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arXiv 2023年

作者： Lau, Nuno Reis, Luís Paulo Simões, David Kasaei, Mohammadreza Abreu, Miguel Silva, Tiago Resende, Francisco DETI/UA Electronics Telecommunications and Informatics Dep. University of Aveiro Portugal DEI/FEUP Informatics Engineering Dep. Faculty of Engineering The University of Porto Portugal IEETA Institute of Electronics and Telematics Engineering of Aveiro Portugal LIACC Artificial Intelligence and Computer Science Lab. University of Porto Portugal

The FC Portugal 3D team is developed upon the structure of our previous Simulation league 2D/3D teams and our standard platform league team. Our research concerning the robot low-level skills is focused on developing behaviors that may be applied on real robots with minimal adaptation using model-based approaches. Our research on high-level soccer coordination methodologies and team playing is mainly focused on the adaptation of previously developed methodologies from our 2D soccer teams to the 3D humanoid environment and on creating new coordination methodologies based on the previously developed ones. The research-oriented development of our team has been pushing it to be one of the most competitive over the years (World champion in 2000 and Coach Champion in 2002, European champion in 2000 and 2001, Coach 2nd place in 2003 and 2004, European champion in Rescue Simulation and Simulation 3D in 2006, World Champion in Simulation 3D in Bremen 2006 and European champion in 2007, 2012, 2013, 2014 and 2015). This paper describes some of the main innovations of our 3D simulation league team during the last years. A new generic framework for reinforcement learning tasks has also been developed. This paper also includes general information related to the design of our agent architecture. The current research is focused on improving the above-mentioned framework by developing new learning algorithms to optimize low-level skills, such as running and sprinting. We are also trying to increase student contact by providing reinforcement learning assignments to be completed using our new framework, which exposes a simple interface without sharing low-level implementation details. © 2023, CC BY-NC-ND.

关键词： Reinforcement learning

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Systematic review of application of artificial intelligence tools in architectural, engineering and construction

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Frontiers in engineering and Built Environment 2021年第2期1卷 203-216页

作者： Momade, Mohammed Hamza Durdyev, Serdar Estrella, Dave Ismail, Syuhaida School of Civil Engineering Universiti Teknologi Malaysia Skudai Malaysia Engineering and Architectural Studies Ara Institute of Canterbury Christchurch New Zealand Faculty of Business AMA Computer University Quezon City Philippines Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Skudai Malaysia

Purpose: This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry. Design/methodology/approach: A thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study. Findings: The following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario. Practical implications: The findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field. Originality/value: There are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from. © 2021, Mohammed Hamza Momade, Serdar Du

关键词： AI tools Architectural engineering and construction industry Forecasting Prediction

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An Integrated E-Learning Fuzzy Expert System for Acute Pharyngitis Diagnosis 6

An Integrated E-Learning Fuzzy Expert System for Acute Phary...

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6th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2022

作者： Istiadi Sulistiarini, Emma Budi Joegijantoro, Rudy Suksmawati, Affi Nizar Nugroho, Kuncahyo Setyo Akbar, Ismail Widyagama University of Malang Department of Informatics Engineering Malang Indonesia Widyagama University of Malang Department of Industrial Engineering Malang Indonesia Widyagama Husada College of Health Department of Environmental Health Science Malang Indonesia Gadjah Mada University Faculty of Mathematics and Natural Sciences Yogyakarta Indonesia Brawijaya University Faculty of Computer Science Malang Indonesia Maulana Malik Ibrahim State Islamic University Faculty of Science and Technology Malang Indonesia

ISBN: (纸本)9798350399615

A sore throat, or acute pharyngitis, is an inflammatory disease caused by viral and bacterial infections. Clinically, the causes of acute viral and bacterial pharyngitis are difficult to distinguish. In addition, there are differences in treatment for viral and bacterial acute pharyngitis. This study integrates expert and e-learning systems to diagnose acute pharyngitis. The Fuzzy Mamdani method is used for the disease diagnosis process in the expert system. System integration aims to optimize the database of disease diagnosis history results in fuzzy expert systems. Thus, the results of the history of disease diagnosis can be generated into test questions that will be retested on students. The results of testing the fuzzy expert system on 25 samples give it an accuracy of 84% The test results are strongly influenced by establishing a rule base between the premise and the consequent that is consistent and interrelated. © 2022 IEEE.

关键词： Bacterial Pharyngitis E-Learning System Expert System Mamdani Fuzzy Inference System Viral Pharyngitis

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Reducing the Number of Qubits from n2 to nlog2(n) to Solve the Traveling Salesman Problem with Quantum computers: A Proposal for Demonstrating Quantum Supremacy in the NISQ Era

arXiv

引用

arXiv 2024年

作者： Ramezani, Mehdi Salami, Sadegh Shokhmkar, Mehdi Moradi, Morteza Bahrampour, Alireza Department of Physics Sharif University of Technology Tehran14588 Iran Centre for Quantum Engineering and Photonics Technology Sharif University of Technology Tehran14588 Iran Department of Computer Engineering Sharif University of Technology Tehran14588 Iran Institute of Informatics Faculty of Mathematics Informatics and Mechanics University of Warsaw Banacha 2 Warsaw02-097 Poland

In our pursuit of quantum supremacy during the NISQ era, this research introduces a novel approach rooted in the Quantum Approximate Optimization Algorithm (QAOA) framework to address the Traveling Salesman Problem (TSP). By strategically reducing the requisite qubit count from n2 to n log2 (n), our QAOA-based algorithm not only contributes to the ongoing discourse on qubit efficiency but also demonstrates improved performance based on established metrics, underscoring its potential for achieving NISQ-era supremacy in solving real-world optimization challenges. © 2024, CC BY-NC-ND.

关键词： Traveling salesman problem

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Multiple Teachers-Meticulous Student: A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image Classification

arXiv

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arXiv 2024年

作者： Nabavi, Shahabedin Hamedani, Kian Anvari Moghaddam, Mohsen Ebrahimi Abin, Ahmad Ali Frangi, Alejandro F. Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Division of Informatics Imaging and Data Sciences Schools of Computer Science and Health Sciences The University of Manchester Manchester United Kingdom Electrical Engineering and Cardiovascular Sciences Departments KU Leuven Leuven Belgium Alan Turing Institute London United Kingdom

Background: Image classification can be considered one of the key pillars of medical image analysis. Deep learning (DL) faces challenges that prevent its practical applications despite the remarkable improvement in medical image classification. The data distribution differences can lead to a drop in the efficiency of DL, known as the domain shift problem. Besides, requiring bulk annotated data for model training, the large size of models, and the privacy-preserving of patients are other challenges of using DL in medical image classification. This study presents a strategy that can address the mentioned issues simultaneously. Method: The proposed domain adaptive model based on knowledge distillation can classify images by receiving limited annotated data of different distributions. The designed multiple teachers-meticulous student model trains a student network that tries to solve the challenges by receiving the parameters of several teacher networks. The proposed model was evaluated using six available datasets of different distributions by defining the respiratory motion artefact detection task. Results: The results of extensive experiments using several datasets show the superiority of the proposed model in addressing the domain shift problem and lack of access to bulk annotated data. Besides, the privacy preservation of patients by receiving only the teacher network parameters instead of the original data and consolidating the knowledge of several DL models into a model with almost similar performance are other advantages of the proposed model. Conclusions: The proposed model can pave the way for practical clinical applications of deep classification methods by achieving the mentioned objectives simultaneously. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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An Enhanced Residual Networks Based Framework for Early Alzheimer’s Disease Classification and Diagnosis 6th

An Enhanced Residual Networks Based Framework for Early Alzh...

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6th International Conference on Communication, Devices and Networking, ICCDN 2022

作者： Awotunde, Joseph Bamidele Sur, Nath Samarendra Imoize, Agbotiname Lucky Misra, Sanjay Gaber, Tarek Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University East Sikkim Rangpo Majitar737136 India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany Department of Applied Data Science Institute of Energy Technology Halden Norway Faculty of Computers and Informatics Suez Canal University Ismailia41522 Egypt School of Science Engineering and Environment University of Salford ManchesterM5 4WT United Kingdom

ISBN: (纸本)9789819919826

Alzheimer's disease (AD) is a long-lasting, degenerative brain illness for which there is now no successful treatment. However, there are medications that can delay its growth and stop its tracks. Therefore, stopping and regulating the progression of AD depends greatly on its early diagnosis. computer-aided diagnosis (CAD) in health care has become more and more dependent on deep learning technologies. This has help in early diagnosis and detection of various disease in health care. New techniques for the early diagnosis of neurocognitive abnormalities are now possible thanks to advancements in medical imaging and computational capacity in an effort to stop or slow down cognitive aging. Therefore, this study proposes a residual network (ResNet) type 50 for early diagnosis of AD. Convolutional neural networks (CNNs) are first used in the study for features extraction and then make the ultimate diagnosis using the ResNet-50. To create a better architecture, the problem of AD diagnosis will be addressed by utilizing and incorporating ResNet-50, which improves the current categorization problem's accuracy to its maximum. With a 98.8% accuracy rate, the proposed model using ResNet-50 produces encouraging findings for AD prediction. The study reveals that the model can effectively circumvent CNNs for the diagnostic issue with AD. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer aided diagnosis

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Tuning Accuracy-Diversity Trade-off in Neural Network Ensemble via Novel Entropy Loss Function 13

Tuning Accuracy-Diversity Trade-off in Neural Network Ensemb...

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13th International Conference on Electrical and Electronics engineering, ELECO 2021

作者： Ali, Muhammad Ammar Sahin, Yusuf Özögür-Akyüz, Sureyya Ünal, Gozde Otar, Buse Cisil Graduate School of Natural and Applied Sciences Bahcesehir University Department of Computer Engineering Istanbul Turkey Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Department of Mathematics Istanbul Turkey Graduate School of Natural and Applied Sciences Bahcesehir University Department of Industrial Engineering Istanbul Turkey

ISBN: (纸本)9786050114379

Ensemble methods are used in machine learning by combining several models which produce an optimal predictive model. Neural network ensemble learning is a technique, which uses multiple individual deep neural networks (DNNs). Ensemble pruning methods are used to reduce the computational complexity of ensemble models. In this study, a novel optimization model is proposed to increase error independence in classifiers via entropy measurement and thus better prune the ensemble. An ensemble of 300 DNNs is trained and tested on the CIFAR-10 dataset and results show an increase in accuracy while main-taining a level of relative entropy measured by Kullback-Leibler divergence (KL-divergence). © 2021 Chamber of Turkish Electrical Engineers.

关键词： Deep neural networks

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Statistical Distance-Guided Unsupervised Domain Adaptation for Automated Multi-Class Cardiovascular Magnetic Resonance Image Quality Assessment

arXiv

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arXiv 2024年

This study proposes an attention-based statistical distance-guided unsupervised domain adaptation model for multi-class cardiovascular magnetic resonance (CMR) image quality assessment. The proposed model consists of a feature extractor, a label predictor and a statistical distance estimator. An annotated dataset as the source set and an unlabeled dataset as the target set with different statistical distributions are considered inputs. The statistical distance estimator approximates the Wasserstein distance between the extracted feature vectors from the source and target data in a mini-batch. The label predictor predicts data labels of source data and uses a combinational loss function for training, which includes cross entropy and centre loss functions plus the estimated value of the distance estimator. Four datasets, including imaging and k-space data, were used to evaluate the proposed model in identifying four common CMR imaging artefacts: respiratory and cardiac motions, Gibbs ringing and Aliasing. The results of the extensive experiments showed that the proposed model, both in image and k-space analysis, has an acceptable performance in covering the domain shift between the source and target sets. The model explainability evaluations and the ablation studies confirmed the proper functioning and effectiveness of all the model's modules. The proposed model outperformed the previous studies regarding performance and the number of examined artefacts. The proposed model can be used for CMR post-imaging quality control or in large-scale cohort studies for image and k-space quality assessment due to the appropriate performance in domain shift coverage without a tedious data-labelling process. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Multimodal E-Commerce Product Classification Using Hierarchical Fusion 2

Multimodal E-Commerce Product Classification Using Hierarchi...

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2nd IEEE Conference on Information Technology and Data Science, CITDS 2022

作者： Tashu, Tsegaye Misikir Fattouh, Sara Kiss, Peter Horvath, Tomas Elte - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Pázmány Péter sétány 1/C Budapest1117 Hungary Faculty of Science Institute of Computer Science Pavol Jozef Šafárik University Jesenná5 Košice040 01 Slovakia College of Informatics Kombolcha Institute of Technology Wollo University Kombolcha208 Ethiopia

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

In this work, we present a multi-modal model for commercial product classification, that combines features extracted by multiple neural network models from textual (Camem-BERT and FlauBERT) and visual data (SE-ResNeXt-50), using simple fusion techniques. The proposed method significantly outperformed the performance of the unimodal models, as well as the reported performance of similar models on our specific task. We made experiments with multiple fusing techniques, and found, that the best preforming technique to combine the individual embedding of the unimodal network is based on the combination of concatenation and averaging the feature vectors. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the performance of multi-label and multimodal classification problems. © 2022 IEEE.

关键词： Neural network models

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Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration

Skill-based Multi-objective Reinforcement Learning of Indust...

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IEEE International Conference on Robotics and Biomimetics

作者： Matthias Mayr Faseeh Ahmad Konstantinos Chatzilygeroudis Luigi Nardi Volker Krueger Department of Computer Science Faculty of Engineering (LTH) Lund University Lund SE Sweden Computer Engineering and Informatics Department (CEID) University of Patras Greece Department of Computer Science and Electrical Engineering Stanford University CA USA

ISBN: (纸本)9781665481106

In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handling forces and torques, these systems often fall short. We introduce an approach that provides a combination of task-level planning with targeted learning of scenario-specific parameters for skill-based systems. We propose the following pipeline: the user provides a task goal in the planning language PDDL, then a plan (i.e., a sequence of skills) is generated and the learnable parameters of the skills are automatically identified, and, finally, an operator chooses reward functions and parameters for the learning process. Two aspects of our methodology are critical: (a) learning is tightly integrated with a knowledge framework to support symbolic planning and to provide priors for learning, (b) using multi-objective optimization. This can help to balance key performance indicators (KPIs) such as safety and task performance since they can often affect each other. We adopt a multi-objective Bayesian optimization approach and learn entirely in simulation. We demonstrate the efficacy and versatility of our approach by learning skill parameters for two different contact-rich tasks. We show their successful execution on a real 7-DOF KUKA-iiwa manipulator and outperform the manual parameterization by human robot operators.

关键词： Service robots Biomimetics Pipelines Key performance indicator Reinforcement learning Manipulators Planning

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