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检索条件"任意字段=IEEE International Conference on Computer Research and Development"

共 367092 条记录，以下是51-60 订阅

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Identifying Neural Biomarkers of Confirmation Bias 13

Identifying Neural Biomarkers of Confirmation Bias

引用

13th international Winter conference on Brain-computer Interface, BCI 2025

作者： Singh, Avinash Liu, Jia Voinov, Alexey Lin, Chin-Teng School of Computer Science University of Technology Sydney UltimoNSW Australia School of Biomedical Engineering University of Sydney CamperdownNSW Australia Faculty of Engineering Technology University of Twente Netherlands

ISBN: (纸本)9798331521929

It has been observed that individuals prefer to favour information that strengthens their beliefs or values, while information that contradicts them causes unpleasant feelings and is more likely to be ignored. This is known as confirmation bias, and it is usually difficult to dislodge once affirmed. Confirmation bias has been shown to lead to flawed decisions in political, organizational, financial and scientific domains. To better understand the origins and detection of neural biomarkers for confirmation bias, this article presents a study that employed electroencephalography (EEG) signals from 10 participants. The study revealed an apparent increase in theta activity (phase-resetting) and prediction error negativity (PEN) among participants exhibiting confirmation bias. These findings have the potential to contribute to the development of novel brain-computer interface systems capable of detecting confirmation bias. Such systems may help mitigate social polarization and the negative effects of biased decision-making. © 2025 ieee.

关键词： Economic and social effects

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Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospects 7

Advancing Extended Reality with 3D Gaussian Splatting: Innov...

引用

7th ieee international conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Qiu, Shi Xie, Binzhu Liu, Qixuan Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong HKSAR China Institute of Medical Intelligence and XR The Chinese University of Hong Kong HKSAR China

ISBN: (纸本)9798331521578

3D Gaussian Splatting (3DGS) has attracted significant attention for its potential to revolutionize 3D representation, rendering, and interaction. Despite the rapid growth of 3DGS research, its direct application to Extended Reality (XR) remains underexplored. Although many studies recognize the potential of 3DGS for XR, few have explicitly focused on or demonstrated its effectiveness within XR environments. In this paper, we aim to synthesize innovations in 3DGS that show specific potential for advancing XR research and development. We conduct a comprehensive review of publicly available 3DGS papers, with a focus on those referencing XR-related concepts. Additionally, we perform an indepth analysis of innovations explicitly relevant to XR and propose a taxonomy to highlight their significance. Building on these insights, we propose several prospective XR research areas where 3DGS can make promising contributions, yet remain rarely touched. By investigating the intersection of 3DGS and XR, this paper provides a roadmap to push the boundaries of XR using cutting-edge 3DGS techniques. © 2025 ieee.

关键词： 3D modeling

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research on Speech Recognition Model Optimization and Real-Time Speech Interaction System Based on Deep Learning 5

Research on Speech Recognition Model Optimization and Real-T...

引用

5th ieee international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Jiang, Dongyu Yu, Xiaoting Harbin Huade University Harbin China

ISBN: (纸本)9798331533694

Voice recognition has been widely used in intelligent human-computer interaction, especially in the areas of voice assistant, intelligent house and autonomous driving. Due to the rapid development of deep learning, the precision and applicability of speech recognition have been greatly improved. However, in the complicated noise environment, different accents and high speed of speech, there are some problems in traditional speech recognition systems, such as poor recognition precision and slow response. In order to solve these problems, a novel voice recognition model is proposed, which combines CNN with self-attention. The model uses CNN to extract the local characteristics of voice signals, and then makes use of Transformer's self-attention mechanism to model the long-term dependency of voice data, so as to improve the precision and robustness of voice recognition. Experiments show that the proposed model achieves over 95% recognition accuracy over HMM-based speech data sets, which is 15% higher than the conventional one. Moreover, the proposed model has a good performance in real time speech interaction systems, and it can react rapidly and achieve high efficiency in speech recognition. Compared with the traditional methods, the proposed system is more adaptable in complicated environment, and it can deal with various kinds of background noise, speed difference and accent variation. © 2025 ieee.

关键词： Real time systems

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VR Cybersickness Classification using Machine Learning Models on Open-Access EEG Datasets from the Human Vestibular Network 7

VR Cybersickness Classification using Machine Learning Model...

引用

7th ieee international conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Li, Gang Byeon, Hae-Rin Cho, Sung-Bae Pollick, Frank University of Bath Department of Computer Science Bath United Kingdom Yonsei University Department of Computer Science and Engineering Seoul Korea Republic of School of Pscyhology and Neuroscience University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9798331521578

This paper addresses key challenges in EEG-based cybersickness classification using machine learning (ML) models. Despite significant research in this area four critical issues remain unresolved: 1) the availability of open-access EEG datasets;2) imbalanced data distribution;3) limited generalizability testing, and 4) insufficient exploration of personalized EEG data. © 2025 ieee.

关键词： Adversarial machine learning

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Predicting Workload in Virtual Flight Simulations Using EEG Spectral and Connectivity Features 7

Predicting Workload in Virtual Flight Simulations Using EEG ...

引用

7th ieee international conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Verkennis, Bas Van Weelden, Evy Marogna, Francesca L. Alimardani, Maryam Wiltshire, Travis J. Louwerse, Max M. Tilburg University Department of Cognitive Science and Artificial Intelligence Tilburg Netherlands Vrije Universiteit Amsterdam Faculty of Science Computer Science Amsterdam Netherlands

ISBN: (纸本)9798331521578

Effective cognitive workload management has a major impact on the safety and performance of pilots. Integrating brain-computer interfaces (BCIs) presents an opportunity for real-time workload assessment. Leveraging cognitive workload data from high-fidelity virtual reality (VR) flight simulations allows for dynamic adjustments to training scenarios. While prior studies have predominantly concentrated on EEG spectral power for workload prediction, delving into intra-brain connectivity may yield deeper insights. This study assessed the predictive value of EEG spectral and connectivity features in distinguishing high vs. low workload periods during simulated flight in VR and Desktop conditions. Using an ensemble approach, a stacked classifier was trained to predict workload from the EEG signals of 52 participants. Results showed that the mean accuracy of the model incorporating both spectral and connectivity features improved by 28% compared to the model that solely relied on spectral features. Further research on other connectivity metrics and deep learning models in a large sample of pilots is essential to validate the potential of a real-time workload-prediction BCI. This could contribute to the development of an adaptive training system for safety-critical operational environments. © 2025 ieee.

关键词： NASA

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Tactile Sensing of Extensor Tendons and Near-Sensor Gesture Recognition for Control of Robotic Hand 38

Tactile Sensing of Extensor Tendons and Near-Sensor Gesture ...

引用

38th ieee international conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Hu, Yushen Zhang, Zhejun Lei, Tengteng Wong, Man The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Guangzhou Hkust Fok Ying Tung Graduate School Guangzhou511458 China

ISBN: (纸本)9798331508890

A flexible active-matrix piezoelectric tactile sensor array is fabricated and affixed on the back of a hand to monitor the location and deformation of the extensor-tendons associated with a hand gesture. Based on the same thin-film transistor technology, an artificial neural network (ANN) allowing real-time and near-sensor classification of sensor signals is implemented. The integration of the tactile sensor array and an ANN facilitates the development of a compact human-machine interface system for recognition of hand gestures and their mimicry by a robotic hand. © 2025 ieee.

关键词： Thin film transistors

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Leveraging Virtual Prototypes for Training Data Collection in LLM-Based Voice User Interface development for Machines 7

Leveraging Virtual Prototypes for Training Data Collection i...

引用

7th ieee international conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Häfner, Polina Eisenlohr, Frithjof Karande, Abhijit Grethler, Michael Mukherjee, Avik Tran, Nhung Karlsruhe Insitute of Technology Karlsruhe Germany Ees Beratungsgesellschaft MbH Ettlingen Germany Rptu Kaiserslautern - Fbk Kaiserslautern Germany

ISBN: (纸本)9798331521578

Voice User Interfaces (VUIs) are becoming increasingly valuable in industrial applications, offering hands-free control in complex environments. However, developing and validating VUIs for such applications faces challenges, including limited access to physical prototypes and high testing costs. This paper presents a methodology that utilizes virtual reality (VR) prototypes to collect training data for large language model (LLM)-based VUIs, allowing early-stage voice control development before physical prototypes are accessible. Through an immersive Wizard-of-Oz (WoZ) method, participants interact with a virtual reality representation of a machine, generating realistic, scenario-based conversational data. This combined WoZ and VR approach enables high-quality data collection and iterative model training, offering an effective solution that can be applied across various types of machine. Preliminary findings demonstrate the viability of VR in generating diverse and robust data sets that closely simulate real-world dialogs for voice interactions in industrial settings. © 2025 ieee.

关键词： Virtual environments

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Compact and Secure Image Encryption for IoT Systems Employing ECC and AES Hybrid Cryptography

Compact and Secure Image Encryption for IoT Systems Employin...

引用

2025 ieee international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Wandile, Piyush S Singh Kirar, Bhupendra Jain, Saurabh Sahu, Yatendra Computer Science and Engineering Indian Institute of Information Technology Madhya Pradesh Bhopal India Electronics and Communications Engineering Indian Institute of Information Technology Madhya Pradesh Bhopal India

ISBN: (纸本)9798331529833

This study focuses on development of a secure and efficient image cryptosystem optimized for IoT devices. The proposed technique addresses the and resource constraints of IoT devices, it introduces a hybrid encryption approach where an amalgam of algorithms such as Arnold's Cat Map, ECC, Diffie-Hellman (ECDH), AES is used for optimized encryption for the image data. This combination ensures compact, secure, and resource-efficient encryption suitable for IoT systems. The system is evaluated through various performance metrics demonstrating its effectiveness in lightweight cryptography applications while ensuring high levels of security against common cryptanalytic attacks. © 2025 ieee.

关键词： Encryption algorithms

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NL2Code: A Hybrid NLP and Model-Driven Framework for Automated Code Generation from Natural Language and UML

NL2Code: A Hybrid NLP and Model-Driven Framework for Automat...

引用

2025 ieee international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Nair, Reshma P Thushara, M.G. Amrita School of Computing Amrita Department of Computer Science & Engineering Kerala Vishwa Vidyapeetham Amritapuri Kollam India Amrita School of Computing Amrita Department of Computer Science & Applications Kerala Vishwa Vidyapeetham Amritapuri Kollam India

ISBN: (纸本)9798331529833

The process of converting natural language requirements and visual models into executable software code remains an ongoing challenge in software engineering. We developed an intelligent system that adopts Natural Language Processing (NLP) and Model-Driven Engineering (MDE) to generate code from natural language requirements and UML diagrams. It then applies advanced NLP techniques for natural language specification, such as tokenization, POS tagging, semantic analysis, and information extraction. This information can be transformed into computer language construct and design patterns with the help of MDE concepts. It generates code templates, skeleton code, and real code and incorporates key principles of software engineering and technological specializations. Case studies explain how the system can improve the software development lifecycle for a development company by automatically translating the natural language and the visual models into code. The system synthesized code with 92-95 % accuracy and 92-93 % completeness in three particular cases of use, proving the efficiency of the translation process of natural language requirements and UML diagrams into executable code. The present work enriches software engineering, NLP, as well as the integration of MDE, and code generation, reducing time consumption, manual efforts, and fast communication in the Software development Process. © 2025 ieee.

关键词： Semantics

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Optimizing Brain Tumor Classification with ResNet-50 Feature Extraction and Machine Learning Algorithms 19

Optimizing Brain Tumor Classification with ResNet-50 Feature...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Mufila Gaffar, Andi Widya Azmi, Nurul Alwi, Erick Irawadi Abdullah, Syahrul Mubarak Adawiyah, Rabiatul Widyawati, Dewi Faculty of Computer Science Universitas Muslim Indonesia Makassar Indonesia

ISBN: (纸本)9798331507817

MRI is a very important diagnostic tool in finding brain tumors, but recently the development of manual interpretation of MRI images has developed some challenges, such as diagnosis is often delayed, and there is a high chance of making mistakes. Recently, to hurry up the process, brain tumor detection has started applying machine learning methods. The classification of MRI images for brain tumors is done in this research paper by extracting their features with the ResNet-50 model. The classical machine learning algorithms that have been applied in the paper for classifying the tumors using the extracted features include Naïve Bayes, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest. In this respect, SVM and KNN have yielded the highest accuracy of 0.95 and 0.96 respectively, hence are the best methods in this task. The study's findings contribute to the development of quicker and more precise methods to help with brain tumor diagnosis in a medical context. © 2025 ieee.

关键词： Support vector machines

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