Reviewerly/exHarmony huggingface dataset:
The impact of students’ generating and answering questions on their performance

The aim of the research is to measure the effect of students' use of the strategy of writing questions and answering them on their achievement performance. The researcher used the achievement test to measure the differences between variables, and checked the research tool from validity-consistency-experimental application. But for the implementation of the research experiment, the students were divided into three groups, namely: The experimental group 1 (generating questions and answering them by the learners in groups), the experimental group 2 (forming and answering the questions of the learners individually), and the control group (forming questions by the teacher). In addition, the researcher used the mean and standard deviation to answer the research question and compare student performance in all three research groups. The results showed that there were significant differences between the averages of the research groups, as the experimental group 2 achieved a higher average (26,474) than the experimental group 1 (24,333) and the control group (24,857).
Real-Time 2D-to-3D Conversion for Television Broadcasting Based on Embedded System

In this article, we present an efficient 3-D display technique that employs current 2-D television (TV) channels. First, a gradient background depth is generated for the still region. To create a stereo effect, multiple objects are detected and segmented using the binary format. Object depth is estimated according to the object size and its location. Depth value is assigned as a proportion of the object size using the linear quantitative method. Each object size is calculated and its depth value is proportional to the size parameter. If an object is the largest one and is located in the central region, the maximum depth value is assigned. For real-time implementation, dual advanced RISC machine (ARM) cores perform the task using the algorithm of the proposed stereo conversion. Our results show that a low-cost embedded system can perform the proposed 3-D algorithm, with a processing time of 43 ms per frame. The signal from a TV channel can be sampled and converted to a 3-D format display on a conventional LCD screen.
A Novel Framework for Semantic Image Segmentation using Generative Adversarial Networks and Neural Architecture Search

Semantic segmentation is a fundamental task in image processing and computer vision domains. Therefore, it is a core component of most emerging industry applications such as medical imaging, autonomous driving, and agriculture. The last five years have witnessed a huge growth in the computer vision domain with the introduction of Generative Adversarial Networks (GANs) due to their ability to learn deep representations by a competitive process between two neural networks called the Generator the Discriminator. Neural Architecture Search (NAS) is undergoing a revolution aimed at automating the architectural engineering process. This paper discusses implementing a novel framework for semantic image segmentation called iSegmentor using NAS and GANs. The proposed approach is the first framework that utilizes NAS in GANs for semantic image segmentation to the best of our knowledge. The iSegmentor framework was evaluated using mean Intersection over Union (mIoU) and Mean Pixel Accuracy (MPA) metrics. The iSegmentor was mainly tested and evaluated for the autonomous driving domain. With the analysis of the past five-year literature, Cityscapes and CamVid datasets were chosen. The iSegmentor achieved 81.73 MPA and 71.90 mIoU for the Cityscapes dataset, whereas CamVid achieved 80.41 MPA and 70.63 mIoU. Further, we present future avenues that we identified with the conducted experiments to assist other researchers to extend the research area of NAS in GANs in semantic segmentation.
Advancement of Quantum Methods in Principal Component Analysis

Quantum principal component analysis (QPCA) is analogous to the classical method with additive speedup. This is based on the unsupervised dimensionality reduction mechanism that transforms exponentially high dimensional data points into low dimensional space by retaining significant features with the largest variance. To our knowledge, quantum computations are highly exponential and supports implicit parallelism. Therefore, quantum method for finding principal components are proved to achieve exponential speedup over existing classical solution. An advantage of quantum superposition is utilized for the Eigen decomposition of density matrix which is identical to covariance, however, a way through which its computation happen, is truly an interesting factor. This paper presents basic methodological framework to design QPCA algorithm. Our framework includes the methods for quantum data encoding, quantum memory processing, and the requisite quantum circuit implementations to realize QPCA algorithms. In addition, we categorized existing QPCA methods in view of recent advances. We then proposed the possible advancements that can benefit to design quantum based efficient algorithms for principal component analysis. At the last, suggested concepts are compared with existing methods based on complexities and other factors to justify our proposal as significant in perspective to future directions.
Foreground-Guided Facial Inpainting with Fidelity Preservation

Facial image inpainting, with high-fidelity preservation for image realism, is a very challenging task. This is due to the subtle texture in key facial features (component) that are not easily transferable. Many image inpainting techniques have been proposed with outstanding capabilities and high quantitative performances recorded. However, with facial inpainting, the features are more conspicuous and the visual quality of the blended inpainted regions are more important qualitatively. Based on these facts, we design a foreground-guided facial inpainting framework that can extract and generate facial features using convolutional neural network layers. It introduces the use of foreground segmentation masks to preserve the fidelity. Specifically, we propose a new loss function with semantic capability reasoning of facial expressions, natural and unnatural features (make-up). We conduct our experiments using the CelebA-HQ dataset, segmentation masks from CelebAMask-HQ (for foreground guidance) and Quick Draw Mask (for missing regions). Our proposed method achieved comparable quantitative results when compare to the state of the art but qualitatively, it demonstrated high-fidelity preservation of facial components.
Vector field encoding approach to secure color image

This paper describes a method based on arbitrary vector light field having spatially variable polarization to encrypt a color image. The information can be encoded into the single polarization component of a vector field using a computer generated hologram.
Weakly Supervised Image Annotation and Segmentation

The various aspects in the processing of an image include object recognition, object classification, image segmentation, and attribute learning, are closely related to each other. In this paper, we proposed a Bayesian Non-parametric (BN) approach to solve the complex visual tasks using the non-parametric property to regulate the model's constraint. A Chinese Restaurant Process Stacked with Weakly Supervised Markov Random Field (WS-MRF-CRP) is developed, which uses Markov Random Field (MRF) for low-level and Chinese Restaurant Process (CRP) for high-level. The proposed approach learns and incorporates association between various object and attribute classes. The input image is clustered into individual components using the MRF, and then the CRP is used for merging the components and generating the image-attribute association. Experiments performed on the Berkeley Segmentation dataset demonstrated that the proposed model performs better than other existing weakly supervised models.
Poster: Preventing Spatial and Privacy Attacks in Mobile Augmented Reality Technologies

The growing popularity of applications featuring Mobile Augmented Reality (MAR) raises serious concerns regarding the use of such a game-changing technology inside sensitive physical spaces, e.g., memorials, hospitals, museums, etc., such that the safety and privacy of users is preserved. To address such concerns, we present our ongoing work for mediating the way MAR Content, e.g., digital objects rendered on top of a video stream, is generated, distributed, and consumed by applications. We introduce a theoretical model, a supporting framework, as well as SpaceMediator, a proof-of-concept application implementing our approach.
Retracted: SeqGAN-APG: Sequential Generative Adversarial Networks for Automatic Patch Generation

As software systems grow in size and complexity, the task of manual debugging consumes more time in the development cycle and opens up to more severe and frequent human error. Researchers have developed systems for automated patch generation to address this problem, which attempt to reduce the manual work required for diagnosing and addressing faulty code. Machine Learning models, which can leverage large amounts of information, for example, from large code repositories, promise one possible set of solutions for automated patch generation. In particular, researchers have recently used sequential generative adversarial networks (GANs) successfully in text-based prediction tasks. We thus propose a model for sequential generative adversarial networks for automated patch generation (SeqGAN-APG), which divides the task of patch generation into a two-part adversarial framework that learns the distribution of sequential features in non-buggy code while also learning to generate sequential features which mimic that distribution. Our approach is novel not only in terms of its application of GANs to an untested kind of sequential data but also in terms of the kinds of sequential features which we implement, going beyond the typical token-based approach.
3D Hand Pointing Recognition over a Wide Area using Two Fisheye Cameras

In this paper, we propose a user interface for large displays that allows pointing operations from a wide area. Two fisheye cameras installed on both sides of the display are used to capture images of a wide area in front of the display, and the system recognizes the user’s skeleton and allows the user to perform pointing operation. Due to the wide viewing angle of the fisheye cameras, the baseline length between the cameras can be long. The experimental result showed that the pointing accuracy was higher with the fisheye cameras than with the standard cameras when operating at the same distance.
Optimizing Solar Panel Tilt Using Machine Learning Techniques

As the world seeks to produce energy from renewable sources, the number of photovoltaic installations has increased. With this, there arises a need for maximizing power output. Higher availability of data and computational units have helped machine learning algorithms to make predictions faster. The need to predict the optimum tilt angle for maximum power generation is essential. Choosing an optimum angle of inclination of a solar panel often involves theoretical calculations which have interdependent and location specific variables. This paper presents a machine learning model that takes other environmental weather dependent factors and changing seasons across the year to compare and presents a model that can estimate the inclination angle for optimum generation of power by the solar array. We use algorithms like gradient boosting, extreme gradient boosting and multilayer perceptrons to establish a relationship between inclination angle and power generation. With our system, we predict an increase of 8.44%-11.8% with the predicted optimised angle. Our models predict an optimized inclination with a variation of 1.5% in the final value. We also present an analytical model which has been used to corroborate the results obtained from the machine learning models.
Cognitive Painting as a Broadly Applicable Method of Constructing a Positive Cognitive-Emotional Space for Young Children and Adolescents under Conditions of the Coronavirus Pandemic

The paper introduces an original, broadly applicable method of creating a highly useful online prop being appropriate for the particular situation when children and adolescents find themselves in the pandemic restrictions launched into the digital cosmos without being trained to bear a flight lasting almost half a year, being a member of a busy crew, though they have never had an experience of being together with for twenty four hours a day. This method is called cognitive painting. Within an online course in addition to the usual tasks we have started up a project dealing with creating a new year story underpinned by students' own perception of snow (in case of snowy winters), their love towards their favorite Christmas toys, their Christmas wishes and hopes and underpinned by sound reasoning about Christmas gift in general, Christmas miracles a and Santa Clause. 62 school children took part in this work (the age from 7 to 18 years). As a result, we have a 92-pages printed book as an output of that co-creative work. A very precious feature of our method is that it can be effectively applied in arbitrary countries, taking into account the peculiarities of national culture and nature.
PENGARUH MODEL CONTEXTUAL TEACHING AND LEARNING (CTL) BERBANTU WORD SQUARE TERHADAP HASIL BELAJAR EKONOMI PESERTA DIDIK KELAS X SMA MUHAMMADIYAH 1 BANDAR SRIBHAWONO TAHUN PELAJARAN 2018/2019

The problem that occurred at SMA Muhammadiyah 1 Bandar Sribhawono is that there are still many learners who have not been able to learn the results of learning economic subjects. To solve the problem, researchers provide one of the learning models that can be applied i.e. model Contextual Teaching and Learning (CTL). The purpose of this research is to know the influence of the Contextual Teaching and Learning (CTL) model of Word square to the outcome of the students ' economic study of class X in SMA Muhammadiyah 1 Bandar Sribhawono Year lesson 2018/2019. This type of research is a quantitative study with the design of experimental Quasi research and using Non-Equivalent Group Design Research design using two classes of experimental and control classes. The sampling techniques used are saturated sampling. This is demonstrated by the test results (pre test) where the &lt; This (0.112 &lt; 2.006) Thitung is received and the H1 is rejected which means there is no difference between the average of learners (pre test) students in the control class with the experimental class. As for after treatment, the student test result (post test) is shown by the value Thitung &gt; This (-2.067 &gt; 2.006) then H0 rejected and H1 accepted. The results showed that there was an influence on Contextual Teaching And Learning (CTL) learning model that helped word square towards economic outcomes of students of class X SMA Muhammadiyah 1 Bandar Sribhawono learning year 2018/2019
Detection of Economy-Related Turkish Tweets Based on Machine Learning Approaches

Conducting NLP for Turkish is a lot harder than other Latin-based languages such as English. In this study, by using text mining techniques, a pre-processing frame is conducted in which TF-IDF values are calculated in accordance with a linguistic approach on 7,731 tweets shared by 13 famous economists in Turkey, retrieved from Twitter. Then, the classification results are compared with four common machine learning methods (SVM, Naive Bayes, LR, and integration LR with SVM). The features represented by the TF-IDF are experimented in different N-grams. The findings show the success of a text classification problem is relative with the feature representation methods, and the performance superiority of SVM is better compared to other ML methods with unigram feature representation. The best results are obtained via the integration method of SVM with LR with the Acc of 82.9%. These results show that these methodologies are satisfying for the Turkish language.
A Multiple Instance Learning Algorithm Using Graph Convolutional Network for Speech Content Classification

Recently, speech content classification begins to be formulated as a multiple instance learning (MIL) problem by some researchers. Under the MIL framework, a speech signal is considered as a bag with labels, and speech fragments are regarded as instances without corresponding labels. On this basis, we propose a new algorithm, named MI-CNN-G, for speech content classification using Graph Convolutional Network (GCN) under the MIL framework. First, we only train the MIL classifier, whose architecture is based on Convolutional Neural Network, for bag classification. Second, we utilize GCN to handle graphs composed of instance features that are the outputs of the MIL classifier for correcting the predicted probabilities of the classifier and then updated the classifier and GCN together. Finally, we employ the MI-CNN-G model to predict the labels of unknown bags. The experimental results show that GCN is effective in improving the performance of the MIL classifier on speech content classification and the MI-CNN-G model outperforms the baseline approaches in the Average Precision metric.
Reservoir Computing in Reduced Order Modeling for Chaotic Dynamical Systems

The mathematical concept of chaos was introduced by Edward Lorenz in the early 1960s while attempting to represent atmospheric convection through a two-dimensional fluid flow with an imposed temperature difference in the vertical direction. Since then, chaotic dynamical systems are accepted as the foundation of the meteorological sciences and represent an indispensable testbed for weather and climate forecasting tools. Operational weather forecasting platforms rely on costly partial differential equations (PDE)-based models that run continuously on high performance computing architectures. Machine learning (ML)-based low-dimensional surrogate models can be viewed as a cost-effective solution for such high-fidelity simulation platforms. In this work, we propose an ML method based on Reservoir Computing - Echo State Neural Network (RC-ESN) to accurately predict evolutionary states of chaotic systems. We start with the baseline Lorenz-63 and 96 systems and show that RC-ESN is extremely effective in consistently predicting time series using Pearson's cross correlation similarity measure. RC-ESN can accurately forecast Lorenz systems for many Lyapunov time units into the future. In a practical numerical example, we applied RC-ESN combined with space-only proper orthogonal decomposition (POD) to build a reduced order model (ROM) that produces sequential short-term forecasts of pollution dispersion over the continental USA region. We use GEOS-CF simulated data to assess our RC-ESN ROM. Numerical experiments show reasonable results for such a highly complex atmospheric pollution system.
Textual Element Entity Extraction of Official Documents Based on Pre-training BERT and Improved CRF Model

In the task of textual element entity extraction in official documents, the rule-based method needs to build a large number of templates and regular expressions, which has a weak generalization. Inspired by the in-domain named entity recognition, this work inputs the text sequences of preprocessed open-source document data into the BERT model to get the semantic embeddings; then inputs the embeddings into the BiLSTM network with CRF layer to get the results. Additionally, this paper proposes Constrained-CRF to improve the constraint conditions of CRF model. Experiments show that our model has a better performance than the rule-based method in textual element entity extraction tasks.
Computer-assisted Music Composition Algorithm Design Dependent on Interactive Genetic Algorithm with Interval Fitness

Abstract Computer-assisted music composition refers to computer-assisted music composition with the participation of people. However, there are problems such as style and expression. In this paper, a computer-assisted music composition algorithm based on the interactive genetic algorithm with interval fitness is proposed. A new music prediction model is established by integrating melody units and rhythms into traditional models with only notes or rhythms as units. Moreover, the generated music phrases are optimized by the interactive genetic algorithmphrase. The simulation results suggest that the proposed algorithm can generate music phrases quickly with a certain melody logic that conforms to the personal demand of users using a small data set.
Mean Mixtures in Directional Statistics

We construct a family of distributions on the unit hyper-sphere by conditioning a spherical normal mean mixture distribution with a general weight function. The resulting family of distributions accounts for symmetry, asymmetry, unimodality and bimodality depending on the choice of the weight function and parameter set. The problem of identifiability is partially addressed by re-parameterization. The circle and sphere are considered as special cases with various weight functions in a numerical illustration. A synopsis of the estimation procedure utilized in the numerical study is given. The fit of the proposed model to two real data sets as well as a simulated data set is evaluated and compared to well known circular models.
Visual Object Detection - an Overview of Algorithms and Results

Object detection is an active area of research with rapid growth in the number of new publications. It is a computer vision technique widely used in various domains and it forms a foundation for a variety of applications, such as face detection and autonomous driving. The precondition for successful object detection are efficient algorithms that are able to detect features related to a certain object in static or moving images and the processing power which ensures a fast execution. This paper provides an overview of the current most significant visual object detection algorithms, their essential features, their advantages, limitations, as well as datasets used for their training and evaluation. Based on the provided overview, this paper attempts to also recognize directions for further development and contribution to the area.
Underwater Image Enhancement Using SIFT and HOG Transformation

An image fusion method combines the data from a sample consisting of source images using the features of information/pixel or interference level approaches. The image obtained by this method is a single image. The purpose of an image fusion algorithm is to integrate the redundant and matching data collected from the source representative to produce a novel picture. This picture gives better interpretation of vision for person or system point of view. It is possible to refine the quality of undersea pictures using white balance and image blending methodologies. To perform image blending, the feature matching approach can be applied. To do feature matching, the HOG and SIFT methods can be used. After being implemented in MATLAB software, the methodologies presented in this work are evaluated by means of specific metrics.
Diagnosing Evolution of Cloud Cluster via Spatio-temporal Trace Analysis

To analyze inner-enterprise cloud cluster performance, the role of workload analysis is of paramount interest to system designers. However, the ever-evolving nature of inner-enterprise cloud platforms such as diversity and spatio-temporal nature of workloads makes evolution diagnosing a challenging task. In this paper, we propose MuCoTrAna-Inner, an evolution diagnosing approach for a large-scale cloud data center based on comparative spatio-temporal trace analysis. Moreover, we present a case study on two representative big traces: Alibaba 2017 trace, and Alibaba 2018 trace. Novel quantitative findings along with the performance bottleneck inferences and recommendations based on workload analysis are provided. Our multifaceted analyses of the traces and new findings not only reveal interesting insights that are of interest to system designers and administrators, but also establish a new view to diagnosing the evolution of inner-enterprise cloud cluster based on trace analysis.