3,832 research outputs found
Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review
Animals play a profoundly important and intricate role in our lives today.
Dogs have been human companions for thousands of years, but they now work
closely with us to assist the disabled, and in combat and search and rescue
situations. Farm animals are a critical part of the global food supply chain,
and there is increasing consumer interest in organically fed and humanely
raised livestock, and how it impacts our health and environmental footprint.
Wild animals are threatened with extinction by human induced factors, and
shrinking and compromised habitat. This review sets the goal to systematically
survey the existing literature in smart computing and sensing technologies for
domestic, farm and wild animal welfare. We use the notion of \emph{animal
welfare} in broad terms, to review the technologies for assessing whether
animals are healthy, free of pain and suffering, and also positively stimulated
in their environment. Also the notion of \emph{smart computing and sensing} is
used in broad terms, to refer to computing and sensing systems that are not
isolated but interconnected with communication networks, and capable of remote
data collection, processing, exchange and analysis. We review smart
technologies for domestic animals, indoor and outdoor animal farming, as well
as animals in the wild and zoos. The findings of this review are expected to
motivate future research and contribute to data, information and communication
management as well as policy for animal welfare
FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices
Deep neural networks show great potential as solutions to many sensing
application problems, but their excessive resource demand slows down execution
time, pausing a serious impediment to deployment on low-end devices. To address
this challenge, recent literature focused on compressing neural network size to
improve performance. We show that changing neural network size does not
proportionally affect performance attributes of interest, such as execution
time. Rather, extreme run-time nonlinearities exist over the network
configuration space. Hence, we propose a novel framework, called FastDeepIoT,
that uncovers the non-linear relation between neural network structure and
execution time, then exploits that understanding to find network configurations
that significantly improve the trade-off between execution time and accuracy on
mobile and embedded devices. FastDeepIoT makes two key contributions. First,
FastDeepIoT automatically learns an accurate and highly interpretable execution
time model for deep neural networks on the target device. This is done without
prior knowledge of either the hardware specifications or the detailed
implementation of the used deep learning library. Second, FastDeepIoT informs a
compression algorithm how to minimize execution time on the profiled device
without impacting accuracy. We evaluate FastDeepIoT using three different
sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus.
FastDeepIoT further reduces the neural network execution time by to
and energy consumption by to compared with the
state-of-the-art compression algorithms.Comment: Accepted by SenSys '1
Modus Operandi of Crowd Workers : The Invisible Role of Microtask Work Environments
The ubiquity of the Internet and the widespread proliferation of electronic devices has resulted in flourishing microtask
crowdsourcing marketplaces, such as Amazon MTurk. An aspect that has remained largely invisible in microtask crowdsourcing
is that of work environments; defined as the hardware and software affordances at the disposal of crowd workers which are used
to complete microtasks on crowdsourcing platforms. In this paper, we reveal the significant role of work environments in the
shaping of crowd work. First, through a pilot study surveying the good and bad experiences workers had with UI elements in
crowd work, we revealed the typical issues workers face. Based on these findings, we then deployed over 100 distinct microtasks
on CrowdFlower, addressing workers in India and USA in two identical batches. These tasks emulate the good and bad UI
element designs that characterize crowdsourcing microtasks. We recorded hardware specifics such as CPU speed and device
type, apart from software specifics including the browsers used to complete tasks, operating systems on the device, and other
properties that define the work environments of crowd workers. Our findings indicate that crowd workers are embedded in a
variety of work environments which influence the quality of work produced. To confirm and validate our data-driven findings we
then carried out semi-structured interviews with a sample of Indian and American crowd workers from this platform. Depending
on the design of UI elements in microtasks, we found that some work environments are more suitable than others to support
crowd workers. Based on our overall findings resulting from all the three studies, we introduce ModOp, a tool that helps to
design crowdsourcing microtasks that are suitable for diverse crowd work environments. We empirically show that the use of
ModOp results in reducing the cognitive load of workers, thereby improving their user experience without effecting the accuracy
or task completion time
Multi-task Self-Supervised Learning for Human Activity Detection
Deep learning methods are successfully used in applications pertaining to
ubiquitous computing, health, and well-being. Specifically, the area of human
activity recognition (HAR) is primarily transformed by the convolutional and
recurrent neural networks, thanks to their ability to learn semantic
representations from raw input. However, to extract generalizable features,
massive amounts of well-curated data are required, which is a notoriously
challenging task; hindered by privacy issues, and annotation costs. Therefore,
unsupervised representation learning is of prime importance to leverage the
vast amount of unlabeled data produced by smart devices. In this work, we
propose a novel self-supervised technique for feature learning from sensory
data that does not require access to any form of semantic labels. We learn a
multi-task temporal convolutional network to recognize transformations applied
on an input signal. By exploiting these transformations, we demonstrate that
simple auxiliary tasks of the binary classification result in a strong
supervisory signal for extracting useful features for the downstream task. We
extensively evaluate the proposed approach on several publicly available
datasets for smartphone-based HAR in unsupervised, semi-supervised, and
transfer learning settings. Our method achieves performance levels superior to
or comparable with fully-supervised networks, and it performs significantly
better than autoencoders. Notably, for the semi-supervised case, the
self-supervised features substantially boost the detection rate by attaining a
kappa score between 0.7-0.8 with only 10 labeled examples per class. We get
similar impressive performance even if the features are transferred from a
different data source. While this paper focuses on HAR as the application
domain, the proposed technique is general and could be applied to a wide
variety of problems in other areas
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