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An Efficient Data Aggregation Solution for Smart Meters Based on Cloud-Edge Collaboration 20th

An Efficient Data Aggregation Solution for Smart Meters Bas...

20th International Conference on Services Computing, SCC 2023 held as Part of the Services Conference Federation, SCF 2023

作者： Xia, Zhuoqun Zhang, Li Sangaiah, Arun Kumar The School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410000 China Department of Electrical and Computer Engineering Lebanese American University Byblos83990 Lebanon

ISBN: (纸本)9783031516733

Smart meters are an important component of the smart grid, and the large-scale deployment of meters on the user side generates a large amount of data that brings huge expenses to the smart grid. In addition, attackers can monitor users’ electricity consumption based on the transmitted data, which poses a privacy threat. To solve these problems, this paper proposes a secure and efficient data aggregation scheme for smart meters based on cloud-side collaboration. Based on cloud-edge collaboration, the cloud server uses double Z-Score standardization to process the electricity consumption feature data, and at the same time uses improved Euclidean distance to obtain the number of clusters and classification results of the feature distinctions, and interacts with the edge device to respond. Based on the response information, the power provider generates confusion parameters and perturbation factors with classification information, which are sent to the meter for generating convergence values to minimize Gaussian noise and attached to the meter data to achieve the confusion processing of meter data. The aggregator receives the meter and cloud information for classification and aggregation and generates an aggregation signature to transmit the data. The results show that the scheme has high privacy security and operational efficiency. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart meters

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Fuzzy-NMS: Improving 3D Object Detection With Fuzzy Classification in NMS

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Wang, Li Zhang, Xinyu Zhao, Fachuan Wu, Chuze Wang, Yichen Song, Ziying Yang, Lei Xu, Bin Li, Jun Ge, Shuzhi Sam School of Machanical Engineering Beijing Institute of Technology Beijing China School of Vehicle and Mobility Tsinghua University Beijing China School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering Interactive Digital Media Institute Social Robotics Laboratory National University of Singapore Singapore

Non-maximum suppression (NMS) is an essential post-processing module in many 3D object detection frameworks to remove overlapping candidate bounding boxes. However, an overreliance on classification scores and difficulties in determining appropriate thresholds can affect the resulting accuracy directly. To address these issues, we introduce fuzzy learning into NMS and propose a novel generalized Fuzzy-NMS module to achieve finer candidate bounding box filtering. The proposed Fuzzy-NMS module combines the volume and clustering density of candidate bounding boxes, refining them with a fuzzy classification method and optimizing the appropriate suppression thresholds to reduce uncertainty in the NMS process. Adequate validation experiments use the mainstream KITTI and large-scale Waymo 3D object detection benchmarks. The results of these tests demonstrate the proposed Fuzzy-NMS module can improve the accuracy of numerous recently NMS-based detectors significantly, including PointPillars, PV-RCNN, and IA-SSD, etc. This effect is particularly evident for small objects such as pedestrians and bicycles. As a plug-and-play module, Fuzzy-NMS does not need to be retrained and produces no obvious increases in inference time. IEEE

关键词： Object detection

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Lightweight Look-ahead Multi-context Vision Transformer

Lightweight Look-ahead Multi-context Vision Transformer

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Byeon, Keunho Lim, Jeewoo Lee, Jaeung Han, Chiwon Bay, SungYun Park, Giho Kwak, Jin Tae School of Electrical Engineering Korea University Seoul Korea Republic of Sejong University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798331510756

Vision Transformer (ViT) has been successfully applied to various vision tasks, outperforming convolutional neural networks due to their ability to capture global dependencies through the self-attention mechanism. However, it has been shown that ViT struggles with high model complexity and low computational efficiency, and thus it becomes less practical for deployment on edge devices. To address these issues, various studies have sought to enhance the efficiency of ViT, in particular focusing on hybrid models and self-attention mechanism. Herein, we propose a lightweight look-ahead multi-context ViT, which integrates convolutional neural networks and ViT in a hybrid, sequential design to achieve compact size and superior performance on vision tasks. The proposed model introduces a novel self-attention mechanism, which is built based upon a modified Nyström method with advanced landmark selection to provide multi-scale and multi-context information. Experimental results on image recognition dataset demonstrate the superior performance and the effectiveness of the proposed model. © 2025 IEEE.

关键词： Convolutional neural networks

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On Properties of Doeblin Coefficients

On Properties of Doeblin Coefficients

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Makur, Anuran Singh, Japneet Purdue University School of Electrical and Computer Engineering Department of Computer Science West LafayetteIN47907 United States Purdue University School of Electrical and Computer Engineering West LafayetteIN47907 United States

ISBN: (纸本)9781665475549

Doeblin coefficients are a classical tool to study the ergodicity of Markov chains. Propelled by recent works on contraction coefficients of strong data processing inequalities, we investigate whether Doeblin coefficients also exhibit some of the notable properties of canonical contraction coefficients. Specifically, we present various new structural and geometric properties of Doeblin coefficients. Then, by establishing an extremal coupling characterization, we show that Doeblin coefficients generalize the well-known total variation (TV) distance to a multi-way divergence, enabling us to measure the distance between multiple distributions rather than just two. We also demonstrate that Doeblin coefficients exhibit contraction properties over Bayesian networks similar to other canonical contraction coefficients. Finally, we discuss how Doeblin coefficients can be used to define a new rule for fusion of probability mass functions. © 2023 IEEE.

关键词： Bayesian networks

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Development of Dementia Care Training System Using AR and Large Language Model

Development of Dementia Care Training System Using AR and La...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Miyawaki, Tomoya Nishiura, Yuuki Fukuda, Ryouta Nakashima, Kazuto Kurazume, Ryo Graduate School of Information Science and Electrical Engineering Kyushu University Japan Kyushu University Faculty of Engineering Department of Electrical Engineering and Computer Science Japan Kyushu University Faculty of Information Science and Electrical Engineering Japan

ISBN: (纸本)9781665410205

We have developed HEARTS, a dementia care training system using augmented reality based on Humanitude. Humanitude is a multimodal comprehensive care technique for dementia, and has attracted attention as a method to reduce the burden on both caregivers and patients. However, the HEARTS developed so far could not evaluate 'speaking' skills based on the content of conversations among 'seeing,' 'touching,' and 'speaking,' all of which are fundamental skills in Humanitude. Therefore, we attempted a new quantitative evaluation of trainees' 'speaking' skills by estimating the emotional value of conversational content using GPT-4 named HEARTS 5. A survey of caregivers was conducted using the developed system and was well received by the participants. We also developed a HEARTS 5 conversational version based on HEARTS 5, with the addition of GPT-4 conversation generation and Azure Text-to-Speech. © 2024 IEEE.

关键词： Augmented reality

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SEPARATE AND DIFFUSE: USING A PRETRAINED DIFFUSION MODEL FOR BETTER SOURCE SEPARATION 12

SEPARATE AND DIFFUSE: USING A PRETRAINED DIFFUSION MODEL FOR...

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12th International Conference on Learning Representations, ICLR 2024

作者： Lutati, Shahar Nachmani, Eliya Wolf, Lior Department of Computer Science Tel Aviv University Israel School of Electrical Engineering Tel Aviv University Israel

The problem of speech separation, also known as the cocktail party problem, refers to the task of isolating a single speech signal from a mixture of speech signals. Previous work on source separation derived an upper bound for the source separation task in the domain of human speech. This bound is derived for deterministic models. Recent advancements in generative models challenge this bound. We show how the upper bound can be generalized to the case of random generative models. Applying a diffusion model Vocoder that was pretrained to model single-speaker voices on the output of a deterministic separation model leads to state-of-the-art separation results. It is shown that this requires one to combine the output of the separation model with that of the diffusion model. In our method, a linear combination is performed, in the frequency domain, using weights that are inferred by a learned model. We show state-of-the-art results on 2, 3, 5, 10, and 20 speakers on multiple benchmarks. In particular, for two speakers, our method is able to surpass what was previously considered the upper performance bound. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Source separation

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Ontology-Based Reinforcement Learning for Semantic Offloading in MEC-Enabled 6G IoT Networks

Ontology-Based Reinforcement Learning for Semantic Offloadin...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Gyamfi, Eric Ansere, James Adu Golpayegani, Fatemeh School of Computer Science University College Dublin Ireland Sunyani Technical University Department of Electrical Engineering Ghana

ISBN: (纸本)9798350364910

The proliferation of ultra-low-latency and faster convergence applications in sixth-generation (6G) networks necessitates mobile edge computing (MEC) for offloading computationally intensive tasks from user devices to the network edge. However, traditional offloading approaches ignore the semantic aspects of tasks and resources. This paper proposes a novel ontology-based reinforcement learning (ORL) approach for optimal semantic offloading decisions in evolving dynamic MEC-enabled 6G networks, considering network uncertainty and time variation conditions. The proposed ORL approach integrates a dynamic learning mechanism that adjusts and captures semantics task resources, enabling adaptive real-time offloading decisions to satisfy task processing requirements and user demands. The numerical results demonstrate significant improvements in latency reduction, responsiveness, and adaptability compared to traditional offloading approaches. © 2024 IEEE.

关键词： Adversarial machine learning

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New Delay Doppler Communication Paradigm in 6G Era:A Survey of Orthogonal Time Frequency Space(OTFS)

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China Communications 2023年第6期20卷 1-25页

作者： Weijie Yuan Shuangyang Li Zhiqiang Wei Yuanhao Cui Jiamo Jiang Haijun Zhang Pingzhi Fan Department of Electronic and Electrical Engineering Southern University of Science and TechnologyShenzhenChina Faculty of Electrical Engineering and Computer Science Technical University of BerlinBerlinGermany School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’anChina Department of Communication Engineering Beijing University of Posts and TelecommunicationsBeijingChina China Academy of Information and Communications Technology BeijingChina School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina School of Information Science and Technology Southwest Jiaotong UniversityChengduChina

In the 6G era,Space-Air-Ground Integrated Network(SAGIN)are anticipated to deliver global coverage,necessitating support for a diverse array of emerging applications in high-mobility,hostile *** such conditions,conventional orthogonal frequency division multiplexing(OFDM)modulation,widely employed in cellular and Wi-Fi communication systems,experiences performance degradation due to significant Doppler *** overcome this obstacle,a novel twodimensional(2D)modulation approach,namely orthogonal time frequency space(OTFS),has emerged as a key enabler for future high-mobility use ***,OTFS modulates information within the delay-Doppler(DD)domain,as opposed to the timefrequency(TF)domain utilized by *** offers advantages such as Doppler and delay resilience,reduced signaling latency,a lower peak-to-average ratio(PAPR),and a reduced-complexity *** studies further indicate that the direct interplay between information and the physical world in the DD domain positions OTFS as a promising waveform for achieving integrated sensing and communications(ISAC).In this article,we present an in-depth review of OTFS technology in the context of the 6G era,encompassing fundamentals,recent advancements,and future *** objective is to provide a helpful resource for researchers engaged in the field of OTFS.

关键词： OTFS 6G delay-Doppler(DD)domain

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Robust Sparse Regression with Non-Isotropic Designs 38

Robust Sparse Regression with Non-Isotropic Designs

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Chih-Hung Novikov, Gleb Department of Electrical Engineering National Taiwan University Taiwan Lucerne School of Computer Science and Information Technology Switzerland

We develop a technique to design efficiently computable estimators for sparse linear regression in the simultaneous presence of two adversaries: oblivious and adaptive. Consider the model y∗ = X∗β∗ + η where X∗ is an n × d random design matrix, β∗ ∈ Rd is a k-sparse vector, and the noise η is independent of X∗ and chosen by the oblivious adversary. Apart from the independence of X∗, we only require a small fraction entries of η to have magnitude at most 1. The adaptive adversary is allowed to arbitrarily corrupt an Ε-fraction of the samples (X1∗, y1∗), ..., (Xn∗, yn∗ ). Given the Ε-corrupted samples (X1, y1), ..., (Xn, yn), the goal is to estimate β∗. We assume that the rows of X∗ are iid samples from some d-dimensional distribution D with zero mean and (unknown) covariance matrix Σ with bounded condition number. We design several robust algorithms that outperform the state of the art even in the special case of Gaussian noise η ∼ N(0, 1)n. In particular, we provide a polynomial-time algorithm that with high probability recovers β∗ up to error O(√Ε) as long as n ≥ O∼ (k2/Ε), only assuming some bounds on the third and the fourth moments of D. In addition, prior to this work, even in the special case of Gaussian design D = N(0, Σ) and noise η ∼ N(0, 1), no polynomial time algorithm was known to achieve error o(√Ε) in the sparse setting n 2. We show that under some assumptions on the fourth and the eighth moments of D, there is a polynomial-time algorithm that achieves error o(√Ε) as long as n ≥ O∼ (k4/Ε3). For Gaussian distribution D = N(0, Σ), this algorithm achieves error O(Ε3/4). Moreover, our algorithm achieves error o(√Ε) for all log-concave distributions if Ε ≤ 1/polylog(d). Our algorithms are based on the filtering of the covariates that uses sum-of-squares relaxations, and weighted Huber loss minimization with 1 regularizer. We provide a novel analysis of weighted penalized Huber loss that is suitable for heavy-tailed designs in the presence of two adversaries

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A novel threshold changeable secret sharing scheme

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Frontiers of computer science 2022年第1期16卷 127-133页

作者： Lein HARN Chingfang HSU Zhe XIA Department of Computer Science Electrical Engineering University of Missouri-Kansas CityKansas CityMO64110USA Computer School Central China Normal UniversityWuhan430079China Department of Computer Science Wuhan University of TechnologyWuhan430071China

A(t,n)threshold secret sharing scheme is a fundamental tool in many security applications such as cloud computing and multiparty *** conventional threshold secret sharing schemes,like Shamir’s scheme based on a univariate polynomial,additional communication key share scheme is needed for shareholders to protect the secrecy of their shares if secret reconstruction is performed over a *** the secret reconstruction,the threshold changeable secret sharing(TCSS)allows the threshold to be a dynamic value so that if some shares have been compromised in a given time,it needs more shares to reconstruct the ***,a new secret sharing scheme based on a bivariate polynomial is proposed in which shares generated initially by a dealer can be used not only to reconstruct the secret but also to protect the secrecy of shares when the secret reconstruction is performed over a *** this paper,we further extend this scheme to enable it to be a TCSS without any *** proposed TCSS is dealer-free and *** generated by a dealer in our scheme can serve for three purposes,(a)to reconstruct a secret;(b)to protect the secrecy of shares if secret reconstruction is performed over a network;and(c)to enable the threshold changeable property.

关键词： cryptography 94A60 authentication and secret sharing 94A62

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