6,968 research outputs found
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent âdevicesâ, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew âcognitive devicesâ are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We introduce Deep Thermal Imaging, a new approach for close-range automatic
recognition of materials to enhance the understanding of people and ubiquitous
technologies of their proximal environment. Our approach uses a low-cost mobile
thermal camera integrated into a smartphone to capture thermal textures. A deep
neural network classifies these textures into material types. This approach
works effectively without the need for ambient light sources or direct contact
with materials. Furthermore, the use of a deep learning network removes the
need to handcraft the set of features for different materials. We evaluated the
performance of the system by training it to recognise 32 material types in both
indoor and outdoor environments. Our approach produced recognition accuracies
above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584
images of 17 outdoor materials. We conclude by discussing its potentials for
real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing
System
Supporting lay users in privacy decisions when sharing sensitive data
The first part of the thesis focuses on assisting users in choosing their privacy settings, by using machine learning to derive the optimal set of privacy settings for the user. In contrast to other work, our approach uses context factors as well as individual factors to provide a personalized set of privacy settings. The second part consists of a set of intelligent user interfaces to assist the users throughout the complete privacy journey, from defining friend groups that allow targeted information sharing; through user interfaces for selecting information recipients, to find possible errors or unusual settings, and to refine them; up to mechanisms to gather in-situ feedback on privacy incidents, and investigating how to use these to improve a userâs privacy in the future. Our studies have shown that including tailoring the privacy settings significantly increases the correctness of the predicted privacy settings; whereas the user interfaces have been shown to significantly decrease the amount of unwanted disclosures.Insbesondere nach den jĂŒngsten Datenschutzskandalen in sozialen Netzwerken wird der Datenschutz fĂŒr Benutzer immer wichtiger. Obwohl die meisten Benutzer behaupten Wert auf Datenschutz zu legen, verhalten sie sich online allerdings völlig anders: Sie lassen die meisten Datenschutzeinstellungen der online genutzten Dienste, wie z. B. von sozialen Netzwerken oder Diensten zur Standortfreigabe, unberĂŒhrt und passen sie nicht an ihre Datenschutzanforderungen an. In dieser Arbeit werde ich einen Ansatz zur Lösung dieses Problems vorstellen, der auf zwei verschiedenen SĂ€ulen basiert. Der erste Teil konzentriert sich darauf, Benutzer bei der Auswahl ihrer Datenschutzeinstellungen zu unterstĂŒtzen, indem maschinelles Lernen verwendet wird, um die optimalen Datenschutzeinstellungen fĂŒr den Benutzer abzuleiten. Im Gegensatz zu anderen Arbeiten verwendet unser Ansatz Kontextfaktoren sowie individuelle Faktoren, um personalisierte Datenschutzeinstellungen zu generieren. Der zweite Teil besteht aus einer Reihe intelligenter BenutzeroberflĂ€chen, die die Benutzer in verschiedene Datenschutzszenarien unterstĂŒtzen. Dies beginnt bei einer OberflĂ€che zur Definition von Freundesgruppen, die im Anschluss genutzt werden können um einen gezielten Informationsaustausch zu ermöglichen, bspw. in sozialen Netzwerken; ĂŒber BenutzeroberflĂ€chen um die EmpfĂ€nger von privaten Daten auszuwĂ€hlen oder mögliche Fehler oder ungewöhnliche Datenschutzeinstellungen zu finden und zu verfeinern; bis hin zu Mechanismen, um In-Situ- Feedback zu Datenschutzverletzungen zum Zeitpunkt ihrer Entstehung zu sammeln und zu untersuchen, wie diese verwendet werden können, um die PrivatsphĂ€reeinstellungen eines Benutzers anzupassen. Unsere Studien haben gezeigt, dass die Verwendung von individuellen Faktoren die Korrektheit der vorhergesagten Datenschutzeinstellungen erheblich erhöht. Es hat sich gezeigt, dass die BenutzeroberflĂ€chen die Anzahl der Fehler, insbesondere versehentliches Teilen von Daten, erheblich verringern
VANET Applications: Hot Use Cases
Current challenges of car manufacturers are to make roads safe, to achieve
free flowing traffic with few congestions, and to reduce pollution by an
effective fuel use. To reach these goals, many improvements are performed
in-car, but more and more approaches rely on connected cars with communication
capabilities between cars, with an infrastructure, or with IoT devices.
Monitoring and coordinating vehicles allow then to compute intelligent ways of
transportation. Connected cars have introduced a new way of thinking cars - not
only as a mean for a driver to go from A to B, but as smart cars - a user
extension like the smartphone today. In this report, we introduce concepts and
specific vocabulary in order to classify current innovations or ideas on the
emerging topic of smart car. We present a graphical categorization showing this
evolution in function of the societal evolution. Different perspectives are
adopted: a vehicle-centric view, a vehicle-network view, and a user-centric
view; described by simple and complex use-cases and illustrated by a list of
emerging and current projects from the academic and industrial worlds. We
identified an empty space in innovation between the user and his car:
paradoxically even if they are both in interaction, they are separated through
different application uses. Future challenge is to interlace social concerns of
the user within an intelligent and efficient driving
Countering the Novelty Effect: A Tutorial for Immersive Virtual Reality Learning Environments
Immersive Virtual Reality (iVR) is a new technology, the novelty effect of which can reduce
the enjoyment of iVR experiences and, especially, learning achievements when presented in the
classroom; an effect that the interactive tutorial proposed in this research can help overcome. Its
increasingly complex levels are designed on the basis of Mayerâs Cognitive Theory of Multimedia
Learning, so that users can quickly gain familiarity with the iVR environment. The tutorial was
included in an iVR learning experience for its validation with 65 users. It was a success, according to
the user satisfaction and tutorial usability survey. First, it gained very high ratings for satisfaction,
engagement, and immersion. Second, high skill rates suggested that it helped users to gain familiarity
with controllers. Finally, a medium-high value for flow pointed to major concerns related to skill
and challenges with this sort of iVR experience. A few cases of cybersickness also arose. The survey
showed that only intense cybersickness levels significantly limited performance and enjoyment; low
levels had no influence on flow and immersion and little influence on skill, presence, and engagement,
greatly reducing the benefits of the tutorial, despite which it remained useful.This work was partially supported by the ACIS project (Reference Number INVESTUN/21/BU/0002) of the Consejeria de Empleo e Industria of the Junta de Castilla y León (Spain), the Erasmus+ RISKREAL Project (Reference Number 2020-1-ES01-KA204-081847) of the European Commission and the HumanAid Project (Reference Number TED2021-129485B-C43) of the Proyectos Estratégicos Orientados a la Transición Ecológica y a la Transición Digital of the Spanish Ministry of Science and Innovation
SciTech News Volume 71, No. 3 (2017)
Columns and Reports
From the Editor.........................3
Division News
Science-Technology Division....5
Chemistry Division....................8
Conference Report, Marion E, Sparks Professional Development Award Recipient..9
Engineering Division................10
Engineering Division Award, Winners Reflect on their Conference Experience..15
Aerospace Section
of the Engineering Division .....18
Architecture, Building Engineering, Construction, and Design Section of the Engineering Division................20
Reviews
Sci-Tech Book News Reviews...22
Advertisements
IEEE..........................................
A review of augmented reality applications for ship bridges
We present a state-of-the art analysis of Augmented Reality (AR) applications for ship bridge operation. We compiled and reviewed what type of use cases were published, what type of maritime applications have been adapted to AR, how they were prototyped and evaluated and what type of technology was used. We also reviewed the user interaction mechanisms, information display and adaptation to maritime environmental conditions.
Our analysis shows that although there are many examples of AR applications in ship bridges, there is still much work that needs to be done before these solutions can be suitably adapted to commercial settings. In addition, we argue there is a need to develop design requirements and regulations that can guide the safe development of AR
Towards Improving Proactive Dialog Agents Using Socially-Aware Reinforcement Learning
The next step for intelligent dialog agents is to escape their role as silent
bystanders and become proactive. Well-defined proactive behavior may improve
human-machine cooperation, as the agent takes a more active role during
interaction and takes off responsibility from the user. However, proactivity is
a double-edged sword because poorly executed pre-emptive actions may have a
devastating effect not only on the task outcome but also on the relationship
with the user. For designing adequate proactive dialog strategies, we propose a
novel approach including both social as well as task-relevant features in the
dialog. Here, the primary goal is to optimize proactive behavior so that it is
task-oriented - this implies high task success and efficiency - while also
being socially effective by fostering user trust. Including both aspects in the
reward function for training a proactive dialog agent using reinforcement
learning showed the benefit of our approach for more successful human-machine
cooperation
- âŠ