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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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Noncured graphene thermal interface materials: Minimizing the thermal contact resistance

arXiv

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arXiv 2021年

作者： Sudhindra, Sriharsha Kargar, Fariborz Balandin, Alexander A. Center Department of Electrical and Computer Engineering Materials Science and Engineering Program University of California RiversideCA92521 United States

We report on experimental investigation of thermal contact resistance, Rc, of the noncuring graphene thermal interface materials with the surfaces characterized by different degree of roughness, Rc. It is found that the thermal contact resistance depends on the graphene loading, N, non-monotonically, achieving its minimum at the loading fraction of N 15 Rc.%. Increasing the surface roughness by Sq~1 μm results in approximately the factor of ×2 increase in the thermal contact resistance for this graphene loading. The obtained dependences of the thermal conductivity, KTIM, thermal contact resistance, Rc, and the total thermal resistance of the thermal interface material layer on and Rc indicate the need for optimization of the loading fraction of graphene for specific materials and roughness of the connecting surfaces. Our results are important for the thermal management of high-power-density electronics. Copyright © 2021, The Authors. All rights reserved.

关键词： Surface roughness

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Performance Analysis and Optimization for Jammer-Aided Multi-Antenna UAV Covert Communication

arXiv

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arXiv 2022年

作者： Du, Hongyang Niyato, Dusit Xie, Yuan-Ai Cheng, Yanyu Kang, Jiawen Kim, Dong In The School of Computer Science and Engineering Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The Institute of Electrical Engineering Yanshan University Qinhuangdao066004 China Alibaba-NTU Singapore Joint Research Institute Nanyang Technological University Singapore639798 Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Unmanned aerial vehicles (UAVs) have attracted a lot of research attention because of their high mobility and low cost in serving as temporary aerial base stations (BSs) and providing high data rates for next-generation communication networks. To protect user privacy while avoiding detection by a warden, we investigate a jammer-aided UAV covert communication system, which aims to maximize the user's covert rate with optimized transmit and jamming power. The UAV is equipped with multi-antennas to serve multi-users simultaneously and enhance the Quality of Service. By considering the general composite fading and shadowing channel models, we derive the exact probability density (PDF) and cumulative distribution functions (CDF) of the signal-to-interference-plus-noise ratio (SINR). The obtained PDF and CDF are used to derive the closed-form expressions for detection error probability and covert rate. Furthermore, the covert rate maximization problem is formulated as a Nash bargaining game, and the Nash bargaining solution (NBS) is introduced to investigate the negotiation among users. To solve the NBS, we propose two algorithms, i.e., particle swarm optimization-based and joint two-stage power allocation algorithms, to achieve covertness and high data rates under the warden's optimal detection threshold. All formulated problems are proven to be convex, and the complexity is analyzed. The numerical results are presented to verify the theoretical performance analysis and show the effectiveness and success of achieving the covert communication of our algorithms. © 2022, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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A comprehensive review of solid-state lithium batteries: Fast Charging characteristics and in-operando diagnostics

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Nano Energy 2025年 142卷

作者： Ruoxu Shang Talyah Nelson Thanh Vy Nguyen Charlotte Nelson Harsha Antony Brian Abaoag Mihrimah Ozkan Cengiz S. Ozkan Materials Science and Engineering Program University of California Riverside CA USA Department of Mechanical Engineering University of California Riverside CA USA Department of Bioengineering University of California Riverside CA USA Department of Electrical and Computer Engineering University of California Riverside CA USA

This comprehensive review article delves into the evolving landscape of solid-state batteries (SSBs), presenting a critical evaluation beyond the conventional lithium-ion technology. It meticulously explores the performance analysis, fast-charging capabilities, and in-operando characterization of SSBs. Amid growing demands for safer, higher energy density, and more sustainable energy storage solutions, SSBs emerge as a compelling alternative, promising to revolutionize applications from portable electronics to electric vehicles. This review begins with a detailed examination of the fundamental principles underpinning SSBs, including their unique architecture and the roles of solid electrolytes in enhancing safety and energy density. It then shifts focus to the pivotal aspect of fast charging – a critical parameter for consumer acceptance and market penetration of electric vehicles. The analysis extends to the intricacies of in-operando characterization techniques, which are essential for understanding the dynamic processes within SSBs during operation, thus offering insights into optimizing performance and longevity. By juxtaposing SSBs with conventional lithium-ion batteries, the review highlights the technological strides and hurdles ahead. Furthermore, it discusses the latest advancements in materials science, engineering strategies, and characterization methods that potentially address the current limitations of SSBs. Conclusively, this review not only underscores the significant progress in SSB technology but also outlines the roadmap and future directions for researchers and industry stakeholders aiming to harness the full potential of SSBs in next-generation energy storage systems.

关键词： Solid-state batteries (SSBs) Fast charging Operando Lithium-ion Batteries

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engineering Andreev Bound States for Thermal Sensing in Proximity Josephson Junctions

arXiv

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arXiv 2025年

作者： Jung, Woochan Arnault, Ethan G. Huang, Bevin Park, Jinho Jang, Seong Watanabe, Kenji Taniguchi, Takashi Englund, Dirk Fong, Kin Chung Lee, Gil-Ho Physics Department Pohang University of Science and Technology Pohang37673 Korea Republic of Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Intelligence Community Postdoctoral Research Fellowship Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Columbia University New YorkNY10027 United States Research Center for Functional Materials National Institute for Materials Science Tsukuba Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba Japan Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States Department of Physics Northeastern University BostonMA02115 United States Quantum Materials and Sensing Institute BurlingtonMA01803 United States

The thermal response of proximity Josephson junctions (JJs) is governed by the temperature (T)- dependent occupation of Andreev bound states (ABS), making them promising candidates for sensitive thermal detection. In this study, we systematically engineer ABS to enhance the thermal sensitivity of critical current (lc) of proximity JJs, quantified as |dlc/dT/| for threshold readout scheme and |dlc/***−1| for inductive readout scheme. Using a gate-tunable graphene-based JJ platform, we explore the impact of key parameters—including channel length, transparency, carrier density, and superconducting material—on the thermal response. Our results reveal that the proximity-induced superconducting gap plays a crucial role in optimizing thermal sensitivity. Notably, we see a maximum |dlc/***−1| value of 0.6 K−1 at low temperatures with titanium-based graphene JJs. By demonstrating a systematic approach to engineering ABS in proximity JJs, this work establishes a versatile framework for optimizing thermal sensors and advancing the study of ABS-mediated transport. © 2025, CC BY.

关键词： Superconducting materials

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis

Digital Technology in Health Education Trend Mapping through...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Fairuz Iqbal Maulana Dian Lestari Vandha Pradwiyasma Widartha Nirwana Haidar Wahyu Dirgantara Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Medicine Airlangga University Surabaya Indonesia Information System of Industrial Engineering Faculty Telkom University Bandung Indonesia Informatics Engineering Study Program University of Madura Pamekasan Indonesia Department of Electrical Engineering University of Merdeka Malang Malang Indonesia

ISBN: (纸本)9781665473286

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012–2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9 % ), Conference Paper (4.2 % ), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), engineering (3.9%), computer science (2.6 % ), Environmental science (2.6 % ), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector.

关键词： Training Pediatrics Instruments Education Bibliometrics Transportation Psychology

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Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon Coupling in Hybrid Organic-Inorganic Perovskites 16

Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Kim, Dasom Hou, Jin Lee, Geon Agrawal, Ayush Kim, Sunghwan Zhang, Hao Bao, Di Baydin, Andrey Wu, Wenjing Tay, Fuyang Huang, Shengxi Hagenmüller, Daivd Chia, Elbert E. M. Kim, Dai-Sik Seo, Minah Mohite, Aditya D. Kono, Junichiro Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States Sensor System Research Center Korea Institute of Science and Technology Seoul02792 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Department of Chemical and Biomolecular Engineering Rice University HoustonTX77005 United States Ulsan44919 Korea Republic of Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Smalley-Curl Institute Rice University HoustonTX77005 United States Université de Strasbourg Cnrs Strasbourg67000 France KU-KIST Graduate School of Converging Science and Technology Korea University Seoul02841 Korea Republic of Department of Physics and Astronomy Rice University HoustonTX77005 United States

Methods for harnessing vibrational states are desired for phonon-based technologies. We realized ultrastrong coupling of two phonon modes in perovskite materials induced by ultrastrong coupling with a common photonic ... 详细信息

ISBN: (纸本)9798350372076

关键词： Organic-inorganic materials

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Machine Learning Based Classification of Micro-Doppler Radar Responses of Hands and Harmonically-Tagged Held Objects

TechRxiv

引用

TechRxiv 2022年

作者： Nourshamsi, Neda Maaz, Mohammad Jafari, Amir Hossein Nanzer, Jeffrey A. Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States Data Science program The George Washington University WashingtonDC20052 United States

We explore the differentiability of the micro-Doppler signal responses of human hand motion and held objects using harmonic radar and harmonic tags. When observing moving hands and objects tagged with harmonic radio-frequency tags, the harmonic radar detects motion (Doppler) responses at the fundamental frequency (scattered from the hand) and at the harmonic frequency (generated by the tag). In homodyne harmonic radars, both frequency responses manifest at baseband. In this work we explore the feasibility of differentiating the micro-Doppler and harmonic micro-Doppler baseband responses using machine learning based classification. For this purpose, three different classification algorithms in parallel with Adam optimization have been deployed. Results are presented in terms of confusion matrix and receiver operating curve (ROC). Overall, Convolution neural network (CNN) provides more accurate performance compared to the other algorithms, demonstrating the first step in classifications of human and hand-held objects. This work provides a framework for future research in harmonic micro-Doppler signal classification. © 2022-11-13, CC BY.

关键词： Deep neural networks

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Guest Editorial AI-Generated Content Empowered Healthcare Electronics

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1319-1321页

作者： Gwanggil Jeon Joel Rodrigues Shiping Wen Junxin Chen Nan Ji Abdellah Chehri Department of Embedded Systems Engineering Incheon National University Incheon South Korea Post-Graduation Program in Electrical Engineering Federal University of Piauí Teresina Brazil Instituto de Telecomunicações Universidade de Aveiro Aveiro Portugal Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW Australia School of Software Dalian University of Technology Dalian China Hong Kong Centre for Cerebro-Cardiovascular Health Engineering City University of Hong Kong Hong Kong China Department of Mathematics and Computer Science Military College of Canada Kingston ON Canada

The rapid advancements in AI-Generated Content (AIGC) have revolutionized various domains, including consumer electronics and healthcare technologies. AIGC’s ability to generate high-quality text, images, and videos within seconds has reshaped human-computer interactions, from intelligent customer service to immersive virtual experiences. More importantly, its application in Healthcare Electronics (HE) has opened new frontiers, facilitating automated diagnostics, medical data synthesis, and intelligent healthcare predictions.

关键词： Special issues and sections Generative AI Medical services Artificial intelligence Medical diagnostic imaging Brain modeling Knowledge graphs Electroencephalography Wearable devices Electronic medical records

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Development of Support Vector Machine Method to Forecast Dengue-Endemic Areas

Development of Support Vector Machine Method to Forecast Den...

引用

International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Hetty Meileni Ermatita Abdiansah Nyayu Latifah Husni Desmawati Doctoral Program in Engineering Science Universitas Sriwijaya (Politeknik Negeri Sriwijaya) Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Artificial Intelligence Research Development (AIRD) Universitas Sriwijaya Palembang Indonesia Department of Electrical Engineering Politeknik Negeri Sriwijaya Palembang Palembang Indonesia Faculty of Health Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia

The cases of dengue hemorrhagic fever (DHF) in Indonesia have increased significantly since 2020. Data shown by the Central Statistics Agency, for example, in South Sumatra Province, there were 6,348 cases of DHF during that period. To suppress the case number of DHF, forecasting areas that are susceptible to this disease is urgently needed. This research aims to develop a machine learning method Support Vector Machine (SVM). The data used include area data, population density data, weather data, and dengue fever case data. The development of the SVM model is carried out using a machine learning method that combines regional data, population density data, weather data, and dengue case data. In the end, using the SVM method can be used to forecast dengue-endemic areas, so that the government and society can take further action to reduce the negative impact of DHF.

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