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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xinzheng Li Yuhong Zhang Peipei Li Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Cross-network node classification aims to use a labeled source network to classify nodes in an unlabeled target network. Most of the existing cross-network node classification methods learn the network representations by capturing the node neighborhood and train the classifier on these representations. The performance is highly dependent on the high-quality neighborhood in the network. However, in applications, the degree of nodes generally follows a long-tail distribution, i.e., a significant proportion of nodes are tail nodes with sparse neighborhood. It poses a challenge to existing methods. To this end, a structure similarity graph for cross-network node classitication method (SCNC) is proposed in this paper. Firstly, the potential links between nodes are predicted with the structural similarity metric to construct structure similarity graph, which can enrich the neighborhood of tail nodes. Then, the embedding representations of the structural similarity graph are learned to capture more neighborhood information. Finally, the adversarial is used to learn the domain invariant representations to address cross-network divergence. Extensive experimental results show that our SCNC outperforms the state-of-the-art methods.

关键词： Network topology Tail Learning (artificial intelligence) Prediction algorithms Classification algorithms Noise measurement Task analysis

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Which Companies are Likely to Invest: knowledge-graph-based Recommendation for Investment Promotion

Which Companies are Likely to Invest: Knowledge-graph-based ...

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IEEE International Conference on data Mining (ICDM)

作者： Chenyang Bu Jiawei Zhang Xingchen Yu Le Wu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology China and Institute of Big Knowledge Science Hefei University of Technology Hefei China

Investment promotion refers to the process by which the government uses disposable resources to attract investors to the region for production and business activities. The existing basic mode of attracting investment is to collect information about enterprises and entrepreneurs through manual methods, determine the target enterprise from the list of enterprises, and then attract investment through visits, negotiations, and other methods. As contacting and visiting companies one by one requires huge amounts of manpower and time, the choice of target companies is critical for attracting investments. However, to the best of our knowledge, no study has conducted research from the perspective of knowledge-graph-based recommendation. In this study, we define the problem of target company recommendation in the process of investment promotion, and analyze the characteristics of the problem and the challenges it faces based on the background of the actual problem. Then, a two-tier model for solving this problem is provided from the perspective of knowledge graph reasoning. Aiming at the problem that the knowledge graph will frequently change, the model is designed based on the idea of combining the advantages of global and local link prediction. The experimental results on real-world data demonstrate the effectiveness of the proposed model.

关键词： Economics knowledge acquisition Government Companies Production Manuals Predictive models

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Wild Mushroom Image Segmentation Algorithm Based on Improved DeepLabV3+

Wild Mushroom Image Segmentation Algorithm Based on Improved...

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Computers and Artificial Intelligence Technologies (CAIT), International Conference on

作者： Fangrong Zhu Benliang Xie Kaixuan Zhao Lin Qin Tingchi Wen College of Big Data and Information Engineering Guizhou University Power Semiconductor Device Reliability Engineering Center of the Ministry of Education Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China The Engineering and Research Center of Southwest Bio-Pharmaceutical Resource Guiyang China

ISBN: (数字)9798331530891

ISBN: (纸本)9798331530907

Wild mushrooms in China are diverse and widely distributed, particularly in southwestern regions like Yunnan and Guizhou, where many inedible species pose a risk of poisoning upon accidental ingestion. To mitigate these incidents, researchers are increasingly focused on species recognition. However, the natural growth environments of wild mushrooms introduce significant background disturbance, complicating accurate identification. Consequently, separating the background from the image is crucial for enhancing recognition accuracy. To solve the above problems, this paper takes the common wild mushrooms in Guizhou province as the research object and proposes a deep learning network based on the improved DeepLabV3+ for the background segmentation task of wild mushroom images. Firstly, the network Xception backbone feature extraction network of the original DeepLabV3+ is replaced with MobileNetV2 network, which makes the network more lightweight and suitable for deployment into mobile devices. Secondly, the Feature Pyramid Networks feature fusion branch is proposed to realize the fusion of different levels of feature maps, so that the network can extract richer wild mushroom features and improve the wild mushroom image segmentation accuracy. And then, a multi-layer cascade superposition is used to replace the original Atrous Spatial Pyramid Pooling module with Dense Atrous Spatial Pyramid Pooling as an enhanced feature extraction network to increase the perceptual field of the network and obtain the multi-scale information of the wild mushroom image. Finally, the ResNet-18 network was used to identify the segmented images. Experimental results show that the improved DeepLabV3+ has better segmentation performance. The average segmentation accuracy of the algorithm is 96.81%, the mean Intersection over Union is 92.36%, and the average accuracy after ResNet-18 recognition reaches 93.8%. In the comparison experiments, our segmentation algorithm has a higher segmentation acc

关键词： Deep learning Computers Image segmentation Accuracy Image recognition Fuses Feature extraction Mobile handsets data mining Artificial intelligence

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Inverse Markov Process Based Constrained Dynamic Graph Layout

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Journal of Computer Science & Technology 2021年第3期36卷 707-718页

作者： Shi-Ying Sheng Sheng-Tao Chen Xiao-Ju Dong Chun-Yuan Wu Xiao-Ru Yuan ParisTech Elite Institute of Technology Shanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Machine Perception(Ministry of Education) National Engineering Laboratory for Big Data Analysis and ApplicationPeking UniversityBeijing 100080China

In online dynamic graph drawing,constraints over nodes and node pairs help preserve a coherent mental map in a sequence of *** the constraints is challenging due to the requirements of both preserving mental map and satisfying the visual aesthetics of a graph *** existing algorithms basically depend on local changes but fail to do proper evaluations on the global propagation when setting *** solve this problem,we introduce a heuristic model derived from PageRank which simulates the node movement as an inverse Markov process hence to give a global analysis of the layout's change,according to which different constraints can be *** constraints,along with stress function,generate layouts maintaining spatial positions and shapes of relatively stable substructures between adjacent *** demonstrate that our method preserves both structure and position similarity to help users track graph changes visually.

关键词： graph drawing data stream dynamic graph layout

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DanceFix: An Exploration in Group Dance Neatness Assessment Through Fixing Abnormal Challenges of Human Pose 39

DanceFix: An Exploration in Group Dance Neatness Assessment ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Huangbiao Ke, Xiao Wu, Huanqi Xu, Rui Li, Yuezhou Xu, Peirong Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou 350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou 350116 China

ISBN: (纸本)157735897X

The fair and objective assessment of performances and competitions is a common pursuit and challenge in human society. The application of computer vision technology offers hope for this purpose, but it still faces obstacles such as occlusion and motion blur. To address these hindrances, our DanceFix proposes a bidirectional spatial-temporal context optical flow correction (BOFC) method. This approach leverages the consistency and complementarity of motion information between two modalities: optical flow, which excels at pixel capture, and lightweight skeleton data. It enables the extraction of pixel-level motion changes and the correction of abnormal skeleton data. Furthermore, we propose a part-level dance dataset (Dancer Parts) and part-level motion feature extraction based on task decoupling (PETD). This aims to decouple complex whole-body parts tracking into fine-grained limb-level motion extraction, enhancing the confidence of temporal information and the accuracy of correction for abnormal data. Finally, we present the DNV dataset, which simulates fully neat group dance scenes and provides reliable labels and validation methods for the newly introduced group dance neatness assessment (GDNA). To the best of our knowledge, this is the first work to develop quantitative criteria for assessing limb and joint neatness in group dance. We conduct experiments on DNV and video-based public JHMDB datasets. Our method effectively corrects abnormal skeleton points, flexibly embeds, and improves the accuracy of existing pose estimation algorithms. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Novelty detection technology for Alzheimer's disease based on autoencoder feature extractor and MCD classification 7

Novelty detection technology for Alzheimer's disease based o...

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Real-time Photonic Measurements, data Management, and Processing VII 2023

作者： Zhang, Shuheng Zhang, Yan Pan, Jie Lin, Xintian Ding, Xuemei Yang, Hongqin Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education Provincial Key Laboratory for Photonics Technology Fujian Normal University Fuzhou350117 China Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application Fujian Normal University Fuzhou350117 China Xiamen Jingyi zhikang Technology Co. Ltd. Xiamen361000 China

ISBN: (纸本)9781510667938

Alzheimer's disease (AD) is a neurodegenerative disorder that affects the life quality of millions of people worldwide. To diagnose new cases in a timely manner, we propose a new novelty detection technique that combines Autoencoder and Minimum Covariance Determinant (MCD). The technique consists of two steps: first, we use an Autoencoder to extract low-dimensional and discriminative features from the publicly available ADNI dataset, where we only train the Autoencoder with normal data, making the abnormal data more distinguishable in the feature space;second, based on the features of normal data, we use MCD to construct a decision boundary, and judge the degree of abnormality by the distance of the test point to the boundary. Compared with traditional methods without using Autoencoder, our technique has significant advantages in terms of accuracy and sensitivity, and can effectively deal with data imbalance problem. Experimental results show that our method can efficiently detect novel AD cases, and has a wide range of application prospects. © 2023 SPIE.

关键词： Neurodegenerative diseases

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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Sequence-to-Sequence knowledge Graph Completion Based on Gated Attention Unit 29

Sequence-to-Sequence Knowledge Graph Completion Based on Gat...

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

作者： Yi, Fengge Wei, Xiumei Liu, Xiaojing Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China 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 State Key Laboratory of High-end Server & Storage Technology Jinan China

ISBN: (纸本)9798350330717

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5's proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment. © 2023 IEEE.

关键词： gated attention unit knowledge graph completion sequence-to-sequence model

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Terahertz metamaterial narrowband absorber based on metal split ring

Terahertz metamaterial narrowband absorber based on metal sp...

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2023 Applied Optics and Photonics China: Optical Sensing, Imaging, and Display Technology and Applications;and Biomedical Optics, AOPC 2023

作者： Ji, Xiaodi Ge, Hongyi Jiang, Yuying Wu, Xuyang Jia, Zhiyuan Sun, Zhenyu Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Henan China Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control Henan China College of Information Science and Engineering Henan University of Technology Henan Zhengzhou450001 China School of Artificial Intelligence and Big Data Henan University of Technology Henan Zhengzhou450001 China

ISBN: (纸本)9781510672321

Multi-band terahertz metamaterial absorbers offer new perspectives to achieve perfect absorption and multipoint information matching, which enable an ever-growing number of applications. In this study, a dual-band terahertz metamaterial absorber based on the metal split ring is designed. The absorber has perfect absorption peaks at 1.15 THz and 2.47 THz, and the absorption rate is more than 99%. The absorber produces a harp peak with a bandwidth of 0.008 at 2.47 THz, which has an extremely high quality factor of 308. The distribution of the electric field and surface current at two resonance points is analyzed using the finite element integration method. Through full wave simulation calculation, the maximum sensitivity of the analyte refractive index of the absorber is 400 GHz/RIU, and the maximum sensitivity of the thickness is 35 GHz/μm. The results show that the absorber can achieve highly sensitive detection of trace substances. © 2023 SPIE. All rights reserved.

关键词： Metamaterials

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China 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 Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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