检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Liu, Xiaowen Zeng, Xueting Chu, Mengyuan Li, Qian Liu, Gang Li, Tao Ding, Xiangdong Mi, Yang Key Lab of Smart Agriculture Systems Ministry of Education China Agricultural University Beijing100083 China Key Laboratory of Agricultural Information Acquisition Technology Ministry of Agriculture and Rural Affairs of China China Agricultural University Beijing100083 China Henan Fengyuan Hepu Agricultural and Animal Husbandry Co. Ltd Xinyang464000 China College of Animal Science and Technology China Agricultural University Beijing100193 China National Engineering Laboratory for Animal Breeding Laboratory of Animal Genetics Breeding and Reproduction Ministry of Agriculture and Rural Affairs Beijing100193 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing100083 China College of Information and Electrical Engineering China Agricultural University China

Body temperature detection in pigs is vital for effective pig breeding, particularly in large-scale farming where traditional infrared thermal imaging methods typically measure only one pig at a time, leading to inefficient monitoring. This study introduces a novel approach for detecting the body temperature of group-raised pigs by leveraging an image super-resolution (SR) algorithm alongside an instantaneous posture recognition technique. The Porcine-ESRGAN was employed to preprocess thermal infrared images, addressing issues related to low resolution and improving the characterization of target areas. Additionally, an improved You Only Look Once v7 (YOLOv7) based on pig posture classification was proposed and named ITG-YOLOv7. ITG-YOLOv7 was developed to identify the instantaneous posture of pigs and measure their ear root or eye temperature. This approach effectively resolves the challenge of obtaining the temperatures of pigs in a lying position. The ITG-YOLOv7 model integrates multi-scale features through the Scale Sequence Feature Fusion (SSFF) module and optimizes parameters with the Generalized Efficient Layer Aggregation Network (GELAN). The incorporation of Shape-IoU as the anchor box loss function enhances bounding box regression accuracy, thus improving detection precision. Performance assessment of Porcine-ESRGAN using thermal infrared images before and after preprocessing. The processed images significantly improve the detection precision (P), recall (R), and mean average precision (mAP) for lying pigs by 3.1%, 11.5%, and 6.9%, indicating that the method is effective in enhancing the temperature detection accuracy for lying pigs. The proposed ITG-YOLOv7 model achieves an accuracy of 93.2%, a R of 95.4%, and an mAP of 97.5% with a model size of 61.9 MB when used in conjunction with the Porcine-ESRGAN model. By correlating maximum head temperatures with ear root and eye temperatures, the maximum head temperature was identified as the body temperature ind

关键词： Thermography (imaging)

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PAM: A Propagation-Based Model for Segmenting Any 3D Objects across Multi-Modal Medical Images

arXiv

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arXiv 2024年

作者： Chen, Zifan Nan, Xinyu Li, Jiazheng Zhao, Jie Li, Haifeng Lin, Ziling Li, Haoshen Chen, Heyun Liu, Yiting Tang, Lei Zhang, Li Dong, Bin Center for Data Science Peking University Beijing China Department of Radiology Key Laboratory of Carcinogenesis and Translational Research Ministry of Education Peking University Cancer Hospital and Institute Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

Background: Volumetric segmentation is crucial for medical imaging applications but faces significant challenges. Current approaches often require extensive manual annotations and scenario-specific model training, limiting their transferability across different tasks or modalities. While general segmentation models offer some versatility in natural image processing, they struggle with the unique characteristics of medical images. There is an urgent need in clinical practice for a new segmentation approach that can effectively handle medical imagery features while maintaining adaptability across various three-dimensional objects and imaging modalities. Methods: We introduce PAM (Propagating Anything Model), a propagation-based segmentation approach that operates on 3D medical image volumes using a 2D prompt (bounding box or sketch mask). PAM extrapolates this initial input to generate a complete 3D segmentation by modeling inter-slice structural relationships, establishing a continuous information flow within 3D medical structures. This approach enhances segmentation effectiveness across various imaging modalities by focusing on structural and semantic continuities rather than isolating specific objects. The model combines a CNN-based UNet architecture for intra-slice information processing with a Transformer-based attention module to facilitate inter-slice propagation. This innovative framework results in a method with unique generalizability, capable of segmenting diverse 3D objects across different medical imaging modalities. Results: PAM demonstrated superior performance on 44 diverse medical datasets, notably improving the dice similarity coefficient (DSC) for hundreds of segmentation object types and various medical imaging modalities. Compared to modern models like MedSAM and SegVol, PAM achieved an average DSC improvement of over 18.1%, while maintaining stable predictions despite prompt deviation (one-way ANOVA test, P ≥ 0.5985) and varying propagation confi

关键词： Semantic Segmentation

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Multi-fuzzy-constrained graph pattern matching with big graph data

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Intelligent data Analysis 2020年第4期24卷 941-958页

作者： Liu, Guliu Li, Lei Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei Anhui 230009 China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Mininglamp Academy of Sciences Mininglamp Technologies Beijing China

Graph pattern matching has been widespread used for protein structure analysis, expert finding and social group selection, ect. Recently, the study of graph pattern matching using the abundant attribute information of vertices and edges as constraint conditions has attracted the attention of scholars, and multi-constrained simulation has been proposed to address the problem in contextual social networks. Actually, multi-constrained graph pattern matching is an NP-complete problem and the fuzziness of constraint variables may exist in many applications. In this paper, we introduce a multi-fuzzy-constrained graph pattern matching problem in big graph data, and propose an efficient first-k algorithm Fuzzy-ETOF-K for solving it. Specifically, exploration-based method based on edge topology is adopted to improve the efficiency of edge connection, and breadth-first bounded search is used for edge matching instead of shortest path query between two nodes to improve the efficiency of edge matching. The results of our experiments conducted on three datasets of real social networks illustrate that our proposed algorithm Fuzzy-ETOF-K significantly outperforms existing approaches in efficiency and the introduction of fuzzy constraints makes our proposed algorithm more efficient and effective. © 2020 - IOS Press and the authors. All rights reserved.

关键词： big graph data Graph pattern matching multi-fuzzy-constrained

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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Efficient Pre-trained Semantics Refinement for Video Temporal Grounding

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Li, Ao Liu, Huijun Zhu, Yiqing Ge, Yongxin Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400044 China Chongqing University School of Big Data and Software Engineering Chongqing401331 China Chongqing University College of Computer Science Chongqing401331 China Chongqing University National Elite Institute of Engineering Chongqing401331 China

Video Temporal Grounding (VTG) is a fine-grained video understanding task that aims to ground the relevant video moments corresponding to given language queries. Most existing approaches utilize powerful Vision-Language Models (VLMs), augmented with additional network architectures or specialized modules to supplement temporal reasoning capabilities. Despite achieving impressive performance, these approaches tend to overlook a critical issue that pre-trained visual and textual representations are not specifically optimized for VTG. Particularly, such representations often suffer from inter-modal semantic misalignment referring to the inconsistency between different modalities, and intra-modal semantic confusion, caused by insufficient discriminability within visual modality. To address these limitations, we propose an efficient semantics refinement framework built upon pre-trained models, featuring two core components. First, the Modal knowledge Bidirectional Propagation (MKBP) component promotes inter-modal semantic alignment via bidirectional enrichment of textual and visual semantics, exploiting their complementary strengths without introducing additional parameters. Second, the Content Context Contrast Learning (C3L) component alleviates intra-modal semantic confusion within visual modality by bringing query-specific visual features closer while separating irrelevant ones. Comprehensive experiments on six benchmark datasets demonstrate the superior performance of our proposed methods. © 1991-2012 IEEE.

关键词： C (programming language)

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Sub-Loc:predicting protein sub-mitochondrial localization based on sequence embedding

Sub-Loc:predicting protein sub-mitochondrial localization ba...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoting Wang Juan Wang Haodong Bian Maozu Guo School of Computer Science Inner Mongolia University and Ecological Big Data Engineering Research Center of the Ministry of Education Hohhot China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing P.R. China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing P.R. China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Mitochondria are subcellular organelles existing in most eukaryotic organisms. They have a pivotal role in lots of bio-chemical processes for cells. Proteins in different compartments of mitochondria have their transport routes. Locating proteins in mitochondria can provide a solid foundation for mitochondrial pathologies. So far, there have been several computational methods for solving the issue. However, their accuracy is so low that they do not identify the localization precisely. We develop an unsupervised learning model to represent mitochondrial proteins as n-dimensional vectors, called sequence embedding, which can learn the global and context information from mitochondrial proteins. We design a new model, called Sub-Loc, to predict the sub-mitochondrial localization of proteins using the SVM classifier and the sequence embedding method. The sequence embedding method and the Sub-Loc are tested by experiments. Experimental results show the sequence embedding method remarkably enhances the performance of prediction for sub-mitochondrial localization of proteins compared with other feature representations. The Sub-Loc outperforms other approaches for predicting sub-mitochondrial localization of proteins.

关键词： Proteins Location awareness Support vector machines Deep learning Pathology Predictive models Feature extraction

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Decentralized and Lightweight Cross-Chain Transaction Scheme Based on Proxy Re-signature

Decentralized and Lightweight Cross-Chain Transaction Scheme...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Huiying Zou Jia Duan Xi Liu Wei Ren Tao Li Xianghan Zheng Kim-Kwang Raymond Choo School of Computer Science China University of Geosciences Wuhan China The Third Surveying and Mapping Institute of Hunan Province Hunan Engineering Research Center of Geographic Information Security and Application Hunan China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China College of Computer and Big Data Fuzhou University School of Information and Intelligent Engineering Sanya College China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio USA

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the widespread application of digital assets and the rapid development of blockchain technology, achieving secure and efficient transactions between different blockchain networks has become an urgent challenge. Existing cross-chain methods impose limitations, e.g., hash lock technology exhibits low scalability, side-chain technology is overly complex, and notary schemes pose centralization risks. In order to address these issues, we propose a cross-chain transaction solution based on proxy re-signature technology. To tackle the centralization concerns, we employ proxy re-signature technology to decentralize the authority of notaries among transaction participants, minimizing the potential for centralization at a low cost. We further present an extended scheme that can guarantee more requirements such as higher transaction amount and shorter transaction time. Furthermore, we analyze and compare the signature technologies used in the proposed solution and provide a systematic proof of our approach’s security.

关键词： Privacy Systematics Costs Scalability Blockchains Security

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Privacy-Preserved Neural Graph Similarity Learning

Privacy-Preserved Neural Graph Similarity Learning

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IEEE International Conference on data Mining (ICDM)

作者： Yupeng Hou Wayne Xin Zhao Yaliang Li Ji-Rong Wen Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education Alibaba Group

To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years. Although GSL models are frequently deployed in privacy-sensitive scenarios, the user privacy protection of neural GSL models has not drawn much attention. To comprehensively understand the privacy protection issues, we first introduce the concept of attackable representation to systematically characterize the privacy attacks that each model can face. Inspired by the qualitative results, we propose a novel P rivacy-P reserving neural G raph M atching network model, named PPGM, for graph similarity learning. To prevent reconstruction attacks, the proposed model does not communicate node-level representations between devices. Instead, we learn multi-perspective graph representations based on learnable context vectors. To alleviate the attacks to graph properties, the obfuscated features that contain information from both graphs are communicated. In this way, the private properties of each graph can be difficult to infer. Based on the node-graph matching techniques while calculating the obfuscated features, PPGM can also be effective in similarity measuring. To quantitatively evaluate the privacy-preserving ability of neural GSL models, we further propose an evaluation protocol via training supervised black-box attack models. Extensive experiments on widely-used benchmarks show the effectiveness and strong privacy-protection ability of the proposed model PPGM. The code is available at: https://***/RUCAIBox/PPGM.

关键词： Training Performance evaluation knowledge engineering Privacy Protocols Neural networks Closed box

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Timing-Driven High-Level Synthesis for Continuous-Flow Microfluidic Biochips

Timing-Driven High-Level Synthesis for Continuous-Flow Micro...

引用

IEEE International Symposium on Quality Electronic Design

作者： Zhengyang Ye Zhisheng Chen Youlin Pan Genggeng Liu Wenzhong Guo Tsung-Yi Ho Xing Huang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China School of Computer Science Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

As continuous-flow microfluidic biochips technology rapidly advances, its automated synthesis methods require adapting to increasingly complex and stringent biochemical applications. In particular, with the introduction of time-sensitive bioassays, in this work, we present a timing-driven high-level synthesis method to optimize the resource efficiency of bioassays considering the time constraints. Specifically, we investigate the existing forms of time constraints and propose a corresponding flexible modeling approach based on reaction-control which can achieve downward compatibility for physical design. Additionally, we also analyze the impact of storage and wash on time constraints. The experimental results show that our method achieves efficient execution and low devices cost while satisfying time constraints.

关键词： Costs Biochips Time factors Microfluidics Physical design

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Joint Optimization of UAV-Carried IRS for Urban Low Altitude mmWave Communications with Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Xie, Wenwen Sun, Geng Liu, Bei Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Kim, Dong In The College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Emerging technologies in sixth generation (6G) of wireless communications, such as terahertz communication and ultra-massive multiple-input multiple-output, present promising prospects. Despite the high data rate potential of millimeter wave communications, millimeter wave (mmWave) communications in urban low altitude economy (LAE) environments are constrained by challenges such as signal attenuation and multipath interference. Specially, in urban environments, mmWave communication experiences significant attenuation due to buildings, owing to its short wavelength, which necessitates developing innovative approaches to improve the robustness of such communications in LAE networking. In this paper, we explore the use of an unmanned aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) to support low altitude mmWave communication. Specifically, we consider a typical urban low altitude communication scenario where a UAV-carried IRS establishes a line-of-sight (LoS) channel between the mobile users and a source user (SU) despite the presence of obstacles. Subsequently, we formulate an optimization problem aimed at maximizing the transmission rates and minimizing the energy consumption of the UAV by jointly optimizing phase shifts of the IRS and UAV trajectory. Given the non-convex nature of the problem and its high dynamics, we propose a deep reinforcement learning-based approach incorporating neural episodic control, long short-term memory, and an IRS phase shift control method to enhance the stability and accelerate the convergence. Simulation results show that the proposed algorithm effectively resolves the problem and surpasses other benchmark algorithms in various performances. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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