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PIT Performance Measurement using NFD Pipeline Parameters in Named Data Networking (NDN)

PIT Performance Measurement using NFD Pipeline Parameters in...

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International Conference on Wireless and Telematics (ICWT)

作者： Adi Sucipto Syaiful Ahdan Iskandar Nana Rachmana Syambas School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Indonesia

ISBN: (数字)9798350377057

ISBN: (纸本)9798350377064

Although NDN is an entirely new architecture, its design principles are based on the success of the current Internet, they represent our knowledge of the advantages and disadvantages of the current Internet architecture, and they can be implemented gradually using the operations of the current Internet. The Pending Interest Table (PIT), the Forwarding Information Base (FIB), and the Content Store (CS) are the three data structures found inside the NDN router. Interest sent upstream to the content source is tracked by the Pending Interest Table (PIT), for data to be transmitted to requesters downstream. PIT is also used for measurement, loop detection, and the containment of recently satisfied interest. In this study, we did the simulation to measure and analyze the performance of PIT in NDN using NFD Pipeline Measurements. The NDNSim simulator was used in the research to observe PIT performance. The findings of the simulation and analysis indicated that in an NDN network, Satisfied Interest was influenced by the granularity of a prefix. At the number of interests 1000, the results showed a decrease in packets, including a decrease of around 21% in the Ininterest packet, and around 30% inSatisfiedInterest and InData. The effect of Prefix length also decreased in Ininterest ranging from 0.014-0.015%, while in inSatisfiedInterest and InData it ranged from 4.2-8.5%. The PIT’s performance was influenced by the length of the prefix and the number of interest messages the customer sent.

关键词： Wireless communication Simulation Pipelines Memory management Machine learning Telematics Data structures

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Atomic Lifecycle for Cyber Threat Intelligence

Atomic Lifecycle for Cyber Threat Intelligence

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Information Security and Cryptology (ISCTURKEY), International Conference on

作者： Süleyman Muhammed Arikan Aynur Koçak Mustafa Alkan Computer Forensics Institute of Informatics Gazi University Ankara Turkiye Electrical and Electronics Engineering Faculty of Technology Gazi University Ankara Turkiye

ISBN: (数字)9798331533649

ISBN: (纸本)9798331533656

The utilisation of cyber threat intelligence provides a multitude of advantages in the assurance of security. A growing body of research is being published on the subject of cyber threat intelligence. However, the precise sequence of steps involved in the cyber threat intelligence life cycle remains unclear. The life cycle presented in each study is not consistent. Moreover, the transition from data to intelligence throughout the life cycle is not sufficiently delineated. The intelligence produced in many studies can be of different types. In our study, all existing lifecycles were examined and their naming approaches were analysed. The objective was to present a cyber threat intelligence life cycle that is as atomic as possible, with each step clearly expressed. This should facilitate a more rigorous comparison of studies in the existing literature on cyber threat intelligence. Furthermore, the life cycle can be utilised in new cyber threat intelligence studies. Researchers who aspire to implement best practices in their study are able to run more effective processes by following the life cycle.

关键词： Accuracy Cyber threat intelligence Security Best practices Standards

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Robustness challenges in Reinforcement Learning based time-critical cloud resource scheduling: A Meta-Learning based solution

Robustness challenges in Reinforcement Learning based time-c...

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作者： Liu, Hongyun Chen, Peng Ouyang, Xue Gao, Hui Yan, Bing Grosso, Paola Zhao, Zhiming Informatics Institute University of Amsterdam Amsterdam1098 XH Netherlands Graduate School of Informatics University of Amsterdam Amsterdam1098 XH Netherlands School of Computer and Software Engineering Xihua University Chengdu610039 China School of Computer Science National University of Defense Technology Changsha410073 China College of Electrical and Control Engineering Shaanxi University of Science and Technology Xi'an710021 China School of Electrical and Electronic Engineering University of Adelaide Adelaide South Australia5005 Australia

Cloud computing attracts increasing attention in processing dynamic computing tasks and automating the software development and operation pipeline. In many cases, the computing tasks have strict deadlines. The cloud resource manager (e.g., orchestrator) effectively manages the resources and provides tasks Quality of Service (QoS). Cloud task scheduling is tricky due to the dynamic nature of task workload and resource availability. Reinforcement Learning (RL) has attracted lots of research attention in scheduling. However, those RL-based approaches suffer from low scheduling performance robustness when the task workload and resource availability change, particularly when handling time-critical tasks. This paper focuses on both challenges of robustness and deadline guarantee among such RL, specifically Deep RL (DRL)-based scheduling approaches. We quantify the robustness measurements as the retraining time and investigate how to improve both robustness and deadline guarantee of DRL-based scheduling. We propose MLR-TC-DRLS, a practical, robust Meta Deep Reinforcement Learning-based scheduling solution to provide time-critical tasks deadline guarantee and fast adaptation under highly dynamic situations. We comprehensively evaluate MLR-TC-DRLS performance against RL-based and RL advanced variants-based scheduling approaches using real-world and synthetic data. The evaluations validate that our proposed approach improves the scheduling performance robustness of typical DRL variants scheduling approaches with 97%–98.5% deadline guarantees and 200%–500% faster adaptation. © 2023 The Author(s)

关键词： Reinforcement learning

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Novel SSD-Based Models for Detection of Multidimensional Pulmonary Nodules in CT Images 7

Novel SSD-Based Models for Detection of Multidimensional Pul...

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7th International Conference on Signal Processing and Information Security, ICSPIS 2024

作者： Ji, Zhanlin Hao, Shengnan Pang, Jianhua Ganchev, Ivan Zhejiang A&F University College of Mathematics and Computer Science Hangzhou China North China University of Science and Technology Department of Artificial Intelligence Tangshan China University of Plovdiv 'Paisii Hilendarski' Department of Computer Systems Plovdiv Bulgaria Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia Bulgaria University of Limerick Telecommunications Research Centre Limerick Ireland

ISBN: (纸本)9798350368673

Lung cancer, as one of the diseases with the highest incidence and mortality rates in the world, has always been a big challenge that humans are constantly striving to address. Pulmonary nodules, as one of the early manifestations of lung cancer, constitute a breakthrough in early detection of this disease and its prompt treatment. Currently, significant progress has been made in object detection models based on deep learning in the context of increasingly scarce medical resources. To make pulmonary nodule detection more efficient and accurate, this paper proposes two novel models, based on the Single Shot MultiBox Detector (SSD) model. The proposed models adapt to the detection of small pulmonary nodules by changing the size of the feature map of the input prediction module. In addition, the prediction frame is modified to make greater use of the shallow feature layer. A group of up-sampling modules is added by increasing asymmetric convolution and a feature pyramid network (FPN) mechanism is used for improving the detection performance. The experimental results, obtained on the LUNA16 dataset, demonstrate that the proposed models achieve great performance improvement compared to the baseline (SSD). © 2024 IEEE.

关键词： Lung cancer

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Joint Packet Scheduling and UAV Trajectory Design in Air-Ground Integrated Network

Joint Packet Scheduling and UAV Trajectory Design in Air-Gro...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Li, Shichao Yu, Zhiqiang Chen, Hongbin Zhang, Ning Dong, Mianxiong School of Information and Communication Guilin University of Electronic Technology Guilin541004 China University of Windsor Department of Electrical and Computer Engineering WindsorONN9B3P4 Canada Muroran Institute of Technology Department of Sciences and Informatics Muroran050-8585 Japan

ISBN: (纸本)9798350370218

Compared with the terrestrial network, the air-ground integrated network consisting of unmanned aerial ve-hicles (UAV s) and high altitude platforms (HAPs) offers the advantages of large coverage, high capacity, and seamless connection, which can provide effective communication services for the Internet of remote things (IoRT). In this paper, we investigate the joint packet scheduling and UAV trajectory design problem, with the objective of minimizing the average packet queue delay of IoRT in the air-ground integrated network. Since the problem is a non-convex in nature, we reformulate it into a Markov decision process (MDP) firstly. Then, based on the multi-agent deep deterministic policy gradient (MADDPG) method, we propose a joint packet scheduling and UAV trajectory design (JPSTD) algorithm. Simulation results show that the proposed JPSTD algorithm outperforms the benchmark algorithms in terms of reducing the average packet queue delay. © 2023 IEEE.

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Exploring Computational Linguistics Techniques for Enhanced Outing Planning: A Comprehensive Review

Exploring Computational Linguistics Techniques for Enhanced ...

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Advanced Research in Computing (ICARC), International Conference on

作者： Faizan Muthaliff T.G.D.K. Sumanathilaka School of Computing Informatics Institute of Technology Colombo Sri Lanka Department of Computer Science Swansea University Swansea Wales United Kingdom

In this day and age where convenience is king a new competitor in the form of chat-bots has appeared which requires users to only have prior knowledge on the language the chat-bot converses in. When it comes to planning outings there are a multitude of criteria to be wary of to determine the ideal solution. This review paper will address the common issues with extracting meaningful information from the user utilizing modern NLP techniques, the required criteria in addition to the user input such as, real time weather information and communicable diseases pertaining to the outing location, to determine an ideal suggestion and finally the method in which said ideal suggestion is determined after prioritizing criteria. The conclusion underscores that while NLP applications might not entirely replace existing menu-based systems for outing planning, they excel in certain scenarios. Notably, NLP's innate capacity to comprehend user preferences and context allows it to offer tailored outing suggestions. Furthermore, the application's unique feature of dynamically adapting to user requests without necessitating a return to the home page adds a distinctive advantage to its usability.

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Comparative Analysis of the Use of Variational Autoencoders and Generative Adversarial Networks for Anomalous Image Detection Tasks 7

Comparative Analysis of the Use of Variational Autoencoders ...

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7th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

作者： Lytvynenko, Volodtmyr Olszewski, Serge Osypenko, Volodymyr Lurie, Iryna Demchenko, Violeta Dontsova, Dariia Kherson National Technical University Department of Informatics and Computer Science Kherson Ukraine Taras Shevchenko National University Department of Radio Engineering and Electronic Systems Kyiv Ukraine Kyiv National University of Technology and Design Department of Computer Science Kyiv Ukraine State Institution Kundiiev Institute of Occupational Health National Academy of Medical Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798331534141

This paper presents a comparative analysis of the application of Variational Autoencoders (VAE) and Wasserstein Generative Adversarial Networks (WGAN) for detecting anomalous images. The performance evaluation of the models is conducted using metrics such as AUC, Precision, Recall, and F1-Score. The results indicate that VAEs provide high Recall in anomaly detection but with low Precision, whereas WGANs demonstrate more balanced results with fewer false positives. Recommendations are proposed for selecting an appropriate model depending on the specific task requirements and the need to minimize false alarms or achieve high detection completeness. © 2024 IEEE.

关键词： Generative adversarial networks

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Advancing Color Perception: An IoT-driven Pedagogical System for Visually Impaired Children

Advancing Color Perception: An IoT-driven Pedagogical System...

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Advances in Signal Processing, Power, Communication, and Computing (ASPCC), IEEE International Conference on

作者： Prasandika Balage Nuwan Jayawardene School of Computer Science and Engineering University of Westminster London United Kingdom Department of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798350355093

ISBN: (纸本)9798350355109

Visual impairment is characterized by the loss of vision, encompassing both complete blindness and partial vision loss. Studies reveal a notable prevalence of visual impairment among school-aged children. The significance of colors in acquiring and discerning visual information is crucial, aiding children in effectively perceiving and comprehending their surroundings. This study presents an Internet of Things (IoT) system designed for teaching colors to visually impaired children. The system achieved an accuracy rate surpassing 77.8%, and it is operated effectively regardless of surrounding lighting conditions. Given that the target users of the system are visually impaired children, this research was conducted in close alignment with Human-computer Interaction (HCI) principles.

关键词： Human computer interaction Visualization Accuracy Visual impairment Education Lighting Color Blindness Signal processing Internet of Things

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MAX CUT in Weighted Random Intersection Graphs and Discrepancy of Sparse Random Set Systems 32

MAX CUT in Weighted Random Intersection Graphs and Discrepan...

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32nd International Symposium on Algorithms and Computation, ISAAC 2021

作者： Nikoletseas, Sotiris Raptopoulos, Christoforos Spirakis, Paul Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute Patras Greece Computer Engineering and Informatics Department University of Patras Greece Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9783959772143

Let V be a set of n vertices, M a set of m labels, and let R be an m × n matrix of independent Bernoulli random variables with probability of success p;columns of R are incidence vectors of label sets assigned to vertices. A random instance G(V, E, RT R) of the weighted random intersection graph model is constructed by drawing an edge with weight equal to the number of common labels (namely [RT R]v,u) between any two vertices u, v for which this weight is strictly larger than 0. In this paper we study the average case analysis of Weighted Max Cut, assuming the input is a weighted random intersection graph, i.e. given G(V, E, RT R) we wish to find a partition of V into two sets so that the total weight of the edges having exactly one endpoint in each set is maximized. In particular, we initially prove that the weight of a maximum cut of G(V, E, RT R) is concentrated around its expected value, and then show that, when the number of labels is much smaller than the number of vertices (in particular, m = nα, α Θ(1)n), we show that with high probability, a majority type randomized algorithm outputs a cut with weight that is larger than the weight of a random cut by a multiplicative constant strictly larger than 1. Then, we formally prove a connection between the computational problem of finding a (weighted) maximum cut in G(V, E, RT R) and the problem of finding a 2-coloring that achieves minimum discrepancy for a set system Σ with incidence matrix R (i.e. minimum imbalance over all sets in Σ). We exploit this connection by proposing a (weak) bipartization algorithm for the case m = n, p = Θ(1)n that, when it terminates, its output can be used to find a 2-coloring with minimum discrepancy in a set system with incidence matrix R. In fact, with high probability, the latter 2-coloring corresponds to a bipartition with maximum cut-weight in G(V, E, RT R). Finally, we prove that our (weak) bipartization algorithm terminates in polynomial time, with high probability, at least w

关键词： Graph theory

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An Approach for Constructing Multi-Modal Spatio-Temporal Ontology of Ship Communication 26

An Approach for Constructing Multi-Modal Spatio-Temporal Ont...

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26th ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Xu, Junfeng Zhang, Yitao Lu, Wangping Ning, Da Duan, Yucong Xu, Ruiqing Feng, Zaiwen No.722 Research Institute of Cssc Wuhan430205 China College of Informatics Huazhong Agricultural University Wuhan430070 China College of Plant Science and Technology Huazhong Agricultural University Wuhan430070 China School of Computer Science and Technology Hainan University Haikou570228 China

ISBN: (纸本)9798350345865

Ship communication situational analysis is an important component of current maritime traffic management. However, it faces some challenges and difficulties. Firstly, the heterogeneity and diversity of ship communication data make data integration and analysis difficult. Secondly, traditional data analysis methods often ignore the spatio-temporal relationships and contextual information between data, resulting in inaccurate and incomplete analysis results. In addition, existing ship communication situational analysis methods often only consider single-modal data, which cannot fully reflect the multi-modal characteristics of maritime traffic. To address these issues, we propose a ship communication situational analysis method based on a multi-modal spatio-temporal knowledge graph. This method can effectively integrate heterogeneous data and model and analyze spatio-temporal relationships and contextual information between data, thus achieving more accurate and comprehensive situational analysis. In conclusion, a multi-modal spatio-temporal knowledge graph is an effective method for ship communication situational analysis, which can overcome the current challenges and difficulties and provide more accurate and comprehensive data support for maritime traffic management. © 2023 IEEE.

关键词： Ships

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