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

arXiv

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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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Digital Map Based on Unmanned Aerial Vehicle

Digital Map Based on Unmanned Aerial Vehicle

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2020 International Conference on computer engineering, Network, and Intelligent Multimedia, CENIM 2020

作者： Sunarya, I Made Gede Al Affan, Muhammad Rusydi Kurniawan, Arief Yuniarno, Eko Mulyanto Universitas Pendidikan Ganesha Department of Informatic Engineering of Education Singaraja81161 Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9781728182834

Institut Teknologi Sepuluh Nopember (ITS) is one of the state universities in Surabaya, East Java. The main campus of ITS occupies an area of 180 hectares of land where there are scattered buildings of service offices, lecture buildings, laboratories, and other supporting facilities. From this, it is felt necessary to make an accurate digital map to help plan and introduce ITS. The development of photogrammetry by utilizing Unmanned Aerial Vehicle (UAV) as the data maker, makes digital map-making activities faster and more accurate than before. The activity of processing map images produced by UAV currently still requires software with a relatively high price. So it is necessary to make another alternative to making the overall ITS map by adding accurate coordinates to the result. The error hole in the orthophoto map produced was 2.0690%, while the orthophoto map produced by Agisoft PhotoScan was 24.5370%. © 2020 IEEE.

关键词： Image processing

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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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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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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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Anomaly Detection for Internet of Things (IoT) Using an Artificial Immune System 12th

Anomaly Detection for Internet of Things (IoT) Using an Arti...

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12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020

作者： Elisa, Noe Yang, Longzhi Chao, Fei Naik, Nitin Department of Computer and Information Sciences Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Department of AI Xiamen University Xiamen China School of Informatics and Digital Engineering Aston University BirminghamB4 7ET United Kingdom

ISBN: (纸本)9783030736880

Internet of Things (IoT) have demonstrated significant impact on all aspects of human daily lives due to their pervasive applications in areas such as telehealth, home appliances, surveillance, and wearable devices. The number of IoT devices and sensors connected to the Internet across the world is expected to reach over 50 billion by the end of 2020. The connection of such rapidly increasing number of IoT devices to the Internet leads to concerns in cyber-attacks such as malware, worms, denial of service attack (DoS) and distributed DoS attack (DDoS). To prevent these attacks from compromising the performance of IoT devices, various approaches for detecting and mitigating cyber security threats have been developed. This paper reports an IoT attack and anomaly detection approach by using the dendritic cell algorithm (DCA). In particular, DCA is an artificial immune system (AIS), which is developed from the inspiration of the working principles and characteristic behaviours of the human immune system (HIS), specifically for the purpose of detecting anomalies in networked systems. The performance of the DCA on detecting IoT attacks is evaluated using publicly available IoT datasets, including DoS, DDoS, Reconnaissance, Keylogging, and Data exfiltration. The experimental results show that, the DCA achieved a comparable detection performance to some of the commonly used classifiers, such as decision trees, random forests, support vector machines, artificial neural network and naïve Bayes, but with reasonably high computational efficiency. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Software Architecture Reconstruction via a Genetic Algorithm: Applying the Move Class Refactoring 24

Software Architecture Reconstruction via a Genetic Algorithm...

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24th Pan-Hellenic Conference on informatics, PCI 2020

作者： Maikantis, Theodoros Tsintzira, Angeliki-Agathi Ampatzoglou, Apostolos Arvanitou, Elvira-Maria Chatzigeorgiou, Alexander Stamelos, Ioannis Bibi, Stamatia Deligiannis, Ignatios Apostolos Ampatzoglou Elvira-Maria Ar-Vanitou Alexander Chatzigeorgiou Department of Applied Informatics University of Macedonia Thessaloniki Greece Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece

ISBN: (纸本)9781450388979

Modularity is one of the four key principles of software design and architecture. According to this principle, software should be organized into modules that are tightly linked internally (high cohesion), whereas at the same time as independent from other modules as possible (low coupling). However, in practice, this principle is violated due to poor architecting design decisions, lack of time, or coding shortcuts, leading to a phenomenon termed as architectural technical debt (ATD). To alleviate this problem (lack of architectural modularity), the most common solution is the application of a software refactoring, namely Move Class - i.e., moving classes (the core artifact in object-oriented systems) from one module to another. To identify Move Class refactoring opportunities, we employ a search-based optimization process, relying on optimization metrics, through which optimal moves are derived. Given the extensive search space required for applying a brute-force search strategy, in this paper, we propose the use of a genetic algorithm that re-arranges existing software classes into existing or new modules (software packages in Java, or folders in C++). To validate the usefulness of the proposed refactorings, we performed an industrial case study on three projects (from the Aviation, Healthcare, and Manufacturing application domains). The results of the study indicate that the proposed architecture reconstruction is able to improve modularity, improving both coupling and cohesion. The obtained results can be useful to practitioners through an open source tool;whereas at the same point, they open interesting future work directions. © 2020 ACM.

关键词： Genetic algorithms

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Smart Health Monitoring System with Wireless Networks to Detect Kidney Diseases

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Computational Intelligence and Neuroscience 2022年第0期2022卷 3564482页

作者： Dhanke, Jyoti Rathee, Naveen Vinmathi, M.S. Janu Priya, S. Abidin, Shafiqul Tesfamariam, Mikiale Bharati Vidyapeeth's College of Engineering Lavale Maharashtra Pune412115 India Department of Electronics and Communication Engineering IIMT College of Engineering Uttar Pradesh Greater Noida201310 India Department of CSE Panimalar Engineering College Bangalore Trunk Road Nazarethpet Poonamallee Tamilnadu Chennai600123 India Department of Electronics and Communication Engineering K. Ramakrishnan College of Engineering Samayapuram Tamilnadu Tiruchirappalli621112 India Department of Computer Science Aligarh Muslim University Uttar Pradesh Aligarh202002 India Department of Software Engineering College of Computing and Informatics Haramaya University Dire Dawa Ethiopia

It is essential to change health services from a hospital to a patient-centric platform since medical costs are steadily growing and new illnesses are emerging on a worldwide scale. This study provides an optimal decision support system based on the cloud and Internet of Things (IoT) for identifying Chronic Kidney Disease (CKD) to provide patients with efficient remote healthcare services. To identify the presence of medical data for CKD, the proposed technique uses an algorithm named Improved Simulated Annealing-Root Mean Square -Logistic Regression (ISA-RMS-LR). The four subprocesses that make up the proposed model are a collection of data, preprocessing, feature selection, and classification. The incorporation of Simulated Annealing (SA) during Feature Selection (FS) enhances the ISA-RMS-LR model's classifier outputs. Using the CKD benchmark dataset, the ISA-RMS-LR model's efficacy has been verified. According to the experimental findings, the proposed ISA-RMS-LR model effectively classifies patients with CKD, with high sensitivity at 99.46%, accuracy at 99.26%, Specificity at 98%, F-score at 99.63%, and kappa value at 98.29%. The proposed system has many benefits including the fast transmission of medical data to the medical personnel, real-time tracking, and registration condition of the patient through a medical record. Potential enhancement of the performance measures the provider system's hospital capacity and monitoring of a significant number of patients with a concentrated average delay. © 2022 Jyoti Dhanke et al.

关键词： Simulated annealing

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