Reviewerly/exHarmony huggingface dataset:
Resilient Backpropagation Neural Network on Prediction of Poverty Levels in South Sulawesi

Poverty is a topic that continues and is always discussed up to this time, as a benchmark indicator of how the level of welfare and prosperity in the lives of people in a country. Several attempts have been made by the central and regional governments to reduce poverty levels, including “Bantuan Langsung Tunai” (BLT) and the “Program Keluarga Harapan” (PKH). However, poverty reduction in Indonesia is still slowing down, including in South Sulawesi. Based on this, this study aims to predict poverty levels in South Sulawesi. Factors thought to influence poverty levels are the Human Development Index (HDI), the Open Unemployment Rate (TPT), and the Gross Regional Domestic Product (GRDP). The data used are data from 2010 to 2014. The method used is a backpropagation neural network with a resilient algorithm or better known as a resilient backpropagation neural network (RBNN). The results of the prediction of poverty levels using predictors of HDI, TPT, and GRDP showed that the analysis of the RBNN reached its optimum using architecture [3- 9 - 1] and reached convergence at the 81th iteration with an accuracy rate of 95.34%.
Sandbox security model for Hadoop file system

Abstract Extensive usage of Internet based applications in day to day life has led to generation of huge amounts of data every minute. Apart from humans, data is generated by machines like sensors, satellite, CCTV etc. This huge collection of heterogeneous data is often referred as Big Data which can be processed to draw useful insights. Apache Hadoop has emerged has widely used open source software framework for Big Data Processing and it is a cluster of cooperative computers enabling distributed parallel processing. Hadoop Distributed File System is used to store data blocks replicated and spanned across different nodes. HDFS uses an AES based cryptographic techniques at block level which is transparent and end to end in nature. However cryptography provides security from unauthorized access to the data blocks, but a legitimate user can still harm the data. One such example was execution of malicious map reduce jar files by legitimate user which can harm the data in the HDFS. We developed a mechanism where every map reduce jar will be tested by our sandbox security to ensure the jar is not malicious and suspicious jar files are not allowed to process the data in the HDFS. This feature is not present in the existing Apache Hadoop framework and our work is made available in github for consideration and inclusion in the future versions of Apache Hadoop.
An Analysis of Code-Mixing Usage in WhatsApp Groups Conversation among Lecturers of Universitas Pamulang

This study was aimed to find out the kinds of code-mixing used by lecturers from some departments and the factors of using code mixing. This study was a descriptive qualitative analysis that attempted to describe the kinds of code-mixing and the factors. The participants of this study were lecturers from 4 different groups of Universitas Pamulang. The participants were selected based on the purposive sampling in order to support the research finding. The data were taken from questionnaire and observation on WhatsApp groups to seek the factors and to classify the types of code-mixing. As the results, the writers found the group of Pelatih TOEFL produced 15 congruent lexicalization, 4 insertion and 0 alternation. The group of Penelitian Internal Unpam produced 15 congruent lexicalization, 6 insertion and 2 alternation. The group of Sastra Unpam produced 9 congruent lexicalization, 10 insertion and 5 alternation. The group of Prodi Sasing produced 17 congruent lexicalization, 3 insertion and 5 alternation. In addition, the distribution of questionnaire resulted the factors of applying code-mixing on all WhatsApp groups. Bilingualism (38%), speaker and interlocutor (4%), situation (5%), vocabulary (46%) and prestige (8%) are the factors found in Pelatih dan Pengawas TOEFL group. Bilingualism (21%), speaker and interlocutor (11.11%), situation (22%), vocabulary (38%) and prestige (8%) are the factors found in Peenelitian Internal Unpam group. Bilingualism (48%), speaker and interlocutor (5%), situation (13%), vocabulary (34%) and prestige (0%) are the factors found in Sastra Unpam group. Bilingualism (55%), speaker and interlocutor (5%), situation (7%), vocabulary 67%) and prestige (12%) are the factors found in Sastra Unpam group.
Application of Rasch Model to Develop a Questionnaire for Evaluating the Quality of Teaching for Students’ Creativity Development

The purpose of this research is to develop a questionnaire for evaluating the quality of teaching for creativity development.We use three methods for creating the questionnaire, namely, interviews, literature review, and expert consultation.The Rasch model was applied to assess the psychometric properties of the questionnaire in the first phase with the participation of 112 students.The results of the study indicated that all items had a point-measure correlation coefficient ranging between 0.312 and 0.757, Infit and Outfit MNSQ values were in the range of 0.62 to 1.54, item difficulty values were in the range from -1.54 to 1.12.The overall reliability index of the questionnaire was 0.955, the separation was 4.582, the scale's number of strata was 6.442.Cronbach's alpha coefficient of the scale was 0.963.The results of the study showed that the questionnaire was in good compliance with the Rasch model requirements.We used the questionnaire to assess the current status of teaching for creativity development in the teaching of Pedagogy in the second phase.This phase involved 300 first-and second-year students who were studying "Pedagogy" and they were grouped in 10 groups.A total of 10 teachers were evaluated, 1 teacher was evaluated by 1 group.The results showed that the quality of teachers' teaching for creativity development was at an acceptable level.We believe that teachers' preparation according to the evaluation criteria for teachers' quality teaching for creativity development is needed to improve teachers' teaching for students' creativity development.
Progressive Growth of ETL Tools: A Literature Review of Past to Equip Future

ETL is the bedrock of a data warehouse. As the data accelerates in the diversified fields, it is substantive to integrate the data to extract the prerequisites for business advancement and policy formation in the arena of health care, education, smart cities, transportation, and many other areas. ETL (Extract–Transform–Load) is the process used to incorporate a range of data sources in the data warehouse for business intelligence. As per the requirement of data integration and analysis, several categories of ETL tools have developed like code-based, GUI-based, cloud-based, Metadata support, Real-time support, and batch processing. Selecting appropriate ETL tools is a crucial task in any business, with each tool having its dominant and stumbling blocks. This paper aims to first, the study of existing ETL tools and its features, and secondly, how to identify vital functions of the ETL tool for an organization.
Searching surveillance video contents using convolutional neural network

Manual video inspection, searching, and analyzing is exhausting and inefficient. This paper presents an intelligent system to search surveillance video contents using deep learning. The proposed system reduced the amount of work that is needed to perform video searching and improved the speed and accuracy. A pre-trained VGG-16 CNNs model is used for dataset training. In addition, key frames of videos were extracted in order to save space, reduce the amount of work, and reduce the execution time. The extracted key frames were processed using the sobel operator edge detector and the max-pooling in order to eliminate redundancy. This increases compaction and avoids similarities between extracted frames. A text file, that contains key frame index, time of occurrence, and the classification of the VGG-16 model, is produced. The text file enables humans to easily search for objects of interest. VIRAT and IVY LAB datasets were used in the experiments. In addition, 128 different classes were identified in the datasets. The classes represent important objects for surveillance systems. However, users can identify other classes and utilize the proposed methodology. Experiments and evaluation showed that the proposed system outperformed existing methods in an order of magnitude. The system achieved the best results in speed while providing a high accuracy in classification.
Palaeoenvironmental Interpretation of the Upper Triassic Baluti Formation in two selected sections at Amediya District, Kurdistan Region- Iraq: Insights from Palynofacies Study

Palynofacies analysis was carried out on 28 outcrop samples from the Upper Triassic Baluti Formation from Zewa and Sararu Sections in south and northwestern of Amediya District respectively, Kurdistan Region- Iraq. Based on the studied palynofacies, both sections are quite different in terms of depositional environment. Four types of palynofacies were recognized as following: the palynofacies type IV (Pl.1) is observed in the lower, middle and upper portions of upper part of Zewa Section in which, is characterized by high amount of phytoclasts, medium percentage of amorphous organic matter (AOM) and palynomorphs. This facies is referred to a depositional area that is located relatively nearshore with an arid hinterland, the palynofacies type V (Pl.2) was observed in lowermost and middle portions of upper part of Zewa Section, which is characterized by high domination of palynomorphs (mainly bisaccates and monosaccate pollen grains) and a less amount of phytoclasts and AOM. This facies indicates the domination of shallow marine to continent environment. The palynofacies type VI (Pl.3) was observed in middle and upper portions of upper part of the Zewa Section and lower and upper parts of the Sararu Section. This facies showed less amount or absence of palynomorphs, and relatively high amount of AOM and phytoclasts. In addition to this, a remarkable percentage of dinoflagellate cysts in Sararu Section has been found in this facies. This facies indicates proximal suboxic-anoxic shelf, and palynofacies type IX (Pl.4) was observed in middle to upper part of Sararu Section and characterized by less amount or absence of palynomorphs with high amounts of AOM and phytoclasts, which represents a deep environment of deposition.
ASSESSMENT OF THE LEVEL OF HEAVY METAL CONCENTRATION IN THE STREET DUST IN SOME SELECTED LOCATIONS IN ZARIA METROPOLIS, KADUNA STATE, NIGERIA

The study investigate the level of heavy metal concentration in street dust in some selected locations in Zaria metropolis, by monitoring the concentration of Chromium (Cr), Cadmium (Cd), Nickel (Ni), Lead (Pb), and Copper (Cu) for eight weeks. Street dust samples were collected by placing polyethylene bags on randomly selected roof tops and road sides at 20 cm to 30 cm to form composite sample. Sample collected were pre-treated and digested using standard procedure. Atomic Absorption Spectrophotometer (AAS) was used to determine the concentration of heavy metals. Results obtained showed that the concentration of Cr range from 16.670 ±2.20 μg/g to 31.895 ±1.845 μg/g, Cd concentration range from 0.280 ±0.042 μg/g to 1.900 ± 1.202 μg/g, Ni concentration range from 1.560 ±0.254 μg/g to 5.880 ±1.173 μg/g, Pb concentration range from 22.290 ±2.248 μg/g to 95.485 ±3.019 μg/g, Cu concentration range from 0.225 ±0.063 μg/g to 1.280 ±0.353 μg/g. The concentration of all heavy metals are below the limit set be World Health Organization (WHO) and therefore do not pose any health risk to humans. In sampled areas, the main sources of these heavy metals were anthropogenic activities such as open waste incineration, vehicle traffic, and ongoing construction at certain locations.
Comparison of REST and GraphQL web technology performance

The aim of the study was to compare the performance of two data exchange styles commonly used in web applications, i.e. REST and GraphQL. For the purposes of the study two test applications were developed containing the same functionalities, one of which was REST and the other one was GraphQL. They were used for performance tests done with the help of the JMeter tool, during which measurements of the total processing time of requests and the volume of data downloaded and sent were performed. An experiment was developed that tested the basic operations found in most network services: display, add, update, and delete data. The most attention was devoted to the information display operation in the case of which load tests were done. On the basis of performed studies and obtained results, no differences in performance during the operation of adding, editing and deleting data by applications based on REST API and GraphQL were found. During the display operation under heavy load conditions and while downloading small portions of data, the service using GraphQL had a better performance. When downloading large portions of data, the REST-based service exhibited a higher performance.
The Use of Mobile-Based Augmented Reality in Science Learning to Improve Learning Motivation

This study aimed to examine the effect of the use of mobile-based Augmented Reality media to improve This study aimed to examine the effect of the use of mobile-based Augmented Reality media to improve student learning motivation in SMP Muhammadiyah Surakarta on the science subject. The study used a quasi-experimental method with pretest-posttest control design. The tests of mobile-based augmented reality media in learning and learning motivation were conducted on the research subjects, seventh-grade students of Muhammadiyah 1 Junior High School Surakarta, with cluster random sampling. It was found that, based on the scores obtained from the statistical test with SPSS 20 program, the sig. level was 0.002, smaller than 0.025 (0.000&amp;lt;0.025). This shows that there is a different significant mean score between the control class and the experimental class. Therefore, the mobile-based augmented reality media influences the classroom learning process, which is shown by the increase in student learning motivation in the learning process.
Fighting COVID-19 and helping economy reopen by using blockchain technology

This paper introduced a best practice on personal health data management on blockchain technology.When the global community is dealing with COVID-19 pandemic and economy reopen strategy, a solid solution is needed to cover various aspects: health data collection and monitoring, data privacy control, immutable data record, global identity and cross region collaboration.By using distributed ledger technology, decentralized identity, verifiable credential, and distributed storage, we built the GreenPass solution to address COVID-19 mitigation and economy reopen issues at the community level.We also envision GreenPass solution as the personal data vault with data ownership and permission management, as a green passport for a person to the future of "data as a personal property right" new world.
Azure-Kinect and Augmented Reality for learning Basic Mathematics - A Case Study

The way in which the human being learns certain complex contents has always been a focus of interest and a challenge for researchers. Given the fact that children's cognitive abilities do not fully develop until a certain age, this topic is particularly important in the young children's learning scope as they do not correctly and easily learn some content of abstract nature, such as contents in math class. This work presents the results of the use of an application called "Mathematics Learning System with Augmented Reality based on Kinect" (SAM-RAK by its acronym in Spanish), which was designed to cover basic topics of mathematics in the Basic General Education level (EGB by its acronym in Span-ish) in Ecuador. The research was carried out under an experimental quantitative approach with 30 children (18 girls and 12 boys), who study in third grade of EGB level, from 2 different educational in-stitutions in Riobamba city. In order to obtain the results that evaluate the developed application, a pre-test and a post-test were applied, which were contrasted with the student&amp;rsquo;s t-test for paired samples. The statistical evidence suggests that the proposed computer system had a positive effect on children's performance, when it was used as a support tool in the classroom. The system was more effective in low performance children compared to those of high performance. It was also proved that children were motivated and showed positive attitudes when using the proposed System.
Evolutionary Algorithms with Self-adjusting Asymmetric Mutation

Evolutionary Algorithms (EAs) and other randomized search heuristics are often considered as unbiased algorithms that are invariant with respect to different transformations of the underlying search space. However, if a certain amount of domain knowledge is available the use of biased search operators in EAs becomes viable. We consider a simple (1+1) EA for binary search spaces and analyze an asymmetric mutation operator that can treat zero- and one-bits differently. This operator extends previous work by Jansen and Sudholt (ECJ 18(1), 2010) by allowing the operator asymmetry to vary according to the success rate of the algorithm. Using a self-adjusting scheme that learns an appropriate degree of asymmetry, we show improved runtime results on the class of functions OneMax $$_a$$ describing the number of matching bits with a fixed target $$a\in \{0,1\}^n$$ .
Image completion via transformation and structural constraints

Abstract Image completion is an approach to fill a damaged region (hole) in an image. In this study, we adopt a novel method which can repair a target region with structural constraints in an architectural scene. An objective function that consists of three terms is proposed to solve the image completion problem. In color term, we compute a parameterized transformation model using detected plane parameters and measure the distance between the target patch and transformed source patch. This model helps to extend the patch search space and find an optimal solution. To improve the patch matching accuracy, we add a guide term that includes structure term and consistency term. The structure term encourages sampling patches along the structural direction, and the consistency term is used to maintain the texture consistency. Considering the color deviation between patches, we add a gradient term into a framework that can solve more challenging problems. Compared with previous methods, the proposed method has good performance in preserving global structure and reasonably estimating perspective distortions. Moreover, we obtain acceptable results in natural scenes. The experimental results illustrate that this novel method is a potential tool for image completion.
Robust Constrained-MDPs: Soft-Constrained Robust Policy Optimization under Model Uncertainty

In this paper, we focus on the problem of robustifying reinforcement learning (RL) algorithms with respect to model uncertainties. Indeed, in the framework of model-based RL, we propose to merge the theory of constrained Markov decision process (CMDP), with the theory of robust Markov decision process (RMDP), leading to a formulation of robust constrained-MDPs (RCMDP). This formulation, simple in essence, allows us to design RL algorithms that are robust in performance, and provides constraint satisfaction guarantees, with respect to uncertainties in the system's states transition probabilities. The need for RCMPDs is important for real-life applications of RL. For instance, such formulation can play an important role for policy transfer from simulation to real world (Sim2Real) in safety critical applications, which would benefit from performance and safety guarantees which are robust w.r.t model uncertainty. We first propose the general problem formulation under the concept of RCMDP, and then propose a Lagrangian formulation of the optimal problem, leading to a robust-constrained policy gradient RL algorithm. We finally validate this concept on the inventory management problem.
The Exploration and Practice of IT Solutions for Online Classes in Higher Education During COVID-19 Pandemic

Due to the COVID-19, the Ministry of Education of China announced that the 2020 spring semester will be postponed, on-site classes should be cancelled and schools should give online classes instead.It's the first time that online classes have been given priority to higher education, which is a new challenge to both schools and online learning platforms.It's necessary to carry out stable and efficient IT solutions to secure the smooth process of online classes.This paper takes South China University of Technology as an example to put forward some IT solutions and ways of improving teaching quality of online classes for higher education during COVID-19 pandemic.
Synchronization precision analysis of a fractional-order hyperchaos with application to image encryption

This paper proposes an effective image encryption algorithm based on the transformational prospective synchronization of a fractional-order hyperchaotic system. Compared with other chaos-based algorithms, fractional orders and synchronization precision are added as secret keys. It is shown that fractional-order, in comparison with its integer counterpart, has bigger complexity and larger key-space. Numerical simulation test results and security analyses demonstrate good performance of the proposed algorithm by encrypting the color image, gray medical image, and binary image. Furthermore, it is found that the synchronization precision accounted for in the decryption process has a significant effect on the decryption resolution.
An ontology-based framework for automated code generation of Web AR applications

In this paper, we propose an ontology-based framework for automated Web AR application code generation leveraging semantic descriptions of application structure and behavior in synergy with domain knowledge and annotations of building information model (BIM). Three case studies are presented: smart home energy consumption monitoring application, robotics testbed companion and virtual music mixing interface. The presented approach significantly reduces the time needed for Web AR application development.
Hierarchical Classification and Visualization Method of Social Simulation Logs Reflecting Multiple Analytical Interests

The simulations of complex social systems involving diverse stakeholders can be evaluated from multiple analytical interests. In this paper, we propose a method to classify social simulation logs hierarchically along with multiple analytical interests and visualize them based on the frequency of classification results. The proposed method introduces the concept of cladistic phylogeny and classifies social simulation logs hierarchically, in such a way as to classify species. In the proposed method, analysts make a hierarchical classification diagram of social simulation logs called a possibility cladogram and understand the frequency of possible results from the viewpoint of analytical interests intuitively. We applied the proposed method to Schelling's segregation model. We confirmed that the proposed method helps intuitively understand the frequency of possible results from the viewpoint of analytical interests and also give assistance to run efficient microdynamics analysis by referring to the classification result of simulation logs on the possibility cladogram. We evaluated the proposed method from two perspectives: realization of visualization benefits corresponding to usefulness and validity as a visualization method.