期刊：
IEEE TRANSACTIONS ON SERVICES COMPUTING,2023年16(6):4102-4114 ISSN：1939-1374
通讯作者：
Guo, YM
作者机构：
[Guo, Yimin; Guo, YM] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan 430073, Hubei, Peoples R China.;[Guo, Yajun] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Guo, YM ] Z;Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan 430073, Hubei, Peoples R China.
关键词：
Internet of Things (IoT);authentication;robust synchronization;anonymity;perfect forward secrecy
摘要：
Anonymity, robust synchronization, and perfect forward secrecy are the most important security properties of authenticated key agreement (AKA) protocols. Designing AKA protocols that simultaneously achieve these security properties and availability in the IoT environment is a challenging task. AKA protocols built using public key cryptographic primitives have advantages in achieving these critical security properties, but performing expensive public-key cryptographic operations is inefficient for resource-constrained IoT devices. The authentication protocols based on symmetric cryptographic primitives are often subject to various attacks. This paper proposes a secure lightweight AKA protocol with critical security properties (called CS-LAKA) for IoT environments without using public-key cryptographic primitives. LAKA cleverly achieves the security goals of anonymity, robust synchronization, and perfect forward secrecy by embedding dynamic identities in authenticators, and a few additional exchange messages are added. This enables LAKA to have both robust security and high efficiency. Subsequently, we perform a formal security analysis to prove that LAKA is secure, and compared with existing related schemes, LAKA has obvious advantages in terms of security, functionality and running performance.
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.
摘要：
A direction-aware augmented spatial keyword top- $k$ query (DAT $k\text{Q}$ ) returns the top- $k$ objects based on a ranking function that considers spatial distance, textual similarity, query numeric attributes, and query direction. When a user initiates a DAT $k\text{Q}$ , some user-desired objects (missing objects) may not appear in the query result set, and then the user wonders why they do not appear, which is called the why-not question. This paper focuses on answering why-not questions on DAT $k$ Qs. We first discuss how to obtain the refined query direction by analyzing the position relationship between missing objects and original query direction in Polar coordinates. Then a DAPC index structure is designed, which can cut down irrelevant search space based on not only conventional distance pruning, keyword pruning, and attribute pruning but also query direction pruning. Particularly, by comparing the position relationship between the query direction and the sector (sector ring) region segmented by the DAPC-based method, the search space that does not meet the query direction is pruned. In addition, we discuss the applicability of our scheme for handling why-not questions on regional spatial keyword queries (SKQ), ordinary direction-aware top- $k$ SKQ queries and complex scoring SKQ queries. Finally, a series of experiments are conducted on two real datasets to show the efficiency of our DAPC-based method.
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：
[Hsu, Chingfang] Computer School, Central China Normal University , Wuhan 430079 , China;[Li, Zixuan] Wuhan Britain-China School , Wuhan 430022 , China;[Xia, Zhe] Department of Computer Science, Wuhan University of Technology , Wuhan 430071 , China;[Harn, Lein] Department of Computer Science Electrical Engineering, University of Missouri-Kansas City , Kansas City, MO 64110 , USA
通讯机构：
[Chingfang Hsu] C;Computer School, Central China Normal University , Wuhan 430079 , China
摘要：
In this paper, we propose a new cryptographic primitive, called multiple blind signature (MBS), which is designed based on the integration of both normal blind signature scheme and dual signature. The major difference between a normal blind signature and an MBS is that using a normal blind signature, only one message, |$m$|, can be verified, but using an MBS, any subset, |${M}^{\prime }$|, of multiple messages in a set, |$M$|, where |${M}^{\prime}{\subseteq} M$|, can be verified. With this additional property, we will show that MBS is especially suitable for e-voting and e-cash applications. In other words, we classify these processes in two applications into two phases, on-line and off-line phases. One unique property of this design is that most time-consuming computation and interaction can be performed in advance in off-line phase. There is no cost of computation and interaction in the online phase.
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：
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.
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：
Identifying temporal and subevent relationships between different events (i.e., event relation extraction) is an important step towards event-centric natural language processing, which can help understand how events evolve and potentially facilitate many downstream tasks, such as timeline generation and event knowledge graph construction. Existing work has extensively leveraged external knowledge to improve the performance of relation extraction. Despite the progress made, the current knowledge-enhanced approach still has some shortcomings, e.g., knowledge missing, knowledge noise, and suboptimal knowledge injection. In this paper, we propose OntoEnhance, a novel event relation extraction framework that fuses semantic information from event ontologies to enhance event representation. Specifically, we first inject the latent knowledge in the event ontology into the prompt text to address the issue of knowledge missing. Then a dual-stack attention fusion mechanism is further introduced to enhance the injection of key knowledge to alleviate knowledge noise. In order to prevent the knowledge in the event ontology from being wrongly dominated, we use the event direction induction mechanism to obtain the event context-based relational sequence representation. Finally, a gate mechanism is used to fuse ontology-based knowledge and context-based event features dynamically. Extensive experiments demonstrate that OntoEnhance outperforms all comparison baselines by a large margin on all four datasets under both standard and few-shot settings.
期刊：
Journal of Information Security and Applications,2023年77:103576 ISSN：2214-2126
通讯作者：
Hsu, CF
作者机构：
[Harn, Lein] Univ Missouri Kansas City, Dept Comp Sci Elect Engn, Kansas City, MO 64110 USA.;[Hsu, Chingfang] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Hubei, Peoples R China.;[Zeng, Shuchang; Xu, Hang; Pang, Fengling; Hsu, Chingfang] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Xia, Zhe] Wuhan Univ Technol, Dept Comp Sci, Wuhan 430071, Peoples R China.
通讯机构：
[Hsu, CF ] C;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Hubei, Peoples R China.
关键词：
General construction;Anonymously releasing data;Secure multiparty computation;Dealer-free;Non-interaction;TCSS
摘要：
Shamir's (t, n) threshold secret sharing (SS) is one of the most important cryptographic primitives in various security applications designed to protect highly sensitive information, such as intellectual property and network security. A dealer in a (t, n) threshold SS divides the secret into n shares such that with t or more than t of these shares, the secret can be retrieved; but with less than t of these shares, no information of the secret can be revealed. In order to adapt the dynamic environment in various applications, the threshold changeable SS (TCSS) allows the threshold of the original secret to be dynamically adjusted. Note that Shamir's original SS is unable to achieve this property. In the literature, most existing TCSS schemes are suffering from limitations that either it requires a trusted dealer to generate and distribute new shares, or requires interactions among shareholders to generate new shares. In this paper, we propose a novel idea to construct a dealer-free and non-interactive TCSS based on pairwise keys between users. Research papers related to pairwise key distribution schemes have been published over last 40 years and it has become one of fundamental tools used in design of cryptographic solutions. First, we show that our idea can be used to construct a scheme for anonymously releasing data and a secure multiparty computation scheme. Both schemes are very simple and security analysis can be understood easily. Second, we demonstrate that a TCSS can also be designed based on same approach. Our design of TCSS is novel and it can be applied to convert any existing SS into a TCSS.
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):103469 ISSN：0306-4573
通讯作者：
Po Hu
作者机构：
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, Hubei, China;National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan, 430079, Hubei, China;[Hao Fei] School of Computing, National University of Singapore, 117583, Singapore;[Ling Zhuang; Po Hu] 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, Hubei, China<&wdkj&>National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan, 430079, Hubei, China
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, Hubei, China<&wdkj&>National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan, 430079, Hubei, China
期刊：
Artificial Intelligence in Medicine,2023年145:102677 ISSN：0933-3657
通讯作者：
Jiang, XP
作者机构：
[Fu, Chengcheng] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Jiang, Xingpeng; Fu, Chengcheng; He, Tingting] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.;[Fu, Chengcheng; van Harmelen, Frank; Huang, Zhisheng] Vrije Univ Amsterdam, Dept Comp Sci, Amsterdam, Netherlands.;[Fu, Chengcheng; He, Tingting; Jiang, Xingpeng] Cent China Normal Univ, Natl Language Resources Monitor Res Ctr Network Me, Wuhan, Peoples R China.;[Huang, Zhisheng] Tongji Univ, Sch Med, Clin Res Ctr Mental Disorders, Shanghai Pudong New Area Mental Hlth Ctr, Shanghai, Peoples R China.
通讯机构：
[Jiang, XP ] C;Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.
关键词：
Food;Gut microbiota;Knowledge graph;Mental health
作者机构：
[Wang, Chao; Zhang, Jiaxu; Tu, Zhigang] Wuhan Univ, State Key Lab Informat Engn Surveying, Wuhan 430072, Hubei, Peoples R China.;[Xie, Wei] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Tu, Ruide] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Chao Wang; Ruide Tu] S;State Key Laboratory of Information Engineering in Surveying, Wuhan University, Wuhan, China<&wdkj&>School Of Information Management, Central China Normal University, Wuhan, China
关键词：
Skeleton action recognition;Visual transformer;Graph-aware transformer;Velocity information of human body joints;Graph neural network
摘要：
Recently, graph convolutional networks (GCNs) play a critical role in skeleton-based human action recognition. However, most GCN-based methods still have two main limitations: (1) The semantic-level adjacency matrix of the skeleton graph is difficult to be manually defined, which restricts the perception field of GCN and limits its ability to extract the spatial–temporal features. (2) The velocity information of human body joints cannot be efficiently used and fully exploited by GCN, because GCN does not represent the correlation between the velocity vectors explicitly. To address these issues, we propose a graph-aware transformer (GAT), which can make full use of the velocity information and learn discriminative spatial–temporal motion features from the sequence of the skeleton graphs in a data-driven way. Besides, similar to the GCN-based model, our GAT also considers the prior structures of the human body including the link-aware structure and the part-aware structure. Extensive experiments on three large-scale datasets, i.e., NTU-RGB+D 60, NTU-RGB+D 120, and Kinetics-Skeleton, demonstrated that the proposed GAT obtains significant improvement compared to the GCN-based baseline for skeleton action recognition.
摘要：
Cervical cancer seriously endangers the health of the female reproductive system and even risks women's life in severe cases. Optical coherence tomography (OCT) is a non-invasive, real-time, high-resolution imaging technology for cervical tissues. However, since the interpretation of cervical OCT images is a knowledge-intensive, time-consuming task, it is tough to acquire a large number of high-quality labeled images quickly, which is a big challenge for supervised learning. In this study, we introduce the vision Transformer (ViT) architecture, which has recently achieved impressive results in natural image analysis, into the classification task of cervical OCT images. Our work aims to develop a computer-aided diagnosis (CADx) approach based on a self-supervised ViT-based model to classify cervical OCT images effectively. We leverage masked autoencoders (MAE) to perform self-supervised pre-training on cervical OCT images, so the proposed classification model has a better transfer learning ability. In the fine-tuning process, the ViT-based classification model extracts multi-scale features from OCT images of different resolutions and fuses them with the cross-attention module. The ten-fold cross-validation results on an OCT image dataset from a multi-center clinical study of 733 patients in China indicate that our model achieved an AUC value of 0.9963 ± 0.0069 with a 95.89 ± 3.30% sensitivity and 98.23 ± 1.36 % specificity, outperforming some state-of-the-art classification models based on Transformers and convolutional neural networks (CNNs) in the binary classification task of detecting high-risk cervical diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer. Furthermore, our model with the cross-shaped voting strategy achieved a sensitivity of 92.06% and specificity of 95.56% on an external validation dataset containing 288 three-dimensional (3D) OCT volumes from 118 Chinese patients in a different new hospital. This result met or exceeded the average of four medical experts who have used OCT for over one year. In addition to promising classification performance, our model has a remarkable ability to detect and visualize local lesions using the attention map of the standard ViT model, providing good interpretability for gynecologists to locate and diagnose possible cervical diseases.
