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arXiv 2024年

作者： Piao, Chengzhe Zhu, Taiyu Baldeweg, Stephanie E. Taylor, Paul Georgiou, Pantelis Sun, Jiahao Wang, Jun Li, Kezhi Institute of Health Informatics University College London LondonNW1 2DA United Kingdom Department of Psychiatry University of Oxford OxfordOX3 7JX United Kingdom Department of Diabetes & Endocrinology University College London Hospitals LondonNW1 2PG United Kingdom Centre for Obesity & Metabolism Dept of Experimental & Translational Medicine University College London LondonWC1E 6JF United Kingdom Centre for Bio-Inspired Technology Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom FLock.io LondonWC2H 9JQ United Kingdom Department of Computer Science University College London LondonWC1E 6EA United Kingdom

Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with diabetes, thereby reducing complications and improving quality of life. The state of the art of BG prediction has been achieved by leveraging advanced deep learning methods to model multi-modal data, i.e., sensor data and self-reported event data, organised as multi-variate time series (MTS). However, these methods are mostly regarded as "black boxes" and not entirely trusted by clinicians and patients. In this paper, we propose interpretable graph attentive recurrent neural networks (GARNNs) to model MTS, explaining variable contributions via summarizing variable importance and generating feature maps by graph attention mechanisms instead of post-hoc analysis. We evaluate GARNNs on four dataset, representing diverse clinical scenarios. Upon comparison with twelve well-established baseline methods, GARNNs not only achieve the best prediction accuracy but also provide high-quality temporal interpretability, in particular for postprandial glucose levels as a result of corresponding meal intake and insulin injection. These findings underline the potential of GARNN as a robust tool for improving diabetes care, bridging the gap between deep learning technology and real-world healthcare solutions. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Leveraging randomized compiling for the quantum imaginary-time-evolution algorithm

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Physical Review Research 2022年第3期4卷 033140-033140页

作者： Jean-Loup Ville Alexis Morvan Akel Hashim Ravi K. Naik Marie Lu Bradley Mitchell John-Mark Kreikebaum Kevin P. O'Brien Joel J. Wallman Ian Hincks Joseph Emerson Ethan Smith Ed Younis Costin Iancu David I. Santiago Irfan Siddiqi Quantum Nanoelectronics Laboratory Dept. of Physics University of California at Berkeley Berkeley California 94720 USA Computational Research Division Lawrence Berkeley National Lab Berkeley California 94720 USA Materials Sciences Division Lawrence Berkeley National Lab Berkeley California 94720 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Inst. for Quantum Computing and Dept. of Applied Mathematics University of Waterloo Waterloo Ontario Canada N2L 3G1 Quantum Benchmark Inc. Kitchener Ontario Canada N2H 5G5

Recent progress in noisy intermediate-scale quantum (NISQ) hardware shows that quantum devices may be able to tackle complex problems even without error correction. However, coherent errors due to the increased complexity of these devices is an outstanding issue. They can accumulate through a circuit, making their impact on algorithms hard to predict and mitigate. Iterative algorithms like quantum imaginary time evolution are susceptible to these errors. This article presents the combination of both noise tailoring using randomized compiling and error mitigation with purification. We also show that cycle benchmarking gives an estimate of the reliability of the purification. We apply this method to the quantum imaginary time evolution of a transverse field Ising model and report an energy estimation error and a ground-state infidelity both below 1%. Our methodology is general and can be used for other algorithms and platforms. We show how combining noise tailoring and error mitigation will push forward the performance of NISQ devices.

关键词： Measurement-based quantum computing Quantum algorithms Quantum benchmarking Quantum computation Quantum control Quantum information processing Quantum simulation

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Air-to-air communications beyond 5G: A novel 3D CoMP transmission scheme

arXiv

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arXiv 2020年

作者： Li, Yan Miridakis, Nikolaos I. Tsiftsis, Theodoros A. Yang, Guanghua Xia, Minghua School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519082 China Institute of Physical Internet School of Electrical and Information Engineering Jinan University Zhuhai519070 China Dept. of Informatics and Computer Engineering University of West Attica 12243 Greece Dept. of Informatics University of Piraeus 18534 Greece School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China Institute of Physical Internet Jinan University Zhuhai519070 China

In this paper, a novel 3D cellular model consisting of aerial base stations (aBSs) and aerial user equipments (aUEs) is proposed, by integrating the coordinated multi-point (CoMP) transmission technique with the theory of stochastic geometry. For this new 3D architecture, a tractable model for aBSs' deployment based on binomial Delaunay-tetrahedralization is developed, which ensures seamless coverage for a given space. In addition, a versatile and practical frequency allocation scheme is designed to eliminate the inter-cell interference effectively. Based on this model, performance metrics including the ergodic data rate and coverage probability are derived for two types of aUEs: i) general aUE (i.e., an aUE having distinct distances from its serving aBSs) and ii) the worst-case aUE (i.e., an aUE having equal distances from its serving aBSs). Simulation and numerical results demonstrate that the proposed approach emphatically outperforms the conventional binomial-Voronoi tessellation without CoMP. Insightfully, it provides a similar performance as to the binomial-Voronoi tessellation which utilizes the conventional CoMP scheme;yet, introducing a considerably reduced computational complexity and backhaul/signaling overhead. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Boundary value analysis testing on augmented reality of indonesian fruit recognition at mekarsari tourist park using cloud method on android mobile devices

Boundary value analysis testing on augmented reality of indo...

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International Conference on Information System, computer Science and engineering 2018, ICONISCSE 2018

作者： Dewi Agushinta, R. Hustinawaty Jatnika, Ihsan Medyawati, Henny Dept. of Information System Faculty of Computer Science and Information Technology Gunadarma University Jl. Margonda Raya No. 100 Pondok Cina Depok West Java16424 Indonesia Dept. of Informatics Engineering Faculty of Industrial Technology Gunadarma University Jl. Margonda Raya No. 100 Pondok Cina Depok West Java16424 Indonesia

Software testing is a process of implementing a program with the aim of finding an error. A good test case is if the test has the possibility of finding an uncovered error. A successful test is if the test finds an error that was not initially found. One of the testing types available is black box testing. This paper proposes testing using black box testing technique. The black box testing method consists of several ways including equivalence partitioning, boundary value analysis, comparison testing, sample testing, robustness testing, and others. Among the many methods of testing, Boundary Value Analysis was chosen in this study. Boundary Value Analysis is a method of testing by determining the value of the lower limit and upper limit of the data that will be tested. This test is performed on the functions of Augmented Reality prototype of Indonesia fruit recognition by using the cloud method on Android mobile devices. From testing the distance of marker objects to Android mobile devices on cloud recognition using an Android camera shows that the higher the augmentable rating of the target image and the more the number of markers features detected, the easier the image will be tracked by AR. If the distance between the camera and the real object gets farther away, then the virtual object cannot be displayed. Testing with mobile devices using HSDPA, 3G and WIFI networks connecting a cloud database server to display virtual objects shows the average results of devices that use WIFI networks provide the fastest performance. © Published under licence by IOP Publishing Ltd.

关键词： Testing

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

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Blockchain-based consents management for personal data processing in the IoT ecosystem 15

Blockchain-based consents management for personal data proce...

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15th International Joint Conference on e-Business and Telecommunications, ICETE 2018

作者： Rantos, Konstantinos Drosatos, George Demertzis, Konstantinos Ilioudis, Christos Papanikolaou, Alexandros Dept. of Computer and Informatics Engineering Eastern Macedonia and Thrace Institute of Technology Kavala Greece Dept. of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Dept. of Information Technology Alexander Technological Educational Institute of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9789897583193

In the Internet of Things (IoT) ecosystem the volume of data generated by devices in the user’s environment is constantly increasing and becoming of particular value. In such an environment the average user is bound to face considerable difficulties in understanding the size and scope of his/her collected data. However, the provisions of the European General Data Protection Regulation (GDPR) require data subjects to be able to control their personal data, be informed and consent to its processing in an intelligible manner. This paper proposes ADVOCATE, a framework that facilitates GDPR-compliant processing of personal data in IoT environments. The present work aims to assist stakeholders, i.e. Data Controllers and Processors, satisfy GDPR requirements, such as informing data subjects in a transparent and unambiguous manner about the data they will manage, the processing purposes and periods. Respectively, data subjects will be promptly and comprehensively informed about any processing requests addressed to them, create and edit processing policies, exercise their rights in access, correction, deletion, restriction and opposition to data processing. Simultaneously, a notary service using blockchain infrastructures will ensure consents’ security and an intelligent service will inform data subjects about the quality of their consents. Copyright © 2018 by SCITEPRESS – Science and technology Publications, Lda. All rights reserved

关键词： Internet of things

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Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

arXiv

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arXiv 2021年

作者： Shoeibi, Afshin Sadeghi, Delaram Moridian, Parisa Ghassemi, Navid Heras, Jonathan Alizadehsani, Roohallah Khadem, Ali Kong, Yinan Nahavandi, Saeid Zhang, Yu-Dong Gorriz, Juan M. The Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran The Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran The Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran The Department of Mathematics and Computer Science University of la Rioja La Rioja Spain Deakin University VIC3217 Australia The School of Engineering Macquarie University Sydney2109 Australia The Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States The Department of Informatics University of Leicester Leicester United Kingdom The Department of Signal Theory Networking and Communications Universidad de Granada Spain The Department of Psychiatry. University of Cambridge United Kingdom

Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1DCNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25. © 2021, CC BY-NC-ND.

关键词： Diseases

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Roadmap on Deep Learning for Microscopy

arXiv

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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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JMentor: An Ontology-Based Framework for Software Understanding and Reuse

JMentor: An Ontology-Based Framework for Software Understand...

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ACS/IEEE International Conference on computer Systems and Applications

作者： Majdi Rawashdeh Awny Alnusair Muder Almiani Lina Sawalha Dept. of Business Information Technology Princess Sumaya University for Technology Amman Jordan Dept. of Informatics & Computer Science Indiana University Kokomo Kokomo IN USA Computer Information Systems Dept. Al-Hussein Bin Talal University Ma'an Jordan Dept. of Electrical and Computer Engineering Western Michigan University Kalamazoo MI USA

ISBN: (数字)9781728150529

ISBN: (纸本)9781728150536

Understanding and reusing complex APIs is a difficult and time consuming process, especially in legacy code and open-source software libraries and frameworks. While some primary API features are documented properly, most others lack enough documentation and design knowledge that can help developers find, understand, and reuse the needed software components and other library features. As such, the source-code itself is often the only reliable means for programmers to rely on when performing their reuse practices. In order to leverage program understanding and software reuse, this paper describes an ontology-based system named JMentor that we have developed for this purpose. Firstly, JMentor is capable of recovering the lost design decisions by reverse engineering design patterns. Secondly, JMentor provides a mechanism for locating and retrieving reusable software components. Thirdly, JMentor provides an approach for automatically recommending source-code snippets that show an example of how the user can properly reuse the retrieved components. JMentor mechanisms are based on ontology and semantic reasoning techniques. Its evaluation case studies show evidence that such techniques can improve software understanding and reuse practices.

关键词： Ontologies Software Semantics Object oriented modeling Software libraries Task analysis

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An implementation of data exchange in environmental monitoring using authenticated attribute-based encryption with revocation

An implementation of data exchange in environmental monitori...

引用

2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2018

作者： Munsyi Sudarsono, Amang Harun Al Rasvid, M. Udin Dept. of Informatics and Computer Engineering Magister Program of Engineering Technology Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Universitas Muhammadiyah Banjarmasin Banjarmasin Indonesia

ISBN: (纸本)9781538680797

Internet of things era grown very rapidly in Industrial Revolution 4.0, there are many researchers use the Wireless Sensor Network (WSN) technology to obtain the data for environmental monitoring. The data obtained from WSN will be sent to the Data Center, where users can view and collect all of data from the Data Center using end devices such as personal computer, laptop, and mobile phone. The Data Center would be very dangerous, because everyone can intercept, track and even modify the data. Security requirement to ensure the confidentiality all of stored data in the data center and give the authenticity in data has not changed during the collection process. Ciphertext Policy Attribute-Based Encryption (CP-ABE) can become a solution to secure the confidentiality for all of data. Only users with appropriate rule of policy can get the original data. To guarantee there is no changes during the collection process of the data then require the time stamp digital signature for securing the data integrity. To protect the confidentiality and data integrity, we propose a security mechanism using CP-ABE with user revocation and Time Stamp Digital Signature using Elliptic Curve Cryptography (ECC) 384 bits. Our system can do the revocation for the users who did the illegal access. Our system is not only securing the data but also providing the guarantee that is no changes during the collection process of the data from the Data Center. © 2018 IEEE.

关键词： Wireless sensor networks

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