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16th International Conference on Intelligent Computing, ICIC 2020

作者： Peng, Xiaoxiao Du, Jixiang Zhang, Hongbo Department of Computer Science and Technology Huaqiao University Quanzhou China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Quanzhou China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Quanzhou China

ISBN: (纸本)9783030607951

Temporal action localization is a challenging task in video understanding. Although great progress has been made in temporal action localization, the most advanced methods still have the problem of sharp performance degradation when an action proposal generated. Most methods use sliding windows method or simply group frames according to frame-level scores. These methods are not enough to provide accurate action boundary and maintain reasonable temporal structure. In order to solve these problems, we propose a novel proposal optimization network to generate start score, end score, action score and regression score, and then remove the redundancy by NMS algorithm. In the proposed method, we introduce a metric loss function to maintain the temporal structure of action proposal in the training process. To verify the effectiveness of the proposed method, we have made comparative experiments on ActivityNet-1.3 dataset respectively, and the proposed method has surpassed some of the state-of-the-art methods on the dataset. © 2020, Springer Nature Switzerland AG.

关键词： Passive optical networks

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Who Distributes It? Privacy-Preserving Image Sharing Scheme with Illegal Distribution Detection 13th

Who Distributes It? Privacy-Preserving Image Sharing Scheme ...

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13th International Conference on security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2020

作者： Deng, Tianpeng Li, Xuan Jin, Biao Xiong, Jinbo College of Mathematics and Informatics Fujian Normal University Fuzhou350117 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Digital Fujian Big Data Security Technology Institute Fuzhou350117 China

ISBN: (纸本)9783030688837

In image sharing schemes, the privacy of shared images can be protected by sending encrypted images to the receiver. However, the decrypted image cannot avoid being illegally distributed by receivers for their own benefits. Some methods have been proposed to solve this problem by embedding query user’s identification into the image. However, spatial domain embedding are usually vulnerable to attacks, such as Least Significant Bit (LSB) replacement attacks. We propose a privacy-preserving image sharing scheme for detecting illegal distributors based on edge detection and Arnold transformation. This scheme can adaptively embed the query user’s authentication information into 1 to 5 LSBs of each pixel of the image according to the embedded position map. After receiving the scrambled encrypted image and embedded position map, the cloud server will generate the embedded value map and embed in the encrypted domain. The image owner can identify the distributor’s identification with the help of cloud server when a suspected illegally distributed image is found. The scheme is robust under different attacks as shown by experiments. © 2021, Springer Nature Switzerland AG.

关键词： Embeddings

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Multitasking differential evolution with difference vector sharing mechanism

Multitasking differential evolution with difference vector s...

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IEEE Symposium Series on Computational intelligence (SSCI)

作者： Yiqiao Cai Deining Peng Shunkai Fu Hui Tian Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

As a new emerging research topic in the field of evolutionary computation, evolutionary multitasking optimization (EMTO) is presented to solve multiple optimization tasks concurrently by transferring knowledge across them. However, the promising search directions found during the evolutionary process have not been shared and utilized effectively in most EMTO algorithms. Therefore, this paper puts forward a difference vector sharing mechanism (DVSM) for multitasking differential evolution (MDE), with the purpose of capturing, sharing and utilizing the useful knowledge across different tasks. The performance of the proposed algorithm, named MDE with DVSM (MDE-DVSM), is evaluated on a suite of single-objective multitasking benchmark problems. The experimental results have demonstrated the superiority of MDE-DVSM when compared with other competitive algorithms.

关键词： Task analysis Optimization Multitasking Sociology Statistics Cultural differences Genetics

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Network configuration approach based on the reliability service 12

Network configuration approach based on the reliability serv...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Fengfei, Kuang Yaojm, Lin Xiehua, Yu Meilong, Chen University of Fujian Province Key Laboratory of Big Data and Artificial Intelligence MinNan Science and Technology University Fujian Quanzhou362332 China Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China

ISBN: (纸本)9798350310153

Wireless communication network plays an important role in various industries. One of the key concern in wireless communication is network security. This paper proposes an optimal transmission lines assignment with maximal reliabilities in the wireless communication. Firstly, a reliability evaluation metric with a transmission line assignment is proposed for calculating the reliability service under some constraints. Secondly, based on the reliability, non-dominated sorting Genetic algorithm is used for working out the optimal solution via considering the network security. Some simulated experiments are conducted that the proposed approach is able to obtain the reasonable solution under the time constraints. © 2022 IEEE.

关键词： Reliability

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Public auditing for real‑time medical sensor data in cloud‑assisted HealthIIoT system

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Frontiers of Optoelectronics 2022年第3期15卷 1-14页

作者： Weiping Ye Jia Wang Hui Tian Hanyu Quan College of Computer Science and Technology Huaqiao UniversityXiamen 361021China Wuhan National Laboratory for Optoelectronics Wuhan 430074China Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen 361021China Fujian Key Laboratory of Big Data Intelligence and Security Xiamen 361021China

With the advancement of industrial internet of things(IIoT),wireless medical sensor networks(WMSNs)have been widely introduced in modern healthcare systems to collect real-time medical data from patients,which is known as *** the limited computing and storage capabilities of lightweight HealthIIoT devices,it is necessary to upload these data to remote cloud servers for storage and ***,there are still some serious security issues within outsourcing medical sensor data to the *** of the most signifcant challenges is how to ensure the integrity of these data,which is a prerequisite for providing precise medical diagnosis and *** meet this challenge,we propose a novel and efcient public auditing scheme,which is suitable for cloud-assisted HealthIIoT ***,to address the contradiction between the high real-time requirement of medical sensor data and the limited computing power of HealthIIoT devices,a new online/ofine tag generation algorithm is designed to improve preprocessing efciency;to protect medical data privacy,a secure hash function is employed to blind the data *** formally prove the security of the presented scheme,and evaluate the performance through detailed experimental comparisons with the state-of-the-art *** results show that the presented scheme can greatly improve the efciency of tag generation,while achieving better auditing performance than previous schemes.

关键词： Healthcare industrial internet of things(HealthIIoT) Medical sensor data Online/ofine signature Public auditing

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Secure YOLOv3-SPP: Edge-Cooperative Privacy-preserving Object Detection for Connected Autonomous Vehicles

Secure YOLOv3-SPP: Edge-Cooperative Privacy-preserving Objec...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Zhou, Yongjie Xiong, Jinbo Bi, Renwan Tian, Youliang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Fujian Provincial Key Laboratory of Network System Information Security College of Computer and Cyberspace Security Fujian Normal University FJ Fuzhou350117 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

The connected autonomous vehicles (CAVs) are a key component of intelligent transportation systems (ITS) where vehicles communicate with each other to exchange sensing data from on-board sensors (e.g., high-definition cameras and lidar). For the sake of the category and location privacy leakage of sensing images shared by CAVs and computational inefficiency of privacy-preserving object detection framework in edge computing environment, we propose a lightweight and privacy-preserving detection framework (PPDF) to support secure extraction, classification and detection of image features, and achieve the goal of data transmission and computing security under the collaborative detection process of edge nodes. Particularly, we design a secure clustering prediction protocol of object anchors, a secure object classification and regression protocol, secure object upsampling and secure feature fusion protocols. Finally, PPDF based on edge-cooperation was constructed and two non-collusive edge servers were used to perform PPDF. Theoretical analysis of correctness, security and complexity indicate that PPDF can not only realize correctness of object detection, but also let category and location privacy of images are protected effectively and have excellent accuracy. Actual performance evaluation shows that PPDF can achieve the same detection accuracy as original YOLOv3-SPP model. At the same time, compared with homomorphic encryption and multi-round iterative calculation schemes, PPDF has obvious advantages in terms of computational cost and communication overhead. © 2022 IEEE.

关键词： Autonomous vehicles

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Adaptive offloading scheme of fine-grained tasks based on reinforcement learning in multi-access edge computing

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Simulation Modelling Practice and Theory 2025年 143卷

作者： Jie Li Ge Chen Kansong Chen School of Computer Science Hubei University Wuhan 430062 China Hubei Key Laboratory of Big Data Intelligent Analysis and Application (Hubei University) Wuhan 430062 China Engineering Research Center of Hubei Province in Intelligent Government Affairs and Application of Artificial Intelligence Wuhan 430062 China Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan 430062 China

In the context of multi-access edge computing, the constraints on device resources and energy pose a challenge in meeting the requirements of delay-sensitive and high-resource demand tasks. In order to address this issue, this paper puts forth an adaptive offloading decision based on fine-grained task sequencing (AOD-FTS). To enhance task execution efficiency and resource utilization, we design scheduling optimization for subtasks algorithm within the fine-grained task model to optimize the execution order of subtasks and improve subtask parallelism. Additionally, to reduce task delay and energy consumption, we propose an adaptive multi-objective reinforcement learning offloading algorithm based on ordered subtasks, formulated through multi-objective Markov decision processes. The scheme dynamically adjusts optimization preferences according to environmental changes and task completion states to make offloading decisions. Simulation results demonstrate that compared with two benchmark algorithms, the proposed AOD-FTS scheme achieves: (1) higher task completion rates, (2) improved resource utilization efficiency, (3) 81.66 % and 51.53 % reductions in delay respectively, and (4) 39.72 % and 71.36 % reductions in energy consumption respectively.

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A Two-Stage Transfer Learning Method for Tea Disease Identification 13

A Two-Stage Transfer Learning Method for Tea Disease Identif...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： He, Yina Luo, Zhiming Lin, Lihui Li, Shaozi Xiamen University Department of Artificial Intelligence Xiamen China Wuyi University Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyishan China

ISBN: (纸本)9798350319156

Tea has been widely planted across the world, while the identification of tea disease is mainly based on human expertise. In recent years, there has been an increasing use of deep learning techniques for the recognition of plant diseases. However, persistent challenges such as the limited availability of comprehensive datasets and the need to improve recognition accuracy still exist in tea disease identification. To address these issues, we collect a small-scale tea disease image dataset, containing 203 images covering six common tea disease. Then, we proposed a two-stage transfer learning approach for improving the accuracy. Firstly, we modified the first stage in ResNet50 to better extracting image feature. Next, we leverage a two-stage transfer learning approach which first transfers to a related intermediate PlantVillage dataset. The proposed model achieved a recognition accuracy of 87.67% when trained from scratch on the tea leaf dataset, which is 5.46% higher than the ResNet50 model. Furthermore, the two-stage transfer algorithm can further improve the recognition accuracy to 90.64%. © 2023 IEEE.

关键词： Image classification

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Teaching resource recommendation for OBE based on kernel canonical correlation analysis 12

Teaching resource recommendation for OBE based on kernel can...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Changqian, Wu Shaozi, Li Yaojin, Lin Xiehua, Yu MinNan Science and Technology Key Laboratory of Big Data and Artificial Intelligence University José Rizal University University of Fujian Province Quanzhou China Xiamen University School of Informatics Xiamen China Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China MinNan Science and Technology University Key Laboratory of Big Data and Artificial Intelligence University of Fujian Province Quanzhou China

ISBN: (纸本)9798350310153

data mining is used to extract information from OBE teaching resources, then the teaching resources and user personality database are established to improve the effectiveness of resources recommendation on the online learning platform. The properties of teaching resources and user personality traits are analyzed using the kernel canonical correlation analysis algorithm, and teaching materials with a high correlation coefficient are chosen to recommend to users. The simulation results demonstrate that this proposed method has higher accuracy and more accurate recommended resources when compared to the widely used teaching resource recommendation techniques. © 2022 IEEE.

关键词： data mining

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Frequency-Aware Attentional Feature Fusion for Deepfake Detection 48

Frequency-Aware Attentional Feature Fusion for Deepfake Dete...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Tian, Cheng Luo, Zhiming Shi, Guimin Li, Shaozi Xiamen University Department of Artificial Intelligence Xiamen China Wuyi University Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyishan China

ISBN: (纸本)9781728163277

Various face manipulation techniques develop rapidly and can easily generate high-quality fake images or videos, posing significant ethical concerns when used for malicious purposes. Although recent works achieve significant performance in deepfake detection, they still suffer from overfitting issues. To deal with this problem, we propose a novel framework to aggregate diverse information for deepfake detection from both RGB and frequency. Specially, we first introduce a channel attention module to assemble local and global contexts to overcome the potential semantic inconsistency on local artifacts and global features. Then we design a spatial-frequency feature fusion module to fuse the RGB-frequency information comprehensively. Moreover, a variant attention module is further proposed to improve feature discrimination. Extensive experiments demonstrate that our method maintains comparable performance in intra-dataset and cross-dataset evaluation. © 2023 IEEE.

关键词： DeepFake Detection Feature Fusion

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