2,261 research outputs found
Using the Proteus virtual environment to train future IT professionals
Abstract. Based on literature review it was established that the use of augmented reality as an innovative technology of student training occurs in following
directions: 3D image rendering; recognition and marking of real objects; interaction of a virtual object with a person in real time. The main advantages of using AR and VR in the educational process are highlighted: clarity, ability to simulate processes and phenomena, integration of educational disciplines, building an open education system, increasing motivation for learning, etc. It has
been found that in the field of physical process modelling the Proteus Physics Laboratory is a popular example of augmented reality. Using the Proteus
environment allows to visualize the functioning of the functional nodes of the computing system at the micro level. This is especially important for
programming systems with limited resources, such as microcontrollers in the process of training future IT professionals. Experiment took place at Borys
Grinchenko Kyiv University and Sumy State Pedagogical University named after A. S. Makarenko with students majoring in Computer Science (field of knowledge is Secondary Education (Informatics)). It was found that computer modelling has a positive effect on mastering the basics of microelectronics. The ways of further scientific researches for grounding, development and
experimental verification of forms, methods and augmented reality, and can be used in the professional training of future IT specialists are outlined in the article
Secure -ish Nearest Neighbors Classifier
In machine learning, classifiers are used to predict a class of a given query
based on an existing (classified) database. Given a database S of n
d-dimensional points and a d-dimensional query q, the k-nearest neighbors (kNN)
classifier assigns q with the majority class of its k nearest neighbors in S.
In the secure version of kNN, S and q are owned by two different parties that
do not want to share their data. Unfortunately, all known solutions for secure
kNN either require a large communication complexity between the parties, or are
very inefficient to run.
In this work we present a classifier based on kNN, that can be implemented
efficiently with homomorphic encryption (HE). The efficiency of our classifier
comes from a relaxation we make on kNN, where we allow it to consider kappa
nearest neighbors for kappa ~ k with some probability. We therefore call our
classifier k-ish Nearest Neighbors (k-ish NN).
The success probability of our solution depends on the distribution of the
distances from q to S and increase as its statistical distance to Gaussian
decrease.
To implement our classifier we introduce the concept of double-blinded
coin-toss. In a doubly-blinded coin-toss the success probability as well as the
output of the toss are encrypted. We use this coin-toss to efficiently
approximate the average and variance of the distances from q to S. We believe
these two techniques may be of independent interest.
When implemented with HE, the k-ish NN has a circuit depth that is
independent of n, therefore making it scalable. We also implemented our
classifier in an open source library based on HELib and tested it on a breast
tumor database. The accuracy of our classifier (F_1 score) were 98\% and
classification took less than 3 hours compared to (estimated) weeks in current
HE implementations
SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning
Performing machine learning (ML) computation on private data while
maintaining data privacy, aka Privacy-preserving Machine Learning~(PPML), is an
emergent field of research. Recently, PPML has seen a visible shift towards the
adoption of the Secure Outsourced Computation~(SOC) paradigm due to the heavy
computation that it entails. In the SOC paradigm, computation is outsourced to
a set of powerful and specially equipped servers that provide service on a
pay-per-use basis. In this work, we propose SWIFT, a robust PPML framework for
a range of ML algorithms in SOC setting, that guarantees output delivery to the
users irrespective of any adversarial behaviour. Robustness, a highly desirable
feature, evokes user participation without the fear of denial of service.
At the heart of our framework lies a highly-efficient, maliciously-secure,
three-party computation (3PC) over rings that provides guaranteed output
delivery (GOD) in the honest-majority setting. To the best of our knowledge,
SWIFT is the first robust and efficient PPML framework in the 3PC setting.
SWIFT is as fast as (and is strictly better in some cases than) the best-known
3PC framework BLAZE (Patra et al. NDSS'20), which only achieves fairness. We
extend our 3PC framework for four parties (4PC). In this regime, SWIFT is as
fast as the best known fair 4PC framework Trident (Chaudhari et al. NDSS'20)
and twice faster than the best-known robust 4PC framework FLASH (Byali et al.
PETS'20).
We demonstrate our framework's practical relevance by benchmarking popular ML
algorithms such as Logistic Regression and deep Neural Networks such as VGG16
and LeNet, both over a 64-bit ring in a WAN setting. For deep NN, our results
testify to our claims that we provide improved security guarantee while
incurring no additional overhead for 3PC and obtaining 2x improvement for 4PC.Comment: This article is the full and extended version of an article to appear
in USENIX Security 202
Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems
Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been
demonstrated to perform efficiently in a variety of applications, such as
dimensionality reduction, feature learning, and classification. Their
implementation on neuromorphic hardware platforms emulating large-scale
networks of spiking neurons can have significant advantages from the
perspectives of scalability, power dissipation and real-time interfacing with
the environment. However the traditional RBM architecture and the commonly used
training algorithm known as Contrastive Divergence (CD) are based on discrete
updates and exact arithmetics which do not directly map onto a dynamical neural
substrate. Here, we present an event-driven variation of CD to train a RBM
constructed with Integrate & Fire (I&F) neurons, that is constrained by the
limitations of existing and near future neuromorphic hardware platforms. Our
strategy is based on neural sampling, which allows us to synthesize a spiking
neural network that samples from a target Boltzmann distribution. The recurrent
activity of the network replaces the discrete steps of the CD algorithm, while
Spike Time Dependent Plasticity (STDP) carries out the weight updates in an
online, asynchronous fashion. We demonstrate our approach by training an RBM
composed of leaky I&F neurons with STDP synapses to learn a generative model of
the MNIST hand-written digit dataset, and by testing it in recognition,
generation and cue integration tasks. Our results contribute to a machine
learning-driven approach for synthesizing networks of spiking neurons capable
of carrying out practical, high-level functionality.Comment: (Under review
An embodied model for handwritten digits recognition in a cognitive robot
This paper presents an embodied model for recognition of handwritten digits in a cognitive developmental robot scenario. Inspired by neuro-psychological data, the model integrates three modules: a stacked auto-encoder network to process the visual information, a feedforward neural controller for the fingers, and a generalized regression network that associates number digits to finger configurations. Results from developmental learning experiments show an improvement in the digits' recognition rate thanks to the inclusion of the robot fingers in the training especially in its early stages (epochs) or with a low number of examples. This behaviour can be linked to that observed in psychological studies with children, who seem to benefit of finger counting only in the initial stage of mathematical learning. These results suggest the potential of the embodied approach to favour the creation of a psychologically plausible developmental model for mathematical cognition in robots and to support the creation of more complex models of human-like behaviours
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