Reviewerly/exHarmony huggingface dataset:
Android game development

Mobile devices that are today at the global level with the largest usage rates are precisely those devices that are based on the Android OS platform. The work was based on the description of the created project, that is, the android game. Android games are the areas that are most represented in the IT development world, that is, the world of the mobile development application. Here, the focus is on that part of the application software, which is an integral part of the entire android package. The android system itself provides the opportunity as an open platform programming application solutions and install them on a mobile device. Through the work, its content and code in the form of scripts will be explained.
Statistical Analysis and Comparison of Survey Result on English Teaching of Two Groups after Experimental Teaching by SPSS 23

In order to improve current higher vocational English teaching, a comparative teaching featuring dynamic class teaching has been conducted for a semester. This paper constitutes a report on the survey results on different states of higher vocational English teaching of experimental group and control group after an experimental teaching with different teaching models, and provides a thorough statistical analysis by SPSS 23. The objective is to compare teaching effect of the two different teaching models and prove the strength of dynamic class model.
Research on Construction of the Course "University Computer Fundamentals" in Colleges and Universities Based on Big Data Analysis

This article focuses on the construction of the curriculum to study the teaching of basic computer courses in colleges and universities, discusses how to realize the cultivation of students&#x2019; values and patriotism in the silent moisturizing of things, and uses data analysis and visualization technology to show the specific implementation in the teaching organization and teaching effectiveness. This thesis proposes to solve worries in cultivating students with excellent teacher ethics, promoting the construction of the faculty team; to build a curriculum view of cultivating and educating people with morality and war; to optimize the content of the course by teaching knowledge points, including longitudinally organizing the history of computer science, telling stories about scientists&#x2019; exploration and discovery to build up confidence; to introduce information technology into the classroom, compare and analyze with foreign countries, increasing responsibility; Big data analysis is adopted to analyze and present the competition results in a multi-dimensional manner, providing more focus and reference for the subsequent optimization of course teaching, and laying a solid practical foundation for further improving the quality of course teaching.
Pembiayaan Pendidikan dan Kualitas Pendidikan di SMK Swasta Methodist Tanjung Morawa

The purpose of this research was to measure the extent to which the influence of education financing on the quality ofeducation at the Methodist Vocational School of Tanjung Morawa. The respondents of this study were all teachers and the ata were collected through a questionnaire with a Likert scale. The data then were tested and analyzed with the Ms. Excel 2019 and simple linear regression was implemented to analyze the data. The results showed that education financing influenced 63% on the quality of education and the calculated Fvalue was 0.0006 or less than the alpha value = 0.05 which indicated that there was an effect of financing on education on the education quality at the Methodist Tanjung Morawa Vocational School. The regression equation Y = 3.41 + 0.74x, indicated that there was a positive influence of the education financing variable on the education quality variable and the results of the ttest using a significance level of 0.05, the Pvalue obtained was lessr than 0.05 indicating the influence of education financing on the quality of education in Methodist Vocational School of Tanjung Morawa.
THE USE OF YOUTUBE VIDEO BY NON-ENGLISH MAJOR STUDENTS FOR SPEAKING SKILLS

This study was conducted to investigate the use of social media especially YouTube by students the Non-English Major of the Faculty of Economics at the University of Palangka Raya. It was also to see the impact of using social media especially YouTube to improve their speaking ability. The study design of this study was descriptive qualitative. Fifteen students of the Faculty of Economics were selected as participants to speak in front of a handphone camera with the topics already prepared. Five online meetings were done to know their progress. The Semi-structured interview was also done to collect the supporting data of deeper information needed. The result showed that the use of social media especially YouTube gave a high impact on the speaking ability of Non-English Major students as seen from their video and deep information from the interview. However, there was various resistance dealing with the efforts to increase their speaking ability, they are psychological factors (lessmotivated, not confident),environment factor (the relationship with their environment), linguistic factors (finding references, vocabulary selection, speaking style, and gesture). The use of social media especially YouTube might make the students feel easier in overcoming the various resistance to improve speaking ability.
A Flexible Remote Laboratory Platform for Interactive AI Experiments with Hardware and Software Facilities

With the rapid development of AI technology, colleges and universities are gradually starting to build AI laboratories to meet the growing demand for AI talent cultivation. AI experiments include a variety of hardware and software facilities, and the operation methods of different facilities are different. So this requires that the AI experimental platforms must adapt to different experimental facilities conveniently and flexibly, and realize the sharing of different types of experimental resources. Based on Social computing technology, this paper implements a flexible remote laboratory platform for interactive AI experiments with hardware and software facilities. The platform is compatible with a variety of experimental equipments and a unified communication module, which can flexibly integrate different kinds of AI experiments and various platform experimental resources. At the same time, the platform implements a new online programmable experiment environment based on the Wiki system, which makes students' online experiments easier. The platform implemented in this paper has a strong teaching effect and provides a very convenient remote teaching way for the AI experimental course.
Introduction

Graphs are a kind of data structure which models a set of objects (nodes) and their relationships (edges). Recently, researches of analyzing graphs with machine learning have received more and more attention because of the great expressive power of graphs, i.e., graphs can be used as denotation of a large number of systems across various areas including social science (social networks) [Hamilton et al., 2017b, Kipf and Welling, 2017], natural science (physical systems [Battaglia et al., 2016, Sanchez et al., 2018] and protein-protein interaction networks [Fout et al., 2017]), knowledge graphs [Hamaguchi et al., 2017] and many other research areas [Khalil et al., 2017]. As a unique non-Euclidean data structure for machine learning, graph draws attention on analyses that focus on node classification, link prediction, and clustering. Graph neural networks (GNNs) are deep learning-based methods that operate on graph domain. Due to its convincing performance and high interpretability, GNN has been a widely applied graph analysis method recently. In the following paragraphs, we will illustrate the fundamental motivations of GNNs.
Conclusions

This book has addressed the application of statistical significance testing to NLP research. The perspective we took is that of a comparison between two algorithms based on their performance on one or several datasets, which is the common evaluation setup in experimental NLP papers.
Forecasting of Spatial Geodesy Data and Investigation of the Relationship with the Solar Activity

This research aims to analyze the parameters that measure the movement of the planet and the natural phenomena that interfere in the variation of the duration of the terrestrial days, in order to predict the future situation in the coming months.The proposed methodology consists in collecting data of the day's variation on the IERS website (International Earth Rotation and Reference Systems Service), sunspot data on the SILSO website (Sunspot index and Long-term Solar Observations) and the Sun's polar magnetic field data on the WSO website (Wilcox Solar Observatory) using Webscrapping technique and store this data in a non-relational database.The predictions of the planet's rotation movement were made using the forecasting of time series through the auto regressions and autoregressive vectors, using the data collected from the VLBI (Very Long Baseline Interferometry).Through statistical analyzes with the graphs, the results show a 22-year LOD cycle and a 22-year PMF cycle, half the 11year sunspot cycle; and the periodicity of the day's variation and its forecasts resulted an RMSE (root mean squared error) in the order of only 0.2 milliseconds error.Another result was the introduction of the Sun's polar magnetic field (PMF) variable that made possible to create a new forecast curve for the variation of the length of the day (LOD).
Network Embedding for Graphs with Node Attributes

Most NE methods investigate network structures for learning. In reality, nodes in a graph usually contain rich feature information, which cannot be well applied with typical representation learning methods. Taking text feature as an example, we will introduce text-associated DeepWalk (TADW) model for learning NEs with node attributes in this chapter. Inspired by the proof that DeepWalk, a state-of-the-art network representation method, is actually equivalent to matrix factorization (MF), TADW incorporates text features of vertices into network representation learning under the framework of matrix factorization. We evaluate TADW and various baseline methods by applying them to the task of multi-class classification of vertices. The experimental results show that, TADW outperforms other baselines on all three datasets, especially when networks are noisy and training ratio is small.
Conclusions and Outlook

The previous chapters provided an in-depth discussion of the state of the art on external labeling algorithms. Our literature review identified that two mostly independent research communities have studied external labeling problems extensively: the visual computing community as well as the algorithms community.
Network Embedding for Graphs with Node Labels

Aforementioned NE methods learned low-dimensional representations for nodes in networks in a fully unsupervised manner. However, the learned representations usually lack the ability of discrimination when applied to machine learning tasks, such as node classification. In this chapter, we overcome this challenge by proposing a novel semi-supervised model, max-margin DeepWalk (MMDW). MMDW is a unified NRL framework that jointly optimizes the max-margin classifier and the aimed NE model. Influenced by the max- margin classifier, the learned representations not only contain the network structure, but also have the characteristic of discrimination. The visualizations of learned representations indicate that MMDW is more discriminative than unsupervised ones, and the experimental results on node classification demonstrate that MMDW achieves a significant improvement than previous NE methods.
Use of the mixed reality tool "VSI Patient Education" on the HoloLens 2 for more illustrative patient education before functional endoscopic sinus surgery (FESS)

Introduction Applications for mixed reality glasses both within medical education as well as in the preoperative and intraoperative phases have long exceeded the field of research. Regarding its use as a method for patient education before surgical interventions, however, available and reliable data remains very limited. Thus, the goal of this prospective randomized clinical study was to investigate if the use of the mixed reality tool "VSI Patient Education" (VSI PE) – as an application on the HoloLens 2 from Microsoft prior to FESS – is a superior alternative to the conventional form of patient education using CT images on the PC screen.
FREEDOM TO CHOOSE READING MATERIAL AND ITS EFFECTIVENESS TO READING COMPREHENSION

This research elaborates readers’ independency to choose the reading material and its effectiveness towards reading comprehension. It highlights the correlation of students’ independency in deciding the reading material they are going to read and how it correlates with the reading comprehension. Reading is considered as one of the language skills with easier access to be developed, both in numbers and quality of the sources. The research is quasi-experimental research with 21 participants from reading class students in English Education Program of State Islamic University Datokarama Palu. During the six weeks meeting the students are divided into two groups: experimental group and control group. Treatment was given by giving students numbers of reading materials and let them choose and read freely. The data were then analysed quantitatively using independent sample t-test. The result of the test shows that the significant value is 0.309 or classified as no significance between the readers’ freedom to choose the reading material with their reading comprehension.
Exploring Teachers and Students’ Perceptions towards Emergency Online Learning Intensive English Writing Course during COVID-19 Pandemic

For ages, teaching and learning face-to-face has been very popular and considered the best way to achieve knowledge and accomplish academic activities. However, during the Covid-19 Pandemic, where on the one side, the whole world was searching for a solution to cope with the situation on the other side online learning demonstrated a substantial alternative. The present paper aimed to investigate the teachers and students’ perceptions of the emergency online learning process, especially for intensive English writing courses. The study is descriptive using the qualitative research methodology. The two focus groups (teacher and student) are interviewed, consisting of 42 participants, asking open-ended questions mainly about the benefits and challenges of the writing course during the Pandemic. The paper ends with the teachers and students’ positive response that indicates the thrilled and jubilant welcome of the synchronized online/virtual learning in the latest situation that emerged due to the Pandemic. Eventually, the study discussed the quality assurance efforts, particularly towards the spontaneous conduction of lectures infused with motivation, responsibility, discipline, and flexibility offered without any interruption. The Internet proved a great learning tool in assuring many resources.
AUDIT LAYANAN TEKNOLOGI INFORMASI RUMAH SAKIT XYZ MENGGUNAKAN FRAMEWORK COBIT 5.0 (CONTROL OBJECTIVE FOR INFORMATION AND RELATED TECHNOLOGY)

XYZ Hospital requires information technology to carry out all activities as supporting health services. Information technology service audit is needed to determine the extent of information technology services at the XYZ Hospital. This study uses the COBIT 5 framework to determine the extent of information technology capability levels at the XYZ Hospital. Data from this study were obtained based on the results of interviews, observations, and questionnaires. The results show that the capability level of the findings in the company is 2.7 which means it is at level 3 (Established). Thus, the applied process conditions already have a standard and are able to achieve the results of the processes implemented. The results of the capability level of the whole process have not reached the desired level. This difference occurs because several processes in the IT field have not been carried out maximally, there is no scheduling, review, and report on the results of problem solving.
An Intelligent Vision-Based Tracking Method for Underground Human Using Infrared Videos

The underground mine environment is dangerous and harsh, tracking and detecting humans based on computer vision is of great significance for mine safety monitoring, which will also greatly facilitate identification of humans using the symmetrical image features of human organs. However, existing methods have difficulty solving the problems of accurate identification of humans and background, unstable human appearance characteristics, and humans occluded or lost. For these reasons, an improved aberrance repressed correlation filter (IARCF) tracker for human tracking in underground mines based on infrared videos is proposed. Firstly, the preprocess operations of edge sharpening, contrast adjustment, and denoising are used to enhance the image features of original videos. Secondly, the response map characteristics of peak shape and peak to side lobe ratio (PSLR) are analyzed to identify abnormal human locations in each frame, and the method of calculating the image similarity by generating virtual tracking boxes is used to accurately relocate the human. Finally, using the value of PSLR and the highest peak point of the response map, the appearance model is adaptively updated to further improve the robustness of the tracker. Experimental results show that the average precision and success rate of the IARCF tracker in the five underground scenarios reach 0.8985 and 0.7183, respectively, and the improvement of human tracking in difficult scenes is excellent. The IARCF tracker can effectively track underground human targets, especially occluded humans in complex scenes.
GAMES IN TEACHING SIMPLE PRESENT TENSE OF THE EIGHTH GRADE STUDENTS AT SMP NEGERI 7 AMPANA TETE

This research aims to find out whether students’ understanding in learning on simple present tense can be improved by using games or not. This research employed pre-experimental design. The variables of this research were games as the independent variable and the students’ understanding in learning simple present tense as the dependent variable. The population of this research was the VIII grade of SMP Negeri 7 Ampana Tete which consists of twenty five students. The technique sampling was total sampling, which means that all population was taken as the sample of this research. The researcher used test as the main instrument in this research which was divided into pre-test and post-test. The data analysis of this research used statistic calculation. The result of the research, the mean score of pre-test was 55.8 while the mean score in post-test was 87.2 and the testing hypothesis, the t-counted score was 11.80, while t-table for degree of freedom 24 and the level of significance = 0.05 was 1.710. It indicated that the hypothesis was accepted because games have improved students’ understanding in learning on simple present tense.
Semantic Segmentation with Multi-Source Domain Adaptation for Radiological Images

Differences in digitization equipment and techniques in radiology may hamper the use of data-driven deep learning approaches. In order to mitigate this limitation, in this work we merge generative image translation networks with supervised semantic segmentation architectures, yielding two semisupervised methods for domain adaptation in medical images. We compare our methods with traditional baselines in the literature using 3 image domains, 16 datasets and 8 segmentation tasks organized into three sets of experiments. Analysis of the results showed that the proposed methods for Domain Adaptation often reached Jaccard scores of 0.9 or higher in unsupervised or semi-supervised settings. We observe that unsupervised domain adaptation performance is close to the performance of fully supervised adaptation in most cases, bridging an important gap in the efficacy of neural networks between labeled and unlabeled datasets.
The information and knowledge landscapes of mutual aid: how librarians can use participatory action research to support social movements in community development

Mutual aid - including and beyond the COVID-19 pandemic - is a case study in how social movements can facilitate transformative community development through grassroots knowledge production, reliant on information management. Their approach aligns with participatory action research (PAR), which can provide a framework for librarians and other knowledge professionals to support the social movements in their communities by leveraging relevant expertise in solidarity-minded ways.
Improved Estimation of the Covariance Matrix using Reinforcement Learning

We propose a data-driven methodology to shrink the covariance matrix using reinforcement learning (RL). Our approach does not impose any assumption about the stock returns and can be applied to any covariance matrix target/prior. We focus on the special case of the global minimum variance portfolio and investigate the economic value of our methodology using two priors. The first is the identity matrix \citep{ledoit2002some}, whereas the other builds on the idea of text-based networks \citep{hoberg2016text}. The portfolio selection problem, hence, boils down to determining the optimal shrinkage policy using RL. The proposed methodology outperforms state-of-the-art shrinkage techniques in terms of out-of-sample Sharpe ratio (net of transaction costs). Overall, our research demonstrates that RL delivers a time-consistent shrinkage policy that incorporates future hedging/rebalancing needs.
Using seagull optimisation algorithm to select mobile service in cloud and edge computing environment

With the rapid development of edge computing, more and more services are deployed on edge servers. Compared with traditional cloud computing, services in the edge computing environment are closer to users, which bring benefits of high performance and low latency to the user-service interactions. However, due to the limited resources of edges, services provided by edges alone may fail to meet increasingly complex mobile computing requirements; therefore, services on clouds become an effective supplement. With the massive increment of services in the mobile internet, selecting proper services to fulfil mobile users' requests becomes a key research field. This paper proposes a service selection model for mobile service selection problem in cloud and edge computing environment. The proposed model combines the seagull optimisation algorithm and the simulated annealing algorithm. Through comparative experiments on simulation datasets with referencing to some other service selection models, it can be inferred that the proposed selection model finds a solution with better QoS performance in fewer iterations.