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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Marten Franke; Vaishnavi Gopinath; Danijela Ristić-Durrant; Kai Michels;

    This paper presents a novel method for generation of synthetic images of obstacles on and near rail tracks over long-range distances. The main goal is to augment the dataset for autonomous obstacle detection (OD) in railways, by inclusion of synthetic images that reflect the specific need for long-range OD in rail transport. The presented method includes a novel deep learning (DL)-based rail track detection that enables context- and scale-aware obstacle-level data augmentation. The augmented dataset is used for retraining of a state-of-the-art CNN for object detection. The evaluation results demonstrate significant improvement of detection of distant objects by augmentation of training dataset with synthetic images.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Applied Sciences; Op...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Applied Sciences
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Applied Sciences; Op...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: BIMprove Consortium;

    Our fourth newsletter including information about the main activities and achievements of the project during the last months.

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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Papaioannou, Thanasis G.; Kochovski, Petar; Shkembi, Klevis; Barelle, Caroline; +4 Authors

    Multiple threats have been identified when citizens interact with online services such as unknown provenance of information, unknown quality of service providers, spread of fake news, fraud, personal privacy violation, centralisation of power to name a few. Blockchain has been considered as key technology to address many of these challenges; however, in reality, building trustworthy decentralized applications (Dapps) is not straightforward as much blockchain-based functionality needs to be developed from scratch and combined with data semantics. In this paper, we propose a new software framework, namely ONTOCHAIN, that leverages semantic web and blockchain technology to build, as distinct value for the Next Generation Internet, fundamental support for trustworthy data/services exchange and trustworthy content handling. It comprises a novel protocol suite grouped into high-level application protocols, such as data provenance, reputation models, decentralised oracles, market mechanisms, ontology representation and management, privacy aware and secure data exchange, multi-source identity verification, value sharing and incentives, and lower-level core protocols that include authorisation, certification, privacy-aware data processing, cross-chain gateways, identity management, secure data exchange, and data semantics in smart contracts. We demonstrate that these protocols are already available and combined to implement interesting NGI Dapps.

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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Aleksandar Dragan Petrović; Milan Banić; Miloš Simonović; Dušan Stamenković; +3 Authors

    One of the most challenging technical implementations of today is self-driving vehicles. An important segment of self-driving is the ability of the computer to “see/detect” objects of interest at a distance which enables safe vehicle operation. An algorithm for the detection of railway infrastructure objects, namely, track and signals, is proposed in this paper to enable detection of signals which are relevant for the track the train is moving along. The algorithm integrates traditional computer vision (CV) algorithms, including Canny edge detection, Hough transform, and You Only Look Once (YOLO) algorithm, based on convolutional neural networks (CNNs). Each of the concepts (CV and CNNs) deals with a different object of detection which together form a unique system that aims to detect both the rails and the relevant signals. This approach ensures that the artificial intelligence (AI) system is “aware” of which route the signal belongs to. The reliability of the proposed algorithm in detection of a relevant signal, verified by the performed tests, is up to 99.7%. The metric method used for validation was intersection over union (IoU). The obtained value of IoU applied on the entire validation dataset exceeds 0.7. Calculated values of average precision and recall were 0.89 and 0.76, respectively. The algorithm created in this way solves the problem of detection of relevant signals along the train route, especially in multitrack scenarios such as stations and yards.

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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Applied Sciences
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      Applied Sciences
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Bernardini, Giulia; Holub, Jan; Conte, Alessio; Gabory, Esteban; +6 Authors

    Let Substr_k(X) denote the set of length-k substrings of a given string X for a given integer k > 0. We study the following basic string problem, called z-Shortest 𝒮_k-Equivalent Strings: Given a set 𝒮_k of n length-k strings and an integer z > 0, list z shortest distinct strings T₁,…,T_z such that Substr_k(T_i) = 𝒮_k, for all i ∈ [1,z]. The z-Shortest 𝒮_k-Equivalent Strings problem arises naturally as an encoding problem in many real-world applications; e.g., in data privacy, in data compression, and in bioinformatics. The 1-Shortest 𝒮_k-Equivalent Strings, referred to as Shortest 𝒮_k-Equivalent String, asks for a shortest string X such that Substr_k(X) = 𝒮_k. Our main contributions are summarized below: - Given a directed graph G(V,E), the Directed Chinese Postman (DCP) problem asks for a shortest closed walk that visits every edge of G at least once. DCP can be solved in 𝒪̃(|E||V|) time using an algorithm for min-cost flow. We show, via a non-trivial reduction, that if Shortest 𝒮_k-Equivalent String over a binary alphabet has a near-linear-time solution then so does DCP. - We show that the length of a shortest string output by Shortest 𝒮_k-Equivalent String is in 𝒪(k+n²). We generalize this bound by showing that the total length of z shortest strings is in 𝒪(zk+zn²+z²n). We derive these upper bounds by showing (asymptotically tight) bounds on the total length of z shortest Eulerian walks in general directed graphs. - We present an algorithm for solving z-Shortest 𝒮_k-Equivalent Strings in 𝒪(nk+n²log²n+zn²log n+|output|) time. If z = 1, the time becomes 𝒪(nk+n²log²n) by the fact that the size of the input is Θ(nk) and the size of the output is 𝒪(k+n²). LIPIcs, Vol. 223, 33rd Annual Symposium on Combinatorial Pattern Matching (CPM 2022), pages 16:1-16:17

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ OpenAIREarrow_drop_down
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    OpenAIRE
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    Authors: BIMprove Consortium;

    Our third newsletter including information about our main achievements during the second 6 months project period.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lalit M. Kandpal; Muhammad A. Munnaf; Cristina Cruz; Abdul M. Mouazen;

    Previous works indicate that data fusion, compared to single data modelling can improve the assessment of soil attributes using spectroscopy. In this work, two different kinds of proximal soil sensing techniques i.e., mid-infrared (MIR) and X-ray fluorescence (XRF) spectroscopy were evaluated, for assessment of seven fertility attributes. These soil attributes include pH, organic carbon (OC), phosphorous (P), potassium (K), magnesium (Mg), calcium (Ca) and moisture contents (MC). Three kinds of spectra fusion (SF) (spectra concatenation) approaches of MIR and XRF spectra were compared, namely, spectra fusion-Partial least square (SF-PLS), spectra fusion-Sequential Orthogonalized Partial least square (SF-SOPLS) and spectra fusion-Variable Importance Projection-Sequential Orthogonalized Partial least square (SF-VIP-SOPLS). Furthermore, the performance of SF models was compared with the developed single sensor model (based on individual spectra of MIR and XRF). Compared with the results obtained from single sensor model, SF models showed improvement in the prediction performance for all studied attributes, except for OC, Mg, and K prediction. More specifically, the highest improvement was observed with SF-SOPLS model for pH [R2p = 0.90, root mean square error prediction (RMSEP) = 0.15, residual prediction deviation (RPD) = 3.30, and ratio of performance inter-quantile (RPIQ) = 3.59], successively followed by P (R2p = 0.91, RMSEP = 4.45 mg/100 g, RPD = 3.53, and RPIQ = 4.90), Ca (R2p = 0.92, RMSEP = 177.11 mg/100 g, RPD = 3.66, and RPIQ = 3.22) and MC (R2p = 0.80, RMSEP = 1.91%, RPD = 2.31, RPIQ = 2.62). Overall the study concluded that SF approach with SOPLS attained better performance over the traditional model developed with the single sensor spectra, hence, SF is recommended as the best SF method for improving the prediction accuracy of studied soil attributes. Moreover, the multi-sensor spectra fusion approach is not limited for only MIR and XRF data but in general can be extended for complementary information fusion in order to improve the model performance in precision agriculture (PA) applications.

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    Authors: Staniša Perić; Marko Milojković; Sergiu-Dan Stan; Milan Banić; +1 Authors

    Object recognition and classification as well as obstacle distance calculation are of the utmost importance in today’s autonomous driving systems. One such system designed to detect obstacle and track intrusion in railways is considered in this paper. The heart of this system is the decision support system (DSS), which is in charge of making complex decisions, important for a safe and efficient autonomous train drive based on the information obtained from various sensors. DSS determines the object class and its distance from a running train by analyzing sensor images using machine learning algorithms. For the quality training of these machine learning models, it is necessary to provide training sets with images of adequate quality, which is often not the case in real-world railway applications. Furthermore, the images of insufficient quality should not be processed at all in order to save computational time. One of the most common types of distortion which occurs in real-world conditions (train movement and vibrations, movement of other objects, bad weather conditions, and day and night image differences) is blur. This paper presents an improved edge-detection method for the automatic detection and rejection of images of inadequate quality regarding the blur level. The proposed method, with its improvements convenient for railway application, is compared with several other state-of-the-art methods for blur detection, and its superior overall performance is demonstrated.

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    Authors: Jocaille, Laurence;

    This is the first issue of the GERONTE H2020 project newsletter. The newsletter aims to communicate to all interested project research and innovation news and activities as they happen. The GERONTE Newsletter is published twice a year and archived on ZENODO for long-term open accessibility. To receive GERONTE newsletter by email on first publication, please subscribe on GERONTE website at https://geronteproject.eu

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    Authors: Neumann, E.; Schreeck, F.; Herberg, J.; Jacqz Aigrain, E.; +8 Authors

    The safety and efficacy of pharmacotherapy in children, particularly preterms, neonates and infants, is limited by a paucity of good-quality data from prospective clinical drug trials. A specific challenge is the establishment of valid biomarkers. OMICs technologies may support these efforts by complementary information about targeted and nontargeted molecules through systematic characterization and quantitation of biological samples. OMICs technologies comprise at least genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics in addition to the patient's phenotype. OMICs technologies are in part hypothesis-generating, allowing an in depth understanding of disease pathophysiology and pharmacological mechanisms. Application of OMICs technologies in paediatrics faces major challenges before routine adoption. First, developmental processes need to be considered, including a subdivision into specific age groups as developmental changes clearly impact OMICs data. Second, compared to the adult population, the number of patients is limited as are the type and amount of necessary biomaterial, especially in neonates and preterms. Thus, advanced trial designs and biostatistical methods, noninvasive biomarkers, innovative biobanking concepts including data and samples from healthy children, as well as analytical approaches (eg liquid biopsies) should be addressed to overcome these obstacles. The ultimate goal is to link OMICs technologies with innovative analysis tools, such as artificial intelligence at an early stage. The use of OMICs data based on a feasible approach will contribute to the identification complex phenotypes and subpopulations of patients to improve the development of medicines for children with potential economic advantages. Contains fulltext : 287161.pdf (Publisher’s version ) (Open Access)

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    British Journal of Clinical Pharmacology; OpenAIRE
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      British Journal of Clinical Pharmacology; OpenAIRE
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    Authors: Marten Franke; Vaishnavi Gopinath; Danijela Ristić-Durrant; Kai Michels;

    This paper presents a novel method for generation of synthetic images of obstacles on and near rail tracks over long-range distances. The main goal is to augment the dataset for autonomous obstacle detection (OD) in railways, by inclusion of synthetic images that reflect the specific need for long-range OD in rail transport. The presented method includes a novel deep learning (DL)-based rail track detection that enables context- and scale-aware obstacle-level data augmentation. The augmented dataset is used for retraining of a state-of-the-art CNN for object detection. The evaluation results demonstrate significant improvement of detection of distant objects by augmentation of training dataset with synthetic images.

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    Authors: BIMprove Consortium;

    Our fourth newsletter including information about the main activities and achievements of the project during the last months.

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    Authors: Papaioannou, Thanasis G.; Kochovski, Petar; Shkembi, Klevis; Barelle, Caroline; +4 Authors

    Multiple threats have been identified when citizens interact with online services such as unknown provenance of information, unknown quality of service providers, spread of fake news, fraud, personal privacy violation, centralisation of power to name a few. Blockchain has been considered as key technology to address many of these challenges; however, in reality, building trustworthy decentralized applications (Dapps) is not straightforward as much blockchain-based functionality needs to be developed from scratch and combined with data semantics. In this paper, we propose a new software framework, namely ONTOCHAIN, that leverages semantic web and blockchain technology to build, as distinct value for the Next Generation Internet, fundamental support for trustworthy data/services exchange and trustworthy content handling. It comprises a novel protocol suite grouped into high-level application protocols, such as data provenance, reputation models, decentralised oracles, market mechanisms, ontology representation and management, privacy aware and secure data exchange, multi-source identity verification, value sharing and incentives, and lower-level core protocols that include authorisation, certification, privacy-aware data processing, cross-chain gateways, identity management, secure data exchange, and data semantics in smart contracts. We demonstrate that these protocols are already available and combined to implement interesting NGI Dapps.

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    Authors: Aleksandar Dragan Petrović; Milan Banić; Miloš Simonović; Dušan Stamenković; +3 Authors

    One of the most challenging technical implementations of today is self-driving vehicles. An important segment of self-driving is the ability of the computer to “see/detect” objects of interest at a distance which enables safe vehicle operation. An algorithm for the detection of railway infrastructure objects, namely, track and signals, is proposed in this paper to enable detection of signals which are relevant for the track the train is moving along. The algorithm integrates traditional computer vision (CV) algorithms, including Canny edge detection, Hough transform, and You Only Look Once (YOLO) algorithm, based on convolutional neural networks (CNNs). Each of the concepts (CV and CNNs) deals with a different object of detection which together form a unique system that aims to detect both the rails and the relevant signals. This approach ensures that the artificial intelligence (AI) system is “aware” of which route the signal belongs to. The reliability of the proposed algorithm in detection of a relevant signal, verified by the performed tests, is up to 99.7%. The metric method used for validation was intersection over union (IoU). The obtained value of IoU applied on the entire validation dataset exceeds 0.7. Calculated values of average precision and recall were 0.89 and 0.76, respectively. The algorithm created in this way solves the problem of detection of relevant signals along the train route, especially in multitrack scenarios such as stations and yards.

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