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

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

引用

2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： He, Jiaming Liu, Cheng Hou, Guanyu Jiang, Wenbo Li, Jiachen 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: (纸本)9798350368741

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. © 2025 ieee.

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A Novel Channel Selection Method for Near-Infrared Spectroscopy-based Brain-computer Interfaces 13

A Novel Channel Selection Method for Near-Infrared Spectrosc...

引用

13th international Winter conference on Brain-computer Interface, BCI 2025

作者： Lee, Hyung-Tak Hwang, Han-Jeong Dept. Electronics and Information Engineering Korea University Sejong Korea Republic of

ISBN: (纸本)9798331521929

Brain-computer interface (BCI) studies have focused on enhancing performance, reducing computational costs, and minimizing equipment size for practical applications. One effective approach to address these challenges is minimizing the number of recording channels. Among various neuroimaging modalities used in BCI development, near-infrared spectroscopy (NIRS) has unique characteristics, requiring two optodes to form a single channel. As a result, reducing the number of NIRS channels does not necessarily decrease the total number of optodes, potentially leaving the equipment bulky and inconvenient. In this study, we propose a novel channel selection algorithm, optode-based sequential forward selection (oSFS), which enhances the traditional SFS (tSFS) algorithm by accounting for the number of optodes. The proposed oSFS explores the optimal placement of optodes by simultaneously evaluating optode pairings and classification accuracy. Both tSFS and oSFS significantly improved classification accuracy compared to using all channels, but no significant difference was found between the two methods. Notably, tSFS reduced the number of optodes by only 16% compared to using all channels, whereas oSFS reduced by 52% when obtained the highest accuracy. Our study introduces the first channel selection method tailored to the unique characteristics of NIRS, making a significant step toward the practical application of practical application of NIRS-based BCIs. © 2025 ieee.

关键词： Near infrared spectroscopy

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A Certain Investigation of Gesture based Control System in Infotainment of Hybrid Car Using Internet of Things

A Certain Investigation of Gesture Based Control System in I...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Vinoth Kumar, K. Thangamani, R. Rengarajan, V. Achinth, H.S. Agratha, N. Prithviraj, Jaison Joel New Horizon College of Engineering Department of Eee Bengaluru India New Horizon College Department of Bca Bengaluru India Velammal Engineering College Department of Eee Chennai India New Horizon College of Engineering Department of Me Bengaluru India

ISBN: (纸本)9798331509828

Multiple devices while driving can lead to a loss of concentration on the road, which contributes to about a third of accidents. To address this problem, significant research is being devoted to developing human-machine interfaces that allow drivers to control the vehicle's electronic components without distraction. In this paper, a complete system that allows control of the infotainment system with hand gestures. The system focuses on the shift stick area and leverages the car roof with a visible and infrared camera integrated with a novel computer vision algorithm. © 2025 ieee.

关键词： Gesture recognition

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IoT based Line of Control Monitoring System 2

IoT based Line of Control Monitoring System

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Petkar, Taniya Faculty of Engineering and Technology Department of Computer Science And Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This paper presents a novel line-of-control (LoC) monitoring system that leverages the Internet of Things (IoT) to improve border security. The system creates a strong infrastructure for real-time monitoring throughout critical border regions, especially the Line of Control, by utilizing IoT devices, sensors, and communication technologies. The coverage, timeliness, and data quality of conventional border surveillance systems are frequently compromised. As opposed to this, the suggested system makes use of a wirelessly networked network of intelligent sensors, such as infrared, acoustic, and video cameras. By utilizing sophisticated image processing algorithms and machine learning to analyze the gathered data, the system is able to differentiate between regular activities and any security risks. To help security staff make decisions quickly, real-time updates are transmitted to a centralized control center. Integration with cloud computing also makes data analysis, retrieval, and storage simpler and guarantees data accessibility for forensic investigations and trend research. © 2025 ieee.

关键词： Network security

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Segmentation of Ship Propeller Cavitation Area Using Image-based Anomaly Detection Network 6

Segmentation of Ship Propeller Cavitation Area Using Image-B...

引用

6th ieee international conference on Image Processing, Applications and systems, IPAS 2025

作者： Jeong, Wonje Shin, Yong-Jin Park, Soon-Yong Kyungpook National University School of Electronic and Electrical Engineering Daegu41566 Korea Republic of Samsung Heavy Industries Co. Ltd. Daejeon34051 Korea Republic of

ISBN: (纸本)9798331506520

Cavitation is the formation of vapor-filled cavities in the liquid. Cavitation in rotating propeller can cause various engineering problems to ships, such as hull vibration, noise, thrust reduction, and propeller corrosion. To address this cavitation problem in designing ship propellers, it needs to analyze the cavitation phenomenon in real propellers. In this study, we introduce a computer vision method to detect and segment cavitation areas in a rotating propeller. The pro-posed method captures the images of a high-speed rotating propeller in a water tunnel and detect the image area of the cavitation. As the first study in the im-age-based automatic cavitation detection, we employ a deep neural network, an anomaly detection method called DDAD (Anomaly Detection with Conditioned Denoising Diffusion Models). In addition, we also use a STN (Spatial Transformer Networks) network to compensate the image shaking caused by the vibration from the high-speed propeller and water flow. After the STN compensation, normal and abnormal images are automatically aligned to find cavitation areas more accurately. The proposed method is the first to use an anomaly detection network for ship propeller cavitation detection. Experimental results show that the proposed method can automatically detect the cavitation area without any annotation supervision. © 2025 ieee.

关键词： Cavitation

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IoT based Portable Multi-Purpose Biometric Authentication System

IoT Based Portable Multi-Purpose Biometric Authentication Sy...

引用

2025 ieee international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Khobragade, Aman Wyawahare, Nikhil Gonnade, Priyanka G.H. Raisoni College of Engineering and Management Department of Cse Nagpur India Nagpur India G H Raisoni College of Engineering and Management Department of Cse Nagpur India

ISBN: (纸本)9798331529833

This research paper aims to contribute to the field of biometric attendance systems. Biometric system which uses unique biological traits like fingerprints for identification and authentication, offers several benefits over traditional authentication methods like passwords or ID cards. The benefits of using biometric system can be enhanced security, convenience and high efficiency, reduced risk of fraud, cost-effective in long run, accountability, scalability, multi-factor authentication, etc. In most of the organizations or institutes attendance is taken by the traditional methods like register book but this method takes time then attendance from these registers is uploaded to server for further attendance report of students. To withstand this problem, we have proposed an IoT based multipurpose biometric authentication system. We have developed a portable system that includes fingerprint sensors to recognize the fingerprint of various students and show the details of the students whose fingerprints have been scanned. The data from the fingerprint scanner is sent to the Raspberry PI server using Wi-Fi. We also have also designed a user interface for students and faculty to view and manage the students and their attendance. The attendance report can be exported to excel file any time faculty want the report. The biometric system provides 80% efficiency over the regular attendance methods. © 2025 ieee.

关键词： Query languages

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Dynamic Street Lighting based on Adaptive Learning

Dynamic Street Lighting based on Adaptive Learning

引用

2025 international conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Biswas, Suvrojyoti Sinha, Mourani Techno India University Department of Computer Science and Engineering West Bengal Kolkata India Techno India University Department of Mathematics West Bengal Kolkata India

ISBN: (纸本)9798331513894

Smart street light solution utilizing Long Range Wide Area Network (LoRaWAN) is one of the highly interesting topics in the field of Internet of Things (IoT) automation engineering. It is seen that many prototypes and modelling were served with IoT based sensors in this existing research idea globally and in fact Narrow Band IoT is used in some cases for data sharing along with secure communication channels. But some factors other than sensory technology associated with it will become more crucial to consider as those are emerging now like street light controlling as per traffic loads, adjustment of illumination power dynamically, and some changes of road condition if possible to distribute the data under a fault-tolerant architecture. In this paper, there is proposed an adaptive algorithm for street light control with decision-making logic based on Reinforcement Learning in some of the road cases as discernment. And with it traffic pattern observation to feed the dataset further using a deep learning technique. Also, simulate a Deep Q-learning algorithm that is necessary to be shown here and the most enthusiastic thing of implementation is to observe the road patterns and vehicular movements and note them clearly. © 2025 ieee.

关键词： Adaptive algorithms

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Adaptive Kernel Optimization for Probabilistic Learning: Integrating Support Vector Machines with Gaussian Process Frameworks 1

Adaptive Kernel Optimization for Probabilistic Learning: Int...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Kanth, Tatiraju V. Rajani Singh, Balveer Singh, Yaspal Bhutada, Sunil Yamaganti, Rohita P.K. University Department of Computer Science Engineering Shivpuri India P.K. University Department of Computer Science Engineering Shivpuri India P.K. University Department of Information Science Department of Computer Science Engineering Shivpuri India

ISBN: (纸本)9798331529246

With its robust capabilities for non-linear regression and classification, kernel-based learning has emerged as a fundamental component of state-of-the-art machine learning approaches. In order to improve probabilistic learning, this study investigates Adaptive Kernel Optimization (AKO), a new method that combines the best features of the Support Vector Machine (SVM) and the Gaussian Process (GP) frameworks. Achieving better flexibility in modeling complicated data distributions while keeping computational efficiency is achieved by employing adaptive kernel functions in the suggested strategy. Quantifying uncertainty in addition to deterministic SVM classifications is made possible with the incorporation of GP kernels, which offer probabilistic insights. The suggested approach guarantees resilience across varied and high-dimensional datasets by dynamically adjusting kernel parameters according to data properties. Extensive testing on benchmark datasets shows that, in comparison to conventional SVM and GP approaches, our model generalizability, classification accuracy, and interpretability are much improved. Autonomous systems, healthcare diagnostics, and financial sectors can all benefit from the scalable, adaptive, and probabilistic learning models that this study establishes. © 2025 ieee.

关键词： Adversarial machine learning

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A Review of Modern Computing Paradigms: Cloud, Fog, and Edge

A Review of Modern Computing Paradigms: Cloud, Fog, and Edge

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Sharma, Neha Choudhary, Monika Srivastava, Prakash Prabha, Chander Sharma, Himanshu Cgc Department of Computer Science and Engineering Punjab Landran India Department of Computer Science & Engineering Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331509828

In the current competitive environment, businesses are looking at how to lower their IT expenses while simultaneously improving their productivity and flexibility to respond quickly to changes in their operational procedures. One of the most widely accepted information technology-based services is cloud computing, which has garnered interest from both the academic and business communities as well as the general public-characteristics such as pay-per-use and subscription options ease of use, elasticity, and low entry costs. Low entry cost in fog, cloud, and edge computing is achieved by utilizing pay-as-you-go pricing models, leveraging open-source technologies, and optimizing resource allocation. By emphasizing how this technology differs from the rest of the current technology and concentrating on its numerous benefits and areas of application-which provide proof of its growing popularity-this paper presents the development of modern computer paradigms and associated areas of research interest. A discussion of each paradigm is done by clearly defining its main ideas and how they relate to the others. The paper presents a comparative assessment of Cloud, Edge, and Fog computing indicating their diverse areas of applicability. © 2025 ieee.

关键词： Fog computing

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Particle-based Data-driven Nonlinear State Estimation of Model-free Process from Nonlinear Measurements

Particle-based Data-driven Nonlinear State Estimation of Mod...

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ghosh, Anubhab Eldar, Yonina C. Chatterjee, Saikat Digital Futures Centre School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Faculty of Mathematics and Computer Science The Weizmann Institute of Science Israel

ISBN: (纸本)9798350368741

We consider the problem of causal filtering of a model-free process from (noisy) nonlinear measurements. The 'model-free process' means that we do not have a state-space model (SSM) of the process dynamics, limiting the use of traditional model-driven filters, such as unscented Kalman filter (UKF) and particle filter (PF). To address the problem we propose a particle-based data-driven nonlinear state estimation (pDANSE) method. In pDANSE, a recurrent neural network (RNN) provides the statistical parameters of a Gaussian prior of the underlying state, and particles are then drawn from the prior to compute the posterior moments. pDANSE is typically trained in a semi-supervised fashion. For our experiments we study the use of half-wave rectification as a nonlinear transformation of measurements. We first show that an unsupervised learning-based method under-performs, and subsequently the semi-supervised learning-based pDANSE performs satisfactorily. Using Lorenz-63 system as benchmark, pDANSE is found to be competitive against a model-driven PF that knows the exact SSM. © 2025 ieee.

关键词： deep neural network Kalman filter particle filter semi-supervised learning State estimation

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