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AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models

AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Larg...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hong, Xin Gong, Yuan Sethu, Vidhyasaharan Dang, Ting School of Computing and Information Systems University of Melbourne Melbourne Australia CSAIL SLS Massachusetts Institute of Technology Boston United States School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia

ISBN: (纸本)9798350368741

Recent advancements in Large Language Models (LLMs) have demonstrated great success in many Natural Language Processing (NLP) tasks. In addition to their cognitive intelligence, exploring their capabilities in emotional intelligence is also crucial, as it enables more natural and empathetic conversational AI. Recent studies have shown LLMs' capability in recognizing emotions, but they often focus on single emotion labels and overlook the complex and ambiguous nature of human emotions. This study is the first to address this gap by exploring the potential of LLMs in recognizing ambiguous emotions, leveraging their strong generalization capabilities and in-context learning. We design zero-shot and few-shot prompting and incorporate past dialogue as context information for ambiguous emotion recognition. Experiments conducted using three datasets indicate significant potential for LLMs in recognizing ambiguous emotions, and highlight the substantial benefits of including context information. Furthermore, our findings indicate that LLMs demonstrate a high degree of effectiveness in recognizing less ambiguous emotions and exhibit potential for identifying more ambiguous emotions, paralleling human perceptual capabilities. © 2025 IEEE.

关键词： ambiguous emotion emotion recognition large language models multimodal prompt design

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Research on crime risk prediction model based on machine learning 24

Research on crime risk prediction model based on machine lea...

引用

13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Hou, Xuehui Rexiti, Kudelaiti Aizezi, Yasen School of Resources and Environment University of Electronic Science and Technology of China Chengdu Sichuan China Faculty of Information Security and Engineering Xinjiang Police College Urumqi Xinjiang China

ISBN: (纸本)9798400717482

Crime prediction is an important method for public security departments to conduct crime early warning and investigation. According to the multidimensional characteristics of criminals, machine learning classification algorithm is used to establish a prediction model of whether a criminal commits a crime again, so as to quantitatively evaluate the possibility of the criminal commits a crime again, and provide a feasible scheme reference for preventing and reducing the recurrence of crime. Analysis of urban crime data in Boston, using Naive Bayes, Random Forest and XGboost algorithms to build a crime type prediction model, we can get the urban crime type tendency, help the public security department to predict the future crime, take effective measures to reduce the urban crime rate. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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AI Integrated Healthcare System for Disease Predictions and Virtual Consultations 3

AI Integrated Healthcare System for Disease Predictions and ...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Al Jabri, Jaber Saleh Salim University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India

ISBN: (纸本)9798331509675

AI-based Disease Prediction and Online Consultation System aims to redesign the way people experience healthcare. The deep growth of machine learning opened doors for new opportunities, such as this system that carefully inputs and validates patients' symptoms, and subsequently predicts possible diseases. In addition, it fosters remote consultation in a user-friendly manner over the internet and erases the barriers of distance and mobility. It is expected that this combination of data-driven forecasting and logic will enhance the treatment accuracy in terms of increased availability of healthcare services. AI-based Disease Prediction and Online Consultation System is a practical showcase on how to transform equity in healthcare. The increasing change in technology can give us possibilities for the utmost usage of this system. In this regard, the complex predictive analytics associated with the system may in effect change how HEIs (health education institutions) practice healthcare management. Patients will be offered a practice which supports the idea of delivering the right information on time and stress the universal right of access to healthcare. Its transformational power has far more prospects than it seems at first. At the core it sets the landscape and, indeed, the stage for the most equal, efficient and inclusive healthcare ecosystem that could ever exist. © 2025 IEEE.

关键词： Electronic health record

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Enhancing Web Security: A Phishing Detection System Integrated with Password Managers 6

Enhancing Web Security: A Phishing Detection System Integrat...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Subbulakshmi, T. Meghana, G. Sunkara, S. Ravi Sailendra Shanmukha Sai, V. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331522667

Phishing attacks have become a widespread and intricate threat, targeting individuals and organizations through deceptive URLs to steal sensitive information such as login credentials and financial data. These attacks exploit human vulnerabilities and frequently employ advanced techniques like URL obfuscation and social engineering to evade traditional security measures. Despite ongoing advancements in cybersecurity, phishing remains a persistent issue due to its adaptability and the continual evolution of attack strategies. This paper introduces a comprehensive phishing detection system that implements both rule-based analysis with machine learning models to address these challenges effectively. The system utilizes YARA rules to detect known phishing patterns and integrates real-time threat intelligence feeds for up-to-date phishing data. Additionally, it employs machine learning algorithms to enhance detection accuracy and adapt to emerging threats. The system is further strengthened by its integration with a password manager, providing real-time URL validation and alerts to users during their online activities. This multi-faceted approach aims to improve the accuracy of phishing detection and offer robust protection against the increasingly advanced methods employed by attackers. © 2025 IEEE.

关键词： Phishing

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The Deepfake Dilemma: Enhancing Deepfake Detection with Vision Transformers 3

The Deepfake Dilemma: Enhancing Deepfake Detection with Visi...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Sumathi, D. Singh, Ashu Sinha, Arpita Aditya, D. Mohammed Riyaan, K.F. Alliance University Department of Computer Science and Engineering Bangalore India Alliance University Department of Information Technology Bangalore India

ISBN: (纸本)9798331515911

The emergence of deepfake videos at an alarming pace has compromised the integrity of digital multimedia and mandates progressive research into detection strategies. A new forensic method for subjecting face tampering detection in videos using the FaceForensics++ dataset is introduced in this work. A Convolutional Neural Network (CNN) based architecture is enhanced with Vision Transformer encoders for identifying facial manipulations introduced at microscopic precision. The hybrid model integrates CNNs and ViTs, allowing for the capturing of both local and global features that help in detecting subtle manipulations. ViTs have a built-in self-attention mechanism, which allows the model to concentrate on essential facial characteristics, thereby enhancing precision even if the manipulations are confirmed in less than optimal situations. Additionally, the pre-trained Gemini 1.5 Pro model is fine-tuned for optimal performance. The classification of faces into authentic or fake is accurately done using the key points by 90%accuracy as shown through experimentation. Moreover, the Haar cascade method is used for human face detection technique as part of integrated design which augments its complete application in real-world functionalities. © 2025 IEEE.

关键词： Convolutional neural networks

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Impact of news on stock prices of mid-cap companies in India 3

Impact of news on stock prices of mid-cap companies in India

引用

3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Gupta, Abhijeet Kumar Hariharan, S. Aggarwal, Mansha Doiphode, Shivani Pulukkul, Yuktha Mohan Jadhav, Anil Symbiosis Centre for Information Technology Pune India

ISBN: (纸本)9798331515911

The objective of this study is to analyze how sentiment in news articles effect mid-cap stock prices in India. In this regard, it is important for investors to learn how such headlines correlate with stock movements. News headlines were used and subjected to sentiment study in order to measure the level of polarity and subjectivity. These features of sentiment were then merged with the opening, closing and adjusted prices. Multiple machine learning models such as logistic regression, decision trees, random trees and support vector machine SVM were used for predicting the target stock price movement. The stock market sentiment and factors related to the stock being analyzed were integrated in such a way as to assess its predictive capability about the stock price movement. The summary from the analysis suggests that stock price measures such as the opening and closing prices of the stock are usually catalytic in making predictions however the stock price moving average has also influenced in predicting future price movements in relation to price direction. This paper suggests that news sentiment should be included in stock price predictions of mid-cap companies, which will benefit the investors as addition in analyzing the market price. © 2025 IEEE.

关键词： Decision trees

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PP-3FAT Scheme for 5G-IoHT Telemedicine Systems 2nd

PP-3FAT Scheme for 5G-IoHT Telemedicine Systems

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2nd international conference on Security and information Technologies with AI, Internet computing and Big-data Applications, SITAIBA 2023

作者： Lin, Tzu-Wei i.School Feng Chia University Taichung City Taiwan Information Security Office Office of Information Technology Feng Chia University Taichung City Taiwan

ISBN: (纸本)9789819777853

5G networks have many attractive features which can improve capacity and speed to provide reliable and speedy connectivity to thousands of devices of Internet of Things. Telemedicine relies on mobile healthcare system which includes alarm system, clinical data storage, and wireless communication where transmitted data might be damaged or leaked without proper security measures. This research proposed a privacy-preserved three-factor authentication with time-bound (PP-3FAT) scheme for 5G-IoHT telemedicine systems to achieve security requirements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G mobile communication systems

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Use of Search Tools in Software Development: A Study of Microservice-Based Team Projects 17

Use of Search Tools in Software Development: A Study of Micr...

引用

17th international conference on Computer Supported Education, CSEDU 2025

作者： Lau, Yi Meng Koh, Christian Michael Jiang, Lingxiao School of Computing & Information Systems Singapore Management University Singapore Singapore

ISBN: (纸本)9789897587467

Universities are increasingly integrating real-world projects into software engineering curricula to prepare students for careers involving complex concepts like Microservices Architecture (MSA). Students frequently struggle with such concepts within limited class time and turn to various search tools and online resources for additional help. Search tools are also widely used in the software development industry. While search engines, like Google and Yahoo!, can provide quick solutions, they pose the risk of information overload. Large Language Models (LLMs) such as ChatGPT, offer the advantage of delivering more precise answers. Studies have shown that LLMs can comprehend codes, assist in system architectural design, and suggest solutions, potentially enhancing the learning experience for students. This study aims to determine how students make use of search tools for their team projects in a software development course that teaches MSA. It will also explore if search tools can enhance learning in team projects by facilitating collaborative, peer, and self-directed learning, and propose methods to address any limitations. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Software design

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Application of Personal information Privacy Protection Based on Artificial Intelligence Algorithms 14th

Application of Personal Information Privacy Protection Based...

引用

14th international conference on Frontier computing, FC 2024

作者： Mu, Xilin Jilin Police College Jilin Changchun130117 China

ISBN: (纸本)9789819627936

Personal information privacy protection is an important issue in the digital age. Artificial intelligence technology has been rapidly developed and widely used, and the data analysis and processing capabilities of artificial intelligence have been continuously improved. Despite this, privacy security risks remain. Abuses of personal information can cause security problems, such as abuse of user identity and privacy information leakage. In this article, we describe a personal privacy information protection application based on artificial intelligence algorithms, including a method and research conclusions. The application recognizes the personal information sensitivity during the process of entering user data and desensitizes the data to protect user personal privacy information. The experiments showed that the application had good performance in personal information sensitivity recognition, privacy protection effectiveness verification, and prediction accuracy. Additionally, the application is customized and expandable to support different data types and use scenarios. The experimental results show that personal privacy information protection based on an artificial intelligence algorithm can provide an effective scheme and guarantee for the protection of a user's personal privacy information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Predicting methane adsorption ability of MOFs based on the crystal structural information by convolutional neural network 5

Predicting methane adsorption ability of MOFs based on the c...

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5th international conference on Material Science and technology, ICMST 2025

作者： Lu, Gang Jiang, Xiuyi Yang, Qingyuan Zhao, Zhengyang Gu, Weiwei Li, Ruiqi College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China State Key Laboratory of Organic-Inorganic Composites Beijing University of Chemical Technology Beijing100029 China

ISBN: (纸本)9781510691742

High-throughput computational screening (HTCS) methods have been widely used in addressing the challenges of screening metal-organic framework (MOF) materials with high performance from large datasets. Accurately predicting the performance of MOFs with hypothetical structures, such as adsorption capacity for methane, can be a great help to HTCS. Traditional molecular dynamics simulation methods are computationally cumbersome and costly. Feature engineering-based machine learning methods have a high reliance on expert experience to extract effective features, some of which can be time-consuming to calculate. Although some classical machine learning models can already achieve good performance, the heavy burden for features computation prior to model training would eventually limit their practical applications, flexibility and generality. In this paper, an approach is proposed for predicting the methane adsorption ability of MOFs directly from their crystal structural information recorded in Crystallographic information File (CIF)-formatted files, without the need for molecular dynamics simulation or calculating any physical or chemical properties. By expressing the structural information in the crystal cells of MOFs as matrices, a deep convolutional neural network model based on residual structure is applied to train an end-to-end model to predict their methane adsorption ability. The experimental results show that the model accomplishes the prediction task with high accuracy, giving the mean square correlation coefficient (R2) as 0.925 and the mean absolute error (MAE) as 22.28 cm3g-1, which demonstrates a good performance over baselines. © 2025 SPIE. All rights reserved.

关键词： Convolutional neural networks

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