Reviewerly/exHarmony huggingface dataset:
Manipulative Media Technology for Addition and Subtraction of Integers in Elementary Schools

Abstract The purpose of this study was to produce manipulative media to understand the concept of addition and subtraction of integers in grade VI students. This research method refers to Borg and Gall’s theory in ten research steps including information gathering, research planning, initial product development, initial field trials, revision of trial results, main product field testing, revision of field product test results, large-scale field trials. final product revision. Data collection techniques were carried out through observation, interviews, and questionnaires. Data analysis used qualitative and quantitative analysis techniques including learning observations, and validation of manipulative media products. Sources of data in this study were teachers and students of grade VI SDN Sumbangrejo and SDN Segoromulyo. The results of this development research indicate that the media developed in the form of manipulative media made of wood can be said to be suitable for use in learning. Based on the validation from the expert, it was noted that the product was in the good category with an average score of 87%. whereas from the results of student responses the average percentage of responses to the media reached 90%. So that learning integers about addition and subtraction with manipulative media runs effectively.
EDUCATIONAL MATERIALS WITH AUGMENTED REALITY IN HIGHER EDUCATION

Objective : to determine the character and forms of the new generation educational materials in the conditions of a new reality - the dominance of clip consciousness, because it is focused on the perception of changing visual images and it limits the formation of systemic knowledge about the subject of study. 2) to make an overview of augmented reality (AR) programs and applications that can be used in the preparation of educational materials. Applied method : analysis of documents, including publications of domestic and foreign authors, describing the experience of introducing virtual (VR), augmented (AR) and mixed (MR) realities technologies into the educational process and educational materials; online sources for the latest augmented reality learning software. Results : textbooks with AR enable to observe objects in 3D in real time and in a real physical environment using smartphones, special glasses; it will allow them to gain practical knowledge and experience. Educational materials with AR and VR will become more in demand in technical / engineering and architectural and construction education, they will allow to visualize projects, to view the architectural models in detail. Conclusions : the use of augmented reality elements has a positive effect on the quality of education, but its effectiveness for students of different specialties is different. The greatest effectiveness of AR in the process of teaching undergraduate students of engineering and architectural specialties was revealed. In general, the impact of virtual and augmented reality on consumers is ambiguous. The problem requires further study, in particular, in the aspect of technostress.
English Character Image Feature Semantic Block Processing for English-Chinese Machine Translation

English character image feature semantic block processing for English-Chinese machine translation is studied in this paper. Prediction result fusion is a means implemented in the decoding process, by integrating the probability distributions obtained by different models to obtain new solutions, and then predict the next target end word. This paper enhances the GANs model to obtain the efficient data mining framework. As use the different model structures and the different random initialization seeds. Because only the initialization parameters of the model are changed, the structural differences of similar network models are too small. In order to increase the differences between models, this article uses integrated learning to build a smart translation system. The experiment results have proven the performance of the proposed model.
Use of Visual Analytics (VA) in Explainable Artificial Intelligence (XAI): A Framework of Information Granules

Research work has been increasing in Visual AnalyticsVisual analytics (VA) since it was first defined in 2005. The techniques of VA that integrated Machine LearningMachine learning (ML) models and interactive visualizationsVisualization have formed a human-centered machine learningMachine learning approach to assist in Data Analytics. VA aims to interpret the complexities of Big Data and underlying ML models by engaging analysts in an iterative process of observing, interpreting, and evaluating inputs, outputs, and architectures of these models. The process then subsequently provides guidance to users, interaction techniques to control AI, and information about inner workings that are often hidden. This chapter defines underlying stages of ML pipeline in feature selection and model performance as components of Information Granules (IG)Information granules in VA; it also explores the use of VA in XAIXAI. This study reviews 13 top-tier publications in recent VA literature to demonstrate (1) the interoperability strategies of VA in feature relevance, model performance, and model architecture; (2) global and local interpretabilityInterpretability in information and visual scalability; and (3) stability of explanations through user case studies, reusability, and design goals. The chapter also analyzes the current stage of VA for granular computingGranular computing in the end. The future work of VA scientists will be to focus on broader behaviors of ML models, particularly in Neural Network, to gain public trust for AI.
A Survey on Evolution of Cloud Technology and Virtualization

The term cloud is termed as a collection of resources both physical and virtual. The customers across the globe are accessing the services of the cloud. The key elements of the data center formed by the cloud are compute, network, storage, application and DBMS (Database Management System). The services provided by a real machine are imitated by the virtual machines. The services offered are for resources like CPU (Central Processing Unit), NIC (Network Interface Card), memory and hard disk. NIC is used to connect the system to the network. The virtual machines are created by software’s like hypervisor. The hypervisor serves as the Virtual Machine Monitor (VMM). It forms as an intermediate between the physical machine and virtual machine. VMware Workstation Pro, Oracle Virtual Box are the examples of hypervisor. The operating system (OS) of the real machine is called as “host OS”. “Guest OS” is the operating system of the virtual machine formed. The number of virtual machines created depends on the underlying hardware of the physical machine with sophisticated hard disks like SSD (Solid State Drive). A virtualized data center with resources is simulated using CloudSim and the results are interpreted.
Introducing a student-led application of Google Expeditions: an exploratory study

Technology integration in classroom settings has become a popular concept, especially due to the growth of the digital native population of learners and the novelty of technology tools. This exploratory study aimed at introducing a low-cost Virtual Reality kit, Google Expeditions, in a language classroom and explore its potential as a student-led tool for supporting language learning and teaching. The use of Google Expeditions was guided by teach-back, a pedagogical methodology which emphasises communication and comprehension through spoken interaction. Data were collected through interviews, focus groups, questionnaires, observations and video-taped sessions. Findings provide information on the patterns followed when pedagogically implementing Google Expeditions under teach-back. What is more, evidence is provided on how collaboration is encouraged and on the positive learning experiences of the students in the Google Expeditions sessions. Implications for researchers and instructional designers are also provided.
A Novel Ant Colony Optimization Strategy for the Quantum Circuit Compilation Problem

Quantum Computing represents the most promising technology towards speed boost in computation, opening the possibility of major breakthroughs in several disciplines including Artificial Intelligence. This paper investigates the performance of a novel Ant Colony Optimization (ACO) algorithm for the realization (compilation) of nearest-neighbor compliant quantum circuits of minimum duration. In fact, current technological limitations (e.g., decoherence effect) impose that the overall duration (makespan) of the quantum circuit realization be minimized, and therefore the production of minimum-makespan compiled circuits for present and future quantum machines is of paramount importance. In our ACO algorithm (QCC-ACO), we introduce a novel pheromone model, and we leverage a heuristic-based Priority Rule to control the iterative selection of the quantum gates to be inserted in the solution. The proposed QCC-ACO algorithm has been tested on a set of quantum circuit benchmark instances of increasing sizes available from the recent literature. We demonstrate that the QCC-ACO obtains results that outperform the current best solutions in the literature against the same benchmark, succeeding in significantly improving the makespan values for a great number of instances and demonstrating the scalability of the approach.
Tamper Detection and Localization for Quranic Text Watermarking Scheme Based on Hybrid Technique

The text of the Quran is principally dependent on the Arabic language. Therefore, improving the security and reliability of the Quran’s text when it is exchanged via internet networks has become one of the most difficult ch... | Find, read and cite all the research you need on Tech Science Press
Stereo Matching Method Based on Space-Aware Network Model

The stereo matching method based on a space-aware network is proposed, which divides the network into three sections: Basic layer, scale layer, and decision layer. This division is beneficial to integrate residue network and ... | Find, read and cite all the research you need on Tech Science Press
Optimal algorithms for differentially private stochastic monotone variational inequalities and saddle-point problems

Abstract In this work, we conduct the first systematic study of stochastic variational inequality (SVI) and stochastic saddle point (SSP) problems under the constraint of differential privacy (DP). We propose two algorithms: Noisy Stochastic Extragradient (NSEG) and Noisy Inexact Stochastic Proximal Point (NISPP). We show that a stochastic approximation variant of these algorithms attains risk bounds vanishing as a function of the dataset size, with respect to the strong gap function; and a sampling with replacement variant achieves optimal risk bounds with respect to a weak gap function. We also show lower bounds of the same order on weak gap function. Hence, our algorithms are optimal. Key to our analysis is the investigation of algorithmic stability bounds, both of which are new even in the nonprivate case. The dependence of the running time of the sampling with replacement algorithms, with respect to the dataset size n , is $$n^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> for NSEG and $${\widetilde{O}}(n^{3/2})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>O</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mrow> <mml:mn>3</mml:mn> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> for NISPP.
Design of subsidized fertilizer prediction information system with safety stock methodology

Abstract The purpose of this research is to design a subsidized fertilizer information system equipped with a prediction system with a safety stock method. This study uses the waterfall methodology with stages of Analysis, Design, Coding, and Testing, as well as the safety stock method for prediction. By testing using black-box testing with system modeling using the Unified Modelling Language (UML) and Business Process Model and Notation (BPMN). The results of this research are a prototype of a fertilizer information system for data collection activities, fertilizer expenditure from the warehouse, and fertilizer orders based on predictions. This research takes a case study at PT. Anugerah Ihsan Makmur, which is a company engaged in the distribution of subsidized fertilizers in the Garut region. This information system can help record the distribution of fertilizer, determine the amount of fertilizer procurement that must be ordered to suppliers to meet inventory needs so that there are no shortages, and can use as a management report on the distribution of subsidized fertilizers to the government.
Detecting Xenophobic Hate Speech in Spanish Tweets Against Venezuelan Immigrants in Ecuador Using Natural Language Processing

In recent reports, Ecuador and Venezuela are located as the countries with the worst social indicators, showing ethnic and racial discrimination between both countries, one possible cause is a large number of Venezuelan immigrants in Ecuador. The present work has the goal of determining the existence of xenophobic content from a set of tweets collected around Venezuelan immigrants in Ecuador, using the diverse phases of the Knowledge Discovery in Text (KDT) methodology. Identifying xenophobia by mean of Natural Language Processing (NLP) is not an easy task; nonetheless, with the use of techniques as Synthetic Minority Oversampling (SMOTE) and Crowdsourcing it is possible to make it. The feelings classification: xenophobic, offensive and other are possible thanks to executing of three supervised classification algorithms: Logistic Regression, Support Vector Machines (SVM) and Naive Bayes. As a result of the execution of the three algorithms, SVM algorithm obtains a better performance with an F1-score of 98%. On the other hand, of the 100% of data analysed, it is determinate that there exist a 5.76% of xenophobic sentiments, 31.23% of offensive emotions and 63% contains other feelings.
Hybrid International Data Encryption Algorithm for Digital Image Encryption

Image security isBongale, Anupkumar M. one of the prominent research areas in the field of information security.Bhamidipati, Kishore In this paper,Bongale, Arunkumar M. a new image encryption method called hybrid international data encryption algorithm (HIDEA) is proposed. In the proposed methodology,Kumar, Satish image encryption takes place in different phases. In the first phase, the input image is transformed into a scrambled image by using Arnold transformation. Secondly, the scrambled image is encrypted using 128-bit key based on international data encryption algorithm (IDEA) encryption algorithm. Same process is applied in the reverse order to obtain the original image from the cipher image. The encryption algorithm has been thoroughly validated based on key sensitivity analysis and additive noise attack analysis and found that the encryption methodology is stable and robust.
Spherical K-Means method to determine earthquake clusters

Abstract Spherical clustering is a grouping technique for spherical data. A vector data set is grouped into clusters where the distance used to group the vectors is the angle between the vectors. Earthquake events are directional data that can be analyzed in three-dimensional (spherical) form because the epicenter of the earthquake spreads is very long, and the time difference between events is different. Therefore, this study aims to determine earthquake clusters in Bengkulu Province and the surrounding areas. The data used is data on earthquake events in Bengkulu Province and surrounding areas from 1970 to 2019, including latitude, longitude, magnitude, and depth, sourced from the USGS website. The analysis used in determining the earthquake clusters in Bengkulu Province is the Spherical K-means cluster analysis. The results showed that there were six earthquake clusters in Bengkulu Province and its surroundings. Most of the clustering results are in the sea.
Co-similar malware infection patterns as a predictor of future risk

The internet is flooded with malicious content that can come in various forms and lead to information theft and monetary losses. From the ISP to the browser itself, many security systems act to defend the user from such content. However, most systems have at least one of three major limitations: 1) they are not personalized and do not account for the differences between users, 2) their defense mechanism is reactive and unable to predict upcoming attacks, and 3) they extensively track and use the user's activity, thereby invading her privacy in the process. We developed a methodological framework to predict future exposure to malicious content. Our framework accounts for three factors-the user's previous exposure history, her co-similarity to other users based on their previous exposures in a conceptual network, and how the network evolves. Utilizing over 20,000 users' browsing data, our approach succeeds in achieving accurate results on the infection-prone portion of the population, surpassing common methods, and doing so with as little as 1/1000 of the personal information it requires.
SANS: Setwise Attentional Neural Similarity Method for Few-Shot Recommendation

Recommender systems generate personalized recommendations for users based on their historical data. However, if some users have few interactions in the training data, i.e., few-shot users, recommendations for them will be inaccurate. In this paper, we propose a setwise attentional neural similarity method (SANS) for the few-shot recommendation problem. Unlike general recommendation algorithms, we eliminate direct representations of few-shot users. First, a neural similarity method is proposed to effectively estimate the correlation between items. Then, we propose a setwise attention mechanism to obtain recommendation scores by aggregating the correlations between a candidate item and items in a candidate user’s historical interactions. To facilitate model training in the few-shot scenario, training samples are generated by episode sampling, and each training sample is assigned with an adaptive weight to emphasize the importance of few-shot users. We simulate the few-shot recommendation problem on three real-world datasets and extensive results show that SANS can outperform the state-of-the-art recommendation algorithms in few-shot recommendation.
Image Background Subtraction and Partial Stylization Based on Style Representation of Convolutional Neural Networks

Image background subtraction refers to the technique which eliminates the background in an image or video to extract the object (foreground) for further processing like object recognition in surveillance applications or object editing in film production, etc. The problem of image background subtraction becomes difficult if the background is cluttered, and therefore, it is still an open problem in computer vision. For a long time, conventional background extraction models have used only image-level information to model the background. However, recently, deep learning based methods have been successful in extracting styles from the image. Based on the use of deep neural styles, we propose an image background subtraction technique based on a style comparison. Furthermore, we show that the style comparison can be done pixel-wise, and also show that this can be applied to perform a spatially adaptive style transfer. As an example, we show that an automatic background elimination and background style transform can be achieved by the pixel-wise style comparison with minimal human interactions, i.e., by selecting only a small patch from the target and the style images.
A Parallel Algorithm for Computing the Weight Spectrum of Binary Linear Codes

We present a parallel SIMD (Single Instruction Multiple Data) algorithm for computing the weight spectrum of binary linear [n,k] codes using GPU (Graphics Processing Units) and CUDA (Compute Unified Device Architecture) programming platform. This algorithm is based on characteristic vector and Walsh-Hadamard transform and does not depend on the length of the code. We compare it with standard consecutive algorithms and give some experimental results.
A Research to Design, Develop and Implementation of Android Application System for Waste Bank Sharia Community at Kampung Hijau Kemuning

Abstract The problem of garbage is a behavioral problem, therefore the need for a change in public behavior to discipline throwing garbage in place, and the problem of garbage becomes one of the problems in major cities and other metropolitan cities, including in Tangerang area. Household waste is one type of garbage that can cause increasing problems facing the government in dealing with it. Kampung Hijau Kemuning is one of the residential settlements in Binong Village, Curug Sub-District that manages sharia-based waste banks aimed at encouraging environmental rescue to be green, clean, reduce waste and become an additional source of income for the residents of Kampung Hijau Kemuning. In the process of waste bank management requires the development of either the use of computer applications based on android or in the process of financial management that is clean and free from usury in accordance with islamic (sharia) rules. The purpose of this research is to build an Android-based Sharia Waste Bank application with a user-friendly view using the Waterfall method. The waterfall model provides a sequential or sequenced approach to software life flow. The results of this research will help the process of managing innovative waste bank activities and become input to increase the empowerment of micro-business communities in an activity, especially in Tangerang area.
Image Aesthetic Assessment: A Deep Learning Approach Using Class Activation Map

Aesthetics is concerned with the beauty and art of things in the world. Judging the aesthetics of images is a highly subjective task. Recently, deep learning-based approaches have achieved great success in image aesthetic assessment problem. In this paper, we have implemented various multi-channel Convolution Neural Network (CNN) architectures to classify images in high and low aesthetic quality. Class activation maps of images are used as input to one channel along with variation of raw images in the proposed two-channel deep network architecture. Various pre-trained deep learning models such as VGG19, InceptionV3, Resnet50 have been implemented in the proposed multi-channel CNN architecture. Experiments are reported on the AVA dataset, which shows improvement in the image aesthetic assessment task over existing approaches.
WITHDRAWN: Retrieval of software components using NLP based IR model

Software engineering is one of the traditional domains that integrates and updates its techniques with all the novel methodologies time to time. There are different process models to develop software and CBD (Component Based Development) is one among them. CBD is an agile model that uses a component to develop the software instead of doing it from scratch. The component used in the proposed work is a piece of code.This paper explains a component based software reuse based on the NLP (Natural language processing) based IR (Information retrieval) technique.