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arXiv 2024年

作者： Balko, Martin Hliněný, Petr Masařík, Tomáš Orthaber, Joachim Vogtenhuber, Birgit Wagner, Mirko H. Department of Applied Mathematics Faculty of Mathematics and Physics Charles University Czech Republic Faculty of Informatics Masaryk University Brno Czech Republic Institute of Informatics Faculty of Mathematics Informatics and Mechanics University of Warsaw Poland Institute of Software Technology Graz University of Technology Austria Institute of Computer Science Osnabrück University Germany

Visualizing a graph G in the plane nicely, for example, without crossings, is unfortunately not always possible. To address this problem, Masařík and Hliněný [10] recently asked for each edge of G to be drawn without crossings while allowing multiple different drawings of G. More formally, a collection D of drawings of G is uncrossed if, for each edge e of G, there is a drawing in D such that e is uncrossed. The uncrossed number unc(G) of G is then the minimum number of drawings in some uncrossed collection of G. No exact values of the uncrossed numbers have been determined yet, not even for simple graph classes. In this paper, we provide the exact values for uncrossed numbers of complete and complete bipartite graphs, partly confirming and partly refuting a conjecture posed by Hliněný and Masařík [10]. We also present a strong general lower bound on unc(G) in terms of the number of vertices and edges of G. Moreover, we prove NP-hardness of the related problem of determining the edge crossing number of a graph G, which is the smallest number of edges of G taken over all drawings of G that participate in a crossing. This problem was posed as open by Schaefer in his book [18].MSC Codes 68R10 Copyright © 2024, The Authors. All rights reserved.

关键词： Graph theory

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MetaSignal: Meta Reinforcement Learning for Traffic Signal Control via Fourier Basis Approximation

TechRxiv

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TechRxiv 2023年

作者： Huang, Shuning Ota, Kaoru Dong, Mianxiong Zhou, Huan Department of Sciences and Informatics Muroran Institute of Technology Hokkaido Muroran Japan College of Computer and Information Technology China Three Gorges University Yichang China

Traffic signal control plans significantly impact transportation system efficiency by regulating traffic conditions at intersections. Adaptive traffic plans that can adjust to real-time road conditions are more effective as a result. Reinforcement learning succeeds at adapting strategies based on feedback derived from the environment, and is thus proficient in dealing with complex traffic scenarios that change dynamically. However, current RL methods rely on significant computational periods to obtain precise functioning mechanisms within the scenarios, posing barriers to their adoption for new scenarios. In addition to directly optimizing the RL model itself to enable fast learning from scratch, another idea is to make the model transferable or reusable with the learned experience. Given the diversity of migration scenarios, the underlying control algorithm should guarantee convergence and endeavor to be parameter-insensitive. From the above concern, we proposed MetaSignal, an efficient meta-reinforcement learning method for traffic signal control. Specifically, our approach utilizes the Fourier basis as the value function approximation in reinforcement learning, distinguishing it from methods like neural network approximation. This linear approximation offers advantages such as convergence facilitation, error bound achievement, and reduced parameter dependence. The meta-learning framework adopts a model-agnostic approach, enabling effective adaptation of the base model to the target scenario with limited training cost. Empirically, the proposed method shows promising and stable performance for traffic signal control through comprehensive comparison experiments in both synthetic and real-world traffic networks. © 2023, CC BY.

关键词： Reinforcement learning

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Cognitive Radio Technique for Blockchain-based Vehicles with IoT 5

Cognitive Radio Technique for Blockchain-based Vehicles with...

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5th International Conference on Contemporary Computing and informatics, IC3I 2022

作者： Veerraju, Sampenga Ghantasala, G S Pradeep Verma, Ekta Kumar, Ajay Akram, Shaik Vaseem Pant, Kumud Dadi Institute of Engineering and Technology Department of Electronics and Communication Engineering Andhra Pradesh Anakapalle India Chitkara Institute of Engineering and Technology Department of Computer Science and Engineering Chitkara University Punjab India Scientific Officer National Informatics Center New Delhi India JECRC University Computer Science and Engineering Rajasthan Jaipur India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

In the In the Internet of Vehicles (IoV), intelligent decision-making is performed by objects with cognition. by comprehending both the social and physical worlds, Cognition Radio Network (CRN) is seen as a workable option. The existence of dangerous devices (MD) further impairs network performance, although For services to be improved and safety metrics to be driven, spectrum availability and information exchange among vehicles are crucial. Recently, a blockchain approach has been added to CRN-based IoV to stop data modification from these Entities and enable the cars to monitor both legal and illegitimate network activity. In this paper, we provide security to IoV throughout spectrum sensors and details transfer using CRN by sensing the paths through with a decision-making technique called Method for Order Personal taste by Similarity to a Solution (topsis (TOPSIS), a technique that inspires the confidence of its Behavioral Users (CU) by analysing certain predefined attributes. The network also maintains a blockchain to track every action involving information that has been saved. Using different frequency sensing and security settings in comparison to an IoV baseline solution, the suggested method is thoroughly evaluated against a number of security criteria. Numerous simulations indicate that when compared to the baseline system, our suggested approach is around 70% more effective at identifying malicious nodes and DoS threats. © 2022 IEEE.

关键词： Internet of things

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Metaverse for Education: Developments, Challenges, and Future Direction

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computer Applications in Engineering Education 2025年第3期33卷

作者： Chamola, Vinay Peelam, Mritunjay Shall Mittal, Uday Hassija, Vikas Singh, Anushka Pareek, Ritawari Mangal, Poorvi Sangwan, Devika de Albuquerque, Victor Hugo C. Mahmud, Mufti Brown, David J. Department of Electrical and Electronics Engineering Internet of Things Research Group APPCAIR BITS-Pilani Rajasthan Pilani India Department of Electrical and Electronics Engineering BITS-Pilani Rajasthan Pilani India School of Computer Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar India Department of Computer Science and Engineering Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Humanities and Social Sciences BITS-Pilani Rajasthan Pilani India Fortaleza Brazil Department of Computer Science Computing and Informatics Research Centre Medical Technologies Innovation Facility at Nottingham Trent University Clifton Lane Nottingham United Kingdom Department of Computer Science Computing and Informatics Research Centre at Nottingham Trent University Clifton Lane Nottingham United Kingdom

The rapid advancements in digital technologies such as artificial intelligence (AI), virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), and the internet of things (IoT) have revolutionized various sectors, including education. Metaverse, a convergence of these transformative technologies, offers immersive, personalized, and interactive experiences, making it a powerful tool in modern education. This paper explores the Metaverse's role in enhancing education by examining its architecture, types, and components while addressing practical implementation challenges, and follows a structured review protocol to ensure a comprehensive analysis, including systematic research, paper selection, and a critical examination of relevant studies from reputable databases such as Google Scholar, IEEE Xplore, ACM, and Springer. The research objectives focus on evaluating the Metaverse's applications in education, ethical challenges, technological limitations, and potential strategies for sustainable integration. Key research questions address the need for Metaverse adoption in education, its benefits, challenges, and future directions. The Metaverse cultivates essential skills such as empathy, ethical reasoning, and effective communication by providing students with customized, immersive learning environments. However, ethical concerns, technical barriers, and infrastructural costs pose significant obstacles to its widespread adoption. It discusses strategies to solve these barriers, explores applications in distance learning, and proposes future research directions to create scalable and sustainable educational models in the Metaverse. Through this structured inquiry, the paper establishes the Metaverse as a transformative force in education, blending technological innovation with instructional advancement. © 2025 Wiley Periodicals LLC.

关键词： Contrastive Learning

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Decomposition of Differences of Independent and Identically Distributed Random Variables

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Moscow University Computational Mathematics and Cybernetics 2025年第1期49卷 67-72页

作者： Ushakov, V. G. Ushakov, N. G. Department of Computational Mathematics and Cybernetics Moscow State University Moscow Russia Institute of Informatics Problems Federal Research Center ‘‘Computer Science and Control’’ Russian Academy of Sciences Moscow Russia Norwegian University of Science and Technology Trondheim Norway

The authors consider conditions where the distribution of the components of the difference between two independent identically distributed random variables can be uniquely reconstructed with an accuracy of up to a shift and reflection. This uniqueness is essential for solving a number of characterization problems in mathematical statistics. An algorithm for estimating the components is presented for when data are given in a symmetrized form.

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Erdős-Pósa property of tripods in directed graphs

arXiv

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arXiv 2024年

作者： Briański, Marcin Hatzel, Meike Okrasa, Karolina Pilipczuk, Michal Department of Theoretical Computer Science Faculty of Mathematics and Computer Science Jagiellonian University Kraków Poland Daejeon Korea Republic of University of Oxford United Kingdom & Warsaw University of Technology Poland Institute of Informatics University of Warsaw Poland

Let D be a directed graphs with distinguished sets of sources S ⊆ V (D) and sinks T ⊆ V (D). A tripod in D is a subgraph consisting of the union of two S-T-paths that have distinct start-vertices and the same end-vertex, and are disjoint apart from sharing a suffix. We prove that tripods in directed graphs exhibit the Erdős-Pósa property. More precisely, there is a function f : → such that for every digraph D with sources S and sinks T, if D does not contain k vertex-disjoint tripods, then there is a set of at most f(k) vertices that meets all the tripods in D. Copyright © 2024, The Authors. All rights reserved.

关键词： Directed graphs

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Evading IoT Intrusion Detection Systems with GAN

Evading IoT Intrusion Detection Systems with GAN

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Asia Joint Conference on Information Security (Asia JCIS)

作者： Mariama Mbow Rodrigo Roman Takeshi Takahashi Kouichi Sakurai Department of Informatics Kyushu University Japan Network Information and Computer Security (NICS) Lab University of Malaga Spain National Institute of Information and Communications Technology Tokyo Japan

ISBN: (数字)9798350380149

ISBN: (纸本)9798350380156

Network intrusion detection systems are essential tools used in cybersecurity to detect malicious traffic. They are used in the Internet of Things (IoT) to monitor the network traffic and identify infected IoT devices. Recently, machine learning and deep learning models have been increasingly adopted to enhance detection systems. However, these model-based detectors are vulnerable to adversarial attacks. This paper investigates the vulnerability of these machine learning-based NIDS models in the IoT environment. We propose a practical targeted black-box attack based on Generative Adversarial Networks to generate adversarial DDoS and Mirai traffic that can evade the detector. Experimental results show that the proposed approach deceived the detector to misclassify the attack traffic as benign and achieved a 100% evasion success rate. Also, the proposed approach can be transferred to other models, achieving different levels of effectiveness.

关键词： Deep learning Network intrusion detection Closed box Detectors Telecommunication traffic Generative adversarial networks Internet of Things Object recognition computer crime Monitoring

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Architecture Open Workspace Concept for Prescriptive Model of Digital Twin 9

Architecture Open Workspace Concept for Prescriptive Model o...

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9th International Conference on ICT for Smart Society, ICISS 2022

作者： Supangkat, Suhono Harso Supangkat, Suhono Harso Leksono, Edi Ramelan, Agus Latifah, Ayu School of Electrical and Informatics Engineering Bandung Institute of Technology Bandung Indonesia Engineering Physics Faculty of Industrial Technology Bandung Institute of Technology Bandung Indonesia Electrical Engineering Departement Universitas Sebelas Maret Solo Indonesia Department of Computer Science Institut Teknologi Garut Garut Indonesia

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

The workspace is a portion of a structure that can currently apply a smart building system adaption for more ideal design outcomes. Millions of hours are reportedly wasted each year as a result of the harmful effects that poor environmental quality has on workers' productivity and health, according to the findings of several studies. Therefore, a centralized approach is required to be able to give quick and accurate information. This may be accomplished via digital twin technology, which will result in a workspace design that is more ideal in the future. The major goal of this research is to create a digital twin prescriptive model in a physical workspace environment that can create a strategic space design plan and be flexible enough to react to changes in the physical environment, which will improve user comfort and productivity. A life cycle model was employed as the study methodology, which begins with the identification of the model's requirements and ends with its maintenance. This model is employed to offer suggestions for preventative and remedial activities on actual things. © 2022 IEEE.

关键词： Productivity

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Reducing the Percentage of Defective Castings Using the Neural Network Modeling Method

Reducing the Percentage of Defective Castings Using the Neur...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Yasnitsky, Leonid N. Rabchevsky, Andrey N. Martynenko, Sergey V. Perm State University Department of Applied Mathematics and Informatics United States National Research University 'Higher School of Economics' Department of Information Technologies Perm Russia Computer Science and Technology Institute Perm State University Perm Russia Ural Federal University Named After. B.N YeItsin Branch in Nizhny Tagil Department of Metallurgical Technologies Nizhny Tagil Russia

ISBN: (纸本)9798350395044

The article is devoted to the current problem of reducing the percentage of defective products manufactured at serial steelmaking plants. To study the patterns of defect formation, a neural network was created that predicts the formation of defects such as 'Cracks' in castings produced by the open-hearth method. To train the neural network, statistical data taken under the conditions of the current production process was used, which resulted in a high degree of information noise. Outliers of statistical information were detected and removed with the original author's neural network technique. By conducting virtual computer experiments on a neural network model, some important dependences of the probability of the defect formation on the content of manganese, phosphorus, silicon, chromium, and sulfur were identified. Based on that, practical recommendations have been developed to reduce defects by adjusting the chemical composition of the metal. Despite the relatively low accuracy of the created neural network model, the application of the practical recommendations made it possible to reduce the percentage of defective products manufactured at a serial plant by half. © 2024 IEEE.

关键词： Statistics

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Application of Neural Network in the Prediction Models of Machine Learning Based Design

Application of Neural Network in the Prediction Models of Ma...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Gehlot, Anita Ansari, Bushra Khatoon Arora, Deepika Anandaram, Harishchander Singh, Bhagwant Arias-Gonzáles, José Luis Uttaranchal University Uttaranchal Institute of Technology India Institute of Engineering and Technology AKTU Department of Civil Engineering Lucknow India Lloyd Institute of Engineering & Technology Computer Science & Engineering Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India SS Informatics Cluster School of Computer Science Uttarakhand Dehradun248007 India Pontificia Universidad Católica Del Peru Peru

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

This research examines way to pass, among the most dynamic and quickly-evolving technology fields in the telecommunication sector. Data may be sent between two locations wirelessly without the usage of digital downloads like wires and cables. Business telecom companies have advanced as a consequence and joined the big data age. Among the most potential machine learning methods for interpreting this deluge of information is computer science, which is employed in a range of business and scholarly research situations. This paper gives a broad overview of technology advancements and big data analytics, as well as possible uses for them in succeeding cellular networks. Apply sophisticated analytics to determine what mobile users' demands are, and then utilise that information to raise the effectiveness of "social wireless channels of communication."The huge, integrated, information machine educational approach it offers, which consists of extracting the features, simulation models, and forecasting enhancements, is particularly noteworthy. The key benefit of the proposed methodology is the logic of robust mathematical methods, issue conceptualization within the context of cellular networks, and reliance on a vast amount of data that represents both the bandwidth as well as other stringent user needs. and the ability to create a procedure. In a communication network, data is normally sent from the transmitter to the receiver across a distance. Using communication systems, the transmitter may be positioned somewhere within a few metres. We will examine existing strategies, their advantages and disadvantages, and potential developments in underwater sensors right now. We had discovered a fresh method of investigation. © 2022 IEEE.

关键词： Information management

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