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StyleKQC: A Style-Variant Paraphrase Corpus for Korean Questions and Commands

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arXiv 2021年

作者： Cho, Won Ik Moon, Sangwhan Kim, Jong In Kim, Seok Min Kim, Nam Soo Department of Electrical and Computer Engineering INMC Seoul National University Korea Republic of Department of Computer Science Tokyo Institute of Technology Japan Google LLC Interdisciplinary Program in Cognitive Science Seoul National University Korea Republic of

Paraphrasing is often performed with less concern for controlled style conversion. Especially for questions and commands, style-variant paraphrasing can be crucial in tone and manner, which also matters with industrial applications such as dialog systems. In this paper, we attack this issue with a corpus construction scheme that simultaneously considers the core content and style of directives, namely intent and formality, for the Korean language. Utilizing manually generated natural language queries on six daily topics, we expand the corpus to formal and informal sentences by human rewriting and transferring. We verify the validity and industrial applicability of our approach by checking the adequate classification and inference performance that fit with conventional fine-tuning approaches, at the same time proposing a supervised formality transfer task. © 2021, CC BY-SA.

关键词： Natural language processing systems

Naked-eye observation of water-forming reaction on palladium etalon:transduction of gas-matter reaction into light-matter interaction

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PhotoniX 2023年第1期4卷 349-363页

作者： Jongsu Lee Eui-Sang Yu Taehyun Kim In Soo Kim Seok Chung Seung Jae Kwak Won Bo Lee Yusin Pak Yong-Sang Ryu Sensor System Research Center Korea Institute of Science and TechnologySeoul 02792Republic of Korea Department of Electrical and Computer Engineering Seoul National UniversitySeoul 08826Republic of Korea Smart Materials Research Section Electronics and Telecommunications Research InstituteDaejeon 34129Republic of Korea Department of Micro/Nano Systems Korea University02841 SeoulRepublic of Korea Nanophotonics Research Center Korea Institute of Science and TechnologySeoul 02792Republic of Korea School of Mechanical Engineering Korea UniversitySeoul 02841Republic of Korea School of Chemical and Biological Engineering Seoul National UniversitySeoul 08826Republic of Korea Interdisciplinary Program in Precision Public Health Korea UniversitySeoul 02481Republic of Korea School of Biomedical Engineering Korea UniversitySeoul 02481Republic of Korea

Palladium is the most prominent material in both scientific and industrial research on gas storage,purification,detection,and catalysis due to its unique properties as a catalyst and hydrogen *** the dynamic optical phenomena of palladium reacting with hydrogen,transduction of the gas-matter reaction into light-matter interaction is attempted to visualize the dynamic surface chemistry and reaction *** simple geometry of the metal-dielectric-metal structure,Fabry-Perot etalon,is employed for a colorimetric reactor,to display the catalytic reaction of the exposed gas via water-film/bubble formation at the dielectric/palladium *** adsorption/desorption behavior and catalytic reaction of hydrogen and oxygen on the palladium surface display highly repeatable and dramatic color changes based on two distinct water formation trends:the foggy effect by water bubbles and the whiteout effect by water film *** and experiments demonstrate the robustness of the proposed Fabry-Perot etalon as an excellent platform for monitoring the opto-physical phenomena driven by heterogeneous catalysis.

关键词： Palladium Hydrogen Oxygen Fabry-Perot resonators Colorimetric sensors Water-forming reaction

Caco-2 Cell-Derived Biomimetic Electrochemical Biosensor for Cholera Toxin Detection

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SSRN

SSRN 2022年

作者： Kim, Yonghwan Lee, Dongtak Seo, Youngjun Jung, Hyo Gi Jang, Jae Won Park, Dongsung Kim, Insu Lee, Gyudo Hwang, Kyo Seon Kim, Seung-Hyun Lee, Sang Won Lee, Jeong Hoon Yoon, Dae Sung School of Biomedical Engineering Korea University Seoul02841 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02841 Korea Republic of Department of Clinical Pharmacology and Therapeutics College of Medicine Kyung Hee University Seoul02447 Korea Republic of Department of Biotechnology and Bioinformatics Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of School of Engineering Brown University ProvidenceRI02912 United States Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Department of Electrical Engineering Kwangwoon University Seoul01897 Korea Republic of Astrion Inc Seoul02841 Korea Republic of

Cholera is a highly contagious and lethal waterborne disease induced by an infection with Vibrio cholerae (V. cholerae) secreting cholera toxin (CTx). Cholera toxin subunit B (CTxB) from the CTx specifically binds with monosialo-tetra-hexosyl-ganglioside (GM1) found on the exterior cell membrane of an enterocyte. Owing to the pathological process of CTx, a biomimetic electrochemical biosensor (CCB) was developed by applying GM1-expressing Caco-2 cell membrane (CCM) on the electrode surface of the biosensor for CTx detection. Briefly, the electrode surface was functionalized with CCM using the vesicle fusion method. The specific binding between GM1 and CTxB on the electrode surface was verified using confocal laser scanning microscopy and atomic force microscopy. The CTxB detection performances of the CCB, including sensitivity, selectivity, and long-term stability, were determined using electrochemical impedance spectroscopy (EIS). Based on the results, the CCB had an excellent limit of detection of ~11.46 nM and a detection range spanning 100 ng/mL - 1 mg/mL. Further, the CCB had high selectivity for the CTxB compared to different interfering molecules, which are abundant constituents of intestinal fluid and fecal matter in the human body. The long-term stability of the CCBs was also verified for 3 weeks using EIS. Overall, the CCB was found to have an excellent potential for application to point-of-care and cost-effective testing for CTxB detection in developing countries. © 2022, The Authors. All rights reserved.

关键词： Electrochemical impedance spectroscopy

Meta-optic Accelerators for Object Classifiers

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arXiv 2022年

作者： Zheng, Hanyu Liu, Quan Zhou, You Kravchenko, Ivan I. Huo, Yuankai Valentine, Jason Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37212 United States Department of Computer Science Vanderbilt University NashvilleTN37212 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37212 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in real-time decision making when computation resources are limited. Here, we demonstrate a meta-optic based neural network accelerator that can off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both spatial multiplexing and additional information channels, such as polarization, in object classification. End-to-end design is used to co-optimize the optical and digital systems resulting in a robust classifier that achieves 95% accurate classification of handwriting digits and 94% accuracy in classifying both the digit and its polarization state. This approach could enable compact, high-speed, and low-power image and information processing systems for a wide range of applications in machine-vision and artificial intelligence. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy utilization

Optimal targeted advertising strategy for secure wireless edge metaverse

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arXiv 2021年

作者： Du, Hongyang Niyato, Dusit Kang, Jiawen Kim, Dong In Miao, Chunyan The School of Computer Science and Engineering The Energy Research Institute NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of of Automation Guangdong University of Technology China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Recently, Metaverse has attracted increasing attention from both industry and academia, because of the significant potential to integrate real and digital worlds ever more seamlessly. By combining advanced wireless communications, edge computing and virtual reality (VR) technologies into Metaverse, a multidimensional, intelligent and powerful wireless edge Metaverse is created for future human society. In this paper, we design a privacy preserving targeted advertising strategy for the wireless edge Metaverse. Specifically, a Metaverse service provider (MSP) allocates bandwidth to the VR users so that the users can access Metaverse from edge access points. To protect users’ privacy, the covert communication technique is used in the downlink. Then, the MSP can offer high-quality access services to earn more profits. Motivated by the concept of "covert", targeted advertising is used to promote the sale of bandwidth and ensure that the advertising strategy cannot be detected by competitors who may make counter-offer and by attackers who want to disrupt the services. We derive the best advertising strategy in terms of budget input, with the help of the Vidale-Wolfe model and Hamiltonian function. Furthermore, we propose a novel metric named Meta-Immersion to represent the user’s experience feelings. The performance evaluation shows that the MSP can boost its revenue with an optimal targeted advertising strategy, especially compared with that without the advertising. © 2021, CC BY.

关键词： Bandwidth

Navigating the landscape: Safeguarding privacy and security in the era of ambient intelligence within healthcare settings

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Cyber Security and Applications

Cyber Security and Applications 2024年 2卷

作者： Vats, Tarun Kumar, Sudhakar Singh, Sunil K. Madan, Uday Preet, Mehak Arya, Varsha Bansal, Ritika Almomani, Ammar Department of CSE Chandigarh College of Engineering and Technology Panjab University Chandigarh India International Center for AI and Cyber Security Research and Innovations (CCRI) Asia University Taiwan Insights2Techinfo India School of Computing Skyline University College Sharjah P.O. Box 1797 United Arab Emirates & Al- Balqa Applied University Jordan Department of Electrical and Computer Engineering Lebanese American University Lebanon Beirut 1102 Lebanon Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India UCRD Chandigarh University Chandigarh India

Ambient intelligence technologies have the potential to transform healthcare by providing personalized, context-aware, and proactive support for patients and healthcare providers. However, the use of these technologies in healthcare settings raises important privacy and security concerns that must be addressed to ensure patient trust and acceptance. This paper explores the privacy and security considerations related to the utilization of ambient intelligence in healthcare, aiming to address the associated risks and establish a robust security infrastructure. By reviewing the inherent privacy and security risks in healthcare settings employing ambient intelligence, discussing the ethical and legal considerations, and proposing mitigation strategies, the focus is on ensuring patient trust and *** architecture that is being presented is a comprehensive one with interconnected layers that guarantees data confidentiality, integrity, and privacy in the ambient intelligence healthcare system. This protects sensitive data and maintains its continuous availability. This research helps to establish a safe environment that supports the transformational potential of ambient intelligence in healthcare while putting patient privacy and data protection first by thoroughly addressing privacy and security concerns. © 2024

关键词： Ambient intelligence healthcare privacy security

Economics of Semantic Communication System: An Auction Approach

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arXiv 2022年

作者： Liew, Zi Qin Du, Hongyang Yang, Wei Lim, Bryan Xiong, Zehui Niyato, Dusit Miao, Chunyan Kim, Dong In Alibaba-NTU Singapore Joint Research Institute Nanyang Technological Univerity Singapore The School of Computer Science and Engineering The Energy Research Institute NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Nanyang Technological University Singapore639798 Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Semantic communication technologies enable wireless edge devices to communicate effectively by transmitting semantic meaning of data. Edge components, such as vehicles in next-generation intelligent transport systems, use well-trained semantic models to encode and decode semantic information extracted from raw and sensor data. However, the limitation in computing resources makes it difficult to support the training process of accurate semantic models on edge devices. As such, edge devices can buy the pretrained semantic models from semantic model providers, which is called "semantic model trading". Upon collecting semantic information with the semantic models, the edge devices can then sell the extracted semantic information, e.g., information about urban road conditions or traffic signs, to the interested buyers for profit, which is called "semantic information trading". To facilitate both types of the trades, effective incentive mechanisms should be designed. Thus, in this paper, we propose a hierarchical trading system to support both semantic model trading and semantic information trading jointly. The proposed incentive mechanism helps to maximize the revenue of semantic model providers in the semantic model trading, and effectively incentivizes model providers to participate in the development of semantic communication systems. For semantic information trading, our designed auction approach can support the trading between multiple semantic information sellers and buyers, while ensuring individual rationality, incentive compatibility, and budget balance, and moreover, allowing them achieve higher utilities than the baseline method. Copyright © 2022, The Authors. All rights reserved.

关键词： Commerce

STEIN LATENT OPTIMIZATION FOR GENERATIVE ADVERSARIAL NETWORKS

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arXiv 2021年

作者： Hwang, Uiwon Kim, Heeseung Jung, Dahuin Jang, Hyemi Lee, Hyungyu Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of AIIS ASRI INMC ISRC NSI Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

Generative adversarial networks (GANs) with clustered latent spaces can perform conditional generation in a completely unsupervised manner. In the real world, the salient attributes of unlabeled data can be imbalanced. However, most of existing unsupervised conditional GANs cannot cluster attributes of these data in their latent spaces properly because they assume uniform distributions of the attributes. To address this problem, we theoretically derive Stein latent optimization that provides reparameterizable gradient estimations of the latent distribution parameters assuming a Gaussian mixture prior in a continuous latent space. Structurally, we introduce an encoder network and novel unsupervised conditional contrastive loss to ensure that data generated from a single mixture component represent a single attribute. We confirm that the proposed method, named Stein Latent Optimization for GANs (SLOGAN), successfully learns balanced or imbalanced attributes and achieves state-of-the-art unsupervised conditional generation performance even in the absence of attribute information (e.g., the imbalance ratio). Moreover, we demonstrate that the attributes to be learned can be manipulated using a small amount of probe data. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

Diffusion-Stego: Training-free diffusion generative steganography via message projection

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Information Sciences 2025年 718卷

作者： Daegyu Kim Chaehun Shin Jooyoung Choi Dahuin Jung Sungroh Yoon Data Science and AI Laboratory Electrical and Computer Engineering Seoul National University Seoul 08826 Republic of Korea School of Computer Science and Engineering Soongsil University Seoul 06978 Republic of Korea Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul 08826 Republic of Korea AIIS ASRI INMC and ISRC Seoul National University Seoul 08826 Republic of Korea

Generative steganography is the process of hiding secret messages in generated images instead of cover images. Existing studies on generative steganography use GAN or Flow models to obtain high hiding message capacity and anti-detection ability over cover images. However, they create relatively unrealistic stego images because of the inherent limitations of generative models. We propose Diffusion-Stego, a generative steganography approach based on diffusion models that outperform other generative models in image generation. Diffusion-Stego projects secret messages into the latent noise of diffusion models and generates stego images with an iterative denoising process. Since the naive hiding of secret messages into noise boosts visual degradation and decreases extracted message accuracy, we introduce message projection, which hides messages into noise space while addressing these issues. We suggest three options for message projection to adjust the trade-off between extracted message accuracy, anti-detection ability, and image quality. Diffusion-Stego is a training-free approach, so we can apply it to pre-trained diffusion models that generate high-quality images, or even large-scale text-to-image models, such as Stable diffusion. Diffusion-Stego achieved a high capacity of messages as well as high quality that makes it challenging to distinguish from real images in the PNG format.

关键词： Diffusion models Generative models Generative steganography