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arXiv 2018年

作者： Tonelli, R. Destefanis, G. Marchesi, M. Ortu, M. Dept. of Matehmatics and Informatics University of Cagliari Italy School of Computer Science University of Hertfordshire United Kingdom Dept. of Electric and Electronic Engineering University of Cagliari Italy

Smart contracts (SC) are software codes which reside and run over a blockchain. The code can be written in different languages with the common purpose of implementing various kinds of transactions onto the hosting blockchain, They are ruled by the blockchain infrastructure and work in order to satisfy conditions typical of traditional contracts. The software code must satisfy constrains strongly context dependent which are quite different from traditional software code. In particular, since the bytecode is uploaded in the hosting blockchain, size, computational resources, interaction between different parts of sofware are all limited and even if the specific software languages implement more or less the same constructs of traditional languages there is not the same freedom as in normal software development. SC software is expected to reflect these constrains on SC software metrics which should display metric values characteristic of the domain and different from more traditional software metrics. We tested this hypotesis on the code of more than twelve thousands SC written in Solidity and uploaded on the Ethereum blockchain. We downloaded the SC from a public repository and computed the statistics of a set of software metrics related to SC and compared them to the metrics extracted from more traditional software projects. Our results show that generally Smart Contracts metrics have ranges more restricted than the corresponding metrics in traditional software systems. Some of the stylized facts, like power law in the tail of the distribution of some metrics, are only approximate but the lines of code follow a log normal distribution which reminds of the same behavior already found in traditional software systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Smart contract

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Compressing and filtering medical data in a low cost health monitoring system 2017

Compressing and filtering medical data in a low cost health ...

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21st Pan-Hellenic Conference on informatics, PCI 2017

作者： Petrellis, N. Kosmadakis, I.-E. Vardakas, M. Gioulekas, F. Birbas, M. Lalos, A. Computer Engineering Dept. ATEI of Thessaly Larissa Greece Electrical and Computer Engineering Dept University of Patras Greece Sub-Direc. of Informatics University General Hospital of Larissa Larissa Greece

ISBN: (纸本)9781450353557

Current work evaluates the precision of low-cost medical sensors, which are incorporated in an e-health platform presented recently by the authors. The sensors' accuracy is an important issue that is investigated in this paper in order to highlight the medical cases where the low-cost developed e-health platform can be used in a fairly reliable way. Specifically, the sensor values obtained from the e-health platform were filtered using the methods of moving average window (MAW), Principal component analysis (PCA) and simplified Kalman filter. It is shown that although moving average window achieves a significant error reduction, the produced output introduces a latency penalty in the original sensor signal. Kalman filter exhibits worse performance from both the MAW and the PCA methods. Finally, it is demonstrated that the PCA method sustains advanced compression of about 30% while in the same time reduces the error of the primary signal measurement, thus improving the sensor accuracy.

关键词： Data compression

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A Data Mining Approach for Biomarker Discovery Using Transcriptomics in Endometriosis

A Data Mining Approach for Biomarker Discovery Using Transcr...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Sadia Akter Dong Xu Susan C. Nagel Trupti Joshi Informatics Institute University of Missouri Dept. of Electrical Engineering and Computer Science Informatics Institute Christopher S. Bond Life Sciences Center University of Missouri Dept. of Obstetrics Gynecology and Women's Health School of Medicine Dept. of Health Management and Informatics Informatics Institute Christopher S. Bond Life Sciences Center University of Missouri

ISBN: (纸本)9781538654897

Endometriosis is a complex and common gynecological disorder affecting 5-10% of reproductive age women. Due to the lack of definitive diagnostic symptoms and expensive invasive procedures for diagnosing endometriosis, the average time for the diagnosis can be up to 10 years. This diagnostic latency has a very significant impact on endometriosis patients, and early diagnosis is desired in order to increase quality of life. In this study, we analyzed 38 RNA-seq transcriptomics samples (16 endometriosis and 22 controls) and identified genomic signatures as potential biomarkers. We applied innovative data mining approaches including a combination of a normalization techniques, generalized linear model (GLM) for identifying the differentially expressed genes and a decision tree algorithm for constructing models with higher predictive performance. A total of 5 candidate genes were identified as potential biomarkers of endometriosis, which outperformed the results from the Biosigner tool using a leave-one-out cross-validation technique. Our data mining approach can successfully distinguish the endometriosis patients from the non-endometriosis and can be potentially used as a prediction-based diagnostic tool for other diseases in future.

关键词： Endometriosis Transcriptomic Biomarker discovery Data mining Predictive diagnostic modeling Decision tree Endometriosis data mining Decision Trees transcriptomics Biological Markers

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A de-centralized framework for data sharing, ontology matching and distributed analytics 1

A de-centralized framework for data sharing, ontology matchi...

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1st International Workshop on Semantic Web Technologies for Health Data Management, SWH 2018

作者： Pezoulas, Vasileios C. Kourou, Konstantina D. Exarchos, Themis P. An-dronikou, Vassiliki Varvarigou, Theodora Tzioufas, Athanasios G. De Vita, Salvatore Fotiadis, Dimitrios I. Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering University of Ioannina IoanninaGR 45110 Greece Dept. of Informatics Ionian University CorfuGR 49100 Greece Division of Communication Electronic and Information Engineering School of Electrical and Computer Engineering National Technical University of Athens AthensGR 15780 Greece Dept. of Pathophysiology Faculty of Medicine National and Kapodistrian University of Athens AthensGR 15772 Greece Clinic of Rheumatology Dept. of Medical and Biological Sciences Udine University UdineIT 33100 Italy

In this paper we present the fundamental concept, as well as, the objectives and the architecture of HarmonicSS EU project. The proposed architecture envisages to address (i) the lack of patient stratification in large patient cohorts, (ii) the dataset heterogeneity across these cohorts, and (iii) the incomplete understanding of disease pathogenesis. In order to do so, ontology matching mechanisms are recruited for harmonizing the clinical data, distributed data analytics services have been designed for the de-centralized analysis of patient data, and different health policies can be assessed, all of them with respect to the data protection guidelines posed by the upcoming General Data Protection Regulation (GDPR). The HarmonicSS architecture has been employed towards the development of a data sharing, harmonization and distributed analytics framework for the recruitment and analysis of heterogeneous regional, national and international longitudinal cohorts of patients with primary Sjögren’s syndrome (pSS). The outcomes can be used by researchers, clinicians, patients, and health policy makers. © 2018 CEUR-WS. All rights reserved.

关键词： Large dataset

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Game-based system for learning labanotation using Microsoft Kinect 6

Game-based system for learning labanotation using Microsoft ...

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6th IEEE Global Conference on Consumer Electronics, GCCE 2017

作者： Ballas, Anastasios Santad, Tossaporn Sookhanaphibarn, Kingkarn Choensawat, Worawat Dept. of Computer Engineering and Informatics University of Patras Greece School of Information Technology and Innovation Bangkok University Thailand

ISBN: (纸本)9781509040452

This paper presents a serious game-based system for dance notation training named LabanDance. The system tracks the full body motion via Microsoft Kinect of the user while performing the movement according to the dancing score. The system provides two training modes which a practice mode and an examination mode. The practice mode is designed for users with less knowledge in the notation where movement of a virtual instructor is displayed according to the score for a user to follow. The second mode is the examination mode where users must perform the body movement according to the displayed Labanotation symbols within the provided time frame. The experiment showed that users without knowledge of Labanotation can enjoy the game while learning the fundamental of the Labanotation. © 2017 IEEE.

关键词： Serious games

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Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

arXiv

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arXiv 2021年

作者： Hering, Alessa Hansen, Lasse Mok, Tony C.W. Chung, Albert C.S. Siebert, Hanna Häger, Stephanie Lange, Annkristin Kuckertz, Sven Heldmann, Stefan Shao, Wei Vesal, Sulaiman Rusu, Mirabela Sonn, Geoffrey Estienne, Théo Vakalopoulou, Maria Han, Luyi Huang, Yunzhi Yap, Pew-Thian Brudfors, Mikael Balbastre, Yaël Joutard, Samuel Modat, Marc Lifshitz, Gal Raviv, Dan Lv, Jinxin Li, Qiang Jaouen, Vincent Visvikis, Dimitris Fourcade, Constance Rubeaux, Mathieu Pan, Wentao Xu, Zhe Jian, Bailiang De Benetti, Francesca Wodzinski, Marek Gunnarsson, Niklas Sjölund, Jens Grzech, Daniel Qiu, Huaqi Li, Zeju Thorley, Alexander Duan, Jinming Großbröhmer, Christoph Hoopes, Andrew Reinertsen, Ingerid Xiao, Yiming Landman, Bennett Huo, Yuankai Murphy, Keelin Lessmann, Nikolas van Ginneken, Bram Dalca, Adrian V. Heinrich, Mattias P. Institute of Medical Informatics Universität zu Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany Department of Radiology and Nuclear Medicine Radboud University Medical Center GA Nijmegen6525 Netherlands The Institute of Medical Informatics Universität zu Lübeck Lübeck23562 Germany The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany The Department of Radiology Stanford University StanfordCA94305 United States The Department of Urology Stanford University StanfordCA94305 United States The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette 91190 France Université Paris-Saclay Institut Gustave Roussy Inserm Radiothérapie Moléculaire et Innovation Thérapeutique Villejuif94800 France The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette91190 France The Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen6525 GA Netherlands The School of Automation Nanjing University of Information Science and Technology Nanjing210044 China The School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital United States Harvard Medical School Boston United States The King’s College London United Kingdom The Tel Aviv University The Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Hubei Wuhan430074 China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Sci

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://***. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods. © 2021, CC BY.

关键词： Image registration

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Rich dynamics and anticontrol of extinction in a prey-predator system

arXiv

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arXiv 2019年

作者： Danca, Marius-F Fečkan, Michal Kuznetsov, Nikolay Cheng, Guanrong Dept. of Mathematics and Computer Science Avram Iancu University of Cluj- Napoca Romania Romanian Institute of Science and Technology Cluj-Napoca Romania Dept. of Mathematical Analysis and Numerical Mathematics Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Mathematical Institute Slovak Academy of Sciences Dept. of Applied Cybernetics Saint-Petersburg State University Russia Dept. of Mathematical Information Technology University of Jyväskylä Finland Dept. of Electronic Engineering City University of Hong Kong Hong Kong

This paper reveals some new and rich dynamics of a two-dimensional prey-predator system and to anticontrol the extinction of one of the species. For a particular value of the bifurcation parameter, one of the system variable dynamics is going to extinct, while another remains chaotic. To prevent the extinction, a simple anticontrol algorithm is applied so that the system orbits can escape from the vanishing trap. As the bifurcation parameter increases, the system presents quasiperiodic, stable, chaotic and also hyperchaotic orbits. Some of the chaotic attractors are Kaplan-Yorke type, in the sense that the sum of its Lyapunov exponents is positive. Also, atypically for undriven discrete systems, it is numerically found that, for some small parameter ranges, the system seemingly presents strange nonchaotic attractors. It is shown both analytically and by numerical simulations that the original system and the anticontrolled system undergo several Neimark-Sacker bifurcations. Beside the classical numerical tools for analyzing chaotic systems, such as phase portraits, time series and power spectral density, the’0-1’ test is used to differentiate regular attractors from chaotic attractors. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectral density

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Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 — Ex vivo imaging: data processing, comparisons with microscopy, and tractography

arXiv

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arXiv 2024年

作者： Schilling, Kurt G. Howard, Amy F.D. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio van de Looij, Yohan Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Dell' Acqua, Flavio Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Jelescu, Ileana O. Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Bioengineering Imperial College London London United Kingdom FMRIB Centre Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Res

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging. © 2024, CC BY.

关键词： Data Sharing

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BUDAMAF data management in cloud federations

arXiv

引用

arXiv 2018年

作者： Psomakelis, Evangelos Tserpes, Konstantinos Anagnostopoulos, Dimosthenis Varvarigou, Theodora Dept. of Informatics and Telematics Harokopio University of Athens Omirou 9 Tavros Greece Dept. of Electrical and Computer Engineering National Technical University of Athens Iroon Polytecniou 9 Zografou Greece

Data management has always been a multi-domain problem even in the simplest cases. It involves, quality of service, security, resource management, cost management, incident identification, disaster avoidance and/or recovery, as well as many other concerns. In our case, this situation gets ever more complicated because of the divergent nature of a cloud federation like BASMATI. In this federation, the BASMATI Unified Data Management Framework (BUDaMaF), tries to create an automated uniform way of managing all the data transactions, as well as the data stores themselves, in a polyglot multi-cloud, consisting of a plethora of different machines and data store systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Information management

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A compact tree representation of an antidictionary

A compact tree representation of an antidictionary

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作者： Ota, Takahiro Morita, Hiroyoshi Dept. of Computer and Systems Engineering Nagano Prefectural Institute of Technology Ueda-shi386-1211 Japan Dept. of Computer and Network Engineering Graduate School of Informatics and Engineering University of Electro-Communications Chofu-shi182-8585 Japan

In both theoretical analysis and practical use for an anti-dictionary coding algorithm, an important problem is how to encode an antidictionary of an input source. This paper presents a proposal for a com-pact tree representation of an antidictionary built from a circular string for an input source. We use a technique for encoding a tree in the compression via substring enumeration to encode a tree representation of the antidic-tionary. Moreover, we propose a new two-pass universal antidictionary coding algorithm by means of the proposal tree representation. We prove that the proposed algorithm is asymptotic optimal for a stationary ergodic source. Copyright © 2017 The Institute of Electronics, Information and Communication Engineers.

关键词： Forestry

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