期刊：
INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS AND NETWORKING,2023年41(1):42-58 ISSN：1542-0973
通讯作者：
Jianqun Cui
作者机构：
[Wu, Jike; Cui, Jianqun; Zhang, Ruijie; Chang, Yanan; Wan, Qiyun] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Zhou, Hao] Wuhan Polytech Univ, Network & Informatizat Ctr, Wuhan, Hubei, Peoples R China.
通讯机构：
[Jianqun Cui] S;School of Computing, Central China Normal University, Wuhan, Hubei, China
关键词：
communication capability;delay-tolerant network;message priority;message strength;relay node selection strategy
摘要：
In delay-tolerant networks (DTN), node connection time and message transmission time are two important influencing factors that can improve the delivery rate. In this paper, we first define a new concept called communication capability (CC) and then apply this concept to the delivery predictability formulation in Prophet and improve it. Then, in Prophet, the selection of relay nodes relies only on the delivery predictability and ignores the caching and forwarding capability of the node. Therefore, we combine delivery predictability, buffering, and forwarding capability to develop a new adaptive relay node selection strategy. Subsequently, we define two metrics called message priority (MP) and message strength (MS). The node forwards messages sequentially based on message priority and discards messages based on message strength. Finally, we present a probabilistic routing algorithm based on node communication capability and message strength (CAMS). The simulation results show that compared with traditional routing algorithms, the CAMS can effectively improve the message delivery rate, reduce the overhead ratio, and keep average hop counts low.
摘要：
Metaverse is the fusion of cyber–physical–social intelligence, and the fusion becomes the core and fundamental property of the metaverse. As an important part of social operationalization, the education domain leads to the birth of the education metaverse. This article answers three basic questions about smart services in the education metaverse: 1) learning scene; 2) technical framework; and 3) initial expansion. Specifically, four key elements constitute the learning scene in the education metaverse: 1) the learner; 2) its time; 3) space; and 4) learning event. In this learning scene, we propose a novel data-knowledge-driven group intelligence framework, aiming to transform data in the education metaverse into knowledge, and intersect and integrate intelligence with knowledge; based on this framework, we apply it to specific services, i.e., transaction and management services. We hope that our work opens the door to research on smart services in the education metaverse and more scholars will work for these challenges.
作者机构：
[Wang, Wenshuo; Li, Zengyang; Wang, Sicheng; Mo, Ran] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.;[Wang, Wenshuo; Li, Zengyang; Wang, Sicheng; Mo, Ran] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.;[Liang, Peng; Li, Bing] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.;[Liang, Peng] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.
会议名称：
31st IEEE/ACM International Conference on Program Comprehension (ICPC)
会议时间：
MAY 15-16, 2023
会议地点：
Melbourne, AUSTRALIA
会议主办单位：
[Li, Zengyang;Wang, Sicheng;Wang, Wenshuo;Mo, Ran] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.^[Li, Zengyang;Wang, Sicheng;Wang, Wenshuo;Mo, Ran] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.^[Liang, Peng;Li, Bing] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.
会议论文集名称：
International Conference on Program Comprehension
关键词：
Deep Learning Framework;Bug;Multiple-Programming-Language Software System;Empirical Study
摘要：
Deep learning frameworks (DLFs) have been playing an increasingly important role in this intelligence age since they act as a basic infrastructure for an increasingly wide range of AI-based applications. Meanwhile, as multi-programming-language (MPL) software systems, DLFs are inevitably suffering from bugs caused by the use of multiple programming languages (PLs). Hence, it is of paramount significance to understand the bugs (especially the bugs involving multiple PLs, i.e., MPL bugs) of DLFs, which can provide a foundation for preventing, detecting, and resolving bugs in the development of DLFs. To this end, we manually analyzed 1497 bugs in three MPL DLFs, namely MXNet, PyTorch, and TensorFlow. First, we classified bugs in these DLFs into 12 types (e.g., algorithm design bugs and memory bugs) according to their bug labels and characteristics. Second, we further explored the impacts of different bug types on the development of DLFs, and found that deployment bugs and memory bugs negatively impact the development of DLFs in different aspects the most. Third, we found that 28.6%, 31.4%, and 16.0% of bugs in MXNet, PyTorch, and TensorFlow are MPL bugs, respectively; the PL combination of Python and C/C++ is most used in fixing more than 92% MPL bugs in all DLFs. Finally, the code change complexity of MPL bug fixes is significantly greater than that of single-programming-language (SPL) bug fixes in all the three DLFs, while in PyTorch MPL bug fixes have longer open time and greater communication complexity than SPL bug fixes. These results provide insights for bug management in DLFs.
期刊：
Information Processing & Management,2023年60(5):103418 ISSN：0306-4573
通讯作者：
Po Hu
作者机构：
PKU-Wuhan Institute for Artificial Intelligence, Wuhan, 100080, China;College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, 430079, China;School of Computer Science, Central China Normal University, Wuhan, 430079, China;[Guo, Xuan] The Computer Science and Engineering Department, University of North Texas, Denton, 76203, United States
通讯机构：
[Po Hu] 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
关键词：
Heterogeneous social network;Link prediction;Meta-learning;Newly emerged link types
期刊：
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2023年20:1-5 ISSN：1545-598X
通讯作者：
Zhang, M.
作者机构：
[Tang, Ping; Zhang, Meng] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;[Liu, Zhihui] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.;[Song, Rong] Cent China Normal Univ, Sch Marxism, Wuhan 430079, Peoples R China.
通讯机构：
[Zhang, M.] C;Central China Normal University, China
摘要：
Convolutional neural networks (CNNs) have become one of the most popular tools to tackle hyperspectral image (HSI) classification tasks. However, CNN suffers from the long-range dependencies problem, which may degrade the classification performance. To address this issue, this letter proposes a transformer-based backbone network for HSI classification. The core component is a newly designed double-attention transformer encoder (DATE), which contains two self-attention modules, termed spectral attention module (SPE) and spatial attention module (SPA). SPE extracts the global dependency among spectral bands, and SPA mines the local features of spatial correlation information among pixels. The local spatial tokens and the global spectral token are fused together and updated by SPA. In this way, DATE can not only capture the global dependence among spectral bands but also extract the local spatial information, which greatly improves the classification performance. To reduce the possible information loss as the network depth increases, a new skip connection mechanism is devised for cross-layer feature fusion. Experimental results in several datasets indicate that the new algorithm holds very competitive classification performance compared to the state-of-the-art methods.
期刊：
IEEE Transactions on Geoscience and Remote Sensing,2023年61:1-16 ISSN：0196-2892
作者机构：
[Chen, Wenjing] School of Computer Science, Hubei University of Technology, Wuhan, China;[Yao, Huaxiong; Chen, Renyi; Sun, Hao; Xie, Wei] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, School of Computer Science, National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan, China;[Lu, Xiaoqiang] College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
摘要：
Recently, pseudolabel-based deep learning methods have shown excellent performance in semi-supervised hyperspectral image (HSI) classification. These methods usually select high-confidence unlabeled samples to help optimize backbone classification networks. However, a large number of remaining low-confidence unlabeled samples, which contain rich land-covers information, are underused. In this article, we propose a pseudolabel-based unreliable sample learning (PUSL) method to fully exploit low-confidence unlabeled samples for semi-supervised HSI classification. First, to avoid overfitting the spatial distribution of labeled samples, we build a position-free transformer (PFT) as the backbone classification network. Second, PFT is initially trained with labeled samples in a supervised learning manner to obtain an initial classifier, which is then used to split unlabeled samples into reliable and unreliable unlabeled samples based on the predicted confidence. Third, reliable unlabeled samples participate in training along with labeled samples. Finally, unreliable unlabeled samples are treated as negative samples for the corresponding categories to improve the discrimination of PFT in a contrastive learning paradigm. Extensive experiments on three HSI datasets demonstrate that PUSL outperforms the compared methods.
期刊：
Multimedia Tools and Applications,2023年82(1):1105-1129 ISSN：1380-7501
通讯作者：
Cong Jin
作者机构：
[Jin, Cong] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
通讯机构：
[Cong Jin] S;School of Computer, Central China Normal University, Wuhan, People’s Republic of China
关键词：
Cross-database;Facial expression recognition;Database adaptation;Learning feature;Images in the wild
摘要：
Since the labeled wild facial expression database is relatively rare, the existing Facial Expression Recognition (FER) models based on machine learning can only be trained with a relatively limited number of samples and whether the trained FER model can have satisfactory recognition performance is a challenge. In this paper, the facial expression database from the Laboratory Environment (LE) is used as the source domain, and the facial expression database from the wild is used as the target domain. Based on these two different databases, a hybrid improved unsupervised Cross-Domain Adaptation (CDA) approach is proposed, which can not only match the data distribution between different databases, but also maximize the correlation of data between different databases, and also maximize data separability on the source database. In the proposed CDA approach, the objective functions of the two improved techniques and those of traditional CDA are to achieve the simultaneous optimization of the three objective functions. After that, the proposed CDA approach was used for Cross-domain FER (CFER) task. To confirm the effectiveness of the proposed CFER model, some experiments are implemented on four cross-database pairs. The comparison and analysis of experimental results show that, compared with other existing CFER models, the proposed CFER model can realize the reuse of LE facial expression data and achieve better recognition performance for wild facial expression data.
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.
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.
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.
