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A hybrid attention model based on first-order statistical features for smoke recognition

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Science China(Technological Sciences) 2024年第3期67卷 809-822页

作者： GUO Nan LIU JiaHui DI KeXin GU Ke QIAO JunFei Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Artificial Intelligence InstituteBeijing 100124China faculty of Information Technology Beijing University of TechnologyBeijing 100124China

Smoke and fire recognition are of great importance on foreseeing fire disasters and preventing environmental pollution by monitoring the burning process of objects(e.g., straw, fuels). However, since fire images suffer from problems like the variability of the features, complexity of scenarios, interference from background, changeable weather conditions as well as image quality problems, identifying smoke and fire accurately and promptly from a given image still remains a substantial challenge. Automatically learning the features of smoke images by CNNs has improved the target recognition ability compared to traditional approaches,nonetheless, convolutions and pooling operations in CNNs may cause severe information loss which may lead to *** tackle the above problems, this paper proposed a hybrid attention model based on the characteristics of smoke images. This model adopted multiple optimized attention mechanism in several stages to quickly and precisely capture the important features,achieving state-of-the-art performance on smoke and fire recognition in terms of accuracy and speed. Our proposed module mainly consists of two stages: pooling and attention. In the first stage, we conducted several newly proposed first-order pooling methods. Through traversing the data space in a larger scope, features are better reserved, thus constructing a more intact feature space of smoke and fire in an image. In the second stage, feature maps are aggregated together to perform channel and spatial attention. The channel and spatial dependencies allow us to quickly catch the important features presented in an image. By fully exploring the feature space and prominent salient features, characteristics of smoke and fire are better presented so as to obtain better smoke and fire detection results. Experiments have been conducted on public smoke detection dataset and new proposed fine-grained smoke and fire detection database. Experimental results revealed that the propose

关键词： hybrid attention first-order pooling smoke and fire detection deep convolutional neural networks

SwinVid:Enhancing Video Object Detection Using Swin Transformer

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Computer Systems Science & Engineering 2024年第2期48卷 305-320页

作者： Abdelrahman Maharek Amr Abozeid Rasha Orban Kamal ElDahshan Computer Science Department Faculty of Artificial Intelligence and InformaticsBenhaEgypt Mathematics Department Faculty of SciencesAl-Azhar UniversityCairoEgypt Department of Computer Science College of Science and Arts in QurayyatJouf UniversitySakakaSaudi Arabia

What causes object detection in video to be less accurate than it is in still images?Because some video frames have degraded in appearance from fast movement,out-of-focus camera shots,and changes in *** reasons have made video object detection(VID)a growing area of research in recent *** object detection can be used for various healthcare applications,such as detecting and tracking tumors in medical imaging,monitoring the movement of patients in hospitals and long-term care facilities,and analyzing videos of surgeries to improve technique and ***,it can be used in telemedicine to help diagnose and monitor patients *** VID techniques are based on recurrent neural networks or optical flow for feature aggregation to produce reliable features which can be used for *** of those methods aggregate features on the full-sequence level or from nearby *** create feature maps,existing VID techniques frequently use Convolutional Neural Networks(CNNs)as the backbone *** the other hand,Vision Transformers have outperformed CNNs in various vision tasks,including object detection in still images and image *** propose in this research to use Swin-Transformer,a state-of-the-art Vision Transformer,as an alternative to CNN-based backbone networks for object detection in *** proposed architecture enhances the accuracy of existing VID *** ImageNet VID and EPIC KITCHENS datasets are used to evaluate the suggested *** have demonstrated that our proposed method is efficient by achieving 84.3%mean average precision(mAP)on ImageNet VID using less memory in comparison to other leading VID *** source code is available on the website https://***/amaharek/SwinVid.

关键词： Video object detection vision transformers convolutional neural networks deep learning

Automated evaluation of parapapillary choroidal microvasculature in crowded optic discs:a controlled,optical coherence tomography angiography study

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International Journal of Ophthalmology(English edition) 2024年第1期17卷 113-118页

作者： Hatice Arda Hidayet Sener Ozge Temizyurek Hatice Kubra Sonmez Duygu Gulmez Sevim Cem Evereklioglu Fatih Horozoglu Ayse Busra Gunay Sener Department of Ophthalmology Erciyes University Medical FacultyKayseri 38090Turkey Department of Medical informatics and Biostatistics Erciyes University Medical FacultyKayseri 38090Turkey

AIM:To compare superficial and deep vascular properties of optic discs between crowded discs and controls using optical coherence tomography angiography(OCT-A).METHODS:Thirty patients with crowded discs,and 47 control subjects were enrolled in the *** eye of each individual was included and OCT-A scans of optic discs were obtained in a 4.5×4.5 mm^(2) rectangular *** peripapillary capillary(RPC)density,peripapillary retinal nerve fiber layer(pRNFL)thickness,cup volume,rim area,disc area,cup-to-disc(c/d)area ratio,and vertical c/d ratio were obtained automatically using device *** parapapillary choroidal microvasculature(PPCMv)density was calculated using MATLAB *** the vertical c/d ratio of the optic disc was absent or small cup,it was considered as a crowded ***:The mean signal strength index of OCT-A images was similar between the crowded discs and control eyes(P=0.740).There was no difference in pRNFL between the two groups(P=0.102).There were no differences in RPC density in whole image(P=0.826)and peripapillary region(P=0.923),but inside disc RPC density was higher in crowded optic discs(P=0.003).The PPCMv density in the inner-hemisuperior region was also lower in crowded discs(P=0.026).The pRNFL thickness was positively correlated with peripapillary RPC density(r=0.498,P<0.001).The inside disc RPC density was negatively correlated with c/d area ratio(r=-0.341,P=0.002).CONCLUSION:The higher inside disc RPC density and lower inner-hemisuperior PPCMv density are found in eyes with crowded optic discs.

关键词： crowded optic disc ischemic optic neuropathy optical coherence tomography angiography parapapillary choroidal microvasculature

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

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Computers, Materials & Continua 2024年第1期78卷 873-897页

作者： Shereen K.Refaay Samia A.Ali Moumen T.El-Melegy Louai A.Maghrabi Hamdy H.El-Sayed faculty of Computers and Artificial intelligence Sohag UniversitySohag82524Egypt faculty of Engineering Assiut UniversityAssiut711745Egypt College of Engineering University of Business and TechnologyJeddah23411Saudi Arabia

The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s *** studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor *** paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial *** proposed algorithms lack algorithm-specific parameters and metaphorical *** proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned *** proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing *** compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)*** efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base *** experimental results were compared with those obtained using the Jaya optimization *** proposed algorithms exhibited the best *** proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base sta

关键词： Wireless sensor networks Rao algorithms optimization LEACH PEAGSIS

Graph-decomposed k-NN searching algorithm on road network

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Frontiers of Computer Science 2024年第3期18卷 243-245页

作者： Wei JIANG Bo NING Guanyu LI Mei BAI Xiao JIA Fangliang WEI faculty of Information Science and Technology Dalian Maritime UniversityDalian 116026China Jiangxing intelligence Inc. Shenzhen 518100China

1 Introduction The k-NN(k Nearest Neighbors)searching problem on road network constitutes a crucial aspect of location-oriented services,which has boasted the practical implications.

关键词： services searching network

Enhancing low-resource cross-lingual summarization from noisy data with fine-grained reinforcement learning

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Frontiers of Information Technology & Electronic Engineering 2024年第1期25卷 121-134页

作者： Yuxin HUANG Huailing GU Zhengtao YU Yumeng GAO Tong PAN Jialong XU faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650504China Yunnan Key Laboratory of Artificial intelligence Kunming University of Science and TechnologyKunming 650504China

Cross-lingual summarization(CLS)is the task of generating a summary in a target language from a document in a source ***,end-to-end CLS models have achieved impressive results using large-scale,high-quality datasets typically constructed by translating monolingual summary corpora into CLS ***,due to the limited performance of low-resource language translation models,translation noise can seriously degrade the performance of these *** this paper,we propose a fine-grained reinforcement learning approach to address low-resource CLS based on noisy *** introduce the source language summary as a gold signal to alleviate the impact of the translated noisy target ***,we design a reinforcement reward by calculating the word correlation and word missing degree between the source language summary and the generated target language summary,and combine it with cross-entropy loss to optimize the CLS *** validate the performance of our proposed model,we construct Chinese-Vietnamese and Vietnamese-Chinese CLS *** results show that our proposed model outperforms the baselines in terms of both the ROUGE score and BERTScore.

关键词： Cross-lingual summarization Low-resource language Noisy data Fine-grained reinforcement learning Word correlation Word missing degree

Semantic-aware entity alignment for low resource language knowledge graph

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Frontiers of Computer Science 2024年第4期18卷 97-106页

作者： Junfei TANG Ran SONG Yuxin HUANG Shengxiang GAO Zhengtao YU faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Artificial intelligence Kunming University of Science and TechnologyKunming 650500China

Entity alignment(EA)is an important technique aiming to find the same real entity between two different source knowledge graphs(KGs).Current methods typically learn the embedding of entities for EA from the structure of KGs for *** EA models are designed for rich-resource languages,requiring sufficient resources such as a parallel corpus and pre-trained language ***,low-resource language KGs have received less attention,and current models demonstrate poor performance on those low-resource ***,researchers have fused relation information and attributes for entity representations to enhance the entity alignment performance,but the relation semantics are often *** address these issues,we propose a novel Semantic-aware Graph Neural Network(SGNN)for entity ***,we generate pseudo sentences according to the relation triples and produce representations using pre-trained ***,our approach explores semantic information from the connected relations by a graph neural *** model captures expanded feature information from *** results using three low-resource languages demonstrate that our proposed SGNN approach out performs better than state-of-the-art alignment methods on three proposed datasets and three public datasets.

关键词： graph neural network knowledge graph entity alignment low-resource language

Personality Trait Detection via Transfer Learning

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Computers, Materials & Continua 2024年第2期78卷 1933-1956页

作者： Bashar Alshouha Jesus Serrano-Guerrero Francisco Chiclana Francisco P.Romero Jose A.Olivas Department of Information Technologies and Systems University of Castilla-La ManchaCiudad Real13071Spain Institute of Artificial intelligence School of Computer Science and InformaticsDe Montfort UniversityThe GatewayLeicesterLE19BHUK

Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human ***-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their *** study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality *** models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term *** makes them suitable to classify multi-label personality traits from reviews while mitigating computational *** focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from ***,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook *** status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality *** test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted *** results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of *** findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality *** findings represent substantial a

关键词： Personality trait detection pre-trained language model big five model transfer learning

Predicting outcomes after kidney transplantation: Can Pareto’s rules help us to do so?

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World Journal of Transplantation 2024年第1期14卷 9-11页

作者： Fernando M Gonzalez Francisca Gonzalez Cohens Department of Nephrology Faculty of MedicineUniversidad de ChileSantiago 7500922Chile Web intelligence Centre Faculty of Physics and Mathematical SciencesSantiago 7500922Chile

Kidney transplantation is the best option for kidney replacement therapy,even considering that most of the times the grafts do not survive as long as their *** the Khalil et al's experience,published in this issue of the Journal,they analyze their second kidney graft survival and describe those significant predictors of early *** editorial comments on the results and put in perspective that most of the times,long-term graft survival could be inadvertently jeopardized if the immunosuppressive therapy is reduced or withdrawn for any reason,and that it could happen frequently if the transplant physician intends to innovate with the clinical care without proper evidence-based data.

关键词： Kidney transplantation Graft survival Acute rejection Interstitial fibrosis and tubular atrophy Immunosuppression