6,097 research outputs found

    Socially Constrained Management Of Power Resources For Social Mobile Robots

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    Autonomous robots acting as companions or assistants in real social environments should be able to sustain and operate over an extended period of time. Generally, autonomous mobile robots draw power from batteries to operate various sensors, actuators and perform tasks. Batteries have a limited power life and take a long time to recharge via a power source, which may impede human-robot interaction and task performance. Thus, it is important for social robots to manage their energy, this paper discusses an approach to manage power resources on mobile robot with regard to social aspects for creating life-like autonomous social robots

    IoT-Enabled Social Relationships Meet Artificial Social Intelligence

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    With the recent advances of the Internet of Things, and the increasing accessibility of ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as relationship explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as Artificial Social Intelligence (ASI) that has the potential to tackle the social relationship explosion problem. This paper discusses the role of IoT in social relationships detection and management, the problem of social relationships explosion in IoT and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.Comment: Submitted to IEEE internet of things journa

    Mitigations to Reduce the Law of Unintended Consequences for Autonomy and Other Technological Advances

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    The United Nations states that Earths population is expected to reach just under 10 billion people (9.7) by the year 2050. To meet the demands of 10 billion people, governments, multinational corporations and global leaders are relying on autonomy and technological advances to augment and/or accommodate human efforts to meet the required needs of daily living. Genetically modified organisms (GMOs), Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) gene-edited plants and cloning will be utilized to expand human food supply. Biomimetic implants are expected to improve life expectancy with 3D printed body parts. Human functioning will be extended with wearables and cybernetic implants continuing humanitys path toward transhumanism. Families will be strengthened with 3 parent households. Disease will surely be eradicated using the CRISPR-CAS9 genetic engineering revolution to design out undesirable human traits and to design in new capabilities. With autonomous cars, trucks and buses on our roads and on-demand autonomous aircraft delivering pizzas, medical prescriptions and groceries in the air and multi-planet vehicles traversing space, utopia will finally arrive! Or will it? All of these powerful, man-made, technological systems will experience unintended consequences with certainty. Instead of over-reacting with hysteria and fear, we should be seeking answers to the following questions - What skills are required to architect socially-healthy technological systems for 2050? What mindsets should we embody to ameliorate hubris syndrome and to build our future technological systems with deliberation, soberness and social responsibility

    Social Impact of Recharging Activity in Long-Term HRI and Verbal Strategies to Manage User Expectations During Recharge

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    Social robots perform tasks to help humans in their daily activities. However, if they fail to fulfill expectations this may affect their acceptance. This work investigates the service degradation caused by recharging, during which the robot is socially inactive. We describe two studies conducted in an ecologically valid office environment. In the first long-term study (3 weeks), we investigated the service degradation caused by the recharging behavior of a social robot. In the second study, we explored the social strategies used to manage users’ expectations during recharge. Our findings suggest that the use of verbal strategies (transparency, apology, and politeness) can make robots more acceptable to users during recharge

    The Role of Edge Robotics As-a-Service in Monitoring COVID-19 Infection

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    Deep learning technology has been widely used in edge computing. However, pandemics like covid-19 require deep learning capabilities at mobile devices (detect respiratory rate using mobile robotics or conduct CT scan using a mobile scanner), which are severely constrained by the limited storage and computation resources at the device level. To solve this problem, we propose a three-tier architecture, including robot layers, edge layers, and cloud layers. We adopt this architecture to design a non-contact respiratory monitoring system to break down respiratory rate calculation tasks. Experimental results of respiratory rate monitoring show that the proposed approach in this paper significantly outperforms other approaches. It is supported by computation time costs with 2.26 ms per frame, 27.48 ms per frame, 0.78 seconds for convolution operation, similarity calculation, processing one-minute length respiratory signals, respectively. And the computation time costs of our three-tier architecture are less than that of edge+cloud architecture and cloud architecture. Moreover, we use our three-tire architecture for CT image diagnosis task decomposition. The evaluation of a CT image dataset of COVID-19 proves that our three-tire architecture is useful for resolving tasks on deep learning networks by edge equipment. There are broad application scenarios in smart hospitals in the future

    Design of cloud robotic services for senior citizens to improve independent living in multiple environments

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    The paper proposed a cloud robotic solution for the healthcare management of senior citizens, to demonstrate the opportunity to remotely provide continuous assistive robotic services to a number of seniors regardless to their position in the monitored environment. In particular, a medication reminding, a remote home monitoring and an user indoor localization service were outsourced in the cloud and provided to the robots, users and caregivers on request. The proposed system was composed of a number of robotic agents distributed over two smart environments: a flat at the Domocasa Lab (Peccioli, IT) and a condominium at the Angen site of the Orebro science park (Orebro, SE). The cloud acquired data from remote smart environments and enabled the local robots to provide advanced assistive services to a number of users. The proposed smart environments were able to collect raw data for the environmental monitoring and the localization of the users by means of wireless sensors, and provide such data to the cloud. On the cloud, specific algorithms improved the local robots, by providing event scheduling to accomplish assistive services and situation awareness on the users position and environments’ status. The indoor user localization service, was provided by means of commercial and ad-hoc sensors distributed over the environments and a sensor fusion algorithm on the cloud. The entire cloud solution was evaluated in terms of Quality of Service (QoS) to estimate the effectiveness of the architecture

    Intelligent Transportation System for Smart-Cities using Fuzzy Logic

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    According to United Nations population statistics 2017, the world population is 7.6 billion and is growing rapidily alomost 11 billion by end of 21 century with a 70% chance of continued growth, this rapid increasing population have created low standards of living in cities. Smart Cities are facing pressures associated with due innovations and globalization to improve their citizens life. Computational intelligence is the study of adaptive mechanism to facilitate intelligent behavior in changing and complex environments. Traffic congestion and monitoring has become one of the critical issues in big cities. The adaptive mechanism of computational intelligence in changing the behavior of complex environments of smart city is very effective. The developing framework and services for smart-city requires sound infrastructure, latest current technology adoption. A framework model with the integration of cloud-data, social network (SN) services that is collecting stream data with smart sensors in the context of smart cities is proposed. The adaptive mechanism of computational intelligence in changing thebehavior of complex environments of smart city is very effective. A radical framework that enables the analysis of big-data sets stemming from Social Networking (SN) sites. Smart cities understanding is a broad concept only city transportation sector is focused in this article. Fuzzy logic modeling techniques are used in many fields i.e. medical, engineering. business and computing related problems. To solve various traffic management issues in cities a detailed analysis of fuzzy logic system is proposed. This paper presents an analysis of the results achieved using Fuzzy Logic System (FLS) for smart cities. The results are verified using MATLAB Simulation
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