The widespread use of social networks has brought to the foreground a very important issue, the veracity of the information circulating within them. Many natural language processing methods have been proposed in the past to assess a post’s content with respect to its reliability; however, end-to-end approaches are not comparable in ability to human beings. To overcome this, in this paper, we propose the use of a more modular approach that produces indicators about a post’s subjectivity and the stance provided by the replies it has received to date, letting the user decide whether (s)he trusts or does not trust the provided information. To this end, we fine-tuned state-of-the-art transformer-based language models and compared their performance with previous related work on stance detection and subjectivity analysis. Finally, we discuss the obtained results.

In this paper, a proposal on the evolution of Internet of Things towards a Social Network's inspired environment (Social Internet of Things-SIoT) is presented. An architecture for supporting the SIoT based on the use of agents, which enable device to device and human to device “social” communication, is presented, along with a simulator (ASSIST) which enables the visualization of the way the proposed architecture works. The use of rules in our proposal comes to enable the communication and operation of agents on the basis of needs and services, while a use case is presented, demonstrating the use of the simulator to create links between SIoT Agents that assist them to discover the services they need.

Graffiti is common in many communities and even affects our historical and heritage structures. This leads to a decrease in the revenue associated with commercial activities or services (e.g., shops, restaurants, residences), and potentially reduces tourism in a region. Visual data, in the form of photographs, is becoming an efficient mechanism to record information. Photographs can be quickly captured, and are already frequently posted online by ordinary citizens (e.g., tourists, residents, visitors). Exploiting image data through automation and computer vision provides a new opportunity to simplify the current manual graffiti-monitoring processes, enabling automated detection, localization, and quantification of such markings. In this study, we developed a vision-based graffiti-detection technique using a convolutional neural network. Images collected from historical structures of interest within a community can be utilized to automatically inspect for graffiti markings. In the case in which citizens collect and contribute data, there is a high degree of duplication and repetition, and potentially a lack of GPS information. These hinder the direct use of the images for automating the process. To address these challenges, we built high-resolution, single-view façade images (orthophotos) before applying our robust graffiti detector. The robust graffiti detector was built using a database with 1022 images of damaged or contaminated structures gathered during a recent European Union project, entitled “Safeguarding Cultural Heritage through Technical and Organisational Resources Management” (STORM). A total of 818 images were used for training (10% of the training set was randomly chosen for the validation set), achieving 88% accuracy among the remaining 204 samples for testing. Using the trained detector, the technique developed was demonstrated using data collected from the Church of Agios Nikolaos (Leontariou), Kantza, Greece.

Continuous monitoring of Hearth Rate (HR) based on photoplethysmography (PPG) sensors is an essential capability of nearly all wrist-worn devices. However, arm movements lead to the creation of Motion Artifacts (MA), affecting the accuracy of HR tracking using PPG sensors. This problem is commonly tackled by exploiting the recorded accelerometer data to correlate them with the PPG signal and eventually clean it. Thus, automatic fusion techniques based on Deep Learning (DL) algorithms have been proposed, but they are considered too large and complex to be deployed on wearable devices. The current work presents a novel and lightweight DL architecture, PULSE, improving sensor fusion by applying a multi-head cross-attention layer to the extracted temporal features. Moreover, we propose a relation-based knowledge distillation mechanism to pass PULSE's knowledge to a student network that uses modality-wise convolutions to replace the attention module and mimic the teacher's performance with 5× fewer parameters. The teacher and student are evaluated on two datasets: a) PPG-DaLiA the most extensive available dataset, with PULSE achieving close performance to the best state-of-the-art model, and b) WESAD with PULSE reducing the mean absolute error by 22.6%. The student model is further compressed using post-training quantization and deployed on two commercial-off-the-shelf microcontrollers, demonstrating its suitability for real-time execution, having a close-to-state-of-the-art MAE of 4.81 BPM (+0.40 BPM) on the PPG-DaLiA, but a 10.9× lower memory footprint of 37.9 kB, and consuming 45.9× lower energy (0.577 mJ).