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Correction: A meta-classification-based approach for outlier identification in GNSS networks

GPS Solutions 2025年第2期29卷 1-1页

作者： Suraci, Stefano Sampaio de Oliveira, Leonardo Castro Klein, Ivandro Goldschmidt, Ronaldo Ribeiro Rofatto, Vinicius Francisco Defense Engineering Department Military Institute of Engineering Leonardo Castro de Oliveira Rio de Janeiro Brazil Defense Engineering Program Military Institute of Engineering Rio de Janeiro Brazil Graduate Program in Geodetic Sciences Federal University of Paraná Curitiba Brazil Dept. of Civil Construction Federal Institute of Santa Catarina Florianopolis Brazil Systems and Computing Program Military Institute of Engineering Rio de Janeiro Brazil Institute of Geography Federal University of Uberlândia Monte Carmelo Brazil

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Explainability of Intelligent Transportation systems using Knowledge Compilation: a Traffic Light Controller Case

arXiv

引用

arXiv 2020年

作者： Wollenstein-Betech, Salomón Muise, Christian Cassandras, Christos G. Paschalidis, Ioannis Ch. Khazaeni, Yasaman Dept. of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA United States School of Computing Queen’s University KingstonON Canada IBM Research AI CambridgeMA United States

Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainability to the controller’s decision given the state of the system. For this, we use simulated historical state-action data as input and build a compact and structured representation which relates states with actions. We implement this method in a Traffic Light Control scenario where the controller selects the light cycle by observing the presence (or absence) of vehicles in different regions of the incoming roads. Copyright © 2020, The Authors. All rights reserved.

关键词： Controllers

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Search@Home: A commercial off-the-shelf environment for investigating optimization problems

arXiv

引用

arXiv 2020年

作者： Fredericks, Erik M. Moore, Jared M. Dept. of Computer Science & Engineering Oakland University RochesterMI48309 United States School of Computing and Information Systems Grand Valley State University AllendaleMI49401 United States

Search heuristics, particularly those that are evaluation-driven (e.g., evolutionary computation), are often performed in simulation, enabling exploration of large solution spaces. Yet simulation may not truly replicate real-world conditions. However, search heuristics have been proven to be successful when executed in real-world constrained environments that limit searching ability even with broad solution spaces. Moreover, searching in situ provides the added benefit of exposing the search heuristic to the exact conditions and uncertainties that the deployed application will face. Software engineering problems can benefit from in situ search via instantiation and analysis in real-world environments. This paper introduces Search@Home, an environment comprising heterogeneous commercial off-the-shelf devices enabling rapid prototyping of optimization strategies for real-world problems. Copyright © 2020, The Authors. All rights reserved.

关键词： Heuristic algorithms

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On the Philosophical, Cognitive and Mathematical Foundations of Symbiotic Autonomous systems (SAS)

arXiv

引用

arXiv 2021年

作者： Wang, Yingxu Karray, Fakhri Kwong, Sam Plataniotis, Konstantinos N. Leung, Henry Hou, Ming Tunstel, Edward Rudas, Imre J. Trajkovic, Ljiljana Kaynak, Okyay Kacprzyk, Janusz Zhou, Mengchu Smith, Michael H. Chen, Philip Patel, Shushma University of Calgary Canada FIEEE Dept. of Electrical & Computer Engineering University of Waterloo ON Canada FIEEE Department of Computer Science City University of Hong Kong Hong Kong FIEEE Dept. of Electrical & Computer Engineering University of Toronto ON Canada SMIEEE Toronto Research Centre DRDC Canada FIEEE Autonomous & Intelligent Systems Dept. Raytheon Technologies Research Center United States Óbuda University Budapest Hungary FIEEE School of Engineering Science Simon Fraser University BurnabyBC Canada FIEEE Bogazici University Bebek Istanbul Turkey FIEEE Systems Research Institute Polish Academy of Sciences Warsaw Poland FIEEE Dept. of Computer Science New Jersey Institute of Technology NJ United States SMIEEE Furaxa Inc. OrindaCA United States FIEEE School of Computer Science and Engineering South China University of Technology Guangzhou China FBCS Faculty of Computing Engineering and Media De Montfort University LeicesterLE1 9BH United Kingdom

Symbiotic Autonomous systems (SAS) are advanced intelligent and cognitive systems exhibiting autonomous collective intelligence enabled by coherent symbiosis of human-machine interactions in hybrid societies. Basic research in the emerging field of SAS has triggered advanced general AI technologies functioning without human intervention or hybrid symbiotic systems synergizing humans and intelligent machines into coherent cognitive systems. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences. SAS are characterized by the composition of autonomous and symbiotic systems that adopt bio-brain-social-inspired and heterogeneously synergized structures and autonomous behaviors. This paper explores their cognitive and mathematical foundations. The challenge to seamless human-machine interactions in a hybrid environment is addressed. SAS-based collective intelligence is explored in order to augment human capability by autonomous machine intelligence towards the next generation of general AI, autonomous computers, and trustworthy mission-critical intelligent systems. Emerging paradigms and engineering applications of SAS are elaborated via an autonomous knowledge learning system that symbiotically works between humans and cognitive robots. © 2021, CC BY-NC-ND.

关键词： Cognitive systems

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Detecting personal protective equipment (PPE) utilising YOLOv8 in a federated learning environment

引用

AIP Conference Proceedings 2024年第1期3220卷

作者： Ioannis Makris Anastasios Lytos Konstantinos Kyranou Vasileios Argyriou Thomas Lagkas Konstantinos-Filippos Kollias George F. Fragoulis Panagiotis Sarigianndis METAMIND INNOVATIONS IKE KILA - Administration Building of Univ. of Western Macedonia Kozani Greece SIDROCO Holdings LTD Karyatis 8 Leoforo Kyriakou Matsi 23 4th Floor Office 401 Nicosia Cyprus Dept. of Computing & Information Systems Penrhyn Road Kingston Univ. London London United Kingdom Dept. of Computer Science School of Science International Hellenic University Kavala Campus Kavala Greece Dept. of Electrical And Computer Engineering ZEP Campus University of Western Macedonia Kozani Greece

In the construction industry, where worker safety is a paramount concern, the effective use of PPE is crucial. This study delves into an innovative approach by exploring the integration of Federated Learning (FL) with the YOLOv8 architecture for enhanced PPE object detection. A specialized construction safety dataset was utilized to conduct the corresponding experiments, comparing FL against conventional local training methodologies. The results have clearly shown the superiority of FL in terms of accuracy improvement of PPE object detection which opens a lot of paths for creative solutions leading to the progress of safety performance at construction sites, at the same time, while observing the strictest privacy and security issues for personal data that is a primary consideration in our data-driven era. The proposed solution achieved an average mAP of 79.52%, and an average Recall of 78.49%, proving its effectiveness over the centralized training.

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Computational Approaches to Explainable Artificial Intelligence: Advances Intheory, Applications and Trends

SSRN

引用

SSRN 2023年

作者： Górriz, Juan Manuel Álvarez-Illán, I. Álvarez-Marquina, A. Arco, J.E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S.B. Contreras, R. Cuadra, J.M. Domínguez, E. Domínguez-Mateos, F. Duro, R.J. Elizondo, D. Fernández-Caballero, A. Fernandez-Jover, E. Formoso, M.A. Gallego-Molina, N.J. Gamazo, J. García González, J. Garcia-Rodriguez, J. Garre, C. Garrigós, J. Gómez-Rodellar, A. Gómez-Vilda, P. Graña, M. Guerrero-Rodriguez, B. Hendrikse, S.C.F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J.J. Martinez-Murcia, F.J. Martínez-Tomás, R. Mekyska, J. Nalepa, G.J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M.A. Ponticorvo, M. Psarrou, A. Ramírez, J. Rincón, M. Rodellar-Biarge, V. Rodríguez-Rodríguez, I. Roelofsma, P.H.M.P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S.H. Wang, W. Zhang, Y.D. Zhu, H. Zhu, Z. Ferrández-Vicente, J.M. SiPBA at Data Science and Computational Intelligence Institute University of Granada Granada Spain University of Granada Granada Spain Dept. of Psychiatry University of Cambridge Cambridge United Kingdom Dept. of Communications Engineering University of Malaga Malaga Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Semantic Information Systems Group Osnabrück University Osnabrück Germany Osnabrück Germany Dept. of Electronics Computer Technology and Projects Universidad Politécnica de Cartagena Cartagena Spain Universitat Politècnica de València València Spain Institute of Applied Computer Science Jagiellonian University Kraków Poland Dept. of Information and Communication Engineering University of Murcia Murcia Spain Dept. of Computer Science and Information Technologies University of A Coruña A Coruña Spain Dept. of Computer Science University of Oviedo Oviedo Spain Buenos Aires Argentina Buenos Aires Argentina Eindhoven University of Technology Eindhoven5612 AZ Netherlands Dept. of Computer Science University of Malaga Malaga Spain De Montfort University Leicester United Kingdom Dept. of Computer Science Faculty of Engineering Universidad de Concepción Concepción Chile Neurocognition and Emotion Research Unit Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain Dept. of Educational Informatics Universidad de Concepción Concepción Chile Madrid Spain Dept. of Humanistic Studies University of Naples Federico II Napoli Italy Escuela Técnica Superior de Ingeniería Informática Universidad Rey Juan Carlos Campus de Móstoles Móstoles Madrid28933 Spain Neuromorphic Speech Processing Lab Center for Biomedical Technology Universidad Politécnica de Madrid Campus de Montegancedo Pozuelo de Alarcón Madrid28223 Spain Biomedical Research Networking Center in Mental Health Instituto de Salud Carlos III Madrid28016 Spain Usher Institute Faculty o

Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). DL algorithms are groundedin non-linear and complex artificial neural systems that progressively extract higher-level features from *** is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. Itis also a turning point in solving otherwise intractable problems in the design of virtual agents or robotics;orin providing novel insight into disease progress in genomics and Neuroimaging (NI), understanding humanbrain function, the automation of industrial processes based on computer vision, and so on. In this paper, themost relevant advances from the last few years in AI and several applications to neuroscience, neuroimaging,computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-ofthe-art in AI methods, models and applications within a collection of works presented at the 9th InternationalConference on the Interplay between Natural and Artificial Computation (IWINAC). The works presentedin this paper are excellent examples of new scientific discoveries made in laboratories that are successfullytransferred to real-life applications. © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

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Deep residual dense U-net for resolution enhancement in accelerated MRI acquisition

arXiv

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arXiv 2020年

作者： Ding, Pak Lun Kevin Li, Zhiqiang Zhou, Yuxiang Li, Baoxin School of Computing Informatics Decision Systems Engineering Arizona State University TempeAZ85281 United States Dept. of Neuroradiology Barrow Neurological Institute PhoenixAZ85013 United States Dept. of Radiology Mayo Clinic Arizona PhoenixAZ85054 United States

Typical Magnetic Resonance Imaging (MRI) scan may take 20 to 60 minutes. Reducing MRI scan time is beneficial for both patient experience and cost considerations. Accelerated MRI scan may be achieved by acquiring less amount of k-space data (down-sampling in the k-space). However, this leads to lower resolution and aliasing artifacts for the reconstructed images. There are many existing approaches for attempting to reconstruct high-quality images from down-sampled k-space data, with varying complexity and performance. In recent years, deep-learning approaches have been proposed for this task, and promising results have been reported. Still, the problem remains challenging especially because of the high fidelity requirement in most medical applications employing reconstructed MRI images. In this work, we propose a deep-learning approach, aiming at reconstructing high-quality images from accelerated MRI acquisition. Specifically, we use Convolutional Neural Network (CNN) to learn the differences between the aliased images and the original images, employing a U-Net-like architecture. Further, a micro-architecture termed Residual Dense Block (RDB) is introduced for learning a better feature representation than the plain U-Net. Considering the peculiarity of the down-sampled k-space data, we introduce a new term to the loss function in learning, which effectively employs the given k-space data during training to provide additional regularization on the update of the network weights. To evaluate the proposed approach, we compare it with other state-of-the-art methods. In both visual inspection and evaluation using standard metrics, the proposed approach is able to deliver improved performance, demonstrating its potential for providing an effective solution. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Urban Intelligence: Toward the Digital Twin of Matera and Catania

Urban Intelligence: Toward the Digital Twin of Matera and Ca...

引用

Blockchain for Renewables Integration (BLORIN), Workshop on

作者： Giordana Castelli Amedeo Cesta Mario Ciampi Riccardo De Benedictis Giuseppe De Pietro Matteo Diez Giovanni Felici Roberto Malvezzi Barbara Masini Riccardo Pellegrini Andreas Scalas Giuseppe Stecca Lucanos Strambini Gabriella Tognola Paolo Ravazzani Emilio Fortunato Campana Engineering ICT and Technology for Energy and Transportation Dept. (DIITET) National Research Council (CNR) Rome Italy Institute of Cognitive Sciences and Technologies (ISTC) National Research Council (CNR) Rome Italy Institute for High Performance Computing and Networking (ICAR) National Research Council (CNR) Naples Italy Institute of Marine Engineering (INM) National Research Council (CNR) Rome Italy Institute for Systems Analysis and Computer Science (IASI) National Research Council (CNR) Rome Italy Engineering ICT and Technology for Energy and National Research Council (CNR) Rome Italy Institute of Electronics Computer and Telecommunication Engineering (IEIIT) National Research Council (CNR) Bologna Italy Institute for Applied Mathematics and Information Technologies (IMATI) National Research Council (CNR) Genoa Italy Institute for Systems Analysis and Computer Science “Antonio Ruberti” (IASI) National Research Council (CNR) Rome Italy Institute of Electronics Computer and Telecommunication Engineering (IEIIT) (CNR) Pisa Italy

ISBN: (纸本)9781665472500

The Urban Intelligence (UI) paradigm conceived by CNR consists of an ecosystem of digital technologies joined within a Digital Twin (DT) of the city aimed at improving the city governance towards goals addressed also by the UN Agenda 2030, such as urban environment, sustainability and resilience, wellbeing and quality of life, local development, and social inclusion. In particular, UI provides a set of candidate policies in complex scenarios, and supports policy makers and stakeholders in designing shared, evidence-based, and integrated solutions. UI is being applied for the first time to two Italian cities, Matera and Catania, paving the way for a deeper scientific framing of the paradigm, as well as for the technological development and testing of the core UI ecosystem in real-life situations. The paper introduces the UI key-concepts and components, illustrates the ongoing experimentations in these pilot cities related to the development of two DTs on parts of the urban areas, and presents some initial results.

关键词： Renewable energy sources Urban areas Ecosystems Lakes Digital twins Sensors Stakeholders

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Reporting of Scope2 Co2-Emissions and Electricity Consumption in 111 Indian Ocean Cities between 2015-2022

SSRN

引用

SSRN 2024年

作者： Nematchoua, Modeste Kameni Buratti, Cinza Orosa, Jose A. Attia, Shady Teller, Jacques Bemanana, Muriel Akinola, Olatunji Bernard, Andrianirina Charles Sendrahasina, Rakotomalala Minoson Roy, Sambatra Eric Jean Falisoa, Rafanotsimiva Liva Jean-Pierre, Messina Reiter, Sigrid School of Informatics Computing and Cyber Systems Northern Arizona University 1295 S. Knoles Dr. Building 90 Room 320 FlagstaffAZ86011 United States Department of Engineering University of Perugia Via G. Duranti 63 Perugia06125 Italy Department of N. S. and Marine Engineering Escuela Técnica Superior de N. y M University of A Coruña Paseo de Ronda 51 A Coruna15011 Spain Sustainable Building Design Lab Dept. UEE Applied Sciences University of Liege Belgium LEMA ArGEnCo Department University of Liege Belgium Higher Polytechnic School University of Antsiranana Antsiranana Madagascar Science and Technology’s Higher Institute University of Mahajanga Mahajanga Madagascar University of Antananarivo Po Box 566 Antananarivo101 Madagascar Department of Industrial Engineering Higher Institute of Technology Antsiranana Antsiranana201 Madagascar Faculty of Architecture and Construction Notre-Dame du Kasaï University Congo

Several sources related that the electricity sector emits almost a quarter of greenhouse gases each year in the world. It is therefore one of the important sectors to take into account to limit global warming. Indian Ocean cities produce significant CO2 emissions during electricity consumption. Their volume and accuracy remain practically unknown and untested. Indeed, until now, there is no methodology suggested by the researchers to evaluate Fossil Fuel carbon dioxide (FFCO2) emission, and electricity consumption in this region. Aware of these crucial problems, this study was carried out to assess and analyse CO2 emissions coming from Electricity consumption (called Scope2) in 111 cities located in the Indian Ocean from 55 Power plants between 2015-2022 (08 years) and in four sectors (Residential, Commercial, Industrial, and On-road). To carry out a good comparison, all the data were grouped into three categories, Before COVID (2015-2018);During COVID (2019-2020);and After COVID (2021-2022)). The results showed that the CO2 emission difference is significant in the residential and commercial sectors. It was observed that CO2 emissions increased in 2019-2022 in the residential, industrial, and on-road sectors whereas, simultaneously during the same period, it decreased in the commercial sector. During the three periods, the CO2 emissions rate was the highest in the residential sector (around 52%), and the least on-road (around 1%). The significant difference in the commercial sector suggests a decrease in electricity consumption during the peak of the pandemic due to reduced business activities. Businesses adapted to new operating conditions, such as reduced hours or enhanced energy efficiency measures, which also contributed to the change in consumption patterns. © 2024, The Authors. All rights reserved.

关键词： Low emission

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Quantum latent semantic analysis

arXiv

引用

arXiv 2019年

作者： González, Fabio A. Caicedo, Juan C. Mindlab Research Group Computing Systems and Industrial Engineering Dept. National University of Colombia

The main goal of this paper is to explore latent topic analysis (LTA), in the context of quantum information retrieval. LTA is a valuable technique for document analysis and representation, which has been extensively used in information retrieval and machine learning. Different LTA techniques have been proposed, some based on geometrical modeling (such as latent semantic analysis, LSA) and others based on a strong statistical foundation. However, these two different approaches are not usually mixed. Quantum information retrieval has the remarkable virtue of combining both geometry and probability in a common principled framework. We built on this quantum framework to propose a new LTA method, which has a clear geometrical motivation but also supports a well-founded probabilistic interpretation. An initial exploratory experimentation was performed on three standard data sets. The results show that the proposed method outperforms LSA on two of the three datasets. These results suggests that the quantum-motivated representation is an alternative for geometrical latent topic modeling worthy of further exploration. Copyright © 2019, The Authors. All rights reserved.

关键词： Singular value decomposition

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