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IEEE Symposium on computers and Communications (ISCC)

作者： Yunting Tao Fanyu Kong Yuliang Shi Jia Yu Hanlin Zhang Huiyi Liu School of Software Shandong University College of Information Engineering Binzhou Polytechnic College of Computer Science and Technology Qingdao University Shandong Sansec Information Technology Co. Ltd

The proliferation of Internet of Things (IoT) devices has led to the generation of massive amounts of data that require efficient aggregation for analysis and decision-making. However, multi-tier IoT systems, which involve multiple layers of devices and gateways, face more complex security challenges in data aggregation compared to ordinary IoT systems. In this paper, we propose an efficient privacy-preserving multi-functional data aggregation scheme for multi-tier IoT architecture. The scheme supports privacy-preserving calculation of mean, variance, and anomaly proportion. The scheme uses the Paillier cryptosystem and the BLS algorithm for encryption and signature, and uses blinding techniques to keep the size of the IoT system secret. In order to make the Paillier algorithm more suitable for the IoT scenario, we also improve its efficiency of encryption and decryption. The performance evaluation shows that the scheme improves encryption efficiency by 43.7% and decryption efficiency by 45% compared to the existing scheme.

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Detection of Cassava Leaf Diseases Using Self-supervised Learning 2

Detection of Cassava Leaf Diseases Using Self-supervised Lea...

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2nd International Conference on computer Science and Management technology, ICCSMT 2021

作者： Zhang, Hengyi Xu, Yushen Sun, Jialiang School of Automation and Software Engineering Shanxi University Taiyuan China Foshan China Computer Science and Technology College Cumt Luoyang China

ISBN: (纸本)9781665420631

Identifying infected plants such as cassava leaves in advance and removing them early can effectively increase production. The traditional approach is to use many image sets to improve the classification accuracy of neural networks. However, this must be established on the premise that the data sets have enough labels that botanists can classify. We applied self-supervised learning to image classification and trained a reliable cassava disease detection model to reduce the difficulty and cost of collecting these tags. We propose a new hybrid loss that combines the classification and contrastive losses for the whole classification process. Experiments show that our method performs well in the lacking of labeled data. Specifically, the model uses 2/3 of the total data set and reaches an accuracy (90%) close to supervised learning (91.59%) by adding the contrastive term. In addition, we also prove that the change of the percentage of data without labels is linearly independent of the model detection accuracy. © 2021 IEEE.

关键词： Supervised learning

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Efficient Public Key Cryptography Scheme with Equality Test for Heterogeneous Systems in IIoT 13th

Efficient Public Key Cryptography Scheme with Equality Test ...

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

作者： Hassan, Abdelrhman Elhabob, Rashad Ibrahim, Umar Wang, Yong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9783030688837

Industrial Internet of Things (IIoT) incorporates various varieties of smart devices and communication technologies that allow organizations to move from conventional industries to smart industries. IIoT provides cost-saving and collaboration since it relies on the availability, flexibility, and powerful processing capabilities of the cloud server. Considering the cloud’s untrusted nature, it is essential to protect the IIoT data before uploading it to the server. However, encryption yields a further critical issue. Due to the "All-or-Nothing" decryption characteristic, it is impractical for the users of another public-key cryptosystem to download all the data stored on the server to obtain their needs. In resolving this issue, this paper proposes Efficient Public-Key Cryptography with Equality Test scheme for Heterogeneous Systems (PKC-ET-HS). The PKC-ET-HS scheme incorporates the notions of Certificateless Public-Key Encryption with Equality Test (CL-PKE-ET) as well as the Identity-Based Encryption with Equality Test (IBE-ET). The scheme enables the cloud server to check whether two ciphertexts having their encryptions performed under a heterogeneous systems contain identical messages. Moreover, in the Random Oracle Model (ROM), the modified Bilinear Diffie-Hellman Intractable (mDBHI) assumption is stated to construct the security of the suggested scheme. Ultimately, the performance evaluation authenticates that the suggested scheme is practicable and convenient for IIoT environments. © 2021, Springer Nature Switzerland AG.

关键词： Public key cryptography

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Container Scheduling Strategy Based on Image Layer Reuse and Sequential Arrangement in Mobile Edge Computing

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IEEE Transactions on Mobile Computing 2025年

作者： Wu, Haijie Lin, Weiwei Zhang, Haotong Shi, Fang Shen, Wangbo Li, Keqin Zomaya, Albert Y. South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Pengcheng Laboratory Shenzhen518066 China South China University of Technology School of Software Engineering Guangzhou510006 China South China Agricultural University College of Mathematics and Informatics Guangzhou510642 China State University of New York Department of Computer Science New PaltzNY12561 United States University of Sydney School of Computer Science SydneyNSW2006 Australia

In Mobile Edge Computing (MEC) scenarios, computational tasks are popularly deployed using containerization to isolate the runtime environment. To complete the execution of the task, the edge server first pulls the image, then instantiates and runs the container. Since it takes a lot of time for the edge server to download the image from the cloud, image reuse reduces the pulling latency significantly. However, the limited storage capacity of edge servers hinders image reuse. Recent works have enhanced reuse efficiency by leveraging the hierarchical structure of images and caching high-value layers. However, their efficiency remains limited due to the lack of multi-container collaboration. This paper proposes a novel container scheduling strategy based on image layer reuse and sequence arrangement (ILR-SA) for MEC scenarios, which achieves efficient scheduling by collaborating multiple containers. First, containers are greedily deployed into the edge cluster. Then, the execution sequence of containers is modeled as an optimal Hamiltonian path problem, efficiently solved by our proposed decomposition algorithm. Finally, an efficient image layer update strategy is used to achieve layer reuse. We conduct rigorous experiments to demonstrate that our proposed container scheduling strategy reduces the computational task completion time by up to 91.3% compared to existing approaches. © 2002-2012 IEEE.

关键词： Mobile edge computing

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GuidedNet: Semi-Supervised Multi-Organ Segmentation via Labeled Data Guide Unlabeled Data

arXiv

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arXiv 2024年

作者： Zhao, Haochen Meng, Hui Yang, Deqian Xie, Xiaozheng Wu, Xiaoze Li, Qingfeng Niu, Jianwei School of Computer Science and Engineering Beihang University Beijing China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China Institute of Software Hangzhou Institute for Advanced Study University of the Chinese Academy of Sciences Hangzhou China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Software Beihang University Beijing China

Semi-supervised multi-organ medical image segmentation aids physicians in improving disease diagnosis and treatment planning and reduces the time and effort required for organ annotation. Existing state-of-the-art methods train the labeled data with ground truths and train the unlabeled data with pseudo-labels. However, the two training flows are separate, which does not reflect the interrelationship between labeled and unlabeled data. To address this issue, we propose a semi-supervised multi-organ segmentation method called GuidedNet, which leverages the knowledge from labeled data to guide the training of unlabeled data. The primary goals of this study are to improve the quality of pseudo-labels for unlabeled data and to enhance the network's learning capability for both small and complex organs. A key concept is that voxel features from labeled and unlabeled data that are close to each other in the feature space are more likely to belong to the same class. On this basis, a 3D Consistent Gaussian Mixture Model (3D-CGMM) is designed to leverage the feature distributions from labeled data to rectify the generated pseudo-labels. Furthermore, we introduce a Knowledge Transfer Cross Pseudo Supervision (KT-CPS) strategy, which leverages the prior knowledge obtained from the labeled data to guide the training of the unlabeled data, thereby improving the segmentation accuracy for both small and complex organs. Extensive experiments on two public datasets, FLARE22 and AMOS, demonstrated that GuidedNet is capable of achieving state-of-theart performance. The source code with our proposed model are available at https://***/kimjisoo12/GuidedNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Life on land: An accurate centralized system for ensuring the safety and wellbeing of domestic and wild animals

TechRxiv

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TechRxiv 2023年

作者： Heshan, Adithya B.M. Wickramathilake, Shamilka K.W.D. Gunathilake, Dulmi W.M.P. Herath, Hiruni H.M.S. Samarasekara, Piyumika de Silva, Dilshan Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

The web application described above has the potential to make a significant contribution to the conservation of endangered wildlife and the responsible management of domestic animals. The application addresses a critical issue that has far-reaching implications for the health of ecosystems and the well-being of animals. By providing users with tools to track and manage animal populations, monitor their health and behavior, and implement conservation efforts, the application empowers individuals and organizations to take action and make a positive impact on the environment. In addition to its practical benefits, the web application also includes educational resources and supports community engagement to raise awareness about the importance of preserving biodiversity and protecting animal welfare. This holistic approach recognizes the complex and interconnected nature of conservation and management efforts, and highlights the need for collaboration and cooperation among stakeholders. The user-friendly interface of the application ensures that it is accessible to a wide range of users, including individuals with limited technical expertise. The ability to collaborate and share data also promotes transparency and accountability, which are essential for effective conservation and management efforts. Overall, this web application has the potential to be a valuable tool for those involved in animal conservation and management, and it offers hope for the protection of endangered wildlife and the responsible management of domestic animals. © 2023, CC BY.

关键词： Animals

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Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting

arXiv

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arXiv 2024年

作者： Ruan, Weilin Wang, Wenzhuo Zhong, Siru Chen, Wei Liu, Li Liang, Yuxuan The Hong Kong University of Science and Technology Guangzhou China The Computer Science Department Jinan University Guangzhou China The School of Big Data and Software Engineering Chongqing University Chongqing China

Predicting spatio-temporal traffic flow presents significant challenges due to complex interactions between spatial and temporal factors. Existing approaches often address these dimensions in isolation, neglecting their critical interdependencies. In this paper, we introduce the Spatio-Temporal Unitized Model (STUM), a unified framework designed to capture both spatial and temporal dependencies while addressing spatio-temporal heterogeneity through techniques such as distribution alignment and feature fusion. It also ensures both predictive accuracy and computational efficiency. Central to STUM is the Adaptive Spatio-temporal Unitized Cell (ASTUC), which utilizes low-rank matrices to seamlessly store, update, and interact with space, time, as well as their correlations. Our framework is also modular, allowing it to integrate with various spatio-temporal graph neural networks through components such as backbone models, feature extractors, residual fusion blocks, and predictive modules to collectively enhance forecasting outcomes. Experimental results across multiple real-world datasets demonstrate that STUM consistently improves prediction performance with minimal computational cost. These findings are further supported by hyperparameter optimization, pre-training analysis, and result visualization. We provide our source code for reproducibility at https://***/r/STUM-E4F0. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Generating synthetic data on agricultural crops with DCGAN

Generating synthetic data on agricultural crops with DCGAN

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International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)

作者： Fieter Brain Pasaribu Luh Joni Erawati Dewi Kadek Yota Ernanda Aryanto Ketut Agus Seputra Pariwate Varnakovida Ni Ketut Kertiasih Computer Science Universitas Pendidikan Ganesha Singaraja Indonesia Software Engineering Universitas Pendidikan Ganesha Singaraja Indonesia Faculty of Science King Mongkut's University of Technology Thonburi Thailand

ISBN: (数字)9798331520311

ISBN: (纸本)9798331520328

Convolutional Neural Network (CNN) is one of the deep learning architectures that is very effective for handling images. CNN is able to automatically extract important features from images, making it very suitable for various image processing tasks such as classification, object detection, and segmentation. However, even though CNN has great capabilities, one important thing to note is the amount of data. A considerable amount of data is needed for the CNN model to work optimally and avoid overfitting. To handle this problem, a synthetic data augmentation process is used using the Deep Convolutional Generative Adversarial Network (DCGAN) method. The generator network contained in the DCGAN model has a latent space dimension input whose value can vary. The size of the latent space dimension is very important in enabling data or image reconstruction during the training process. This study tested latent space dimension values on a corn plant dataset totaling 9159. The latent space values used in this experiment were 64, 100, and 128. In addition, this study also tested different batch sizes, namely 64 and 128. The model was evaluated using Fre'chet Inception Distance (FID) and Inception Score (IS). From the evaluation results, the best score on FID was 0.018001 and IS 1.239421. The greater the latent space value, the more realistic and clear the image results will be. Likewise, the smaller the batch size value, the more realistic and clear the image results.

关键词： Training Plants (biology) Generative adversarial networks Feature extraction Data augmentation Size measurement Data models Stability analysis Convolutional neural networks Synthetic data

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Learning-Based Directional Improvement Prediction for Dynamic Multiobjective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Ye, Yulong Liu, Songbai Zhou, Junwei Lin, Qiuzhen Jiang, Min Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China School of Informatics Xiamen University Fujian China Department of Computing The Hong Kong Polytechnic University Hong Kong China

In recent years, dynamic multiobjective evolutionary algorithms (DMOEAs) using the prediction strategy have shown promising performance for solving dynamic multiobjective optimization problems (DMOPs), as they can predict environmental changing trends in advance. However, most of them follow a regular change pattern and thus their performance is compromised when solving DMOPs with irregular change patterns (e.g., nonlinear correlations). To alleviate this challenge, this article proposes a DMOEA with a learnable prediction for tackling DMOPs. Specifically, a neural network is designed to effectively capture diverse change patterns of the environment. Based on the change patterns learned, a directional improvement prediction (DIP) is developed to guide the evolutionary search toward promising directions in the decision space. In this way, a superior initial population with good convergence and diversity is predicted by DIP, which can be more effective for solving various DMOPs. Comprehensive empirical studies show that the proposed DIP is effective and the proposed algorithm has some advantages over five competitive DMOEAs when solving three commonly used benchmarks and one real-world problem. IEEE

关键词： Neural networks

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An incentive mechanism for crowdsourcing markets with social welfare maximization in cloud-edge computing

An incentive mechanism for crowdsourcing markets with social...

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作者： Xu, Xiaolong Cai, Qing Zhang, Guoming Zhang, Jie Tian, Wei Zhang, Xiaorui Liu, Alex X. School of Computer and Software Nanjing University of Information Science and Technology Jiangsu China Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Jiangsu China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu China Department of Computer Science and Engineering Michigan State University MI United States

Crowdsourcing is emerging as a powerful paradigm that utilizes the distributed devices to sense, collect, and upload data to satisfy the requirements of the users. Currently, with the popularity of edge computing, edge device users can act as recruiters or participants to publish or perform crowdsourcing tasks and share feedback. However, due to the individual selfishness, it is still a challenge to maximize the social welfare distribution of all the participants and the recruiters for the crowdsourcing market. In view of this challenge, an incentive mechanism for the crowdsourcing market with social welfare maximization in the cloud-edge computing is designed in this paper. Technically, a double action model under the cloud-edge computing framework is proposed first, which aims to maximize the social welfare maximization and meanwhile meet the demands of incentive compatibility, individual rationality, market clearing, and budget constraint. Secondly, a corresponding incentive mechanism is designed based on the double auction model to achieve the market fairness. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of the mechanism. © 2018 John Wiley & Sons, Ltd.

关键词： Crowdsourcing

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