2023 International Conference on Computing, Networking and Communications (ICNC)最新文献_第10页

Preliminary Analysis of Dietary Management Support Method for Improving the Symptoms in Irritable Bowel Syndrome 饮食管理支持方法对改善肠易激综合征症状的初步分析

2023 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2023-02-20 DOI: 10.1109/ICNC57223.2023.10073984

Takuya Yamanaka, Da Li, Shinichi Nakajima

引用次数: 0

CMRCV: Causal Modeling to Localize Failed Equipment by Representative Nodes and Contribution Values CMRCV:基于代表性节点和贡献值的故障设备定位因果模型

2023 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2023-02-20 DOI: 10.1109/ICNC57223.2023.10074021

Yoichi Matsuo, Yuusuke Nakano, Keishiro Watanabe

引用次数: 0

Augmenting Campus Wireless Architectures with SDN 用SDN增强校园无线架构

2023 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2023-02-20 DOI: 10.1109/ICNC57223.2023.10074202

William Brockelsby, R. Dutta

引用次数: 0

Guiding Interactive Film With Emotion-Profiling Chatbots 用情感分析聊天机器人指导互动电影

2023 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2023-02-20 DOI: 10.1109/ICNC57223.2023.10074513

Celina Ma, Haohong Wang, Mea Wang

引用次数: 2

Hensel’s Compression-Based Dimensionality Reduction Approach for Privacy Protection in Federated Learning Hensel基于压缩的联邦学习隐私保护降维方法

2023 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2023-02-20 DOI: 10.1109/ICNC57223.2023.10074197

Ahmed El Ouadrhiri, A. Abdelhadi, Phu H. Phung

{"title":"Hensel’s Compression-Based Dimensionality Reduction Approach for Privacy Protection in Federated Learning","authors":"Ahmed El Ouadrhiri, A. Abdelhadi, Phu H. Phung","doi":"10.1109/ICNC57223.2023.10074197","DOIUrl":"https://doi.org/10.1109/ICNC57223.2023.10074197","url":null,"abstract":"Differential privacy (DP) is considered a de-facto standard for protecting users’ privacy in data analysis, machine, and deep learning. Existing DP-based privacy-preserving approaches, in federated learning, consist of adding noise to the clients’ gradients before sharing them with the server. However, implementing DP on the gradient is inefficient as the privacy leakage increases by increasing the synchronization training epochs due to the composition theorem. Recently, researchers were able to recover images of the training dataset using a Generative Regression Neural Network (GRNN). In this work, we propose a novel approach using two layers of privacy protection to overcome the limitations of the existing DP-based methods. The first layer leverages Hensel’s Lemma to reduce the training dataset’ s dimension. The new dimensionality reduction method reduces the dimension of a dataset without losing information since Hensel’s Lemma guarantees uniqueness. The second layer applies DP to the compressed dataset generated by the first layer. The proposed approach overcomes the problem of privacy leakage due to composition by applying DP only once before the training. Therefore, clients train their local model on the privacy-preserving dataset generated by the second layer. Experimental results show that the proposed approach ensures strong privacy protection while achieving high accuracy. In particular, the new dimensionality reduction method achieves an accuracy of 97%, with only 25% of the original dataset size.","PeriodicalId":174051,"journal":{"name":"2023 International Conference on Computing, Networking and Communications (ICNC)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122979619","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

Consensus algorithms for Opt-in/Opt-out and proximity marketing context 选择加入/选择退出和邻近营销环境的共识算法

2023 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2023-02-20 DOI: 10.1109/ICNC57223.2023.10074503

Fatima Chahal, H. Fouchal, D. Gaïti

引用次数: 0

Transmission-Cost Minimization for Packet-level Coding on Multi-path Wireless Networks 多路径无线网络中分组级编码的传输成本最小化

2023 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2023-02-20 DOI: 10.1109/ICNC57223.2023.10074036

Wei Mao, Shu-ping Yeh, Jing Zhu, Hosein Nikopour, S. Talwar

{"title":"Transmission-Cost Minimization for Packet-level Coding on Multi-path Wireless Networks","authors":"Wei Mao, Shu-ping Yeh, Jing Zhu, Hosein Nikopour, S. Talwar","doi":"10.1109/ICNC57223.2023.10074036","DOIUrl":"https://doi.org/10.1109/ICNC57223.2023.10074036","url":null,"abstract":"Topological redundancies in modern wireless infrastructures provide opportunities to enhance reliability and latency performances through multiple data paths, however, existing standards for wireless access networks cannot fully utilize such an added degree of freedom. Packet-level coding, which proactively adds redundancy to the transmitted data at the packet level, can efficiently utilize the added bandwidth from all data paths and enhance reliability with low latency. Thus it is a good candidate to supplement single-link/physical layer techniques (e.g., channel coding) and provide a better chance of realizing new reliability- and delay-critical services, such as URLLC. In this paper we study the problem of how to optimally design the coding rate and packet distribution rule over multiple data paths to transmit a set of data packets under stringent reliability and delay constraints, so that radio resource cost is minimized. Since such problems are often NP-hard and obtaining optimal solutions usually requires very high-complexity exhaustive search, we propose novel heuristic algorithms that use the path resources in the order of their cost-effectiveness scores to approximately solve this problem. Numerical simulations show that our proposed algorithms can drastically reduce the computational complexity while still able to achieve near-optimal performance.","PeriodicalId":174051,"journal":{"name":"2023 International Conference on Computing, Networking and Communications (ICNC)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132351142","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

The Hysteresis Effect of Momentum Spillover in Asset Pricing via Spatial-Temporal Graph Learning 基于时空图学习的资产定价动量溢出的滞后效应

2023 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2023-02-20 DOI: 10.1109/ICNC57223.2023.10074006

Chenhao He, Qing Li, Rui Cheng, Jun Wang, Jinghua Tan

{"title":"The Hysteresis Effect of Momentum Spillover in Asset Pricing via Spatial-Temporal Graph Learning","authors":"Chenhao He, Qing Li, Rui Cheng, Jun Wang, Jinghua Tan","doi":"10.1109/ICNC57223.2023.10074006","DOIUrl":"https://doi.org/10.1109/ICNC57223.2023.10074006","url":null,"abstract":"Asset pricing is a long-standing topic in finance. To capture the momentum spillovers of listed firms, recent pilot studies have been studying graph neural networks to lead the influence propagation among listed firms and predict stock movement. However, all these works ignored the hysteresis effect of momentum spillover where the future return of a target firm lags behind the historical momentum spillovers which would cause hysteresis influence. In this study, we argue that the hysteresis effect of momentum spillover is critical in asset pricing and propose a novel hysteresis spatial-temporal graph learning (HysGL) for it. For spatial learning, we dynamically estimate the momentum spillover generated at each moment to represent the influence propagated from changing relevant firms. For temporal learning, we progressively propose the time-sensitive and state-sensitive mechanisms to explicitly model the influence pattern of hysteresis effect by calculating the response strength of a target firm to all historical momentum spillovers over the time dimension, based on which the cumulative hysteresis influence received by the target firm is obtained and then sequentially embedded to extract the hidden state preserving the temporal dependency of momentum spillovers. Extensive experiments performed on two real-world financial datasets demonstrate the superiority of the HysGL over the state-of-the-art algorithms, including TGC, FinGAT and AD-GAT.","PeriodicalId":174051,"journal":{"name":"2023 International Conference on Computing, Networking and Communications (ICNC)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131194455","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

A Zero-Trust Framework for Industrial Internet of Things 工业物联网零信任框架

2023 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2023-02-20 DOI: 10.1109/ICNC57223.2023.10074295

Adel Atieh, P. Nanda, M. Mohanty

{"title":"A Zero-Trust Framework for Industrial Internet of Things","authors":"Adel Atieh, P. Nanda, M. Mohanty","doi":"10.1109/ICNC57223.2023.10074295","DOIUrl":"https://doi.org/10.1109/ICNC57223.2023.10074295","url":null,"abstract":"Interactions between different types of systems from various environments are increasing continuously due to the nature of business and commercial requirements. All of these interactions require a level of trust given for each system in order to enable essential operations and functions. Traditional trust models and frameworks implemented in different environments define static levels of trust given to users and systems. This includes the Defence-in-depth security model that is typically implemented in industrial control systems (ICS) environments. While this model and other security models provide an outstanding level of restriction and security if implemented correctly, they can still allow unauthorised access to sensitive data through compromised trust devices. Industrial Internet of Things (IIoT) solutions are actively being deployed in different sectors. Despite the criticality of the environments IIoT solutions serve, these solutions require more integrated connectivity that ICS environment due to cloud connectivity. This research paper proposes a zero-trust framework for IIoT and explores how this framework could mitigate the existing risks within IIoT solutions. Moreover, this research paper proposes a zero-trust anatomy for IIoT and explores the potential performance and/or complexity overhead resulted from the use of this model.","PeriodicalId":174051,"journal":{"name":"2023 International Conference on Computing, Networking and Communications (ICNC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129051272","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

Channel Allocation Algorithm Of Wi-Fi Wi-Fi的信道分配算法

2023 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2023-02-20 DOI: 10.1109/ICNC57223.2023.10074461

Jin Wang, Xiaofeng Zhong, Shidong Zhou

引用次数: 0