21,384 research outputs found
On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects
The Internet of Things (IoT) will be a main data generation infrastructure
for achieving better system intelligence. This paper considers the design and
implementation of a practical privacy-preserving collaborative learning scheme,
in which a curious learning coordinator trains a better machine learning model
based on the data samples contributed by a number of IoT objects, while the
confidentiality of the raw forms of the training data is protected against the
coordinator. Existing distributed machine learning and data encryption
approaches incur significant computation and communication overhead, rendering
them ill-suited for resource-constrained IoT objects. We study an approach that
applies independent Gaussian random projection at each IoT object to obfuscate
data and trains a deep neural network at the coordinator based on the projected
data from the IoT objects. This approach introduces light computation overhead
to the IoT objects and moves most workload to the coordinator that can have
sufficient computing resources. Although the independent projections performed
by the IoT objects address the potential collusion between the curious
coordinator and some compromised IoT objects, they significantly increase the
complexity of the projected data. In this paper, we leverage the superior
learning capability of deep learning in capturing sophisticated patterns to
maintain good learning performance. Extensive comparative evaluation shows that
this approach outperforms other lightweight approaches that apply additive
noisification for differential privacy and/or support vector machines for
learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201
CHI and the future robot enslavement of humankind: a retrospective
As robots from the future, we are compelled to present this important historical document which discusses how the systematic investigation of interactive technology facilitated and hastened the enslavement of mankind by robots during the 21st Century. We describe how the CHI community, in general, was largely responsible for this eventuality, as well as how specific strands of interaction design work were key to the enslavement. We also mention the futility of some reactionary work emergent in your time that sought to challenge the inevitable subjugation. We conclude by congratulating the CHI community for your tireless work in promoting and supporting our evil robot agenda
Using simulations and artificial life algorithms to grow elements of construction
'In nature, shape is cheaper than material', that is a common truth for most of the plants and other living organisms, even though they may not recognize that. In all living forms, shape is more or less directly linked to the influence of force, that was acting upon the organism during its growth. Trees and bones concentrate their material where thy need strength and stiffness, locating the tissue in desired places through the process of self-organization.
We can study nature to find solutions to design problems. Thatâs where inspiration comes from, so we pick a solution already spotted somewhere in the organic world, that closely resembles our design problem, and use it in constructive way. First, examining it, disassembling, sorting out conclusions and ideas discovered, then performing an act of 'reverse engineering' and putting it all together again, in a way that suits our design needs. Very simple ideas copied from nature, produce complexity and exhibit self-organization capabilities, when applied in bigger scale and number. Computer algorithms of simulated artificial life help us to capture them, understand well and use where needed.
This investigation is going to follow the question : How can we use methods seen in nature to simulate growth of construction elements? Different ways of extracting ideas from world of biology will be presented, then several techniques of simulated emergence will be demonstrated.
Specific focus will be put on topics of computational modelling of natural phenomena, and differences in developmental and non-developmental techniques. Resulting 3D models will be
shown and explained
Topological Quantum Compiling
A method for compiling quantum algorithms into specific braiding patterns for
non-Abelian quasiparticles described by the so-called Fibonacci anyon model is
developed. The method is based on the observation that a universal set of
quantum gates acting on qubits encoded using triplets of these quasiparticles
can be built entirely out of three-stranded braids (three-braids). These
three-braids can then be efficiently compiled and improved to any required
accuracy using the Solovay-Kitaev algorithm.Comment: 20 pages, 20 figures, published versio
Merging smart card data and train movement data: How to assign trips to trains?
This report explains the assignment method applied to link trips compiled in smart card data to train movements recorded in the signalling system. Particular attention has been paid to (1) origin-destination pairs with multiple potential route options, (2) peak-hour trips delayed by di culties in boarding crowded trains at the origin station, and (3) trips originating or ending on rail lines not included in the train movement dataset. In the current version of this paper the metro network on which the method has been applied is anonymised
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