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:
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.
作者:
Zhenyu Lu;Zhou Zhao;Tianqi Yue;Xu Zhu;Ning Wang
期刊:
IEEE Transactions on Cognitive and Developmental Systems,2023年:1-1 ISSN:2379-8920
作者机构:
[Tianqi Yue; Xu Zhu] Department of Engineering Mathematics and Bristol Robotics Laboratory, University of Bristol, Bristol, UK;[Zhenyu Lu; Ning Wang] Faculty of Environment and Technology and Bristol Robotics Lab, University of the West of England, Bristol, UK;[Zhou Zhao] School of Computer Science, Central China Normal University, Wuhan, China
摘要:
This paper presents a new bio-inspired tactile sensor that is multi-functional and has different sensitivity contact areas. The TacTop area is sensitive and is used for object classification when there is a direct contact. On the other hand, the TacSide area is less sensitive and is used to localize the side contact areas. By connecting tendons from the TacSide area to the TacTop area, the sensor is able to perform multiple detection functions using the same expression region. For the mixed contacting signals collected from the expression region with numerous markers and pins, we build a modified DenseNet121 network which specifically removes all fully connected layers and keeps the rest as a sub-network. The proposed model also contains a global average pooling layer with two branching networks to handle different functions and provide accurate spatial translation of the extracted features. The experimental results demonstrate a high prediction accuracy of 98% for object perception and localization. Furthermore, the new tactile sensor is utilized for obstacle avoidance, where action skills are extracted from human demonstrations and then an action dataset is generated for reinforcement learning to guide robots towards correct responses after contact detection. To evaluate the effectiveness of the proposed framework, several simulations are performed in the MuJoCo environment.
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.