Reviewerly/exHarmony huggingface dataset:
Usability and Performance of the Web-based Frontline Services and Laboratories System

Objectives:The study developed a Web-based Frontline Services and Laboratories Evaluation System for NIPSC to identify the needs and concerns of attending students in NIPSC by giving ratings and posting comments or suggestions in laboratories and frontline services.The main objectives of the research were to determine the level of usability of the developed features of a Webbased Frontline Services and Laboratories Evaluation system for NIPSC.Evaluate the system's performance in terms of functionality, reliability, and efficiency of the information provided to clienteles.Methods: Descriptive and developmental research designs were employed in this study.It also helps the researcher analyze the information by the deployed standard survey questions to ensure the developed system meets its developed features and functions.The researcher used the Rapid Application Development (RAD) model as the software development life cycle for the software development activities.The researcher gets the total sampling size using sloven's formula.Findings: The finding implied was very good in using the developed system to get feedback from the students.The developed system gives satisfaction to the users in which they can easily rate and give their comments and suggestions to the services and laboratories of the NIPSC.The administrator can easily gather feedback from the students in helping them to know what were the lacking needs of their services are.Using the developed system, it can continuously progress and outgrow the services of Northern Iloilo Polytechnic State College when it comes to evaluating the school's frontline services and laboratories.The system's overall performance in terms of functionality, reliability, and efficiency implied that using the developed system is very good in getting feedback from the students.Novelty: The developed system can evaluate the comments and suggestions of the students of NIPSC using their mobile phones and tablets.Be able to give results quickly, and it is user-friendly.It is very good if the school may continue to update the developed system, which may help to maximize its potential and improve its functionalities, reliability, and efficiency.
Executing Quality Management Tools to Enhance Customer’s Journey at a Clothes Laundry Firm

Purpose: This project aims to assess and enhance the customer’s journey at a laundry company in Saudi Arabia from when the customer arrives at the parking lot of the laundry store until receiving the services and payment. Design, methodology, approach: A mixed-method approach was employed, in which qualitative data were gathered from focus group interviews and solo interviews, and quantitative data were gathered through the survey. Moreover, Quality Management Tools were used to build the action plan and offer conclusions and recommendations that would enhance the customer’s journey and improve satisfaction. Findings: Services quality issues are categorized into five categories: promotions, human resources; services; detergent products, and facilities. House of Quality represents the highest 15 prioritized solutions. These recommended solutions' relative weights range between 9% to 4%. The use of these tools highlights areas for improvement and the root causes of each issue. The seven quality tools are trustworthy tools to conquer challenges faced by the company and may be effective in improving service quality to positively strengthen organizational performance, customer satisfaction, and success. Originality, value: There are limited studies practically employing the seven Quality Management Tools to enhance customer satisfaction and improve their journey. The Saudi laundry market specifically has a dearth of this type of study. Furthermore, this market has seen rapid growth since it is known as a part of the SME sector in recent few years.
Credit Card Fraud Detection Techniques: A Survey

Fraud events are increasing on day to day bases. Credit card fraud is the most frequent fraud that is making a big financial loss on a global level. Researchers have implemented many machine learning algorithms to detect the credit card frauds. This research has briefly described several algorithms and compares the performance of Random Forest, Naïve Bayes, K-Nearest Neighbor, Logistic Regression and Multilayer Perceptron. Algorithms are also used to classify the real transactions or fraudulent transactions. These datasets are compared on the basis of accuracy, precision, recall &amp; false positive rate. Comparison results show that Random Forest performs best in credit card fraud detection dataset among others. Research shows that any ML algorithm can be used to demonstrate the classification of fraud detection.
Media Interaktif dan Power Point terhadap Motivasi dan Hasil Belajar Anatomi Fisiologi Mahasiswa

This study aims to determine the effect of interactive media and motivation on anatomy and physiology learning outcomes in health polytechnic students of the Jambi obstetrics department. This research method is quasi-experimental, using a pre-test, post-test control group design. The results showed that the pre-test value of the experimental class group was 2238, with an average value of 74.60 with a standard deviation of 3.024. While the post-test in this practical class obtained a total of 2373, the average value was 79.10 with a standard deviation of 3.736. Meanwhile, the total pre-test score for the control group was 2000, with an average score of 66.67 with a standard deviation of 5.287. At the same time, the post-test in the control class obtained a total score of 2112 and an average value of 70.40 with a standard deviation of 7.700. In conclusion, there is an effect of using interactive media and motivation to increase learning outcomes of anatomy and physiology of Jambi Health Polytechnic students, Department of Midwifery.&#x0D; &#x0D; Keywords: Learning Outcomes, Interactive Media, Motivation, PowerPoint
THE SPECIFICS OF PSYCHOLOGICAL ANALYSIS IN THE TRILOGY «LOZINSKOE FIELD» BY GRIGORY MEDVEDEV

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PROBLEMATIKA KOLABORASI PEMBELAJARAN DARING DAN LURING DI SDN 09 MUARADUA KABUPATEN OKU SELATAN

This research discusses the problem that occurs in the implementation of 2 ways of lessons. namely online learning and offline learning at the 09 Muaradua Elementary School, South OKU Regency.This research uses a qualitative research approach with a type of case study. Data collection techniques with observation, interviews, and documentation. The data analysis method used consists of three stages, namely data collection, data reduction, and concluding the final result of the study.The application of online and offline learning collaborations is carried out by dividing the time between online and offline learning. The problems during online learning, namely signal constraints, many students do not have cellphones, teachers and students are still a little technologically illiterate. The problem when learning offline is that students are sometimes reluctant to follow health protocols such as wearing masks and keeping clean their hands. With various problems from each of these lessons, it causes less conducive collaboration between these two lessons. Solutions from various parties in the school environment to overcome all problems by helping each other in the learning process.
PENGARUH PENERAPAN METODE PERMAINAN BINGO DALAM MENINGKATKAN HASIL BELAJAR SISWA PADA MATA PELAJARAN FIQIH DIKELAS VIII MTs NEGERI 1 MODEL PALEMBANG

Difficulties of students in understanding the subject matter well and different levels of intelligence as well as a lack of participation and response of students. so students find it difficult to focus in accepting the explanations that are submitted by the teacher. So the method of learning is needed in improving student learning outcomes. This study aims to determine whether or not there is an influence of the application of the Bingo game method in improving student learning outcomes in the subject of Fiqh. This research is using experimental method. The research sample is students of MTs N 1 Palembang model consisting of 43 experimental class students and 44 control class students. Data obtained using tests, and documentation. Then the data collected was analyzed by t test statistics. The results of this study indicate: (1) The average experimental class learning outcomes with a percentage level namely: high category 8 students (19%), 31 students (72%), low 4 students (9%). (2) The averagecontrol class learning outcomes with a percentage level namely:high category 8 students (18%), medium 30 students (68%), low 6 students (14%). (3) There is a significant influence between the learning outcomes of the experimental class students compared to the learning outcomes of the control class. Because the price of t = 5.47&gt; from the significant level of 1.99.&#x0D;
STUDI KOMPARATIF SELF REGULATED LEARNING SISWA ASRAMA DAN NON-ASRAMA DI SMA IT IZZUDDIN PALEMBANG

In IT Izzuddin High School Palembang, after the learning activities are finished students/students have a different environment. Consists of boarding students and non-boarding students. The existence of environmental differences will certainly provide different support and obstacles. Starting from this the researcher wants to compare the self-regulated learning of boarding and non-boarding students with the title “Comparative Study of Self Regulated Learning of Dormitory and Non-Boarding Students at IT Izzuddin Palembang High School”. This study aims to determine whether there are differences in self-regulated learning between boarding students and non-boarding students at IT Izzuddin Palembang High School.This research method uses a quantitative approach. This type of research is comparative research. The population in this study were all students of IT Izzuddin Palembang High School it the 2019/2020 school years, totaling 140 students, consisting of 69 boarding students and 71 non-boarding students. From this population, a sample of 60 boarding students and 63 non-boarding students was taken. The sampling technique is proportionate stratified random sampling. After the content validity test, Cronbach's alpha coefficient of 0.730 was obtained. The result is &gt; 0.6 then the instrument is reliable. Methods of data analysis using t-test and then given an interpretation of the results.Based on the results og the hypothesis test, t = 2.777 with p = 0.006. Because the value of p &lt; 5% (0.006 &lt; 0.05) then H0 is rejected and H1 is accepted, there is a significant difference in self-regulated learning between boarding and non-boarding students at IT Izzuddin Palembang High School where the level of self-regulated learning of boarding students is higher compared to no
Modeling Mobile Interface Tappability Using Crowdsourcing and Deep Learning

Tapping is an immensely important gesture in mobile touchscreen interfaces, yet people still frequently are required to learn which elements are tappable through trial and error. Predicting human behavior for this everyday gesture can help mobile app designers understand an important aspect of the usability of their apps without having to run a user study. In this paper, we present an approach for modeling tappability of mobile interfaces at scale. We conducted large-scale data collection of interface tappability over a rich set of mobile apps using crowdsourcing and computationally investigated a variety of signifiers that people use to distinguish tappable versus not-tappable elements. Based on the dataset, we developed and trained a deep neural network that predicts how likely a user will perceive an interface element as tappable versus not tappable. Using the trained tappability model, we developed TapShoe, a tool that automatically diagnoses mismatches between the tappability of each element as perceived by a human user---predicted by our model, and the intended or actual tappable state of the element specified by the developer or designer. Our model achieved reasonable accuracy: mean precision 90.2\% and recall 87.0\%, in matching human perception on identifying tappable UI elements. The tappability model and TapShoe were well received by designers via an informal evaluation with 7 professional interaction designers.
Research results of Samara State University of Social Sciences and Education in 2018

In 2018, the team of scientific and pedagogical workers of the Samara State Social and Pedagogical University worked to fulfill the tasks set by the President of the Russian Federation in national projects, in particular in the Science project.&#x0D; SGSPU, being a pedagogical university, was and remains an educational institution of a humanitarian orientation. In the past year, the university carried out four state assignments of the Ministry of Education and Science of the Russian Federation in the field of scientific activity.
Extended Gaze Following: Detecting Objects in Videos Beyond the Camera Field of View

In this paper we address the problems of detecting objects of interest in a video and of estimating their locations, solely from the gaze directions of people present in the video. Objects can be indistinctly located inside or outside the camera field of view. We refer to this problem as extended gaze following. The contributions of the paper are the followings. First, we propose a novel spatial representation of the gaze directions adopting a top-view perspective. Second, we develop several convolutional encoder/decoder networks to predict object locations and compare them with heuristics and with classical learning-based approaches. Third, in order to train the proposed models, we generate a very large number of synthetic scenarios employing a probabilistic formulation. Finally, our methodology is empirically validated using a publicly available dataset.
Prony methods for extracting excited states

We propose an algebraic method for extracting excited states from lattice gauge theory correlation functions. Instead of fitting to a sum of decaying exponentials, we adopt a variant of Prony's method to obtain $M$ energies (exponential decay rates) by finding the roots of an $M^{\rm th}$ order polynomial, and then solving for the amplitudes linearly. The resulting states tend to have overlapping error ellipses, making identification of states ambiguous. This is especially problematic at large Euclidean times where the signal to noise may be low, as well as when many states are extracted. We propose a variation of K-means clustering to identify each extracted state.
A Note on Using the Resistance-Distance Matrix to solve Hamiltonian Cycle Problem

An instance of Hamiltonian cycle problem can be solved by converting it to an instance of Travelling salesman problem, assigning any choice of weights to edges of the underlying graph. In this note we demonstrate that, for difficult instances, choosing the edge weights to be the resistance distance between its two incident vertices is often a good choice. We also demonstrate that arguably stronger performance arises from using the inverse of the resistance distance. Examples are provided demonstrating benefits gained from these choices.
An Editorial Network for Enhanced Document Summarization

We suggest a new idea of Editorial Network - a mixed extractive-abstractive summarization approach, which is applied as a post-processing step over a given sequence of extracted sentences. Our network tries to imitate the decision process of a human editor during summarization. Within such a process, each extracted sentence may be either kept untouched, rephrased or completely rejected. We further suggest an effective way for training the "editor" based on a novel soft-labeling approach. Using the CNN/DailyMail dataset we demonstrate the effectiveness of our approach compared to state-of-the-art extractive-only or abstractive-only baseline methods.
Faster randomized block Kaczmarz algorithms

The Kaczmarz algorithm is a simple iterative scheme for solving consistent linear systems. At each step, the method projects the current iterate onto the solution space of a single constraint. Hence, it requires very low cost per iteration and storage, and it has a linear rate of convergence. Distributed implementations of Kaczmarz have become, in recent years, the de facto architectural choice for large-scale linear systems. Therefore, in this paper we develop a family of randomized block Kaczmarz algorithms that uses at each step a subset of the constraints and extrapolated stepsizes, and can be deployed on distributed computing units. Our approach is based on several new ideas and tools, including stochastic selection rule for the blocks of rows, stochastic conditioning of the linear system, and novel strategies for designing extrapolated stepsizes. We prove that randomized block Kaczmarz algorithm converges linearly in expectation, with a rate depending on the geometric properties of the matrix and its submatrices and on the size of the blocks. Our convergence analysis reveals that the algorithm is most effective when it is given a good sampling of the rows into well-conditioned blocks. Besides providing a general framework for the design and analysis of randomized block Kaczmarz methods, our results resolve an open problem in the literature related to the theoretical understanding of observed practical efficiency of extrapolated block Kaczmarz methods.
Event-triggered distributed Bayes filter

The aim of this paper is to devise a strategy that is able to reduce communication bandwidth and, consequently, energy consumption in the context of distributed state estimation over a peer-to-peer sensor network. Specifically, a distributed Bayes filter with event-triggered communication is developed by enforcing each node to transmit its local information to the neighbors only when the Kullback-Leibler divergence between the current local posterior and the one predictable from the last transmission exceeds a preset threshold. The stability of the proposed eventtriggered distributed Bayes filter is proved in the linear-Gaussian (Kalman filter) case. The performance of the proposed algorithm is also evaluated through simulation experiments concerning a target tracking application.
On Quantum Advantage in Information Theoretic Single-Server PIR

In (single-server) Private Information Retrieval (PIR), a server holds a large database $DB$ of size $n$, and a client holds an index $i \in [n]$ and wishes to retrieve $DB[i]$ without revealing $i$ to the server. It is well known that information theoretic privacy even against an `honest but curious' server requires $\Omega(n)$ communication complexity. This is true even if quantum communication is allowed and is due to the ability of such an adversarial server to execute the protocol on a superposition of databases instead of on a specific database (`input purification attack'). Nevertheless, there have been some proposals of protocols that achieve sub-linear communication and appear to provide some notion of privacy. Most notably, a protocol due to Le Gall (ToC 2012) with communication complexity $O(\sqrt{n})$, and a protocol by Kerenidis et al. (QIC 2016) with communication complexity $O(\log(n))$, and $O(n)$ shared entanglement. We show that, in a sense, input purification is the only potent adversarial strategy, and protocols such as the two protocols above are secure in a restricted variant of the quantum honest but curious (a.k.a specious) model. More explicitly, we propose a restricted privacy notion called \emph{anchored privacy}, where the adversary is forced to execute on a classical database (i.e. the execution is anchored to a classical database). We show that for measurement-free protocols, anchored security against honest adversarial servers implies anchored privacy even against specious adversaries. Finally, we prove that even with (unlimited) pre-shared entanglement it is impossible to achieve security in the standard specious model with sub-linear communication, thus further substantiating the necessity of our relaxation. This lower bound may be of independent interest (in particular recalling that PIR is a special case of Fully Homomorphic Encryption).
MUREL: Multimodal Relational Reasoning for Visual Question Answering

Multimodal attentional networks are currently state-of-the-art models for Visual Question Answering (VQA) tasks involving real images. Although attention allows to focus on the visual content relevant to the question, this simple mechanism is arguably insufficient to model complex reasoning features required for VQA or other high-level tasks. In this paper, we propose MuRel, a multimodal relational network which is learned end-to-end to reason over real images. Our first contribution is the introduction of the MuRel cell, an atomic reasoning primitive representing interactions between question and image regions by a rich vectorial representation, and modeling region relations with pairwise combinations. Secondly, we incorporate the cell into a full MuRel network, which progressively refines visual and question interactions, and can be leveraged to define visualization schemes finer than mere attention maps. We validate the relevance of our approach with various ablation studies, and show its superiority to attention-based methods on three datasets: VQA 2.0, VQA-CP v2 and TDIUC. Our final MuRel network is competitive to or outperforms state-of-the-art results in this challenging context. Our code is available: https://github.com/Cadene/murel.bootstrap.pytorch
Lost in Machine Translation: A Method to Reduce Meaning Loss

A desideratum of high-quality translation systems is that they preserve meaning, in the sense that two sentences with different meanings should not translate to one and the same sentence in another language. However, state-of-the-art systems often fail in this regard, particularly in cases where the source and target languages partition the "meaning space" in different ways. For instance, "I cut my finger." and "I cut my finger off." describe different states of the world but are translated to French (by both Fairseq and Google Translate) as "Je me suis coupe le doigt.", which is ambiguous as to whether the finger is detached. More generally, translation systems are typically many-to-one (non-injective) functions from source to target language, which in many cases results in important distinctions in meaning being lost in translation. Building on Bayesian models of informative utterance production, we present a method to define a less ambiguous translation system in terms of an underlying pre-trained neural sequence-to-sequence model. This method increases injectivity, resulting in greater preservation of meaning as measured by improvement in cycle-consistency, without impeding translation quality (measured by BLEU score).
A Review on the Application of Blockchain for the Next Generation of Cybersecure Industry 4.0 Smart Factories

Industry 4.0 is a concept devised for improving the way modern factories operate through the use of some of the latest technologies, like the ones used for creating Industrial Internet of Things (IIoT), robotics or Big Data applications. One of such technologies is blockchain, which is able to add trust, security and decentralization to different industrial fields. This article focuses on analyzing the benefits and challenges that arise when using blockchain and smart contracts to develop Industry 4.0 applications. In addition, this paper presents a thorough review on the most relevant blockchain-based applications for Industry 4.0 technologies. Thus, its aim is to provide a detailed guide for future Industry 4.0 developers that allows for determining how blockchain can enhance the next generation of cybersecure industrial applications.