Reviewerly/exHarmony huggingface dataset:
Students Perception about Entrepreneurship Course Using Quantum Learning Model

Learning model are useful for helping students to learn and to provide an environment for interactive students experiences.Therefore, learning model has a strategic role especially for the achievement of entrepreneurial character building in students.Furthermore, the challenges that occur in the internalization of entrepreneurial values at University of Muhammadiyah Bangka Belitung (UNMUH BABEL) were that the character of students still does not show entrepreneurial stretching, such as students experience and interest in business-related activities.Whereas the expected learning environment such as student cooperatives is not yet available, that students only get experience from the courses and students actively participating in student activity units related to entrepreneurship and their activities are not optimal because only a small proportion are active.That situation made entrepreneurship course must be good managed as well as possible which requires a learning model accompanied by the ability of the lecturer to maximize the internalization process.The quantum learning model was a model that could anticipate of the challenges found in entrepreneurship learning, especially for Primary School Teacher Study Program students.The result of this research used a basic study for further entrepreneurship development model in UNMUH BABEL.The aim of this research was to know of the students perceptions of the quantum learning model used in the Entrepreneurship course.Furthermore, to describe the learning process that has been carried out.This research uses descriptive quantitative method.The data were collected through questionnaire of students perception.The subject in this study were 50 students form a total of 124 students who have experienced entrepreneurship learning.The result of this research showed that perception of Enroll (75%); Demonstrate (74%); Review (75%); and Celebrate (77%) were included good category.However the Experiences (54%) and the Label (41%) were included passably category.The conclusion of this research was quantum learning model could be to improve the students entrepreneurial skills based on their perception and experiences.
Using Reading Aloud Technique to Stimulate Students Reading Comprehension

This article aims to stimulate students' reading comprehension skills using the Reading Aloud technique. Reading aloud is the main axis connecting reading and speaking English. Reading aloud not only increases reading and basic knowledge but also improves oral expression. How the Read aloud technique can stimulate students' reading skills. Reading plays an important role as one of four abilities in improving students' English performance. This study involved 16 respondents who were 5th-semester students of the English Language Education Program at Ibn Khaldun University. This research was a descriptive study in which the researcher used a qualitative method. Data collected by interview, questionnaire, and observation. The results of the study showed that most of them did not agree with the techniques from Reading aloud. Based on respondents' statements, we found that the use of the reading method with hard reading techniques helped students to stimulate reading comprehension, especially to help students in pronunciation.&#x0D;
Effects of Non- Performing Loans on Economic Growth Evidence from four African countries: (Ghana, Nigeria, South Africa and Kenya)

The central focus of this study is to find out how non-performing banking loans affect economic growth in four major African economies since this could help in significant macro and micro policy directions in the countries under consideration.The study employed panel regression analysis and adopted random effect model to find the relationship between the key variables of interest; economic growth (GDP) and Non-performing loans accounting for the heterogeneity that exists among the countries; Ghana, Nigeria, South Africa and Kenya using data from the world bank data catalogue from the period 1999 to 2019.The study established that Non-performing loans (NPL) and Bank Liquidity Reserve to Bank Assets ratio (Banksliq) negatively and significantly affects economic growth however, Inflation negatively affects economic growth but statistically insignificant.The results from this study therefore constitute an important call on policy makers and managers of these economies to adhere strictly to the laws regulating their financial and banking sectors especially on the issues of NPL and Banksliq.The governments and monetary policy authorities in these respective countries must also collaborate actively with the financial sector players to monitor and improve the control of access to the limited investment funds.This is to say that access to investment capital should be based on market principles.Fiscal policy should also be carefully anchored with monetary policy to avoid inflationary pressure since it also exposes borrowers to default risk.
Innovative Trends and Approaches in Integrating Technology in Education: An International Comparison

The modern information society has had a significant impact on the education systems in many countries around the world. It has enabled openness, accessibility, mobility and lifelong education. However, two decades after the first mainstream rollout of computers in schools, we have learned many significant lessons about Information-Communication Technologies (ICT) in Education and their potential transformational impact on national education systems. ICT is a term used in European countries and is similar to Information Technology (IT) in North America. This research will focus on some challenges faced with modern trends in technology in different educational public schools systems in Russia and in Texas, USA. We surveyed over 180 Russian students and 100 Texas students, all in the middle grades, on their attitudes and preferences on use of technology inside and outside of the classroom. Our results showed, with respect to listening to music, playing computer games, and reasons for internet searches, Russian and Texas students are similar. Differences of uses of multimedia, drawing and graphic editing, and use of game servers were found between these students. Analysis of the standards and approaches in technology integration in the Russian and American education systems, and the comparative results of the surveys from both countries, show modern trends in the use of technology.
Type inference as constraint solving: how GHC’s type inference engine actually works

The Haskell compiler GHC includes a type inference engine for a rather sophisticated type system.  You might worry that a complicated type sytsem leads to a very complicated type inference engine. You’d be right, but we have learned a lot about how to structure type inference so that the complexity does not get out of control. In this talk, given at Zurihac 2019, I explain how GHC’s type inference engine actually works by first generating constraints and then solving them.
Learning Probabilities: Towards a Logic of Statistical Learning

We propose a new model for forming beliefs and learning about unknown probabilities (such as the probability of picking a red marble from a bag with an unknown distribution of coloured marbles). The most widespread model for such situations of 'radical uncertainty' is in terms of imprecise probabilities, i.e. representing the agent's knowledge as a set of probability measures. We add to this model a plausibility map, associating to each measure a plausibility number, as a way to go beyond what is known with certainty and represent the agent's beliefs about probability. There are a number of standard examples: Shannon Entropy, Centre of Mass etc. We then consider learning of two types of information: (1) learning by repeated sampling from the unknown distribution (e.g. picking marbles from the bag); and (2) learning higher-order information about the distribution (in the shape of linear inequalities, e.g. we are told there are more red marbles than green marbles). The first changes only the plausibility map (via a 'plausibilistic' version of Bayes' Rule), but leaves the given set of measures unchanged; the second shrinks the set of measures, without changing their plausibility. Beliefs are defined as in Belief Revision Theory, in terms of truth in the most plausible worlds. But our belief change does not comply with standard AGM axioms, since the revision induced by (1) is of a non-AGM type. This is essential, as it allows our agents to learn the true probability: we prove that the beliefs obtained by repeated sampling converge almost surely to the correct belief (in the true probability). We end by sketching the contours of a dynamic doxastic logic for statistical learning.
Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation

Unsupervised domain adaptation (UDA) for semantic segmentation has been attracting attention recently, as it could be beneficial for various label-scarce real-world scenarios (e.g., robot control, autonomous driving, medical imaging, etc.). Despite the significant progress in this field, current works mainly focus on a single-source single-target setting, which cannot handle more practical settings of multiple targets or even unseen targets. In this paper, we investigate open compound domain adaptation (OCDA), which deals with mixed and novel situations at the same time, for semantic segmentation. We present a novel framework based on three main design principles: discover, hallucinate, and adapt. The scheme first clusters compound target data based on style, discovering multiple latent domains (discover). Then, it hallucinates multiple latent target domains in source by using image-translation (hallucinate). This step ensures the latent domains in the source and the target to be paired. Finally, target-to-source alignment is learned separately between domains (adapt). In high-level, our solution replaces a hard OCDA problem with much easier multiple UDA problems. We evaluate our solution on standard benchmark GTA to C-driving, and achieved new state-of-the-art results.
Multi-Task Learning using Dynamic Task Weighting for Conversational Question Answering

Conversational Question Answering (ConvQA) is a Conversational Search task in a simplified setting, where an answer must be extracted from a given passage. Neural language models, such as BERT, fine-tuned on large-scale ConvQA datasets such as CoQA and QuAC have been used to address this task. Recently, Multi-Task Learning (MTL) has emerged as a particularly interesting approach for developing ConvQA models, where the objective is to enhance the performance of a primary task by sharing the learned structure across several related auxiliary tasks. However, existing ConvQA models that leverage MTL have not investigated the dynamic adjustment of the relative importance of the different tasks during learning, nor the resulting impact on the performance of the learned models. In this paper, we first study the effectiveness and efficiency of dynamic MTL methods including Evolving Weighting, Uncertainty Weighting, and Loss-Balanced Task Weighting, compared to static MTL methods such as the uniform weighting of tasks. Furthermore, we propose a novel hybrid dynamic method combining Abridged Linear for the main task with a Loss-Balanced Task Weighting (LBTW) for the auxiliary tasks, so as to automatically fine-tune task weighting during learning, ensuring that each of the task’s weights is adjusted by the relative importance of the different tasks. We conduct experiments using QuAC, a large-scale ConvQA dataset. Our results demonstrate the effectiveness of our proposed method, which significantly outperforms both the single-task learning and static task weighting methods with improvements ranging from +2.72% to +3.20% in F1 scores. Finally, our findings show that the performance of using MTL in developing ConvQA model is sensitive to the correct selection of the auxiliary tasks as well as to an adequate balancing of the loss rates of these tasks during training by using LBTW.
INSTRUCTIONAL MEDIA USED IN TEACHING ENGLISH

This study investigates (1) the use of instructional media by teachers in teaching English to students. This research was conducted at a private elementary school in Cimahi, namely the Harapan Bangsa Creative Elementary School. To collect data, researchers used a case study approach with observation and interview techniques. The results showed that there are three types of learning media that are generally used by teachers, consisting of: (1) books (2) pictures and (3) videos. Each of these media is used in many different ways according to the needs of teaching and learning activities. In this case the teacher feels difficult to face challenges in the process of selecting and using instructional media due to lack of resources and enthusiasm from students so that teaching and learning activities become less effective and efficient. The solution used to solve the problem of learning media during the selection process is to utilize the media available at school. In the field observation process, the media that has been used by the teacher is a textbook. From the use of instructional media using textbooks only 75% of students are able to survive with these media. Keywords: Instructional Media, English
Image Filter With Logistic Functions

Image enhancement is one of the most important issue of image processing. During acquisition of image, undesirable signals could be added into digital image. Image enhancement algorithms are used to clean the relevant noises. The well-known image filters in frequency domain are ideal, Butterworth and Gauss type filters. Each of them has its own characteristics and parameters to be selected by users. Depending parameters preferred, different responds are obtained. In this study, a novel image filter with logistic functions has been proposed. The developed filter has capability of satisfying the characteristics of conventional filters with single function: ideal, Butterworth and Gauss lowpass filter. The devised filter has been tested and compared with conventional filters. Furthermore, the obtained results have been given.
Range Resolution in Quantum Noise Radar

Recently proposed methods for generation of entangled (quantum correlated) signals directly in microwave frequency range give more chances for design of QR for long-range applications. In such applications, the range resolution capability is of a great importance. Using analogy of signal generation and processing in Noise Radar and Quantum Radar we describe a novel concept for Stepped-Frequency QR (SF-QR) design which enables preserving quantum properties of probing signals and provide range resolution capability in SF-QR.
AN EMPIRICAL INVESTIGATION OF LANGUAGE MODEL BASED REVERSE TURING TEST AS A TOOL FOR KNOWLEDGE AND SKILLS ASSESSMENT

Automating assessment of person’s skills is an important area of study in artificial intelligence and natural language processing. In this work we conduct empirical study of a recently proposed Reverse Turing Test for Knowledge Assessment approach—a completely automated domain agnostic method of knowledge assessment that can operate completely without human assessor involvement. Our study involved 53 participants and three different knowledge domains. We conclude that this method can reliably differentiate between expertise levels and therefore can be a compelling alternative to human grading and multiple-choice tests in many domains.
Augmented Reality Application for Surgery Simulation: Circumcision Augmented Reality Simulation (CARS)

As one of the most common minor surgical procedure performed globally, male circumcision has some complications when performed by medically trained providers and non-medically trained providers. Presently, there is no standardized circumcision training approach, and there have been variable resources and materials used. We constructed a low-cost, and mobile circumcision training application augmented reality called Circumcision Augmented Reality Simulation (CARS). CARS is built in a computer with minimum processor 4Ghz (RAM 4GB, VGA 2GB) and smartphone with Blender, Inkscape, Audacity and Unity3D software. Features that are provided in CARS are including 3D models, animation and sound of circumcision procedure. CARS was tested through several steps including black box technique, response time loading and layer resolution examination. The trial is also done with various smartphones. The angles detected ranging from 20o-90o, with a maximum distance of 4 m using markers measuring 20x22 cm. Based on the percentage of the enclosed barrier, the marker can be detected between 0-90 percent. CARS as an example of augmented reality application for surgery simulation has been successfully built with features performing circumcision simulation and suitable for potential users (medical students and doctors). Implications for future research include assessing reliability and validity by growing its use to a broader population, creating another surgery procedure simulation with augmented reality technology using smartphone.
Analysis of the possibilities and peculiarities of programming cryptology problems on a quantum computer

This paper is devoted to detailing the possibilities and features of quantum computer use for cryptological problems, their demonstration, justification of approaches to the possibilities analysis and studying features of cryptanalysis problems programming on quantum computers. The possibilities and availability of hardware for solving cryptanalysis problems through quantum methods are analyzed and existing restrictions of their use are determined. The quantum computer and quantum computer programming features are considered. The possibilities of quantum computer use for cryptanalysis are also considered with the Grover’s method example. The essence of Grover’s method and features of its application for cryptanalysis are given. An example of its application to the search space which is represented by a quantum register of 56 qubits is given as well. The quantum computer application of Grover’s method on quantum computer accessible through a loud service is considered. Schemes for conducting a search by Grover's method for quantum computer application are developed, containing a different number of Grover iterations to study the need for executing a full cycle, the possibility to stop and evaluate search results at a certain stage. The developed circuits are tested on quantum computers with different architectures and on a quantum simulator provided for the analysis of circuits intended to run on a quantum computer. The comparison of the expected and obtained results of the Grover’s method application at different search stages on quantum computer is given.
Processing effort is a poor predictor of cross-linguistic word order frequency

Some have argued that word orders which are more difficult to process should be rarer cross-linguistically. Our current study fails to replicate the results of Maurits, Navarro, and Perfors (2010), who used an entropy-based Uniform Information Density (UID) measure to moderately predict the Greenbergian typology of transitive word orders. We additionally report an inability of three measures of processing difficulty — entropy-based UID, surprisal-based UID, and pointwise mutual information — to correctly predict the correct typological distribution, using transitive constructions from 20 languages in the Universal Dependencies project (version 2.5). However, our conclusions are limited by data sparsity.
Effect of Environmental Cost Disclosure on Profitability of Listed Oil and Gas Firms in Nigeria

The objective of this study was to ascertain the effect of environmental cost disclosure on profitability of oil and gas firms listed on Nigeria Stock Exchange between 2010 and 2019.Eleven (11) listed oil and gas firms were purposively sampled.The proxies for environmental cost disclosure include waste management cost disclosure, employee health and safety cost disclosure and environmental remediation cost, while net profit margin was employed as profitability measure.Content analysis was employed while Pearson Correlation Coefficient and Panel Least Square (PLS) Regression analysis via STATA 13 statistical software were used to test the hypotheses of the study.The result of this study showed that waste management cost disclosure, employee health and safety cost disclosure and environmental remediation cost disclosure have a significant positive effect on net profit margin at 5% level of significance respectively.This study therefore recommends inter alia that since environmental cost is value relevant in making strategic business decision.Thus, oil and gas firms should constantly reposition their accounting system in order to provide information on environmental cost so that the true costs in an organization can be ascertained and properly allocated.
Searching via Nonlinear Quantum Walk on the 2D-Grid

We provide numerical evidence that the nonlinear searching algorithm introduced by Wong and Meyer, rephrased in terms of quantum walks with effective nonlinear phase, can be extended to the finite 2-dimensional grid, keeping the same computational advantage with respect to the classical algorithms. For this purpose, we have considered the free lattice Hamiltonian, with linear dispersion relation introduced by Childs and Ge The numerical simulations showed that the walker finds the marked vertex in O(N1/4log3/4N) steps, with probability O(1/logN), for an overall complexity of O(N1/4log5/4N), using amplitude amplification. We also proved that there exists an optimal choice of the walker parameters to avoid the time measurement precision affecting the complexity searching time of the algorithm.
Recent Developments in Cloud Computing Environment: Current Challenges and Future Prospects

Cloud computing provides various services which have changed the concept of information storage and retrieval recently. The benefits of cloud environments have arisen a technological shift from centralized computing to distributed and online capacities. These changes are of concern at present to industry and academia as some obvious security are compromised with benefits. In this presented work, we discuss the security risks and major challenges for the cloud computing environment. Some open research problems are also identified for future work and those who research in this field look in that direction.
Building and Deploying Models in SageMaker

In this chapter, we will be exploring some of SageMaker’s built-in algorithms that are widely used in the industry. We will be exploring the algorithms from the general domain, natural language processing domain, computer vision domain, and forecasting domain.