2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)最新文献_第3页

Combining 3D Surveying with Archaeological Uncertainty: The Metopes of the Athenian Treasury at Delphi 结合三维测量与考古的不确定性:在德尔菲雅典国库的墙面

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555568

K. Mania, Athanasia Psalti, D. Lala, M. Tsakoumaki, A. Polychronakis, Anastasia Rempoulaki, Michael Xinogalos, E. Maravelakis

引用次数: 3

Bayesian optimization for the design of deep neural networks 深度神经网络设计中的贝叶斯优化

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555533

Nikolas Giannakis, N. Gorgolis, I. Hatzilygeroudis

{"title":"Bayesian optimization for the design of deep neural networks","authors":"Nikolas Giannakis, N. Gorgolis, I. Hatzilygeroudis","doi":"10.1109/IISA52424.2021.9555533","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555533","url":null,"abstract":"The design of deep neural networks (DNNs), which in essence concerns the choice of specific values for their hyperparameters, is a very involved process that provides very big challenges to researchers and designers. The fact that there are strong problem-specific dependencies and intuition/experience has been typically used by the designers, has led to the consideration of it as more of an art issue than a well structured and stardardized procedure. The aim of this work is to introduce some structure on the above design process by considering it as a function to be optimized, which takes as input a specific set of hyperparameter values and returns the accuracy of the designed DNN. The process of Bayesian optimization, using Gaussian processes as modeling functions, is employed to fine tune the hyperparameters of DNNs. We arrived at some very promising results. Comparing the proposed process to the random choice of hyperparameters from a specific set, much better accuracy is achieved at no significant extra time cost. Also, the process produces neural network architectures that mimic very closely the known best performing architectures for specific problem sets within the given constraints.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114569819","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

Fuzzy Integration of kernel-based Gaussian Processes applied to Anomaly Detection in Nuclear Security 基于核的高斯过程模糊集成在核安全异常检测中的应用

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555524

M. Alamaniotis

{"title":"Fuzzy Integration of kernel-based Gaussian Processes applied to Anomaly Detection in Nuclear Security","authors":"M. Alamaniotis","doi":"10.1109/IISA52424.2021.9555524","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555524","url":null,"abstract":"Advances in artificial intelligence (AI) have provided a variety of solutions in several real-world complex problems. One of the current trends contains the integration of various AI tools to improve the proposed solutions. The question that has to be revisited is how tools may be put together to form efficient systems suitable for the problem at hand. This paper frames itself in the area of nuclear security where an agent uses a radiation sensor to survey an area for radiological threats. The main goal of this application is to identify anomalies in the measured data that designate the presence of nuclear material that may consist of a threat. To that end, we propose the integration of two kernel modeled Gaussian processes (GP) by using a fuzzy inference system. The GP models utilize different types of information to make predictions of the background radiation contribution that will be used to identify an anomaly. The integration of the prediction of the two GP models is performed with means of fuzzy rules that provide the degree of existence of anomalous data. The proposed system is tested on a set of real-world gamma-ray spectra taken with a low-resolution portable radiation spectrometer.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127799703","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

Short Survey of Artificial Intelligent Technologies for Defect Detection in Manufacturing 制造业缺陷检测的人工智能技术综述

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555499

E. Papageorgiou, T. Theodosiou, George Margetis, N. Dimitriou, P. Charalampous, D. Tzovaras, Ioannis Samakovlis

引用次数: 10

Bayesian Optimization in High-Dimensional Spaces: A Brief Survey 高维空间中的贝叶斯优化:综述

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555522

Mohit Malu, Gautam Dasarathy, A. Spanias

引用次数: 18

Web of Things Functionality in IoT: A Service Oriented Perspective 物联网中的物联网功能:面向服务的视角

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555578

Aimilios Tzavaras, E. Petrakis

引用次数: 1

Motivating Item Annotations In Cultural Portals With UI/UX Based On Behavioral Economics 基于行为经济学的UI/UX文化门户项目注释激励

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555569

G. Drakopoulos, Yorghos Voutos, Phivos Mylonas, S. Sioutas

{"title":"Motivating Item Annotations In Cultural Portals With UI/UX Based On Behavioral Economics","authors":"G. Drakopoulos, Yorghos Voutos, Phivos Mylonas, S. Sioutas","doi":"10.1109/IISA52424.2021.9555569","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555569","url":null,"abstract":"Digital repositories and cultural content delivery systems built on top of them are integral parts in the current form of cultural landscape. In these systems netizen engagement is paramount and it can take many forms ranging from participation to digital for a to custom multimedia creation. One important engagement manifestation is the annotation of cultural items stored in the portal. This allows the discovery of additional item aspects, properties, semantics, topical variations, and latent connections to other items and hence it is paramount in many technological and commercial levels. In order to ensure a sufficiently high level of netizen activity, UI/UX design guidelines based on behavioral economics principles can be integrated into digital repositories transforming the traditional one way interaction to a novel fully bidirectional experience and making thus netizens part of both the long term cultural preservation and the insight gain processes. This conference paper proposes a set of such guidelines along with best practices stemming from the worldwide use of digital repositories and cultural portals.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132343972","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: 3

Hybrid Quantum Differential Evolution 混合量子微分演化

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555505

C. Pizzuti

引用次数: 3

Semantic Information in Gating Patterns of Dynamic Convolutional Neural Networks 动态卷积神经网络门控模式中的语义信息

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555567

Ilias Theodorakopoulos, G. Economou

{"title":"Semantic Information in Gating Patterns of Dynamic Convolutional Neural Networks","authors":"Ilias Theodorakopoulos, G. Economou","doi":"10.1109/IISA52424.2021.9555567","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555567","url":null,"abstract":"Dynamic Convolutional Neural Networks are an emerging class of models characterized by their ability to dynamically adjust inference complexity at run-time, by identifying parts of the model with minimal contribution to the result and skipping the corresponding computations. A prominent such category includes models that generate binary gating signals indicating whether specific convolutional kernels need to be computed or can be omitted based on the characteristics of each processed datum. These signals are usually generated by branches of the same model which are typically learned simultaneously to the main task, with their main objective being to enable good performance with parsimony of computations. We argue that such objective incentivizes the model to implicitly optimize and utilize kernels in class/concept –specific groups, hence ascribing semantic information to the gating signals. We demonstrate this behavior by studying the characteristics of such signals for popular CNN architectures in the ImageNet database. By comparing the relationship between gating signals from different visual categories in the ImageNet hierarchy, it is shown that the gating patterns’ dissimilarity correlates well with semantic span of the underlying classes. It is also demonstrated that through appropriate distance measures, gating patterns can be used for ranking classes’ similarity with comparable performance to that off standard CNN-generated image descriptors, but in a significantly more compact representation due to their binary nature. (Abstract)","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114542463","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

Forecasting of short-term PV production in energy communities through Machine Learning and Deep Learning algorithms 通过机器学习和深度学习算法预测能源社区的短期光伏产量

2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2021-07-12 DOI: 10.1109/IISA52424.2021.9555544

Nikos Dimitropoulos, Nikolaos Sofias, Panagiotis Kapsalis, Z. Mylona, Vangelis Marinakis, Niccolo Primo, H. Doukas

{"title":"Forecasting of short-term PV production in energy communities through Machine Learning and Deep Learning algorithms","authors":"Nikos Dimitropoulos, Nikolaos Sofias, Panagiotis Kapsalis, Z. Mylona, Vangelis Marinakis, Niccolo Primo, H. Doukas","doi":"10.1109/IISA52424.2021.9555544","DOIUrl":"https://doi.org/10.1109/IISA52424.2021.9555544","url":null,"abstract":"Photovoltaic (PV) modules and solar plants are one of the main drivers towards zero-carbon future. Energy communities that are engaging citizens through collective energy actions can reinforce positive social norms and support the energy transition. Furthermore, by incorporating Artificial Intelligence (AI) techniques, innovative applications can be developed with huge potential, such as supply and demand management, energy efficiency actions, grid operations and maintenance actions. In this context, the scope of this paper is to present an approach for forecasting an energy cooperative’s solar plant short term production by using its infrastructure and monitoring system. More specifically, four Machine Learning (ML) and Deep Learning (DL) algorithms are proposed and trained in an operational solar plant producing high accuracy short-term forecasts up to 6 hours. The results can be used for scheduling supply of the energy communities and set the base for more complex applications that require accurate short-term predictions, such as predictive maintenance.","PeriodicalId":437496,"journal":{"name":"2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132779821","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: 5