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Constructing a Secure Charity NFT Auction Platform Using Fisco Bcos Blockchain for Enhancing Transparency and Traceability

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IEEE Access 2024年 12卷 36924-36941页

作者： Chen, Chin-Ling Zhan, Wan-Bing Tsaur, Woei-Jiunn Huang, Der-Chen Liu, Ling-Chun Changchun Sci-Tech University Changchun School of Information Engineering Jilin130600 China Chaoyang University of Technology Department of Computer Science and Information Engineering Taichung413310 Taiwan Xiamen University of Technology Xiamen School of Computer and Information Engineering Fujian361024 China National Taipei University Computer Center New Taipei City237303 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan National Chung Hsing University Department of Computer Science and Engineering Taichung402202 Taiwan

In the charity sector, fundraising and transparency have long been key issues. Charity NFT (Non-Fungible Token) auctions, an emerging charity fundraising model integrating blockchain and NFT concepts, bring opportunities and challenges. Blockchain provides distributed data integrity and transparency via cryptography-linked data blocks, while NFTs enable unique digital ownership representation. This study designs a charity NFT auction platform on the Fisco Bcos blockchain, using multi-signature algorithms to ensure NFT authenticity, ECDSA (Elliptic Curve Digital Signature Algorithm) signatures for transaction integrity and traceability, NFTs and virtual currencies to reduce costs, and IPFS (InterPlanetary File System) for storage. The implemented system achieves 2104 TPS throughput with 492-millisecond latency, increasing transaction processing with low latency. Overall, the platform aims to address charity issues like opaque fund flows, high costs, and fake initiatives through the strategic application of blockchain and NFT functionality. © 2013 IEEE.

关键词： Smart contract

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Generative Adversarial Networks (GANs) for Education: State-Of-Art and Applications

Generative Adversarial Networks (GANs) for Education: State-...

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International Conference on Power engineering and Intelligent Systems, PEIS 2024

作者： Kavyashree, K.N. Shidaganti, Ganeshayya Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9789819767090

In the ever-changing educational landscape, technology integration has become essential to improving students’ understanding of basic topics. The COVID-19 pandemic hastened the transition to online education while posing new difficulties for teachers and students alike. The disruption of traditional classroom dynamics was exacerbated by the lack of digital innovation and interactive instructional tools, which made student participation less effective. Traditional teaching paradigms must make way for ones that encourage self-directed learning and fortify technical infrastructure in order to handle these difficulties. Generative Adversarial Networks (GANs), a cutting-edge use of deep learning, are starting to revolutionize educational technology. With the use of GANs, a range of features that are tailored to the individual learning preferences of each student may be created, thereby bridging knowledge gaps and providing a more dynamic and interesting learning environment for students. This research investigates several state-of-the-art features that leverage technology linked to GANs to enhance students’ virtual learning environments. The novel applications of generative adversarial networks are explored, with a focus on applications for student observation, academic performance, questioning and answering, and personalized learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Reinforcement Learning Based Dark Image Enhancement Through Color Feature Balancing 3

Reinforcement Learning Based Dark Image Enhancement Through ...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Rab, Raqeebir Ahmed, Towkir Alam, Arfayet Rahman, Ashikur Leshob, Abderrahmane Ahsanullah University of Science and Technology Department of Computer Science and Engineering Tejgaon Dhaka1208 Bangladesh Bangladesh University of Engineering and Technology Department of Computer Science and Engineering West Palashi Dhaka1000 Bangladesh University of Quebec in Montreal Department of Computer Science and Engineering Montreal Canada

ISBN: (纸本)9798350388282

Image enhancement refers to the process of manipulating an image to improve the visual information it contains. Images can degrade due to various factors, such as operator incompetence or low-quality picture- recording devices. The degraded images exhibit color imbalance, unwanted noise, and hue saturation disparity, especially in dark images. Furthermore, any photograph taken in low-light conditions typically falls short of attaining the desired level of visibility and essential details of the image. Most image enhancement systems depend on a predetermined set of instructions. Deep learning approaches instruct and execute certain actions during training without knowledge of the properties of individual images. Insufficient knowledge occasionally leads to the distortion of images. Conversely, an agent that utilizes Reinforcement Learning (RL) can make decisions about which actions to perform based on the input image. In order to address the aforementioned challenges, we present a novel approach in this study that use Reinforcement Learning to enhance dark images. This method accurately emulates the sequential process employed by humans during retouching. Our reinforcement learning-based agent, similar to a human expert, aims to evaluate the present state of the image prior to making any decision. Upon conducting a comprehensive analysis, we have determined that our agent, which utilizes reinforcement learning (RL), has demonstrated exceptional efficacy in maintaining the authenticity and color equilibrium of dark images. In general, the experimental results demonstrate the outstanding efficacy of the proposed approach. © 2024 IEEE.

关键词： Image enhancement

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A Vulnerability Detection Method for Smart Contracts Based on Dynamic Meta Optimizer

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IEEE Internet of Things Journal 2025年第13期12卷 24904-24915页

作者： He, Daojing Gong, Wei Chan, Sammy Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Jiangxi University of Science and Technology School of Information Engineering Jiangxi Ganzhou China City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong

With the increasingly complex blockchain technology environment and emerging security threats, the detection and prevention of vulnerabilities in blockchain smart contracts have become crucial for ensuring the healthy development of blockchain technology and avoiding substantial economic losses. Recently developed vulnerability detection methods for smart contracts suffer from the drawbacks of insufficient feature extraction and inadequate multitask detection. This paper proposes a multifaceted learning model, DLR, based on a dual-loop architecture of meta learning to address these issues by adopting model-agnostic meta-learning techniques. This model employs a syntax analyzer for targeted feature extraction, with graph information used as a supporting tool during the initial stage of feature engineering. Combined with an improved optimizer algorithm in the inner loop, the model can effectively learn and adjust based on the specific requirements of each task. In the outer loop, the model achieves efficient learning rate adjustment for multi-task learning by integrating the adaptive learning rate of the Adam optimizer with a global adjustment using simulated annealing strategy, thereby enhancing performance across diverse tasks. Experimental results demonstrate significant improvements in detection accuracy over state-of-the-art methods for three types of vulnerabilities, with our method achieving detection accuracies of 94.40%, 93.36%, and 94.33% for reentrancy, timestamp dependence, and integer overflow vulnerabilities, respectively. © 2014 IEEE.

关键词： Contrastive Learning

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A Hybrid Deep Learning Approach for Automatic Melanoma Diagnosis: Integrating Convolutional Neural Networks with Vision Transformers 4

A Hybrid Deep Learning Approach for Automatic Melanoma Diagn...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Priyanka, G. Mahalakshmi, A. Priya, N. Sivaranjani, N. Archana, B. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Hindusthan college of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

The most lethal type of skin lesion is melanoma. The likelihood of survival for melanoma is significantly increased by early detection. Nevertheless, a number of characteristics, such as diminished contrast between the skin and abrasion and visual similarities between non-melanoma and melanoma, make it challenging to identify melanoma with accuracy. The novel melanoma recognition method described in this study consists of three primary phases: preprocessing, segmentation and classification. To maximize image quality and get the data ready for segmentation, the preprocessing step included denoising techniques, grayscale conversion, Gaussian thresholding, and Contrast Limited Adaptive Histogram Equalization (CLAHE). Three widely used edge detection algorithms-Canny, Sobel, and Prewitt-were applied to the segmentation process in order to identify lesion boundaries and extract features unique to melanoma. Performance criteria such as Root Mean Square Error (RMSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR) were used to assess the accuracy of the segmentation. Based on the comparison analysis, Sobel was found to be the best algorithm for correctly identifying melanoma lesions. The outcomes showed how well the preprocessing methods worked to enhance image quality and how well the segmentation algorithms identified melanoma lesions. This research's subsequent phase will concentrate on classifying images using deep learning models. The segmented images are used to train deep learning model namely Convolutional Neural Networks with Vision Transformer. Superior performance is obtained in terms of accuracy, precision, recall, and F1 score when denoising, segmentation, and classification techniques are combined. This highlights the potential of deep learning and image processing fusion in improving skin disease recognition and performance of classification systems. © 2024 IEEE.

关键词： Dermatology

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A Scrutiny of Machine Learning Methods for the Detection and Identification of Cyber Intrusion

A Scrutiny of Machine Learning Methods for the Detection and...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Eluri, Rama Krishna Valicharla, Karunakumar Divya, Modalavalasa Anusha, K.B. Narasaraopeta Engineering College Department Of Computer Science & Engineering Narasaraopet India Aditya Institute Of Technology And Management Department Of Computer Science & Engineering Tekkali India

ISBN: (纸本)9798350371567

This study investigates the interruption identification problem for organization safe havens;the main goal is to classify network behavior as normal or abnormal while minimizing misclassification. In this investigation, two effective information mining techniques are combined to identify network disruption. This methodology makes use of the two computations while avoiding their flaws, combining SVM, Decision Tree, and K Means together for effective information arrangement. This approach is implemented and surveyed using the standard benchmarking KDDCUP99 informational collecting. The experimental findings outperformed the other methods in terms of precision rate and run duration productivity, and this computation is suitable for detecting novel attack kinds. © 2024 IEEE.

关键词： Decision trees

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An Investigative Approach to Knee Osteoarthritis Detection Using Deep Learning on X-ray Radiographs

An Investigative Approach to Knee Osteoarthritis Detection U...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Ritom, Taki Tazwar Ahmed, Gazi Fayaz Sourov, Md. Arash Kursy Ahammed, Fahad Jennifer, Sanjeda Sara Morshed, Md. Adnan Reza, Ahmed Wasif Arefin, Mohammad Shamsul East West University Department of Computer Science and Engineering Dhaka Bangladesh Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331529765

A chronic joint condition called osteoarthritis affects the ligaments, capsule, synovium, subchondral bone, and articular cartilage. Osteoarthritis most frequently develops in the knee joints. A scoring system developed by Kellgren and Lawrence (KL) is used to measure the severity of osteoarthritis. The range of the KL score is 0-4. Grades 0 through 4 represent normal, doubtful mild, moderate, and severe, respectively. In this work, we classified 3 levels of Osteoarthritis by modifying the KL scoring system. This research identifies grade 0 for normal and doubtful, grade 1 for mild and moderate, and grade 2 for severe. To identify these classes from knee radiograph images, we used a deep-learning architecture namely ResNet-50, and customized it. Using our proposed model, we achieved 97% training accuracy. Clinical researchers will find this research useful in effectively determining the degree of osteoarthritis in the knee. © 2024 IEEE.

关键词： X ray radiography

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CR-SAM: Curvature Regularized Sharpness-Aware Minimization 38

CR-SAM: Curvature Regularized Sharpness-Aware Minimization

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Wu, Tao Luo, Tie Wunsch, Donald C. Department of Computer Science Missouri University of Science and Technology United States Department of Electrical and Computer Engineering Missouri University of Science and Technology United States

ISBN: (纸本)1577358872

The capacity to generalize to future unseen data stands as one of the utmost crucial attributes of deep neural networks. Sharpness-Aware Minimization (SAM) aims to enhance the generalizability by minimizing worst-case loss using one-step gradient ascent as an approximation. However, as training progresses, the non-linearity of the loss landscape increases, rendering one-step gradient ascent less effective. On the other hand, multi-step gradient ascent will incur higher training cost. In this paper, we introduce a normalized Hessian trace to accurately measure the curvature of loss landscape on both training and test sets. In particular, to counter excessive non-linearity of loss landscape, we propose Curvature Regularized SAM (CR-SAM), integrating the normalized Hessian trace as a SAM regularizer. Additionally, we present an efficient way to compute the trace via finite differences with parallelism. Our theoretical analysis based on PAC-Bayes bounds establishes the regularizer’s efficacy in reducing generalization error. Empirical evaluation on CIFAR and ImageNet datasets shows that CR-SAM consistently enhances classification performance for ResNet and Vision Transformer (ViT) models across various datasets. Our code is available at https://***/TrustAIoT/CR-SAM. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Monolithic full-color active-matrix micro-LED microdisplay using InGaN/AlGaInP heterogeneous integration

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Light(science & Applications) 2023年第11期12卷 2459-2467页

作者： Longheng Qi Peian Li Xu Zhang Ka Ming Wong Kei May Lau Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina

A prototype of full-color active-matrix micro-light-emitting diode(micro-LED)micro-display with a pixel density of 391 pixel per inch(ppi)using InGaN/AlGaInP heterogeneous integration is *** blue/green dual-color micro-LED arrays realized on a single metal organic chemical vapor deposition(MOCVD)-grown GaN-on-Si epiwafer and AlGaInP red micro-LED arrays are both monolithically fabricated,followed by the integration with a common complementary metal oxide semiconductor(CMOS)backplane via flip-chip bonding technology to form a doublelayer thin-film display ***-color images with decent color gamut and brightness are successfully displayed through the fine adjustment of driving current densities of RGB *** full-color display combines the advantages of high quantum efficiency of InGaN material on blue/green light and AlGaInP material on red light through heterogeneous integration and high pixel density through monolithic fabrication approach,demonstrating the feasibility and prospects of high brightness,good color performance,and high-resolution micro-LED microdisplays in future metaverse applications.

关键词： AlGaInP integration matrix

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Poster: MMEdge: Real-time Multimodal Inference via Cross-Modal Speculative Sensing and Encoding 26

Poster: MMEdge: Real-time Multimodal Inference via Cross-Mod...

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26th International Workshop on Mobile Computing Systems and Applications, HOTMOBILE 2025

作者： Huang, Runxi Ouyang, Xiaomin Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798400714030

Achieving real-time multimodal inference on devices is crucial for time-critical and privacy-sensitive applications. Existing methods primarily focus on optimizing the latency of model inference while neglecting the data collection process. We introduce MMEdge, an on-device, end-to-end multimodal inference framework. MMEdge employs a cross-modality speculative sensing and encoding approach to enhance inference efficiency and robustness at runtime. © 2025 Copyright held by the owner/author(s).

关键词： Edge computing

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