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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Zhou, Yao Qin, Yusong Wang, Song Huang, Zengxi Zhang, Dazhi Xihua University School of Computer Science and Software Engineering Chengdu China Xihua University School of Computer and Software Engineering and the Sichuan Xihua Jiaotong Forensics Center Chengdu China Sichuan Police College Criminal Science and Technology Department Luzhou China

ISBN: (纸本)9798350325485

Person Re-Identification (ReID) is a crucial task in computer vision that aims to match persons captured from non-overlapping camera views. In this paper, to alleviate the impacts of background and occlusion, we propose to use instance segmentation and pose estimation methods to create masks for global feature extraction. Furthermore, we divide the pedestrian images into three regions according to pedestrian keypoints, trying to eliminate the alignment errors. This part-based matching strategy also helps to address the occlusion issue. Overall, we construct a deep learning network with three branches, including two global branches and a part-based branch. The two global branches extract global features using segmentation-based mask and the mask derived from pedestrian keypoint heatmaps, respectively. In the end, a weighted fusion strategy is used to combine the global scores and part-based scores for final classification. This network enables us to acquire robust global feature of pedestrians by excluding background and occlusion, and simultaneously address the alignment errors to some extent. Experimental results on these three widely used datasets demonstrate the effectiveness of our method: Specifically, it achieves 64.5% rank-1 accuracy and 54.3% mAP on Occluded-Duke, and 94.4% and 87.1% rank-1 accuracies on Market-1501 and DukeMTMC-reID, respectively. © 2023 IEEE.

关键词： Deep learning

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Performance-Based Scheduling in Distributed Mixed Criticality Systems

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IEEE Access 2025年 13卷 95321-95340页

作者： Ali, Amjad Wasly, Saud Khattak, Asad Masood Ali, Ihsan Iqbal, Shahid Hayat, Bashir University of Swat Department of Computer and Software Technology Swat Khyber Pakhtunkhwa Saidu Sharif19130 Pakistan King Abdulaziz University Electrical and Computer Engineering Department Jeddah21589 Saudi Arabia Zayed University College of Technological Innovation Dubai United Arab Emirates Institute of Management Sciences Peshawar School of Computer Science and IT Peshawar25000 Pakistan

With a focus on computationally intensive, distributed, and parallel workloads, scheduling in mixed-criticality distributed systems presents significant challenges due to shared memory and resources, as well as the diverse demands of tasks. The system’s efficiency is heavily dependent on the overall scheduling duration (make span), while individual task deadlines impose strict timing constraints. When the tasks need to simultaneously access the shared memory, then these tasks interfere the execution of one another. For managing the scheduling of interfering tasks in distributed mixed-criticality systems, a novel Interference-Aware Partitioning Fixed Priority (IAP-FP) approach is proposed, which effectively handles task partitioning among cores while considering interference, ensuring better performance and adherence to critical deadlines. This method reduces task waiting times, which minimizes the scheduling duration and improving the schedulability of the task set. The novel proposed approach is compared with Mixed-Criticality Multicore Compositional Earliest Deadline First (MMC-EDF), Global and Mixed Criticality Partitioning (MC-Partitioning) approaches to show the efficiency. The proposed approach schedules 75% tasks while the MMC-EDF, Global and MC-Partitioning approaches schedules 65%, 0% and 18% tasks respectively for target utilization U=0.8. As the utilization of mixed criticality (MC) workload increases, the schedulability of MC task sets decreases, but still the proposed approach performs better than the MMC-EDF, Global and MC-partitioning approaches. © 2013 IEEE.

关键词： Embedded systems

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GMA: Graph Multi-agent Microservice Autoscaling Algorithm in Edge-Cloud Environment

GMA: Graph Multi-agent Microservice Autoscaling Algorithm in...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Tong, Ganghao Meng, Chunyang Song, Shijie Pan, Maolin Yu, Yang Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Sun Yat-sen University School of Software Engineering Zhuhai China

ISBN: (纸本)9798350304855

The emerging edge-cloud computing paradigm, comprising cloud centers and multiple distributed edge servers, extends the computing capability from the cloud center to a range of servers. Although the microservice autoscaling problem has been intensively studied in the context of cloud computing, existing algorithms in most cases cannot be effectively migrated to the edge-cloud environment because servers are geographically distributed and heterogeneous, and information is not synchronized between servers. Existing works, however, mainly focus on centralized strategies with time-consuming synchronization methods, i.e. strategies shared by all servers, without comprehensively considering the heterogeneity and distribution of the environment. Soft information synchronization, autonomy and collaboration is proposed to tackle the aforementioned issues, and refer to it as SAC paradigm. According to the SAC paradigm, each server with inferred information of other servers can collaborate with others by a dedicated autoscaling strategy, that is, server collaboration. The microservice autoscaling problem is then transformed into the Graph-based Jointly Microservice Autoscaling (GJMA) problem based on spectral graph theory. GJMA problem aims to minimize average waiting time of microservice-based application while reducing service-level agreement(SLA) violation rate and fluctuations in the autoscaling process, taking into account resource heterogeneity. Graph-based Multi-agent Algorithm(GMA), an implementation of SAC paradigm based on graph convolutional networks and multi-agent reinforcement learning, is implemented to solve GJMA problem. Experimental results show that the proposed algorithm for the edge-cloud environment is always efficient to find a better autoscaling strategy compared to the implemented comparison algorithms. © 2023 IEEE.

关键词： Convolution

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Low-Quality Ultrasound Image Enhancement Using Spatially Variable Neural Implicit Network

Low-Quality Ultrasound Image Enhancement Using Spatially Var...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Xiaojie Xia, Jing Wu, Mei Gao, Shanshan Zhou, Yuanfeng Shandong University School of Software Jinan China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore The Second Hospital of Shandong University Ultrasound Department Jinan China Shandong University of Finance and Economics School of Computer Science and Technology Jinan China

ISBN: (纸本)9798350386226

Ultrasound images are widely used because they are easy to use and non-invasive. However, differences in acquisition equipment and frequency settings can result in varying image quality. To address this issue, we propose an ultrasound image enhancement method based on spatially variable neural implicit networks. Our approach consists of several steps: First, the encoder of an arbitrary-scale super-resolution network processes low-quality ultrasound images to generate a feature matrix. Concurrently, the parameter generation network decomposes the low-quality image into a parameter matrix. The pixel prediction module of the arbitrary-scale super-resolution network then uses the parameter matrix to map encoded features to pixel values. To further align the predicted pixel values with the distribution of high-quality ultrasound images and reduce interference from artifacts, we design a spectrum discriminator network based on the fast Fourier transform. This network is highly effective at capturing high-quality information and minimizing the impact of redundant information. In both qualitative and quantitative comparisons with current state-of-the-art image enhancement methods and arbitrary magnification techniques, our approach demonstrates superior performance. © 2024 IEEE.

关键词： Ultrasonic imaging

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CEMA-LSTM:Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

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computer Modeling in engineering & sciences 2023年第4期135卷 45-64页

作者： Zhiyun Yang Qi Liu HaoWu Xiaodong Liu Yonghong Zhang School of Computer and Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK School of Automation Nanjing University of Information Science TechnologyNanjing210044China

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy *** relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation,where radar echo maps were used to predict their consequent moment,so as to recognize potential severe convective weather ***,these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation,due to the size limitation of convolution filter,lack of global feature,and less attention to features from previous *** address the problems,this paper proposes a CEMA-LSTM recurrent unit,which is embedded with a Contextual Feature Correlation Enhancement Block(CEB)and a Multi-Attention Mechanism Block(MAB).The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction;the MAB uses a position and channel attention mechanism to capture global features of radar *** practical radar echo datasets were used involving the FREM and CIKM 2017 *** quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTMover recentmodels,e.g.,PhyDNet,MIM and PredRNN++,*** particular,compared with the second-rankedmodel,its average POD,FAR and CSI have been improved by 3.87%,1.65%and 1.79%,respectively on the FREM,and by 1.42%,5.60%and 3.16%,respectively on the CIKM 2017.

关键词： Radar echo extrapolation attention mechanism long short-term memory deep learning

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Attention Enhanced Feature Fusion for Mental Stress Level Classification 21

Attention Enhanced Feature Fusion for Mental Stress Level Cl...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Sahlu, Ameneshewa Abush Yohannes, Gobena Mulugeta Dawit, Bekalu Nigus Teshome, Molla Woretaw School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798331519254

Mental stress poses significant health risks, manifesting in various psychological and physical issues such as depression, anxiety, and cardiovascular complications. Establishing a reliable method for swiftly and accurately classifying mental stress levels is crucial due to its widespread impact. Existing classification approaches predominantly rely on either data-intensive traditional machine learning algorithms or opaque deep learning models, hindering real-world use and interpretability. This study addresses these limitations by proposing a novel approach for stress level classification using the WESAD dataset. Our method combines an attention-based ResN et with CBAM. This innovative approach synergistically integrates shallow and deep features, harnessing both low-level details and high-level semantic information to effectively classify stress levels into four distinct states: baseline, stress, amusement, and meditation, or three states excluding the meditation state. The experimental result shows 92.74% accuracy and 91.19% F1 score for the 4-class setup, and 97.58% accuracy and 97.51 % F1 score for the 3-class setup. Our approach offers a more robust and interpretable framework for mental stress classification, with potential applications in health care and stress management. © 2024 IEEE.

关键词： Deep learning

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A Text-to-SQL Query Parser with Integrating Instances

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第6期52卷 1764-1771页

作者： Cui, Xuxu Shen, Derong School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning114051 China School of Computer Science and Engineering Northeastern University Liaoning114051 China

Text-to-SQL is a natural language processing task focused on translating natural language queries into Structured Query Language (SQL), aiming to facilitate interaction between non-technical users and relational databases. Currently, many existing Text-to-SQL models based on deep learning methods concentrate on encoding natural language questions and database schemas, while often neglecting the inclusion of database instance information. Additionally, these models are constrained by SQL syntax during the decoding process, which limits their generalization ability. This paper presents a Text-to-SQL query parser that integrates database instances to address these limitations. By incorporating database instance information, both natural language queries and database schema-instance pairs are represented as graph structures for encoding. Predefined relationships are utilized to unify the encoding of natural language questions, database schemas, and database instances. An instance-aware query parsing method is then applied during the decoding phase, allowing the model to fully leverage database instance information when generating SQL queries, which improves its scalability. The method is evaluated using the cross-domain Spider dataset, and pretrained language models such as GloVe, GAP, and BERT are employed to enhance model performance. Experimental results show that the proposed approach significantly improves prediction accuracy in Text-to-SQL tasks. © (2025), (International Association of Engineers). All rights reserved.

关键词： Structured Query Language

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HeGTa: Leveraging Heterogeneous Graph-enhanced Large Language Models for Few-shot Complex Table Understanding 39

HeGTa: Leveraging Heterogeneous Graph-enhanced Large Languag...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jin, Rihui Li, Yu Qi, Guilin Hu, Nan Li, Yuan-Fang Chen, Jiaoyan Wang, Jianan Chen, Yongrui Min, Dehai Bi, Sheng School of Computer Science and Engineering Southeast University China Ministry of Education China Monash University Australia The University of Manchester United Kingdom Alibaba Group China Law and Innovation Lab Law School Southeast University China State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)157735897X

Table Understanding (TU) has achieved promising advancements, but it faces the challenges of the scarcity of manually labeled tables and the presence of complex table structures. To address these challenges, we propose HeGTa, a heterogeneous graph (HG)-enhanced large language model (LLM) designed for few-shot TU tasks. This framework aligns structural table semantics with the LLM's parametric knowledge through soft prompts and instruction tuning. It also addresses complex tables with a multi-task pre-training scheme by incorporating novel multi-granularity self-supervised HG pretext tasks. We empirically demonstrate the effectiveness of HeGTa, showing that it outperforms the SOTA for few-shot complex TU on several benchmarks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Staging of Different Tumor by Utilizing Laser Guidance (@405 nm) in CT Scan Images Along with Statistical Analysis

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Lasers in engineering 2025年第4-6期59卷 309-325页

作者： Amjad, M. Ullah, H. Siddique, M.A. Majid, M.A. Aleem, A. Akhtar, M. Gilanie, G. Abdullah-Al-Wadud, M. Farhan, M. Akram, A. Batool, Z. Nazir, A. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

Early detection of lung cancer, liver cancer (HCC), and pancreatitis is crucial for effective treatment. This study assessed the use of laser guidance CT scans for diagnosis and staging. 26 patients of CT scan which were collected from BVH and civil hospital Bahawalpur, Pakistan. Visible light with a 405 nm wavelength is used. Results showed staging of lung cancer (2 at stage 1, 2 at stage 2, 3 at stage 4) and HCC (stages 1 to 4a). Pancreatic diseases detected included acute pancreatitis (44%), chronic pancreatitis (22%), gallstones (22%), and pancreatic tumors (11%). Alkaline phosphatase, SGOT, and blood urea levels were notably elevated in all three cancers compared to controls. CT scans show promise in identifying cancer stages and diagnosing pancreatitis, aiding in tailored treatment. Statistical analysis shows that alkaline phosphatase emerges as a potential biomarker for these conditions. ©2025 Old City Publishing, Inc.

关键词： Image analysis

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LKAW: A Robust Watermarking Method Based on Large Kernel Convolution and Adaptive Weight Assignment

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computers, Materials & Continua 2023年第4期75卷 1-17页

作者： Xiaorui Zhang Rui Jiang Wei Sun Aiguo Song Xindong Wei Ruohan Meng Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Instrument Science and Engineering Southeast UniversityNanjing211189China School of Teacher Education Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Science Engineering Nanyang Technological UniversitySingapore

Robust watermarking requires finding invariant features under multiple attacks to ensure correct *** learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread *** existing methods use 3×3 small kernel convolution to extract image features and embed the ***,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the *** address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss *** uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel ***,the modification of the embedded watermarking on the cover image is extended to more *** the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight ***,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image *** experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise.

关键词： Robust watermarking large kernel convolution adaptive loss weights high-frequency wavelet loss deep learning

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