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FDSC-YOLOv8:Advancements in Automated Crack Identification for Enhanced Safety in Underground Engineering

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computer Modeling in Engineering & Sciences 2024年第9期140卷 3035-3049页

作者： Rui Wang Zhihui Liu Hongdi Liu Baozhong Su Chuanyi Ma Key Laboratory of Computing Power network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan250353China School of Qilu Transportation Shandong UniversityJinan250100China Geotechnical and Structural Engineering center Shandong UniversityJinan250061China Shandong Lairong High-Speed Railway Co. Ltd.Weihai264200China Shandong High-Speed Group Co. Ltd.Jinan250014China

In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel ***,the dim and dusty environment inherent to under-ground engineering poses considerable challenges to crack *** paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8(FDSC-YOLOv8)specifically designed for underground engineering structural ***,to improve the extraction of multi-layer convolutional features,the fixed convolutional module is replaced with a deformable convolutional ***,the model’s receptive field is enhanced by introducing a multi-branch convolutional module,improving the extraction of shallow features for small ***,the Dynamic Snake Convolution module is incorporated to enhance the extraction capability for slender and weak ***,the Convolutional Block Attention Module(CBAM)module is employed to achieve better target *** FDSC-YOLOv8s algorithm’s mAP50 and mAP50-95 reach 96.5%and 66.4%,according to the testing data.

关键词： Crack segmentation improved YOLOv8 engineering applications feature extraction

A Hybrid and Lightweight Device-to-Server Authentication Technique for the Internet of Things

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computers, Materials & Continua 2024年第3期78卷 3805-3823页

作者： Shaha Al-Otaibi Rahim Khan Hashim Ali Aftab Ahmed Khan Amir Saeed Jehad Ali Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of computer Science Abdul Wali Khan University MardanMardan23200Pakistan Department of computer Science and IT UET PeshawarJalozai Campus PeshawarPeshawar24240Pakistan Department of AI Convergence network Ajou UniversitySuwon16499South Korea

The Internet of Things(IoT)is a smart networking infrastructure of physical devices,i.e.,things,that are embedded with sensors,actuators,software,and other technologies,to connect and share data with the respective server *** IoTs are cornerstones in different application domains,the device’s authenticity,i.e.,of server(s)and ordinary devices,is the most crucial issue and must be resolved on a priority ***,various field-proven methodologies were presented to streamline the verification process of the communicating devices;however,location-aware authentication has not been reported as per our knowledge,which is a crucial metric,especially in scenarios where devices are *** paper presents a lightweight and location-aware device-to-server authentication technique where the device’s membership with the nearest server is subjected to its location information along with other ***,Media Access Control(MAC)address and Advance Encryption Scheme(AES)along with a secret shared key,i.e.,λ_(i) of 128 bits,have been utilized by Trusted Authority(TA)to generate MaskIDs,which are used instead of the original ID,for every device,i.e.,server and member,and are shared in the offline ***,TA shares a list of authentic devices,i.e.,server S_(j) and members C_(i),with every device in the IoT for the onward verification process,which is required to be executed before the initialization of the actual communication ***,every device should be located such that it lies within the coverage area of a server,and this location information is used in the authentication process.A thorough analytical analysis was carried out to check the susceptibility of the proposed and existing authentication approaches against well-known intruder attacks,i.e.,man-in-the-middle,masquerading,device,and server impersonations,etc.,especially in the IoT ***,proposed authentication and existing state-of-the-art approaches have been s

关键词： Internet of things authenticity security location communication

Deep simulated annealing for the discovery of novel dental anesthetics with local anesthesia and anti-inflammatory properties

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Acta Pharmaceutica Sinica B 2024年第7期14卷 3086-3109页

作者： Yihang Hao Haofan Wang Xianggen Liu Wenrui Gai Shilong Hu Wencheng Liu Zhuang Miao Yu Gan Xianghua Yu Rongjia Shi Yongzhen Tan Ting Kang Ao Hai Yi Zhao Yihang Fu Yaling Tang Ling Ye Jin Liu Xinhua Liang Bowen Ke State Key Laboratory of Oral Diseases and National Clinical Research center for Oral Diseases West China Hospital of StomatologySichuan UniversityChengdu 610041China College of computer Science Sichuan UniversityChengdu 610065China Department of Anesthesiology Laboratory of Anesthesia and Critical Care MedicineNational-Local Joint Engineering Research Centre of Translational Medicine of AnesthesiologyFrontiers Science Center for Disease-Related Molecular NetworkWest China HospitalSichuan UniversityChengdu 610041China

Multifunctional therapeutics have emerged as a solution to the constraints imposed by drugs with singular or insufficient therapeutic *** primary challenge is to integrate diverse pharmacophores within a single-molecule *** address this,we introduced DeepSA,a novel edit-based generative framework that utilizes deep simulated annealing for the modification of articaine,a wellknown local *** integrates deep neural networks into metaheuristics,effectively constraining molecular space during compound *** framework employs a sophisticated objective function that accounts for scaffold preservation,anti-inflammatory properties,and covalent *** a sequence of local editing to navigate the molecular space,DeepSA successfully identified AT-17,a derivative exhibiting potent analgesic properties and significant anti-inflammatory activity in various animal *** insights into AT-17 revealed its dual mode of action:selective inhibition of NaV1.7 and 1.8 channels,contributing to its prolonged local anesthetic effects,and suppression of inflammatory mediators via modulation of the NLRP3 inflammasome *** findings not only highlight the efficacy of AT-17 as a multifunctional drug candidate but also highlight the potential of DeepSA in facilitating AI-enhanced drug discovery,particularly within stringent chemical constraints.

关键词： Multifunctional drugs Deep simulated annealing Molecule generation Articaine derivatives AI-enhanced drug discovery

Cross-Dimension Attentive Feature Fusion network for Unsupervised Time-Series Anomaly Detection

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computer Modeling in Engineering & Sciences 2024年第6期139卷 3011-3027页

作者： Rui Wang Yao Zhou Guangchun Luo Peng Chen Dezhong Peng School of computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of computer and Software Engineering Xihua UniversityChengdu610039China School of computer Science Sichuan UniversityChengdu610065China National Innovation center for UHD Video Technology Chengdu610095China

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series *** to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly ***,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time *** this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as ***,a series and feature mixing block is introduced to learn representations in 1D ***,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature ***,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly *** results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

关键词： Time series anomaly detection unsupervised feature learning feature fusion

TGAIN: Geospatial Data Recovery Algorithm Based on GAIN-LSTM

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computers, Materials and Continua 2024年第1期81卷 1471-1489页

作者： Yang L. Li L. Ma S. School of Soft Engineering Jinling Institute of Technology Nanjing 211169 China School of computer Jinling Institute of Technology Nanjing 211169 China School of network Security Jinling Institute of Technology Nanjing 211169 China

Accurate geospatial data are essential for geographic information systems (GIS), environmental monitoring, and urban planning. The deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial data. In this paper, we consider abnormal spatial data as missing data and focus on abnormal spatial data recovery. Existing geospatial data recovery methods require complete datasets for training, resulting in time-consuming data recovery and lack of generalization. To address these issues, we propose a GAIN-LSTM-based geospatial data recovery method (TGAIN), which consists of two main works: (1) it uses a long-short-term recurrent neural network (LSTM) as a generator to analyze geospatial temporal data and capture its temporal correlation; (2) it constructs a complete TGAIN network using a cue-masked fusion matrix mechanism to obtain data that matches the original distribution of the input data. The experimental results on two publicly accessible datasets demonstrate that our proposed TGAIN approach surpasses four contemporary and traditional models in terms of mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), mean absolute percentage error (MAPE), coefficient of determination (R2) and average computational time across various data missing rates. Concurrently, TGAIN exhibits superior accuracy and robustness in data recovery compared to existing models, especially when dealing with a high rate of missing data. Our model is of great significance in improving the integrity of geospatial data and provides data support for practical applications such as urban traffic optimization prediction and personal mobility analysis. © 2024 The Authors. Published by Tech Science Press.

关键词： data recovery generative adversarial networks Geospatial data temporal correlation

A novel structural variant of RET causes familial Hirschsprung's disease via nonsense-mediated mRNA decay

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Genes & Diseases 2024年第1期11卷 7-10页

作者： Qi Wang Siyu Pu Bo Xiang Jing Chen Department of Pediatric Surgery and Laboratory of Pediatric Surgery Rare Diseases CenterInstitute of Immunology and InflammationFrontiers Science Center for Disease-Related Molecular NetworkWest China HospitalSichuan UniversityChengduSichuan 610041China

Hirschsprung's disease(HsCR)is the most common type of developmental gastrointestinal malformation causing intestinal obstruction in children,with an incidence of 1/5000 in live births.1 it is characterized by the absence of intrinsic ganglion cells in the myenteric and submucosal plexuses of the distal gastrointestinal tract,leading to the dysregulation of smooth muscle contraction/relaxation and resulting in intestinal *** on the length of the aganglionic intestine,HSCR can be categorized into short-segmental HSCR(S-HSCR),which affects up to the sigmoid colon from the anus,and long-segmental HSCR(LHSCR),which affects beyond the sigmoid colon.1 In some rare conditions,aganglionic lesions can affect the full length of the colon or even the whole intestine.

关键词： intestine colon relaxation

A New Solution to Intrusion Detection Systems Based on Improved Federated-Learning Chain

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computers, Materials & Continua 2024年第6期79卷 4491-4512页

作者： Chunhui Li Hua Jiang School of computer and Electronic Information Guangxi UniversityNanning530000China Cyber Security and Information center Guangxi UniversityNanning530000China

In the context of enterprise systems,intrusion detection(ID)emerges as a critical element driving the digital transformation of *** systems spanning various sectors of enterprises geographically dispersed,the necessity for seamless information exchange has surged *** existing cross-domain solutions are challenged by such issues as insufficient security,high communication overhead,and a lack of effective update mechanisms,rendering them less feasible for prolonged application on resource-limited *** study proposes a new cross-domain collaboration scheme based on federated chains to streamline the server-side *** this framework,individual nodes solely engage in training local data and subsequently amalgamate the final model employing a federated learning algorithm to uphold enterprise systems with efficiency and *** curtail the resource utilization of blockchains and deter malicious nodes,a node administration module predicated on the workload paradigm is introduced,enabling the release of surplus resources in response to variations in a node’s contribution *** encountering an intrusion,the system triggers an alert and logs the characteristics of the breach,facilitating a comprehensive global update across all nodes for collective *** results across multiple scenarios have verified the security and effectiveness of the proposed solution,with no loss of its recognition accuracy.

关键词： Cross-domain collaboration blockchain federated learning contribution value node management release slack resources

Complementary monogamy and polygamy properties among multipartite systems

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Chinese Physics B 2024年第3期33卷 207-213页

作者：李陶周静怡孙琪靳志祥梁登峰罗婷 School of Mathematics and Statistics Beijing Technology and Business UniversityBeijing 100048China School of computer Science and Techonology Dongguan University of TechnologyDongguan 523808China People's Public Security University of China Academy of Information Network SecurityBeijing 100038China

Monogamy and polygamy relations are essential properties of quantum entanglement,which characterize the distributions of entanglement in multipartite *** this paper,we establish the general monogamy relations forγ-th(0≤γ≤α,α≥1)power of quantum entanglement based on unified-(q,s)entanglement and polygamy relations forδ-th(δ≥β,0≤β≤1)power of entanglement of assistance based on unified-(q,s)entanglement of assistance,which provides a complement to the previous research in terms of different parameter regions ofγandδ.These results are then applied to specific quantum correlations,e.g.,entanglement of formation,Renyi-q entanglement of assistance and Tsallis-q entanglement of assistance to get the corresponding monogamy and polygamy ***,typical examples are presented for illustration.

关键词： monogamy relation polygramy relation

Multi-party privacy-preserving decision tree training with a privileged party

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Science China(Information Sciences) 2024年第8期67卷 253-268页

作者： Yiwen TONG Qi FENG Min LUO Debiao HE Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Key Laboratory of Computing Power network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology(Shandong Academy of Sciences)

Currently, a decision tree is the most commonly used data mining algorithm for classification tasks. While a significant number of studies have investigated privacy-preserving decision trees, the methods proposed in these studies often have shortcomings in terms of data privacy breach or efficiency. Additionally,these methods typically only apply to symmetric frameworks, which consist of two or more parties with equal privilege, and are not suitable for asymmetric scenarios where parties have unequal privilege. In this paper,we propose SecureCART, a three-party privacy-preserving decision tree training scheme with a privileged party. We adopt the existing pMPL framework and design novel secure interactive protocols for division,comparison, and asymmetric multiplication. Compared to similar schemes, our division protocol is 93.5–560.4× faster, with the communication overhead reduced by over 90%; further, our multiplication protocol is approximately 1.5× faster, with the communication overhead reduced by around 20%. Our comparison protocol based on function secret sharing maintains good performance when adapted to pMPL. Based on the proposed secure protocols, we implement SecureCART in C++ and analyze its performance using three real-world datasets in both LAN and WAN environments. he experimental results indicate that SecureCART is significantly faster than similar schemes proposed in past studies, and that the loss of accuracy while using SecureCART remains within an acceptable range.

关键词： privacy protection decision trees secure multi-party computation secret sharing privileged party