Reviewerly/exHarmony huggingface dataset:
Rapid prototyping of XR experiences

course Share on Rapid prototyping of XR experiences Authors: Mark Billinghurst University of South Australia and University of Auckland University of South Australia and University of AucklandView Profile , Michael Nebeling University of Michigan University of MichiganView Profile Authors Info & Claims SIGGRAPH '22: ACM SIGGRAPH 2022 CoursesAugust 2022 Article No.: 13Pages 1–124https://doi.org/10.1145/3532720.3535684Published:02 August 2022Publication History 0citation246DownloadsMetricsTotal Citations0Total Downloads246Last 12 Months246Last 6 weeks65 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Korea-China Bilateral Relations using the GDELT big data: 1992-2021

For the past 30 years since the establishment of diplomatic relations between Korea and China, various relationship has been made in different fields, such as economic cooperation, cultural/academic exchanges, and military affairs have varied according to the situation of the times. One option to analyse this is to use data objectively with the analysis being based on computer-recognized information&#x0D; In order to better understand the relationship between Korea and China objectively through data can be guaranteed if human intervention or interference is prevented and analyzed based on information recognized by computers, and the results of this analysis can be useful in many fields.&#x0D; This study analyzed big data-based time series event data (GDELT) and analyzed the conflict-cooperation relationship between Korea and China for the past 30 years after the establishment of diplomatic ties. Based on the Goldstein value of the conflict-cooperation spectrum, the annual average conflict-cooperation degree was measured. In addition, the tone of the news article was analyzed and schematized based on the emotional index with the negative-positive spectrum.&#x0D; This study tracked the process of changes in the conflict-cooperation index and emotional index between the two countries from 1992 to 2021, analyzed the change in relations between the two countries and the psychological distance between the two countries, and verified that GDELT is useful. Simultaneously this study was written to contribute to finding a more effective approach in determining future policies toward China using GDELT based on the validity and reliability of the analysis.
IMPROVING ENGLISH SPEAKING SKILLS THROUGH SMALL GROUP DISCUSSION

This research is aimed at inquiring the use of small group discussions in the improvement of speaking skills studied in classroom action research (CAR). The population of the research was the eighth-grade students of SMPN 2 Curug consist of thirty students. The researchers used one class as a control group. Based the result of this study, the researchers implemented the action in two cycles; every cycle consists of planning, acting, observing and reflecting. The first is planning, the researchers identified a problem. The second is acting, the researchers collected and analysed the data. The third is observing, in this stage the researchers developed a plan to make change and implemented these changes. This cycle reflects the components of action research. It is critical to consider the impact of the changes made when reflecting. The result of this research is the mean score of students on the speaking test before carrying out the Class Action Research (CAR) is 57,5 for pretest and post-test 64,7 in the first cycle, this is the student’s treatment. And in the second cycle 70,6 after using small group discussion. It means that the mean score of the post test is greater than the pre-test. Thus, this research that using small group discussion can improve students’ speaking ability for the eighth-year students of SMPN 2 Curug.
Teaching Procedure Text By Using Youtube As A Media In English Language Teaching: EFL Students' Perspectives

This study aims to determine students' perceptions of the use of Youtube application for learning procedure text. The method used was qualitative using a descriptive approach. The population in this study were students of SMPN 06 Lebong and the sample used was purposive sampling for class VII students of SMPN 06 Lebong. The data collection technique in this study was used a questionnaire given to students using a Google form given in the third week after learning the text procedure was completed by using Youtube video as a media during learning, as well as interviews with several selected students which were carried out using Whatsapp to find out their perceptions of the use of Youtube media in learning procedure text. The data is explained in detail according to the statement given by the respondent. The results showed that the use of the Youtube application as a medium for learning procedure texts could make it easier for students to understand the material related to procedure texts, besides that students found it easier to access information related to learning materials so that they were able to explain and make procedural texts properly and correctly. Thus, the use of the Youtube application in the procedural text learning process can help students understand the procedural text material well and can have a positive impact on English learning activities.
RCS Distribution Fitting using Improved Nonparametric Model

An accurately established model of radar cross section (RCS) distribution is significant to target detection. Aimed at the problems that typical fluctuation models have inadequate accuracy in characterizing the RCS distribution to real targets, and the nonparametric model in forms of Legendre polynomials performs well but is in difficulty of giving the optimal order, an improved nonparametric model is proposed and verified in this work. Firstly, some deficiencies to determination of polynomial order in the nonparametric model are pointed out. And then, a dichotomous finite comparison method is presented to quickly search the optimal model order. Finally, some RCS measurements are analyzed while the fitting errors and computational complexity of several models to RCS distribution fitting are compared, together with the Kolmogorov-Smirnov test for goodness of fit. Data results show that the improved nonparametric model has good fitting effect and moderate computational complexity for RCS distribution.
Lecturer Strategy in Assignment Management as the Implementation of Academic Service Quality in the Covid-19 Period

The formation of interaction, communication, and collaboration between lecturers and students, and or students in assignment management during the Covid-19 period requires lecturers to change their pedagogy quckly through choosing the right strategy. This study aims to present information as a measure of the success of lecturers’ strategies in managing assignments to write books with ISBN as an implementation of the quality of academic services during the Covid-19 period. This cross-sectional descriptive survey research method was conducted on 128 fifth semester students of A-D class, Elementary School Teacher Education Study Program, Muhammadiyah University of Tangerang, Academic Year 2020/2021, with 52 web-based questionnaires distributed online using the google form application, measured using 4 response point Likert data format without middle category, using 4 answer options: Strongly Agree, Agree, Disagree, and Strongly Disagree. The management of the assignments to write books with ISBN is very appropriate to do as a lecturer strategy to maximize the quality of academic services in curriculum development courses during the Covid-19 period.
Guidelines for Subject Analysis in Subject Cataloging

The representation of information in subject cataloging as a result of subject analysis will depend on the cataloger’s prior knowledge, influenced by subjectivity. The subject analysis in cataloging is a central theme of this investigation with the aim to elaborate guidelines for subject analysis in cataloging. For this purpose, how books are cataloged in university libraries has been verified. The Individual Verbal Protocol was applied with catalogers from Brazilian and Spanish University Libraries. Directions for the elements and variables of the subject analysis and procedures for good development were obtained to constitute the Guidelines of Subject Analysis in Cataloging. It is concluded that the guidelines formed by four sections are indicated for incorporation in subject cataloging procedure manuals for the purpose of improving the levels of representation and information retrieval results.
Three-photon Fock-state Master Equations for Multi-emitter Chiral Waveguide Quantum Electrodynamics

We report the derivation of a Fock-state master equation apt to describe the time evolution of three-photon entanglement in multi-emitter chiral waveguide quantum electrodynamics with possible applications in unidirectional quantum networking and long-distance quantum communications.
New Explainable Deep CNN Design for Classifying Breast Tumor Response Over Neoadjuvant Chemotherapy

To reduce breast tumor size before surgery, Neoadjuvant Chemotherapy (NAC) is applied systematically to patients with local breast cancer. However, with the existing clinical protocols, it is not yet possible to have an early prediction of the effect of chemotherapy on a breast tumor. Predicting the response to chemotherapy could reduce toxicity and delay effective treatment. Computational analysis of Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE-MRI) through Deep Convolution Neural Network (CNN) has proved a significant performance in classifying responders and no responder's patients. This study intends to present a new explainable Deep Learning (DL) model predicting the breast cancer response to chemotherapy based on multiple MRI inputs.In this study, a cohort of 42 breast cancer patients who underwent chemotherapy was used to train and validate the proposed DL model. This dataset was provided by the Jules Bordet institute of radiology in Brussels, Belgium. 14 external subjects were used to validate the DL model to classify responding or non-responding patients on temporal DCE-MRI volumes. The model performance was assessed by the Area Under the receiver operating characteristic Curve (AUC), accuracy, and features map visualization according to pathological complete response (Ground truth).The developed deep learning architecture was able to predict the responding breast tumors to chemotherapy treatment in the external validation dataset with an AUC of 0.93 using parallel learning MRI images acquired at different moments. The visual results showed that the most important extracted features from non-responding tumors are in the peripheral and external tumor regions. The model proposed in this study is more efficient compared to those proposed in the literature.Even with a limited training dataset size, the developed multi-input CNN model using DCE-MR images acquired before and following the first chemotherapy was able to predict responding and non-responding tumors with higher accuracy. Thanks to the visualization of the extracted characteristics by the DL model on the responding and non-responding tumors, the latter could be used henceforth in clinical analysis after its evaluation based on more extra data.
Analyzing Search Techniques for Autotuning Image-based GPU Kernels: The Impact of Sample Sizes

Modern computing systems are increasingly more complex, with their multicore CPUs and GPUs accelerators changing yearly, if not more often. It thus has become very challenging to write programs that efficiently use the associated complex memory systems and take advantage of the available parallelism. Autotuning addresses this by optimizing parameterized code to the targeted hardware by searching for the optimal set of parameters. Empirical autotuning has therefore gained interest during the past decades. While new autotuning algorithms are regularly presented and published, we will show why comparing these autotuning algorithms is a deceptively difficult task. In this paper, we describe our empirical study of state-of-the-art search techniques for autotuning by comparing them on a range of sample sizes, benchmarks and architectures. We optimize 6 tunable parameters with a search-space size of over 2 million. The algorithms studied include Random Search (RS), Random Forest Regression (RF), Genetic Algorithms (GA), Bayesian Optimization with Gaussian Processes (BO GP) and Bayesian Optimization with Tree-Parzen Estimators (BO TPE). Our results on the ImageCL benchmark suite suggest that the ideal autotuning algorithm heavily depends on the sample size. In our study, BO GP and BO TPE outperform the other algorithms in most scenarios with sample sizes from 25 to 100. However, GA usually outperforms the others for sample sizes 200 and beyond. We generally see the most speedup to be gained over RS in the lower range of sample sizes (25–100). However, the algorithms more consistently outperform RS for higher sample sizes (200–400). Hence, no single state-of-the-art algorithm outperforms the rest for all sample sizes. Some suggestions for future work are also included.
High-Performance GraphBLAS Backend Prototype for NEC SX-Aurora TSUBASA

Developing efficient graph algorithm implementations is a significant important problem of modern computer science since graphs are frequently used in various real-world applications. Graph algorithms typically belong to the data-intensive class, and thus using architectures with high-bandwidth memory potentially allows to solve many graph problems signif-icantly faster than modern multicore CPUs. Among other su-percomputer architectures, SX-Aurora TSUBASA vector engines are equipped with high-bandwidth memory, and thus, potentially allow to accelerate various graph applications. However, very few existing graph processing frameworks can efficiently utilize SX-Aurora hardware capabilities. GraphBLAS standard proposes a convenient way to develop graph algorithms in terms of linear algebra and allows its users not todeeply understand vector architectures hardware features. This paper describes a world-first attempt to implement an optimized prototype of the GraphBLAS backend for SX-Aurora TSUBASA. Our backend prototype can achieve performance, comparable to the existing Vector Graph Library (VGL) based implementations for SX-Aurora TSUBASA, and also outperform the existing GraphBLAS backends for NVIDIA GPUs. We also discuss the roadmap and challenges for creating the full GraphBLAS implementation for SX-Aurora TSUBASA vector engines.
PLSSVM: A (multi-)GPGPU-accelerated Least Squares Support Vector Machine

Machine learning algorithms must be able to effi-ciently cope with massive data sets. Therefore, they have to scale well on any modern system and be able to exploit the computing power of accelerators independent of their vendor. In the field of supervised learning, Support Vector Machines (SVMs) are widely used. However, even modern and optimized implementations such as LIBSVM or ThunderSVM do not scale well for large non-trivial dense data sets on cutting-edge hardware: Most SVM implementations are based on Sequential Minimal Optimization, an optimized though inherent sequential algorithm. Hence, they are not well-suited for highly parallel GPUs. Furthermore, we are not aware of a performance portable implementation that supports CPUs and GPUs from different vendors. We have developed the PLSSVM library to solve both issues. First, we resort to the formulation of the SVM as a least squares problem. Training an SVM then boils down to solving a system of linear equations for which highly parallel algorithms are known. Second, we provide a hardware independent yet efficient implementation: PLSSVM uses different interchange-able backends—OpenMP, CUDA, OpenCL, SYCL—supporting modern hardware from various vendors like NVIDIA, AMD, or Intel on multiple GPUs. PLSSVM can be used as a drop-in replacement for LIBSVM. We observe a speedup on CPUs of up to 10 compared to LIBSVM and on GPUs of up to 14 compared to ThunderSVM. Our implementation scales on many-core CPUs with a parallel speedup of 74.7 on up to 256 CPU threads and on multiple GPUs with a parallel speedup of 3.71 on four GPUs. The code, utility scripts, and documentation are all available on GitHub: https://github.com/SC-SGS/PLSSVM.
RRT-based Path Planning for Car-Like Vehicles with Nonholonomic Constraints

In this paper, for car-like mobile robots, a novel path planning algorithm is proposed. The algorithm is based on rapidly-exploring random trees (RRT) with fixed nodes (RRT*FN). An improvement on the RRT*FN is proposed to abide by the non-holonomic motion constraints and RRT*FN non-holonomic (RRT*FN-NH) is introduced. The new path planning algorithm handles the non-holonomic constraints as well as the constraints on the velocity and acceleration of the vehicle. The performance of the proposed algorithm RRT*FN-NH is tested on two maps obtained from Google maps at three different speed profiles.
Low-Complexity Compression with Random Access

We investigate the problem of variable-length compression with random access for stationary and ergodic sources, wherein short substrings of the raw file can be extracted from the compressed file without decompressing the entire file. It is possible to design compressors for sequences of length n that achieve compression rates close to the entropy rate of the source, and still be able to extract individual source symbols in time $\theta(1)$ under the word-RAM model. In this article, we analyze a simple well-known approach used for compression with random access. We theoretically show that this is suboptimal, and design two simple compressors that simultaneously achieve entropy rate and constant-time random access. We then propose dictionary compression as a means to further improve performance, and experimentally validate this on various datasets.
Evaluation on the Effects of 2D Animation as a Verbal Apraxia Therapy for Children with Verbal Apraxia of Speech

This article presents an evaluation of 2D Animation of Learning Numbers and Letters for Children withVerbal Apraxia.The developed application provides some knowledge and encourage children with verbal apraxia to learn and know about numbers and letters.An experimental testing was conducted to evaluate the usability of the developed application, aimed as a therapy for the children who suffer this apraxia across all age.Five important evaluation components such as learnability, usability, accessibility, functionality, and effectiveness were included in this testing to investigate the user engagement and satisfaction of the proposed medical and educational learning system.Online questionnaires were distributed as a method to collect user testing outputs.A total of 33 respondents from multimedia designers, practitioners, psychologists, and parents were involved in this survey.The results of the testing indicate that majority of respondents are satisfied with the outcomes of the 2D animation video.The results presented may facilitate improvements in the teaching syllabus for students with speech and language disorder and produce a great visual animation treatment to the users.
TEC Forecasting using Optimized Variational Mode Decomposition and Elman Neural Networks

Forecasting the ionosphere layer's total electronic content (TEC) is crucial for its impact on satellite signals and global positioning systems (GPS) and the ability to predict earthquakes.The existing statistical-based forecasting models such as ARMA, ARIMA, and HW suffered from the TEC nonstationarity nature, which requires algorithmic handling of the forecasting and the mathematical part.This study proposes a hybrid method that incorporates several components and is designated as Optimized Variational Mode Decomposition with Recursive Neural Network Forecasting (OVMD-RNN) to forecast TEC.Before using the Elman Network to train each component, Variational Mode Decomposition (VMD) was used to decompose the signal into its essential stationary components.In addition, the proposed method includes an optimization algorithm for determining the best VMD decomposer parameters.The GPS Ionospheric Scintillation and TEC Monitor (GISTM) at Universiti Kebangsaan Malaysia station have been used to evaluate the method based on collected datasets for three years, 2011, 2012, and 2013.The experiment findings show that the model has successfully tracked all the up and down patterns in the time series.The results also reveal that VMD-based training might not always provide good results due to the residual signal.Finally, the evaluation focused on generating loss value and comparing it to the ARIMA benchmark.It showed that OVMD-RNN had accomplished a maximum improvement percentage of ARIMA with a value of (99%).
Data scaling performance on various machine learning algorithms to identify abalone sex

This study aims to analyze the performance of machine learning algorithms with the data scaling process to show the method's effectiveness. It uses min-max (normalization) and zero-mean (standardization) data scaling techniques in the abalone dataset. The stages carried out in this study included data normalization on the data of abalone physical measurement features. The model evaluation was carried out using k-fold cross-validation with the number of k-fold 10. Abalone datasets were normalized in machine learning algorithms: Random Forest, Naïve Bayesian, Decision Tree, and SVM (RBF kernels and linear kernels). The eight features of the abalone dataset show that machine learning algorithms did not too influence data scaling. There is an increase in the performance of SVM, while Random Forest decreases when the abalone dataset is applied to data scaling. Random Forest has the highest average balanced accuracy (74.87%) without data scaling.
NewsImages

We present NewsImages, a dataset of online news items, and the related NewsImages rematching task. The goal of NewsImages is to provide researchers with a means of studying the depiction gap, which we define to be the difference between what an image literally depicts and the way in which it is connected to the text that it accompanies. Online news is a domain in which the image-text connection is known to be indirect: The news article does not describe what is literally depicted in the image. We validate NewsImages with experiments that show the dataset's and the task's use for studying occurring connections between image and text, as well as addressing the depiction gap, which include sparse data, diversity of content, and importance of background knowledge.
THE USE OF DEVICES IN E-LEARNING AS A SOURCE OF LITERATURE AT SMKN 1 BAKUNG

The Covid-19 epidemic, which started in Wuhan, China in 2019, has spread to a number of countries, including Indonesia. Learning from Home is another term for online learning (BDR). To show support for government policies and to assist students in meeting their educational needs, SMKN 1 Bakung adopts an online learning strategy using e-learning. The aim of this article is to show many types of literature and the use of devices in e-learning as a source of literature. Qualitative research is used for this research. After analyzing the data, it could be concluded that in e-learning SMKN 1 Bakung, there are some types of works of literature. They are separated based on their characteristics and locations. Due to the characteristic, they are textual, non-textual, and mixed documents. While based on the location, they are general and references collections. The literature is mostly in form of pdf to make them easily accessed by students. The use of devices in e-learning is as follows: accessing courses, accessing exams/tests as well as accessing teleconference.