Reviewerly/exHarmony huggingface dataset:
Biconditional Generative Adversarial Networks for Multiview Learning with Missing Views

In this paper, we present a conditional GAN with two generators and a common discriminator for multiview learning problems where observations have two views, but one of them may be missing for some of the training samples. This is for example the case for multilingual collections where documents are not available in all languages. Some studies tackled this problem by assuming the existence of view generation functions to approximately complete the missing views; for example Machine Translation to translate documents into the missing languages. These functions generally require an external resource to be set and their quality has a direct impact on the performance of the learned multiview classifier over the completed training set. Our proposed approach addresses this problem by jointly learning the missing views and the multiview classifier using a tripartite game with two generators and a discriminator. Each of the generators is associated to one of the views and tries to fool the discriminator by generating the other missing view conditionally on the corresponding observed view. The discriminator then tries to identify if for an observation, one of its views is completed by one of the generators or if both views are completed along with its class. Our results on a subset of Reuters RCV1/RCV2 collections show that the discriminator achieves significant classification performance; and that the generators learn the missing views with high quality without the need of any consequent external resource.
SCRUTINIZING DIRECTED READING THINKING ACTIVITY (DRTA) STRATEGY ON STUDENTS' READING COMPREHENSION

The aim of this research was to examine the directed reading thinking activity (DRTA) strategy on students’ reading skills. This research was classified as experimental research applying a quasi-experimental design. This study involved 60 students of second-grade students of SMPN 1 Jonggat. The students were divided into two groups, the experimental and control groups. The experimental group was subjected to DRTA strategy, while the control group was treated using the conventional learning strategy. The data were obtained by using a reading test. The data were analyzed using descriptive and inferential statistics. The data were calculated using SPSS 19.0 for Windows. The descriptive statistic was aimed at finding out the students’ mean score, mode, median, and standard deviation. Hence, the inferential statistic was used to test the research hypothesis using the independent sample t-test. The results of the research showed that the DRTA strategy in teaching reading had a more positive effect than the conventional learning strategy. It was proven that the students’ mean score in the experimental class (78.80) was higher than those in the control group (73.73). In addition, the score of 2-tailed (0.032) was lower than the sig. level 0.05. Therefore, the hypothesis was accepted. It means that the directed reading thinking (DRTA) strategy had a significant effect on students’ reading comprehension.
Using real-time online preprocessed mouse tracking for lower storage and transmission costs

Abstract Pageview is the most popular webpage analytic metric in all sectors including blogs, business, e-commerce, education, entertainment, research, social media, and technology. To perform deeper analysis, additional methods are required such as mouse tracking, which can help researchers understand online user behavior on a single webpage. However, the geometrical data generated by mouse tracking are extremely large, and qualify as big data. A single swipe on a webpage from left to right can generate a megabyte (MB) of data. Fortunately, the geometrical data of each x and y point of the mouse trail are not always needed. Sometimes, analysts only need the heat map of a certain area or perhaps just a summary of the number of activities that occurred on a webpage. Therefore, recording all geometrical data is sometimes unnecessary. This work introduces preprocessing during real-time and online mouse tracking sessions. The preprocessing that is introduced converts the geometrical data from each x and y point to a region-of-interest concentration, in other words only heat map areas that the analyzer is interested in. Ultimately, the approach used here is able to greatly reduce the storage and transmission cost of real-time online mouse tracking.
THE ACTIVITY APPROACH AS A BASIS FOR PRESCHOOL TEACHERS’ METHODOLOGICAL ACTIVITIES

The article analyzes the content of the activity component of the methodological competence of the teachers of pre-school educational institutions in the system of continuous education and noted that the activity approach provides the opportunity to develop holistic integration models as well as to identify basic functions, elements, components, their relationships and interconnections, system-forming factors and the functioning conditions in static and dynamic aspects.All professional competence structural components are aimed at the preschool teacher`s practical activity, in particular, the capacity for solving specific pedagogical situations.The objectives of methodological competence development in preschool teachers are achieved in the process of professional training and retraining, the improvement of methodological tools for methodological work.The activity approach is the basis of preschool teachers` methodological activities and should ensure the formation of readiness for self-development and continuing education, modelling and construction of social environment for personal development in the education system, active cognitive activity.The principles of methodological work organization: relevance, unity of theory and practice; scientific character to the conformity with modern scientific achievements in various fields; the unity and interconnection of all directions of teachers` professional development; goal orientation, consistency, succession, a mass character, and collectivity; the methodological work transformation into a part of the continuing education system, teachers engagement into various forms of methodological; creation of favorable working conditions, the availability of free time for the teacher's creative activity; efficiency, flexibility, mobility and an individual approach requiring, the creative nature of methodological work, the creation of a methodological work system in a preschool institution; continuing teachers` self-education, qualified assistance provision both in theoretical and in practical matters; improving teaching effectiveness.
Label Propagation-Based Parallel Graph Partitioning for Large-Scale Graph Data

The increasing importance of graph data in various fields requires large-scale graph data to be processed efficiently. Furthermore, well-balanced graph partitioning is a vital component of parallel/distributed graph processing. The goal of graph partitioning is to obtain a well-balanced graph topology, where the size of each partition is balanced while the number of edge cuts is reduced. Moreover, a graph-partitioning algorithm should achieve high performance and scalability. In this study, we present a novel graph-partitioning algorithm that ensures a high edge cutting quality and excellent parallel processing performance. We apply formulas based on the label propagation algorithm to improve the quality of edge cuts and achieve fast convergence. In our approach, the necessity of applying the label propagation process for all vertices is removed, and the process is applied only for candidate vertices based on a score metric. Our proposed algorithm introduces a stabilization phase in which remote and highly connected vertices are relocated to prevent the algorithm from becoming trapped in local optima. Comparison results show that a prototype based on the proposed algorithm outperforms well-known parallel graph-partitioning frameworks in terms of speed and balance.
<b>ManifoldOptim</b>: An <i>R</i> Interface to the <b>ROPTLIB</b> Library for Riemannian Manifold Optimization

Manifold optimization appears in a wide variety of computational problems in the applied sciences.In recent statistical methodologies such as sufficient dimension reduction and regression envelopes, estimation relies on the optimization of likelihood functions over spaces of matrices such as the Stiefel or Grassmann manifolds.Recently, Huang, Absil, Gallivan, and Hand (2016) have introduced the library ROPTLIB, which provides a framework and state of the art algorithms to optimize real-valued objective functions over commonly used matrix-valued Riemannian manifolds.This article presents Mani-foldOptim, an R package that wraps the C++ library ROPTLIB.ManifoldOptim enables users to access functionality in ROPTLIB through R so that optimization problems can easily be constructed, solved, and integrated into larger R codes.Computationally intensive problems can be programmed with Rcpp and RcppArmadillo, and otherwise accessed through R. We illustrate the practical use of ManifoldOptim through several motivating examples involving dimension reduction and envelope methods in regression.
A New method for Image Encryption Using Chaotic Permutation

With the extensive recent development of communication methods and resulting increase in data surveillance and espionage, the need for reliable data encryption methods is greater than ever.Conventional encryption calculations, for example, DES and RSA, are not beneficial in the field of picture encryption because of some inherent characteristics of pictures such as bulk data size and high redundancy, which are problematic for conventional encryption.Many researchers have proposed different image encryption schemes to overcome image encryption problems.In the last two decades, more and more studies have looked to incorporate conventional encryption methods and the complex behavior of chaotic signals.In this paper, a novel image encryption algorithm is proposed based on pixel chaotic permutation.A chaotic logistic map and Ikeda map are used to design a new pseudo-random bit generator, and a novel permutation scheme is used to modify pixel values.Then, a new permutation algorithm based on a traditional Japanese game called Amidakuji is used for pixel scrambling.Different statistical manners, such as correlation coefficient, NPCR (Number of Pixels Change Rate), UACI (Unified Average Changing Intensity), and entropy, are used to provide analysis of the effectiveness of the proposed encryption methods.Our example reveals that the proposed encryption method can obtain highly secure encrypted images using a novel chaotic permutation method based on Amidakuji.
A hybrid collaborative filtering recommendation algorithm: integrating content information and matrix factorisation

Matrix factorisation is a one of the most popular techniques in recommendation systems. However, matrix factorisation still suffers from cold start problem and needs complicated computation. In this paper, we present a hybrid recommendation algorithm, which integrates user and item content information and matrix factorisation. First, based on user or item content information, biases of user or item can be evaluated in advance. Incorporating user and item biases into matrix factorisation model, we can obtain final prediction model. At last, momentum stochastic gradient descent method is used to optimise other parameters. Experimental results on a real data set have shown best performance of our algorithm in terms of MAE and RMSE when compared with other classical matrix factorisation recommendation algorithms.
Mobile Application Development for Technology Enhanced Learning: An Applied Study on the Students of the College of Mass Communication at Ajman University

Educational applications play an important role in modern education. Based on this, the objectives of this study are as follows: analyze a student interview (focus group) that has been conducted among University student of college of Mass Communication at Ajman University; determine the characteristics of educational applications; find out possible advantages and disadvantages of educational applications; formulate, including based on a student interview, criteria for choosing educational applications; divide educational applications into different categories to more fully see the academic possibilities of their use; develop student skills. Based on the objectives, it is decided to divide the study into two parts. The first step is to analyze semi-structured interviews that have been conducted with students. In the second part of the study, a list of educational applications is compiled, indicating their educational categories.
HSV image classification of ancient script on copper Kintamani inscriptions using GLRCM and SVM

The problem of inscription physical damage as one of the historical heritages can be overcome using an image processing technique. The purpose of this study is to design a segmentation application for ancient scripts on inscriptions to recognize the character patterns on the inscriptions in digital form. The preprocessing was carried out to convert images from RGB to HSV. The application used the gray level run length matrix (GLRLM) to extract texture features and the support vector machine (SVM) method to classify the results. The inscription image segmentation was carried out through the pattern detection process using the sliding window method. The application obtained 88.32 % of accuracy, 0.87 of precision, and 0.94 of sensitivity.
Study of naturalness in tone-mapped images

Nowadays, images can be obtained in various ways such as capturing photos in single-exposure mode, applying Multiple Exposure Fusion algorithms to generate an image from multiple shoots of the same scene, mapping High Dynamic Range (HDR) images to Standard Dynamic Range (SDR) images, converting raw formats to displayable formats, or applying post-processing techniques to enhance image quality, aesthetic quality,…When looking at some photos, one might have a feeling of unnaturalness. This paper deals with the problem of developing a model firstly to estimate if an image looks natural or not to humans and the second purpose is to try to understand how the unnaturalness feeling is induced by a photo: Are there specific unnaturalness clues or is unnaturalness a general feeling when looking at a photo? The study focuses on SDR images, especially on tone-mapped images. The first contribution of the paper is the setting of an experiment gathering human naturalness opinions on 1900 SDR images mainly obtained from tone mapping operators. Based on the collected data, the second contribution is to study the efficiency of different feature types including handcrafted features and learned features for image naturalness analysis. A binary classification model is then developed based on the determined features to classify if an image looks natural or unnatural.
Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology

Recent advances in computational and algorithmic power are evolving the field of medical imaging rapidly. In cancer research, many new directions are sought to characterize patients with additional imaging features derived from radiology and pathology images. The emerging field of Computational Pathology targets the high-throughput extraction and analysis of the spatial distribution of cells from digital histopathology images. The associated morphological and architectural features allow researchers to quantify and characterize new imaging biomarkers for cancer diagnosis, prognosis, and treatment decisions. However, while the image feature space grows, exploration and analysis become more difficult and ineffective. There is a need for dedicated interfaces for interactive data manipulation and visual analysis of computational pathology and clinical data. For this purpose, we present IIComPath, a visual analytics approach that enables clinical researchers to formulate hypotheses and create computational pathology pipelines involving cohort construction, spatial analysis of image-derived features, and cohort analysis. We demonstrate our approach through use cases that investigate the prognostic value of current diagnostic features and new computational pathology biomarkers.
Efficient Batch-Incremental Classification Using UMAP for Evolving Data Streams

Learning from potentially infinite and high-dimensional data streams poses significant challenges in the classification task. For instance, k-Nearest Neighbors (kNN) is one of the most often used algorithms in the data stream mining area that proved to be very resource-intensive when dealing with high-dimensional spaces. Uniform Manifold Approximation and Projection (UMAP) is a novel manifold technique and one of the most promising dimension reduction and visualization techniques in the non-streaming setting because of its high performance in comparison with competitors. However, there is no version of UMAP that copes with the challenging context of streams. To overcome these restrictions, we propose a batch-incremental approach that pre-processes data streams using UMAP, by producing successive embeddings on a stream of disjoint batches in order to support an incremental kNN classification. Experiments conducted on publicly available synthetic and real-world datasets demonstrate the substantial gains that can be achieved with our proposal compared to state-of-the-art techniques.
An updated LLVM-based quantum research compiler with further OpenQASM support

Abstract Quantum computing is a rapidly growing field with the potential to change how we solve previously intractable problems. Emerging hardware is approaching a complexity that requires increasingly sophisticated programming and control. Scaffold is an older quantum programming language that was originally designed for resource estimation for far-future, large quantum machines, and ScaffCC is the corresponding LLVM-based compiler. For the first time, we provide a full and complete overview of the language itself, the compiler as well as its pass structure. While previous works Abhari et al (2015 Parallel Comput. 45 2–17), Abhari et al (2012 Scaffold: quantum programming language https://cs.princeton.edu/research/techreps/TR-934-12 ), have piecemeal descriptions of different portions of this toolchain, we provide a more full and complete description in this paper. We also introduce updates to ScaffCC including conditional measurement and multidimensional qubit arrays designed to keep in step with modern quantum assembly languages, as well as an alternate toolchain targeted at maintaining correctness and low resource count for noisy-intermediate scale quantum (NISQ) machines, and compatibility with current versions of LLVM and Clang. Our goal is to provide the research community with a functional LLVM framework for quantum program analysis, optimization, and generation of executable code.
An optimal resource allocation scheme for virtual machine placement of deploying enterprise applications into the cloud

The emergence of cloud computing can help enterprises reduce their hardware and software investment and save their own operation and maintenance costs, thus more and more enterprises deploy their applications into the cloud. Generally, components of enterprise applications are resided in virtual machines and then hosted by physical machines. In order to achieve the efficiency and utilization of physical machines, reasonable virtual machines placement becomes very important. In this paper we propose a scheme of resource allocation model for virtual machines placement and investigate it with convex optimization approach. We also present a heuristic algorithm to achieve the optimal resource allocation and discuss its equilibrium and stability by applying the asymptotic stability of the continuous dynamic system of Lyapunov stability theory. Finally, we give some numerical examples to illustrate the performance of the resource allocation scheme and confirm its convergence with a certain number of iterations.
Meningkatkan Kosakata Anak Usia Dini Melalui Permainan Chinese Whispers

Vocabulary are words that someone has to socialize with other people. The process of acquiring a child's vocabulary can be influenced by external factors as well as from factors within the child itself. The topics contained in this article include the game Chinese whispers to improve vocabulary in children. Chinese whispes game is a game that is done in groups, by whispering to convey messages in sequence until the last player. In principle the game Chinese Whipers is able to train children in improving vocabulary, because in this game the child will mention the words instructed by the teacher or friend.
Evolutionary Context-Integrated Deep Sequence Modeling for Protein Engineering

Protein engineering seeks to design proteins with improved or novel functions. Compared to rational design and directed evolution approaches, machine learning-guided approaches traverse the fitness landscape more effectively and hold the promise for accelerating engineering and reducing the experimental cost and effort.
Displaying Health Status Based IoT

Displaying Health Status Based IoT&#x0D; Students have many activity in their daily life. There are many things to do like going to class, meeting for group assignment, shopping for food and so on. If the student is unwell then need to go to the clinic for treatment and leaving letter. So in order to reduce the risk of receiving illness, researchers propose Displaying Health Status Based on the Internet of Things Concept. This tool will help students by knowing the pulse rates is normal or not. Displaying Health Status Based on the Internet of Things is designed to facilitate students to see their pulse rate. This tool will also help students to develop their potential and facilitate the health of students themselves by knowing their pulse rate at the proper level. Students will always keep their health healthy for everyday life. Additionally, this tool can alert students that health is essential for life. The results of this research show that this tool can be used when exercising to determine the pulse rate is normal or not.
NCoV–Based Considerations on Online- English Teaching and Traditional Classroom-English Teaching in China

Facing the Novel Coronal Virus (NCoV) in China, teachers, and students are required to realize the idea “Class-stop means not stopping learning” so that all schools in China start the online teaching. This paper, from the view of NCoV , based on the educational principles and features of online English teaching and traditional classroom English teaching, contrastively discusses what goes on in these two teaching ways and attempts to prove that online English teaching is only used as a kind of teaching aids and cannot be considered as the equal status as the classroom English teaching, which may offer the sign both for design the teaching software and for the reform of our country’s education system.
Image Quality Assessment Based on Quaternion Singular Value Decomposition

We propose an image quality assessment metric based on quaternion singular value decomposition that represents a color image as a quaternion matrix, separates image noise information using singular value decomposition and extracts features from both the whole image and its noise information. In the proposed method, the color image and its local variance are represented by using quaternion and then performing singular value decomposition. Later, 75% of singular values are taken as image noise information. We extract a luminance comparison, contrast comparison, structure comparison, phase congruency and gradient magnitude from whole color images and extract the peak signal-to-noise ratio from image noise information as features. Finally, these features are used as the input to a kernel extreme learning machine to predict the quality of the tested images. Extensive experiments performed on four benchmark image quality assessment databases demonstrate that the proposed metric achieves high consistency with the subjective evaluations and outperforms state-of-the-art image quality assessment metrics.