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

Availability Analysis of a Drone System with Proactive Offloading for Software Life-extension 无人机系统主动卸载延长软件寿命的可用性分析

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/coins54846.2022.9854966

Kengo Watanabe, F. Machida

{"title":"Availability Analysis of a Drone System with Proactive Offloading for Software Life-extension","authors":"Kengo Watanabe, F. Machida","doi":"10.1109/coins54846.2022.9854966","DOIUrl":"https://doi.org/10.1109/coins54846.2022.9854966","url":null,"abstract":"Real-time image processing on a drone to recognize the real-world environment has become popular recently in many applications. However, continuous image processing on a drone may entail the degradation of performance and reliability over the long-time operation, also known as software aging. Since the degradation due to software aging progresses with the amount of the workload to process, offloading the image processing tasks to other computers can mitigate the progression of the software aging. In this paper, we propose a new software life-extension method to counteract software aging on a drone image processing system by means of proactive task offloading. To evaluate the effectiveness of the proposed method, we develop continuous-time Markov chains (CTMCs) to analyze the stochastic behaviors of the system. Through numerical experiments, we show that proactive offloading improves the steady-state availability, the mean time to down (MTTD), and the average throughput by 1.85%, 1.57x, 1.48x, respectively. We also show that the combination of offloading and software rejuvenating further improves the steady-state availability and the average throughput.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"1 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":"129996746","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

Global Aggregation Node Selection Scheme in Federated Learning for Vehicular Ad Hoc Networks (VANETs) 基于联邦学习的车载Ad Hoc网络全局聚合节点选择方案

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854941

Z. Trabelsi, Tariq Qayyum, Kadhim Hayawi, M. Ali

{"title":"Global Aggregation Node Selection Scheme in Federated Learning for Vehicular Ad Hoc Networks (VANETs)","authors":"Z. Trabelsi, Tariq Qayyum, Kadhim Hayawi, M. Ali","doi":"10.1109/COINS54846.2022.9854941","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854941","url":null,"abstract":"Federated learning allows multiple users and parties to collaborate and train machine learning models in a distributed and privacy-preserving manner in Vehicular Adhoc Networks VANETs. This computing paradigm addresses privacy concerns; however, it comes at a considerable cost of network resources. After training the machine learning models in conventional federated learning frameworks, devices share that model with a central server, mostly cloud, where the global aggregation is performed. Multiple devices communicating with a central server raise network bandwidth and congestion concerns. To solve this problem, we proposed a federated learning framework for VANETs where instead of using a fixed global aggregator, we used variable global aggregation nodes. The global aggregation node is selected based on communication delay and workload in the proposed framework. We also believe that, in a vehicular Adhoc network, all network nodes cannot participate in the learning process due to network, computation, and energy resource limitations. We Also proposed a client selection algorithm that adapts itself and selects some clients based on specific criteria. Finally, the proposed technique is compared with the hierarchical federated learning framework (HFL) and FedAvg where proposed method outperformed in terms of accuracy.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"31 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":"134437278","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 the Performance of Multi-Label Classifiers via Label Space Reduction 通过标签空间约简提高多标签分类器的性能

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854940

J. M. Moyano, J. M. Luna, Sebastián Ventura

引用次数: 0

Profiling the real world potential of neural network compression 剖析现实世界中神经网络压缩的潜力

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854973

Joe Lorentz, Assaad Moawad, Thomas Hartmann, Djamila Aouada

引用次数: 0

A review of CNN accelerators for embedded systems based on RISC-V 基于RISC-V的嵌入式系统CNN加速器综述

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9855006

Alejandra Sánchez-Flores, Lluc Alvarez, Bartomeu Alorda-Ladaria

引用次数: 2

Automatic Speech Recognition in German: A Detailed Error Analysis 德语语音自动识别:详细的错误分析

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854978

Johannes Wirth, R. Peinl

引用次数: 2

Dynamic Airline Discounts using an Evolutionary Subgroup Discovery Methodology 基于进化子群发现方法的动态航空折扣

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854942

Marco Antonio Barrón, J. M. Luna, Sebastián Ventura

引用次数: 0

Electronically Foveated Dynamic Vision Sensor 电子注视点动态视觉传感器

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9855009

T. Serrano-Gotarredona, F. Faramarzi, B. Linares-Barranco

引用次数: 3

Multi-view Deep Neural Networks for multiclass skin lesion diagnosis 多视图深度神经网络在多类别皮肤病变诊断中的应用

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854997

Eduardo Pérez, S. Ventura

{"title":"Multi-view Deep Neural Networks for multiclass skin lesion diagnosis","authors":"Eduardo Pérez, S. Ventura","doi":"10.1109/COINS54846.2022.9854997","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854997","url":null,"abstract":"Early diagnosis is still the best method to face skin cancer. The diagnosis of skin lesions remains as a challenge for physicians and researchers. In the past few years, it has benefited from computer-aided diagnosis methods that successfully apply classic Machine Learning techniques and more recently Convolutional Neural Networks. This work is aimed at discovering architectures that best fuse clinical records and medical images for the diagnosis of skin lesions. As a result, a genetic algorithm is designed in order to select how to combine such information and the main details of the new architecture. The architecture is able to cope with multiple inputs and learn multiple outputs, proving flexibility by sharing network parameters, which implicitly mitigates the overfitting of the model. An extensive experimental study was conducted on the well-known ISIC2019 dataset, where the models were trained with a total of 72,106 images and meta-data, including the augmented images. The proposal outperformed the baseline state-of-the-art model while diagnosing from eight skin lesion categories. Furthermore, the discovered architecture achieved 85%, 94%, and 84% of recall score when diagnosing malignant lesions - melanoma, basal cell carcinoma, and squamous cell carcinoma, respectively. Finally, the results showed the suitability of the proposed genetic algorithm, which was able to automatically build a multimodal fusion architecture for the diagnosis of skin lesions.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"3 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":"128810822","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

Two-dimensional Dataset Reduction in Data-Driven Fault Detection for IoT-based Cyber Physical Systems 基于物联网的网络物理系统数据驱动故障检测中的二维数据集缩减

2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) Pub Date : 2022-08-01 DOI: 10.1109/COINS54846.2022.9854937

Georgios Tertytchny, M. Michael

{"title":"Two-dimensional Dataset Reduction in Data-Driven Fault Detection for IoT-based Cyber Physical Systems","authors":"Georgios Tertytchny, M. Michael","doi":"10.1109/COINS54846.2022.9854937","DOIUrl":"https://doi.org/10.1109/COINS54846.2022.9854937","url":null,"abstract":"Internet of Things (IoT)-based Cyber-Physical Systems (CPS) are the integration of computational, networking and physical processes. IoT devices are used to enhance monitoring and control of such systems. CPS normal operation might be altered by physical component faults, which can be exploited to generate abnormal behaviour. Data-driven fault detection can be used in CPS to take advantage of the large amount of data being generated by IoT devices. However, the data can be noisy, corrupted or redundant, which can lead to misclassification and/or degradation in the performance of the detectors. A proper selection of data in terms of features and instances can improve the quality of the data and enhance the performance of such detectors. Moreover, it can allow for the implementation of lightweight but still accurate fault detectors, which are required in resource constrained edge-based IoT environments. This work studies the unified problem of instance and feature selection, for the purpose of two-dimensional reduction, in class-based fault detection datasets used in IoT-based CPS. To avoid the high complexity required by an optimal two-dimensional reduction approach, we examine order-based feature and instance dataset reduction and evaluate the new fault detectors which are trained based on the reduced datasets, in terms of accuracy gain/loss. A new weighted metric which combines accuracy and dataset reduction rates is introduced that enables the selection of an appropriate reduction scheme per application. Experimental results suggest that a proper selection of instance and feature selection algorithms can significantly reduce dataset size by up to 90%.","PeriodicalId":187055,"journal":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","volume":"42 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":"123332833","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