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arXiv 2023年

作者： Lampropoulos, Konstantinos Kosmidis, Thanos Autexier, Serge Savić, Miloš Athanatos, Manos Kokkonidis, Miltiadis Koutsouri, Tzortzia Vizitiu, Anamaria Valachis, Antonios Padron, Miriam Quintero Department of Electrical & Computer Engineering University of Patras Patras Greece CareAcross Research CareAcross LTD London United Kingdom Bremen Germany Division of Informatics Faculty of Sciences University of Novi Sad Novi Sad Serbia Institute of Computer Science Foundation for Research and Technology – Hellas Heraklion Crete Greece Research & Innovation Development INTRASOFT International S.A. Luxembourg Luxembourg SPHYNX Research SPHYNX Technology Solutions AG Zug Switzerland Department of Automation and Information Technology Transilvania University of Brasov Brasov Romania Advanta Siemens SRL Brasov Romania Department of Oncology Faculty of Medicine and Health Örebro University Örebro Sweden Atos Research Innovation Atos S.A.E Madrid Spain

The latest cancer statistics indicate a decrease in cancer-related mortality. However, due to the growing and ageing population, the absolute number of people living with cancer is set to keep increasing. This paper presents ASCAPE, an open AI infrastructure that takes advantage of the recent advances in Artificial Intelligence (AI) and Machine Learning (ML) to support cancer patients’ quality of life (QoL). With ASCAPE health stakeholders (e.g. hospitals) can locally process their private medical data and then share the produced knowledge (ML models) through the open AI infrastructure. Copyright © 2023, The Authors. All rights reserved.

关键词： Intelligent systems

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ReSSL: relational self-supervised learning with weak augmentation 21

ReSSL: relational self-supervised learning with weak augment...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Mingkai Zheng Shan You Fei Wang Chen Qian Changshui Zhang Xiaogang Wang Chang Xu School of Computer Science Faculty of Engineering The University of Sydney and SenseTime Research SenseTime Research and Department of Automation Tsinghua University Institute for Artificial Intelligence Tsinghua University (THUAI) Beijing National Research Center for Information Science and Technology (BNRist) University of Science and Technology of China SenseTime Research Department of Automation Tsinghua University Institute for Artificial Intelligence Tsinghua University (THUAI) Beijing National Research Center for Information Science and Technology (BNRist) SenseTime Research and The Chinese University of Hong Kong School of Computer Science Faculty of Engineering The University of Sydney

ISBN: (纸本)9781713845393

Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most of methods mainly focus on the instance level information (i.e., the different augmented images of the same instance should have the same feature or cluster into the same class), but there is a lack of attention on the relationships between different instances. In this paper, we introduced a novel SSL paradigm, which we term as relational self-supervised learning (ReSSL) framework that learns representations by modeling the relationship between different instances. Specifically, our proposed method employs sharpened distribution of pairwise similarities among different instances as relation metric, which is thus utilized to match the feature embeddings of different augmentations. Moreover, to boost the performance, we argue that weak augmentations matter to represent a more reliable relation, and leverage momentum strategy for practical efficiency. Experimental results show that our proposed ReSSL significantly outperforms the previous state-of-the-art algorithms in terms of both performance and training efficiency.

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Developing an AI literacy test for junior secondary students: The first stage

Developing an AI literacy test for junior secondary students...

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： King Woon Yau Ching Sing Chai Thomas K. F. Chiu Helen Meng Irwin King Savio Wai-Ho Wong Chandni Saxena Yeung Yam Faculty of Engineering The Chinese University of Hong Kong Shatin Hong Kong China Department of Curriculum and Instruction The Chinese University of Hong Kong Shatin Hong Kong China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Shatin Hong Kong China Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong China Department of Educational Psychology The Chinese University of Hong Kong Shatin Hong Kong China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong China

Artificial Intelligence (AI) education for K-12 is increasingly important as the emerging AI technologies are pervasively infused into human daily lives. Eleven countries have endorsed and implemented the AI curricula at K-12 levels [1]. The "Five Big Ideas" about AI, as proposed by the Association for the Advancement of Artificial Intelligence (AAAI) and the computer Science Teacher Association (CSTA) have emerged to form the core components of K-12 AI education [2], [3]. However, the assessment of students’ learning in AI education for K-12 is still in its initial stage. Surveys and interviews are the major assessing tools based on students’ self- perceptions. Standardized tests on students’ AI literacy are still rare. This paper focuses on the first stage of an AI literacy test based on the Five Big Ideas for Grade 7 to Grade 9 students. The test aims to measure the changes of students’ AI literacy before and after attending a one-year AI course. The preliminary findings show the students have increased in their AI literacy after attending the course. The test can provide insights for understanding students’ competencies and needs in AI literacy and improving the curriculum guidelines for students’ effective learning.

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PCB Defect detection using denoising convolutional autoencoders

arXiv

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arXiv 2020年

作者： Khalilian, Saeed Hallaj, Yeganeh Balouchestani, Arian Karshenas, Hossein Mohammadi, Amir Faculty of Computer Engineering University of Isfahan Isfahan Iran Industrial Automation Unit Pardisan Rayaneh System Company Isfahan Iran

Printed Circuit boards (PCBs) are one of the most important stages in making electronic products. A small defect in PCBs can cause significant flaws in the final product. Hence, detecting all defects in PCBs and locating them is essential. In this paper, we propose an approach based on denoising convolutional autoencoders for detecting defective PCBs and to locate the defects. Denoising autoencoders take a corrupted image and try to recover the intact image. We trained our model with defective PCBs and forced it to repair the defective parts. Our model not only detects all kinds of defects and locates them, but it can also repair them as well. By subtracting the repaired output from the input, the defective parts are located. The experimental results indicate that our model detects the defective PCBs with high accuracy (97.5%) compare to state of the art works. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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INSTANCE SEGMENTATION AND TEETH CLASSIFICATION IN PANORAMIC X-RAYS

arXiv

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arXiv 2024年

作者： Budagam, Devichand Ghosh, Sayan Imanbayev, Azamat Zhanatuly Kaplun, Dmitrii Rychenkov, Artem Sinitca, Aleksandr Kumar, Ayush Shrivastav, Anuj Akhmetov, Iskander Rafailovich Antonov, Sergey Cyganov, Gleb Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Chemical Engineering Indian Institute of Technology Kharagpur Kharagpur India School of IT and Engineering Kazakh-British Technical University Almaty Kazakhstan Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Centre for Digital Telecommunication Technologies St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Department of Humanities and Social Sciences Indian Institute of Technology Kharagpur Kharagpur India Department of Architecture and Regional Planning Indian Institute of Technology Kharagpur Kharagpur India Faculty of Digital Transformation ITMO University St. Petersburg Russia

Teeth segmentation and recognition are critical in various dental applications and dental diagnosis. Automatic and accurate segmentation approaches have been made possible by integrating deep learning models. Although teeth segmentation has been studied in the past, only some techniques were able to effectively classify and segment teeth simultaneously. This article offers a pipeline of two deep learning models, U-Net and YOLOv8, which results in BB-UNet, a new architecture for the classification and segmentation of teeth on panoramic X-rays that is efficient and reliable. We have improved the quality and reliability of teeth segmentation by utilising the YOLOv8 and U-Net capabilities. The proposed networks have been evaluated using the mean average precision (mAP) and dice coefficient for YOLOv8 and BB-UNet, respectively. We have achieved a 3% increase in mAP score for teeth classification compared to existing methods, and a 10-15% increase in dice coefficient for teeth segmentation compared to U-Net across different categories of teeth. A new Dental dataset was created based on UFBA-UESC dataset with Bounding-Box and Polygon annotations of 425 dental panoramic X-rays. The findings of this research pave the way for a wider adoption of object detection models in the field of dental diagnosis. © 2024, CC BY.

关键词： Object detection

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Josephson junction based on high critical-temperature superconductors: analysis, microcontroller implementation, and suppression of coexisting and chaotic attractors

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The European Physical Journal B 2022年第9期95卷 1-13页

作者： Ngongiah, Isidore Komofor Ramakrishnan, Balamurali Natiq, Hayder Pone, Justin Roger Mboupda Kuiate, Gaetan Fautso Department of Physics Faculty of Science University of Bamenda Bamenda Cameroon Center for Nonlinear Systems Chennai Institute of Technology Chennai India Information Technology College Imam Ja’afar Al-Sadiq University Baghdad Iraq Research Unit of Automation and Applied Computer (RUAIA) Electrical Engineering Department of IUT-FV University of Dschang Bandjoun Cameroon Department of Physics Higher Teacher Training College University of Bamenda Bamenda Cameroon Department of Mechanical and Industrial Engineering National Higher Polytechnic Institute University of Bamenda Bamenda Cameroon

A Josephson junction (JJ) based on high critical-temperature superconductors described by a linear resistive–capacitive–inductance shunted junction (LRCLSJ) model with unharmonic current-phase relation (UCPR) is theoretically and experimentally investigated in this paper. The numerical simulations indicate that JJ based on high critical-temperature superconductors exhibits excitable mode, regular spiking, periodic bursting, relaxation oscillations, chaotic attractors, and coexisting attractors. The theoretical investigations are verified experimentally through the microcontroller implementation. In addition, the coexistence between chaotic and limit cycle attractors found in JJ based on high critical-temperature superconductors is controlled to the desired trajectory using the linear augmentation control method. Finally, analytical calculations and numerical simulations are carried out to show the serviceableness of the two designed single controllers in suppressing chaos in JJ based on high critical-temperature superconductors.

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A parallel uncertain frequent itemset mining algorithm with spark 20

A parallel uncertain frequent itemset mining algorithm with ...

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20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2019

作者： Ding, Jiaman Li, Haibin Wang, Yuanyuan Jia, Lianyin You, Jinguo Yang, Yang Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China College of Engineering and Computer Science Australian National University Canberra Australia

ISBN: (纸本)9781728126166

Frequent Itemset Mining (FIM) from large-scale databases has emerged as an important problem in the data mining and knowledge discovery research community. However, FIM suffers from three important limitations with the rapidly expanding of big data in all domains. First, it assumes that all items have the same importance. Second, it ignores the fact that data collected in a real-life environment is often inaccurate. Third, it is also a data-intensive and computation-intensive process which makes the FIM algorithm very time-consuming over large datasets. To address these issues, we propose a Parallel uncertain frequent itemset mining algorithm with spark (Pufim). Pufim firstly expresses item uncertainty by considering both the probability and weight, and calculates the maximum probability weight value of 1-items. Next, a distributed Pufim-tree structure is designed inspiring by FP-Tree for reducing the times of scanning the databases. Each node of Pufim-tree stores an item and its maximum probability weight value. Finally, experiments on publicly available UCI datasets demonstrate that Pufim achieves more prominent results than other related approaches across various metrics. In addition, the empirical study also shows Pufim has a good scalability. © 2019 IEEE.

关键词： Data mining

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Input Data Clustering for the Efficient Operation of Renewable Energy Sources in a Distributed Information System 15

Input Data Clustering for the Efficient Operation of Renewab...

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15th IEEE International Scientific and Technical Conference on computer Sciences and Information Technologies, CSIT 2020

作者： Kiktev, Nikolay Osypenko, Volodymyr Shkurpela, Nataliia Balaniuk, Anastasiia National University of Life and Environmental Sciences of Ukraine Department of Automation and Robotic Systems Kyiv Ukraine Kyiv National University of Technologies and Design Institute of Computer Engineering and Information Technologies Kyiv Ukraine Taras Shevchenko National University of Kyiv Faculty of Information Technology Kyiv Ukraine

ISBN: (纸本)9781728174433

In this work a local database of the Microsoft SQL Server type to store meteorological data for any region received from the Internet was designed and implemented. Each of the tables has fields describing the weather conditions necessary to solve the problem determining the source of electricity, using of which is cost-effective in a given period of the year, time of day, geographical location and weather conditions. A database application in C # using the Windows Forms Application template was developed. The distribution of temperature indicators depending on the time of the studies for a certain period using cluster analysis was implemented. © 2020 IEEE.

关键词： Cluster analysis

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An improved pareto Ant colony algorithm for interference vehicle scheduling problem

An improved pareto Ant colony algorithm for interference veh...

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International Conference on Mechatronics and Intelligent Robotics, ICMIR 2018

作者： Rui, Wang Yumei, Luo Ning, Guo Faculty of Computer Information Kunming Metallurgy College Kunming650033 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9783030002138

In order to solve the vehicle routing problem with time windows of interference in the process of logistics distribution such as time window, demand changes and so on, an improved Pareto ant colony algorithm is proposed to minimize the length in this paper. Firstly, according to the customer time window changes and the characteristics of this kind of interference, two kinds of jamming events disturbance recovery strategies are put forward. Then the ant transition probability is defined based on the principles of saving, and the Pareto optimal solution set of global pheromone updating rule is used to improve the performance of the standard ant colony algorithm. Through the experiment, the simulation result shows that the algorithm improves the global search ability of the algorithm which effectively avoids the algorithm falling into local optimum and reduces the total distribution. © Springer Nature Switzerland AG 2019.

关键词： Ant colony optimization

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Accurate Discharge Coefficient Prediction of Streamlined Weirs by Coupling Linear Regression and Deep Convolutional Gated Recurrent Unit

arXiv

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arXiv 2022年

作者： Chen, Weibin Sharifrazi, Danial Liang, Guoxi Band, Shahab S. Chau, Kwok Wing Mosavi, Amir College of Computer Science and Artificial Intelligence Wenzhou University Zhejiang Wenzhou325035 China Department of Computer Engineering Shiraz Branch Islamic Azad University Shiraz Iran Department of Artificial Intelligence Wenzhou Polytechnic Wenzhou325035 China Future Technology Research Center College of Future National Yunlin University of Science and Technology Yunlin County Taiwan Department of Civil and Environmental Engineering Hong Kong Polytechnic University Hong Kong John von Neumann Faculty of Informatics Obuda University Budapest Hungary Institute of Information Engineering Automation and Mathematics Slovak University of Technology Bratislava Slovakia

Streamlined weirs, as a nature-inspired type of weirs, has gained tremendous attention among hydraulic engineers mainly due to their well-known performance with high discharge coefficient. Computational fluid dynamic (CDF) is considered as a robust tool to predict discharge coefficient. To bypass the computational cost of CFD-based assessment, the present study proposes data-driven modelling techniques, as an alternative to CFD simulation, to predict discharge coefficient based on an experimental dataset. To this end, after splitting the dataset by k-fold cross-validation technique, the performance assessment of classical and hybrid machine-deep learning (ML-DL) algorithms is undertaken. Amongst ML techniques, linear regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (KNN), and decision tree (DT) algorithms are studied . In the context of DL, long short-term memory (LSTM), convolutional neural network (CNN), gated recurrent unit (GRU) and their hybrid forms such as LSTM-GRU, CNN-LSTM and CNN-GRU techniques are compared by different error metrics. It is found that the proposed three-layer hierarchical DL algorithm consisting of a convolutional layer coupled with two subsequent GRU levels, which is also hybridized by LR method (i.e., LR-CGRU), leads to lower error metrics. This paper paves the way for data-driven modelling of streamlined *** Codes 68T05 © 2022, CC BY.

关键词： Deep neural networks

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