Reviewerly/exHarmony huggingface dataset:
Students' Perceptions on E-Learning Use in Fourth and Sixth Semesters of English Department in UMPO

This research is qualitative descriptive research with the subject are the fourth and sixth semesters of the English Department at the Muhammadiyah University of Ponorogo (UMPO). Semesters four and six each consist of 16 students. So the total number of respondents was 32 students. The data collections were questionnaires, observation, and interviews to find out student's perception of E-learning use, E-learning element, and the impact of students in E-learning use. The results of the study show that according to students' perception E-learning is effectively carried out in lectures because with E-learning, students can access lecture material wherever and whenever. Besides, by using E-learning, the learning resources and learning activities of students are also diverse because E-learning is equipped with attractive features. One of the E-learning platforms used by English Department lecturers at Muhammadiyah University in Ponorogo (UMPO) is Bebas UMPO (MOODLE), Google Classroom, Edmodo, and Whatsapp. There are some standard features from applications/platforms that used by lecturer: Features from Bebas UMPO are  Forums, Quizzes, SCORM packages, Resources, and Assignments ; feature from Google classroom is  Classwork ; part from Edmodo are Chatting, and Quiz and feature from Whatsapp is Group chat . The result of the study is students more active in responding during lecture activities using E-learning. Keywords : Students' perception, E-learning
Modelling Fringe Projection Based on Linear Systems Theory and Geometric Transformation

Fringe projection measurement techniques offer fast, non-contact measurements of the surface form of manufactured parts at relatively low cost. Recent advances in fringe projection have reduced measurement errors from effects such as multiple surface reflections and projector defocus. However, there is no standardised calibration framework for fringe projection systems and an uncertainty estimation of surface measurements is rarely carried out in practice. A calibration framework for estimating spatial frequency-dependent measurement uncertainty built on solid theoretical foundations is required. To move towards traceable surface measurement using fringe projection techniques, we are developing a measurement model to accurately predict the captured image and include all major uncertainty contributors, i.e. a virtual fringe projection system. The first step of the model is to calculate the optical field distribution using the three-dimensional optical transfer function of the projector. Next, a camera image is built up using a ray-tracing model to probe the optical field distribution at the measurement surface boundary. The results are compared to an experimental fringe projection system. The intention is to use this model within a Monte-Carlo framework to move towards estimating the uncertainty at each point-cloud data point.
Fordham University Web Governance Guideline

Fordham University Web Governance Guidelines are intended to set standards for acceptable use of the Fordham University web infrastructure and resources, clarify responsibilities, and encourage accurate, useful, and appropriate presentations of information.
Research Guides: Knowledge Organization and Metadata Services: Home

Outlines the mission and responsibilities of the department and provides access to the tools used to accomplish this work.
A Mobile Design Environment for Building Form Generation

Computer technology has affected architectural studies as well as other professions. Architectural tools are used in every stage of the design and their primary goals are transferring and sharing the ideas of the architects’ mind. Nevertheless, in the early design phase, digital design tools remain ineffective in terms of idea development. Current design software and modeling tools are insufficient for the architect to quickly share ideas and generate alternative suggestions for fast sketching and modeling. In this paper, a mobile design application is developed. It aims to support open-ended design thinking and to be fast and effective in terms of improving ideas. It is based on augmented reality and it works on mobile phones. In order to evaluate the application, a set of images consisting of tall buildings are shown to users. Then they are asked to model a similar form of their own. At the end, results are assessed with a questionnaire. Using the obtained data, the effectiveness of the digital mobile tool in the early design stage is discussed.
The machine training in problems of satellite images’s processing

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.
Performance of Multi-threaded Web Applications using Web Workers in Client-side JavaScript

Context - Software applications on the web are more commonly used nowadays than before. As a result of this, the performance needed to run the applications is increasing. One method to increase per ...
Adversarial Semi-supervised Multi-domain Tracking

Neural networks for multi-domain learning empowers an effective combination of information from different domains by sharing and co-learning the parameters. In visual tracking, the emerging features in shared layers of a multi-domain tracker, trained on various sequences, are crucial for tracking in unseen videos. Yet, in a fully shared architecture, some of the emerging features are useful only in a specific domain, reducing the generalization of the learned feature representation. We propose a semi-supervised learning scheme to separate domain-invariant and domain-specific features using adversarial learning, to encourage mutual exclusion between them, and to leverage self-supervised learning for enhancing the shared features using the unlabeled reservoir. By employing these features and training dedicated layers for each sequence, we build a tracker that performs exceptionally on different types of videos.
Grid-Based Line Following and Obstacle Avoiding Robot

The world is expanding quickly, and it is always in a rush. In this fast-growing world, people always try to save time as much as they can. One of the crucial needs of people is that everyone here wants to reduce stress and get the maximum output. As a result, robot is becoming popular day by day  being capable of performing a variety of tasks and applications. This report is about a robot that can move to a particular target through a specific line of a grid by avoiding collisions. The IR sensor is applied to follow a path and the Sonar sensor is used to avoid any obstacle. The robot can move from its origin to a selected target of the grid, and it can get back to the source by the same path it traveled. In this paper, general software architecture and algorithm of the desired robot are explained briefly.
Finding Critical and Gradient-Flat Points of Deep Neural Network Loss Functions

Author(s): Frye, Charles Gearhart | Advisor(s): Bouchard, Kristofer E; DeWeese, Michael R | Abstract: Despite the fact that the loss functions of deep neural networks are highly non-convex,gradient-based optimization algorithms converge to approximately the same performancefrom many random initial points. This makes neural networks easy to train, which, combined with their high representational capacity and implicit and explicit regularizationstrategies, leads to machine-learned algorithms of high quality with reasonable computational cost in a wide variety of domains.One thread of work has focused on explaining this phenomenon by numerically characterizing the local curvature at critical points of the loss function, where gradients are zero.Such studies have reported that the loss functions used to train neural networks have nolocal minima that are much worse than global minima, backed up by arguments fromrandom matrix theory. More recent theoretical work, however, has suggested that badlocal minima do exist.In this dissertation, we show that one cause of this gap is that the methods used tonumerically find critical points of neural network losses suffer, ironically, from a bad localminimum problem of their own. This problem is caused by gradient-flat points, wherethe gradient vector is in the kernel of the Hessian matrix of second partial derivatives.At these points, the loss function becomes, to second order, linear in the direction of thegradient, which violates the assumptions necessary to guarantee convergence for secondorder critical point-finding methods. We present evidence that approximately gradient-flatpoints are a common feature of several prototypical neural network loss functions.
Improving Zero-Shot Neural Architecture Search with Parameters Scoring

The exceptional success of deep learning comes at the cost of long training sessions, and a slow iterative process of proposing new architectures that have to be hand-engineered through years of experience. Neural Architecture Search (NAS) is the line of research that tries to automatically design architectures with better performances at a given task. The performance of a network in a task can be predicted by a score, even before the network is trained: this is referred to as zero-shot NAS. However, the existing score remains unreliable for architectures with high accuracy. We develop in this direction by exploring different related scores. We study their time efficiency and we improve on their dependence with the final accuracy, especially for high values of the score. We propose a monotonicity metric to evaluate the adequate relative scoring of the architectures, as a way to avoid imposing a linearity assumption too early. We find that our use of noise improves the score, but a more substantial improvement comes when the evaluation of the score is done in the parameter space. We hope this effort will help clarify promising directions to speed up automatic discovery of good neural architectures without training.
Drug Side Effect Frequency Mining over a Large Twitter Dataset Using Apache Spark

Despite clinical trials by pharmaceutical companies as well as current Food and Drug Administration reporting systems, there are still drug side effects that have not been caught. To find a larger sample of reports, a possible way is to mine online social media. With its current widespread use, social media such as Twitter has given rise to massive amounts of data, which can be used as reports for drug side effects. To process these large datasets, Apache Spark has become popular for fast, distributed batch processing. To extract the frequency of drug side effects from tweets, a pipeline can be used with sentimental analysis-based mining and text processing. Machine learning in the form of a new ensemble classifier using a combination of sentiment analysis features to increase the accuracy of identifying drug-caused side effects. In addition, the frequency count for the side effects is also provided. Furthermore, we have also implemented the same pipeline in Apache Spark to improve the speed of processing of tweets by 2.5 times, as well as to support the process of large tweet datasets. As the frequency count of drug side effects opens a wide door for further analysis, we present a preliminary 234study on this issue, including the side effects of simultaneously using two drugs, and the potential danger of using less common combination of drugs. We believe the pipeline design and the results present in this work would have helpful implication on studying drug side effects and on big data analysis in general. With the help of domain experts, it may be used to further analyze drug side effects, medication errors, and drug interactions.
Implementasi Registrasi Online Seleksi Penerimaan Karyawan Baru Pada PT Lion Parcel

The use of technology in a current system can speed up the work process, including in the new employee recruitment system. This research is intended to describe the new employee recruitment system and evaluate the new employee recruitment process implemented by PT Lion Express (Lion Parcel). The method applied in data collection is by interviewing and seeing firsthand the process of recruiting new employees at PT Lion Express (Lion Parcel). In accordance with the results of research that has been done by the author, it can be concluded that the employee recruitment process carried out by the personnel department already has a systematic stage. In the recruitment process there are still several problems that hamper as long as the process can be an evaluation of the company for the future. Obstacles encountered include the process of registering prospective employees for the implementation of psychological tests which are considered ineffective because they are still based on emails received and announcements of applicant data completeness are still done one-by-one by e-mail to applicants, thus consuming the work of HR department personnel. Therefore, a web-based Smart system is needed that can facilitate registration and announcement of lack of data completeness online. So that companies can maximize the process of hiring new employees, it is recommended that the selection of new employees use a system that can accommodate the obstacles that often occur in the process of hiring new employees. The system design uses UML diagrams and PHP programming languages.
Research Guides: Electronic Resources at The Evergreen Library: Technology Support

This guide aims to bring together all the electronic resources (e-books, e-journals, databases, streaming media) and digital content available through The Evergreen State College Library.
Effect of Malaria Parasite on Haematological Parameters: An Institutional Experience

Objectives: Hematological changes are among the most common complications encountered in malaria.This study analyzes and statistically evaluates the hematological changes as a diagnostic test for malaria in patient with acute febrile illness.Method: Hundred samples were diagnosed positive by the Leishman's stained thick blood film.The data were collected from the central lab, Uttar Pradesh University of Medical Sciences, Saifai, Etawahduring May 2017 to August, 2019.Laboratory records of people suspected with malaria infection such as fever other signs and symptoms as medical doctor recommended were reviewed. Results:The finding showed that 100 patients were diagnosed to have malaria by positive smear report by microscopy.Of these were 4 were caused byP.falciparum, whereas 96 were caused by P.vivax .No patient with co-infection of P.falciparum and P.vivax were found.The following parameters were significantly lower in malaria-infected patients; red blood cells (RBCs) count, hemoglobin (Hb), platelets count, white blood cells (WBCs) counts, neutropil, monocyte, lymphocyte and eosinophil counts, while Mean corpuscular volume, Mean corpuscular hemoglobin, Mean corpuscular hemoglobin concentration, neutrophil-lymphocyte ratio (NLR) and monocyte-lymphocyte ratio (MLR) were higher in comparison to non-malaria infected patients.Thrombocytopenia was present in 91% of malaria infected patients. Conclusion:Patients infected with malaria exhibited important changes in most of hematological parameters with low hemoglobin, platelets, WBCs and lymphocyte counts being the most important predictors of malaria infection.
Software Options for Data Visualization

Whether it is an article on your group webpage, a graphical abstract for a scientific journal or your spin-​off company portfolio, presenting your scientific data outside from the conventional journal article becomes more and more important. Choosing the correct tool to transport your scientific data is therefore a major key factor in Science Communication. Nowadays, simple diagrams like bar plots or curves often seem very basic – if not boring – compared to all the opportunities that digitalization offers. But which tools exist and which would be the most suitable to present your data within a given framework?In his talk, Oliver Humbelin will cover the basics of tooling, its prerequisites and opportunities with respect to the available data. New and innovative tools, e.g. D3 (Data Driven Documents), a tool to produce dynamic, interactive data visualizations will be discussed in more detail. By means of examples, you will be introduced to and inspired by a completely new approach of scientific data visualization!
On a radical extension of the field of rational functions in several variables

Let F be a field and let F(X1,…,Xn) be the field of rational functions in n variables X1,…,Xn over F. Let T=X1+⋯+Xn∈F(X1,…,Xn) and let m be a positive integer such that charF∤m. Is it possible to express each Xi as a rational function in X1m…,Xnm and T over F? It is not difficult to prove that this can be done but it is another matter to show how this is done. We answer the above question affirmatively with a nonconstructive proof and a constructive proof.
Technology Sensor Net (TechNet) / Muhammad Izzat Izzuddin Sapari

Technology is a huge thing within sport, it varies from mobile phones, televisions, sport equipment and more, there is no doubt that the importance of technologies used has increased over the years since sports have become a globally activity. With the implementation of technology, correct decisions have been made, improve technical development and sport performance. TechNet is one of the product renovations from normal net in order to improve performance in netting sports such as badminton, table tennis, volleyball and else. The aim of this product focuses on statistical information and observation for sport performance. This product also can connect with application software in many devices such as mobile phones, computer and else. Therefore, our objective also to make customer satisfaction with our good service and quantity product. Hence, attract more customers and build a long good relationship with them. Combination of net product and sensor technology create new idea of technology in sports. Hence, this product will ease sport analyst to observe and analyse information during games without using complicated and old method. This product also has higher sensitivity to detect and classified information such as ball pass or something hit the net. In this project, we take design inspiration from the existing net product with the combination of sensor technologies which are touch sensor and motion detector. Then we start to aim to design portable, simple and easy to carry the device. Besides that, we are also always considering the technical and economic criteria such as functionality, working principle, aesthetic value and the most important is the safety of the product. In our new product development of Technology Sensor Net, the reason we design this product with simple and portable because we want to make this product more portable that can easily carry by the customer who want to use it anytime and anywhere like training or observation purpose.
Incorporation of time in data analysis for surface mineralogical mapping with multispectral remote sensing

Geological time is very different from the general time; changes in geology do not happen in a month or a year, unlike its surroundings. As a result, the applications of multi-temporal remote sensing in mineralogical mapping are minimal. Although geology does not change in short periods, its surroundings – vegetation cover, atmospheric and illumination conditions change rapidly with time. With these changes in surroundings happening, the question ‘Do remotely obtained spectra of geological objects – rocks, outcrops, soil surfaces, etc. change with time?’ raises. Vegetation cover is considered one of the toughest challenges in Geological Remote Sensing (GRS), which is also the reason for most GRS studies to take place in desert-like areas. In most GRS studies, pixels rich in vegetation are masked out. Techniques such as spectral unmixing, principal component analysis, regression models, etc. were developed over the years to tackle the influence of vegetation cover on spectral signatures of geological objects. This research attempts to take on the challenge of vegetation cover by incorporating the paradigm of time. The abilities of time in tackling vegetation cover in geological remote sensing using multi-temporal Sentinel-2 imagery are evaluated in this research. Changes in remotely sensed spectra of geological objects due to natural vegetation over a period of thirteen months were observed to understand the level of information that can be retrieved from pixels with mixtures of vegetation and geological objects. Forty Sentinel 2 level 2A images over Rodalquilar, southeast Spain, were analyzed using Google Earth Engine; pixels were selected for tracing changes over time, and classified into ‘pure geology,’ ‘mixed,’ and ‘vegetation’ pixels. Time-series analysis was then performed on spectra, band ratios, and reflectance values of individual bands of all the selected pixels. Results from this research show that spectra of geological objects change with time; both pure geology and mixed pixels showed variations over time. Seasonal differences were observed in spectra and band ratios of mixed pixels; the trends were similar to that of the variation of vegetation in the region. Time helped in differentiating between signal due to different parts (vegetation, geology) in mixed pixels. Several possibilities of misinterpretations in the traditional GRS approach were observed. This research demonstrated the advantages of using multi-temporal remote sensing applications in surface mineralogical mapping and contributes to understanding the variations in remotely sensed spectra of geological objects over time.
The Implementation of English Language Teaching Management from An English-mediated Early Childhood Education School in Salatiga

English is a language that has been widely studied at the educational level. The introduction of English at kindergarten level is also growing these days. In practice, the use of English in kindergarten needs to be optimized, especially with good governance of English learning. This study aims to capture best practice ideas from an accredited private kindergarten school in the city of Salatiga. Respondents of this study were the principal, teacher representatives and parents in the research object. Data collection techniques in this study used semi-structured interviews. Acquisition data were analyzed qualitatively by reducing and categorizing according to management aspects. Research results showed that at the planning stage, the school properly manages the vision and mission, curriculum, and school policies that support learning with English as the language of instruction. At the organizing stage, the school regulates the recruitment of human resources for educators and external parties who collaborate in learning at the school so that language communication qualifications are maintained. For the implementation stage, various interesting learning methods were developed and added to the exposure of English content so that children were used to it and continued to develop their language skills. Then, at the supervision stage, schools assess the learning process and teacher qualifications including providing assistance to maintain the quality they already have.