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Incentive Scheme for Shared Parking Space based on NFT 2

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Incentive Scheme for Shared Parking Space based on NFT

2nd International Conference on computer science and Blockchain, CCSB 2022

作者： Cai, Fugang Pan, Heng Si, Xueming Wu, Zhili Qian, Haiyang Henan International Joint Laboratory of Blockchain and Data Sharing Zhengzhou Henan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan China Zhongyuan University of Technology The Frontier Information Technology Research Institute China Shanghai Jiao Tong University Department of Computer Science and Engineer Shanghai China

ISBN: (纸本)9798350331882

The lack of centralized management causes problems in the system of distributed parking spaces, such as unsecured revenues, poor service quality and malicious valuation. Therefore, this paper proposes a parking sharing scheme based on non-fungible tokens. Firstly, we propose the calculation method of parking space renter reputation value, parking space provider reputation value and the reputation incentive algorithm of parking space sharing, which can ensure the service quality of distributed parking space share and prevent malicious evaluation and implement bidirectional constraints on parking space provider and renter. Secondly, we propose a parking space sharing model with non-fungible tokens as sharing credentials to map parking space assets to on-chain assets via non-fungible tokens and introduce timers based on hash time locks. We propose an intelligent parking rental contract based on improved hash lock to guarantee the revenue of parking space providers in a distributed environment. Finally, experiments based on Ethereum show that the scheme can realize shared parking space at a lower cost;the proposed incentive algorithm can effectively prevent parking space providers and renters from misbehaving. © 2022 IEEE.

关键词： Smart contract

A Novel Hybrid Optimizer Based on Coati Optimization Algorithm and Differential Evolution for Global Optimization and Constrained Engineering Problems

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International Journal of Computational Intelligence Systems 2025年第1期18卷 1-70页

作者： Biswas, Saptadeep Maiti, Binanda Singh, Gyan Ezugwu, Absalom E. Saleem, Kashif Abualigah, Laith Smerat, Aseel Bera, Uttam Kumar Department of Mathematics National Institute of Technology Agartala Tripura India Unit for Data Science and Computing North-West University Potchefstroom South Africa Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh Saudi Arabia Computer Science Department Al al-Bayt University Mafraq Jordan Faculty of Educational Sciences Al-Ahliyya Amman University Amman Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India Computer Technologies Engineering Mazaya University College Nasiriyah Iraq

This paper presents a novel hybrid metaheuristic, the Hybrid Coati Optimization Algorithm with Differential Evolution (HCOADE), developed to address complex global optimization tasks and constrained engineering design problems. HCOADE integrates the exploration-driven behaviour of the Coati Optimization Algorithm (COA)-inspired by the social foraging and predation strategies of coatis-with the powerful mutation and crossover mechanisms of Differential Evolution (DE), thereby achieving a balanced and adaptive search process. The hybridization enhances global exploration and local exploitation, enabling the algorithm to efficiently navigate diverse and challenging optimization landscapes. To rigorously evaluate its performance, HCOADE is tested on benchmark suites from CEC 2014, 2017, 2020, and 2022, which encompass unimodal, multimodal, hybrid, and composition functions. It is also applied to real-world constrained engineering problems, such as pressure vessel design, cantilever beam optimization, and reinforced concrete beam design. Comparative experiments against state-of-the-art algorithms—including COA, DE, RSA, PSO, SSA, BBO, QIO, DMOA, and others—demonstrate that HCOADE consistently delivers superior solution quality, faster convergence, and higher robustness. Quantitative results show that HCOADE achieved the 1st place average rank across all four benchmark suites. It obtained top performance on 80% of the functions in CEC 2014, 66.7% in CEC 2017, 70% in CEC 2020, and 66.7% in CEC 2022. Furthermore, HCOADE outperformed or matched CEC competition-winning algorithms such as LSHADE-cnEpSin, LSHADE-SPACMA, and CMA-ES on numerous challenging functions of CEC 2017. Statistical analyses, including Wilcoxon Rank Sum Tests and ranking evaluations, confirm the significance and reliability of HCOADE’s performance. Furthermore, convergence behaviour, measurement of exploration and exploitation, and sensitivity analysis highlight the algorithm’s adaptability and stability

关键词：

The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Ma, Jiating Zhou, Yipeng Li, Qi Sheng, Quan Z. Cui, Laizhong Liu, Jiangchuan Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen518060 China Macquarie University School of Computing Faculty of Science and Engineering Macquarie ParkNSW2119 Australia Tsinghua University Institute for Network Sciences and Cyberspace Beijing100190 China Beijing National Research Center for Information Science and Technology Beijing100190 China Simon Fraser University School of Computing Science BurnabyBCV5A 1S6 Canada

To preserve the data privacy, the federated learning (FL) paradigm emerges in which clients only expose model gradients rather than original data for conducting model training. To enhance the protection of model gradients in FL, differentially private federated learning (DPFL) is proposed which incorporates differentially private (DP) noises to obfuscate gradients before they are exposed. Yet, an essential but largely overlooked problem in DPFL is the heterogeneity of clients' privacy requirement, which can vary significantly between clients and extremely complicates the client selection problem in DPFL. In other words, both the data quality and the influence of DP noises should be taken into account when selecting clients. To address this problem, we conduct convergence analysis of DPFL under heterogeneous privacy, a generic client selection strategy, popular DP mechanisms and convex loss. Based on convergence analysis, we formulate the client selection problem to minimize the value of loss function in DPFL with heterogeneous privacy, which is a convex optimization problem and can be solved efficiently. Accordingly, we propose the DPFL-BCS (biased client selection) algorithm. The extensive experiment results with real datasets under both convex and non-convex loss functions indicate that DPFL-BCS can remarkably improve model utility compared with the SOTA baselines. © 2004-2012 IEEE.

关键词： Convex optimization

An Algorithm for Detecting Surface Defects in Industrial Strip Steel based on Receptive Field and Feature Information Supplementation

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An Algorithm for Detecting Surface Defects in Industrial Str...

International Conference on computer Supported Cooperative Work in Design

作者： Qingyu Xu Jiguo Yu Anming Dong Zihao Shang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Data Intelligence Information Technology Co. Ltd. Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the context of Industry 4.0 and the rise of intelligent manufacturing, the quality of industrial products is becoming more and more important. Strip steel surface defect detection, as a key link in industrial production, is crucial to ensure the quality of industrial products. However, due to the irregularity of the defect scale and the inconspicuous defect features in the surface defect image of strip steel, it is difficult for the existing detection algorithms to realize the effective detection of defects. In order to better extract the features of defects and improve the network’s ability to detect defects, this paper proposes an algorithm for detecting surface defects on industrial strip steel based on receptive field and feature information supplementation. First, we design a receptive field (RF) module to replace the residual structure in the C3 module, which we name C3RF. This module can effectively increase the network’s receptive field, allowing the network to fully capture irregular defect features without increasing the cost. Second, for the characteristics that defective features are not obvious and tend to lose detail information as the network deepens, an extra information supplemental branching feature fusion pyramid (EFPN) is proposed on the basis of the original PAFPN architecture to compensate for the detail information that is lost by the fragile features in deeper layers. Finally, convolutional block attention module (CBAM) is introduced to replace the spatial pooling pyramid (SPPF) in the baseline network, which enhances the contrast between defects and backgrounds, and improves the classification and localization ability of the network. Our network achieves an accuracy of 82.2% on the publicly available strip steel defect detection dataset, which is a 4.0% improvement over the baseline. The results show that the network constructed in this paper can realize effective defect detection.

关键词： Radio frequency Location awareness Strips Production Feature extraction Real-time systems Hardware

Disentangling degree and tie strength heterogeneity in egocentric social networks

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arXiv 2024年

作者： Heydari, Sara Iñiguez, Gerardo Kertész, János Saramäki, Jari Department of Computer Science Aalto University School of Science Aalto00076 Finland Faculty of Information Technology and Communication Sciences Tampere University Tampere33720 Finland Department of Network and Data Science Central European University Vienna1100 Austria Centro de Ciencias de la Complejidad Universidad Nacional Autonóma de México Ciudad de México04510 Mexico Complexity Science Hub Vienna1080 Austria

The structure of personal networks reflects how we organise and maintain social relationships. The distribution of tie strengths in personal networks is heterogeneous, with a few close, emotionally intense relationships and a larger number of weaker ties. Recent results indicate this feature is universal across communication channels. Within this general pattern, there is a substantial and persistent inter-individual variation that is also similarly distributed among channels. The reason for the observed universality is yet unclear-one possibility is that people's traits determine their personal network features on any channel. To address this hypothesis, we need to compare an individual's personal networks across channels, which is a non-trivial task: while we are interested in measuring the differences in tie strength heterogeneity, personal network size is also expected to vary a lot across channels. Therefore, for any measure that compares personal networks, one needs to understand the sensitivity with respect to network size. Here, we study different measures of personal network similarity and show that a recently introduced alter-preferentiality parameter and the Gini coefficient are equally suitable measures for tie strength heterogeneity, as they are fairly insensitive to differences in network size. With these measures, we show that the earlier observed individual-level persistence of personal network structure cannot be attributed to network size stability alone, but that the tie strength heterogeneity is persistent too. We also demonstrate the effectiveness of the two measures on multichannel data, where tie strength heterogeneity in personal networks is seen to moderately correlate for the same users across two communication channels (calls and text messages). Copyright © 2024, The Authors. All rights reserved.

关键词： Communication channels (information theory)

Bayesian Optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction Model 5

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Bayesian Optimization with Stacked Sparse Autoencoder based ...

5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Shaikh, Irfan Abdul Karim Krishna, P. Vamsi Biswal, Swagat Gourav Kumar, A. Sasi Baranidharan, S. Singh, Kamlesh Faculty of Business Bahrain Polytechnic Isa Town Bahrain School of Management Malla Reddy University Telangana Hyderabad India Odisha University of Tecnology and Research Department of Electrical Engineering Orissa Bhubaneswar India Institute of Engineering and Technology Srinivas University Mentor-IT-Inurture Education Solutions Pvt Ltd Bangalore Department of Cloud Technology and Data Science Karnataka Dakshina Kannada India Department of Mba and Research Center Karnataka Bangalore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun Campus Dehradun India

ISBN: (纸本)9781665474672

Digital currency is a way of currency utilized in the digital world namely electronic devices or digital forms. Many terms are alternative words for digital currency such as cyber cash, digital money, and electronic money. Cryptocurrency is a type of asset that has developed due to the progression of financial technology and it has made a tremendous chance for research workers. Cryptocurrency price prediction is challenging because of the dynamism and price volatility. The electronic economy is severely hazardous and should be advanced with greater caution, to minimize or avoid the risk that occurs in this case. Therefore, this study develops a new Bayesian optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction (BOSSAE-CPP) model. The major intention of the BOSSALCPP technique lies in the effectual prediction of cryptocurrency prices. To attain this, the BOSSAE-CPP technique exploits SSAE model for price prediction process. Moreover, the BO technique is used to optimally choose the hyperparameter values of the SSAE model and results in enhanced predictive outcomes. To deliberate the enhanced outcomes of the BOSSALCPP technique, extensive experimentation study is made. The comparison study highlighted the improved performance of the BOSSAE-CPP technique. © 2023 IEEE.

关键词： Electronic money

ACTC: Active Threshold Calibration for Cold-Start Knowledge Graph Completion

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arXiv 2023年

作者： Sedova, Anastasiia Roth, Benjamin Research Group Data Mining and Machine Learning University of Vienna Austria UniVie Doctoral School Computer Science University of Vienna Austria Faculty of Philological and Cultural Studies University of Vienna Austria

Self-supervised knowledge-graph completion (KGC) relies on estimating a scoring model over (entity, relation, entity)-tuples, for example, by embedding an initial knowledge graph. Prediction quality can be improved by calibrating the scoring model, typically by adjusting the prediction thresholds using manually annotated examples. In this paper, we attempt for the first time cold-start calibration for KGC, where no annotated examples exist initially for calibration, and only a limited number of tuples can be selected for annotation. Our new method ACTC finds good per-relation thresholds efficiently based on a limited set of annotated tuples. Additionally to a few annotated tuples, ACTC also leverages unlabeled tuples by estimating their correctness with Logistic Regression or Gaussian Process classifiers. We also experiment with different methods for selecting candidate tuples for annotation: density-based and random selection. Experiments with five scoring models and an oracle annotator show an improvement of 7% points when using ACTC in the challenging setting with an annotation budget of only 10 tuples, and an average improvement of 4% points over different budgets. © 2023, CC BY.

关键词： Calibration

Improving Matrix-vector Multiplication via Lossless Grammar-Compressed Matrices

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arXiv 2022年

作者： Ferragina, Paolo Gagie, Travis Köppl, Dominik Manzini, Giovanni Navarro, Gonzalo Striani, Manuel Tosoni, Francesco Department of Computer Science University of Pisa Italy Faculty of Computer Science Dalhousie University Canada M&D Data Science Center Tokyo Medical Dental University Japan Millennium Institute for Foundational Research on Data Department of Computer Science University of Chile Chile Department of Sciences and Technological Innovation University of Piemonte Orientale Italy

As nowadays Machine Learning (ML) techniques are generating huge data collections, the problem of how to efficiently engineer their storage and operations is becoming of paramount importance. In this article we propose a new lossless compression scheme for real-valued matrices which achieves efficient performance in terms of compression ratio and time for linear-algebra operations. Experiments show that, as a compressor, our tool is clearly superior to gzip and it is usually within 20% of xz in terms of compression ratio. In addition, our compressed format supports matrix-vector multiplications in time and space proportional to the size of the compressed representation, unlike gzip and xz that require the full decompression of the compressed matrix. To our knowledge our lossless compressor is the first one achieving time and space complexities which match the theoretical limit expressed by the k-th order statistical entropy of the input. To achieve further time/space reductions, we propose column-reordering algorithms hinging on a novel column-similarity score. Our experiments on various data sets of ML matrices show that, with a modest preprocessing time, our column reordering can yield a further reduction of up to 16% in the peak memory usage during matrix-vector multiplication. Finally, we compare our proposal against the state-of-the-art Compressed Linear Algebra (CLA) approach showing that ours runs always at least twice faster (in a multi-thread setting) and achieves better compressed space occupancy for most of the tested data sets. This experimentally confirms the provably effective theoretical bounds we show for our compressed-matrix approach. © 2022, CC BY-NC-ND.

关键词： Matrix algebra

Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal

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arXiv 2023年

作者： Sedova, Anastasiia Zellinger, Lena Roth, Benjamin Research Group Data Mining and Machine Learning University of Vienna Austria UniVie Doctoral School Computer Science University of Vienna Austria Faculty of Philological and Cultural Studies University of Vienna Austria

An accurate and substantial dataset is essential for training a reliable and well-performing model. However, even manually annotated datasets contain label errors, not to mention automatically labeled ones. Previous methods for label denoising have primarily focused on detecting outliers and their permanent removal – a process that is likely to over- or underfilter the dataset. In this work, we propose AGRA: a new method for learning with noisy labels by using Adaptive GRAdient-based outlier removal1. Instead of cleaning the dataset prior to model training, the dataset is dynamically adjusted during the training process. By comparing the aggregated gradient of a batch of samples and an individual example gradient, our method dynamically decides whether a corresponding example is helpful for the model at this point or is counter-productive and should be left out for the current update. Extensive evaluation on several datasets demonstrates AGRA’s effectiveness, while a comprehensive results analysis supports our initial hypothesis: permanent hard outlier removal is not always what model benefits the most from. © 2023, CC BY.

关键词： Statistics