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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Maciej Szymkowski Włodzimierz Woźniak Bartosz Jura Faculty of Computer Science Białystok University of Technology Białystok Poland Computer Science Research Group Artificial Intelligence Development division Łukasiewicz Research Network – Poznań Institute of Technology Poznań Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

Nowadays, rails are one of the most popular forms of transportation. It is related to the relatively lower costs of the tickets in comparison to airplanes or even costs connected with travelling by car. It needs to be pointed out that this type of transportation has also one important issue that must be addressed. It is connected to the selection of the appropriate train type for all interested passengers. It is extremely hard especially when passengers’ pileups are observed. In this work, we would like to propose an idea to consume time-series data for prediction of the passengers’ flow in one, selected train station that is Łόdź, Poland. Diversified algorithms that can be used directly in these data were presented and our own ideas are given. We would like to also propose our own pipeline built on the basis of all analyzed approaches.

关键词： Rails Surveys Costs Time series analysis Pipelines Transportation Predictive models

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HaWANet: Road Scene Understanding with Multi-modal Sensor data Using Height-Width-Driven Attention network 23rd

HaWANet: Road Scene Understanding with Multi-modal Sensor D...

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23rd International Conference of the Italian Association for Artificial Intelligence, AIxIA 2024

作者： Chatterjee, Soumick Xu, Jiahua Kuzhipathalil, Adarsh Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Hertie Institute for Clinical Brain Research Tuebingen Germany XenomatiX NV Leuven Belgium

ISBN: (纸本)9783031806063

In recent years, the field of autonomous vehicles and driverless technology has seen remarkable advancements, driven by contributions from mainstream automotive manufacturers and open-source projects. This research aims to develop a pipeline for road scene understanding through semantic segmentation. The proposed pipeline utilises a multi-modal segmentation model, incorporating greyscale images and point cloud data from Xenolidar, specifically designed to capture the structural priors of highway road scenes. The fusion of input modalities and the design of an encoder-decoder architecture with a novel attention scheme called HaWANet is introduced, which focuses on the height and width contextual information to improve the accuracy of road segmentation, are the primary aspects explored for the proposed model. The output of the encoder is a two-dimensional point cloud, which effectively represents the road’s planar nature, and is crucial for improving the accuracy of road segmentation, particularly in edge cases, addressing current challenges in autonomous driving research. This research, aimed at addressing the segmentation problem for multimodal sensor data, has presented significant performance improvement over single-modal approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Artificial Intelligence models for prediction of passengers’ flow in rail transport

Artificial Intelligence models for prediction of passengers...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

Nowadays, one of the most popular and ecological forms of passengers’ transport is rail. In the last couple of years (especially after COVID-19 pandemic) many more passengers selected this type of transportation rather than cars or even airplanes. It is connected with much higher travel comfort as well as the possibility of effective work on the train. However, due to the higher popularity, one extremely important problem was observed. It is connected with the passengers’ flow that can lead even to traffic. Sometimes it is hard to select an appropriate type of train to effectively serve all the interested people. In this work, we would like to propose not only the survey under the latest AI-based models for prediction of passengers’ pileups but also provide information about possible ideas that will be tested on the real data, collected from one of the trains stations in Poland.

关键词： Rails Surveys COVID-19 Pandemics Biological system modeling Transportation Predictive models

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A Novel Framework for Detection of Digital Face Video Manipulation using Deep Learning 3

A Novel Framework for Detection of Digital Face Video Manipu...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Chandra, Sanskriti Saxena, Shivam Kumar, Santosh Chaube, Mithilesh Kumar Srinivas, K.G. Alsamhi, Saeed Hamood Curry, Edward Saif, Abdu IIIT-Naya Raipur Department of Data Science and Artificial Intelligence Chhattisgarh India IIIT-Naya Raipur Department of Computer Science and Engineering Chhattisgarh India IIIT-Naya Raipur Department of Mathematical Sciences Chhattisgarh India University of Galway Insight Sfi Research Centre for Data Analytics Galway Ireland Faculty of Engineering & Information Technology Taiz Yemen

ISBN: (纸本)9798350321487

Digital face manipulation and classification have recently attracted the attention of academia and industry worldwide. researchers have developed deep learning and computer vision techniques for detecting face manipulations, and it has become a challenging task to differentiate between authentic and manipulated face images manually. The challenge results in the decline of authenticity in digital media content. In this paper, we propose a framework for the classification of manipulating face images using the EfficientNet learning model. The proposed framework takes four digital facial forgeries: Face-Swap, Face-2-Face, DeepFakes, and neural textures. Multiple manipulation techniques are used to process manipulated faces, such as the Blaze-face tracking method, to determine the locations of the face images and pixel coordinates. The proposed framework is used first to identify the type of face manipulation and then to perform detection of the tampered regions in the face images. The proposed framework provided an automated benchmark that considers all four modification techniques in a realistic situation. The results show that the proposed framework outperforms existing approaches regarding accuracy and efficiency. Furthermore, the proposed framework is suitable for detecting digital face video manipulation in various applications, including forensics and security. © 2023 IEEE.

关键词： Textures

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Multi-Temporal Cloud Removal of Satellite Image for Surface Water Segmentation

Multi-Temporal Cloud Removal of Satellite Image for Surface ...

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International Conference on computer, Control, Informatics and Its Applications (IC3INA)

作者： Suhardiman Roni Salambue Andre Sihombing Ira Maryati Lia Sadita Aruni Yasmin Azizah Zul Indra Gita Sastria Department of Computer Science Universitas Riau Pekanbaru Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Bandung Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

Satellite imagery utilization for water surface monitoring faces challenges in the presence of clouds that interfere with the segmentation process. This study aims to overcome the problem by implementing and developing the Multi-temporal Cloud Removal MCR method at the pre-processing stage. This research uses (MCR) approach to produce cloud-free satellite images. The difference from the current MCR method comes from using a red band in the cloud detection step, adding a dilation process on the cloud masking result, and the pixel mapping process. The best MCR results are obtained from cloud cover percentages below 20% with an NRMSE value of 0.156 and a PSNR of 29.815. In addition, this research compares the accuracy of the U-net water segmentation model between the original satellite image and the resulting MCR data. The model evaluation process uses binary cross-entropy loss, dice coefficient, and intersection over union (IoU) to check the model's performance. The model achieved a Dice coefficient of 0,842, IoU of 0.913 and a binary cross-entropy loss of 0,137 for MCR images. For images with cloud cover below 20%, the Dice coefficient and IoU slightly decreased to 0.819 and 0.892 (loss: 0.1563). As cloud cover increased to 20-40%, these values dropped to 0.687 and 0.812 (loss: 0.25) and further decreased to 0.546 and 0.706 (loss: 0.2656) for 40-60% cloud cover. The results demonstrate that the percentage of cloud cover in satellite images significantly influences the accuracy of surface water segmentation.

关键词： Digital control Image segmentation Accuracy data models Satellite images Informatics Surface treatment Monitoring Faces

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Study of Whisker Growth from SAC0307-SiC Composite Solder Alloy 46

Study of Whisker Growth from SAC0307-SiC Composite Solder Al...

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46th International Spring Seminar on Electronics Technology, ISSE 2023

作者： Choi, Halim Skwarek, Agata Hurtony, Tamás Byun, Jaeduk Illés, Balázs Budapest University of Technology and Economics Faculty of Electrical Engineering and Computer Science Department of Electronics Technology Budapest Hungary Lukasiewicz Research Network - Institute of Microelectronics and Photonics Department of Microelectronics Kraków Poland Dankook University College of Science & Technology Department of Physics Cheonan Korea Republic of

ISBN: (纸本)9798350334845

In the current study, tin (Sn) whisker growth was observed on the surface of the SAC0307-SiC composite solder joint. A commercial SAC0307 solder alloys were reinforced with SiC nanophases to produce composite solder joints. SiC nanoparticles and nanowires were mixed with SAC0307 solder paste using the ball milling method. The solder paste was deposited by stencil printing on the test PCBs, and 0805 chip resistors were soldered by IR reflow method. The solder joints facilitated the growth of the Sn whisker through accelerated lifetime tests. The samples were observed by scanning electron microscopy (SEM) every 1000 hours of the test. On the SiC composite solder joints, Sn whiskers and large corrosion spots were observed after 1000 hours of the THB test. In the case of SAC0307 reference samples, only minor corrosion signs were found after 1000 hours of the THB test. The main finding was that the addition of SiC nanophases promoted Sn whisker growth and decreased the corrosion resistance of the composite solder. © 2023 IEEE.

关键词： Silicon carbide

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Real-Time data-Driven Maintenance Logistics: A Public-Private Collaboration

Real-Time Data-Driven Maintenance Logistics: A Public-Privat...

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2024 Commit2data, C2D 2024

作者： van Jaarsveld, Willem Bliek, Laurens de Weerdt, Mathijs Kapodistria, Stella Pronk, Verus Verleijsdonk, Peter Voorberg, Simon Akçay, Alp da Costa, Paulo Eshuis, Rik Kaymak, Uzay van Houtum, Geert-Jan Verwer, Sicco Zhang, Yingqian Department of Industrial Engineering and Innovation Sciences Eindhoven University of Technology Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Philips Research Laboratories Eindhoven Netherlands Department of Information Systems Supply Chain Management and Decision Analysis NEOMA Business School Rouen France Etsy Inc. Dublin Ireland Jheronimus Academy of Data Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9783959773515

The project "Real-time data-driven maintenance logistics" was initiated with the purpose of bringing innovations in data-driven decision making to maintenance logistics, by bringing problem owners in the form of three innovative companies together with researchers at two leading knowledge institutions. This paper reviews innovations in three related areas: How the innovations were inspired by practice, how they materialized, and how the results impact practice. © Willem van Jaarsveld, Alp Akçay, Laurens Bliek, Paulo da Costa, Mathijs de Weerdt, Rik Eshuis, Stella Kapodistria, Uzay Kaymak, Verus Pronk, Geert-Jan van Houtum, Peter Verleijsdonk, Sicco Verwer, Simon Voorberg, and Yingqian Zhang.

关键词： Scheduled maintenance

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ML_Recognition: A Robust Model for Handwritten Digit Recognition Using Machine Learning Classifiers 3

ML_Recognition: A Robust Model for Handwritten Digit Recogni...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Zeid, Magdy Abd-Elghany Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Ibrahim Abd Elatif Gomaa Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

ISBN: (纸本)9798350306231

Handwritten digit recognition is a branch of machine learning in which a computer is taught to recognize hand-written numbers. Classification and regression are applied using deep learning and machine learning algorithms. In this paper, we discussed the efficiency of different algorithms: Random Forest, KNN, Naive Bayes, SVM, CNN, and Decision Tree Algorithm. Those algorithms were applied in the handwritten digit recognition process using the MINIST data set. By comparing each algorithm by training every one of them on the data set, then by testing, we got the best algorithm to get the optimal results with the highest accuracy. After comparing the results of each algorithm, it has shown that the neural network has the best results with 97.3% classification accuracy followed by K- the nearest neighbor with 97.2%. © 2023 IEEE.

关键词： Support vector machines

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A Fast Optimistic Method for Monotone Variational Inequalities

arXiv

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arXiv 2023年

作者： Sedlmayer, Michael Nguyen, Dang-Khoa Bot, Radu Ioan Research Network Data Science University of Vienna Vienna Austria Faculty of Mathematics University of Vienna Vienna Austria

We study monotone variational inequalities that can arise as optimality conditions for constrained convex optimisation or convex-concave minimax problems and propose a novel algorithm that uses only one gradient/operator evaluation and one projection onto the constraint set per iteration. The algorithm, which we call fOGDA-VI, achieves a o(1/k) rate of convergence in terms of the restricted gap function as well as the natural residual for the last iterate. Moreover, we provide a convergence guarantee for the sequence of iterates to a solution of the variational inequality. These are the best theoretical convergence results for numerical methods for (only) monotone variational inequalities reported in the literature. To empirically validate our algorithm we investigate a two-player matrix game with mixed strategies of the two players. Concluding, we show promising results regarding the application of fOGDA-VI to the training of generative adversarial nets. Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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CFPChain: An Optimization of research Funding Selection System Employing A Consortium Blockchain Based Approach 10

CFPChain: An Optimization of Research Funding Selection Syst...

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10th International Conference on computer, Control, Informatics and its Applications, IC3INA 2023

作者： Ramdhani, Taufik Iqbal Basuki, Akbari Indra Susanto, Heru Umbara, Raditya Panji Arifin, Samsul Faiza, Ninon Nurul Research Center for Artificial Intelligence and Cyber Security National Research and Innovation Agency Bandung Indonesia Research Center for Geological Disaster National Research and Innovation Agency Tangerang Selatan Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Data and Information Center National Research and Innovation Agency Jakarta Indonesia Faculty Computer Science Institut Teknologi Tangerang Selatan Indonesia Department of Informatics Faculty of Science Nad Technology Universitas Al Azhar Indonesia Jakarta Indonesia Department of Informatics Universitas Bunda Mulia Tangerang Selatan Indonesia

ISBN: (纸本)9798350394870

This paper addresses the challenges in the current centralized research funding selection system and proposes a solution using permissioned blockchain technology. The existing system suffers from drawbacks such as concealed data modification and lack of interoperability. The Blockchain approach, based on the principles of Bitcoin technology, offers high security, auditability, traceability, and integrity. By utilizing interconnected blocks and cryptography, the proposed system ensures the authenticity and consistency of data over time. The chosen blockchain platform, Hyperledger Fabric, provides crash-fault-tolerance consensus and private communication options for enhanced security and efficiency. The objective of this study is to develop a consortium blockchain-based research funding platform that enables collaboration among researchers from public agencies, universities, and companies, with funding provided by financial organizations. The proposed architecture model ensures tamper-proof data, accountability, and interoperability, benefiting researchers, reviewers, auditors, funders, and companies. © 2023 IEEE.

关键词： Smart contract

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