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6th International Conference on Natural Language Processing and Information Retrieval, NLPIR 2022

作者： Hloch, Mark Van Meegen, Markus Kubek, Mario Unger, Herwig Faculty of Electrical Engineering and Computer Science Hochschule Niederrhein - University of Applied Sciences Krefeld Germany Department of Computer Science Georgia State University Atlanta United States Fern Universität Hagen Chair of Computer Engineering Hagen Germany

ISBN: (纸本)9781450397629

Classical tasks of a librarian, such as screening and categorizing new documents based on their content, are increasingly replaced by search engines or through the use of cataloging software. A first overview of a corpus topical orientation can be achieved by combining graph-based search engines and clustering methods. Existing classical clustering methods, however, often require an a priori specification of the desired number of clusters to be output and do not consider term relationships in graphs, which is deficient from a practical point of view. Therefore, fully unsupervised graph-based clustering approaches at the term level offer new possibilities that mitigate these shortcomings. Within this work, a set of novel graph-based clustering algorithms have been developed. The hierarchical clustering algorithm (HCA) forms term hierarchies by iteratively isolating nodes of a given co-occurrence graph based on the evaluation of the edge weight between the nodes. Based on the co-occurrence graph inherent relationships of terms, a new graph is built agglomerative forming individual term clusters of related terms. The feasibility of the outlined methods for text analysis is shown. © 2022 ACM.

关键词： Cluster analysis

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A review: Challenges and opportunities for artificial intelligence and robotics in the offshore wind sector

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Energy and AI 2022年第2期8卷 177-212页

作者： Daniel Mitchell Jamie Blanche Sam Harper Theodore Lim Ranjeetkumar Gupta Osama Zaki Wenshuo Tang Valentin Robu Simon Watson David Flynn Smart Systems Group Institute of SensorsSignals and SystemsSchool of Engineering and Physical SciencesHeriot-Watt UniversityEdinburghEH144ASUnited Kingdom Centre for Mathematics and Computer Science Intelligent and Autonomous Systems GroupCWIAmsterdam1098 XG Netherlands Algorithms Group Faculty of Electrical EngineeringMathematics and Computer Science(EEMCS)Delft University of Technology(TU Delft)Delft2628 XE Netherlands The University of Manchester Department of Electrical and Electronic EngineeringOxford RoadManchesterM139PL United Kingdom James Watt School of Engineering University of GlasgowG128QQUnited Kingdom

The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.

关键词： Artificial intelligence Autonomous systems Digitalization Offshore renewable energy Offshore wind farms Robotics

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Foreword

Foreword

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作者： Abawajy, Jemal Xu, Zheng Atiquzzaman, Mohammed Zhang, Xiaolu Faculty of Science Engineering and Built Env ironment Deakin University GeelongVIC Australia School of Computer Engineering and Sciences Shanghai Polytechnic University Shanghai China School of Computer Science University of Oklahoma NormanOK United States Department of Information Systems and Cyber Security The University of Texas at San Antonio San AntonioTX United States

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Hybrid Development in Flutter and its Widgits

Hybrid Development in Flutter and its Widgits

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2022 International Conference on Cyber Resilience, ICCR 2022

作者： Sharma, Swati Khare, Shanu Unival, Vaibhav Verma, Sahil Chandigarh University Department of Computer Science Punjab Gharuan India Uttaranchal University Dehradun248007 India Faculty of Computer Science and Engineering Sgt University Gurugram122505 India

ISBN: (纸本)9781665461221

The development of Cross-platform mobile applications is a goal of every client in today's world. Engineers are forced to build the same system multiple times for different OS (operating systems). Google provides a solution by introducing Flutter. It is an open-source SDK for improving high performance and the most reliable mobile apps for apps like iOS, Android, Linux, web and windows. It provides feature of timely compiling using a computer code that includes integration during the execution of the program in working time instead of previous practice. Typically, this includes byte-code translation known as source code to machine code, which is done continuously. Ahead of time integration includes advanced programming language such as C or C ++ [17]. Flutter provides different frameworks, widgets that makes it easy to use and implement code. In this Research paper we are gone discuss about flutter and about its widgets. © 2022 IEEE.

关键词： Integration

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Arabic Fake News Detection Using Machine Learning Approach

Arabic Fake News Detection Using Machine Learning Approach

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Duridi, Tasneem Eleyan, Derar Eleyan, Amna Bejaoui, Tarek Palestine Technical University Department of Computer Science Tulkarm Palestine Nablus University for Technical and Vocational Education Faculty of Telecomm. and Information Technology Nablus Palestine Palestine Technical University - Kadoorie Department of Computer Science Tulkarm Palestine Manchester Metropolitan University Department of Computing and Mathematics ManchesterM1 5GD United Kingdom University of Carthage Computer Engineering Department Tunisia

ISBN: (纸本)9798350364910

In the contemporary digital realm, the widespread dissemination of false Arabic news is a significant social concern laden with various risks. Recognizing the seriousness of this issue, our research utilizes cutting-edge technologies, specifically Machine Learning, to distinguish between authentic Arabic news and deceptive counterparts. The consequences of propagating misinformation go beyond compromising social cohesion;they extend to the erosion of digital information's credibility, fostering an atmosphere of mistrust and deception that undermines the very foundations of societal bonds. Through the application of contemporary technologies, this study aims to identify and underscore Arabic news that embodies such risks. The intricate characteristics of Arabic language morphology, marked by words carrying multiple meanings based on inflectional forms and the prevalence of numerous diacritical marks, intensify the challenges of text classification. Despite contending with these linguistic intricacies, modern natural language processing approaches offer practical solutions. Notably, our methodology relies on the preprocessing of two available datasets for training and testing, a crucial step for seamlessly integrating a range of Machine Learning techniques, including Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and Multinomial Naive Bayes (NB). Importantly, the Logistic Regression technique emerged as the most effective, achieving an accuracy of 95.92% in the SANAD dataset for discerning nuanced Arabic news © 2024 IEEE.

关键词： Adversarial machine learning

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Distributed Sequential Receding Horizon control of Multi-Agent systems Under Recurring Signal Temporal Logic

Distributed Sequential Receding Horizon Control of Multi-Age...

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European control Conference (ECC)

作者： Eleftherios E. Vlahakis Lars Lindemann Dimos V. Dimarogonas Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science Viterbi School of Engineering University of Southern California Los Angeles CA USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

We consider the synthesis problem of a multi-agent system under signal temporal logic (STL) specifications representing bounded-time tasks that need to be satisfied recurrently over an infinite horizon. Motivated by the limited approaches to handling recurring STL systematically, we tackle the infinite-horizon control problem with a receding horizon scheme equipped with additional STL constraints that introduce minimal complexity and a backward-reachability-based terminal condition that is straightforward to construct and ensures recursive feasibility. Subsequently, we decompose the global receding horizon optimization problem into agent-level programs the objectives of which are to minimize local cost functions subject to local and joint STL constraints. We propose a scheduling policy that allows individual agents to sequentially optimize their control actions while maintaining recursive feasibility. This results in a distributed strategy that can operate online as a model predictive controller. Last, we illustrate the effectiveness of our method via a multi-agent system example assigned a surveillance task.

关键词： Surveillance Decentralized control Europe Predictive models Fasteners Cost function Infinite horizon

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A Detailed Study of Blockchain and dapps

A Detailed Study of Blockchain and dapps

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2022 International Conference on Cyber Resilience, ICCR 2022

作者： Verma, Sourav Dash, Sonali Joshi, Ankita Kavita Chandigarh University Department of Computer Science Punjab Gharuan India Uttaranchal University Dehradun248007 India Sgt University Faculty of Computer Science and Engineering Gurugram122505 India

ISBN: (纸本)9781665461221

With the rise of Bitcoin, blockchain technology has gained a lot of attention from the financial world. While the underlying distributed ledger technology has many potential applications, Bitcoin remains the most well-known and widely- adopted use case. As a result, Bitcoin is often seen as a bellwether for the wider blockchain industry. However, there is another side to blockchain that is often overlooked: dapps. Decentralized apps (dapps) are applications that run on a decentralized network, such as Ethereum. Unlike traditional apps, which are controlled by a central authority, dapps are open source and controlled by no one. This makes them incredibly appealing to developers who value decentralization and censorship-resistance. The readers will get an overview of blockchain and dApps research. © 2022 IEEE.

关键词： Smart contract

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Hunger games search optimization with deep learning model for sustainable supply chain management

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Discover Internet of Things 2023年第1期3卷 10页

作者： Xu, Zheng Jain, Deepak Kumar Neelakandan, S. Abawajy, Jemal School of Computer and Information Engineering Institute for Artificial Intelligence Shanghai Polytechnic University Shanghai201209 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China School of Artificial Intelligence Dalian China Symbiosis Institute of Technology Symbiosis International University Pune India Department of Computer Science and Engineering R.M.K Engineering College Taluk India Deakin University Geelong Australia

The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is difficult, cryptographic methods are commonly used in existing works. Effective supply chain management (SCM) offers various benefits to organizations, such as enhanced customer satisfaction, increased operational efficiency, competitive advantage, and cost reduction. Potential SCM for agricultural and food supply chains needs distributors, coordination and collaboration among farmers, retailers, and stakeholders. The use of technology like Block Chain (BC), sensors, and data analytics, can boost traceability and visibility, decrease waste, and ensure safety and quality throughout the supply chain. Therefore, this study develops a Hunger Games Search Optimization with Deep Learning Model for Sustainable agricultural and food Supply Chain Management (HGSODL-ASCM) technique. The fundamental goal of the HGSODL-ASCM technique is to improve decision-making processes for agricultural and food commodity production and storage in order to optimise revenue. In the provided HGSODL-ASCM technique, a bidirectional long short-term memory (Bi-LSTM) model is built to determine the amount of productivity and storage required to maximise profit. In order to boost the performance of the Bi-LSTM classification process, the HGSO algorithm has been utilized in this work. The presented HGSODL-ASCM technique can independently achieve the SCM policies via interaction with complicated and adaptive environments. A brief set of simulations were performed to ensure the improved performance of the HGSODL-ASCM technique. The simulation results demonstrated how superior the HGSODL-ASCM method is to other methods already in use. © The Author(s) 2023.

关键词： Food supply

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DEVELOPABLE RULED SURFACES GENERATED BY THE CURVATURE AXIS OF A CURVE

arXiv

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arXiv 2025年

作者： Taş, Ferhat Ziatdinov, Rushan Department of Computer Science Faculty of Science Istanbul University Istanbul34143 Turkey Department of Industrial Engineering College of Engineering Keimyung University Daegu704-701 Korea Republic of

MSC Codes 53Axx, 65D17Ruled surfaces play an important role in various types of design, architecture, manufacturing, art, and sculpture. They can be created in a variety of ways, which is a topic that has been the subject of much discussion in mathematics and engineering journals. In geometric modelling, ideas are successful if they are not too complex for engineers and practitioners to understand and not too difficult to implement, because these specialists put mathematical theories into practice by implementing them in CAD/CAM systems. Some of these popular systems such as AutoCAD, Solidworks, CATIA, Rhinoceros 3D, and others are based on simple polynomial or rational splines and many other beautiful mathematical theories that have not yet been implemented due to their complexity. Based on this philosophy, in the present work, we investigate a simple way to generate ruled surfaces whose generators are the curvature axes of curves. We show that this type of ruled surface is a developable surface and that there is at least one curve whose curvature axis is a line on the given developable surface. In addition, we discuss the classifications of developable surfaces corresponding to space curves with singularities, while these curves and surfaces are most often avoided in practical design. Our research also contributes to the understanding of the singularities of developable surfaces and, in their visualisation, proposes the use of environmental maps with a circular pattern that creates flower-like structures around the singularities. Copyright © 2025, The Authors. All rights reserved.

关键词： Visualization

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Facial Expression Detection using Convolutional Neural Network

Facial Expression Detection using Convolutional Neural Netwo...

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2022 International Conference on Cyber Resilience, ICCR 2022

作者： Mudit Divya, K. Joshi, Sanjeev Kumar Verma, Sahil Chandigarh University Department of Computer Science Punjab Gharuan India Uttaranchal University Dehradun248007 India Sgt University Faculty of Computer Science and Engineering Gurugram122505 India

ISBN: (纸本)9781665461221

A face expression is a clear representation of an individual's affective state, cognitive activity, intention, personality, psychopathology, and it serves as a means of communication in interpersonal relationships. Automatic facial expression recognition is a critical segment of usual interface between human and a machine, and it must be used for behavioristic psychology therapeutic practice as well. Face identification and placement in a chaotic scene, facial feature extraction, and facial feature categorization are all tasks that an autonomous face features Recognition system must complete. Convolution Neural Networks are used to create a facial expression detection system. LeNet Architecture is the foundation of the CNN model. During this experiment, we have used the Kaggle facial features dataset in which we acquire seven types of face expressions which indicates our emotions swiftly. Kaggle facial feature data set with 7 face expressions labeled as (happy, sad, surprise, fear, rage, disgust, neutral) was taken for the research. Through this paper we are able to obtain 56.7% accuracy and 0.576 precision while testing the dataset. © 2022 IEEE.

关键词： Face recognition

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