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DeBERTa-GRU: Sentiment Analysis for Large Language Model

computers, Materials & Continua 2024年第6期79卷 4219-4236页

作者： Adel Assiri Abdu Gumaei Faisal Mehmood Touqeer Abbas Sami Ullah department of Informatics for Business College of BusinessKing Khalid UniversityAbha61421Saudi Arabia department of computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia School of electrical and Information engineering Zhengzhou UniversityZhengzhou450001China department of computer Science and Technology Beijing University of Chemical TechnologyBeijing100029China department of computer Science Government College University FaisalabadFaisalabadPunjab38000Pakistan

Modern technological advancements have made social media an essential component of daily *** media allow individuals to share thoughts,emotions,and *** analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the *** analysis is essential in business and society because it impacts strategic *** analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance *** execution time increases due to the sequential processing of the sequence ***,the calculation times for the Transformer models are reduced because of the parallel *** study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their *** particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment *** the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics *** proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

关键词： DeBERTa GRU Naive Bayes LSTM sentiment analysis large language model

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OptoGPT: A foundation model for inverse design in optical multilayer thin film structures

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Opto-Electronic Advances 2024年第7期7卷 4-16页

作者： Taigao Ma Haozhu Wang L.Jay Guo department of Physics University of MichiganAnn ArborMichigan 48109USA department of electrical engineering and computer Science University of MichiganAnn ArborMichigan 48109USA

Optical multilayer thin film structures have been widely used in numerous photonic ***,existing inverse design methods have many drawbacks because they either fail to quickly adapt to different design targets,or are difficult to suit for different types of structures,e.g.,designing for different materials at each *** methods also cannot accommodate versatile design situations under different angles and *** addition,how to benefit practical fabrications and manufacturing has not been extensively considered *** this work,we introduce OptoGPT(Opto Generative Pretrained Transformer),a decoder-only transformer,to solve all these drawbacks and issues simultaneously.

关键词： multilayer thin film structure inverse design foundation models deep learning structural color

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Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

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computers, Materials & Continua 2024年第7期80卷 139-161页

作者： Roseline Oluwaseun Ogundokun Joseph Bamidele Awotunde Hakeem Babalola Akande Cheng-Chi Lee Agbotiname Lucky Imoize department of Multimedia engineering Kaunas University of TechnologyKaunas44249Lithuania department of computer Science Landmark UniversityOmu Aran251103Nigeria department of computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria department of Telecommunication Science University of IlorinIlorin230003Nigeria department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina department of computer Science and Information engineering Fintech and Blockchain Research CenterAsia UniversityTaichung City41354TaiwanChina department of electrical and Electronics engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria department of electrical engineering and Information Technology Institute of Digital CommunicationRuhr UniversityBochum44801Germany

Mobile technology is developing *** phone technologies have been integrated into the healthcare industry to help medical ***,computer vision models focus on image detection and classification ***2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to *** leads to increased *** biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational ***,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is *** pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory *** proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and *** contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable *** model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class *** the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is *** testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,a

关键词： Retinal images ocular disorder deep transfer learning disease identification mobile device

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Template-assisted growth of Co-BaTiO_(3) vertically aligned nanocomposite thin films with strong magneto-optical coupling effect

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Nano Research 2024年第4期17卷 3130-3138页

作者： Zedong Hu Juanjuan Lu Hongyi Dou Jianan Shen James PBarnard Juncheng Liu Xinghang Zhang Haiyan Wang School of electrical and computer engineering Purdue UniversityWest LafayetteIN 47906USA School of Materials engineering Purdue UniversityWest LafayetteIN 47906USA

Oxide-metal based nanocomposite thin films have attracted great interests owing to their unique anisotropic structure and physical properties.A wide range of Au-based oxide-metal nanocomposites have been demonstrated,while other metal systems are scarce due to the challenges in the initial nucleation and growth as well as possible interdiffusions of the metallic *** this work,a unique anodic aluminum oxide(AAO)template was used to grow a thin Co seed layer and the following self-assembled metal-oxide(Co-BaTiO_(3))vertically aligned nanocomposite thin film *** AAO template allows the uniform growth of Co-seeds and successfully deposition of highly ordered Co pillars(with diameter<5 nm and interval between pillars<10 nm)inside the oxide *** magnetic anisotropy and strong magneto-optical coupling properties have been observed.A thin Au-BaTiO_(3) template was also later introduced for further enhanced nucleation and ordered growth of the *** the advantage of such a unique nanostructure,a large out-of-plane(OP)coercive field(Hc)of~5000 Oe has been achieved,making the nanocomposite an ideal candidate for high density perpendicular magnetic tunneling junction(p-MTJ).A strong polar magneto-optical Kerr effect(MOKE)has also been observed which inspires a novel optical-based reading method of the MTJ states.

关键词： vertically aligned nanocomposite(VAN) Co BaTiO_(3) thin film magneto-optical coupling magnetic anisotropy

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Effect of granular shape on radial segregation in a two-dimensional drum

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Chinese Physics B 2024年第4期33卷 509-514页

作者：徐悦李然迟志鹏修文正孙其诚杨晖 School of Optical-electrical and computer engineering University of Shanghai for Science and TechnologyShanghai 200093China School of electrical and Electronic engineering Shandong University of TechnologyZibo 255049China State Key Laboratory of Hydroscience and engineering Tsinghua UniversityBeijing 100084China College of Medical Instruments Shanghai University of Medicine and Health SciencesShanghai 201318China

Granular segregation is widely observed in nature and *** research has focused on segregation caused by differences in the size and density of spherical ***,due to the fact that grains typically have different shapes,the focus is shifting towards shape *** this study,experiments are conducted by mixing cubic and spherical *** results indicate that spherical grains gather at the center and cubic grains are distributed around them,and the degree of segregation is *** experiments,a structured analysis of local regions is conducted to explain the inability to form stable segregation patterns with obviously different geometric ***,through simulations,the reasons for the central and peripheral distributions are explained by comparing velocities and the number of collisions of the grains in the flow layer.

关键词： granular materials cubic grains shape segregation segregation mechanism

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New Results in Cooperative Adaptive Optimal Output Regulation

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Journal of Systems Science & Complexity 2024年第1期37卷 253-272页

作者： DONG Yuchen GAO Weinan JIANG Zhong-Ping State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China department of electrical and computer engineering Tandon School of EngineeringNew York UniversityBrooklynNY 11201USA

This paper investigates the cooperative adaptive optimal output regulation problem of continuous-time linear multi-agent *** the multi-agent system dynamics are uncertain,solving regulator equations and the corresponding algebraic Riccati equations is challenging,especially for high-order *** this paper,a novel method is proposed to approximate the solution of regulator equations,i.e.,gradient descent *** is worth noting that this method obtains gradients through online data rather than model information.A data-driven distributed adaptive suboptimal controller is developed by adaptive dynamic programming,so that each follower can achieve asymptotic tracking and disturbance ***,the effectiveness of the proposed control method is validated by simulations.

关键词： Adaptive dynamic programming cooperative output regulation gradient descent method multi-agent systems.

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Superior efficiency and stability of YbO_(x)-enhanced perovskite solar cells

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Science China Materials 2024年第4期67卷 1356-1358页

作者： Qifeng Zhang Guozhong Cao department of electrical and computer engineering North Dakota State UniversityFargoND 58108-6050USA department of Materials Science and engineering University of WashingtonSeattleWA 98195-2120USA

Perovskite solar cells represent a revolutionary class of photovoltaic devices that have gained substantial attention for their exceptional performance and potential to provide an affordable and efficient solution for harnessing solar energy. These cells utilize perovskite-structured materials, typically hybrid organicinorganic lead halide compounds, as the light-absorbing layer.

关键词： energy perovskite stability

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Temporally consistent video colorization with deep feature propagation and self-regularization learning

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Computational Visual Media 2024年第2期10卷 375-395页

作者： Yihao Liu Hengyuan Zhao Kelvin CKChan Xintao Wang Chen Change Loy Yu Qiao Chao Dong Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of sciences Beijing 100049China department of electrical&computer engineering Nanyang Technological University50 Nanyang AvenueSingapore 639798 Applied Research Center Tencent PCGShenzhenChina

Video colorization is a challenging and highly ill-posed *** recent years have witnessed remarkable progress in single image colorization,there is relatively less research effort on video colorization,and existing methods always suffer from severe flickering artifacts(temporal inconsistency)or unsatisfactory *** address this problem from a new perspective,by jointly considering colorization and temporal consistency in a unified ***,we propose a novel temporally consistent video colorization(TCVC)*** effectively propagates frame-level deep features in a bidirectional way to enhance the temporal consistency of ***,TCVC introduces a self-regularization learning(SRL)scheme to minimize the differences in predictions obtained using different time *** does not require any ground-truth color videos for training and can further improve temporal *** demonstrate that our method can not only provide visually pleasing colorized video,but also with clearly better temporal consistency than state-of-the-art methods.A video demo is provided at https://***/watch?v=c7dczMs-olE,while code is available at https://***/lyh-18/TCVC-Tem porally-Consistent-Video-Colorization.

关键词： video colorization temporal consistency feature propagation self-regularization

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Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar department of computer Science and engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan department of computer Science National University of PakistanRawalpindiPunjab46000Pakistan department of computer Software engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan department of computer engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia department of computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK department of computer engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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Memory-enhanced text style transfer with dynamic style learning and calibration

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Science China(Information sciences) 2024年第4期67卷 181-196页

作者： Fuqiang LIN Yiping SONG Zhiliang TIAN Wangqun CHEN Diwen DONG Bo LIU College of computer Science and Technology National University of Defense Technology department of computer Science and engineering The Hong Kong University of Science and Technology Strategic Assessments and Consultation Institute Academy of Military Sciences

Text style transfer aims to rephrase a sentence to match the desired style while retaining the original content. As a controllable text generation task, mainstream approaches use content-independent style embedding as control variables to guide stylistic generation. Nonetheless, stylistic properties are contextsensitive even under the same style. For example, “delicious” and “helpful” convey positive sentiments,although they are more likely to describe food and people, respectively. Therefore, desired style signals must vary with the content. To this end, we propose a memory-enhanced transfer method, which learns fine-grained style representation concerning content to assist transfer. Rather than employing static style embedding or latent variables, our method abstracts linguistic characteristics from training corpora and memorizes subdivided content with the corresponding style representations. The style signal is dynamically retrieved from memory using the content as a query, providing a more expressive and flexible latent style space. To address the imbalance between quantity and quality in different content, we further introduce a calibration method to augment memory construction by modeling the relationship between candidate *** results obtained using three benchmark datasets confirm the superior performance of our model compared to competitive approaches. The evaluation metrics and case study also indicate that our model can generate diverse stylistic phrases matching context.

关键词： style transfer memory-enhanced method text generation deep learning text representation

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