Reviewerly/exHarmony huggingface dataset:
Museum of Geology and Mineralogy of I.V. Bel’kov (Geological Institute of the KSC RAS): on the threshold of the 90<sup>th</sup> anniversary

Abstract Musium of Geology and Mineralogy of I.V. Bel’kov of the Geological Institute (GI) of the Kola Science Centre of the RAS is located in Apatity city, Murmansk region, in the FRC KSC RAS building. Officially, the Museum GI was created in 1957, although it has been leading its history since the 30s of 20 th century. The history of the Museum is closely connected with the names of such outstanding researchers of the Kola North as academician A. E. Fersman and doctor of geological and mineralogical sciences I. V. Bel’kov. In 2009, the Museum was named after I. V. Bel’kov. The exposition of the Museum is the most complete collection of minerals and rocks of the Kola region. Among them are rare and new minerals, which are unique in their assiciation, forms of crystal, sizes and beauty of colors. This give a great value to the collections and arouse great interest of visitors. Samles of minerals and rock come to Museum from different geological objects in the Kola region exclusively: Khibiny and Lovozero, Kovdor, Keivy, Pechenga etc. The presented samples of minerals, rocks and ores allow us to estimate the richness and diversity of the stone world of the Kola region. The Museum workers are engaged not only in accounting, but also in scientific and educational activities. Constant work with old collections leads to the discovery of previously unknown minerals in the region, and with the participation of employees of the GI CSC RAS, new mineral species are discovered. Every five years there is a “Catalogue of mineral species of the Kola region”, which is constantly updated. In 2020, it will be 90 years since the formation of the first Museum of geology and mineralogy in the Kola region.
Enabling Designers to Generate Concepts of Interactive Product Behaviours: A Mixed Reality Design Approach

Abstract To design interactive behaviours for their products designers/makers have to use high fidelity tools like ‘electronic prototyping kits’, involving sensors and programming to incorporate interactions in their products and are dependent on availability of hardware. Not every designer is comfortable using such tools to ideate and test their concept ideas, eventually slowing them down in the process. Thus, there is a need for a design tool that reduces dependence on complex components of such tools while exploring new concepts for product design at an early stage. In this work, we propose a Mixed Reality system that we developed to simulate interactive behaviours of products using designed visual interaction blocks. The system is implemented in three stages: idea generation, creating interactions and revision of interactive behaviours. The implemented virtual scenario showed to elicit high motivation and appeal among users resulting in inventive and creative design experience at the same time. As a result, designers will be able to create and revise their interaction-behavioural design concepts virtually with relative ease, resulting in higher concept generation and their validation.
Adaptive Updating Siamese Network with Like-Hood Estimation for Surveillance Video Object Tracking

Surveillance video object tracking is considered vital with the expanding of surveillance services. Many recent trackers have achieved advanced performance while model drift is still a challenging topic in surveillance scenes because of background interference and object appearance variation. In this paper, we propose an ensemble framework consisting of two branches to avoid model drift. In the correlation branch, a well-designed Siamese network is presented which employs a spatial channel attention module to promote tracking performance and adopts novel criterions to adap tively update filter templates thus preventing model drift. In the like-hood branch, a like-hood estimation method is proposed to correct object position which effectively reduces similar backgrounds' interference. Then two branches combine to get the ultimate response. Experimental results on four challenging surveillance video related benchmarks show that our approach has state-of-the-art tracking performance while runs at 65 frames per second speeds showing great potential in practical tracking application.
The Relationship Between Culture and Human Development

The human development is used to evaluate the richness of human life, focusing on the people, on their opportunities and choices, rather than simply on the richness of economies. As for national culture, it is understood as a set of characteristics that distinguish members and that may influence all aspects of social and individual life. This study hypothesizes that national culture, measured using Hofstede's six cultural dimensions, has an impact on corruption and on innovation, and that less corrupt and more innovative nations create better welfare conditions and human development for their habitants. To test the proposed framework, data were obtained from Hofstede's, Transparency International, Global Innovation, and United Nations Development Programme websites for the year 2012. Using PLS-SEM, the results show that cultural factors play a smaller role on determining innovation than corruption, and that decreasing corruption is more important to improve human development than increasing innovation.
Quality guided image recognition towards industrial materials diffusion

Ordered porous materials, especially mesoporous materials, molecular sieves and organometallic framework materials, are widely used in the fields of adsorption, membrane separation and catalytic reaction processes due to their unique properties. Molecular sieve porous material is a new type of engineering material with excellent performance, and its microstructure is one of the key factors affecting macroscopic physical properties and use effect. At present, in order to further broaden its application in the fields of separation, catalysis, sensors and micro-devices, at present, microscopic molecular level control of porous material composition and structure, macroscopically controlling its appearance and appearance has become a research of porous materials. An important development direction. However, the quantitative characterization of the molecular structure of molecular sieve porous materials and its influence on physical properties has always been the focus and difficulty in the field of materials science and engineering. At the same time, the microstructure of porous materials of molecular sieves is the key factor affecting its macroscopic physical properties, and the analysis of spatial structure characteristics. Has important research significance. Based on this paper, a method for predicting effective diffusion coefficient in porous materials by convolutional neural network is proposed. The training samples of porous material microstructure are generated by computer stochastic simulation, and the corresponding effective diffusion coefficients are calculated by finite element method. The image quality subjective evaluation model is used to control the microscopic picture precision of the molecular sieve porous material. The combination of the two methods can quickly and accurately calculate the effective diffusion coefficient.
Unsupervised Domain Adaptation for Disguised Face Recognition

Disguised face recognition (DFR) is an extremely challenging task due to the numerous variations that can be introduced with different disguises. Most existing disguised face recognition approaches follow a supervised learning framework. However, due to the domain shift problem, the Convolutional Neural Networks (CNN) model trained on one dataset often fail to generalize well to another dataset. In our attempt, we formulate the DFR as an unsupervised learning problem and propose a unified deep learning architecture Unsupervised Domain Adaptation Model (UDAM) with three merits. Firstly, UDAM is a unified deep architecture, containing a Domain Style Adaptation subNet (DSN) and an Attention Learning subNet (ALN), which jointly learn from end-to-end. Secondly, DSN is a well-design generative adversarial network which simultaneously translate the labeled image from the source to the target domain in an unsupervised manner and maintain the ID label after translation. Thirdly, ALN is a Convolutional Neural Network (CNN) for disguised face recognition with our proposed attention transfer strategy. Extensive experiments using Simple and Complex Face Disguise Dataset and the IIIT-Delhi Disguise Version 1 Face Database have demonstrated that the proposed method yields a consistent and competitive performance for disguised face recognition.
Toward a Workload Allocation Optimizer for Power Saving in Data Centers

The number and scale of data centers are both rapidly increasing due to a continuously growing demand for cloud computing services from many areas. Cloud computing infrastructure relies on a massive amount of HPC servers to process millions of tasks and consumes an enormous amount of power. The implementation of advanced task allocation technology provides a solution for energy efficiency and has therefore become an essential goal for data centers. In this paper, we propose a novel CPU-intensive workload allocation optimizer (WAO) for the task of power saving within data centers. There are three major contributions to this research. First, a data center monitoring module, which continually reports the latest status of the data center and stores operational data. Second, we propose an accurate and efficient server power prediction model for all servers in the HPC clusters. Third, we provide an optimal task assignment engine that evaluates and assigns tasks to the most appropriate server to facilitate minimal power consumption. Our experimental results show that our proposed WAO can obtain about 29.6% power savings and 26% more productivity in a real data center.
The application of UAV remote sensing in natural disasters emergency monitoring and assessment

Fast acquisition and processing of effective data sources are a heated topic in remote sensing image processing research. Unmanned aerial vehicle (UAV) remote sensing system has the advantages of maneuverability, rapidity and economical, it has become a hot topic in the world. The study analyzes the characteristics of remote sensing image and the characteristics of UAV remote sensing system, and refers a variety of images fast processing algorithms to explore the rapid remote sensing images stitching and rapid information extraction methods. Based on the analysis of the relevant research at home and abroad, this paper draws lessons from some image processing ideas of modern photogrammetry, and proposes a fast image stitching method of UAV remote sensing images based on SURF (Speed Up Robust Features) feature description. This method is applied to UAV remote sensing fast image stitching to achieve high-quality UAV remote sensing images for fast and automatic splicing. The stitching speed of this method is much faster than that of SIFT (Scale-invariant feature transform) algorithm. And the splicing effect of this method is satisfactory.
Fast method of video genre categorization for temporally aggregated broadcast videos

The automatic detection of video genre is very desirable and necessary for further analysis of videos mainly when the video processing methods should be parameterized according to the specific video features. It improves first of all the efficiency of temporal segmentation. Temporal segmentation is usually the initial stage for the analysis of edited videos, for such processes as highlights detection, removing of undesirable parts like publicity, as well as selection of play segments in sports videos, etc. Then the temporal aggregation method based on the analysis of shot length and consisting in shot grouping into scenes of a given category can be applied to significantly reduce processing time. The analyses and the observations of videos confirm that the editions of videos and the video structures significantly depend on the video genre. Many processes can be better performed if the genre of video is known and the methods and their parameters are adequate to the video genre. The paper presents the analyses and tests in the AVI Indexer showing the impact of shot length on the results of temporal segmentation, temporal aggregation, and genre detection of video edited in a standard and typical way for a given video genre.
Decision-based behavior modeling of software-intensive systems

This paper assumes a software-intensive system embedded in an environment of entities and their attributes. Behavior of the system, for which software is responsible, is its effect on selected attributes in the environment. A useful model would record relevant entities and attributes; distinguish attributes the system affects; record that effect with sufficient precision to be executed; organize it to maintain intellectual control and constrain the impact of change during development, sustainment, and assurance; and organize it to make efficient use of processing and communication resources.
Analysis and Visualization of User Navigations on Web

The web is the largest repository of data. The user frequently navigates on the web to access the information. These navigational patterns are stored in weblogs which are growing exponentially with time. This increase in voluminous weblog data raises major challenges concerning handling big data, understanding navigation patterns and the structural complexity of the web, etc. Visualization is a process to view the complex large web data graphically to address these challenges. This chapter describes the various aspects of visualization with which the novel insights can be drawn in the area of web navigation mining. To analyze user navigations, visualization can be applied in two stages: post pre-processing and post pattern discovery. First stage analyses the website structure, website evolution, user navigation behaviour, frequent and rare patterns and detecting noise. Second stage analyses the interesting patterns obtained from prediction modelling of web data. The chapter also highlights popular visualization tools to analyze weblog data.
An Edge Computing Marketplace for Distributed Machine Learning

There is an increasing demand among machine learning researchers for powerful computational resources to train their machine learning models. In order to train these models in a reasonable amount of time, the training is often distributed among multiple machines; yet paying for such machines is costly. DeepMarket attempts to reduce these costs by creating a marketplace that integrates multiple computational resources over a distributed tensorflow framework. Instead of requiring users to rent expensive resources from a third party cloud provider, DeepMarket will allow users to lend their computing resources to each other when they are available. Such a marketplace, however, requires a credit mechanism that ensures users receive resources in proportion to the resources they lend to others. Moreover, DeepMarket must respect users' needs to use their own resources and the resulting limits on when resources can be lent to others. This Demo will introduce the audience to PLUTO: DeepMarket's intuitive graphical user interface. The audience will be able to see how PLUTO in coordination with DeepMarket servers tracks the performance of each user's training jobs, matches jobs to resources made available by other users, and tracks the resulting credits that regulate the exchange of resources.
Application of a Probabilistic Model to High Frequency Spectrum Occupancy

Spectrum occupancy measurements and statistical models for spectrum occupancy data have been subject of interest since many years and are important as the spectrum becomes more and more crowded. Even with the unexpected growth of mobile communications, High Frequency (HF) radio links have advantages that make them unique in some application areas, justifying the development of spectrum occupancy models for the HF domain. In this paper a bidimensional Markov model for spectrum occupancy in HF is presented. The model takes into account both the time dimension and the frequency dimension one. The model is validated using real HF data acquired in the western part of Romania, using a Universal Software Radio Peripheral and a HF antenna. The spectrum occupancy values have been computed off-line for a set of frequency bands according to ITU frequency allocation. The results confirm the validity of the bidimensional Markov model as a useful tool to capture the statistical properties of spectrum occupancy in HF domain.
Determination of Guaranteed Stability Regions of Systems with a PID Controller and a Parametrically Perturbed Control Object

The paper discusses the problem of designing the linear controllers for non-stationary control objects. The stability regions on the plane of the controller coefficients have been studied. The method of calculating full stability areas based on spline Lyapunov functions is proposed. It is shown that full stability areas are bigger than areas determined by the classical frequency methods. This advantage is used to achieve better accuracy and quality of control. Numerical experiments with non-stationary objects of different types are given.
Complex-Valued-based Learning Classifier System for POMDP Environments

This paper proposes Complex-Valued-based Learning Classifier System (CVLCS) that can learn an appropriate policy for the POMDP environments by extending Complex-Valued Reinforcement Learning (CVRL). Concretely, CVLCS explores the optimal policy by not only evolving classifiers but also updating Q-values (i.e., strength) of evolved ones, while CVRL explores the optimal policy by only updating Q-values of the state-action pairs prepared beforehand. To investigate the effectiveness of CVLCS, this paper applies it to various types of the POMDP environments. The experimental results have revealed that (1) CVLCS can derive the good performance which is close to the optimal one and shows such a performance faster than the conventional methods (i.e., Q-Learning as one of CVRL and ZCSM as one of LCS) and (2) CVLCS can stably derive the good performance even in the difficult environments where the conventional methods fail to derive good performance.
Learning to Write Anywhere with Spatial Transformer Image-to-Motion Encoder-Decoder Networks

Learning to recognize and reproduce handwritten characters is already a challenging task both for humans and robots alike, but learning to do the same thing for characters that can be transformed arbitrarily in space, as humans do when writing on a blackboard for instance, significantly ups the ante from a robot vision and control perspective. In previous work we proposed various different forms of encoder-decoder networks that were capable of mapping raw images of digits to dynamic movement primitives (DMPs) such that a robot could learn to translate the digit images into motion trajectories in order to reproduce them in written form. However, even with the addition of convolutional layers in the image encoder, the extent to which these networks are spatially invariant or equivariant is rather limited. In this paper, we propose a new architecture that incorporates both an image-to-motion encoder-decoder and a spatial transformer in a fully differentiable overall network that learns to rectify affine transformed digits in input images into canonical forms, before converting them into DMPs with accompanying motion trajectories that are finally transformed back to match up with the original digit drawings such that a robot can write them in their original forms. We present experiments with various challenging datasets that demonstrate the superiority of the new architecture compared to our previous work and demonstrate its use with a humanoid robot in a real writing task.
Tensor Super-Resolution with Generative Adversarial Nets: A Large Image Generation Approach

Deep generative models have been successfully applied to many applications. However, existing methods experience limitations when generating large images (the literature usually generates small images, e.g., $$32 \times 32$$ or $$128 \times 128$$ ). In this paper, we propose a novel scheme using tensor super-resolution with adversarial generative nets (TSRGAN), to generate large high-quality images by exploring tensor structures. Essentially, the super resolution process of TSRGAN is based on tensor representation. First, we impose tensor structures for concise image representation, which is superior in capturing the pixel proximity information and the spatial patterns of elementary objects in images, over the vectorization preprocess in existing works. Secondly, we propose TSRGAN that integrates deep convolutional generative adversarial networks and tensor super-resolution in a cascading manner, to generate high-quality images from random distributions. More specifically, we design a tensor super-resolution process that consists of tensor dictionary learning and tensor coefficients learning. Finally, on three datasets, the proposed TSRGAN generates images with more realistic textures, compared with state-of-the-art adversarial autoencoders and super-resolution methods. The size of the generated images is increased by over 8.5 times, namely $$374\times 374$$ in PASCAL2.
A Method to Introduce &amp; Evaluate Motion Parallax with Stereo for Medical AR/MR

Incorrect depth perception and lack of good evaluation systems are major barriers in clinical translation of augmented and mixed reality AR/MR. Thus, a systematic study of depth cues is necessary. Therefore, in the current paper we present a method to introduce the quantitative depth cue Motion Parallax (MP) in surgical scenes and study its effect on depth perception when combined with binocular disparity. In addition to this we present an innovative virtual tool method to evaluate depth. To introduce MP, we reconstructed the tissue surface using structure from motion technique. Then to get accurate absolute scale of the reconstructed surface stereo-triangulation was used. The simulated tumor was rendered beneath the reconstructed point-cloud by rendering a hole for `X-ray' like vision. The MP was introduced by rotating the entire scene from side-to-side with a tumor-surface-point as pivot for maximum impact. Finally for evaluation, we used a virtual surgical tool rendered using real-time da Vinci surgical API's forward kinematics data. In total, 12 subjects participated in a within-subjects-experiment design to study four cases, i.e., Stereo + MP ( S+MP), Mono + MP ( M+MP), Stereo + No MP (S+N-MP) and Mono + No MP (M+N-MP). The subjects significantly overestimated Judged Percentage of True Distance in M+MP when compared to M+N-MP (probability (p)=0.000, Number of Samples (N))=120) and S+N-MP cases ( p=0.001, N = 120). Furthermore, the observed VariableError was less in S+MP and S+N-MP cases when compared to M+MP and M+N-MP cases. The use of Motion Parallax in console interfaces for surgical robotics showed overestimation of judged distance. But to our knowledge it is the first work studying the effect of motion parallax and stereo in the surgical context. Therefore, its further study is warranted.