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31st International Conference on Computational Linguistics, COLING 2025

作者： Sun, Xinyi Tan, Hongye Guo, Yaxin Qiang, Pengpeng Li, Ru Zhang, Hu School of Computer and Information Technology Shanxi University Taiyuan China Institute of Intelligent Information Processing Shanxi University Taiyuan China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China

ISBN: (纸本)9798891761964

Data-driven pre-trained language models typically perform shortcut learning wherein they rely on the spurious correlations between the data and the ground truth. This reliance can undermine the robustness and generalization of the model. To address this issue, data augmentation emerges as a promising solution. By integrating anti-shortcut data to the training set, the models' shortcut-induced biases can be mitigated. However, existing methods encounter three challenges: (1) Manual definition of shortcuts is tailored to particular datasets, restricting generalization. (2) The inherent confirmation bias during model training hampers the effectiveness of data augmentation. (3) Insufficient exploration of the relationship between the model performance and the augmented data quantity may result in excessive data consumption. To tackle these challenges, we propose a method of Smart Data Augmentation based on Large Language Models (SAug-LLM). It leverages the LLMs to autonomously identify shortcuts and generate their anti-shortcut counterparts. In addition, the dual validation is employed to mitigate the confirmation bias during the model retraining. Furthermore, the data augmentation process is optimized to effectively rectify model biases while minimizing data consumption. We validate the effectiveness and generalization of our method through extensive experiments across various natural language processing tasks, demonstrating an average performance improvement of 5.61%. © 2025 Association for Computational Linguistics.

关键词： Data assimilation

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FSA-Net:A Cost-efficient Face Swapping Attention Network with Occlusion-Aware Normalization

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intelligent Automation & Soft computing 2023年第7期37卷 971-983页

作者： Zhipeng Bin Huihuang Zhao Xiaoman Liang Wenli Chen College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

The main challenges in face swapping are the preservation and adaptive superimposition of attributes of two *** this study,the Face Swapping Attention Network(FSA-Net)is proposed to generate photoreal-istic face *** existing face-swapping methods ignore the blending attributes or mismatch the facial keypoint(cheek,mouth,eye,nose,etc.),which causes artifacts and makes the generated face silhouette *** address this problem,a novel reinforced multi-aware attention module,referred to as RMAA,is proposed for handling facial fusion and expression occlusion *** framework includes two *** the first stage,a novel attribute encoder is proposed to extract multiple levels of target face attributes and integrate identities and attributes when synthesizing swapped *** the second stage,a novel Stochastic Error Refinement(SRE)module is designed to solve the problem of facial occlusion,which is used to repair occlusion regions in a semi-supervised way without any *** proposed method is then compared with the current state-of-the-art *** obtained results demonstrate the qualitative and quantitative outperformance of the proposed *** details are provided at the footnote link and at https://***/view/fsa-net-official.

关键词： Attention face-swapping neural network face manipulation identity swap image translation

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An Algebraic Multigrid-Based Physical Factorization Preconditioner for the Multi-Group Radiation Diffusion Equations in Three Dimensions

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Numerical Mathematics(Theory,Methods and Applications) 2023年第2期16卷 410-432页

作者： Xiaoqiang Yue Zekai Zhang Xiaowen Xu Shuying Zhai Shi Shu National Center for Applied Mathematics in Hunan Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationHunan Key Laboratory for Computation and Simulation in Science and EngineeringXiangtan UniversityXiangtan 411105China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100094China School of Mathematical Sciences Huaqiao UniversityQuanzhou 362021China.

The paper investigates the robustness and parallel scaling properties of a novel physical factorization preconditioner with algebraic multigrid subsolves in the iterative solution of a cell-centered finite volume discretization of the threedimensional multi-group radiation diffusion *** key idea is to take advantage of a particular kind of block factorization of the resulting system matrix and approximate the left-hand block matrix selectively spurred by parallel processing *** spectral property of the preconditioned matrix is then *** practical strategy is considered sequentially and in ***,numerical results illustrate the numerical robustness,computational efficiency and parallel strong and weak scalabilities over the real-world structured and unstructured coupled problems,showing its competitiveness with many existing block preconditioners.

关键词： Radiation diffusion equations physical factorization preconditioning algebraic multigrid parallel and distributed computing

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EBVS: An Efficient Pseudonymous Batch Verification Scheme for Vehicular Networks 2nd

EBVS: An Efficient Pseudonymous Batch Verification Scheme fo...

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2nd International Conference on Data Security and Privacy Protection, DSPP 2024

作者： Wang, Ping Wang, Shuokang College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen518060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Guangdong Shenzhen518060 China

ISBN: (纸本)9789819785452

With the rapid development of intelligent transportation systems, the security of vehicular network communication architectures has become a focal point of research. This paper introduces a vehicular network communication system based on cloud computing and blockchain technology, aimed at enhancing the efficiency and security of vehicle privacy protection and message authentication. The system architecture comprises five core components: the Traffic Safety Management Center (TSMC), Cloud Node Servers (CNS), Roadside Units (RSU), Vehicles (V), and the Traffic Safety Blockchain (TSB). In the system initialization phase, public-private key pairs based on elliptic curves and bilinear mappings are generated to provide fundamental encryption and authentication mechanisms. During the identity registration stage, vehicles acquire a pseudonymous identity (PID), used for subsequent message authentication, ensuring vehicle privacy while maintaining identity legitimacy. Through vehicle message authentication and the traffic safety blockchain, the system facilitates efficient batch processing of messages and immutable data records, enhancing the transparency and credibility of traffic data. Furthermore, the system conducts a detailed analysis of potential external threats and malicious vehicle behaviors, proposing corresponding countermeasures such as enhanced data integrity monitoring, adaptive risk management, and collaborative governance strategies. These measures enable the system not only to effectively defend against external attacks but also to monitor and prevent malicious vehicle operations in real-time, thereby ensuring the safe and stable operation of intelligent transportation systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Risk management

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Deep Multi-image Hiding with Random key 1

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1st International Conference on Artificial Intelligence Security and Privacy, AIS and P 2023

作者： Zhang, Wei Tang, Weixuan Rao, Yuan Li, Bin Huang, Jiwu Institute of Artificial Intelligence Guangzhou University Guangzhou China Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China

ISBN: (数字)9789819997855

ISBN: (纸本)9789819997848

Multi-image hiding is the technique of hiding multiple secret images within one cover image. In most existing methods, it is possible for one receiver to reveal other receivers’ secret images. To improve the privacy and secrecy among different receivers, one possible solution is to introduce the key mechanism, wherein only the receiver with private key has the permission to reveal the corresponding secret image. In this paper, a multiple image hiding method called DEMIHAK (Deep Multiple Image Hiding with Random key) is proposed, which utilizes deep neural networks to implement a secure key verification. From the side of the sender, each secret image is assigned with a random key, according to which can sample a key map. Then, BindNet is utilized to incorporate a secret image and its key map into a processed secret image, and HideNet is adopted to conceal multiple processed secret images within cover image and generate a stego image. From the side of the receiver, according to a transmitted private key, RevealNet can be applied to reveal the corresponding secret image from the stego image. Experimental results show that DEMIHAK outperforms existing method from the perspective of visual quality, security, and secrecy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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An Enhanced Particle Swarm Algorithm Based on Fitness Landscape information 13

An Enhanced Particle Swarm Algorithm Based on Fitness Landsc...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Diao, Zhenya Yu, Fei Guan, Jian Liu, Meitong School of Physics and Information Engineering Minnan Normal University Key Lab of Intelligent Optimization and Information Processing Zhangzhou36300 China School of Physics and Information Engineering Minnan Normal University Key Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province Key Lab of Intelligent Optimization and Information Processing Zhangzhou36300 China

ISBN: (纸本)9798350308365

In the original Particle Swarm Optimization (PSO), the selection of parameters significantly impacts the search capability of PSO. The paper introduces an innovative algorithm called Fitness Landscape-based PSO (FLPSO) to address the impact of parameter selection on the search capability of PSO. The algorithm focuses on acquiring fitness landscape information and analyzing the population state to intelligently select appropriate parameters in each generation. FLPSO defines four primary states: exploration, exploitation, convergence, and escaping state. In the convergence state, an elite learning strategy is incorporated to help the algorithm escape potential local optima by targeting the global best particle. To validate the efficacy of FLPSO, the paper conducted experiments on 30 different characteristic test functions from CEC2017. FLPSO was compared with six existing algorithms: PSO, PSO-TVAC, APSO, AWPSO, RPSO and ASRPPSO. The experimental results showed significant improvements in FLPSO, providing promising evidence for its effectiveness. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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A Local Regression-Based SLA-Aware Energy-Efficient Virtual Machine Consolidation Algorithm in Data Centers 9

A Local Regression-Based SLA-Aware Energy-Efficient Virtual ...

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9th International Conference on Big Data computing and Communications, BigCom 2023

作者： Wu, Xiaodong Wang, Ronghai Lin, Guoxin Quanzhou Normal University School of Mathmatics and Computer Science China Fujian Province University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian China Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

ISBN: (纸本)9798350331240

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms. © 2023 IEEE.

关键词： Virtual machine

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Permittivity inversion of GPR images based on DeepLab 3

Permittivity inversion of GPR images based on DeepLab

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3rd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2022

作者： Liu, Hongwei Wang, Jinpeng Talukdar, Masud Yuan, Da Key Laboratory of Intelligent Information Processing Shandong Technology and Business University Yantai Shandong264005 China

ISBN: (纸本)9781510663473

Ground Penetrating Radar (GPR) is usually used to detect unknown underground structure information, but it is very difficult to extract the underground target structure information from original GPR signal. This paper aims to solve the inversion problem by deep learning method. The two-dimensional ground penetrating radar B-scan signal is converted into intuitive underground structure information by neural network. In this paper, the DeepLab network proposed by Google is improved to solve the problem of permittivity inversion of GPR signal images. We verify the network using simulated data, which is generated by Finite Difference Time Domain (FDTD) algorithm. Finally we quantitatively evaluate the performance of our network by comparing it with some existing deep learning inversion networks. © 2023 SPIE.

关键词： Permittivity

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Near-field Beamforming Technique Based on 3-D Cylindrical Array: Enlarging Region and Improving Spectral Efficiency 12

Near-field Beamforming Technique Based on 3-D Cylindrical Ar...

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12th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2024

作者： Gao, Shuai Zhou, Kai Wang, Ying Xiang, Jianhong Zhong, Yu Harbin Engineering University College of Information and Communication Engineering Harbin China Agile and Intelligent Computing Key Laboratory Chengdu China

ISBN: (纸本)9798350351019

For large incidence/emergence angles in uniform linear arrays, the near-field region shrinks sharply. In this paper, we present Uniform 3D Cylindrical Array (UCA-3D), which can enlarge the near-field region and improve the spectral efficiency. Specifically, using the rotational symmetry of UCA-3D, we can obtain near-field regions that are uniformly enlarged at all angles. As UCA-3D can generate orthogonal near-field beams along the same direction, we can improve the spectral efficiency. The simulation results prove that the theory is valid, and the UCA-3D is feasible. © 2024 IEEE.

关键词： Beamforming

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Efficient and fine-grained access control with fully-hidden policies for cloud-enabled IoT

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Digital Communications and Networks 2025年第2期11卷 473-481页

作者： Qi Li Gaozhan Liu Qianqian Zhang Lidong Han Wei Chen Rui Li Jinbo Xiong School of Computer Science Nanjing University of Posts and Telecommunications Key Laboratory of Cryptography of Zhejiang Province Hangzhou Normal University Tongda College of Nanjing University of Posts and Telecommunications Henan Key Laboratory of Network Cryptography Technology School of Computer Science and Technology Xidian University State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Faculty of Software Fujian Normal University

Ciphertext-Policy Attribute-Based Encryption(CP-ABE) enables fine-grained access control on ciphertexts, making it a promising approach for managing data stored in the cloud-enabled Internet of Things. But existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute content. Additionally, resource-constrained IoT devices, especially those adopting wireless communication, frequently encounter affordability issues regarding decryption costs. In this paper, we propose an efficient and fine-grained access control scheme with fully hidden policies(named FHAC). FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them. A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access policy. Dictionary attacks are thwarted by providing unauthorized users with invalid values. The heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud servers. Additionally, users can verify the correctness of multiple outsourced decryption results simultaneously. Security analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption.

关键词： Access control Policy hiding Verifiable outsourced computation Cloud IoT

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