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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Yuchen Liang, Hu Qiu, Ran Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network's lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments. © 2023 IEEE.

关键词： Detail-aware Image recognition Lightweight

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Research on Task Allocation Method of Mobile Swarm Intelligence Perception Based on Hybrid Artificial Fish Swarm Algorithm

Research on Task Allocation Method of Mobile Swarm Intellige...

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作者： Li, Jianjun Su, Fangyuan Yang, Yu Liu, Junjun School of Computer and Information Engineering Harbin University of Commerce Harbin150028 China Heilongjiang Cultural Big Data Theory Application Research Center Harbin150028 China Heilongjiang Key Laboratory of E-Commerce and Information Processing Harbin150028 China

In this study, the task allocation mechanism in mobile swarm intelligence perception is studied and analyzed. Considering how to allocate tasks under specified time constraints and optimize for the goal, a hybrid artificial fish swarm algorithm is proposed by using swarm intelligence algorithm. The inertia index of particle swarm optimization algorithm is pulled into the typical artificial fish swarm algorithm, which is proved by simulation experiments. The hybrid artificial fish swarm algorithm greatly improves the convergence speed, avoids the shortage that the artificial fish swarm algorithm often stop when it obtains the local optimization, and this make the algorithm obtains the effect of global optimization. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Swarm intelligence

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Towards Exploratory Query Optimization for Template-Based SQL Workloads

Towards Exploratory Query Optimization for Template-Based SQ...

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International Conference on data engineering

作者： Jieming Feng Zhanhuai Li Qun Chen School of Computer Science and Engineering Northwestern Polytechnical University Xi'an Shaanxi China Ministry of Industry and Information Technology Key Laboratory of Big Data Storage and Management Northwestern Polytechnical University Xi'an Shaanxi China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

SQL query optimization aims to choose an optimal Query Execution Plan (QEP) for a query. The existing optimizers usually choose the plan with the minimal execution cost. However, in some real scenarios (e.g., cloud OLAP), it is a business imperative to minimize the cost of an entire workload. Unfortunately, the greedy optimizers are prone to generating execution plans that are only suboptimal from the workload perspective. In this paper, we propose a novel QEP optimization approach for template-based SQL that aims to minimize the execution cost of an entire workload instead of individual queries. While choosing an execution plan for a query, the proposed approach considers a plan's impact on future query execution as well as its own execution cost. We first define an exploratory metric, Plan Exploration Value (PEV), to measure a plan's potential benefit to future query execution. Then, we model exploratory plan selection as an optimization problem with cost constraints, and present an efficient algorithm based on dynamic programming for its solution. Finally, we evaluate the performance of the proposed approach by a comparative study on benchmark workloads. Our extensive experiments have shown that it generates more efficient workload execution plans than the existing alternatives.

关键词： Measurement Maximum likelihood estimation Costs Query processing Heuristic algorithms Current measurement Benchmark testing

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GAF-GAN: Gated Attention Feature Fusion Image Inpainting Network Based on Generative Adversarial Network 29

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Liang, Hu Liu, Yuchen Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures. © 2023 IEEE.

关键词： Dense feature fusion module Gated attention mechanism Image inpainting Underwater environment

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Research on the Effect of Surface Roughness of Molybdenum Shield on the Sapphire Crystal Growth

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Integrated Ferroelectrics 2025年

作者： Wang, DongYang Jiang, JianWei Liu, KaiGe Ruan, ShuangChen Zhai, JianPang College of Big Data and Internet Shenzhen Technology University Shenzhen China Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen China College of New Materials and New Energies Shenzhen Technology University Shenzhen China Key Laboratory of Advanced Optical Precision Manufacturing Technology of Guangdong Higher Education Institutes Shenzhen Technology University Shenzhen China

The temperature field determines the environment in which sapphire crystals are grown, and the molybdenum shield is one of the core components to maintain a stable temperature field. In production, it has been found that the appearance of the molybdenum shield surface changes as the use time increases. In this study, the surface roughness and reflectivity of old and new molybdenum shield samples were tested, and it was found that the surface roughness of the old molybdenum shield was larger and the reflectivity was lower, and the reflectivity of the molybdenum shield was negatively correlated with its surface roughness. Based on the test data, the sapphire crystal growth process was simulated numerically, and the crystal growth experiments were carried out using the old and new molybdenum shield respectively. The old molybdenum shield with low reflectivity would increase the power of the single crystal furnace, change the radial and axis temperature distribution of the temperature field, and make the crystal-melt growth interface too flat and the stress value too large, resulting in increasing the risk of cracking and bubbles in the crystal, leading to the degradation of crystal quality. © 2025 Taylor & Francis Group, LLC.

关键词： Single crystals

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Joint Multi-Scale Contextual and Noise Suppression for Group Emotion Recognition

Joint Multi-Scale Contextual and Noise Suppression for Group...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wangdong Guo Qing Zhu Qirong Mao School of Computer Science and Communication Engineering Jiangsu University Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Group Emotion Recognition (GER) seeks to identify emotional states within multi-person groups. The complexity of the environment and the reliance on a single group emotion label often result in noisy individuals with inconsistent emotional expressions, hampering accurate emotion classification. Mainstream GER networks attempt to downweight noisy individuals, but when their numbers are high and over-relying on a single group label, the negative impact of noise on individuals remains significant, further complicating classification and diminishing accuracy. To address these limitations, we propose the Multi-Scale Contextual and Noise Suppression Model (MCon-NSM), a novel framework that enhances GER by capturing fine-grained interaction contexts and collaboratively suppressing noise at both the individual and group label levels. Extensive experiments on the GAF series datasets demonstrate that our method achieves results comparable to state-of-the-art techniques, validating its effectiveness in mitigating noise in GER.

关键词： Emotion recognition Analytical models Accuracy Noise Noise reduction Speech recognition Complexity theory Noise measurement Speech processing Context modeling

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Source Code Summarization in the Era of Large Language Models

Source Code Summarization in the Era of Large Language Model...

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International Conference on Software engineering (ICSE)

作者： Weisong Sun Yun Miao Yuekang Li Hongyu Zhang Chunrong Fang Yi Liu Gelei Deng Yang Liu Zhenyu Chen College of Computing and Data Science Nanyang Technological University Singapore Singapore State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Engineering University of New South Wales Sidney Australia School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798331505691

ISBN: (纸本)9798331505707

To support software developers in understanding and maintaining programs, various automatic (source) code summarization techniques have been proposed to generate a concise natural language summary (i.e., comment) for a given code snippet. Recently, the emergence of large language models (LLMs) has led to a great boost in the performance of coderelated tasks. In this paper, we undertake a systematic and comprehensive study on code summarization in the era of LLMs, which covers multiple aspects involved in the workflow of LLMbased code summarization. Specifically, we begin by examining prevalent automated evaluation methods for assessing the quality of summaries generated by LLMs and find that the results of the GPT-4 evaluation method are most closely aligned with human evaluation. Then, we explore the effectiveness of five prompting techniques (zero-shot, few-shot, chain-of-thought, critique, and expert) in adapting LLMs to code summarization tasks. Contrary to expectations, advanced prompting techniques may not outperform simple zero-shot prompting. Next, we investigate the impact of LLMs' model settings (including top_p and temperature parameters) on the quality of generated summaries. We find the impact of the two parameters on summary quality varies by the base LLM and programming language, but their impacts are similar. Moreover, we canvass LLMs' abilities to summarize code snippets in distinct types of programming languages. The results reveal that LLMs perform suboptimally when summarizing code written in logic programming languages compared to other language types (e.g., procedural and object-oriented programming languages). Finally, we unexpectedly find that CodeLlamaInstruct with 7B parameters can outperform advanced GPT-4 in generating summaries describing code design rationale and asserting code properties. We hope that our findings can provide a comprehensive understanding of code summarization in the era of LLMs.

关键词： Computer languages Codes Systematics Source coding Large language models Object oriented modeling Software Prompt engineering Object oriented programming Software engineering

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Digital twins in smart farming:An autoware-based simulator for autonomous agricultural vehicles

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International Journal of Agricultural and Biological engineering 2023年第4期16卷 184-189页

作者： Xin Zhao Wanli Wang Long Wen Zhibo Chen Sixian Wu Kun Zhou Mengyao Sun Lanjun Xu Bingbing Hu Caicong Wu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural AffairsBeijing 100083China The Bureau of Agriculture and Rural Affairs of Urumchi Xinjiang Uygur Autonomous RegionUrumchi 830000China Research&Advanced Engineering AGCO A/SDK-8930 RandersDenmark Beijing Agricultural Machinery Experiment Appraisal Extension Station Beijing 100079China Kunlun Beidou Intelligence Technologies Co.Ltd. Beijing 102200China

Digital twins can improve the level of control over physical entities and help manage complex systems by integrating a range of *** autonomous agricultural machine has shown revolutionary effects on labor reduction and utilization rate in field *** vehicles in precision agriculture have the potential to improve competitiveness compared to current crop production methods and have become a research ***,the development time and resources required in experiments have limited the research in this *** tools in unmanned farming that are required to enable more efficient,reliable,and safe autonomy are increasingly *** by the recent development of an open-source virtual simulation platform,this study proposed an autoware-based simulator to evaluate the performance of agricultural machine guidance based on digital *** photogrammetry using drones is used to construct threedimensional maps of fields at the same scale as reality.A communication format suitable for agricultural machines was developed for data input and output,along with an inter-node communication *** conversion,publishing,and maintenance of multiple coordinate systems were completed based on ROS(Robot Operating System).Coverage path planning was performed using hybrid curves based on Bézier curves,and it was tested in both a simulation environment and actual fields with the aid of Pure Pursuit algorithms and PID controllers.

关键词： autoware simulation platform autonomous agricultural vehicle digital twin autonomous robots

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Application Research of Vertical Federated Learning Technology in Banking Risk Control Model Strategy 21

Application Research of Vertical Federated Learning Technolo...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Luo, Yong Lu, Zhi Yin, Xiaofei Lu, Songfeng Weng, Yiting Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China China Minsheng Banking Corp. Ltd. Beijing China Shenzhen Huazhong University of Science and Technology Research Institute Wuhan China Shu Lun Pan Beijing China

ISBN: (纸本)9798350329223

This study centers on the application of vertical federated learning technology in the context of Internet banking loans, with a particular focus on innovations in data privacy protection, risk control model algorithms, and secure multi-party computation. Currently, banking risk control strategies mainly rely on traditional data processing technologies, which often fall short in protecting user privacy and ensuring data usage efficiency. We adopt vertical federated learning technology, offering an innovative solution for the Internet banking loan scenario. Firstly, regarding data privacy protection, we propose a differential privacy mechanism to safeguard user-sensitive data. Secondly, we innovatively apply risk control model algorithms, facilitating collaborative modeling across multiple Internet loan platforms through federated learning. Furthermore, we introduce secure multi-party computation technology to ensure the secure transmission of data and confidentiality of computation processes during federated learning. Through empirical experiments on real Internet loan datasets, we validate the effectiveness and feasibility of our proposed methods. After implementing our risk control model, the credit approval rate increased from 3.44% to 18.2%, with a single-day high reaching 25.53%. The average loan amount increased by 7,700 yuan, and the average interest rate slightly declined by 0.48%, marking a significant improvement and breakthrough compared to traditional risk control models. This study offers innovative solutions for data privacy protection and risk control in the Internet loan scenario, providing safer and more reliable services for financial institutions and users. Moreover, our methods possess high practicality and promotional value. The potential widespread impact on the industry is profound. © 2023 IEEE.

关键词： Risk management

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Generation of functional oligopeptides that promote osteogenesis based on unsupervised deep learning of protein IDRs

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Bone Research 2022年第2期10卷 354-366页

作者： Mingxiang Cai Baichuan Xiao Fujun Jin Xiaopeng Xu Yuwei Hua Junhui Li Pingping Niu Meijing Liu Jiaqi Wu Rui Yue Yong Zhang Zuolin Wang Yongbiao Zhang Xiaogang Wang Yao Sun Department of Oral Implantology School of StomatologyTongji UniversityShanghai Engineering Research Center of Tooth Restoration and RegenerationShanghai 200072China The First Affiliated Hospital of Jinan University School of StomatologyClinical Research Platform for Interdiscipline of StomatologyJinan UniversityGuangzhou 510630China Key Laboratory of Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China Guangzhou Laboratory Bioland LaboratoryGuangzhou Regenerative Medicine and Health Guangdong LaboratoryGuangzhou 510320China Institute for Regenerative Medicine Shanghai East HospitalShanghai Key Laboratory of Signaling and Disease ResearchFrontier Science Center for Stem Cell ResearchSchool of Life Sciences and TechnologyTongji UniversityShanghai 200092China

Deep learning(DL)is currently revolutionizing peptide drug development due to both computational advances and the substantial recent expansion of digitized biological ***,progress in oligopeptide drug development has been limited,likely due to the lack of suitable datasets and difficulty in identifying informative features to use as inputs for DL ***,we utilized an unsupervised deep learning model to learn a semantic pattern based on the intrinsically disordered regions of~171 known osteogenic ***,oligopeptides were generated from this semantic pattern based on Monte Carlo simulation,followed by in vivo functional characterization.A five amino acid oligopeptide(AIB5P)had strong bone-formation-promoting effects,as determined in multiple mouse models(e.g.,osteoporosis,fracture,and osseointegration of implants).Mechanistically,we showed that AIB5P promotes osteogenesis by binding to the integrinα5 subunit and thereby activating FAK *** summary,we successfully established an oligopeptide discovery strategy based on a DL model and demonstrated its utility from cytological screening to animal experimental verification.

关键词： functional thereby utilized

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