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Microscopy and Microanalysis 2019年第S2期25卷 600-601页

作者： Julia I. Deitz John M. Walls Jake W. Bowers Tyler J. Grassman David W. McComb Center for Electron Microscopy and Analysis Department of Materials Science and Engineering The Ohio State University Columbus OH United States. Centre for Renewable Energy Systems Technology Dept. of Mechanical Electrical and Manufacturing Engineering Loughborough University Loughborough Leicestershire UK. Dept. of Electrical & Computer Engineering The Ohio State University Columbus OH USA. Dept. of Materials Science & Engineering The Ohio State University Columbus OH USA.

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

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

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

Sharing of vulnerability information among companies - A survey of Swedish companies

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arXiv 2019年

作者： Olsson, Thomas Hell, Martin Host, Martin Franke, Ulrik Borg, Markus Software and Systems Engineering Lab RISE Research Institutes of Sweden Lund Dept. of Electrical and Information Technology Lund University Lund Dept. of Computer Science Lund University Lund Software and Systems Engineering Lab RISE Research Institutes of Sweden Kista

Software products are rarely developed from scratch and vulnerabilities in such products might reside in parts that are either open source software or provided by another organization. Hence, the total cybersecurity of a product often depends on cooperation, explicit or implicit, between several organizations. We study the attitudes and practices of companies in software ecosystems towards sharing vulnerability information. Furthermore, we compare these practices to contemporary cybersecurity recommendations. This is performed through a questionnairebased qualitative survey. The questionnaire is divided into two parts: the providers' perspective and the acquirers' perspective. The results show that companies are willing to share information with each other regarding vulnerabilities. Sharing is not considered to be harmful neither to the cybersecurity nor their business, even though a majority of the respondents consider vulnerability information sensitive. However, the companies, despite being open to sharing, are less inclined to proactively sharing vulnerability information. Furthermore, the providers do not perceive that there is a large interest in vulnerability information from their customers. Hence, the companies' overall attitude to sharing vulnerability information is passive but open. In contrast, contemporary cybersecurity guidelines recommend active disclosure and sharing among actors in an ecosystem. Copyright © 2019, The Authors. All rights reserved.

关键词： Surveys

A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations

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arXiv 2024年

作者： Garrucho, Lidia Kushibar, Kaisar Reidel, Claire-Anne Joshi, Smriti Osuala, Richard Tsirikoglou, Apostolia Bobowicz, Maciej del Riego, Javier Catanese, Alessandro Gwoździewicz, Katarzyna Cosaka, Maria-Laura Abo-Elhoda, Pasant M. Tantawy, Sara W. Sakrana, Shorouq S. Shawky-Abdelfatah, Norhan O. Abdo-Salem, Amr Muhammad Kozana, Androniki Divjak, Eugen Ivanac, Gordana Nikiforaki, Katerina Klontzas, Michail E. García-Dosdá, Rosa Gulsun-Akpinar, Meltem Lafcı, Oğuz Mann, Ritse Martín-Isla, Carlos Prior, Fred Marias, Kostas Starmans, Martijn P.A. Strand, Fredrik Díaz, Oliver Igual, Laura Lekadir, Karim Barcelona Spain Department of Oncology-Pathology Karolinska Institutet Stockholm Sweden Institute of Machine Learning in Biomedical Imaging Helmholtz Center Munich Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany 2nd Dept. of Radiology Medical University of Gdansk Gdansk Poland Parc Taulí Hospital Universitari Sabadell Spain Hospital Germans Trias i Pujol Badalona Spain Centro Mamario Instituto Alexander Fleming Buenos Aires Argentina Department of Diagnostic & Interventional Radiology and Molecular Imaging Faculty of Medicine Ain Shams University Cairo Egypt Department of Radiology University Hospital of Heraklion Stavrakia Greece Department of Diagnostic and Interventional Radiology University Hospital Dubrava Zagreb Croatia University of Zagreb School of Medicine Zagreb Croatia Computational BioMedicine Laboratory Institute of Computer Science Foundation for Research and Technology—Hellas Heraklion Greece Department of Radiology School of Medicine University of Crete Heraklion Greece Medical Imaging and Radiology Universitary and Politechnic Hospital La Fe Valencia Spain Department of Radiology Hacettepe University Faculty of Medicine Sihhiye Ankara Turkey Department of Biomedical Imaging and Image-guided Therapy Medical University of Vienna Vienna Austria Department of Radiology and Nuclear Medicine Radboud University Medical Center Netherlands University of Arkansas for Medical Sciences Little RockAR United States Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece Heraklion Greece Department of Radiology and Nuclear Medicine Erasmus MC Cancer Institute University Medical Center Rotterdam Rotterdam Netherlands Department of Pathology Erasmus MC Cancer Institute University Medical Center Rotterdam Rotterdam Netherlands Breast Radiology Karolinska University Hospital Stockholm Sweden Passeig Lluís Companys 23 Barcelona Sp

Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a multicenter dataset of 1506 pretreatment T1-weighted dynamic contrast-enhanced MRI cases, including expert annotations of primary tumors and non-mass-enhanced regions. The dataset integrates imaging data from four collections in The Cancer Imaging Archive (TCIA), where only 163 cases with expert segmentations were initially available. To facilitate the annotation process, a deep learning model was trained to produce preliminary segmentations for the remaining cases. These were subsequently corrected and verified by 16 breast cancer experts (averaging 9 years of experience), creating a fully annotated dataset. Additionally, the dataset includes 49 harmonized clinical and demographic variables, as well as pre-trained weights for a baseline nnU-Net model trained on the annotated data. This resource addresses a critical gap in publicly available breast cancer datasets, enabling the development, validation, and benchmarking of advanced deep learning models, thus driving progress in breast cancer diagnostics, treatment response prediction, and personalized care. Copyright © 2024, The Authors. All rights reserved.

关键词： Dynamic contrast enhanced MRI

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

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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)

Efficient experimental quantum fingerprinting with wavelength division multiplexing

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arXiv 2020年

作者： Zhong, Xiaoqing Xu, Feihu Lo, Hoi-Kwong Qian, Li Center for Quantum Information and Quantum Control Dept. of Physics University of Toronto TorontoONM5S 1A7 Canada Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics University of Science and Technology of China Hefei230026 China Center for Quantum Information and Quantum Control Dept. of Electrical & Computer Engineering University of Toronto TorontoONM5S 3G4 Canada

Quantum communication complexity studies the efficiency of information communication (that is, the minimum amount of communication required to achieve a certain task) using quantum states. One representative example is quantum fingerprinting, in which the simultaneous message passing model is considered and the minimum amount of communication could be exponentially smaller than the classical fingerprinting protocol. Experimental demonstrations based on a practical quantum fingerprinting protocol where coherent states are used to construct the fingerprints, have successfully shown the superiority of quantum fingerprinting. However, as a consequence of using coherent states, the communication time in this coherent quantum fingerprinting (CQF) protocol is increased. Moreover, the minimum amount of information communicated in these experimental demonstrations is largely limited by the dark counts of the single photon detectors. Here, we propose to enhance the performance of the existing CQF protocol through applying wavelength division multiplexing (WDM) and simultaneous detection of multiple wavelength channels. We show that the new WDM-CQF protocol can potentially reduce the communication time significantly. More importantly, with the same experimental parameters, the amount of communication is much reduced in the new scheme compared with the original CQF protocol. The more wavelength channels are used, the less communication is required by WDM-CQF protocol. We also perform a proof-of-concept experimental demonstration of the new WDM-CQF protocol with 6 wavelength channels. The experimental results further validate that the new scheme with WDM not only beats the classical protocol, but also reduces the amount of communication required by the original CQF protocol by more than half. Copyright © 2020, The Authors. All rights reserved.

关键词： Demonstrations

P10.12.A CDK12/CDK13 inhibition disrupts a transcriptional program critical for glioblastoma survival

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Neuro-Oncology 2022年第SUPPLEMENT_2期24卷 ii51–ii51页

作者： Lier, S Rein, I Lund, S Lång, A Lång, E Meyer, N Dutta, A Anand, S Nesse, G Johansen, R Klungland, A Rinholm, J Bøe, S Anand, A Pollard, S Lerdrup, M Pandey, D Institute of Basic Medical Sciences University of Oslo Oslo Norway Dept. of Microbiology Oslo University Hospital Oslo Norway Dept. of Radiation Biology Oslo University Hospital Oslo Norway Dept. of Computer Science and Engineering Indian Institute of Technology Guwahati Assam India Department of Informatics Systems and Communications University of Milano-Bicocca Milan Italy Dept. of Biosciences University of Oslo Oslo Norway MRC Centre for Regenerative Medicine SCRM Building University of Edinburgh Edinburgh United Kingdom Center for Chromosome Stability (CCS) Department of Cellular and Molecular Medicine University of Copenhagen Copenhagen Denmark

Single perceptron operating at 12 GigaOPs based on a Kerr soliton crystal microcomb for versatile, high-speed, scalable, optical neural networks

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TechRxiv

TechRxiv 2021年

作者： Moss, David J. Tan, M. Xu, X. Wu, J. Boes, A. Corcoran, B. Nguyen, T.G. Chu, S.T. Little, B.E. Morandotti, R. Mitchell, A. Hicks, D.G. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia Dept. of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia School of Engineering RMIT University MelbourneVIC3001 Australia Department of Physics and Material Science City University of Hong Kong Tat Chee Avenue Hong Kong Xi’an Institute of Optics and Precision Mechanics of CAS Xi’an China INRS-Énergie Matériaux et Télécommunications 1650 Boulevard Lionel-Boulet Varennes QCJ3X 1S2 Canada University of Electronic Science and Technology of China Chengdu610054 China Bioinformatics Division Walter & Eliza Hall Institute of Medical Research ParkvilleVIC3052 Australia

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 109 operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing. © 2021, CC BY.

关键词： Data handling

The CARAMEL Project: a Secure Architecture for Connected and Autonomous Vehicles

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The CARAMEL Project: a Secure Architecture for Connected and...

European Conference on Networks and Communications (EuCNC)

作者： Christian Vitale Nikos Piperigkos Christos Laoudias Georgios Ellinas Jordi Casademont Pouria Sayyad Khodashenas Andreas Kloukiniotis Aris S. Lalos Konstantinos Moustakas Pablo Barrientos Lobato Javier Moreno Castillo Petros Kapsalas Klaus-Peter Hofmann KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Industrial Systems Institute Athena Research and Innovation Center Patras Greece Dept. of Electrical and Computer Engineering University of Patras Patras Greece i2CAT Foundation Barcelona Spain Universitat Politècnica de Catalunya Barcelona Spain Atos IT Solutions and Services Iberia S.L. Madrid Spain FICOSA Barcelona Spain Panasonic Automotive Langen Germany T-Systems International GmbH Frankfurt Germany

ISBN: (数字)9781728143552

ISBN: (纸本)9781728143569

The main goals of the CARAMEL project are to enhance the protection of modern vehicles against cybersecurity threats related to automated driving, smart charging of Electric Vehicles, and communication among vehicles or between vehicles and the roadside infrastructure. This work focuses on the latter and presents the CARAMEL architecture for improving the security and privacy of communication for connected and autonomous driving. The proposed architecture includes: (i) multi-radio access technology capabilities, with simultaneous 802.11p and LTE-Uu support; (ii) a MEC platform, where algorithms for detecting attacks are implemented; (iii) an intelligent On-Board Unit with anti-hacking features inside the vehicle; (iv) a Public Key Infrastructure that validates in real-time the integrity of vehicle's data transmissions. As an indicative application scenario, the interaction between the entities of the CARAMEL architecture is showcased in the case that the GPS locations used by vehicles are spoofed.

关键词： computer architecture Europe computer security Privacy Safety Servers

An early warning tool for predicting mortality risk of COVID-19 patients using machine learning

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arXiv 2020年

作者： Chowdhury, Muhammad E.H. Rahman, Tawsifur Khandakar, Amith Al-Madeed, Somaya Zughaier, Susu M. Doi, Suhail A.R. Hassen, Hanadi Islam, Mohammad T. Department of Electrical Engineering Qatar University Doha2713 Qatar Department of Biomedical Physics & Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering Qatar University Doha2713 Qatar Department of Basic Medical Sciences College of Medicine QU Health Qatar University Doha2713 Qatar Department of Population Medicine College of Medicine QU Health Qatar University Doha2713 Qatar Dept. of Electrical Electronics and Systems Engineering Universiti Kebangsaan Malaysia Bangi Selangor43600 Malaysia

Background COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. Methods In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics, and patient outcomes were investigated using machine learning tools to identify key biomarkers to predict the mortality of individual patient. A nomogram was developed for predicting the mortality risk among COVID-19 patients. Results Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high sensitive C-reactive protein, and age - acquired at hospital admission were identified as key predictors of death by multi-tree XGBoost model. The area under curve (AUC) of the nomogram for the derivation and validation cohort were 0.961 and 0.991, respectively. An integrated score (LNLCA) was calculated with the corresponding death probability. COVID-19 patients were divided into three subgroups: low-, moderate- and high-risk groups using LNLCA cut-off values of 10.4 and 12.65 with the death probability less than 5%, 5% to 50%, and above 50%, respectively. Conclusions The prognostic model, nomogram and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting