Reviewerly/exHarmony huggingface dataset:
TRMOR: a finite-state-based morphological analyzer for Turkish

Morphological analysis is an important component of natural language processing systems like spelling correction tools, parsers, machine translation systems, and dictionary tools.In this paper, we present TRMOR, a morphological analyzer for Turkish, which uses the SFST tool (Stuttgart Finite-State Transducer).TRMOR can be freely used for academic research (see http://www.cis.uni-muenchen.de/schmid/tools/SFST/).It covers a large part of Turkish morphology including inflection, derivation, and some compounding.It uses morphotactic and morphophonological rules and a stem lexicon.We describe the morphological structure of Turkish, explain the phonological and morphological rules implemented in TRMOR, evaluate the system, and test it in special cases.The evaluation of TRMOR was executed on gold-standard words.One thousand words were randomly selected from Wikipedia word lists.For those words, we achieved gold-standard analysis.TRMOR has 94.12% precision on these 1000 words that were randomly selected from Wikipedia word lists.Morphological analyses of Turkish are prepared for the gold-standard version since, to our knowledge, there is no gold-standard segmentation available for Turkish morphological analyzers for noncommercial purposes.
A Low-cost Mobile VR Walkthrough System for Displaying Multimedia Works Based on Unity3D

Virtual reality technology has developed rapidly in recent years, and VR walkthrough system is used widely, as an important branch. This project is the application of VR walkthrough. We designed the new digital exhibition hall by modeling the three-dimensional space with a sense of reality and immersion. It focuses on multimedia works show and art education of universities, at the same time, it is an excellent way to demonstrate teaching achievements. The system runs on Android mobile phone and users can wear VR display equipment Cardboard to look around in the exhibition hall. In addition, users can move freely through the gamepad to view works in different areas. Meanwhile, they can choose works to play, which improves the interactivity between the system and users.
A Personalized Collaborative Filtering Recommendation Algorithm Based on Linear Regression

This paper attempts to solve the problems with linear regression-based collaborative filtering recommendation algorithm, namely, the difficulty in extracting eigenvalues, the low accuracy and the poor interpretability.For this purpose, the tag weights were introduced as eigenvalues and the prediction accuracy was improved by the principle of collaborative filtering recommendation algorithm, creating a personalized collaborative filtering recommendation algorithm based on linear regression (PCFLR).Firstly, the tag weights for users were computed by term frequency-inverse document frequency (TF-IDF), and taken as the eigenvalues of the linear regression model.Then, the linear regression model was constructed based on the users' historical scores.After that, the cost function was set up by the least squares method, and regularized to prevent over-fitting.Next, the optimal value of the cost function was computed by gradient descent method, yielding the tag weights for items.On this basis, the predicted scores of all unrated items were obtained considering the linear relationship between the tag weights for users and those for items.The mean absolute error (MAE) between the predicted and actual scores was computed, and used to adjust the predicted scores into the final results.In addition, the set of recommendable items for the target user was produced based on the scores rated by all neighboring users, and coupled with the linearly regressed scores to make recommendations to the target user.The experimental results show that the PCFLR outperformed the traditional recommendation algorithms in accuracy and interpretability.
Phase-to-depth calibration in fringe projection profilometry based on blockwise surface fitting

A new classification method is proposed in this paper which categorizes the phase-to-depth calibration methods for fringe projection profilometry into pixel-dependent (P-D) ones and pixel-independent (P-I) ones. In P-D methods, depth is expressed as a function of the unwrapped phase, and parameters of the function are different from pixel to pixel. In P-I calibration methods, depth is expressed as a function of both phase and pixel coordinate, and there is only one set of parameters which applies to all pixels. P-D method is resistant to distortion of the projector or the camera but is time-consuming because a number of phase maps need to be recorded; P-I method requires only two or three phase maps but is sensitive to distortion. To address this problem, we further propose a blockwise calibration method. It requires the same number of phase maps as the P-I method and meanwhile can be as resistant to distortion as the P-D method. Simulation results show that when phase maps are contaminated by random additive noise, error produced by the proposed blockwise method is even less than that of the P-D method which uses 18 phase maps. In the experiment the proposed method and the P-D method were utilized to calibrate a fiber-optic fringe projection profilometer and then heights of a 4 mm and a 7 mm gauge block were measured. Difference between results of the two methods was less than the accuracy of the translation stage (0.01 mm), which verified the effectiveness of the proposed method.
Multiscale SAR image segmentation by combining curvelet transform and GMTRJ algorithms

To integrate multiscale analysis within region-based segmentation framework, this paper presents multiscale SAR image segmentation method. First, curvelet transform is used to obtain a collection of the decomposed SAR images on multiple scales. Their domain is partitioned into a set of blocks by a regular tessellation, in which the number of blocks is assumed to be a random variable with Poisson distribution. On the partitioned domain, the Bayesian paradigm is followed to build the region- and multiscale-based image segmentation model with unknown number of classes. Further, a Generalized Multiple-Try Reversible Jump (GMTRJ) algorithm is designed to simulate the segmentation model. During the procedure of iterative simulation for the segmentation model, the segmentation result on the current scale is regarded as the initial segmentation on next scale. The segmented results corresponding to the finest scale is considered as the optimal image segmentation. The proposed method is validated using SAR images. The Kappa coefficient of the test simulated SAR image is up to 0.998, and the Kappa coefficients of the test real SAR images are up or equal to 0.903. From the test results of the quantitative and qualitative evaluation, it can be found that the proposed method can not only determine the number of classes, but also segment homogeneous regions well.
Bibliometric Analysis of Research Output on the Internet of Things in the Arab World

&#x0D; &#x0D; &#x0D; Arab countries understand the importance of the Internet of things for the development of society. This entails many efforts to conduct research related to the Internet of things in the Arab countries. Analysing contributions of researchers to the Internet of things research could be useful to go further in this filed. In this study, bibliometric techniques are used to investigate publications indexed in the Scopus database with the affiliation to the Arab countries. The paper used research outputs of Arab nations as quantitative measure, while it also used citation rates and citation scores for qualitative measure. Data analysis focuses on the growth in the Internet of things research, types of publication, core journals, subject and keyword analysis, top productive authors, top productive institutes and countries, and collaborative countries with the Arab countries. Findings of the study highlighted the differences among the Arab group countries with regard to IoT research. In addition, the Arab nations appeared performing better in IoT research than the environmental and medical research.&#x0D; &#x0D; &#x0D;
A New Genetic Algorithm with Agent-Based Crossover for Generalized Assignment Problem

Generalized assignment problem (GAP) considers finding minimum cost assignment of n tasks to m agents provided each task should be assigned to one agent only. In this study, a new Genetic Algorithm (GA) with some new methods is proposed to solve GAPs. The agent-based crossover is based on the concept of dominant gene in genotype science and increases fertility rate of feasible solutions. The solutions are classified as infeasible, feasible and mature with reference to their conditions. The new local searches provide not only feasibility in high diversity but high profitability for the solutions. A solution is not given up through maturation-based replacement until it reaches its best. Computational results show that the agent-based crossover has much higher fertility rate compared to classical crossover. Also, the proposed GA creates either optimal or approximately optimal solutions.
RESEARCH OF MULTIPLE PLANS IN MULTI-FACTOR EXPERIMENTS WITH A MINIMUM NUMBER OF TRANSITIONS OF LEVELS OF FACTORS

Contex. The actual problem of reducing the set of plans for multivariate experiments in searching for the best in price costs hasbeen solved.Objective is the synthesis and study of a variety of experimental plans with a minimum number of transitions of factor levels.Methods. The use of experimental design methods allows reducing the price and time costs in the study of various technologicalprocesses, devices and systems.Minimizing the number of transitions of levels of factors in terms of the experiment, in turn, leads to a decrease in the cost (time)of its implementation. One of the methods for reducing the number of transitions of levels of factors is the use of the Gray code whenconstructing a plan of an experiment.It is shown that multi-factor experiments plans constructed using the Gray code have a minimum number of transitions of factorlevels, but are not always optimal in terms of the cost (time) of the experiment.For the synthesis of many experimental plans with a minimum number of transitions of levels of factors in searching for the optimalplan for cost (time) costs, a method based on the generation of binary code variants is proposed. Analysis of their characteristicsand the choice of sequences that meet specified requirements were conducted. The formation of test plans for an experiment iscarried out according to the method based on the generation of variants for constructing plans, determining equivalence classes withrespect to a given group P of transformations and forming a set of typical representatives for the selected equivalence classes.Results. Software that implements the proposed methods, which is used in the construction of a set of experimental plans for thenumber of factors K = 3 with the minimum number of level transitions was developed.Conclusions. The experiments, which were carried out, confirmed the efficiency of the proposed methods and the software implementingthem makes it possible to reduce the set of experiment plans for finding the optimal one. The scientific originality of theresearch is presented by the methods which allow to synthesize many plans of multifactor experiments to reduce the search for optimalplans in price (time) cost. The practical significance of the research results is in the developed software which implements theproposed methods. It can be widely used in the study of technological processes, devices and systems, on which the implementationof an active experiment is possible.
ICT Integrated Pedagogy in a Multicultural Classroom: Experiences of Mathematics Teacher

Multiculturalism in the present context is a global issue in education. It has not yet been materialized into the education system in Nepal. The education system of a country should prepare students to function in today’s multicultural society. In this regards, this paper focuses on the issues of the management of multicultural classes, and the role of ICT-integrated pedagogy to manage such a classroom context. It also discusses the context, and experiences of teachers that can bring the multicultural classroom situation into the right track. For this purpose, I apply narrative inquiry of two mathematics teachers (one male and one female) of University Campus Kirtipur with more than seven-year experience. In-depth interview, related to their experiences and practices in multicultural mathematics classes, was conducted and found that they face problems to teach students in a multicultural classes such as language problem, content problem, and level of understanding. There are two types of multiculturalism in our classes they are critical multiculturalism, and Liberal multiculturalism. The management of multiculturalism is a great challenge for teachers. Although teachers are less familiar with the management of multicultural class, they try to manage such types of diversity by supporting their students personally through ICT-integrated pedagogy. The finding of the study shows that ICT-integrated pedagogy is the best strategy to manage multicultural classes by respecting students’ personal feelings.
The Effect of Trade Openness on Income Inequality with the Role of Institutional Quality

This study investigates the effect of trade openness on income inequality using the panel system generalised method of moments (GMM). The sample countries consist of 65 developed and developing countries and the time period covers from 1984 to 2012. This study also provides new evidence that sheds light on the role of institutional quality in influencing the effectof trade openness on income inequality. The empirical results reveal that trade openness tends to increase income inequality. In addition, the marginal effect also revealed that institutional quality has a corrective effect on the trade openness – income inequality nexus.
KLASIFIKASI PEMINJAMAN NASABAH BANK MENGGUNAKAN METODE NEURAL NETWORK

Payment of loans that experience difficulties in repayment or often called bad credit is a very detrimental thing for the bank, with the occurrence of bad credit the bank does not have the maximum ability to make money for investment. Choosing the right customer must go through the right analysis because the decision to approve or disagree with the loan is the main point that determines the possibility of bad credit. This study aims to classify eligible customers to obtain loans by taking into account existing parameters such as age, total income, number of families, monthly expenditure average, education level and others. This study uses a data mining classification method with a neural network model, to assess the accuracy of data processing using rapid miners then proceed with measurements using confusion matrix, ROC curve. The results of the neural network algorithm after going through confusion matrix testing, the ROC curve shows a very high accuracy value, and the dominant value of AUC and algorithm. The accuracy value is 98.24% with AUC of 0.979
Moments of friction in virtual reality: How feeling histories impact experience

This article examines embodied interaction in a virtual reality learning environment. Studies of embodied interaction in immersive learning environments, like virtual reality, tend to treat all bodies the same without considering the nuanced cultural histories those bodies have with being mobile, especially within—and beyond—technology-mediated environments. In response, this study pivots from perspectives on embodied interaction that underscore the inextricable link between mind and body in favor of sociocultural perspectives to embodiment that emphasize the cultural-historical production of embodied interaction across space and over time. Through multimodal analysis of 10 learners’ experiences in a virtual reality experience called Thought for Food, this article contributes (1) an overt focus on the importance of feeling histories—embodied ways of sensing, feeling, and moving within digital environments—of learners engaging in virtual reality environments in order to promote equitable learning opportunities and (2) argues for future designs that are attuned to frictions—contestations between bodies and interfaces—that potentially collide with learners’ feeling histories.
Beating the probabilistic lower bound on perfect hashing

For an integer q 2, a perfect q-hash code C is a block code over [q] := {1, . . ., q} of length n in which every subset {c 1 , c 2 , . . ., c q } of q elements is separated, i.e., there exists i ∈ [n] such that {proj i (c 1 ), . . ., proj i (c q )} = [q], where proj i (c j ) denotes the ith position of c j .Finding the maximum size M (n, q) of perfect q-hash codes of length n, for given q and n, is a fundamental problem in combinatorics, information theory, and computer science.In this paper, we are interested in asymptotical behavior of this problem.More precisely speaking, we will focus on the quantity R q := lim sup n→∞ log 2 M (n,q) n .A well-known probabilistic argument indicates R q 1 q-1 log 2
Adaptive Performance Modeling and Prediction of Applications in Multi-Tenant Clouds

Clouds have been adopted by many organizations as their computing infrastructure due to the support for flexible re-source demands and low cost, which is normally achieved through sharing the underlying hardware among multiple cloud tenants. However, such sharing can result in large variations for the performance of applications running in virtual machines (VMs) on same hosts. In this paper, we propose User-APMP, a user-level application performance modeling and prediction frame-work, based on micro-benchmarks and regression techniques for applications that run repetitively in clouds (such as on-line data analytics). Specifically, a few micro-benchmarks are devised to probe the in-situ perceivable performance of CPU, memory and I/O components of the target VM. Then, based on such probe information and in-place measured performance of applications, the performance model can be adaptively developed and refined at runtime with regression techniques. Moreover, sliding-windows are exploited to control the number of historical data items to retrain the model. We evaluate the prediction accuracy for the considered benchmark applications. The evaluation results show that, the prediction error of User-APMP generally decreases with higher adaptation frequencies and more historical data points, which however leads to higher runtime overhead. With only 100 data points, the average prediction errors can reach 25%.
Bringing critical literacy into tertiary EFL reading class

Critical literacy (CL) is an area that remains under-researched regardless of the growing acknowledgement on its importance. Previously published works on CL have provided depictions of classroom implementations but they appeared to be lacking a guiding framework thus tended to be haphazard. This study addressed the gap by exploring the implementation of CL using a prototype model that combines the Four Resources Framework (FRF) as a theoretical frame and Survey, Question, Read, Recite, and Review (SQ3R) as a working model. The study aims at uncovering how CL could be infused into regular EFL Reading class using the model and how students developed their CL. The investigation involved 39 university students in Indonesia. The data were collected using multiple methods: observation, tests, interview, artifact, and questionnaire. Quantitative and qualitative data were concurrently analyzed by using statistical pack and interactive model, respectively. The study confirmed that questions and materials played key roles in turning a conventional Reading class into a CL class. Having exposed to critical questions, the students indicated progress in their practice as text users and text analysts. The analysis revealed that the students at differing baselines addressed the four roles differently in terms of the extent and consistency. Students with high proficiency tended to be more consistent in addressing the text user and text analyst roles either individually or as part of the group. In contrast, students with lower proficiency indicated inconsistent engagement with the two roles, particularly in individual work. Generally, the students’ ability to build critical stance to text was also determined by the complexity of the material. The easier the text was comprehended, the more critical the students were toward the text. The study suggests that it be necessary to include explicit teaching and sufficient provision of time in CL teaching to produce an automated critical response to texts.
Analysis of Braycurtis, Canberra and Euclidean Distance in KNN Algorithm

Classification is a technique used to build a classification model from a sample of training data. One of the most popular classification techniques is The K-Nearest Neighbor (KNN). The KNN algorithm has important parameter that affect the performance of the KNN Algorithm. The parameter is the value of the K and distance matrix. The distance between two points is determined by the calculation of the distance matrix before classification process by the KNN. The purpose of this study was to analyze and compare performance of the KNN using the distance function. The distance functions are Braycurtis Distance, Canberra Distance and Euclidean Distance based on an accuracy perspective. This study uses the Iris Dataset from the UCI Machine Learning Repository. The evaluation method used id 10-Fold Cross-Validation. The result showed that the Braycurtis distance method had better performance that Canberra Distance and Euclidean Distance methods at K=6, K=7, K=8 ad K=10 with accuracy values of 96 %.
Feature Extraction Method GLCM and LVQ in Digital Image-Based Face Recognition

The face is one of the media to identify someone, a human face has a very high level of variability. Many methods have been introduced by researchers and scientists in recognizing one's face, one of the methods introduced is the Feature Extraction of Gray Level Co-Occurrence Matrix (GLCM) and Learning Vector Quantization (LVQ). GLCM feature extraction is used for data extraction/learning process whereas a data analysis process (face recognition, cropping and storing data) the LVQ method is used for the data training process where the data that has been processed in GLCM feature extraction which still has large dimensions are processed to be smaller dimensions. So this test uses data of 190 photos and gets a match of 90%, the authors conclude that the GLCM feature extraction and LVQ method can very well recognize faces contained in the database.
Joint deep learning of angular loss and hard sample mining for person re-identification

Person re-identification (ReID) is a critical work in the field of intelligent image processing and deep learning, which has attracted the attention of industry application. Person ReID focuses on matching person images obtained from non-overlapping camera views and finding the person-of-interest. An important unresolved problem is to obtain efficient metric for measuring the similarity among pedestrian images. Lately, deep learning with metric learning has become a general method for person ReID. Yet, previous methods mainly used a variety of distance to measure the similarity among samples. The way of distance measure is more sensitive when the scale changes. In this paper, we propose angular loss with hard sample mining (ALHSM) to learn better similarity metric for the person ReID. Our work uses the angular relationship in triangles as a measure of similarity, minimizing the angle at the negative point of the triangle. ALHSM combines with hard negative mining strategies, which learn better similarity metric and achieve advanced performance on several benchmark datasets. The experimental results show that our work is competitive compared to the state-of-the-art.