Reviewerly/exHarmony huggingface dataset:
Employees’ Perception towards Online Training Program: A Case from A Hotel

Training has become a fundamental aspect in maintaining and even developing human resources in an organization. Training is a serious program that cost a lot, therefore the training program provided should be formulated based on the employee’s needs. Online training is one of the methods that is expected to give more flexibility and efficiency to the employees and the employers. As a luxury resort, Hotel X conducts online training and sets minimum hours to be achieved by the employees. However, based on the data obtained, most of employees do not achieve the minimum hours. Therefore, this research aims to analyse the online training program conducted at Hotel X in Bali. This research employed quantitative and qualitative methods to obtain the employees’ perceptions regarding the training course, content, instructor, and its relevance to their job. The quantitative data were obtained through a set of questionnaires administered to 180 samples and qualitative data were obtained by conducting interview with some employees and some managerial and senior staff. The result showed that, the training course and the training content provided are very good, the training instructor is qualified and very good, and the online training materials are relevant to the respondent’s job.
Factors Related to Adherence to Taking Anti-Tuberculosis (OAT) Drugs in Pulmonary Tuberculosis Patients at Cut Meutia General Hospital, North Aceh Regency

Tuberculosis (TB) is a disease with a high risk of transmission and is one of the diseases whose cases continue to increase and cause death worldwide. Until now, pulmonary tuberculosis has been difficult to treat because of the patient's non-compliance with taking anti-tuberculosis drugs (OAT). This study aims to investigate the factors associated with adherence to taking OAT drugs in tuberculosis patients at Cut Meutia General Hospital. This research is an observational study with a cross-sectional design. The study was conducted at the pulmonary disease polyclinic of Cut Meutia General Hospital, North Aceh Regency. The study was conducted on 100 patients, with the sampling technique being total sampling. The sample inclusion criteria in this study were patients who had been diagnosed with pulmonary tuberculosis, were willing to participate in the study, and had signed an agreement to participate in the study. A total of 100 patients participated in this study. Most of the patients were male (64%), aged over 46 years (52%), had low education (75%), had difficult hospital access (70%), and had poor knowledge regarding TB treatment (60%). In conclusion, the factors of age, gender, and distance of residence are related to adherence to taking OAT. Meanwhile, education and knowledge are not related to adherence to taking OAT in pulmonary TB patients at Cut Meutia General Hospital, North Aceh Regency.
Sistem Informasi Manajemen

Information systems in the processing of student value using the program the computer can facilitate teachers in processing student value more quickly and effectively. So as to produce information quickly and accurately in searching the data value of students. One is the use of E-report cards in processing grades at MAN 1 Sungai Panuah. The main focus of this research is "Processing Student Value of E-Grade X Class at MAN 1 Sungai Panuah" This research was conducted at MAN 1 Sungai Panuah by using Qualitative Descriptive approach. Data collection method used is interview
Web-based Platform - a Defining Factor for Career Orientation Under the Conditions of a Pandemic

The flow of information exchange between employers and job seekers - students, PhD students, alumni - are analyzed. The tasks of the career centres to organize and direct the information flows for vacancies in the conditions of a pandemic are presented. A web-based platform is presented, which supports a database for students, PhD students and alumni. The platform allows employers to prepare and send messages to students, taking their professional fields and specialties into account. The relational schema of the database is also presented, as well as the process of publishing a message in the system.
Comparison of algorithms for elimination the outliers in 3D modelling from video

In this paper, we make a comparison of different algorithms for the elimination of outliers in the process of 3D modeling from video. The process of 3D modeling from video uses specific algorithms for detecting and matching key points. The first step in this process is obtaining a cloud of points, then matching the key points, obtaining the network, and converting the network into a polygonal model. There are two types of points: inliers (points that describe the model) and outliers (unnecessary points). From the practical examples, we got results and conclusions for comparison of Random sample consensus (RANSAC), MEstimator Sample Consensus (MSAC), and Maximum Likelihood Sample Consensus (MLESAC) algorithms. Using different algorithms for eliminating the outliers in practical experiments we get different quality of the final 3D model.
Detection protocol for non-Gaussian quantum steering

Recent developments, such as the experimental realization of large-scale cluster states, have built a valid case for continuous-variable quantum optics as a promising platform for quantum information processing. The capability of creating non-Gaussian states is key to building a universal quantum computer and achieving a quantum computational advantage. On the other hand, quantum correlations are also at the core of current developments in quantum technologies. Yet, quantum correlations in non-Gaussian states are still poorly understood for continuous-variable systems. In this contribution we will focus on quantum steering, where Alice and Bob each share a part of bipartite quantum state and perform local measurements on their respective subsystem. Quantum steering from Alice to Bob occurs when Bob can exploit information from Alice’s measurements to infer the outcome of his observables’ measurement more precisely than allowed by classical correlations. The paradigmatic example for this phenomenon is found when Alice and Bob both measure field quadratures. In this case, Bob can construct conditional variances that violate Heisenberg’s inequality. This violation, known as Reid’s criterion, is a signature of quantum steering that relies purely on Gaussian features of the state. More generally, we speak of Gaussian steering when we can violate steering inequality using only information from the state’s covariance matrix. For non-Gaussian states, the covariance matrix only offers limited information about the state, and many properties remain under the radar. Therefore, certification protocols of quantum steering for non-Gaussian states are scarce and generally highly demanding from an experimental point of view. In this contribution, we use a recently established connection between quantum steering and the (quantum) Fisher information to develop a new protocol for detection of quantum steering in non-Gaussian. This protocol relies exclusively on homodyne measurements.
The implementation of Listen-Read-Discuss Strategy to improve the eleventh-grade students’ reading comprehension at SMAN 1 PenawarAji

The aim of this research was to find out whether there was an improvement in students' reading comprehension after they were taught by using Listen-Read-Discuss (LRD) Strategy.The sample of this research was 30 students ineleventh grade of IPS 3 in SMA Negeri 1 Penawar Aji in academic year 2019/2020. The data were obtained after conducting the pretest and posttest.The result of this research shows that there is a significant improvement in students’ reading comprehension. It can be seen in the mean score of pretest which was 63.4 that improved to 71.2 in posttest and the t-value is 22.015 with df 19 meanwhile in t-table of 19 is 2.0930. It means that t-value is higher than t-table (22.015 &gt; 2.0930). The significant value was 0.00, which was lower than 0.05. It indicated that H1 is accepted, and that there was a significant improvement in students’ reading comprehension after they were taught using Listen-Read-Discuss Strategy. It can be concluded that Listen-Read-Discuss Strategy could be used as an alternative technique to improve students’ reading comprehension. Keywords: listen-read-discuss, Listen-Read-Discuss Strategy, reading comprehension.
Defining Artificial Intelligence

Artificial Intelligence (AI) has grown to become a research area that provides key technologies relevant across many disciplines and applications. This chapter briefly outlines the history of AI. The main areas of today's AI research landscape are described and their relation to each other is pointed out.
Ground Robot Path Planning on 3D Mesh Surfaces Using Bi-directional RRT based on Local Regions

The increasing application of ground robots requires efficient path planning algorithms in three-dimensional (3D) environments containing non-spherical topology. Path planning on surface meshes is possible, however, expensive computation of geodesics is required. To reduce the length and, hence, cost of the geodesics, a growing submesh based on local regions is used. Rapidly-exploring Random Trees (RRT) with local regions are computed and compared with the bi- directional variant, based on RRT-Connect. Results show that RRT-Connect with local regions reduces the computational burden for mesh-based path planning.
Open Innovation, Informal Competition, Corruption and Power Outages in Africa.

Existing studies on the effect of open innovation (OI) has been contradictory due to empirical and theoretical reasons. Empirically, aside from the dearth of studies on Africa, prior studies have focused on different contexts and different operationalisation of OI. Theoretically, there are contingent factors that could moderate this relationship that has not been given due consideration in existing research. This research, therefore, examines the moderation effects of power outages, corruption and informal competition on the relationship between OI and firm performance in Africa. To this end, we draw on country-level data for 43 African countries from the World Bank Enterprise Survey (WBES) to test our hypotheses. The findings suggest a high level of power outages and corruption weakens the effect of foreign licence technology on firm performance and the effect of informal competition was negative but not significant. The above result contributes to the existing literature by developing and testing a framework that examines how the effect of FLT on firm performance could be contingent on some institutional factors and doing so in a context (Africa) where existing studies are limited though highly needed.
Revamping DDL: Affordances of Digital Technology

Data-driven learning (DDL), introduced in 1990 by Tim Johns, has come in multiple guises ranging from serendipitous corpus exploration to focus on pre-selected linguistic aspects. In the present chapter, I will show that despite the variety of approaches used, DDL is still mainly conceptualised as the analysis of written concordance lines and has not taken advantage of the numerous advances in digital technology over the last 30 years (e.g. multimodality, mobility, etc.). After framing DDL in the light of some current theoretical frameworks of language pedagogy, I will discuss the norms, types of corpora, users, and technologies that are suitable for DDL (low tech, high tech, and wild tech options). I will show, with the help of concrete examples, that DDL can be revamped to make full use of the affordances of new technologies. Exploiting such affordances will hopefully also boost the use of DDL by and for many more learner profiles, such as younger learners or less favoured populations, thereby promoting more equitable multilingualism.
The Development of World Cybersecurity

AbstractPresently, new technology and new application like big data, cloud computing, AI, 5G and IoT breed new forms of digital economy. Digital form, platform-oriented form, intelligent form and ecological form re-forge society. Cyberspace has become a major field in which leading countries contend with each other. In 2020, the outbreak of COVID-19 enormously impacted on world cybersecurity, which signaled a new stage for world cybersecurity development.
The Shareholder Value Implications of Data Breaches

Digitalization in operations and supply chain management entails various opportunities for performance improvement. However, the increasing reliance on data introduces new risks for firms, such as data breaches. In this study, we assess the financial implications of data breaches and identify a set of factors influencing these implications. Based on a sample of 219 data breach announcements by publicly traded U.S. firms, we conduct an event study to examine the stock market reaction to data breaches. We further estimate a series of regression models to identify important influencing factors in the stakeholder environment of the firm, including several robustness checks and post-hoc analyses. We find a significantly negative stock market reaction to announcements of data breaches within a two-day event window. Our post-hoc analysis indicates a sustained negative impact on shareholder value. The negative stock market reaction is stronger when customer data is breached, the breach occurred along the supply chain, and the firm receives more media attention in general. This study adds to the emerging topics of cyber risk in operations and supply chain management, and to the literature on data breaches specifically.
Lung Cancer Detection using Machine Learning

Cancer is a disease in which cells in the body grow out of control. When cancer starts in the lungs, it is called lung cancer. Lung cancer begins in the lungs and may spread to lymph nodes or other organs in the body, such as the brain. Cancer from other organs also may spread to the lungs. When cancer cells spread from one organ to another, they are called metastases.
ACCELERATED VIRTUALIZATION OF HIGHER EDUCATION IN TIMES OF PANDEMIC: THE CASE OF AN ECUADORIAN UNIVERSITY

"The purpose of the paper is to present and discuss the impact, challenges, and opportunities that the COVID-19 pandemic and related lockdowns have caused to the te aching and learning model of an Ecuadorian Higher Education Institution: the Universidad Andina Simón Bolivar (UASB). The discussion will be led from the perspective of various educational actors and different levels of decision-making. At the beginning of 2020, the COVID-19 pandemic that confined a large part of the world's population to their homes and paralyzed activity in practically all areas of human action, forced a rethinking of education at all levels. In addition, the massive closure of schools and universities led to the deployment of distance learning modalities through a variety of formats and online platforms, replacing traditional educational processes. Experts have called emergency remote teaching for this sudden and unforeseen shift of face-to-face classes to virtual modalities. This name comes to highlights that the solutions adopted in a very short period, in many cases, could not be based on well-founded models of e-learning and a well-planned learning process. Still, rather traditional practices were transferred from face-to-face education to online platforms, maintaining their conventional forms in terms of methodologies, teacher and student roles, typologies of activities, evaluation models, and so on. The work presents the case study of the successful transformation process of a Higher Education institution based on a face-to-face model to a remote model to continue providing training to its students despite the disruption caused by the confinement derived from the health crisis of COVID-19. The analysis of the data collected through a variety of methods that included interviews with faculty, the analysis of institutional documents, and a student survey highlights the complexity and adequacy of the strategies implemented by the educational community to make the transition from an emergency remote teaching to a quality online learning ecosystem."
Two improved metaheuristic techniques and their applications in automated cryptanalysis of knapsack cryptosystems

During the past decade, considerable improved versions of real particle swarm optimisation (PSO) have been proposed in the literature. However, only a few significant improved versions of binary PSO (BPSO) have been reported. For efficiently solving binary optimisation problems, this paper proposes an improved-BPSO technique in which an improved idea for updating particles' velocity is proposed. To escape from the local optimum, an on/off mutation strategy is also introduced. Thereafter, the proposed strategy is utilised to solve two reduced knapsack cryptosystems. For a fair assessment of the proposed technique two relatively new methods are also utilised, namely, novel-BPSO and modified-BPSO. This paper also proposes an improved genetic algorithm (improved-GA) for solving the considered knapsack cryptosystems. Finally, the experimental results are analysed by performing f-test and t-test. The outcomes obtained indicate that the improved-GA and improved-BPSO strategies solve the considered automated cryptanalysis problem efficiently in terms of accuracy and convergence.
Image Matching and Registration

AbstractWhen we compare two images, we are faced with the following basic question: when are two images the same or similar, and how can this similarity be measured? Of course one could trivially define two images I1, I2 as being identical when all pixel values are the same (i.e., the difference I1−I2 is zero). Although this kind of definition may be useful in specific applications, such as for detecting changes in successive images under constant lighting and camera conditions, simple pixel differencing is usually too inflexible to be of much practical use. Noise, quantization errors, small changes in lighting, and minute shifts or rotations can all create large numerical pixel differences for pairs of images that would still be perceived as perfectly identical by a human viewer. Obviously, human perception incorporates a much wider concept of similarity and uses cues such as structure and content to recognize similarity between images, even when a direct comparison between individual pixels would not indicate any match. The problem of comparing images at a structural or semantic level is a difficult problem and an interesting research field, for example, in the context of image-based searches on the Internet or database retrieval.
APPTeK: Agent-Based Predicate Prediction in Temporal Knowledge Graphs

In temporal Knowledge Graphs (tKGs), the temporal dimension is attached to facts in a knowledge base resulting in quadruples between entities such as (Nintendo, released, Super Mario, Sep-13-1985), where the predicate holds within a time interval or at a timestamp. We propose a reinforcement learning agent gathering temporal relevant information about the query entities' neighborhoods, simultaneously. We refer to the encodings of the explored graph structures as fingerprints which are used as input to a Q-network. Our agent decides sequentially which relation type needs to be explored next to expand the local subgraphs of the query entities. Our evaluation shows that the proposed method yields competitive results compared to state-of-the-art embedding algorithms for tKGs, and we additionally gain information about the relevant structures between subjects and objects.
The Birth of Bias: A case study on the evolution of gender bias in an English language model

Detecting and mitigating harmful biases in modern language models are widely recognized as crucial, open problems. In this paper, we take a step back and investigate how language models come to be biased in the first place. We use a relatively small language model, using the LSTM architecture trained on an English Wikipedia corpus. With full access to the data and to the model parameters as they change during every step while training, we can map in detail how the representation of gender develops, what patterns in the dataset drive this, and how the model's internal state relates to the bias in a downstream task (semantic textual similarity). We find that the representation of gender is dynamic and identify different phases during training. Furthermore, we show that gender information is represented increasingly locally in the input embeddings of the model and that, as a consequence, debiasing these can be effective in reducing the downstream bias. Monitoring the training dynamics, allows us to detect an asymmetry in how the female and male gender are represented in the input embeddings. This is important, as it may cause naive mitigation strategies to introduce new undesirable biases. We discuss the relevance of the findings for mitigation strategies more generally and the prospects of generalizing our methods to larger language models, the Transformer architecture, other languages and other undesirable biases.
Topological Mapping for Manhattan-like Repetitive Environments

We showcase a topological mapping framework for a challenging indoor warehouse setting. At the most abstract level, the warehouse is represented as a Topological Graph where the nodes of the graph represent a particular warehouse topological construct (e.g. rackspace, corridor) and the edges denote the existence of a path between two neighbouring nodes or topologies. At the intermediate level, the map is represented as a Manhattan Graph where the nodes and edges are characterized by Manhattan properties and as a Pose Graph at the lower-most level of detail. The topological constructs are learned via a Deep Convolutional Network while the relational properties between topological instances are learnt via a Siamese-style Neural Network. In the paper, we show that maintaining abstractions such as Topological Graph and Manhattan Graph help in recovering an accurate Pose Graph starting from a highly erroneous and unoptimized Pose Graph. We show how this is achieved by embedding topological and Manhattan relations as well as Manhattan Graph aided loop closure relations as constraints in the backend Pose Graph optimization framework. The recovery of near ground-truth Pose Graph on real-world indoor warehouse scenes vindicate the efficacy of the proposed framework.