THE SAS STRATEGY TRAINING FOR CHILDREN WITH ASD IN INCLUSIVE EDUCATIONAL ROBOTICS ACTIVITIES

Educational Robotics in inclusive learning environments creates a wide area of research where innovative teaching practices and theoretical approaches are developed and investigated in order for the new growing educational challenges to be met. In this context, an educational intervention research was carried out using mixed research methodology. The aim of the research was to investigate the effect of the“SAS Strategy Training” a strategy that developed to foster the participation of children with autism, at level 2, in inclusive teamwork with peers during construction and programming LEGO Mindstorms. 2 children, 10-11 years old, diagnosed with autism, at level 2, participated in two inclusive educational robotics teams with typical peers. Τhe SaS Strategy has been integrated in the collaboration script that was designed to support the interaction between the team members during their collaboration on programming LEGO Mindstorms. The “SaS Strategy Training” had encouraging results in reducing the barriers of the participation of the children with autism in teamwork with their typical peers. Critical questions, reflections and new research horizons emerged.

COLLABORATIVE EDUCATIONAL ROBOTICS FOR THE INCLUSION OF CHILDREN WITH DISABILITIES

This literature review aims to collect systematically peer-reviewed research articles published in scientific journals that focus on the participation of children with disabilities in collaborative educational robotics activities. After systematic research, using three major digital scientific libraries, we full-screened eleven research studies that were implemented in inclusive or special settings and published during the period 2010-2022. These articles were selected as they described in detail the psycho-pedagogical methods that were used by the researchers to foster the participation of children with disabilities in collaborative educational robotics activities. The psycho-pedagogical methods have been categorized and presented critically in relation to the research methods and results. Finally, the discussion section of this review highlights the need of promoting collaborative methods in the context of inclusive educational robotics environments

Artificial Intelligence, Transport and the Smart City:Definitions and Dimensions of a New Mobility Era

Artificial intelligence (AI) is a powerful concept still in its infancy that has the potential, if utilised responsibly, to provide a vehicle for positive change that could promote sustainable transitions to a more resource-efficient livability paradigm. AI with its deep learning functions and capabilities can be employed as a tool which empowers machines to solve problems that could reform urban landscapes as we have known them for decades now and help with establishing a new era; the era of the “smart city”. One of the key areas that AI can redefine is transport. Mobility provision and its impact on urban development can be significantly improved by the employment of intelligent transport systems in general and automated transport in particular. This new breed of AI-based mobility, despite its machine-orientation, has to be a user-centred technology that “understands” and “satisfies” the human user, the markets and the society as a whole. Trust should be built, and risks should be eliminated, for this transition to take off. This paper provides a novel conceptual contribution that thoroughly discusses the scarcely studied nexus of AI, transportation and the smart city and how this will affect urban futures. It specifically covers key smart mobility initiatives referring to Connected and Autonomous Vehicles (CAVs), autonomous Personal and Unmanned Aerial Vehicles (PAVs and UAVs) and Mobility-as-a-Service (MaaS), but also interventions that may work as enabling technologies for transport, such as the Internet of Things (IoT) and Physical Internet (PI) or reflect broader transformations like Industry 4.0. This work is ultimately a reference tool for researchers and city planners that provides clear and systematic definitions of the ambiguous smart mobility terms of tomorrow and describes their individual and collective roles underpinning the nexus in scope

Comparative Evaluation of Machine Learning Inference Machines on Edge-class Devices

Computer science and engineering have evolved rapidly over the last decade offering innovative Machine Learning frameworks and high-performance hardware devices. Executing data analytics at the edge promises to transform the mobile computing paradigm by bringing intelligence next to the end user. However, it remains an open question to explore if, and to what extent, today's Edge-class devices can support ML frameworks and which is the best configuration for efficient task execution. This paper provides a comparative evaluation of Machine Learning inference machines on Edge-class compute engines. The testbed consists of two hardware compute engines (i.e., CPU-based Raspberry Pi 4 and Google Edge TPU accelerator) and two inference machines (i.e., TensorFlow-Lite and Arm NN). Through an extensive set of experiments in our bespoke testbed, we compared three setups using TensorFlow-Lite ML framework, in terms of accuracy, execution time, and energy efficiency. Based on the results, an optimized configuration of the workload parameters can increase accuracy by 10%, and in addition, the class of the Edge compute engine in combination with the inference machine affects execution time by 86% and power consumption by almost 145%.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Embedded and Networked System

Wireless Sensor Network Technologies for Condition Monitoring of Industrial Assets

Part IV: ICT and Emerging Technologies in Production ManagementInternational audienceSystematic and robust condition monitoring of crucial equipment is the cornerstone of any successful preventive maintenance policy in the industrial environment. Recent advances in low-cost wireless sensor network (WSN) technologies and products indicate a promising future for a cost-effective, wider and more permanent deployment of a distributed sensing and processing infrastructure. This paper aims to provide a comprehensive assessment of main WSN technology alternatives available today, based on a qualitative and quantitative analysis of the typical range of requirements in the specific application domain of industrial machine condition monitoring

Cooperative Task Execution for Object Detection in Edge Computing:An Internet of Things Application

The development of computer hardware and communications has brought with it many exciting applications in the Internet of Things. More and more Single Board Computers (SBC) with high performance and low power consumption are used to infer deep learning models at the edge of the network. In this article, we investigate a cooperative task execution system in an edge computing architecture. In our topology, the edge server offloads different workloads to end devices, which collaboratively execute object detection on the transmitted sets of images. Our proposed system attempts to provide optimization in terms of execution accuracy and execution time for inferencing deep learning models. Furthermore, we focus on implementing new policies to optimize the E2E execution time and the execution accuracy of the system by highlighting the key role of effective image compression and the batch sizes (splitting decisions) received by the end devices from a server at the network edge. In our testbed, we used the You Only Look Once (YOLO) version 5, which is one of the most popular object detectors. In our heterogeneous testbed, an edge server and three different end devices were used with different characteristics like CPU/TPU, different sizes of RAM, and different neural network input sizes to identify sharp trade-offs. Firstly, we implemented the YOLOv5 on our end devices to evaluate the performance of the model using metrics like Precision, Recall, and mAP on the COCO dataset. Finally, we explore optimal trade-offs for different task-splitting strategies and compression decisions to optimize total performance. We demonstrate that offloading workloads on multiple end devices based on different splitting decisions and compression values improves the system’s performance to respond in real-time conditions without needing a server or cloud resources.</p

AUGMENTED REALITY FOR TEACHING EDUCATIONAL ROBOTICS ONLINE:DESIGN DECISIONS WITH GROUP CONCEPT MAPPING

Hands-on experiments in science, technology, engineering and mathematics (STEM) education for experiencing direct effects of actions with tangible educational robotics become challenging when students, teachers and concrete artifacts cannot be in the same place and time during educational activities, or when direct interaction between different actors, resources and materials in real time is not possible. Temporary, improvised solutions developed locally under the stress of the COVID-19 pandemic helped to resolve some pressing bottlenecks in educational domains where interaction with objects and artifacts is essential for learning. Such practices can serve as inspiration for designing new approaches in which face-to-face, online or blended forms of instruction coexist, making education more resilient to unexpected calamities and enriching the repertoire of 21st century teaching and learning.This paper reports on the initial steps in the development of such an approach, framing it from the theoretical perspectives and elaborating the initial results concerning requirements for the design of instructional solutions for teaching educational robotics (ER) using Augmented Reality (AR).Within the framework of an Erasmus+ collaboration, researchers from three European research centers together with representatives from primary and secondary schools and a science museum collected input from the target population (primary and secondary school teachers, researchers, technologists and designers) on the desired and expected characteristics of AR-enhanced instructional solutions for teaching ER online. Group Concept Mapping (GCM) methodology was used for this purpose, as it supports the active participation of problem owners and stakeholders in the design process and decision-making.Six thematic groups or clusters of ideas were distinguished, including ideas on generic educational benefits of combining AR and ER, particularly for STEM learning; ideas on feedback and feedback-related affordances in the context of AR use for ER; ideas on user interaction design; ideas on AR functionalities and hardware; ideas on ER platforms; and conceptual design ideas.These clusters of ideas represent different perspectives on the question how the teaching of ER can be supported with AR. They inform design in different but complementary ways. Moreover, they reflect the shared views of the stakeholders involved about the potential of using AR to teach ER, and shed light on existing gaps and educational challenges that need to be addressed

Comparative Evaluation of Machine Learning Inference Machines on Edge-class Devices

Computer science and engineering have evolved rapidly over the last decade offering innovative Machine Learning frameworks and high-performance hardware devices. Executing data analytics at the edge promises to transform the mobile computing paradigm by bringing intelligence next to the end user. However, it remains an open question to explore if, and to what extent, today's Edge-class devices can support ML frameworks and which is the best configuration for efficient task execution. This paper provides a comparative evaluation of Machine Learning inference machines on Edge-class compute engines. The testbed consists of two hardware compute engines (i.e., CPU-based Raspberry Pi 4 and Google Edge TPU accelerator) and two inference machines (i.e., TensorFlow-Lite and Arm NN). Through an extensive set of experiments in our bespoke testbed, we compared three setups using TensorFlow-Lite ML framework, in terms of accuracy, execution time, and energy efficiency. Based on the results, an optimized configuration of the workload parameters can increase accuracy by 10%, and in addition, the class of the Edge compute engine in combination with the inference machine affects execution time by 86% and power consumption by almost 145%.</p

Task Allocation Methods and Optimization Techniques in Edge Computing:A Systematic Review of the Literature

Task allocation in edge computing refers to the process of distributing tasks among the various nodes in an edge computing network. The main challenges in task allocation include determining the optimal location for each task based on the requirements such as processing power, storage, and network bandwidth, and adapting to the dynamic nature of the network. Different approaches for task allocation include centralized, decentralized, hybrid, and machine learning algorithms. Each approach has its strengths and weaknesses and the choice of approach will depend on the specific requirements of the application. In more detail, the selection of the most optimal task allocation methods depends on the edge computing architecture and configuration type, like mobile edge computing (MEC), cloud-edge, fog computing, peer-to-peer edge computing, etc. Thus, task allocation in edge computing is a complex, diverse, and challenging problem that requires a balance of trade-offs between multiple conflicting objectives such as energy efficiency, data privacy, security, latency, and quality of service (QoS). Recently, an increased number of research studies have emerged regarding the performance evaluation and optimization of task allocation on edge devices. While several survey articles have described the current state-of-the-art task allocation methods, this work focuses on comparing and contrasting different task allocation methods, optimization algorithms, as well as the network types that are most frequently used in edge computing systems