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Resilient TCP Variant Enabling Smooth Network Updates for Software-Defined Data Center Networks

Tsinghua science and Technology 2024年第5期29卷 1615-1632页

作者： Abdul Basit Dogar Sami Ullah Yiran Zhang Hisham Alasmary Muhammad Waqas Sheng Chen Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China and also with Department of Informatics and SystemsUniversity of Management and TechnologyLahore 54660Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir 18050Pakistan School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha 61421Saudi Arabia Department of Computer Engineering Faculty of Information TechnologyUniversity of BahrainSakheer 32038Bahrainand also with School of EngineeringEdith Cowan UniversityPerth WA 6027Australia School of Electronics and Computer Science University of SouthamptonSouthamptonSO171BJUK

Network updates have become increasingly prevalent since the broad adoption of software-defined networks(SDNs)in data *** TCP designs,including cutting-edge TCP variants DCTCP,CUBIC,and BBR,however,are not resilient to network updates that provoke flow *** this paper,we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates,because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance *** look into the causes and propose a network update-friendly TCP(NUFTCP),which is an extension of the DCTCP variant,as a *** are used to assess the proposed *** findings reveal that NUFTCP can more effectively manage the problems of out-of-order packets and packet drops triggered in network updates,and it outperforms DCTCP considerably.

关键词： software defined data center networks network updates DCTCP out-of-order packets packet drop SDN

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3D reconstruction of spherical images:a review of techniques,applications,and prospects

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地球空间信息科学学报（英文版） 2024年第6期27卷中插13,1959-1988页

作者： San Jiang Kan You Yaxin Li Duojie Weng Wu Chen School of Computer Science China University of GeosciencesWuhanChina Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityHong KongChina cHubei Luojia Laboratory Wuhan UniversityWuhanChina School of Computer Science China University of GeosciencesWuhanChina Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityHong KongChina

3D reconstruction plays an increasingly important role in modern photogrammetric *** satellite or aerial-based remote sensing(RS)platforms can provide the necessary data sources for the 3D reconstruction of large-scale landforms and *** with low-altitude Unmanned Aerial Vehicles(UAVs),3D reconstruction in complicated situations,such as urban canyons and indoor scenes,is challenging due to frequent tracking failures between camera frames and high data collection ***,spherical images have been extensively used due to the capability of recording surrounding environments from one *** contrast to perspective images with limited Field of View(FOV),spherical images can cover the whole scene with full horizontal and vertical FOV and facilitate camera tracking and data acquisition in these complex *** the rapid evolution and extensive use of professional and con-sumer-grade spherical cameras,spherical images show great potential for the 3D modeling of urban and indoor *** 3D reconstruction pipelines,however,cannot be directly used for spherical ***,there exist few software packages that are designed for the 3D reconstruction from spherical *** a result,this research provides a thorough survey of the state-of-the-art for 3D reconstruction from spherical images in terms of data acquisition,feature detection and matching,image orientation,and dense matching as well as presenting promising applications and discussing potential *** anticipate that this study offers insightful clues to direct future research.

关键词： Spherical image equirectangular projection 3D reconstruction structure from motion simultaneous localization and mapping dense matching image matching

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A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

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computers, Materials & Continua 2023年第10期77卷 115-137页

作者： Bishrul Haq Sher Muhammad Daudpota Ali Shariq Imran Zenun Kastrati Waheed Noor Department of Computer Science Sukkur IBA UniversitySukkur65200Pakistan Department of Computer Science Norwegian University of Science and Technology(NTNU)Gjøvik2815Norway Department of Informatics Linnaeus UniversityVäxjö35195Sweden Department of Computer Science&IT University of BalochistanQuetta87300Pakistan

The Internet has become one of the significant sources for sharing information and expressing users’opinions about products and their interests with the associated *** is essential to learn about product reviews;however,to react to such reviews,extracting aspects of the entity to which these reviews belong is equally ***-based Sentiment Analysis(ABSA)refers to aspects extracted from an opinionated *** literature proposes different approaches for ABSA;however,most research is focused on supervised approaches,which require labeled datasets with manual sentiment polarity labeling and aspect *** study proposes a semisupervised approach with minimal human supervision to extract aspect terms by detecting the aspect ***,the study deals with two main sub-tasks in ABSA,named Aspect Category Detection(ACD)and Aspect Term Extraction(ATE).In the first sub-task,aspects categories are extracted using topic modeling and filtered by an oracle further,and it is fed to zero-shot learning as the prompts and the augmented *** predicted categories are the input to find similar phrases curated with extracting meaningful phrases(e.g.,Nouns,Proper Nouns,NER(Named Entity Recognition)entities)to detect the aspect *** study sets a baseline accuracy for two main sub-tasks in ABSA on the Multi-Aspect Multi-Sentiment(MAMS)dataset along with SemEval-2014 Task 4 subtask 1 to show that the proposed approach helps detect aspect terms via aspect categories.

关键词： Natural language processing sentiment analysis aspect-based sentiment analysis topic-modeling POS tagging zero-shot learning

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Autonomous machine learning for early bot detection in the internet of things

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Digital Communications and Networks 2023年第6期9卷 1301-1309页

作者： Alex Medeiros Araujo Anderson Bergamini de Neira Michele Nogueira Department of Informatics Federal University of Parana(UFPR)CuritibaBrazil Department of Computer Science Federal University of Minas Gerais(UFMG)Belo HorizonteBrazil

The high costs incurred due to attacks and the increasing number of different devices in the Internet of Things(IoT)highlight the necessity of the early detection of botnets(i.e.,a network of infected devices)to gain an advantage against ***,early botnet detection is challenging because of continuous malware mutations,the adoption of sophisticated obfuscation techniques,and the massive volume of *** literature addresses botnet detection by modeling the behavior of malware spread,the classification of malicious traffic,and the analysis of traffic *** article details ANTE,a system for ANTicipating botnEt signals based on machine learning *** system adapts itself to different scenarios and detects different types of *** autonomously selects the most appropriate Machine Learning(ML)pipeline for each botnet and improves the classification before an attack effectively *** system evaluation follows trace-driven experiments and compares ANTE results to other relevant results from the literature over four representative datasets:ISOT HTTP Botnet,CTU-13,CICDDoS2019,and *** show an average detection accuracy of 99.06%and an average bot detection precision of 100%.

关键词： Network security Bot early detection Autonomous machine learning Network traffic analysis

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Credit card default prediction using ML and DL techniques

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 293-306页

作者： Wahab, Fazal Khan, Imran Sabada, Sneha Corvit Systems Peshawar Pakistan Department of Informatics University of Sussex United Kingdom Department of Engineering and Computer Science University of Hertfordshire United Kingdom

The banking sector is widely acknowledged for its intrinsic unpredictability and susceptibility to risk. Bank loans have emerged as one of the most recent services offered over the past several decades. Banks typically serve as intermediaries for loans, investments, short-term loans, and other types of credit. The usage of credit cards is experiencing a steady increase, thereby leading to a rise in the default rate that banks encounter. Although there has been much research investigating the efficacy of conventional Machine Learning (ML) models, there has been relatively less emphasis on Deep Learning (DL) techniques. The application of DL approaches to credit card default prediction has not been extensively researched despite their considerable potential in numerous fields. Moreover, the current literature frequently lacks particular information regarding the DL structures, hyperparameters, and optimization techniques employed. To predict credit card default, this study evaluates the efficacy of a DL model and compares it to other ML models, such as Decision Tree (DT) and Adaboost. The objective of this research is to identify the specific DL parameters that contribute to the observed enhancements in the accuracy of credit card default prediction. This research makes use of the UCI ML repository to access the credit card defaulted customer dataset. Subsequently, various techniques are employed to preprocess the unprocessed data and visually present the outcomes through the use of exploratory data analysis (EDA). Furthermore, the algorithms are hypertuned to evaluate the enhancement in prediction. We used standard evaluation metrics to evaluate all the models. The evaluation indicates that the AdaBoost and DT exhibit the highest accuracy rate of 82 % in predicting credit card default, surpassing the accuracy of the ANN model, which is 78 %. © 2024 The Authors

关键词： Prediction models

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A Hybrid Extreme Gradient Boosting and Long Short-Term Memory Algorithm for Cyber Threats Detection

Mendel

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Mendel 2023年第2期29卷 307-322页

作者： Amin, Reham El-Taweel, Ghada Ali, Ahmed Fouad Tahoun, Mohamed Department of Computer Science Faculty of Computers and Informatics Suez Canal University Ismailia41522 Egypt

The vast amounts of data, lack of scalability, and low detection rates of traditional intrusion detection technologies make it impossible to keep up with evolving and increasingly sophisticated cyber threats. Therefore, there is an urgent need to detect and stop cyber threats early. Deep Learning has greatly improved intrusion detection due to its ability to self-learn and extract highly accurate features. In this paper, a Hybrid XG Boosted and Long Short-Term Memory algorithm (HXG-BLSTM) is proposed. A comparative analysis is conducted between the computational performance of six established evolutionary computation algorithms and the recently developed bio-inspired metaheuristic algorithm called Zebra Optimisation Algorithm. These algorithms include the Particle Swarm Optimisation Algorithm, the Bio-inspired Algorithms, Bat Optimisation Algorithm, Firefly Optimisation Al-gorithm, and Monarch Butterfly Optimisation Algorithm, as well as the Genetic Algorithm as an Evolutionary Algorithm. The dimensionality curse has been miti-gated by using these metaheuristic methods for feature selection, and the results are compared with the wrapper-based feature selection XGBoost algorithm. The proposed algorithm uses the CSE-CIC-IDS2018 dataset, which contains the lat-est network attacks. XGBoost outperformed the other FS algorithms and was used as the feature selection algorithm. In evaluating the effectiveness of the newly proposed HXGBLSTM, binary and multi-class classifications are considered. When comparing the performance of the proposed HXGBLSTM for cyber threat detection, it outperforms seven innovative deep learning algorithms for binary classification and four of them for multi-class classification. Other evaluation criteria such as recall, F1 score, and precision have been also used for comparison. The results showed that the best accuracy for binary classification is 99.8%, with F1-score of 99.83%, precision of 99.85%, and recall of 99.82%, in extensive and detaile

关键词： Intrusion detection

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Exploring the Intersection of Generative AI and Cognitive science: Insights and Implications 3

Exploring the Intersection of Generative AI and Cognitive Sc...

引用

3rd International Conference on Intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Sukthong, Napat Department of Computer Science Faculty of Informatics Mahasarakham University Mahasarakham Thailand

ISBN: (纸本)9798331529222

The rapid advancements in Generative Artificial Intelligence (AI) have revolutionized domains such as natural language processing, computer vision, and creative content generation. Simultaneously, Cognitive science seeks to understand the mechanisms of human cognition, including memory, decision-making, and creativity. This paper explores the intersection of these fields, investigating how Generative AI models can simulate cognitive processes and how Cognitive science insights can inform AI development. Methodologies include experiments with Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and GPT-3 to assess simulations of memory, creativity, and decision-making. Empirical findings demonstrate how VAEs enable memory reconstruction, GANs simulate decision-making processes, and Transformer-based models like GPT-3 exhibit creative capabilities. This study provides valuable insights into advancing AI research while deepening the theoretical understanding of human cognition. ©2024 IEEE.

关键词： Generative adversarial networks

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Blockchain-Empowered Secure Data Communication for UAV

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SN computer science 2024年第8期5卷 1163页

作者： Kumar, Ajay Bhatt, Dheeraj Singh, Yashwant Kumar, Neerendra Department of Computer Science and Informatics Central University Himachal Pradesh Dharamshala India Department of Computer Science and Information Technology Central University of Jammu Jammu India

Unmanned Aerial Vehicles (UAVs), originally used for agriculture, military and typical videography applications, now expanded into many exciting areas but also carries potential security risks. Blockchain, widely renowned for its decentralized and secure properties will materialize the cure for these security threats faced by UAV networks. In this research, we introduced a blockchain based UAV communication algorithm in which both are jointly used to ensure a secure data exchange between drones. This algorithm makes sure that only validated data blocks are added to the Blockchain and ensures integrity & security during communication. By conducting an in-depth literature review, we have addressed some of the issues and challenges propagated by UAV systems. To summarize, the findings lay out that a combination of blockchain and UAV communication can enhance data integrity mechanisms as well as security layers;hence proving to be an optimal design for shaping even trustable solutions in wider domains dealing with professional aerial systems operation. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Algorithm Blockchain Decentralized Drone Security UAV

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Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

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computers, Materials & Continua 2024年第12期81卷 4787-4832页

作者： Israa Ibraheem Al Barazanchi Wahidah Hashim Reema Thabit Mashary Nawwaf Alrasheedy Abeer Aljohan Jongwoon Park Byoungchol Chang College of Computing and Informatics Universiti Tenaga Nasional(UNITEN)Kajang43000Malaysia College of Engineering University ofWarith Al-AnbiyaaKarbala56001Iraq Faculty of Information Science&Technology Universiti Kebangsaan MalaysiaBangi43600Malaysia Department of Computer Science Applied CollegeUniversity of HailHail55424Saudi Arabia Department of Computer Science and Informatics Taibah UniversityMedina42353Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Republic of Korea

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning ***,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state *** models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal *** diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed *** goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic *** propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies ***,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN *** further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and *** validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’***,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model *** findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

关键词： computer science clinical decision support system(CDSS) medical queries healthcare deep learning recurrent neural network(RNN) long short-term memory(LSTM)

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Current Status of Analytical FPGA Placement 9

Current Status of Analytical FPGA Placement

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9th South-East Europe Design Automation, computer Engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Kariotis, Kosmas Dadaliaris, Antonios N. Kranas, George K. Dimitriou, George Dossis, Michael F. University of Thessaly Department of Informatics and Telecommunications Lamia Greece University of Thessaly Department of Computer Science and Biomedical Informatics Lamia Greece University of Western Macedonia Department of Computer Science Kastoria Greece

ISBN: (纸本)9798350342482

This work examines the advancements and challenges in analytical placement algorithms for Field-Programmable Gate Arrays (FPGAs), a pivotal aspect of FPGA design. The review highlights the transition from traditional placement methodologies to analytical methods, which offer enhanced solution quality, efficiency, and flexibility. The paper discusses the importance of considering the complexities inherent to FPGA design, including routability, clock signals, congestion, and heterogeneity. Furthermore, the paper emphasizes the use of advanced computing techniques, such as parallelization and deep learning, in FPGA placement. Despite recent advances, the paper identifies remaining gaps in fully addressing resource heterogeneity, clock feasibility, and scalability challenges, suggesting these as areas for future research. © 2024 IEEE.

关键词： Clocks

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