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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Zhiqian Jia Wei Li Zuoyong Li Zhipeng Mu Lihui Lin Shenghua Teng College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Xiamen Air Traffic Management Station of CAAC Xiamen China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Data Science MinjiangUniversity Fuzhou China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry School of Mathematics and Computer Science Wuyi University Fujian China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

In recent years, significant advancements have been made in the field of anomaly detection. Expanding anomaly detection tasks to new scenarios has become a development avenue. Anomaly detection for the outer packaging of supermarket goods is a challenging task due to the diversity of goods’ appearances, which results in some detection methods suffering from limited sensitivity to anomalies and inadequate target mining. To address these issues, we propose a novel unsupervised anomaly detection method based on multi-branch feature extraction and foreground cosine structure mining strategy. Firstly, we use anomaly synthesis technique to enhance the model’s perception of anomalies and construct more precise decision boundaries in the feature space. Secondly, a multi-branch feature extractor extracts and integrates multi-level and multi-scale features, providing rich texture and semantic information to improve the model’s adaptability in real-world scenarios. Finally, the foreground cosine loss focuses on target mining while supplementing local structural information, further enhancing the model’s ability to detect subtle anomalies. To validate the effectiveness of the proposed method, extensive experiments were conducted on the GoodsAD dataset, sampled from supermarket scenarios. Experimental results show that this method achieves significant performance in image-level detection and pixel-level localization.

关键词： Location awareness Adaptation models Sensitivity Semantics Education Packaging Feature extraction information technology Anomaly detection Biomedical imaging

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TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation

arXiv

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arXiv 2023年

作者： Li, Jiang Su, Xiangdong Zhang, Fujun Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology China

This paper presents a translation-based knowledge geraph embedding method via efficient relation rotation (TransERR), a straightforward yet effective alternative to traditional translation-based knowledge graph embedding models. Different from the previous translation-based models, TransERR encodes knowledge graphs in the hypercomplex-valued space, thus enabling it to possess a higher degree of translation freedom in mining latent information between the head and tail entities. To further minimize the translation distance, TransERR adaptively rotates the head entity and the tail entity with their corresponding unit quaternions, which are learnable in model training. We also provide mathematical proofs to demonstrate the ability of TransERR in modeling various relation patterns, including symmetry, antisymmetry, inversion, composition, and subrelation patterns. The experiments on 10 benchmark datasets validate the effectiveness and the generalization of TransERR. The results also indicate that TransERR can better encode large-scale datasets with fewer parameters than the previous translation-based models. Our code and datasets are available at https://***/dellixx/TransERR. © 2023, CC BY-NC-ND.

关键词： Knowledge graph

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Achromatic on-chip focusing of graphene plasmons for spatial inversions of broadband digital optical signals

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Advanced Photonics Nexus 2023年第5期2卷 56-63页

作者： Zhiyong Wu Zhengji Xu Sun Yat-sen University School of Microelectronics Science and TechnologyZhuhaiChina Sun Yat-sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and SystemsZhuhaiChina

On-chip focusing of plasmons in graded-index lenses is important for imaging,lithography,signal processing,and optical interconnects at the deep subwavelength ***,owing to the inherent strong wavelength dispersion of plasmonic materials,the on-chip focusing of plasmons suffers from severe chromatic *** the well-established planar dielectric grating,a graded-index waveguide array lens(GIWAL)is proposed to support the excitation and propagation of acoustic graphene plasmon polaritons(AGPPs)and to achieve the achromatic on-chip focusing of the AGPPs with a focus as small as about 2%of the operating wavelength in the frequency band from 10 to 20 THz,benefiting from the wavelength-independent index profile of the *** analytical theory is provided to understand the on-chip focusing of the AGPPs and other beam evolution behaviors,such as self-focusing,self-collimation,and pendulum effects of Gaussian beams as well as spatial inversions of digital optical ***,the possibility of the GIWAL to invert spatially broadband digital optical signals is demonstrated,indicating the potential value of the GIWAL in broadband digital communication and signal processing.

关键词： achromatic lens self-focusing lens graded-index lens waveguide array broadband focusing graphene plasmon

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A Novel Image Encryption Scheme Based on Poker Cross-Shuffling and Fractional Order Hyperchaotic System 10th

A Novel Image Encryption Scheme Based on Poker Cross-Shuffli...

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10th International Conference on computer Engineering and Networks, CENet 2020

作者： Chen, Zhong Zhao, Huihuang Chen, Junyao College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China Department of Computer Engineering AjouUniversity Suwon16499 Korea Republic of

ISBN: (纸本)9789811584619

A novel digital image encryption scheme based on Poker shuffling and fractional order hyperchaotic system is proposed. Poker intercrossing operation has nonlinearity and periodicity, and can form permutation group. Firstly, the plain image is transformed into block image, and each block is confused by Poker shuffling operation with key. Secondly, bit plane of every pixel in each block is confused. Lastly, the scrambled image is encrypted by fractional order hyperchaotic sequence. This encryption scheme provides a secure and efficient key stream, and guarantees a large key space. The experimental results and security analysis imply that the new encryption method has secure encryption effect and property of resisting common attacks. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Lightweight Monocular 3D Object Detection Based on Attention Mechanism

Lightweight Monocular 3D Object Detection Based on Attention...

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Image processing, Electronics and computers (IPEC), Asia-Pacific Conference on

作者： Feng Zhan Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan Hubei China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

In the field of autonomous driving, 3D target detection is an important technology. In view of the shortcomings of existing monocular 3D detection algorithms in terms of accuracy and real-time performance, we propose a lightweight 3D target detection algorithm based on the attention mechanism. In order to ensure the real-time performance of the model, we use depthwise separable convolutions in the backbone network. The feature maps of each scale are then connected to an EMA module, which can fuse spatial attention and cross-dimensional Coordinate attention is used to enhance the feature extraction capability of the network, and BiFPN is subsequently used for multi-scale feature fusion. Finally, experiments were conducted on the KITTI dataset, and the results showed that our algorithm was superior to other methods in both accuracy and speed.

关键词： Three-dimensional displays Attention mechanisms Accuracy Head Fuses Object detection Feature extraction Real-time systems Data mining Visual perception

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A Blockchain-Assisted key Generation Electric Health Records Sharing Scheme 8th

A Blockchain-Assisted Key Generation Electric Health Records...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Zhang, Qiao Chen, Xiubo Ahmad, Haseeb Xu, Gang Yang, Yixian Guizhou Provincial Key Laboratory of Public Big Data GuiZhou University Guiyang Guizhou550025 China State Key Laboratory of Networking and Switching Technology Information Security Center Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science National Textile University Faisalabad37610 Pakistan School of Information Science and Technology North China University of Technology Beijing100144 China

ISBN: (纸本)9783031067631

Electronic health records (EHRs) contain a large amount of private data of patients. Once these data are compromised during the process of sharing, it may threaten the patients’ privacy. In this paper, a novel blockchain-assisted electronic health record sharing scheme is proposed, which utilizes attribute-based encryption (ABE) based on the consortium blockchain to realize the privacy protection in the sharing process of EHRs. Firstly, the master key is negotiated by all consensus nodes of consortium blockchain, no one knows the specific value of the master key. Consensus nodes are acted by medical institutions, they also responsible for generating and managing users’ private keys. Secondly, with the help of powerful cloud services, pre-decryption and ciphertext retrieval assignments are outsourced to cloud services for reducing the burden of blockchain. Thirdly, this scheme is also based on searchable encryption, which allows for quick ciphertext lookup from cloud services. Data owners can generate ciphertext indexes for their private data, and data users with retrieval trapdoors can quickly retrieve ciphertexts. Finally, the security and performance of blockchain-assisted electronic health record sharing scheme are analyzed in detail, the results show that our scheme is safe and feasible. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Distributed Traffic Engineering in Hybrid Software Defined Networks: A Multi-agent Reinforcement Learning Framework

arXiv

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arXiv 2023年

作者： Guo, Yingya Tang, Qi Ma, Yulong Tian, Han Chen, Kai The College of Computer and Data Science Fuzhou University China The Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China The Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fujian 350003 China The Department of Computer Science & Engineering Hong Kong University of Science & Technology Hong Kong

Traffic Engineering (TE) is an efficient technique to balance network flows and thus improves the performance of a hybrid Software Defined Network (SDN). Previous TE solutions mainly leverage heuristic algorithms to centrally optimize link weight setting or traffic splitting ratios under the static traffic demand. Note that as the network scale becomes larger and network management gains more complexity, it is notably that the centralized TE methods suffer from a high computation overhead and a long reaction time to optimize routing of flows when the network traffic demand dynamically fluctuates or network failures happen. To enable adaptive and efficient routing in TE, we propose a Multi-agent Reinforcement Learning method CMRL that divides the routing optimization of a large network into multiple small-scale routing decision-making problems. To coordinate the multiple agents for achieving a global optimization goal, we construct an interactive environment for training the routing agents that own partial link utilization observations. To optimize credit assignment of multi-agent, we introduce the difference reward assignment mechanism for encouraging agents to take better action. Extensive simulations conducted on the real traffic traces demonstrate the superiority of CMRL in improving TE performance, especially when traffic demands change or network failures happen. Copyright © 2023, The Authors. All rights reserved.

关键词： Global optimization

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Unified-modal Salient Object Detection via Adaptive Prompt Learning

arXiv

引用

arXiv 2023年

作者： Wang, Kunpeng Li, Chenglong Tu, Zhengzheng Liu, Zhengyi Luo, Bin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China The Institute of Physical Science and Information Technology Anhui University Hefei230601 China

Existing single-modal and multi-modal salient object detection (SOD) methods focus on designing specific architectures tailored for their respective tasks. However, developing completely different models for different tasks leads to labor and time consumption, as well as high computational and practical deployment costs. In this paper, we attempt to address both single-modal and multi-modal SOD in a unified framework called UniSOD, which fully exploits the overlapping prior knowledge between different tasks. Nevertheless, assigning appropriate strategies to modality variable inputs is challenging. To this end, UniSOD learns modality-aware prompts with task-specific hints through adaptive prompt learning, which are plugged into the proposed pre-trained baseline SOD model to handle corresponding tasks, while only requiring few learnable parameters compared to training the entire model. Each modality-aware prompt is generated from a switchable prompt generation block, which adaptively performs structural switching based on single-modal and multi-modal inputs without human intervention. Through end-to-end joint training, UniSOD achieves overall performance improvement on 14 benchmark datasets for RGB, RGB-D, and RGB-T SOD, which demonstrates that our method effectively and efficiently unifies single-modal and multi-modal SOD tasks. The code and results are available at https://***/Angknpng/UniSOD. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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VMINer: Versatile Multi-view Inverse Rendering with Near-and Far-field Light Sources

VMINer: Versatile Multi-view Inverse Rendering with Near-and...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Fan Fei Jiajun Tang Ping Tan Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University Light Illusions Hong Kong University of Science and Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This paper introduces a versatile multi-view inverse rendering framework with near-and far-field light sources. Tackling the fundamental challenge of inherent ambiguity in inverse rendering, our framework adopts a lightweight yet inclusive lighting model for different near-and far-field lights, thus is able to make use of input images under varied lighting conditions available during capture. It leverages observations under each lighting to disentangle the intrinsic geometry and material from the external lighting, using both neural radiance field rendering and physically-based surface rendering on the 3D implicit fields. After training, the reconstructed scene is extracted to a textured triangle mesh for seamless integration into industrial rendering soft-ware for various applications. Quantitatively and qualitatively tested on synthetic and real-world scenes, our method shows superiority to state-of-the-art multi-view inverse rendering methods in both speed and quality.

关键词： Geometry Training Surface reconstruction Three-dimensional displays Lighting Rendering (computer graphics) Neural radiance field

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From Data Integrity to Global ModeI Integrity for Decentralized Federated Learning: A Blockchain-based Approach

From Data Integrity to Global ModeI Integrity for Decentrali...

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International Joint Conference on Neural Networks (IJCNN)

作者： Na Wang Yao Zhao Youyang Qu Lei Cui Bai Li Longxiang Gao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of IT Deakin University VIC Australia Zhejiang Tianheng Information Technology Company China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Decentralized Federated Learning (DFL) is extensively applied in various areas, e.g., healthcare, finance, and Internet of Things (loT), offering practical solutions for distributed intelligent applications and data collaboration. In DFL systems, participants, e.g., edge devices, organizations, or nodes, collaborate in the training of a shared global model by aggregating local models from various participants. During this process, participants need to communicate frequently with a central authority/node/server to share model parameters. Such communication is vulnerable to malicious attacks or tampering, posing a significant threat to the integrity of model training. The integrity verification method can provide an integrity guarantee for the global model of DFL. However, most of the existing integrity verification schemes are centralized and not suitable for resource-constrained DFL scenarios. Therefore, how to verify the integrity of the global model becomes an important issue in DFL. To address it, we devise a global model integrity verification method for DFL. Specifically, we generate a digital signature for each global model parameter as proof of integrity, while improving the efficiency of integrity verification by electing delegates to conduct the verification process. A series of experiments is conducted to validate the performance of the proposed method. The experimental results demonstrate that our approach not only effectively ensures the integrity of the global model but also functions well under limited resources.

关键词： Training Federated learning Data integrity Neural networks Finance Distributed databases Organizations

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