作者机构：
[Liao, Kaibo; Liao, KB; Peng, Xi] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;[Peng, Xi] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan, Hubei, Peoples R China.
通讯机构：
[Liao, KB ] C;Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
摘要：
Underwater images are often scattered due to suspended particles in the water, resulting in light scattering and blocking and reduced visibility and contrast. Color shifts and distortions are also caused by the absorption of different wavelengths of light in the water. This series of problems will make the underwater image quality greatly impaired, resulting in some advanced visual work can not be carried out underwater. In order to solve these problems, this paper proposes an underwater image enhancement method based on multi-task fusion, called MTF. Specifically, we first use linear constraints on the input image to achieve color correction based on the gray world assumption. The corrected image is then used to achieve visibility enhancement using an improved type-II fuzzy set-based algorithm, while the image is contrast enhanced using standard normal distribution probability density function and softplus function. However, in order to obtain more qualitative results, we propose multi-task fusion, in which we solve for similarity, then we obtain fusion weights that guarantee the best features of the image as much as possible from the obtained similarity, and finally we fuse the image with the weights to obtain the output image, and we find that multi-task fusion has excellent image enhancement and restoration capabilities, and also produces visually pleasing results. Extensive qualitative and quantitative evaluations show that MTF method achieves optimal results compared to ten state-of-the-art underwater enhancement algorithms on 2 datasets. Moreover, the method can achieve better results in application tests such as target detection and edge detection.
摘要：
Identification of potential human-virus protein-protein interactions (PPIs) contributes to the understanding of the mechanisms of viral infection and to the development of antiviral drugs. Existing computational models often have more hyperparameters that need to be adjusted manually, which limits their computational efficiency and generalization ability. Based on this, this study proposes a kernel Bayesian logistic matrix decomposition model with automatic rank determination, VKBNMF, for the prediction of human-virus PPIs. VKBNMF introduces auxiliary information into the logistic matrix decomposition and sets the prior probabilities of the latent variables to build a Bayesian framework for automatic parameter search. In addition, we construct the variational inference framework of VKBNMF to ensure the solution efficiency. The experimental results show that for the scenarios of paired PPIs, VKBNMF achieves an average AUPR of 0.9101, 0.9316, 0.8727, and 0.9517 on the four benchmark datasets, respectively, and for the scenarios of new human (viral) proteins, VKBNMF still achieves a higher hit rate. The case study also further demonstrated that VKBNMF can be used as an effective tool for the prediction of human-virus PPIs.
作者机构：
[Hua, Shiqi; Sun, Hao; Jin, Lianghao; Xie, Wei] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan, Peoples R China.;[Hua, Shiqi; Jin, Lianghao; Xie, Wei; Sun, Hao] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.;[Hua, Shiqi; Jin, Lianghao; Xie, Wei; Sun, Hao] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan, Peoples R China.;[Sun, B; Sun, Bo] Dalian Med Univ, Affiliated Hosp 1, Dalian, Peoples R China.;[Tu, Zhigang] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan, Peoples R China.
通讯机构：
[Sun, B ] D;[Sun, H ] C;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan, Peoples R China.;Dalian Med Univ, Affiliated Hosp 1, Dalian, Peoples R China.
关键词：
image segmentation;medical image processing
摘要：
Abstract Subarachnoid haemorrhage (SAH), mostly caused by the rupture of intracranial aneurysm, is a common disease with a high fatality rate. SAH lesions are generally diffusely distributed, showing a variety of scales with irregular edges. The complex characteristics of lesions make SAH segmentation a challenging task. To cope with these difficulties, a u‐shaped deformable transformer (UDT) is proposed for SAH segmentation. Specifically, first, a multi‐scale deformable attention (MSDA) module is exploited to model the diffuseness and scale‐variant characteristics of SAH lesions, where the MSDA module can fuse features in different scales and adjust the attention field of each element dynamically to generate discriminative multi‐scale features. Second, the cross deformable attention‐based skip connection (CDASC) module is designed to model the irregular edge characteristic of SAH lesions, where the CDASC module can utilise the spatial details from encoder features to refine the spatial information of decoder features. Third, the MSDA and CDASC modules are embedded into the backbone Res‐UNet to construct the proposed UDT. Extensive experiments are conducted on the self‐built SAH‐CT dataset and two public medical datasets (GlaS and MoNuSeg). Experimental results show that the presented UDT achieves the state‐of‐the‐art performance.
期刊：
Pervasive and Mobile Computing,2024年98:101877 ISSN：1574-1192
通讯作者：
Guo, YM
作者机构：
[Guo, Yimin; Zhang, Chengde; Guo, YM; Yang, Fan; Xiong, Ping] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan, Peoples R China.;[Guo, Yajun] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.
通讯机构：
[Guo, YM ] Z;Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan, Peoples R China.
关键词：
Tactile Internet;Authentication;Key agreement;Security;Industrial Internet of Things
摘要：
In the Industrial Internet of Things (IIoT), haptic control of machines or robots can be managed remotely. However, with the emergence of Tactile Industrial Internet of Things (TIIoT), the transmission of haptic data over public channels has raised security and privacy concerns. In such an environment, mutual authentication between haptic users and remotely controlled entities is crucial to prevent illegal control by adversaries. Therefore, we propose an endto -end authentication scheme, SecTIIoT, to establish secure communication between haptic users and remote IoT devices. The scheme addresses security issues by using lightweight hash cryptographic primitives and employs a useful piggyback strategy to improve authentication efficiency. We demonstrate that SecTIIoT is resilient to various known attacks with formal security proofs and informal security analysis. Furthermore, our detailed performance analysis shows that SecTIIoT outperforms existing lightweight authentication schemes as it provides more security features while reducing computation and communication costs.
摘要：
Abstract Event detection plays an essential role in the task of event extraction. It aims at identifying event trigger words in a sentence and classifying event types. Generally, multiple event types are usually well‐organized with a hierarchical structure in real‐world scenarios, and hierarchical correlations between event types can be used to enhance event detection performance. However, such kind of hierarchical information has received insufficient attention which can lead to misclassification between multiple event types. In addition, the most existing methods perform event detection in Euclidean space, which cannot adequately represent hierarchical relationships. To address these issues, we propose a novel event detection network HyperED which embeds the event context and types in Poincaré ball of hyperbolic geometry to help learn hierarchical features between events. Specifically, for the event detection context, we first leverage the pre‐trained BERT or BiLSTM in Euclidean space to learn the semantic features of ED sentences. Meanwhile, to make full use of the dependency knowledge, a GNN‐based model is applied when encoding event types to learn the correlations between events. Then we use a simple neural‐based transformation to project the embeddings into the Poincaré ball to capture hierarchical features, and a distance score in hyperbolic space is computed for prediction. The experiments on MAVEN and ACE 2005 datasets indicate the effectiveness of the HyperED model and prove the natural advantages of hyperbolic spaces in expressing hierarchies in an intuitive way.
作者机构：
[Zhao, Weizhong; He, Tingting; Jiang, Xingpeng; Wu, Junze] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, P.R. China;[Zhao, Weizhong] School of Computer, Central China Normal University, Wuhan, Hubei 430079, P.R. China;[Zhao, Weizhong] National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, P.R. China;[Hu, Xiaohua] College of Computing & Informatics, Drexel University, Philadelphia, PA 19104, United States
通讯机构：
[Weizhong Zhao; Xingpeng Jiang] H;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University , Wuhan, Hubei 430079, P.R. China<&wdkj&>Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University , Wuhan, Hubei 430079, P.R. China<&wdkj&>School of Computer, Central China Normal University , Wuhan, Hubei 430079, P.R. China<&wdkj&>National Language Resources Monitoring & Research Center for Network Media, Central China Normal University , Wuhan, Hubei 430079, P.R. China
摘要：
MOTIVATION: The crisis of antibiotic resistance, which causes antibiotics used to treat bacterial infections to become less effective, has emerged as one of the foremost challenges to public health. Identifying the properties of antibiotic resistance genes (ARGs) is an essential way to mitigate this issue. Although numerous methods have been proposed for this task, most of these approaches concentrate solely on predicting antibiotic class, disregarding other important properties of ARGs. In addition, existing methods for simultaneously predicting multiple properties of ARGs fail to account for the causal relationships among these properties, limiting the predictive performance. RESULTS: In this study, we propose a causality-guided framework for annotating properties of ARGs, in which causal inference is utilized for representation learning. More specifically, the hidden biological patterns determining the properties of ARGs are described by a Gaussian Mixture Model, and procedure of causal representation learning is used to derive the hidden features. In addition, a causal graph among different properties is constructed to capture the causal relationships among properties of ARGs, which is integrated into the task of annotating properties of ARGs. The experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework on the task of annotating properties of ARGs. AVAILABILITY AND IMPLEMENTATION: The data and source codes are available in GitHub at https://github.com/David-WZhao/CausalARG.
摘要：
Complex knowledge graph question answering (KGQA) aims at answering natural language questions by entities retrieving from a knowledge graph (KG). Recently, the relation path-based models have shown the unique advantage for complex KGQA. However, these existing models ignore the dependency between different relation paths, which leads to aimless reasoning over the KG. To resolve this issue, we propose the question-directed reasoning with relation-aware graph attention network (QRGAT) that encodes the reasoning process as a reasoning graph. The relation-aware GAT can recognize neighbor entities along with the corresponding relations for each entity. With the relation-aware GAT stacked in multiple layers, it can collaboratively capture the dependency of different relation paths for each entity. The question-directed reasoning utilizes the information learned by the relation-aware GAT to solve the aimless reasoning on the KG by constructing a reasoning graph. Extensive experiments demonstrate that our QRGAT outperforms the baseline models on both popular datasets WebQuestionsSP and ComplexWebQuestions. Compared with the strong GNN-based baseline NSM<inline-formula><tex-math notation="LaTeX">$_{+h}$</tex-math></inline-formula>, our QRGAT achieves the performance improvements of 2.3% on WebQuestionsSP and 3.6% on ComplexWebQuestions by the metric Hits@1. Complex knowledge graph question answering (KGQA) aims at answering natural language questions by entities retrieving from a knowledge graph (KG). Recently, the relation path-based models have shown the unique advantage for complex KGQA. However, these existing models ignore the dependency between different relation paths, which leads to aimless reasoning over the KG. To resolve this issue, we propose the question-directed reasoning with relation-aware graph attention network (QRGAT) that encodes the reasoning process as a reasoning graph. The relation-aware GAT can recognize neighbor entities along with the corresponding relations for each entity. With the relation-aware GAT stacked in multiple layers, it can collaboratively capture the dependency of different relation paths for each entity. The question-directed reasoning utilizes the information learned by the relation-aware GAT to solve the aimless reasoning on the KG by constructing a reasoning graph. Extensive experiments demonstrate that our QRGAT outperforms the baseline models on both popular datasets WebQuestionsSP and ComplexWebQuestions. Compared with the strong GNN-based baseline NSM<inline-formula><tex-math notation="LaTeX">$_{+h}$</tex-math></inline-formula>, our QRGAT achieves the performance improvements of 2.3% on WebQuestionsSP and 3.6% on ComplexWebQuestions by the metric Hits@1.
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.
摘要：
由于容迟网络的间歇性连接等特点，以及节点自身缓存、能量等资源受限的原因，DTN中的节点往往会表现出一定的自私性。自私节点的存在可能会提高网络的开销，降低消息的成功投递率。为了促进自私节点参与合作，提出了一种基于虚拟货币交易的高效率路由算法PVCT(Efficient Routing Protocol Based on Virtual Currency Transaction in DTN)，并结合容迟网络的小世界特性，以提高路由算法的效率。该算法利用虚拟货币交易的方式，并根据节点的基本属性、位置属性、社会属性等进行定价，节点根据设计的价格函数给出对应的报价，并利用价格函数合理地分配消息副本数。在PVCT策略中，节点根据判断情况分为正常节点和自私节点，当消息的跳数小于等于两跳时，按照概率路由的策略进行转发；反之，当消息的跳数大于两跳时，若遇到的为自私节点，则执行虚拟货币交易的路由算法。携带消息节点的出价若高于转发节点的价格，则进行交易，更新各自的收益状态；否则，进入二次价格调整阶段来协调节点双方之前的虚拟报价。仿真实验表明，PVCT路由算法在DTN中能更好地促进消息的转发，从而提高网络的整体性能。
关键词：
Session-based recommendationheterogeneous hypergraphshypergraph neural networksinformation lossadditional information
摘要：
In recent years, session-based recommendation (SBR), which seeks to predict the target user’s next click based on anonymous interaction sequences, has drawn increasing interest for its practicality. The key to completing the SBR task is modeling user intent accurately. Due to the popularity of graph neural networks (GNNs), most state-of-the-art (SOTA) SBR approaches attempt to model user intent from the transitions among items in a session with GNNs. Despite their accomplishments, there are still two limitations. First, most existing SBR approaches utilize limited information from short user–item interaction sequences and suffer from the data sparsity problem of session data. Second, most GNN-based SBR approaches describe pairwise relations between items while neglecting complex and high-order data relations. Although some recent studies based on hypergraph neural networks have been proposed to model complex and high-order relations, they usually output unsatisfactory results due to insufficient relation modeling and information loss. To this end, we propose a category-aware lossless heterogeneous hypergraph neural network (CLHHN) in this article to recommend possible items to the target users by leveraging the category of items. More specifically, we convert each category-aware session sequence with repeated user clicks into a lossless heterogeneous hypergraph consisting of item and category nodes as well as three types of hyperedges, each of which can capture specific relations to reflect various user intents. Then, we design an attention-based lossless hypergraph convolutional network to generate sessionwise and multi-granularity intent-aware item representations. Experiments on three real-world datasets indicate that CLHHN can outperform the SOTA models in making a better tradeoff between prediction performance and training efficiency. An ablation study also demonstrates the necessity of CLHHN’s key components. In recent years, session-based recommendation (SBR), which seeks to predict the target user’s next click based on anonymous interaction sequences, has drawn increasing interest for its practicality. The key to completing the SBR task is modeling user intent accurately. Due to the popularity of graph neural networks (GNNs), most state-of-the-art (SOTA) SBR approaches attempt to model user intent from the transitions among items in a session with GNNs. Despite their accomplishments, there are still two limitations. First, most existing SBR approaches utilize limited information from short user–item interaction sequences and suffer from the data sparsity problem of session data. Second, most GNN-based SBR approaches describe pairwise relations between items while neglecting complex and high-order data relations. Although some recent studies based on hypergraph neural networks have been proposed to model complex and high-order relations, they usually output unsatisfactory results due to insufficient relation modeling and information loss. To this end, we propose a category-aware lossless heterogeneous hypergraph neural network (CLHHN) in this article to recommend possible items to the target users by leveraging the category of items. More specifically, we convert each category-aware session sequence with repeated user clicks into a lossless heterogeneous hypergraph consisting of item and category nodes as well as three types of hyperedges, each of which can capture specific relations to reflect various user intents. Then, we design an attention-based lossless hypergraph convolutional network to generate sessionwise and multi-granularity intent-aware item representations. Experiments on three real-world datasets indicate that CLHHN can outperform the SOTA models in making a better tradeoff between prediction performance and training efficiency. An ablation study also demonstrates the necessity of CLHHN’s key components.
期刊：
ACM Transactions on Knowledge Discovery from Data,2024年18(1):1–22 ISSN：1556-4681
通讯作者：
Hu, P
作者机构：
[Wang, Huan] Huazhong Agr Univ, PKU Wuhan Inst Artificial Intelligence, Coll Informat, Wuhan 430070, Hubei, Peoples R China.;[Liu, Guoquan; Hu, Po; Hu, P] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Hu, P ] C;Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
关键词：
Transfer learning;link prediction;type-shared knowledge
摘要：
Link prediction has received increased attention in social network analysis. One of the unique challenges in heterogeneous social networks is link prediction in new link types without verified link information, such as recommending products to new overseas groups. Existing link prediction models tend to learn type-specific knowledge on specific link types and predict missing or future links on the same link types. However, because of the uncertainty of new link types in the evolving process of social networks, it is difficult to collect sufficient verified link information in new link types. Therefore, we propose the Transferable Domain Adversarial Network (TDAN) based on transfer learning to handle the challenge. TDAN exploits transferable type-shared knowledge in historical link types to help predict the unobserved links in new link types. TDAN mainly comprises a structural encoder, a domain discriminator, and an optimization decoder. The structural encoder learns the link representations in a heterogeneous social network. Subsequently, to learn transferable type-shared knowledge, the domain discriminator distinguishes link representations into different link types while minimizing the differences between type-specific knowledge in adversarial training. Inspired by the denoising auto-encoder, the optimization decoder reconstructs the learned type-shared knowledge to eliminate the noise generated during the adversarial training. Extensive experiments on Facebook and YouTube show that TDAN can outperform the state-of-the-art models.
期刊：
IEEE INTERNET OF THINGS JOURNAL,2024年11(2):3348-3361 ISSN：2327-4662
通讯作者：
Guo, YM
作者机构：
[Guo, Yimin; Guo, YM; Xiong, Ping] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan 430073, Peoples R China.;[Zhang, Zhenfeng] Chinese Acad Sci, Trusted Comp & Informat Assurance Lab, Inst Software, Beijing 100045, 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, Peoples R China.
关键词：
Authentication;blockchain;fog-enabled Internet of Things (IoT);key agreement;physical unclonable functions (PUFs)
摘要：
The insufficient trustworthiness of fog nodes in fog computing leads to new security and privacy problems in communication between entities. Existing authentication schemes rely on a trusted third party, or assume that fog nodes are trustworthy, or the authentication overhead is high, which is inconsistent with the characteristics of fog computing. To solve the problem of secure communication in the fog computing environment, we propose an efficient blockchain-based secure remote authentication protocol for the fog-enabled Internet of Things (BSRA). Specifically, blockchain is introduced to construct distributed trust for the fog computing environment. Only lightweight cryptographic primitives, such as physical unclonable functions (PUFs) and cryptographic hash functions, are exploited to design the authentication scheme. In addition, we use temporary identities and the authentication-piggybacking-synchronization to ensure the anonymity and effectiveness of the authentication scheme. We conduct security analysis to demonstrate that BSRA can provide guarantees against various known attacks. We also evaluate the performance of BSRA from several aspects, and the results show that BSRA is effective.
摘要：
Brain storm optimization (BSO) is a population-based intelligence algorithm for optimization problems, which has attracted researchers' growing attention due to its simplicity and efficiency. An improved BSO, called CIBSO, is presented in this article. First of all, a new grouping method, in which the population is partitioned into chunks according to the fitness and recombined to groups, is developed to balance each group with same quality-level. Afterwards, a new mutation strategy is designed in CIBSO and a learning mechanism is used to adaptively select appropriate strategy. Experiments on the CEC2014 test suite indicate that CIBSO is better or at least competitive performance against the compared BSO variants.
摘要：
Temporal knowledge graph (TKG) reasoning aims to infer the missing links from the massive historical facts. One of the big issues is that how to model the entity evolution from both the local and especially global perspectives. The primary temporal dependency models often fail to disentangle both perspectives due to the lack explicit annotations to distinguish the boundary of these two representations. To address these limitations, we propose a contrastive learning framework to Disentangle Local and Global perspectives for TKG Reasoning with selfsupervision framework (DLGR). Our proposed DLGR can jointly utilize the local and global perspectives on two separate graphs and disentangle them in a self -supervised manner. Firstly, we construct a temporal subgraph and a temporal unified graph to effectively learn the local and global perspective representations, respectively. Second, we extract proxies regarding the different neighbors as pseudo labels to supervise the local and global disentanglement in a contrastive manner. Finally, we adaptively fuse the learned two perspective representations for TKG reasoning. The empirical results show that our DLGR significantly outperforms other baselines (e.g., compared to the strong baseline HGLS, our DLGR achieves 4.3%, 3.4%, 1.6% and 1.1% improvements on ICEWS14, ICEWS18, YAGO and WIKI using MRR).
作者机构：
[Zhong X.] South China University of Technology, Shien-Ming Wu School of Intelligent Engineering, Guangzhou, 510640, China;[Lu, Tao] Wuhan Institute of Technology, Hubei Key Laboratory of Intelligent Robot, Wuhan, 430073, China;[Zhong, Rui; Zhong, Xiaoda] Central China Normal University, School of Computer Science, Wuhan, 430079, China
通讯机构：
[Xiao, D.] C;Central China Normal University, China
关键词：
3D CNNs;compression;Lenslet image;reinforcement learning;VVC
摘要：
Nowadays, with continuous integration of big data, artificial intelligence and cloud computing technologies, there are increasing demands and specific requirements for data sharing in sustainable smart cities: (1) practical data sharing should be implemented in the non-interactive fashion without a trusted third party to be involved; (2) dynamic thresholds are preferred since the participants may join or leave at any time; (3) multi-secret sharing is desirable to increase the packing capacity. To fulfil these requirements, we propose a general construction of ideal threshold changeable multi-secret sharing scheme (TCMSS) with information-theoretic security, in which polynomials are employed to achieve dealer-free and non-interactive in the secret reconstruction phase. The TCMSS scheme can be built on any existing linear secret sharing scheme, and it is simpler and more efficient than the existing TCSS schemes in the literature. The main difference between TCMSS and Shamir's SS is that univariate polynomial is used in Shamir's SS to generate the shares for all shareholders; while in TCMSS, each shareholder can recover her own univariate polynomial using her share. This article demonstrates that with this novel modification, the classic polynomial-based SS can be transformed into an ideal TCMSS. Moreover, the TCMSS scheme is lightweight and it can resist both internal and external attacks. It does not require pairwise key distribution and its secret reconstruction phase is improved with enhanced properties. Therefore, the designed proposal is fairly suitable and attractive to be deployed in sustainable cities.
期刊：
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2023年20:1-5 ISSN：1545-598X
通讯作者：
Yu, J
作者机构：
[Sun, Hao] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Sch Comp, Wuhan 430079, Peoples R China.;[Sun, Hao] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Peoples R China.;[Li, Qianqian; Zhou, Dongbo] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.;[Yu, Jie] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China.;[Yu, Jie] Wuhan Univ, Off Sci & Technol Dev, Wuhan 430072, Peoples R China.
通讯机构：
[Yu, J ] W;Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China.
关键词：
Deep learning;feature reconstruction;open-set classification;remote-sensing imagery
摘要：
Existing remote-sensing scene image (RSSI) classification methods usually rely on the static closed-set assumption that testing samples do not belong to unknown classes. However, practical applications are usually the open-set classification problem, which means that RSSIs from unknown classes will appear in the testing set. Most existing methods are prone to forcibly misclassify RSSIs of unknown classes into known classes, resulting in poor practical performance. In this letter, a deep feature reconstruction learning (DFRL) framework is proposed for the open-set classification of RSSIs (OSC-RSSIs). The proposed DFRL unifies discriminative feature learning and feature reconstruction into an end-to-end network. First, a feature extraction module is utilized to project raw input data from the image space to the feature space to extract deep features. Then, the deep features are fed to a deep feature reconstruction module for distinguishing known and unknown classes based on feature-level reconstruction errors. The feature-level reconstruction can effectively suppress the interference of complex backgrounds. In addition, a sparse regularization is introduced to improve the discrimination of image representation. Experiments on three RSSI datasets demonstrate the effectiveness of DFRL for OSC-RSSIs.
