Reviewerly/exHarmony huggingface dataset:
HIGH FUNCTIONING AUTISTIC STUDENTS’ LEARNING STRATEGIES IN WRITING TEXTS IN INCLUSIVE SCHOOLS

The aims of this study were to: 1) describe the process of how HFA students learn descriptive texts in inclusive classes, 2) disclose the influencing factors of HFA students’ success in writing descriptive texts in inclusive classes, 3) examine the learning strategies used by the HFA students in writing descriptive texts in inclusive classes.&#x0D; A qualitative study was used as a method of this study. The participants of this study were four HFA students at the seventh grade of inclusive schools. This study was conducted in two schools in Junior High School of Yogyakarta and Purwokerto, Central Java. Interview, observation, and documentation were used to collect the data. Moreover, this study used data condensation, data display, conclusion drawing, or verification to analyze the data.&#x0D; The results generally showed that the pictures were employed by HFA students as inspiration to enhance their ability in writing descriptive texts. Involving them into fun activities could enhance social skill dealing with their social impairments. HFA students choose particular strategies to accomplish particular task. There are some crucial factors that determine the success of autistic students in writing English descriptive texts. They are internal factors and external factors. Internal factors cover: motivation 1) autistic students’ perception on learning strategies used, 3) learning experiences. Internal factors consist of: 1) environment; 2) teacher’s strategies; 4) role of special guidance teachers. In addition, two dominant strategies are used when learning English. They are memory strategies and cognitive strategies. The last, one of the factors that influence the students’ choice of learning strategies is teacher’s teaching strategies.&#x0D; Keywords: Learning Strategies, English Learning Strategies, Memory strategies, Cognitive Strategies, Autistic Students, HFA Students.
고교학점제와 2022 개정 교육과정에 대비한 과학과 융합선택과목 재구조화 방안 탐색

The goal of this study is to explore ways to restructure Convergence Elective Courses in science in preparation for the high school credit system, ahead of the 2022 revised science curriculum. This study started from the problem that the 2015 revised science curriculum has not guaranteed science subject choice for students with non-science/engineering career aptitudes. To this end, a survey was conducted by randomly sampling high schools across the country. A total of 1,738 students responded to the questionnaire of 3 science elective courses such as Science History, Life & Science, Convergence Science. In addition, in-depth interviews with 12 science teachers were conducted to examine the field operation of these three courses, which will be classified and revised as Convergence Elective subjects in the 2022 revised curriculum. According to the results of the study, high school students perceive these three courses as science literacy courses, and find these difficult to learn due to lack of personal interest, and difficulties in content itself. The reason students choose these three courses is mainly because they have aptitude for science, or these courses have connection with their desired career path. Teachers explained that students mainly choose Life & Science, and both teachers and students avoid Science History because the course content is difficult. Based on the research results, we suggested ways to restructure Convergence Electives for the 2022 revised curriculum including developing convergence electives composed of interdisciplinary convergence core concepts with high content accessibility, developing convergence electives with core concepts related to AI or advanced science, developing module-based courses, and supporting professional development of teachers who will teach interdisciplinary convergence electives.
PENINGKATKAN KEMAMPUAN SISWA MEMAHAMI POKOK BERITA DENGAN MENGGUNAKAN MODEL PEMBELAJARAN KOOPERATIF TIPE TALKING STICK DI KELAS X SMA NEGERI I RANTAU SELATAN TAHUN PEMBELAJARAN 2020 - 2021

The problem in this study is that the ability to understand student news is still low. To overcome this, a learning model isneeded that can improve students' ability to understand the subject, the learning model in question is the talking sticklearning model. The purpose of this study is to find out whether talking stick-type cooperative learning models can improvethe ability to understand the subject matter. This research is a PTK (Class Action Research) study by giving treatment to thegroup conducted using the talking stick learning model. The population of this study was all 40 high school students, whilethe sample of this study was as many as 40 students. The instruments used to capture the study data are observations andtests. Based on the results of the research obtained, it was concluded that the application of talking stick learning models canimprove students' ability to understand the subject matter, this is seen based on test results. In the pre-cycle, students whocompleted by 45%, when compared to the criteria of the level of completion of learning are at the criteria less once. Then incycle I increases to 70%, when compared to the completion criteria then it is on the criteria enough. In cycle II completionincreased back to 90%, when compared to the criteria of learning completion level is at very good criteria.
Using Audio Middleware

While game engines are impressive in their power, they lack many important features for composers and sound designers. Often, a composer must spend several hours accomplishing a seemingly simple task in <italic>Unity</italic> that could be achieved quite easily with other methods. This chapter demonstrates the benefits of audio middleware, discussing its functionality and the practical reasons why it is ideal to implement middleware to create game audio whenever possible. The reader will learn how to use <italic>Wwise</italic> to create interactive music structures and seamless transitions.
Getting Started with Python Programs in Visual Studio Code

In the previous chapter, we covered the basics of VS Code. In this chapter, we are going to explain how to set up your VS Code for Python development. It can be easily downloaded and installed for all the major operating systems such as Windows, Linux, and macOS.
한국어와 반투어의 격 표지 대조 - 쇼나어를 중심으로 -

The purpose of this study is to do a contrastive analysis on case marking in Korean and Shona- a bantu language. To achieve this, the structure, method of presentation and function of these were analyzed and the similarities and differences were presented. Korean’s case marking is realized through a postpositional particle on the argument, whereas in Shona, a prefix allomorph attached to the verb complex for subject marking and an infix on the post-initial position of the verb complex for object marking is employed. In Korean the choice between ‘-i’ or ‘-ka’ for subject marking , and between ‘-eul’ or ‘-reul’ for object marking relies on the phonological characteristics of the language. In Shona on the other hand, the choice of case marking is dependent on up to 20 noun classes and save for the position, the morphology of both subject and object markers are considerably similar. Korean and Shona case marking also differ in terms of function, namely, Korean’s case markers serve a ‘distinguishing role’, whilst Shona’s serve an ‘identifying’ role. While Korean’s structural case particles are commonly dropped in spoken language, Shona’s subject markers cannot be dropped, and the object marker, depending on the animacy, definiteness or information structure it represents, is used optionally. Knowing the differences in Korean’s and Shona’s case marking will aid Shona native speakers learning Korean in their understanding of Korean’s sentence structure.
Complementary Object Tracking Using Average Peak-to-Correlation Energy

In recent years, template-based methods such as Siamese network trackers and Correlation Filter (CF) based trackers have achieved state-of-the-art performance in several benchmarks. Recent Siamese network trackers use deep features extracted from convolutional neural networks to locate the target. However, the tracking performance of these trackers decreases when there are similar distractors to the object and the target object is deformed. On the other hand, correlation filter (CF)-based trackers that use handcrafted features (e.g., HOG features) to spatially locate the target. These two approaches have complementary characteristics due to differences in learning methods, features used, and the size of search regions. Also, we found that these trackers are complementary in terms of performance in benchmarking. Therefore, we propose the “Complementary Tracking framework using Average peak-to-correlation energy” (CTA). CTA is the generic object tracking framework that connects CF-trackers and Siamese-trackers in parallel and exploits the complementary features of these. In CTA, when a tracking failure of the Siamese tracker is detected using Average peak-to-correlation energy (APCE), which is an evaluation index of the response map matrix, the CF-trackers correct the output. In experimental on OTB100, CTA significantly improves the performance over the original tracker for several combinations of Siamese-trackers and CF-rackers.
Automatic Generation of Security-Centric Description for Cyber Threats

It is essential to ensure the users comprehend the security information so that they can apply the advice it contains in practice. However, the security-centric descriptions generated by recent techniques do not always address users' concerns and they are generally too technical to be understood by ordinary users. The thesis presents how we study users’ understanding of cybersecurity texts and how we design the tool to automatically generate understandable descriptions to help the users comprehend the cybersecurity information and raise their security awareness. Our developed tool is designed with educational purposes in mind to help users increase their security knowledge.
Dissecting Convolutional Neural Networks

I examined the hidden layers of a convolutional neural network for MNIST handwritten digit classification. I found that it is possible to view how backpropagation affects nodes in the activation maps of hidden layers by visualizing their outputs as images across training epochs. This can allow one to look inside the black box of neural networks and to gain a deeper understanding of their mechanics.
Pemanfaatan Internet Sebagai Sumber Belajar Pada SMU Negeri 2 Pematangsiantar

The learning process today is inseparable from the use of technology as a learning resource. The internet as a medium of information that is able to penetrate the boundaries of space and time has a lot of potential that can support a better educational process. The amount of information available can be used as literature to broaden horizons. Utilization of the internet as a learning medium is one component of learning resources and at the same time a form of problem solving learning that can be considered as a new thing. This phenomenon is expected to be a solution to the problems that have occurred so far, such as limited manpower, distance, geographical location, high costs and limited study time.
Robustness of Neural Networks

This paper analyzes the robustness characteristics of Neural Networks (NN) using linear systems theory. Robustness is defined as the ability of a neural network to map untrained data (data not used during training) within an error tolerance. An induced Euclidean matrix norm is used to derive error bounds for NN with activation functions that predominantly exhibit linear behavior. Lyapunov stability theory is used to derive bounds on the non-linear variations in NN with activation functions that exhibit non-linear behavior. A Monte Carlo simulation analysis is conducted to examine the robustness characteristics of fully and sparsely connected networks. The following conclusions are drawn based on the above analysis: (a) sparsity in the NN connection topology is highly desirable to achieve robustness; (b) two hidden layer networks with equal number of neurons in each layer exhibit very poor robustness; (c) a fully forward-connected network with sparsity is the most robust and accurate for a given number of neurons; and (d) for NN with many neurons, highly non-linear activation functions exhibit very poor robustness.
About the literary design of scientific works

Our generation is an active participant in a magnificent historical process. Capitalist economic forms are being broken, the chains of religion are thrown off, the old, superstitious, dark is burnt in the fire of the class struggle. A new socialist culture is born, a new communist morality, a new person.
P-086. Establishment of prediction model for hypertensive disorders of pregnancy by time series analysis

This paper investigates a novel PDE-constrained optimization model with discontinuous variable exponent p(x) identification. Since the parameter p is always related to a better approximation of the image gradient, its computation plays a critical role in preserving the image texture. Analytically, we include results on the approximation of this parameter as well as the resolution of the encountered PDE in a well posed framework. In addition, to resolve the PDE-constrained minimization problem, we proposed a modified primal-dual algorithm. Finally, numerical results are provided to compute the parameter p and also to remove high intensity of noise. The proposed algorithm simultaneously keep safe fine details and important features in medical image applications (Magnetic Resonance Images (MRI)) with numerous comparisons to show the performance of the proposed approach.
IMPLEMENTATION OF KUBERNETES CLUSTER USING LXD CONTAINER

The use of cloud computing service enables clients to access data and information without having to install an application which increases the number of client requests that leads to the increase of server load and could result in overload. In clustering technology, the server workload will be distributed evenly to each server. To run clustering, a platform called Kubernetes is needed. Kubernetes is used for container management. To run a clustering with Kubernetes requires virtualization using LXD. This final project compares the performance of conventional model services and Kubernetes clusters. The test results of two servers in standby condition, the technology that uses the highest CPU and memory is the Kubernetes cluster which reached 104% on CPU usage, while memory usage is 28%. It is because of the nodes that are running on the virtual machine. In busy server conditions, conventional technology is the one that uses the highest CPU and memory due to the high, which reached 1289% on CPU usage while memory usage is 46%. Number of client requests that increase CPU and memory resources.
The Bright Side of COVID-19: Implications of the Pandemic on E-Learning and the Educational Sector- A deep look into the Action Plan of the Jordanian Ministry of Higher Education

A plethora of research have been published on COVID-19 focusing only on its negative impact on every aspect of our lives. Undoubtedly, COVID-19 has been one of the worst health emergencies that caused millions of deaths around the world and disastrous effects on economies and various sectors. However, research so far has completely ignored the other part of the story. Therefore, this study turns the scales upside down and focuses on the bright side of COVID-19 by exploring its positive impact on e-learning and the educational sector in Jordan. This study discusses the action plan for e-learning set by the Jordanian Ministry of Higher Education for the period 2021-2023. The results demonstrate that COVID-19 has done to e-learning more than any strategy or plan by the Jordanian Ministry of Higher Education and all higher education institutions. It has opened the eyes on the potential and promises of e-learning for the higher education sector in Jordan.
Expository text and propositional text processing

The topic of this paper is whether it is worth applying the propositional approach to text processing and readability to the improvement of longer expository texts. Two versions of a biology text were constructed: a coherent one would, according to the propositional approach, lead to better learning outcomes, than a non-coherent one. To check for instructional alternatives, a reward for good learning results was either announced or not In a field experiment, 167 students at school read one text under one condition and had to give a written reproduction immediately after reading and again after two weeks. Reproductions were merely weakly influenced by the texts’ propositional structure; visible effects disappeared after two weeks. In contrast, reward caused strong effects, which even became stronger in time. Finally, some reasons are presented, which account for the fact that previous results within the propositional approach might not be valid for longer expository texts.
Neural networks for short term pattern storage

An analysis of long-term memory processes indicates a need for short-term mechanisms that store arbitrary spatial patterns of neural activity for substantial lengths of time. This paper analyzes the short-term pattern storage properties of generalized on-center, off-surround neural networks. Except for special cases, such networks cannot store patterns for arbitrarily long lengths of time (to equilibrium). However, bounds of pattern degradation show that networks close (in a precise sense) to these special cases may store arbitrary spatial patterns of activity for substantial lengths of time with good accuracy.
The Model of Engineering Inventories Design with Pieces Abd Telos System

The purpose of this study is to design an accounting and engineering information system at Burger Sandwich Steak Restaurants in Indonesia. This study is an application study. The focus of the study is Burger Sandwich Steak Restaurants in Indonesia. Methods of data collection consisted of interviews, observations and observations. The analysis method used is the PIECES analysis, system requirements analysis, and performance analysis of the TELOS system. The software used is SQL Server and Microsoft Visual Studio. The results of this study show that: Details of accounting and engineering at Burger Sandwich Steak Restaurants in Indonesia are still uncommon. The accounting-related functions are front-end, kitchen, bar, cashier, and accounting, while those for startups are engineering and accounting. Documents used in the accounting system for accounting orders and purchases made, and engineered inventions are purchase notes. The records used in accounting details are sales records, and the inventory of engineering is in stock cards. The design of the system includes: goods, menu, detail menu, supplier, purchase, sale, start-up and customization. (c) The output design consists of: Inventory reports for engineering, purchase, and sales (d) Implementation results indicate that the system can work well and the modifications used for direct conversion.
ON APPLICATION OF KALMAN FILTER METHOD TO HIV/AIDS EPIDEMIC

Abstract:The epidemic of HIV infection and AIDS is still growing and has been steadily raising the national rankings of causes of death. The composition of AIDS population is changing both in geographic area and risk group. The changing behaviour of the epidemic necessitates our better understandings of the disease and the ways of intervention. One of the tools to facilitate this task is by the use of modelling the natural history of the epidemic. In recent years many factors, including therapy and behavioural changes have modified the course of the HIV/AIDS epidemic. To include these modifications in HIV/AIDS models, a recessive estimation technique such as Kalman Filter is used. The Kalman Filter approach was applied to a simple differential model to describe the observed HIV/AIDS epidemic in the homo/bisexual male community.
Google, Yahoo and Bing Search Engines' Performance in the Persian Information Retrieval: A Fuzzy and Classical Evaluation

Purpose: Evaluating the Fuzzy and classical search engines' performance in Persian information retrieval to determine the false drop rate and select the best in retrieving the lowest duplicate records. Methodology: In this applied research, the semi-experimental, evaluative and comparative methods are adopted. Research samples are selected according to purposeful sampling, based on the popularity of search engines. The data collection tool is a researcher-devised checklist with 20 questions. Findings: It is revealed that Google out performs Yahoo and Bing in both the fuzzy and classical evaluations. The obtained precision ratio of the fuzzy evaluation of search engines is greater than of the classical evaluation. In both evaluations, Google, Yahoo and Bing have the lowest rate of false drop, respectively. In the fuzzy evaluation, this false drop ratio is less than of the classical evaluation. Google has the lowest and Yahoo has the highest duplicate records' count. Conclusion: The obtained findings from the fuzzy and classical evaluation reveal that Fuzzy evaluation increases the precision rate and reduces false drop in search engines. Moreover the Fuzzy evaluation provides a more accurate and realistic precision and false drop rate by introducing a spectrum of relevance rate of retrieved records. It is recommended that researchers apply fuzzy evaluation when evaluating the search engines' performance. In general, Google has better performance than Bing and Yahoo based on three measured criteria. Consequently, users are advised to apply this search engine when searching for Persian information on the web to save time and money.
Feature Reuse Across Software Releases During Software Evolution1

Software evolution relies on storing component versions along with delta-changes in a repository of a version control tool such a centralized CVS in old days, or decentralized Git today. Code implementing various software features (e.g., requirements) often spreads over multiple software components, and across multiple versions of those components. Not having a clear picture of feature implementation and evolution may hinder software reuse which most often is concerned with feature reuse across system releases, and components are just means to that end. Much research on feature location shows how important and difficult is to find feature-related code buried in program components post mortem. We propose to avoid creating the problem in the first place, by explicating feature-related code in component versions at the time of their implementation. To do that, we complement traditional version control approach with generative mechanisms. We describe salient features of such an approach realized in ART (Adaptive Reuse Technology, http://art-processor.org), and explain its role in easing comprehending software evolution and feature reuse. Advanced commercial version control tools make a step towards easing the evolution problems addressed in this paper. Our approach is an alternative way of addressing the same problem on quite a different ground.