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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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Suitability of SDN and MEC to facilitate digital twin communication over LTE-A

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Digital Communications and Networks 2024年第2期10卷 347-354页

作者： Hikmat Adhami Mohammad Alja’afreh Mohamed Hoda Jiaqi Zhao Yong Zhou Abdulmotaleb El Saddik Mohamed bin Zayed University of Artificial Intelligence Masdar CityAbu DhabiUnited Arab Emirates School of Computer Science and Technology China University of Mining and TechnologyXuzhou221116China School of Electrical Engineering and Computer Science University of OttawaOttawaONK1N 6N5Canada

Haptic is the modality that complements traditional multimedia,i.e.,audiovisual,to evolve the next wave of innovation at which the Internet data stream can be exchanged to enable remote skills and control *** will require ultra-low latency and ultra-high reliability to evolve the mobile experience into the era of Digital Twin and Tactile *** the 5th generation of mobile networks is not yet widely deployed,Long-Term Evolution(LTE-A)latency remains much higher than the 1 ms requirement for the Tactile Internet and therefore the Digital *** work investigates an interesting solution based on the incorporation of Software-defined networking(SDN)and Multi-access Mobile Edge Computing(MEC)technologies in an LTE-A network,to deliver future multimedia applications over the Tactile Internet while overcoming the QoS *** network scenarios were designed and simulated using Riverbed modeler and the performance was evaluated using several time-related Key Performance Indicators(KPIs)such as throughput,End-2-End(E2E)delay,and *** best scenario possible is clearly the one integrating MEC and SDN approaches,where the overall delay,jitter,and throughput for haptics-attained 2 ms,0.01 ms,and 1000 packets per *** results obtained give clear evidence that the integration of,both SDN and MEC,in LTE-A indicates performance improvement,and fulfills the standard requirements in terms of the above KPIs,for realizing a Digital Twin/Tactile Internet-based system.

关键词： LTE-A 5G Digital twin Tactile Internet KPIs SDN MEC NFV Haptic

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Water Quality Monitoring and Control in Urban Areas in Real-Time via IoT and Mobile Applications 3

Water Quality Monitoring and Control in Urban Areas in Real-...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohanasundaram, R. Sagar, Raghav Kumar Khandelwal, Animan Upadhyay, Diwas Poddar, Harshit Rajagopal, Sivakumar Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798350372120

The presence of impurities in water resulting from the expansion of industrial activities and the disregard of regulations aimed at protecting the environment presents considerable threats to the well-being of the general population. The establishment of reliable access to uncontaminated drinking water assumes paramount importance in the realm of disease prevention. The continuous monitoring of the quality of water in real-time serves as an indispensable measure for ensuring its safety. The objective of this article is to introduce a system that capitalizes on the Internet of Things (IoT) to automate the distribution and management of water resources within urban areas. By making use of embedded technology, this system allows for the uninterrupted surveillance and regulation of water quality. To further streamline the management process, an application compatible with the Android operating system has been developed to promptly notify users of any irregularities, thereby enabling remote control. Moreover, a web-based application has been designed to cater to the needs of iOS users, thereby broadening accessibility. This comprehensive approach not only heightens the level of user awareness but also provides them with greater control over their water supply, thereby enhancing public health outcomes. © 2024 IEEE.

关键词： Internet of things

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Point cloud upsampling generative adversarial network based on residual multi-scale off-set attention

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Virtual Reality & Intelligent Hardware 2023年第1期5卷 81-91页

作者： Bin SHEN Li LI Xinrong HU Shengyi GUO Jin HUANG Zhiyao LIANG School of Computer and Artificial Intelligence Wuhan Textile UniversityHubei 430200China School of Computer Science and Engineering Macao University of Science and TechnologyMacao China

Background Owing to the limitations of the working principle of three-dimensional(3D) scanning equipment, the point clouds obtained by 3D scanning are usually sparse and unevenly distributed. Method In this paper, we propose a new generative adversarial network(GAN) that extends PU-GAN for upsampling of point clouds. Its core architecture aims to replace the traditional self-attention(SA) module with an implicit Laplacian offset attention(OA) module and to aggregate the adjacency features using a multiscale offset attention(MSOA)module, which adaptively adjusts the receptive field to learn various structural features. Finally, residual links are added to create our residual multiscale offset attention(RMSOA) module, which utilizes multiscale structural relationships to generate finer details. Result The results of several experiments show that our method outperforms existing methods and is highly robust.

关键词： Point cloud upsampling Generative adversarial network Attention

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Adaptive Edge Caching in mmWave Integrated Access and Backhaul Networks 11

Adaptive Edge Caching in mmWave Integrated Access and Backha...

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11th International Symposium on Telecommunication, IST 2024

作者： Rashidi, Zahra Nazarifard, Fatemeh Sadat Hashemi Hakami, Vesal Iran University of Science and Technology School of Computer Engineering Tehran Iran Iran University of Science and Technology Center of Excellence in Future Networks School of Computer Engineering Tehran Iran

ISBN: (纸本)9798350356250

Caching popular files at the small base stations has proved to be an effective strategy for reducing the content delivery delay in cellular networks and alleviating backhaul congestion. The challenging characteristics of radio propagation, the use of highly directional transmission in future-generation cellular networks, and the popularity of content lead to more complex and critical problems in content placement. In this paper, we propose a mathematical formulation for the centralized optimization of content placement at integrated access and backhaul (IAB) nodes to minimize the average content delivery latency in millimeter wave (mmWave) IAB cellular communications. We consider the dynamics of links and the time-varying popularity of contents as Markov decision processes (MDP) and propose a deep reinforcement learning framework to obtain a solution with low computation complexity. Simulation experiments are conducted to investigate the effectiveness of the proposed learning algorithm as well as to compare it against some schemes with different levels of adaptation to the system dynamics. © 2024 IEEE.

关键词： Markov processes

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid Data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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Prediction of railway track irregularity based on TCN-BiLSTMs-Attention model 24

Prediction of railway track irregularity based on TCN-BiLSTM...

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3rd International Conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Wu, Chen Lu, Xiaofeng Wang, Yiyun Pang, Tiantian School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an China

ISBN: (纸本)9798400710834

Track irregularities can significantly reduce the comfort and safety of train operation. If the development trend of track irregularities can be predicted, the railway management department can issue early warnings to ensure the safe and smooth operation of trains. The sequence data associated with track irregularities exhibit complex nonlinear and non-stationary characteristics, and predictions based solely on the track quality index (TQI) do not take into account the influence of other factors. To address this issue, this paper proposes a model based on TCN-BiLSTMs-Attention for predicting the development trends of track irregularities. First, the Time Convolutional Network (TCN) is employed to extract features from the preprocessed multivariate input data of track irregularities, so as to capture the impact of various indicators on TQI values and provide richer inputs for subsequent model stages. The data is then fed into a multi-layer bidirectional long short-term memory network (BiLSTM) for training. The stacked BiLSTM layers utilize the non-linear changes and long-term dependencies of TQI sequence data that LSTM networks can learn, as well as the bidirectional LSTM, to simultaneously process past and future information. This increases the depth of the model, further improving its learning ability. Subsequently, the attention mechanism module is used for information extraction, enhancing the model’s sensitivity to key information and its ability to capture long-distance dependencies. Finally, the prediction results are output through a fully connected layer. Comparative experiments with multiple models have shown that the proposed TCN-BiLSTMs-Attention model yields more accurate predictions and has strong robustness. © 2024 Copyright held by the owner/author(s).

关键词： Railroads

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Even Search in a Promising Region for Constrained Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 474-486页

作者： Fei Ming Wenyin Gong Yaochu Jin School of Computer Science China University of GeosciencesWuhan 430074China IEEE Faculty of Technology Bielefeld UniversityNorth Rhine-Westphalia 33619Germany School of Engineering Westlake UniversityHangzhou 310030China

In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general ***, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.

关键词： Constrained multi-objective optimization even search evolutionary algorithms promising region real-world problems

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The Effect of Artificial Intelligence on Data Security Systems 5th

The Effect of Artificial Intelligence on Data Security Syste...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Chaure, Suvarna Punjabi, Sunil Department of Computer Engineering SIES Graduate School of Technology Maharashtra Nerul India

ISBN: (纸本)9783031770807

Artificial intelligence (AI) in its many forms is leading the charge in spurring breakthroughs in digital security in response to the emerging challenges in the post-COVID environment. On the one hand, businesses are finding it challenging to handle security risks related to a range of concerns, including web domain, decision-making, quality control, and system openness, to name a few. However, to comprehend the relationship between AI and those problems, research over the past ten years has concentrated on security capabilities based on instruments like platform complacency, intelligent trees, modeling techniques, and outage management systems. The literature has long acknowledged how important artificial intelligence will be in directing industries and reshaping the transportation, health, and education sectors. This paper presents an Artificial Intelligence-based Security method for Banking Sector (AISBS). AI is one of the best technologies for mapping and preventing unforeseen hazards from consuming an organization. The proposed method can be used to categorize, and resolve cyberattack issues. Algorithms like the Enhanced Encryption Standard (EES) encrypt and decrypt data to guarantee the security of financial sector data. The K-Nearest Neighbor (KNN) algorithm generates predictions by using its training data to make predictions. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Decision making

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Data Driven Approach for Analysing the Relationship between Weather Patterns and Energy Consumption in Residential Buildings for Improved Energy Management 5

Data Driven Approach for Analysing the Relationship between ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kowshik, S.B. Ramakrishnan, R. Amutha, S. Dhanush, R. Akshay, P. Preetam Kalyaan, P. School of Electronics Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331518097

Residential buildings are the most significant providers of global energy consumption. Hence, studying the weather patterns and energy use in residential buildings remains vital for effective energy management strategies. This work explores a data-driven approach to analyse the complex interplay between weather and energy consumption in residential settings. Traditional methods for understanding building energy use often rely on physical models or occupant surveys, which can be time-consuming and expensive. This research addresses the need for a more efficient and comprehensive approach to analyse the effect of weather patterns on energy consumption. Following deep learning sequence models were used to most accurately predict the energy consumption - LSTM (Long Short Term Memory), Bi-LSTM (Bidirectional LSTM) and Attention Based LSTM (ABLSTM) and find the ABLSTM model most accurate and suited for our requirement with RMAE value as 0.3086, R2 as 0.9990. This research provides individuals and communities with the knowledge to make informed decisions about their energy usage, potentially leading to reduced energy costs through optimized consumption and can contribute to improved energy security. © 2024 IEEE.

关键词： Building energy prediction Data driven Deep learning Energy shortage LSTM Sustainability Sustainable energy

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