Model-driven engineering techniques and tools for machine learning-enabled IoT applications: A scoping review

This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In particular, since a significant portion of the IoT data is sequential time series data, such as sensor data, time series analysis techniques are required. Therefore, IoT modeling languages and tools are expected to support DAML methods, including time series analysis techniques, out of the box. In this paper, we study and classify prior work in the literature through the mentioned lens and following the scoping review approach. Hence, the key underlying research questions are what MDE approaches, tools, and languages have been proposed and which ones have supported DAML techniques at the modeling level and in the scope of smart IoT services.info:eu-repo/semantics/publishedVersio

Bridging Community, History, and Culture in Personal Informatics Tools: Insights from an Existing Community-Based Heart Health Intervention for Black Americans

A healthy diet and increased physical activity are essential for reducing the prevalence of cardiovascular disease and related deaths, a worldwide public health concern that disproportionately affects Black American communities. Still, Black Americans can face unique challenges meeting dietary and physical activity requirements due to inequities in access and quality of care, environmental and local factors, and difficulties in changing individual health behaviors. Personal informatics and self-tracking tools are one way of increasing awareness of health behaviors to motivate behavior change. However, there are still gaps in knowledge about what encourages different users to engage with personal informatics tools over time, particularly when used in collaborative, community-health settings. This paper contributes a nuanced understanding of fifteen participants' reasons for engaging in an existing community-based health education and behavior change program that combines collaborative self-tracking with culturally relevant content and social engagement to motivate heart-healthy behaviors. We illustrate participants' positive and negative experiences engaging in self-tracking and collaborative tasks during the program. We also discuss how participants envision that integrating technology might support or hinder participant engagement and the work of deploying community-based public health interventions. Finally, we discuss design implications for culturally informed, community-based personal informatics tools that engage Black American's in heart-healthy activities

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202