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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Guo, Wangdong Zhu, Qing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (纸本)9798350368741

Group Emotion Recognition (GER) seeks to identify emotional states within multi-person groups. The complexity of the environment and the reliance on a single group emotion label often result in noisy individuals with inconsistent emotional expressions, hampering accurate emotion classification. Mainstream GER networks attempt to downweight noisy individuals, but when their numbers are high and over-relying on a single group label, the negative impact of noise on individuals remains significant, further complicating classification and diminishing accuracy. To address these limitations, we propose the Multi-Scale Contextual and Noise Suppression Model (MCon-NSM), a novel framework that enhances GER by capturing fine-grained interaction contexts and collaboratively suppressing noise at both the individual and group label levels. Extensive experiments on the GAF series datasets demonstrate that our method achieves results comparable to state-of-the-art techniques, validating its effectiveness in mitigating noise in GER. © 2025 IEEE.

关键词： Emotion analysis Group emotion recognition Multi-Scale context learning Noise suppression

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Arc plasma-deposited Co single-atom catalysts supported on an aligned carbon nanofiber for hydrogen peroxide electrosynthesis and an electro-Fenton process

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Carbon Energy 2024年第11期6卷 234-255页

作者： Chang‐Kyu Hwang Sooyeon Kim Ki Ro Yoon Thao Thi Le Chinh V.Hoang Jae Won Choi Wenjun Zhang Sae Yane Paek Chung Hyeon Lee Ji Hyun Lee Keun Hwa Chae Sohee Jeong Seung Yong Lee Byeong-Kwon Ju Sang Hoon Kim Sang Soo Han Jong Min Kim Materials Architecturing Research Center Korea Institute of Science and Technology (KIST))seculSecrngbuk-guRepublic of Korea Department of Micro/Nano Systems Korea UniversitySeoulSeongbuk-guRepublic of Korea Computational Science Research Center Korea Institute of Science and Technology (KIST)SeculSecrngbuk-guRepublic of Korea Advanced Textile R&D Department Korea Institute of Industrial Technology (KITECH)AmsanGyeonggi-doRepublic of Korea Extreme Materials Research Center Korea Institute of Science and Technology (KIST)SeoulSeongbuk:guRepubic of Korea Nanoscience and Technology KIST School University of Science and TechnologySeoulRepublic of Korea Advanced Analysis and Data Center Korea Institute of Science and TechnologySeoulSeongbuk-guRepublic of Korea Display and Nanosensor Laboratory School of Electrical Engineering Korea UniversitySeoulSeongbuk guRepublic of Korea KHU-KIST Department of Converging Science and Technology Kyung Hee UniversitySeoulSeongbuk-guRepubic of Korea

Atomically dispersed single-atom catalysts(SACs)on carbon supports show great promise for H_(2)O_(2) electrosynthesis,but conventional wet chemistry methods using particulate carbon blacks in powder form have limited their potential as two-electron(2^(e−))oxygen reduction reaction(ORR)***,we demonstrate high-performance Co SACs supported on a free-standing aligned carbon nanofiber(CNF)using electrospinning and arc plasma deposition(APD).Based on the surface oxidation treatment of aligned CNF and precise control of the deposition amount in a dry-based APD process,we successfully form densely populated Co SACs on aligned *** experimental analyses and density functional theory calculations,we reveal that Co SAC has a Co–N_(2)–O_(2) moiety with one epoxy group,leading to excellent 2^(e−)ORR ***,the aligned CNF significantly improves mass transfer in flow cells compared to randomly oriented CNF,showing an overpotential reduction of 30 mV and a 1.3-fold improvement(84.5%)in Faradaic efficiency,and finally achieves an outstanding production rate of 15.75 mol gcat^(−1) h^(−1) at 300 mA cm^(−2).The high-performance Co SAC supported on well-aligned CNF is also applied in an electro-Fenton process,demonstrating rapid removal of methylene blue and bisphenol F due to its exceptional 2e^(−)ORR activity.

关键词： arc plasma deposition electro-Fenton flow reactor hydrogen peroxide production oxygen reduction reaction single-atom catalysts

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Time Series Analysis and Forecasting of Air Quality Index of Dhaka City of Bangladesh

Time Series Analysis and Forecasting of Air Quality Index of...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Sakib, Sheikh Rahmatulla Sara, Kamarun Nahar Hossain Rasel, Md. Tahmid Asif, Md. Masudul Islam Nahid, Md. Aynul Hasan Rahman, Md. Saifur Mridha, M.F. Islam, Ashraful Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Department of Computer Science Dhaka Bangladesh Independent University Bangladesh Center for Computational and Data Sciences Dhaka Bangladesh

ISBN: (纸本)9798350337617

In Dhaka, the capital city of Bangladesh, various sources including vehicle emissions, industrial activities, brick kilns, building sites, and open rubbish burning contribute to the air pollution problem. To assess the air quality, the Air Quality Index (AQI) is utilized, which categorizes air quality based on pollutant concentration. In this study, we have built ARIMA, Auto-ARIMA, SARIMAX, and VAR models to predict the air quality of Dhaka. Unlike previous studies, we have utilized hourly air pollutants factors such as PM2.5, PM10, SO2, CO, NO2, and O3 to forecast air quality. Our novel approach enables us to predict the monthly and weekly air quality of Dhaka city. Our analysis reveals that the SARIMAX model, which takes into account seasonal patterns, trends, and external factors, is the most accurate in predicting Dhaka city's air quality. The model's prediction performance is assessed using statistical indicators such as mean absolute percentage error and root mean square error. The study highlights that the SARIMAX model could aid policymakers in evaluating the efficacy of air pollution control measures. © 2023 IEEE.

关键词： Air quality

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Pelvic bone region segmentation (PBRS) from X-ray image using convolutional neural network (CNN) 26

Pelvic bone region segmentation (PBRS) from X-ray image usin...

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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Ul Alam, Syed Alif Binte Alam, Saadia Saha, Sourav Haque, Mahmudul Rahman, Rashedur Kobashi, Syoji Independent University Bangladesh Department of Computer Science and Engineering Dhaka1229 Bangladesh Independent University Bangladesh Center for Computational & Data Sciences Dhaka1229 Bangladesh University of Hyogo Graduate School of Engineering Shosha Hyogo Himeji2167 Japan

ISBN: (纸本)9798350359015

Pelvic and hip fractures offer considerable public health risks with high morbidity and mortality rates. Because of the complicated bone structure of the pelvic bone region, detecting fractures is difficult. Though X-ray imaging is routinely utilised for detecting fractures, manual fracture diagnosis is prone to inaccuracies. This paper proposes the use of deep learning algorithms for automated segmentation of the pelvic bone region in X-ray images. In our work, we have investigated U-Net based pelvic area segmentation models with various convolutional neural network (CNN) backbones. The DenseNet121-based U-Net design emerged as the most optimal model, establishing a compromise between performance and computational efficiency. Although it had a modest loss in IoU and F1 scores when compared to InceptionNetV3, it had a remarkable 59.44% reduction in the number of parameters. © 2023 IEEE.

关键词： deep learning pelvic bone segmentation x-ray

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Towards understanding structure–property relations in materials with interpretable deep learning

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npj computational Materials 2023年第1期9卷 157-168页

作者： Tien-Sinh Vu Minh-Quyet Ha Duong-Nguyen Nguyen Viet-Cuong Nguyen Yukihiro Abe Truyen Tran Huan Tran Hiori Kino Takashi Miyake Koji Tsuda Hieu-Chi Dam Japan Advanced Institute of Science and Technology NomiIshikawaJapan HPC SYSTEMS Inc. MinatoTokyoJapan Applied Artificial Intelligence Institute Deakin UniversityGeelongVICAustralia School of Materials Science and Engineering Georgia Institute of TechnologyAtlantaGAUSA Research and Services Division of Materials Data and Integrated System National Institute for Materials ScienceTsukubaIbarakiJapan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and TechnologyTsukubaIbarakiJapan Department of Computational Biology and Medical Sciences Graduate School of Frontier SciencesUniversity of TokyoKashiwa-shiChiba-kenJapan International Center for Synchrotron Radiation Innovation Smart(SRIS) Tohoku UniversityAoba-kuSendaiJapan

Deep learning(DL)models currently employed in materials research exhibit certain limitations in delivering meaningful information for interpreting predictions and comprehending the relationships between structure and material *** address these limitations,we propose an interpretable DL architecture that incorporates the attention mechanism to predict material properties and gain insights into their structure–property *** proposed architecture is evaluated using two well-known datasets(the QM9 and the Materials Project datasets),and three in-house-developed computational materials ***–test–split validations confirm that the models derived using the proposed DL architecture exhibit strong predictive capabilities,which are comparable to those of current state-of-the-art ***,comparative validations,based on first-principles calculations,indicate that the degree of attention of the atoms’local structures to the representation of the material structure is critical when interpreting structure–property relationships with respect to physical *** properties encompass molecular orbital energies and the formation energies of *** proposed architecture shows great potential in accelerating material design by predicting material properties and explicitly identifying crucial features within the corresponding structures.

关键词： properties property structure

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SETAR Model Research Based on Support Vector Regression Parameter Estimation Method and Empirical Analysis 3

SETAR Model Research Based on Support Vector Regression Para...

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2021 3rd International Conference on Robotics, Intelligent Control and Artificial Intelligence, ICRICA 2021

作者： Li, Yun Yin, Xiangfeng Wen, Xueting School of Mathematics and Computational Science Hunan University of Science and Technology Xiangtan China Sch. of Mathematics and Computational Science Hunan University of Science and Technology Hunan Engineering Research Center for Big Data and Intelligent Decision-making of Industrial Development Xiangtan China

In this paper, we use support vector regression (SVR) to estimate the parameters of the SETAR model for predicting the log-return of stock price data. SETAR model is usually estimated using maximum likelihood (ML) and minimize conditional sum-of-squares (CSS), assuming that the data are normal distribution. SVR is a powerful tool for regression estimation. Therefore, this paper proposes to use SVR instead of maximum likelihood and conditional least squares to estimate the parameters of the SETAR model. Empirical analysis on the two stock price data of China Unicom and ICBC, the results show that on the data of China Unicom, the prediction error of the Gaussian kernel SVR-SETAR model is 8% lower than that of the ML-SETAR model and the CSS-SETAR model. And on ICBC data, the prediction error of the Gaussian kernel SVR-SETAR model is 26.32% and 20% lower than that of the ML-SETAR model and CSS-SETAR model, respectively. This suggests that SVR can be used to estimate the parameters of the SESAT model and the SVR-SETAR model has higher predictive power. © Published under licence by IOP Publishing Ltd.

关键词： Normal distribution

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Improving Autoencoder-Based Recommendation Systems 7th

Improving Autoencoder-Based Recommendation Systems

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7th International Conference on data Management, Analytics and Innovation, ICDMAI 2023

作者： Sinhababu, Nilanjan Sarma, Monalisa Samanta, Debasis Center for Computational and Data Sciences Indian Institute of Technology Kharagpur India Subir Chowdhury School of Quality and Reliability Indian Institute of Technology Kharagpur India Computer Science and Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9789819914135

Recommendation systems help in providing suggestions regarding the products/services that the users might be interested in by analyzing ratings, reviews, and other feedback obtained from the users on their previous purchases. These types of systems can be seen in domains like e-commerce, entertainment, news, etc. The ratings and reviews in e-commerce are obtained from user feedback which are far less in number when compared with the users and items count. This leads to data sparsity issues where the recommendation systems are prone to provide in-accurate recommendations. To counter this issue, in our study, we used both autoencoder and sentiment analysis on the reviews using the BERT language model to generate a more accurate version of the rating matrix. The empirical studies done on the Amazon baby product reviews dataset have shown that the approach has significantly increased the accuracy of predicted ratings. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Model-Free Control for Drop-on-Demand Droplet Generation Using Reinforcement Learning

Model-Free Control for Drop-on-Demand Droplet Generation Usi...

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European Control Conference (ECC)

作者： Mikhail Vulf Anton Bolychev Dmitry Kolomenskiy Pavel Osinenko Center for Digital Engineering Skolkovo Institute of Science and Technology Moscow Russia Center for Computational and Data Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia Center for Materials Technologies Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Recently we have developed a Drop-on-Demand droplet generator which allows to generate coarse suspension droplets in a wide range of sizes (from 0.75 to 4.40 mm), However, there is a common problem that it is difficult for different liquids to generate one drop of the desired size. To solve this problem, an auto-calibration and dynamic control box is being developed. In the present work, we simulate the dynamics of a hydraulic part of the droplet generator by an experimentally verified model. This simulation is used as a black box for model-free control developed by reinforcement learning approach. The obtained results are consistent with previous droplet generation experiments. The proposed model-free control method can be used for automatic parameter adjustment for generating a single drop of different liquids.

关键词： Liquids Electronic equipment Microcontrollers Flight recording Process control Europe Hydraulic systems

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Importance of alternative pathways to STEM through analysis of longitudinal student dataset

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Discover Education 2024年第1期3卷 1-18页

作者： AlHmoud, Issa W. Poudel, Samin Deshmukh, Sulochana Booth, Caroline S. Monty, Greg Bikdash, Marwan Department of Computational Data Science and Engineering North Carolina A & T State University Greensboro NC United States Department of Counseling North Carolina A & T State University Greensboro NC United States Center for Energy Research and Technology North Carolina A & T State University Greensboro NC United States

Using a longitudinal national educational dataset, data science methods were applied to explain students’ educational trajectories and determine the most predictive variables in STEM degree attainment. Challenging the notion of the STEM pipeline, an Alternative Pathways to STEM (APS) model was proposed. Using a foundation of Social Cognitive Career Theory, academic, demographic, and economic variables were selected for analysis and Imbalanced dataset and random forest data analytic techniques were implemented. Results supported the consideration of the Alternate Pathways to STEM model given the varied educational pathways identified throughout STEM matriculation. Additionally, multiple academic variables, except for career interest, were found to be most predictive of STEM graduation, while demographic and economic variables were not significant. These findings have important implications for recruiting STEM students and increasing STEM graduation rates. Additional research is recommended using these data extraction and analysis methodologies on other datasets to further test the efficacy of the Alternative Pathways to STEM model. © The Author(s) 2024.

关键词： Education Imbalanced dataset Pathways Random forest STEM STEM pipeline

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Quantization of Bandlimited Graph Signals

Quantization of Bandlimited Graph Signals

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2023 International Conference on Sampling Theory and Applications, SampTA 2023

作者： Krahmer, Felix Lyu, He Saab, Rayan Veselovska, Anna Wang, Rongrong Technical University of Munich and Munich Center for Machine Learning Department of Mathematics & Munich Data Science Institute Garching/Munich Germany University of California San Diego Department of Mathematics & Halicioglu Data Science Institute San Diego United States Michigan State University Department of Computational Mathematics Science and Engineering Department of Mathematics East Lansing United States

ISBN: (纸本)9798350328851

Graph models and graph-based signals are becoming increasingly important in machine learning, natural sciences, and modern signal processing. In this paper, we address the problem of quantizing bandlimited graph signals. We introduce two classes of noise-shaping algorithms for graph signals that differ in their sampling methodologies. We demonstrate that these algorithms can be efficiently used to construct quantized representatives of bandlimited graph-based signals with bounded amplitude. Moreover, for one of the algorithms, we provide theoretical guarantees on the relative error between the quantized representative and the true signal. © 2023 IEEE.

关键词： Quantization (signal)

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