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Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Yu Wang Xiaoming Wu Qile Yuan Yunfeng Dong Xiangzhi Liu 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

As blockchain technology becomes widely applied, handling high volumes of transactions has become a critical challenge. Sharding has emerged as an effective solution to enhance the throughput of blockchain systems. Yet, this approach introduces challenges related to the frequency of cross-shard transactions and workload balancing. High rates of cross-shard transactions can slow overall processing speed and reduce throughput, while uneven load distribution can overburden some shards and underutilize others, impacting overall efficiency. To address these issues, this paper proposes a sharding method named LCLB. This method employs the concept of label propagation to pre-shard accounts with frequent transactions into a designated shard, ensuring a lower ratio of cross-shard transactions. It also uses an elastic load balancing algorithm to dynamically adjust the distribution of accounts, ensuring a relatively balanced processing load across all shards. Our results indicate that LCLB significantly enhances blockchain sharding performance,not only enhancing transaction throughput but also significantly reducing transaction confirmation latency, while ensuring a balanced load across various shards.

关键词： Sharding Heuristic algorithms Throughput Load management Blockchains Partitioning algorithms Delays Resource management Robots Intelligent control

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TSFE: Research on Transformer and Feature Enhancement Methods for Image-Text Retrieval

TSFE: Research on Transformer and Feature Enhancement Method...

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International Conference on computer Supported Cooperative Work in Design

作者： Jinxiao Yang Xingang Wang Qingao Wang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer 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

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In recent years, cross-modal retrieval has a wide range of applications in fields such as information retrieval and multimedia processing, where the key is to find relevant data in another modality through one modal query. In cross-modal graphic retrieval, mutual attention mechanisms are commonly used to interact image and text features. However, this interaction mechanism makes it difficult to extract separate visual and textual features for subsequent indexing steps, especially in large-scale retrieval tasks. Furthermore, in image representation, non-semantic information in image features can lead to inaccuracies in the alignment of image and text features. Therefore this paper proposes a Transformer and Feature Enhancement Method (TSFE) for Image-Text Retrieval. The method better fuses the underlying features and the higher level features by using a hierarchical Transformer coding structure on two separate branches, image and text, to proofread and localise the final output features. The Feature Enhanced Attention (FEA) module is designed in order to infer non-semantic information in the image and enhance the features of salient regions. Channel-space attention and max pooling is introduced on top of Transformer encoder self-attention to filter redundant and interfering information from channel and spatial dimensions. Experiments on Flickr30k dataset and MS-COCO dataset in this paper show improved performance in cross-modal retrieval, thus validating the effectiveness and sophistication of the proposed method.

关键词： Visualization Semantics Pipelines Noise Image representation Transformers Feature extraction

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A Blockchain-Based Hierarchical Dynamic Collaborative Storage Model

A Blockchain-Based Hierarchical Dynamic Collaborative Storag...

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Smart World Congress (SWC), IEEE

作者： Qile Yuan Xiangzhi Liu Yu Wang Yunfeng Dong Haoyu Jia Xiaoming Wu 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The pervasive application of blockchain technology has led to a significant increase in data volume, presenting significant challenges to existing storage architectures in environments characterized by large volumes of data and highfrequency transactions. To enhance the scalability of blockchain, this study introduces an innovative framework, Hierarchical Dynamic Storage Management Chain Model, named HDSMC. The model consists of two stages. In the first phase of the model, when multimodal data files are uploaded, LBA techniques are employed to securely eliminate unnecessary redundancy, effectively preventing the storage of duplicate data. In the second phase, further processing is conducted on the generated blocks. This involves analyzing the frequency of block access to compute the variation in block “heat”. Based on the Block Heat (BH), the number of replicas for each block across participating nodes is then determined. Furthermore, the framework implements an adaptive tiering strategy based on data access frequency and its importance, which facilitates the timely migration of data to an appropriate storage level. A simulated Hyperledger Fabric environment was employed to deploy the HDSMC framework and to assess its performance against current storage methodologies. The empirical outcomes indicated that this approach could achieve lower storage expenditures while ensuring high reliability. This research not only presents a novel strategy for blockchain storage management but also suggests potential applications for storage optimization in other big data scenarios.

关键词： Heating systems Time-frequency analysis Sharding Scalability Storage management Redundancy Fabrics Blockchains Reliability Optimization

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Multi-Attention Gate Based U-net For Retinal Vessel Segmentation

Multi-Attention Gate Based U-net For Retinal Vessel Segmenta...

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

作者： Yu Zhu Rui Li Shenghua Teng Xinrong Cao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

Retinal blood vessel segmentation images can be used to detect and evaluate various cardiovascular and ophthalmic diseases. However, due to the intricate vessel structures and blurred boundaries of vessels, it is a huge challenge to efficiently and accurately segment blood vessels. To deal with the above problems, this paper improves on the U-net by firstly using multi-scale feature convolution with kernels of varying size for feature extraction. Second, a non-local attention mechanism is applied to obtain richer global semantic information. Then multi-attention gate is used in the skip connection part by inputting feature maps of various scales and dimensions and selectively learning the interrelated regions, which improves the segmentation ability of the network model for the tiny structure of blood vessels. Quantitative and qualitative experimental results on two public datasets, DRIVE and CHASE_DB1, demonstrate the effectiveness of the proposed method.

关键词： Image segmentation Semantics Logic gates Blood vessels Network architecture Feature extraction Retinal vessels

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SGNet: A Multi-Scale Method for Steel Surface Defect Detection

SGNet: A Multi-Scale Method for Steel Surface Defect Detecti...

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Smart World Congress (SWC), IEEE

作者： Shanshan Liu Xiangzhi Liu Jiazheng Man Bingyang Cao Yunfeng Dong Xiaoming Wu 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The large variation in target scales within steel surface defect images often results in low detection accuracy. To address this issue, we have developed a multi-scale defect detection method named SGNet. Initially, we introduced a Self-Guided Bi-Level Routing Transformer (SG-BiFormer) as the backbone network of our model. SG-BiFormer utilizes a self-guided strategy to model global information and dynamically reallocates tokens based on the importance of image regions, increasing token quantity in significant areas to preserve fine-grained details and reducing it in less critical areas. This strategy significantly enhances the accuracy and efficiency of the model in handling multi-scale targets. Subsequently, we incorporated Grouped Separable Convolutions (GSConv) to manage local information of multi-scale features. GSConv automatically adjusts the size and shape of the convolution kernels according to different input features. This design not only improves global information processing capabilities but also ensures sensitive and precise detail capture. Lastly, we employed MPDIoU as the loss function for our model, which optimizes corner positioning to tackle the challenges of regression tasks, substantially improving the accuracy of the model in handling complex defects. Experimental evidence confirms the effectiveness of this method. Our SGNet achieved a 77.1% mAP on the NEU-DET dataset, demonstrating outstanding performance.

关键词： Visualization Accuracy Shape Detectors Transformers Routing Feature extraction Steel Kernel Defect detection

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A Network Combining CNN and Transformer for Blind Image Super-Resolution

A Network Combining CNN and Transformer for Blind Image Supe...

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

作者： Shuhao Zhang Zuoyong Li Shenghua Teng Kun Zeng College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

Blind super-resolution (SR) requires not only estimating blur kernel, but also super-resolving low-resolution image based on estimated blur kernel. Most blind SR methods use convolutional neural networks (CNNs) for kernel estimation, which cannot exploit long-range dependency within image domain, thus failing to predict blur kernel accurately. To address this issue, we propose a network combining CNN and transformer named NCCT for kernel estimation. By modeling local and non-local image priors simultaneously, NCCT outperforms other blind SR methods in terms of kernel estimation accuracy. Moreover, we design a network module named RRFDB for constructing lightweight blind SR network, which runs faster and achieves comparative SR performance with fewer parameters compared with other state-of-the-art blind SR methods.

关键词： Superresolution Education Estimation Transformers Feature extraction Convolutional neural networks Kernel

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Overcoming Data Limitations and Cross-Modal Interaction Challenges in Medical Visual Question Answering

Overcoming Data Limitations and Cross-Modal Interaction Chal...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yurun Bi Xingang Wang Qingao Wang Jinxiao Yang 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 Engineering Research Center of Big Data Applied Technology Faculty of Computer 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

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Medical Visual Question Answering (Med-VQA) is a sophisticated multimodal technology within the medical domain that utilizes natural language processing and artificial intelligence techniques. It serves as a tool in the medical field to provide information and answers, aiming to correctly respond to medical questions related to specific professional medical images. The primary goal is to assist patients in understanding their physical condition and provide decision support for healthcare professionals. While Generalized Visual Question Answering has seen significant advancements, challenges persist in the medical domain. Firstly, medical data has inherent limitations, such as the scarcity of annotated medical datasets and the complexity of medical images (including factors like noise, artifacts, and low contrast). Secondly, in terms of cross-modal fusion, there exists an intricate relationship between medical images and textual information. The complexity of the relationship between medical images and medical text, as well as the incorporation of global contextual details, pose challenges. To address these issues, this paper proposes the Feature-Enhanced Network with Conditional Mixing. This innovative approach aims to overcome the scarcity and complexity of medical images without the need for additional datasets. Simultaneously, to tackle the challenge of cross-modal fusion and information interaction and to address the complex relationship between medical images and text, we propose the Cross-Adaptive Fusion Network. Experimental results conducted on the VQA-RAD dataset demonstrate noticeable improvements in accuracy compared to mainstream Med-VQA models.

关键词： Visualization Accuracy Noise Neural networks Medical services Question answering (information retrieval) Data models

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Research and Application of Speech Transmission Index in Acoustic Leakage Detection

Research and Application of Speech Transmission Index in Aco...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Qiuyun Hao FuQiang Wang Peng Zhang XiaoFeng Ma Jianqiang Zhang Wei Zhao 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Acoustic information in confidential premise can be leaked through channels such as walls, pipes, doors and windows. Acoustic leakage level needs to be measured through specific indicators. Acoustic leakage evaluation indicator includes subjective evaluation indicator and objective evaluation indicator. The subjective evaluation indicator is a qualitative evaluation of acoustic leakage through subjective auditory perception of human ear, which can directly reflect the degree of acoustic leakage in confidential premise. At present, the objective evaluation indicator is mainly sound transmission loss (STL) to reflect objective degree of acoustic leakage in confidential premise. Therefore, it is relatively singular. Through study of Speech Transmission Index (STI), speech transmission index for public address systems (STIPA), which is a simplified form of FULL STI, is applied to acoustic leakage detection in this paper. STI has been proposed as an objective evaluation indicator and applied to acoustic leakage detection and acoustic information security. In order to confirm the effectiveness of objective evaluation indicator, a large number of related experimental studies have been conducted in the paper. Through experiments, it has been proven that STI can objectively evaluate level of acoustic leakage and level of acoustic information security in confidential premise. STI can better predict level of Chinese clarity under various complex transmission conditions. It provides a reliable basis for speech privacy of confidential premise and evaluation of architectural acoustic, sound reinforcement system and voice communication system.

关键词： Privacy Communication systems information security Ear Signal processing Propagation losses Acoustics Indexes Reliability Informatics

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Gaf-Net: Graph Attention Fusion Network for Multi-View Semi-Supervised Classification

SSRN

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SSRN 2023年

作者： Song, Na Du, Shide Wu, Zhihao Zhong, Luying Wang, Shiping School of Computer Science and Technology Hainan University Hainan570228 China School of Mechanical Electrical and Information Engineering Putian University Putian351100 China College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

Multi-view semi-supervised classification is a typical task to classify data using a small amount of supervised information, which has attracted a lot of attention from researchers in past years. In practice, existing methods focus more on extracting spatial or spectral features using graph convolutional neural networks without considering the diversity and variability of graph structures and the contributions of different views. To address this challenge, a network framework termed graph attention fusion network is proposed, which is comprised of two phases: view-specific feature embedding and graph embedding fusion. In the former feature extraction stage, view-specific feature embedding module can flexibly focus on the neighborhood calculation operation to learn a weight for each neighboring node. In the latter feature fusion stage, the graph embedding fusion module is performed by complementarity and consistency to fuse these embeddings for semi-supervised classification tasks. We conduct extensive experiments in terms of semi-supervised classification on real-world datasets to validate the superiority of the proposed method over various existing state-ofthe-art methods. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Expert-level vision-language foundation model for real-world radiology and comprehensive evaluation

arXiv

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arXiv 2024年

作者： Liu, Xiaohong Yang, Guoxing Luo, Yulin Mao, Jiaji Zhang, Xiang Gao, Ming Zhang, Shanghang Shen, Jun Wang, Guangyu Department of Radiology Sun Yat-Sen Memorial Hospital Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Medical Research Center Sun Yat-Sen Memorial Hospital Sun Yat-Sen University Guangzhou China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China

Radiology is a vital and complex component of modern clinical workflow and covers many tasks. Recently, vision-language (VL) foundation models in medicine have shown potential in processing multimodal information, offering a unified solution for various radiology tasks. However, existing studies either pre-trained VL models on natural data or did not fully integrate vision-language architecture and pretraining, often neglecting the unique multimodal complexity in radiology images and their textual contexts. Additionally, their practical applicability in real-world scenarios remains underexplored. Here, we present RadFound, a large and open-source vision-language foundation model tailored for radiology, that is trained on the most extensive dataset of over 8.1 million images and 250,000 image-text pairs, covering 19 major organ systems and 10 imaging modalities. To establish expert-level multimodal perception and generation capabilities, RadFound introduces an enhanced vision encoder to capture intra-image local features and inter-image contextual information, and a unified cross-modal learning design tailored to radiology. To fully assess the models' capability, we construct a benchmark, RadVLBench, including radiology interpretation tasks like medical vision-language question-answering, as well as text generation tasks ranging from captioning to report generation. We also propose a human evaluation framework. When evaluated on the real-world benchmark involving three representative modalities, 2D images (chest X-rays), multi-view images (mammograms), and 3D images (thyroid CT scans), RadFound significantly outperforms other VL foundation models on both quantitative metrics and human evaluation. In summary, the development of RadFound represents an advancement in radiology generalists, demonstrating broad applicability potential for integration into clinical workflows. © 2024, CC BY.

关键词： Visual languages

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