2022 26th International Conference on Information Technology (IT)最新文献

An example of SMS service development at the University of Montenegro Information System 黑山大学信息系统短信服务发展的一个例子

2022 26th International Conference on Information Technology (IT) Pub Date : 2022-02-16 DOI: 10.1109/IT54280.2022.9743529

M. Zarubica, Slobodan Dukanović, Lidija Milosavljević, Jelena N. Terzić, Vladimir Gazivoda, Luka Filipović

引用次数: 0

EEG Signal Classification with Deep Neural Networks using Visibility Graphs 基于可见性图的深度神经网络脑电信号分类

2022 26th International Conference on Information Technology (IT) Pub Date : 2022-02-16 DOI: 10.1109/IT54280.2022.9743535

Turan Goktug Altundogan, Mehmet Karaköse

引用次数: 0

[Copyright notice] (版权)

2022 26th International Conference on Information Technology (IT) Pub Date : 2022-02-16 DOI: 10.1109/it54280.2022.9743532

引用次数: 0

Regulated Output Synchronization of Multi-Agent Systems via Output Feedback 基于输出反馈的多智能体系统调节输出同步

2022 26th International Conference on Information Technology (IT) Pub Date : 2022-02-16 DOI: 10.1109/IT54280.2022.9743524

Luka Martinović, Ž. Zečević, B. Krstajić

引用次数: 0

Speed-Up of Machine Learning for Sound Localization via High-Performance Computing

2022 26th International Conference on Information Technology (IT) Pub Date : 2022-02-16 DOI: 10.1109/IT54280.2022.9743519

E. M. Sumner, Marcel Aach, A. Lintermann, Runar Unnthorsson, M. Riedel

{"title":"Speed-Up of Machine Learning for Sound Localization via High-Performance Computing","authors":"E. M. Sumner, Marcel Aach, A. Lintermann, Runar Unnthorsson, M. Riedel","doi":"10.1109/IT54280.2022.9743519","DOIUrl":"https://doi.org/10.1109/IT54280.2022.9743519","url":null,"abstract":"Sound localization is the ability of humans to determine the source direction of sounds that they hear. Emulating this capability in virtual environments can have various societally relevant applications enabling more realistic virtual acoustics. We use a variety of artificial intelligence methods, such as machine learning via an Artificial Neural Network (ANN) model, to emulate human sound localization abilities. This paper addresses the particular challenge that the training and optimization of these models is very computationally-intensive when working with audio signal datasets. It describes the successful porting of our novel ANN model code for sound localization from limiting serial CPU-based systems to powerful, cutting-edge High-Performance Computing (HPC) resources to obtain significant speed-ups of the training and optimization process. Selected details of the code refactoring and HPC porting are described, such as adapting hyperparameter optimization algorithms to efficiently use the available HPC resources and replacing third-party libraries responsible for audio signal analysis and linear algebra. This study demonstrates that using innovative HPC systems at the Jülich Supercomputing Centre, equipped with high-tech Graphics Processing Unit (GPU) resources and based on the Modular Supercomputing Architecture, enables significant speed-ups and reduces the time-to-solution for sound localization from three days to three hours per ANN model.","PeriodicalId":335678,"journal":{"name":"2022 26th International Conference on Information Technology (IT)","volume":"165 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125964659","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

Distributed Deep Learning Approach for Optimal Hyper-Parameter Values 最优超参数值的分布式深度学习方法

2022 26th International Conference on Information Technology (IT) Pub Date : 2022-02-16 DOI: 10.1109/IT54280.2022.9743528

Ziya Tan, M. Karakose

{"title":"Distributed Deep Learning Approach for Optimal Hyper-Parameter Values","authors":"Ziya Tan, M. Karakose","doi":"10.1109/IT54280.2022.9743528","DOIUrl":"https://doi.org/10.1109/IT54280.2022.9743528","url":null,"abstract":"With the development of artificial intelligence, there are great changes especially in technology and industry sectors. The fact that deep learning and reinforcement learning studies are popular topics by researchers accelerates this change. In this article, a distributed system is presented to determine the hyper-parameters of the deep learning algorithm used for object detection at the most accurate value. One of the most important factors affecting the accuracy rate in object recognition approaches using deep learning algorithms is the determination of hyper-parameters with correct values. It may be necessary to carry out very long experiments to determine the optimum of these parameters. To solve this problem, a deep learning network used for object detection has been trained by combining the RAY distributed architecture with a deep learning algorithm. The accuracy rate is observed by changing the parameters in each iteration. For object detection, the training of the neural network we created with the CIFAR-10 dataset was carried out using CPU. In addition, thanks to the distributed architecture, each process is trained by 4 different workers. The training results and the properties of the artificial neural network are given in detail in the following sections. Accordingly, we can highlight the main contributions of this article in three points. Firstly; to show that long processes are completed in a short time, thanks to the integration of deep learning algorithms with the distributed system; training the model used to determine the optimal hyper-parameter values and the third is the presentation of the distributed deep learning approach.","PeriodicalId":335678,"journal":{"name":"2022 26th International Conference on Information Technology (IT)","volume":"118 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132227939","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 New Framework for Quantum Image Processing and Application of Binary Template Matching 量子图像处理新框架及二值模板匹配的应用

2022 26th International Conference on Information Technology (IT) Pub Date : 2022-02-16 DOI: 10.1109/IT54280.2022.9743534

Hasan Yetiş, Mehmet Karaköse

引用次数: 2

Cloud-in-the-Loop simulation of C-V2X application relocation distortions in Kubernetes based Edge Cloud environment 基于Kubernetes的边缘云环境下C-V2X应用重定位失真的云在环仿真

2022 26th International Conference on Information Technology (IT) Pub Date : 2022-02-16 DOI: 10.1109/IT54280.2022.9743520

L. Maller, Péter Suskovics, L. Bokor

引用次数: 2

The use of micro:bit in practical classes micro:bit在实践课上的运用

2022 26th International Conference on Information Technology (IT) Pub Date : 2022-02-16 DOI: 10.1109/IT54280.2022.9743546

Ivana Cavor, Ilija Knežević, Nemanja Pudar, Lazar Mrdović, Tatijana Dlabač

引用次数: 0

An Internet of Things System for Environmental Monitoring Based on ESP32 and Blynk 基于ESP32和Blynk的物联网环境监测系统

2022 26th International Conference on Information Technology (IT) Pub Date : 2022-02-16 DOI: 10.1109/IT54280.2022.9743538

Dejan Babic, I. Jovović, Tomo Popović, N. Kovač, Stevan Cakic

引用次数: 0