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Electronics and Renewable Systems (ICEARS), International Conference on

作者： P. Chinnasamy Subashini. B Nijanthan Nijanthan Madasamy Raja. G Devulapally Swetha Sreenivasulu Reddy L Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Data Science and Business Systems School of Computing SRMIST India Computer Science and Engineering Paavai Engineering College Namakkal Tamilnadu India Information Technology Paavai Engineering College Namakkal Tamilnadu India Dept. of Computer Science and Engineering Vaagdevi Engineering College Telangana India Department of Mathematics School of Advanced Sciences Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms.

关键词： Patents Technological innovation Costs Smart contracts Intellectual property Product development Blockchains Security Protection Resilience

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PRESS: Defending Privacy in Retrieval-Augmented Generation via Embedding Space Shifting

PRESS: Defending Privacy in Retrieval-Augmented Generation v...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiaming He Cheng Liu Guanyu Hou Wenbo Jiang Jiachen Li School of Computer Science and Engineering University of Electronic Science and Technology of China China College of Computer Science and Cyber Security (Oxford Brookes College) Chengdu University of Technology China School of Data Science The Chinese University of Hong Kong Shenzhen China School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Retrieval-augmented generation (RAG) expands the capabilities of large language models (LLMs) in various applications by integrating relevant information retrieved from external data sources. However, the RAG systems are exposed to substantial privacy risks during the information retrieval process, leading to potential data leakage of private information. In this work, we present a Privacy-preserving Retrieval-augmented generation via Embedding Space Shifting (PRESS), systematically exploring how to protect privacy in RAG systems. Specifically, we first conduct proximal policy optimization (PPO) based training on pre-trained language models to generate target training samples. Then we employ a purposive shift fine-tuning on the text embedding model with the generated samples for guiding the RAG system to map potential privacy leaking queries to safe target in embedding space. Extensive experimental results on representative models and datasets demonstrate that our protection method achieves high defense performance with high efficiency while keeping the normal functionality of the RAG system.

关键词： Training Privacy data privacy Presses Soft sensors Retrieval augmented generation Signal processing Speech processing Protection Optimization

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AI-Empowered ClimateClean: Wastewater-to-Energy Solutions for a Resilient and Sustainable Future

AI-Empowered ClimateClean: Wastewater-to-Energy Solutions fo...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Saeed Hamood Alsamhi Ammar Hawbani Neil J Rowan Mamoona Naveed Asghar Niall O'Brolchain Eoghan Clifford Insight Centre for Data Analytics University of Galway Galway Ireland School of Computer Science Shenyang Aerospace University Shenyang China Centre for Sustainable Disinfection and Sterilization TUS Athlone Ireland School of Computer Science University of Galway Ireland School of Engineering University of galway Galway Ireland

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Wastewater treatment has emerged as a critical area of interest for sustainable resource management due to its ability to provide both environmental repair and produce renewable energy. Using artificial intelligence (AI) to boost efficacy and efficiency, this study explores wastewater-to-energy solutions from the standpoint of sustainable development and climate change mitigation. We analyse the energy outputs, advantages, and climatic implications, including Microbial Fuel Cells (MFC), Thermal Conversion (TC), Anaerobic Digestion (AD), and Hydrothermal Processes (HP) while providing a taxonomy and integrating AI-driven strategies for optimisation. By utilizing state-of-the-art research and practical implementations, we clarify optimal procedures, valuable insights, and critical success elements influencing the implementation. To manage resources sustainably, this paper emphasizes incorporating wastewater-to-energy solutions into current infrastructure wastewater-to-energy paradigms, encouraging multi-stakeholder involvement, and igniting circular economy ideas. It provides strategic guidance for negotiating complex technical and legal environments, pointing the way toward the scalable and significant implementation of wastewater-to-energy breakthroughs to achieve a more sustainable, resilient, and clean future in the face of climate change.

关键词： Climate change Technological innovation Renewable energy sources Scalability Wastewater Resource management Artificial intelligence Wastewater treatment Sustainable development Resilience

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A Communication-Aware and Energy-Efficient Genetic Programming Based Method for Dynamic Resource Allocation in Clouds 28th

A Communication-Aware and Energy-Efficient Genetic Programm...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Fang, Zhengxin Ma, Hui Chen, Gang Hartmann, Sven Chen, Shiping Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Informatics Clausthal University of Technology Clausthal-Zellerfeld Germany CSIRO Data61 Sydney Australia

ISBN: (纸本)9783031900648

Loosely coupled microservices have emerged as a new paradigm for efficiently deploying applications in clouds. However, dynamic resource allocation in clouds introduces significant challenges to microservice application deployment. On the one hand, frequent invocations between microservices may lead to substantial communication overhead if microservices are not allocated properly. On the other hand, the increasing number of microservices in modern applications makes it very challenging to minimize the energy consumption of a cloud data center as it introduces a bi-level optimization problem with an extremely large search space. In this paper, we propose a new communication-aware and energy-efficient genetic programming based method that automatically learns heuristics for dynamic resource allocation to jointly minimize the communication overhead and the energy consumption. Comprehensive experiments using real-world datasets show that our proposed method can evolve effective heuristics that noticeably outperform existing approaches for dynamic microservice deployment in clouds. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic programming

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A Reinforcement Learning Agent Controlled Multi-branch Small Object Detection Framework

A Reinforcement Learning Agent Controlled Multi-branch Small...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Junkun Hong Yitian Long Yueyi Luo Liujie Hua Jun Long Qianqian Qi School of Computer Science and Engineering Central South University Changsha China Data Science Institute Vanderbilt University Nashville Tennessee United States School of Mathematics and Statistics Central South University Changsha China Big Data Institution Central South University Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The past few years have witnessed the immense development of small object detection, which is aimed at detecting size-limited targets in high-resolution images. The prevailing methods focus on extracting fine-grained information by expanding the receptive fields and then generating the potential small object region. However, these solutions inevitably add sophisticated detectors and extra learning components, which incur time-consuming and computation-costing. Meanwhile, we observe that it’s suboptimal to extract fine features in such a generic way. To alleviate the issues, we propose a multi-branch small object detection framework with a regular-scale detection branch and a small-scale detection branch. Specifically, we design and pre-train a reinforcement learning agent to control feature extractors in both branches according to the results of small object areas. Moreover, we present a region clipping algorithm to rebuild the small object to regular size, which can be input into a mature detector directly. The extensive experiments on COCO, VisDrone, SODA-D, and our collecting TVDS datasets demonstrate our method outperforms the state-of-the-art methods in several metrics.

关键词： Measurement Pipelines Signal processing algorithms Object detection Reinforcement learning Detectors Signal processing Feature extraction data mining Speech processing

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DeepVec: State-Vector Aware Test Case Selection for Enhancing Recurrent Neural Network

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IEEE Transactions on Software engineering 2025年

作者： Jiang, Zhonghao Yan, Meng Huang, Li Sun, Weifeng Liu, Chao Sun, Song Lo, David Chongqing University School of Big Data and Software Engineering China Chongqing Normal University School of Computer and Information Science China Singapore Management University School of Information Systems Singapore

Deep Neural Networks (DNN) have realized significant achievements across various application domains. There is no doubt that testing and enhancing a pre-trained DNN that has been deployed in an application scenario is crucial, because it can reduce the failures of the DNN. DNN-driven software testing and enhancement require large amounts of labeled data. The high cost and inefficiency caused by the large volume of data of manual labeling, and the time consumption of testing all cases in real scenarios are unacceptable. Therefore, test case selection technologies are proposed to reduce the time cost by selecting and only labeling representative test cases without compromising testing performance. Test case selection based on neuron coverage (NC) or uncertainty metrics has achieved significant success in Convolutional Neural Networks (CNN) testing. However, it is challenging to transfer these methods to Recurrent Neural Networks (RNN), which excel at text tasks, due to the mismatch in model output formats and the reliance on image-specific characteristics. What’s more, balancing the execution cost and performance of the algorithm is also indispensable. In this paper, we propose a state-vector aware test case selection method for RNN models, namely DeepVec, which reduces the cost of data labeling and saves computing resources and balances the execution cost and performance. DeepVec selects data using uncertainty metric based on the norm of the output vector at each time step (i.e., state-vector), and similarity metric based on the direction angle of the state-vector. Because test cases with smaller state-vector norms often possess greater information entropy and similar changes of state-vector direction angle indicate similar RNN internal states. These metrics can be calculated with just a single inference, which gives it strong bug detection and model improvement capabilities. We evaluate DeepVec on five popular datasets, containing images and texts as well as commonl

关键词： Model checking

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Robust Optical Quantum Imaging Framework with Entangled Photons in Oceanic Turbulent Environments

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IEEE Transactions on Communications 2025年

作者： Cao, Jingyang Zhou, Mu Zhang, Ruichen Niyato, Dusit Nie, Wei Han, Zhu Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing China Nanyang Technological University College of Computing and Data Science Nanyang China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

As an important part of underwater optical technology, underwater imaging plays a crucial role in accurately capturing underwater targets and environmental features. Facing the challenges of complex ocean environments and severe photon attenuation, quantum imaging breaks through the limitations of traditional optical imaging technology by utilizing the characteristics of two-photon entanglement and time-space correlation, thus offering a new perspective on ocean turbulence. To this end, we propose a new underwater entangled-photon quantum imaging system. Specifically, we first construct an entangled photon quantum imaging physical model through ocean long-exposure turbulence and then exploit an entangled light co-incidence imaging reconstruction method to image the target object. Furthermore, in response to the problem that ocean environment has a great impact on photons, we develop a photon capture probability method based on entangled photon pairs to reduce the impact of the ocean turbulence noise on imaging and improve the underwater target imaging resolution. We experimentally demonstrate the effectiveness of our method by showing that even in harsh ocean environments, quantum imaging performs superior resolution capabilities over traditional light source imaging techniques. © 1972-2012 IEEE.

关键词： Quantum entanglement

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AD-Det: Boosting Object Detection in UAV Images with Focused Small Objects and Balanced Tail Classes

arXiv

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arXiv 2025年

作者： Li, Zhenteng Lian, Sheng Pan, Dengfeng Wang, Youlin Liu, Wei College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350002 China School of Software East China Jiaotong University Nanchang330013 China

Object detection in Unmanned Aerial Vehicle (UAV) images poses significant challenges due to complex scale variations and class imbalance among objects. Existing methods often address these challenges separately, overlooking the intricate nature of UAV images and the potential synergy between them. In response, this paper proposes AD-Det, a novel framework employing a coherent coarse-to-fine strategy that seamlessly integrates two pivotal components: Adaptive Small Object Enhancement (ASOE) and Dynamic Class-balanced Copy-paste (DCC). ASOE utilizes a high-resolution feature map to identify and cluster regions containing small objects. These regions are subsequently enlarged and processed by a fine-grained detector. On the other hand, DCC conducts object-level resampling by dynamically pasting tail classes around the cluster centers obtained by ASOE, maintaining a dynamic memory bank for each tail class. This approach enables AD-Det to not only extract regions with small objects for precise detection but also dynamically perform reasonable resampling for tail-class objects. Consequently, AD-Det enhances the overall detection performance by addressing the challenges of scale variations and class imbalance in UAV images through a synergistic and adaptive framework. We extensively evaluate our approach on two public datasets, i.e., VisDrone and UAVDT, and demonstrate that AD-Det significantly outperforms existing competitive alternatives. Notably, AD-Det achieves a 37.5% Average Precision (AP) on the VisDrone dataset, surpassing its counterparts by at least 3.1%. Code will be available at https://***/gentlezachary/AD-Det. Copyright © 2025, The Authors. All rights reserved.

关键词： Vehicle detection

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FedCosA: Optimised Federated Learning Model Using Cosine Annealing

FedCosA: Optimised Federated Learning Model Using Cosine Ann...

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Intelligent Systems (ETIS), Emerging Technologies for

作者： Binu John Varun G. Menon Mainak Adhikari Department of Computer Science & Engineering SCMS School of Engineering and Technology Ernakulam Kerala India APJ Abdul Kalam Technological University Thiruvananthapuram Kerala India School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram Kerala India

ISBN: (数字)9798331507541

ISBN: (纸本)9798331507558

The Federated Learning paradigm allows multiple devices or nodes to contribute to model training without sharing raw data, ensuring that sensitive information remains on the client side while still benefiting from collective learning. However, FL suffers several problems, including slow convergence, overfitting, and communication cost, particularly in Non-Independent and Identically Distributed (Non-IID) data settings. To solve these concerns, we offer an improved Federated Averaging (FedAvg) method that includes the Adam optimizer, cosine annealing learning rate scheduler, and weight decay (L2 regularisation). The Adam optimizer generates adaptive learning rates, but the cosine annealing schedule refines the learning rate over time for greater stability. Weight decay reduces overfitting by penalizing big model parameters. Furthermore, picking a subset of customers in each cycle lowers communication costs. Experimental findings on the MNIST dataset show that our strategy outperforms existing FL algorithms like FedAvg, FedSGD, FedNova, and FedSAM in test accuracy and loss minimization while significantly lowering communication load and training time. These enhancements make the suggested approach more effective and scalable for FL across several settings.

关键词： Training Adaptation models Schedules Annealing Costs Federated learning Minimization data models Overfitting Load modeling

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space 26

Safe Reinforcement Learning for Social Motor Coordination in...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Yang, Fan Huang, Yuening Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing HoustonTX United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (纸本)9798331521950

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints. © 2025 IEEE.

关键词： Reinforcement learning

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