Second Screen User Profiling and Multi-level Smart Recommendations in the context of Social TVs

In the context of Social TV, the increasing popularity of first and second screen users, interacting and posting content online, illustrates new business opportunities and related technical challenges, in order to enrich user experience on such environments. SAM (Socializing Around Media) project uses Social Media-connected infrastructure to deal with the aforementioned challenges, providing intelligent user context management models and mechanisms capturing social patterns, to apply collaborative filtering techniques and personalized recommendations towards this direction. This paper presents the Context Management mechanism of SAM, running in a Social TV environment to provide smart recommendations for first and second screen content. Work presented is evaluated using real movie rating dataset found online, to validate the SAM's approach in terms of effectiveness as well as efficiency.Comment: In: Wu TT., Gennari R., Huang YM., Xie H., Cao Y. (eds) Emerging Technologies for Education. SETE 201

Calibrating mini-mental state examination scores to predict misdiagnosed dementia patients

Mini-Mental State Examination (MMSE) is used as a diagnostic test for dementia to screen a patient’s cognitive assessment and disease severity. However, these examinations are often inaccurate and unreliable either due to human error or due to patients’ physical disability to correctly interpret the questions as well as motor deficit. Erroneous data may lead to a wrong assessment of a specific patient. Therefore, other clinical factors (e.g., gender and comorbidities) existing in electronic health records, can also play a significant role, while reporting her examination results. This work considers various clinical attributes of dementia patients to accurately determine their cognitive status in terms of the Mini-Mental State Examination (MMSE) Score. We employ machine learning models to calibrate MMSE score and classify the correctness of diagnosis among patients, in order to assist clinicians in a better understanding of the progression of cognitive impairment and subsequent treatment. For this purpose, we utilize a curated real-world ageing study data. A random forest prediction model is employed to estimate the Mini-Mental State Examination score, related to the diagnostic classification of patients.This model uses various clinical attributes to provide accurate MMSE predictions, succeeding in correcting an important percentage of cases that contain previously identified miscalculated scores in our dataset. Furthermore, we provide an effective classification mechanism for automatically identifying patient episodes with inaccurate MMSE values with high confidence. These tools can be combined to assist clinicians in automatically finding episodes within patient medical records where the MMSE score is probably miscalculated and estimating what the correct value should be. This provides valuable support in the decision making process for diagnosing potential dementia patients. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Socialising around media. Improving the second screen experience through semantic analysis, context awareness and dynamic communities

SAM is a social media platform that enhances the experience of watching video content in a conventional living room setting, with a service that lets the viewer use a second screen (such as a smart phone) to interact with content, context and communities related to the main video content. This article describes three key functionalities used in the SAM platform in order to create an advanced interactive and social second screen experience for users: semantic analysis, context awareness and dynamic communities. Both dataset-based and end user evaluations of system functionalities are reported in order to determine the effectiveness and efficiency of the components directly involved and the platform as a whole

Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical records

Background: Dementia develops as cognitive abilities deteriorate, and early detection is critical for effective preventive interventions. However, mainstream diagnostic tests and screening tools, such as CAMCOG and MMSE, often fail to detect dementia accurately. Various graph-based or feature-dependent prediction and progression models have been proposed. Whenever these models exploit information in the patients’ Electronic Medical Records, they represent promising options to identify the presence and severity of dementia more precisely. Methods: The methods presented in this paper aim to address two problems related to dementia: (a) Basic diagnosis: identifying the presence of dementia in individuals, and (b) Severity diagnosis: predicting the presence of dementia, as well as the severity of the disease. We formulate these two tasks as classification problems and address them using machine learning models based on random forests and decision tree, analysing structured clinical data from an elderly population cohort. We perform a hybrid data curation strategy in which a dementia expert is involved to verify that curation decisions are meaningful. We then employ the machine learning algorithms that classify individual episodes into a specific dementia class. Decision trees are also used for enhancing the explainability of decisions made by prediction models, allowing medical experts to identify the most crucial patient features and their threshold values for the classification of dementia. Results: Our experiment results prove that baseline arithmetic or cognitive tests, along with demographic features, can predict dementia and its severity with high accuracy. In specific, our prediction models have reached an average f1-score of 0.93 and 0.81 for problems (a) and (b), respectively. Moreover, the decision trees produced for the two issues empower the interpretability of the prediction models. Conclusions: This study proves that there can be an accurate estimation of the existence and severity of dementia disease by analysing various electronic medical record features and cognitive tests from the episodes of the elderly population. Moreover, a set of decision rules may comprise the building blocks for an efficient patient classification. Relevant clinical and screening test features (e.g. simple arithmetic or animal fluency tasks) represent precise predictors without calculating the scores of mainstream cognitive tests such as MMSE and CAMCOG. Such predictive model can identify not only meaningful features, but also justifications of classification. As a result, the predictive power of machine learning models over curated clinical data is proved, paving the path for a more accurate diagnosis of dementia

Information exchange in business collaboration using grid technologies

With the emergence of service provisioning environments and new networking capabilities, antagonistic businesses have been able to collaborate securely by sharing information in order to have a beneficial result for all. This collaboration has sometimes been imposed by state legislation and sometimes been desirable by the firms themselves so as to resolve frequently occurring abnormalities. In any case, as information exchange takes place between antagonistic firms, security and privacy issues arise. In the context of this paper, a collaborative environment has been analyzed for enterprises that set out in the banking sector. A Grid-based Anti-Money Laundering (AML) system has been developed in an effort to take advantage of the Grid infrastructure, supporting the secure and trustful exchange of information between financial institutions and ensuring the confidentiality of the data transferred and the authentication of the users to whom they are available. Special emphasis is put on security mechanisms for supporting identity and privacy management as well as in Service Level Agreements (SLA) enforcement for enabling a trust enforcement platform in a collaboration business mode

Quality of service evaluation in real time service oriented infrastructures

55 σ.Με την εμφάνιση των υπηρεσιοστρεφών τεχνολογιών και υποδομών και την υιοθέτηση των ηλεκτρονικών συμβάσεων μεταξύ παρόχων υπηρεσιών και πελατών, εμφανίστηκε η ανάγκη ελέγχου και επικύρωσης της προσφερόμενης ποιότητας καθ' όλη τη διάρκεια του κύκλου ζωής μιας υπηρεσίας. Η παρούσα διατριβή εστιάζει στα συστήματα παροχής "Εφαρμογής ως Υπηρεσία" και επιδιώκει την εισαγωγή και ανάλυση καινοτόμων μηχανισμών για την αποτελεσματική αξιολόγηση της παρεχόμενης ποιότητας. Στο πλαίσιο που τέθηκε παραπάνω, το πρώτο σκέλος της παρούσας ερευνητικής εργασίας ασχολείται με το πρόβλημα επιλογής μιας υπηρεσίας από μια πληθώρα διαθέσιμων εφαρμογών που διατίθενται για τον πελάτη. Αξιοποιώντας την εμπειρία των πελατών που εμφανίζουν παρόμοια συμπεριφορά, προτείνεται ένα μοντέλο συνεργατικής αξιολόγησης των παρεχόμενων υπηρεσιών, με βαθμολόγηση των παρόχων και χρήση τεχνικών συσχέτισης, ώστε να προβλεφθεί επιτυχώς η μελλοντική αξιολόγηση υπηρεσιών που δεν έχουν χρησιμοποιηθεί από τους πελάτες. Στο δεύτερο σκέλος της διατριβής, παρουσιάζεται ένας μηχανισμός δυναμικής διαχείρισης πόρων για περιβάλλοντα παροχής "Εφαρμογής ως Υπηρεσία", με στόχο τη βέλτιστη αξιοποίησή τους κάτω από μεγάλο φόρτο εργασίας. Χρησιμοποιώντας την ελαστικότητα των σύγχρονων εικονικοποιημένων υποδομών, το προτεινόμενο μοντέλο υπαγορεύει ότι οι πόροι του συστήματος θα πρέπει να αξιοποιηθούν πλήρως από τις εισερχόμενες σε αυτό εργασίες, ακόμα και στην περίπτωση που δεν πληρούνται οι απαιτήσεις τους, με χρήση τεχνικών διαχείρισης ρίσκου. Οι τεχνικές αυτές χρησιμοποιούν την πιθανότητα παραβίασης των ηλεκτρονικών συμφωνιών, που προκύπτει από το ιστορικό εκτελέσεων, για την αξιολόγηση της παρεχόμενης ποιότητας ανά πάσα στιγμή και την καλύτερη δυνατή κατανομή των πόρων. Το πρόβλημα της κατανομής ανάγεται στο γνωστό πρόβλημα του σακιδίου και μια ευριστική λύση σχεδιάζεται και υλοποιείται σε ένα υπάρχον υπηρεσιοστρεφές σύστημα.With the emergence of service provisioning technologies and infrastructures and the adoption of Service Level Agreements between service providers and customers, the need to control and validate the offered quality has appeared throughout the service lifecycle. This thesis focuses on "Software as a Service" systems and seeks to introduce and analyze innovative mechanisms for the effective evaluation of service quality. In that frame, the first part of the current research work tackles the problem of selecting a service from a plethora of available ones for a customer. Exploiting the experience of users that present similar behavior, a collaborative evaluation model of the provided services is proposed, using correlation techniques and rating the providers, to successfully predict future assessment of services that have not been used by customers yet. The second part of the thesis presents a dynamic resource management mechanism for "Software as a Service" systems in order to maximize resource utilization under a heavy load. Employing the elasticity of virtualized infrastructures, the proposed model dictates that system resources must be fully exploited by incoming jobs, even if they do not satisfy their requirements completely, using risk management techniques. Those techniques use the violation probability for Service Level Agreements, apparent from the service history, to evaluate the quality offered at any time and allocate resources in an optimum way. The resource allocation problem is deducted to the well known Knapsack Problem and a heuristic solution is designed and implemented as part of an existing service oriented system.Φώτης Α. Αίσωπο

A novel high-throughput implementation of a partially unrolled SHA-512

A design approach to create small-sized high-speed implementation of the new version of Secure Hash Algorithm is proposed. The resulted design can be easily embedded to operate in HMAC IP cores, providing a high degree of security. The proposed implementation does not introduce significant area penalty, compared to other competitive designs. However the achieved throughput presents an increase compared to commercially available IP cores that range from 48%-1912%. © 2006 IEEE

Real-time QoS monitoring from the end user perspective in large scale social networks

Social networking (SN) activities account for a major fraction of the time that internet users collectively spend on the web and they represent a valuable source of information and services. The SocIoS project defined an API that enables the aggregation of data and functionality of underlying SN services (SNS) APIs and allows their combination, so to build new application workflows and/or to complement existing ones. While this scheme provides SN users with a tool that has a dramatic potential in terms of productivity, it also introduces a dependency on external SNS, over which the SocIoS API end user has limited control. In this context, the availability of a dependable (i.e., unbiased, reliable, and timely) facility for continuous monitoring of the QoS being actually delivered by external SNS is thus of paramount importance. Such a facility is implemented by the QoS-MONaaS component, a portable architecture developed within the context of the SRT-15 FP7 project