摘要：
Group signatures allow users to sign messages on behalf of a group without revealing authority is capable of identifying the user who generated it. However, the exposure of the user's signing key will severely damage the group signature scheme. In order to reduce the loss caused by signing key leakage, Song proposed the first forward-secure group signature. If a group signing key is revealed at the current time period, the previous signing key will not be affected. This means that the attacker cannot forge group signatures regarding messages signed in the past. To resist quantum attacks, many lattice-based forward-secure group signatures have been proposed. However, their key-update algorithm is expensive since they require some costly computations such as the Hermite normal form (HNF) operations and conversion from a full-rank set of lattice vectors into a basis.
In this paper, we propose the group signature with forward security from lattice. In comparison with previous works, we have several advantages: Firstly, our scheme is more effective since we only need to sample some vectors independently from a discrete Gaussian during the key-update algorithm. Secondly, the derived secret key size is linear instead of quadratic with the lattice dimensions, which is more friendly towards lightweight applications. Anonymous authentication plays an increasingly critical role in protecting privacy and security in the environment where private information could be collected for intelligent analysis. Our work contributes to the anonymous authentication in the post-quantum setting, which has wide potential applications in the IoT environment.
作者：
Zengyang Li;Wenshuo Wang;Sicheng Wang;Peng Liang;Ran Mo
期刊：
2023 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM),2023年:1-11
作者机构：
[Peng Liang] School of Computer Science, Wuhan University, Wuhan, China;[Zengyang Li; Wenshuo Wang; Sicheng Wang; Ran Mo] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, School of Computer Science, Central China Normal University, Wuhan, China
摘要：
Background: In modern software systems, more and more systems are written in multiple programming languages (PLs). There is no comprehensive investigation on the phenomenon of multi-programming-language (MPL) bugs, which resolution involves source files written in multiple PLs. Aim: This work investigated the characteristics of bug resolution in MPL software systems and explored the reasons why bug resolution involves multiple PLs. Method: We conducted an empirical study on 54 MPL projects selected from 655 Apache OSS projects, of which 66,932 bugs were analyzed. Results: (1) the percentage of MPL bugs (MPLBs) in the selected projects ranges from 0.17% to 42.26%, and the percentage of MPLBs for all projects as a whole is 10.01%; (2) 95.0% and 4.5% of all the MPLBs involve source files written in 2 and 3 PLs, respectively; (3) the change complexity resolution characteristics of MPLBs tend to be higher than those of single-programming-language bugs (SPLBs); (4) the open time for MPLBs is 19.52% to 529.57% significantly longer than SPLBs regarding 9 PL combinations; (5) the reopen rate of bugs involving the PL combination of JavaScript and Python reaches 20.66%; (6) we found 6 causes why the bug resolution involves multiple PLs and identified 5 cross-language calling mechanisms. Conclusion: MPLBs are related to increased development difficulty.
期刊：
JOURNAL OF SUPERCOMPUTING,2023年79(12):13724-13743 ISSN：0920-8542
通讯作者：
Xiang Li
作者机构：
[Wu, Fei; Li, Xiang; Zhang, Maoyuan] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Hubei, Peoples R China.;[Wu, Fei; Li, Xiang; Zhang, Maoyuan] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Wu, Fei; Li, Xiang; Zhang, Maoyuan] Cent China Normal Univ, Natl Language Resources Monitor & Res Ctr Network, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Xiang Li] H;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China<&wdkj&>School of Computer, Central China Normal University, Wuhan, China<&wdkj&>National Language Resources Monitor and Research Center for Network Media, Central China Normal University, Wuhan, China
摘要：
Cross-domain sentiment analysis (CDSA) aims to overcome domain discrepancy to judge the sentiment polarity of the target domain lacking labeled data. Recent research has focused on using domain adaptation approaches to address such domain migration problems. Among them, adversarial learning performs domain distribution alignment via domain confusion to transfer domain-invariant knowledge. However, this method that transforms feature representations to be domain-invariant tends to align only the marginal distribution, and may inevitably distort the original feature representations containing discriminative knowledge, thus making the conditional distribution inconsistent. To alleviate this problem, we propose adversarial domain adaptation with model-oriented knowledge adaptation (Moka-ADA) for the CDSA task. We adopt the adversarial discriminative domain adaptation (ADDA) framework to learn domain-invariant knowledge for marginal distribution alignment, based on which knowledge adaptation is conducted between the source and target models for conditional distribution alignment. Specifically, we design a dual structure with similarity constraints on intermediate feature representations and final classification probabilities, so that the target model in training learns discriminative knowledge from the trained source model. Experimental results on a publicly available sentiment analysis dataset show that our method achieves new state-of-the-art performance.
期刊：
Pervasive and Mobile Computing,2023年95:101843 ISSN：1574-1192
通讯作者：
Guo, YJ
作者机构：
[Guo, Yajun; Yang, Huan] Cent China Normal Univ, Sch Comp, Luoyu Rd 152, Wuhan 430079, Peoples R China.;[Guo, Yimin] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, South Lake Ave 182, Wuhan 430073, Peoples R China.
通讯机构：
[Guo, YJ ] C;Cent China Normal Univ, Sch Comp, Luoyu Rd 152, Wuhan 430079, Peoples R China.
关键词：
Authentication;Security;Fog computing;Physical unclonable functions;Smart home
摘要：
With the rise of Internet of Things (IoT), the smart home is another emerging concept and application of IoT, where security and private data of devices are important. In this paper, fog computing is applied to the smart home environment, where fog can provide many smart features and services to the smart home. Fog computing has many advantages, such as low latency and real-time interaction. However, when fog computing is combined with smart home, it also faces some security threats: first, some fog nodes and smart home devices are deployed in public places, vulnerable to damage or theft by attackers, not considered fully trusted, and vulnerable to device loss/theft attacks, impersonation attacks, and message tampering attacks, etc. These threats can lead to adversaries controlling devices in the smart home or modifying messages to make smart home devices execute wrong commands, causing irreparable damage; Second, the smart home system should have good real-time interaction, and the authentication process using the low latency feature of fog computing should not be involved by the cloud. Considering these, it is necessary to design a secure and effective fog-enabled smart home authentication system that is secure against various known attacks, especially when the fog node is not fully trusted or the smart home device is captured as well. Finally, the authentication scheme should also be lightweight due to the limited resources of many smart home devices. To address these issues, this paper proposes a lightweight authentication scheme for the fog-enabled smart home system. It also employs a physical unclonable function to achieve mutual authentication among three parties: smart home devices, fog nodes and users. Formal security analysis under the Real-Or-Random model shows that this scheme is provably secure. And informal security analysis shows that our scheme is robust against various known attacks. At the same time, the proposed scheme requires less computation cost (8.239 ms) and is approximately 40% to 390% faster than existing related schemes. Although the communication cost is slightly higher (4512 bits), it is reasonable because the proposed scheme implements fog/gateway node compromised attack that has not been achieved by any other existing related schemes.
期刊：
Peer-to-Peer Networking and Applications,2023年16(3):1340-1353 ISSN：1936-6442
通讯作者：
Wu, AML
作者机构：
[Wu, AML; Wu, Anmulin; Guo, Yajun] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;[Guo, Yimin] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan 430073, Peoples R China.
通讯机构：
[Wu, AML ] C;Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
关键词：
Internet of Vehicles;Blockchain;Mobile edge computing;Authentication mechanism;Privacy protection
摘要：
Blockchain technology can provide excellent support for identity authentication and access control mechanisms. In particular, blockchain technology can ensure that large amounts of confidential data generated by the Internet of Vehicles devices are stored and transmitted in a safe and reliable environment, which is the key to making system services optimal. In addition, mobile edge computing is the best solution for IoV applications to deal with low latency and limited computing and storage capacity of vehicle-mounted devices. Mobile edge computing can help IoV systems achieve a variety of functions and features, the most important of which is the ability to process terminal data in real-time. Even though the amount of data generated by IoV devices is growing rapidly, the system is still characterized by low latency and high efficiency. Because the communication between IoV devices is carried out in an untrusted environment, it is particularly important to design a secure and effective identity authentication scheme. Therefore, this paper proposes an efficient, safe, and time-sensitive authentication mechanism for devices on the Internet of Vehicles, which applies to a large number of scenarios. The mechanism is based on the blockchain concept and mobile edge computing technology. Security analysis shows that the proposed scheme meets the security requirements of the Internet of Vehicles and is resistant to many known attacks. By comparing with existing advanced IoT authentication schemes, the performance evaluation of the mechanism shows that the scheme enhances security features while reducing computation and communication overhead.
作者机构：
[Guo, Yajun; Duan, Xinrui] Cent China Normal Univ, Sch Comp Sci, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;[Guo, Yimin] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, South Lake Ave, Wuhan 430073, Hubei, Peoples R China.
通讯机构：
[Guo, YJ ] C;Cent China Normal Univ, Sch Comp Sci, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
关键词：
Authentication protocol;Vehicular ad hoc networks;Blockchain;Physically unclonable function;Vehicular fog services;Conditional anonymity
摘要：
With the advances in smart vehicles and fog computing, Fog computing is extended to traditional Vehicular Ad Hoc Networks (VANETs). As a geographically distributed paradigm, Vehicle Fog Service (VFS) overcomes the limitations of VANETs in real-time response and location awareness. It supports a wide range of traffic information services, such as road warnings, congestion control, and autonomous driving. Secure communication between VFS entities is a critical problem in an open network. Meanwhile, most fog nodes are deployed in the public domain and are vulnerable to physical attacks. This paper proposes a secure authentication scheme for VFS to address the above issues. The scheme combines blockchain and physical unclonable function (PUF) to achieve two-way authentication of on-board units (OBU) and road side units (RSU) with the untrusted fog nodes. Our scheme provides conditional anonymity and non-repudiation, offering recourse in case of malicious behavior. Unlike other schemes, the proposed scheme only needs to determine whether the pseudo-identity has a revocation tag instead of scanning the whole certificate revocation list (CLS), significantly reducing the computational overhead. In addition, we use the Real-Or-Random ROR model and formally prove that the proposed scheme is provably secure, and informal security analysis shows that the scheme is robust to various known attacks. Finally, compared with existing schemes, our scheme maintains lower communication and computation costs and provides more security features, which shows that our scheme is more suitable for secure VFS environments.
作者机构：
[Peng, Pai; Li, Le; Chen, Qiyuan] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.;[Yang, Haitong] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.;[Li, Le] Hubei Key Lab Math Sci, Wuhan 430072, Peoples R China.
通讯机构：
[Li, L.] C;Central China Normal University, China
作者机构：
[Hu, Zhanxuan; Ning, Hailong] Xian Univ Posts & Telecommun, Sch Comp Sci & Technol, Shaanxi Key Lab Network Data Anal & Intelligent Pr, Xian, Peoples R China.;[Hu, Zhanxuan; Ning, Hailong; An, Mengyuan] Xian Key Lab Big Data & Intelligent Comp, Xian, Peoples R China.;[Lei, Tao] Shaanxi Univ Sci & Technol, Sch Elect Informat & Artificial Intelligence, Xian, Peoples R China.;[Sun, Hao] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Nandi, Asoke K.] Brunel Univ London, Dept Elect & Elect Engn, London, England.
通讯机构：
[Tao Lei; Tao Lei Tao Lei Tao Lei] S;School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an, China
关键词：
deep learning;image analysis;image classification;information fusion
摘要：
Aerial scene recognition (ASR) has attracted great attention due to its increasingly essential applications. Most of the ASR methods adopt the multi-scale architecture because both global and local features play great roles in ASR. However, the existing multi-scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features, leading to a limited ability to deal with challenges of large-scale variation and complex background in aerial scene images. In addition, existing methods may suffer from poor generalisations due to millions of to-be-learnt parameters and inconsistent predictions between global and local features. To tackle these problems, this study proposes a scale-wise interaction fusion and knowledge distillation (SIF-KD) network for learning robust and discriminative features with scale-invariance and background-independent information. The main highlights of this study include two aspects. On the one hand, a global-local features collaborative learning scheme is devised for extracting scale-invariance features so as to tackle the large-scale variation problem in aerial scene images. Specifically, a plug-and-play multi-scale context attention fusion module is proposed for collaboratively fusing the context information between global and local features. On the other hand, a scale-wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during training. Comprehensive experimental results show the proposed SIF-KD network achieves the best overall accuracy with 99.68%, 98.74% and 95.47% on the UCM, AID and NWPU-RESISC45 datasets, respectively, compared with state of the arts.
期刊：
BRIEFINGS IN BIOINFORMATICS,2023年24(2) ISSN：1467-5463
通讯作者：
Weizhong Zhao
作者机构：
[Shen, Xianjun; Zhao, Weizhong; Yuan, Xueling; He, Tingting; Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Shi, Chuan] Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
通讯机构：
[Weizhong Zhao] H;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University , Wuhan, Hubei 430079, P R China<&wdkj&>School of Computer Science, Beijing University of Posts and Telecommunications , Beijing, 100876, P R China<&wdkj&>National Language Resources Monitoring & Research Center for Network Media, Central China Normal University , Wuhan, Hubei 430079, P R China
关键词：
drug–drug interaction;heterogeneous information network;meta-path-based information fusion
摘要：
Drug-drug interactions (DDIs) are compound effects when patients take two or more drugs at the same time, which may weaken the efficacy of drugs or cause unexpected side effects. Thus, accurately predicting DDIs is of great significance for the drug development and the drug safety surveillance. Although many methods have been proposed for the task, the biological knowledge related to DDIs is not fully utilized and the complex semantics among drug-related biological entities are not effectively captured in existing methods, leading to suboptimal performance. Moreover, the lack of interpretability for the predicted results also limits the wide application of existing methods for DDIs prediction. In this study, we propose a novel framework for predicting DDIs with interpretability. Specifically, we construct a heterogeneous information network (HIN) by explicitly utilizing the biological knowledge related to the procedure of inducing DDIs. To capture the complex semantics in HIN, a meta-path-based information fusion mechanism is proposed to learn high-quality representations of drugs. In addition, an attention mechanism is designed to combine semantic information obtained from meta-paths with different lengths to obtain final representations of drugs for DDIs prediction. Comprehensive experiments are conducted on 2410 approved drugs, and the results of predictive performance comparison show that our proposed framework outperforms selected representative baselines on the task of DDIs prediction. The results of ablation study and cold-start scenario indicate that the meta-path-based information fusion mechanism red is beneficial for capturing the complex semantics among drug-related biological entities. Moreover, the results of case study demonstrate that the designed attention mechanism is able to provide partial interpretability for the predicted DDIs. Therefore, the proposed method will be a feasible solution to the task of predicting DDIs.
摘要：
The development of decision-making systems based on artificial intelligence can lead to achieving optimal solutions water-land-food nexus. In this paper, an extreme learning machine model was developed with the objective function of wheat production maximization. The constraints defined for this problem are divided into three categories: technical parameters of production in agriculture, climatic stress on water resources and land limits. The water, land and food nexus was simulated using 23 experimental farms in Henan province during the 2021–2022 cultivation year. Root-mean-square error was used as an error criterion, and Pearson's coefficient was incorporated into the decision-making system as a correlation index of variables. Harvest index, length of the growth period, cultivation costs and irrigation water were the criteria to evaluate the impact of the sustainable model. The harvest index and the length of the growth period showed the highest and lowest correlation with the production rate, respectively. Furthermore, the optimal management of irrigation water and cost had the most significant impact on increasing crop production. The method proposed in this paper can be a virtual cropping model by changing the area under cultivation of a crop in the different farms of a study area, which increases yield production.
期刊：
IEEE Transactions on Geoscience and Remote Sensing,2023年61:1-11 ISSN：0196-2892
通讯作者：
Fu, LH
作者机构：
[Fu, Lihua; Chen, Xingrong; Xu, Yuejiao; Niu, Xiao] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.;[Zhang, Meng] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Fu, LH ] C;China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China.
关键词：
Tensors;Three-dimensional displays;Matrix decomposition;Correlation;Singular value decomposition;Frequency-domain analysis;Spectral analysis;3-D seismic data reconstruction;fully connected tensor network (FCTN);Hankel tensor;low rank
摘要：
Rank-reduction approaches assume that seismic data in the frequency–space domain is of low-rank after a specific pretransformation. The presence of noise or missing traces will increase the rank; therefore, seismic data can be denoised and recovered via rank-reduction techniques. The iterative weighted projection onto convex sets (POCS) framework can be used for noise attenuation and data reconstruction simultaneously. Multichannel singular spectrum analysis (MSSA) is a classic 3-D seismic data reconstruction algorithm that rearranges the temporal frequency slices of the data with missing traces into a block Hankel matrix and then uses randomized singular value decomposition (RSVD) to interpolate slices. To further improve the efficiency and precision of 3-D seismic data reconstruction, we introduce the fully connected tensor network (FCTN) decomposition over the Hankel tensor of the frequency slices. We show that our novel rank-reduction method estimates fewer parameters than MSSA, yielding more accurate and robust results. Moreover, FCTN decomposes a fourth-order tensor into four factor contractions, which breaks the limitations that traditional tensor decomposition methods, such as CANDECOMP/PARAFAC (CP) and Tucker decomposition, cannot establish the connections between different factors and are less effective at characterizing relationships. The newly proposed approach does not require singular value decomposition (SVD), leading to an overall reduction in computational complexity. Synthetic and field examples are used to compare the performance of our method with MSSA, and our numerical results reveal the better performance of the proposed FCTN decomposition method for seismic data with large gaps or a high missing ratio.
作者机构：
[Zhou, Jin] Cent China Normal Univ CCNU, Sch Educ Informat Technol, Wuhan, Peoples R China.;[Ye, Jun-min] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.
通讯机构：
[Zhou, Jin] C;Cent China Normal Univ CCNU, Sch Educ Informat Technol, Wuhan, Peoples R China.
摘要：
延迟容忍网络(delay tolerant network,DTN)中,由于节点的移动性、休眠调度、资源受限以及网络误码率较高等因素,消息成功投递的可能性较低.为了解决上述DTN中存在的问题,有大量的路由算法被提出,其中...展开更多 延迟容忍网络(delay tolerant network,DTN)中,由于节点的移动性、休眠调度、资源受限以及网络误码率较高等因素,消息成功投递的可能性较低.为了解决上述DTN中存在的问题,有大量的路由算法被提出,其中Prophet路由算法作为DTN中重要的路由之一,主要思想是根据DTN中节点的相遇频率进行节点之间消息的传递,但该算法没有考虑节点的交互意愿和中继节点的缓存大小.针对这一问题,本文提出一种基于连接分离时间的概率路由算法P-AVF(Prophet routing based on Average fluctuation).该算法主要根据节点在时间窗口T内的连接分离时间以及节点间运动轨迹的差异性来定义节点连接的平均波动,进而引出节点连接紧密性与可靠性的概念,使得连接能力更优异的节点与它相遇过的节点保持更大的投递预测值,从而能综合挑选出合适转发消息的中继节点.同时利用消息接收节点的缓存占用比和该节点与其他节点连接分离的总时间作为影响该节点投递预测值衰减的一部分,使得投递预测值的衰减更准确.仿真结果表明,基于连接分离时间的概率路由算法P-AVF在消息的投递率、网络负载率和平均跳数等方面均优于其他对比路由算法.收起
作者：
Yao, Shixiong;Tian, Xingjian;Chen, Jiageng*;Xiong, Yi
期刊：
International Journal of Network Management,2023年33(3):e2193- ISSN：1055-7148
通讯作者：
Chen, Jiageng
作者机构：
[Xiong, Yi; Yao, Shixiong; Chen, Jiageng] Cent China Normal Univ, Comp Sch, 152 Luoyu Rd, Wuhan, Hubei, Peoples R China.;[Yao, Shixiong] Wuhan Univ, Key Lab Aerosp Informat Secur & Trust Comp, Minist Educ, Wuhan, Peoples R China.;[Tian, Xingjian] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan, Peoples R China.
通讯机构：
[Chen, Jiageng] C;Cent China Normal Univ, Comp Sch, 152 Luoyu Rd, Wuhan, Hubei, Peoples R China.
摘要：
Smart grid has drawn a lot of attention and investment in recent years, which not only helps the modern generation and distribution of traditional power but also highly widens the application of renewable energy sources. However, the main challenges in the application of smart grid are 1. the privacy preservation of users' information and 2. the trustful transmission channel among peers. In order to solve these problems, VPN and blockchain can be considered since they have some features perfectly suitable for these situations. In this paper, we propose a smart grid system based on WireGuard and Hyperledger Fabric to solve the problems mentioned above. And we also implement the whole system and give a view by web application. What's more, all the functionalities are displayed and tested, including building a smart device simulator, deploying data visualization and making some performance evaluations about transactions and WireGuard communication. Experiment results show that the introduction of WireGuard into network infrastructure does not cause too much loss of bandwidth and delay, but it ensures a certain degree of communication security. And Fabric provides the consistency and traceability of transactions in smart grid system.
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.
