Text categorization and sentiment analysis are two of the most typical natural language processing tasks with various emerging applications implemented and utilized in different domains, such as health care and policy making. At the same time, the tremendous growth in the popularity and usage of social media, such as Twitter, has resulted on an immense increase in user-generated data, as mainly represented by the corresponding texts in users’ posts. However, the analysis of these specific data and the extraction of actionable knowledge and added value out of them is a challenging task due to the domain diversity and the high multilingualism that characterizes these data. The latter highlights the emerging need for the implementation and utilization of domain-agnostic and multilingual solutions. To investigate a portion of these challenges this research work performs a comparative analysis of multilingual approaches for classifying both the sentiment and the text of an examined multilingual corpus. In this context, four multilingual BERT-based classifiers and a zero-shot classification approach are utilized and compared in terms of their accuracy and applicability in the classification of multilingual data. Their comparison has unveiled insightful outcomes and has a twofold interpretation. Multilingual BERT-based classifiers achieve high performances and transfer inference when trained and fine-tuned on multilingual data. While also the zero-shot approach presents a novel technique for creating multilingual solutions in a faster, more efficient, and scalable way. It can easily be fitted to new languages and new tasks while achieving relatively good results across many languages. However, when efficiency and scalability are less important than accuracy, it seems that this model, and zero-shot models in general, can not be compared to fine-tuned and trained multilingual BERT-based classifiers.

Finding new ways to cost-effectively facilitate population screening and improve cancer diagnoses at an early stage supported by data-driven AI models provides unprecedented opportunities to reduce cancer related mortality. This work presents the INCISIVE project initiative towards enhancing AI solutions for health imaging by unifying, harmonizing, and securely sharing scattered cancer-related data to ensure large datasets which are critically needed to develop and evaluate trustworthy AI models. The adopted solutions of the INCISIVE project have been outlined in terms of data collection, harmonization, data sharing, and federated data storage in compliance with legal, ethical, and FAIR principles. Experiences and examples feature breast cancer data integration and mammography collection, indicating the current progress, challenges, and future directions.

More than 50 billion devices are expected to be internet enabled by 2020, ranging from wearable devices to sensors monitoring industrial processes. As a result, emerging back-end support technologies not only have to anticipate this dramatic increase in connected devices, but also the heterogeneity and the reliability of these devices that are of major importance. For that purpose, this paper proposes an innovative mechanism for recognizing, connecting all these devices of both known and unknown nature, and capturing their reliability, so as to finally consider whether they will be considered as reliable or not, and thus their data will be kept for further analysis. In this context, in this mechanism four (4) discrete stages are implemented, facilitating the devices recognition accompanied with their specifications classification, and their reliability estimation and validation, whose combination makes finally feasible the manipulation of these devices and the estimation of their overall reliability levels.

Edge computing has become a prominent solution when it comes to mobile applications and data management, due to its ability to considerably reduce data transmission costs, and to analyze data requiring fewer computing resources, since the analysis occurs at lower data volumes, without having to relocate data to centralized infrastructures. One major challenge, indicates the optimal data placement regarding data-intensive applications and, in general, applications requiring vast transmission of large amount of data. In this paper, we propose a novel user mobility-based data placement strategy, considering a trade-off between latency and data migration, which has not been investigated before. We classify the users into three mobility classes; namely static, local or mobile, via the use of a causal-aware Deep Learning network. This information is then exploited in order to optimize the data placement through specific data placement and retrieval algorithms for each mobility class. We evaluate the performance of the proposed solution using simulations, and prove that our solution manages to reduce the average data accessing cost by 60% for static or local users and 10% for mobile users, while the average path length is reduced by 50% for static and local users, and by 12% for mobile users.

A key feature of fifth generation (5G) and Software Defined Networking (SDN) is the assurance of high levels of the quality of service (QoS). To this end, Service Level Agreements (SLAs) are introduced in order to fulfill the gap between network operators and their customers. An SLA is a contract between the operator and the internal or external customer, which determines what Network Services (NSs) are offered and the guaranteed level of performance. Taking into consideration the above-mentioned needs, in this paper, we are introducing a fully integrated SLA management framework in a real 5G environment. In this demonstration we aim to bind business requirements as Service Level Objectives (SLOs) between network operators and the customers, with measurable recourse attributes. To achieve this, we allow network operators to choose between different SLOs during the SLA Template generation, and then automatically formulate an Agreement, based on each network slice instantiation and the corresponding NS. Finally, we provide a monitoring system in order to detect and alert for any violations.