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Reducing Client-Side Cost in Secure Inner Product Evaluations 39

Reducing Client-Side Cost in Secure Inner Product Evaluation...

引用

39th International Conference on Information Networking, ICOIN 2025

作者： Hahn, Changhee Koo, Dongyoung Hur, Junbeom Seoul National University of Science and Technology Dept. Electrical and Information Engineering Korea Republic of Hansung University Dept. Convergence Security Korea Republic of Korea University Dept. Computer Science and Engineering Korea Republic of

ISBN: (纸本)9798331506940

In the era of ubiquitous data collection and analysis, preserving privacy while utilizing data, such as inner product evaluations, poses a significant challenge. One such method is inner product functional encryption (IPFE), which enables the computation of inner products on encrypted vectors without exposing the vectors themselves. However, the computational intensity of IPFE decryption poses challenges, particularly for resource-limited client devices and high-dimensional vectors. To facilitate partial decryption, prior IPFE schemes require the client to derive a partial decryption key from an assigned secret key, incurring computation costs linear to the vector dimension. In this paper, we provide a framework for the challenges of IPFE, enabling efficient decryption outsourcing, maintaining data privacy, and facilitating key generation outsourcing in IPFE schemes. In the proposed framework, outsourced decryption using a partial decryption key generates a masked plaintext that only the client can unmask. Furthermore, the process of deriving the partial decryption key can be fully outsourced, thereby ensuring that the client's computational burden remains constant. © 2025 IEEE.

关键词： Sensitive data

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The Role of Aggregator Topology on the Impact of False Data Injection Attacks

The Role of Aggregator Topology on the Impact of False Data ...

引用

Intelligent Data and Security (IDS), IEEE International Conference on

作者： Javad Mokhtari Koushyar Mina Guirguis George Atia Dept. of Computer Science Texas State University San Marcos TX USA Dept. of Electrical & Computer Engineering Dept. of Computer Science University of Central Florida Orlando FL USA

ISBN: (数字)9798331596613

ISBN: (纸本)9798331596620

Data integrity in smart power grid is crucial for accurate billing, demand prediction, and the overall grid stability. Aggregators in the power grid are entities that aggregate power data measurements from consumers and relay this information to upper levels to guide power forecasting, generation and distribution. It has been shown that attackers mounting False Data Injection Attacks (FDIA) can disrupt the process of power state estimation while evading detection. In this paper, we study the role of the topology – the manner in which consumers are connected to substations and aggregators – on the impact of FDIA on the power grid. Through simulation experiments, we demonstrate the impact of compromised aggregators on demand forecasting leading to power loss and power outages.

关键词： Accuracy Substations Data integrity Vectors Stability analysis Topology Smart grids Power system reliability Security State estimation

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Synergia: Device-Edge Server Association for ISAC-assisted Mobile Edge Computing systems

Synergia: Device-Edge Server Association for ISAC-assisted M...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Panagiotis Charatsaris Arianna Santamaria Penafiel Maria Diamanti Eirini Eleni Tsiropoulou Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The efficient operation of the unified Integrated Sensing and Communication (ISAC) – Mobile Edge Computing (MEC) systems is important for enhancing data sensing, communication, and computation processes in next-generation wireless systems. Despite prior research focusing on these systems, little attention has been given to optimizing the device-edge server associations. This paper addresses this gap by introducing the novel two-stage device-edge server association Synergia framework. Firstly, representative utility functions capture the characteristics of the devices and MEC servers by jointly considering their sensing, communication, and computation characteristics. Secondly, the Estimated Synergia framework leverages the Matching Theory to rapidly determine an initial device-server matching by disregarding the devices’ externalities, i.e., the matching decisions of other devices. Thirdly, the Accurate Synergia model refines and improves this matching by using the coalition formation games, while considering the devices’ externalities in optimizing the utilities of both the devices and the MEC servers. Extensive numerical evaluations demonstrate the Synergia’s operational efficiency and scalability, outperforming reinforcement learningbased approaches. Also, a real-world application involving car accident detection validates its applicability.

关键词： Wireless communication Multi-access edge computing Scalability Games Pricing Integrated sensing and communication Numerical models Servers Global communication Next generation networking

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Dengue Cases Prediction and Analysis using LSTM and Region-specific Data

Dengue Cases Prediction and Analysis using LSTM and Region-s...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ud Deen Shafique, Hafiz Noor Ali, Nauman Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Ashraf, Farhan Khan, Zohaib Ahmed Javaid, Arslan University Lahore Department of Computer Engineering Information Technology Punjab Pakistan Bahria University Lahore Campus Dept. of Computer Science Zunnurain Pakistan University of Central Punjab Lahore Faculty of Information Technology Department of Computer Science Pakistan National College of Business Administration and Economics Department of Computer Science Lahore Pakistan Information Technology University Lahore Pakistan

ISBN: (纸本)9798350373783

Dengue Fever presents a substantial global health challenge, necessitating the development of accurate predictive models for effective disease management. This research paper conducts a comprehensive analysis of a Long ShortTerm Memory (LSTM) neural network-based model for forecasting Dengue cases in the Philippines. The study includes data preprocessing, model development using TensorFlow and Keras, and thorough evaluation using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Rsquared (R2). The dataset, 'Dengue Cases in the Philippines,' provides a detailed historical record of Dengue fever cases and fatalities across various regions, enabling a thorough analysis of temporal and spatial dynamics. Data preprocessing involves label encoding, normalization, and careful data splitting to ensure accurate model training and testing. The LSTM neural network architecture is optimized through hyperparameter tuning, including learning rate, batch size, and epochs, to improve prediction accuracy. Model performance is rigorously evaluated, with an MAE of 913, indicating areas for further improvement. While the model shows promise, there is room for refinement. This study contributes to the field by exploring the feasibility of LSTM models for Dengue prediction, with insights that may inform future research in disease forecasting and public health strategies. Limitations, ethical considerations, and recommendations for further work are discussed. © 2024 IEEE.

关键词： Electronic health record

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A Lightweight CNN for Real-Time Pre-Impact Fall Detection

A Lightweight CNN for Real-Time Pre-Impact Fall Detection

引用

2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Turetta, Cristian Toqeer Ali, Muhammad Demrozi, Florenc Pravadelli, Graziano University of Verona Dept. of Computer Science Italy University of Stavanger Dept. of Electrical Engineering and Computer Science Norway University of Verona Dept. of Engineering for Innovation Medicine Italy

ISBN: (纸本)9783982674100

Falls can have significant and far-reaching effects on various groups, particularly the elderly, workers, and the general population. These effects can impact both physical and psychological well-being, leading to long-term health problems, reduced productivity, and a decreased quality of life. Numerous fall detection systems have been developed to prompt first aid in the event of a fall and reduce its impact on people's lives. However, detecting a fall after it has occurred is insufficient to mitigate its consequences, such as trauma. These effects can be further minimized by activating safety systems (e.g., wearable airbags) during the fall itself - specifically in the pre-impact phase - to reduce the severity of the impact when hitting the ground. Achieving this, however, requires recognizing the fall early enough to provide the necessary time for the safety system to become fully operational before impact. To address this challenge, this paper introduces a novel lightweight convolutional neural network (CNN) designed to detect pre-impact falls. The proposed model overcomes the limitations of current solutions regarding deployability on resource-constrained embedded devices, specifically for controlling the inflation of an airbag jacket. We extensively tested and compared our model, deployed on an STM32F722 microcontroller, against state-of-the-art approaches using two different datasets. © 2025 EDAA.

关键词： Safety devices

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The Development of an Autonomous Robotic Scanning System with the Laser Rangefinder

The Development of an Autonomous Robotic Scanning System wit...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Ko, Hung-Chin Chen, Syuan-Yi He, Jyun-Hong Chiang, Hsin-Han National Taiwan Normal University Dept. Electrical Engineering Taipei Taiwan National Taipei University of Technology Dept. Computer Science and Information Engineering Taipei City Taiwan National Taipei University of Technology Dept. Vehicle Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

Due to the rise of Industry 4.0, many industries have begun to replace traditional human labor with automated equipment to achieve greater productivity and more accurate inspection results. Due to its advantages of high stability, high accuracy, and low repetition error, many manufacturing industries have chosen to use robotic arms to replace human labor in production and inspection tasks to obtain faster and more accurate work results. Based on the above advantages, this experiment combines a seven-axis robot arm with 2D LiDAR to scan the object to be inspected, and the algorithm is used to analyze, calculate and plan the inspection path of the arm so that the robot arm can automatically scan the surface of the object to be inspected in an equal speed, smooth, and reproducible way. © 2023 IEEE.

关键词： Inspection

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A New Compensation Topology For Inductive Power Transfer (IPT) systems 49

A New Compensation Topology For Inductive Power Transfer (IP...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Huang, You-Chun Madawala, Udaya Baguley, Craig A. Hsieh, Yao-Ching National Sun Yat-sen University Dept. of Electrical Engineering Kaohsiung Taiwan University of Auckland Dept. of Electrical Computer and Software Engineering Auckland New Zealand Auckland University of Technology Dept. of Electrical and Electronic Engineering Auckland New Zealand

ISBN: (纸本)9798350331820

This paper presents a new IPT compensation topology that can be operated at two different frequencies to form either primary series and secondary parallel (S-P) or primary parallel and secondary parallel (P-P) compensation. Model are presented to demonstrate how this transformation between S-P and P-P compensations with dual frequency control. The simulated results of a 1 kW IPT system have verified that the proposed system can remain output power with phase-shift modulation (PSM) and allows for efficient power transfer despite large pad misalignments by operating at different compensation. © 2023 IEEE.

关键词： Energy transfer

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Comparative Analysis of Binary and Multiclass Activity Recognition in High-Quality Newborn Resuscitation Videos 5

Comparative Analysis of Binary and Multiclass Activity Recog...

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5th Northern Lights Deep Learning Conference, NLDL 2024

作者： García-Torres, Jorge Meinich-Bache, Øyvind Brunner, Sara Rettedal, Siren Kibsgaard, Amalie Engan, Kjersti Dept. Electrical Engineering and Computer Science University of Stavanger Norway Laerdal Medical AS Stavanger Norway Dept. of Pediatrics Stavanger University Hospital Norway Faculty of Health Sciences University of Stavanger Norway Dept. of Research Stavanger University Hospital Norway

Globally, 3-10% of newborns do not breathe spontaneously at birth and need resuscitation. Prompt initiation of resuscitative interventions such as tactile stimulation and positive pressure ventilation can reduce neonatal mortality and morbidity associated with birth asphyxia. Automated video analysis of resuscitation episodes may be beneficial for evaluation and debriefing purposes. In this work, a dataset of 220 newborn resuscitation videos collected at the Stavanger University Hospital (Norway) is used to develop NBT-I3D, a deep neural network pipeline to automatically recognize resuscitation activities. To assess the task, both binary and multiclass networks have undergone training, allowing for a comparison of the two approaches. Results obtained for binary classification show a mean precision and recall of 84.76% and 80.92%, respectively. For multiclass, a mean precision and recall of 72.26% and 74.80% are reported. © NLDL 2024. All rights reserved.

关键词： Resuscitation

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TRUSTCACHE: Trust-based Content Caching in Information-Centric Networks

TRUSTCACHE: Trust-based Content Caching in Information-Centr...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Sean Tsikteris Aisha B Rahman Md Sadman Siraj Panagiotis Charatsaris Eirini Eleni Tsiropoulou Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The Information-Centric Networking (ICN) paradigm has reshaped the modern network architectures and promises efficient content delivery to the end-users. This paper introduces TRUSTCACHE, a novel framework enabling the content caching within ICNs and focusing on the trust-based ICN selection and optimal cache memory allocation to the Content Providers (CPs). The TRUSTCACHE framework incorporates the Optimistic Q-learning with Upper Confidence Bound reinforcement learning algorithm that enables the CPs to autonomously select ICNs based on their cache memory availability and trust levels. Also, TRUSTCACHE enables the CPs to jointly consider the reliability of the ICNs and their cache memory availability by integrating a novel trust model. Furthermore, TRUSTCACHE leverages the multilateral bargaining principles in order to ensure the optimal cache memory allocation among the CPs, in terms of aligning with their profit margin characteristics. Simulation-based experiments validate TRUSTCACHE’s operational efficiency across diverse CP profit margin profiles and highlight its superiority over alternative models lacking trust-based ICN selection or employing proportional fairness strategies for cache memory allocation.

关键词： Q-learning Cache memory Information-centric networking Focusing Resource management Reliability Global communication Game theory

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A Unified Deep Learning Approach for Integrating Retinal Image Diagnosis and Surgical Tool Classification 1

A Unified Deep Learning Approach for Integrating Retinal Ima...

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1st IEEE International Conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Jesica, J. Anitha, J. Sujitha Juliet, D. Belfin, R.V. Karunya Institute of Technology and Sciences Dept. of Data Science and Cyber Security Coimbatore India Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India University of North Carolina Dept. of Neurology Chapel Hill United States

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

Early and accurate diagnosis by using retinal image processing is critical for enabling optimized patient care. Existing techniques for the diagnosis in medical image processing often face limitations. This research study leverages the comparative analysis of advanced Convolutional Neural Networks (CNNs), including AlexNet, InceptionV3, Xception, and EfficientNetB3;out of which EfficientNetB3 gives the comparatively higher accuracy of 84%. This research work presents a novel deep learning-based approach for detecting diseases along with the integration of Lenscraft AI. Lenscraft AI helps to segment surgical masks and identify the surgical tools used in surgical recordings using YOLOv8. The developed tool and tracking method offers a higher precision value of 0.8863 and efficiency in identifying the surgical instruments and potentially improving surgical technique for the trainee surgeons and patient safety. © 2024 IEEE.

关键词： Convolutional neural networks

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