AUTOMATIC CONTROL AND COMPUTER SCIENCES最新文献_第4页

Deep Learning Approaches for Real-Time Affect Recognition 实时情感识别的深度学习方法

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2026-06-22 DOI: 10.3103/S0146411626700070

R. Selvi, C. Vijayakumaran

{"title":"Deep Learning Approaches for Real-Time Affect Recognition","authors":"R. Selvi, C. Vijayakumaran","doi":"10.3103/S0146411626700070","DOIUrl":"10.3103/S0146411626700070","url":null,"abstract":"Emotions are extensive psycho-physiological processes associated with various physical changes within and outside the body. In addition to being vital for human–machine interfaces, they are crucial for human–human interaction. However, when people unintentionally or purposefully hide their feelings, these indicators may be unhelpful since they cannot convey underlying emotions. Since emotions are typically uncontrollable, research has shown that examining brain activity and physiological signals can result in more accurate emotion detection. The effectiveness and challenges in identifying emotions from various media, utilizing brain activity and other physiological signals, are explored and discussed in this research. The purpose of the suggested multimodal emotion recognition experiment using the ASCERTAIN dataset is to evaluate our method’s effectiveness. The suggested model detects emotions by using the spectrogram of the EEG, ECG, and GSR signals as input. Our experimental findings show that our proposed CNNBiL. (1-dConvolutionalNeuralNetwork + Bidirectional-LongShortTermMemory) The approach has a recognition accuracy of 82.8% in multiclass emotion recognition tasks.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"60 1","pages":"60 - 72"},"PeriodicalIF":0.5,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148292375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Artificial Intelligence Algorithm Optimized the Spanish Pronunciation of Label Terms in New Energy Equipment 人工智能算法优化新能源设备标签词的西班牙语发音

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2026-06-22 DOI: 10.3103/S0146411626700082

Xingying Fan

引用次数: 0

Research on the Communication Mode of Chinese Traditional Costume Culture Based on Digital Technology 基于数字技术的中国传统服饰文化传播模式研究

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2026-06-22 DOI: 10.3103/S0146411626700094

Fei Guo

引用次数: 0

Face Recognition under the Influence of Occlusion Based on OpenCV and MTCNN 基于OpenCV和MTCNN的遮挡影响下的人脸识别

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2026-06-22 DOI: 10.3103/S0146411626700100

Qin Dong

{"title":"Face Recognition under the Influence of Occlusion Based on OpenCV and MTCNN","authors":"Qin Dong","doi":"10.3103/S0146411626700100","DOIUrl":"10.3103/S0146411626700100","url":null,"abstract":"In facial recognition, occlusion has an impact on the accuracy of facial detection. OpenCV and multi-task convolutional neural network are used to analyze the impact of occlusion on facial recognition. Three networks of the multi-task convolutional neural network, namely P-Net, R-Net, and O-Net are cascaded for training. Face detection network is trained using the Wider Face dataset, and key point detection network is trained using the CNN_ FacePoint dataset. The results showed that each network achieved recognition accuracy of 91.63, 95.68, and 99.26%, respectively. Compared with classical algorithms, the multi-task convolutional neural network performs outstandingly under occlusion, with better recognition accuracy and recall than traditional convolutional neural networks and Viola-Jones algorithms. The structure of MTCNN balances classification error, bounding box regression error, and key point localization error across various networks, improving the face recognition performance in the occlusions. The research has promoted the development of facial recognition technology in key applications such as public safety monitoring and identity verification, especially in situations where masks are often worn in public places, which has greater potential for practical and future applications. The research provides innovative and practical solutions for facial recognition technology.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"60 1","pages":"93 - 104"},"PeriodicalIF":0.5,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148292370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Protecting Smart City Blockchain Systems from Selfish Mining Attacks 保护智慧城市区块链系统免受自私的挖矿攻击

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2026-02-24 DOI: 10.3103/S0146411625701044

M. O. Kalinin, A. S. Konoplev

引用次数: 0

Application of Large Language Models in the Problem of Event Forecasting 大型语言模型在事件预测问题中的应用

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2026-02-24 DOI: 10.3103/S0146411625701111

A. D. Dakhnovich, V. M. Bogina, A. A. Makeeva

{"title":"Application of Large Language Models in the Problem of Event Forecasting","authors":"A. D. Dakhnovich, V. M. Bogina, A. A. Makeeva","doi":"10.3103/S0146411625701111","DOIUrl":"10.3103/S0146411625701111","url":null,"abstract":"This paper presents a study of the application of large language models (LLMs) to predict events based on LLM agents—autonomous systems that use LLMs for reasoning, decision making, and interaction with the environment. Various architectures of LLM agents are analyzed: cooperative systems (ChatDev, MetaGPT), multiagent debates (MAD, ChatEval), agents for web tasks (WebAgent, WebVoyager), and simulation agents (Generative Agents, EconAgent). Particular attention is paid to the features of predictive modeling based on LLMs, where classical approaches (regression, time series) are replaced by agent-based modeling and predictive engineering. This article presents the results of an experiment on predicting the outcome of a selected conflict using an LLM agent (Mistral, DeepSeek) and the retrieval-augmented generation (RAG) approach based on data from analytical agencies, opinion leaders, and news sources. The convergence of forecast estimates of polarized sources is revealed and requirements for forecasting systems are formulated: weighting sources according to expert significance, filtering neutral data, and sample balancing. Requirements are put forward for the selection of data assessed by simulation LLM agents.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"59 8","pages":"1536 - 1543"},"PeriodicalIF":0.5,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147341471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Combination of Methods for Selective Teacher Intervention in the Student’s Learning Process and Low-Rank Adaptation in the Knowledge Distillation Models 教师选择性干预学生学习过程的方法与知识蒸馏模型中的低阶适应的结合

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2026-02-24 DOI: 10.3103/S0146411625700919

I. A. Sikarev, T. M. Tatarnikova, V. M. Abramov

引用次数: 0

Study of Adversarial Attacks on Classical Machine Learning Models in the Context of Network Threat Detection 网络威胁检测中经典机器学习模型的对抗性攻击研究

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2026-02-24 DOI: 10.3103/S0146411625700981

P. E. Yugai

引用次数: 0

Fuzz Testing of Closed-Source Software Using Large Language Models 使用大型语言模型的闭源软件模糊测试