Intelligent and Converged Networks最新文献_第4页

RecBERT: Semantic Recommendation Engine with Large Language Model Enhanced Query Segmentation for k-Nearest Neighbors Ranking Retrieval RecBERT：采用大语言模型增强查询分割的语义推荐引擎，用于 k 近邻排序检索

Intelligent and Converged Networks Pub Date : 2024-01-09 DOI: 10.23919/ICN.2024.0004

Richard Wu

{"title":"RecBERT: Semantic Recommendation Engine with Large Language Model Enhanced Query Segmentation for k-Nearest Neighbors Ranking Retrieval","authors":"Richard Wu","doi":"10.23919/ICN.2024.0004","DOIUrl":"https://doi.org/10.23919/ICN.2024.0004","url":null,"abstract":"The increasing amount of user traffic on Internet discussion forums has led to a huge amount of unstructured natural language data in the form of user comments. Most modern recommendation systems rely on manual tagging, relying on administrators to label the features of a class, or story, which a user comment corresponds to. Another common approach is to use pre-trained word embeddings to compare class descriptions for textual similarity, then use a distance metric such as cosine similarity or Euclidean distance to find top \u0000<tex>$k$</tex>\u0000 neighbors. However, neither approach is able to fully utilize this user-generated unstructured natural language data, reducing the scope of these recommendation systems. This paper studies the application of domain adaptation on a transformer for the set of user comments to be indexed, and the use of simple contrastive learning for the sentence transformer fine-tuning process to generate meaningful semantic embeddings for the various user comments that apply to each class. In order to match a query containing content from multiple user comments belonging to the same class, the construction of a subquery channel for computing class-level similarity is proposed. This channel uses query segmentation of the aggregate query into subqueries, performing k-nearest neighbors (KNN) search on each individual subquery. RecBERT achieves state-of-the-art performance, outperforming other state-of-the-art models in accuracy, precision, recall, and F1 score for classifying comments between four and eight classes, respectively. RecBERT outperforms the most precise state-of-the-art model (distilRoBERTa) in precision by 6.97% for matching comments between eight classes.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"5 1","pages":"42-52"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10387238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140321731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey on Intelligence-Endogenous Network: Architecture and Technologies for Future 6G 智能内生网络调查：未来 6G 的架构和技术

Intelligent and Converged Networks Pub Date : 2023-12-05 DOI: 10.23919/ICN.2024.0005

Lanlan Li

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CNNs-Based End-to-End Asymmetric Encrypted Communication System 基于 CNN 的端到端非对称加密通信系统

Intelligent and Converged Networks Pub Date : 2023-12-01 DOI: 10.23919/ICN.2023.0026

Yongli An;Zebing Hu;Haoran Cai;Zhanlin Ji

引用次数: 0

Total Contents 总内容

Intelligent and Converged Networks Pub Date : 2023-12-01 DOI: 10.23919/TUP-ICN.2023.10379049

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A Systematic Review: Detecting Phishing Websites Using Data Mining Models 系统回顾：利用数据挖掘模型检测钓鱼网站

Intelligent and Converged Networks Pub Date : 2023-12-01 DOI: 10.23919/ICN.2023.0027

Dina Jibat;Sarah Jamjoom;Qasem Abu Al-Haija;Abdallah Qusef

{"title":"A Systematic Review: Detecting Phishing Websites Using Data Mining Models","authors":"Dina Jibat;Sarah Jamjoom;Qasem Abu Al-Haija;Abdallah Qusef","doi":"10.23919/ICN.2023.0027","DOIUrl":"https://doi.org/10.23919/ICN.2023.0027","url":null,"abstract":"As internet technology use is on the rise globally, phishing constitutes a considerable share of the threats that may attack individuals and organizations, leading to significant losses from personal and confidential information to substantial financial losses. Thus, much research has been dedicated in recent years to developing effective and robust mechanisms to enhance the ability to trace illegitimate web pages and to distinguish them from non-phishing sites as accurately as possible. Aiming to conclude whether a universally accepted model can detect phishing attempts with 100% accuracy, we conduct a systematic review of research carried out in 2018–2021 published in well-known journals published by Elsevier, IEEE, Springer, and Emerald. Those researchers studied different Data Mining (DM) algorithms, some of which created a whole new model, while others compared the performance of several algorithms. Some studies combined two or more algorithms to enhance the detection performance. Results reveal that while most algorithms achieve accuracies higher than 90%, only some specific models can achieve 100% accurate results.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"4 4","pages":"326-341"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physical Layer Authentication of MIMO-STBC Systems Based on Constellation Dithering 基于星座抖动的 MIMO-STBC 系统物理层验证

Intelligent and Converged Networks Pub Date : 2023-12-01 DOI: 10.23919/ICN.2023.0029

Yongli An;Haifei Bai;Shikang Zhang;Zhanlin Ji

{"title":"Physical Layer Authentication of MIMO-STBC Systems Based on Constellation Dithering","authors":"Yongli An;Haifei Bai;Shikang Zhang;Zhanlin Ji","doi":"10.23919/ICN.2023.0029","DOIUrl":"https://doi.org/10.23919/ICN.2023.0029","url":null,"abstract":"Most of the existing physical layer watermarking authentication schemes are based on a single-input single-output system and require pre-issue of shared keys. To address these problems, in this thesis, a physical layer authentication scheme without the distribution keys is proposed based on the constellation dithering physical layer authentication watermarking mechanism with a multiple-input multiple-output (MIMO) system, and space-time block coding (STBC) is used to improve the robustness of transmission. Specifically, the legitimate node obtains channel state information (CSI) through channel probing and couples CSI with the message signal using a hash function to generate an authentication tag, which is then embedded through constellation dithering. The receiver extracts the tag and authenticates it using hypothesis testing. Performance analysis shows that the scheme is resistant to various attacks such as replay, interference, tampering, and forgery. Simulation results show that the use of MIMO multi-antenna diversity with STBC coding technique reduces the bit error rate (BER) of message signals and tag signals and improves the detection rate of legitimate signals.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"4 4","pages":"355-365"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Navigating the Ethical and Privacy Concerns of Big Data and Machine Learning in Decision Making 驾驭决策中的大数据和机器学习所带来的伦理和隐私问题

Intelligent and Converged Networks Pub Date : 2023-12-01 DOI: 10.23919/ICN.2023.0023

Hamed Taherdoost

{"title":"Navigating the Ethical and Privacy Concerns of Big Data and Machine Learning in Decision Making","authors":"Hamed Taherdoost","doi":"10.23919/ICN.2023.0023","DOIUrl":"https://doi.org/10.23919/ICN.2023.0023","url":null,"abstract":"In recent years, the fields of big data and machine learning have gained significant attention for their potential to revolutionize decision-making processes. The vast amounts of data generated by various sources can provide valuable insights to inform decisions across a range of domains, from business and finance to healthcare and social policy. Machine learning algorithms enable computers to learn from data and improve their performance over time, thereby enhancing their ability to make predictions and identify patterns. This article provides a comprehensive overview of how big data and machine learning can improve decision-making processes between 2017–2022. It covers key concepts and techniques involved in these tools, including data collection, data preprocessing, feature selection, model training, and evaluation. The article also discusses the potential benefits and limitations of these tools and explores the ethical and privacy concerns associated with their use. In particular, it highlights the need for transparency and fairness in decision-making algorithms and the importance of protecting individuals' privacy rights. The review concludes by highlighting future research opportunities and challenges in this rapidly evolving field, including the need for more robust and interpretable models, as well as the integration of human decision making with machine learning algorithms. Ultimately, this review aims to provide insights for researchers and practitioners seeking to leverage big data and machine learning to improve decision-making processes in various domains.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"4 4","pages":"280-295"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of the Internet of Smart Orchard Things Based on Multi-Sensors and LoRa Technology 基于多传感器和 LoRa 技术的智能果园物联网开发

Intelligent and Converged Networks Pub Date : 2023-12-01 DOI: 10.23919/ICN.2023.0028

Pingchuan Zhang;Xu Chen;Shan Li;Caihong Zhang;Yanjun Hu

{"title":"Development of the Internet of Smart Orchard Things Based on Multi-Sensors and LoRa Technology","authors":"Pingchuan Zhang;Xu Chen;Shan Li;Caihong Zhang;Yanjun Hu","doi":"10.23919/ICN.2023.0028","DOIUrl":"https://doi.org/10.23919/ICN.2023.0028","url":null,"abstract":"With the rapid growth of science and technology, the Internet of Things (IoT) technology has matured and attracted the attention of many researchers. The development of agricultural modernization leads to the gradual emergence of intelligent management gradually taking root in agricultural production. Among many technologies in the IoT technologies, low-power Wide Area Network (WAN) technology has the characteristics of reliable and stable transmission with long distance and low power consumption. This is very useful for data transmission in special environments, especially for orchards in mountainous areas. This paper proposed a new agricultural Internet of Things in orchard management based on multi-sensors, such as DHT11 for temperature/humidity and GY-30 for illumination, the Long Range (LoRa) technology for transmitting the collected data or control command between the terminal and data cloud center, etc. Setting a low-power IoT sensor network in the orchard can remotely measure the parameters in the orchard. LoRa WAN is used to transmit data to the central node. In order to reduce power consumption and cost, a single monitoring node selects two power supplies, a solar power supply and a power supply, and the power supply can be turned on remotely by users in special circumstances. Experiments in different environments in the peach orchard show that the monitoring system has enough reliability and accuracy, and is suitable for environmental monitoring in orchards in remote areas or areas with complex terrain.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"4 4","pages":"342-354"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Resource Allocation in Edge and Fog-Cloud Computing with Genetic Algorithm and Particle Swarm Optimization 利用遗传算法和粒子群优化加强边缘和雾云计算中的资源分配

Intelligent and Converged Networks Pub Date : 2023-12-01 DOI: 10.23919/ICN.2023.0022

Saad-Eddine Chafi;Younes Balboul;Mohammed Fattah;Said Mazer;Moulhime El Bekkali

{"title":"Enhancing Resource Allocation in Edge and Fog-Cloud Computing with Genetic Algorithm and Particle Swarm Optimization","authors":"Saad-Eddine Chafi;Younes Balboul;Mohammed Fattah;Said Mazer;Moulhime El Bekkali","doi":"10.23919/ICN.2023.0022","DOIUrl":"https://doi.org/10.23919/ICN.2023.0022","url":null,"abstract":"Evolutionary algorithms have gained significant attention from researchers as effective solutions for various optimization problems. Genetic Algorithm (GA) is widely popular due to its logical approach, broad applicability, and ability to tackle complex issues encountered in engineering systems. However, GA is known for its high implementation cost and typically requires a large number of iterations. On the other hand, Particle Swarm Optimization (PSO) is a relatively new heuristic technique inspired by the collective behaviors of real organisms. Both GA and PSO algorithms are prominent heuristic optimization methods that belong to the population-based approaches family. While they are often seen as competitors, their efficiency heavily relies on the parameter values chosen and the specific optimization problem at hand. In this study, we aim to compare the runtime performance of GA and PSO algorithms within a cutting-edge edge and fog cloud architecture. Through extensive experiments and performance evaluations, the authors demonstrate the effectiveness of GA and PSO algorithms in improving resource allocation in edge and fog cloud computing scenarios using FogWorkflowSim simulator. The comparative analysis sheds light on the strengths and limitations of each algorithm, providing valuable insights for researchers and practitioners in the field.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"4 4","pages":"273-279"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Analysis of a Recommendation System Based on Collaborative Filtering Techniques for Big Data 基于协作过滤技术的大数据推荐系统的设计与分析

Intelligent and Converged Networks Pub Date : 2023-12-01 DOI: 10.23919/ICN.2023.0024

Najia Khouibiri;Yousef Farhaoui;Ahmad El Allaoui

{"title":"Design and Analysis of a Recommendation System Based on Collaborative Filtering Techniques for Big Data","authors":"Najia Khouibiri;Yousef Farhaoui;Ahmad El Allaoui","doi":"10.23919/ICN.2023.0024","DOIUrl":"https://doi.org/10.23919/ICN.2023.0024","url":null,"abstract":"Online search has become very popular, and users can easily search for any movie title; however, to easily search for moving titles, users have to select a title that suits their taste. Otherwise, people will have difficulty choosing the film they want to watch. The process of choosing or searching for a film in a large film database is currently time-consuming and tedious. Users spend extensive time on the internet or on several movie viewing sites without success until they find a film that matches their taste. This happens especially because humans are confused about choosing things and quickly change their minds. Hence, the recommendation system becomes critical. This study aims to reduce user effort and facilitate the movie research task. Further, we used the root mean square error scale to evaluate and compare different models adopted in this paper. These models were employed with the aim of developing a classification model for predicting movies. Thus, we tested and evaluated several cooperative filtering techniques. We used four approaches to implement sparse matrix completion algorithms: \u0000<tex>$k$</tex>\u0000-nearest neighbors, matrix factorization, co-clustering, and slope-one.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"4 4","pages":"296-304"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0