摘要：
Temporal knowledge graph embedding (TKGE) aims to learn the embedding of entities and relations in a temporal knowledge graph (TKG). Although the previous graph neural networks (GNN) based models have achieved promising results, they cannot directly capture the interactions of multi-facts at different timestamps. To address the above limitation, we propose a time-aware relational graph attention model (TARGAT), which takes the multi-facts at different timestamps as a unified graph. First, we develop a relational generator to dynamically generate a series of time-aware relational message transformation matrices, which jointly models the relations and the timestamp information into a unified way. Then, we apply the generated message transformation matrices to project the neighborhood features into different time-aware spaces and aggregate these neighborhood features to explicitly capture the interactions of multi-facts. Finally, a temporal transformer classifier is applied to learn the representation of the query quadruples and predict the missing entities. The experimental results show that our TARGAT model beats the GNN-based models by a large margin and achieves new state-of-the-art results on four popular benchmark datasets.
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：
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.
期刊：
BRIEFINGS IN BIOINFORMATICS,2023年24(2) ISSN：1467-5463
通讯作者：
Xingpeng Jiang
作者机构：
[Wang, Haodong; Wang, Yue; Xiao, Zhen; Huang, Xiaoyun; He, Tingting; Jiang, Xingpeng; Sun, Han] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China;[Wang, Haodong; Wang, Yue; Xiao, Zhen; Huang, Xiaoyun; He, Tingting; Jiang, Xingpeng; Sun, Han] School of Computer Science, Central China Normal University, Wuhan 430079, China;[Xiao, Zhen; Sun, Han] School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China;[Huang, Xiaoyun] Collaborative & Innovative Center for Educational Technology, Central China Normal University, Wuhan 430079, China;[He, Tingting; Jiang, Xingpeng] National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan 430079, China
通讯机构：
[Xingpeng Jiang] H;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University , Wuhan 430079 , China<&wdkj&>School of Computer Science, Central China Normal University , Wuhan 430079 , China<&wdkj&>National Language Resources Monitoring & Research Center for Network Media, Central China Normal University , Wuhan 430079 , China
关键词：
Kernel machine regression;Microbiome-based association test;Multinomial logit model;Ordinal/Nominal multicategory phenotypes
摘要：
Microbes can affect the metabolism and immunity of human body incessantly, and the dysbiosis of human microbiome drives not only the occurrence but also the progression of disease (i.e. multiple statuses of disease). Recently, microbiome-based association tests have been widely developed to detect the association between the microbiome and host phenotype. However, the existing methods have not achieved satisfactory performance in testing the association between the microbiome and ordinal/nominal multicategory phenotypes (e.g. disease severity and tumor subtype). In this paper, we propose an optimal microbiome-based association test for multicategory phenotypes, namely, multiMiAT. Specifically, under the multinomial logit model framework, we first introduce a microbiome regression-based kernel association test for multicategory phenotypes (multiMiRKAT). As a data-driven optimal test, multiMiAT then integrates multiMiRKAT, score test and MiRKAT-MC to maintain excellent performance in diverse association patterns. Massive simulation experiments prove the success of our method. Furthermore, multiMiAT is also applied to real microbiome data experiments to detect the association between the gut microbiome and clinical statuses of colorectal cancer as well as for diverse statuses of Clostridium difficile infections.
作者机构：
[Hsu, Chingfang] Cent China Normal Univ, Comp Sch, 152 Luoyu Ave, Wuhan 430079, Peoples R China.;[Xia, Zhe] Wuhan Univ Technol, Dept Comp Sci, 122 Luoshi Ave, Wuhan 430071, Peoples R China.;[Cheng, Tianshu] Cent China Normal Univ, Middle Sch 1, 281 Zhuodaoquan Ave,45100 Rockhill Rd, Wuhan 430079, Peoples R China.;[Harn, Lein] Univ Missouri, Dept Comp Sci Elect Engn, Kansas City, MO 64110 USA.
通讯机构：
[Zhe Xia] D;Department of Computer Science, Wuhan University of Technology , 122 Luoshi Avenue, Wuhan 430071 , China
关键词：
RSE toward 5G;secure group communications;membership authenticated group key agreement;symmetric bivariate polynomial;logic XOR operation;lightweight and constant-round
摘要：
With rapid development of next-generation mobile networks and communications (5G networks), group-oriented applications in resource-constrained smart environments (RSEs), such as smart homes and smart classrooms, have attracted great attentions. Due to the insecure communications between resource-constrained devices, secure group communications in RSE toward 5G face many challenges. In RSE toward 5G, lightweight communications and low computational overheads are crucial. Besides, the private tokens used to generate the group key are expected to be reused multiple times. However, the conventional frameworks for secure group communications cannot meet these requirements. A practical construction of extremely lightweight constant-round membership authenticated group key establishment framework is proposed in this paper for RSE toward 5G, which not only implements identity authentication among the members and group key establishment but also ensures extremely lightweight computation and communication costs by each group member. In our proposed scheme, the increase in the number of group members will not lead to a linear or logarithmic increase in the communication and calculation costs at the member side. Our framework also resists external and internal attacks and meets all the desirable security features. In this framework, the privacy of tokens can be well protected, so that they can be reused for multiple times. Therefore, our scheme significantly reduces the costs of communication and calculation, and it is more efficient compared with the related schemes in the literature. This proposal is fairly suitable for lightweight membership authentication and group key establishment in RSE toward 5G.
作者机构：
[Pi, Chenchen; Xie, W; Xie, Wei; Sun, Hao] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.;[Pi, Chenchen; Xie, W; Xie, Wei; Sun, Hao] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Pi, Chenchen; Xie, W; Xie, Wei; Sun, Hao] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netw, Wuhan, Peoples R China.
会议名称：
IEEE International Conference on Multimedia and Expo (ICME)
会议时间：
JUL 10-14, 2023
会议地点：
Brisbane, AUSTRALIA
会议主办单位：
[Sun, Hao;Pi, Chenchen;Xie, Wei] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.^[Sun, Hao;Pi, Chenchen;Xie, Wei] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.^[Sun, Hao;Pi, Chenchen;Xie, Wei] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netw, Wuhan, Peoples R China.
会议论文集名称：
IEEE International Conference on Multimedia and Expo
摘要：
Pseudo-labels are popular in semi-supervised facial expression recognition. Recent methods usually exploit the confidence as the criterion for pseudo-label generation, and utilize the high-confidence pseudo-labels as the ground-truth for training. However, high confidence cannot guarantee the correctness of pseudo-labels. False pseudo-labels can weaken the feature discrimination and degrade recognition performance. In this paper, we propose a Critical Feature Refinement Network (CFRN) to alleviate the interference of false pseudo-labels on the model performance. Specially, a feature dropout module and a feature emphasis module are proposed to improve the feature discrimination of CFRN. Then, a mean-absolute error loss is further exploited to improve the robustness against false pseudo-labels. Experimental results on three challenging datasets RAF-DB, SFEW and Affectnet demonstrate that the proposed CFRN outperforms the state-of-the-art methods.
作者机构：
[Mo, Ran] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.
会议名称：
38th IEEE/ACM International Conference on Automated Software Engineering (ASE)
会议时间：
SEP 11-15, 2023
会议地点：
Echternach, LUXEMBOURG
会议主办单位：
[Mo, Ran] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.^Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.
会议论文集名称：
IEEE ACM International Conference on Automated Software Engineering
摘要：
With the continuous improvement of artificial intelligence technology, autonomous driving technology has been greatly developed. Hence automated driving software has drawn more and more attention from both researchers and practitioners. Code clone is a commonly used to speed up the development cycle in software development, but many studies have shown that code clones may affect software maintainability. Currently, there is little research investigating code clones in automated driving software. To bridge this gap, we conduct a comprehensive experience study on the code clones in automated driving software. Through the analysis of Apollo and Autoware, we have presented that code clones are prevalent in automated driving software. about 30% of code lines are involved in code clones and more than 50% of files contain code clones. Moreover, a notable portion of these code clones has caused bugs and co-modifications. Due to the high complexity of autonomous driving, the automated driving software is often designed to be modular, with each module responsible for a single task. When considering each module individually, we have found that Perception, Planning, Canbus, and Sensing modules are more likely to encounter code clones, and more likely to have bug-prone and co-modified clones. Finally, we have shown that there exist cross-module clones to propagate bugs and co-modifications in different modules, which undermine the software's modularity.
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：
With the rapid developments of Internet of Things (IoT) technologies, the security of sensitive data has attracted more and more attention for many resource-asymmetric smart environments, such as smart home, smart agriculture and so on. The resource-asymmetry environment refers to the uneven distribution of resources on different devices side, which is specifically manifested as gateway side is resource-rich, user side and device side are resource-restricted. Hence, a secure and practical authentication key establishment scheme for such smart environments is urgently needed. Recently many researchers have designed authentication and key establishment schemes for security purpose, however most of them cannot consider the excess of gateway resources and guarantee the anonymity of user, and further, they are not suitable for resource-asymmetric smart environments because they are not lightweight enough in user side and smart device side. Due to the fact that Rabin cryptosystem has the large difference in time-consuming between encryption and decryption, it is extremely suitable for constructing authentication and key establishment scheme for resource-asymmetric smart environments. So, a new practical authentication and key establishment scheme based on the Rabin cryptosystem for resource-asymmetric smart environments is proposed, which can make better use of the advantages of abundant gateway resources and realize the lightweight operations on device side and user side, and at the same time can provide user anonymity. With Proverif and BAN logic, we can prove that our solution not only provides anonymity, but also satisfies all defined security features. Simultaneously, compared with latest similar protocols in computation cost and communication overhead, the results show that our scheme is more effective. Hence, our design has more attraction for authentication and key establishment scheme in resource-asymmetric smart environments.
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(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
