Reviewerly/exHarmony huggingface dataset:
A Self-Adaptive Resource Provisioning Approach using Fuzzy Logic for Cloud-Based Applications

The resource adaptation on demand is an important factor in the field of cloud computing.During runtime, autonomic resource provisioning is not an easy task to choose the accurate amount of resources for service-based cloud applications.For this reason, it is required to guess the future demands for self-adaptive resources to deal with the irregular requests based on runtime workload changes of service-based cloud applications.In this paper, an efficient approach to increase the utilization of resources is proposed that is based on self-adaptive computing with fuzzy logic.Additionally, the proposed fuzzy logic approach enhanced the performance of planning phase for better decision making.Based on fuzzy logic, cloud applications having self-learning provisioning resources outperformed the hybrid resource provisioning approach.To calculate the quality of the proposed technique, real-world ClarkNet and NASA workload traces are used.The results of experiments show that the proposed technique has decreased the entire cost and has boosted the utilization of resources as compared to the other contemporary techniques.
Nonstationary multiresolution analysis on local fields of prime characteristic

The concepts of multiresolution analysis (MRA) and wavelet have been generalized to a local field K of positive characteristic by using a prime element p of such a field. A MRA is a sequence of closed subspaces of L 2 (K) satisfying certain properties. In this paper, we are interested in a nonstationary MRA and related wavelets on local fields of positive characteristic.
The <b>JuliaConnectoR</b>: A Functionally-Oriented Interface for Integrating <i>Julia</i> in <i>R</i>

Like many groups considering the new programming language Julia, we faced the challenge of accessing the algorithms that we develop in Julia from R. Therefore, we developed the R package JuliaConnectoR, available from the Comprehensive R Archive Network (CRAN), the official R package repository, and from GitHub (https://github. com/stefan-m-lenz/JuliaConnectoR), in particular for making advanced deep learning tools available. For maintainability and stability, we decided to base communication between R and Julia on the transmission control protocol, using an optimized binary format for exchanging data. Our package also specifically contains features that allow for a convenient interactive use in R. This makes it easy to develop R extensions with Julia or to simply call functionality from Julia packages in R. Interacting with Julia objects and calling Julia functions becomes user-friendly, as Julia functions and variables are made directly available as objects in the R workspace. We illustrate the further features of our package with code examples, and also discuss advantages over the two alternative packages JuliaCall and XRJulia. Finally, we demonstrate the usage of the package with a more extensive example for employing neural ordinary differential equations, a recent deep learning technique that has received much attention. This example also provides more general guidance for integrating deep learning techniques from Julia into R.
Subjective Evaluation of User Quality of Experience for Omnidirectional Video Streaming

This paper reports on the results of subjective testing of user Quality of Experience (QoE) for omnidirectional video (ODV) streaming quality. The test was conducted among 20 test subjects who watched three ODVs using a Head Mounted Display (HMD) system. The length of the videos was between two and three minutes. The first video was used for training purposes and contained no quality degradations. The quality of the other two ODVs was degraded by manipulating the resolution or by introducing different frame drop patterns. While watching the pre-prepared videos the subjects indicated if they noticed the changes in the quality and then rated it. After watching each video, the subjects completed a separate questionnaire, which evaluated their level of enjoyment and discomfort with the video. The results showed that the degradation of both objective parameters (video resolution and frame rate) impacted the subjects’ perception of quality; however, the impact was somewhat alleviated in ODV which contained dynamic scenes and fast camera movements.
A two-dimensional material recognition image algorithm based on deep learning

The Van der Waals heterojunction is composed of stacked atomically thin two-dimensional materials. As a new type material, it has many unprecedented characteristics. However, owing to the position and shape of the two-dimensional material peeling are random during the preparation process, the preparation efficiency of Van der Waals heterojunction is always low. Finding the peeled two-dimensional material quickly has become the key to improve the preparation speed of Van der Waals heterojunctions. In recent years, deep learning has become more and more excellent in the field of image recognition, and the application of deep learning on two-dimensional material recognition has achieved a very good effect. In this paper, we use deep learning-based semantic segmentation network to identify and segment two-dimensional materials in images collected under optical microscope. Moreover, the network architecture was improved to achieve an average accuracy of 97.17%. Since deep learning algorithms can extract high-level features of images through training with a large amount of data, compared with traditional graphics algorithms, the deep learning algorithm have good generalization capabilities and are robust to changes in conditions during detection, which can avoid errors caused by traditional algorithms that require manual adjustment of parameters.
Comparison of C4.5 and Naïve Bayes Algorithm for Mustahik Classification

Zakat is one of the pillars of Islam that must be done for all Muslims.People who fulfill zakat are called muzakki and people who receive zakat are called mustahik.However there's main problems of the Amil Zakat Agency related to distribution of zakat funds which are sometimes not fulfill on the target needed.Based on these problems, this study aims to analyze the comparison of the C4.5 and Naive Bayes algorithms for the classification of mustahik determination.The results of the study concluded that it was found that the use of the C4.5 algorithm is better than the Naive Bayes algorithm, proved by the level of accuracy starting from 75% -100%, while using the Naive Bayes algorithm the level of accuracy starts from 50% -100% and the execution time of each algorithm was similar which is 0 s.The results of the accuracy in this research are obtained through RapidMinerStudio tools using split validation.
Teacher and Students’ Politeness Strategies in EFL Classroom Interactions

This study aims to explore the use of politeness strategies which occur in EFL classroom interactions in a senior high school. This study applied a descriptive qualitative research design to explore the politeness strategies used by the teacher and the students in their interactions. The participants in this study were an English teacher and 30 students. The data in this study were in the form of utterances which contain politeness strategies. The data were taken from a 90-minutes English lesson which was video-recorded. The findings showed there were a total of 13 excerpts containing three politeness strategies: positive politeness strategy, negative politeness strategy, and bald-on-record strategy. The interactions were dominated by the teacher. Moreover, the politeness strategies which occurred in the classroom interactions were influenced by some factors such as age difference, institutional position, power, and social distance.
The Comparison of Apriori Algorithm with Preprocessing and FP-Growth Algorithm for Finding Frequent Data Pattern in Association Rule

Association Rules is a data mining method to find the relation between items called rules.Finding rules in the association method can be divided into two phases.The first phase is finding the frequent pattern which satisfies specified minimum frequent, and the second phase is finding strict rules from the frequent pattern which satisfy the minimum support and confidence.The main problem of Association Rules is based on the algorithm used, and this method takes a large amount of memory and time-consuming.This study aims to add preprocessing using the aggregate function on the Apriori Algorithm and therefore improve the memory and time consumption for finding a large number of rules.
Theoretical perspectives for the study of the contamination between physical and virtual teaching/learning environments

We propose an analysis of the effects on the teaching/learning practices of mathematics that stem from digital technologies in school classrooms. We describe two episodes - an exchange of messages in the Stream of Google Classroom and an extract from a private Whatsapp chat between two classmates. We analyze such episodes with a theoretical lens based on the Chevallard Triangle of Didactics (Chevallard &amp; Joshua, 1982) and the sociological perspective, proposed by D’Amore (2005), considering the classroom as a society. Digital technologies enable new complex teaching/learning environments whose study require a systemic and relational approach as the one offered by the concept of Triangle. Moreover, we show how such environments in turn can be considered societies with their constitutive practices and extra-functional meta-practices that are linked with the societies that originate in the school classrooms. We show how the interaction between these theoretical perspectives allows us to characterize teaching/learning processes when physical and virtual environments contaminate each other.
Gender Classification and Age Estimation using Neural Networks: A Survey

Researchers have shown more interest in soft biometrics area to fill the commination gaps between humans and machines with the growth of real-world application has increased day to day life.Soft-biometric consists of age, gender, ethnicity, height, facial measurements and etc.This paper contains a detail discussion about the contribution of the researchers in the area of gender classification and age estimation using neural networking.Most of the work is done using Convolutional neural networks and auto encoders.Various elements related to neural network model such as dataset, findings, calculative metrics and results are embraced for effortless interpretation of tabular correlation research.Finally, the authors summarize germane tasks for future various research aspects.
A Dynamically Reconfigurable BbNN Architecture for Scalable Neuroevolution in Hardware

In this paper, a novel hardware architecture for neuroevolution is presented, aiming to enable the continuous adaptation of systems working in dynamic environments, by including the training stage intrinsically in the computing edge. It is based on the block-based neural network model, integrated with an evolutionary algorithm that optimizes the weights and the topology of the network simultaneously. Differently to the state-of-the-art, the proposed implementation makes use of advanced dynamic and partial reconfiguration features to reconfigure the network during evolution and, if required, to adapt its size dynamically. This way, the number of logic resources occupied by the network can be adapted by the evolutionary algorithm to the complexity of the problem, the expected quality of the results, or other performance indicators. The proposed architecture, implemented in a Xilinx Zynq-7020 System-on-a-Chip (SoC) FPGA device, reduces the usage of DSPs and BRAMS while introducing a novel synchronization scheme that controls the latency of the circuit. The proposed neuroevolvable architecture has been integrated with the OpenAI toolkit to show how it can efficiently be applied to control problems, with a variable complexity and dynamic behavior. The versatility of the solution is assessed by also targeting classification problems.
The Diversity of Labuhanbatu Community Culture in Android-Based Applications

Android is an application system which has been developed by many people so that people can use the Android system in a work which can help them solve problems in their work. Android systems in smartphones can support complete facilities with Android. Labuhan Batu area is divided into three regions, because of this, knowledge is needed to explains about the culture of the people of Labuhanbatu main district separately of the people in the other districts. In this research, the author describes the research methods used in system design into two parts of method namely the research results obtained by methods using descriptive methods and the research conducted based on actual data by comparing theories and then drawing conclusions. The benefit of the authors working on this research is to make an Android-based application which eases the public to find out the information about the culture that exists in each district in Labuhanbatu Regency. Labuhanbatu Regency Cultural Information Application based on Android can be used by students and even the public, using an Android-based smartphone.
Black-Box Test-Cost Reduction Based on Bayesian Network Models

The growing complexity of circuit boards makes manufacturing test increasingly expensive. In order to reduce test cost, a number of test selection methods have been proposed in the literature. However, only few of these methods can be applied to black-box test-cost reduction. In this article, we propose a novel black-box test selection method based on Bayesian networks (BNs), which extract the strong relationship among tests. First, the problem of reducing the black-box test cost is formulated as a constrained optimization problem. Next, multiple structure learning and transfer learning algorithms are implemented to construct BN models. Based on these BN models, we propose an iterative test selection method with a new metric, Bayesian index, for test-cost reduction. In addition, averaging strategies are applied to enhance the reduction performance. Finally, a robust model selection framework is proposed to select the optimal BN model for test-cost reduction. Two case studies with production test data demonstrate that when no prior information is provided, our proposed approach effectively reduces the test cost by up to 14.7%, compared to the state-of-the-art greedy algorithm. Moreover, our proposed approach further reduces the test cost by up to 7.1% when prior information is provided from similar products.
End-to-End Quantum Key Distribution (QKD) from Metro to Access Networks

Derived from quantum cryptography, quantum key distribution (QKD) can deliver secret keys between two users to secure communication under a certain scale. Nowadays, QKD integrated in backbone, metro, and access areas has been respectively studied and tested around the world. Although the experiments in various scenarios can verify the feasibility of QKD, the situation of QKD between two end users from metro to access networks has not been considered up to now. In this paper, we propose a QKD Network (QKDN) architecture for end-to-end QKD from metro to access networks in a costefficient manner. The cost efficiency can be achieved by sharing Quantum Receivers (Qrecs) in the metro network with the Quantum Transmitters (Qtras) in various access networks since the distance between any two end users within a metro network permits to distribute quantum keys. Under such an architecture, an end-to-end QKD scheme is designed to provide secret keys for end users in different access networks, by mapping Qrecs and allocating time slots according to security requests. Through the end-to-end QKD scheme, the occupation of Qrecs and time slots can be appropriately improved. To verify the effectiveness of the scheme, simulation is conducted on two different topologies respectively. Performances are evaluated in terms of three parameters: i) the failure ratio of end-to-end QKD (FQ), ii) the utilization ratio of time slots (TU), and iii) the utilization ratio of Qrecs (QU). Simulation results show that the topology with more nodes will lower the QU by distributing requests to more numbers of Qrecs. Moreover, the topology with bigger node connectivity can lower FQ since a shorter path will be selected to save the unnecessary occupation of link resources for other end-to-end requests.
Combination of spatially enhanced bag-of-visual-words model and genuine difference subspace for fake coin detection

Fake coins are harmful for society, the detection of which is of paramount importance. Due to the large quantities of fake coins in the real world, it is impossible to examine them manually. To address this issue, we present an intelligent system to automatically detect fake coins based on their images. The intelligent system consists of two components: coin image representation and classifier learning. To represent the coin image, a new spatially enhanced bag-of-visual-words model, called SEBOVW model, is proposed. Afterwards, we improve the representation by building a genuine difference subspace. The coin is finally represented based on its projection onto this subspace. In order to discriminate between genuine and fake coins, we train a classifier using the subspace representations. A thorough evaluation of the proposed intelligent system has been conducted on four coin datasets, consisting of thousands of coins of different denominations and from two countries. Promising experimental results in excess of 98% accuracy demonstrate its effectiveness and validity.
Image Steganography Analysis Based on Deep Learning

Despite the fact that steganalysis has grown quickly lately, it despite everything faces numerous troubles and difficulties.This paper proposed a steganalysis based on deep learning technique.The convolutional neural network method is used for steganalysis approach.The steganalysis approach utilizes the deep features extracted from the stego image.The approach uses global information for feature learning.The steganalysis is used to recognize the various kinds of steganalysis calculations.This paper also proposed a steganalysis based on low embedding rate images and multi-class steganography.The study on image steganogrphy using deep learning is discussed with their pros and cons.
Ethnopedagogy Integration to Mobile Learning for Improving Student Learning Achievement in Remote Areas

The aim of this study is to integrate mobile learning with an ethnopedadogy approach to improve teaching and learning in remote areas in Lombok.The research method used in this study is a mixed-method approach following the steps of research and development (RnD), and by quantitative and qualitative data for implementing the product.The study was conducted at three high schools in a remote area of East Lombok, West Nusa Tenggara Province, Indonesia.The research helped develop a prototype of mobile learning media for chemistry learning by integrating the Ethnopedagogy approach.Mobile learning was implemented using three approaches complementary to the desired outcomes.The research revealed that the prototype of mobile learning can be implemented in chemistry learning in remote areas.The effectiveness of using mobile learning integrated with an ethnopedagogy approach shows that learning by using fully mobile learning can provide better results than using blended learning and mobile learning as a complementary.Results indicate that the use of mobile learning integrated with an ethnopedagogy approach can provide students' in remote areas with meaningful learning experiences to improve achievement.The approach is suitable for use with students in remote areas who face difficulties in accessing learning resources, in engaging in local culture, in schools with limited teaching staff, with students suffering low motivation and who have little parental support.
Benchmarking neural embeddings for link prediction in knowledge graphs under semantic and structural changes

Recently, link prediction algorithms based on neural embeddings have gained tremendous popularity in the Semantic Web community, and are extensively used for knowledge graph completion. While algorithmic advances have strongly focused on efficient ways of learning embeddings, fewer attention has been drawn to the different ways their performance and robustness can be evaluated. In this work we propose an open-source evaluation pipeline, which benchmarks the accuracy of neural embeddings in situations where knowledge graphs may experience semantic and structural changes. We define relation-centric connectivity measures that allow us to connect the link prediction capacity to the structure of the knowledge graph. Such an evaluation pipeline is especially important to simulate the accuracy of embeddings for knowledge graphs that are expected to be frequently updated.
ONTOLOGY-BASED GENERATION OF MULTILINGUAL QUESTIONS FOR ASSESSMENT IN MEDICAL EDUCATION

The requirements of state-of-the-art curricula and teaching processes in medical education have brought both new and improved the existing assessment methods. Recently, several promising methods have emerged, among them the Comprehensive Integrative Puzzle (CIP), which shows great potential. However, the construction of such questions requires high efforts of a team of experts and is time-consuming. Furthermore, despite the fact that English language is accepted as an international language, for educational purposes there is also a need for representing data and knowledge in native language. In this paper, we present an approach for automatic generation of CIP assessment questions based on using ontologies for knowledge representation. In this way, it is possible to provide multilingual support in the teaching and learning process because the same ontological concept can be applied to corresponding language expressions in different languages. The proposed approach shows promising results indicated by dramatic speeding up of construction of CIP questions compared to manual methods. The presented results represent a strong indication that adoption of ontologies for knowledge representation may enable scalability in multilingual domain-specific education regardless of the language used. High level of automation in the assessment process proven on the CIP method in medical education as one of the most challenging domains, promises high potential for new innovative teaching methodologies in other educational domains as well.