Reviewerly/exHarmony huggingface dataset:
Intellectual Property in the Age of 3D Printing

The paper discusses the features of three-dimensional printing and the innovations that it brought to the field of copyright, industrial property rights, in particular industrial design, and means of individualization. The concept and some features of three-dimensional printing technology are disclosed. The author studies the exclusive rights to which results of intellectual activity can be violated when using the technology of three-dimensional printing. It is concluded that exclusive rights to an industrial design, trademarks, and to copyright may be violated. The author investigates the actions that will not constitute a violation of the exclusive right to the considered results of intellectual activity. It is concluded that not every action would constitute a violation of exclusive rights, in particular, this applies to the use of a trademark for purposes not related to the introduction of goods into circulation; use of industrial design for purposes not related to the implementation of entrepreneurial or other income-generating activities.
The auxiliary function method for waveform based earthquake location

This paper introduces the auxiliary function method, a novel, fast and simple approach for waveform based earthquake location. From any initial hypocenter and origin time, we can construct the auxiliary function, whose zero set contains the real earthquake hypocenter and the origin time. This translates the earthquake location problem into the problem of searching the common zeros of the auxiliary functions. The computational cost of constructing the auxiliary functions is close to the cost of one single iteration of the traditional iterative method. And the cost of searching the common zeros of the auxiliary functions is almost negligible. Thus, the overall cost of this new method is significantly less than that of the iterative methods. Moreover, there is only one common zero point of the auxiliary functions in most practical situations. This means that the new method only requires one round of calculation to obtain an accurate earthquake hypocenter and origin time from arbitrary initial values. According to our numerical tests, even for large data noise, the method can still achieve good location results.
Short-term Load Forecasting Based on R-SDBN

This paper proposes a self-organizing deep belief network with robust locally weighted scatterplot smoothing method (R-SDBN) for short-term load forecasting, which can not only organize the structure of short-term load forecasting automatically but also denoise the input load data. The deep belief network (DBN) integrates deep learning and feature learning. It can quickly analyze a large number of data and has a strong data fitting ability. And the robust locally weighted scatterplot smoothing (RLWSS) method is introduced to weaken the noise and correct the outlies of load data. However, the optimal structure of DBN and the optimal span of RLWSS are different given specific applications. In the proposed model, the structure of DBN and the span of RLWSS are selforganized with minimum root mean square error as objective, not selected by experience, during the training process, and they are modified after each forecasting. Taking the actual load of a certain city in China as an example, the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the maximum absolute error (MAE) of the 24hour forecasting is 4.38MW, 6.54%, and 12.48MW respectively. Compared with the backward propagation neural network, support vector machine (SVM), and DBN, R-SDBN has remarkable advantages.
The Research of Forensic Voiceprint Identification Based on WMFCC

The characteristic comparison of formants is an important method for voice identification. In order to solve the key problems of voice identification, a weighted Mel Frequency Cepstrum Coefficient (WMFCC) which can be used for formats extraction from the voice signal is proposed in this paper. The WMFCC coefficient which contains the main components of the frequency spectrum can be obtained by analyzing short-time voice signal. The candidate formants can be obtained from the formant position of spectral envelope parameters. The accurate formant location would be fixed by analyzing the main components of signal spectrum. The resonance formats analysis of Nasal vowels; the vowel resonance formants contrast and Long Term Mean Power Spectrum Analysis were used for the Voiceprint identification. The experiment shows fewer errors of the formants based on WMFCC were smaller, and it shows better robustness in noisy environment.
DEVSR: Dynamically Enhanced Video Super-Resolution

Super-resolution algorithms have been getting better with both computational performance and reconstruction accuracy for both single image super-resolution and video super-resolution. We propose an enhancing methodology for existing super-resolution algorithms. Our method is applicable only for videos and could speed up the computation time for any existing single image super-resolution algorithm (SISR). We consider the individual frames of a video and use the method of differences by finding a delta image for each consecutive frame after the origin frame. The non-zero values in delta frame are the only parts of that frame which are up-scaled whereas the rest of the parts are copied from the up-scaled origin image. Furthermore, the origin image is replaced with a new one after every time a scene is changed with rest of the process being the same. This algorithm could even optimize computation performance and quality, or accuracy based on the need of the viewer, that is quality could be increased or decreased at the expense of computation time. We explore up-scaling intermittent delta frames for much faster video super-resolution. Our proposed approach improves the computation time of any single image super-resolution algorithm by 40% or more for most videos when up-scaling every frame of that complete video.
A SECURITY Sketch-Based Image Retrieval System Using Multi-edge Detection and Scale Inverant Feature Transform Algorithm

Drawing is still the best communication channel among the different civilization and languages. As technology is advancing the demand on sketch-based image retrieval system is one of the technologies increasing every day. Through the last years, numerous researchers have presented various techniques and several algorithms for correct and dependable sketch-based image retrieval system. In this paper, a proposed sketch-based image retrieval system is represented. Framework occurs within two stages: creating the sketch dataset stage and implementing the proposal system stage. The sketch dataset is created by selecting 100 colored image that passes through preprocessing stage which includes a comparison of multi-edge detection operators and then choosing the best edge detection; the proposal phase trend to entering a line-based/hand-drawing sketch and apply the SIFT algorithm to match among the input sketch and all sketches in the dataset. SIFT is one of the main and efficient algorithms that are used in the proposal to make detection and description, since it works on large keypoints. This research trend to retrieve images depending on sketch image, the result of matching will retrieve images that are approximate to the entered sketch. The proposed system is evaluated according to the measures that are used in detection, description, and matching fields which are precision, recall, and accuracy measures, also it uses the MSE and PSNR to measure the best edge detection algorithm. The suggested system achieves between (96%) accuracy for line-based sketches and (84%) for hand drawing since the detection is identical. In addition, the suggested system decreases the needed time that SIFT algorithm desires for making the detection.
Improved Adaptive Spectrum Scale-Space in Frequency Domain for Saliency Detection

Saliency detection is a process to find the significant region on the images, which is popular in the image processing area. It has been extended and used in many applications in computer vision systems. Many researches have attempted to propose effective model to detect the saliency area which is corresponding to human perception. Therefore, this research focuses on improving the saliency detection process by proposing the improved adaptive spectrum scale-space (IASSS). The main contributions of the proposed method include (i) scale-and-space Gaussian filter (AS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> G filter) and (ii) the new method for saliency map selection based on local entropy. Firstly, the AS2G filter is used to suppress the non-saliency amplitude spectrum to extract the saliency map. Then, the best saliency map is selected from the results of the RGB and Lab color images by using the local entropy criteria. Then, the experimental results based on 235 images show that the overall performance of the proposed IASSS method outperforms the baseline methods including Itti, SR, SUN, SAL, HFT, and MSS methods.
Learning to Track by Bi-Directional Long Short-Term Memory Networks

Invisualtracking, deeplearningbasedtrackershave demonstrated strong competitiveness under many challenging conditions such as background clutters and occlusions. Several recent studies have devoted to defeating such conditions by using Recurrent Neural Networks (RNNs) to explore temporal smoothness and stable deep representation. In this paper, we propose a tracker based on Bi-directional Long Short-Term Memory network (Bi-LSTM) under the tracking-by-detection paradigm. In particular, the proposed tracker adapts the YOLO algorithm to detect the target in single frame, and combines both detected target and tracked target in Bi-LSTM. Since BiLSTM learns the representation of current frame from both historical frames and subsequent frames, the proposed tracker can simultaneously incorporate forward plays and backward plays. This brings two advancements: 1) Bi-LSTM extends visual features into temporal domain and controls motions of the target, and 2) Bi-LSTMp redicts reliable target location by incorporating both past and future motions. We evaluated the proposed tracker on the visual benchmark OTB challenge videos and confirmed its effectiveness by comparing with the representative trackers to illustrate the feasibility of managing trace memory.
A Probabilistic Scheme for Representation Learning with Radial Transform Images

Data representation can facilitate training of deep neural network when limited data is available. We have previously proposed the radial transform sampling method as a data representation technique for training neural networks. In this paper, a probabilistic framework to analyze radial transform is presented. To further elaborate it, performance of training deep neural networks on radial transform generated images for semantic segmentation of kidneys in abdominal computed tomography is evaluated. Our results show that the proposed representation method can achieve higher performance than other similar methods by translating a semantic segmentation problem to a classification problem when limited annotated images are available.
Efficient Belief Propagation for Graph Matching

In this short note we derive a novel belief propagation algorithm for graph matching and we numerically evaluate it in the context of matching random graphs. The derived algorithm has a lower asymptotic time-complexity without significantly compromising the accuracy compared to leading available algorithms in the literature. An extended version of this article, with further theory and numerical simulations is in preparation.
State Management of the Educational Services Market in a Multicultural Region as a Tool of Provision of Population’s High Living Standards

The purpose of the research is to determine scenarios and to develop a “road map” of implementing the optimal scenario of state management of the educational services market in multicultural regions of Russia in the interests of increasing the population’s living standards. It is substantiated that standardization (development and usage of common requirements to all universities) hinders the implementation of the potential of the educational services market in stimulating the increase of living standards in Russia’s regional economy. It is necessary to limit standardization in the Russia’s educational services market. The quality of provision of educational services (characteristics of the educational process) and their results (mastered competencies) should be standardized, but universities should have larger independence for their provision.
Research on Calibration Method of Camera Stabilizer Based on Binocular Vision

The calibration of the camera stabilizer is the basis of precision measurement. The traditional dual target needs to establish a complex mathematical model with large calculation, low real-time performance, slow speed and low precision. The neural network can effectively deal with the nonlinear mapping problem, which can well describe the nonlinear relationship between the three-dimensional spatial feature point coordinates and the corresponding points of two cameras in binocular vision. Therefore, this paper proposes an improved RBF binocular vision neural network calibration method, and introduces genetic algorithm and firefly algorithm to optimize the learning algorithm of radial basis function network. The improved RBF binocular visual neural network has less error in testing, uses fewer neurons, and has shorter training time. It can better meet the requirements of real-time online calibration, and has strong self-organization, self-learning, self-adaptation and strong The nonlinear mapping ability accurately establishes the relationship between the three-dimensional object point coordinates and the two camera coordinates in binocular vision. Compared with the traditional method, the method has the advantages of fast reconstruction speed, high calculation precision, small calculation amount and good real-time performance.
Adapting distributed stream processing technologies for the automation of modern health care systems

With the increase in population, there is an increasing number of patients. Subsequently, we also see an increase in the amount of patient data that needs to be processed, further emphasizing the need for new systems and developments that can handle such large quantities of big data. To this end, this paper proposed a potential solution to this problem in the form of a system that can analyze a patient's data in real-time, providing doctors and other intended healthcare personnel with an immediate report of a patient's situation, allowing for a quicker response time, better treatment, and the first step towards a grand realized smart hospital system in the long-term. This system, aided by the rapid analysis of Apache Flink, produces the requested data to the doctor as intended, enabling for a swift response time to patient issues, thus highlighting a unique approach to the field, a contrast to other previous research in this field where there is a lack of said provisions.
Semi-Regular Geometric Kernel Encoding &amp; Reconstruction for Video Compression

Conventional video coding schemes employ a hybrid motion prediction / residual transform coding paradigm, which only exploits redundancy in individual pairs of video frames for compression gain. However, rigid geometric structures in 3D space—e.g., a building in a scene’s background—persist across time in a large frame group. Thus if one can extract and encode the geometric structure, then redundancy across the entire frame group can be removed in one shot. In this paper, we extract a best-fitting "semi-regular" geometric structure from a target spatial region in a frame group, which is encoded separately as a unified signal predictor for these frames. By semi-regular, we mean its geometry is simple enough that its shape parameters can be encoded cheaply. This semi-regular structure kernel approximates the 3D shape of an object in the video, on which we project pixels from the frame group to a carefully spaced 2D grid overlaid on the kernel. We encode the projected pixels as an intra-frame using HEVC. The decoded pixels are then back-projected to each frame as the predictor, and the resulting prediction residuals are transform-coded. Experimental results show that employing a semi-regular geometric kernel—a folded 2D plane in our realization—improves coding performance over native HEVC implementation and our previous regular kernel based scheme.
A New Technique of Image Enhancement by Equalizing the Bivariate Probability Density of Brightness

Histogram equalization is the most well-known intensity transformation technique and is widely used to enhance images owing to high efficiency, simplicity, and low computational cost. However, the traditional approach to image histogram equalization has a number of disadvantages, which significantly limit the possibilities of its use for images enhancement in automatic mode. In this paper, we propose a new technique of image intensity transformation based on equalizing the bivariate probability density of brightness. This approach provides effective image enhancement without the appearance of unwanted artifacts. A generalized description for intensity transformation based on equalizing the joint bivariate brightness distribution is presented. The relationship between the proposed generalized description and the traditional definition of histogram equalization is shown. The proposed technique provides an increase in the efficiency of image enhancement compared to the traditional technique of histogram equalization.
Multi-Task Learning Via SA-FPN and EJ-Head

As a concise framework, Mask R-CNN achieves promising performance in object detection and instance segmentation. However, there is room for improvement in two aspects. One is that performing multi-task prediction needs more credible feature extraction and multi-scale features integration to handle objects with varied scales. We address this problem by using a novel neck module called SA-FPN (Scale Aware Feature Pyramid Networks), which can accurately help detect and segment the objects of multiple scales. The other is that the isolation between detection and instance segmentation branch exists, causing the gap between training and testing processes. So we propose a unified head module named EJ-Head (Effective Joint Head) to combine two branches into one head, not only realizing the interaction between two tasks, but also enhancing the effectiveness of multi-task learning. Comprehensive experiments on MS-COCO benchmark show that our proposed methods bring noticeable gains for both object detection and instance segmentation. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Effective Combination of Multiple Evidences for I-vector Based Limited Data Speaker Verification

The performance of automatic speaker verification (ASV) system always depends upon the amount of information (speech sample) used in the process. ASV system's performance suffers when the information provided to the system is limited, even though the methodology is remain same. The issue of limited information can be resolved to some extend by using multiple evidences. In general, score level combination scheme is widely used to combine the effect of multiple evidences, where a decision is made based on the independent opinions of the evidences. We conjecture that the collectively contributed decisions may be more effective and propose a new combination scheme for limited data speaker verification task. In the proposed work, we have used mel frequency cepstral coefficient (MFCC) and residual MFCC (RMFCC) as representation of the vocal tract and excitation source information. The experiments are conducted with well-known NIST-2003 speaker recognition evaluation (SRE) database. The score level combination scheme provide a relative improvement of 14.93% in extremely limited data condition (≃ 2 sec), on an average 15.57% for all limited data conditions. In comparison, the proposed scheme provides 28.40% and 29.02%, respectively. Thus proposed method provides a relative gain of 13.47% for extremely limited data condition and on an average 13.42% for other limited data conditions. These experimental results signify the importance of using proposed combination scheme over the popular score level combination scheme.
On the Specifics of Entrepreneurship Perception by University Students

On the base of the survey in two universities in Voronezh (680 students) the hypothesis is rejected that students, in accordance with J. Schumpeter's interpretation of entrepreneurial activity, associate entrepreneurship with innovations. It is found that the student's perception of entrepreneurship in the Russian cultural and linguistic environment differs from the Schumpeterian one. Although the inclination to joining entrepreneurship in the future is widespread among the students, they are not ready to innovate. Students perceive entrepreneurship primarily as a pathway to financial security and personal well-being. Respondents tend to neglect socially significant aspects of entrepreneurship.It is revealed that the students miss a holistic system of views and priorities regarding to their careers. Personal interests and motivations prevail over business inclinations. This is expressed in the students' career preferences, which incline to the ability to combine business and personal life, to the pursuit of financial security, material well-being, avoiding difficulties. As a rule, students prefer interesting and creative work. Hard work and education as factors of success in their future professional careers are valued low.At the same time, weak social inclinations are manifested. Mutual assistance and cooperation as factors of professional progress are rated low. Also, the students are not inclined to work much and hard for the sake of public benefits. Prevailing not only personal interests as compared to the social, but even personal as compared to professional is an obstacle to the students joining the innovative entrepreneurship, requiring the priority of business and professional interests. The low interest to continuing family business reflects the young people's desire to independence, to free choice of path. This indirectly indicates their deviation from traditional values (which are considered to be characteristic of the Russian culture in the past) and about the growth of their individualism.
Comparison of Deterministic and Geomorphic Methods for Determining Spatial Variations of Nitrate in Ghaemshahr-Juybar Plan

Comparison of Deterministic and Geomorphic Methods for Determining Spatial Variations of Nitrate in Ghaemshahr-Juybar Plan
Three Satellites Detection Trajectory Estimation Algorithm Based on the Thrust Acceleration Template

In order to improve the trajectory estimation accuracy of the three satellites detection, an estimation algorithm based on the thrust acceleration template is proposed. Firstly, using STK to analyze the SBIRS's regional coverage capability, about 43% of the time can achieve complete coverage of the selected region by more than three satellites. Secondly, constructing the thrust acceleration template by using the ballistic missile prior information, after that, extending the thrust direction angles to the components of the target's state vector, then a parametric dynamic model with high precision is established. Furthermore, the thrust direction is approximated by the missile initial position direction. Finally, using the UKF to estimate the missile motion state in real time. Experimental results show that, compared with the algorithm based on the CS model, the estimation errors of this algorithm are significantly reduced, and this algorithm is more robust.