained in meta-paths. We conduct comprehensive experiments, and the results demonstrate the effectiveness of MPGNN-DSA, suggesting that the proposed method will be a feasible solution to the task of DSAs prediction.
期刊：
Journal of Computing in Higher Education,2023年:1-24 ISSN：1042-1726
通讯作者：
Yu, S;Wang, JH
作者机构：
[Yu, Shuang; Liu, Qingtang; Yu, S; Zheng, Xinxin; Wu, Linjing] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China.;[Ye, Junmin] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Wang, Jianhu] Xinjiang Normal Univ, Coll Educ Sci, Urumqi, Peoples R China.
通讯机构：
[Yu, S ] C;[Wang, JH ] X;Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China.;Xinjiang Normal Univ, Coll Educ Sci, Urumqi, Peoples R China.
摘要：
Interdisciplinary collaboration is widely used in research, industry, and education. Understanding the differences in cognitive processes between cross-discipline and same-discipline groups can improve instruction in collaborative learning. In this study, students volunteered to participate in cross-discipline or same-discipline collaborative learning groups to collaboratively complete three assignments. Epistemic network analysis was employed to identify the differences in cognitive engagement, the roles of group leaders, and the trajectory differences between these two kinds of groups. The results showed that compared to same-discipline groups, cross-discipline groups have lower-order cognitive engagement. Group leaders in cross-discipline groups invested most of their energy into inquiring or starting the discussion. In addition, cognitive engagement of cross-discipline groups dropped sharply over time in collaborative learning. These differences imply that to achieve better collaboration in cross-discipline groups, teachers should provide more interventions, such as evaluation and analytical support, to help students reach high-level cognitive engagement.
作者：
Ye, Shengwei;Zhao, Weizhong;Shen, Xianjun;Jiang, Xingpeng;He, Tingting
期刊：
Methods,2023年218:48-56 ISSN：1046-2023
通讯作者：
Zhao, WZ
作者机构：
[Shen, Xianjun; Zhao, Weizhong; Zhao, WZ; He, Tingting; Jiang, Xingpeng; Ye, Shengwei] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Hubei, Peoples R China.;[Shen, Xianjun; Zhao, Weizhong; He, Tingting; Jiang, Xingpeng; Ye, Shengwei] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Shen, Xianjun; Zhao, Weizhong; He, Tingting; Jiang, Xingpeng; Ye, Shengwei] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Zhao, WZ ] C;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Hubei, Peoples R China.
关键词：
Drug repurposing;Drug-disease associations prediction;Graph convolutional network;Heterogeneous information network;Multi-task learning
摘要：
Drug repurposing, which typically applies the procedure of drug-disease associations (DDAs) prediction, is a feasible solution to drug discovery. Compared with traditional methods, drug repurposing can reduce the cost and time for drug development and advance the success rate of drug discovery. Although many methods for drug repurposing have been proposed and the obtained results are relatively acceptable, there is still some room for improving the predictive performance, since those methods fail to consider fully the issue of sparseness in known drug-disease associations. In this paper, we propose a novel multi-task learning framework based on graph representation learning to identify DDAs for drug repurposing. In our proposed framework, a heterogeneous information network is first constructed by combining multiple biological datasets. Then, a module consisting of multiple layers of graph convolutional networks is utilized to learn low-dimensional representations of nodes in the constructed heterogeneous information network. Finally, two types of auxiliary tasks are designed to help to train the target task of DDAs prediction in the multi-task learning framework. Comprehensive experiments are conducted on real data and the results demonstrate the effectiveness of the proposed method for drug repurposing.
作者机构：
[Wan, Cuihong; Peng, Zhao] Cent China Normal Univ, Sch Life Sci, Wuhan 430079, Hubei, Peoples R China.;[Wan, Cuihong; Peng, Zhao] Cent China Normal Univ, Hubei Key Lab Genet Regulat & Integrat Biol, Wuhan 430079, Hubei, Peoples R China.;[Li, Jiaqiang; Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Li, Jiaqiang; Jiang, Xingpeng] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Xingpeng Jiang; Cuihong Wan] S;School of Computer, and Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University , Wuhan 430079, Hubei , People's Republic of China<&wdkj&>School of Life Sciences, and Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University , Wuhan 430079, Hubei , People's Republic of China
摘要：
As one of the essential life forms in the biosphere, research on cyanobacteria has been growing remarkably for decades. Biological functions in organisms are often accomplished through protein-protein interactions (PPIs), which help to regulate interacting proteins or organize them into an integral machine. However, the study of PPIs in cyanobacteria falls far behind that in mammals and has not been integrated for ease of use. Thus, we built CyanoMapDB (http://www.cyanomapdb.msbio.pro/), a database providing cyanobacterial PPIs with experimental evidence, consisting of 52,304 PPIs among 6,789 proteins from 23 cyanobacterial species. We collected available data in UniProt, STRING, and IntAct, and mined numerous PPIs from co-fractionation MS data in cyanobacteria. The integrated data are accessible in CyanoMapDB (http://www.cyanomapdb.msbio.pro/), enabling users to easily query proteins of interest, investigate interacting proteins with evidence from different sources, and acquire a visual network of the target protein. We believe that CyanoMapDB will promote research involved with cyanobacteria and plants.
作者机构：
[Xia, Zhe] Department of Computer Science , Wuhan University of Technology , 122 Luoshi Road, Wuhan 430071, China;[Chen, Jingxue] Guangxi Key Laboratory of Trusted Software , Guilin University of Electronic Technology , 1 Jinji Road, Guilin 541004, China;[Hsu, Chingfang; Cui, Jianqun] Computer School , Central China Normal University , 152 Luoyu Road, Wuhan 430079, China;[Harn, Lein] Department of Computer Science Electrical Engineering , University of Missouri-Kansas City , 5100 Rockhill Road, Kansas City, MO 64110, USA
通讯机构：
[Chingfang Hsu] C;Computer School , Central China Normal University , 152 Luoyu Road, Wuhan 430079, China
摘要：
The next generation of Internet of Things (IoT) networks and mobile communications (5G IoT networks) has the particularity of being heterogeneous, therefore, it has very strong ability to compute, store, etc. Group-oriented applications demonstrate its potential ability in 5G IoT networks. One of the main challenges for secure group-oriented applications (SGA) in 5G IoT networks is how to secure communication and computation among these heterogeneous devices. Conventional protocols are not suitable for SGA in 5G IoT networks since multiparty joint computation in this environment requires lightweight communication and computation overhead. Furthermore, the primary task of SGA is to securely transmit various types of jointly computing data. Hence, membership authentication and secure multiparty joint arithmetic computation become two fundamental security services in SGA for 5G IoT networks. The membership authentication allows communication entities to authenticate their communication partners and the multiparty joint computations allow a secret output to be shared among all communication entities. The multiparty joint computation result can be used to protect exchange information in the communication or be used as a result that all users jointly compute by using their secret inputs. A novel construction of computation/communications-efficient membership authenticated joint arithmetic computation is proposed in this paper for 5G IoT networks, which not only integrates the function of membership authentication and joint arithmetic computation but also realizes both computation and communication efficiency on each group member side. Our protocol is secure against inside attackers and outside attackers, and also meets all the described security goals. Meanwhile, in this construction the privacy of tokens can be well protected so tokens can be reused multiple times. This proposal is noninteractive and can be easily extended to joint arithmetic computation with any number of inputs. Hence, our design has more attraction for lightweight membership authenticated joint arithmetic computation in 5G IoT networks.
通讯机构：
[Wang, R ] C;Cent China Normal Univ, Sch Comp Sci, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Hubei, Peoples R China.
关键词：
Graph information bottleneck;contextual feature transformation (CFT);spatial attention model;video summarization;Bi-LSTM
摘要：
End-to-end Long Short-Term Memory (LSTM) has been successfully applied to video summarization. However, the weakness of the LSTM model, poor generalization with inefficient representation learning for inputted nodes, limits its capability to efficiently carry out node classification within user-created videos. Given the power of Graph Neural Networks (GNNs) in representation learning, we adopted the Graph Information Bottle (GIB) to develop a Contextual Feature Transformation (CFT) mechanism that refines the temporal dual-feature, yielding a semantic representation with attention alignment. Furthermore, a novel Salient-Area-Size-based spatial attention model is presented to extract frame-wise visual features based on the observation that humans tend to focus on sizable and moving objects. Lastly, semantic representation is embedded within attention alignment under the end-to-end LSTM framework to differentiate indistinguishable images. Extensive experiments demonstrate that the proposed method outperforms State-Of-The-Art (SOTA) methods.
期刊：
Information Processing & Management,2023年60(2):103207 ISSN：0306-4573
通讯作者：
Chen Qiu
作者机构：
[Gu, Jinguang; Qiu, Chen; Xu, Zhaoyang; Liu, Maofu; Fu, Haidong] Wuhan Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430081, Peoples R China.;[Gu, Jinguang; Qiu, Chen; Xu, Zhaoyang; Liu, Maofu; Fu, Haidong] Wuhan Univ Sci & Technol, Inst Big Data Sci & Engn, Wuhan 430081, Peoples R China.;[Zhou, Guangyou] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Chen Qiu] S;School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430081, China<&wdkj&>Institute of Big Data Science and Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China
期刊：
Information Processing & Management,2023年60(1):103114 ISSN：0306-4573
通讯作者：
Weizhong Zhao
作者机构：
[Zhao, Weizhong; Xia, Jun; He, Tingting; Jiang, Xingpeng] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Hubei, Peoples R China.;[Zhao, Weizhong] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Zhao, Weizhong] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Weizhong Zhao] H;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, Hubei, China<&wdkj&>School of Computer, Central China Normal University, Wuhan 430079, Hubei, China<&wdkj&>National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan 430079, Hubei, China
关键词：
Deep knowledge tracing;Forgetting and learning mechanisms;Intelligent education
