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An efficient data aggregation scheme with local differential privacy in smart grid

Digital Communications and Networks 2022年第3期8卷 333-342页

作者： Na Gai Kaiping Xue Bin Zhu Jiayu Yang Jianqing Liu Debiao He School of Cyber Science and Technology University of Science and Technology of ChinaHefeiAnhui230027China Department of Electronic Engineering and Information Science University of Science and Technology of ChinaHefeiAnhui230027China Department of Electrical and Computer Engineering University of Alabama in HuntsvilleHuntsvilleAL35899USA Key Laboratory of Aerospace Information Security and Trusted Computing of Ministry of Education School of Cyber Science and EngineeringWuhan UniversityWuhanBeihu430072China

By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the smart grid collect the users' power usage data on a regular basis and upload it to the control center to complete the smart grid data acquisition. The control center can evaluate the supply and demand of the power grid through aggregated data from users and then dynamically adjust the power supply and price, etc. However, since the grid data collected from users may disclose the user's electricity usage habits and daily activities, privacy concern has become a critical issue in smart grid data aggregation. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring a trusted third party.

关键词： Local differential privacy Data aggregation Smart grid Privacy preserving

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Large Model Collaborative Optimization Enhanced by Mobile Consumer Electronics Constructed Federated ML

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IEEE Transactions on Consumer Electronics 2024年

作者： Bu, Chao Li, Jianlong Wang, Jinsong Tianjin University of Technology Key Laboratory of Computer Vision and System of Ministry of Education School of Computer Science and Engineering Tianjin300384 China Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology School of Computer Science and Engineering Tianjin300384 China

With the rapid development of internet of everything, current Mobile Consumer Electronics (MCEs) can support complex computing due to their specialized data-handling capacity. Thus, federated Machine Learning (ML) and edge computing are integrated to leverage such capacity of MCEs to enhance large model training. This paper studies motivations of all participants that take part in large model collaborative optimization from a global perspective, and proposes the positive promotion pattern that collaborates MCEs, Edge computing Servers (ECSs), and the centralized computing center to support large model collaborative optimization by introducing the idea of computing economic. We firstly propose the novel three-layered framework for large model collaboratively optimizing based on MCEs constructed federated ML. Secondly, we introduce the economic factor into the large model optimization by devising algorithms to assess consumptions of all participants and dynamically adjust incentive benefits according to the qualities of trained models. Finally, we design the scheme to encourage more MCEs to train local models by adaptively adjusting the ECS pricing strategy with the ECS future economic benefit trend considered. Experimental results validate that our proposed approach is able to optimize the global accuracy and improve the economic benefit more efficiently than the state of the art. © 1975-2011 IEEE.

关键词： Mobile edge computing

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Personality recognition based on ECG in response to VR emotional stimuli 23

Personality recognition based on ECG in response to VR emoti...

引用

3rd International Conference on Bioinformatics and Intelligent computing, BIC 2023

作者： Lan, Ping Xie, Jialan Hu, Zhaonian Liu, Guangyuan College of Electronic and Information Engineering Southwest University Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing400715 China Institute of Affective Computing and Information Processing Southwest University Chongqing400715 China Key Laboratory of Cognition and Personality Ministry of Education Faculty of Psychology Southwest University Chongqing400715 China

ISBN: (纸本)9798400700200

This paper explores the feasibility and effect of using ECG signals in response to virtual reality(VR) emotional stimuli for personality recognition. Personality represents an individual''s behavior pattern, so it has extensive research significance to identify personality. In the field of personality recognition, there have been researches on the use of physiological signals to recognize personality recently, but there have been no studies on personality recognition in VR scenarios. VR technology has been applied in many fields, and in the field of emotion recognition, the advantage of VR video as emotional arousal material has been proved. We designed the emotional stimulation experiment in VR scene to collect ECG signals, and constructed the personality recognition model. The results showed that the F1 scores of neuroticism, extraversion, openness, agreeableness and conscientiousness using only ECG were 0.79, 0.80, 0.81, 0.73 and 0.81, respectively, which were much higher than the random level. And we found that in VR scenes, negative emotional stimulation can achieve better personality recognition effect. Our results confirm the feasibility of personality recognition using ECG signals in response to VR emotional stimuli. © 2023 ACM.

关键词： Electrocardiograms

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CLIP-Driven Multi-Scale Instance Learning for Weakly Supervised Video Anomaly Detection

CLIP-Driven Multi-Scale Instance Learning for Weakly Supervi...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhangbin Qian Jiawei Tan Zhilong Ou Hongxing Wang School of Big Data and Software Engineering Chongqing University China Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Existing weakly supervised video anomaly detection methods mainly employ Multiple Instance Learning (MIL) to identify abnormal snippets in untrimmed videos. However, the semantics and presentations of anomalies frequently exhibit ambiguity that MIL is difficult to tackle. Moreover, MIL suffers from false alarms due to its independent optimization of each instance, neglecting temporal correlation between adjacent snippets. Consequently, we badly need to better connect abnormal presentations and their semantics, as well as to enable multi-temporal-scale anomaly discovery. This paper proposes a CLIP-Driven Multi-Scale Instance Learning (CMSIL) framework with two branches including Vision-Language (VL) and Multi-Scale Instance Learning (MSIL). The VL branch leverages the powerful visual concept priors from Contrastive Language-Image Pre-training (CLIP) to generate pseudo anomalies, thereby providing suspected anomaly cues for model training guidance. The MSIL branch utilizes a feature pyramid to fully mine fine-grained temporal dependencies by employing MIL within each pyramid level to learn anomalous patterns across different temporal scales. By collaborating with the two branches, the proposed CMSIL shows better proficiency in handling anomalies with varying durations. Extensive experiments on the XD-Violence and UCF-Crime datasets demonstrate the superior performance of our method. The code is available at https://***/casperZB/CMSIL.

关键词： Training Visualization Correlation Codes Semantics Focusing Streaming media Anomaly detection Optimization Context modeling

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A Versatile Framework for Multi-scene Person Re-identification

arXiv

引用

arXiv 2024年

作者： Zheng, Wei-Shi Yan, Junkai Peng, Yi-Xing The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China The Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education 510006 China

Person Re-identification (ReID) has been extensively developed for a decade in order to learn the association of images of the same person across non-overlapping camera views. To overcome significant variations between images across camera views, mountains of variants of ReID models were developed for solving a number of challenges, such as resolution change, clothing change, occlusion, modality change, and so on. Despite the impressive performance of many ReID variants, these variants typically function distinctly and cannot be applied to other challenges. To our best knowledge, there is no versatile ReID model that can handle various ReID challenges at the same time. This work contributes to the first attempt at learning a versatile ReID model to solve such a problem. Our main idea is to form a two-stage prompt-based twin modeling framework called VersReID. Our VersReID firstly leverages the scene label to train a ReID Bank that contains abundant knowledge for handling various scenes, where several groups of scene-specific prompts are used to encode different scene-specific knowledge. In the second stage, we distill a V-Branch model with versatile prompts from the ReID Bank for adaptively solving the ReID of different scenes, eliminating the demand for scene labels during the inference stage. To facilitate training VersReID, we further introduce the multi-scene properties into self-supervised learning of ReID via a multi-scene prioris data augmentation (MPDA) strategy. Through extensive experiments, we demonstrate the success of learning an effective and versatile ReID model for handling ReID tasks under multi-scene conditions without manual assignment of scene labels in the inference stage, including general, low-resolution, clothing change, occlusion, and cross-modality scenes. Codes and models are available at https://***/iSEE-laboratory/VersReID. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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Cdunet: A U-Shaped Encoder-Decoder Network with Channel Attention Block and Dynamic Feature Extraction Block for Ldct Denoising

SSRN

引用

SSRN 2024年

作者： Li, Minghao Teng, Yueyang Zhou, Jie Li, Mingrui Zhu, Ruiming Wang, Haoran College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110169 China

Background and Objective: Computer tomography (CT) has become an important tool of clinical diagnosis. However, X-ray radiation related to CT can cause harm to patients. Low-dose CT (LDCT) can reduce radiation, however it can bring noise. How to remove noise from LDCT is a worthwhile research topic. In recent years, deep learning has shown outstanding performance in the field of LDCT ***: This article proposes a U-shaped encoder-decoder network CDUNet with channel attention block (CAB) and dynamic feature extraction block (DFEB). The CAB assigns different weights to feature maps to better utilize multi-channel information. The DFEB uses CondConv, which is able to respond differently to different inputs. When processing CT images, DEFB can perform targeted processing on different CT slices and different parts within each slice to achieve better feature extraction. At the same time, we also use the Sobel operator to calculate the gradient of the image, combining gradient loss with Mean Square Error (MAE) loss to alleviate the smoothing problem and restore the structural information of the image ***: The experiments on the 2016 NIH-AAPM-Mayo Clinic LDCT Grand Challenge show that our method has better results compared with state-of-the-art (SOTA) methods. Specifically, our method not only has the best visual effects, but also outperforms other algorithms in terms of evaluation ***: Experiments shows that our method is reasonable and effective. The code is available at https://***/xxxxxxmmmmmm/CDUNet. © 2024, The Authors. All rights reserved.

关键词： Network coding

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Implementation Analysis of Reachability Graph and Its Application in Net Learning

Implementation Analysis of Reachability Graph and Its Applic...

引用

Computer, Control and Robotics (ICCCR), International Conference on

作者： Mingjian Guang Chungang Yan Zihao Lu Rui Duan Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

Reachability represents the most fundamental dynamic property of Petri nets. The reachability graph serves as a crucial tool for analyzing reachability, and its generation algorithm has garnered widespread attention. However, most of the existing work has focused primarily on the accuracy of the generation algorithm, often overlooking computational and time costs. Therefore, this paper concentrates on the implementation of reachability graphs and proposes three implementation methods: definition-based (DB), matrix-based (MB), and a fast generation method without self-loops (FG). Through comprehensive experiments, we evaluate and analyze the running speed and applicable scenarios of these three methods. Furthermore, we have successfully applied the proposed methods in net learning.

关键词： Digital control Costs Accuracy Heuristic algorithms Petri nets Benchmark testing Computational efficiency

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A Shortest Path Query Approach for Encrypted Graphs Based on Padding Dictionary Structure

A Shortest Path Query Approach for Encrypted Graphs Based on...

引用

2023 China Automation Congress, CAC 2023

作者： Yang, Ming Zhang, Kaiyang Mu, Chao Wang, Xin Liu, Yuanlong Zhang, Heng Key Laboratory of Computing Power Network and Information Security Shandong Computer Science Center Ministry of Education Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Grid Shandong Electric Power Company Jinan China School of Computer Engineering Jiangsu Ocean University Lianyungang China

ISBN: (纸本)9798350303759

Hybrid intelligence has emerged as an innovative approach that synergistically combines the strengths of human intelligence and artificial intelligence (AI) to address complex problems. In this paradigm, knowledge graphs play a pivotal role by supplying vital background information and contextual understanding to hybrid intelligence systems. However, due to limitations in local storage capacity, managing large-scale graph data often requires outsourcing to cloud servers. Nevertheless, the use of untrusted cloud servers poses a risk of potential user data leaks. To address these concerns, this paper presents an encryption scheme specifically designed to protect graph data, enabling queries involving intermediate nodes in the shortest path between two nodes within the graph. The proposed scheme ensures the privacy and security of outsourced data by employing a novel method that utilizes padding dictionary structure encryption. This method ensures strict limitations on the cloud server's capabilities, restricting it solely to query operations, it effectively prevents any compromise of user data or unauthorized access to sensitive information, users can securely store and process their graph data on servers, significantly reducing the potential risk of data leakage and unauthorized breaches. To validate the feasibility and effectiveness of the proposed solution, practical experiments were conducted using real-world data. © 2023 IEEE.

关键词： Cryptography

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ACDR-CRAFF Net: A Multi-Scale Network Based on Adaptive Channel and Coordinate Relational Attention Network for Remote Sensing Scene Classification

引用

IET Image Processing 2025年第1期19卷

作者： Dai, Wei Xu, Haixia Shi, Furong Yuan, Liming Wang, Xinyu Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

Accurate classification of remote sensing scene images is crucial for diverse applications, from environmental monitoring to urban planning. While convolutional neural networks (CNNs) have dramatically improved classification accuracy, challenges remain due to the complex distribution of small objects, varied spatial configurations, and intra-class multimodality in remote sensing images. In this work, we make three key contributions to address these challenges. (1) We propose the adaptive channel and coordinate relational attention network (ACDR-CRAFF), a novel multi-scale feature fusion framework designed to enhance feature representation across scales. (2) We introduce two innovative modules: the adaptive channel dimensionality reduction (ACDR) module, which dynamically adjusts channel representations to retain essential low-dimensional features, and the coordinate relational attention multi-scale feature fusion (CRAFF) module, which effectively captures and transfers spatial information between feature levels. (3) By integrating ACDR and CRAFF, our model achieves a progressive fusion of local to global features, ensuring robust feature expressiveness at multiple scales. Experimental results on four widely used benchmark datasets demonstrate that ACDR-CRAFF consistently outperforms several state-of-the-art methods, achieving significant improvements in classification accuracy and setting a new benchmark for complex remote sensing scene classification tasks. These results underscore the effectiveness of our approach in addressing the limitations of existing methods and advancing the state of the art in remote sensing image analysis. © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Photointerpretation

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General Sparse Normalization and Proportionate ASE Algorithms 5

General Sparse Normalization and Proportionate ASE Algorithm...

引用

5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Hadi, Abdul Peng, Yan Qadir, Abdul Li, Yingsong Khan, Khushal College of Information and Communication Engineering Harbin Engineering University Harbin China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn230088 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China College of Intelligent System and Control Science Engineering Harbin Engineering University China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

Andrew's Sine Estimator (ASE) has recently been used to invent adaptive filtering, which can combat more kind of noises than conventional estimators. Inspired by the LMS and its sparse forms, normalization and proportionate ASE algorithms with Lawson constraints are constructed with the respective given names of Lawson normalization ASE (LNASE) and Lawson proportionate NASE (LPNASE). Simulations are performed to analyze the performance of these algorithms under impulsive noise. © 2022 IEEE.

关键词： Impulse noise

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