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A Method of Multimodal Emotion Recognition in Video Learning Based on Knowledge Enhancement

Computer systems science & engineering 2023年第11期47卷 1709-1732页

作者： Hanmin Ye Yinghui Zhou Xiaomei Tao School of Information Science and Engineering Guilin University of TechnologyGuilin541004China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of TechnologyGuilin541004China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal UniversityGuilin541004China

With the popularity of online learning and due to the significant influence of emotion on the learning effect,more and more researches focus on emotion recognition in online *** of the current research uses the comments of the learning platform or the learner’s expression for emotion *** research data on other modalities are *** of the studies also ignore the impact of instructional videos on learners and the guidance of knowledge on *** of the need for other modal research data,we construct a synchronous multimodal data set for analyzing learners’emotional states in online learning *** data set recorded the eye movement data and photoplethysmography(PPG)signals of 68 subjects and the instructional video they *** the problem of ignoring the instructional videos on learners and ignoring the knowledge,a multimodal emotion recognition method in video learning based on knowledge enhancement is *** method uses the knowledge-based features extracted from instructional videos,such as brightness,hue,saturation,the videos’clickthrough rate,and emotion generation time,to guide the emotion recognition process of physiological *** method uses Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM)networks to extract deeper emotional representation and spatiotemporal information from shallow *** model uses multi-head attention(MHA)mechanism to obtain critical information in the extracted deep ***,Temporal Convolutional Network(TCN)is used to learn the information in the deep features and knowledge-based ***-based features are used to supplement and enhance the deep features of physiological ***,the fully connected layer is used for emotion recognition,and the recognition accuracy reaches 97.51%.Compared with two recent researches,the accuracy improved by 8.57%and 2.11%,*** the four public data sets,our proposed method also achieves bett

关键词： Emotion recognition video learning physiological signal knowledge enhancement deep learning CNN LSTM TCN

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ColorRefineNet: Color Refining Underwater Image Enhancement based on Attention Network 5

ColorRefineNet: Color Refining Underwater Image Enhancement ...

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5th International Conference on Advanced information Technologies, ICAIT 2024

作者： Tun, May Thet Shimamura, Tetsuya University of Information Technology Vision and Learning Lab Yangon Myanmar Saitama University Graduate School of Science and Engineering Saitama Japan

ISBN: (纸本)9798331510046

In recent times, the challenges associated with underwater images have gained more attention, particularly in color enhancement, which are significant obstacles for high-level computer vision tasks. To solve these issues, this paper proposes a color refinement network, named ColorRefineNet, which is designed to enhance the brightness of color and contrast of underwater images while preserving structure details, sharpness, and colorfulness. In ColorRefineNet, a channel and spatial attention-based Convolutional Block Attention Module (CBAM) attention module is incorporated into each ConvBlock of the baseline Shollow-UWnet. Additionally, a structural similarity (SSIM) loss function is integrated as an additional loss to enhance the color and contrast of underwater images. Comprehensive experiments on eight underwater image datasets, such as EUVP-Dark, UIEB, UFO-120, U45, C60, T90, ColorChecker, and OceanDark, show that ColorRefineNet outperforms UCIQE scores than the baseline Shallow-UWnet and state-of-the-art (SOTA) methods. © 2024 IEEE.

关键词： Underwater photography

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Efficient Approach for Virtual Machine and Resource Allocation in Cloud Computing 1

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5th International Conference on information systems and Management science, ISMS 2022

作者： Saxena, Rajat Dubey, Shatendra Sada, Rajesh Cloud Computing Lab Department of Computer Science and Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeth Vishwavidyalaya Indore India Cloud Computing Lab Department of Information Technology NRI Institute of Information Science and Technology Bhopal India

ISBN: (数字)9783031311536

ISBN: (纸本)9783031311529

Globalization has brought many changes in modern business world. Problems of enterprise oriented software applications like distribution and configuration of resources presents challenge to the traditional software sales model. Cloud computing presents the solution for such problems. With the help of cloud-based model one can earn good return by making the QoS demands of their users (customers) satisfied. In order to fulfill the infrastructure demands (like network, storage, etc.) to their users, service providers have to maintain their own hardware or they lease it from the IaaS providers. For maintaining their own hardware SaaS providers will have to incur an extra cost, but if taken on lease, zero maintenance cost involved. Moreover, if provider wants to optimize cost and gain customer satisfaction, they will have to satisfy their customers/users by maintaining service level objectives. This paper presents a model for resource allocation in such a way as to minimize cost and maximize profit by satisfying the service level needs of the customers. The model is designed to enable the service providers with an ability to cope with the changing needs of customers, mapping customer requests to infrastructure level parameters and better handling of heterogeneous VMs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Software as a service (SaaS)

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FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures 23

FlexiFed: Personalized Federated Learning for Edge Clients w...

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2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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A Graph Neural Network Recommendation Based on Long-and Short-Term Preference

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Computer systems science & engineering 2023年第12期47卷 3067-3082页

作者： Bohuai Xiao Xiaolan Xie Chengyong Yang School of Information Science and Engineering Guilin University of TechnologyGuilin541004China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of TechnologyGuilin541004China Network and Information Center Guilin University of TechnologyGuilin541004China

The recommendation system(RS)on the strength of Graph Neural Networks(GNN)perceives a user-item interaction graph after collecting all items the user has interacted *** the RS performs neighborhood aggregation on the graph to generate long-term preference representations for the user in quick ***,user preferences are *** the passage of time and some trend guidance,users may generate some short-term preferences,which are more likely to lead to user-item interactions.A GNN recommendation based on long-and short-term preference(LSGNN)is proposed to address the above *** consists of four modules,using a GNN combined with the attention mechanism to extract long-term preference features,using Bidirectional Encoder Representation from Transformers(BERT)and the attention mechanism combined with Bi-Directional Gated Recurrent Unit(Bi-GRU)to extract short-term preference features,using Convolutional Neural Network(CNN)combined with the attention mechanism to add title and description representations of items,finally inner-producing long-term and short-term preference features as well as features of items to achieve *** experiments conducted on five publicly available datasets from Amazon,LSGNN is superior to state-of-the-art personalized recommendation techniques.

关键词： Recommendation systems graph neural networks deep learning data mining

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Precise mode control of laser-written waveguides for broadband,low-dispersion 3D integrated optics

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Light(science & Applications) 2024年第6期13卷 1186-1195页

作者： Yuying Wang Lijing Zhong Kuen Yao Lau Xuhu Han Yi Yang Jiacheng Hu Sergei Firstov Zhi Chen Zhijun Ma Limin Tong Kin Seng Chiang Dezhi Tan Jianrong Qiu College of Optical Science and Engineering Zhejiang University310027HangzhouChina Institute of Light+X Science and Technology College of Information Science and EngineeringNingbo University315211NingboChina School of Optoelectronic Science and Engineering&Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University215006SuzhouChina Prokhorov General Physics Institute of the Russian Academy of Sciences Dianov Fiber Optics Research Center38 Vavilov str.Moscow119333Russia Zhejiang Lab 311121HangzhouChina College of Materials Science and Engineering Key Laboratory of Advanced Materials of Yunnan ProvinceKunming University of Science and Technology650093KunmingYunnanChina Department of Electrical Engineering City University of Hong Kong83 Tat Chee AvenueKowloonHong Kong SARChina School of Materials Science and Engineering Zhejiang University310027HangzhouChina

Three-dimensional(3D)glass chips are promising waveguide platforms for building hybrid 3D photonic circuits due to their 3D topological capabilities,large transparent windows,and low coupling *** present,the key challenge in scaling down a benchtop optical system to a glass chip is the lack of precise methods for controlling the mode field and optical coupling of 3D waveguide ***,we propose an overlap-controlled multi-scan(OCMS)method based on laser-direct lithography that allows customizing the refractive index profile of 3D waveguides with high spatial precision in a variety of *** the basis of this method,we achieve variable mode-field distribution,robust and broadband coupling,and thereby demonstrate dispersionless LP21-mode conversion of supercontinuum pulses with the largest deviation of<0.1 dB in coupling ratios on 210 nm *** approach provides a route to achieve ultra-broadband and low-dispersion coupling in 3D photonic circuits,with overwhelming advantages over conventional planar waveguide-optic platforms for on-chip transmission and manipulation of ultrashort laser pulses and broadband supercontinuum.

关键词： waveguide mode dispersion

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Consensus Control of Multi-Agent systems Using Fault-Estimation-in-the-Loop:Dynamic Event-Triggered Case

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1440-1451页

作者： Yamei Ju Derui Ding Xiao He Qing-Long Han Guoliang Wei Shanghai Key Lab of Modern Optical System the Department of Control Science and EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Department of Control Science and Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia Department of Automation BNRistTsinghua UniversityBeijing 100084China College of Science University of Shanghai for Science and TechnologyShanghai 200093China

The paper develops a novel framework of consensus control with fault-estimation-in-the-loop for multi-agent systems(MASs)in the presence of faults.A dynamic event-triggered protocol(DETP)by adding an auxiliary variable is utilized to improve the utilization of communication ***,a novel estimator with a noise bias is put forward to estimate the existed fault and then a consensus controller with fault compensation(FC)is adopted to realize the demand of reliability and safety of addressed ***,a novel consensus control framework with fault-estimation-in-the-loop is developed to achieve the predetermined consensus performance with the l_(2)-l_(∞)constraint by employing the variance analysis and the Lyapunov stability ***,the desired estimator and controller gains are obtained in light of the solution to an algebraic matrix equation and a linear matrix inequality in a recursive way,***,a simulation result is employed to verify the usefulness of the proposed design framework.

关键词： Consensus control dynamic event-triggered protocol(DETP) fault compensation(FC) fault estimation multi-agent systems(MASs)

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Pindel-TD:A Tandem Duplication Detector Based on A Pattern Growth Approach

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Genomics, Proteomics & Bioinformatics 2024年第1期22卷 99-104页

作者： Xiaofei Yang Gaoyang Zheng Peng Jia Songbo Wang Kai Ye School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Center for Mathematical Medical the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China Faculty of Science Leiden UniversityLeiden 2311 EZNetherland

Tandem duplication(TD)is a major type of structural variations(SVs)that plays an important role in novel gene formation and human ***,TDs are often missed or incorrectly classified as insertions by most modern SV detection methods due to the lack of specialized operation on TD-related mutational ***,we developed a TD detection module for the Pindel tool,referred to as Pindel-TD,based on a TD-specific pattern growth ***-TD is capable of detecting TDs with a wide size range at single nucleotide *** simulated and real read data from HG002,we demonstrated that Pindel-TD outperforms other leading methods in terms of precision,recall,F1-score,and ***,by applying Pindel-TD to data generated from the K562 cancer cell line,we identified a TD located at the seventh exon of SAGE1,providing an explanation for its high ***-TD is available for non-commercial use at https://***/xjtu-omics/pindel.

关键词： Tandem duplication Pattern growth Short-read sequencing Structural variation SAGE1

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Automated Detection of Harmful Insects in Agriculture: A Smart Framework Leveraging IoT, Machine Learning, and Blockchain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4787-4798页

作者： Rahman, Wahidur Hossain, Muhammad Minoar Hasan, Md. Mahedi Iqbal, Md. Sadiq Rahman, Mohammad Motiur Fida Hasan, Khondokar Moni, Mohammad Ali Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Uttara University Department of Computer Science and Engineering Dhaka1230 Bangladesh Prime University Department of Management and Information System Mirpur Dhaka1216 Bangladesh Bangladesh University Department of Computer Science and Engineering Dhaka1207 Bangladesh School of Professional Studies CanberraNSW2052 Australia Artificial Intelligence and Cyber FuturesInstitute Charles Sturt University AI & Digital Health Technology BathurstNSW2795 Australia

Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a real-time framework that incorporates the Internet of Things (IoT), blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the IoT and blockchain technologies facilitates stablishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed. Besides, an IoT-based system is embodied with a camera module and microprocessor, accompanied by some apparatus required to automate the whole system. In addition, the research also includes the blockchain to secure each individual data transmission among the several IoT components and the cloud server. While examining the proposed solution, various experimental data have been systematically documented and analyzed. The proposed framework attained a peak accuracy of 98.91% using the VGG19 model and ensemble classifiers to detect the pest with a specificity of 99.14% and a precision of 98.21%. The study additionally quantifies the mean duration of the cloud response when integrated with IoT, yielding an average time of 1.71 s after pest identification. Nevertheless, the system has exhibited a high level of efficacy in the context of real-time monitoring and automation of paddy farms. © 2020 IEEE.

关键词： Blockchain

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