Reviewerly/exHarmony huggingface dataset:
Creative learning in VR: An antidisciplinary approach

Joichi Ito, MIT Media Lab Director, suggests that the way ahead in education is to support endeavors where learning processes and peer collaborations are valued above end products such as exam scores. He terms this antidisciplinary [1]. To engage students in an antidisciplinary construction of their learning environments, a 3D virtual Fukushima Dai-ichi nuclear power plant scenario is designed for familiarity with operator challenges, nuclear plant risks, and basic nuclear power content [2]. Donning the Oculus Rift HMD, students are immersed in the Fukushima nuclear power plant and tasked with retrieving 5 radioactive bins randomly positioned throughout the plant. Due to the radiation levels, students must maneuver a robot throughout the plant. To locate the bins, the students can maneuver a drone over the plant. While retrieving the bins the student also collects virtual `Information cards' for later questioning. In addition, the student must locate the entrance (a teleport) to Reactor 2 and turn on the cooling water pump. The activity is timed so that on 8 minutes a tsumani alarm is sounded and water rises after 10 minutes. To learn about the accident, the student enters a Control Room and undertakes a `cause-and-effect' quiz utilizing the collected Information cards.
A new approach to robust fundamental matrix estimation using an analytic objective function and adjusted gradient projection

In this paper we propose a new approach to tackling the challenging problem of robust fundamental matrix estimation from corrupted correspondences. Compared with traditional robust methods, the proposed approach achieves enhanced estimation accuracy and stability. These achievements are attributed mainly to two novelties contributed by the new approach. Firstly, a new, more easily-solvable analytic objective function is proposed to well consider both the presence of correspondence outliers and the computational convenience. Secondly, an adjusted gradient projection method is developed to provide a more stable solver for robust estimation. Experimental results show that the proposed approach performs better than traditional robust methods RANSAC, MSAC, LMEDS and MLESAC, in particular when correspondences were seriously corrupted.
TEKNIK MENULIS YANG EFISIEN DAN EFEKTIF DI MEDIA ONLINE

Writing news in online media are still many that do not meet the requirements. News is written too long and follows the writing style in print media. Writing news in online media must be efficient and effective to be easily scanned by readers. Efficient and effective is different from print and television media. Online media space is very limited compared to the two media. If the news is made too long and long-winded, the reader will quickly saturate and make them reluctant to read it. In fact, the language of news in online media must be pleasing, readable and easy to understand. This is what underlies the writing training in online media for Inews TV employees in December 2018. The training is conducted by providing material on how to write good news in online media. After that, the news will be analyzed in the Inews TV online media, whether it has fulfilled the provisions. The results of the analysis are monitored together, is there still a lack of writing in the next edition. This training is expected to make employees more adept at writing news in online media. After several times the training began to show better writing changes Keywords: Online media, efficient, effective
GraphSW: a training protocol based on stage-wise training for GNN-based Recommender Model.

Recently, researchers utilize Knowledge Graph (KG) as side information in recommendation system to address cold start and sparsity issue and improve the recommendation performance. Existing KG-aware recommendation model use the feature of neighboring entities and structural information to update the embedding of currently located entity. Although the fruitful information is beneficial to the following task, the cost of exploring the entire graph is massive and impractical. In order to reduce the computational cost and maintain the pattern of extracting features, KG-aware recommendation model usually utilize fixed-size and random set of neighbors rather than complete information in KG. Nonetheless, there are two critical issues in these approaches: First of all, fixed-size and randomly selected neighbors restrict the view of graph. In addition, as the order of graph feature increases, the growth of parameter dimensionality of the model may lead the training process hard to converge. To solve the aforementioned limitations, we propose GraphSW, a strategy based on stage-wise training framework which would only access to a subset of the entities in KG in every stage. During the following stages, the learned embedding from previous stages is provided to the network in the next stage and the model can learn the information gradually from the KG. We apply stage-wise training on two SOTA recommendation models, RippleNet and Knowledge Graph Convolutional Networks (KGCN). Moreover, we evaluate the performance on six real world datasets, Last.FM 2011, Book-Crossing,movie, LFM-1b 2015, Amazon-book and Yelp 2018. The result of our experiments shows that proposed strategy can help both models to collect more information from the KG and improve the performance. Furthermore, it is observed that GraphSW can assist KGCN to converge effectively in high-order graph feature.
Optimization of Software Estimation Models

In software engineering, estimations are frequently used to determine expected but yet unknown properties of software development processes or the developed systems, such as costs, time, number of developers, efforts, sizes, and complexities. Plenty of estimation models exist, but it is hard to compare and improve them as software technologies evolve quickly. We suggest an approach to estimation model design and automated optimization allowing for model comparison and improvement based on commonly collected data points. This way, the approach simplifies model optimization and selection. It contributes to a convergence of existing estimation models to meet contemporary software technology practices and provide a possibility for selecting the most appropriate ones.
PENERAPAN SISTEM INFORMASI AKUNTANSI PENERIMAAN DAN PENGELUARAN KAS PADA PT. KRESNA REKSA FINANCE CABANG SAMARINDA

The Acceptance Accounting Information System and the Samarinda Branch Cash flow, under the guidance of Mrs. Titin Ruliana and Mrs. Rina Masithoh Haryadi. The purpose of this research is to design information systems for acceptance and flow at PT. Kresna Reksa Finance Samarinda Branch. In accordance with the purpose of this study, the main points raised are: (1) To know and release the application of the cash accounting information system at PT. Kresan Reksa Finance, (2) To find out and analyze the application of information systems at PT. Kresan Reksa Finance . The analytical tool used in this study is comparing measurements and analysis with internal theories applied by PT. Kresna Reksa Finance. After going through the stages of research, analysis and discussion of internal problems that PT. Kresna Reksa Finance is quite in line with the numbers - internal internships, especially for 68% results which means it's good enough.
Outlier Robust Extreme Learning Machine for Multi-Target Regression

The popularity of algorithms based on Extreme Learning Machine (ELM), which can be used to train Single Layer Feedforward Neural Networks (SLFN), has increased in the past years. They have been successfully applied to a wide range of classification and regression tasks. The most commonly used methods are the ones based on minimizing the $\ell_2$ norm of the error, which is not suitable to deal with outliers, essentially in regression tasks. The use of $\ell_1$ norm was proposed in Outlier Robust ELM (OR-ELM), which is defined to one-dimensional outputs. In this paper, we generalize OR-ELM to deal with multi-target regression problems, using the error $\ell_{2,1}$ norm and the Elastic Net theory, which can result in a more sparse network, resulting in our method, Generalized Outlier Robust ELM (GOR-ELM). We use Alternating Direction Method of Multipliers (ADMM) to solve the resulting optimization problem. An incremental version of GOR-ELM is also proposed. We chose 15 public real-world multi-target regression datasets to test our methods. Our conducted experiments show that they are statistically better than other ELM-based techniques, when considering data contaminated with outliers, and equivalent to them, otherwise.
Companions, Causality and Codensity

In the context of abstract coinduction in complete lattices, the notion ofcompatible function makes it possible to introduce enhancements of thecoinduction proof principle. The largest compatible function, called thecompanion, subsumes most enhancements and has been proved to enjoy many goodproperties. Here we move to universal coalgebra, where the corresponding notionis that of a final distributive law. We show that when it exists, the finaldistributive law is a monad, and that it coincides with the codensity monad ofthe final sequence of the given functor. On sets, we moreover characterise thiscodensity monad using a new abstract notion of causality. In particular, werecover the fact that on streams, the functions definable by a distributive lawor GSOS specification are precisely the causal functions. Going back toenhancements of the coinductive proof principle, we finally obtain that anycausal function gives rise to a valid up-to-context technique.
IMPLEMENTASI ALGORITMA SAX DAN RANDOM PROJECTION UNTUK TIME SERIES MOTIF DISCOVERY PADA BIG DATA PLATFORM STUDI KASUS: RESONANSI ELEMEN ORBITAL ASTEROID

Fenomena Big Data telah terjadi pada banyak bidang pengetahuan, salah satunya adalah pada bidang astronomi. Data yang memiliki jumlah yang sangat banyak pada bidang astronomi salah satunya adalah data resonansi elemen orbital asteroid. Data tersebut dapat diolah sehingga para ilmuwan dapat mencari mean motion resonance pada suatu partikel asteroid untuk mengetahui pada tahun keberapa asteroid akan beresonansi dengan planet planet tertentu. Untuk itu, penelitian ini membuat sebuah model komputasi untuk mendapatkan mean motion resonance secara cepat dan efektif dengan memodifikasi dan mengimplementasikan algoritma SAX dan algoritma motif discovery random projection pada Big Data Platform yaitu Apache Hadoop dan Apache Spark. Hasil penelitian ini menunjukkan adanya percepatan yang sangat signifikan antara penggunaan stand alone dan penggunaan Big Data platform dengan perancangan 2 skenario. Skenario pertama yaitu penggunaan cluster dengan 4 cores dan beberapa worker node dan skenario kedua yaitu penggunaan cluster dengan 2 worker node dan beberapa jumlah core. Penelitian ini juga membuktikan bahwa model komputasi yang dibangun dibandingkan dengan kelauran software SwiftVis mendapatkan rata-rata akurasi sebesar 83%. The Big Data phenomenon has occurred in many fields of knowledge, one of them is in the field of astronomy. One of data that has a very large amount in the astronomy field is the resonance data of asteroid orbital elements. The data can be processed so that scientists can find the mean motion resonance in an asteroid particle to find out in what year the asteroid will resonate with a particular planet. But now, it cannot be denied that to process the data with a large amount of data will take a lot of time. For this reason, this research makes a computational model to get mean motion resonance quickly and effectively by modifying and implementing the SAX algorithm and motif discovery random projection algorithm on the Big Data platform, using Apache Hadoop and Apache Spark. The results of this study indicate a very significant acceleration between standalone use and the use of Big Data platforms by designing 2 scenarios. The first scenario is the use of clusters with 4 cores and several worker nodes and the second scenario is the use of clusters with 2 worker nodes and a number of cores. This study also proves that the computational model compared to the result from SwiftVis software gets an average accuracy of 83%.
Running with Scrum

The first course in software engineering is the most critical. Education must start from an understanding of the heart of software development, from familiar ground that is common to all software development endeavors. This book is an in-depth introduction to software engineering that uses a systematic, universal kernel to teach the essential elements of all software engineering methods. This kernel, Essence, is a vocabulary for defining methods and practices. Essence was envisioned and originally created by Ivar Jacobson and his colleagues, developed by Software Engineering Method and Theory (SEMAT) and approved by The Object Management Group (OMG) as a standard in 2014. Essence is a practiceindependent framework for thinking and reasoning about the practices we have and the practices we need. Essence establishes a shared and standard understanding of what is at the heart of software development. Essence is agnostic to any particular method, lifecycle independent, programming language independent, concise, scalable, extensible, and formally specified. Essence frees the practices from their method prisons. The first part of the book describes Essence, the essential elements to work with, the essential things to do and the essential competencies you need when developing software. The other three parts describe more and more advanced use cases of Essence. Using real but manageable examples, it covers the fundamentals of Essence and the innovative use of serious games to support software engineering. It also explains how current practices such as user stories, use cases, Scrum, and microservices can be described using Essence, and illustrates how their activities can be represented using the Essence notions of cards and checklists. The fourth part of the book offers a vision how Essence can be scaled to support large, complex systems engineering. Essence is supported by an ecosystem developed and maintained by a community of experienced people worldwide. From this ecosystem, professors and students can select what they need and create their own way of working, thus learning how to create ONE way of working that matches the particular situation and needs.
Software Applications Used For The Educational Activity Management Of Students

Dedicated software solutions have also made a difference in education. Efficient administration, structural assistance, quick sign-up procedures, accreditation management and other long-term transactions have seen a substantial improvement in their operation. Consistency, productivity, efficiency have structured various cumbersome procedures, making everything more competent. Key points showing how school management software plays an important role in school development are: Communication; Managing finance without error; Organization of school programs. This paper presents a software solution dedicated to teachers in pre-university education. The application is useful because it provides teachers with a managerial role, teacher, school director, the possibility of improved educational management, much faster, with fewer errors. The application is a catalogue database, made in a very commonly used Microsoft Office Access programming environment. The school management software program in a class provides access to the teachers of each discipline. They can complete, each for his discipline, notes and absences. The master teacher can retrieve reports and perform statistics. By connecting several such programs, the manager of the school may have a general overview of the whole school activity of both students and teachers. Communication with parents or tutors of students is implemented at the level of the class master or school manger. Thus, parents receive reports on childrens activity by email and SMS notifications.
LibGuides: Databases: Interlibrary Loan

What is a library database? There is a wealth of information available to you from library databases accessible from SAPL.
An Innovative Lossless Image and Video Compression Using Revised S Transformation

In this age of digital era as the consumption of videos and images increasing exponentially, image compression is widely used approach to utilize bandwidth of transmission and storage space efficiently. Even though lossless image compression techniques already exist, the challenge is to reduce computational complexity along with preserving quality of image. The proposed solution uses pyramidal approach to compress images. A revised S-transform is used to form pyramid structure. Set of pixels in the upper level of pyramid is compressed using mean and gradient. The same mean and gradient values are used to reconstruct the original image. Using the above techniques lossless compression is achieved with minimal computational complexity. Further same technique can be extended to video compression by converting it into sequence of images. This method can also achieve parallelism, which will result in much faster execution times and throughput.
Intelligent Human–Computer Interaction Design Based on Multi-modality Technology Applied to Shipborne Command and Control System

This paper is aimed to solve the problems of that high load of human–computer interaction and lack of quantitative capability. This paper according to multi-view coordination association and F + C interaction technology and intelligent fusion technology of dynamic adaptation and multi-modality aimed to build the task-oriented multi-modality intelligent fusion interaction technology applied to shipborne command and control system. Then taking the eye movement modality as an example, analysis visualization techniques are based on two phases of training and application, which can make combat data show a higher level of intelligence to build a dynamic adaptation and multi-modality intelligent fusion interaction technology applied to shipborne command and control system.
A pilot study of gaze-gait relations analysis in a VR environment using HMD and LRF

In this paper, we propose a VR-based experimental environment for analyzing the relationship among gaze and body movements using a head-mounted camera (HMD) and laser range finders (LRF). So far, we have analyzed the relations among gaze and whole body movements using a treadmill-based environment. Since the participants walked on treadmills during experiments, walking on a treadmill may be different from natural walking. Thus, in this research, we create a VR-based environment combined with LRF for tracking participants's positions. We first estimate human positions by using LRF, then generate a virtual environment corresponding to estimated human positions. The generated environment with the gaze targets is presented via HMD. We conducted a pilot experiment in this environment and show the experimental results.
Matching vs. Non-Matching Visuals and Shape for Embodied Virtual Healthcare Agents

Embodied virtual agents serving as patient simulators are widely used in medical training scenarios, ranging from physical patients to virtual patients presented via virtual and augmented reality technologies. Physical-virtual patients are a hybrid solution that combines the benefits of dynamic visuals integrated into a human-shaped physical form that can also present other cues, such as pulse, breathing sounds, and temperature. Sometimes in simulation the visuals and shape do not match. We carried out a human-participant study employing graduate nursing students in pediatric patient simulations comprising conditions associated with matching/non-matching of the visuals and shape.
LibGuides: Children Literature Genres: Citations

This guide will help readers, educators, and parents identify common children literature genres so that the reading experience can be diversified by genre or to help readers figure out what genre they enjoy reading.
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation

Inspired by the success of adversarial learning, we propose a new end-to-end unsupervised deep learning framework for monocular depth estimation consisting of two Generative Adversarial Networks (GAN), deeply coupled with a structured Conditional Random Field (CRF) model. The two GANs aim at generating distinct and complementary disparity maps and at improving the generation quality via exploiting the adversarial learning strategy. The deep CRF coupling model is proposed to fuse the generative and discriminative outputs from the dual GAN nets. As such, the model implicitly constructs mutual constraints on the two network branches and between the generator and discriminator. This facilitates the optimization of the whole network for better disparity generation. Extensive experiments on the KITTI, Cityscapes, and Make3D datasets clearly demonstrate the effectiveness of the proposed approach and show superior performance compared to state of the art methods. The code and models are available at this https URL 3dv---coupled-crf-disparity.
The Complexity of The Mathematics Education Within Early Education Conceptual field

Starting from the kindergarten to the university level, among of the education vital roles which must be fulfilled, such as the development of constructively and critically thinking students, the empowerment of citizenship towards the creation of a better society, generation of awareness of issues of equality and justice, this has to promote the vital fact that in a society there are “responsibilities” as well as “rights”. Not in the last, a better society is based on the technological progress which are accomplished through the sciences and mathematics knowledge. In this framework, the education brings its major role for the society within the school environment by formatting the students for that they will become the future engineers, teachers, doctors, economics, etc. for a future society. The goal of our paper is to analyse the professional development of the teachers for the kindergarten (educators) and primary school (teachers) in the context of the current reforms in science education from the perspective of teachers’ mathematics knowledge. At least in our country, in the international framework of the reform efforts for the STEM goals have often been unsuccessful because they failed because those teachers knowledge, beliefs, and attitudes that should to consider. But do academics and university professors have no essential role in building the pre-university education system? Is this an issue that we should submit to an education research? Who has the scientific and honorary responsibilities to answer these questions?
Utility of automated hematology analyzer in diagnosis of malarial parasite

Utility of automated hematology analyzer in diagnosis of malarial parasite - IJPO- Print ISSN No: - 2394-6784 Online ISSN No:- 2394-6792 Article DOI No:- 10.18231/j.ijpo.2019.082, Indian Journal of Pathology and Oncology-Indian J Pathol Oncol