Reviewerly/exHarmony huggingface dataset:
Order or Chaos: The Case of Cryptocurrency Platform

The purpose of this study is to explore the market maturity of cryptocurrency trading platforms based on the information transmission perspective of financial market price volatility. This study uses the volatility spillovers index proposed by Diebold and Yilmaz [1][2] and uses the bitcoin trading platform to measure the total price volatility of cryptocurrency trading platforms, and the directional spillovers among the trading platforms. The sample period is from January 1, 2015 to September 30, 2018. In the empirical process, each sub-sample is taken every three months. The argument of this research indicated that if the cryptocurrency trading platforms' total spillover effects, with the rolling of the sub-sample period, show the increasing trend, and the trading platform has a staggered spillover effect with each other, indicating that cryptocurrency trading platforms exist the chaotic phenomenon, and the cryptocurrency market is in a stage of low maturity. On the contrary, if the total spillover effects are showing a decreasing trend and the spillover effects are mainly from a certain minority trading platforms, indicating that the cryptocurrency trading platforms present the order phenomenon, and cryptocurrency market is at a stage of high maturity. The contribution of this research is to identify the market maturity of the cryptocurrency trading platform, and to promote policy makers to propose a market-building mechanism for the market situation, so that the cryptocurrency has the opportunity to become a mainstream trading tool.
Research of infrared image super-resolution reconstruction based on improved FSRCNN

A multi-channel1 infrared image super-resolution reconstruction algorithm based on FSRCNN (Fast Super Resolution Convolutional Neural Network) was proposed. The improvements include two aspects: firstly, a multi-scale feature extraction channel was designed according to the characteristics of infrared image, enriched the detailed information of image reconstruction, and introduced residual channel to improve learning efficiency; secondly, improved activation function to make it more representative in negative regions to improve the performance of network. Experimental results showed that the PSNR of infrared image by improved activation function was improved by 3.06 dB, 1.22 dB, 0.51 dB, and the SSIM was improved by 0.0583, 0.0137, 0.0046, compared with the Sigmoid, ReLU and PReLU functions. The PSNR after image reconstruction was improved by 5.4 dB, 2.08 dB, 1.2 dB, the SSIM was improved by 0.2044, 0.0417, and 0.0258, compared with the traditional Bicubic, SRCNN and FSRCNN, which improved the reconstruction quality of infrared images.
Applying logarithmic density function to develop a segmentation model for intensity and noisy inhomogeneity images: towards informed decisions in computational and applied mathematics

Abstract This study examined the how a logarithmic intensity function could be used to develop a segmentation model for intensity and noisy homogeneity images. Whereas inhomogeneous images demand the use of local image data, it remains notable that it remains defective for noisy images. The eventuality is that active contour motions tend to be misguided by the local information. In the proposed model, the logarithmic function was able to capture minute details contained in selected images and also counter or ignore the perceived noise. As such, the model proved effective and robust, worth applying to such images. To verify the effectiveness of the model, its results were compared to the experimental outcomes previously reported after employing local Chan-Vese Model. Indeed, the proposed model exhibited superior performance in relation to the treatment of intensity and noisy inhomogeneity images.
Research on 24-h forecasting of solar irradiance based on multilayer perceptron model

Accurate forecast of solar irradiance is significant for related domains. Because accurate forecasts can help relevant researchers plan the management and application of solar energy that can be used in nuclear power stations and power plant. In this paper, an approach of solar irradiance forecasting based on artificial neural network (ANN) is adopted. The dataset from April 1st, 2017 to May 31st, 2018 was measured by the meteorological station in Yunnan Normal University. Multilayer perceptron model (MLP) and the variables, such as daily solar irradiance, air humidity, and relevant time parameter are employed to forecast solar irradiance in future 24 h. Moreover, the method of cross-validation is used to guarantee the robustness of experimental results. The results show the normalized root means square error (nRMSE) between the measured data and forecasted data is about 1.8~20.07% (1.8~10.6% for the sunny day, 11.6~20.07% for the cloudy day). Compared with ANN model, the nRMSE on the model of K-Nearest Neighbor (KNN), Linear Regression (LR), Ridge Regression (RR), Lasso Regression, Auto-Regressive and Moving Average (ARMA) and Decision Tree Regression (DTR), are 35%, 31%, 30%, 26%, 23% and 11% (unstable) respectively. It means that the performance of our model satisfies related applications.
Secure and Traceable Medical Image Sharing Using Enigma in Cloud?

The introduction of patient's electronic health care records results in a danger to their protection as resentful exercises would make genuine mischief the individuals who have legitimately or in a round about way identified with their information. Current methodologies to feasibly supervise and guarantee electronic health care records have been ended up being lacking. In our thesis, we propose the electronic healthcare record share which tends to the issue in sharing the medicinal information among the restorative huge information caretakers in a trustless condition. The framework is based on enigma and gives information surveying, scalability, decentralization and access control for shared electronic healthcare records in cloud archives among enormous information. Among information advances and sharing from one substance to the next, it invigilates the activities performed on the framework. The structure utilizes secret contracts and access control system to follow the conduct of the information and deny access to offending substances on the identification of infringement of authorizations on information. Our proposed idea is the present front line answers for information sharing among cloud specialist. Information surveying, versatility and access control while imparting medicinal services information to substances, for example, research and therapeutic foundations with insignificant hazard to information security can be accomplished by actualizing our proposed idea.
Topic Clustering Analysis of Online Judge System

With the continuous development of computer education, programming teaching as a core course of computer elementary science education is receiving more and more attention. Colleges and universities have started to combine the Online Judge System (OJ) to develop programming skills of students. OJ platforms have been domestically and internationally developed, and there is any corresponding problem solving reports on the Internet. However, these resources currently are not well organised and used. Therefore, it could be meaningful to cluster these dissociative problem solution reports. The OJ problem, short text classification is usually used to refer to the algorithm. In this paper, the clustering algorithm is utilized to transform the problem into a succinct text clustering problem. The research results can not only serve as an effective guide for OJ problem solving, but also provide a more scientific match for learning algorithms, and can provide a valuable reference for participants in various programming competitions.Data availability: https://pan.baidu.com/s/1Dk6qeFTyTgV4Cj00jMIP3A.
A filter for syntactically incomparable parallel sentences

Abstract Massive automatic comparison of languages in parallel corpora will greatly speed up and enhance comparative syntactic research. Automatically extracting and mining syntactic differences from parallel corpora requires a pre-processing step that filters out sentence pairs that cannot be compared syntactically, for example because they involve “free” translations. In this paper we explore four possible filters: the Damerau-Levenshtein distance between POS-tags, the sentence-length ratio, the graph-edit distance between dependency parses, and a combination of the three in a logistic regression model. Results suggest that the dependency-parse filter is the most stable throughout language pairs, while the combination filter achieves the best results.
Learning Representations for Vietnamese Sentence Classification (Extended Abstract)

In this study, we propose a new deep language model that taking the advantage of Transformer model towards the task of Vietnamese sentence classification. We construct a new Vietnamese dataset for evaluating the model. We also conduct experiments on English corpora to evaluate our proposed model.
Integrated Visualization with Controllable Deep Linking for Distributed Datasets

With visual analysis of a large IoT system where structured and unstructured datasets are collected from various distributed data sources or their edge processing units, it is analytically meaningful to have an integrated visualization composed of multiple coordinated charts and to deduce insight from datasets with visual analysis. But such a unified ensemble is difficult due to being short of explicitly available relationships among these distributed datasets. In this paper, we present an integrated visualization framework with deep linking on based of analytical relationship among distributed datasets. Considering the try-and-error aspect of visual analysis, we furtherly leverage the integrated visualization with comprehensible user interface, and implement brushing and linking individually or collaboratively.
Homoglyph attack detection with unpaired data

The human eyes fall prey to cyber-attacks designed to visually deceive us. One such attack that has been effective is named spoofing or homoglyph attack. A homoglyph attack employs a simple trick to deceive us by using a spoof domain or process (file) name that is hard to distinguish from the legitimate names. Due to this, a user might be drawn to click on the spoof process or domain names, and in worst-case it results in triggering any malicious malware planted in them. As a result, their sensitive personal information might be at risk of being exposed.
SmartSim: Combination of Vibro-Vestibular Wheelchair and Curved Pedestal of Self-Gravitational Acceleration for Road Property and Motion Feedback

We developed a vehicle ride simulation system for immersive virtual reality, consisting of a wheelchair for vibration and vestibular sensation, and a pedestal with a curved surface for the wheelchair to run on, utilizing a gravitational component. Vehicle motion feedback systems often use a six degrees of freedom motion platform to induce virtual vehicle acceleration on the user’s body. However, because motion platforms are typically complex and expensive, their use is limited to relatively large-scale systems. The proposed system enables the presentation of variety of road property sensations as well as continuous acceleration of vehicle motion using high-bandwidth wheel torque produced by two direct-current motors. Our unique combination of a wheel and a pedestal can present vibration and vestibular sensations of vehicle acceleration with simple, light-weight, and low-cost equipment. In our demonstration experience, users can perceive the sensation of various road properties, such as uneven surfaces, and continuous acceleration of a car or roller coaster.
The Maintenance’s Strategy & the 4th Industrial Revolution

As we are thinking about the 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> . Industrial Revolution and how our engineering products/units will be operated and will perform the required functions, it's with a great importance to think about the maintenance's strategies and concepts which to be compatible with like those products as well. The new product will be more accurate, safe, and intelligent, also faster and better in performance. The artificial intelligence is to be the cornerstone in this revolution, and the maintenance process has to be with the same level. For that we may need to develop the concepts and theories of maintenance so that it will be scientifically acceptable with those new tools.For that, we will act with those concepts through this paper: i)The Engineering unit/Product & the Technical Systems: When we act with the maintenance process, in a strategic view, we act with an (engineering unit/product) which consists of multi integrated systems. In fact we will not find an engineering unit/ product with only one system. We will discuss and explain this topic, through which we will derivative a developed definition for the maintenance process. ii)The factors or basis of the functionality efficiency: That is the main factors affect the functionality efficiency of the systems & the engineering units/ products, then by this way we can conclude a technical definition of defects and how they occur. iii)The legality of occurrence of defects (Legal defects & Illegal defects): with which we assume that all the factors of the functionality efficiency been applied, and then we will discuss the results. iv)The Technical State Functions: The Technical Surplus, the Functional Span Age, The defects and The Failure State concepts: With this topic we will discuss the following: The General Equation of Maintenance, the Initial Power Function of the system, the Defect and the Defect's Index relations, and the Failure definition and its relations, and how it will be a term of the Reliability theorems. Some mathematical examples are included to illustrate those concepts.
Integrating hyperspectral and radiometric remote sensing, spatial topographic analysis and surface geochemistry to assist mineral exploration

SummaryExploration and mining companies in Australia are faced with the challenge of locating deep ore deposits potentially buried under hundreds of metres of cover sequence materials. This research aims to investigate how the integration of hyperspectral remote sensing and surface geochemistry can be used to recognise signatures of alteration potentially associated with buried mineralisation. While each of these methods are individually mature, their combination for the purpose of mineral exploration is novel. Integration of methods including classification of spatial data and radiometric imagery, hyperspectral alteration mineral mapping and lithogeochemical analysis has produced results that might benefit exploration within the study area of the Central Gawler Gold Province. Further, the methods developed have the potential to be applied in other areas and therefore benefit the wider Australian and international minerals industry, especially in areas where there is no obvious expression of alteration at the surface.
Fast Local VM Migration Against Hypervisor Corruption

Virtual machine migration is an established technique for tolerating hardware faults affecting the virtualization infrastructure. Normally migration is performed between different physical hosts and hypervisors, which requires the memory state to be eventually sent over the network, thereby causing performance degradation in the migrated and co-located virtual machines, particularly when the migrated VMs are running IO-and memory-heavy workloads. Since most of the hardware faults are transient and can be recovered from by refreshing the affected component, we propose and evaluate a technique for migrating virtual machines over the same physical host almost instantly and with no overhead, by avoiding memory copy and taking advantage of Intel EPT's inner workings. This technique can be employed for refreshing the VMs' state held by the hypervisor with a lower VM downtime and performance overhead than what would be possible using traditional live migration.
SOCIAL INFOGRAPHIC SERVICES FOR SCHOOL EDUCATION

Informatization of school education in Russia is experiencing another round of development, teachers of all subjects use information and communication technologies in the educational process. There are more and more free Internet services, the tools of which allow you to present educational information in a graphic and interactive way, contributing to a better perception of educational material. The article discusses a number of these services interesting for school education.
In load collapse prevention system

In recent years, the demand for the logistics industry has increased enormously due to the spread of Internet mail order. On the other hand, the number of fatal accidents caused by trucks is more than 10,000 a year in Japan. Above all, there is a problem that there is an annual fatal and fatal accident due to truck load collapse. In this study, using the drive recorder, we calculated the inclination of the package photographed during driving by using KCF of object tracking method of image processing. Comparing the change of the Luggage tilt and the amount of change in 3-axis acceleration attached to the vehicle/time of each frame in all the data, frames at each maximum value point almost agreed at the time of " When riding over convex part of inclined road". On the other hand, "When a sudden steering wheel during driving", a point where the absolute value of the change in package inclination during one frame reaches 0.005 ° and the point where the maximum change occurs after that. It almost coincided with the point where the curve was reached in the video. As for the 3-axis acceleration sensor, there is no difference in tendency between the curve and the straight running, and it is considered as the main cause that the influence of the roughness of the road is greatly influenced rather than the influence at the time of the curve.
Adaptive Model-updated Correlation Filters for Real-time Visual Tracking

The Correlation Filters has excellent performance in the field of visual tracking. However, most of the model-updated methods adopt the linear interpolation with a fixed learning rate. Although the tracked object information can be retained in a period of time, it is easy to learn the background information into the model due to a fixed learning rate. In this paper, a new adaptive Model-updated method is proposed. Firstly, the similarity metric method is used to construct the confidence function, and then the high confidence prediction image frame is used to construct the discriminant dataset. Finally, the adaptive learning rate is computed according to the confidence value of the predicted image. Perform an adaptive update of the model. The experimental results show that the proposed algorithm can be well embedded in the correlation filters tracker. The proposed method is embedded in the KCF and Staple algorithms and tested on three challenging benchmark datasets OTB2013, OTB100 and TC128, and the tracking performance is improved.
Text Relation Extraction Using Sentence-Relation Semantic Similarity

There is a huge amount of available information stored in unstructured plain text. Relation Extraction (RE) is an important task in the process of converting unstructured resources into machine-readable format. RE is usually considered as a classification problem where a set of features are extracted from the training sentences and thereafter passed to a classifier to predict the relation labels. Existing methods either manually design these features or automatically build them by means of deep neural networks. However, in many cases these features are general and do not accurately reflect the properties of the input sentences. In addition, these features are only built for the input sentences with no regard to the features of the target relations. In this paper, we follow a different approach to perform the RE task. We propose an extended autoencoder model to automatically build vector representations for sentences and relations from their distinctive features. The built vectors are high abstract continuous vector representations (embeddings) where task related features are preserved and noisy irrelevant features are eliminated. Similarity measures are then used to find the sentence-relation semantic similarities using their representations in order to label sentences with the most similar relations. The conducted experiments show that the proposed model is effective in labeling new sentences with their correct semantic relations.
The work efficiency of scientific-pedagogical staff of universities as the basis of the competitiveness of the universities

The interrelations between the efficiency of higher education workers and the competitiveness of the Universities have been analyzed. The system of remuneration of workers in the field of education has been evaluated. The relationship between the average salary of higher education workers and the average salary in the economy, as well as the rating indicators of Russian universities on the basis of indicative indicators, - has been investigated. The goals of transition to the system of effective contract have been considered and systematized. The specificity of the implementation of institutional changes, that ensure the qualitative transformation of higher education in the transition to the system of effective employment, has been revealed. The interrelation of the model of effective employment and competitiveness of the leading Russian universities in the world scientific and educational space has been traced.
Plot Digitizing over Big Data Using Beam Search

In recent years, scholars have proposed different plot digitizing algorithms. Shen proposed a node-based algorithm, which used the topological structure to correct the clarity of the curve to some extent but could not separate the different curve segments. Shi proposed the tracking of sparse pixel traversal, which only applicable to the solid curve and cannot recognize the dotted curve. This paper studies a digital image processing problem for digitizing plot images, in which contains multiple curves and noise. The objective is to completely digitizing the data from the plot images, whether dotted curves or solid curves, and resists interference such as coordinate axes and other noises. A mathematical programing model is presented to describe the problem. One heuristic procedure based on beam search is developed aiming at quickly seeking optimal or near-optimal solutions. Computational experiments show that the proposed algorithm perform well, which was statistically whether identical or better than other approaches.