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2023 IEEE Asia-Pacific Conference on computer science and Data Engineering, CSDE 2023

作者： Chandra, Abel Sharma, Alok Dehzangi, Iman Tsunoda, Tatsuhiko Sattar, Abdul Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Rutgers University Department of Computer Science CamdenNJ United States Center for Computational and Integrative Biology Rutgers University Camden United States Riken Center for Integrative Medical Sciences Laboratory for Medical Science Mathematics Yokohama Japan School of Science The University of Tokyo Laboratory for Medical Science Mathematics Department of Biological Sciences Tokyo Japan Graduate School of Frontier Sciences The University of Tokyo Laboratory for Medical Science Mathematics Department of Computational Biology and Medical Sciences Tokyo Japan

ISBN: (纸本)9798350341072

Understanding protein sequences can advance treatments for various diseases. However experimentally obtaining this information is laborious, time-consuming, and expensive. Traditional machine learning techniques, like support vector machine, random forest and logistic regression, offer potential to fast-track this process but are sometimes limited by data complexity. Deep learning algorithms, in contrast, tend to yield higher performance. In this study, we employed a convolutional neural network to predict protein phosphoglycerylation. Features were extracted from pre-trained transformer models and compared with conventional features, such as evolutionary information and physicochemical/biochemical properties. Our results indicate significant performance improvements across all feature types, with the combination of transformerbased features and the convolutional neural network being especially effective. This methodology holds potential for other protein property prediction tasks. Our software and datasets used in this study are publicly available at https://***/abelavit/***. © 2023 IEEE.

关键词： Convolutional neural networks

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Enhancing UAV Autonomous Navigation in Indoor Environments Using Reinforcement Learning and Convolutional Neural Networks 22

Enhancing UAV Autonomous Navigation in Indoor Environments U...

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22nd IEEE International Symposium on intelligent systems and Informatics, SISY 2024

作者： Safarpour Motealegh Mahalegi, Homayoun Farhadi, Amirfarhad Molnar, Gyorgy Nagy, Eniko Obuda University Institute of Mechatronics and Vehicle Engineering Budapest Hungary Islamic Azad University Department of Computer Engineering Science and Research Branch Tehran Iran Obuda University Hungary Szechenyi University Apaczai Csere János Faculty of Humanities Education and Social Sciences Kando Kalman Faculty of Electrical Engineering Budapest Gyor Hungary Obuda University Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350385601

In recent years, the number of unmanned aerial vehicle (UAV) applications has increased. However, navigating them indoors is still tricky because no GPS signals are available, and the obstacles constantly change. This study exploits the collaboration between convolutional neural networks (CNNs) and reinforcement learning (RL) to overcome these challenges, improving the level of independence of UAVs in indoor settings. We present a new technique known as potential-based reward shaping, which directly incorporates expert knowledge into the reinforcement learning framework. This technique efficiently addresses the problem of sparse rewards that often hinder learning in conventional RL configurations. In addition, we have created a reflective reinforcement learning agent that systematically assesses its actions to prioritize those that result in the most substantial enhancements. We have extensively evaluated the performance of our method with the High-Definition Indoor Navigation (HDIN) dataset to show that our method continuously outperformed current non-introspective RL techniques in terms of decision-making speed and navigation accuracy in this evaluation. The results show that this new CNN-RL combination is feasible and suggests a scalable method for complex indoor navigation problems. © 2024 IEEE.

关键词： Convolutional neural networks

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Modern Manufacturing for Alloy Wheel Defect Detection using Image Processing and Application

Modern Manufacturing for Alloy Wheel Defect Detection using ...

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IEEE International Conference on computer Communication and the Internet (ICCCI)

作者： Tuaniai Archevapanich Woranidtha Krungseanmuang Vasutorn Chaowalittawin Posathip Sathaporn Punyisa Chaowalittawin Boonchana Purahong Department of Electronics and Communication Engineering Faculty of Engineering and Architecture Rajamagala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Robotics and Computational Intelligent Systems School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Indusstrail Physics School of Science Bangkok Thailand Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

This paper presents an innovative approach to identifying defects in alloy wheel production by integrating image processing techniques with a mobile application platform. The system receives X-ray alloy images from the factory via mobile phone, processes them using image processing techniques to enhance clarity and readiness for defect detection, and then transmits the processed images to a Django framework via a uniform resource locator (URL). Subsequently, the system detects defects in the images, encodes them in Base64 format, and sends them to the mobile application through an API (Application Program Interface) for display on the user interface. This well-designed system architecture offers manufacturers a comprehensive solution to ensure product quality, reduce costs, and enhance customer satisfaction.

关键词： Uniform resource locators Image processing Metals Wheels systems architecture User interfaces Mobile applications

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A Toolkit to Generate Social Navigation Datasets 21st

A Toolkit to Generate Social Navigation Datasets

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21st International Workshop of Physical Agents, WAF 2020

作者： Baghel, Rishabh Kapoor, Aditya Bachiller, Pilar Jorvekar, Ronit R. Rodriguez-Criado, Daniel Manso, Luis J. Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India Department of Computer Science and Information Systems Birla Institute of Technology and Science Goa Pilani India Robotics and Artificial Vision Laboratory University of Extremadura Badajoz Spain Department of Computer Engineering Pune Institute of Computer Technology Pune India Department of Computer Science College of Engineering and Physical Sciences Aston University Birmingham United Kingdom

ISBN: (纸本)9783030625788

Social navigation datasets are necessary to assess social navigation algorithms and train machine learning algorithms. Most of the currently available datasets target pedestrians’ movements as a pattern to be replicated by robots. It can be argued that one of the main reasons for this to happen is that compiling datasets where real robots are manually controlled, as they would be expected to behave when moving, is a very resource-intensive task. Another aspect that is often missing in datasets is symbolic information that could be relevant, such as human activities, relationships or interactions. Unfortunately, the available datasets targeting robots and supporting symbolic information are restricted to static scenes. This paper argues that simulation can be used to gather social navigation data in an effective and cost-efficient way and presents a toolkit for this purpose. A use case studying the application of graph neural networks to create learned control policies using supervised learning is presented as an example of how it can be used. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Navigation

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Position: understanding LLMs requires more than statistical generalization 24

Position: understanding LLMs requires more than statistical ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Patrik Reizinger Szilvia Ujváry Anna Mészáros Anna Kerekes Wieland Brendel Ferenc Huszár Max Planck Institute for Intelligent Systems Tübingen Germany University of Cambridge UK and AI Center UCL London UK University of Cambridge UK Department of Computer Science ETH Zurich and Max Planck ETH Center for Learning Systems Max Planck Institute for Intelligent Systems Tübingen Germany and ELLIS Institute Tübingen Germany and Tübingen AI Center Germany

The last decade has seen blossoming research in deep learning theory attempting to answer, "Why does deep learning generalize?" A powerful shift in perspective precipitated this progress: the study of overparametrized models in the interpolation regime. In this paper, we argue that another perspective shift is due, since some of the desirable qualities of LLMs are not a consequence of good statistical generalization and require a separate theoretical explanation. Our core argument relies on the observation that AR probabilistic models are inherently non-identifiable: models zero or near-zero KL divergence apart--thus, equivalent test loss--can exhibit markedly different behaviors. We support our position with mathematical examples and empirical observations, illustrating why non-identifiability has practical relevance through three case studies: (1) the nonidentifiability of zero-shot rule extrapolation; (2) the approximate non-identifiability of in-context learning; and (3) the non-identifiability of fine-tunability. We review promising research directions focusing on LLM-relevant generalization measures, transferability, and inductive biases.

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Comparative Analysis of Compromise Approaches in Multiple Rankings Decision-Making: A Case Study on Performance of Electric Cars

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Procedia computer science 2024年 246卷 5408-5417页

作者： Bartosz Paradowski Mariusz Gajewski Andrzej Wilczyński Wojciech Sałabun National Institute of Telecommunications National Research Institute ul. Szachowa 1 04-894 Warsaw Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. żołnierska 49 71-210 Szczecin Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 31-155 Krakow Poland

As civilization advances, decision-making problems become increasingly complex, posing significant challenges in deriving optimal solutions. The field of multi-criteria decision analysis (MCDA) has evolved to address these complexities; however, single-solution approaches are often inadequate in multi-decision-maker scenarios, necessitating group decision-making methods. This study proposes an alternative compromise solution for evaluating electric car performance, examining various compromise approaches, including voting methods like Borda and Copeland counts, and more advanced techniques such as Iterative Compromise Ranking Analysis (ICRA), Improved Borda, HQ compromise, Dominance-directed graph, and Rank position methods. These methods were assessed using two ranking correlation coefficients to evaluate their effectiveness in multi-decision-maker scenarios. Results indicate that while minor differences in rankings were observed based on the chosen compromise method, these differences could influence decision-making outcomes. The analysis highlights the importance of selecting methods that align with specific needs and expectations. Future research should focus on simulation-based studies to better understand the characteristics of each method and apply these methods to real-world case studies to determine their suitability in various contexts.

关键词： multiple rankings comparative analysis MCDM compromise decision-making

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D2NT: A High-Performing Depth-to-Normal Translator

D2NT: A High-Performing Depth-to-Normal Translator

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yi Feng Bohuan Xue Ming Liu Qijun Chen Rui Fan Robotics & Artificial Intelligence Laboratory (RAIL) the State Key Laboratory of Intelligent Autonomous Systems and Frontiers Science Center for Intelligent Autonomous Systems the College of Electronic & Information Engineering Tongji University Shanghai P. R. China Department of Computer Science & Engineering the Hong Kong University of Science and Technology (Guangzhou) Hong Kong SAR P. R. China Robotics & Autonomous Systems Thrust of the Systems Hub the Hong Kong University of Science and Technol-ogy (Guangzhou) Nansha Guangzhou P. R. China

Surface normal holds significant importance in visual environmental perception, serving as a source of rich geometric information. However, the state-of-the-art (SoTA) surface normal estimators (SNEs) generally suffer from an unsatisfactory trade-off between efficiency and accuracy. To resolve this dilemma, this paper first presents a superfast depth-to-normal translator (D2NT), which can directly translate depth images into surface normal maps without calculating 3D coordinates. We then propose a discontinuity-aware gradient (DAG) filter, which adaptively generates gradient convolution kernels to improve depth gradient estimation. Finally, we propose a surface normal refinement module that can easily be integrated into any depth-to-normal SNEs, substantially improving the surface normal estimation accuracy. Our proposed algorithm demonstrates the best accuracy among all other existing real-time SNEs and achieves the SoTA trade-off between efficiency and accuracy.

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Subjective weight determination methods in multi-criteria decision-making: a systematic review

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Procedia computer science 2024年 246卷 5396-5407页

作者： Bartłomiej Kizielewicz Tomasz Tomczyk Michał Gandor Wojciech Sałabun National Institute of Telecommunications National Research Institute ul. Szachowa 1 04-894 Warsaw Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 31-155 Krakow Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. żołnierska 49 71-210 Szczecin Poland

The determination of weights is a critical step in Multi-Criteria Decision-Making (MCDM) processes, and subjective methods play a significant role in this area. This paper provides a comprehensive review of subjective weight determination methodologies, focusing primarily on sources from the Scopus database. Through a systematic literature review, this study analyzes 33,434 publications related to subjective weight determination, providing insights into the trends, advancements, and applications of these methodologies. The review underscores the importance of expert knowledge in contemporary decision-making and suggests potential areas for future research, including the integration of subjective methods with emerging technologies and the development of more robust and consistent weighting frameworks. This paper aims to serve as a valuable resource for researchers and practitioners in the field of MCDM, offering a detailed overview of the current state of subjective weight determination methods and their practical implications.

关键词： Subjective weighting AHP BWM DEMATEL MCDM MCDA Mutli-criteria

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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Few-shot learning based histopathological image classification of colorectal cancer

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intelligent Medicine 2024年第4期4卷 256-267页

作者： Rui Li Xiaoyan Li Hongzan Sun Jinzhu Yang Md Rahaman Marcin Grzegozek Tao Jiang Xinyu Huang Chen Li Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110167China Cancer Hospital China Medical UniversityShenyangLiaoning 110122China Shengjing Hospital China Medical UniversityShenyang.Liaoning 110000China Institute for Medical Informatics University of LuebeckGermany Department of Knowledge Engineering University of Economics in KatowicePoland School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic *** histopathologic image classification is often inefficient and *** some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their *** address these challenges,this paper proposes a method based on few-shot *** This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant *** model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy *** this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test *** Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per *** documented the model’s loss,accuracy,and the confusion matrix of the ***,we employed the t-SNE algorithm to analyze and assess the model’s classification *** The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot *** consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization *** research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.

关键词： Colorectal cancer Few-shot learning Transfer learning Contrastive learning Histopathological images Benign and malignant categories

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