CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks

The unprecedented increase in the usage of computer vision technology in society goes hand in hand with an increased concern in data privacy. In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity. We propose and develop CIAGAN, a model for image and video anonymization based on conditional generative adversarial networks. Our model is able to remove the identifying characteristics of faces and bodies while producing high-quality images and videos that can be used for any computer vision task, such as detection or tracking. Unlike previous methods, we have full control over the de-identification (anonymization) procedure, ensuring both anonymization as well as diversity. We compare our method to several baselines and achieve state-of-the-art results.Comment: CVPR 202

Socio-Anthropological Problems of Education in the Consumer Society and Information Technologies

This paper aims to show the inconsistency of the process and results of informatization for people, culture, and society, where consumption becomes its characteristic feature. We note that with the wide spread of education in the modern world, which to a certain extent became possible due to the development of information technologies, its depth disappears, and it becomes less qualitative. The research is based on the dialectical method, and it allows us to identify both positive and negative aspects of the introduction of information technology in the educational process. The system and structural-functional methods proved to be useful for a comprehensive analysis of education in the context of social changes, establishing a number of relationships and transforming the cognitive abilities of a person as a subject of education. The research novelty lies in the identification of changes that occur in the subjects of the educational process under the influence of the introduction of information technologies. Such socio-anthropological changes became an important philosophical and scientific problem that requires further interdisciplinary research

Deep Appearance Maps

We propose a deep representation of appearance, i. e., the relation of color, surface orientation, viewer position, material and illumination. Previous approaches have useddeep learning to extract classic appearance representationsrelating to reflectance model parameters (e. g., Phong) orillumination (e. g., HDR environment maps). We suggest todirectly represent appearance itself as a network we call aDeep Appearance Map (DAM). This is a 4D generalizationover 2D reflectance maps, which held the view direction fixed. First, we show how a DAM can be learned from images or video frames and later be used to synthesize appearance, given new surface orientations and viewer positions. Second, we demonstrate how another network can be used to map from an image or video frames to a DAM network to reproduce this appearance, without using a lengthy optimization such as stochastic gradient descent (learning-to-learn). Finally, we show the example of an appearance estimation-and-segmentation task, mapping from an image showingmultiple materials to multiple deep appearance maps

Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis

The advent of data-driven technology solutions is accompanied by an increasing concern with data privacy. This is of particular importance for human-centered image recognition tasks, such as pedestrian detection, re-identification, and tracking. To highlight the importance of privacy issues and motivate future research, we motivate and introduce the Pedestrian Dataset De-Identification (PDI) task. PDI evaluates the degree of de-identification and downstream task training performance for a given de-identification method. As a first baseline, we propose IncogniMOT, a two-stage full-body de-identification pipeline based on image synthesis via generative adversarial networks. The first stage replaces target pedestrians with synthetic identities. To improve downstream task performance, we then apply stage two, which blends and adapts the synthetic image parts into the data. To demonstrate the effectiveness of IncogniMOT, we generate a fully de-identified version of the MOT17 pedestrian tracking dataset and analyze its application as training data for pedestrian re-identification, detection, and tracking models. Furthermore, we show how our data is able to narrow the synthetic-to-real performance gap in a privacy-conscious manner

Granulation of nanocomposites based on glauconite and urea: binding materials and characterization of activated mineral fertilisers

Relevance. Development of controlled release fertilisers and their granulation is at the forefront of agriculture and environment. With growing world population and increasing food demand, agriculture faces the challenge of efficient resource management and increased crop yields. In this environmentally friendly and easy to obtain and use fertilisers become a key element for sustainable development of agribusinesses. Aim. To study the complete cycle of creation of granular fertilisers based on new materials, including aggregation of mineral particles with different binding solutions and mechanochemical activation of initial mixtures of glauconite and urea. Potassium-containing clay mineral – glauconite of Karinskiy deposit (Russia) was used as a "container" material. Urea containing up to 46 wt % of nitrogen was used as an additive (nutrient) component for fertilisers. The following parameters were stable during mechanochemical preparation: mineral/urea ratio and abrasion type. Solutions with different nitrogen concentrations were used as binders during granulation. The methodology for investigating the characteristics of the resulting nanocomposites included particle size analysis, X-ray diffraction analysis, scanning electron microscopy, and FTIR spectrometer. Results. The advantages of different granulation options for mechanochemically activated composites were investigated. The authors have produced f full cycle of controlled-release granular fertiliser. The average nitrogen content in glauconite particles reaches 5.5 wt %. The granule size depends on the content of urea gel concentrate in the binder. The maximum strength and proportionality of granules are achieved, when using a binder consisting of 100% urea gel concentrate. Determination of the optimal content of gel concentrate in the binder used in granulation plays a key role in obtaining strong fertiliser granules

Synthesis of Tungsten Diselenide Nanoparticles by Chemical Vapor Condensation Method

Crystalline tungsten diselenide (WSe2) nanoparticles have been synthesized by a gas phase reaction using tungsten hexacarbonyl and elemental selenium as precursors. The WSe2 nanoparticle morphology varies from the spherical shape to flake-like layered structures. Mean size in smaller dimension are less than 5 nm and the number of layers decreased linearly with decreasing of reaction time and concentration of carbonyl in the gas phase. The mean value of interlayer distance in &lt;0001&gt; direction is comparable with the microscopic values. The selenium-to-tungsten atomic ratios of 2.07, 2.19 and 2.19 were determined respectively, approach to the stoichiometric ratio of 2:1. Main impurities are oxygen and carbon and strongly interrelated with carbonyl concentration in the gas phase.DOI: http://dx.doi.org/10.5755/j01.ms.21.3.7356</p

LIME: Live Intrinsic Material Estimation

We present the first end to end approach for real time material estimation for general object shapes with uniform material that only requires a single color image as input. In addition to Lambertian surface properties, our approach fully automatically computes the specular albedo, material shininess, and a foreground segmentation. We tackle this challenging and ill posed inverse rendering problem using recent advances in image to image translation techniques based on deep convolutional encoder decoder architectures. The underlying core representations of our approach are specular shading, diffuse shading and mirror images, which allow to learn the effective and accurate separation of diffuse and specular albedo. In addition, we propose a novel highly efficient perceptual rendering loss that mimics real world image formation and obtains intermediate results even during run time. The estimation of material parameters at real time frame rates enables exciting mixed reality applications, such as seamless illumination consistent integration of virtual objects into real world scenes, and virtual material cloning. We demonstrate our approach in a live setup, compare it to the state of the art, and demonstrate its effectiveness through quantitative and qualitative evaluation.Comment: 17 pages, Spotlight paper in CVPR 201

BRCA1 4153delA founder mutation in Russian ovarian cancer patients

The BRCA1 4153delA allele is frequently referred to as the Russian founder mutation, as it was initially detected in several cancer families from Moscow. Our earlier studies have demonstrated 1% occurrence of BRCA1 4153delA heterozygosity in familial and/or early-onset and/or bilateral Russian breast cancer (BC) patients. Since literature data suggest that the 4153delA variant is more associated with ovarian cancer (OC) than with BC, we expected to reveal a highly elevated frequency of this genotype in Russian ovarian cancer series. However, real-time allele-specific PCR genotyping has detected only two BRCA1 4153delA carriers out of 177 unselected OC patients (1.1%). Both these carriers were early-onset and had serous carcinomas of grade 3. Thus, our study supports neither the Russian origin of BRCA1 4153delA mutation, nor its selectivity towards ovarian versus breast cancer predisposition