Users often begin exploratory visual analysis (EVA) without clear analysis goals but iteratively refine them as they learn more about their data. As an essential step in data science, researchers want to aid EVA by de...
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Today, technology plays a pivotal role in our daily lives, significantly impacting individuals. Individuals with muscular dystrophy, such as Amyotrophic Lateral Sclerosis (ALS), benefit from eye-tracking technology, a...
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ISBN:
(纸本)9798400714092
Today, technology plays a pivotal role in our daily lives, significantly impacting individuals. Individuals with muscular dystrophy, such as Amyotrophic Lateral Sclerosis (ALS), benefit from eye-tracking technology, allowing them to interact with computers. This technology empowers them to control computer functions using only their eye movements, eliminating the need for traditional mouse and keyboard inputs. Despite these advancements, ALS patients still face challenges with certain tasks. Our study focuses on a specific individual with ALS who utilizes eye-tracking technology to navigate Adobe Premiere's video editing software. Our objective is to address these challenges by providing a solution that leverages the shortcut commands already available in Adobe Premiere Pro while also creating a simplified interface tailored for the individual we are designing for. This approach facilitates easier access to these shortcuts during his work. Our research emphasizes the potential to adapt and personalize assistive eye-tracking technology, creating more opportunities for individuals with disabilities.
This paper introduces an extension to the Framework for Interaction Fidelity Analysis (FIFA) that incorporates a new dimension called Feedback Symmetry. While FIFA effectively evaluates how closely Mixed Reality (MR) ...
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Wearable sensory devices are becoming the enabling technology for many applications in healthcare and well-being, where computational elements are tightly coupled with the human body to monitor specific events about t...
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Wearable sensory devices are becoming the enabling technology for many applications in healthcare and well-being, where computational elements are tightly coupled with the human body to monitor specific events about their subjects. Classification algorithms are the most commonly used machine learning modules that detect events of interest in these systems. The use of accurate and resource-efficient classification algorithms is of key importance because wearable nodes operate on limited resources on one hand and intend to recognize critical events (e.g., falls) on the other hand. These algorithms are used to map statistical features extracted from physiological signals onto different states such as health status of a patient or type of activity performed by a subject. Conventionally selected features may lead to rapid battery depletion, mainly due to the absence of computing complexity criterion while selecting prominent features. In this paper, we introduce the notion of power-aware feature selection, which aims at minimizing energy consumption of the data processing for classification applications such as action recognition. Our approach takes into consideration the energy cost of individual features that are calculated in real-time. A graph model is introduced to represent correlation and computing complexity of the features. The problem is formulated using integer programming and a greedy approximation is presented to select the features in a power-efficient manner. Experimental results on thirty channels of activity data collected from real subjects demonstrate that our approach can significantly reduce energy consumption of the computing module, resulting in more than 30 percent energy savings while achieving 96.7 percent classification accuracy.
As computers become more pervasive, they become more personal. The ultimate pervasive computer would be as unique as the individual it serves. Unfortunately, consumer products are typically closed to protect the manuf...
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As computers become more pervasive, they become more personal. The ultimate pervasive computer would be as unique as the individual it serves. Unfortunately, consumer products are typically closed to protect the manufacturer''s intellectual property
human-centered computing and human-centered multimedia computing (HCMC) have emerged as important subfields of computational science which leverage the social and behavioral sciences toward improving the usability of ...
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human-centered computing and human-centered multimedia computing (HCMC) have emerged as important subfields of computational science which leverage the social and behavioral sciences toward improving the usability of technologies and multimedia systems. While technological solutions have made significant strides for the broader population, individuals with disabilities have been largely ignored, often having to force-fit or adapt themselves to available solutions. The authors first introduced a methodology to enrich HCMC by considering perspectives from individuals with disabilities. They subsequently introduced a person-centered approach to HCMC known as person-centered multimedia computing (PCMC). In the proposed work, they seek to further enrich the PCMC methodology by incorporating interdisciplinary inspirations that take into account the diverse challenges associated with assistive technology design and deployment. Several applications are presented, highlighting how considerations of technology, adaptation and policy from a disability perspective can enrich the design of person-centered accessible technologies. This approach has been implemented through ongoing work on a NSF IGERT project, "Alliance for Person-centered Accessible Technologies," details of which are also provided in this paper.
Physiological and affective computing propose methods to improve human-machine interactions by adapting machines to the users' states. Recently, social signal processing (SSP) has proposed to apply similar methods...
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Physiological and affective computing propose methods to improve human-machine interactions by adapting machines to the users' states. Recently, social signal processing (SSP) has proposed to apply similar methods to human-human interactions with the hope of better understanding and modeling social interactions. Most of the social signals employed are facial expressions, body movements and speech, but studies using physiological signals remain scarce. In this paper, we motivate the use of physiological signals in the context of social interactions. Specifically, we review studies which have investigated the relationship between various physiological indices and social interactions. We then propose two main directions to apply physiological SSP: using physiological signals of individual users as new social cues displayed in the group and using inter-user physiology to measure properties of the interactions such as conflict and social presence. We conclude that physiological measures have the potential to enhance social interactions and to connect people.
Social computing is ubiquitous and intensifying in the 21st *** used to reference computational augmentation of social interaction through collaborative filtering,social media,wikis,and crowdsourcing,here I propose to...
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Social computing is ubiquitous and intensifying in the 21st *** used to reference computational augmentation of social interaction through collaborative filtering,social media,wikis,and crowdsourcing,here I propose to expand the concept to cover the complete dynamic interface between social interaction and computation,including computationally enhanced sociality and social science,socially enhanced computing and computer science,and their increasingly complex combination for mutual *** recommends that we reimagine Computational Social Science as Social computing,not merely using computational tools to make sense of the contemporary explosion of social data,but also recognizing societies as emergent computers of more or less collective intelligence,innovation and *** further proposes we imagine a socially inspired computer science that takes these insights into account as we build machines not merely to substitute for human cognition,but radically complement *** leads to a vision of social computing as an extreme form of human computer interaction,whereby machines and persons recursively combine to augment one another in generating collective intelligence,enhanced knowledge,and other social goods unattainable without each *** the example of science and technology,I illustrate how progress in each of these areas unleash advances in the others and the beneficial relationship between the technology and science of social computing,which reveals limits of sociality and computation,and stimulates our imagination about how they can reach past those limits together.
We discuss the existing facilities at the Design computing and Extended Reality (DCXR) Lab at George Mason University, which comprise mostly commercial off-the -shelf computing and extended reality devices, for conduc...
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ISBN:
(纸本)9798350348392
We discuss the existing facilities at the Design computing and Extended Reality (DCXR) Lab at George Mason University, which comprise mostly commercial off-the -shelf computing and extended reality devices, for conducting research on virtual reality -based training, We also share thoughts on extending the facilities for conducting more sophisticated virtual reality (VR) training research in the future, which features more advanced functionali ties such as remote VR training, adaptive training, and co-training in VR. In particular, we discuss a remote VR training platform to be established between George Mason University and Purdue University.
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