Setting a Baseline for long-shot real-time Player and Ball detection in Soccer Videos

Players and ball detection are among the first required steps on a football analytics platform. Until recently, the existing open datasets on which the evaluations of most models were based, were not sufficient. In this work, we point out their weaknesses, and with the advent of the SoccerNet v3, we propose and deliver to the community an edited part of its dataset, in YOLO normalized annotation format for training and evaluation. The code of the methods and metrics are provided so that they can be used as a benchmark in future comparisons. The recent YOLO8n model proves better than FootAndBall in long-shot real-time detection of the ball and players on football fields.Comment: 6 pages, 4 figures, 1 table. 14th International Conference on Information,Intelligence, Systems and Applications (IISA 2023) , Thessaly, Volos, Greece, 10-12 July 202

KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database

<p>Abstract</p> <p>Background</p> <p>The KEGG Pathway database is a valuable collection of metabolic pathway maps. Nevertheless, the production of simulation capable metabolic networks from KEGG Pathway data is a challenging complicated work, regardless the already developed tools for this scope. Originally used for illustration purposes, KEGG Pathways through KGML (KEGG Markup Language) files, can provide complete reaction sets and introduce species versioning, which offers advantages for the scope of cellular metabolism simulation modelling. In this project, KEGGconverter is described, implemented also as a web-based application, which uses as source KGML files, in order to construct integrated pathway SBML models fully functional for simulation purposes.</p> <p>Results</p> <p>A case study of the integration of six human metabolic pathways from KEGG depicts the ability of KEGGconverter to automatically produce merged and converted to SBML fully functional pathway models, enhanced with default kinetics. The suitability of the developed tool is demonstrated through a comparison with other state-of-the art relevant software tools for the same data fusion and conversion tasks, thus illustrating the problems and the relevant workflows. Moreover, KEGGconverter permits the inclusion of additional reactions in the resulting model which represent flux cross-talk with neighbouring pathways, providing in this way improved simulative accuracy. These additional reactions are introduced by exploiting relevant semantic information for the elements of the KEGG Pathways database. The architecture and functionalities of the web-based application are presented.</p> <p>Conclusion</p> <p>KEGGconverter is capable of producing integrated analogues of metabolic pathways appropriate for simulation tasks, by inputting only KGML files. The web application acts as a user friendly shell which transparently enables the automated biochemically correct pathway merging, conversion to SBML format, proper renaming of the species, and insertion of default kinetic properties for the pertaining reactions. The tool is available at: <url>http://www.grissom.gr/keggconverter</url></p

KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database

Journal URL: http://www.biomedcentral.com/bmcbioinformatics

Εφυής εξόρυξη βιοϊατρικών δεδομένων για τη δημιουργία ολοκληρωμένων μοντέλων φυσιολογίας

In the context of contributing to the creation of Integrated Physiological Models, three novel approaches are proposed and developed, targeting at different levels of biomedical modelling. At the lower molecular level, the integration of metabolic pathways in a way capable of deriving descriptive and predictive dynamic models resulted in the creation of the KEGGconverter tool. KEGGconverter is capable of producing integrated analogues of metabolic pathways appropriate for simulation tasks, by inputting only KGML files and having an SBML model as output. The automated introduction of four case-specific default kinetic mechanisms in the models provides for the addition of the layer of kinetic equations, which is directed by a rule-based algorithm that was implemented for the specific task. At a higher level, the novel GOrevenge algorithm exploits the Gene Ontology to map genes to specific cellular pathways and vice versa in order to further infer the putative functional role of specific genes, through an associative context, starting from the results of various statistical enrichment analyses. Although this implementation utilizes the GO annotations, the algorithm can be generically extended to accommodate any biological ontology or controlled vocabulary definition. This is due to the exploitation of the encapsulated underlying topological network information and the interplay among the annotations and the annotated subjects. Lastly, a novel methodology on multi-modal data fusion regarding separate datasets has been developed utilizing a dataset of features from skin lesion images and a dataset regarding microarray data. Both datasets were the output from studies on cutaneous melanoma, but involved different patients. The multivariate analysis applied to the unified datasets that were produced by this method indicated the better discrimination performance achieved in predicting the class of healthy/disease samples. This could lead not only to the creation of better analytical models of the specific disease, but also in dealing with modelling other complex diseases having multi-modal datasets as well.Στο πλαίσιο συνεισφοράς στη κατασκευή Ολοκληρωμένων Μοντέλων Φυσιολογίας, αναπτύχθηκαν τρεις νέες προσεγγίσεις, στοχεύοντας σε διαφορετικά επίπεδα βιοϊατρικής μοντελοποίησης. Στο χαμηλότερο μοριακό επίπεδο, η ολοκλήρωση μεταβολικών μονοπατιών με ένα τρόπο ικανό στην παραγωγή περιγραφικών και προγνωστικών δυναμικών μοντέλων, είχε σαν αποτέλεσμα την δημιουργία του εργαλείου KEGGconverter. Με τον KEGGconverter είναι δυνατή η παραγωγή ολοκληρωμένων ισοδύναμων μεταβολικών μονοπατιών, κατάλληλων για εργασίες προσομοίωσης, έχοντας σαν είσοδο μόνο αρχεία KGML και έχοντας σαν έξοδο μοντέλα SBML. Η αυτοματοποιημένη εισαγωγή στα μοντέλα τεσσάρων κινητικών μηχανισμών κατά περίπτωση, παρέχει την προσθήκη του επιπέδου κινητικών εξισώσεων, κατευθυνόμενο από έναν αλγόριθμο βασιζόμενο σε κανόνες, ο οποίος αναπτύχθηκε για το συγκεκριμένο έργο. Σε υψηλότερο επίπεδο, ο νέος αλγόριθμος GOrevenge χρησιμοποιεί την Gene Ontology ώστε να αντιστοιχίσει γονίδια σε ορισμένα κυτταρικά μονοπάτια και αντίστροφα, με σκοπό να αναδείξει περεταίρω τον λειτουργικό ρόλο συγκεκριμένων γονιδίων, μέσα σε ένα πλαίσιο συσχετισμών, ξεκινώντας από τα αποτελέσματα διαφόρων στατιστικών αναλύσεων εμπλουτισμού. Αν και η συγκεκριμένη υλοποίηση χρησιμοποιεί τους επιχαρακτηρισμούς GO, ο αλγόριθμος μπορεί γενικά να επεκταθεί ώστε να συμπεριλάβει οποιαδήποτε βιολογική οντολογία ή ορισμό περιορισμένου λεξικού. Αυτό συμβαίνει λόγο της εκμετάλλευσης της περικλείουσας υποκείμενης πληροφορίας του τοπολογικού δικτύου και την αλληλεπίδραση μεταξύ των επιχαρακτηρισμών και των επιχαρακτηρισμένων αντικειμένων. Τέλος, αναπτύχθηκε μια νέα μεθοδολογία στον συγκερασμό πολύτροπων δεδομένων, χρησιμοποιώντας ένα σύνολο χαρακτηριστικών από εικόνες μορφωμάτων στο δέρμα, και ένα σχετικό σύνολο δεδομένων από μικροσυστοιχίες. Η ανάλυση πολλαπλών μεταβλητών που εφαρμόστηκε στο ενοποιημένο σύνολο δεδομένων όπως παράχθηκε από αυτή τη μέθοδο, ανέδειξε την καλύτερη απόδοση διαχωρισμού που επιτεύχθηκε στην πρόγνωση της κλάσης υγειών/ασθενών δειγμάτων. Αυτό θα μπορούσε να οδηγήσει όχι μόνο στην δημιουργία καλύτερων αναλυτικών μοντέλων της συγκεκριμένης ασθένειας, αλλά επίσης στον χειρισμό μοντέλων άλλων πολύπλοκων ασθενειών που περιγράφονται από πολύτροπα σύνολα δεδομένων

D6.9 INTEGRATION OF RESULTS: POLICYCLOUD COMPLETE ENVIRONMENT M36

This deliverable has been released in December 2022, at M36 of the project, and its main objective is to specify the final integration results between the PolicyCLOUD components. This deliverable will follow the methodology of D6.2 and D6.8 that were respectively submitted in M12 (December 2020) and M24 (December 2021) which have two main pillars: Define common practices for integration and validation of the outcomes of the project Detail the cloud environment the project will make use of to demonstrate the results Regarding the former, GitLab will be the base code repository for the project, where the project already owns an organizational account. Over GitLab [1], the trunk-based development branching policy has been applied, as we considered it the most suitable policy given the project characteristics. Also, GitLab’s issue reporting tool has been adopted, as it is fully integrated with GitLab’s features. The test bed to support the demonstrators has been deployed over EGI’s (EGI) infrastructure where flexibility is one of the critical features. This deliverable abstractly incorporates all the changes and implementations that WP2, WP3, WP4 and WP5 had made during the second year of the project. More details about the components and the actual implementation can be found in the related WP deliverables [7] [8] [9]. In detail, the schemas of the data have been finalized so the standard version that we defined initiated the data import to the repository of PolicyCLOUD. Moreover, the infrastructure (IaaS) and the platform deployment (PaaS/ Serverless) have been restructured and reshaped based on the latest needs of the components. EGI deployed the new flavour of PolicyCLOUD to the Openstack Infrastructure and IBM made the proper changes to the Openwhisk middleware for the serverless and other services. The related WP deliverables highlight detailed information and instructions for each component change that in total orchestrate the PolicyCLOUD engine.This deliverable is submitted to the EC, not yet approved

D2.7 CONCEPTUAL MODEL & REFERENCE ARCHITECTURE

The third and final version of the PolicyCLOUD Conceptual Model & Reference Architecture (originally submitted as Deliverable D2.2 in September 2020 [20] with the second version submitted as D2.6 in June 2021 [21]) is presented in this document. The PolicyCLOUD Conceptual Model presents the overall project concept along 2 main axes. Along the first data axis PolicyCLOUD delivers Cloud Gateways and APIs to access data sources and adapt to their interfaces so as to simplify interaction and data collection from any source. Along the second main axis, the Policies Management Framework of PolicyCLOUD allows the definition of forward-looking policies as well as their dynamic adaptation and refocusing to the population they are applied on. Based on the project’s offerings along the main two axes of the Concept, five main building blocks (in a layered manner) define its Architecture: (1) The Cloud Based Environment and Data Acquisition, (2) Data Analytics, (3) the Policies Management Framework, (4) the Policy Development Toolkit and (5) The Marketplace. The architecture also includes a Data Governance Model, Protection and Privacy Enforcement and the Ethical Framework as depicted in Figure 2. The architecture allows for integrated data acquisition and analytics. It also allows data fusion with processing and initial analytics (see 7.6.5) as well as seamless analytics (see 7.6.6) on hybrid data at rest. Integration in PolicyCLOUD follows three directions: (i) architecture integration, (ii) integration with the cloud infrastructure and (iii) integration with Use Case scenarios through the implementation of end-to- end scenarios. Additional integration activities take place along the two frameworks of PolicyCLOUD, (a) the Data Governance model, protection and privacy enforcement mechanism and (b) the Ethical and Legal Compliance framework. For end-to-end data path analysis we have used two Use Case scenarios: (i) the scenario of Use Case 1: “Radicalization incidents” and the scenario of Use Case 2: “Visualization of negative and positive opinions on social networks for different products”. The new updates in this final document provide the following: Analysis of how External Frameworks can be integrated with PolicyCLOUD (section 7.6.11.4); Presentation of the overall Conceptual View and architecture of the Data Marketplace (section 7.9.1); Outline of the mechanisms developed for initialising the Policy Development Toolkit with Policy Model components and the visualization of results (section 7.8.3); Analysis of the Ethical and Legal Compliance Framework positive interventions to the PolicyCLOUD architecture, including the addition of specific fields/parameters to the registration Application Programming Interfaces to be populated with details regarding each individual analytics tool and dataset/data source (section 7.5); Presentation of the integration of the Data Governance model, protection and privacy enforcement mechanisms with the Policy Development Toolkit, the cloud gateways and the marketplace (section 7.10.2), and within the same context, the integration of EGI-Check-in with Keycloak including the integration of the Data Governance model, protection and privacy enforcement mechanisms with the Kubernetes cluster. The document also addresses the Reviewers’ comments to the previous version of the deliverable (Deliverable D2.6), included in the second review report. In order to address these comments, additional updates of Deliverable D2.7 include: (i) links to specific user/stakeholder requirements (D2.5), (ii) descriptions and implementation details for the two remaining pilot Use Cases (Sofia and London) and (iii) reference to EOSC and to the role of the Conceptual Model & Reference Architecture document for the identification of the relevant services and of their providers, and description of the onboarding process based on Deliverable D3.4 [22].This deliverable is submitted to the EC, not yet approved