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Understanding interfacial engineering of surface functionalized boron nitride nanosheets within thermal interface materials

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Science China Materials 2025年第4期68卷 1300-1302页

作者： Gaojie Han Yuezhan Feng Changyu Shen State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002China

The continuous miniaturization and high-power development of electronic devices have given rise to severe interface thermal issues,which urgently demand highly thermally conductive thermal interface materials(TIMs)to ... 详细信息

关键词： interfacial engineering heat accumulation interface thermal issueswhich thermal interface materials surface functionalized boron nitride nanosheets electronic devices thermal conductivity

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LOL: a highly flexible framework for designing stream ciphers

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Science China(Information Sciences) 2024年第9期67卷 62-75页

作者： Dengguo FENG Lin JIAO Yonglin HAO Qunxiong ZHENG Wenling WU Wenfeng QI Lei ZHANG Liting ZHANG Siwei SUN Tian TIAN State Key Laboratory of Cryptology PLA Strategic Support Force Information Engineering University Institute of Software Chinese Academy of Sciences Westone Cryptologic Research Center School of Cryptology University of Chinese Academy of Sciences

In this paper, we propose LOL, a general framework for designing blockwise stream ciphers. The proposed framework achieves ultrafast software implementations for ubiquitous virtual networks in 5G/6G environments and high-security levels for post-quantum cryptography. The LOL framework is structurally strong; furthermore, this framework and all its components enjoy high flexibility with various extensions. On the basis of the LOL framework, we propose new stream-cipher designs called LOL-MINI and LOL-DOUBLE with the support of the AES-NI and single instruction multiple data instructions. The former applies the basic LOL single mode, while the latter uses the extended parallel-dual mode. LOL-MINI and LOL-DOUBLE support 256-bit key length. Our thorough evaluations revealed that these cipher designs have 256-bit security margins against all existing cryptanalysis methods, including differential, linear, and integral. The software performances of LOL-MINI and LOL-DOUBLE can reach 89 and 135 Gbps. In addition to pure encryptions,the LOL-MINI and LOL-DOUBLE stream ciphers can be applied in a stream-cipher-then-MAC strategy to make AEAD schemes.

关键词： stream cipher 5G/6G mobile system fast software implementation

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Contour Detection Algorithm forαPhase Structure of TB6 Titanium Alloy fused with Multi-Scale Fretting Features

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Chinese Journal of Mechanical engineering 2024年第5期37卷 499-509页

作者： Fei He Yan Dou Xiaoying Zhang Lele Zhang School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004China Hebei Provincial Key Laboratory of Software Engineering Yan shan University Qinhuangdao 066004China School of Mechanical Electronic and Control EngineeringBeijing Jiaotong UniversityBeijing 100044China

Aiming at the problems of inaccuracy in detecting theαphase contour of TB6 titanium *** combining computer vision technology with human vision mechanisms,the spatial characteristics of theαphase can be simulated to obtain the contour ***,an algorithm forαphase contour detection of TB6 titanium alloy fused with multi-scale fretting features is ***,through the response of the classical receptive field model based on fretting and the suppression of new non-classical receptive field model based on fretting,the information maps of theαphase contour of the TB6 titanium alloy at different scales are obtained;then the information map of the smallest scale contour is used as a benchmark,the neighborhood is constructed to judge the deviation of other scale contour information,and the corresponding weight value is calculated;finally,Gaussian function is used to weight and fuse the deviation information,and the contour detection result of TB6 titanium alloyαphase is *** the Visual Studio 2013 environment,484 metallographic images with different temperatures,strain rates,and magnifications were *** results show that the performance evaluation F value of the proposed algorithm is 0.915,which can effectively improve the accuracy ofαphase contour detection of TB6 titanium alloy.

关键词： TB6 titanium alloyαphase Multi-scale fretting features Contour detection

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CTNet:A Convolutional Transformer Network for Color Image Steganalysis

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Journal of Computer Science & Technology 2025年第2期40卷 413-427页

作者： Kang-Kang Wei Wei-Qi Luo Shun-Quan Tan Ji-Wu Huang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen UniversityGuangzhou 510006China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhen 518060China Shenzhen Key Laboratory of Media Security Shenzhen UniversityShenzhen 518060China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129China

Compared with convolutional neural network(CNN),Transformer can obtain global receptive field features more effectively and has recently achieved great success in natural language processing and computer *** to the particularity of steganography,however,almost all existing steganalytic networks just employ CNN with local receptive fields to detect embedding *** this paper,we propose a novel convolutional Transformer network for color image ***,we firstly obtain various image residuals for each color channel of an input image in the pre-processing *** capture more comprehensive steganalytic features,the truncated residuals after channel concatenation will pass through a feature extraction module composed of a CNN group and a Transformer *** CNN group aims to extract local receptive fields features,while the Transformer group with multi-head self-attention as the key tries to extract global steganalytic ***,we employ a global covariance pooling(GCP)and two fully-connected(FC)layers with dropout for *** comparative experiments demonstrate that the proposed method can significantly improve the detection performances in color image steganalysis and achieve state-of-the-art *** the proposed method is originally designed for color images,it can also obtain competitive results for grayscale images compared with the current best *** addition,we provide numerous ablation studies to verify the rationality of the proposed network architecture.

关键词： steganalysis steganography convolutional neural network Transformer color image

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DTCC:Multi-level dilated convolution with transformer for weakly-supervised crowd counting

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Computational Visual Media 2023年第4期9卷 859-873页

作者： Zhuangzhuang Miao Yong Zhang Yuan Peng Haocheng Peng Baocai Yin Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial IntelligenceFaculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Taiji Computer Corporation Ltd. China

Crowd counting provides an important foundation for public security and urban *** to the existence of small targets and large density variations in crowd images,crowd counting is a challenging *** methods usually apply convolution neural networks(CNNs)to regress a density map,which requires annotations of individual persons and ***-supervised methods can avoid detailed labeling and only require counts as annotations of images,but existing methods fail to achieve satisfactory performance because a global perspective field and multi-level information are usually *** propose a weakly-supervised method,DTCC,which effectively combines multi-level dilated convolution and transformer methods to realize end-to-end crowd *** main components include a recursive swin transformer and a multi-level dilated convolution regression *** recursive swin transformer combines a pyramid visual transformer with a fine-tuned recursive pyramid structure to capture deep multi-level crowd features,including global *** multi-level dilated convolution regression head includes multi-level dilated convolution and a linear regression head for the feature extraction *** module can capture both low-and high-level features simultaneously to enhance the receptive *** addition,two regression head fusion mechanisms realize dynamic and mean fusion *** on four well-known benchmark crowd counting datasets(UCF_CC_50,ShanghaiTech,UCF_QNRF,and JHU-Crowd++)show that DTCC achieves results superior to other weakly-supervised methods and comparable to fully-supervised methods.

关键词： crowd counting transformer dilated convolution global perspective field pyramid

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Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space 39

Learning with Open-world Noisy Data via Class-independent Ma...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Pan, Linchao Gao, Can Zhou, Jie Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

ISBN: (纸本)157735897X

Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. However, in real-world scenarios, noisy labels from similar unknown classes, i.e., open-set noise, may occur during the training and inference stage. Such open-world noisy labels may significantly impact the performance of LNL methods. In this study, we propose a novel dual-space joint learning method to robustly handle the open-world noise. To mitigate model overfitting on closed-set and open-set noises, a dual representation space is constructed by two networks. One is a projection network that learns shared representations in the prototype space, while the other is a One-Vs-All (OVA) network that makes predictions using unique semantic representations in the class-independent space. Then, bi-level contrastive learning and consistency regularization are introduced in two spaces to enhance the detection capability for data with unknown classes. To benefit from the memorization effects across different types of samples, class-independent margin criteria are designed for sample identification, which selects clean samples, weights closed-set noise, and filters open-set noise effectively. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods and achieves an average accuracy improvement of 4.55% and an AUROC improvement of 6.17% on CIFAR80N. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Constructing uniform design tables based on restart discrete dynamical evolutionary algorithm

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Soft Computing 2024年第19期28卷 11515-11534页

作者： Zhao, Yuelin Wu, Feng Yang, Yuxiang Wei, Xindi Hu, Zhaohui Yan, Jun Zhong, Wanxie State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Department of Engineering Mechanics Faculty of Vehicle Engineering and Mechanics Dalian University of Technology Dalian116023 China

Generating uniform design tables (UDTs) is the first step to experimenting efficiently and effectively, and is also one of the most critical steps. Thus, the construction of uniform design tables has received much attention over the past decades. This paper presents a new algorithm for constructing uniform design tables: restart discrete dynamical evolutionary algorithm (RDDE). This algorithm is based on a well-designed dynamical evolutionary algorithm and utilizes discrete rounding technology to convert continuous variables into discrete variables. Considering the optimization of UDT is a multi-objective optimization problem, RDDE uses Friedman rank to select the optimal solution with better comprehensive comparison ranking. RDDE also utilizes a simulated annealing-based restart technology to select control parameters, thereby increasing the algorithm's ability to jump out of local optima. Comparisons with state-of-the-art UDTs and two practical engineering examples are presented to verify the uniformity of the design table constructed by RDDE. Numerical results indicate that RDDE can indeed construct UDTs with excellent uniformity at different levels, factors, and runs. Especially, RDDE can flexibly construct UDTs with unequal intervals of factors that cannot be directly processed by other designs of experiment. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Simulated annealing

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Learning to represent code semantics

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Science China(Information Sciences) 2025年

作者： Fang LIU Ge LI Qianhui ZHAO Li ZHANG State Key Laboratory of Complex & Critical Software Environment School of Computer Science & Engineering Beihang University Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University

Code semantic learning serves as the basis of many program analysis tasks. Researchers have paid much effort to build robust and effective code representation models over the years. One line of work focuses on introducing the code structure into the representations. To further improve the robustness of the code representation, approaches based on compiler intermediate representations(IRs) are proposed. However, these IR-based models suffer from heavy computational costs and memory overhead. How to represent program semantics effectively and efficiently still remains a challenge. To this end, we propose EECS, an effective and efficient code semantic representation approach based on compiler IRs and a hybrid attention mechanism. For input representation, to address the unlimited vocabulary size issue in IR, we propose a variable identification strategy to allocate each register variable to a new ID that can represent their relative positions. Besides, we also extract the data flow information among the code blocks. Then we build a hierarchical multi-layer Transformer encoder to capture the data dependency information as well as the code semantics through a hybrid attention mechanism. To enable EECS to learn code semantics and functionality better, we optimize three objectives jointly during the training *** results on three code semantic understanding tasks show that EECS performs better than the state-of-the-art techniques, demonstrating the remarkable capability of EECS on program semantics understanding.

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MARL-Based Joint Optimization of Service Migration and Resource Allocation in MEC 20th

MARL-Based Joint Optimization of Service Migration and Res...

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Lin, Ziqi He, Zhenli Zhai, Xiaolong Xiao, Yuanfei School of Software Yunnan University Kunming650504 China Yunnan Key Laboratory of Software Engineering Yunnan University Kunming China Engineering Research Center of Cyberspace Yunnan University Kunming China

ISBN: (纸本)9789819628636

Mobile Edge Computing (MEC) extends computational resources to the network edge, enabling low-latency services for users and IoT devices. While existing research advances service migration, it often isolates migration from resource allocation, leading to suboptimal edge server utilization and increased response delays. Moreover, the impact of migrating user-specific service contexts within network topologies is often overlooked, which undermines the effectiveness of migration strategies and increases costs. To address these issues, we propose a joint optimization of service migration and resource allocation in MEC, modeled as a Multi-Agent Markov Decision Process (MAMDP). We propose an algorithm based on Multi-Agent Reinforcement Learning (MARL). By employing Multi-Agent Proximal Policy Optimization (MAPPO) and Karush-Kuhn-Tucker (KKT) conditions, our approach optimizes both service migration and resource allocation strategies, thereby enhancing service quality. Simulations demonstrate that our method significantly outperforms benchmarks, achieving substantial reductions in response delays, service failures, and migration costs. © IFIP International Federation for Information Processing 2025.

关键词： Markov processes

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Personalized Federated Learning with Feature Alignment via Knowledge Distillation 21st

Personalized Federated Learning with Feature Alignment via...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Qi, Guangfei Qu, Zhihao Lyu, Shen-Huan Jia, Ninghui Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9789819601189

Personalized Federated Learning (PFL) has gained significant attention for its ability to handle heterogeneous data effectively. Parameter decoupling is a typical approach to PFL. It decouples the model into a feature extractor and a classifier head, where the feature extractor is trained collaboratively to learn a common representation and the classifier head is personalized for local data. Since local training only learns personalized feature information and ignores global information, the generalization ability of the feature extractor is limited. To improve the performance of the local model, a feasible approach is to make the local feature extractor more generalized. However, prior work requires the transmission of additional feature data beyond the transmission of model parameters, which leads to privacy leakage and higher communication overhead. To address these shortcomings, we propose a PFL algorithm with feature alignment via knowledge distillation, named PFAKD. PFAKD enhances the training of local feature extractors by explicitly aligning each sample’s local features with global features, providing more detailed guidance. Meanwhile, it avoids additional communication overhead and the risk of privacy leakage. We conduct extensive experiments in heterogeneous data scenarios. PFAKD outperforms other state-of-the-art methods by up to 4.35% in terms of model accuracy. Our code is available at https://***/fei0829/PFAKD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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