Data Collection and Utilization Framework for Edge AI Applications

As data being produced by IoT applications continues to explode, there is a growing need to bring computing power closer to the source of the data to meet the response time, power dissipation and cost goals of performance-critical applications in various domains like the Industrial Internet of Things (IIoT), Automated Driving, Medical Imaging or Surveillance among others. This paper proposes a data collection and utilization framework that allows runtime platform and application data to be sent to an edge and cloud system via data collection agents running close to the platform. Agents are connected to a cloud system able to train AI models to improve overall energy efficiency of an AI application executed on an edge platform. In the implementation part, we show the benefits of FPGA-based platform for the task of object detection. Furthermore, we show that it is feasible to collect relevant data from an FPGA platform, transmit the data to a cloud system for processing and receiving feedback actions to execute an edge AI application energy efficiently. As future work, we foresee the possibility to train, deploy and continuously improve a base model able to efficiently adapt the execution of edge applications

Identifying gender bias in blockbuster movies through the lens of machine learning

The problem of gender bias is highly prevalent and well known. In this paper, we have analysed the portrayal of gender roles in English movies, a medium that effectively influences society in shaping people's beliefs and opinions. First, we gathered scripts of films from different genres and derived sentiments and emotions using natural language processing techniques. Afterwards, we converted the scripts into embeddings, i.e. a way of representing text in the form of vectors. With a thorough investigation, we found specific patterns in male and female characters' personality traits in movies that align with societal stereotypes. Furthermore, we used mathematical and machine learning techniques and found some biases wherein men are shown to be more dominant and envious than women, whereas women have more joyful roles in movies. In our work, we introduce, to the best of our knowledge, a novel technique to convert dialogues into an array of emotions by combining it with Plutchik's wheel of emotions. Our study aims to encourage reflections on gender equality in the domain of film and facilitate other researchers in analysing movies automatically instead of using manual approaches

Towards Integrated Digital-Twins: An Application Framework for Autonomous Maritime Surface Vessel Development

The use of digital twins for the development of Autonomous Maritime Surface Vessels (AMSVs) has enormous potential to resolve the increasing need for water-based navigation and safety at the sea. Aiming at the problem of lack of broad and integrated digital twin implementations with live data along with the absence of a digital twin-driven framework for AMSV design and development, an application framework for the development of a fully autonomous vessel using an integrated digital twin in a 3D simulation environment has been presented. Our framework has 4 layers which ensure that simulation and real-world vessel and the environment are as close as possible. Åboat, an in-house, experimental research platform for maritime automation and autonomous surface vessel applications, equipped with two trolling electric motors, cameras, LiDARs, IMU and GPS has been used as the case study to provide a proof of concept. Åboat, its sensors, and the environment have been replicated in a commercial, 3D simulation environment, AILiveSim. Using the proposed application framework, we develop obstacle detection and path planning systems based on machine learning which leverage live data from a 3D simulation environment to mirror the complex dynamics of the real world. Exploiting the proposed application framework, the rewards across training episodes of a Deep Reinforcement Learning model are evaluated for live simulated data in AILiveSim

Towards Integrated Digital-Twins: An Application Framework for Autonomous Maritime Surface Vessel Development

The use of digital twins for the development of Autonomous Maritime Surface Vessels (AMSVs) has enormous potential to resolve the increasing need for water-based navigation and safety at the sea. Aiming at the problem of lack of broad and integrated digital twin implementations with live data along with the absence of a digital twin-driven framework for AMSV design and development, an application framework for the development of a fully autonomous vessel using an integrated digital twin in a 3D simulation environment has been presented. Our framework has 4 layers which ensure that simulation and real-world vessel and the environment are as close as possible. Åboat, an in-house, experimental research platform for maritime automation and autonomous surface vessel applications, equipped with two trolling electric motors, cameras, LiDARs, IMU and GPS has been used as the case study to provide a proof of concept. Åboat, its sensors, and the environment have been replicated in a commercial, 3D simulation environment, AILiveSim. Using the proposed application framework, we develop obstacle detection and path planning systems based on machine learning which leverage live data from a 3D simulation environment to mirror the complex dynamics of the real world. Exploiting the proposed application framework, the rewards across training episodes of a Deep Reinforcement Learning model are evaluated for live simulated data in AILiveSim

Olfactory receptors in pulmonary arterial hypertension: A novel pathway of vascular remodeling?

Pulmonary arterial hypertension (PAH) is due to progressive obstruction of pulmonary arteries, thus leading to right heart failure and death. Breath volatile organic compounds (VOCs) can discriminate PAH and controls. Thus, a unique breath-print of PAH is detected using an artificial nose. VOCs target olfactory receptors (ORs) in olfaction. Interestingly, ORs are detected in peripheral tissues not related to olfaction and their deregulation is associated to cancer development. PSGR, encoded by the OR51E2 gene, is one of the ORs. Because vascular cells in PAH exhibit properties of cancer cells, we propose the ground-breaking hypothesis that ORs participate to vascular remodeling leading to PAH. Thus we aim to determine whether a deregulated expression and function of the pulmonary vascular PSGR could participate to the pathological phenotype of vascular cells, and its potential use as a novel therapeutic target in PAH. PSGR gene and protein expression were assessed in total lung, distal pulmonary arteries and PASMCs from PAH patients compared to controls using qRT-PCR and western blot. We evaluated proliferation (Ki67) and apoptosis (TMRM) after siRNA-directed silencing of PSGR expression in PASMCs. We demonstrate that PSGR expression is significantly increased (50%) in PASMC, in total lung and isolated pulmonary arteries from PAH patients compared to controls. PSGR silencing in PAH-PASMCs decreased both cell proliferation (20%) and resistance to apoptosis (25%). This deregulated OR expression in PAH PASMCs opens a new avenue in PAH pathophysiology. The whole spectrum of ORs is currently investigated using microarrays and deregulated ORs will be evaluated both in vitro and in vivo