2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)最新文献_第3页

A Comprehensive Blockchain Framework for (COVID-19)Vaccine Program Registration, Supply Chain and Side Effects (COVID-19)疫苗项目注册、供应链和副作用的综合区块链框架

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854996

Alia Al Sadawi, M. Ndiaye

{"title":"A Comprehensive Blockchain Framework for (COVID-19)Vaccine Program Registration, Supply Chain and Side Effects","authors":"Alia Al Sadawi, M. Ndiaye","doi":"10.1109/COINS54846.2022.9854996","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854996","url":null,"abstract":"The history of mankind witnessed a lot of pandemics throughout its years. Many of which caused fatalities and created severe consequences socially and economically. The latest example is the COVID-19 pandemic that got the world into a heated race to contain by developing immunization vaccines. Many of the vaccines came into production in record time which raises questions about their side effects. Also, since vaccines are sensitive medical products, they require continuous cold supply chain monitoring and tracking. It is clear how vital it is to link any occurring side effects to their causes to determine whether it is a manufacturing problem or a supply chain management issue. Additionally, and to properly establish this link, an effective beneficiaries’ registration process is required. Therefore, it is necessary to establish a comprehensive system that combines vaccines cold supply chain management solution with side-effects follow-up data and individuals registration records. To achieve these goals, the technology of blockchain was selected to develop a framework for the vaccines program. Blockchain is known as the architecture behind cryptocurrencies, however, it proved its reliability and effectiveness in different use cases. Blockchain special characteristics were the reason behind adopting it to establish a blockchain-based platform for vaccines cold supply chain associated with traceable side-effects information records combined with beneficiaries registration data. The connection between the three parts of our system framework is important to achieving the utmost benefits out of the vaccine program in terms of compliance with standard measures and its link to side-effects and beneficiaries metadata.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130867987","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

COINS 2022 Cover Page 硬币2022封面

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/coins54846.2022.9854968

引用次数: 0

Water economy with smart water system in the City of Carouge 卡鲁日市智能水系统的水经济

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854990

E. Kim, R. Rossi, Betty Cortiñas-Lorenzo, B. Magoutas, A. Martinez, Cédric Crettaz, E. Bothos, J. M. F. Montenegro

引用次数: 0

A Study on Employing UPTANE for Secure Software Update OTA in Drone Environments 无人机环境下使用UPTANE进行安全软件更新OTA的研究

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854983

Shamma Al Blooshi, Kyusuk Han

引用次数: 2

Encoding categorical variables in physics-informed graphs for Bayesian Optimization 编码分类变量的物理通知图贝叶斯优化

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854962

Jan Krummenauer, Nesrine Kammoun, Benedikt Stein, Juergen Goetze

{"title":"Encoding categorical variables in physics-informed graphs for Bayesian Optimization","authors":"Jan Krummenauer, Nesrine Kammoun, Benedikt Stein, Juergen Goetze","doi":"10.1109/COINS54846.2022.9854962","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854962","url":null,"abstract":"The handling of categorical variables is a major challenge while using Bayesian Optimization (BO). Recent publications address this problem by introducing novel BO-frameworks adjusted specifically for categorical inputs. A promising approach, named \"Gryffin\", highlights the importance of using prior physical knowledge about categorical variables and shows significant performance improvements in comparison to other state of the art frameworks. However, the runtime of this approach increases significantly when the number of categorical choices is large. Therefore, we propose an alternative method for utilizing prior physical knowledge in BO by using physics-informed graphs. Our approach is based on a BO framework named \"Combo\", which allows handling graph-structures as input variables. In contrast to handling the categorical variables in complete graphs as originally proposed, we present a method for reshaping and simplifying the graph-structures based on prior physical knowledge. Physical similarities of categorical choices are encoded in graph edges, inducing proximity in the search space and enabling more precise predictions of a surrogate model. The optimization performance improves in comparison to the default Combo approach and other state of the art optimization techniques.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"35 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121005391","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

Research opportunities in the application of blockchain in video games: A scoping review 在电子游戏中应用区块链的研究机会:范围审查

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854939

J. Arnedo-Moreno, Victor Garcia-Font

引用次数: 1

Incorporating Reinforcement Learning for Quality-aware Sample Selection in Deep Architecture Training 在深度架构训练中结合强化学习进行质量意识样本选择

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854971

Gereziher W. Adhane, Mohammad Mahdi Dehshibi, D. Masip

{"title":"Incorporating Reinforcement Learning for Quality-aware Sample Selection in Deep Architecture Training","authors":"Gereziher W. Adhane, Mohammad Mahdi Dehshibi, D. Masip","doi":"10.1109/COINS54846.2022.9854971","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854971","url":null,"abstract":"Many samples are necessary to train a convolutional neural network (CNN) to achieve optimum performance while maintaining generalizability. Several studies, however, have indicated that not all input data in large datasets are informative for the model, and using them for training can degrade the model’s performance and add uncertainty. Furthermore, in some domains, such as medicine, there is insufficient labelled data to train a deep learning model from scratch, necessitating the use of transfer learning to fine-tune a pretrained model in another domain. This paper proposes a transfer learning strategy based on partially supervised reinforcement learning (RL) to address these concerns by selecting the most informative samples while avoiding negative transfers from the dataset. We conducted several experiments on the benchmark image classification databases MNIST, Fashion-MNIST, and CIFAR-10 to create a fair test harness for assessing the performance of the proposed strategy, which can be extended to explore other domains in the future. The results show that the proposed strategy outperforms the classical training methods.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116047295","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

Time and Energy trade-off analysis for Multi-Installment Scheduling with result retrieval strategy for Large Scale data processing 基于结果检索策略的大规模数据调度时间与能量权衡分析

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854989

Gokul Madathupalyam Chinnappan, B. Veeravalli

{"title":"Time and Energy trade-off analysis for Multi-Installment Scheduling with result retrieval strategy for Large Scale data processing","authors":"Gokul Madathupalyam Chinnappan, B. Veeravalli","doi":"10.1109/COINS54846.2022.9854989","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854989","url":null,"abstract":"On network-based computing systems, multi-instalment scheduling (MIS) has been shown to be an efficient method for reducing the time needed for processing large-scale divisible loads. Because the size of the output compared to the size of the input was considered to be minimal, the result backpropagation was not considered by traditional MIS techniques. Although the MIS-RR strategy incorporated the result retrieval phase along with a periodic MIS strategy, the energy effects of the strategy are yet to be studied. In this paper, we develop a comprehensive simulation framework using SimPy and use it to study the real-world time and energy effects of the MIS-RR strategy. We demonstrate the time and energy trade-offs that are required for an effective schedule. We analyse the strategy based on a variety of influencing factors such as network scalability, number of instalments, and overheads, and then try to determine the maximum number of processors to employ for a given load size and a given energy budget. This latter finding is crucial for system administrators when determining the number of resources to be deployed for a given load size and a power budget.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114766495","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

IoT based solution for seed collection management of genebank 基于物联网的基因库种子采集管理解决方案

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854980

A. Radziuk, Grzegorz Gryziak, Juliusz Pukacki, W. Podyma, Kazimierz Wilk, Radoslaw Taracinski, Michal Blaszczak, J. Czembor

引用次数: 1

Detection and Classification of Human Activities using Distributed Sensing of Environmental Vibrations 利用环境振动的分布式感知来检测和分类人类活动

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854970

Marcel Koch, Fabian Schlenke, Fabian Kohlmorgen, Markus Kuller, J. Bauer, Hendrik Wöhrle

引用次数: 1