Reviewerly/exHarmony huggingface dataset:
Geometric wavelet scattering on graphs and manifolds

Convolutional neural networks (CNNs) are revolutionizing imaging science for two- and three-dimensional images over Euclidean domains. However, many data sets are intrinsically non-Euclidean and are better modeled through other mathematical structures, such as graphs or manifolds. This state of affairs has led to the development of geometric deep learning, which refers to a body of research that aims to translate the principles of CNNs to these non-Euclidean structures. In the process, various challenges have arisen, including how to define such geometric networks, how to compute and train them efficiently, and what are their mathematical properties. In this letter we describe the geometric wavelet scattering transform, which is a type of geometric CNN for graphs and manifolds consisting of alternating multiscale geometric wavelet transforms and nonlinear activation functions. As the name suggests, the geometric wavelet scattering transform is an adaptation of the Euclidean wavelet scattering transform, first introduced by S. Mallat, to graph and manifold data. Like its Euclidean counterpart, the geometric wavelet scattering transform has several desirable properties. In the manifold setting these properties include isometric invariance up to a user specified scale and stability to small diffeomorphisms. Numerical results on manifold and graph data sets, including graph and manifold classification tasks as well as others, illustrate the practical utility of the approach.
Indonesia’s Inflation Analysis Using Hybrid Fourier - Wavelet Multiscale Autoregressive Method

Abstract Regression analysis is a statistical method to determine the relationship between predictor variables and response variables. Regression approach can be done with parametric and nonparametric approaches. The parametric approach is rigorous with assumptions and must fulfillthe assumptions to get a good model.Meanwhile, the nonparametric approach is not rigorous with assumptions because the method is based on an unknown curve shape approach. Nonparametric regression can be done with several approaches including Fourier and Wavelet methods. The Fourier method is a method based on cosine and sine series. It is very suitable for data that has repetitive or periodic patterns. The Fourier series modeling is less efficient because it requires many coefficients to obtain a good model and it is less able to handle data with sharp jumps. Recently, there has been a combination of two methods called hybrid methods that give better results. In this paper, Indonesia’s inflation data modelling is performed by using hybrid-wavelet. First, the data is modeled using Fourier with small K; then the error Fourier model is modeled by using multiscale waveletautoregressive. In this study, the value of Inflation in Indonesia was modeled from January 2007 to August 2017. The response variable was the inflation value, while the predictor variable was time. The Fourier method with K = 100 generated MSE of 0.846216 and R 2 of 80.12%. The Fourier-Wavelet hybrid model with K = 1 generated MSE of 0.31 and R 2 of 95%. So that in inflation modeling in Indonesia, the Fourier-Wavelet hybrid regression model generated a better model than the wavelet model with fewer parameters than the Fourier method.
Using Conceptors to Transfer Between Long-Term and Short-Term Memory

We introduce a model of working memory combining short-term and long-term components. For the long-term component, we used Conceptors in order to store constant temporal patterns. For the short-term component, we used the Gated-Reservoir model: a reservoir trained to hold a triggered information from an input stream and maintain it in a readout unit. We combined both components in order to obtain a model in which information can go from long-term memory to short-term memory and vice-versa.
State Strategy of Russian Universities and Technological Business Companies for the Transfer of Bioinformatics Knowledge

This article presents examples of optimization of IT-infrastructure. Issues of information security at the time of digital economy are discussed. The article lists modern trends of optimization at IT-infrastructure use of bioinformatics knowledge. Examples of cloud services and the use of hybrid clouds are given. The information system of the future is defined. There is an integration of the existing system into a single digital platform and a description is given of ensuring the work of the social bloc of the country. The author defines the software visualizer, which opens unlimited possibilities for manipulating disk resources.
A Single-Camera 3D Microscope Scanner with Image Stitching and Stereo Matching

Image stitching is a commonly used method to produce high resolution mosaic images of objects that would not be able to fit in a single image at a desired resolution. In this paper, we present a low-cost 3D microscope scanning system that expands on this procedure. Our system uses the relative position information calculated during the stitching process to compute local depth maps for each pair of adjacent images via stereo matching. These local depth maps are then merged into a single depth map of the entire scene. Additionally, the color and depth data are combined to generate a 3D point cloud of the scanned object. The experimental results show that the system has the capability to produce seamless mosaic images and precise depth maps with high efficiency.
Automated Training Algorithms of Dialog Systems

Purpose of research. The research described in this article is conducted within the Salebot.pro project (on the https://salebot.pro resource) and aimed at development of simple and effective realization of a dialog system. Methods . The research plan provided the analysis of various methods of natural processing languages and machine learning languages. Implementation of these methods was taken from popular libraries with an open source code. The model of a dialog system was made in two options: on the basis of Spacy freymvork and metric assessment algorithm, on the basis of Levenstein's distance. Simplicity of implementation and costs on training of a system and personnel were compared. Results. The algorithms described in article compare the most similar words from two texts and count average percent of coincidence. Such approach provides a possibility of acceptable work in languages with free word order. Russian is one such languages. The executed research allowed developing an automated training algorithm of dialog systems in real time without context loss. On the same basis training algorithm of a dialog system in dialog history is developed. It is offered to use these algorithms together. It is originally necessary to train it at history of dialogues during creation of a dialogue system. And then it is necessary to train it permanently in real time. Conclusion. The advantage of the developed algorithm is ease in implementation and low cost of infrastructure which is necessary for model training and its service and also operation simplicity. Approach which differs from training with the teacher allows accelerating training process and input of new data into the system. Specific feature of the developed algorithms is ignoring of text semantics that makes training automated but not automatic.
An Abstract Domain for Objects in Dynamic Programming Languages

Dynamic languages, such as JavaScript, PHP, Python or Ruby, provide a memory model for objects data structures allowing programmers to dynamically create, manipulate, and delete objects’ properties. Moreover, in dynamic languages it is possible to access and update properties by using strings: this represents a hard challenge for static analysis. In this paper, we exploit the finite state automata abstract domain, approximating strings, in order to define a novel abstract domain for objects. We design an abstract interpreter useful to analyze objects in a toy language, inspired by real-word dynamic programming languages. We then show, by means of minimal yet expressive examples, the precision of the proposed abstract domain.
Faults Prediction and Diagnoses of Shield Machine Based on LSTM

Because the environment where shield machine is running is full of complexity and risk, it's possible for some serious faults to occur. And if operators could not find and solve the problems timely, it may lead to accidents. This paper applies long short-term network to predict and diagnose some common faults including the formation of mud cake, the wear of cutters, the blockage of slurry pipe and the subsidence of land by time series sensor data from shield machine. The simulation verifies the feasibility of our method. In this paper, a LSTM network is improved and we demonstrate that compared with LSTM model and GRU(Gated Recurrent Unit) model, the loss function of the improved model converge faster in the same epoch and achieve higher accuracy. The results show that our method to predict and diagnose the faults is satisfying.
Underwater Image Saliency Detection Based on Improved Histogram Equalization

In order to solve the problem of unsatisfactory detection effect of underwater visual saliency map, an image saliency detection algorithm based on improved histogram equalization is proposed. Underwater images are often not clear enough because the refraction of light underwater causes insufficient image resolution. Therefore, in order to solve the existing problems of traditional histogram equalization algorithm, an improved histogram equalization method is proposed to enhance the quality of images, which makes the saliency regions smoother and clearer. In this paper, the simulation experiments were conducted on UIEBD dataset and DLOU_underwater dataset. The experimental results show the effectiveness, robustness and accuracy of the proposed algorithm.
A Machine Learning Based Image Inpainting System on Mobile Device

Removing unneeded objects from the picture is an increasing demand for image processing applications now when more and more people take photos by mobile phones. This paper proposed a machine learning based approach to solve this problem, based on which an application is developed. The application can detect all the objects in an image. Then, user can choose some of detected objects and then the application will remove them from the picture and inpaint the image. Simple operations and good results make the proposed application superior to other similar applications.
Detecting Defects Based on Mining and Confirming Programming Patterns From Different Versions of Projects

Many implied programming patterns are contained in large-scale software. Most of the implied programming patterns are missing proper documentation. Defects would be brought into the software, if any of the patterns is violated by programmers. To alleviate this problem, many works are proposed to find defects by mining programming patterns from the software. However, a great many of candidate patterns and defects are reported by these approaches. These patterns and defects need to be manually confirmed, and the applicability and scalability of these approaches are restricted by this problem. In view of this problem, this paper proposes an approach to automated mining, confirming, filtering function call sequence patterns (FCSPs), and detecting defects which violate the patterns. At first, FCSPs are mined from a previous stable version and an update version under analyzing, respectively; then, the FCSPs are confirmed by analyzing correlations; after that, useful FCSPs are filtered with respect to the FCSPs mined from the previous version; finally, the version under analyzing is scanned for suspicious defects against the filtered FCSPs. 3 open source projects are selected as the experimental subjects to evaluate the approach. As the experimental result shows, the efficiency of defect detection is improved by the proposed approach. It confirms programming candidate FCSPs with 82% F1-measure and 77% accuracy, and eliminates 55% suspicious defects without sacrificing the performance.
Data Interpolation Method Based on Minimum Total Variation Method

In order to solve the problem of uniform and non-uniform seismic data interpolation, the proposed method converts the data into curvelet domain and carries on the gaussian sampling, then regards the data recovery as an inversion problem which can be solved by using the minimum total variation method. Finally, the interpolated data can be obtained by inverse curvelet transform. The theoretical data processing shows that the algorithm has better interpolation effect for uniform and non-uniform missing data, and the latter is better. In order to further verify the validity of the algorithm, the actual data of a region are used for interpolation processing. The results before and after interpolation show that this method is a fairly good interpolation algorithm.
Contiguous Sequence Mining Approach for Program Procedure Pattern

The program procedure patterns exist in the software development process. This paper gives a program procedure pattern mining approach based on MCSPAN (Maximal Contiguous Sequential pattern mining). First the program structure features are mined. Structure feature mining is transformed into a frequent sequence mining problem with contiguous constrains and maximal constrains and an algorithm: MCSPAN is given to obtain the program structure candidate patterns as follows. Then a filtering algorithm with the constraint of the data flow feature is given to filter the structure candidate patterns and then the program structure relationship candidate patterns are obtained which have the program procedure pattern form. For clarifying the function semantics, some heuristic rules are applied in the structure relation candidate patterns filtering and finally the program procedure patterns are obtained. Through mining more than 100,100 lines of java code, about 180 kinds of program procedure patterns are discovered. This mining approach is effective through analyzing the recall and precision rate in the experiment. And the availability of the mined program procedure patterns is analyzed.
Establishing feature point correspondences between multisensor images with a robust feature matching strategy

Establishing cross-spectral feature point correspondences is a challenging problem due to high textural and intensity changes between multisensor images. To overcome such changes, a new feature matching strategy is proposed. The proposed strategy is based on Gaussian mixture model-universal background model (GMM-UBM). GMM-UBM is widely used as a classifier in speech-related applications. GMM-UBM is used to establish feature point correspondences between multisensor images. Experiments were performed using 17 different state-of-the-art feature points and GMM-UBM was validated on two different image datasets. The experimental results show that the proposed GMM-UBM method outperforms traditional Euclidean distance-based feature matching strategies and provides better results on multisensor images.
Research on Condition-based Maintenance Technology for Power Transformer

In the basis of analyzing the importance of power transformer in substation and power system, the development process of operation and maintenance technology for power transformer are introduced in detail. And the meaning of condition parameters used for characterizing the state of power transformer were illustrated, for example, the temperature of winding hot spot, casing insulation, the micro water and gas content in oil and the vibration. Taking ATmegal 16L microcontroller as the core, the whole structure of condition monitoring system, the power supply unit and signal processing unit were detailed designed.
Extracting Structure from Contaminated Symbolic Data

Symbolic data is the epitome of modern biological datasets. Modern sequencing technologies produce millions of reads giving insights on genome sequence, transcription levels, epigenetic modifications, and much more. To analyze those sequences one usually makes assumptions on their underlying structure, e.g., that the number of reads has Poisson distribution, or that transcription factor binding events occur independently at nonoverlapping promoters. These types of assumptions are often not exactly correct in reality. In fact, even when they are valid, a small amount of data "contamination" may make them appear untrue. The traditional approach to questioning assumptions on data has been hypothesis testing. This approach has various shortcomings, however. Particularly, its Boolean nature does not give room for a null hypothesis to be "approximately true.'' This tutorial introduces a methodology to assess statistical assumptions on symbolic data that may be contaminated. It will give a general overview on how to approach these problems numerically, and present analytical results for some special classes of structured probability distributions. It will demonstrate the applicability of this rather new methodology with DNA methylation data to question the common but unconscious assumption that methylation of CpGs is exchangeable, and transcription factor binding site k-mers to question the use of logoplots in summarizing TFBS behaviour. Data and code for this tutorial, in the form an iPython Notebook, will be made available via GitHub. This work is in collaboration with M. E. Lladser.
Being a Curator

Human curation has earned itself a permanent place in the streaming media world, though it wasn’t always clear whether or how that would happen. For a time, it seemed like tech didn’t want to justify the use of human labour if the future could be won without it. That time seems to have passed, and with handmade playlists and other editorial products, human curators have a unique ability to help the widest-ever audiences find great music. People from different backgrounds are more able now to hear each other’s sounds than ever before. Whether the streamlining of these services matters to a given user is wholly subjective, since the joy of discovering new music is often so inefficient and chaotic. It was never clear that this same sense of joy would make it into streaming products – for many of us, it cannot. And that’s okay. Services offer only what they can.
ANALYSIS OF THE READINESS OF GENERAL SECONDARY EDUCATION INSTITUTIONS TO INTRODUCE DIGITAL EDUCATION

The influence of digital technologies on the educational sector, the requirements of educational reform in the direction of introducing digital education in the educational process of institutions of general secondary education are examined. It is established that the basis of the digital space is the information and educational environment, which consists of information resources, pedagogical technologies, technological means and means of maintaining the system’s performance. It turned out that a modern teacher, regardless of the subject he teaches, should have informational and digital competencies. The article presents the results of a study of the existing conditions for the introduction of digital education in institutions of general secondary education at the regional level. An insufficient level of resource provision of institutions, a low level of teachers' knowledge of digital information competencies was found, problems were formulated that needed to be solved. An adaptive approach to the organization of teacher training courses on the formation of information and digital competencies is proposed.
Light-diffusing composite material based on polystyrene, polyethylene and hollow glass microspheres

Light scattering composites based on polystyrene, polyethylene and glass hollow microspheres have been developed. Composites with the optimal ratio of light transmission and light scattering coefficients has been produced.
Action Units Classification Using ClusWiSARD

This paper presents the use of WiSARD and ClusWiSARD weightless neural networks models for the classification of the contraction and extension of Action Units, the facial muscles involved in emotive expressions. This is a complex problem due to the large number of very similar classes, and because it is a multi-label classification task, where the positive expression of one class can modify the response of the others. WiSARD and ClusWiSARD solutions are proposed and validated using the CK+ dataset, producing responses with accuracy of 89.66%. Some of the major works in the field are cited here, but a proper comparison is not possible due to a lack of appropriate information about such solutions, such as the subset of classes used and the time of training/testing. The contribution of this paper is in the pioneering use of weightless neural networks in an AUs classification task, in the unpublished application of the WiSARD and ClusWiSARD models in multi-label tasks and in the new unsupervised expansion of ClusWiSARD proposed here.
Research on Face Recognition Algorithm for Robot Vision

Aiming at the face recognition system in robot vision, an improved Convolutional Neural Network face recognition method is proposed. This method aims at improving the accuracy and stability of face recognition system in robot vision. According to the size of the training network database, through the design of feature extraction layer by layer reverse order, a variety of different network structures are used to construct the network structure, and multi-layer small convolution kernel is added into the network structure. Experimental results show that this method has higher recognition accuracy and faster recognition rate than the traditional Convolutional Neural Network face recognition method.
Combining Deep Learning and (Structural) Feature-Based Classification Methods for Copyright-Protected PDF Documents

This document describes the implementation of a copyright classification process for user-contributed Portable Document Format (PDF) documents. The implementation employs two ways to classify documents as copyright-protected or non-copyright-protected: first, using structural features extracted from the document metadata, content and underlying document structure; and second, by turning the documents into images and using their pixels to generate features for semi-supervised deep convolutional networks.
Designing to support uncomfortable breathing exercises

Under certain conditions, breathing abnormally fast or slow can improve health and performance. Even breathing in a way that intentionally restricts the amount of oxygen delivered to the body can be desirable in some situations. However, while these various "uncomfortable" breathing exercises can lead to benefits when practiced in a controlled and supervised setting, they can also be potentially dangerous if practiced incorrectly. In this paper, we discuss ethical considerations for ensuring end-user safety when designing technology-mediated breathing exercises.
Expectation Minimax Algorithm for the Two-Player Military Chess Game

The two-player military chess is a typical incomplete information computer game. This paper proposes an expectation minimax algorithm for the two-player military chess. The principle structure of expectation minimax algorithm is given based on the traditional model, a chessman's priority probability evaluation algorithm and a situation evaluation function for two-player military chess game are used for the implementation of the two-payer military chess game. Experiments show that the proposed expectation minimax algorithm is verified effective which can improve the performance of two-payer military chess game.