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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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Electron charge qubit with 0.1 millisecond coherence time

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Nature Physics 2024年第1期20卷 116-122页

作者： Zhou, Xianjing Li, Xinhao Chen, Qianfan Koolstra, Gerwin Yang, Ge Dizdar, Brennan Huang, Yizhong Wang, Christopher S. Han, Xu Zhang, Xufeng Schuster, David I. Jin, Dafei Center for Nanoscale Materials Argonne National Laboratory LemontIL United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions CambridgeMA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States James Franck Institute and Department of Physics University of Chicago ChicagoIL United States Department of Electrical and Computer Engineering Northeastern University BostonMA United States Department of Applied Physics Stanford University StanfordCA United States Department of Physics and Astronomy University of Notre Dame Notre DameIN United States

Electron charge qubits are compelling candidates for solid-state quantum computing because of their inherent simplicity in qubit design, fabrication, control and readout. However, electron charge qubits built on conventional semiconductors and superconductors suffer from severe charge noise that limits their coherence time to the order of one microsecond. Here we report electron charge qubits that exceed this limit, based on isolated single electrons trapped on an ultraclean solid neon surface in a vacuum. Quantum information is encoded in the motional states of an electron that is strongly coupled with microwave photons in an on-chip superconducting resonator. The measured relaxation and coherence times are both on the order of 0.1 ms, surpassing all existing charge qubits and rivalling state-of-the-art superconducting transmon qubits. The simultaneous strong coupling of two qubits with a common resonator is also demonstrated, as the first step towards two-qubit entangling gates for universal quantum computing. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

关键词： Superconducting resonators

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3D2-Actor: Learning Pose-Conditioned 3D-Aware Denoiser for Realistic Gaussian Avatar Modeling

arXiv

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arXiv 2024年

作者： Tang, Zichen Yang, Hongyu Zhang, Hanchen Chen, Jiaxin Huang, Di School of Artificial Intelligence Beihang University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China School of Computer Science and Engineering Beihang University Beijing China

Advancements in neural implicit representations and differentiable rendering have markedly improved the ability to learn animatable 3D avatars from sparse multi-view RGB videos. However, current methods that map observation space to canonical space often face challenges in capturing pose-dependent details and generalizing to novel poses. While diffusion models have demonstrated remarkable zero-shot capabilities in 2D image generation, their potential for creating animatable 3D avatars from 2D inputs remains underexplored. In this work, we introduce 3D2-Actor, a novel approach featuring a pose-conditioned 3D-aware human modeling pipeline that integrates iterative 2D denoising and 3D rectifying steps. The 2D denoiser, guided by pose cues, generates detailed multi-view images that provide the rich feature set necessary for high-fidelity 3D reconstruction and pose rendering. Complementing this, our Gaussian-based 3D rectifier renders images with enhanced 3D consistency through a two-stage projection strategy and a novel local coordinate representation. Additionally, we propose an innovative sampling strategy to ensure smooth temporal continuity across frames in video synthesis. Our method effectively addresses the limitations of traditional numerical solutions in handling ill-posed mappings, producing realistic and animatable 3D human avatars. Experimental results demonstrate that 3D2-Actor excels in high-fidelity avatar modeling and robustly generalizes to novel poses. Code is available at: https://***/silence-tang/GaussianActor. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital elevation model

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DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition

DrFER: Learning Disentangled Representations for 3D Facial E...

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International Conference on Automatic Face and Gesture Recognition

作者： Hebeizi Li Hongyu Yang Di Huang School of Computer Science and Engineering Beihang University Beijing China Institute of Artificial Intelligence Beihang University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

Facial Expression Recognition (FER) has consistently been a focal point in the field of facial analysis. In the context of existing methodologies for 3D FER or 2D+3D FER, the extraction of expression features often gets entangled with identity information, compromising the distinctiveness of these features. To tackle this challenge, we introduce the innovative DrFER method, which brings the concept of disentangled representation learning to the field of 3D FER. DrFER employs a dual-branch framework to effectively disentangle expression information from identity information. Diverging from prior disentanglement endeavors in the 3D facial domain, we have carefully reconfigured both the loss functions and network structure to make the overall framework adaptable to point cloud data. This adaptation enhances the capability of the framework in recognizing facial expressions, even in cases involving varying head poses. Extensive evaluations conducted on the BU-3DFE and Bosphorus datasets substantiate that DrFER surpasses the performance of other 3D FER methods.

关键词： Point cloud compression Three-dimensional displays Face recognition Disentangled representation learning Gesture recognition Feature extraction Robustness

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Cycle Self-Refinement for Multi-Source Domain Adaptation 38

Cycle Self-Refinement for Multi-Source Domain Adaptation

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38th AAAI Conference on artificial intelligence, AAAI 2024

作者： Zhou, Chaoyang Wang, Zengmao Du, Bo Luo, Yong School of Computer Science Wuhan University China National Engineering Research Center for Multimedia Software Wuhan University China Institute of Artificial Intelligence Wuhan University China Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Hubei Luojia Laboratory China

ISBN: (纸本)1577358872

Multi-source domain adaptation (MSDA) aims to transfer knowledge from multiple source domains to the unlabeled target domain. In this paper, we propose a cycle self-refinement domain adaptation method, which progressively attempts to learn the dominant transferable knowledge in each source domain in a cycle manner. Specifically, several source-specific networks and a domain-ensemble network are adopted in the proposed method. The source-specific networks are adopted to provide the dominant transferable knowledge in each source domain for instance-level ensemble on predictions of the samples in target domain. Then these samples with high-confidence ensemble predictions are adopted to refine the domain-ensemble network. Meanwhile, to guide each source-specific network to learn more dominant transferable knowledge, we force the features of the target domain from the domain-ensemble network and the features of each source domain from the corresponding source-specific network to be aligned with their predictions from the corresponding networks. Thus the adaptation ability of source-specific networks and the domain-ensemble network can be improved progressively. Extensive experiments on Office-31, Office-Home and DomainNet show that the proposed method outperforms the state-of-the-art methods for most tasks. Copyright © 2024, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Forecasting

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CapText: Large Language Model-based Caption Generation From Image Context and Description

arXiv

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arXiv 2023年

作者： Ghosh, Shinjini Anupam, Sagnik Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States

While deep-learning models have been shown to perform well on image-to-text datasets, it is difficult to use them in practice for captioning images. This is because captions traditionally tend to be context-dependent and offer complementary information about an image, while models tend to produce descriptions that describe the visual features of the image. Prior research in caption generation has explored the use of models that generate captions when provided with the images alongside their respective descriptions or contexts. We propose and evaluate a new approach, which leverages existing large language models to generate captions from textual descriptions and context alone, without ever processing the image directly. We demonstrate that after fine-tuning, our approach outperforms current state-of-the-art image-text alignment models like OSCAR-VinVL on this task on the CIDEr metric. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Deriving Language Models from Masked Language Models

arXiv

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arXiv 2023年

作者： Hennigen, Lucas Torroba Kim, Yoon Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory United States

Masked language models (MLM) do not explicitly define a distribution over language, i.e., they are not language models per se. However, recent work has implicitly treated them as such for the purposes of generation and scoring. This paper studies methods for deriving explicit joint distributions from MLMs, focusing on distributions over two tokens, which makes it possible to calculate exact distributional properties. We find that an approach based on identifying joints whose conditionals are closest to those of the MLM works well and outperforms existing Markov random field-based approaches. We further find that this derived model's conditionals can even occasionally outperform the original MLM's conditionals. © 2023, CC BY-SA.

关键词： Markov processes

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Inspecting and Editing Knowledge Representations in Language Models

arXiv

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arXiv 2023年

作者： Hernandez, Evan Li, Belinda Z. Andreas, Jacob Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology United States

Neural language models (LMs) represent facts about the world described by text. Sometimes these facts derive from training data (in most LMs, a representation of the word banana encodes the fact that bananas are fruits). Sometimes facts derive from input text itself (a representation of the sentence I poured out the bottle encodes the fact that the bottle became empty). We describe REMEDI, a method for learning to map statements in natural language to fact encodings in an LM's internal representation system. REMEDI encodings can be used as knowledge editors: when added to LM hidden representations, they modify downstream generation to be consistent with new facts. REMEDI encodings may also be used as probes: when compared to LM representations, they reveal which properties LMs already attribute to mentioned entities, in some cases making it possible to predict when LMs will generate outputs that conflict with background knowledge or input text. REMEDI thus links work on probing, prompting, and LM editing, and offers steps toward general tools for fine-grained inspection and control of knowledge in LMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Bottles

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JTMA: Joint Multimodal Feature Fusion and Temporal Multi-head Attention for Humor Detection 4

JTMA: Joint Multimodal Feature Fusion and Temporal Multi-hea...

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4th Multimodal Sentiment Analysis Challenge and Workshop, MuSe 2023, In conjunction with ACM Multimedia 2023

作者： Li, Qi Xu, Yangyang Zhao, Zhuoer Tang, Shulei Zhang, Feixiang Wang, Ruotong Sun, Xiao Wang, Meng AHU-IAI AI Joint Laboratory Anhui University HeFei China Institute of Advanced Technology University of Science and Technology of China HeFei China School of Computer Science and Information Engineering Hefei University of Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center ZhongJuYuan Intelligent Technology Co Ltd HeFei China School of Computer Science and Information Engineering Hefei University of Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center HeFei China

ISBN: (纸本)9798400702709

In this paper, we propose a model named Joint multimodal feature fusion and Temporal Multi-head Attention (JTMA) to solve the MuSe-Humor sub-challenge in Multimodal Sentiment Analysis Challenge 2023. The goal of MuSe-Humor sub-challenge is to predict whether humor occurs in the given dataset that includes data from multiple modalities (e.g., text, audio and video). The cross-cultural testing presents a new challenge that makes it different from the previous years. To solve the above problems, the proposed model JTMA firstly uses a 1-D CNN to aggregate temporal information within the unimodal feature. Then the interactions of inter-modality and intra-modality are performed by the multimodal feature encoder module. Finally, we integrate the high-level representations learned from multiple modalities to accurately predict humor. The effectiveness of our proposed model is demonstrated through experimental results obtained on the official test set. Our model achieves an impressive AUC score of 0.8889, surpassing the performance of all other participants in the competition, and securing the Top 1 ranking. © 2023 ACM.

关键词： Sentiment analysis

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Temporal-Aware Multimodal Feature Fusion for Sentiment Analysis 4

Temporal-Aware Multimodal Feature Fusion for Sentiment Analy...

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4th Multimodal Sentiment Analysis Challenge and Workshop, MuSe 2023, In conjunction with ACM Multimedia 2023

作者： Li, Qi Tang, Shulei Zhang, Feixiang Wang, Ruotong Xu, Yangyang Zhao, Zhuoer Sun, Xiao Wang, Meng AHU-IAI AI Joint Laboratory Anhui University HeFei China Institute of Advanced Technology University of Science and Technology of China HeFei China School of Computer Science and Information Engineering Hefei University of Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center ZhongJuYuan Intelligent Technology Co Ltd HeFei China School of Computer Science and Information Engineering Hefei University of Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center HeFei China

ISBN: (纸本)9798400702709

In this paper, we present a solution to the MuSe-Personalisation sub-challenge in the Multimodal Sentiment Analysis Challenge 2023. The task of MuSe-Personalisation aims to predict a time-continuous emotional value (i.e., arousal and valence) by using multimodal data. The MuSe-Personalisation sub-challenge faces the individual variations problem, resulting in poor generalization on unknown test sets. To solve the above problem, we first extract several informative visual features, and then propose a framework containing feature selection, feature learning and fusion strategy to discover the best combination of features for sentiment analysis. Finally, our method achieved the Top 1 performance in the MuSe-Personalisation sub-challenge, and the result in the combined CCC of physiological arousal and valence was 0.8681, outperforming the baseline system by a large margin (i.e., 10.42%) on the test set. © 2023 ACM.

关键词： Sentiment analysis

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