203 research outputs found
Steganographic Generative Adversarial Networks
Steganography is collection of methods to hide secret information ("payload")
within non-secret information "container"). Its counterpart, Steganalysis, is
the practice of determining if a message contains a hidden payload, and
recovering it if possible. Presence of hidden payloads is typically detected by
a binary classifier. In the present study, we propose a new model for
generating image-like containers based on Deep Convolutional Generative
Adversarial Networks (DCGAN). This approach allows to generate more
setganalysis-secure message embedding using standard steganography algorithms.
Experiment results demonstrate that the new model successfully deceives the
steganography analyzer, and for this reason, can be used in steganographic
applications.Comment: 15 pages, 10 figures, 5 tables, Workshop on Adversarial Training
(NIPS 2016, Barcelona, Spain
An industry case of large-scale demand forecasting of hierarchical components
Demand forecasting of hierarchical components is essential in manufacturing.
However, its discussion in the machine-learning literature has been limited,
and judgemental forecasts remain pervasive in the industry. Demand planners
require easy-to-understand tools capable of delivering state-of-the-art
results. This work presents an industry case of demand forecasting at one of
the largest manufacturers of electronics in the world. It seeks to support
practitioners with five contributions: (1) A benchmark of fourteen demand
forecast methods applied to a relevant data set, (2) A data transformation
technique yielding comparable results with state of the art, (3) An alternative
to ARIMA based on matrix factorization, (4) A model selection technique based
on topological data analysis for time series and (5) A novel data set.
Organizations seeking to up-skill existing personnel and increase forecast
accuracy will find value in this work
Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough
The COVID-19 pandemic created a significant interest and demand for infection
detection and monitoring solutions. In this paper we propose a machine learning
method to quickly triage COVID-19 using recordings made on consumer devices.
The approach combines signal processing methods with fine-tuned deep learning
networks and provides methods for signal denoising, cough detection and
classification. We have also developed and deployed a mobile application that
uses symptoms checker together with voice, breath and cough signals to detect
COVID-19 infection. The application showed robust performance on both open
sourced datasets and on the noisy data collected during beta testing by the end
users
Dielectric properties of the tissues with different histological structure: Ex vivo study
This study aimed to estimate the dielectric properties of tissues with different histological structures. For this, specimens of fibrous (n=9), muscular (n=7), and fatty (n=11) human tissues were studied. The estimation of dielectric permittivity and conductivity of these specimens was tested with a program and apparatus device for near-field resonance microwave sensing, including 5 applicators with different depths of study. Results of the study demonstrated that this technology can visualize the shape, localization, and linear decisions of biological objects. The currently used method allows distinguishing the tissue histological type. It was stated that fibrous tissue has a maximal level of median and highest dielectric permittivity, and the minimal value of this parameter was fixed for fatty specimens (in 4.26 and 4.53 times lower than in fibrous one, respectively). Muscular tissue has an intermediate value of dielectric permittivity, approaching a level close to fibrous tissue
Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting
Topological Data Analysis (TDA) is a recent approach to analyze data sets
from the perspective of their topological structure. Its use for time series
data has been limited. In this work, a system developed for a leading provider
of cloud computing combining both user segmentation and demand forecasting is
presented. It consists of a TDA-based clustering method for time series
inspired by a popular managerial framework for customer segmentation and
extended to the case of clusterwise regression using matrix factorization
methods to forecast demand. Increasing customer loyalty and producing accurate
forecasts remain active topics of discussion both for researchers and managers.
Using a public and a novel proprietary data set of commercial data, this
research shows that the proposed system enables analysts to both cluster their
user base and plan demand at a granular level with significantly higher
accuracy than a state of the art baseline. This work thus seeks to introduce
TDA-based clustering of time series and clusterwise regression with matrix
factorization methods as viable tools for the practitioner
Recommender systems in antiviral drug discovery
Recommender systems (RSs), which underwent rapid development and had an enormous impact on e-commerce, have the potential to become useful tools for drug discovery. In this paper, we applied RS methods for the prediction of the antiviral activity class (active/inactive) for compounds extracted from ChEMBL. Two main RS approaches were applied: Collaborative filtering (Surprise implementation) and content-based filtering (sparse-group inductive matrix completion (SGIMC) method). The effectiveness of RS approaches was investigated for prediction of antiviral activity classes ("interactions") for compounds and viruses, for which some of their interactions with other viruses or compounds are known, and for prediction of interaction profiles for new compounds. Both approaches achieved relatively good prediction quality for binary classification of individual interactions and compound profiles, as quantified by cross-validation and external validation receiver operating characteristic (ROC) score >0.9. Thus, even simple recommender systems may serve as an effective tool in antiviral drug discovery
Special Economic Zones as a Factor in Ensuring Security of Social and Economic Development of the Territory
This article analyzes the tools of special economic zones as a means of ensuring security of socio-economic development of the territory. Considered Canadian experience of regional development on the basis of special economic zones and project forms of public-private partnership. Particular attention is paid to the interaction of special economic zones with different management tools of territorial development, in particular the system of indicative planning
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