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IEEE Conference on Global Communications (GLOBECOM)

作者： Guangyuan Liu Hongyang Du Dusit Niyato Jiawen Kang Zehui Xiong Boon Hee Soong Energy Research Institute@NTU Interdisciplinary Graduate Program School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering Nanyang Technological University Singapore

The popularity of Metaverse as an entertainment, social, and work platform has led to a great need for seamless avatar integration in the virtual world. In Metaverse, avatars must be updated and rendered to reflect users' behaviour. Achieving real-time synchronization between the virtual bilocation and the user is complex, placing high demands on the Metaverse Service Provider (MSP)'s rendering resource allocation scheme. To tackle this issue, we propose a semantic communication framework that leverages contest theory to model the interactions between users and MSPs and determine optimal resource allocation for each user. To reduce the consumption of network resources in wireless transmission, we use the semantic communication technique to reduce the amount of data to be transmitted. Under our simulation settings, the encoded semantic data only contains 51 bytes of skeleton coordinates instead of the image size of 8.243 megabytes. Moreover, we implement Deep Q-Network to optimize reward settings for maximum performance and efficient resource allocation. With the optimal reward setting, users are incentivized to select their respective suitable uploading frequency, reducing down-sampling loss due to rendering resource constraints by 66.076% compared with the traditional average distribution method. The framework provides a novel solution to resource allocation for avatar association in VR environments, ensuring a smooth and immersive experience for all users.

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Design of Runge-Kutta optimization for fractional input nonlinear autoregressive exogenous system identification with key-term separation

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Chaos, Solitons and Fractals 2024年 182卷

作者： Khan, Taimoor Ali Chaudhary, Naveed Ishtiaq Khan, Zeshan Aslam Mehmood, Khizer Hsu, Chung-Chian Raja, Muhammad Asif Zahoor International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Electrical and Computer Engineering International Islamic University Islamabad Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Information Management National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

Population-based metaheuristic algorithms have gained significant attention in research community due to its effectiveness in solving complex optimization problems in diverse fields. In this study, knacks of population-based Runge-Kutta optimizer (RUN) are exploited for the identification of fractional input non-linear exogenous auto-regressive (FINARX) system with key term separation. The fractional order calculus operator of the Grünwald-Letnikov derivative is exploited to develop FINARX from a conventional non-linear auto-regressive exogenous system. The identification scheme for FINARX model is implemented through a mean-square-error-based fitness function. RUN utilizes the slope variations calculated by the well-known Runge-Kutta method for an effective search mechanism in the exploration and exploitation phases. Moreover, an enhanced solution quality mechanism is employed for speedy convergence and keeping the movement toward the best solution by escaping the local optima. The robustness of the algorithm is analyzed by multiple variations of non-linearity as well as different noise scenarios. The performance of the RUN to identify the FINARX system is validated in terms of convergence rate, fitness value, robustness, and accuracy in weight estimation. The effectiveness of the RUN is further assessed through exhaustive simulations with their statistics as well as comparison with the standard recent counterparts, including the Whale optimization algorithm, Reptile Search algorithm, and Aquila optimizer on different performance indices for the FINARX system. © 2024 Elsevier Ltd

关键词： Mean square error

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Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakes

arXiv

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arXiv 2024年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei11529 Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu300044 Taiwan Department of Computer Science National Chengchi University Taipei11529 Taiwan Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Institute of Information Science Academia Sinica Taipei11529 Taiwan

The emergence of contemporary deepfakes has attracted significant attention in machine learning research, as artificial intelligence (AI) generated synthetic media increases the incidence of misinterpretation and is difficult to distinguish from genuine content. Currently, machine learning techniques have been extensively studied for automatically detecting deepfakes. However, human perception has been less explored. Malicious deepfakes could ultimately cause public and social problems. Can we humans correctly perceive the authenticity of the content of the videos we watch? The answer is obviously uncertain;therefore, this paper aims to evaluate the human ability to discern deepfake videos through a subjective study. We present our findings by comparing human observers to five state-of-the-art deepfake detection models. To this end, we used gamification concepts to provide 110 participants (55 native English speakers and 55 non-native English speakers) with a web-based platform where they could access a series of 40 videos (20 real and 20 fake) to determine their authenticity. Each participant performed the experiment twice with the same 40 videos in different random orders. The videos are manually selected from the FakeAVCeleb dataset. We found that all AI models performed significantly better than humans when evaluated on the same 40 videos. The study also reveals that while deception is not impossible, humans tend to overestimate their detection capabilities. Our experimental results may help benchmark human versus machine performance, advance forensics analysis, and enable adaptive countermeasures. © 2024, CC BY.

关键词： Audiovisual

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A Genetic Algorithm approach for optimization of geothermal power plant production: Case studies of direct steam cycle in Kamojang

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South African Journal of Chemical engineering 2023年 45卷 1-9页

作者： Rudiyanto, Bayu Birri, Mochammad Syahrul Widjonarko Avian, Cries Kamal, Dianta Mustofa Hijriawan, Miftah Energy Engineering Laboratory Department of Renewable Energy Engineering Politeknik Negeri Jember Jl. Mastrip No.164 Krajan Timur Jember 68121 Indonesia Graduate School of Department of Renewable Energy Engineering Politeknik Negeri Jember Jl. Mastrip No.164 Krajan Timur Jember 68121 Indonesia Department of Electrical Engineering Universitas Jember Jl. Kalimantan 37 Tegalboto Jember 68121 Indonesia Departement of Electronic and Computer Engineering National Taiwan University of Science and Technology No.43 Keelung Rd. Sec.4 Da'an Dist. Taipei City 106335 Taiwan Department of Mechanical Engineering Politeknik Negeri Jakarta Jl. Prof. Dr. G. A. Siwabessy Depok 16425 Indonesia Graduate Program of Mechanical Engineering Universitas Sebelas Maret Jl. Ir. Sutami 36 Surakarta 57126 Indonesia

Indonesia has enormous geothermal potential, but it only contributes 5% to Indonesia's energy matrix. During 37 years of operation, PT. Pertamina Geothermal Energy Kamojang area has been operating to produce electricity and is currently capable of supplying and distributing electricity to the Java-Bali area with a capacity of 60 MWe. However, this can run into a decrease in the efficiency and effectiveness of system performance due to energy losses in several geothermal power plant components during energy conversion. In this case, exergy analysis at PT. Pertamina Geothermal Energy Kamojang area Unit 4 direct-dry steam cycle was done on each component and state. This aims to know the energy and exergy stream and where it happened irreversibly at the component. The biggest irreversibility value occurred at the turbine and main condenser, with a value of 21,693.890 kW and 21,688.148 kW. The total irreversibility of all systems is 58,326.201 kW, while the total exergy inlet systems is 119,308.457 kW, so the value efficiency exergy obtained is 51.13%. Based on the environment as dead state analysis, an efficiency exergy value is inversely proportional to the irreversibility value and ascending environment temperature. System optimization was done with the genetic algorithm method, with variable values at the pressure wellhead and inlet turbine for the overall exergy efficiency value. The value obtained from optimization is 11.98 bar at the wellhead and 10.023 bar at the inlet turbine, and the overall efficiency exergy increased by 51.22%. © 2023 The Author(s)

关键词： Dry steam cycle Exergy Genetic Algorithm Geothermal power plant Optimization

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Understanding Audiovisual Deepfake Detection: Techniques, Challenges, Human Factors and Perceptual Insights

arXiv

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arXiv 2024年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min The Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei11529 Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu300044 Taiwan The Department of Computer Science National Chengchi University Taipei11529 Taiwan The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan The Institute of Information Science Academia Sinica Taipei11529 Taiwan

Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. Despite extensive research on unimodal deepfake detection, identifying complex deepfakes through joint analysis of audio and visual streams remains relatively unexplored. To fill this gap, this survey first provides an overview of audiovisual deepfake generation techniques, applications, and their consequences, and then provides a comprehensive review of state-of-the-art methods that combine audio and visual modalities to enhance detection accuracy, summarizing and critically analyzing their strengths and limitations. Furthermore, we discuss existing open source datasets for a deeper understanding, which can contribute to the research community and provide necessary information to beginners who want to analyze deep learning-based audiovisual methods for video forensics. By bridging the gap between unimodal and multimodal approaches, this paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity. © 2024, CC BY.

关键词： Audiovisual

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Mobility-aware Decentralized Federated Learning for Autonomous Underwater Vehicles

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IEEE Transactions on Wireless Communications 2025年

作者： He, Hongyi Du, Jun Jiang, Chunxiao Wang, Jintao Song, Jian Han, Zhu Tsinghua University Department of Electronic Engineering Beijing100084 China Tsinghua University Department of Electronic Engineering State Key laboratory of Space Network and Communication Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology and State Key laboratory of Space Network and Communication Beijing100084 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The underwater Internet of Things (UIoT) is crucial in developing marine resources. However, due to the low data rate of underwater channels, it is difficult to have a central server to process data from numerous devices as using terrestrial communications. Therefore, decentralized federated learning (DFL) with communication-efficient modifications is a promising alternative to empower UIoT with artificial intelligence and collaborative training. However, existing DFL strategies rely on a carefully designed small aggregation weight when aggregating parameters from neighbor nodes to mitigate the compression error, resulting in a slow convergence rate. In addition, the effect of data compression under time-varying topologies is not considered in current DFL algorithms. In response to these problems, this work studies a DFL framework with underwater acoustic channel and time-varying topology. Firstly, considering the low data rate and dynamics of the acoustic channel, we propose a practical scheme for adaptive compression and device connectivity. Moreover, we combine data compression and the error-compensation technique with time-varying topology and propose a DFL algorithm with aggregation weights decaying over time to achieve fast convergence under non-independent and identically distributed (non-IID) data. We derive a convergence bound for the proposed algorithm with respect to compression and time-varying topology and demonstrate that it achieves the same asymptotic convergence rate as centralized FL with perfect communication. Simulation results show that, compared with DFL algorithms without decaying aggregation weights and centralized FL schemes, the proposed algorithm exhibits higher accuracy and faster convergence rate in underwater environments. © 2002-2012 IEEE.

关键词： Autonomous underwater vehicles

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Automatic melanoma detection using discrete cosine transform features and metadata on dermoscopic images

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Journal of King Saud University - computer and Information sciences 2024年第2期36卷

作者： Yousefi, Shamim Najjar-Ghabel, Samad Danehchin, Ramin Band, Shahab S. Hsu, Chung-Chian Mosavi, Amir Department of Computer Engineering University of Mohaghegh Ardabili Ardabil Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan John von Neumann Faculty of Informatics Budapest Hungary Ludovika University of Public Service Budapest Hungary

Machine learning contributes in improving the accuracy of melanoma detection. There are extensive studies in classic and deep learning-based approaches for melanoma detection in the literature. Still, they are not accurate or require high learning data. This paper proposes a hybrid mechanism for automated melanoma detection on dermoscopic images based on Discrete Cosine Transform features and metadata. It is composed of three steps. First, extra information/artifacts are deleted;the remaining pixels are standardized for accurate processing. Second, the reliability of the mechanism is improved by the Radon transform, extra data is removed using the Top-hat filter, and the detection rate is increased by Discrete Wavelet Transform and Discrete Cosine Transform. Then, the number of features is reduced by Locality Sensitive Discriminant Analysis. The third step divides the images into learning and test ones to create image-based models using learning data. Finally, the best model is selected based on test data and improved by a metadata-based model. Simulation results show that the decision tree provides the most practical image-based model by improving accuracy and sensitivity. Besides, the comparison results demonstrate that our model improves the F-Value to detect melanoma superior to other approaches. © 2024 The Author(s)

关键词： Artificial intelligence Big data Data science Dermoscopic image Discrete cosine transform features Machine learning Melanoma detection Metadata

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A Large Reservoir Computing Forecasting Method Based on Randomized Fuzzy Cognitive Maps

A Large Reservoir Computing Forecasting Method Based on Rand...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Omid Orang Fabricio J. Erazo-Costa Petrônio C. L. Silva Guilherme de Alencar Barreto Frederico Gadelha Guimarães Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte Brazil Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Federal Institute of Northern Minas Gerais Januária Campus Brazil Department of Teleinformatics Engineering Universidade Federal do Ceará Fortaleza Brazil

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

This paper introduces a novel forecastings technique based on randomized fuzzy cognitive maps (FCM), called LRHFCM (or large reservoir of randomized high-order FCM) for predicting univariate time series. LR-HFCM is a hybrid method combining fuzzy time series (FTS), FCMs, and reservoir computing. It is a type of echo state network (ESN) consisting of the input layer, intermediate (or large reservoir) layer, and output layer, where LASSO regression is applied to train the output layer. The novelty of this approach is that the internal layer includes a very large reservoir, considering different combinations from the sets of concepts and order using a certain number of sub-reservoirs to capture different dynamics of input time series. It is important to highlight that the weights within each sub-reservoir are chosen randomly and remain constant throughout the training process. The validity of the LR-HFCM approach is evaluated across 15 different time series datasets. The results highlight the outperformance of the LR-HFCM technique in comparison to various baseline models.

关键词： Training Adaptation models Adaptive systems Computational modeling Time series analysis Fuzzy cognitive maps Reservoirs

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Single-molecule localization microscopy of expanded mammalian centrioles unravels the ultrastructural constitution of distal appendages

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Biophysical journal 2023年第3S1期122卷 17a页

作者： Chang, Ting-Jui B Hsu, Jimmy C-C Yang, Tony Physics National Taiwan University Taipei City Taiwan Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica Taipei City Taiwan Electrical Engineering National Taiwan University Taipei City Taiwan Electrical Engineering National Taiwan University Taipei City Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei City Taiwan

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Non-Hermitian selective thermal emitter for thermophotovoltaics

Non-Hermitian selective thermal emitter for thermophotovolta...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Ciril S. Prasad Samuel C. Raphaelson Gururaj V. Naik Department of Electrical and Computer Engineering Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Material Science and NanoEngineering Rice University Houston TX USA

Selective thermal emitters can boost the efficiency of heat-to-electricity conversion in thermophotovoltaic systems only if their spectral selectivity is high. We demonstrate a non-Hermitian metasurface-based selectiv... 详细信息

ISBN: (纸本)9781957171258

关键词： Emissivity Light intensity Optical constants Silicon Temperature Thermal emission

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