Reviewerly/exHarmony huggingface dataset:
THE CORRELATION BETWEEN VOCABULARY MASTERY AND READING COMPREHENSION AT THE SECOND YEAR STUDENTS OF SMPN 3 GUNUNGSARI

The research entitled The Correlation Between Vocabulary Mastery and Reading Comprehension aims at finding out there is whether any correlation between vocabulary mastery and reading comprehension at the second year students of SMPN 3 Gunungsari. This research was designed by using descriptive quantitative method. The research took 30 students as sample of this study. To analyses the data this research used statistic computation by product moment correlation, to get the final score. The data found that the mean score of reading comprehension was 54.4 and vocabulary was 53,6. After computing by the statistical analysis of Product Moment, the value shows 1.631 while the t-table was 1.671 and 2.390. This result of this investigation indicated that there is no significant between vocabulary and reading comprehension. The value of t-test was 1.631 is lower than 1.671 and 2.390 in 0.05 and 0.01 significant level. While the degree of freedom (df) = 60. It can be concluded that there is no correlation between vocabulary mastery and reading comprehension.
Homogeneous universal $H$-fields

We consider derivations $\partial$ on Conwayâs field $\mathbf {No}$ of surreal numbers such that the ordered differential field $(\mathbf {No},\partial )$ has constant field $\mathbb {R}$ and is a model of the model companion of the theory of $H$-fields with small derivation. We show that this determines $(\mathbf {No},\partial )$ uniquely up to isomorphism and that this structure is absolutely homogeneous universal for models of this theory with constant field $\mathbb {R}$.
Students’ Perception on The Implementation of Orai Application In CLL Method in Teaching Speaking

This study aims to examine EFL learners’ perception toward the application of Orai App in CLL method in teaching speaking. The study employed likert-scale questionnaires to enable researchers to collect massive data. The total of 24 first year students of a private university in East Java – Indonesia, comprising of 5 male and 19 female students participated in this study. Data were analyzed using descriptive statistic by finding out the total score per item. The results of this study illustrated that Orai application helps students alleviate the fear of speaking. It can also help students in increasing their speaking ability on the aspects of pronunciation, intonation as well as the speaking speed. Based on the data analysis, CLL method assisted by Orai application is strongly recommended to be used to assist the students to be better speaker.
A Technique for Improving Web Mining using Enhanced Genetic Algorithm

World Wide Web is growing at a very fast pace and makes a lot of information available to the public. Search engines used conventional methods to retrieve information on the Web; however, the search results of these engines are still able to be refined and their accuracy is not high enough. One of the methods for web mining is evolutionary algorithms which search according to the user interests. The proposed method based on genetic algorithm optimizes important relationships among links on web pages and also presented a way for classifying web documents. Likewise, the proposed method also finds the best pages among searched ones by engines. Also, it calculates the quality of pages by web page features independently or dependently. The proposed algorithm is complementary to the search engines. In the proposed methods, after implementation of the genetic algorithm using MATLAB 2013 with crossover rate of 0.7 and mutation rate of 0.05, the best and the most similar pages are presented to the user. The optimal solutions remained fixed in several running of the proposed algorithm.
The effects of mobile learning for nursing students: an integrative evaluation of learning process, learning motivation, and study performance

This paper reports a study on the relationships between the variables of nursing students' learning motivation, perception of their mobile learning practice and their study performance. A total of 225 undergraduate nursing students participated in the research. They used a mobile app for fostering clinical knowledge and skills acquired in class, and took part in a questionnaire survey as well as a before-and-after test of their study performance. The results showed that, despite a statistically significant improvement in their study performance after using mobile devices for learning, this improvement was positively correlated with only some of the motivational factors (i.e., mastery goal orientation and intrinsic value) and certain variables (e.g., learners' prior knowledge) in the Framework for the Rational Analysis of Mobile Education used for conceptualising mobile learning. Also, the results of factor analysis suggested three latent factors among all the variables in the model.
Significance of Chromite, G5 Mg-Almandine Garnet, Zircon and Tourmaline in Heavy Mineral Detection of Diamond Bearing Lamproite

A project, with the objective of identifying heavy minerals that could be utilized in heavy mineral prospecting programs to detect diamondiferous lamproite, was completed by the Geological Survey of Canada and C.F. Mineral Research Ltd. consultants in 1990
Determinants of Foreign Direct Investment in Ethiopia: Systematic Review

The determinants of FDI include any host country's situations that affects the inflow of FDI, like market size, the economic growth rate, real growth domestic product, infrastructure, natural resource, the political situation etc.In recent years, Ethiopia has started encouraging the inflow of FDI by improving the investment climate and by providing different incentive packages.The focus of this review is to identify the major determinants of foreign direct investment (FDI) in Ethiopia.The results show that the variables like real growth domestic product, liberalization, exchange rate devaluation and trade openness are significant and have positive correlation with the inflow of FDI in Ethiopia.On the other hand, variables like inflation, poor infrastructure; volatile and high lending interest rate has significant and negative association with the inflow of foreign direct investment.Finally, the study recommend possible intervention of the government through infrastructure development and formulation sound fiscal and monetary policies to control the negative impact of inflation, interest rate and other macro variables.
INVERSE COOKING RECIPE GENERATION FROM FOOD IMAGES

Food photography is appreciated by many as it showcases the beauty of food.However, food images do not provide any information about the preparation process and the complexity of the recipe behind each dish.An inverse cooking system that generates cooking recipes from food images is developed using Convolutional Neural Network (CNN).The system utilizes a unique architecture to predict ingredients and their dependencies without imposing any order.It then generates cooking instructions by simultaneously considering the image and inferred ingredients.The system's performance was extensively evaluated on the Recipe1M dataset, demonstrating an improvement in ingredient prediction compared to previous methods.The system was also able to generate high-quality recipes by leveraging both the image and inferred ingredients, and according to human evaluation, produced more compelling recipes than retrieval-based approaches.
Optimized decoherence suppression of tripartite entanglement in finite temperature environments using weak measurement and quantum measurement reversal

In practical application, since quantum entanglement is inevitably destroyed by thermal noise resulted from environment, protecting entanglement from decoherence in thermal bath environment becomes a critically important issue in the field of quantum information. In this work, we propose a scheme to protect the tripartite Greenberger–Horne–Zeilinger (GHZ) entanglement state subjected to the generalized amplitude damping (GAD) channel based on the quantum technique of weak measurement (WM) and quantum measurement reversal (QMR). By optimizing the strengths of WM and QMR, the decoherence caused by the channel can be effectively suppressed. The results show that after performing the optimal pre- and post-channel weak measurement, the negativity of the GHZ state can be effectively improved with a certain probability, and the phenomenon of sudden death can be delayed to a certain degree. This scheme can be applied to the decoherence suppression of GAD channel at finite temperature.
BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection

Multimodal representation learning is gaining more and more interest within the deep learning community. While bilinear models provide an interesting framework to find subtle combination of modalities, their number of parameters grows quadratically with the input dimensions, making their practical implementation within classical deep learning pipelines challenging. In this paper, we introduce BLOCK, a new multimodal fusion based on the block-superdiagonal tensor decomposition. It leverages the notion of block-term ranks, which generalizes both concepts of rank and mode ranks for tensors, already used for multimodal fusion. It allows to define new ways for optimizing the tradeoff between the expressiveness and complexity of the fusion model, and is able to represent very fine interactions between modalities while maintaining powerful mono-modal representations. We demonstrate the practical interest of our fusion model by using BLOCK for two challenging tasks: Visual Question Answering (VQA) and Visual Relationship Detection (VRD), where we design end-to-end learnable architectures for representing relevant interactions between modalities. Through extensive experiments, we show that BLOCK compares favorably with respect to state-of-the-art multimodal fusion models for both VQA and VRD tasks. Our code is available at this https URL.
Web Service Composition in Cloud: A Fuzzy Rule Model

Background: Cloud Computing has drawn much attention in the industry due to its costefficient schema along with more prospects, such as elasticity and scalability nature of Cloud Computing. One of the main service models of a Cloud is software as a service, where many web services are published and hosted in the Cloud environment. Many web services offered in a Cloud have similar functionality, with different of characteristics non-functional requirements such as Quality of Service (QoS). In addition, as individual web services are limited in their capability. Therefore, there is a need for composing existing services to create new functionality in the form of composite service to fulfill the requirements of Cloud user for certain processes. Methods: This paper introduces a fuzzy rule approach to compose web service based on QoS from different Cloud Computing providers. The fuzzy rule is generated based on QoS of discovered web service from Cloud in order to compose web services that only match user requirements. The proposed model is based on an agent that is responsible for discovering and composing web service that only stratified user requirements. Results: the experimental result shows that the proposed model is efficient in terms of time and the use of fuzzy rules to compose web services from different Cloud providers under different specifications and configurations of Cloud Computing environment. Conclusion: In this paper, an agent-based model was proposed to compose web services based on fuzzy rule in Cloud environment. The agent is responsible for discovering web services and generating composition plans based on offered QoS for each web service. The agent employs a set of fuzzy rules to carry out an intelligent selection to select the best composition plan that fulfills the requirements of the end user. The model was implemented on CloudSim to ensure the validity of the proposed model and performance time analysis was performed that showed good result in terms of time with regard to the Cloud Computing configuration.
Video Frame Interpolation Using Deep Convolutional Neural Network

Video frame interpolation fuse several low-resolution (LR) frames into one high-resolution (HR) frame. The existing methods for video frame interpolation use optical flow method to determine motion in a scene, but computation using optical flow method is difficult, which can lead to artifacts in the output video. In many applications where we use video footages, there is a similarity in the content of footages. This similarity in content recommends that using some kind of context-aware approach can do better interpolation than the different existing interpolation techniques. We propose such a context-aware approach for video interpolation, the video frame interpolation using convolutional neural networks. In this proposed method, neighboring images are given as input to an end-to-end convolutional neural network which interpolates a frame between them. A comparative analysis of video interpolation technique using proposed RGB model and HSV model using metric standards such as SSIM, PSNR, and MSE is also included in the proposed method.
An Improved Fully Homomorphic Encryption Scheme for Cloud Computing

Business in cloud computing is very popular among Small and Medium Enterprises (SMEs). By leveraging services from the cloud, such companies can migrate all of their in-house operations to cloud at low costs with minimum IT facility requirements such as desktop machines and the Internet. Even though the cloud promises tremendous advantages in terms of computing resources and storage spaces, some of the companies are still reluctant to adopt such a technology because of security concerns. To overcome such problems, a fully homomorphic encryption (FHE) scheme with improved efficiency can be implemented as the scheme allows computation on encrypted data without decryption. In this paper, we propose an improved FHE scheme that uses a symmetric key for encryption together with a protocol to implement the scheme. Furthermore, we also provide an analysis regarding to the noise growth in the processed ciphertext and squashing technique that is required to reduce the noise. This analysis is essential to improve the efficiency of the scheme as the squashing technique is time-consuming.
A Study on the Network Catchphrases from the Perspective of Memetics

Memetics is a new theory in linguistic research. It was put forward by Richard Dawkins in order to explain cultural evolutionism. Meme refers to imitation, replication and propagation, which plays an important role in language and cultural development. Memetics has already attracted many scholars’ attentions and has become more and more popular. With the thriving of the internet, netizens make changes in word selection and sentence patterns, so that meme is suitable for potential hosts. Network language is replicated quickly and disseminated widely, which influences people’s daily life. Therefore, the study of network catchphrase memes is valuable and meaningful. Memetics can explain cultural inheritance and language variation. It can also help people find the potential law of network language transmission. This thesis shows the characteristics of memes and network catchphrases respectively. Then the author analyzes the ways and factors of network catchphrases’ dissemination from the perspective of memetics, and discusses factors that affect the dissemination of network catchphrase memes. Therefore, the study on network language, especially catchphrases, has theoretical significance and practical value.
A Pipeline Approach to Free‐Description Question Answering in Chinese Gaokao Reading Comprehension

This study attempted to answer complicated free-description questions in Chinese Gaokao Reading comprehension (RC) tasks. We found that quite a few questions can be answered by extracting sentences from the document and combining them, so we used a pipeline approach with two components: Answer sentence extraction (ASE) and Answer sentence fusion (ASF). Semantic vector similarity and topical distribution similarity were explored for ASE. Integer linear programming strategy was used for ASF, which combined dependencies with the language model, based on word importance. As a first step towards the new challenge, we obtained some encouraging results on actual exam questions in Chinese subject's RC tasks of Beijing Gaokao, which helped us obtain insights into techniques needed to solve real-word complex questions.
Semi-Unsupervised Learning with Deep Generative Models: Clustering and Classifying using Ultra-Sparse Labels.

We introduce $\textit{semi-unsupervised learning}$, an extreme case of semi-supervised learning with ultra-sparse categorisation where some classes have no labels in the training set. That is, in the training data some classes are sparsely labelled and other classes appear only as unlabelled data. Many real-world datasets are conceivably of this type. We demonstrate that effective learning in this regime is only possible when a model is capable of capturing both semi-supervised and unsupervised learning. We develop two deep generative models for classification in this regime that extend previous deep generative models designed for semi-supervised learning. By changing their probabilistic structure to contain a mixture of Gaussians in their continuous latent space, these new models can learn in both unsupervised and semi-unsupervised paradigms. We demonstrate their performance both for semi-unsupervised and unsupervised learning on various standard datasets. We show that our models can learn in an semi-unsupervised manner on Fashion-MNIST. Here we artificially mask out all labels for half of the classes of data and keep $2\%$ of labels for the remaining classes. Our model is able to learn effectively, obtaining a trained classifier with $(77.2\pm1.3)\%$ test set accuracy. We also can train on Fashion-MNIST unsupervised, obtaining $(75.2\pm1.5)\%$ test set accuracy. Additionally, doing the same for MNIST unsupervised we get $(96.3\pm0.9)\%$ test set accuracy, which is state-of-the art for fully probabilistic deep generative models.
QIM - Based Audio Watermarking with Combination Technique of DCT-QR-CPT

ABSTRAKAudio watermarking adalah teknik memasukkan informasi ke dalam file audio dan untuk melindungi hak cipta data digital dari distribusi ilegal. Makalah ini memperkenalkan audio stereo watermarking berdasarkan Quantization Index Modulation (QIM) dengan teknik gabungan Discrete Cosine Transform (DCT) - QRCartesian Polar Transform (CPT). Host audio dibagi menjadi beberapa frame, selanjutnya setiap frame ditransformasi oleh DCT, kemudian output DCT diuraikan menjadi matriks orthogonal dan matriks segitiga menggunakan metode QR. Selanjutnya, CPT mengubah dua koefisien kartesian dari matriks segitiga (R) pada posisi (1,1) dan (2,2) menjadi koefisien polar. Setelah itu, penyisipan dilakukan pada koefisien polar oleh QIM. Hasil simulasi menunjukkan bahwa imperseptibilitas audio terwatermark berkualitas baik dengan Signal to Noise Ratio (SNR)&gt; 20, Mean Opinion Score (MOS)&gt; 4 dan tahan terhadap serangan seperti Low Pass Filter (LPF) dan Band Pass Filter (BPF) dengan cut off 25-6k, resampling, Linear Speed Change (LSC) dan MP3 Compression dengan rate diatas 64 kbps.Kata kunci: Audio Watermarking, CPT, DCT, QIM, QR ABSTRACTAudio watermarking is a technique for inserting information into an audio file and to protect the copyright of digital data from illegal distribution. This paper introduces a stereo audio watermarking based on Quantization Index Modulation (QIM) with combined technique Discrete Cosine Transform (DCT) – QR – Cartesian Polar Transform (CPT). Each frame of a host audio is transformed by DCT, then DCT output is decomposed using QR method. Next, CPT transform two cartesian coefficients from triangular matrix (R) in position (1,1) and (2,2) to polar coefficients. After that, embedding is executed on polar coefficients by QIM. The simulation result shows that the imperceptibility is good with Signal to Noise Ratio (SNR)&gt;20, Mean Opinion Score (MOS)&gt;4 and it is robust against attacks such as Low Pass Filter (LPF) and Band Pass Filter (BPF) with cut off 25-6k, Resampling, Linear Speed Change and MP3 Compression with rate 64 kbps and above. Keywords: Audio Watermarking, CPT, DCT, QIM, QR
The Effects of Conventional Method, Mind Mapping Method, and Learning Motivation on Thematic Learning Outcomes in 3rd Grade of Elementary School

This study aims to determine the effect of mind mapping vs. conventional methods and learning motivation on learning outcomes in the thematic subjects of energy themes and changes in energy change sub-themes 2 in third grade students of SD Muhammadiyah 1 and SD Muhammadiyah 2 Waru 2017/2018 academic year. Sources of data were obtained from students of SD Muhammadiyah 1 Waru with a total of 47 students and SD Muhammadiyah 2 Waru with 38 students. This study uses experimental research design with the form of Factorial Design with the sampling technique is Cluster Sampling. The results of the study concluded that the experimental group students showed better learning outcomes compared to the control group. Students who had high learning motivation towards thematic subjects had better learning outcomes compared to students who had low learning motivation on thematic subjects, and not there is interaction between mind mapping vs. conventional methods and student learning motivation on thematic subjects in grade 3 of Muhammadiyah 1 Waru Elementary School and Muhammadiyah 2 Waru Elementary School in the academic year 2017/2018.
Credulous Acceptability, Poison Games and Modal Logic

The Poison Game is a two-player game in which players alternatively move a token on a graph's nodes and such that one player can influence which edges the other player is able to traverse. It operationalizes the notion of existence of credulously acceptable arguments in an argumentation framework or, equivalently, the existence of non-trivial semi-kernels. We develop a modal logic (poison modal logic, PML) tailored to represent winning positions in such a game, thereby identifying the precise modal reasoning that underlies the notion of credulous acceptability in argumentation. We study model-theoretic and decidability properties of PML, and position it with respect to recently studied logics at the cross-road of modal logic, argumentation, and graph games.
Single‐image super‐resolution using online kernel adaptive filters

The online kernel adaptive filters are non-linear filters which provide impulse response and are more efficient compared to other kernel algorithms. The performance of kernel adaptive filters depends on dictionary size. Here the single-image super-resolution using online kernel adaptive filters is a learning-based method. The algorithm generates a sparser solution for obtaining high-resolution image from a low-resolution image. It finds out a dictionary with most significant set of basis vectors using the spatial similarity among the dictionaries created from the low-resolution and high-resolution image patches in the training set. The dictionary is utilised to generate the high-resolution image. The algorithm is analysed on three different kernel adaptive filters, extended kernel recursive least squares, kernel recursive least squares tracker and naive online regularised risk minimisation algorithm. The performance of the super-resolution method is evaluated on a large number of images and is compared with the state-of-the art non-linear solutions to the super-resolution. The results show a better progress in peak signal-to-noise ratio up to 1.2 dB.