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International Conference on Radioelektronika

作者： Mohammad Alibakhshikenari Peiman Parand Ali Zidour Bal S. Virdee Lida Kouhalvandi Patrick Longhi Takfarinas Saber Ernesto Limiti Electronics Engineering Department University of Rome “Tor Vergata” Rome Italy Department of Electrical Systems Engineering Faculty of Technology University of M'hamed Bougara Boumerdes Algeria Center for Communications Technology London Metropolitan University London UK Department of Electrical and Electronics Engineering Dogus University Istanbul Turkiye Lero Science Foundation Ireland Centre for Software School of Computer Science University of Galway Galway Ireland

ISBN: (数字)9798331544478

ISBN: (纸本)9798331544485

This paper presents an innovative H-plane crossover based on groove gap-waveguide (GGW) technology for high-performance millimeter-wave (mm-wave) circuits. The design facilitates the development of key transmission components, such as Butler matrices (BMs) and beamforming feeding networks (BFNs), for multi-beam antenna systems operating in the V-band spectrum (40–50 GHz). The proposed crossover is built by cascading two identical 3-dB/90° hybrid couplers. Each coupler is designed with GGW unit-cells constructed from metallic pins spaced less than a quarter-wavelength apart. This configuration creates a wide stopband of 20–57 GHz, ensuring minimal signal interference and strong impedance matching. The coupler achieves 90° phase shift, 50 dB isolation, and low insertion loss of 0.02 dB at 45 GHz, with a fractional bandwidth of 22.22%. The crossover demonstrates excellent performance over the entire V-band, making it suitable for advanced antenna systems in satellite communications and space applications. The design reduces complexity, cost, and losses typically associated with 3D and multilayer crossover technologies, providing a compact and efficient solution for mm-wave networks.

关键词： Butler matrices Satellite antennas Millimeter wave circuits Three-dimensional displays Array signal processing Impedance matching Transmitting antennas Gap waveguide Couplers Millimeter wave communication

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Expert Phishing Detection System

Expert Phishing Detection System

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Ayoub Alsarhan Issa Al-Aiash Dimah Al-Fraihat Mohammad Aljaidi Dena Abd Al Hakem Abu Laila Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Cybersecurity Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Security Faculty of Information Technology Zarqa Technical Intermediate College Zarqa Jordan

ISBN: (数字)9798350353464

ISBN: (纸本)9798350353471

Phishing attacks are a threat that continuously emphasizes the need for effective detection methods. This paper presents a machine learning detection method that employs a random forest classifier, which is an adaptable algorithm that can handle a variety of feature sets. The dataset used in this work offers a range of features extracted from various URLs, addressing elements such as URL length, domain structure, and keyword presence. We deployed a computational approach for feature engineering. The random forest classifier is employed for both prediction and to assess feature importance, guiding an automated feature selection process. By doing this, the model's performance is optimized, and the most important features are utilized. The findings of this study show the effectiveness of our method with an impressive accuracy of 98.45% on a test set. The classifier exhibits balanced precision and recall for both phishing and legitimate instances. Moreover, the automated feature selection process improves the interpretation of the model, emphasizing key indicators of phishing attacks. The proposed approach not only produces outstanding outcomes but also offers a flexible and scalable approach to address the dynamically evolving threat landscape of phishing attempts.

关键词： Uniform resource locators Visualization Accuracy Machine learning algorithms Phishing Predictive models Feature extraction

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Fair Short Paths in Vertex-Colored Graphs

arXiv

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arXiv 2022年

作者： Bentert, Matthias Kellerhals, Leon Niedermeier, Rolf University of Bergen Department of Informatics Algorithms Norway Technische Universität Berlin Faculty IV Institute of Software Engineering and Theoretical Computer Science Algorithmics and Computational Complexity Germany

The computation of short paths in graphs with arc lengths is a pillar of graph algorithmics and network science. In a more diverse world, however, not every short path is equally valuable. For the setting where each vertex is assigned to a group (color), we provide a framework to model multiple natural fairness aspects. We seek to find short paths in which the number of occurrences of each color is within some given lower and upper bounds. Among other results, we prove the introduced problems to be computationally intractable (NP-hard and parameterized hard with respect to the number of colors) even in very restricted settings (such as each color should appear with exactly the same frequency), while also presenting an encouraging algorithmic result ("fixed-parameter tractability") related to the length of the sought solution path for the general problem. Copyright © 2022, The Authors. All rights reserved.

关键词： Color

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A new lightweight data security system for data security in the cloud computing

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Measurement: Sensors 2023年 29卷

作者： Mohammed, Shameer Nanthini, S. Bala Krishna, N. Srinivas, Inumarthi V. Rajagopal, Manikandan Ashok Kumar, M. University of Technology and Applied Sciences Sur Oman Department of AI & DS Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Thandalam Chennai India Department of CSE Mohan Babu University &SreeVidyanikethan Engineering College Andhrapradesh Tirupati517102 India Mumbai400077 India School of Business and Management Christ University Bangalore560029 India Faculty of Computer Science Department of Computer Science and Software Engineering Skyline University Nigeria Kano Nigeria

In recent decades, data has proved indispensable to all facets of human existence. The development of several applications has resulted in the exponential expansion of data. This information can be encrypted and stored in secure areas. Cloud computing is the technology that can be used to store these massive data sets. The article suggests a Cloud-based Data Security System (C-DSS) that employs a five-tiered trust model for cloud-edge data-sharing architectures. The data owner can select an appropriate trust level and Cyber Threat Information (CTI) sanitization procedure before releasing CTI for analytic strategy. In addition, this cleansing method is conducted either by an end device or by the cloud service supplier, based on the organization's degree of confidence in latter. Research presents the trust architecture, cloud architecture, and installation methodology, all of which are intended to meet widest variety of needs for exchanging secret CTI information. The testing findings high degree of data security and an evident improvement in terms of cypher processing time and security services when compared to the encryption systems that are most often employed in cloud technology. In conclusion, research briefly outlines development and evaluation performed so far by pilot applications confirming the architecture. © 2023 The Authors

关键词： Cryptography

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Enhancing Sovereign Allocation of Resources in Cloud Milieus Using a Causal Dilated Geometric Algebra Approach for Dynamic Scalability

Enhancing Sovereign Allocation of Resources in Cloud Milieus...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Chandrasekhar Rao Katru Sathish Srinivasan Mohan Kumar T G Sunita Yadwad G. Satish Ramya Maranan Lead Software Engineer Indian Land SC USA Oracle Cloud Infrastructure Division Machine Learning & AI Development San Francisco Bay Area California USA Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda Gujarat Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Telangana Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance.

关键词： Cloud computing Energy consumption Algebra Scalability Quality of service Transformers Real-time systems Resource management Time factors Service level agreements

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Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

arXiv

引用

arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

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A Novel Trust Assessment System for Online Social Networking Environment Using Learning Assisted Classification Model

A Novel Trust Assessment System for Online Social Networking...

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

作者： S. Nithya D. Deepa Vunnava Dinesh Babu Pallavi G Hamed Fawareh R. D. Kayalvizhy Department of Computer Science Engineering Saveetha Engineering College Chennai Department of CSE St. Joseph's College of Engineering Chennai Department of CSE RV Institute of Technology Guntur Department of Civil Engineering Laxminarayan Innovation Technological University Nagpur India Software Engineering Dept Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia Department of Electronics and Communication Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Trust is essential in the constantly developing environment of online social networks. The Nexus Terroism Impact and Intro Trust facilitate the communication, decision-making, and maintenance of digital communities among individuals. Trust-based Learning with Classification Principle (TLCP) is introduced in this research to improve the assessment of trust in social networks. To ascertain its efficacy, our proposed methodology undergoes a 10-fold cross-validation against the Trust Assessment Methodology (TAM). The traditional Trust Assessment Model (TAM) evaluates and regulates trust in Internet social networking. This method incorporates trust assessment factors to generate a user dependability judgment that is both discriminatory and dynamic. The Trustworthy Account Management (TAM) challenge is designed to enhance digital trust by addressing disinformation, deception, and hostile conduct. The TLCP is able to accurately evaluate trust by utilizing reputation scoring, community analysis, and user behavior metrics. Trust levels are dynamically adjusted as a result of the identification of trustworthy behavioral patterns and danger indicators by machine learning algorithms and social media data. This research examines the connections between trust management and digital interactions and suggests a general solution to alleviate specific trust evaluation issues across online social networks.

关键词： Measurement Machine learning algorithms Social networking (online) Privacy breach Real-time systems Hazards Maintenance Internet Fake news Trust management

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Variational Optimization of Informational Privacy 11th

Variational Optimization of Informational Privacy

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11th International Workshop on Biological Knowledge Discovery from Data, BIOKDD 2020, the 4th International Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2020, the 2nd International Workshop on Machine Learning and Knowledge Graphs, MLKgraphs2019, held at the 31st International Conference on Database and Expert Systems Applications, DEXA 2020

作者： Kumar, Mohit Brunner, David Moser, Bernhard A. Freudenthaler, Bernhard Faculty of Computer Science and Electrical Engineering University of Rostock Rostock Germany Software Competence Center Hagenberg Hagenberg Austria

ISBN: (纸本)9783030590277

The datasets containing sensitive information cant be publicly shared as a privacy-risk posed by several types of attacks exists. The data perturbation approach uses a random noise adding mechanism to preserve privacy, however, results in distortion of useful data. There remains the challenge of studying and optimizing privacy-utility tradeoff especially in the case when statistical distributions of data are unknown. This study introduces a novel information theoretic framework for studying privacy-utility tradeoff suitable for multivariate data and for the cases with unknown statistical distributions. We consider an information theoretic approach of quantifying privacy-leakage by the mutual information between sensitive data and released data. At the core of privacy-preserving framework lies a variational Bayesian fuzzy model approximating the uncertain mapping between released noise added data and private data such that the model is employed for variational approximation of informational privacy. The suggested privacy-preserving framework consists of three components: 1) Optimal Noise Adding Mechanism;2) Modeling of Uncertain Mapping Between Released Noise Added Data and Private Data;and 3) Variational Approximation of Information Privacy. © 2020, Springer Nature Switzerland AG.

关键词： Mapping

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How inter-rater variability relates to aleatoric and epistemic uncertainty: a case study with deep learning-based paraspinal muscle segmentation

arXiv

引用

arXiv 2023年

作者： Roshanzamir, Parinaz Rivaz, Hassan Ahn, Joshua Mirza, Hamza Naghdi, Neda Anstruther, Meagan Battié, Michele C. Fortin, Maryse Xiao, Yiming Department of Electrical and Computer Engineering Concordia University Montreal Canada Faculty of Health Sciences Western University London Canada Health Kinesiology and Applied Physiology Concordia University Montreal Canada School of Physical Therapy and Western’s Bone and Joint Institute Western University London Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

Recent developments in deep learning (DL) techniques have led to great performance improvement in medical image segmentation tasks, especially with the latest Transformer model and its variants. While labels from fusing multi-rater manual segmentations are often employed as ideal ground truths in DL model training, inter-rater variability due to factors such as training bias, image noise, and extreme anatomical variability can still affect the performance and uncertainty of the resulting algorithms. Knowledge regarding how inter-rater variability affects the reliability of the resulting DL algorithms, a key element in clinical deployment, can help inform better training data construction and DL models, but has not been explored extensively. In this paper, we measure aleatoric and epistemic uncertainties using test-time augmentation (TTA), test-time dropout (TTD), and deep ensemble to explore their relationship with inter-rater variability. Furthermore, we compare UNet and TransUNet to study the impacts of Transformers on model uncertainty with two label fusion strategies. We conduct a case study using multi-class paraspinal muscle segmentation from T2w MRIs. Our study reveals the interplay between inter-rater variability and uncertainties, affected by choices of label fusion strategies and DL *** Codes I.2.1, I.4.6 © 2023, CC BY-NC-SA.

关键词： Uncertainty analysis

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Method for filtering encrypted traffic using a neural network between an Industrial Internet of things system and Digital Twin 21

Method for filtering encrypted traffic using a neural networ...

引用

Proceedings of the 5th International Conference on Future Networks and Distributed Systems

作者： Ivan Luksha Truong Duy Dinh Evgenii Karelin Ruslan Glushakov Ruslan Kirichek Dept. of software engineering and computer science Bonch-Bruevich Saint Petersburg State University of Telecommunications Russian Federation Posts and Telecommunications Institute of Technology Faculty of Information Vietnam Dept. of software engineering and computer science The Bonch-Bruevich Saint Petersburg State University of Telecommunications Russian Federation Saint-Petersburg State Pediatric Medical University Russian Federation The Bonch-Bruevich Saint Petersburg State University of Telecommunications Dept. of software engineering and computer science Russian Federation

ISBN: (纸本)9781450387347

Several traffic optimization approaches are available nowadays, including compression, packet deduplication, specialized hardware and software solutions, application optimization, and neural network utilization. However, the inclination to approach data to consumers and transfer data to the periphery necessitates the employment of novel (perhaps combining the use of known) ways to handle the problem, while keeping in mind that most traffic is encrypted, and the proposed solution should be “transparent” to end-users. The article presents a technique for isolating encrypted IoT traffic, and a study of its effectiveness using a neural network in encrypted SSL sessions has been carried out.

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