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2023 International Conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Zheng, Tianhang Cao, Lili Zhu, Yongjian School of Mechanical and Energy Engineering Zhejiang University of Science and Technology Hangzhou China Zhejiang Provincial Key Laboratory of Food Logistics Equipment and Technology Zhejiang University of Science and Technology Hangzhou China School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9781510677685

In this paper, an uncalibrated photometric stereo network model is proposed. Traditional photometric stereo has precise requirements on light source and object surface reflectance, which greatly limits the usability of photometric stereo, and the ideal network model should be able to handle arbitrary objects under uncalibrated light sources to complete the reconstruction. To solve this problem, we propose an uncalibrated photometric stereo neural network, First, an arbitrary number of images are input to the first-stage neural network system to estimate the light source information and complete the calibration to eliminate the dependence on the light source information. Then the calibration result is input to the second-stage neural network together with the image, and the multi-scale and attention mechanism is used to obtain the object surface texture information to achieve the surface normal reconstruction of the object. Our network (MAPS-Net) is compared with other uncalibrated photometric stereo methods on the DiLiGenT benchmark, and the average MAE achieves an excellent performance of 9.20. © 2024 SPIE.

关键词： Deep neural networks

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STRNet: Semantic Segmentation Based on Small Target Refinement at Large Scale 24

STRNet: Semantic Segmentation Based on Small Target Refineme...

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3rd International Conference on computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Wang, Futian Shi, Tao Luo, Bin Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9798400716942

In the past few years, most high-performance semantic segmentation models have focused on learning semantic information in high-level semantic features, and in order to obtain more features, they will use high-resolution images for high-rate downsampling. Although this will obtain a better segmentation effect, due to the high rate of subsampling, the segmentation effect of small and medium-sized targets in the image is very poor, and because the training is conducted with high-resolution images, it will bring huge pressure on computing resources. To solve this problem, we propose a new two-branch small target refinement model (STRNet), and in order to reduce the high cost of computing resources brought by high resolution images, we use low resolution images for training. A large number of experiments demonstrated that our approach improved Cityscapes and CamVid datasets by 2.0% and 3.8% over the strong baseline, respectively. © 2024 ACM.

关键词： Semantic Segmentation

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Neighbor Load Rank Based Load Balancing Routing in LEO Satellite networks

Neighbor Load Rank Based Load Balancing Routing in LEO Satel...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Lu, Xueyu Wei, Wenting Wang, Kun Fu, Liying Li, Chenxi Wu, Celimuge Xidian University State Key Laboratory of Integrated Service Networks Xi'an China National Key Laboratory of Advanced Communication Networks Shijiazhuang China Xidian University School of Computer Science and Technology Xi'an China University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan

ISBN: (纸本)9798350351255

The emerging space-terrestrial integrated networks (STIN) are envisioned to provide seamless connectivity for global coverage. As a promising key component for STIN, the Low Earth Orbiting (LEO) satellite network still faces significant technological challenges in routing for ensuring both reachability and efficiency, due to frequent topology changes and uneven load distribution. Load-balancing routing is a feasible solution to improve efficiency by alleviating regional overload, however, it may encounter either slow convergences or local perceptions. In this paper, we propose a Neighbor Load Rank (NLR) based load-balancing routing for LEO satellite networks, where potential congestion at the queue buffer of a satellite node is characterized by load scores of its neighboring satellites, so as to reduce the perspective limitation of local load-balancing routing. To accelerate routing convergence and reduce computational complexity, we design region delineation, boundary penalty and directional incentive strategies to obtain the approximate minimum hop count path. Meanwhile, we employ the topology-stabilizing model (TSM) to convert the frequent satellite-ground interconnection changes into traffic fluctuations. Simulations demonstrate that NLR can maintain low-latency capacity with lower transmission overhead and effectively balance the overloaded traffic. © 2024 IEEE.

关键词： Satellite communication systems

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Multi-label Classification of Road Cracks Based on Semantic-Aware Mixup 4

Multi-label Classification of Road Cracks Based on Semantic-...

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4th International Conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Xu, Chengyang Zhou, Chunpeng Yu, Zhi Zhejiang University Zhejiang Provincial Key Laboratory of Service Robot School of Software Technology Hangzhou China Zhejiang University Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Hangzhou China Zhejiang University Zhejiang Provincial Key Laboratory of Service Robot School of Software Technology Ningbo China

ISBN: (纸本)9798350342710

In the realm of road crack detection scenarios, Mixup, a widely used data augmentation technique, exhibits constrained effectiveness in multi-label classification, unlike its performance on general datasets. This paper investigates the reasons behind this limitation, identifying two main factors: (1) the mutually exclusive nature of road crack labels and (2) the potential generation of incorrect labels during image mixing. To address these issues, we propose an approach called Semantic-Aware Mixup (SA-Mixup), which focuses on enhancing crack samples specifically. Experimental evaluations on three distinct road crack datasets demonstrate the effectiveness and simplicity of our proposed method. The results highlight significant improvements in model performance, offering a promising solution for accurate and efficient multi-label classification in road crack detection tasks. © 2023 IEEE.

关键词： Crack detection

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Medical Entity Disambiguation with Medical Mention Relation and Fine-grained Entity Knowledge 30

Medical Entity Disambiguation with Medical Mention Relation ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Lu, Wenpeng Zhang, Guobiao Peng, Xueping Guan, Hongjiao Wang, Shoujin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Data Science Institute University of Technology Sydney Sydney Australia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9782493814104

Medical entity disambiguation (MED) plays a crucial role in natural language processing and biomedical domains, which is the task of mapping ambiguous medical mentions to structured candidate medical entities from knowledge bases (KBs). However, existing methods for MED often fail to fully utilize the knowledge within medical KBs and overlook essential interactions between medical mentions and candidate entities, resulting in knowledge- and interaction-inefficient modeling and suboptimal disambiguation performance. To address these limitations, this paper proposes a novel approach, MED with Medical Mention Relation and Fine-grained Entity Knowledge (MMR-FEK). Specifically, MMR-FEK incorporates a mention relation fusion module and an entity knowledge fusion module, followed by an interaction module. The former employs a relation graph convolutional network to fuse mention relation information between medical mentions to enhance mention representations, while the latter leverages an attention mechanism to fuse synonym and type information of candidate entities to enhance entity representations. Afterwards, an interaction module is designed to employ a bidirectional attention mechanism to capture interactions between medical mentions and entities to generate the matching representation. Extensive experiments on two publicly available real-world datasets demonstrate MMR-FEK's superiority over state-of-the-art(SOTA) MED baselines across all metrics. Our source code is publicly available. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

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Knowledge Graph Link Prediction Model Based on Attention Graph Convolutional Network 9th

Knowledge Graph Link Prediction Model Based on Attention Gr...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Shi, Junkang Li, Ming Zhao, Jing Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (纸本)9783031466731

The main purpose of knowledge graph embedding (KGE) is to complete the missing part of triplets. By learning from existing models, we found that adding graph convolutional networks (GCN) to KGE models achieved good performance. These models mainly use the local structural characteristics of data. However, their main goal is to learn an aggregator rather than a feature vector for each node. To address the problems with existing models, we proposed a knowledge graph link prediction model based on attentional relational graph convolutional networks (ARGCN). First, we capture the relationship attribute features between nodes by adding an attention mechanism and extract important node attribute features for visualization. Then, through a relational graph convolutional neural network, we capture the associated attributes between nodes and extract the feature information that can best represent nodes. Finally, we use DistMult as the scoring function. Our experiments show that our proposed is effective on standard FB15k-237 and WN18RR datasets. Compared with other models, it has relative improvements in Hits@1, Hits@3 and Hits@10. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Knowledge graph

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GrayINT – Detection and Localization of Gray Failures via Hybrid In-band Network Telemetry 24

GrayINT – Detection and Localization of Gray Failures via H...

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24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： Zhang, Kuichao Su, Wei Shi, Huiling Zhang, Kai Zhang, Wei Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Electronics and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9788995004395

As the underlying infrastructure, data center networks (DCNs) provide an important platform for cloud computing and next-generation networks. However, due to the influence of many factors, such as network, hardware, and software, various failures may occur during the operation of the DCN, which brings great challenges to its reliability. Particularly, a more insidious type of network failure, known as gray failure, presents a significant threat to system stability. In response to this challenge, in this work, we propose GrayINT, a gray failure detection and localization system that leverages In-band Network Telemetry (INT) technology to monitor all paths within the data center network in real-time. To verify the feasibility of our design, we build a fat-tree data center network on a virtual network testbed. A large number of experimental results demonstrate that our system can monitor device and link status in real-time, achieving gray failure detection and localization. Copyright 2023 KICS.

关键词： Next generation networks

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Improving the Interpretability through Maximizing Mutual Information for EEG Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 744-757页

作者： Yang, Hua Philip Chen, C.L. Chen, Bianna Zhang, Tong North Carolina State University Department of Computer Science United States South China University of Technology Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition The School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center The Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Trustworthy Graph Neural networks (GNNs) for EEG emotion recognition should identify emotions accurately and elucidate corresponding rationales. Current GNNs have achieved notable performance by dynamically modeling emotional connections between EEG channels. However, these GNNs lack interpretability due to the absence of explicit rationale behind their predictions. This paper conducts a comprehensive identification of important EEG channels to enhance the interpretability of EEG emotion recognition from the perspective of mutual information. Specifically, an Adjacency-Explainable Graph Neural Network (AEG) for ante-hoc interpretability is proposed to capture genuine EEG emotional connections, which gives a theoretical guarantee to remove spurious connections. Moreover, a Channel-wise Adaptive Class Activation Mapping Explainer (CACA) for post-hoc interpretability is developed to locate the EEG channels that contribute most to predictions. Experimental results on three datasets, i.e., SEED, SEED-IV, and DREAMER, prove that imbuing training processes with enhanced interpretability ensures significant performance improvements in emotion recognition. Quantitative comparisons of post-hoc interpretability also demonstrate the superiority of CACA. Furthermore, this paper illustrates two potential applications of the proposed methodologies, showing their broader utility and significance. © 2010-2012 IEEE.

关键词： Graph neural networks

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Coalition game-based clustering algorithm for LEO satellite networks

Coalition game-based clustering algorithm for LEO satellite ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Peixin Wei, Wenting Wang, Kun Liu, Lizhe Qin, Liang Wu, Celimuge Xidian University State Key Laboratory of Integrated Service Networks Xi'an China National Key Laboratory of Advanced Communication Networks Shijiazhuang China Xidian University School of Computer Science and Technology Xian China University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan

ISBN: (纸本)9798350351255

Low Earth Orbit (LEO) satellites are gradually developing towards to a large scale, in order to achieve a desired vision of ubiquitous connectivity and broadband access at anytime and anywhere. However, the increasing scale and highly dynamic nature of LEO constellation pose challenges to network management on its flexibility and scalability. Clustering is introduced as an effective approach to manage LEO satellite networks in a flexible manner. Unfortunately, satellite clusters encounter instability and high communication load due to frequent topology changes and traffic growth. In this paper, we design LEO satellites clustering models that jointly optimize cluster reliability and network communication load in the GEO/LEO network architecture. The coalition game framework is introduced to obtain a stable cluster structure by adopting an automated and centralized approach. A coalition formation algorithm based on the optimization of reliability and communication load is developed for the clustering problem. Finally, numerical simulations are carried out to evaluate the superiority and effectiveness of the proposed grouping and clustering scheme. © 2024 IEEE.

关键词： Satellite communication systems

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Smartphone-based colorimetric detection of formaldehyde in the air

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Building Simulation 2024年第11期17卷 2007-2015页

作者： Meng Yang Jin Ye Tao Yu Ying Song Hua Qian Tianyi Liu Yang Chen Junqi Wang Shi-jie Cao Cong Liu School of Energy and Environment Southeast UniversityNanjing210096China School of Energy and Power Jiangsu University of Science and TechnologyZhenjiangJiangsu212100China Wuhan Second Ship Design and Research Institute Wuhan430205China Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test Hubei Provincial Institute for Food Supervision and TestWuhan430075China Laboratory of Image Science and Technology the School of Computer Science and EngineeringSoutheast UniversityNanjingChina Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast UniversityNanjingChina School of Cyber Science and Engineering Southeast UniversityNanjingChina Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationNanjingChina School of Architecture Southeast UniversityNanjingChina Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control Southeast UniversityNanjingChina Global Centre for Clean Air Research(GCARE) University of SurreyGuildfordUK

Adverse impacts of exposure to formaldehyde on human health significantly increases attention in monitoring formaldehyde concentrations in the *** formaldehyde detection methods typically rely on large and costly instruments and requires high skills of expertise,preventing it from being widely accessible to *** study introduced a novel approach utilizing smartphone-based colorimetric *** of green channel signals of digital images by a smartphone successfully capture variation of purple color of 4-amino-3-hydrazino-5-mercapto-1,2,4-triazol solution,which is proportional to formaldehyde *** is because that green and purple are complimentary color pairs.A calibration curve was established between green channel signals and formaldehyde concentrations,with a correlation coefficient of *** limit of the smartphone-based method is 0.008 mg/m^(3).Measurement errors decrease as formaldehyde concentrations increase,with median relative errors of 34%,17%,and 6%for concentration ranges of 0–0.06 mg/m^(3),0.06–0.12 mg/m^(3),and 0.12–0.35 mg/m^(3),*** method replaced scientific instrumentation with ordinary items,greatly reducing cost and operation *** would provide an opportunity to realize onsite measurements for formaldehyde by occupants themselves and increase awareness of air quality for better health protection.

关键词： indoor air quality VOC RGB color identification machine vision

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