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Data Augmentation via Face Morphing for Recognizing Intensities of Facial Emotions

IEEE Transactions on Affective Computing 2023年第2期14卷 1228-1235页

作者： Huang, Tsung-Ren Hsu, Shin-Min Fu, Li-Chen National Taiwan University Department of Psychology Center for Artificial Intelligence & Advanced Robotics Taipei106319 Taiwan National Taiwan University Center for Artificial Intelligence & Advanced Robotics Department of Electrical Engineering Department of Computer Science and Information Engineering Taipei106319 Taiwan

Being able to recognize emotional intensity is a desirable feature for a facial emotional recognition (FER) system. However, the development of such a feature is hindered by the paucity of intensity-labeled data for model training. To ameliorate the situation, the present study proposes using face morphing as a novel way of data augmentation to synthesize faces that express different degrees of a designated emotion. Such an approach has been successfully validated on humans and machines. Specifically, humans indeed perceived different levels of intensified emotions in these parametrically synthesized faces, and FER systems based on neural networks indeed showed improved sensitivities to intensities of different emotions when additionally trained on the synthesized faces. Overall, the proposed data augmentation method is not only simple and effective but also useful for building FER systems that recognize facial expressions of mixed emotions. © 2010-2012 IEEE.

关键词： Face recognition

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Pseudo Random Binary Sequence using Lattice-based Hard Problem for Quantum Key Distribution 56

Pseudo Random Binary Sequence using Lattice-based Hard Probl...

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56th International Carnahan Conference on Security Technology, ICCST 2023

作者： Pandit, Anupama Arjun Mishra, Arun SoCE&MS Defence Institute of Advanced Technology Department of Computer Science & Engineering Pune India

ISBN: (纸本)9798350315875

Recently, there has been a growing interest among researchers in Pseudo-Random Binary Sequences (PRBS) for Quantum Key Distribution (QKD). QKD is a cryptographic protocol that distributes secret keys between senders and receivers, relying on random numbers to determine the basis for evaluating transmitted bits. Quantum Random Number Generators (QRNG) utilizing natural resources create random bit sequences for QKD. However, QRNGs are susceptible to natural noise, which compromises their security. Additionally, post-processing of QRNG-generated sequences is necessary to reduce noise, but this process slows down the random number generation speed in QKD. Non-deterministic Pseudo-Random Sequence Generators (ND-PRSG) may overcome the QRNG's constraints, post-processing, and speed. Lattice-based NDPRSG provides security and efficiency. The proposed work focuses on generating a uniform PRBS with non-deterministic entropy using Learning with Errors (LWE) and LFSR. NIST Statistical Tests are performed on generated PRBS for randomness analysis, and the results are reported. The proposed PRBS generation can be used for QKD, operating up to 20.17 Mbit/s. © 2023 IEEE.

关键词： Statistical tests

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic 3

Guardians of Truth: Exploring Techniques for Veracity Identi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Suthanthiradevi, P. Revathy, G. Sathish Kumar, P. Karthikeyan, B. Muthu, Lakshmi.V SRM institute of Science and Technology Data Science and Business Systems School of Computing India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering India Rajalakshmi institute of technology Computer and Communication Engineering India SASTRA Deemed University School of Computing India St. Joseph's Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350372120

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score. © 2024 IEEE.

关键词： Social networking (online)

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Retinal Scan Denoising Using Generative Adversarial Networks: A Deep Convolutional Approach 5

Retinal Scan Denoising Using Generative Adversarial Networks...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Gowthami, G. Mano Shankari, J. Babu, Tina St. Francis de Sales College Department of Computer Applications Bengaluru India Alliance School of Advanced Computing Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (纸本)9798331518097

Image denoising is needed if there is a need to remove noise while preserving details or structures in images in computing vision and image processing. Recent advancements in generative adversarial networks (GANs) and convolutional neural networks (CNNs) have only improved the application of denoising techniques in varying ways. These methods give clearer and more accurate images since these approaches are able to separate noise from image substance. One of the main use of application of the image denoising is that they enhance the images in media, movies and also in medical field where high quality of images are required for diagnosing the diseases. This paper examines the significance of image denoising and introduces a novel GAN-based retinal scan denoising technique that outperforms both CNN-based denoising and conventional Gaussian filtering, achieving higher performance (PSNR: 27). It outperforms other approaches with SCD of 10 dB, SSIM of 0.866 and MSE of 18.99. In this paper, we propose a GAN-based solution for denoising images and leveraging deep learning-based techniques for evaluating the quality of retinal scans and we demonstrate the potential of our approach for enhancing the quality of the images. © 2024 IEEE.

关键词： Generative adversarial networks

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A Robust Deep Feature Fusion Model for Skin Cancer Classification 3rd

A Robust Deep Feature Fusion Model for Skin Cancer Classific...

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3rd International Conference on advanced Computing and Applications, ICACA 2024

作者： Mandal, Diptarka Ray, Amartya Sarkar, Sujan Ojjela, Odelu Sarkar, Ram School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Maharashtra Pune411025 India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata700032 India

ISBN: (纸本)9789819747986

Skin cancer ranks among the deadliest forms of cancer worldwide, leading to numerous fatalities annually. Hence, the development of efficient computer-aided diagnosis (CAD) systems has become a pressing need to help medical professionals classify various types of skin cancer. This paper introduces a novel method which combines the outcomes of two deep learning models, leveraging the feature fusion strategy. Our approach utilizes Xception and Google’s Big Transfer (BiT-M) model as base learners, complemented by a squeeze and excitation attention module to enhance the fused feature maps. We also establish a combined loss function to optimize our model. We have evaluated the model on the challenging ISIC 2017 dataset and obtained an impressive accuracy of 0.795, surpassing the performance of many recently published methods within the literature. The source code of the proposed model is available at https://***/Cmatermedicalimageanalysis/ISIC2017. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lung cancer

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Deep Multi-View Clustering via View-Specific Representation and Global Graph 7

Deep Multi-View Clustering via View-Specific Representation ...

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7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023

作者： Pu, Jingyu Cui, Chenhang Ren, Yazhou Wen, Zichen Wu, Tianyi Pu, Xiaorong He, Lifang University of Electronic Science And Technology of China School of Computer Science And Engineering China University of Electronic Science And Technology of China School of Computer Science And Engineering Shenzhen Institute For Advanced Study China Lehigh University Department of Computer Science And Engineering Bethlehem United States

ISBN: (纸本)9798350359145

Multi-view clustering can improve clustering performance by leveraging the complementary information from multiview data and has garnered growing interest. Despite significant advancements has been established in this field, most existing methods primarily concentrate on learning the latent representation, neglecting the latent graph that typically reveals relationships between different instances. To address this limitation, this paper presents a new framework of Deep Multi-View Clustering via View-specific Representation and Global Graph (DVGMVC) to address the aforementioned issue. Our method leverages the latent graph of each view to enhance representation quality and incorporates a global graph that combines discriminative information to enhance the performance of deep embedded clustering. Specifically, we employ autoencoders to learn embedded features and construct latent graphs from each view. Subsequently, a weighted two-layer plain residual network refines the latent feature using topological information derived from the latent graphs. As various views adhere to the same foundational cluster structure across multiple views, DVGMVC merges the embedded features of all views to create a unified feature. By leveraging the Graph Convolutional Network (GCN) module, which exploits information from both the latent graphs and the global graph generated from the global feature, a unified embedded feature is generated to capture comprehensive topological information among samples. This unified embedded feature is then employed to generate pseudo labels in a self-supervised manner, guiding unified embedding learning and clustering. In a self-supervised manner, such pseudo-label information guides unified embedding learning and clustering. In-depth experiments on public datasets has proven the state-of-the-art clustering effectiveness of our proposed method. © 2023 IEEE.

关键词： Embeddings

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Optimizing solar radiation prediction: a novel SVM approach for renewable energy systems

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International Journal of Information Technology (Singapore) 2025年 1-13页

作者： Basha, S. Sharief Rao, A. Nagaraja Department of Mathematics School of Advanced Science Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computational Intelligence School of Computer Science & Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India

The global transition toward renewable energy sources, driven by fossil fuel depletion and environmental imperatives, has positioned solar power as a crucial sustainable alternative. This study presents an advanced methodological framework for solar radiation (SR) prediction utilizing Support Vector Machine (SVM) techniques. The research analyzes comprehensive solar radiation data collected from India’s Vellore district throughout 2023, implementing a novel Support Vector Regression (SVR) approach with both polynomial and radial basis function (RBF) kernels. While conventional methodologies predominantly focus on minimizing training errors, our approach emphasizes reducing the generalization error bound to enhance predictive performance. The proposed model incorporates diverse meteorological parameters for SR prediction, demonstrating superior generalization capabilities compared to traditional approaches. Empirical results indicate that the SVR_rbf model significantly outperformed both SVR_poly and artificial neural network (ANN) models in SR prediction accuracy. The model's efficacy is validated through rigorous performance metrics: RMSE (10.3032), MAE (4.2728), MAPE (0.0152), and R² (0.9795). This research advances the understanding of machine learning applications in renewable energy forecasting, particularly for regions with limited meteorological data availability. Research findings enhance solar energy utilization and contribute to the transition towards sustainable energy. Future research directions may explore the integration of additional environmental parameters and the model’s adaptability across diverse geographical contexts. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Artificial neural network Predictive models Solar radiation Support vector regression

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Conformity, Confabulation, and Impersonation: Persona Inconstancy in Multi-Agent LLM Collaboration 2

Conformity, Confabulation, and Impersonation: Persona Incons...

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2nd Workshop on Cross-Cultural Considerations in NLP, C3NLP 2024

作者： Baltaji, Razan Hemmatian, Babak Varshney, Lav R. Department of Electrical and Computer Engineering United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign United States

ISBN: (纸本)9798891761469

This study explores the sources of instability in maintaining cultural personas and opinions within multi-agent LLM systems. Drawing on simulations of inter-cultural collaboration and debate, we analyze agents’ pre- and post-discussion private responses alongside chat transcripts to assess the stability of cultural personas and the impact of opinion diversity on group outcomes. Our findings suggest that multi-agent discussions can encourage collective decisions that reflect diverse perspectives, yet this benefit is tempered by the agents’ susceptibility to conformity due to perceived peer pressure and challenges in maintaining consistent personas and opinions. Counterintuitively, instructions that encourage debate in support of one’s opinions increase the rate of inconstancy. Without addressing the factors we identify, the full potential of multi-agent frameworks for producing more culturally diverse AI outputs will remain untapped. © 2024 Association for Computational Linguistics.

关键词： Chatbots

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Classification of Animal Species Using a Deep Neural Network-Based Feature Extraction Method 21

Classification of Animal Species Using a Deep Neural Network...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Ibrahim, Mohammed Al-Kubaise, Khamis Alkapti, Ali Almusa, Abdullah Abdelaziz, Osama Al-Maadeed, Somaya Sadasivuni, Kishor Kumar Qatar University Computer Science and Engineering Department Doha Qatar Center for Advanced Materials Qatar University PoBox 2713 Doha Qatar

ISBN: (纸本)9798350378078

This study presents an innovative approach to animal classification and recognition utilizing machine learning and deep learning methodologies. Leveraging advanced algorithms, the proposed system achieves remarkable accuracy in identifying diverse animal species. By integrating sophisticated image processing techniques, the system enhances image quality, improving overall performance. The research demonstrated that the SVM model combined with deep neural network-based feature extraction achieved the highest accuracy of 95.65%. This paper represents a significant stride toward improving the precision and efficiency of animal classification, offering promising applications in biodiversity conservation and ecological monitoring by using advanced feature extraction approach with deep learning. © 2024 IEEE.

关键词： Animal Recognition computer Vision Deep Learning Feature Extraction KNN

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FMCW Monopulse Radar with an Orthogonally Polarized Dual-Beam Dielectric Resonator Antenna

FMCW Monopulse Radar with an Orthogonally Polarized Dual-Bea...

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Lee, Chanhee Shin, Kyeong-Han Lee, Ji-Hoon Lee, Hyo-Won Kim, Jeong-Wook Yu, Jong-Won School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Electrical and Computer Engineering Ajou University Suwon Korea Republic of

ISBN: (纸本)9798350364774

In this paper, frequency-modulated continuous-wave (FMCW) radar system using dielectric resonator antenna (DRA), which has two beams with orthogonal linear polarization to each other, is introduced to function as a monopulse radar. The DRA operates at 24.3 GHz with a 16.8% bandwidth and provides over 40 dB isolation between V-pol and H-pol ports. Simulations demonstrate effective target detection and advanced directional information extraction, similar to monopulse radar, using a signal processing technique to address overlapping beam regions. © 2024 IEEE.

关键词： Monopulse radar

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