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Mendel 2023年第1期29卷 1-6页

作者： Fieri, Brillian Suhartono, Derwin Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Offensive language is one of the problems that have become increasingly severe along with the rise of the internet and social media usage. This language can be used to attack a person or specific groups. Automatic moderation, such as the usage of machine learning, can help detect and filter this particular language for someone who needs it. This study focuses on improving the performance of the soft voting classifier to detect offensive language by experimenting with the combinations of the soft voting estimators. The model was applied to a Twitter dataset that was augmented using several augmentation techniques. The features were extracted using Term Frequency-Inverse Document Frequency, sentiment analysis, and GloVe embedding. In this study, there were two types of soft voting models: machine learning-based, with the estimators of Random Forest, Decision Tree, Logistic Regression, Naïve Bayes, and AdaBoost as the best combination, and deep learning-based, with the best estimator combination of Convolutional Neural Network, Bidirectional Long Short-Term Memory, and Bidirectional Gated Recurrent Unit. The results of this study show that the soft voting classifier was better in performance compared to classic machine learning and deep learning models on both original and augmented datasets. © 2023, Brno University of Technology. All rights reserved.

关键词： Sentiment analysis

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Blockchain with drug traceability in medical supply chain with Henry gas solubility optimisation algorithm

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International Journal of Wireless and Mobile Computing 2025年第2期28卷 135-148页

作者： Devi, P. Yamini Sriramya, P. Department of Computer Science and Engineering Saveetha School of Engineering Thandalam Tamil Nadu Chennai India Department of Data Science Saveetha School of Engineering SIMATS Tamil Nadu Chennai India

The drug traceability model is used for ensuring drug quality and its safety for customers in the medical supply chain. The healthcare supply chain is a complex network, which is susceptible to failures and leakage of information because of cyber security attacks. Hence, this research aims to design a blockchain to perform traceability of drugs using Henry Gas Solubility Optimisation (HGSO). The entities include distributors, wholesalers, retailers, blockchain and patients. This model comprises initialisation, registration, key generation, authentication, drug distribution with traceability and key generation phases. Any exchange of medicines needs the approval of both sender and receiver. The drugs are registered in the blockchain, which then generates a secret key that is obtained using HGSO. The verification and authentication of drugs are performed after the authentication process. The implemented model outperformed with the response time, memory, normalised variance, and privacy of 32.678 sec, 84 MB, 0.958 and 0.958. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： authentication blockchain drug traceability key generation medical supply chain

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Personality Identification from Social Media Using Ensemble BERT and RoBERTa

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Informatica (Slovenia) 2023年第4期47卷 537-544页

作者： Tsani, Eggi Farkhan Suhartono, Derwin Computer Science Department Binus Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Social media growth was fast because many people used it to express their feelings, share information, and interact with others. With the growth of social media, many researchers are interested in using social media data to conduct research about personality identification. The identification result can be used as a parameter to screen candidate attitudes in the company's recruitment process. Some approaches were used for research about personality;one of the most popular is the Big Five Personality Model. In this research, an ensemble model between BERT and RoBERTa was introduced for personality prediction from the Twitter and Youtube datasets. The data augmentation method also introduces to handling the imbalance class for each dataset. Pre-trained model BERT and RoBERTa was used as the feature extraction method and modeling process. To predict each trait in the Big Five Personality, the voting ensemble from BERT and RoBERTa achieved an average f1 score 0,730 for Twitter dataset and 0,741 for Youtube dataset. Using the proposed model, we conclude that data augmentation can increase average performance compared to the model without data augmentation process. © 2023 Slovene Society Informatika. All rights reserved.

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Tensor-based forward-backward algorithms in the physics-informed coupled hidden markov model

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1052-1066页

作者： Zhang, Shunli Yang, Laurence T. Zhang, Yue Lu, Zhixing Cui, Zongmin School of Computer and Big Data Science Jiujiang University Jiujiang332005 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Hainan University Haikou570228 China Department of Computer Science St. Francis Xavier University AntigonishNSB2G2W5 Canada

Imposing data-driven with physical laws for user activity prediction could effectively solve various physical problems such as smart care, surveillance, and human-robot. In the growing field of artificial intelligence, the application of activity prediction based on the physical coupled hidden Markov model (CHMM) and tensor theory with physical properties has attracted increasing attentions. However, existing CHMMs usually only consider the time-series characteristic of data, while ignoring physical characteristics of user activity such as periodicity, timing, and correlation. Moreover, they are all matrix-based models, which could not holistically analyze the dependencies among physical states. The aforementioned disadvantages lead to lower prediction accuracy of the CHMM. To remove these disadvantages, three physics-informed tensor-based CHMMs are first constructed by incorporating prior physical knowledge. Then, the corresponding forward-backward algorithms are designed for resolving the evaluation problem of the CHMM. These algorithms could overall model multiple physical features by imposing physics and prior knowledge into the CHMM during training to improve the precision of probabilistic computing. The algorithms reduce the dependence of the model on training data by adding physical features. Finally, the comparative experiments show that our algorithms have better performances than existing prediction methods in precision and efficiency. In addition, further self-comparison experiments verify that our algorithms are effective and practical. Impact Statement-Through the analysis of users' behavior habits, consumption habits, preferences, etc., users? potential needs may be discovered. This discovery could help predict users' activities. If a waiter predicts the user's next activity. He gives her/him unexpected services to meet users' next needs. Obviously, it would significantly improve user satisfaction. In addition, connecting the front and rear products co

关键词： Hidden Markov models

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A Facial Expression Recognition Method Integrating Uncertainty Estimation and Active Learning

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computers, Materials & Continua 2024年第10期81卷 533-548页

作者： Yujian Wang Jianxun Zhang Renhao Sun School of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China Data Space Research Institute of Hefei Comprehensive National Science Center Hefei230088China

The effectiveness of facial expression recognition(FER)algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression ***,labeling large datasets demands significant human,time,and financial *** active learning methods have mitigated the dependency on extensive labeled data,a cold-start problem persists in small to medium-sized expression recognition *** issue arises because the initial labeled data often fails to represent the full spectrum of facial expression *** paper introduces an active learning approach that integrates uncertainty estimation,aiming to improve the precision of facial expression recognition regardless of dataset scale *** method is divided into two primary ***,the model undergoes self-supervised pre-training using contrastive learning and uncertainty estimation to bolster its feature extraction ***,the model is fine-tuned using the prior knowledge obtained from the pre-training phase to significantly improve recognition *** the pretraining phase,the model employs contrastive learning to extract fundamental feature representations from the complete unlabeled *** features are then weighted through a self-attention mechanism with rank ***,data from the low-weighted set is relabeled to further refine the model’s feature extraction *** pre-trained model is then utilized in active learning to select and label information-rich samples more *** results demonstrate that the proposed method significantly outperforms existing approaches,achieving an improvement in recognition accuracy of 5.09%and 3.82%over the best existing active learning methods,Margin,and Least Confidence methods,respectively,and a 1.61%improvement compared to the conventional segmented active learning method.

关键词： Expression recognition active learning self-supervised learning uncertainty estimation

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Virtual Reality Based Shading Pattern Recognition and Interactive Global Maximum Power Point Tracking in Photovoltaic Systems

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Journal of Modern Power Systems and Clean Energy 2024年第6期12卷 1849-1858页

作者： Kangshi Wang Jieming Ma Xiao Lu Jingyi Wang Ka Lok Man Kaizhu Huang Xiaowei Huang School of Advanced Technology Xi'an Jiaotong-Liverpool UniversitySuzhou 215123China Data Science Research Center Duke Kunshan UniversitySuzhou 215316China School of Computer Science the University of LiverpoolLiverpoolU.K

The performance of photovoltaic(PV)systems is in-fluenced by various factors,including atmospheric conditions,geographical locations,and spatial and temporal ***,the optimization of PV systems relies heavily on the global maximum power point tracking(GMPPT)*** this paper,we adopt virtual reality(VR)technology to visual-ize PV entities and simulate their *** integra-tion of VR technology introduces a novel spatial and temporal dimension to the shading pattern recognition(SPR)of PV sys-tems,thereby enhancing their descriptive ***-more,we introduce an interactive GMPPT(IGMPPT)method based on VR *** method leverages interactive search techniques to narrow down search regions,thereby en-hancing the search *** results demonstrate the effectiveness of the proposed IGMPPT in representing the spatial and temporal characteristics of PV systems and improv-ing the efficiency of GMPPT.

关键词： Photovoltaic(PV)system virtual reality(VR) shading pattern recognition(SPR) global maximum power point tracking(GMPPT)

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Age-of-Information-Aware Federated Learning

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Journal of computer science & Technology 2024年第3期39卷 637-653页

作者：徐殷肖明军吴晨吴杰周津锐孙贺 Student Member CCF 1.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China School of Data Science University of Science and Technology of ChinaHefei 230026China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122U.S.A. CCF IEEE

Federated learning(FL)is an emerging privacy-preserving distributed computing paradigm,enabling numerous clients to collaboratively train machine learning models without the necessity of transmitting clients’private datasets to the central *** most existing research where the local datasets of clients are assumed to be unchanged over time throughout the whole FL process,our study addresses such scenarios in this paper where clients’datasets need to be updated periodically,and the server can incentivize clients to employ as fresh as possible datasets for local model *** primary objective is to design a client selection strategy to minimize the loss of the global model for FL loss within a constrained *** this end,we introduce the concept of“Age of Information”(AoI)to quantitatively assess the freshness of local datasets and conduct a theoretical analysis of the convergence bound in our AoI-aware FL *** on the convergence bound,we further formulate our problem as a restless multi-armed bandit(RMAB)***,we relax the RMAB problem and apply the Lagrangian Dual approach to decouple it into multiple ***,we propose a Whittle’s Index Based Client Selection(WICS)algorithm to determine the set of selected *** addition,comprehensive simulations substantiate that the proposed algorithm can effectively reduce training loss and enhance the learning accuracy compared with some state-of-the-art methods.

关键词： federated learning Age of Information restless multi-armed bandit Whittle’s index

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A novel ensemble causal feature selection approach with mutual information and group fusion strategy for multi-label data

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International Journal of Intelligent Computing and Cybernetics 2024年第4期17卷 671-704页

作者： Yifeng Zheng Xianlong Zeng Wenjie Zhang Baoya Wei Weishuo Ren Depeng Qing School of Computer Science Minnan Normal UniversityZhangzhouChina and Key Laboratory of Data Science and Intelligence ApplicationFujian Province UniversityZhangzhouChina

Purpose-As intelligent technology advances,practical applications often involve data with multiple ***,multi-label feature selection methods have attracted much attention to extract valuable ***,current methods tend to lack interpretability when evaluating the relationship between different types of variables without considering the potential causal ***/methodology/approach-To address the above problems,we propose an ensemble causal feature selection method based on mutual information and group fusion strategy(CMIFS)for multi-label ***,the causal relationship between labels and features is analyzed by local causal structure learning,respectively,to obtain a causal feature ***,we eliminate false positive features from the obtained feature set using mutual information to improve the feature subset ***,we employ a group fusion strategy to fuse the obtained feature subsets from multiple data sub-space to enhance the stability of the ***-Experimental comparisons are performed on six datasets to validate that our proposal can enhance the interpretation and robustness of the model compared with other methods in different ***,the statistical analyses further validate the effectiveness of our ***/value-The present study makes a noteworthy contribution to proposing a causal feature selection approach based on mutual information to obtain an approximate optimal feature subset for multilabel ***,our proposal adopts the group fusion strategy to guarantee the robustness of the obtained feature subset.

关键词： Multi-label learning Feature selection Causal relationship Mutual information Group fusion strategy

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Optimization of semi-supervised generative adversarial network models:a survey

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International Journal of Intelligent Computing and Cybernetics 2024年第4期17卷 705-736页

作者： Yongqing Ma Yifeng Zheng Wenjie Zhang Baoya Wei Ziqiong Lin Weiqiang Liu Zhehan Li School of Computer Science Minnan Normal UniversityZhangzhouChina and Key Laboratory of Data Science and Intelligence ApplicationFujian Province UniversityZhangzhouChina

Purpose-With the development of intelligent technology,deep learning has made significant progress and has been widely used in various *** learning is data-driven,and its training process requires a large amount of data to improve model ***,labeled data is expensive and not readily ***/methodology/approach-To address the above problem,researchers have integrated semisupervised and deep learning,using a limited number of labeled data and many unlabeled data to train *** this paper,Generative Adversarial Networks(GANs)are analyzed as an entry ***,we discuss the current research on GANs in image super-resolution applications,including supervised,unsupervised,and semi-supervised learning ***,based on semi-supervised learning,different optimization methods are introduced as an example of image ***,experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be ***-Following the analysis of the selected studies,we summarize the problems that existed during the research process and propose future research ***/value-This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning *** comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.

关键词： Deep learning Generative adversarial network Semi-supervised learning Model optimization Image classification

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Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative data

Capturing Minds, Not Just Words: Enhancing Role-Playing Lang...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Ran, Yiting Wang, Xintao Xu, Rui Yuan, Xinfeng Liang, Jiaqing Yang, Deqing Xiao, Yanghua School of Data Science Fudan University China School of Computer Science Fudan University China

ISBN: (纸本)9798891761681

Role-playing agents (RPA) have been a popular application area for large language models (LLMs), attracting significant interest from both industry and academia. While existing RPAs well portray the characters' knowledge and tones, they face challenges in capturing their minds, especially for small role-playing language models (RPLMs). In this paper, we propose to enhance RPLMs via personality-indicative data. Specifically, we leverage questions from psychological scales and distill advanced RPAs to generate dialogues that grasp the minds of characters. Experimental results validate that RPLMs trained with our dataset exhibit advanced role-playing capabilities for both general and personality-related evaluations. Code and data are available at https://***/alienet1109/RolePersonality. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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