Social media has become a crucial platform for communication and information sharing, but it also facilitates the spread of misinformation, particularly during crises such as the COVID-19 pandemic undermining public’s trust in science. Especially, battling misinformation on scientific issues is important to bring closer public to science. The present work is a part of the EU funded project VERITY that aims to address this challenge by using a multidisciplinary approach resulting in in development of strategies to enhance public’s trust in science. Among other methodologies, VERITY investigates social media to identify factors that influence trust in science, and this article provides the results of a study on a specific dataset related to COVID-19 vaccination through various methods such as social network analysis, quantitative and qualitative analysis, analysis of the content and the reactions of the messages and the application of deep learning models in order to reveal people’s behaviour and the factors that influence it.

In this work, a novel federated distillation weight aggregation method is proposed. Specifically, an algorithm designed for effective learning in distributed environments is introduced. This algorithm includes an innovative federated distillation scheme, proposing a sophisticated aggregation of model outputs, employing a global server model to manage process. On the server side, feature mixing is employed to aggregate client representations before they are transmitted back to the client side for knowledge distillation. During feature mixing a weight factor is assigned to each client’s logits, penalizing bad quality clients and preserving system’s credibility. Thorough experimentation has been conducted to comprehensively study the issue at hand. Key findings reveal the significant potential of the proposed solution, which achieves robust performance in a federated setting while reducing communication costs.

In this work, a novel federated distillation weight aggregation method is proposed. Specifically, an algorithm designed for effective learning in distributed environments is introduced. This algorithm includes an innovative federated distillation scheme, proposing a sophisticated aggregation of model outputs, employing a global server model to manage process. On the server side, feature mixing is employed to aggregate client representations before they are transmitted back to the client side for knowledge distillation. During feature mixing a weight factor is assigned to each client’s logits, penalizing bad quality clients and preserving system’s credibility. Thorough experimentation has been conducted to comprehensively study the issue at hand. Key findings reveal the significant potential of the proposed solution, which achieves robust performance in a federated setting while reducing communication costs.

Human-like social robots (or humanoids) such as Softbank's NAO6, have been proven valuable assistants, able to advance State-of-the-Art of Technology in Education and Learning (TEL) as they are quite impressive “clones” of human behavior, and due to their relatable form, are often perceived as superior social companions. The rise of accessible Large Language Models and cloud computing, could transform robots like NAO6- a rather obsolete robot with quite low computing capacity (Pentium CPU, 2–4 GB RAM)- into a capable social agent, able to adopt A.I. behavior. In the current paper, we present a solution to enrich Softbank NAO6with A.I. capacity, in order to act as an LLM vessel. Specifically, we managed to connect an augmented AI chatbot to NAO6by deploying corresponding Python APIs. In our showcase, a NAO6acts as the ancient Greek Philosopher Plato that “guides the one who seeks wisdom” based on his theory. Our solution has been evaluated in real crowded settings as a proof-of-concept. Next steps involve to evaluate our solution in school classrooms.

The widespread use of social networks has brought to the fore a very important issue, the reliability of the information circulating within them. This paper presents the developed technologies referring to the visualization of data coming from social media involving the parameter of trustworthiness, and demonstrates the solution of H2020 EUNOMIA project. In particular, EUNOMIA's Digital Observatory is comprised of two tools drawing data from a REST API structure that returns the data in JSON format. A tool provides visualizations about the post's sentiment accompanied by their votes of trustworthiness and a second tool that depicts the most shared posts illustrating their trustworthiness according to their votes in an interactive way that permits user to define the level of trustworthiness.

In the current study, we present how robot-based interventions can improve symbolic play and imitation skills in children with ASD (Autism Spectrum Disorder) by increasing their eye contact, attention, imitation skills, and engagement with a robot. Specifically, we deploy the humanoid NAO 6 (SoftBank Robotics) to enrich ASD students’ symbolic play skills through six education applications developed in Choregraphe software platform. The applications were evaluated in pilot studies with 5 students (2 girls and 3 boys, aged 7-10 years) in a Special Primary School for Children with Autism, for 3 months (Nov2020-Jan2021), in which they constantly interacted with the NAO. Initial results reveal improvements in eye contact, attention, and imitation skills of the children with ASD, prompting for a long-term study in our next steps.

In this paper, we present work in progress on the development of an intelligent interconnected infrastructure for public security and protection domain. In contrast to other Internet of Things (IoT) frameworks, the proposed system aims to effectively combine device and human awareness to achieve situational awareness, so as to provide a protection and security environment for citizens. The emphasis is placed on tourists, by creating the appropriate infrastructure to address a set of urgent situations, such as health-related problems and missing children in overcrowded environments, supporting smart links between humans and entities on the basis of goals, and adapting device operation to comply with human objectives, profiles and privacy. The framework effectively combines state-of-the-art technologies on IoT data collection and analytics, knowledge representation and interoperability, crowdsourcing, data fusion and decision-making.

In this paper, we present a series of tasks that can be combined in a scavenger hunt game, in order to provide a practical training on networking and information security. In combination with a theoretical approach in several security principles, this hands-on experience can be used in a course on network and information security.

This paper studies the use of the Gherkin language in education, as a tool that aims to enhance the logical thinking of the students. To achieve this, the design of a user-friendly application will be presented. This application will allow the students to create their own logical expressions and test different scenarios that can be selected by their instructor, in an effort to familiarize themselves on applying their logic on practical real-life use cases.

Privacy and confidentiality in communication with a public entity, especially when this communication is about issues related to security (i.e. reporting criminal activity), is paramount. The guarantees that the reporting system can provide to end users directly contribute to the overall success of the system while of utmost importance, that can greatly influence whether the system is used or not is too maintain a sense of security that will increarse the user's trust. In this paper, we present an architecture for ensuring privacy and confidentiality in incident reporting taking primarily, under consideration the large number of mobile devices that can be used in creating these reports. The proposed solution consists of two servers and uses identity cloaking and message encryption of the data exchanged.

Recent advances in IoT and pervasive computing have led to the introduction of a plethora of devices, featuring enhanced intelligence in sensing, understanding context and reacting to situations. In the case of smart home environment, the result is the introduction of smart, connected domotic devices, featuring enhanced intelligence. In combination with enhanced capabilities for sensing, controlling and automatic, the power of these devices can be further exploited by the use of a semantic connection layer that can facilitate a goal-oriented collaboration between devices and a meaningful interaction with humans. This paper proposes an integrated platform, enabling dynamic injection of automation rules based on semantic web technologies, in a collective intelligence environment. The role of the human - end user in this environment is supported through a user friendly IDE enabling the easy discovery, access and operation, through the introduction of automation rules.

The contribution of this position paper is twofold: the first one extends the use of social networking in the direction of allowing not only communications among persons but also human-machine interaction and machine to machine collaboration; the second contribution is the presentation of an experimental platform for large-scale testing of innovative mobile social networking applications, including the one described in the first part of the paper.

In an endeavour to speak and prevail over some of the open problems that obstruct efficient networked media, this workshop will fetch together folks from a number of research communities, including but not limited to Multimedia Distribution and Access, Social Network Analysis, Multimedia Content Analysis, Behavioral Analysis, User Modelling Adaptation and Personalization. It is our credence that a synergetic approach involving the above mentioned research areas can surpass their individual potentials, leading to improved networked media access. The main objective of this workshop is to provide a forum to disseminate work that explicitly exploit the synergy between multimedia content analysis, behavioral modelling, personalisation, and next generation networking and community aspects of social networks. This synergetic methodology could produce high quality of experience for personalized multimedia access in networking environment.

In an effort to address and overcome some of the open issues that hinder effective access and interaction of multimedia content, this workshop will bring together individuals from a number of research communities, including but not limited to Multimedia Distribution and Access, Social Network Analysis, Multimedia Content Analysis, and User Modelling Adaptation and Personalization. It is our belief that a synergetic approach involving these areas of work can exceed their individual potentials, leading to improved access, understanding, and retrieval of multimedia content. The main objective of this workshop is to provide a forum to disseminate work that explicitly exploits the synergy between multimedia content analysis, personalisation, and next generation networking and community aspects of social networks. We believe that this integration could result on robust, personalized multimedia services, providing users with an improved multimedia experience.

In this paper, we provide the theoretical evaluation of Icebreaker, a social networking service designed in the scope of IST project Magnet Beyond. We built on previous results of our work that indicate spectral clustering's applicability in regard to the service requirements imposed by the specific application; we extend our approach by provisioning for an online adaptive algorithm that places users into social groups that have previously been assessed through the application of spectral clustering. Experimental results indicate that our approach is able to adhere to the service requirements as new users join the system, without the need of iterative spectral clustering application that is computationally demanding.

In this paper, we introduce and evaluate a framework for a user profile-based socializing application. We model the user profile as an unordered set composed of n keywords that represent users' preferences. We evaluate the effectiveness of two clustering algorithms, k-means and spectral clustering in the scope of social groups' assessment. Through experimental results we substantiate the applicability of spectral clustering in the examined service and we evaluate the impact of profile size in terms of the quality of partitions yielded by spectral clustering. The above are performed using case studies and scenarios developed in the context of 1ST project's MAGNET Beyond pilot services definition.