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International Conference on Microwave and Millimeter Wave technology Proceedings

作者： Yuqi Xiao Kai Lu Kwok Wa Leung Chi-Sing Leung Department of Electrical Engineering City University of Hong Kong Hong Kong SAR China School of Electronics and Information Technology and Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Guangzhou China Department of Electrical Engineering State Key Laboratory of Terahertz and Millimeter Waves City University of Hong Kong Hong Kong SAR China Information and Communication Technology Centre CityU Shenzhen Research Institute Shenzhen China

This research proposes a simple approach to recognize resonate modes in rectangular dielectric resonator antennas (DRAs) with machine learning (ML) techniques. The proposed method utilizes a convolutional neural network (CNN) with a focal loss function to classify various modes present in the antenna. DRAs with different dimensions are simulated with ANSYS HFSS to obtain their electric fields at various resonant frequencies. The resulting dataset comprises 700 fields, manually labeled according to their corresponding resonant modes. After training the CNN model using this dataset, it is capable of accurately classifying 12 low-order resonant modes in rectangular DRAs. The performance of the model is evaluated using a test set, where it achieves a recognition accuracy of 97.8%.

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Affective Image Filter: Reflecting Emotions from Text to Images

Affective Image Filter: Reflecting Emotions from Text to Ima...

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International Conference on computer Vision (ICCV)

作者： Shuchen Weng Peixuan Zhang Zheng Chang Xinlong Wang Si Li 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 Beijing Academy of Artificial Intelligence School of Artificial Intelligence Beijing University of Posts and Telecommunications

Understanding the emotions in text and presenting them visually is a very challenging problem that requires a deep understanding of natural language and high-quality image synthesis simultaneously. In this work, we propose Affective Image Filter (AIF), a novel model that is able to understand the visually-abstract emotions from the text and reflect them to visually-concrete images with appropriate colors and textures. We build our model based on the multi-modal transformer architecture, which unifies both images and texts into tokens and encodes the emotional prior knowledge. Various loss functions are proposed to understand complex emotions and produce appropriate visualization. In addition, we collect and contribute a new dataset with abundant aesthetic images and emotional texts for training and evaluating the AIF model. We carefully design four quantitative metrics and conduct a user study to comprehensively evaluate the performance, which demonstrates our AIF model outperforms state-of-the-art methods and could evoke specific emotional responses from human observers.

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Attribute-Based key Management for Patient-Centric and Trusted Data Access in Blockchain-Enabled IoMT

SSRN

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SSRN 2023年

作者： Xiang, Axin Gao, Hongfeng Tian, Youliang Wang, Linjie Xiong, Jinbo State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Guiyang550025 China Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology Guizhou University Guizhou Guiyang550025 China Network and Information Management Center Guizhou University Guizhou Guiyang550025 China School of Data Science Tongren University Guizhou Tongren554300 China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou350117 China

Internet of Medical Things (IoMT) provides real-time data collection and access services to medical entities by connecting their devices to Internet. However, within such a complex architecture, achieving patient-centric data access still presents trustworthiness challenges. Ciphertext-policy attribute-based encryption (CP-ABE) and blockchain are both the promising cryptographic technologies that support patients with secure and fine-grained access control over their data. Nevertheless, both have their unique key management issues. In this paper, we propose a novel patient-centric and trusted data access (PCT-DA) system in blockchain-enabled IoMT. Firstly, we develop an online/offline encryption and verifiable outsourced decryption CP-ABE scheme to relieves the computation pressure on patients/users. Secondly, we design a flexible attribute-based key management mechanism to overcome the security drawbacks of data encryption keys and user attribute keys. Finally, we build a trusted integrity audit contract to guarantee the incentive fairness of patients/users, allowing them to release/access medical data honestly. Moreover, theoretical analyses prove that the PCT-DA system not only achieves confidentiality, integrity, auditability and forward/backward security, but also guarantees static security under random oracles. Simulation experiments indicate that our proposal reduces the time cost by approximately 14.14 seconds over state-of-the-art and is more flexible in BE-IoMT. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Thinking the Importance of Patient’s Chief Complaint in TCM Syndrome Differentiation

Thinking the Importance of Patient’s Chief Complaint in TCM...

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International Conference on computer Supported Cooperative Work in Design

作者： Zhizhuo Zhao Xueping Peng Yong Li Hao Wu Weiyu Zhang Wenpeng Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) 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 Australian Artificial Intelligence Institute University of Technology Sydney Australia Jinan ChangQing District People’s Hospital School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Traditional Chinese Medicine (TCM) is a natural, safe, and effective therapeutic approach with widespread application worldwide. The unique diagnostic methods of TCM often require a comprehensive analysis of patient information, much of which is contained in clinical text. Numerous studies have demonstrated the effectiveness of natural language processing (NLP) techniques in TCM disease classification. Therefore, this paper focuses on the task of TCM syndrome differentiation, proposing a novel matching score calculation method and a new label attention calculation method to assist the model in focusing on the relationship between TCM syndrome and disease symptom. Specifically, we enhance the model’s attention to the uniqueness of the relationship between TCM syndrome and symptom by introducing a finer-grained token-level matching score. Simultaneously, we improve the model’s attention to the generality of the relationship between TCM syndrome and symptom through a more global label attention mechanism. Additionally, we observe a severe long-tail problem in the dataset. To alleviate this issue, we propose the use of focal loss to help the model pay more attention to challenging samples. Extensive experiments on the TCM-SD dataset indicate that our approach significantly outperforms state-of-the-art baselines 1 .

关键词： Attention mechanisms Federated learning Focusing Natural language processing Task analysis Medical diagnostic imaging Diseases

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A Policy-Controlled Signature Scheme for end-to-end Secure Communication

A Policy-Controlled Signature Scheme for end-to-end Secure C...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Feng Yu Linghui Meng Jing Xi Xianxian Li Qianhui Lu Weidong Zhu Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University GuiLin China Jiangsu Provincial Key Laboratory of Netwrok and Information Security University of SouthEast Nanjing China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University GuiLin China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University GuiLin China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

In the digitalized society, end-to-end security communication is receiving increasing attention. Issues such as ma-licious sender impersonation and over-privilege communication continue to prevail in end-to-end communication. This paper pro-poses a policy-controlled signature scheme aimed at addressing these concerns through identity verification and permission man-agement, thus establishing a secure mode of communication. The scheme designs policy-controlled signatures with partial policy hiding functionality, employing expressive access structures and garbled Bloom Filter (GBF) to protect the sender's attributes. In comparison to related approaches, this scheme provides benefits in terms of signature computation cost, thereby enhancing the resilience and efficiency of the communication system against attacks.

关键词： Privacy Costs Communication systems Prevention and mitigation Big Data Computational efficiency Security Protection Resilience

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CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing

arXiv

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arXiv 2024年

作者： Xian, Xiaole He, Xilin Niu, Zenghao Zhang, Junliang Xie, Weicheng Song, Siyang Yu, Zitong Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Great Bay University China

For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional finetuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. The code is available at https://***/connorxian/CA-Edit. © 2024, CC BY.

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Research and development of advanced computing technologies

Scientific World Journal

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Scientific World Journal 2015年第1期2015卷 239723页

作者： Ding, Shifei Shi, Zhongzhi Azar, Ahmad Taher School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Faculty of Computers and Information Benha University Benha 13511 Egypt

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Rafmnet:Reinforced Attention Fusion and Multiscale Network For Noisy Infrared and Visible Image Fusion

Rafmnet:Reinforced Attention Fusion and Multiscale Network F...

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IEEE International Conference on Image processing

作者： Guohua Lv Xiyan Wang Yongbiao Gao Yi Zhai Guixin Zhao Guangxiao Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Comuter Science and Technology 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 College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The purpose of infrared and visible image fusion is to combine the advantages of different types of images to produce more robust and informative images. However, if the source images are noisy, existing fusion methods may not produce clear results. To address this issue, we propose a novel method for infrared and visible image fusion with noise reduction. This method enhances the visual perception of fused images by integrating features of different scales extracted by the denoising network into the fusion network. By using deformable convolutional denoising networks, noise in images can be removed and features can be enhanced. Then, a set of reinforced attention fusion modules (RAFM) are designed to fuse the features extracted by the denoising network. Experimental results demonstrate the effectiveness of our proposed method, which outperforms existing state-of-the-art methods in terms of fusion accuracy and visual perception.

关键词： Couplings Fuses Noise reduction Noise Feature extraction Noise measurement Task analysis

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Neighborhood collaborative classifiers

Neighborhood collaborative classifiers

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Suping Xu Xibei Yang Eric C. C. Tsang Eric A. Mantey Intelligent Information Processing Key Laboratory of Shanxi Province Shanxi University Taiyuan China School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan China School of Economics and Management Nanjing University of Science and Technology Nanjing China Faculty of Information Technology Macau University of Science and Technology Macau China

ISBN: (纸本)9781509003914

In neighborhood rough set model, the majority rule based neighborhood classifier (NC) is easy to be misjudged with the increasing of the size of information granules. To remedy this deficiency, we propose a neighborhood collaborative classifier (NCC) based on the idea of collaborative representation based classification (CRC). NCC first performs feature selection with neighborhood rough set, and then instead of solving the classification problem by the majority rule, NCC solves a similar problem with collaborative representation among the neighbors of each unseen sample. Experiments on UCI data sets demonstrate that: 1) Our NCC achieves satisfactory performance in larger neighborhood information granules when compared with NC; 2) NCC reduces the size of dictionary when compared with CRC.

关键词： Collaboration Rough sets Training Cybernetics Numerical models Error analysis Measurement

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A Trajectory Tracking System for Zebrafish Based on Embedded Edge Artificial Intelligence

A Trajectory Tracking System for Zebrafish Based on Embedded...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Chuanhao Zang Anming Dong Jiguo Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute 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

Trajectory tracking of zebrafish is an important requirement in studying neurological disorders and developing new psychoactive drug. However, many challenges emerge for stable tracking, since zebrafish are similar in appearance, occlusion, agile, non-linear in moving and easy to swarm, all of which will lead to mistrack for multiple fish. And there is no precedent for tracking zebrafish through embedded edge artificial intelligence device. To overcome these difficulties, we present a tracking system for zebrafish based on RK3588-S. First, We construct an embedded edge AI hardware system consisting of two cameras driven by the RK3588-S, one for the front view and the other for top view of the fish. Then, we develop a 2D tracking algorithm based on YOLOv5 and the Observation-Centric Simple online and real-time tracking (OC-SORT) algorithm, which are transplanted to the RK3588-S for tracking the top and front views of the fish at the edge device. Compared with the previous methods, our method has fewer ID exchanges and highly real-time. Moreover, we apply the MQTT mechanism to establish communication links between the edge and the cloud to reliably transmit data to the cloud. The correspondence between cloud server and embedded AI is 1: N. Finally, we design a multi-view data fusion association algorithm to fuse the data of the two views in the cloud, which are further utilized to build the 3D tracklets of the zebrafish.

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