Reviewerly/exHarmony huggingface dataset:
Visual information and kinesthetics in virtual manufacturing simulation

This research aims to design and assemble complex parts using wearable sensor data, image processing data and integration of Application Programming Interface of 3D CAD system. Experiments were conducted to improve users’ experience to have both kinesthetic and visual interaction with virtual objects. Input data from muscle contraction as well as hand image processing are fed with the data from an Electromyogram (EMG) sensor. This allows virtual objects to be grasped, moved and dropped through muscle force classification based on physical world masses. From the results of this research, the combination of wearable sensor and image processing as an instruction input for virtual manufacturing successfully enhance users’ interaction experiences with virtual objects addressed in a touchless manner.
METHOD OF WRITING DISCURSIVE ESSAYS ON SOCIAL AND PEDAGOGICAL TOPICS

&#x0D; &#x0D; &#x0D; The article discusses essential problems of discursive essay writing method during professional formation of a future teacher, conceptual approaches and technique of step-by-step preparation for its writing, as well as approaches to assessing and analyzing the level of performance using defined set of criteria given specifics of social and pedagogical problems. General theoretical bases of interpretation of discourse features and logic and technological support of its implementation are described, given the specifics of essay writing method, which was not relevant in the system of information and reproductive professional education.&#x0D; &#x0D; &#x0D;
The Design of Learning Methods for Reading Comprehension

The ability to read, in this case is the ability to understand English-language texts (reading comprehension), is one of the skills that must be possessed by students in a college. This relates to their academic activities that require them to struggle to find sources to support lecture activities. The number of reference sources is present in the form of articles, books, journals and proceedings which are often written in English. Interviews were conducted with several students of University of AMIKOM Purwokerto, Department of Informatics, finding out how to overcome the problem, and trying to provide alternative improvements. To overcome the difficulties experienced by students in reading English texts are by discussing a method of learning reading comprehension. This research is a descriptive study, which describes the facts of difficulties experienced by students in the difficulty of understanding the English text. The instrument used in data collection was a matter of English text. In this study, data analysis used consisted of three steps, namely data reduction, data presentation, and completing conclusions or verification.
Preprocessing Techniques in Text Categorization: A Survey

Text Categorization is a process of categorizing or labeling an unstructured Natural Language (NL) text to related categories with the help of a predefined set. In text categorization, pre-processing is a crucial step which is used for extracting non-trivial, interesting and useful input for further stages of the process of text categorization. As the words in text usually contains a lot of structural variations, so before accessing the information from documents, pre-processing techniques are applied on the data to minimize the size of the data which may increase efficacy of the result and better categorize the text. The main objective of this research is to Survey about the pre-processing techniques like Tokenization, Stop-words removing and Stemming. We’ll see how these techniques affect text categorization in good or may be bad ways.
Itinerary books (itineraria) of the Vysotskys’ printing house

The study gives a new outlook on the activities of the first Tyumen printing house, which was founded by K. N. Vysotsky in 1869 and existed until 1909. The scientific novelty of the research is due to the following: 1) book publishing is considered as part of the media revolution carried out by K. N. Vysotsky thanks to the opening of the first photography studio (1866), lithography studio (1867), printing house (1869), and a newspaper publishing news and advertisements (1879) in a provincial Siberian town; 2) publishing activities of K. N. Vysotsky and L. K. Vysotskaya are linked with the history of Siberian steam navigation in the 1870–1890s. The purpose of the article is to characterize Russian dorozhniki - itinerary books as a special type of publications in the Vysotskys’ printing house, to identify their diversity and role in the activities of steamship companies, in particular the Kurbatov and Ignatov Partnership. A bibliological analysis based on the structural-typological method within the context and system approaches allows a new interpretation of the role and place of the Vysotskys’ printing house in the history of Siberian book culture. The author comes to the conclusions that about 20% of the repertoire (11 of 56 books) of the Vysotskys’ printing house are books about steamboats and rivers. They represent a semantic unity, a unique series structured by space (more than 3000 km along West Siberian rivers from Tyumen to Tomsk) and the idea of industrialization and cultural development of new lands. The structural dominance of the series belongs to a special publication type: dorozhnik (an itinerary book), the purpose of which was to indicate the distance between settlements and to serve as a travel guide on Siberian rivers. Publication of such books, which are known to exist since the ancient Rome, testifies to the high print culture of the Vysotskys publishers. These books are very diverse: brief guidebooks coexisted with lithographic cartographic editions and advertising catalogs in the genre of history writing and ethnographic travel essays. The significance of the itinerary books published by the Vysotskys lies in the cultural brand they formed for Tyumen as a town and the birthplace of all navigation along West Siberian rivers.
A Hamilton-Jacobi Formulation for Time-Optimal Paths of Rectangular Nonholonomic Vehicles

We address the problem of optimal path planning for a simple nonholonomic vehicle in the presence of obstacles. Most current approaches are either split hierarchically into global path planning and local collision avoidance, or neglect some of the ambient geometry by assuming the car is a point mass. We present a Hamilton-Jacobi formulation of the problem that resolves time-optimal paths and considers the geometry of the vehicle.
Analysis of Multi Cluster Projection on High Dimensional Data Based on Forest Scenario

Clustering algorithm is frequently used for extending a distance metric or a similarity evaluation for the separation of data from database. The divided data points are more similar and they are categorized significantly to cluster in high dimensional data spaces. Then, clustered data points are projected with different diverse set of proportions. Clustering high dimensional data is a proficient research field. High-dimensional data are wide-ranging in numerous areas of forest scenario, machine learning, signal and image processing, computer vision, pattern recognition, bioinformatics and so on. Let us consider the forest scenario for clustering the information about the trees among dissimilar tree structure. Based on the clustering of forest information, they combine diverse areas of capability and equipment.
REGULATORY AND FINANCIAL REQUIREMENTS TO SWITCH FROM TRADITIONAL PAYMENT TO ELECTRONIC PAYMENT CASE STUDY IN BAGHDAD MUNICIPALITY BAGHDAD WATER DEPARTMENT

The topic of e-payment is an important topic in the light of the rapid developments of information and communication technology.light on the requirements of Walt Nzimih and finance for the shift to electronic payment and the extent provided by the Department of water Baghdad / Baghdad Municipality in order to pay the electronic system adoption in the collection of revenue.The researcher relied on the case study methodology (Case study ) As a curriculum science consistent with the research problem and helps the possibility of providing substantive treatment to them, either the research community was represented by ( the Department of Water Baghdad) , one of the departments of the Secretariat of Baghdad , and used the checklist Ka main instrument has been relied upon to gather information either methods of analysis of data loss Stability coefficient was used ( Cronbach's Alpha(SPIR-MAN grading coefficient, weighted mean, standard deviation and relative importance), and through data collection and analysis, a number of conclusions were reached, most notably that the Baghdad Water Department / Baghdad Municipality possesses the human resources that enable it to shift.To the electronic payment system but suffer from weakness in the human resources training side .The Baghdad Water Department / Baghdad Municipality has the regulatory requirements that enable it to switch to electronic payment system, but suffers from weakness in the financial aspect.
ONDISCOVERY OF RANKING FRAUD FOR MOBILE APPS

Most of us use android and IOS Mobiles these days and also uses the play store or app store capability normally. Both the stores provide great number of application but unluckily few of those applications are fraud. Such applications dose damage to phone and also may be data thefts. Hence, such applications must be marked, so that they will be identifiable for store users. So we are proposing a web application which will process the information, comments and the review of the application. So it will be easier to decide which application is fraud or not. Multiple application can be processed at a time with the web application. Also User cannot always get correct or true reviews about the product on internet. So rating/comments will be judged by the admin and it would be easy for admin to predict the application as Genuine or Fraud.
A Bayesian sparse finite mixture model for clustering data from a heterogeneous population

In this paper, we introduce a Bayesian approach for clustering data using a sparse finite mixture model (SFMM).The SFMM is a finite mixture model with a large number of components k previously fixed where many components can be empty.In this model, the number of components k can be interpreted as the maximum number of distinct mixture components.Then, we explore the use of a prior distribution for the weights of the mixture model that take into account the possibility that the number of clusters k c (e.g., nonempty components) can be random and smaller than the number of components k of the finite mixture model.In order to determine clusters we develop a MCMC algorithm denominated Split-Merge allocation sampler.In this algorithm, the split-merge strategy is data-driven and was inserted within the algorithm in order to increase the mixing of the Markov chain in relation to the number of clusters.The performance of the method is verified using simulated datasets and three real datasets.The first real data set is the benchmark galaxy data, while second and third are the publicly available data set on Enzyme and Acidity, respectively.
Customer Relationship Management Information System in Medika Lestari Hospitals

Marketing strategies based on efforts to create relationships between companies and customers are better known as Customer Relationship Management (CRM). CRM is a corporate strategy used to pamper customers so they don't look away. This study wants to find out and improve relations between hospitals and patients. Besides that, by building this system, can it affect customer / patient satisfaction. The quality of information systems is measured using the ISO 9126 model by adapting four characteristics of software quality: Functionality, Reliability, Usability, and Efficiency. The system development method uses the Rapid Application Development (RAD) model. Researchers will conduct system analysis and design using the Unified Model Language (UML), then coding with the PHP programming language and MySQL database, as well as collecting data using a questionnaire, and testing the system using the Blackbox Testing model. Based on testing using ISO 9126 the results are functionality (83%), Reliability (86%), Usability (87%), and Efficiency (83%). Overall the results are 85% (very good).
Regularizing CNN Transfer Learning with Randomised Regression

This paper is about regularizing deep convolutional networks (CNNs) based on an adaptive framework for transfer learning with limited training data in the target domain. Recent advances of CNN regularization in this context are commonly due to the use of additional regularization objectives. They guide the training away from the target task using some forms of concrete tasks. Unlike those related approaches, we suggest that an objective without a concrete goal can still serve well as a regularized. In particular, we demonstrate Pseudo-task Regularization (PtR) which dynamically regularizes a network by simply attempting to regress image representations to pseudo-regression targets during fine-tuning. That is, a CNN is efficiently regularized without additional resources of data or prior domain expertise. In sum, the proposed PtR provides: a) an alternative for network regularization without dependence on the design of concrete regularization objectives or extra annotations; b) a dynamically adjusted and maintained strength of regularization effect by balancing the gradient norms between objectives on-line. Through numerous experiments, surprisingly, the improvements on classification accuracy by PtR are shown greater or on a par to the recent state-of-the-art methods.
Effects of Applying Flipped Learning to Simulation Training in Nursing Students

Background/Objectives: This study was conducted to confirm the effect of applying flipped learning to simulation training of nursing students.Methods/Statistical analysis: The study was the experiment with pre and post design in single group. The subjects of this study were 88 nursing students who were taking the simulation training course of nursing college. The learning model was designed with a total of 11 programs based on the ‘PRTNER’ model. Collected data were analyzed using descriptive statistics and paired t-test using SPSS 22.0.Findings: Although there was no statistically significant difference in the problem solving ability(t=-1.342, p=.183) between before and after applying flipped learning to simulation training, the significant improvement was achieved in self-directed learning ability(t=-2.187, p=.031), communication ability(t=-5.316, p<.001), and satisfaction of instruction(t=-2.830, p=.006). Therefore, using the teaching and learning method with flipped learning, the learning outcomes can be maximized by improving the core competencies that nursing students should have. These results are useful as basic data for the development of teaching and learning methods that can improve the core competencies of nursing students.Improvements/Applications: Based on this results, we propose the follow-up study to confirm the effectiveness of various teaching and learning methods applying flipped learning to the theoretical curriculum in nursing science.s
THE USE OF DIGITAL VLOGGING TO ENHANCE THE REVIEW TEXT SPEAKING SKILL OF THE TWELFTH GRADERS OF SMAK PENDOWO MAGELANG IN THE SCHOOL YEAR 2018/2019

Internet and information technology can be used for English learning process since they provide a chance to improve students’ ability in English. One of the activities which can be used as a technique in learning speaking skill is digital vlogging. The objectives of this research are to know how great can the use of digital vlogging enhance students’ interest to the review text speaking skill of the twelfth graders of SMAK Pendowo Magelang in the school year 2018/2019 and to know how great is the enhancement of review text speaking skill of the twelfth graders of SMAK Pendowo Magelang in the school year 2018/2019 by using digital vlogging. This study is a classroom action research. It was conducted in SMAK Pendowo Magelang. The subjects of the research were the XII IPA students of SMAK Pendowo Magelang in the school year 2018/2019. The types of the data are quantitative and qualitative data. The quantitative data were obtained from tests and they were in the form of students’ scores. Meanwhile, the qualitative data were collected through observation, questionnaire, and documentation. Digital vlogging brings great enhancement to the students’ interest and the enhancement of review text speaking skill is great. It can be inferred that all implemented actions are successful to enhance the students’ interest and students’ speaking skill of review text. Digital vlogging also makes everyone consider that learning is fun and it increases students’ confidence in speaking English.
Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization

Much progress has been made in text summarization, fueled by neural architectures using large-scale training corpora. However, in the news domain, neural models easily overfit by leveraging position-related features due to the prevalence of the inverted pyramid writing style. In addition, there is an unmet need to generate a variety of summaries for different users. In this paper, we propose a neural framework that can flexibly control summary generation by introducing a set of sub-aspect functions (i.e. importance, diversity, position). These sub-aspect functions are regulated by a set of control codes to decide which sub-aspect to focus on during summary generation. We demonstrate that extracted summaries with minimal position bias is comparable with those generated by standard models that take advantage of position preference. We also show that news summaries generated with a focus on diversity can be more preferred by human raters. These results suggest that a more flexible neural summarization framework providing more control options could be desirable in tailoring to different user preferences, which is useful since it is often impractical to articulate such preferences for different applications a priori.
Towards Robustness against Unsuspicious Adversarial Examples

Despite the remarkable success of deep neural networks, significant concerns have emerged about their robustness to adversarial perturbations to inputs. While most attacks aim to ensure that these are imperceptible, physical perturbation attacks typically aim for being unsuspicious, even if perceptible. However, there is no universal notion of what it means for adversarial examples to be unsuspicious. We propose an approach for modeling suspiciousness by leveraging cognitive salience. Specifically, we split an image into foreground (salient region) and background (the rest), and allow significantly larger adversarial perturbations in the background, while ensuring that cognitive salience of background remains low. We describe how to compute the resulting non-salience-preserving dual-perturbation attacks on classifiers. We then experimentally demonstrate that our attacks indeed do not significantly change perceptual salience of the background, but are highly effective against classifiers robust to conventional attacks. Furthermore, we show that adversarial training with dual-perturbation attacks yields classifiers that are more robust to these than state-of-the-art robust learning approaches, and comparable in terms of robustness to conventional attacks.
OBJECT ORIENTED METRIC BASED ANALYSIS OF TEXT TRANSMISSION IN E-LEARNING THROUGH NATURAL LANGUAGE STEGANOGRAPHY

E-learning is an application of information and communication technology in the field of learning. Through steganography the e-learning institution can provide security to other participants of e-learning like teacher and learner. Here we use text steganography with modified SNOW algorithm while passing secret texts from the administrator to the learner in an e-learning system. In this paper, we calculate the object oriented metric based analysis of CK and MOOD metrics of our proposed model, which ensures the advantages of code redundancy, code reusability, and cost effectiveness and so on.
Analysis of environmental factors effect on cognitive performance and stress level in elementary school students

Room temperature and light intensity are some of the physical work environment factors that affect work performance and human workload. This study aims to determine the effect of room temperature and light intensity on cognitive performance, (speed of response and concentration), and mental load, (the stress levels) of elementary school students. The factorial experimental design method used in this study, in which each factor has three levels to be tested, specifically 21°C, 25°C, and 29°C to room temperature and 50 lux, 150 lux and 300 lux for the light intensity. This study resulted that room temperature and light intensity affecting cognitive performance and stress levels in elementary school students significantly.
Informative Scene Decomposition for Crowd Analysis, Comparison and Simulation Guidance

Crowd simulation is a central topic in several fields including graphics. To achieve high-fidelity simulations, data has been increasingly relied upon for analysis and simulation guidance. However, the information in real-world data is often noisy, mixed and unstructured, making it difficult for effective analysis, therefore has not been fully utilized. With the fast-growing volume of crowd data, such a bottleneck needs to be addressed. In this paper, we propose a new framework which comprehensively tackles this problem. It centers at an unsupervised method for analysis. The method takes as input raw and noisy data with highly mixed multi-dimensional (space, time and dynamics) information, and automatically structure it by learning the correlations among these dimensions. The dimensions together with their correlations fully describe the scene semantics which consists of recurring activity patterns in a scene, manifested as space flows with temporal and dynamics profiles. The effectiveness and robustness of the analysis have been tested on datasets with great variations in volume, duration, environment and crowd dynamics. Based on the analysis, new methods for data visualization, simulation evaluation and simulation guidance are also proposed. Together, our framework establishes a highly automated pipeline from raw data to crowd analysis, comparison and simulation guidance. Extensive experiments and evaluations have been conducted to show the flexibility, versatility and intuitiveness of our framework.
Robust Question Answering Through Sub-part Alignment

Current textual question answering models achieve strong performance on in-domain test sets, but often do so by fitting surface-level patterns in the data, so they fail to generalize to out-of-distribution settings. To make a more robust and understandable QA system, we model question answering as an alignment problem. We decompose both the question and context into smaller units based on off-the-shelf semantic representations (here, semantic roles), and align the question to a subgraph of the context in order to find the answer. We formulate our model as a structured SVM, with alignment scores computed via BERT, and we can train end-to-end despite using beam search for approximate inference. Our explicit use of alignments allows us to explore a set of constraints with which we can prohibit certain types of bad model behavior arising in cross-domain settings. Furthermore, by investigating differences in scores across different potential answers, we can seek to understand what particular aspects of the input lead the model to choose the answer without relying on post-hoc explanation techniques. We train our model on SQuAD v1.1 and test it on several adversarial and out-of-domain datasets. The results show that our model is more robust cross-domain than the standard BERT QA model, and constraints derived from alignment scores allow us to effectively trade off coverage and accuracy.