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Applied Computational Intelligence in Information Systems (ACIIS), International Conference on

作者： Ahmed M. S. Elaklouk Ratna Zuarni Ramli Ibrahim Edris Nena Padilla-Valdez Adel Al Jumaily School of Computing and Informatics Universiti Teknologi Brunei Bandar Seri Begawan Brunei Darussalam College of Computing Informatics and Mathematics Universiti Teknologi MARA Negeri Seremban Malaysia Centre for Communication Universiti Teknologi Brunei Bandar Seri Begawan Brunei Darussalam School of Computing University of Technology Brunei Department of Computer Science & Software Engineering University of Western Australia and ENSTA Bretagne University France

Brain damage resulting from stroke and traumatic brain injury is the primary cause of complicated disability and one of the major causes of mortality in most countries, burdening healthcare systems owing to the growth in the number of brain injury victims. Rehabilitation is required to deal with the brain damage. With fewer therapists available, it is very challenging to manage patients and even motivate them to participate actively in their rehabilitation. Patients have frequently complained that many traditional rehabilitation systems are monotonous and uninteresting. The study’s objective is to propose a telerehabilitation gaming system framework that can serve as guidance for developing an affordable rehabilitation gaming system that can motivate and engage patients and increase rehabilitation effectiveness. The research methodology is based on synthesizing related research and currently available technologies. The proposed framework includes the therapist and patient attached with vital constructs: tailoring tools, instructional content, game characteristics, tailored games, and performance. It is an internet-based communication structure that connects the Rehabilitation Gaming System to the hospital and other care provider networks. The proposed framework is evaluated by a panel of five experts. The results reveal that all experts believe that the proposed framework can serve as a useful guide for creating gaming interventions that can boost the patient’s motivation and engagement and increase the effectiveness of rehabilitation.

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Evaluation of augmentation methods in classifying autism spectrum disorders from fMRI data with 3D convolutional neural networks

arXiv

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arXiv 2021年

作者： Jönemo, Johan Abramian, David Eklund, Anders Division of Medical Informatics Department of Biomedical Engineering Division of Statistics & Machine Learning Department of Computer and Information Science Linköping University Sweden

Classifying subjects as healthy or diseased using neuroimaging data has gained a lot of attention during the last 10 years. Here we apply deep learning to derivatives from resting state fMRI data, and investigate how different 3D augmentation techniques affect the test accuracy. Specifically, we use resting state derivatives from 1,112 subjects in ABIDE preprocessed to train a 3D convolutional neural network (CNN) to perform the classification. Our results show that augmentation only provide minor improvements to the test accuracy. © 2021, CC BY.

关键词： Convolutional neural networks

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Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach

arXiv

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arXiv 2023年

作者： Hong, Haokai Jiang, Min Lin, Qiuzhen Tan, Kay Chen Department of Artificial Intelligence Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism School of Informatics Xiamen University Fujian361005 China College of Computer Science and Software Engineering Shenzhen University China Department of Computing Hong Kong Polytechnic University Hong Kong

We define very large-scale multiobjective optimization problems as optimizing multiple objectives (VLSMOPs) with more than 100,000 decision variables. These problems hold substantial significance, given the ubiquity of real-world scenarios necessitating the optimization of hundreds of thousands, if not millions, of variables. However, the larger dimension in VLSMOPs intensifies the curse of dimensionality and poses significant challenges for existing large-scale evolutionary multiobjective algorithms, rendering them more difficult to solve within the constraints of practical computing resources. To overcome this issue, we propose a novel approach called the very large-scale multiobjective optimization framework (VMOF). The method efficiently samples general yet suitable evolutionary directions in the very large-scale space and subsequently fine-tunes these directions to locate the Pareto-optimal solutions. To sample the most suitable evolutionary directions for different solutions, Thompson sampling is adopted for its effectiveness in recommending from a very large number of items within limited historical evaluations. Furthermore, a technique is designed for fine-tuning directions specific to tracking Pareto-optimal solutions. To understand the designed framework, we present our analysis of the framework and then evaluate VMOF using widely recognized benchmarks and real-world problems spanning dimensions from 100 to 1,000,000. Experimental results demonstrate that our method exhibits superior performance not only on LSMOPs but also on VLSMOPs when compared to existing algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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The OPS-SAT benchmark for detecting anomalies in satellite telemetry

arXiv

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arXiv 2024年

作者： Ruszczak, Bogdan Kotowski, Krzysztof Evans, David Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Prószkowska Str. 76 Opole45-758 Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka Str. 16 Gliwice44-100 Poland KP Labs Bojkowska Str. 37J Gliwice44-100 Poland European Space Agency ESOC Robert-Bosch-Str. 5 Darmstadt64293 Germany

Detecting anomalous events in satellite telemetry is a critical task in space operations. This task, however, is extremely time-consuming, error-prone and human dependent, thus automated data-driven anomaly detection algorithms have been emerging at a steady pace. However, there are no publicly available datasets of real satellite telemetry accompanied with the ground-truth annotations that could be used to train and verify anomaly detection supervised models. In this article, we address this research gap and introduce the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired on board OPS-SAT—a CubeSat mission which has been operated by the European Space Agency which has come to an end during the night of 22–23 May 2024 (CEST). The dataset is accompanied with the baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms for anomaly detection. They were trained and validated using the training-test dataset split introduced in this work, and we present a suggested set of quality metrics which should be always calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible and objective validation procedure that can be used to quantify the capabilities of the emerging anomaly detection techniques in an unbiased and fully transparent way. © 2024, CC BY.

关键词： Anomaly detection

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Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble

arXiv

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arXiv 2024年

作者： Chen, Chuangtao Zhao, Qinglin Zhou, MengChu He, Zhimin Sun, Zhili Situ, Haozhen Faculty of Innovation Engineering Macau University of Science and Technology Macao999078 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States School of Electronic and Information Engineering Foshan University Foshan528000 China Institute of Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China

Classical diffusion models have shown superior generative results. Exploring them in the quantum domain can advance the field of quantum generative learning. This work introduces Quantum Generative Diffusion Model (QGDM) as their simple and elegant quantum counterpart. Through a non-unitary forward process, any target quantum state can be transformed into a completely mixed state that has the highest entropy and maximum uncertainty about the system. A trainable backward process is used to recover the former from the latter. The design requirements for its backward process includes non-unitarity and small parameter count. We introduce partial trace operations to enforce non-unitary and reduce the number of trainable parameters by using a parameter-sharing strategy and incorporating temporal information as an input in the backward process. We present QGDM’s resource-efficient version to reduce auxiliary qubits while preserving generative capabilities. QGDM exhibits faster convergence than Quantum Generative Adversarial Network (QGAN) because its adopted convex-based optimization can result in better convergence. The results of comparing it with QGAN demonstrate its effectiveness in generating both pure and mixed quantum states. It can achieve 53.02% higher fidelity in mixed-state generation than QGAN. The results highlight its great potential to tackle challenging quantum generation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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The equivalence between correctability of deletions and insertions of separable states in quantum codes

arXiv

引用

arXiv 2021年

作者： Shibayama, Taro Ouyang, Yingkai Department of Mathematics and Informatics Graduate School of Science Chiba University Japan Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

In this paper, we prove the equivalence of inserting separable quan-tum states and deletions. Hence any quantum code that corrects dele-tions automatically corrects separable insertions. First, we describe the quantum insertion/deletion error using the Kraus operators. Next, we develop an algebra for commuting Kraus operators corresponding to insertions and deletions. Using this algebra, we prove the equiv-alence between quantum insertion codes and quantum deletion codes using the Knill-Laflamme conditions. Copyright © 2021, The Authors. All rights reserved.

关键词： Algebra

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Effects of a Memory-Engaging Secondary Task in a Virtual Setting for Stimulating the Embodiment of an Artificial Upper Limb

Effects of a Memory-Engaging Secondary Task in a Virtual Set...

引用

IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO

作者： Andrea Lucaroni Andrea Bottino Fabrizio Lamberti Giulia Mariani Andrea Marinelli Nicolò Boccardo Emanuele Gruppioni Lorenzo De Michieli Matteo Laffranchi Giacinto Barresi Rehab Technologies Lab Istituto Italiano di Tecnologia Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering Università degli Studi di Genova Genoa Italy Department of Control and Computer Engineering Politecnico di Torino Turin Italy Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT) Genoa Italy Centro Protesi INAIL Istituto Nazionale per l’Assicurazione contro gli Infortuni sul Lavoro Bologna Italy

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

The embodiment (the sensation that an external object is part of one’s body scheme) is one of the processes targeted by the research in bionic prosthetics as a potential strategy to increase the acceptance of artificial limbs. This study investigates how one of the components of the embodiment, the body ownership (the sensation of possessing the external object), may be affected by a secondary task (possibly competing for cognitive resources requested for the embodiment itself) in a virtual exergame for prosthetic training. In this preliminary study participants, without impairments, are asked to perform activities of pick-and-place in two conditions by using a digital model of the Hannes bionic hand: (i) one is cognitively challenging, requiring to memorize the sequence of objects to move before it disappears; (ii) the other is non-challenging for the subject’s memory, with a constantly visible sequence. In both conditions, each subject is asked to catch objects according to the presented sequence. According to questionnaire data, we noticed how this procedure is feasible. Interestingly, the proprioceptive drift (the person is asked to indicate where they perceive their own real limb without looking at it) in a subsequent Rubber Hand Illusion session with the real bionic hand seems highlighting that the working memory-engaging secondary task can impair the embodiment process in terms of body ownership of the robotic hand. Probably, the memory task actually requires a type of cognitive resources that are necessary for the body ownership process itself during the multisensory stimulation. This allows for envisioning further investigations and novel design concepts for prosthetic training paradigms.

关键词： Training Solid modeling Visualization Biological system modeling Memory management Propioception Virtual environments

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RA-GCN: Graph Convolutional network for disease prediction problems with imbalanced data

arXiv

引用

arXiv 2021年

作者： Ghorbani, Mahsa Kazi, Anees Baghshah, Mahdieh Soleymani Rabiee, Hamid R. Navab, Nassir Department of Computer Engineering Sharif University of Technology Tehran Iran Computer Aided Medical Procedures Department of Informatics Technical University of Munich Germany Whiting School of Engineering Johns Hopkins University Baltimore United States

Disease prediction is a well-known classification problem in medical applications. Graph Convolutional Networks (GCNs) provide a powerful tool for analyzing the patients' features relative to each other. This can be achieved by modeling the problem as a graph node classification task, where each node is a patient. Due to the nature of such medical datasets, class imbalance is a prevalent issue in the field of disease prediction, where the distribution of classes is skewed. When the class imbalance is present in the data, the existing graph-based classifiers tend to be biased towards the major class(es) and neglect the samples in the minor class(es). On the other hand, the correct diagnosis of the rare positive cases (true-positives) among all the patients is vital in a healthcare system. In conventional methods, such imbalance is tackled by assigning appropriate weights to classes in the loss function which is still dependent on the relative values of weights, sensitive to outliers, and in some cases biased towards the minor class(es). In this paper, we propose a Re-weighted Adversarial Graph Convolutional Network (RA-GCN) to prevent the graph-based classifier from emphasizing the samples of any particular class. This is accomplished by associating a graph-based neural network to each class, which is responsible for weighting the class samples and changing the importance of each sample for the classifier. Therefore, the classifier adjusts itself and determines the boundary between classes with more attention to the important samples. The parameters of the classifier and weighting networks are trained by an adversarial approach. We show experiments on synthetic and three publicly available medical datasets. Our results demonstrate the superiority of RA-GCN compared to recent methods in identifying the patient's status on all three datasets. The detailed analysis of our method is provided as quantitative and qualitative experiments on synthetic datasets. © 2021, CC BY

关键词： Forecasting

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Automated detection of vertebral fractures from X-ray images: A novel machine learning model and survey of the field

引用

Neurocomputing 2024年 566卷

作者： Cheng, Li-Wei Chou, Hsin-Hung Cai, Yu-Xuan Huang, Kuo-Yuan Hsieh, Chin-Chiang Chu, Po-Lun Cheng, I-Szu Hsieh, Sun-Yuan Institute of Medical Informatics National Cheng Kung University No. 1 University Road Tainan70101 Taiwan Department of Computer Science and Information Engineering National Chi Nan University No. 1 University Rd. Puli Township Nantou54561 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University No. 1 University Road Tainan70101 Taiwan Department of Orthopedics National Cheng Kung University Hospital College of Medicine National Cheng Kung University No. 138 Sheng Li Road Tainan701 Taiwan Department of Radiology Tainan Hospital Ministry of Health and Welfare No. 125 Jhongshan Rd. West Central Dist. Tainan70043 Taiwan College of Medicine National Cheng Kung University No. 1 University Road Tainan70101 Taiwan Department of Computer Science and Information Engineering Institute of Medical Information Institute of Manufacturing Information and Systems National Cheng Kung University. No. 1 University Road Tainan70101 Taiwan

Vertebral fractures are a common problem and the most prevalent of thoracolumbar compression and burst fractures. However, vertebral fractures are difficult to diagnose: an experienced orthopedist or radiologist is required to detect and determine the type of vertebral fracture. Thus, artificial intelligence methods for diagnosing vertebral fractures are clinically useful. On the basis of a review of 12 studies in the literature, the earliest of which was published in 2020, we propose a machine learning model that detects and determines the type of vertebral fracture on the basis of X-ray data. In this method, YOLOv4 and ResUNet are used to segment vertebral bodies from X-ray images. In evaluation experiments, our method had a precision of 99%, 74%, and 94% in identifying healthy vertebrae, compression fractures, and burst fractures, respectively. © 2023 Elsevier B.V.

关键词： Diagnosis

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Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound

arXiv

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arXiv 2025年

作者： Huang, Yuhao Chang, Ao Dou, Haoran Tao, Xing Zhou, Xinrui Cao, Yan Huang, Ruobing Frangi, Alejandro F. Bao, Lingyun Yang, Xin Ni, Dong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen University Shenzhen China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen China School of Computing University of Leeds Leeds United Kingdom Shenzhen RayShape Medical Technology Co. Ltd Shenzhen China Division of Informatics Imaging and Data Science School of Health Sciences University of Manchester Manchester United Kingdom Department of Computer Science School of Engineering University of Manchester Manchester United Kingdom Department of Electrical Engineering Department of Cardiovascular Sciences KU Leuven Belgium Alan Turing Institute London United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom Department of Ultrasound Affiliated Hangzhou First People’s Hospital School of Medicine Westlake University China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: 1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. 2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. 3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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