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

SoccerNet_v3_H250/

&lt;p&gt;This is our proposed dataset for evaluating long-shot Football (Soccer) player and ball detection models. It is a subset of&nbsp;&lt;a href="https://github.com/SoccerNet/SoccerNet-v3"&gt;SoccerNet-v3&lt;/a&gt;. It consists of the frames in which the length (height) of the bounding box that locates a person does not exceed 250 pixels. The SoccerNet v3 compressed .png images have been extracted to the proper directory structure and converted to .jpg. Additionally, the corresponding annotations of the bounding boxes have been filtered and converted into a format compatible with YOLO annotation. Two classes are present:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;&ldquo;0&rdquo; for ball, and&lt;/li&gt; &lt;li&gt;&ldquo;1&rdquo; for person bounding boxes. As &quot;person&quot; we have included all 7 classes of human annotations.&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;The division into training, validation and testing parts has been preserved, and the corresponding number of images is now: 14,368/2,726/2,692. Relevant code is available at [&lt;a href="https://github.com/kmouts/FootAndBall"&gt;https://github.com/kmouts/FootAndBall&lt;/a&gt;]&lt;/p&gt; &lt;p&gt;More info regarding the dataset is available here: [&lt;a href="https://github.com/kmouts/SoccerNet_v3_H250"&gt;https://github.com/kmouts/SoccerNet_v3_H250&lt;/a&gt;]&lt;/p&gt;Setting a Baseline in long-shot real-time Soccer Player and Ball detectio

PolicyCLOUD: Analytics as a Service Facilitating Efficient Data-Driven Public Policy Management

Part 2: Clustering/Unsupervised Learning/AnalyticsInternational audienceWhile several application domains are exploiting the added-value of analytics over various datasets to obtain actionable insights and drive decision making, the public policy management domain has not yet taken advantage of the full potential of the aforementioned analytics and data models. Diverse and heterogeneous datasets are being generated from various sources, which could be utilized across the complete policies lifecycle (i.e. modelling, creation, evaluation and optimization) to realize efficient policy management. To this end, in this paper we present an overall architecture of a cloud-based environment that facilitates data retrieval and analytics, as well as policy modelling, creation and optimization. The environment enables data collection from heterogeneous sources, linking and aggregation, complemented with data cleaning and interoperability techniques in order to make the data ready for use. An innovative approach for analytics as a service is introduced and linked with a policy development toolkit, which is an integrated web-based environment to fulfil the requirements of the public policy ecosystem stakeholders