A smartwater metering deployment based on the fog computing paradigm

In this paper, we look into smart water metering infrastructures that enable continuous, on-demand and bidirectional data exchange between metering devices, water flow equipment, utilities and end-users. We focus on the design, development and deployment of such infrastructures as part of larger, smart city, infrastructures. Until now, such critical smart city infrastructures have been developed following a cloud-centric paradigm where all the data are collected and processed centrally using cloud services to create real business value. Cloud-centric approaches need to address several performance issues at all levels of the network, as massive metering datasets are transferred to distant machine clouds while respecting issues like security and data privacy. Our solution uses the fog computing paradigm to provide a system where the computational resources already available throughout the network infrastructure are utilized to facilitate greatly the analysis of fine-grained water consumption data collected by the smart meters, thus significantly reducing the overall load to network and cloud resources. Details of the system's design are presented along with a pilot deployment in a real-world environment. The performance of the system is evaluated in terms of network utilization and computational performance. Our findings indicate that the fog computing paradigm can be applied to a smart grid deployment to reduce effectively the data volume exchanged between the different layers of the architecture and provide better overall computational, security and privacy capabilities to the system

Birth to Three Language Acquisition: Influences of Ambient Language in the Montessori Setting

BIRTH TO THREE LANGUAGE ACQUISITION: INFLUENCES OF AMBIENT LANGUAGE IN THE MONTESSORI SETTING Claudine Campanelli Long Island University, New York, 2021 Dissertation Chair: Lynn Cohen, PhD There is an expanse of literature looking at various topics supporting Montessori education, especially in preschool; however, there is a lack of research in infant and toddler Montessori classrooms. Most of the empirical data regarding language acquisition has focused on the child’s acquisition of vocabulary through direct instruction, rather than the learning capability from overhearing a third party in a naturalistic setting. The purpose of this intervention study was to add to the limited empirical research on language acquisition in infant and toddler Montessori environments. More specifically, the intervention assessed if infants and toddlers could indirectly acquire new vocabulary through the Absorbent Mind from teachers and peers’ ambient dialogue during the Montessori three-period lesson. The research utilized a descriptive, correlational pre-and-post quasi-experimental design to assess and analyze vocabulary and ambient language. Data collection occurred in three Association Montessori Internationale (AMI) and American Montessori Society (AMS) infant and toddler mixed-aged environments throughout New York State and Maryland. The Language Environmental Analysis (LENA) system was used to analyze audio recordings. Transcriptions of audio recordings quantified vocabulary acquisition and ambient language. Paired t-tests and ANCOVA were used to analyze children’s acquired vocabulary. A fidelity scale analyzed the extent to which Montessori trained teachers adhered to the three-period lesson intervention. The findings provide opportunities to improve infant and toddler teachers\u27 classroom practice related to language acquisition. Suggestions were offered for early childhood teacher preparation programs

Artificial Intelligence and Ambient Intelligence

This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on “Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules”, presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots’ ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science

Human Action Recognition and Monitoring in Ambient Assisted Living Environments

Population ageing is set to become one of the most significant challenges of the 21st century, with implications for almost all sectors of society. Especially in developed countries, governments should immediately implement policies and solutions to facilitate the needs of an increasingly older population. Ambient Intelligence (AmI) and in particular the area of Ambient Assisted Living (AAL) offer a feasible response, allowing the creation of human-centric smart environments that are sensitive and responsive to the needs and behaviours of the user. In such a scenario, understand what a human being is doing, if and how he/she is interacting with specific objects, or whether abnormal situations are occurring is critical. This thesis is focused on two related research areas of AAL: the development of innovative vision-based techniques for human action recognition and the remote monitoring of users behaviour in smart environments. The former topic is addressed through different approaches based on data extracted from RGB-D sensors. A first algorithm exploiting skeleton joints orientations is proposed. This approach is extended through a multi-modal strategy that includes the RGB channel to define a number of temporal images, capable of describing the time evolution of actions. Finally, the concept of template co-updating concerning action recognition is introduced. Indeed, exploiting different data categories (e.g., skeleton and RGB information) improve the effectiveness of template updating through co-updating techniques. The action recognition algorithms have been evaluated on CAD-60 and CAD-120, achieving results comparable with the state-of-the-art. Moreover, due to the lack of datasets including skeleton joints orientations, a new benchmark named Office Activity Dataset has been internally acquired and released. Regarding the second topic addressed, the goal is to provide a detailed implementation strategy concerning a generic Internet of Things monitoring platform that could be used for checking users' behaviour in AmI/AAL contexts

A Systematic Review of the Literature on Living Labs in Higher Education Institutions:Potentials and Constraints

Living labs (LLs) have emerged as an interface for higher education institutions to collaborate with companies, citizens, non-profit and government organizations to address a variety of problems around social challenges and sustainable development. In this systematic literature review, we summarize the existing knowledge on how universities shape and manage the LLs they are associated with and how they align with their core missions of education and research and—in particular—their social missions. Following PRISMA guidelines for a systematic literature review (SLR), we analyzed journal articles, conference papers, and book chapters published between 2008 and 2020, capturing 93 university-governed LL experiences from across the world. Our findings show that LLs are developing from bottom-up initiatives, often at the fringe of higher education institutions, towards more self-standing entities implementing strategies to undertake social outreach activities. LLs require their host universities to intensify the relationships with their stakeholders and work on capacity building and focus on inter- and transdisciplinary research methodologies. Finally, our literature review points to the need for further research on the hybrid governance approaches displayed by LLs, particularly looking at the roles and responsibilities of academics involved in managing LL initiatives

Technology enhanced learning using humanoid robots

In this paper we present a mixture of technologies tailored for e-learning related to the Deep Learning, Sentiment Analysis, and Semantic Web domains, which we have employed to show four different use cases that we have validated in the field of Human-Robot Interaction. The approach has been designed using Zora, a humanoid robot that can be easily extended with new software behaviors. The goal is to make the robot able to engage users through natural language for different tasks. Using our software the robot can (i) talk to the user and understand their sentiments through a dedicated Semantic Sentiment Analysis engine; (ii) answer to open-dialog natural language utterances by means of a Generative Conversational Agent; (iii) perform action commands leveraging a defined Robot Action ontology and open-dialog natural language utterances; and (iv) detect which objects the user is handing by using convolutional neural networks trained on a huge collection of annotated objects. Each module can be extended with more data and information and the overall architectural design is general, flexible, and scalable and can be expanded with other components, thus enriching the interaction with the human. Different applications within the e-learning domains are foreseen: The robot can either be a trainer and autonomously perform physical actions (e.g., in rehabilitation centers) or it can interact with the users (performing simple tests or even identifying emotions) according to the program developed by the teachers

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

Proceedings Ambient Intelligence: 15th European Conference, AmI 2019, Rome, Italy, November 13-15, 2019

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Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators