2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)最新文献_第6页

Performance Evaluation for End-to-End Slice Management in 5G/B5G Cellular Networks 5G/B5G蜂窝网络端到端切片管理性能评估

2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) Pub Date : 2022-09-22 DOI: 10.23919/softcom55329.2022.9911525

Noor Mohammedali, T. Kanakis, Ali Al-Sherbaz, Michael Opoku Agyeman

{"title":"Performance Evaluation for End-to-End Slice Management in 5G/B5G Cellular Networks","authors":"Noor Mohammedali, T. Kanakis, Ali Al-Sherbaz, Michael Opoku Agyeman","doi":"10.23919/softcom55329.2022.9911525","DOIUrl":"https://doi.org/10.23919/softcom55329.2022.9911525","url":null,"abstract":"End-to-End (E2E) virtual networks represent a key technology in future cellular networks. Generally, the E2E connection means each slice has an independent part of the RAN, User Plane Function (UPF) and the 5G Core. Within each slice, a subscriber may have one or more Quality of Service (QoS) flows. These flows only exist within the slices. According to the 3G Partnership Project (3GPP) Technical Specification (TS), it could be at most eight Single Network Slice Selection Assistance Informations (S- NSSAIs) in the Allowed list. Requested NSSAIs sent in signalling messages; registration request, accept and respectively; between the user and the network. These messages allow the network to select the serving Access and Mobility Management Function (AMF), network slices and Network Slice Instances (NSIs) for the user. The research idea is to improve the Quality of Service (QoS) and the Quality of Experience (QoE) for the user when connecting to different slices on the 5G systems. The slice performance for one slice should not be affected by other slice traffic. This paper evaluates the performance of E2E 5G slicing in terms of throughput, jitter, reliability, transmission rate and mobility under different circumstances. In the proposed system, the performance of the slice is checked when the user connects to eight slices or more at the same time. In addition, we propose a slice termination and connection algorithm that allows the user to register new slices. Moreover, the algorithm allows users who are already registered to be released after using slices, enabling more effective use of the network resources.","PeriodicalId":261625,"journal":{"name":"2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129053220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Teaching & Learning Analytics for Data-Based Optimization of Teaching and Learning Processes in Courses with Blended Learning 混合学习课程中基于数据的教学和学习过程优化的教学和学习分析

2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) Pub Date : 2022-09-22 DOI: 10.23919/softcom55329.2022.9911349

L. Mehnen, B. Pohn, Matthias Blaickner, Thomas Mandl, I. Dregely

引用次数: 0

Empirical Assessment on Interactive Detection of Code Smells 代码气味交互检测的实证评估

2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) Pub Date : 2022-09-22 DOI: 10.23919/softcom55329.2022.9911317

D. Albuquerque, Everton T. Guimarães, Alexandre Braga Gomes, M. Perkusich, H. Almeida, A. Perkusich

{"title":"Empirical Assessment on Interactive Detection of Code Smells","authors":"D. Albuquerque, Everton T. Guimarães, Alexandre Braga Gomes, M. Perkusich, H. Almeida, A. Perkusich","doi":"10.23919/softcom55329.2022.9911317","DOIUrl":"https://doi.org/10.23919/softcom55329.2022.9911317","url":null,"abstract":"Code smell detection is traditionally supported by Non-Interactive Detection (NID) techniques, which enable devel-opers to reveal smells in later software versions. These techniques only reveal smells in the source code upon an explicit developer request and do not support progressive interaction with affect code. The later code smells are detected, the higher the effort to refactor the affected code. The notion of Interactive Detection (ID) has emerged to address NID's limitations. An ID technique reveals code smell instances without an explicit developer request, encouraging early detection of code smells. Even though ID seems promising, there is a lack of evidence concerning its impact on code smell detection. Our research focused on evaluating the effectiveness of the ID technique on code smell detection. For doing so, we conducted a controlled experiment where 16 subjects underwent experimental tasks. We concluded that using the ID technique led to an increase of 60% in recall and up to 13% in precision when detecting code smells. Consequently, developers could identify more refactoring opportunities using the ID technique than the NID.","PeriodicalId":261625,"journal":{"name":"2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)","volume":"1004 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123324591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Siamese Network for Content-Based Image Retrieval: Detection of Alzheimer's Disease from neuroimaging data 基于内容的图像检索的暹罗网络:从神经成像数据检测阿尔茨海默病

2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) Pub Date : 2022-09-22 DOI: 10.23919/softcom55329.2022.9911487

Ivana Marin, T. Marasovic, Sven Gotovac

{"title":"Siamese Network for Content-Based Image Retrieval: Detection of Alzheimer's Disease from neuroimaging data","authors":"Ivana Marin, T. Marasovic, Sven Gotovac","doi":"10.23919/softcom55329.2022.9911487","DOIUrl":"https://doi.org/10.23919/softcom55329.2022.9911487","url":null,"abstract":"In recent years deep-learning methods have demon-strated impressive results in various domains of computer vision, including medical imaging. This paper examines the possibility of leveraging deep-learning concepts in designing a computer system that could help clinicians make accurate Alzheimer disease (AD) diagnosis by retrieving the most similar archived brain scans of patients with already known diagnoses. We implement a siamese network with ResNet-50 twin subnetworks and train it on the MRI data obtained from ADNI (Alzheimer's Disease Neu-roimaging Initiative) dataset. Four different approaches for slice extraction from MRI volume are considered: using the three slices from the same plane (axial, coronal or sagittal) and combining one slice from each plane. The final performance of the CBIR system on new patient's data based only on MR neuroimaging modality shows limited and comparable performance with all four approaches and leaves space for further enhancements, including complementing neuroimaging MRI data with other data modalities relevant for AD detection.","PeriodicalId":261625,"journal":{"name":"2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129558259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Review of Least Action Principle in Electromagnetics: Part II: Derivation of Maxwell's Equations 电磁学最小作用原理回顾:第二部分:麦克斯韦方程组的推导

2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) Pub Date : 2022-09-22 DOI: 10.23919/softcom55329.2022.9911284

D. Poljak

引用次数: 2

Impact of Multi-Layer Recurrent Neural Networks in the Congestion Analysis of TeraHertz B5G/6G MAC Mechanism 多层递归神经网络在太赫兹B5G/6G MAC机制拥塞分析中的影响

2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) Pub Date : 2022-09-22 DOI: 10.23919/softcom55329.2022.9911500

Djamila Talbi, Zoltán Gál

{"title":"Impact of Multi-Layer Recurrent Neural Networks in the Congestion Analysis of TeraHertz B5G/6G MAC Mechanism","authors":"Djamila Talbi, Zoltán Gál","doi":"10.23919/softcom55329.2022.9911500","DOIUrl":"https://doi.org/10.23919/softcom55329.2022.9911500","url":null,"abstract":"Nowadays design of BSG/6G radio technologies require analysis based on simulations to determine optimum functioning properties. We executed ns-3 simulations to generate TeraHertz scale MAC event sequences. Standard communication proposal mechanism, called Adaptive Directional Antenna Protocol for Terahertz (ADAPT), was analysed by extract frame collision behaviour in the control plane of the high-speed channel. Seven step sizes of sector indexes with specific features were used at the base station to give access to the mobile terminals spread in 30 sectors of the circular radio cell. After presenting basic properties of the MAC mechanism we grouped collision sequences into four classes. Testing classifications were performed with three types of recurrent neural networks (RNN). Transfer learning was used to detect influence of the recurrent layers on the performance of the compound multilayer RNN. Complex metric was introduced to quantify the learning efficiency of the RNN. It was found that the proposed metric, called Weighted Accuracy-to- Time Ratio is able to characterize and compare in efficient manner goodness of different deep learning techniques used for evaluation of the ADAPT technology. This new metric quantifies transfer learning property and differentiates applicability of the most popular recurrent neural networks used in practice.","PeriodicalId":261625,"journal":{"name":"2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127842401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Topology Optimization of an Assembled 3D Printed Robot 装配式3D打印机器人的拓扑优化

2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) Pub Date : 2022-09-22 DOI: 10.23919/softcom55329.2022.9911410

I. Chavdarov, Bozhidar Naydenov, Kaloyan Yovchev, Lyubomira Miteva

引用次数: 0

Review of Least Action Principle in Electromagnetics Part I: Derivation of Continuity Equation and Lorentz Force 电磁学最小作用原理述评第一部分:连续性方程和洛伦兹力的推导

2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) Pub Date : 2022-09-22 DOI: 10.23919/softcom55329.2022.9911297

Dragan Poliak

引用次数: 2

An Approach based on vSDN to Optimize Power Consumption 一种基于vSDN的功耗优化方法

2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) Pub Date : 2022-09-22 DOI: 10.23919/softcom55329.2022.9911318

E. P. Neto, G. Callou

{"title":"An Approach based on vSDN to Optimize Power Consumption","authors":"E. P. Neto, G. Callou","doi":"10.23919/softcom55329.2022.9911318","DOIUrl":"https://doi.org/10.23919/softcom55329.2022.9911318","url":null,"abstract":"Recently, Technology and the web had a fast evo-lution. Assuming this, Software-Defined Networking (SDN) has been conceived to offer tenants improved network management, allowing modern services in business features. Additionally, SDNs allow the creation of many different and independent virtual networks, called virtual SDNs (vSDNs). However, some issues are present, e.g., resilience (i.e., recovering after a determined equipment stop working) and power consumption optimization of topologies with idle devices. This paper proposes an approach based on vSDN that optimizes power consumption by switching off idle equipment. This approach assumes multiple tenants and creates vSDNs according to the considered network topology metrics (e.g., power usage effectiveness (PUE), delay, power con-sumption, packet loss, availability, and reliability). In addition, the Network Model Flow (NMF) is proposed to represent the data flow on the network. To optimize power consumption, we proposed an approach that put the idle devices into the sleep mode. In the experiments, the proposed strategy reduced the power consumption upper than 50%.","PeriodicalId":261625,"journal":{"name":"2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125993247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Classification Results in Network Data Analysis using Interpretability Methods 利用可解释性方法改进网络数据分析中的分类结果

2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) Pub Date : 2022-09-22 DOI: 10.23919/softcom55329.2022.9911501

Domazoj Begušić, Luke Frederick Walker, S. Krznarić, D. Pintar

{"title":"Improving Classification Results in Network Data Analysis using Interpretability Methods","authors":"Domazoj Begušić, Luke Frederick Walker, S. Krznarić, D. Pintar","doi":"10.23919/softcom55329.2022.9911501","DOIUrl":"https://doi.org/10.23919/softcom55329.2022.9911501","url":null,"abstract":"Developing network intrusion detection and prevention systems usually leverage a rule-based approach, which is derived from rules defined by network security experts who can utilize logic from both low and high network layers. However, in recent times, machine learning methods have also achieved promising results in developing Network Intrusion Detection Systems, and their popularity is steadily rising. Unfortunately, the usage of these machine learning methods in real-life problems has regularly proved that no good out-of-the-box solution exists for production or deployment. Also, due to the increasing volume and complexity of processed data that machine learning methods are faced with over time, improvements and adaptions are frequently required. As the problem at hand becomes more convoluted, so does the the nature of the applied solution. This complexity is further compounded by the fact that certain machine and deep learning methods intrinsically do not offer a way of understanding how they make decisions, effectively behaving like black boxes. All of this significantly lowers the understandability of implemented solutions in production environments that are already quite complex, which justifies the need of interpretability methods. While interpretability methods are commonly designed to be used by humans, in this paper we propose a way of improving a model's classification performance by applying data mining methods on explanation data generated by interpretability methods. The paper's main contribution is improving on a previously built network intrusion detection system through proposing an automated process of integrating explanations into original data with the purpose of improving the interpretability and score of the used machine learning model","PeriodicalId":261625,"journal":{"name":"2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122297055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0