86,522 research outputs found
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
A Classification Model for Sensing Human Trust in Machines Using EEG and GSR
Today, intelligent machines \emph{interact and collaborate} with humans in a
way that demands a greater level of trust between human and machine. A first
step towards building intelligent machines that are capable of building and
maintaining trust with humans is the design of a sensor that will enable
machines to estimate human trust level in real-time. In this paper, two
approaches for developing classifier-based empirical trust sensor models are
presented that specifically use electroencephalography (EEG) and galvanic skin
response (GSR) measurements. Human subject data collected from 45 participants
is used for feature extraction, feature selection, classifier training, and
model validation. The first approach considers a general set of
psychophysiological features across all participants as the input variables and
trains a classifier-based model for each participant, resulting in a trust
sensor model based on the general feature set (i.e., a "general trust sensor
model"). The second approach considers a customized feature set for each
individual and trains a classifier-based model using that feature set,
resulting in improved mean accuracy but at the expense of an increase in
training time. This work represents the first use of real-time
psychophysiological measurements for the development of a human trust sensor.
Implications of the work, in the context of trust management algorithm design
for intelligent machines, are also discussed.Comment: 20 page
Explorations in engagement for humans and robots
This paper explores the concept of engagement, the process by which
individuals in an interaction start, maintain and end their perceived
connection to one another. The paper reports on one aspect of engagement among
human interactors--the effect of tracking faces during an interaction. It also
describes the architecture of a robot that can participate in conversational,
collaborative interactions with engagement gestures. Finally, the paper reports
on findings of experiments with human participants who interacted with a robot
when it either performed or did not perform engagement gestures. Results of the
human-robot studies indicate that people become engaged with robots: they
direct their attention to the robot more often in interactions where engagement
gestures are present, and they find interactions more appropriate when
engagement gestures are present than when they are not.Comment: 31 pages, 5 figures, 3 table
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 “devices”, 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 “cognitive devices” 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
Wearable and mobile devices
Information and Communication Technologies, known as ICT, have undergone dramatic changes in the last 25 years. The 1980s was the decade of the Personal Computer (PC), which brought computing into the home and, in an educational setting, into the classroom. The 1990s gave us the World Wide Web (the Web), building on the infrastructure of the Internet, which has revolutionized the availability and delivery of information. In the midst of this information revolution, we are now confronted with a third wave of novel technologies (i.e., mobile and wearable computing), where computing devices already are becoming small enough so that we can carry them around at all times, and, in addition, they have the ability to interact with devices embedded in the environment. The development of wearable technology is perhaps a logical product of the convergence between the miniaturization of microchips (nanotechnology) and an increasing interest in pervasive computing, where mobility is the main objective. The miniaturization of computers is largely due to the decreasing size of semiconductors and switches; molecular manufacturing will allow for “not only molecular-scale switches but also nanoscale motors, pumps, pipes, machinery that could mimic skin” (Page, 2003, p. 2). This shift in the size of computers has obvious implications for the human-computer interaction introducing the next generation of interfaces. Neil Gershenfeld, the director of the Media Lab’s Physics and Media Group, argues, “The world is becoming the interface. Computers as distinguishable devices will disappear as the objects themselves become the means we use to interact with both the physical and the virtual worlds” (Page, 2003, p. 3). Ultimately, this will lead to a move away from desktop user interfaces and toward mobile interfaces and pervasive computing
Internet Predictions
More than a dozen leading experts give their opinions on where the Internet is headed and where it will be in the next decade in terms of technology, policy, and applications. They cover topics ranging from the Internet of Things to climate change to the digital storage of the future. A summary of the articles is available in the Web extras section
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