Reviewerly/exHarmony huggingface dataset:
GenExplorer: Visualizing and Comparing Gene Expression Levels via Differential Charts

This paper describes a visual interface for analyzing gene expression data generated from multiple biological samples under different controlled conditions. The tasks are to provide a comprehensive overview of thousands of genes under different states and have an intuitive way to narrow down genes with common behaviors. Our method involves using multidimensional projections and differential charts to help users analyze different data sets via a web-based interface. Incorporating these charts and other visualization techniques into our final design makes the application accessible to genetics analysts, as demonstrated in the two use cases in plant and cancer researches. We further discuss the feedback from domain experts and the limitations of our approach to accommodate gene exploration tasks.
Glue: Enhancing Compatibility and Flexibility of Reinforcement Learning Platforms by Decoupling Algorithms and Environments

Reinforcement Learning (RL) platforms play an important role in translating the rapid advances of RL algorithms into the successes of real-world tasks. These platforms integrate multiple simulation environments, allowing testing, evaluating and finally applying RL algorithms in different scenarios. However, the algorithm code is required to execute in the same runtime system with the underlying environments, which limits platforms' compatibility when adapting an algorithm and flexibility when switching between different algorithms. We propose GLUE to resolve this issue, by decoupling the executions of algorithms and environments first, then leveraging the RPC protocol to orchestrate a seamless workflow between them. GLUE is further implemented as a library, which hides the handling of language-specific RPCs from users. We evaluate GLUE by adapting 6 RL algorithm implementations to a representative RL platform. Compared with the baseline approach, GLUE enables algorithms to achieve competitive performance, but reduces lines of algorithm code to be changed in adaption by 27.77% , at the cost of 5.40% longer training time, on average.
Comparative analysis of students' motivation in Russia and China

The choice of University for training and future profession largely determines the future of a person. The educational environment in which a student is located affects the formation of his personality, character, attitude to work and life. Also very important is the team in which the student is for several years. The training process has a significant impact on the professional development of the future specialist. This study is based on a comparison of the motivation of higher education and choice of future profession among students of the Ural Federal University and the North China University of Water Resources and Electric Power. The article describes the similarity and difference of approaches to the choice of higher education, the reasons for obtaining higher education, the differences in the assessment of the correctness of such a choice. The article was prepared in collaboration with Sun Guofeng, a student (bachelor) of the North China University of Water Resources and Electric Power.
Neural Machine Translation model for University Email Application

Machine translation has many applications such as news translation, email translation, official letter translation etc. Commercial translators, e.g. Google Translation lags in regional vocabulary and are unable to learn the bilingual text in the source and target languages within the input. In this paper, a regional vocabulary-based application-oriented Neural Machine Translation (NMT) model is proposed over the data set of emails used at the University for communication over a period of three years. A state-of-the-art Sequence-to-Sequence Neural Network for ML → EN (Malay to English) and EN → ML (English to Malay) translations is compared with Google Translate using Gated Recurrent Unit Recurrent Neural Network machine translation model with attention decoder. The low BLEU score of Google Translation in comparison to our model indicates that the application based regional models are better. The low BLEU score of English to Malay of our model and Google Translation indicates that the Malay Language has complex language features corresponding to English.
Ceramics making technology from the Eneolithic layers of Rakushechny Yar settlement

The paper contains the results of the technical and technological analysis of ceramics from the Eneolithic layers of the Rakushechny Yar settlement. It is located on the Porechny Island (the Don River, Rostov Region, Russian Federation). The methodological basis of the conducted research is a historical and cultural approach to the study of ancient ceramics, developed in Russian archeology by A.A. Bobrinsky. The methods of the research are binocular microscopy, trasology and experiment (physical modeling). 141 samples of ceramics (conditionally separate vessels) from layers 5-2 of Rakushechny Yar were subjected to a microscopic examination. The obtained technological information allowed us to reconstruct techniques and methods of Eneolithic utensils manufacturing at all stages of production - preparatory, creative and fortifying stages of pottery technology. Based on these data, the paper presents a general description of the Eneolithic pottery. A comparative analysis was made of the pottery technology of the Neolithic and Eneolithic population that inhabited the Rakushechny Yar site. According to its results, the similarities and differences of the Eneolithic and Neolithic pottery traditions are highlighted. Particular attention is paid to the question of the possible origins of new traditions, which became widespread during the Eneolithic. These include silty clays selection traditions, the introduction of artificial additives into the molding composition: crushed shell, bird fluff and sand.
First Gale Western Butte Capping-Unit Compositions, and Relationships to Earlier Units Along Curiosity's Traverse

The Curiosity rover has been traversing through the clay-bearing unit (Glen Torridon; GT), approaching Greenheugh pediment, a large, fan-shaped surface surrounding the mouth of Gediz Vallis on the lower slope of Mt. Sharp. The pediment unconformably overlies the underlying bedrock, and is hence younger than units of the Mt. Sharp group. Orbital imaging of the pediment has shown it to have a slightly lower albedo and higher thermal inertia than neighboring units, to be relatively retentive of craters (e.g., erosion resistant), and to exhibit curved bedforms suggestive of lithified eolian bedforms. No diagnostic spectral signature has been observed from orbit. Recent rover positions allowed remote imaging of the contact between Greenheugh pediment and the eroded Murray formation strata below it, showing that the pediment capping material is cross-bedded and relatively thin (1-3 m), and suggesting that the pediment may have been much larger at one time. As Curiosity approached the edge of the pediment, the team investigated two buttes named Central and Western. The latter butte contains dark capping material that initially looked similar to the pediment cap, but close inspection revealed important physical differences. Here we report on compositions from ChemCam of two float rocks that appear to have rolled down from the capping unit, and on potential relation-ships to other targets along the traverse of the rover.
Computer Fractal Technology Based on MATLAB

This paper analyzes the types of image enhancement technology, mainly from two aspects of image space domain enhancement technology and frequency domain enhancement technology, explores its application methods and effects and puts forward the shortcomings of this technology, which is a kind of promotion for the further development of image enhancement technology. For example, using IFS system theory and l system theory in fractal geometry, using MATLAB language to generate self-similar and exquisite fractal patterns. It provides a new method for landform description, biological simulation research, art design, and other fields.
Design Research: Immersion and Intervention

Abstract Research is part of any design praxis. But there is no consensus about how exactly research should be conducted. This is in part due to the fact that the disciplines of design have varied historically and are today greatly differentiated from one another. This chapter sketches out how design ethnography does research: it does not test previously constructed hypotheses, but rather explores and leads into new territories, which makes it risky and adventurous. It concerns itself with singularities, which means it does not necessarily need to generalize its findings. It can break with convention, push boundaries, and expand conceptual horizons. It is abductive, constructivist, and reflexive. It develops hypotheses that can be transferred into design.
Research on GAN-based Container Code Images Generation Method

Recognizing images based on deep learning algorithms requires sufficient samples as a training dataset. In the port field, there is also a lack of container image datasets for deep learning research. This paper proposes a model based on GAN's container box character sample extended dataset (C-SAGAN), and addresses the problems of container box code character defaced and corrupt caused by the port environment, the generative adversarial network is trained with a small amount of real images to generate container character samples. The C-SAGAN model introduces class tags and self-attention in the generator and discriminator. The class tags can control the image generation process. The self-attention mechanism can extract image features based on global information and generate image samples with clear details. The experimental results show that the quality of the samples generated by the generative adversarial network model proposed in this paper is excellent. The samples are used in the CRNN model as the training dataset and the real images are used as the test sets, won the high recognition rate.
A note on the properties of estimators in missing data analysis

In the missing mechanism, missing at random (MAR) is sometimes assumed when data has missing values. When MAR holds and the true distribution belongs to the assumed statistical model, the maximum likelihood estimator based on the observed data has consistency. Based on a weaker condition than MAR, this study investigates the properties of the estimators obtained by applying the maximum likelihood method and the Bayesian method when the true distribution does not belong to the statistical model.
Data Integrity And Ensuring Computation Using MHT In Cloud Computing

Abstract Basically, the architecture model of cloud computing provides the storage capacity as a service over the internet. The main intent of cloud computing is to provide services of security to the users and owners of data. Several security issues are appeared due to appropriate control of data. Hence in this paper the data integrity and ensuring computation using MHT in cloud computing is developed. In this first the data owner will select the initial parameters and choose the random function. The parameters are selected based on the generation of encrypted data blocks (EDB’s). After choosing function random key is computed. Now the Third Party Auditor (TPA) will initialize the data for verification. All this data is stored in the cloud server. This cloud server will follow the cloud service provider rules. Hence data which is obtained from server will computes and generate the signatures using MHT. After generation the path of the node will be identified. At last the obtained data is tested.
Expert system for risk prediction of cesarean section delivery with Dempster Shafer method

The maternal mortality rate in Indonesia is related to the lack of knowledge of mothers regarding high-risk pregnancies, especially with cesarean delivery. In this case, the prediction system designed is done as an effort to increase the awareness of mothers or women who were preparing for high-risk pregnancies. In this article, an Android-based prediction application will be presented using an expert system with the Dempster Shafer method. This method is based on a mathematical theory which consists of a combination of belief function and plausible reasoning from the risk parameters of each labor class by representing the knowledge obtained from experts. This study uses 16 risk parameters as input based on Poejo Rochati's high-risk pregnancy card with 2 outputs, with risk or no risk of having cesarean delivery. The result obtained from this system is 85%, concludes that the prediction system is able to predict the risk of cesarean delivery.
Assessment of Information Security Management System: A Case Study of Data Recovery Center in Ministry XYZ

The DRC of the Ministry XYZ has suffered from a system breach. The DRC's problem will lead to a lack of system information security, availability, and an increasing threat to the whole system of Ministry XYZ. In 2019, the KAMI Index assessment of the Ministry XYZ stated that the level of maturity and completeness of the application of ISO 27001 standards of the XYZ Ministry were at the level of fulfillment of the basic framework. There is a gap between the assessment result and the operational problem within the DRC of Ministry XYZ due to the lack of an information security management system. Therefore, this study conducts the same KAMI Index assessment within the scope of the DRC only and aims to offer a recommendation based on ISO 27001 as the basis of the KAMI Index assessment. This study used discussion, observation, and KAMI Index assessment tools for collecting data and analyze the result. The assessment result of the DRC showed that the maturity level of the ISO 27001 standard on the DRC is on the application of the basic framework. The suggested recommendations to improve the information security management system of the DRC were mostly in the aspect of the information security framework and assets management.
Application of Cloud Computing Clustering Mining Algorithm in Logistics Scheduling Information

To solve the problem of large amount of logistics data and difficult distribution scheduling, this paper proposes the logistics data clustering analysis based on the combination of Prime algorithm and K-means clustering algorithm based on cloud computing to classify the logistics data of cloud platform. On this basis, considering the cost of distribution, the ant colony algorithm is used to optimize the route of order distribution. Finally, the simulation results show that the algorithm used in this paper has fewer iterations and high clustering accuracy, and the load ratio is more than 80% in order distribution, which effectively reduces the cost of logistics distribution.
The Application of Machine Learning in Gesture Prediction on EMG Dataset

With the development of electric technology, mobile devices share a considerable portion of nowadays' market, and thus hl-tech enterprises have a large amount of investment in auxiliary product attached to smartphones and personal computers. For instance, bracelets are one of those commodities, in which sensing devices are settled to capture users' gestures and other activities. Therefore, large quantities of experimental statistics are required to be analyzed and classified to provide reference to Research and Development departments. However, traditional methods rely on subjective judgement and rules of thumb, leading to a lack of efficiency and accuracy. To avoid aforementioned deficiency, this article adopts several different approaches of machine learning to assure the accuracy and objectivity.
Why the Basics Still Matter: The Cost of Using a Machine to Do Mathematics

Remember the joke 1 + 1 = a window? If you rearrange the numbers and symbols you can draw a simple window. It is a playful answer, and quite creative. This creativity based on playfulness is commonly missing these days from mathematics courses. Dan Finkel (2019), founder of For the Love of Math, says that when a student comes to a teacher with a playful solution such as the one above the response should always be “yes,” because “yes,” compared to “correct,” starts a true dialogue between student and teacher, because “yes” says “I value and accept your idea;” “yes” is a mark of respect, and “no” can be a motivation destroyer.
An ESN based Modeling for Roll-to-Roll Printing Systems

In this paper, a modeling scheme based on Echo State Networks (ESN) is designed and discussed for modeling in Roll-to-Roll (R2R) systems. R2R system involves transport and process of thin, flexible, continuous materials (called webs). An accuracy model is critical to the research of R2R system, such as model-based control and prediction. Existing mechanism modeling methods currently used in R2R systems require complex derivation and do not provide the accuracy performance for changing operating conditions and material properties. The modeling scheme based on ESN utilizes the nonlinear approximation approach where the optimal output connect weights of the network are calculated based on matching of the actual closed-loop R2R printing system. The model established by the proposed method considers the effect of operating conditions and material properties. Experimental data from an industrial printing system is used to corroborate the accuracy of R2R system model can be raised double by the proposed method which compared with mechanism modeling methods.
A Multi-Scan Joint Tracking and Classification Method for Weak Targets

This paper proposes a novel multi-scan algorithm with the task of the joint tracking and classification (JTC) for weak targets to address the limitations existed in the traditional single-scan JTC algorithms based on the threshold detection. At each scan, the measurements both from the radar system and the electronic support measures (ESM) sensor, which is one of the passive and bearing-only sensors, are first adopted. Then, by using these measurements of multiple scans, a fast multi-scan JTC (MS-JTC) algorithm is proposed. It is shown that the MS-JTC can bring about a better signal to noise ratio (SNR) for radar systems and better classification due to the accumulation information of multiple scans. Finally, by comparing with the existing single-scan-based algorithm, simulation experiments are executed to validate the effectiveness of our proposed algorithm.
Crossover Operation of Random Drift Particle Swarm Optimization

In order to enhance the global search capability, a crossover operation based on RDPSO algorithm(CO-RDPSO) is put forward. Through crossover operation, particles can skip the local optimum and strengthen the global search capability. Test CO-RDPSO with twelve functions, the experiment results directions, CO-RDPSO not only obtains better results but also converge faster than other algorithms both on unimodal or multimodal.
Multi-sensor Data Fusion for UAV Landing Guidance Based on Bayes Estimation

The safety issues in the UAV landing process have recently attracted widespread attention. This paper proposes a multi-sensor data fusion algorithm based on Bayes estimation to achieve precise positioning during the autonomous landing of the drone. This method uses outlier detection, state estimation, and data fusion to analyze and process measurement data from multiple sensors in real time to obtain the best real-time data during the autonomous landing of the drone. The simulation results show that this algorithm has good accuracy and robustness in solving the landing guidance problem, can initially achieve autonomous landing guidance for drones, and also has important reference value for the future realization of carrier-based aircraft autonomous landing and fighter precise guidance.
Information and communication technology in the professional activity of higher school teachers

Abstract In modern education, the competence approach is becoming increasingly popular, it involves the implementation of educational programs aimed at developing the ability of an individual to independently apply the knowledge and skills acquired in their professional activities in a certain context. One of the priorities for the development of the modern information society is the Informatization of education–the process of improving the educational process through the introduction of information and communication technologies. Information and communication technologies allow to implement interactive communication of all participants of the educational process with each other, as well as provide students with educational materials and educational information stored on various information devices.
Study of the residual uncertainty of messages circulating in hierarchical systems

It is shown that when performing operational control procedures that ensure the routine operation of hierarchical systems, the task of maintaining controllability is reduced to fulfilling the conditions for isomorphism of the set of its states and the set of control actions. &#x0D; With the implementation of hierarchical management at the tactical and strategic levels, proceeding under conditions of in-complete certainty, the probability of a positive feedback objectively increases, which can lead to a further increase in uncertain-ty and, ultimately, to a loss of control.
Reducing Triangle Inequality Violations with Deep Learning and Its Application to Image Retrieval

Given a distance matrix with triangular inequality violations, the metric nearness problem requires to find the closest matrix that satisfies the triangle inequality. It has been experimentally shown that deep neural networks can be used to efficiently produce close matrices with a fewer number of triangular inequality violations. This paper further extends the deep learning approach to the metric nearness problem by applying it to the content-based image retrieval. Since vantage space representation of an image database requires distances to satisfy triangle inequalities, applying deep learning to the matrices in the vantage space with triangular inequality violations produces distance matrices with a fewer number of violations. Experiments performed on the Corel-1k dataset demonstrate that fully convolutional autoencoders considerably reduce triangular inequality violations on distance matrices. Overall, the image retrieval accuracy based on the distance matrices generated by the deep learning model is better than that based on the original matrices in 91.16% of the time.
An iterative phase-correction method for low-quality phase-shift images and its application

Abstract To solve the issue of phase recovery from low-quality fringe images (where the low quality is caused by noise, non-linear intensity and surface reflectivity changes), an iterative phase-correction method is proposed. The high-phase-precision solution is achieved by regenerating the phase-shift-fringe images from the wrapped phase and performing iterative filtering. In general, compared with conventional methods, the proposed iterative phase-correction method has three main advantages: (a) it suppresses the influence of noise without interference from surface reflectivity; (b) it effectively improves the wrapped phase accuracy, thereby improving 3D measurement accuracy, and (c) it effectively recovers the absolute phase (determined by a multi-frequency heterodyne method) information of surfaces with very low or very high reflectivities, without the additional projection of phase-shift-fringe images. The effectiveness of the proposed iterative phase-correction method is verified by simulation and experiment. The proposed method, applied to binocularly structured light, can improve measurement accuracy and greatly improve the effectiveness of measurement of objects with drastic changes in reflectivity. Based on the calibration accuracy (without gamma calibration of the projector) described in this paper, the root-mean-square deviation of the measurement results for binocularly structured light can reach 0.0094 mm.