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).

Aspect terms extraction (ATE), a key subtask for aspect-based sentiment analysis, opinion summarization, and topic modeling aims at extracting grammatical elements (nouns, phrases, and adjectives) from user reviews that reveal the discussed features of the entity under review. These aspect terms are usually the targets of the opinions expressed. Identifying them requires tackling substantial linguistic challenges but, due to the multiple commercial and social applications, significant research effort has been invested in efficiently mining aspects. Recent advances in ATE address methods that exploit a sentence or a word-level encoding of a user review as a solution. This article proposes a novel and effective word- and sentence-level encoding framework, which utilizes a neural network architecture that learns to extract aspect terms. The main advantage of our approach is that it can extract explicit and implicit aspects (i.e., aspects that are not directly mentioned in the user-generated text). We evaluate our method on four widely used datasets where we prove its efficiency against state-of-the-art alternative approaches.

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 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.

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.