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8th International Workshop on Cultures of Participation in the Digital Age: Differentiating and Deepening the Concept of "End User" in the Digital Age, CoPDA 2024

作者： Mannari, Chiara Anichini, Elisa Bacco, Manlio Ferrari, Alessio Turchi, Tommaso Malizia, Alessio Department of Computer Science University of Pisa Italy Institute of Information Science and Technologies "Alessandro Faedo" - ISTI CNR Pisa Italy Faculty of Logistics Molde University College Norway

Digital technologies show promising potential in the development of sustainable agriculture. For example, the combination of cloud and edge paradigms, 5G, and the Internet of Things (IoT) allows the development of sophisticated applications — e.g., for food traceability, pest detection, and automatic irrigation — with the possibility to also exploit Artificial Intelligence (AI)-powered techniques. At the same time, digitalisation in agriculture is a socio-technical process that involves several classes of stakeholders with diverse backgrounds and skills, e.g., in farming or technology. Model-driven approaches leveraging diagrammatic notations can support information exchange between different domains. In fact, the development of models can be a co-design practice involving end-users throughout all phases of creation because of their expressive power. However, current modelling platforms are typically oriented toward engineers, and there is a lack of tools accessible to end-users for designing and modelling systems. In this position paper, we present ModeLLer, a prototype of a web environment for modelling systems based on an intuitive visual language that can be exported into standard code. The aim is to increasingly involve users in modelling their digital ecosystems as a task for developing digital applications. © 2024 Copyright for this paper by its authors.

关键词： Visual languages

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MSC: A Unique Chameleon Hash-Based Off-Chain Storage Framework for Metaverse Applications

MSC: A Unique Chameleon Hash-Based Off-Chain Storage Framewo...

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作者： Xiong, Chenxi Yang, Ting Mao, Gang School of Computer Science and Technology University of Electronic Science and Technology of China Chengdu China The Second Research Institute of CAAC Civil Aviation Logistics Technology Company Ltd. Chengdu China

Blockchain has evolved into a secure and trustworthy environment for decentralized applications, offering the advantages of tamper-resistant, while simultaneously introducing on-chain overhead issues. The development of metaverse related smart contracts on blockchain has given rise to a compelling research inquiry concerning the secure reduction of on-chain storage overhead. In this research, the Metaverse Off-chain Storage Framework based on Chameleon hash (MSC), a unique framework for decentralized system based on chameleon hash, supports code or stored data updates without changing on-chain data is presented. The index of decentralized applications' data is calculated using the Chameleon hash to ensure that the index remains unchanged during the data modification process. Simultaneously, data can be stored outside of the blockchain with proper authentication mechanisms in place. The experimental results have shown that MSC exhibits reduced on-chain storage requirements when compared to similar frameworks. Furthermore, MSC significantly reduced overhead as compared to the direct storage of data within a smart contract. © 2024 The Author(s). International Journal of Network Management published by John Wiley & Sons Ltd.

关键词： Decentralized systems

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Enhanced and improved garbage identification and classification of YOLOV5 based on data 5

Enhanced and improved garbage identification and classificat...

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5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Wang, Junxi Zhan, Xin Yang, Zicong Li, Yangwen Zhuhai College of Science and Technology Department of Electronic Mechanical Engineering Zhuhai China Zhuhai College of Science and Technology Department of Computer Zhuhai China Zhuhai College of Science and Technology Department of Logistics Zhuhai China

ISBN: (纸本)9798350373820

In the current social background, the problem of garbage classification highlights its importance, and intelligent solutions are urgently needed. This research focuses on the study of a convolutional neural network-based garbage sorting robot detection algorithm model to effectively address this challenge. By using data enhancement technology, this study processed garbage images to improve the adaptability and robustness of the model. The adversarial sample generation technology is introduced to further enhance the ability of the model to resist noise and attack. Combined with YOLOV5 and the CA attention mechanism, the model performs well in garbage sorting tasks. In the training and testing of 3700 images, the garbage classification recognition accuracy rate reached 96.70%. This study not only provides intelligent and efficient solutions for the field of garbage sorting but also highlights the value of scientific literacy in practical problem solving. © 2024 IEEE.

关键词： Sorting

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Fixed-Parameter Tractability of the (1 + 1) Evolutionary Algorithm on Random Planted Vertex Covers 23

Fixed-Parameter Tractability of the (1 + 1) Evolutionary Alg...

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17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, FOGA 2023

作者： Kearney, Jack Neumann, Frank Sutton, Andrew M. Algorithmic Evolution Lab Department of Computer Science University of Minnesota Duluth DuluthMN United States Optimisation and Logistics Group School of Computer Science University of Adelaide AdelaideSA Australia

ISBN: (纸本)9798400702020

We present the first parameterized analysis of a standard (1+1) Evolutionary Algorithm on a distribution of vertex cover problems. We show that if the planted cover is at most logarithmic, restarting the (1+1) EA every O(n log n) steps will find a cover at least as small as the planted cover in polynomial time for sufficiently dense random graphs p > 0.71. For superlogarithmic planted covers, we prove that the (1+1) EA finds a solution in fixed-parameter tractable time in expectation. We complement these theoretical investigations with a number of computational experiments that highlight the interplay between planted cover size, graph density and runtime. © 2023 ACM.

关键词： Evolutionary algorithms

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Design and implementation of OLT switching function in 40/10G TDM-PON experimental system

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Journal of Optical Communications 2023年第4期44卷 437-446页

作者： Han, Juntao Yang, Rongchuan Tang, Chaosheng School of E-Commerce and Logistics Management Henan University of Economics and Law Henan Zhengzhou450046 China School of Computer Science and Technology Henan Polytechnic University Henan Jiaozuo454000 China

The access network is developing towards large capacity, wide-coverage, and low delay, which puts forward higher requirements for network structure and data exchange performance of optical access network equipment. In this paper, we have designed a high-speed optical network device with 40/10G TDM-PON. To effectively solve the problem of network switching capability in high-speed data transmission, we propose a low delay large capacity switching algorithm, which minimizes the network switching delay. We model the network by integer linear programming and simulate the network based on OMNeT++. The simulation results show that the algorithm can effectively solve the problem of network switching delay in the high-speed network environment. © 2021 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Electronic data interchange

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Transformer-Based Cloud Detection Method for High-Resolution Remote Sensing Imagery

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computers, Materials & Continua 2024年第7期80卷 661-678页

作者： Haotang Tan Song Sun Tian Cheng Xiyuan Shu Key Laboratory of GIS Application Research School of Geography and TourismChongqing Normal UniversityChongqing401331China College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Department of Military Installations Army Logistics AcademyChongqing401331China

Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and *** the limitations of conventional convolutional neural networks,we propose an innovative transformer-based *** method leverages transformers,which are adept at processing data sequences,to enhance cloud detection ***,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud *** extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud *** integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation ***,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.

关键词： Cloud transformer image segmentation remotely sensed imagery pyramid vision transformer

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Finding Antimagic Labelings of Trees by Evolutionary Search 23

Finding Antimagic Labelings of Trees by Evolutionary Search

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17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, FOGA 2023

作者： Branson, Luke Sutton, Andrew M. Yan, Xiankun Algorithmic Evolution Lab Department of Computer Science University of Minnesota Duluth DuluthMN United States Optimisation and Logistics Group School of Computer Science University of Adelaide AdelaideSA Australia

ISBN: (纸本)9798400702020

Randomized search heuristics can sometimes be effective verifiers for combinatorial conjectures. In this paper, we demonstrate how a simple evolutionary algorithm can be used to confirm the antimagic tree conjecture for all trees up to order 25. This conjecture, which has been open for over thirty years, is that every tree except K2 has an antimagic labeling: a bijective edge labeling such that the sum of labels assigned to edges incident to a vertex v is unique for all vertices v μ V. Moreover, we formally prove that that simple evolutionary algorithms are guaranteed to find antimagic labelings in expected polynomial time on trees of any order for certain restricted classes (paths, combs, uniform caterpillars, uniform spiders and perfect binary trees). © 2023 ACM.

关键词： Combinatorial optimization

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An Optimal Deep Learning for Cooperative Intelligent Transportation System

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computers, Materials & Continua 2022年第7期72卷 19-35页

作者： K.Lakshmi Srinivas Nagineni E.Laxmi Lydia A.Francis Saviour Devaraj Sachi Nandan Mohanty Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Periyar Maniammai Institute of Science&TechnologyThanjavur613403India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyTelangana500075India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Engineering College of Engineering PunePuneMaharashtra411005India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related *** the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS *** the same time,prediction of short-term traffic flow is highly essential to manage the traffic *** to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow *** paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so *** presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow *** IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count *** addition,deep belief network(DBN)model is applied for the prediction of short-term traffic *** further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of *** results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

关键词： Cooperative intelligent transportation systems traffic flow prediction deep belief network deep learning vehicle counting

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A Meta-Analysis of AI in Fraud Detection: Evaluating the Effectiveness of Different Algorithms and Data Sources 2

A Meta-Analysis of AI in Fraud Detection: Evaluating the Eff...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Chouhan, Nitu Kediya, Shailesh Wagh, Umesh Deshpande, Prasanna Karmore, Pravin Das, Debasis Symbiosis Skills and Professional University School of Ports Terminal Management and Logistics Pune India Symbiosis Skills and Professional University School of Logistics and Supply Chain Management Pune India G.S College of Commerce Wardha India School of Ports Terminal Management and Logistics Symbiosis Skills and Professional University Kiwale Pune India Ramdeobaba University Department of Computer Science and Applications Nagpur India Webster University Department of Computer Science Tashkent Uzbekistan

ISBN: (纸本)9798331528713

The widespread use of artificial intelligence (AI) techniques has changed the way fraud is found in many areas, offering better accuracy and speed compared to older methods. This meta-analysis looks at how well distinctive AI calculations and information sources work at finding tricks. The objective is to deliver a full picture of how things are done presently and discover places where more ponder is required. The think about takes the comes about of numerous diverse inquire about papers and centers on how AI can be utilized to discover tricks in areas like healthcare, managing an account, and e-commerce. A few of the foremost critical AI calculations that are looked at are machine learning models (like Back Vector Machines, Arbitrary Woodlands, and Neural Systems), strategies for finding exceptions (like Segregation Woodland and One-Class SVM), and blended approaches that utilize more than one method to urge way better comes about. To see if each calculation is sweet at finding extortion totally different circumstances, its execution measures are carefully looked at. These incorporate precision, review, F1-score, and range beneath the bend (AUC). The meta-analysis too looks into how distinctive information sources influence how well AI models work. It looks at how to utilize organized and unstructured information, money related records, client behavior designs, content information from client audits, and data from social systems and IoT gadgets approximately the setting of the information. Putting these distinctive sorts of information together is implied to create AI frameworks way better at finding tricks by catching complicated patterns and other ways of assaulting. The audit finds critical patterns in AI-based extortion location, such as the move toward real-time handling and the utilize of logical AI models to create things clearer and simpler to understand. It too talks about problems like information protection issues, uneven datasets, and the ought to keep

关键词： Artificial Intelligence Data Sources Fraud Detection Machine Learning Algorithms Performance Evaluation

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Evaluation of Graduate Employment Ability Based on Ahp Entropy Weight Model 20

Evaluation of Graduate Employment Ability Based on Ahp Entro...

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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Liyin, Liu Jianping, Li Jie, Tian University of Electronic Science and Technology of China Logistics Support Department Chengdu611731 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

In order to achieve a scientific and reasonable evaluation of graduate employability and promote high-quality employment, this article combines the AHP method and entropy weight method to construct an evaluation model for graduate employability. Firstly, a three-layer indicator model for evaluating graduate students' employability is constructed through six dimensions: scientific research ability, learning ability, cognitive ability, practical ability, workplace ability, and job application ability. Then, based on the AHP method and entropy weight method, the weights of various indicators in the criterion layer and indicator layer were analyzed, and the final weights were calculated by combining weights. Finally, it was demonstrated through empirical analysis that the model can provide a scientific and reasonable evaluation of graduate students' employability. Research shows that (1) graduate students' employability is influenced by multiple abilities, among which research ability has a relatively significant impact on employability;(2) Graduate students as a whole exhibit different characteristics under different indicators, and indicators with relatively scattered data have guiding significance for graduate management and employment;(3) The overall score of graduate students reflects their employability, which is beneficial for guiding role model promotion or employment assistance work. © 2023 IEEE.

关键词： Employment

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