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Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution

IEEE Transactions on Multimedia 2025年

作者： Fu, Congrui Yuan, Hui Jiang, Shiqi Zhang, Guanghui Shen, Liquan Hamzaoui, Raouf Center of Experimental Teaching China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China Shandong University School of software Shandong Jinan China Shandong University School of Computer Science and Technology China Shanghai University School of Communication and Information Engineering Shanghai China De Montfort University School of Engineering and Sustainable Development LeicesterLE1 9BH United Kingdom

By converting low-frame-rate, low-resolution videos into high-frame-rate, high-resolution ones, space-time video super-resolution techniques can enhance visual experiences and facilitate more efficient information dissemination. We propose a convolutional neural network (CNN) for space-time video super-resolution, namely GIRNet. Our method combines long-term global information and short-term local information from the video to better extract complete and accurate spatial-temporal information. To generate highly accurate features and thus improve performance, the proposed network integrates a feature-level temporal interpolation module with deformable convolutions and a global spatial-temporal information-based residual convolutional long short-term memory (convLSTM) module. In the feature-level temporal interpolation module, we leverage deformable convolution, which adapts to deformations and scale variations of objects across different scene locations. This provides a more efficient solution than conventional convolution for extracting features from moving objects. Our network effectively uses forward and backward feature information to determine inter-frame offsets, leading to the direct generation of interpolated frame features. In the global spatial-temporal information-based residual convLSTM module, the first convLSTM is used to derive global spatial-temporal information from the input features, and the second convLSTM uses the previously computed global spatial-temporal information feature as its initial cell state. This second convLSTM adopts residual connections to preserve spatial information, thereby enhancing the output features. Experiments on the Vimeo90K dataset show that the proposed method outperforms open source state-of-the-art techniques in peak signal-to-noise-ratio (by 1.45 dB, 1.14 dB, and 0.2 dB over STARnet, TMNet, and 3DAttGAN, respectively), structural similarity index(by 0.027, 0.023, and 0.006 over STARnet, TMNet, and 3DAttGAN, respectiv

关键词： Long short-term memory

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SecPQ: Secure Prediction Queries on Encrypted Outsourced Databases

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Liang, Jinwen Guo, Song Hong, Zicong Zhou, Enyuan Zhang, Chuan Xiao, Bin Hong Kong Polytechnic University Hung Hom Department of Computing Kowloon Hong Kong Hong Kong University of Science and Technology Clear Water Bay Department of Computer Science and Engineering Kowloon Hong Kong Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Guangdong China

Prediction queries have revolutionized data search by integrating machine learning models and traditional data processing operations for advanced analytics. However, existing prediction query frameworks for outsourced databases face a critical security vulnerability: data flows are processed in plaintext on semi-honest servers, making them susceptible to data breaches. The main challenge in achieving secure prediction queries is that machine learning inference and data processing operations are distinct functionalities, while most current cryptographic frameworks support only a single type of operation on specific encrypted data. To bridge this crucial gap, we propose SecPQ, the first framework tailored for secure prediction queries. Our approach unifies decision tree pipelines and data processing operations, such as selection, projection, and equality-joining, through equality matching on encrypted outsourced data. This enables the design of secure prediction queries with decision tree pipelines operating on encrypted data. We provide formal security definitions and proofs for SecPQ. To further optimize the efficiency of secure prediction queries, we leverage order-preserving encryption to construct SecPQope, which offers improved query efficiency at the expense of weaker security properties compared with SecPQ. Extensive experimental evaluations on billions of records demonstrate the feasibility and effectiveness of both SecPQ and SecPQope. © 2004-2012 IEEE.

关键词： Prediction models

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Optimizing Cost Efficiency in Cloud-Based Automation with DevOps Techniques

Optimizing Cost Efficiency in Cloud-Based Automation with De...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Garg, Avni Singh, Tribhuvan Rajiv, Asha Dhamodaran, S. Sahoo, Girija Shankar Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331542375

Cloud automation has created a trend offering several advantages to businesses, including scalability and cost-effectiveness. Although deploying such automation in a cloud setting is fundamental, optimizing its cost efficiency proves challenging, particularly with DEVOPS techniques. DEVOPS integrates development and operations to promote collaboration and automate different parts of the software delivery process. It is also possible to elaborate the DEVOPS techniques by implementing them through cloud-based automation, which can greatly benefit both efficiency and cost. A key area for cost optimization is infrastructure management. By using DEVOPS techniques for the provisioning and deployment of resources, organizations can automate what have previously been tedious and manual processes, which can lead to lower infrastructure costs and more efficiency. Utilization of resources can be enhanced through DEVOPS techniques. Auto-scaling automatically adapts resources based on demand, while load balancing distributes workloads across resources to improve performance and costs. However, one area in which DEVOPS can dramatically enhance companies' cost efficiency is continuous monitoring and optimizing. Automated monitoring tools fire alerts notifying businesses about the most precise area where work is being done inefficiently in real-time, helping companies to save money. © 2025 IEEE.

关键词： Automation Businesses Collaboration Dramatically Efficiency

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UAV Swarm Path Planning for Sensor Data Collection via Double Pre-Partitioned Deep Q-Network

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IEEE Sensors Journal 2025年

作者： Zhang, Heng Gao, Yuan Wang, Xin He, Yunpeng Yang, Ming Wu, Xiaoming Yang, Chaoqun Cao, Xianghui Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China Jiangsu Ocean University School of Computer Engineering Lianyungang222000 China Southeast University School of Automation Nanjing210096 China

Collision-free path planning is a significant challenge in deploying unmanned aerial vehicle (UAV) swarms for sensor data collection tasks, particularly in uncertain environments. This paper addresses the problem of designing flight strategies for energy-constrained UAV swarms using deep reinforcement learning. We propose a locally-aware double pre-partitioned deep Q-network (DPPDQN) for flight planning of multiple UAVs in large-scale uncertain industrial environments. This algorithm aims to collect data from ground sensors with communication constraints while avoiding mutual collisions. In the proposed algorithm, we first introduce a node pre-partitioning scheme using an improved clustering algorithm, which efficiently reduces the number of access points for UAVs through effective data aggregation. Subsequently, we employ an area pre-partitioning algorithm that segments the detection area into several subregions based on the number of UAVs available for operation, thereby preventing collisions among the UAV swarm during task execution. Finally, we propose a deep Q-Network algorithm incorporating a convolutional neural network, which utilizes the local perceptions of UAVs as state inputs to address the 'curse of dimensionality"occurring in large-scale environments. Systematic simulation experiments validate the effectiveness and advantages of the proposed method. © 2025 IEEE.

关键词： Convolutional neural networks

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How small is big enough? Big data-driven machine learning predictions for a full-scale wastewater treatment plant

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Water Research 2025年 274卷 123041页

作者： Ma, Yanyan Qiao, Yiheng Chen, Mengxue Rui, Dongni Zhang, Xuxiang Liu, Weijing Ye, Lin State Key Laboratory of Pollution Control and Resource Reuse School of Environment Nanjing University Nanjing210023 China Nanjing Gaoke Environmental Technology Co. Ltd. Nanjing210038 China Jiangsu Provincial Key Laboratory of Environment Engineering Jiangsu Provincial Academy of Environmental Science Nanjing210036 China

Wastewater treatment plants (WWTPs) generate vast amounts of water quality, operational, and biological data. The potential of these big data, particularly through machine learning (ML), to improve WWTP management is increasingly recognized. However, the costs associated with data collection and processing can rise sharply as datasets grow larger, and research on determining the optimal data volume for effective ML application remains limited. In this study, we comprehensively analyzed water quality, operational, and biological data collected from a full-scale WWTP over 970 days. Our results demonstrate that ML models can predict not only operational and water quality parameters (concentrations of dissolved oxygen and effluent chemical oxygen demand) but also the abundances of functional bacteria. Notably, we discovered that increasing data volume does not always improve model performance, and that data collection intervals do not need to be excessively small, as moderate intervals can still yield reliable predictions. These findings suggest that excessively large datasets may not be necessary for effective ML predictions in WWTPs. Overall, this study underscores the importance of optimizing dataset size to balance computation efficiency and prediction accuracy, providing valuable insights into data management strategies that can enhance the operational efficiency and sustainability of WWTPs. © 2024 Elsevier Ltd

关键词： Data accuracy

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DLPO : Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective

arXiv

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arXiv 2025年

作者： Peng, Dengyun Zhou, Yuhang Chen, Qiguang Liu, Jinhao Chen, Jingjing Qin, Libo Research Center for Social Computing and Interactive Robotics Harbin Institute of Technology China School of Computer Science and Engineering Central South University China Fudan University China

Large Language Models (LLMs) have achieved remarkable success across diverse tasks, largely driven by well-designed prompts. However, crafting and selecting such prompts often requires considerable human effort, significantly limiting its scalability. To mitigate this, recent studies have explored automated prompt optimization as a promising solution. Despite these efforts, existing methods still face critical challenges in robustness, efficiency, and generalization. To systematically address these challenges, we first conduct an empirical analysis to identify the limitations of current reflection-based prompt optimization paradigm. Building on these insights, we propose 7 innovative approaches inspired by traditional deep learning paradigms for prompt optimization (DLPO), seamlessly integrating these concepts into text-based gradient optimization. Through these advancements, we progressively tackle the aforementioned challenges and validate our methods through extensive experimentation. We hope our study not only provides valuable guidance for future research but also offers a comprehensive understanding of the challenges and potential solutions in prompt optimization. Our code is available at https://***/sfasfaffa/DLPO. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework

arXiv

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arXiv 2025年

作者： Li, Xiaolong Wei, Jianhao Wang, Haidong Dong, Li Chen, Ruoyang Yi, Changyan Cai, Jun Niyato, Dusit Shen, Xuemin The Xiangjiang Laboratory Hunan Provincial General University Key Laboratory of IoT Intelligent Sensing and Distributed Collaborative Optimization Hunan University of Technology and Business Changsha410205 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China The Department of Electrical and Computer Engineering University of Concordia Montreal Canada The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada

In intelligent transportation systems (ITSs), incorporating pedestrians and vehicles in-the-loop is crucial for developing realistic and safe traffic management solutions. However, there is falls short of simulating complex real-world ITS scenarios, primarily due to the lack of a digital twin implementation framework for characterizing interactions between pedestrians and vehicles at different locations in different traffic environments. In this article, we propose a surveillance video assisted federated digital twin (SV-FDT) framework to empower ITSs with pedestrians and vehicles in-the-loop. Specifically, SVFDT builds comprehensive pedestrian-vehicle interaction models by leveraging multi-source traffic surveillance videos. Its architecture consists of three layers: (i) the end layer, which collects traffic surveillance videos from multiple sources;(ii) the edge layer, responsible for semantic segmentation-based visual understanding, twin agent-based interaction modeling, and local digital twin system (LDTS) creation in local regions;and (iii) the cloud layer, which integrates LDTSs across different regions to construct a global DT model in realtime. We analyze key design requirements and challenges and present core guidelines for SVFDT’s system implementation. A testbed evaluation demonstrates its effectiveness in optimizing traffic management. Comparisons with traditional terminal-server frameworks highlight SV-FDT’s advantages in mirroring delays, recognition accuracy, and subjective evaluation. Finally, we identify some open challenges and discuss future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Multi-modal Long-Short Distance Attention-based Transformer-GAN for PET Reconstruction with Auxiliary MRI

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Zeng, Pinxian Zeng, Xinyi Wang, Yan Zhou, Luping Zu, Chen Wu, Xi Zhou, Jiliu Shen, Dinggang Sichuan University School of Computer Science China University of Sydney School of Electrical and Information Engineering Australia Department of Risk Controlling Research *** China Chengdu University of Information Technology School of Computer Science China ShanghaiTech University School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd Shanghai200232 China Shanghai Clinical Research and Trial Center Shanghai201210 China

To obtain high-quality PET scans while minimizing potential radiation hazards for patients, various GAN-based methods have been developed to reconstruct high-quality standard-count PET (SPET) images from low-count PET (LPET) ones. While recent efforts try to integrate MRI or CT to enhance reconstruction in a multi-modal way, current architectures mainly face two limitations: (1) CNN backbones or simple Transformer bottleneck layers are insufficient for robust semantic understanding, and (2) the identical strategies for multi-modal feature extraction and fusion overlook each modality's respective importance for the reconstruction task. In this work, we propose the Multi-modal Long-Short Distance Attention-based Transformer-GAN (MLSDA-GAN), a novel network combining 3D transformer and CNN architecture for PET image reconstruction. Specifically, to extract fine-grained features with a small number of parameters, our MLSDA-GAN integrates multi-scale convolution into the embedding part of the transformer. As for our multi-modal design, given the strong correlation between LPET and SPET in structural characteristics, we treat MRI as an auxiliary modality to LPET and achieve effective multi-modal extraction and fusion strategies. These strategies include (1) a PET-specific Self-attention Extraction (PSE) block for comprehensive feature extraction of the primary LPET and (2) a Multi-modality Cross-attention Fusion (MCF) block for effective multi-modal interaction and fusion, enabling us to more efficiently model both long- and short-range relationships in the corresponding feature extraction and fusion processes. Experiments demonstrate superiority of our method quantitatively and qualitatively. Code is available at https://***/Aru321/MLSDA-GAN. © 1991-2012 IEEE.

关键词： Radiation hazards

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Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Huo, Xiao Hou, Junhui Wan, Shuai Yang, Fuzheng Xidian University School of Telecommunication Engineering Xi'an710071 China City University of Hong Kong Department of Computer Science Hong Kong Northwestern Polytechnical University School of Electronics and Information Xi'an710129 China Royal Melbourne Institute of Technology School of Engineering MelbourneVIC3001 Australia

The evolution of 3D visualization techniques has fundamentally transformed how we interact with digital content. At the forefront of this change is point cloud technology, offering an immersive experience that surpasses traditional 2D representations. However, the massive data size of point clouds presents significant challenges in data compression. Current methods for lossy point cloud attribute compression (PCAC) generally focus on reconstructing the original point clouds with minimal error. However, for point cloud visualization scenarios, the reconstructed point clouds with distortion still need to undergo a complex rendering process, which affects the final user-perceived quality. In this paper, we propose an end-to-end deep learning framework that seamlessly integrates PCAC with differentiable rendering, denoted as rendering-oriented PCAC (RO-PCAC), directly targeting the quality of rendered multiview images for viewing. In a differentiable manner, the impact of the rendering process on the reconstructed point clouds is taken into account. Moreover, we characterize point clouds as sparse tensors and propose a sparse tensor-based transformer, called SP-Trans. By aligning with the local density of the point cloud and utilizing an enhanced local attention mechanism, SP-Trans captures the intricate relationships within the point cloud, further improving feature analysis and synthesis within the framework. Extensive experiments demonstrate that the proposed RO-PCAC achieves state-of-the-art compression performance, compared to existing reconstruction-oriented methods, including traditional, learning-based, and hybrid methods. The code will be released at https://***/net-F/***. © 2025 IEEE.

关键词： Rendering (computer graphics)

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An Ensemble Machine Learning Approach for Predicting Sources of Organic Aerosols Measured by Aerosol Mass Spectrometry

ACS ES&T Air

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ACS ES&T Air 2025年第3期2卷 378-385页

作者： Yunjiang Zhang Jie Fang Qingxiao Meng Xinlei Ge Hasna Chebaicheb Olivier Favez Jean-Eudes Petit Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control School of Environmental Science and Engineering Nanjing University of Information Science and Technology Nanjing 210044 China；Institut National de l’Environnement Industriel et des Risques Verneuil-en-Halatte 60550 France Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control School of Environmental Science and Engineering Nanjing University of Information Science and Technology Nanjing 210044 China Institut National de l’Environnement Industriel et des Risques Verneuil-en-Halatte 60550 France Laboratoire des Sciences du Climat et de l’Environnement CEA/Orme des Merisiers Gif sur Yvette 91191 France

Long-term source apportionment of atmospheric organic aerosols (OA) is essential for supporting air pollution management strategies. While aerosol mass spectrometry (AMS) combined with traditional source apportionment tools can accurately identify various OA sources, they face efficiency challenges when processing large volumes of long-term data. This study proposes an ensemble machine learning approach to efficiently apportion OA sources from long-term AMS measurements. Using six-year observation of a simplified version of AMS (i.e., ACSM) in the Paris region along with OA factor data derived from positive matrix factorization analysis, we developed an ensemble machine learning source apportionment model. Compared to individual machine learning algorithms, the ensemble model substantially reduced the root-mean-square error (RMSE) and increased the correlation coefficient in predicting OA sources by approximately 30% and 5%, respectively. Sensitivity analysis with five years of baseline data revealed that model performance relatively stabilizes when the training data exceeds two years, with RMSE values for primary and secondary OA factors at 0.31–0.45 μg/m3and 0.69–0.84 μg/m3, respectively. This ensemble model not only enhances the efficiency of long-term OA source apportionment but also holds potential for near-real-time online applications.

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