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Contextualized Cross-Domain Aspect Sentiment Transformer: A Fine-Grained Aspect-Centric Approach for Enhanced Context-Aware Sentiment Analysis

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Computational Intelligence 2025年第3期41卷

作者： Gaurav Dubey Anupama Chadha Amrita Jyoti Gaurav Raj Kamaljit Kaur Anil Kumar Dubey Department of Computer Science KIET Group of Institutions Delhi-NCR Ghaziabad India Manav Rachna International Institute of Research and Studies MRIIRS Faridabad India Information Technology Department ABES Engineering College Ghaziabad India Department of Computer Science Sharda University Greater Noida India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Department of Computer Science ABES Engineering College Ghaziabad India

Context-aware sentiment analysis (CASA) is increasingly critical due to the complex nature of sentiments in digital communication. Traditional sentiment analysis often fails to capture the nuances in context-rich environments, facing challenges like disentangling sentiments, adapting to dynamic contexts, and handling cross-domain variations. Additionally, data sparsity and subjectivity in sentiment interpretation complicate CASA. To address these challenges, this paper proposed a Contextualized Cross-Domain Aspect Sentiment Transformer Network (CC-ASTN) that integrates BERT-based embeddings with aspect-specific embeddings for nuanced contextual and aspect-specific sentiment details. A core feature of CC-ASTN is its fine-grained sentiment analysis, which begins with word-level analysis to discern subtle emotional cues and modifiers, enabling the model to detect sentiment nuances. A novel dual attention mechanism dynamically adjusts focus based on relevance, resolving ambiguities. advanced domain adversarial training and transfer learning techniques ensure effective cross-domain adaptation, while data augmentation and few-shot learning strategies tackle data sparsity. A hierarchical approach for sentiment analysis breaks down complex sentiments into granular components. The model's robustness is enhanced through dropout, layer normalization, and noise contrastive estimation (NCE), ensuring stability and performance consistency. A composite loss function balances multiple objectives, facilitating precise, domain-neutral sentiment analysis. Additionally, the model integrates real-time feedback mechanisms and leverages a multi-modal approach by incorporating textual, visual, and contextual data for holistic analysis. The CC-ASTN model demonstrates significant efficiency, with training typically taking ˜5 h. Experimental results validate the model's effectiveness, showing significant improvements over existing methods on the SemEval2014 Task 4 and SentiHood datasets. The

关键词： aspect-specific embeddings BERT context-aware sentiment analysis cross-domain adaptation dual attention mechanism transformer architecture

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The Role of Chalcogen Vacancies in Single Photon Emission from Monolayer Tungsten Dichalcogenides

arXiv

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arXiv 2024年

作者： Gavin, S. Carin Zeman, Charles J. Dasgupta, Anushka Liu, Yiying Wu, Wenjing Huang, Shengxi Marks, Tobin J. Hersam, Mark C. Schatz, George C. Stern, Nathaniel P. Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Rice University HoustonTX77705 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Advanced Materials Institute Rice University HoustonTX77005 United States Department of Chemical and Biological Engineering Northwestern University EvanstonIL60208 United States Materials Research Center Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States

Understanding the mechanism of single photon emission (SPE) in two-dimensional (2D) materials is an unsolved problem important for quantum optical materials and the development of quantum information applications. In 2D transition metal dichalcogenides (TMDs) such as tungsten diselenide (WSe2), quantum emission has been broadly attributed to exciton localization from atomic point defects, yet the precise microscopic picture is not fully understood. This work presents a new framework, supported by both computational and experimental evidence, to explain both the origins of facile SPE in WSe2 and the relative scarcity of SPE in the related 2D TMD, tungsten disulfide (WS2). A vertical divacancy configuration of selenium creates a defect-centered, direct energy gap in the band structure of WSe2, giving rise to highly localized, radiative transitions. This configuration is shown to be energetically preferred in the monolayer lattice, which is reflected in the abundant experimental observation of SPE in WSe2 both from the literature and this work. In contrast, the same vertical divacancy configuration in WS2 does not create direct localized transitions, consistent with scarce observations of SPE in that material. By revealing a single mechanism in tungsten-based TMDs that addresses the prevalence of SPE and is consistent between theory and experiment, these results provide a framework for better understanding the rules governing the atomic origins of single photon emission in TMDs. Copyright © 2024, The Authors. All rights reserved.

关键词： Nanocrystals

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SemEval-2020 Task 3: Graded Word Similarity in Context 14

SemEval-2020 Task 3: Graded Word Similarity in Context

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14th International Workshops on Semantic Evaluation, SemEval 2020

作者： Armendariz, Carlos S. Purver, Matthew Pollak, Senja Ljubešić, Nikola Ulčar, Matej Robnik-Šikonja, Marko Vulić, Ivan Pilehvar, Mohammed Taher Cognitive Science Research Group Queen Mary University of London London United Kingdom Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia University of Ljubljana Faculty of Computer and Information Science Slovenia University of Cambridge United Kingdom Tehran Institute for Advanced Studies

ISBN: (纸本)9781952148316

This paper presents the Graded Word Similarity in Context (GWSC) task which asked participants to predict the effects of context on human perception of similarity in English, Croatian, Slovene and Finnish. We received 15 submissions and 11 system description papers. A new dataset (CoSimLex) was created for evaluation in this task: it contains pairs of words, each annotated within two short text passages. Systems beat the baselines by significant margins, but few did well in more than one language or subtask. Almost every system employed a Transformer model, but with many variations in the details: WordNet sense embeddings, translation of contexts, TF-IDF weightings, and the automatic creation of datasets for fine-tuning were all used to good effect. © 2020 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings. All rights reserved.

关键词： Semantics

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Infant vocal tract development analysis and diagnosis by cry signals with CNN Age classification

arXiv

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arXiv 2021年

作者： Ji, Chunyan Pan, Yi Department of Computer Science Georgia State University Atlanta United States Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

From crying to babbling and then to speech, infants' vocal tract goes through anatomic restructuring. In this paper, we propose a non-invasive fast method of using infant cry signals with convolutional neural network (CNN) based age classification to diagnose the abnormality of the vocal tract development as early as 4-month age. We study F0, F1, F2, and spectrograms and relate them to the postnatal development of infant vocalization. A novel CNN based age classification is performed with binary age pairs to discover the pattern and tendency of the vocal tract changes. The effectiveness of this approach is evaluated on Baby2020 with healthy infant cries and Baby Chillanto database with pathological infant cries. The results show that our approach yields 79.20% accuracy for healthy cries, 84.80% for asphyxiated cries, and 91.20% for deaf cries. Our method first reveals that infants' vocal tract develops to a certain level at 4-month age and infants can start controlling the vocal folds to produce discontinuous cry sounds leading to babbling. Early diagnosis of growth abnormality of the vocal tract can help parents keep vigilant and adopt medical treatment or training therapy for their infants as early as possible. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Detector Record Framework Utilizing Network Meet Future Demand For Smart Installation

A Detector Record Framework Utilizing Network Meet Future De...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： R. Kannamma Deepak Kaushal Shaik Vaseem Akram Padala Sravan Saurabh Sharma Soosan Chhabra Prathyusha Engineering College Department of Management Studies Graphic Era Deemed to be University Dehradun Uttarakhand India Uttaranchal Institute of Technology Uttaranchal University School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Research Scholar Amity University Gwalior Lovely Professional University Phagwara

Through into the communication linking physical items through World wide web, the Iot (IoT) is anticipated to provide greater accessibility. However, because when data collected are transmitted between of sink node in wsns, which make up the IoT's state exists, their constraints lead to a compliance issue. An SRS (Sensing Recording Solution) is used to solve this issue. The contradictory observed data's data are kept clean by utilising an SRS. As users drive along roads, their smart phones anticipate where they will be next in the request the Sbs' detected data for that location. That observed data will be lost if the Iot networks in the area where the users travel are unsteady. Think of a scenario where the user travels through hazardous locations. Users can really be notified about a risky scenario if their smartphone is unable to collect instructions. Two innovative SRSs were suggested that employ cellular service to avoid this: one makes use of Potential to excel, while the other makes use of the random subset of the Rs consumers. The exploratory study shown that the suggested strategy can deliver linked to sensory data with ease in any unusual situation.

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Enhancing Flexibility of Smart Bioresorbable Vascular Scaffolds Through 3d Printing Using Polycaprolactone and Polylactic Acid

SSRN

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SSRN 2024年

作者： Wei, Jinlian Oyunbaatar, Nomin-Erdene Jeong, Yun-Jin Park, Jongsung Kim, Su-Hwan Kwon, Kyeongha Lee, Heonzoo Won, Yonggwan Kim, Dong-Su Lee, Dong-Weon School of Mechanical Engineering Chonnam National University Gwangju61186 Korea Republic of Department of Automatic System Chosun College of Science & Technology Gwangju61453 Korea Republic of Department of Precision Mechanical Engineering Kyungpook National University Gyeongsangbuk-do Sangju37224 Korea Republic of School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Intelligent Electronics and Computer Engineering Chonnam National University Gwangju61186 Korea Republic of Gwangju61012 Korea Republic of

With the growing use of stent implantations, the risk of in-stent restenosis represents a major concern for cardiovascular patients. Herein, we propose a hybrid bioresorbable vascular scaffold (H-BVS) integrated with a wireless sensor that could enable significant advances in cardiovascular therapeutics. The H-BVS was manufactured using a state-of-the-art customized 3D printer equipped with dual nozzles to ensure a synergistic combination of polycaprolactone and polylactic acid. Moreover, the H-BVS was integrated with a wireless, battery-less LC-type pressure sensor for real-time monitoring of in-stent restenosis. This wireless sensor was meticulously connected with the H-BVS framework through engineered microstructures, which not only ensured a robust bond but also significantly enhanced the overall integrity and functionality of the device. Furthermore, the combination of the H-BVS and sensor was optimized to exhibit superior bending flexibility and radial strength, which are key factors in ensuring effectiveness and patient safety. The feasibility of the smart H-BVS (SH-BVS) was verified through comprehensive testing within a phantom system designed to simulate real blood flow conditions. We believe that the potential of the SH-BVS system to revolutionize patient monitoring and treatment strategies in the cardiovascular healthcare domain cannot be overstated. © 2024, The Authors. All rights reserved.

关键词： Biodegradable polymers

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Gamma-Ray Burst Light Curve Reconstruction: A Comparative Machine and Deep Learning Analysis

arXiv

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arXiv 2024年

作者： Kaushal, A. Manchanda, A. Dainotti, M.G. Deepu, A. Naqi, S. Felix, J. Indoriya, N. Magesh, S.P. Gupta, H. Gupta, K. Madhan, A. Hartmann, D.H. Pollo, A. Bogdan, M. Prochaska, J.X. Fraija, N. Debnath, D. Department of Computer Science and Engineering UIET-H Panjab University Punjab 146001 India Department of Physics Indian Institute of Technology Telangana Hyderabad502284 India National Astronomical Observatory of Japan 2 Chome-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Astronomical Sciences The Graduate University for Advanced Studies SOKENDAI Shonankokusaimura Miura District Kanagawa Hayama240-0115 Japan Nevada Center for Astrophysics University of Nevada 4505 Maryland Parkway Las VegasNV89154 United States Space Science Institute 4765 Walnut Street Suite B BoulderCO80301 United States Department of Ocean Engineering and Naval Architecture Indian Institute of Technology West Bengal Kharagpur721302 India KNIT Uttar Pradesh Sultanpur228118 India Indian Institute of Technology Kharagpur721302 India Department of Electrical Engineering and Computer Science IISER Madhya Pradesh Bhopal462066 India Indian Institute of Technology Madras600036 India MIT WPU School of Computer Science & Engineering Pune411038 India Department of Computer Science PSIT College Uttar Pradesh Kanpur209305 India Visveswaraya Technological University Karnataka Belagavi590018 India Department of Physics and Astronomy Clemson University ClemsonSC29634 United States Astronomical Observatory of Jagiellonian University in Kraków Orla 171 Kraków30-244 Poland National Centre for Nuclear Research Warsaw02-093 Poland Department of Mathematics University of Wroclaw Wroclaw50-384 Poland University of California Santa Cruz 1156 High Street Santa CruzCA95064 United States National Autonoma University of Mexico Circuito Interior Mexico City04510 Mexico NIT Tripura Agartala799046 India

Gamma-Ray Bursts (GRBs), observed at large redshifts, are probes of the evolution of the Universe and can be used as cosmological tools. To this end, we need tight (with small dispersion) correlations among key parameters. To reduce such a dispersion, we will mitigate gaps in light curves (LCs), including the plateau region, key to building the two-dimensional Dainotti relation between the end time of plateau emission (Ta) to its luminosity (La). We reconstruct LCs using nine models: Multi-Layer Perceptron (MLP), Bi-Mamba, Fourier Transform, Gaussian Process–Random Forest Hybrid (GP-RF), Bidirectional Long Short-Term Memory (Bi-LSTM), Conditional GAN (CGAN), SARIMAX-based Kalman filter, Kolmogorov–Arnold Networks (KANs), and Attention U-Net. These methods are compared to the Willingale model (W07) over a sample of 545 GRBs. MLP and Bi-Mamba outperform other methods, with MLP reducing the plateau parameter uncertainties by 25.9% for log Ta, 28.6% for log Fa, and 37.7% for α (the post-plateau slope in the W07 model), achieving the lowest 5-fold cross validation (CV) mean squared error (MSE) of 0.0275. Bi-Mamba achieved the lowest uncertainty of parameters, a 33.3% reduction in log Ta and log Fa and a 41.9% in α, but with a higher MSE of 0.130. Bi-Mamba brings the lowest outlier percentage for log Ta and log Fa (2.70%), while MLP carry α outliers to 0.900%. The other methods yield MSE values ranging from 0.0339 to 0.174. These improvements in parameter precision are needed to use GRBs as standard candles, investigate theoretical models, and predict GRB redshifts through machine learning. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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Large-scale and high-quality III-nitride membranes through microcavity-assisted crack propagation by engineering tensile-stressed Ni layers

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Opto-Electronic science 2022年第10期1卷 51-61,42-50页

作者： Jung-Hong Min Kwangjae Lee Tae-Hoon Chung Jung-Wook Min Kuang-Hui Li Chun Hong Kang Hoe-Min Kwak Tae-Hyeon Kim Youyou Yuan Kyoung-Kook Kim Dong-Seon Lee Tien Khee Ng Boon S.Ooi Photonics Laboratory ComputerElectrical and Mathematical Sciences and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Department of Electrical Engineering Stanford UniversityStanfordCA 94305USA Light Source Research Division Korea Photonics Technology Institute(KOPTI)Gwangju 61007Republic of Korea School of Electrical Engineering and Computer Science Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Department of Advanced Convergence Technology Research Institute of Advanced Convergence TechnologyKorea Polytechnic University237 Sangidaehak-roSiheung-si 15073Republic of Korea King Abdullah University of Science and Technology(KAUST) Thuwal 23955-6900Saudi Arabia

Epitaxially grown III-nitride alloys are tightly bonded materials with mixed covalent-ionic *** tight bonding presents tremendous challenges in developing III-nitride membranes,even though semiconductor membranes can provide numerous advantages by removing thick,inflexible,and costly ***,cavities with various sizes were introduced by overgrowing target layers,such as undoped GaN and green LEDs,on nanoporous templates prepared by electrochemical etching of n-type *** large primary interfacial toughness was effectively reduced according to the design of the cavity density,and the overgrown target layers were then conveniently exfoliated by engineering tensile-stressed Ni *** resulting III-nitride membranes maintained high crystal quality even after exfoliation due to the use of GaN-based nanoporous templates with the same lattice *** microcavity-assisted crack propagation process developed for the current III-nitride membranes forms a universal process for developing various kinds of large-scale and high-quality semiconductor membranes.

关键词： III-nitride alloys membranes nanoporous Ni stressor light-emitting diodes ultraviolet photodetectors

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Primitive quantum gates for an SU(2) discrete subgroup: Binary octahedral

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Physical Review D 2024年第5期109卷 054503-054503页

作者： Erik J. Gustafson Henry Lamm Felicity Lovelace Superconducting and Quantum Materials System Center (SQMS) Batavia Illinois 60510 USA Fermi National Accelerator Laboratory Batavia Illinois 60510 USA Quantum Artificial Intelligence Laboratory (QuAIL) NASA Ames Research Center Moffett Field California 94035 USA USRA Research Institute for Advanced Computer Science (RIACS) Mountain View California 94043 USA Department of Physics University of Illinois at Chicago Chicago Illinois 60607 USA

We construct a primitive gate set for the digital quantum simulation of the 48-element binary octahedral (BO) group. This non-Abelian discrete group better approximates SU(2) lattice gauge theory than previous work on the binary tetrahedral group at the cost of one additional qubit—for a total of six—per gauge link. The necessary primitives are the inversion gate, the group multiplication gate, the trace gate, and the BO Fourier transform.

关键词： Lattice field theory Lattice gauge theory Non-Abelian gauge theories Quantum circuits Quantum gates Quantum simulation

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Enhanced Feedback Residual Attention Network with Slanted Scarce Auto-Encoder and IoT Based Industrial Waste Water Treatment System for Effective Waste Management

Enhanced Feedback Residual Attention Network with Slanted Sc...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： K.V.S. Prasad T. Gavaskar G. Ravivarman P. Chinniah Sophia S Sathyakala S Department of Basic Sciences and Humanities GMR Institute of Technology Vizianagaram Andhra Pradesh India Department of Mechanical Engineering St. Joseph's College of Engineering Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering ST. Joseph College of Engineering Kanchipuram Tamil Nadu India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of Management Studies Sona College of Technology Salem Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

In the last few decades, rapid industrialization has increased the problem of wastewater treatment and management because of the increasing complexity and toxicity of wastewater that is difficult to treat by any conventional approach. Commonly used waste water treatment systems are unable to treat a variety of pollutants because of deficiency of good classification and optimization processes which results in wastage of resources and harming the environment. Furthermore, existing systems are inflexible, and an ability to change the nature of treatment in real time is impossible, which is crucial for the timely and efficient delivery and adjustment of therapies. To overcome these challenges, there is a need for smart monitoring systems enabled by IoT and effective treatment processes utilizing machine learning for better waste management. This paper comes up with an IoT and Machine Learning based system for the treatment of industrial wastewater that uses an Enhanced Feedback Residual Attention Network with Slanted Scarce Autoencoder to achieve accuracy in pollutant classification and quantification. Fick's Law Optimization is incorporated for optimization which makes the treatment process more efficient by allowing the treatment to be applied only where necessary and in the right amounts. IoT sensors are also part of the system to ensure that the data is collected in real time and the waste is continuously monitored to allow quick reactions to changes in waste. The introduced approach attains higher accuracy as 99%.

关键词： Waste management Accuracy Autoencoders Machine learning Real-time systems Sensor systems Sensors Wastewater Wastewater treatment Optimization

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