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.
期刊:
ACM Transactions on Knowledge Discovery from Data,2024年18(1):1–22 ISSN:1556-4681
作者机构:
[Guoquan Liu; Po Hu] School of Computer Science, Central China Normal University, China;[Huan Wang] College of Informatics, Huazhong Agricultural University, PKU-Wuhan Institute for Artificial Intelligence, China
关键词:
Transfer learning;type-shared knowledge;link prediction
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.
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
作者机构:
[Chen, Renyi; Yao, Huaxiong] Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.;[Yao, Huaxiong] Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.
通讯机构:
[Yao, HX ] ;Cent China Normal Univ, Comp Sch, Wuhan 430079, Peoples R China.
摘要:
Abstract: Obtaining accurate road conditions is crucial for traffic management, dynamic route planning, and intelligent guidance services. The complex spatial correlation and nonlinear temporal dependence pose great challenges to obtaining accurate road conditions. Existing graph-based methods use a static adjacency matrix or a dynamic adjacency matrix to aggregate spatial information between nodes, which cannot fully represent the topological information. In this paper, we propose a Hybrid Graph Model (HGM) for accurate traffic prediction. The HGM constructs a static graph and a dynamic graph to represent the topological information of the traffic network, which is beneficial for mining potential and obvious spatial correlations. The proposed method combines a graph neural network, convolutional neural network, and attention mechanism to jointly extract complex spatial–temporal features. The HGM consists of two different sub-modules, called spatial–temporal attention module and dynamic graph convolutional network, to fuse complex spatial–temporal information. Furthermore, the proposed method designs a novel gated function to adaptively fuse the results from spatial–temporal attention and dynamic graph convolutional network to improve prediction performance. Extensive experiments on two real datasets show that the HGM outperforms comparable state-of-the-art methods. Keywords: traffic prediction; graph neural network; attention
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.
摘要:
A detailed theoretical study is conducted on the nonlinear interference in the same-wavelength bidirectional coherent optical fiber communication systems. The Gaussian noise (GN) model used to evaluate nonlinear interference (NLI) in unidirectional systems is applied and extended to bidirectional transmission scenarios. The extended NLI model shows that in a bidirectional transmission communication system, the backward signal almost does not introduce additional nonlinear crosstalk to the forward signal due to the strong walk-off effect between forward and backward transmitted signals. Specifically, the ratio of the nonlinear crosstalk introduced by the forward and backward signals is about 21 dB, which means that the traditional GN model is also applicable in the bidirectional scenario. This conclusion is validated on the platform of a same-wavelength bidirectional coherent optical communication system based on Optisystem software.
期刊:
IEEE Journal of Biomedical and Health Informatics,2023年PP:1-12 ISSN:2168-2194
作者机构:
[Xueli Pan; Frank van Harmelen] Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China;National Language Resources Monitor Research Center for Network Media, Central China Normal University, Wuhan, China;School of Computer Science, Central China Normal University, Wuhan, China
摘要:
It is commonly known that food nutrition is closely related to human health. The complex interactions between food nutrients and diseases, influenced by gut microbial metabolism, present challenges in systematizing and practically applying knowledge. To address this, we propose a method for extracting triples from a vast amount of literature, which is used to construct a comprehensive knowledge graph on nutrition and human health. Concurrently, we develop a query-based question answering system over our knowledge graph, proficiently addressing three types of questions. The results show that our proposed model outperforms other state-of-art methods, achieving a precision of 0.92, a recall of 0.81, and an F1 score of 0.86in the nutrition and disease relation extraction task. Meanwhile, our question answering system achieves an accuracy of 0.68 and an F1 score of 0.61 on our benchmark dataset, showcasing competitiveness in practical scenarios. Furthermore, we design five independent experiments to assess the quality of the data structure in the knowledge graph, ensuring results characterized by high accuracy and interpretability. In conclusion, the construction of our knowledge graph shows significant promise in facilitating diet recommendations, enhancing patient care applications, and informing decision-making in clinical research.
摘要:
With the development of information networks, the entities from different network domains interact with each other more and more frequently. Therefore, identity management and authentication are essential in cross-domain setting. The traditional Public Key Infrastructure (PKI) architecture has some problems, including single point of failure, inefficient certificate revocation status management and also lack of privacy protection, which cannot meet the demand of cross-domain identity authentication. Blockchain is suitable for multi-participant collaboration in multi-trust domain scenarios. In this paper, a cross-domain certificate management scheme CD-BCM based on the consortium blockchain is proposed. For the issue of Certificate Authority’s single point of failure, we design a multi-signature algorithm. In addition, we propose a unified structure for batch certificates verification and conversion, which improve the efficiency of erroneous certificate identification. Finally, by comparing with current related schemes, our scheme achieves good functionality and scalability in the scenario of cross-domain certificate management.
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:
IEEE Journal of Biomedical and Health Informatics,2023年27(6):3061-3071 ISSN:2168-2194
通讯作者:
Zhao, Weizhong;Shen, XJ
作者机构:
[Shen, Xianjun; Wang, Haodong; Wang, Yue; Zhao, Weizhong; Zhao, WZ; Shen, XJ; Jiang, Xingpeng; Li, Dandan] Cent China Normal Univ, Sch Comp, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China.;[Sun, Han] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.;[Shen, Xianjun; Wang, Haodong; Wang, Yue; Zhao, Weizhong; Zhao, WZ; Shen, XJ; Jiang, Xingpeng; Li, Dandan] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Peoples R China.
通讯机构:
[Zhao, WZ; Shen, XJ ] C;Cent China Normal Univ, Sch Comp, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Peoples R China.
关键词:
Phage-host interactions prediction;graph representation learning;multi-head attention mechanism;heterogeneous information network
摘要:
In the treatment of bacterial infectious diseases, overuse of antibiotics may lead to not only bacterial resistance to antibiotics but also dysbiosis of beneficial bacteria which are essential for maintaining normal human life activities. Instead, phage therapy, which invades and lyses specific pathogenic bacteria without affecting beneficial bacteria, becomes more and more popular to treat bacterial infectious diseases. For the effective phage therapy, it requires to accurately predict potential phage-host interactions from heterogeneous information network consisting of bacteria and phages. Although many models have been proposed for predicting phage-host interactions, most methods fail to consider fully the sparsity and unconnectedness of phage-host heterogeneous information network, deriving the undesirable performance on phage-host interactions prediction. To address the challenge, we propose an effective model called GERMAN-PHI for predicting Phage-Host Interactions via Graph Embedding Representation learning with Multi-head Attention mechaNism. In GERMAN-PHI, the multi-head attention mechanism is utilized to learn representations of phages and hosts from multiple perspectives of phage-host associations, addressing the sparsity and unconnectedness in phage-host heterogeneous information network. More specifically, a module of GAT with talking-heads is employed to learn representations of phages and bacteria, on which neural induction matrix completion is conducted to reconstruct the phage-host association matrix. Results of comprehensive experiments demonstrate that GERMAN-PHI performs better than the state-of-the-art methods on phage-host interactions prediction. In addition, results of case study for two high-risk human pathogens show that GERMAN-PHI can predict validated phages with high accuracy, and some potential or new associated phages are provided as well.
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:
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.