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arXiv 2021年

作者： Liu, Guliu Li, Lei Liu, Guanfeng Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology China Department of Computing Macquarie University Australia Mininglamp Academy of Sciences Mininglamp Technology China

Traditional social group analysis mostly uses interaction models, event models, or other methods to identify and distinguish groups. This type of method can divide social participants into different groups based on their geographic location, social relationships, and/or related events. However, in some applications, it is necessary to make more specific restrictions on the members and the interaction between members of the group. Generally, graph pattern matching (GPM) is used to solve this problem. However, the existing GPM methods rarely consider the rich contextual information of nodes and edges to measure the credibility between members. In this paper, a social group query problem that needs to consider the trust between members of the group is proposed. To solve this problem, we propose a Strong Simulation GPM algorithm (NTSS) based on the exploration of pattern Node Topological ordered sequence. Aiming at the inefficiency of the NTSS algorithm when matching pattern graph with multiple nodes with zero in-degree and the problem of repeated calculation of matched edges shared by multiple matching subgraphs, two optimization strategies are proposed. Finally, we conduct verification experiments on the effectiveness and efficiency of the NTSS algorithm and the algorithms with the optimization strategies on four social network datasets in real applications. Experimental results show that the NTSS algorithm is significantly better than the existing multi-constrained GPM algorithm, and the NTSS_Inv_EdgC algorithm, which combines two optimization strategies, greatly improves the efficiency of the NTSS algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Constrained optimization

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An MRC Framework for Semantic Role Labeling

arXiv

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arXiv 2021年

作者： Wang, Nan Li, Jiwei Meng, Yuxian Sun, Xiaofei Qiu, Han Wang, Ziyao Wang, Guoyin He, Jun Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China China Shannon.AI China Zhejiang University China Tsinghua University China University of Leeds United Kingdom Amazon Alexa AI United States

Semantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a sentence and can be decomposed into two subtasks: predicate disambiguation and argument labeling. Prior work deals with these two tasks independently, which ignores the semantic connection between the two tasks. In this paper, we propose to use the machine reading comprehension (MRC) framework to bridge this gap. We formalize predicate disambiguation as multiple-choice machine reading comprehension, where the descriptions of candidate senses of a given predicate are used as options to select the correct sense. The chosen predicate sense is then used to determine the semantic roles for that predicate, and these semantic roles are used to construct the query for another MRC model for argument labeling. In this way, we are able to leverage both the predicate semantics and the semantic role semantics for argument labeling. We also propose to select a subset of all the possible semantic roles for computational efficiency. Experiments show that the proposed framework achieves state-of-the-art or comparable results to previous work. Code is available at https://***/ShannonAI/MRC-SRL. © 2021, CC BY.

关键词： Semantics

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Siamese Network Visual Tracking Algorithm Based on GCT Attention and Dual-Template Update

Siamese Network Visual Tracking Algorithm Based on GCT Atten...

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Natural Language Processing (ICNLP), International Conference on

作者： Sugang Ma Siwei Sun Lei Pu Xiaobao Yang School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an China School of Operational Support Rocket Force Engineering University Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an China

To address the problem of insufficient representational capability and lack of online update of the Fully-convolutional Siamese Network (SiamFC) tracker in complex scenes, this paper proposes a siamese network visual tracking algorithm based on GCT attention and dual-template update mechanism. First, the feature extraction network is constructed by replacing AlexNet with the VGG16 network and SoftPool is used to replace the maximum pooling layer. Secondly, the attention module is added after the backbone network to enhance the network’s ability to extract object features. Finally, a dual-template update mechanism is designed for response map fusion. Average Peak-to-Correlation Energy (APCE) is used to determine whether to update the dynamic templates, effectively improving the tracking robustness. The proposed algorithm is trained on the Got-10k dataset and tested on the OTB2015 and VOT2018 datasets. The experimental results show that, compared with SiamFC, the success rate and accuracy reach 0.663 and 0.891 on the OTB2015, which improve respectively 7.6% and 11.9%; On the VOT2018 dataset, the tracking accuracy, robustness and EAO are improved respectively by 2.9%, 29% and 14%. The proposed algorithm achieves high tracking accuracy in complex scenes and the tracking speed reaches 52.6 Fps, which meets the real-time tracking requirements.

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Tobacco Curing State Recognition Based on a Multi-Sensor Feature Fusion Approach

Tobacco Curing State Recognition Based on a Multi-Sensor Fea...

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International Conference on Computer and Communications (ICCC)

作者： Jili Zhang Yang Cao Jianyu Wei Jing Yang Mingxiong He Congguang Huang Banghong Wu Yabo Yu Zhengbin Huang Chenglin Rao Shiyong Li China Tobacco Guangxi Industrial Co. Ltd. Guangxi China Mechanical Engineering College Guizhou University Guizhou China State Key Laboratory of Public Big Data Guizhou University Guizhou China Hechi Branch of China Tobacco Guangxi Zhuang Autonomous Region Corporation. Guangxi China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

The accurate identification of tobacco curing status is a complex process involving comprehensive analysis of multiple types of feature data sources. The influence of each type of feature on the state recognition model varies with the change in the curing process, and the existing models cannot reasonably utilize these feature data, which makes it difficult to improve the model recognition accuracy. To address these challenges, this paper proposes a dynamic data fusion scheme based on clustering methods and uses the LightGBM algorithm and data fusion method to build a state recognition fusion model that integrates six state recognition sub-models, which can accurately identify the curing status. SRFM aims to integrate curing expertise and automatically adjust the data fusion strategy according to the changes in multi-source data to meet the needs of comprehensive data analysis in recognition. Through experimental comparison, SRFM outperforms the existing models, with an accuracy of 99.8% in identifying the curing status and a macro F1-Score of 99.9%.

关键词： Temperature distribution Accuracy Computational modeling Heuristic algorithms Soft sensors data preprocessing data integration Curing Feature extraction data models

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Few-shot learning based histopathological image classification of colorectal cancer

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Intelligent Medicine 2024年第4期4卷 256-267页

作者： Rui Li Xiaoyan Li Hongzan Sun Jinzhu Yang Md Rahaman Marcin Grzegozek Tao Jiang Xinyu Huang Chen Li Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110167China Cancer Hospital China Medical UniversityShenyangLiaoning 110122China Shengjing Hospital China Medical UniversityShenyang.Liaoning 110000China Institute for Medical Informatics University of LuebeckGermany Department of Knowledge Engineering University of Economics in KatowicePoland School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic *** histopathologic image classification is often inefficient and *** some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their *** address these challenges,this paper proposes a method based on few-shot *** This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant *** model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy *** this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test *** Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per *** documented the model’s loss,accuracy,and the confusion matrix of the ***,we employed the t-SNE algorithm to analyze and assess the model’s classification *** The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot *** consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization *** research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.

关键词： Colorectal cancer Few-shot learning Transfer learning Contrastive learning Histopathological images Benign and malignant categories

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Virtual&augmented reality for biological microscope in experiment education

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Virtual Reality & Intelligent Hardware 2020年第4期2卷 316-329页

作者： Xiang ZHOU Liyu TANG Ding LIN Wei HAN Key Laboratory of Spatial Data Mining&Information Sharing of MOE Fuzhou UniversityFuzhou 350108China National Engineering Research Center of Geospatial Information Technology Fuzhou UniversityFuzhou 350108China

Background Mixed-reality technologies,including virtual reality(VR)and augmented reality(AR),are considered to be promising potential tools for science teaching and learning processes that could foster positive emotions,motivate autonomous learning,and improve learning *** In this study,a technology-aided biological microscope learning system based on VR/AR is *** structure of the microscope is described in a detailed three-dimensional(3D)model,each component being represented with their topological interrelationships and associations among them being *** interactive behavior of the model was specified,and a standard operating guide was *** motion control of components was simulated based on collision *** with immersive VR equipment and AR technology,we developed a virtual microscope subsystem and a mobile virtual microscope guidance *** The system consisted of a VR subsystem and an AR *** focus of the VR subsystem was to simulate operating the microscope and associated interactive behaviors that allowed users to observe and operate the components of the 3D microscope model by means of natural interactions in an immersive *** AR subsystem allowed participants to use a mobile terminal that took a picture of a microscope from a textbook and then displayed the structure and functions of the instrument,as well as the relevant operating *** flexibly allowed students to use the system before or after class without time and space *** system allowed users to switch between the VR and AR *** The system is useful for helping learners(especially K-12 students)to recognize a microscope's structure and grasp the required operational skills by simulating operations using an interactive *** the future,such technology-assisted education would be a successful learning platform in an open learning space.

关键词： Virtual reality Augmented reality Microscope Operating guide Experiment

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Short-term traffic flow prediction based on BAS-VMD-CEEMDAN and IWOA-RF

Short-term traffic flow prediction based on BAS-VMD-CEEMDAN ...

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Information Systems and Computing Technology (ISCTech), International Conference on

作者： Jia Tian Deyong Wang Yanyun Fn Xueyi Zhao Jian Fang Wenxi Shi School of Information Science and Engineering Xinjiang University Urumqi China Key Laboratory of Big Data of Xinjiang Social Security Risk Prevention and Control Xinjiang Lianhaichuangzhi Information Technology Co. Ltd Urumqi China Beijing Academy of Science and Technology Beijing China

Short-term traffic flow forecasting is crucial for intelligent traffic management. In view of the non-linear and random nature of short-term traffic flow, a prediction model based on BAS-VMD-CEEMDAN and IWOA-RF is proposed. Firstly, the parameters of the VMD, namely the modal number (K) and the value of the quadratic penalty term (α), are optimized using the BAS algorithm, and then the traffic flow is decomposed to obtain several VMF components and one RES component, which extracts the smooth information of the traffic flow. Subsequently, the RES component is decomposed using the decomposition completeness of CEEMDAN to obtain several IMF components, refining the complex information of the RES component. Then, the RF model is constructed according to the characteristics of each component, and then the parameters of RF models are optimized using IWOA. Finally, overlay the predictions of each component superimposed to obtain the final predictions. Experimental results show that the short-term traffic flow forecasting model basedon BAS-VMD-CEEMDAN-IWOA-RF has a high accuracy compared to other decomposition models.

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Seasonal Variation, Gas/Particle Partitioning, Sources and Risks of Polycyclic Aromatic Compounds in Xi’an, Northwest China

SSRN

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SSRN 2023年

作者： Zhang, Jianing Wei, Chong Han, Yongming Lei, Dewen State Key Laboratory of Loess and Quaternary Geology Key Lab of Aerosol Chemistry and Physics Institute of Earth Environment Chinese Academy of Sciences Xi’an710061 China Shanghai Carbon Data Research Center CAS Key Laboratory of Low-Carbon Conversion Science and Engineering Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China Department of Environmental Science and Technology School of Human Settlements and Civil Engineering Xi’an Jiaotong University Xi’an710049 China University of Chinese Academy of Sciences Beijing100049 China

Polycyclic aromatic compounds (PACs), such as polycyclic aromatic hydrocarbons (PAHs,) and their derivatives (oxygenated PAHs, nitrated PAHs and N-heterocyclic PAHs), have received extensive attention due to their carcinogenicity, teratogenicity, and mutagenicity to humans. In this study, gaseous and PM2.5-bound PACs were analysed in four seasons in 2016 in Xi’an and further evaluated for their gas/particle partition, sources, and health risks. Annual average concentrations (gaseous + particulate) of Σ27PAHs, Σ12OPAHs, Σ14NPAHs, and Σ4AZAs were 445.1±428.0, 45.6±62.4, 4.9±6.5, and 6.2±8.6 ng m−3, respectively, with the highest values in winter and the lowest values in summer. Low-molecular-weight PACs mainly occur in gaseous and high-molecular-weight PACs partition in particles, and the major mechanism is absorption into the atmospheric particles. PACs significantly correlated with combustion-derived air pollutants and carbon fractions, suggesting their common sources and transport pathways. The concentration ratios indicated mixed sources of fossil fuel combustion and biomass burning, while the positive matrix factorization model suggested that coal combustion, vehicle exhaust, and biomass burning accounted for 30.0%, 25.8% and 23.6% of PAHs, respectively, and direct emissions contributed 56.1% of OPAHs. The PACs estimated annual lifetime lung cancer risk is 2.67 × 10−5, exceeding the value advocated by the World Health Organization (10−5). © 2023, The Authors. All rights reserved.

关键词： Polycyclic aromatic hydrocarbons

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

arXiv

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arXiv 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fujian Key Laboratory of Network Fuzhou350002 China Computing and Intelligent Information Processing [Fuzhou University Fuzhou350116 China Department of Computer Science Faculty of Environment Science and Economy University of Exeter ExeterEX4 4QF United Kingdom School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Informatics University of Oslo Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Bio-Inspired Visual Neural Network for Moving Pedestrian Detection

SSRN

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SSRN 2023年

作者： Zhao, Qingqing Dai, Kunyu Hu, Bin Yang, Kai State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China College of Computer Science and Technology Guizhou University Guiyang550025 China Department of Information Engineering Guizhou Communications Polytechnic Guiyang550025 China

Reliably detecting pedestrian objects is crucial for building advanced intelligent surveillance systems in the future. In natural environments, traditional pedestrian detection methods are challenged by the irregular features of pedestrian objects in the field of view, such as varying scales, postures, and positions. Inspired by the mammalian visual layered structures and invariant object recognition mechanisms in biological vision systems, a novel feedforward visual neural network is investigated to detect moving pedestrian objects. The proposed model consists of two neural parts: the presynaptic and postsynaptic networks. The former captures visual signals, representing low-level features of a moving object, based on the significant convex and concave regions of the object’s contour;the latter processes these captured signals to form an invariant representation on behalf of a moving object, utilizing the invariant object recognition with sparse representation of biological recognition mechanism. Experimentally, the effectiveness and reliability of the proposed model are sufficiently verified by pedestrian videos in different realistic scenes, and comparative experimental results demonstrate that it outperforms existing state-of-the-art models. This research is a critical step towards the construction of an artificial vision system for pedestrian object perception. © 2023, The Authors. All rights reserved.

关键词： Object detection

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