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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Lieto Wamunyima Haiyi Bian Bobwillie Kessellie Sulonteh Faculty of Electronic Information Engineering Huaiyin Institute of Technology Huai’an City China Faculty of Computer Application and Engineering RIMT University Mandi Gobindgarh Punjab India

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

This study focuses on the recognition of maize haploid seeds using convolutional neural networks (CNNs). A dataset comprising 3,000 maize seed images, consisting of 1,230 haploid maize seed images and 1,770 diploid maize seed images, was utilized for experimentation. Classical CNN models, including VGG, AlexNet, ResNet, and SKNet, were employed to compare their effectiveness in haploid maize seed recognition. Additionally, this study proposes the ECA-SKEnsemble model, which enhances the SKNet architecture by replacing its fully connected layer with a one-dimensional convolution, thus reducing model complexity. The ECA-SKEnsemble integrates multiple instances of the ECA-SKNet model, each trained with different initializations and training techniques. By combining these models using ensemble techniques such as weighted voting or averaging, ECA-SKEnsemble achieves superior performance in maize seed recognition. The model attained an accuracy of 93.04%, outperforming standard CNN architectures, including VGG and ResNet. This work demonstrates the effectiveness of ensemble learning combined with efficient channel attention for improving maize seed recognition accuracy.

关键词： Training Accuracy Convolution Computational modeling computer architecture Convolutional neural networks Ensemble learning Kernel Tuning Standards

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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A multi-view comparative learning method for spatial transcriptomics data clustering

A multi-view comparative learning method for spatial transcr...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Peng, Wei Zhang, Zhihao Dai, Wei Fu, Xiaodong Liu, Li Liu, Lijun Yu, Ning Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming China State University of New York The College at Brockport Department of Computing Sciences BrockportNY14422 United States

ISBN: (纸本)9798350337488

Clustering individual cells or spots based on their gene expression profiles in a spatial context is a powerful approach to uncovering the underlying biological diversity and relationships among cells. The intricate information within spatial transcriptomics data demands sophisticated algorithms that effectively integrate gene expression, cell position, and tissue image data for accurate cell or spot clustering. This work proposes a Multi-View Comparative Learning method for clustering Spatial Transcriptomics data (MVCLST). MVCLST first builds on two data views using gene expression profiles, cell space coordinates, and image features. Then it employs four different encoders to capture the common and private features of the two views. The model employs a contrastive learning loss to encourage effective interaction between the two views and ensure feature consistency. The shared and private features from both views are fused using corresponding decoders. Finally, the model employs the Leiden algorithm for downstream clustering of the learned features. We test the MVCLST method on a human dorsolateral prefrontal cortex dataset. The results show that MVCLST outperforms other state-of-the-art methods in most cases. Additionally, the clusters identified by MVCLST align closely with manual annotations and established neuroscience definitions. © 2023 IEEE.

关键词： contrast learning multiview learning spatial transcriptome data clustering

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Breaking Speaker Recognition with Paddingback

Breaking Speaker Recognition with Paddingback

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhe Ye Diqun Yan Li Dong Kailai Shen Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology China

Machine Learning as a Service (MLaaS) has gained popularity due to advancements in Deep Neural Networks (DNNs). However, untrusted third-party platforms have raised concerns about AI security, particularly in backdoor attacks. Recent research has shown that speech backdoors can utilize transformations as triggers, similar to image backdoors. However, human ears can easily be aware of these transformations, leading to suspicion. In this paper, we propose PaddingBack, an inaudible backdoor attack that utilizes malicious operations to generate poisoned samples, rendering them indistinguishable from clean ones. Instead of using external perturbations as triggers, we exploit the widely-used speech signal operation, padding, to break speaker recognition systems. Experimental results demonstrate the effectiveness of our method, achieving a significant attack success rate while retaining benign accuracy. Furthermore, Padding-Back demonstrates the ability to resist defense methods and maintain its stealthiness against human perception.

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Semi-Supervised Feature Selection With Multi-Scale Fuzzy Information Fusion: From Both Global and Local Perspectives

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1825-1839页

作者： Zhou, Nan Liao, Shujiao Chen, Hongmei Ding, Weiping Lu, Yaqian Minnan Normal University School of Mathematics and Statistics Fujian Key Laboratory of Granular Computing and Applications Zhangzhou363000 China Southwest Jiaotong University School of Computing and Artificial Intelligence National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science 999078 China

In reality, the laborious nature of label annotation leads to the widespread existence of limited labeled data. Moreover, multi-scale data have received widespread attention due to its rich knowledge representation. However, current research on multi-scale data primarily focuses on supervised learning environments, while semi-supervised feature selection for multi-scale data with limited labels remains inadequately explored. Meanwhile, existing studies on multi-scale data often emphasize selecting optimal scales and further conducting feature selection, but this strategy may result in losing potentially valuable information from other scales. To overcome these limitations, by adopting a multi-scale fuzzy information fusion mechanism, this article proposes two new semi-supervised feature selection approaches for multi-scale data with limited labels from both global and local perspectives. Initially, by fusing fuzzy information at various scales, a label learning method is proposed to convert the decision of missing labels into a fuzzy decision of label distribution. Subsequently, label-distributed multi-scale fuzzy global and local rough sets are constructed from global and local perspectives, respectively. Based on the two models, two semi-supervised feature selection algorithms are developed based on global and local multi-scale fuzzy information fusion. Experimental results demonstrate that, compared to other advanced feature selection algorithms, the two proposed algorithms can effectively handle multi-scale data with limited labels and exhibit superior classification performance, computational efficiency and robustness. © 1993-2012 IEEE.

关键词： Self-supervised learning

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Research and development of blockchain consensus algorithms 7

Research and development of blockchain consensus algorithms

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7th International Conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Ma, Xinkun Li, Yingna Wang, Jinyu Faculty of Information Engineering and Automation Kunming University of Science and Technology 727 Jingming South Rd. Chenggong District Kunming650500 China Computer Technology Application Key Lab of the Yunnan Province 727 Jingming South Rd. Chenggong District Kunming650500 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

Blockchain technology is a paradigm of distributed systems, and its consistency problem is solved by consensus algorithms. This paper summarizes the existing consensus algorithms and divides them into three types: proof, voting, and Paxos-like. The implementation methods and advantages and disadvantages of some representative algorithms among the three types of consensus algorithms are introduced. Finally, the research hotspots and development directions of consensus algorithms in terms of performance and scalability are pointed out. © 2023 SPIE. All rights reserved.

关键词： Blockchain

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Foreign object detection of transmission line based on improved Yolov5 7

Foreign object detection of transmission line based on impro...

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7th International Conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Wei, Gengwu Li, Yingna Faculty of Information Engineering and Automation Kunming University of Science and Technology 727 Jingming South Rd.Chenggong District Kunming650500 China Computer Technology Application Key Lab of the Yunnan Province 727 Jingming South Rd.Chenggong District Kunming650500 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

The environment of the transmission line is complex and easy to attach foreign matter, which has always been one of the reasons for the safety hazards to the operation of the transmission line. In view of the current problem that the detection accuracy of the foreign matter on the transmission line needs to be improved, an improved transmission line foreign matter detection method based on the Yolov5 algorithm is proposed. Detection model. First, the RepVGG module is introduced into the feature extraction network to enhance the network feature extraction ability and improve the model reasoning speed;secondly, the ability of the network to identify important feature information is strengthened by integrating the attention mechanism module;finally, by adding the prediction layer and Soft-nms The algorithm processes the target prediction frame to improve the detection accuracy of the model. The experimental results show that the improved Yolov5 transmission line foreign object detection algorithm proposed in this paper has a mAP value of 4.1% higher than the conventional Yolov5, and it also has certain advantages in performance compared with the conventional target detection algorithm. © 2023 SPIE. All rights reserved.

关键词： Object detection

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SQAT-LD: SPeech Quality Assessment Transformer Utilizing Listener Dependent Modeling for Zero-Shot Out-of-Domain MOS Prediction

SQAT-LD: SPeech Quality Assessment Transformer Utilizing Lis...

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IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Kailai Shen Diqun Yan Li Dong Ying Ren Xiaoxun Wu Jing Hu Faculty of Electrical Engineering and Computer Science Ningbo University Zhejiang Key Laboratory of Mobile Network Application Technology

In this paper, we propose the speech quality assessment transformer utilizing listener dependent modeling (SQAT-LD) mean opinion score (MOS) prediction system, which was submitted to the 2023 VoiceMOS Challenge. The system is based on a combination of self-supervised learning (SSL) models and listener-dependent modeling. Due to this challenge’s emphasis on real-world and challenging zero-shot out-of-domain MOS prediction in three different voice evaluation scenarios, we specifically designed a two-branch module to predict scores and weights for each frame, aiming to achieve better generalization. In the challenge, our system achieved fourth place in Track 1a, second place in Track 1b and first place in Track 2. Additionally, we conducted an ablation study to investigate the effectiveness of our proposed method.

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A Cascaded Design of Best Features Selection for Fruit Diseases Recognition

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computers, Materials & Continua 2022年第1期70卷 1491-1507页

作者： Faiz Ali Shah Muhammad Attique Khan Muhammad Sharif Usman Tariq Aimal Khan Seifedine Kadry Orawit Thinnukool Department of Computer Science COMSATS University IslamabadWah Campus47080Pakistan Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia National University of Science and Technology Islamabad IslamabadPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s *** manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an ***,it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf *** to the literature,many automated methods have been developed for the recognition of fruit diseases at the early ***,these techniques still face some challenges,such as the similar symptoms of different fruit diseases and the selection of irrelevant *** processing and deep learning techniques have been extremely successful in the last decade,but there is still room for improvement due to these ***,we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation(ACO)based *** proposed method consists of four fundamental steps:data augmentation to solve the imbalanced dataset,fine-tuned pretrained deep learning models(NasNetMobile andMobileNet-V2),the fusion of extracted deep features using matrix length,and finally,a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis(NCA).The best-selected features were eventually passed to many classifiers for final *** experimental process involved an augmented dataset and achieved an average accuracy of 99.7%.Comparison with existing techniques showed that the proposed method was effective.

关键词： Fruits diseases data augmentation deep learning features fusion feature selection

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Enhancing Building Safety: A Brief Review of Agent-Based Modeling for Fire Evacuation Simulation 16

Enhancing Building Safety: A Brief Review of Agent-Based Mod...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Kasereka, Selain K. Kabwe, Ortega M. Kinyanta, Maria W.K. Kasongo, Audrey L. Ilunga, Godwill W.K. Muhambya, Katya Tashev, Tasho Kyamakya, Kyandoghere Mathematics Statistics and Computer Science Department University of Kinshasa Kinshasa Democratic Republic Congo Faculty of English Engineering Studies Technical University of Sofia Sofia Bulgaria ABIL Research Center Kinshasa Democratic Republic Congo Faculty of Computer Science Université Nouveaux Horizons Lubumbashi Democratic Republic Congo Ganpat University Faculty of Computer Application Gujarat India Université Libre des Pays des Grands Lacs Faculty of Sciences and Technology Goma Democratic Republic Congo Institute of Smart Systems Technologies University of Klagenfurt Klagenfurt Austria Polytechnic Faculty University of Kinshasa Kinshasa Democratic Republic Congo

The issue of building evacuation in the event of a fire is a significant concern in urban planning and architecture. In the absence of appropriate measures, an emergency situation can potentially result in disastrous outcomes. In this review article, we explore the application of agent-based modeling (ABM) in the simulation of building evacuations in the event of a free breakout. To address this issue, we present a synthesis of findings from several studies, highlighting the advantages of ABM in modeling complex evacuation dynamics such as crowd behavior, consideration of multiple parameters, and decision-making processes. Additionally, we provide insight into the simulation tools and techniques used in studies that demonstrate the practical applications of ABM in enhancing evacuation strategies. Furthermore, we identify current challenges, research gaps, and propose future directions to enhance the accuracy and effectiveness of fire evacuation modeling and simulations. This study aims to contribute to the improvement of proposed solutions and models for the development of successful, effective, and reliable evacuation plans to enhance the safety of occupants in buildings. © 2025 The Author(s).

关键词： Urban planning

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