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Emotion recognition in Virtual Reality learning Environments: A Multimodal machine learning Approach 17th

Emotion Recognition in Virtual Reality Learning Environments...

引用

17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and Applications, CIAPP 2024, held in conjunction with Mexican international conference on Artificial Intelligence, MICAI 2024

作者： Romero-Ramos, Luis González-Serna, Gabriel López-Sánchez, Máximo González-Franco, Nimrod Valenzuela-Robles, Blanca Computer Science Department TecNM/CENIDET Morelos Cuernavaca62490 Mexico

ISBN: (纸本)9783031838811

Positive emotions significantly impact learning outcomes as they foster problem-solving and decision-making, while negative emotions can hinder information processing. Traditional methods for detecting emotions in immersive virtual reality (VR) applications, such as electroencephalography (EEG) and electrocardiography (ECG), are often invasive, expensive, and susceptible to noise. this paper introduces a new, non-invasive approach for classifying emotional valence in Virtual Immersive learning Environments (VILE) based on analyzing head and hand movements using machine learning algorithms. A modified Self-Assessment Mannequin collected multimodal behavioral data from 30 students. the results show promising accuracy (71%) in distinguishing between positive and negative emotional states. While this method currently focuses on valence classification rather than specific emotion recognition, it paves the way for developing more accessible and less intrusive solutions for emotional detection in VR learning contexts. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Virtual environments

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Autoencoder for Synthetic to Real Generalization: From Simple to More Complex Scenes 26

Autoencoder for Synthetic to Real Generalization: From Simpl...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Da Cruz, Steve Dias Taetz, Bertram Stifter, thomas Stricker, Didier IEE SA Bissen Luxembourg Univ Kaiserslautern Kaiserslautern Germany German Res Ctr Artificial Intelligence Berlin Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

learning on synthetic data and transferring the resulting properties to their real counterparts is an important challenge for reducing costs and increasing safety in machine learning. In this work, we focus on autoencoder architectures and aim at learning latent space representations that are invariant to inductive biases caused by the domain shift between simulated and real images showing the same scenario. We train on synthetic images only, present approaches to increase generalizability and improve the preservation of the semantics to real datasets of increasing visual complexity. We show that pre-trained feature extractors (e.g. VGG) can be sufficient for generalization on images of lower complexity, but additional improvements are required for visually more complex scenes. To this end, we demonstrate a new sampling technique, which matches semantically important parts of the image, while randomizing the other parts, leads to salient feature extraction and a neglection of unimportant parts. this helps the generalization to real data and we further show that our approach outperforms fine-tuned classification models.

关键词： Visualization Semantics machine learning Feature extraction Complexity theory Safety pattern recognition

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A Privacy Protection Scheme for Power data Based on Blockchain and Privacy-preserving Computation 6

A Privacy Protection Scheme for Power Data Based on Blockcha...

引用

6th international conference on Next Generation data-Driven Networks, NGDN 2024

作者： Ai, Xuhua Yin, Yuan Meng, Qi Huang, Yiting Tu, Denghao Dong, Yun Southern Power Grid Guangxi Power Grid Co. Ltd Nanning China Topsec Network Security Technology Co. Ltd Beijing China

ISBN: (纸本)9798350388374

Although the widespread deployment of smart meters provides advantages such as accurate billing and monitoring data, it is necessary to make electricity data private because the data generated has the potential to reflect a large amount of privacy information that reflects the personal privacy and behavior of consumers. this paper proposes a power data privacy protection scheme based on blockchain and privacy-preserving computing. Firstly, the power data collected by the smart meter is encrypted through Paillier homomorphic encryption to protect the privacy of users. then, the preprocessed datasets required for subsequent tasks (such as machine learning, data mining, and data monitoring) were obtained through ciphertext privacy intersection. In order to ensure that the results of the privacy request cannot be tampered with and can be flexibly accessed, the results of the privacy request are uploaded to the power chain for storage. theoretical analysis and simulation results show the superiority of our scheme in privacy protection and ciphertext privacy set intersection. © 2024 IEEE.

关键词： Differential privacy

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Detection of Children Abuse by Voice and Audio Classification by Short-Time Fourier Transform machine learning Implemented on Nvidia Edge GPU Device 6

Detection of Children Abuse by Voice and Audio Classificatio...

引用

6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Yan, Jiuqi Chen, Yingxian Fok, W.W.T. The University of Hong Kong Hong Kong

ISBN: (纸本)9798350325485

Children's safety in the home has become an increasing social concern, and the purpose of this experiment is to use machine learning to detect child abuse scenarios in order to improve children's safety. this experiment uses machine learning to classify and recognize children's voices and to predict whether the current sound the child is making is crying, screaming or laughing. If a crying or screaming sound is detected from a child, an immediate alert is sent to the relevant personnel so that they can sense what the child may be experiencing and respond in a timely manner in the blind spot of the surveillance. Together with the hybrid use of video image classification, the accuracy of child abuse detection can be greatly improved. this greatly reduces the likelihood of children being violently abused in childcare centers and allows staff to stop impending or recent child abuse incidents in a timely manner. the dataset collected for this experiment was derived exclusively from sounds recorded on-site at the children's home, including cries, laughter, screams, and background noise. In this experiment, the short-Time Fourier transform is used to transform the sound signals into spectrograms, and the image signals are imported into a convolutional neural network for training and prediction, and the final trained model has an accuracy of about 92% for sound detection. © 2023 IEEE.

关键词： Security systems

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Lightweight Conditional Model Extrapolation for Streaming data under Class-Prior Shift 26

Lightweight Conditional Model Extrapolation for Streaming Da...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Tomaszewska, Paulina Lampert, Christoph H. Warsaw Univ Technol Fac Math & Informat Sci Warsaw Poland Inst Sci & Technol Austria ISTA Machine Learning & Comp Vis Grp Klosterneuburg Austria

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

We introduce LIMES, a new method for learning with non-stationary streaming data, inspired by the recent success of meta-learning. the main idea is not to attempt to learn a single classifier that would have to work well across all occurring data distributions, nor many separate classifiers, but to exploit a hybrid strategy: we learn a single set of model parameters from which a specific classifier for any specific data distribution is derived via classifier adaptation. Assuming a multi-class classification setting with class-prior shift, the adaptation step can be performed analytically with only the classifier's bias terms being affected. Another contribution of our work is an extrapolation step that predicts suitable adaptation parameters for future time steps based on the previous data. In combination, we obtain a lightweight procedure for learning from streaming data with varying class distribution that adds no trainable parameters and almost no memory or computational overhead compared to training a single model. Experiments on a set of exemplary tasks using Twitter data show that LIMES achieves higher accuracy than alternative approaches, especially with respect to the relevant real-world metric of lowest within-day accuracy.

关键词： continual learning non-stationary data incremental training meta-learning class-prior shift

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Face Classification Using Stacking Ensemble learning 6

Face Classification Using Stacking Ensemble Learning

引用

6th international conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Arora, Aditi Malik, Indu Gupta, Aanchal Jindal, Bhavya Gupta, Gaurisha Department of CSE ABESEC UP Ghaziabad India

ISBN: (纸本)9798350374629

Developing a high-level automated security system for identification/authentication has long been an active research subject in practically all fields. Traditional security systems utilize a key or a password to secure a process/product, whereas biometric security systems use a person’s physical or behavioral attributes. Face patterns play a significant part in several potential recognition/authentication applications because of their uniqueness, universality, dependability, and stability. this study provides a face identification system that starts with eye detection and ends with face image recognition. AN ensemble stacking approach for face classification is presented in this study that combines various machine learning (ML) algorithms with and without Principal Component Analysis (PCA). the proposed system is trained and tested on a well-known publically available Olivetti Face dataset. the dataset is split into 75% train and 25% test dataset which is also transformed using PCA. the results show that the accuracy of each base model having PCA data is on par or better than the one without PCA. the paper also proves that the performance of the new stacking ensemble system results in an average or better performance than the other base models. For Olivetti face dataset, the proposed Stacked Ensemble learning model’s accuracy with PCA is 94%, which is on average than the accuracy of the other base models, including Neural Network (NN) (100%), Random Forest (92%), Support Vector machine (SVM)(96%), Gaussian Naives Bayes(81%), K-Nearest Neighbour (KNN) (89%), and Logistic Regression (LR) (97%) respectively. ©2024 IEEE.

关键词： Principal component analysis

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Doubly Regularized Least Squares Twin Extreme learning machine for pattern Classification 6

Doubly Regularized Least Squares Twin Extreme Learning Machi...

引用

6th international conference on Artificial Intelligence and pattern recognition, AIPR 2023

作者： Wu, Qing Yu, Ming Chen, Lu School of Automation Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9798400707674

Least squares twin extreme learning machine (LSTELM) possesses better generalization ability than the standard extreme learning machine (ELM) by finding two nonparallel hyperplanes in ELM random feature space. However, LSTELM is not sparse in nature and does not take into account the intrinsic geometric information of the data. To overcome the above, this paper proposes one classification method based on LSTELM, namely doubly regularized least squares twin extreme learning machine (DRLSTELM). DRLSTELM uses L2,1-norm regularization and manifold regularization methods to improve the classification performance. Furthermore, in its nonlinear case, a hybrid kernel function is introduced to improve the generalization and learning ability of the model. To evaluate the classification performance of the proposed algorithm, experiments were conducted on the NDC datasets and the benchmark datasets respectively. the results show that the classification effect of DRLSTELM is significantly better than other state-of-the-art methods. © 2023 ACM.

关键词： Classification (of information)

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A Self-training Approach for Few-Shot Named Entity recognition 1

引用

6th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint international conference on Web and Big data (APWeb-WAIM)

作者： Qian, Yudong Zheng, Weiguo Fudan Univ Sch Data Sci Shanghai Peoples R China

ISBN: (数字)9783031251986

ISBN: (纸本)9783031251979;9783031251986

Named entity recognition (NER) is a basic task in natural language processing and can be used in a wide range of downstream tasks, such as question answering, text summarization, and machine translation. In recent years, deep-learning based methods achieve great performance in the NER task. It often demands a huge amount of data to train models. However, it is very expensive to collect sufficient training data in many real-world applications. thus, it is important to develop NER systems for few-shot settings. In this paper, we propose a self-training approach for NER that employs the framework of the machine reading comprehension model when lacking training samples. Experimental results on NER benchmarks demonstrate that the proposed method in this paper outperforms the state-of-the-art methods.

关键词： Named entity recognition Few-shot learning Semi supervised learning Self-training

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French Natural Language Processing Based on machine learning Algorithms 6

French Natural Language Processing Based on Machine Learning...

引用

6th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2024

作者： Hui, Fenfen Xi’an FANYI University Xi’an710105 China

ISBN: (纸本)9781837242672

With the development of intelligent technology, optimizing the processing of natural language by computers has become one of the current research hotspots. In order to enable computers to effectively recognize natural languages such as French text, a machine learning algorithm with improved K-means clustering has been developed. the study designed a word extraction box for text content using the frequent pattern sequence tree method, which enables accurate word segmentation of text content by computers. At the same time, text based natural language becomes recognizable data content after text preprocessing. By improving the K-means clustering algorithm, the computer's ability to recognize French natural language text content can be enhanced, thus developing natural language processing technology based on French content. the research results show that the number of feature words for text clustering needs to be maintained between 20-35 to ensure the extraction of the text extraction algorithm. Meanwhile, the research method achieved an extraction accuracy range of 88.69% -92.73% for French text, which is 16.26% -31.59% higher than the extraction accuracy of other algorithms. the research algorithm shows significantly better text extraction performance for French natural language than other algorithms. therefore, researching algorithms can provide a French language text processing technique with stronger text mining performance for computers. © the Institution of Engineering & Technology 2024.

关键词： Adversarial machine learning

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Detection of Phishing Websites from URLs Using Hybrid Ensemble-Based machine learning Technique 6th

Detection of Phishing Websites from URLs Using Hybrid Ensemb...

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6th international conference on Soft Computing and data mining (SCDM)

作者： Agagu, Modupe Ogunbiyi, Ibrahin Abayomi Lasisi, Ayodele Omorogiuwa, Osaremwinda Olusegun Agagu Univ Sci & Technol Dept Comp Sci Okitipupa Ondo State Nigeria King Khalid Univ Dept Comp Sci Coll Comp Sci Abha Saudi Arabia Igbined Univ Dept Comp Sci & Informat Technol Okada Edo State Nigeria

ISBN: (纸本)9783031669644;9783031669651

With the increasing advancement in technology, people interact with the internet daily, whether it is to access their bank accounts, social media platforms, etc. Malicious actors often create clones of legitimate websites and then lure users to access these cloned sites so that they can capture user-sensitive information. this research proposes a hybrid ensemble-based machine-learning technique for predicting phishing websites from URLs to address this issue. the research employs three base classifiers: CatBoost, XGBoost, and LightGBM. the voting technique was used to combine the base classifiers to build an ensemble model. Performance metrics such as accuracy, precision, recall, and F-1 score were used to measure the performance of the model. the experimental results of the proposed model demonstrate an outstanding performance of 93.0%, 92.2%, 93.4% and 92.8% for accuracy, recall, precision and fl-score respectively on the dataset used. When compared with some existing models, the model gave an outstanding performance.

关键词： Phishing detection machine learning Ensemble Model CatBoost XGBoost LightGBM

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