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作者： Nazir, Faria Majeed, Muhammad Nadeem Ghazanfar, Mustansar Ali Maqsood, Muazzam Department of Software Engineering University of Engineering and Technology Taxila Taxila Pakistan Department of Data Science University of the Punjab Lahore Pakistan School of Architecture Computing and Engineering University of East London London United Kingdom Department of Computer Science COMSATS University Islamabad Attock Campus Pakistan

Nowadays, the demand for language learning is increasing because people need to communicate with other people belonging to different regions for their business deals, study, etc. During language learning, a lot of pronunciation mistakes occur due to unfamiliarity with a new language and differences in accent. In this paper, we perform speech mistakes analysis using deep feature-based clustering. We proposed two novel methods for speech analysis, one to deal with phonemic errors (confusing phonemes) and the other to deal with the prosodic errors (partially changed pronunciation variation of phones). For accurate and efficient language learning, it is important to learn both phonemic as well as prosodic error corrections. In our first method, we perform speech analysis by combining deep CNN features and clustering algorithm to detect the phonemic errors. We classify the phonemes using K-nearest neighbor, Naïve Bayes, and support vector machine (SVM). We perform experiments on the six most frequently mispronounced confusing pairs of Arabic to handle phonemic errors and achieve an accuracy of 94%. In our second method, we proposed the unsupervised phone variation model (PVM) to detect prosodic errors. In PVM, each phone is extended to represent the different types of pronunciation variation of that phone with different proficiency levels. We use an Arabic dataset of 28 individual phones for speech analysis and provide feedback based on the variation of each phone and achieves an accuracy of 97%. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Error correction

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VIPNet: Combining Viewpoint Information and Shape Priors for Instant Multi-view 3D Reconstruction 17th

VIPNet: Combining Viewpoint Information and Shape Priors fo...

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17th Asian Conference on Computer Vision, ACCV 2024

作者： Ye, Weining Li, Zhixuan Jiang, Tingting School of Computer Science Peking University Beijing100871 China College of Computing and Data Science Nanyang Technological University Singapore Singapore National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing100871 China

ISBN: (纸本)9789819609680

While the multi-view 3D reconstruction task has made significant progress, existing methods simply fuse multi-view image features without effectively leveraging available auxiliary information, especially the viewpoint information for guiding and associating features of different views. To this end, we propose to enhance multi-view 3D reconstruction with the power of viewpoint information. Specifically, a simple-yet-effective viewpoint estimator is designed to learn and provide comprehensive viewpoint knowledge for locating and associating learned features from different views. Moreover, to improve the 3D reconstruction quality when 2D images of only very few viewpoints are available, we propose to learn the shape prior knowledge to provide sufficient shape information for compensating the limited 2D observations. Overall, we present VIPNet, benefiting from Viewpoint Information and Shape Prior learning for high-quality multi-view 3D reconstruction. Extensive experiments validate the effectiveness of the proposed VIPNet, which achieves state-of-the-art performance on challenging datasets and shows well generalization ability in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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A Novel Dense-Net Deep Neural Network Method for Classify Tuberculosis Stages Using Chest X-Ray Images with Enhanced Features

A Novel Dense-Net Deep Neural Network Method for Classify Tu...

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data, Computation and Communication (ICDCC), International Conference on

作者： Ebtasam Ahmad Siddiqui Rajneesh Kumar Patel Artificial Intelligence & Data Science Poornima Institute of Engineering & Technology Jaipur India School of Computing Science & Engineering VIT Bhopa University India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

Although chest X-ray (CXR) imaging is critical for the detection of potentially lethal disorders, interpretation may arise because to similarities between distinct lung conditions. In response, researchers proposed CBAMWDnet, a new deep learning model for tuberculosis (TB) diagnosis based on computed tomography (CXR) images. To successfully collect geographical and contextual data, the model includes the Convolutional Block Attention Module (CBAM) into its architecture, which is based on the Wide Dense Net (WDnet). Pre-processing using an enhanced Gabor filter improves image quality prior to applying the model. We test the model using three well-known datasets: Shenzhen, Montgomery, and Indiana. The results indicate that the Shenzhen dataset performs the best. CBAMWDnet beats previous techniques by better classifying tuberculosis (TB) into normal, primary, latent, and active stages. This complex classification engine improves both accuracy and dependability.

关键词： Image quality Accuracy Sensitivity Tuberculosis Computational modeling Reliability engineering data models Gabor filters X-ray imaging Biomedical imaging

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Performance Bounds for Passive Sensing in Asynchronous ISAC Systems - Appendices

arXiv

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arXiv 2024年

作者： Zhao, Jingbo Lu, Zhaoming Zhang, J. Andrew Li, Weicai Xiong, Yifeng Han, Zijun Wen, Xiangming Gu, Tao School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Electrical and Data Engineering University of Technology Sydney Australia School of Computing Macquarie University Australia

This document contains the appendices for our paper titled "Performance Bounds for Passive Sensing in Asynchronous ISAC Systems." The appendices include rigorous derivations of key formulas, detailed proofs of the theorems and propositions introduced in the paper, and details of the algorithm tested in the numerical simulation for validation. These appendices aim to support and elaborate on the findings and methodologies presented in the main text. All external references to equations, theorems, and so forth, are directed towards the corresponding elements within the main paper. © 2024, CC BY-NC-ND.

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

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IEEE Transactions on Mobile computing 2025年第7期24卷 6315-6332页

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics 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. © 2025 IEEE.

关键词： Convex optimization

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling 38

An Autoencoder-Like Nonnegative Matrix Co-Factorization for ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yu, Shenbao Pan, Yinghui Zeng, Yifeng Doshi, Prashant Liu, Guoquan Poh, Kim-Leng Lin, Mingwei College of Computer and Cyber Security Fujian Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University United Kingdom Intelligent Thought and Action Lab School of Computing University of Georgia United States Financial Technology Research Institute Fudan University China College of Design and Engineering National University of Singapore Singapore

Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models. © 2024 Neural information processing systems foundation. All rights reserved.

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Discovering Cohesive Temporal Subgraphs with Temporal Density Aware Exploration

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Journal of Computer Science & Technology 2022年第5期37卷 1068-1085页

作者： Chun-Xue Zhu Long-Long Lin Ping-Peng Yuan Hai Jin National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Real-world networks,such as social networks,cryptocurrency networks,and e-commerce networks,always have occurrence time of interactions between *** networks are typically modeled as temporal *** cohesive subgraphs from temporal graphs is practical and essential in numerous data mining applications,since mining cohesive subgraphs gets insights into the time-varying nature of temporal ***,existing studies on mining cohesive subgraphs,such as Densest-Exact and k-truss,are mainly tailored for static graphs(whose edges have no temporal information).Therefore,those cohesive subgraph models cannot indicate both the temporal and the structural characteristics of *** this end,we explore the model of cohesive temporal subgraphs by incorporating both the evolving and the structural characteristics of temporal ***,the volume of time intervals in a temporal network is *** a result,the time complexity of mining temporal cohesive subgraphs is *** efficiently address the problem,we first mine the temporal density distribution of temporal *** by the distribution,we can safely prune many unqualified time intervals with the linear time ***,the remaining time intervals where cohesive temporal subgraphs fall in are examined using the greedy *** results of the experiments on nine real-world temporal graphs indicate that our model outperforms state-of-the-art solutions in efficiency and ***,our model only takes less than two minutes on a million-vertex DBLP and has the highest overall average ranking in EDB and TC metrics.

关键词： temporal network temporal feature distribution cohesive subgraph convex property

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Dynamical analysis,control,boundedness,and prediction for a fractional-order financial risk system

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Chinese Physics B 2024年第11期33卷 223-235页

作者： Kehao Yang Song Zheng Tianhu Yu Aceng Sambas Muhamad Deni Johansyah Hassan Saberi-Nik Mohamad Afendee Mohamed School of Data Science Zhejiang University of Finance & EconomicsHangzhou 310018China Department of Mathematics Luoyang Normal UniversityLuoyang 471934China Faculty of Informatics and Computing Universiti Sultan Zainal AbidinBesut Campus22200TerengganuMalaysia Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaTasikmalayaJawa Barat46196Indonesia Department of Mathematics Universitas PadjajdaranJatinangorKabupaten Sumedang 45363Indonesia Department of Mathematics and Statistics University of NeyshaburNeyshabur ***Iran

This paper delves into the dynamical analysis,chaos control,Mittag–Leffler boundedness(MLB),and forecasting a fractional-order financial risk(FOFR)system through an absolute function *** this end,the FOFR system is first proposed,and the adomian decomposition method(ADM)is employed to resolve this fractional-order *** stability of equilibrium points and the corresponding control schemes are assessed,and several classical tools such as Lyapunov exponents(LE),bifurcation diagrams,complexity analysis(CA),and 0–1 test are further extended to analyze the dynamical behaviors of *** the global Mittag–Leffler attractive set(MLAS)and Mittag–Leffler positive invariant set(MLPIS)for the proposed financial risk(FR)system are ***,a proficient reservoir-computing(RC)method is applied to forecast the temporal evolution of the complex dynamics for the proposed system,and some simulations are carried out to show the effectiveness and feasibility of the present scheme.

关键词： FOFR system dynamical analysis control boundedness forecasting

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Hybrid System with Modified Cuckoo Search to Design Airfoil

Hybrid System with Modified Cuckoo Search to Design Airfoil

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2023 International Conference on data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Thinakaran, K. Anand, M. Kumar, T.Rajesh Atham, Saira Banu Subramanian, K.M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Chennai Kattankulathur India Saveetha Institute of Medical and Technical Science Saveetha School of Engineering India Presidency University School of Cse & Is Department of Computer Science and Engineering Bangaluru India Shadan College of Engineering and Technology Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9798350348910

The proposed models can design the airfoil by Cuckoo search with Levenberg-Marquardt. The Neural Network framework has impediments due to over-fitting. This paper proposed a modified cuckoo search. here the aerodynamic coefficient as an input to produce output the airfoil coordinates. The generated airfoil is compared to know its performance metrics. The Cuckoo search with the Feedforward Neural Network model yields the lowest prediction error. © 2023 IEEE.

关键词： Airfoils

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