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SSRN 2023年

作者： Karczmarek, Pawel Gregosiewicz, Adam Lagodowski, Zbigniew A. Dolecki, Michal Galka, Lukasz Powroznik, Pawel Pedrycz, Witold Department of Computational Intelligence Lublin University of Technology ul. Nadbystrzycka 36B Lublin20-618 Poland Department of Mathematics Lublin University of Technology ul. Nadbystrzycka 36B Lublin20-618 Poland Department of Computer Science Lublin University of Technology ul. Nadbystrzycka 36B Lublin20-618 Poland Department of Electrical & Computer Engineering University of Alberta EdmontonT6R 2V4 AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland

In this study, we propose and thoroughly analyze a new concept of aggregation operator based on the generalization of Choquet integral (CI). This approach is based on an application of quadrature formulae to calculate the Choquet integral values. However, we use a novel modification of this method based on a so called smoothing operation. In this manner, the value obtained is more precise and allows to preserve the key features of Choquet integral which can be effectively utilized in a process of aggregation of classifiers, or, in a more general form, in the information fusion processes. Numerical experiments series demonstrate the efficiency of our approach. Moreover, mathematical properties of newly constructed operators are thoroughly discussed. © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Innovative dust detection and efficient cleaning of PV Panels: A CNN‑RF approach using I–V curve data transformed into RGB mosaics

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Energy Conversion and Management: X 2025年 27卷

作者： Safia Babikir Bashir Mena Maurice Farag Abdul-Kadir Hamid Ali A. Adam Ramesh C. Bansal Nsilulu T Mbungu A. Elnady Ahmed G. Abo-Khalil Mousa Hussein Smart Grid Research Group Sustainable Energy and Power Systems Research Center Research Institute of Sciences and Engineering (RISE) Department of Electrical Engineering University of Sharjah Sharjah 27272 United Arab Emirates Smart Automation and Communication Technologies Research Center (SACT) University of Sharjah 27272 Sharjah United Arab Emirates Department of Electrical Electronic and Computer Engineering University of Pretoria Pretoria South Africa Department of Electrical and Communication Engineering United Arab Emirates University Al Ain United Arab Emirates

Photovoltaic (PV) panels are vital for renewable energy generation, yet their efficiency is critically hindered by environmental challenges such as dust accumulation, especially in arid regions like the UAE. Dust buildup can reduce efficiency by up to 30% within a month, threatening the sustainability of solar power, which is projected to supply 10% of global energy by 2030. Existing cleaning methods are unsustainable, consuming an estimated 10 billion gallons of water annually, enough to meet the drinking needs of 2 million people, necessitating the development of a cost-effective, resource-efficient alternative. This research presents a novel machine learning-based system to automate dust detection and optimize cleaning, significantly reducing water consumption while improving power generation efficiency. The methodology transforms I-V curve electrical parameters into RGB mosaic images, enabling precise classification of operational states such as normal operation, dust accumulation, shading, and faults without relying on external imaging devices. The system is built on a hybrid model combining Convolutional Neural Networks (CNN) and Random Forest (RF) classifiers (CNN-RF), where the CNN extracts high-level features from RGB mosaic images, and the RF classifier accurately categorizes operational states. Upon detecting dust accumulation, a secondary CNN-RF model classifies the severity into low, moderate, or heavy, guiding an optimized cleaning process that minimizes water usage while maintaining cleaning effectiveness. The primary CNN-RF model achieved 100% accuracy in classifying operational states using RGB mosaic images, surpassing the 97% accuracy achieved by I–V curve-based methods. Furthermore, the secondary CNN-RF model for dust severity classification attained an accuracy of 98% using RGB mosaic images, compared to only 68% when using traditional I–V curves, highlighting the superior performance of RGB mosaic images in detecting fine-grained dust levels. T

关键词： Automated Cleaning Convolutional Neural Networks Dust Detection Machine Learning Photovoltaics

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Transmission of radiology images over an Unsecure Network Using Hybrid Encryption Schemes

Transmission of radiology images over an Unsecure Network Us...

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2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020

作者： Rahul, N. Manjunath, K.N. Manuel, Manuel Kurady, Rajendra Manipal Institute of Technology Manipal Academy of Higher Education Department of Computer Science and Engineering ManipalKarnataka576104 India Scania CV AB Information Technology Department SódertdäljeSE-15187 Sweden Research and Development RTWO Healthcare Solutions LLP Bengaluru560086 India

ISBN: (纸本)9781728198859

In this paper., we propose a hybrid encryption scheme to transmit the medical image dataset securely in radiology networks. The proposed methodology uses the RSA (Rivest-Shamir-Adleman) encryption technique., XOR technique, and the digitally reconstructed Radiograph (DRR) image from the 3D volume of MRI scan images. As a first step, the volume of interest (VOI) was segmented and then computed the DRR image on the segmented volume in the sagittal direction. The pixels of the DRR image was XORed with all the image slices. All the images and the DRR image were encrypted separately using the RSA technique and transmitted. At the receiver, the XOR was applied to all the received images, the original slices were retained, VOI was segmented again, and the DRR was recomputed. Now, the received DRR and the recomputed DRR were compared for the changes in the image content through histogram comparison, MSE, and Mean absolute deviation. The data integrity violation was tested by adding an image, deleting an image, and modifying the pixels of the image before sending it. The method was applied to fifty (n=50) samples. In all the above test cases performed, the method identified the data integrity violation correctly. © 2020 IEEE.

关键词： Radiology

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Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review

arXiv

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arXiv 2024年

作者： Lopez-Ramos, Luis M. Leiser, Florian Rastogi, Aditya Hicks, Steven Strümke, Inga Madai, Vince I. Budig, Tobias Sunyaev, Ali Hilbert, Adam Holistic Systems department Simula Metropolitan Center for Digital Engineering Oslo Norway Department of Economics and Management Karlsruhe Institute of Technology Germany Department of Neuroradiology University Hospital Bonn Germany Department of Computer Science Faculty of Informatics Norwegian University of Science and Technology Trondheim Norway QUEST Center for Responsible Research Charité - Universitätsmedizin Berlin Berlin Germany School of Computing and Digital Technology Birmingham City University Birmingham United Kingdom ETH Zurich Switzerland School of Computation Information and Technology Technical University of Munich Germany Charité - Universitätsmedizin Berlin Germany

The joint implementation of Federated learning (FL) and Explainable artificial intelligence (XAI) will allow training models from distributed data and explaining their inner workings while preserving important aspects of privacy. Towards establishing the benefits and tensions associated with their interplay, this scoping review maps those publications that jointly deal with FL and XAI, focusing on publications where an interplay between FL and model interpretability or post-hoc explanations was found. In total, 37 studies met our criteria, with more papers focusing on explanation methods (mainly feature relevance) than on interpretability (mainly algorithmic transparency). Most works used simulated horizontal FL setups involving 10 or fewer data centers. Only one study explicitly and quantitatively analyzed the influence of FL on model explanations, revealing a significant research gap. Aggregation of interpretability metrics across FL nodes created generalized global insights at the expense of node-specific patterns being diluted. 8 papers addressed the benefits of incorporating explanation methods as a component of the FL algorithm. Studies using established FL libraries or following reporting guidelines are a minority. More quantitative research and structured, transparent practices are needed to fully understand their mutual impact and under which conditions it happens. © 2024, CC BY.

关键词： Federated learning

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On Jamming Power Optimization for Secure and QoS-Aware Routing in Ad Hoc Networks

On Jamming Power Optimization for Secure and QoS-Aware Routi...

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2020 International Conference on Networking and Network Applications, NaNA 2020

作者： Xu, Yang Yang, Kehan Wang, Yu Li, Zhao Zhang, Tao Liu, Jia School of Economics and Management Xidian University Xi'an710071 China School of Cyber Engineering Xidian University Xi'an710071 China School of Computer Science and Technology Xidian University Xi'an710071 China Center for Cybersecurity Research and Development National Institute of Informatics Tokyo101-8430 Japan

ISBN: (纸本)9781728189543

This paper considers a typical ad hoc network composed of peer legitimate nodes, one eavesdropping node and one cooperative jamming node, and investigates the jamming power setting issue to ensure both security and quality of service (QoS) for end-to-end data transmission therein. We first provide theoretical modelings to reveal how the secrecy outage probability (SOP) and connection outage probability (COP) of a given route are related to the jamming power. Based on the SOP and COP of the route, we then propose the route metric that can flexibly adapt to different security and QoS requirements, as well as formulate the jamming power setting issue as a mathematical optimization problem. By solving this optimization problem, we further determine the jamming power that can achieve the optimal routing performance on the data transmission path. Finally, we present numerical results to demonstrate the impacts of jamming power on the system performance, as well as the behaviors of the optimal jamming power setting. © 2020 IEEE.

关键词： Quality of service

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IMU-Based Monitoring for Assistive Disease Diagnosis and Management of IoHT: A Review

TechRxiv

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TechRxiv 2022年

作者： Bo, Fan Yerebakan, Mustafa Dai, Yanning Wang, Weibing Li, Jia Gao, Shuo Hu, Boyi The Smart Sensing Research and Development Center Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China The School of Microelectronics University of Chinese Academy of Sciences Beijing100049 China The School of Instrumentation and Optoelectronic Engineering Beihang University Beijing100191 China The Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing100191 China The Department of Industrial and Systems Engineering University of Florida GainesvilleFL32611 United States

With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnosis carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement units (IMUs) based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML) based techniques have been proposed to smartly map IMU captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. To help readers comprehensively understand the fundamentals and state-of-the-art techniques, this article systematically reviews this field, by introducing and explaining relevant application scenarios, discussing challenges, and predicting foreseeable future trends. © 2022, CC BY.

关键词： Machine learning

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Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer

arXiv

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arXiv 2024年

作者： Pan, Liangrui Liang, Qingchun Zeng, Wenwu Peng, Yijun Zhao, Zhenyu Liang, Yiyi Luo, Jiadi Wang, Xiang Peng, Shaoliang College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Department of Pathology The Second Xiangya Hospital Central South University Hunan Changsha410011 China Hunan Clinical Medical Research Center for Cancer Pathogenic Genes Testing and Diagnosis Changsha Hunan410011 China Department of Thoracic Surgery The Second Xiangya Hospital Central South University Hunan Changsha410011 China Oncology Department State Key Laboratory of Systems Medicine for Cancer of Shanghai Cancer Institute Renji Hospital School of Medicine Shanghai Jiaotong University Shanghai200127 China

Spread through air spaces (STAS) is a distinct invasion pattern in lung cancer, crucial for prognosis assessment and guiding surgical decisions. Histopathology is the gold standard for STAS detection, yet traditional methods are subjective, time-consuming, and prone to misdiagnosis, limiting large-scale applications. We present VERN, an image analysis model utilizing a feature-interactive Siamese graph encoder to predict STAS from lung cancer histopathological images. VERN captures spatial topological features with feature sharing and skip connections to enhance model training. Using 1,546 histopathology slides, we built a large single-cohort STAS lung cancer dataset. VERN achieved an AUC of 0.9215 in internal validation and AUCs of 0.8275 and 0.8829 in frozen and paraffin-embedded test sections, respectively, demonstrating clinical-grade performance. Validated on a single-cohort and three external datasets, VERN showed robust predictive performance and generalizability, providing an open platform (http://***:5000/) to enhance STAS diagnosis efficiency and accuracy. © 2024, CC BY.

关键词： Lung cancer

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The LogiSmile Project - Piloting Autonomous Vehicles for Last-Mile Logistics in European cities

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Transportation research Procedia 2023年 71卷 180-187页

作者： Clément Lemardelé Albert Baldó Adina Aniculaesei Andreas Rausch Mariona Conill Lars Everding Thomas Vietor Torben Hegerhorst Roman Henze László Mátyus Laia Pagès Vidal Rocag Alberto Sanfeliu Angel Santamaria-Navarro István Tóháti CARNET Barcelona - The Future Mobility Research Hub C. Jordi Girona 29 Nexus II Building - Office 0A 08034 Barcelona Spain Technische Universität Clausthal Institute of Software and Systems Engineering Arnold-Sommerfeld Str. 1 38678 Clausthal-Zellerfeld Germany Àrea Metropolitan de Barcelona carrer 62 16-18 - Zona Franca 08040 Barcelona Spain Technische Universität Braunschweig Institute for Engineering Design Hermann-Blenk-Str. 42 38108 Braunschweig Germany Technische Universität Braunschweig Institute of Automotive Engineering Hans-Sommer-Str. 4 38106 Braunschweig Germany Directorate of innovation and service development DKV Debrecen Exclusive Public Transport Company Ltd. Salétrom utca 3 402 5 Debrecen Hungary Institut de Robòtica i Informàtica Industrial CSIC-UPC C. Llorens i Artigas 4-6 08028 Barcelona Spain

The use of autonomous technologies for last-mile logistics has the potential to reduce operation costs, cut emissions from the delivery sector, improve safety levels in communities, and provide efficient delivery solutions in areas which experience access regulations. Unfortunately, the implementation of real-life, economically-sustainable, and safe use cases in open urban environments remains scarce, especially in Europe. As a consequence, there is a real need for pilot tests of innovative delivery schemes based on autonomous technologies, not only in numerical simulations, but also in representative urban settings throughout Europe. In this paper, we will present the LogiSmile project, in which a consortium of European partners is piloting a transport management system for autonomous last-mile logistics. Beyond describing the delivery characteristics and use cases of this system, we also highlight the lessons learnt during the course of the project about managing innovation in the urban mobility field, gathering insight form the innovation triple helix (academia, industry and public sector).

关键词： CEP market autonomous delivery robot autonomous hub vehicle pilot test innovation triple helix

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Capture Uncertainties in Deep Neural Networks for Safe Operation of Autonomous Driving Vehicles

Capture Uncertainties in Deep Neural Networks for Safe Opera...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Liuhui Ding Dachuan Li Bowen Liu Wenxing Lan Bing Bai Qi Hao Weipeng Cao Ke Pei Southern University of Science and Technology Shenzhen China Sifakis Research Institute for Trustworthy Autonomous Systems Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Huawei Technologies Co. Ltd. Shenzhen China

Uncertainties in Deep Neural Network (DNN)-based perception and vehicle’s motion pose challenges to the development of safe autonomous driving vehicles. In this paper, we propose a safe motion planning framework featuring the quantification and propagation of DNN-based perception uncertainties and motion uncertainties. Contributions of this work are twofold: (1) A Bayesian Deep Neural network model which detects 3D objects and quantitatively capture the associated aleatoric and epistemic uncertainties of DNNs; (2) An uncertainty-aware motion planning algorithm (PU-RRT) that accounts for uncertainties in object detection and ego-vehicle’s motion. The proposed approaches are validated via simulated complex scenarios built in CARLA. Experimental results show that the proposed motion planning scheme can cope with uncertainties of DNN-based perception and vehicle motion, and improve the operational safety of autonomous vehicles while still achieving desirable efficiency.

关键词： Deep learning Solid modeling Uncertainty Three-dimensional displays Object detection Planning Trajectory

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COVID-19 as Opportunity to Test Digital Nomad Lifestyle

COVID-19 as Opportunity to Test Digital Nomad Lifestyle

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International Conference on computer Supported Cooperative Work in Design

作者： Marcos Antonio de Almeida António Correia Daniel Schneider Jano Moreira de Souza Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

ISBN: (纸本)9781728165974;9781728165981

We report the first findings of an empirical study aimed at investigating how COVID-19 pandemic has impacted the work practices and lifestyles of digital nomads (DN). To do this, we analyzed messages, questions and comments posted by digital nomads in a specific online discussion community of the Reddit social network. Preliminary findings indicate COVID-19 as an opportunity to test DN lifestyle by aspiring digital nomads who want to plan their careers and also present evidence of an overload of online channels for actual DNs. On the other hand, we found that much of the literature on digital nomadism is fragmented and scattered through different disciplines and perspectives, with a strong focus on digital nomads' lifestyles. In order to obtain a holistic and unified understanding of digital nomads, we conducted a comprehensive literature review to further conceptualize the phenomenon under study.

关键词： COVID-19 Social networking (online) Pandemics engineering profession Conferences Bibliographies Collaborative work

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