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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gu, Huijie Wang, Hanpu He, Shiwei Li, Mengyan Zhou, Jianmeng Chen, Tong Southwest University College of Electronic and Information Engineering Chongqing400715 China Swu Chongqing Key Laboratory of Generic Technology and System of Service Robots China Southwest University Institute of Legal Psychology and Intelligent Computing Chongqing400715 China

ISBN: (纸本)9798350386226

Masked Facial Expression refers to facial expression that people deliberately make which do not correspond to their true emotions. The purpose of masked facial expression is to conceal one's genuine feelings. Precisely identifying masked facial expression aids in revealing the genuine emotions, and finds applications in diagnosis of mental illness. However, the masked facial expression sequences have redundant information, temporal modeling is difficult, and the expressive ability of AU features needs to be improved. To solve the existing problems, we first propose a key frame Selection and Data Augmentation (KS-DA) method to obtain the key frame in the sequence. In addition, we propose a Global Feature Relation Block (GFR) to aggregate global information and combine it with Temporal Convolutional Networks (TCN) to better realize temporal modeling. Finally, we propose an Adaptive Weight Generation Module (Ada-WGM) to form different weights from adaptation, allowing for better extraction of the temporal changes in AU features. Finally, the experiments have shown that the proposed method has improved the mixed expression classification task (36 task) by 15.23% and the experienced emotions classification task (6E task) by 17.77%, respectively, and obtain sort results. © 2024 IEEE.

关键词： Convolutional neural networks

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Progress on the antiferromagnetic topological insulator MnBi2Te4

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National Science Review 2024年第2期11卷 33-49页

作者： Shuai Li Tianyu Liu Chang Liu Yayu Wang Hai-Zhou Lu X.C.Xie Department of Physics Harbin Institute of Technology Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and Technology(SUSTech) Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area(Guangdong) Shenzhen Key Laboratory of Quantum Science and Engineering International Quantum Academy Beijing Academy of Quantum Information Sciences Beijing Key Laboratory of Optoelectronic Functional Materials&Micro-Nano Devices Department of Physics Renmin University of China State Key Laboratory of Low Dimensional Quantum Physics Department of PhysicsTsinghua University Frontier Science Center for Quantum Information Hefei National Laboratory International Center for Quantum Materials School of Physics Peking University Institute for Nanoelectronic Devices and Quantum Computing Fudan University

Topological materials, which feature robust surface and/or edge states, have now been a research focus in condensed maTher physics. They represent a new class of materials exhibiting nontrivial topological phases,and provide a platform for exploring exotic transport phenomena, such as the quantum anomalous Hall effect and the quantum spin Hall effect. Recently, magnetic topological materials have attracted considerable interests due to the possibility to study the interplay between topological and magnetic *** particular, the quantum anomalous Hall and axion insulator phases can be realized in topological insulators with magnetic order. MnBi2Te4, as the first intrinsic antiferromagnetic topological insulator discovered, allows the examination of existing theoretical predictions; it has been extensively studied, and many new discoveries have been made. Here we review the progress made on MnBi2Te4from both experimental and theoretical aspects. The bulk crystal and magnetic structures are surveyed first, followed by a review of theoretical calculations and experimental probes on the band structure and surface states, and a discussion of various exotic phases that can be realized in MnBi2Te4. The properties of MnBi2Te4thin films and the corresponding transport studies are then reviewed, with an emphasis on the edge state transport. Possible future research directions in this field are also discussed.

关键词： MnBi2Te4 magnetic topological insulator antiferromagnetic quantum anomalous Hall effect axion insulator

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A hierarchical access control and sharing model for healthcare data with on-chain-off-chain collaboration

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Journal of intelligent and Fuzzy Systems 2025年第3期48卷 215-231页

作者： Dong, Jianhang Jiang, Rong Zhang, Yi Tian, Shenghu Wu, Bin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Institute of Intelligence Applications Yunnan University of Finance and Economics Kunming China Yunnan Academy of Scientific and Technical Information Kunming China

Healthcare data is growing rapidly and data sharing is becoming increasingly important among hospitals. However, traditional methods are prone to data leakage and threaten patient privacy. To ensure data security and realize sharing and access control, combining blockchain technology, attribute-based encryption and ciphertext policy (CP-ABE) and AES symmetric encryption algorithm, this paper proposes a hierarchical attribute-based encryption algorithm based on master-sidechain (MSC-CP-ABE) and a collaborative access control and sharing model with on-chain and off-chain (AC-CSS). The algorithm encrypts hierarchically based on data sensitivity, the ciphertext is stored in the interplanetary file system (IPFS), and the encrypted address and key are stored in the side chain, which is convenient for indexing in the main chain. The vectorized access policy implements policy hiding, combines on-chain and off-chain, saves blockchain space, achieves hierarchical access control, and improves security and efficiency. Simulation experiments show that the main sidechain model proposed in this paper has better performance in terms of transaction delay and throughput. Compared with existing models, the algorithm proposed in this paper has certain advantages in data encryption and decryption time and key generation time. © 2025 – IOS Press. All rights reserved.

关键词： Authorization

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Beyond MLE: Convex Learning for Text Generation 37

Beyond MLE: Convex Learning for Text Generation

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37th Conference on Neural information Processing Systems, NeurIPS 2023

作者： Shao, Chenze Ma, Zhengrui Zhang, Min Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Future Science and Engineering Soochow University China

ISBN: (纸本)9781713899921

Maximum likelihood estimation (MLE) is a statistical method used to estimate the parameters of a probability distribution that best explain the observed data. In the context of text generation, MLE is often used to train generative language models, which can then be used to generate new text. However, we argue that MLE is not always necessary and optimal, especially for closed-ended text generation tasks like machine translation. In these tasks, the goal of model is to generate the most appropriate response, which does not necessarily require it to estimate the entire data distribution with MLE. To this end, we propose a novel class of training objectives based on convex functions, which enables text generation models to focus on highly probable outputs without having to estimate the entire data distribution. We investigate the theoretical properties of the optimal predicted distribution when applying convex functions to the loss, demonstrating that convex functions can sharpen the optimal distribution, thereby enabling the model to better capture outputs with high probabilities. Experiments on various text generation tasks and models show the effectiveness of our approach. It enables autoregressive models to bridge the gap between greedy and beam search, and facilitates the learning of non-autoregressive models with a maximum improvement of 9+ BLEU points. Moreover, our approach also exhibits significant impact on large language models (LLMs), substantially enhancing their generative capability on various tasks. Source code is available at https://***/ictnlp/Convex-Learning. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Maximum likelihood estimation

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QSM: A Quick Shuffling Method for Defending Against Low-Rate Denial of Service Attacks in Microservice Systems

QSM: A Quick Shuffling Method for Defending Against Low-Rate...

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2024 International Conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Zhao, Zheng Fan, Qiqi Zhao, Chunchang Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Microservice architecture, renowned for its flexible scalability and loose coupling, has seen widespread adoption. However, this architecture has also become a prime target for network attackers, with low-rate denial of service (LDoS) attacks posing significant threats to microservice systems. Due to their covert nature, traditional passive defense methods often fall short in countering LDoS attacks. To address this challenge, this paper introduces a microservice-oriented quick shuffling method (QSM), based on the moving target defense (MTD) principle. The QSM framework leverages the dynamic and flexible properties of microservices to implement a rapid shuffling mechanism, ensuring the protection of innocent clients. Experimental results demonstrate the effectiveness of QSM in robustly defending microservice systems against LDoS attacks. © 2025 SPIE.

关键词： Denial-of-service attack

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Channel estimation for massive MIMO systems based on adaptive angular domain compensation 5

Channel estimation for massive MIMO systems based on adaptiv...

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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Liu, Ting Wang, Yong Xiang, Jianhong Wang, Ying Liu, Yang College Of Information And Communication Engineering Harbin Engineering University Harbin150001 China Sichuan Key Laboratory Of Agile Intelligent Computing Southwest China Institute Of Electronics Technology Chengdu610036 China

ISBN: (纸本)9798400709272

Millimeter-wave (mmWave) and massive Multiple Input Multiple Output (MIMO) technologies rely heavily on accurate channel state information (CSI) to significantly increase system capacity. However, the large size of the antenna array makes it more difficult to obtain accurate channel state information. We propose to utilize beam domain channels to improve the channel estimation accuracy. Specifically, we first construct the angular domain compensation. By utilizing this angular domain compensation, we further propose an angular domain compensated alternating direction multiplier method (ADC-ADMM) algorithm. The scheme models the channel estimation problem as a bi-objective convex optimization problem with joint angular domain compensation, which can capture channel state information more comprehensively. In addition, the angular domain compensation can provide a priori information and improve the robustness in noisy environments. The simulation results confirm the feasibility of the proposed ADC-ADMM scheme. © 2023 ACM.

关键词： Millimeter waves

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Distributionally Robust Scheduling for Benefit Allocation in Regional Integrated Energy System with Multiple Stakeholders

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Journal of Modern Power Systems and Clean Energy 2024年第5期12卷 1631-1642页

作者： Qinglin Meng Xiaolong Jin Fengzhang Luo Zhongguan Wang Sheharyar Hussain the Comprehensive Service Center State Grid Tianjin Electric Power CompanyTianjin 300010China the Key Laboratory of Smart Grid of Ministry of Education School of Electrical and Information EngineeringTianjin UniversityTianjin 300072China the School of Electrical Engineering Tiangong UniversityTianjin 300387China the Key Laboratory of Smart Energy&Information Technology of Tianjin Municipality Tianjin 300072China the Institute of Marine Electronic and Intelligent System Ocean CollegeZhejiang UniversityZhoushanChina

A distributionally robust scheduling strategy is proposed to address the complex benefit allocation problem in regional integrated energy systems(RIESs)with multiple stakeholders.A two-level Stackelberg game model is established,with the RIES operator as the leader and the users as the *** considers the interests of the RIES operator and demand response users in energy *** leader optimizes time-of-use(TOU)energy prices to minimize costs while users formulate response plans based on prices.A two-stage distributionally robust game model with comprehensive norm constraints,which encompasses the two-level Stackelberg game model in the day-ahead scheduling stage,is constructed to manage wind power ***-Kuhn-Tucker(KKT)conditions transform the two-level Stackelberg game model into a single-level robust optimization model,which is then solved using column and constraint generation(C&CG).Numerical results demonstrate the effectiveness of the proposed strategy in balancing stakeholders'interests and mitigating wind power risks.

关键词： Regional integrated energy system(RIES) distributionally robust scheduling Stackelberg game uncertainty demand response

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Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1105-1121页

作者： Yue Zhou Xin Luo MengChu Zhou the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China the School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

关键词： Big data analysis cryptocurrency transaction network embedding(CTNE) dynamic network network embedding network representation static network

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Target extraction based on hybrid domain features of array GM-APD lidar 10

Target extraction based on hybrid domain features of array G...

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10th Symposium on Novel Optoelectronic Detection technology and Applications

作者： Zhang, Xin Li, Sining Sun, Jianfeng Jiang, Peng Jin, Haowei National Key Laboratory of Laser Spatial Information Institute of Opto-Electronic Harbin Institute of Technology Heilongjiang Harbin150001 China Zhengzhou Research Institute of Harbin Institute of Technology Henan Zhengzhou450000 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing100074 China

ISBN: (数字)9781510688155

ISBN: (纸本)9781510688148

This study proposes a target extraction algorithm for array GM-APD lidar based on hybrid domain features, designed to address the challenges of accurately extracting target region based on the image in complex scenes and high noise interference. We extracted the temporal features of the echo signals and transformed the echo signals into the image domain to enhance the information content and further extract image features. By integrating multi-view K-means clustering and AdaBoost classification methods, we introduced a dynamic weight adjustment mechanism and a region adjustment method based on a correlation coefficient to achieve pixel-type discrimination and target region extraction. Experimental results demonstrate that the proposed method exhibits good robustness and adaptability in complex detection environments. Under conditions of different occlusion, different signal-to-noise ratios, and different target reflectivity, the proposed algorithm significantly outperforms traditional region-growing segmentation methods in terms of accuracy of target region extraction. © 2025 SPIE.

关键词： K-means clustering

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An RCG-based study of aspect-level sentiment analysis for hybrid attention 3

An RCG-based study of aspect-level sentiment analysis for hy...

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3rd International Conference on Machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Lu, Baoqing Yu, Hongzhi Wan, Fucheng Min, Chen Wei, Bin Fen, Yulin Key Laboratory of Linguistic and Cultural Computing Ministry of Education Gansu Lanzhou730000 China Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Gansu Lanzhou730000 China Lanzhou technology and business college Gansu Lanzhou730000 China

ISBN: (纸本)9781510681880

Aspect-based sentiment analysis (ABSA) is a crucial granular task within sentiment analysis, focusing on the precise identification of sentiment orientations for specific aspects within text. Recognizing that identical context words can express opposing sentiment polarities in different situations, it's essential to delve into the nuanced interactions between target and context words. This study introduces an RCG-based Hybrid Attention Network, a novel architecture that adeptly utilizes lexical attention mechanisms to extract lexical features and fortify the relationship between aspects and their corresponding target words. To assess the efficacy of our proposed approach, we conducted experiments on a well-known public dataset. The results show a significant 3.37% enhancement in accuracy and a 1.38% improvement in Macro-F1 scores compared to related methods, affirming the superiority of our technique in enhancing the performance of aspect-level sentiment analysis. © 2024 SPIE.

关键词： Economic and social effects

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