Reviewerly/exHarmony huggingface dataset:
THE ROLE OF EDMODO VIRTUAL CLASS TOWARD THE STUDENTS� WRITING ABILITY IN REMOTE ISLAND

The objectives of this research were to find out (1) whether or not the use of Edmodo virtual class increases the motivation of the students of SMA Negeri 24 Karanrang island Pangkep in writing English and (2) the extent to which Edmodo virtual class increases students writing achievement. The population was all the students of SMA Negeri 24 Karanrang island at Pangkep regency in academic year 2018/2019. The students in this remote area were consisted of 30 students merely. Involving two classes (class X and class XI), selected by using total sampling technique, this research employed quasi experimental design (15 students as experimental class that was taught by using Edmodo virtual class and 15 students as control class that was taught by lecturing way). The data then collected through questionnaire (of writing motivation) and writing test, then analyzed through descriptive statistics and inferential statistic (SPSS) program version 22. The result gained from the data analysis showed that (1) the use of Edmodo virtual class in writing motivation was significantly different from the existing way. (2) The use of Edmodo virtual class in writing achievement increases the students writing achievement
Construction and Promotion of Teachers in Application-Oriented Colleges and Universities

This paper enumerated practical teaching staff in colleges and universities have encountered difficulties in the process of continuous development and bottlenecks, and combing the method and to promote the development of applied university faculty acceleration strategy, strive to applied in colleges and universities in insufficient to absorb and draw lessons from developed countries applied advantages of the construction of teachers team in colleges and universities, to promote rapid development applied in colleges and universities in our country, to cultivate more talents. Contribute to the development of various undertakings in our country.
Optimization of the Size of Thread Pool in Runtime Systems to Enterprise Application Integration: A Mathematical Modelling Approach

ABSTRACT Companies seek technological alternatives that provide competitiveness for their business processes. One of them is integration platforms, software tools that build integration solutions, which allow the different applications that make up the software ecosystem to work synchronously and that new applications or functionality be incorporated with the least impact in the existing ones. The runtime system is the component of the integration platform responsible for managing the computational resources that run the integration solution. Among these resources are the processing units, called threads, or sets of those threads, called thread pools. The performance of the runtime systems is directly related to the number of threads available to run the integration solution, but scaling the number of threads that provide a shorter response time is a challenge for software engineers. If this quantity is undersized, it may cause a delay in the execution; if it is overestimated, it could cause a waste of computational resources. This article presents a mathematical model, defined by differential equations, that establishes the optimum number of threads, which maximizes the expected performance gain by minimizing the execution time of the integration solution. In addition, it presents the mathematical model application, which assists the analysis of the expected gain in different architecture scenarios and quantity of threads.
DEVELOPING ENGLISH WRITING MATERIALS OF PROCEDURE TEXT FOR STUDENTS GRADE 11 OF CULINARY PROGRAM AT SMK NEGERI 10 MEDAN

Students’ worksheet, as one of learning tools that is used to involve the students to work actively during teaching and learning process, should be developed based on the students’ need. Students’ worksheet with scientific approach in listening skill has not applied. A good student’s worksheet must be satisfied criteria of valid, practical and effective. This research is a developmental research using Research and Development (RnD). It has been conducted on 35 students of SMK Negeri 10 Medan grade X for Multimedia Program. In this study, data obtained from Curriculum syllabus analysis, students’ need analysis and results of validation. Students’ need analysis was done to analyze the students’ perception about the previous learning materials and to know what kind of learning material they need. All of student answer the questionnaire in students’ worksheet in listening skill. From their answers are summarized into questions and answers for students’ worksheet, especially in the listening activity. Keywords: Students’ worksheet, scientific approach, listening skill.
Semantic Tagging Framework for Contextually Augmented Features

The value of (semi-)automatically recognizing the meaningful regions on a Computer Aided Design (CAD) model is well documented and has been traditionally studied as Geometric feature recognition.However, the effective tagging of the regions, especially tagging with contextually meaningful (i.e.semantic) keywords, has been elusive.In this paper, we focus on the non-geometric aspects of the tagging problem and present a semantic tagging framework.Specifically, given the contextually augmented instances created from the unidentified regions on a CAD model, the framework tags the instances with semantic keywords defined in the semantic model.This will provide the direct link between a CAD model and semantic models, and thus allow high-level reasoning to support design and manufacturing tasks.The feasibility of the approach has been verified by applying the developed framework to tag the regions on turbine blade models.This paper concludes with the future challenges of the approach.
Alternating Projections Filtering Algorithm to Track Moving Objects

An interest is often present in knowing evolving variables that are not directly observable; this is the case in aerospace, engineering control, medical imaging, or data assimilation. What is at hand, though, are time-varying measured data, a model connecting them to variables of interest, and a model of how to evolve the variables over time. However, both models are only approximation and the observed data are tainted with noise. This is an ill-posed inverse problem. Methods, such as Kalman filter (KF), have been devised to extract the time-varying quantities of interest. These methods applied to this inverse problem, nonetheless, are slow, computation wise, since they require large matrices multiplications and even matrix inversion. Furthermore, these methods are not usually suitable to impose some constraints. This article introduces a new iterative filtering algorithm based on alternating projections. Experiments were run with simulated moving projectiles and were compared with results using KF. The new optimization algorithm proves to be slightly more accurate than KF, but, more to the point, it is much faster in terms of CPU time.
A NEW AND ADAPTIVE SECURITY MODEL FOR PUBLIC COMMUNICATION BASED ON CORRELATION OF DATA FRAMES

Recent developments in communication and information technologies, plus the emerging of the Internet of Things (IoT) and machine to machine (M2M) principles, create the need to protect data from multiple types of attacks. In this paper, a secure and high capacity data communication model is proposed to protect the transmitted data based on identical frames between a secret and cover data. In this model, the cover data does not convey any embedded data (as in normal steganography system) or modify the secret message (as in traditional cryptography techniques). Alternatively, the proposed model sends the positions of the cover frames that are identical with the secret frames to the receiver side in order to recover the secret message. One of the significant advantages of the proposed model is the size of the secret key message which is considerably larger than the cover size, it may be even hundred times larger. Accordingly, the experimental results demonstrate a superior performance in terms of the capacity rate as compared to the traditional steganography techniques. Moreover, it has an advantage in terms of the required bandwidth to send the data or the required memory for saving when compared to the steganography methods, which need a bandwidth or memory up to 3-5 times of the original secret message. Where the length of the secret key (positions of the identical frames) that should be sent to the receiver increases by only 25% from the original secret message. This model is suitable for applications with a high level of security, high capacity rate and less bandwidth of communication or low storage devices.
Reparative Practices: Invitations from mundane urban ecologies

The paper proposes a reparative turn in co-design towards an attention and sensitivity to more-thanhuman world-making practices in our urban environments.The notion of 'reparative' hold strings with the reparative system that an organism starts when damage is experienced.Thinking-with this biological, cultural and performatively, we propose the reparative as the starting point for learning to notice life-giving potentialities in the Anthropocene.Reparative practices are ethical and political in the sense that we are searching for lifegiving practices that can move us beyond design practices in the Anthropocene.Hence, by bringing attention to environmental enchantments related to sensory everyday practices we propose that designers and citizens alike can initiate reparative futures. URBAN ECOLOGIES IN THE ANTHROPOCENEThe consumer culture, waste production, real estate development and fast distribution of goods in urban environments influence the lives and liveability of other species.While humans hold significant abilities to change these systems, non-humans also hold agencies that shall be acknowledged and worked-with.Central to our argument is that it is not humans alone that forms and transforms our habitats.Hence, we must include unruly collaborations with non-human lives as we might learn about planetary survival from living and design with more-than-human inhabitation.
Segmentation of Medical Images using Adaptively Regularized Kernel-based Fuzzy C-Means Clustering

Medical imaging is the process of seeing the internal parts of the body, whose purpose is to maintain health, prevention and treatment of diseases.Nowadays, medical imaging has become a common part of everyday clinical practices.Despite huge progress, there is still no such instrument that can represent all facets of the human body.Image segmentation is the most common method used to analyze and detect distortion in medical images.Clustering is a technique used to group similar data in the same cluster.MRI segmentation is critically important for diagnostic studies and diagnosis.There are many drawbacks in existing methods based on soft clustering, which include low noise and high computational cost in the presence of image noise and artifacts.In this paper, we use a novel method to split brain tissues from magnetic resonance images, which routinely use regularized kernel-based fuzzy-clustering clusters.Adaptive regularized kernel based fuzzy clustering means (ARKFCM) is applied to remove nuclei and non-nuclei images of the Histopathological ROI image.In ARKFCM, results occurred in two sets of images, both images are examined to generate clustering cells.The combined ARKFCM image contains many overlapping areas of cells.
Caracterização de pacientes diabéticos tipo 2 em uso recente de insulina: estilo de vida e sintomas depressivos

AIMS: To characterize type 2 diabetes mellitus patients who have recently initiated treatment with insulin regarding lifestyle and depressive symptoms.METHODS: Cross-sectional study carried out with patients in recent use of insulin, which was determined by the register held in the family health strategy, from January 2017 to August 2018. In home visits, the patients answered questions about lifestyle and characteristics of the disease, and Beck’s Depression Inventory.RESULTS: The sample consisted of 38 type 2 diabetic patients in recent use of insulin. There was a predominance of males (57.9%), married (65.8%) and white (89.5%), the average years of study being 5.8±2.9. 13.2% were considered physically active in leisure time, and only 47.4% were restricting carbohydrates. 65.8% were overweight and 34.2% were obese. The participants had type 2 diabetes mellitus diagnosis for 8.9±7.48 years on average. Regarding the reasons that led to the use of insulin, 92.1% mentioned the lack of control of the disease. As for the classification of depressive symptoms, 47.3% had some psychological alteration. We verified that the presence of depressive symptomswas more prevalent in women (p=0,014).CONCLUSION: Patients with type 2 diabetes on recent insulin use had few changes in lifestyle after being diagnosed with the disease. Most patients started using insulin due to lack of control of the disease. The presence of depressive symptoms was more prevalent in women and in patients who reported dietary changes after the diagnosis of type 2 diabetes mellitus.
Collocation and Selectional Preferences: A Frame-based Approach

Most of the research conducted into collocation and semantic frames has dealt with these phenomena separately. The study of collocation has not figured prominently in the research agenda of frame semantics, and frame semantics has only sporadically been used as an analytical framework for collocation. This article is a contribution to narrowing the gap between the two fields. It does so by addressing key issues in the design of a frame-based approach to collocation, with a special focus on the relation between collocational patterns and semantic valency, and by providing arguments for the efficacy of the frame-semantic theoretical apparatus in explaining verb-adjective links that are not accounted for by the existing models of collocation. The methodology combines lexicographic resources as well as quantitative and qualitative analysis of examples and data from an English web corpus (ukWaC).
The Implementation of Recursive Algorithm to Determine the Determinant of n x n Matrix Using Cofactor Expansion

This research examines the algorithm to determine the Determinant of n x n matrix using cofactor expansion and the implementation using recursive algorithm. Random function is added as an option to generate large matrix. To identified singular matrix and to avoid unnecessary further process, the algorithm searches for rows that are multiples of integer from another row, or columns that are multiples of integer from another column, then returns 0 as a Determinant value. In order to minimize the number of iterations and computations, the algorithm searches for rows or columns having the highest number of zero elements to be expanded. The program will not expand zero element and immediately returns a value of zero for those cofactor. Finally the program calculates the number of iterations performed each time the cofactor expands to calculate the Determinant of 2 x 2 matrix.In conclusion, the data shows that the use of recursice algorithm to iteratively expand cofactor considering the row and column having highest number of zero, will reduce the number of iteration and computation.
Parallel Frequent Itemset Mining on the Intel MIC Accelerators

Association rule mining is one of the basic problems of data mining, which supposes finding strong correlations between itemsets in large transaction database. Association rules are generated from frequent itemsets (itemset is frequent if its items frequent occur together in transactions). The DIC (Dynamic Itemset Counting) algorithm is modification of the classical Apriori algorithm of finding frequent itemsets. DIC tries to reduce the number of passes made over the transaction database while keeping the number of itemsets counted in a pass relatively low. The paper addresses the task of accelerating DIC on the Intel MIC (Many Integrated Core) systems in the case when the transaction database fits into the main memory. The paper presents a parallel implementation of DIC based on OpenMP technology and thread-level parallelism. We exploit the bit-based internal layout for transactions and itemsets. This technique simplifies the support count via logical bitwise operation, and allows for vectorization of such a step. Experiments with large synthetic and real databases showed good performance and scalability of the proposed algorithm.
The World in My Mind: Visual Dialog with Adversarial Multi-modal Feature Encoding

Yiqun Yao, Jiaming Xu, Bo Xu. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
A Method of Non‐textured Regions Matching

It is difficult for existing dense or quasidense matching algorithms to obtain accurate matching results due to the lack of effective feature information in non-textured regions. This may affect the quality of subsequent 3D reconstruction and super-resolution reconstruction. We propose a method of non-textured regions matching. To reduce the impact of noise and illumination, vector sampling normalized cross correlation is proposed to directly measure coherence between two colour images by the effective information of multi-channel features. Three mathematical properties are used in affine transformation: 1) Centroid location is not affected by affine transformation; 2) Affine transformation transforms a straight line into another straight line; 3) Affine transformation maintains the linear relation invariant. In non-textured regions, we can construct texture regions which have affine invariant properties to improve the accuracy of template matching. This could provide more information for the diffusion of dense or quasi-dense matching. In experiments, we demonstrate that the method has high accuracy in cases where there is no big distortion between the two viewing angles from two aspects of simulated images and photographic images.
Using Fuzzy Logic System to Optimize Measuring Porosity by Aerodynamics Sensor

Porosity is a measure of the void spaces in a material and is a fraction of the volume voids over the total volume, between 0 and 1, or as a percentage between 0% and 100%. Many methods have been developed for determining porosity. Most of the methods developed have been designed for small samples, it is the need to determine the bulk volume, pore volume and the volume of its solid matrix for the sample. The direct and indirect methods are based on volume determination and measuring of some properties of the void space respectively. In this paper an aerodynamic sensor related to the indirect determination porosity method was designed to measure in-process grinding wheel porosity. The fuzzy logic control system was used to optimize the best design and dimensions for best transducer reception out of the porosity variations. A computational fluid dynamic package used to design the sensor parameters, and to simulate different pours wall to be measured. An experiment made with different porosity levels grinding wheels to validate the theoretical method. The results provide the ability of the sensor to detectwall porosity up to 0.05 %, also.Composites mesh, sponges, rocks and other pours surfaces can be future research candidates.
The Information Literacy Assessment of Trainees Based on Cluster Analysis

In this paper, the evaluation index system of information literacy of trainees is determined by expert Delphi method. Questionnaires are conducted according to the problems in the second-level indicators, and the results are analyzed by second-order cluster analysis. The information literacy level of trainees is divided into groups, and the existing problems and shortcomings are pointed out, which provides effective guidance for further information literacy education.
Hubungan Motivasi dan Hasil Belajar Siswa Kelas XI MAN Model Makassar

This study discusses the correlation between students’ learning motivation and mathematics learning outcomes. The population of this study is 320 students of class XI MAN Model Makassar. The technique sampling used was random sampling. Data collected by using documentation format and questionnaire. Data analyzed by using descriptive analysis and nonparametric statistical analysis. The result of descriptive analysis showed that 53,75% of students had high learning motivation and 62,5% of students get scores 7-7,4 on 10 scales. The result of nonparametric statistic analysis showed that the coefficient correlation between learning outcomes and motivation was 0,522 by the Kendall test and 0,575 by Spearman test. T = 5,405 by Kendall and t = 6, 208 by Spearman was greater than t table 2,326. It means that there were a positive and significant correlation between students’ motivation and learning outcomes.
Optimal quantum channel for perfect controlled super-dense coding protocol

Perfect super-dense coding protocol allows transmission of two classical bits (cbits) by sending just one quantum bit (qubit) with unit success probability. Perfect controlled super-dense coding protocol should be such that perfect super-dense coding is achieved with the controller's cooperation. There are different possible types of quantum channels to be shared among the authorized parties to perform perfect controlled super-dense coding. Of our interest is the quantum channel which is optimal in the sense that no cbits can be transmitted with certainty without the controller's cooperation. We attack this problem by means of the so-called control power. By calculating and analyzing the control power for specific quantum channels we figure out the one that is optimal. We also address the issue of how to prepare such optimal quantum channel.
Combined feature compression encoding in image retrieval

Recently, features extracted by convolutional neural networks (CNNs) are popularly used for image retrieval. In CNN representation, high-level features are usually chosen to represent the images in coarse-grained datasets, while mid-level features are successfully applied to describe the images for fine-grained datasets. In this paper, we combine these different levels of features as a joint feature to propose a robust representation that is suitable for both coarse-grained and fine-grained image retrieval datasets. In addition, in order to solve the problem that the efficiency of image retrieval is influenced by the dimensionality of indexing, a unified subspace learning model named spectral regression (SR) is applied in this paper. We combine SR and the robust representation of the CNN to form a combined feature compression encoding (CFCE) method. CFCE preserve the information without noticeably impacting image retrieval accuracy. We find the tendency of the image retrieval performance to change the compressed dimensionality of features. We further discover a reasonable dimensionality of indexing in image retrieval. Experiments demonstrate that our model provides state-of-the-art performances across datasets.