Reviewerly/exHarmony huggingface dataset:
Automated method for determination of geological horizons nonconformity according to three-dimensional seismic data

In complicated geological media mapping of faults according to wave seismic field often becomes difficult. In algorithm of preliminary processing of seismic data spatial summation of traces is present that leads to «corrosion» of exact location of the fault. Automated method is proposed for identification of faults in three-dimensional body of seismic data of logarithmic decrements of fading (LDF) obtained solving of inverse problem of seismic. Resolvability of LDF data is comparable with resolving ability of initial seismic records.As a result, the analysis is conducted not only taking into account reflecting features of the medium but also with its absorbing properties.Abrupt change of absorption in minor spatial interval is a good identifying attribute of the presence of non-conformable lean-vertical occurrence of geological horizons. Deep and spatial distribution of identified fault structures is an additional argument supporting their existence.We used as an example the materials of detailed seismic mapping of МОГТ 3D conducted by «Ukrgeophysisc» in thin-layer mining field of «Krasnolimanskaya» mine. Geological section of this area is specific by its complexity because of the presence of thrust-shear geological structures related to tension and subsequent compression of these territories that lead to appearance of large amount of deep faults and local fissures.In consolidated geological medium gradient of change of logarithmic gradient of fading will become minor and function of absorption mainly consists of low-speed harmonics. The presence of shear faults leads whereas to abrupt change of absorbing properties of the medium and to appearance of local high-speed oscillations. It is possible to find out the presence of such low-amplitude non-stationary processes in harmonic functions using wavelet analysis. Therefore the paper presents a method that makes possible to find out fault-related decompaction zones in three-dimensional data 3D CDP of the mine field «Krasnolimanskaya 5,25 km2 consisting of almost 27,5 million values reflecting different physical properties of the medium.
A Two-Stage Fully Automatic Segmentation Scheme Using Both 2D and 3D U-Net for Multi-sequence Cardiac MR

Multi-sequence cardiac magnetic resonance (MR) segmentation is an important medical imaging technology that facilitates intelligent interpretation of clinical MR images. However, fully automatic segmentation of multi-sequence cardiac MR is a challenging task due to the complexity and variability of cardiac anatomy. In this study, we propose a two-stage deep learning scheme for automatic segmentation of volumetric multi-sequence MR images by leveraging both 2D and 3D U-Net. In the first stage, a 2D U-Net model coupled with the iterative randomized Hough transform is employed on the balanced-steady state free precession (bSSFP) MR sequences, so as to find the center coordinates of the left ventricles (LVs). The regions of interest (ROIs) are then localized around the center coordinates on the corresponding late gadolinium enhanced (LGE) MR sequences. In the second stage, a 3D probabilistic U-Net model is performed on the ROIs in the LGE data to segment the LV, right ventricle (RV) and left ventricular myocardium (MYO). Experimental results on the MICCAI 2019 Multi-Sequence Cardiac MR Segmentation (MS-CMRSeg) Challenge show that the proposed scheme performs well with average Dice similarity coefficients of LV, RV and MYO as 0.792, 0.697 and 0.611, respectively.
A Multibranch Search Tree-Based Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data

In the interest of privacy concerns, cloud service users choose to encrypt their personal data before outsourcing them to cloud. However, it is difficult to achieve efficient search over encrypted cloud data. Therefore, how to design an efficient and accurate search scheme over large-scale encrypted cloud data is a challenge. In this paper, we integrate bisecting k -means algorithm and multibranch tree structure and propose the α -filtering tree search scheme based on bisecting k -means clusters. The novel index tree is built from bottom-up, and a greedy depth first algorithm is used for filtering the nonrelevant document cluster by calculating the relevance score between the filtering vector and the query vector. The α -filtering tree can improve the efficiency without the loss of search accuracy. The experiment on a real-world dataset demonstrates the effectiveness of our scheme.
RESEARCH COMPETENCE COMPONENT STRUCTURE OF ACADEMIC AND SCIENTIFIC STAFF

The article reveals the role of research competence in the career growth of academic staff, outlines the functions of research competence in the context of national science entry into the European Research Area. The research competence of an academic is considered in terms of the condition and result of academic’s professional activity. On the basis of literary sources analysis and taking into account the mission of the universities’ scientific activity, the concept of an academic’s research competence as a personal quality, being developed on the basis of a combination of knowledge, skills, values, has been formulated. Cognitive, research and value components have been distinguished in the structure of an academic’s research competence. The cognitive component is: knowledge of research methodology, types and methods and means of research, as well as knowledge of the research area. The activity component is the ability to analyze and evaluate scientific materials, to use knowledge that collectively constitutes the cognitive component, in one’s own research activities and in the education of higher education students through research; the ability to conduct a scientific experiment, to process and present scientific results. The value component is characterized by an awareness of the personal importance of scientific activity, its role in professional activity; ethical relationships shown in research with other researchers, academic integrity and tolerance. The author has expressed the opinion that it is inappropriate to consider motivation for research activity as a separate component of research competence. The argument is that motivation is an incentive to act, not an awareness of something specific, so it should be considered in the structure of readiness for activity. The characteristics of each component of an academic’s research competence have been presented.
Forensic Case Studies

In this chapter, the authors collected and defined different types of case studies based on cyber forensics. They tried to gather the latest as well as the oldest case studies. This chapter will help those who want to study different categories of cyber care and their forensics studies. The following scenarios are specific examples of the problems that have been faced by various organizations in the past. For reasons of client confidentiality and legal sensitivity, actual names have been changed.
THE EFFECT OF USING SMALL GROUP DISCUSSION TECHNIQUE ON STUDENTS' SPEAKING SKILL AT XI GRADE OF MAS KEPENUHAN

The purpose of this research was to find out the effect of Small Group Discussion technique on students’ speaking skill in narrative text. This research was conducted in March 2019. The sample was the eleventh grade students of Madrasah Aliyah Swasta (MAS) Kepenuhan. The research method was Experimental research, the number of the sample joined in this research was 40 students. The researcher divided them into two classes,the research post-test only design to find out the effect of Small Group Discussion technique toward on students’ speaking skill. According to the test given, the researcher found that the students’ post test in the independent sample T-test was 0.000 smaller than 0.005 with probabilitas 2 tailed, So, 0.000/2 = 0,000 smaller than α = 0.005, or 0,000 &lt; 0.05. So.Ho rejected. The df 38. It could be concluded that Ha accepted and Ho rejected. It means small group discussion had significant effect on students speaking skill in the narrative text at Eleventh grade students of Mas Kepenuhan.
Work Stress and Job Satisfaction among Egyptian Employees: The Impact of Stress and Satisfaction on Egyptian Employees

Work Stress and Job Satisfaction among Egyptian Employees: The Impact of Stress and Satisfaction onEgyptian Employees IntroductionJob satisfaction and stress face every individual in his daily life.The effect of them could not be ignored.Nowadays the cultural effect in the societies made job satisfaction and stress different than in the past.This is the main concern of this paper to investigate how satisfaction and stress changed among employees due to changes in national cultural effects.Job satisfaction and stress could differ according to individual's attitudes.Organizations play an important role in the development of a country.The main purpose of a company is to earn good profit in a high level that is why employees job satisfaction is necessary for an organization.If the workers of an organization are not satisfied with their workthen the employees of the organization will not complete their work in a good way and this has a negative impact on the organization.(Riaz et al. 2016).On the other hand, Job stress can be described as the damaging physical and emotional sides of a person that arise when the wishes of the job do not match with the abilities or wants of the employee.Job stress can lead to bad strength and even damage.Job stress is also of great concern and worry, Kreitnes & Kinicki (1992) defined stress as the nonspecific response of the body to any demands made upon it.Signs of stress could be felt from the surrounding environment in the workplace or from psychological and behavioral problems.The link between stress and satisfaction and every aspect in our life caused these definitions to be very difficult.Reaching a world-wide application model to investigate stress and satisfaction is actually very difficult.The world of stress and satisfaction needs a great deal of investigation. Objectives of the StudyThe aim of this paper is to investigate the effect of satisfaction and stress on Egyptian employees and try to reach effective managerial solutions.The tremendous effects of satisfaction and stress led researchers to concentrate on these aspects and investigate them.The core of this research is to investigate aspects of satisfaction and stress in the Egyptian environment and among Egyptian employees.We hope to reach managerial solutions that could reduce the negative effects.The objectives of this study are as follows:  To measure the effect of satisfaction and stress on the Egyptian employees. To reach managerial solutions that acts as a guide towards reducing negative effects of dissatisfaction and stress in the Egyptian environment.
The Application of Fuzzy-Analytical Hierarchy Process Method for Majors Selection at Public Universities

The purpose of this research is to build a decision support model for prospective students in choosing undergraduate programs at state universities after they accomplished their high school. There are more than 800 undergraduate programs from science and technology departments from 40 state universities in Indonesia. In this research, we use the Fuzzy Analytical Hierarchy Process (F-AHP) to model a decision. This method is chosen because able to make comparisons between criteria more complex than other DSS (Decision Support System) methods. For the criteria used in this research is computer-based test result, student capacity in 2019 and the people who interest in those majors in 2018. The main result of this research is a ranking of science and technology departments at 40 state universities in Indonesia. The results of this research are expected to be able to assist students in choosing majors that will be taken after graduation in their school. In addition, the method of this research can also be continued in subsequent researches that are similar (theoretically). Whereas practically, this method can be implemented every year using different data.
Rise the Momentum: A Method for Reducing the Training Error on Multiple GPUs

Deep neural network training is a common issue that is receiving increasing attention in recent years and basically performed on Stochastic Gradient Descent or its variants. Distributed training increases training speed significantly but causes precision loss at the mean time. Increasing batchsize can improve training parallelism in distributed training. However, if the batchsize is too large, it will bring difficulty to training process and introduce more training error. In this paper, we consider controlling the total batchsize and lowering batchsize on each GPU by increasing the number of GPUs in distributed training. We train Resnet50 [4] on CIFAR-10 dataset by different optimizers, such as SGD, Adam and NAG. The experimental results show that large batchsize speeds up convergence to some degree. However, if the batchsize of per GPU is too small, training process fails to converge. Large number of GPUs, which means a small batchsize on each GPU declines the training performance in distributed training. We tried several ways to reduce the training error on multiple GPUs. According to our results, increasing momentum is a well-behaved method in distributed training to improve training performance on condition of multiple GPUs of constant large batchsize.
CONSTRUCTION OF MINIMUM CONNECTED DOMINATING SET IN A GRAPH AND ITS APPLICATION IN WIRELESS SENSOR NETWORKS

It is largely applied in Wireless Sensor Networks (WSN). It acts as a virtual backbone in WSNs.algorithms of finding such minimum connected dominating set was studied. A new algorithm for constructing a routing in WSN using one of the algorithms is proposed and implemented using java language. The sampleoutputs are also included
Recognition of Degraded and Non Degraded Roman Characters Using Different Classifiers

In this paper, we present an efficient and robust technique for the recognition of offline roman characters.The main strategy is to extract statistical and similarity features using a combination of grey level co-occurrence matrix (GLCM) and complementary similarity measure (CSM) method.In this work, the CSM method is used to extract features from binary images and combined with GLCM to boost the accuracy of character recognition.The recognition has been done using four different classifiers i.e. artificial neural network (ANN), Naive Bayes classifier, random forest (RF) and support vector machine (SVM).The standard dataset has been used for experimental work.We have done experiments on the clean and noisy dataset.It achieves the accuracy of 100% for some characters without noise and 94.11% with impulsive noise.A comparison of these four classifiers is recorded with and without a noisy environment.On a clean dataset, the random forest provides the best average recognition accuracy of 84.9% for all characters.On low noise datasets, random forest and artificial neural networks have almost the same recognition accuracy and on high noise datasets, SVM provides the highest recognition accuracy.
Implementasi Algoritma Naive Bayes Untuk Menentukan Lokasi Strategis Dalam Membuka Usaha Menengah Ke Bawah di Kota Medan (Studi Kasus: Disperindag Kota Medan)

The business can be influenced by the location of the location and the type of business that was built in the specified location and can develop over time but it is not impossible for bankruptcy to occur because the type of business is not in the right location and promising due to factors that do not support at that location, include that area where the population is not too dense and the land is not that large. For this reason, the authors conduct research on strategic locations to open a particular type of business depending on the location found by the Naive Bayes method. Where the method is used to predict accurately from the many locations that have been researched after that it is determined which is more appropriate area.
RANCANG BANGUN GAME TREASURE OF LABYRINTH DENGAN ALGORITMA BACKTRACKING BERBASIS ANDROID

Games are one of the entertainment media which is the choice of almost everyone to get rid of boredom or just fill in spare time, there are also games used as hobbies and moreover games are now used as electronic sports (e-sports). Currently the game has many types of games, one of which is a type of puzzle game (puzzle). One puzzle game is a maze game. The labyrinth is a puzzle that has a complex branching form and has many dead ends. At the moment there are many labyrinth type games, but rarely are labyrinth games equipped with characters, enemies and a dynamic maze that can change if the game is repeated. To create a labyrinth game that has a dynamic labyrinth arena requires a labyrinth generator or can be called a labyrinth generator by utilizing the backtracking algorithm. This algorithm is powerful enough to be used in some problem solving and also to provide artificial intelligence in the game. The software methodology used in making games is the GDLC (Game Development Life Cycle). GDLC is a game development model that adopts an iterative approach consisting of 6 development phases, starting from the innitiation, pre-production, production, testing, beta and realese phases. for the generation of the labyrinth arena using the Backtracking algorithm while for testing this game uses the UAT (User Acceptment Test). The results of this study in the form of an adventure game based on a maze puzzle that is applied to mobile android. This game is played to train players in problem solving and entertainment facilities.Keywords: Puzzle Game, GDLC (Game Development Life Cycle), Backtracking Algorithm, Android
A novel method for predicting kidney diseases using optimal artificial neural network in ultrasound images

The main aim of this research is to design and develop an efficient approach for predicting ultrasound kidney diseases using multiple stages. Nowadays, kidney disease prediction is one of the crucial procedures in surgical and treatment planning for ultrasound images. Therefore, in this paper, we propose a novel ultrasound kidney diseases prediction using the artificial neural network (ANN). To achieve the concept, we comprise the proposed system into four modules such as preprocessing, feature extraction, feature selection using OGOA and disease prediction using ANN. Initially, we eliminate the noise present in the input image using the optimal wavelet and bilateral filter. Then, a set of GLCM features are extracted from each input image and then we select the important features using oppositional grasshopper optimisation algorithm (OGOA). To classify the image as normal or abnormal, the proposed method utilises an artificial neural network (ANN). The performance of the proposed method is evaluated using accuracy, sensitivity, and specificity. The experimentation results show that the proposed system attains the maximum accuracy of 95.83% which is high compared to existing methods.
Instantaneous Velocity Estimation of Magnetic Microrobots with Visual Tracking

Motion controlling the magnetic microrobot automatically is a huge challenge. A closed-control of magnetic microrobots actuated by electromagnetic manipulation systems can realize the precise and automatic motion control. In order to control the microrobot automatically, tracking and calculating the instantaneous velocity of microrobots are necessary in closed-loop state. A fast tracking via spatio-temporal context (STC) learning algorithm is applied for real-time microrobot tracking. In precision, success rate and computing times that is better than other tracking methods. The instantaneous velocity of magnetic microrobots for different rotational propulsion frequencies was estimated by STC method in real time. The experimental results show that proposed tracking algorithm can be used for visual servo of magnetic microrobots.
Mean Square Error of Non-Sampled Area in Small Area Estimation

Small area estimation (SAE) is a statistical technique to predict the parameter of subpopulation with small or even zero sample size.An area with zero sample size can be estimated with the support of cluster information.The area random effect assumed has a similarity between region and can be analyzed by clustering the auxiliary variables.In SAE, Mean square error (MSE) is used to compare the precision of parameter estimates.But, there is no study that discuss the MSE of non-sampled area in SAE.The main idea of this research is to modify the existing statistical method by adding the cluster information with the assumption that there are similar characteristics of similar areas.The new method was evaluated by data simulation and case study to check the performance.The data simulation show that all modified methods produce a relatively similar MSE of non-sampled area..
Medical Image Augmentation Using Image Synthesis with Contextual Function

Deep learning technology has been widely used in medical research. For medical images that normally contain more complicated distributions than ordinary images, existing methods have tended to show poor generality when dealing with images of diverse distributions. In recent years, the new method of generative model has begun to receive more and more attention. In this paper, we focus on applications of generative models in medical imaging. We propose a framework with a new contextual loss function that can preserve contexts better than traditional methods. Then we treat it as a data augmentation operation and successfully apply this framework to medical image segmentation. Experiments with generated images and segmentation show that our method is accurate and robust for maintaining semantics, outperforming two existing models under comparison.
Face Detection using Logit Boost Algorithm with YCbCr Color Space

In this paper, for detecting faces in an image hybrid method is used. It is based on joint haar-like features with LogitBoost algorithm and for skin pixel segmentation YCbCr color space is used. Our method is based on the co-occurrence Haar-like features which can capture the structural characteristic of the face, make it possible to construct a more effective weak classifier. As the Haar-like, the joint Haar-like feature can be calculated very effective and has the robustness to the addition of noise and change in illumination. The face detector is learned by stage wise selection which is different from Viola and JonesDetector is that we use LogitBoost.Our experimental results show that the LogitBoost obtain higher performance than AdaBoost.We have confirmed that our method based on LogitBoost and YCbCr color space yielded higher performance than AdaBoost and YCbCr color space based skin pixel segmentation when acted solely.