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2024 5th International Conference on Big Data Economy and Information Management, BDEIM 2024

作者： Hwang, Min-Shiang Yang, Cheng-Ying Chang, Yung-Ling Wu, Chia-Chun Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Computer Science University of Taipei Taipei Taiwan The Ph.D. Program in Artificial intelligence Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (纸本)9798400711862

Recently, Wei & Chen proposed a secure three-party mutual authentication protocol for RFID based on elliptic curve cryptography (ECC). Their approach can allow insecure communication between the reader and the backend, so their approach must achieve mutual authentication between the tag, reader, and backend. In addition, their method also satisfies the essential characteristics of RFID systems, such as confidentiality, anonymity, resistance to tracking attacks, and spoofing attacks. However, this article finds that its method cannot meet the mutual authentication and denial of service attacks. © 2024 Copyright held by the owner/author(s).

关键词： ECC key negotiation RFID/NFC tripartite authentication

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Alzheimer's Disease Prediction using SVM Kernal's with PCA Architecture 21

Alzheimer's Disease Prediction using SVM Kernal's with PCA A...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Patel, Ruchi Verma, Ashok Kumar Kshirsagar, Ravindra V. Sahu, Neelesh Kumar Gyan Ganga Institute of Technology and Sciences Department of Computer Science and Engineering MP Jabalpur India Gyan Ganga Institute of Technology and Sciences Department of Electronics and Communication MP Jabalpur India Gyan Ganga Institute of Technology and Sciences Department of Artificial Intelligence and Robotics MP Jabalpur India

ISBN: (纸本)9798350358230

Memory and other mental processes are both severely disrupted by Alzheimer's disease (AD), a neurodegenerative condition. It interferes with almost every cognitive process that the brain is capable of. This ultimately results in a smaller and less volumetric brain as time passes. There is currently no treatment that can reverse the effects of Alzheimer's disease. The only way for early detection and therapy to be possible is for the symptoms to be recognized promptly. Throughout the years, there has been a significant amount of research conducted on the application of machine learning to the diagnosis and classification of Alzheimer's disease. The performance of linear, polynomial, and RBF (Radial Basis Function) classification kernels in the context of AD classification using the Support Vector Machine (SVM) with the Principal Component Architecture (PCA) is investigated in this study. As a result, a framework is provided for the categorization of AD, which is comprised of the following steps: data input, model building, hyperparameter tuning, prediction on test data, performance evaluation, and selection of the most effective SVM kernel model and PCA for dimension reduction. ADNI and the Alzheimer's MRI Preprocessed dataset that is available on Kaggle are used to conduct an evaluation of the proposed framework. The performance of the model is evaluated using accuracy measure for both the datasets. Across both datasets, the SVM linear kernel with PCA achieved the maximum accuracy, which was 99.99 percent. © 2023 IEEE.

关键词： Support vector machines

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Grating Coupled Attachment of Optical Fiber Arrays for in situ Photonics Experimentation

Grating Coupled Attachment of Optical Fiber Arrays for in si...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Arnold, Kellen P. Slaby, Joel B. Dattilo, Hannah M. Kaylor, C. Alex Schrimpf, Ronald D. Fleetwood, Daniel M. Ralph, Stephen E. Reed, Robert Weiss, Sharon M. Interdisciplinary Materials Science Program Vanderbilt University 2201 West End Ave NashvilleTN37235 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Electrical and Computer Engineering Vanderbilt University 2201 West End Ave NashvilleTN37235 United States

We report a simple, vacuum-compatible fiber attach process for in situ study of grating-coupled photonic devices. The robustness of this technique is demonstrated on grating-coupled waveguides exposed to multiple X-ra... 详细信息

ISBN: (纸本)9781957171395

关键词： X ray lasers

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PhysORD: A Neuro-Symbolic Approach for Physics-infused Motion Prediction in Off-road Driving

PhysORD: A Neuro-Symbolic Approach for Physics-infused Motio...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zhipeng Zhao Bowen Li Yi Du Taimeng Fu Chen Wang Department of Computer Science and Engineering Spatial AI & Robotics (SAIR) Lab Institute for Artificial Intelligence and Data Science University at Buffalo Buffalo NY USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Motion prediction is critical for autonomous off-road driving, however, it presents significantly more challenges than on-road driving because of the complex interaction between the vehicle and the terrain. Traditional physics-based approaches encounter difficulties in accurately modeling dynamic systems and external disturbance. In contrast, data-driven neural networks require extensive datasets and struggle with explicitly capturing the fundamental physical laws, which can easily lead to poor generalization. By merging the advantages of both methods, neuro-symbolic approaches present a promising direction. These methods embed physical laws into neural models, potentially significantly improving generalization capabilities. However, no prior works were evaluated in real-world settings for off-road driving. To bridge this gap, we present PhysORD, a neural-symbolic approach integrating the conservation law, i.e., the Euler-Lagrange equation, into data-driven neural models for motion prediction in off-road driving. Our experiments showed that PhysORD can accurately predict vehicle motion and tolerate external disturbance by modeling uncertainties. It outperforms existing methods both in accuracy and efficiency and demonstrates data-efficient learning and generalization ability in long-term prediction.

关键词： Bridges Uncertainty Accuracy Neural networks Merging Predictive models Mathematical models Dynamical systems Intelligent robots

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ENTROPY IS NOT ENOUGH FOR TEST-TIME ADAPTATION: FROM THE PERSPECTIVE OF DISENTANGLED FACTORS 12

ENTROPY IS NOT ENOUGH FOR TEST-TIME ADAPTATION: FROM THE PER...

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12th International Conference on Learning Representations, ICLR 2024

作者： Lee, Jonghyun Jung, Dahuin Lee, Saehyung Park, Junsung Shin, Juhyeon Hwang, Uiwon Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Division of Digital Healthcare Yonsei University Korea Republic of AIIS ASRI INMC ISRC Seoul National University Korea Republic of

Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online updates, causing error accumulation. To mitigate it, TTA methods have utilized the model output's entropy as a confidence metric that aims to determine which samples have a lower likelihood of causing error. Through experimental studies, however, we observed the unreliability of entropy as a confidence metric for TTA under biased scenarios and theoretically revealed that it stems from the neglect of the influence of latent disentangled factors of data on predictions. Building upon these findings, we introduce a novel TTA method named Destroy Your Object (DeYO), which leverages a newly proposed confidence metric named Pseudo-Label Probability Difference (PLPD). PLPD quantifies the influence of the shape of an object on prediction by measuring the difference between predictions before and after applying an object-destructive transformation. DeYO consists of sample selection and sample weighting, which employ entropy and PLPD concurrently. For robust adaptation, DeYO prioritizes samples that dominantly incorporate shape information when making predictions. Our extensive experiments demonstrate the consistent superiority of DeYO over baseline methods across various scenarios, including biased and wild. Project page is publicly available at https://***/DeYO/. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Entropy

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Automated COVID-19 Detection from Chest X-Ray Images: A High-Resolution Network (HRNet) Approach

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SN computer science 2021年第4期2卷 294页

作者： Ahmed, Sifat Hossain, Tonmoy Hoque, Oishee Bintey Sarker, Sujan Rahman, Sejuti Shah, Faisal Muhammad Hiperdyne Corporation Tokyo Japan Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Department of Robotics and Mechatronics Engineering University of Dhaka Dhaka Bangladesh

The pandemic, originated by novel coronavirus 2019 (COVID-19), continuing its devastating effect on the health, well-being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID-19 in the human body to constraint the exposure and control the spread of the virus. Chest X-Rays are one of the non-invasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time-consuming. Our intensive background studies show that, the works till now are not efficient to produce an unbiased detection result. In this work, we proposed an automated COVID-19 classification method, utilizing available COVID and non-COVID X-Ray datasets, along with High-Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes. To evaluate the proposed method, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got a significant result of 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models. Finally, we conclude that our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X-Ray images into the diagnosis of the disease. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： COVID-19 Healthcare HRNet Pandemic UNet X-Ray

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Continual Facial Features Transfer for Facial Expression Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Maharjan, Rahul Singh Bonicelli, Lorenzo Romeo, Marta Calderara, Simone Cangelosi, Angelo Cucchiara, Rita University of Manchester Centre for Robotics and Ai United Kingdom University of Modena and Reggio Emilia Department of Engineering Enzo Ferrari Modena Italy Heriot-Watt University Department of Computer Science United Kingdom

Facial Expression Recognition (FER) models based on deep learning mostly rely on a supervised train-once-test-all approach. These approaches assume that a model trained on an in-the-wild facial expression dataset with one type of domain distribution will perform well on a test dataset with a domain distribution shift. However, facial images in real-world can be from different domain distributions from which the model has been trained. However, re-training models on only new domain distributions will severely affect the performance of the previous domain. Re-training on all previous and new data can improve overall performance but is computationally expansive. In this study, we oppose the train-once-test-all approach and propose a buffer-based continual learning approach to enhance the performance of multiple in-the-wild datasets. We propose a model that continually leverages attention to important facial features from the pre-trained model to improve performance in multiple datasets. We validated our model using split-in-the-wild datasets where the dataset is provided to the model in an incremental setting instead of all at once. Furthermore, to evaluate the model performance, we continually used three in-the-wild datasets representing different domains (Domain-FER). Extensive experiments on these datasets reveal that the proposed model achieves better results than other Continual FER models. © 2010-2012 IEEE.

关键词： Contrastive Learning

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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Prognostics and System Health Management Conference (PHM)

作者： Congrui Cai Zhonghai Lu Embedded Systems Master Program School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Department of Electrical Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms.

关键词： Performance evaluation Semiconductor device modeling Recurrent neural networks Computational modeling Estimation Power electronics Data models

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Gender-Based Bimodal Analysis of Speech and Text in Non-Contextual Emotion Recognition

Gender-Based Bimodal Analysis of Speech and Text in Non-Cont...

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Informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Abby Rasya Angkasa Amalia Zahra Wai-Keung Fung Computer Science Department BINUS Graduate Program - Master of Computer Science Jakarta Indonesia Engineering Department Cardiff Metropolitan University - Cardiff School of Technologies Cardiff United Kingdom

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

Emotion recognition can help human-computer interactions by enabling systems to respond empathetically and adapt to users' emotional conditions. This capability improves user experience, supporting the development of a more intuitive and emotionally responsive communication system. This study analyzes a bimodal approach based on gender (male and female) to recognize emotions without contextual information in dialogue analysis. Utilizing the Multimodal EmoryNLP dataset extracted from the TV series Friends with acted speech, we focused on four primary emotions: Angry, Neutral, Joy, and Scared. The model used in this study for text classification is RoBERTa, and wav2vec 2.0 is used for audio feature extraction with the Bi-LSTM model for classification. The experiment results using weighted F1-score reveal that data augmentation enhanced the performance of analyzing the original dataset from 0.46% to 0.52% and the male dataset from 0.43% to 0.51 %. In comparison, the female dataset remained consistent at 0.46%. The weighted F1-score and Unweighted Averaged Recall (UAR) from the male dataset are higher, 51 % and 48%, respectively, than those from the female dataset, 46% and 47%, respectively. Gender-based analysis indicated that male and female datasets exhibited distinct performance patterns, highlighting variations in emotional expression and recognition between genders. These findings underscore the effectiveness of multimodal strategies in emotion recognition and suggest that gender-specific factors play a significant role in enhancing classification performance. While these results highlight performance trends, further validation through repeated trials and statistical analyses could provide stronger generalizations and insights into gender-based differences.

关键词： Emotion recognition TV Statistical analysis Text categorization Speech recognition Linguistics Feature extraction Data augmentation Market research User experience

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Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Classification 4

Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Cl...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Fitrianah, Devi Suryaningrum, Kristien Margi Sagala, Noviyanti Tri Maretta Ayumi, Vina Lim, Siew Mooi School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Mathematics Statistics Department Jakarta Indonesia Universitas Mercu Buana Informatics Engineering Program Faculty of Computer Science Jakarta Indonesia Tunku Abdul Rahman University College Faculty of Computing and Information Technology Computer Science Department Kuala Lumpur Malaysia

ISBN: (纸本)9781665473279

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of MobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%. © 2022 IEEE.

关键词： Fish

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