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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Honglei Zhu Anming Dong Jiguo Yu Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security 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

Drug box recognition is an integral part of the pharmacy automation system (PAS), which checks for discrepancies between the drugs provided by the system and the doctor’s prescription with the help of modern image recognition techniques. Although deep learning has facilitated the development of pharmacy automation systems, practical applications still face challenges. First, pharmacies update drugs frequently, and many pharmacies across the country are trained independently, which requires consuming massive computational resources. Secondly, the equipment performance of pharmacies is poor, and it’s hard to guarantee the real-time and accuracy of recognition. Therefore, we propose a cloud-side collaborative drug box recognition system. When new drugs are added, the new model is trained in the cloud and uniformly distributed to each edge through this architecture. In terms of real-time recognition, an improved lightweight YOLOv5 model is added to the edge part, and a text recognition module is deployed in the cloud to validate the recognition results at the edge. This cloud-edge collaboration architecture not only ensures the accuracy of recognition, but also reduces the burden on the compute edge, ensures the real-time *** results show that the proposed cloud-edge architecture reduces the recognition latency by 37.18% and achieves a precision of 97.47%. The improved YOLOv5 model reduces GFLOPs by 45% while achieving a 1.6% precision improvement.

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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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A residual attention-based privacy-preserving biometrics model of transcriptome prediction from genome

A residual attention-based privacy-preserving biometrics mod...

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IEEE International Conference on Trust, security and Privacy in computing and Communications (TrustCom)

作者： Cheng Tian Song Liu Jinbao Li Guangchen Wang Luyue Kong 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 Department of Medicine Shandong Medical College Jinan China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Transcriptome prediction from genetic variation data is an important task in the privacy-preserving and biometrics field, which can better protect genomic data and achieve biometric recognition through transcriptome. Many transcriptome prediction methods have achieved good accuracy from genetic variation data. However, these traditional transcriptome prediction methods have the problems of linear assumption, overfitting, expose personal privacy, and extensive manual optimization. To solve these shortcomings, we propose an attention-based transcriptome prediction model from genetic variation named RATPM that improves the accuracy of transcriptome prediction and protects participant genomic data. In RATPM, we introduce and improve the deep learning model with multi-head self-attention into the transcriptome prediction stage of Predixcan, which uncovers the non-linear relationship between genetic variation and transcriptome. Moreover, we introduce a residual attention module to generate attention-aware features and extract more accurate features at different levels from genetic variation. Furthermore, we introduce the BERT pre-training module to encode genetic variation fully utilizing their contextual information. Our research enables scientific institutions to publish only predicted transcriptomic data for biometric purposes, thus protecting the genomic information of the subjects. Finally, we evaluated our model on the 1000 Genomes and Geuvadis projects datasets to compare with other baseline models.

关键词： Privacy Biometrics (access control) Biological system modeling Genomics Predictive models Feature extraction Gene expression

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Zhiqiang Zhang Yongqiang Jiang Qian Gao Zhipeng Wang Key Laboratory of Computing Power Network and information Security Ministry of Eaucation Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China and 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 and Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Jinan China

Existing multimodal recommendation research still faces some challenges, such as not being able to fully mine the implicit relevance information of neighbor nodes, and the unreasonable weight allocation to imbalanced nodes. To address the aforementioned challenges, this paper introduces a new multimodal recommendation model called NS-MAR+. Specifically, the model firstly constructs a neighbor similarity graph convolutional network to capture the implicit relevance information and reasonably assigns the attention weights through the graph attention mechanism. Secondly, the model introduces a modal alignment and fusion mechanism by using a multilayer perceptron (MLP) to map image and text features into a shared space for comparison and fusion. In addition, the model constructs a user co-interaction graph and an item semantic graph based on the original information and performs graph convolution operations to enhance the preference information of users and items, and to better capture the interactions and internal features between users and items. Finally, MLP is employed to aggregate user and item representations and predict personalized recommendation rankings. To validate the experiment's efficacy, this paper compares with several leading multimodal recommendation models on public datasets with a performance improvement of 1% to 3%. The experimental outcomes indicate that the model in this paper has good superiority and accuracy in multimodal recommendation tasks.

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Generalization Boosted Adapter for Open-Vocabulary Segmentation

arXiv

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arXiv 2024年

作者： Xu, Wenhao Wang, Changwei Feng, Xuxiang Xu, Rongtao Huang, Longzhao Zhang, Zherui Guo, Li Xu, Shibiao School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The 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 Jinan250013 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China The Aerospace Information Research Institute Chinese Academy of Sciences China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Vision-language models (VLMs) have demonstrated remarkable open-vocabulary object recognition capabilities, motivating their adaptation for dense prediction tasks like segmentation. However, directly applying VLMs to such tasks remains challenging due to their lack of pixel-level granularity and the limited data available for fine-tuning, leading to overfitting and poor generalization. To address these limitations, we propose Generalization Boosted Adapter (GBA), a novel adapter strategy that enhances the generalization and robustness of VLMs for open-vocabulary segmentation. GBA comprises two core components: (1) a Style Diversification Adapter (SDA) that decouples features into amplitude and phase components, operating solely on the amplitude to enrich the feature space representation while preserving semantic consistency;and (2) a Correlation Constraint Adapter (CCA) that employs cross-attention to establish tighter semantic associations between text categories and target regions, suppressing irrelevant low-frequency "noise" information and avoiding erroneous associations. Through the synergistic effect of the shallow SDA and the deep CCA, GBA effectively alleviates overfitting issues and enhances the semantic relevance of feature representations. As a simple, efficient, and plug-and-play component, GBA can be flexibly integrated into various CLIP-based methods, demonstrating broad applicability and achieving state-of-the-art performance on multiple open-vocabulary segmentation benchmarks. © 2024, CC BY.

关键词： Semantics

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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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Improvement of Low-Contrast Objective Detecting Capability for YOLOv5 Based on Receptive Field Enhancement and Redundant Feature Reuse

Improvement of Low-Contrast Objective Detecting Capability f...

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International Joint Conference on Neural networks (IJCNN)

作者： Qingyu Xu Jiguo Yu Anming Dong 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

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

YOLOv5s is a classic deep learning target detection framework with balanced speed and performance in recognition, which is widely used in industrial defect inspections. YOLOv5s relies on the residual structure in the C3 module for feature extraction, but due to its simple structure and the small, fixed convolution kernel, it is difficult to accurately and completely extract target features when applied to the detection of defects with inconspicuous features and irregular shapes, which results in a decrease in recognition accuracy. To solve this problem, this paper proposes an improved YOLO architecture based on receptive field enhancement and redundant feature reuse. Firstly, the method designs a redundant feature reuse module (RFRM) to replace the residual structure in the C3 module for feature extraction. This module can deepen the network with a smaller cost and enhance the network’s ability to extract features. Furthermore, the feature reuse process allows the network to deepen while retaining detailed information, enabling the network to classify and localize more accurately. Secondly, a simplified convolutional block attention module (SCBAM) is designed and embedded into the neck of the detection network. This module directs the detection network to focus more on inconspicuous defect features and reduce interference from background noise in the feature map, thereby improving the accuracy of defect classification and localization. Finally, a receptive field enhancement module (RFEM) is designed and inserted between the neck and head of the detection network. This module adds only a small computational cost to expand the receptive field of the network, enabling it to further extract and comb irregular features from the neck in a comprehensive manner, making the features reaching the detection head more complete. Applying the above improvements to defect detection, the experimental results show that the improved YOLO architecture based on receptive field enhancem

关键词： YOLO Head Accuracy Target recognition Shape Computer architecture Feature extraction

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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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On the ADEP and DOR Analysis of RIS-aided URLLC Systems with Partial CSI in Smart Factory

On the ADEP and DOR Analysis of RIS-aided URLLC Systems with...

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IEEE Globecom Workshops

作者： Tingnan Bao Haiming Wang Hong-Chuan Yang Wen-Jing Wang Mazen O. Hasna Lenovo Research + Institute Of Computing Technology Chinese Academy of Sciences Beijing China Lenovo Reseach Lenovo Group Limited Bejiing China Department of Electrical and Computer Engineering University of Victoria Victoria Canada Shaanxi Key Laboratory of Information Communication Network and Security XUPT Xi'an China Department of Electrical Engineering Qatar University Doha Qatar

ISBN: (数字)9781665423908

ISBN: (纸本)9781665423915

Factory automation applications impose stringent quality of service (QoS) over ultra-reliable and low-latency communication (URLLC) system. However, the existing wireless systems cannot meet the requirement for these mission-critical industrial internet-of-things (IIoT) applications. In this paper, we consider the reconfigurable intelligent surface (RIS)-aided URLLC systems in the smart factory. To facilitate the performance analysis of the practical situation, the presence of phase error at RIS is discussed. We analyze and derive approximate closed-form expression of average decoding error probability (ADEP) and the closed-form expression of performance limits de-lay outage rate (DOR) under our proposed scenario, respectively. Through analytical results and associated numerical examples, we demonstrate the effect of the number of reflecting elements at the RIS, channel blocklength and packet size in achieving high reliability and low latency. Therefore, the deployment of RIS can offer an attractive solution to enhance the reliability over URLLC systems in the smart factory.

关键词： Wireless communication Closed-form solutions Error probability Rician channels Quality of service Ultra reliable low latency communication Reconfigurable intelligent surfaces

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A DoS-attack Resilient Control Approach for AIOT-enabled Systems

A DoS-attack Resilient Control Approach for AIOT-enabled Sys...

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Artificial Intelligence of Things and Systems (AIoTSys), International Conference on

作者： Xiaoya Cao Zhenya Chen Weisheng Liu Yuanlong Liu Wenting Wang Xin Wang Ming 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 Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Grid Shandong Electric Power Company Jinan China State Grid Shandong Electric Power Research Institute Jinan China

This paper presents a novel controller design for dealing with the challenges posed by Denial-of-Service (DoS) attacks in the context of Artificial Intelligence of Things (AIOT). The proposed design employs a prediction approach that utilizes uniform quantization for both encoder and decoder, aiming to effectively mitigate data loss during data transmission in scenarios involving DoS attacks. Furthermore, the study investigates the application of dynamic system design as a means to enhance the security defense mechanisms of AIOT, consequently bolstering the overall resilience of the system. The effectiveness and practicality of the proposed approach are validated through comprehensive simulations, providing empirical evidence of its efficacy.

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