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Design of Interactive Digital Virtual Display Application for Chinese Heritage Traditional Herbal Medicine Culture 12th

Design of Interactive Digital Virtual Display Application fo...

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12th International Conference on Culture and Computing (C and C) Held as Part of the 26th International Conference on Human-computer Interaction (HCII)

作者： Li, Haoru Feng, Binlin Su, Mingyang Liu, Zhuohua Shenzhen Univ Shenzhen Peoples R China Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9783031611469;9783031611476

The project focuses on designing a platform using Unity3D technology to preserve intangible cultural heritage, emphasizing the pharmacological efficacy and processing of herbal treatments in traditional Chinese medicine. Traditional Chinese medicine plays a crucial role in cultural continuity and emotional confront, yet its digitization is confronted with the complexity of its theoretical framework, insufficient public awareness, and the neglect of cultural elements during its propagation. In response, the research team has engineered an interactive application that transposes the herbal wisdom cataloged in the "Bencao Gangmu" into a digital milieu. This application's distinctiveness lies in its fusion of time-honored medicinal culture with digital innovation. Furthermore, the design posits a new framework for cultural inheritance that effectively simplifies and disseminates the complex theories of Chinese medicine and herbal knowledge through digital means to the general public. The application establishes a conduit that intricately connects traditional Chinese medicinal culture with contemporary society, offering an educational, accessible, and experiential platform, that serves as an innovative exemplar for the digital preservation and inheritance of intangible cultural heritage.

关键词： Traditional Chinese Herbs Intangible Cultural Heritage Unity3D Technology Digital Conservation Bencao Gangmu Interactive Digital Application

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A New Frontier in Digital Security: Verification for NFT Image Using Deep Learning-Based ConvNeXt Model in Quantum Blockchain 1

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24th World International Conference on Information Security Applications (WISA)

作者： Prihatno, Aji Teguh Suryanto, Naufal Larasati, Harashta Tatimma Oktian, Yustus Eko Le, Thi-Thu-Huong Kim, Howon Pusan Natl Univ Sch Comp Sci & Engn Busan 609735 South Korea Pusan Natl Univ Blockchain Platform Res Ctr Busan 609735 South Korea Pusan Natl Univ IoT Res Ctr Busan 609735 South Korea

ISBN: (数字)9789819980246

ISBN: (纸本)9789819980239;9789819980246

Non-Fungible Tokens (NFTs) have transformed the digital asset landscape with unique ownership verification. However, securing NFT images remains a crucial challenge. This paper proposes a verification framework for NFT images in a quantum blockchain environment. We explore the fundamentals, characteristics, and security challenges of NFT images. We examine the significance of quantum computing for digital security, highlighting vulnerabilities in classical encryption. We discuss existing image verification techniques and their limitations, leading to our proposed methodology that combines quantum-inspired approaches with a Deep Learning-based model. Additionally, we investigate the potential of ConvNeXt as a part of Deep Learning methods to enhance NFT image verification security and trust. Our comprehensive technique combines the Deep Learning-based method with a quantum blockchain to ensure the integrity, scalability, and validity of NFT images. Experimental evaluation demonstrates the feasibility and effectiveness of our approach. We discuss implications, including comparisons, limitations, and future research areas. This research advances digital security, providing insights into NFT image verification in the quantum computing era and laying the foundation for secure NFT ecosystems, promoting adoption across various domains.

关键词： NFT quantum blockchain deep learning digital security ConvNeXt

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Identity-Based Matchmaking Encryption with Enhanced Privacy - A Generic Construction with Practical Instantiations 28th

Identity-Based Matchmaking Encryption with Enhanced Privacy ...

引用

28th European Symposium on Research in computer Security (ESORICS)

作者： Boyen, Xavier Li, Qinyi QUT Brisbane Qld Australia Griffith Univ Brisbane Qld Australia

ISBN: (纸本)9783031514753;9783031514760

Identity-based matchmaking encryption (IB-ME), proposed by Ateniese et al. (Crypto 2019), is a type of matchmaking encryption (ME). In IB-ME, the sender can specify a target identity rcv during encryption, and the receiver can set a target identity snd during decryption. The ciphertext can be decrypted if snd matches the sender's identity s, and rcv matches the receiver's identity rho. The basic security notion of IB-ME is privacy, whose original definition ensures that sigma, rcv, and the message remain hidden as long as rcv not equal rho, regardless the relation between snd and sigma. Francati et al. (IndoCrypt 2021) argue that the original privacy notion is unsatisfactory as it does not match the intuitive privacy guarantee of matching encryption. They revise the original privacy notion with an enhanced privacy notion to characterise meaningful privacy under the condition snd not equal sigma and construct an IB-ME system with the enhanced security in the plain model, albeit under a q-type pairing-based assumption. Chen et al. (AsiaCrypt 2022) leave how to construct IB-ME systems with enhanced privacy as an open problem. In this paper, we solve the problem by a generic construction of IB-ME with enhanced privacy. Instantiating our construction gives practical IB-ME systems with enhanced privacy from various standard assumptions.

关键词： Identity-based Matchmaking encryption Enhanced privacy Quantum resistance Standard assumptions

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scMulan: A Multitask Generative Pre-Trained Language Model for Single-Cell Analysis 28th

scMulan: A Multitask Generative Pre-Trained Language Model f...

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28th Annual International Conference on Research in Computational Molecular Biology (RECOMB)

作者： Bian, Haiyang Chen, Yixin Dong, Xiaomin Li, Chen Hao, Minsheng Chen, Sijie Hu, Jinyi Sun, Maosong Wei, Lei Zhang, Xuegong Tsinghua Univ Dept Automat MOE Key Lab Bioinformat & Bioinformat Div BNRIST Beijing Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ Ctr Synthet & Syst Biol Sch Life Sci & Sch Med Beijing Peoples R China

ISBN: (纸本)9781071639887;9781071639894

Gene expression could be perceived as a form of "cell language", with underlying regulatory mechanisms akin to biological grammar. Decoding this language is critical in understanding cellular functions and behaviors. In this study, we proposed a new pre-training paradigm by integrating rich metadata and pre-training tasks, and developed scMulan, a multitask generative pre-trained language model for single-cell analyses. scMulan can accomplish multiple tasks in zero-shot manner such as cell-type annotation, batch integration, and conditional cell generation, guided by different task prompts. scMulan is also ready to be expanded for novel tasks through fine-tuning.

关键词： Foundation Models Single-cell Data Pretraining

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Maximizing Data Efficiency of HTR Models by Synthetic Text 16th

Maximizing Data Efficiency of HTR Models by Synthetic Text

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16th IAPR International Workshop on Document Analysis Systems (DAS)

作者： Muth, Markus Peer, Marco Kleber, Florian Sablatnig, Robert TU Wien Comp Vis Lab Favoritenstr 9 A-1040 Vienna Austria

ISBN: (纸本)9783031704413;9783031704420

The usability of synthetic handwritten text to improve machine learning models is assessed for the domain of HTR. Synthetic handwritten text is generated using an existing model based on a GAN. The output of this model is then used to train a state-of-the-art HTR model, which is then applied to recognize real datasets. While this results in a CER of 28.3% and a WER of 65.5% for line images of the IAM dataset - more than three times higher than the state-of-the-art result - our experiments show that the amount of real data in a mixed training set can be significantly reduced (70-80%) to achieve comparable CER and WER rates as with real data. Using only 10% of the training data (113 images) from the CVL dataset results in a CER of 54.5% and a WER of 88.8%, pre-training the model with synthetic data results in a CER of 14.6% and a WER of 43.4%.

关键词： Synthetic Data Handwritten Text Recognition Synthetic Text

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Schatten Capped p Regularization for Robust Principle Component Analysis 40th

Schatten Capped p Regularization for Robust Principle Compon...

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40th computer Graphics International Conference (CGI)

作者： Yang, Lan Zhang, Bin Feng, Qingrong Liu, Xinling Wang, Jianjun Southwest Univ Coll Comp & Informat Sci Coll Software Chongqing 400715 Peoples R China Ningxia Med Univ Dept Neurosurg Gen Hosp Yinchuan 750001 Ningxia Peoples R China Southwest Univ Sch Math & Stat Chongqing 400715 Peoples R China

ISBN: (纸本)9783031500787;9783031500770

Robust Principal Component Analysis (RPCA) is widely used for low-rank matrix recovery, which restores low-rank structures in damaged data through matrix decomposition. Existing approaches adopt the nuclear norm as a convex approximation of rank function. However, the nuclear norm treats the different singular values equally, leading to suboptimal matrix representation. To better depict the low-rank part, in this paper, we adopt a better surrogate of rank function, namely Schatten Capped p regularization. Further, the Schatten Capped p regularization-based RPCA model is proposed. And then we propose an efficient Alternating Direction Method of Multiplier (ADMM) algorithm to solve for the resulting optimization model. Experimentally, our algorithm is compared to state-of-the-art methods in practical applications such as image denoising, video background and foreground separation, and face de-shadowing. Especially, our algorithm can separate the noise better than other algorithms in the case of low noise levels in image denoising.

关键词： RPCA schatten capped p regularization ADMM background subtraction image denoising

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A Modified Hopfield Model with Adjustable Activation Function for Buridan's Assay 18th

A Modified Hopfield Model with Adjustable Activation Functio...

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18th International Conference on Neural Networks (ISNN)

作者： Liu, Xingjian Du, Chuangyi Tao, Lingyi Tsinghua Univ Sch Biomed Engn Beijing Peoples R China Tsinghua Univ Sch Social Sci Dept Psychol Beijing Peoples R China

ISBN: (纸本)9789819743988;9789819743995

Previous findings suggest that the sensory neurons behind thirst and hunger are embedded in circulatory networks that project throughout the brain. The Buridan's assay tests how a hungry and thirsty animal makes unitary choice under conflicting needs. In this case, the animals are attracted to two decision alternatives, having the characteristics of the attractor model. Therefore, we constructed a modified Hopfield model to understand the neural mechanism behind decision-making process in Buridan's assay. Importantly, the activation function of the model can be adjusted according to prior decisions, mimicking the effects of reward satiation. Our model successfully reproduced the general neural firing pattern across trials as well as the decision dynamics between trials.

关键词： Hopfield model Buridan's assay decision-making activation function neural mechanism

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A Bibliometric Analysis of Cognitive Load Sensing Methodologies and Its Applications 15th

A Bibliometric Analysis of Cognitive Load Sensing Methodolog...

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15th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management (DHM)

作者： Villarreal, Ryan Thomas Nordstrom, Parker Andrew Duffy, Vincent G. Purdue Univ Coll Engn Sch Ind Engn W Lafayette IN 47907 USA Purdue Univ Coll Engn Ray Ewry Sports Engn Ctr W Lafayette IN 47907 USA

ISBN: (纸本)9783031610592;9783031610608

Cognitive workload refers to the amount of mental resources a person expends while performing a task or processing information. Recent trends in the field have shown that cognitive load can be estimated through the use of physiological sensing techniques such as electroencephalograms (EEG), eye tracking, and electromyography (EMG). As these technologies are developed to be smaller, faster, smarter, and stronger, it has become more feasible to record physiological measurements in natural user environments and contexts, reducing challenges to generalizability and ecological validity. To gain a better understanding of the field and discuss where it is heading, our team completed a bibliometric analysis on the history, current state, and recent trends in the field of cognitive workload sensing and its applications. A literature review was conducted utilizing leading tables to analyze the most influential papers in the field. Further, an analysis of trends in the field is included to discuss the history of the field and its direction. It is shown that the field is still emerging, with a rapid growth of publications starting at the beginning of the 21st century.

关键词： Cognitive Load Ergonomics Cognitive Load Applications

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A Holistic Approach to Elderly Safety: Sensor Fusion, Fall Detection, and Privacy-Preserving Techniques 11th

A Holistic Approach to Elderly Safety: Sensor Fusion, Fall D...

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11th Pacific-Rim Symposium on Image and Video Technology (PSIVT)

作者： Hoa Nguyen ThuGiang Mai Minh Nguyen Auckland Univ Technol Auckland New Zealand

ISBN: (纸本)9789819703753;9789819703760

In light of the rising need for elderly care, this paper introduces a comprehensive integrated system centered on ensuring the safety of solitary senior individuals, leveraging the capabilities of the Internet of Things (IoT), deep learning, and sensor technology. We explore both wearable and non-wearable sensors for continuous monitoring. Recognizing the challenges associated with wearable devices - such as battery constraints, user discomfort, and potential inaccuracies - we highlight the benefits of visual object-based fall detection using environmental sensors. These include visual cameras and depth sensors optimally placed within living spaces to bypass the limitations of wearable devices and elevate monitoring precision. To address potential privacy concerns from ongoing video monitoring, we utilize advanced methods like human skeleton extraction and reversible visual data-hiding schemes. By camouflaging visual data, our proposed method ensures the content remains undetected by conventional means. This data-hiding scheme for videos and images encrypts media in a way that, to the general observer, it appears as random noise, yet it can be securely stored and transferred across platforms. Moreover, our encryption technique draws inspiration from water wave patterns and utilizes circular patterns derived from images, making the chance of brute force decryption nearly impossible. As a result, the system transforms human visuals into anonymous skeletal structures and encrypts visual data, safeguarding both privacy and data integrity. In essence, our holistic system marries technological advancements with the principles of humane care, striving for a harmonious blend of comfort, precise monitoring, and rigorous privacy preservation.

关键词： Fall detection Visual object-based algorithm Human skeleton extraction Privacy preservation Deep learning Environmental sensors Data encryption Reversible data-hiding Visual camouflage

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Key Substructure Learning with Chemical Intuition for Material Property Prediction 29th

Key Substructure Learning with Chemical Intuition for Materi...

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29th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Zhang, Peiliang Yuan, Jingling Li, Lin Luo, Wen Hu, Jiwei Li, Xin Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Wuhan Fiberhome Tech Serv Co Ltd Wuhan 430070 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230026 Peoples R China iFLYTEK Co Ltd Artificial Intelligence Res Inst Hefei 230088 Peoples R China

ISBN: (纸本)9789819755745;9789819755752

Substructures are crucial factors influencing material molecular properties, and the effective identification of molecular substructures can accelerate novel material discovery. The chemical intuition of irregularity and contribution variability existing in substructures present challenges in modeling molecular structural information, resulting in unsatisfactory performance in molecular property prediction. In this paper, we propose Key Substructure Learning with Chemical Intuition for Material Property Prediction (KSCI), including two modules. (1) Graph self-attention-based irregular substructure representation (GSR) adaptively extracts irregular molecular substructures using substructure feature entropy coefficient and multi-hop neighbor feature weighted aggregation. GSR utilizes a novel substructure graph self-attention mechanism, which captures the molecular graph's topology to achieve explicit encoding of substructures. (2) Gain-driven key substructure learning (GKL) calculates the mutual information gain of all molecular substructures to material molecular representation. In this way, the substructures with higher gain to molecular properties can be identified and direct the generation of molecular graph-level embedded representations. Experimental results on four datasets demonstrate that the Mean Absolute Error of KSCI outperforms eleven state-of-the-art material property prediction models. Visualization results show that KSCI exhibits satisfactory performance in irregular key substructure identification.

关键词： Key Substructure Representation Learning Chemical Intuition Irregular Substructure Extraction Material Property Prediction

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