Reviewerly/exHarmony huggingface dataset:
Technique for orthotransformed satellite imagery application in environmental assessment

The article solves an actual scientific and applied task, which is to form a methodological basis for the use of orthotransformed satellite imagery to create information services for environmental monitoring of objects of critical infrastructure.These services are designed to expand the capacity of the National Center for providing the information to central executive authorities about the results of environmental monitoring.The expansion involves the development of methods of using orthotransformed satellite imagery for the assessment of the environmental state.Based on the classification algorithm of enterprises and objects, a database of objects of critical infrastructure of Ukraine classified by their impact on the environment has been developed for the application in GIS-compatible systems.In the formation of the ideology of geographic information systems, the requirements for technical means are justified.These requirements include the deployed service, maintenance, filling and updating of the base of objects of critical infrastructure of Ukraine, the mechanism and order of visualization of impact assessments of all objects of the developed database.The algorithm has been adapted for application in an automated mode as part of GIS-systems.For research, the structure of the classifier using the state OCI register and validation of the developed methodology using the results of space imagery with specific examples for each of the objects of critical infrastructure of Ukraine has been developed.The following object is selected: Darnytsya Power Plant -an enterprise for the production and supply of electric and thermal energy.The study confirmed the relevance, scientific and practical value of using remote sensing methods for studying the ecological status of large areas of the earth's surface.
Optimization of image compression and ciphering based on EZW techniques

This paper presents the design and optimization of image compression and ciphering depend on optimized embedded zero tree of wavelet (EZW) techniques. Nowadays, the compression and ciphering of image have become particularly important in a protected image storage and communication. The challenge is put in application for both compression and encryption where the parameters of images such as quality and size are critical in secure image transmission. A new technique for secure image storage and transmission is proposed in this work. The compression is achieved by remodel the EZW scheme combine with discrete cosine transform (DCT). Encrypted the XOR ten bits by initial threshold of EZW with random bits produced from linear-feedback shift register (LFSR). The obtained result shows that the suggested techniques provide acceptable compression ratio, reduced the computational time for both compression and encryption, immunity against the statistical and the frequency attacks.
COEVOLUTION PATTERNS TO DETECT AND MANAGE UML DIAGRAMS CHANGES

UML diagrams are divided into different perspectives in modelling a problem domain. Preserving coevolution among these diagrams is very crucial so that they can be updated continuously to reflect software changes. Formal methods such as Coloured Petri Nets (CPNs) are widely used in detecting and handling coevolution between software artifacts. Although ample progress has been made, it still remains much work to be done in further improving the effectiveness and accuracy of the state-of-the-art coevolution techniques in managing changes in UML diagrams. In this research, a set of 84 coevolution patterns for supporting coevolution among UML diagrams are proposed to trace the diagrams’ inconsistencies and to determine the change impact incrementally after updating diagrams elements. Coevolution patterns are applied to UML class, object, activity, statechart, and sequence diagrams to cover the different perspectives of UML diagrams. The researcher uses CPNs as a formal language of modelling case study models for the proposed patterns. CPNs tools simulation and monitoring toolboxes are used to validate and monitor the proposed coevolution patterns models and to collect quantitative data about the patterns.
Socio-Economic and Parental Attention toward Learning Achievement with Mediation of Motivation to Learn

This study investigates the influence of parents' socio-economic conditions and attention to motivation to learn and learning achievement, moreover, also examine the mediation of motivation to learn on the influence of socio-economic conditions and parents' attention toward learning achievement. This study uses the ex-post-facto method. The data collection used in this study was questionnaires. The study population was eighth-grade students of junior high school in Medan, Indonesia, as many as 213 people and the samples taken were 138 people. The data analysis technique uses here as path analysis. The findings in the study are the socio-economic conditions and parents' attention directly, positively and significantly influence students' motivation to learn. Then the parents' socio-economic conditions, parents' attention, and motivation to learn have a positive and significant direct effect on learning achievement. Furthermore, motivation to learn is considered to mediate the influence of socio-economic conditions and parents' attention to social studies learning achievement. Parents' attention also mediates the influence of parents' socio-economic conditions on motivation to learn and learning achievement. Learning achievement is more accessible to achieve for families with adequately socio-economic conditions. Social studies learning achievements can be improved by increasing the attention of parents because it will increase motivation and further improve learning achievement.
Flood Disaster Mitigation Policy Analysis ( A Case Study in Pangkalpinang City)

Disaster mitigation in Pangkalpinang City is one of the concerns of the Pangkalpinang City Government after the major floods in 2016.This study aims to look at how disaster mitigation policies are carried out by the Pangkalpinang City Government, especially by the Disaster Management Agency of Pangkalpinang City.This study uses qualitative methods with exploratory models (case studies).Data collection techniques used are through in-depth interviews and FGDs, document studies, field observations..The result of the study shows that several disaster mitigation policies that have had a positive impact on Disaster Management Agency include structural and non-structural mitigation.For example in terms of social, administrative, environmental, structural, legal, and economic aspects.While the obstacles faced by the Disaster Management Agency include the aspect of funding, social aspect, and political aspects.Suggestions that can be given to the Pangkalpinang City Government are the need for more serious attention from the City Government and House of Representatives on the existence of Disaster Management Agency because Pangkalpinang City is now prone to major floods, including stagnant water conditions (small floods).
Unordered Features Selection of Low Birth WeightDatain Indonesiausing the LASSO and Fused LASSO Techniques

This paper aims to analyze the Low Birth Weight (LBW) data of infants in Indonesia by using the LASSO and Fused LASSO techniques.Fused LASSO is usually used to select parameters for ordered features.In this case, the features are unordered.Therefore, this research adopts three techniques in ordering features.Furthermore, all these Fused LASSO techniques and LASSO are compared.This paper utilizes data on 1,176 LBW infants collected from the 2017 Indonesian Demographic and Health Survey (IDHS).The results showed that LASSO has the sparsest solutionbased on the 5-fold cross-validation.Thefeatures that contribute to LBW are mothers' occupation, mothers' age, antenatal care, multiple birth, and birth order.However, Fused LASSO 1 has the lowest AIC and BIC valuecompared to other ordering techniques.Ordering features using the correlation between the features and the outcomes is recommended as an alternative technique to sort unordered features.
Object Scanning of Windows Kernel Driver Based on Pool Tag Quick Scanning

In the memory forensics, the Pool Tag Scanning based on the memory pool tag requires a detailed search of the physical memory when scanning the kernel driver object, which is very inefficient. The object scanning of Windows kernel driver by using the pool tag quick scanning is proposed. The method uses the quick pool tag scanning to reduce the memory range of the scan, and then scan the driver object according to the characteristics of the kernel driver object quickly, to help investigator to determine whether the driver is normal. Experimental results shows that the scanning efficiency for object scanning of kernel driver is improved greatly by using the quick pool tag scanning technology and the time spent in the scanning step is reduced while ensuring the false alarm rate is same.
On a Conjecture Regarding Identification in Hamming Graphs

In 2013, Goddard and Wash studied identifying codes in the Hamming graphs $K_q^n$. They stated, for instance, that $\gamma^{ID}(K_q^n)\leqslant q^{n-1}$ for any $q$ and $n\geqslant 3$. Moreover, they conjectured that $\gamma^{ID}(K_q^3)=q^2$. In this article, we show that $\gamma^{ID}(K_q^3)\leqslant q^2-q/4$ when $q$ is a power of four, which disproves the conjecture. Goddard and Wash also gave the lower bound $\gamma^{ID}(K_q^3)\geqslant q^2-q\sqrt{q}$. We improve this bound to $\gamma^{ID}(K_q^3)\geqslant q^2-\frac{3}{2} q$. Moreover, we improve the above mentioned bound $\gamma^{ID}(K_q^n)\leqslant q^{n-1}$ to $\gamma^{ID}(K_q^n)\leqslant q^{n-k}$ for $n=3\frac{q^k-1}{q-1}$ and to $\gamma^{ID}(K_q^n)\leqslant 3q^{n-k}$ for $n=\frac{q^k-1}{q-1}$, when $q$ is a prime power. For these bounds, we utilize two classes of closely related codes, namely, the self-identifying and the self-locating-dominating codes. In addition, we show that the self-locating-dominating codes satisfy the result $\gamma^{SLD}(K_q^3)=q^2$ related to the above conjecture.
Face Liveness Detection Based On Multiple Feature Descriptors

The face liveness detection module is one of the most important parts in the state-of-the-art face recognition system. In this paper, we present an efficient method to further improve its accuracy by leveraging multiple feature descriptors. Firstly, a data-driven feature descriptor is proposed based on the Karhunen-Loève Transform (KLT) learned from both client and imposter face images. Moreover, the Completed Local Binary Pattern (CLBP) algorithm is utilized to represent the local structure and the high-middle spectra components of 2D Fourier transform are also utilized to reflect the global structure. These features are fed into the support vector machine (SVM) to learn a classifier for face liveness detection. Experimental results on NUAA illustrate that our proposed method outperforms most of the widely utilized feature descriptors.
Imbalanced Data Classification for Multi-Source Heterogenous Sensor Networks

Most of the traditional classification algorithms are based on the uniform distribution of samples, and the effect is not ideal when dealing with such data, which mainly shows that the classification results incline to the majority class. Therefore, we propose the imbalanced multi-source heterogeneous data classification algorithms in this paper, which are mainly based on the expansion and extension of Support Vector Machines. Considering that there are complex connections within multi-source data, express them as a unified, concise and efficient mathematical model can completely retain data information and improve data processing efficiency. We perform tensor representation and feature extraction on the heterogeneous data, and two different classification algorithms are proposed in this paper. In the first method, we represent multi-source heterogeneous data into a unified tensor form directly and obtain a high-quality core data through dimensionality reduction algorithm, then realize data classification by Support Tensor Machine. In the other method, we extract data from different data sources and classify them with Ensemble Deep Support Vector Machine (DSVM), which combined three DSVM with different kernel functions. The algorithms are compared on CUAVE data set, which contains two different modalities of sound and picture.
Consistent Disparity Synthesis for Inter-View Prediction in Lightfield Compression

For efficient compression of lightfields that involve many views, it has been found preferable to explicitly communicate disparity/depth information at only a small subset of the view locations. In this study, we focus solely on inter-view prediction, which is fundamental to multi-view imagery compression, and itself depends upon the synthesis of disparity at new view locations. Current HDCA standardization activities consider a framework known as WaSP, that hierarchically predicts views, independently synthesizing the required disparity maps at the reference views for each prediction step. A potentially better approach is to progressively construct a unified multi-layered base-model for consistent disparity synthesis across many views. This paper improves significantly upon an existing base-model approach, demonstrating superior performance to WaSP. More generally, the paper investigates the implications of texture warping and disparity synthesis methods.
North Frisian dialects: A quantitative investigation using a parallel corpus of translations

North Frisian is well-known for its small-scale variation and the traditional classification found in Århammar (1968) assumes as many as ten dialect groups within a small area. Until this day, however, a dialect classification based on quantitative methods is lacking and the criteria for the traditional classification are also far from clear. In order to address this problem, the paper uses parallel text material (the questionnaires from Georg Wenker's “Sprachatlas des Deutschen Reichs”) and character n-grams (tri­grams). Applying cosine distance to the trigram inventories of 55 North Frisian questionnaires, the paper employs several dimension re­duction techniques, e. g. multidimensional scaling, Neighbor-Net and hierarchical cluster analysis and compares the results with the traditional classification. While the latter can be confirmed to a large extent, the distinctions within Southern Mainland North Frisian seem to be less clear. Using an association measure (log-likelihood), prominent features are extracted for the six main dialect groups that emerge on basis of the aggregated data. Finally, the paper discusses the quality of the North Frisian Wenker questionnaires in the light of these findings.
The Effect of Whatsapp in A Flipped Classroom on Students’ Writing Achievement at MTsN 1 Konawe

This study aims at finding out the significant effect of using WhatsApp as a media in a flipped classroom on students’ writing achievement. This study applied the pre-experimental design. The population was the 2017/2018 second grade students of MTsN 1 Konawe which consists of 240 students. The samples were 30 students of Class VIII-2. The experimental class was taught by using WhatsApp Group chat as a flipped classroom media. The tests consisted of pre-test and post- test. The instrument used in this study was a writing test. The data were analyzed by using paired sample t-test. The hyphotesis of this research stated if there was a significant effect of using WhatsApp on students’ writing achievement at MTsN 1 Konawe (H1). The result of paired sample t-test shown that the asymp sig. is 0.000. It means that the asymp sig. was lower than 0.05 (0.000 &lt; 0.05). Therefore, the H1 is accepted. It can be concluded that WhatsApp has significant effect on students’ writing achievement at the second grade students of MTsN 1 Konawe..
Pelatihan Penulisan Karya Ilmiah untuk Meningkatkan Kemampuan Guru di SMP Negeri 7 Kota Bengkulu

The purpose of this service is to improve the ability of teachers at SMPN 7 Bengkulu City in writing scientific papers. The method used in this pegabdian activity is a training method by way of lectures and direct demonstrations for trainees. Participants in this activity were the board of teachers of SMP Negeri 7 Bengkulu City, totaling 13 people. The instrument used in this training was a questionnaire that was distributed to participants before and after the training to see whether or not there was an increase in the knowledge of the trainees. This training resulted in an increase in teacher knowledge about scientific work, namely knowledge of reference lists and publication journals by 55%, structure knowledge of scientific works by 40%, technical knowledge of writing scientific papers by 50%, and psychological and economic constraints by 50%.
Nursing Care for Patients Submitted to Coronary Transluminal Angioplasty: An Integrative Review

Aims: To verify the scientific evidence in the literature about nursing care after coronary transluminal angioplasty, from 2005 to 2019.&#x0D; Methodology: This is an Integrative Review, performed in the LILACS, MEDLINE and Google Scholar databases, including original and review articles, theses, dissertations and monographs published in Portuguese or English in the last 15 years (2005-2019). Data were collected using the Ursi’s instrument and data analysis was carried out by the use Bardin’s Content Analysis.&#x0D; Results: 06 articles were found and two categories emerged: Nursing diagnoses after coronary transluminal angioplasty and Nursing interventions after coronary transluminal angioplasty. The nursing process should be carried out in its entirety so that the results of nursing care are effective and the improvement of the quality of the services provided is continuous. &#x0D; Conclusion: it is concluded that nursing actions are beyond bedside care, as they also include management services of inputs and human resources.
ANALISIS IMPLEMENTASI PREPROCESSING DENGAN OTSU-GAUSSIAN PADA PENGENALAN WAJAH

In this research, we will focus on facial expressions to detect customer satisfaction in mini markets where the service level is less than optimal. To find out the level of custome satisfaction can be seen through facial recognition tahen through CCTV in the mini market. The problems that occur are many customers who do not directly convey the impression that is felt when shopping, while minimarkets and shopping conters must know the level of customer satisfaction to improve sales strategies. Research to solve the problem is still rerely done, therefore one of the roles of intelligent computing is to solve the problem using Support Vector machine (SVM). The purpose of this study is to improve the accuracy of facial expressions of mini market customers through improved preprocessing. The results of the application of the otsu method and the gaussian function can be used for the preprocessing stage through a threshold image that has good image quality. The otsu-gaussian method is not effectively used for preprocessing data sourced from video or images with poor image quality, making it difficult to recognize faces.
A SURVEY OF QoS IN 5G NETWORK FOR IoT APPLICATIONS

Internet of Things (IoT) is being aopted and used to revolutionize the way the globe can be connected through seamless connection and through the use of smart and heterogeneous devices that are interrelated.Applications of the Internet of Things are transforming the daily lives of people and the activities that are often carried.The future of the applications that rely on the Internet of Things is dependent on the strength of the network connecting devices to each other; therefore the security will be main criteria as millions of devices are interconnected.In this paper we survey different cellular networks when they are applied in the development of the Internet of Things applications, we also discuss security challenges and solutions in cloud as well as security of IoT applications and cloud computing, this implies the discussion of implementing some criteria for Quality of Security (QoS) in 5G network like authentication, confidentiality, integrity and availability using AWS firewall.
Infrastructure as a Service: Cloud Computing Model for Micro Companies

Cloud computing is one of the most promising computing platforms. This paper describes the activities related to the problems in using cloud computing (CC) in micro companies, especially in Poland, specifically infrastructure as a service (IaaS) model. The usage of cloud computing in micro industries has been explained in detail, which also includes advantages and disadvantages. It is observed that the use of cloud computing reduces the significant cost of running a business, mainly in micro companies. This paper also presents the review of the literature in the last few years regarding the use of CC. It is identified that, despite some merits, cloud computing (CC) systems in micro companies are rarely in use. The main approaches explored in this paper are the observations, literature studies based on available sources on cloud computing (CC) including the case studies, and so on. Finally, the work presented in this paper covers the assumptions regarding the future work in the same directions and trends, especially in context to the cloud computing concept for micro companies.
Research on ROI Algorithm of Ship Image Based on Improved YOLO

In this paper, a region of interest (ROI) extraction algorithm based on YOLO algorithm is proposed. The algorithm optimizes the output tensor dimension of YOLO model, generates different image quality images of naval vessels by using image degradation function, and retrains the network by means of migration learning, which enhances the accuracy and detection rate of the algorithm. Compared with the original algorithm, the detection rate of the algorithm is improved by 4.25% on average, which proves the effectiveness of the algorithm.
Optimization-Based Construction of Quadrilateral Table Cartograms

A quadrilateral table cartogram is a rectangle-shaped figure that visualizes table-form data; quadrilateral cells in a table cartogram are transformed to express the magnitude of positive weights by their areas, while maintaining the adjacency of cells in the original table. However, the previous construction method is difficult to implement because it consists of multiple operations that do not have a unique solution and require complex settings to obtain the desired outputs. In this article, we propose a new construction for quadrilateral table cartograms by recasting the construction as an optimization problem. The proposed method is formulated as a simple minimization problem to achieve mathematical clarity. It can generate quadrilateral table cartograms with smaller deformation of rows and columns, thereby aiding readers to recognize the correspondence between table cartograms and original tables. In addition, we also propose a means of sorting rows and/or columns prior to the construction of table cartograms to reduce excess shape deformation. Applications of the proposed method confirm its capability to output table cartograms that clearly visualize the characteristics of datasets.