Reviewerly/exHarmony huggingface dataset:
STUDENT-CENTERED PROJECTS: RURAL HIGH SCHOOL STUDENTS LEADING PROJECTS IN TECHNOLOGY, IDENTITY, AND SOCIAL JUSTICE

"CompuPower is a student-centered program integrating computer technology, identity exploration activities, and community outreach projects. The program was implemented in six rural high schools in Arizona, USA between 2019 and 2021. Students worked in groups in project-based learning environments to create technology-based products (for example, websites, social media campaigns, and public service announcement videos) and to explore their individual identities and skills as they set education and career goals. To understand the CompuPower program from the students’ perspective, our research team conducted a qualitative, retrospective interview study using photo elicitation (Harper, 2002). This paper reports on the experiences of 13 rural high school student participants from diverse backgrounds. It explores students’ CompuPower experiences, including the technical and non-technical skills they gained throughout the program as they worked on community outreach efforts."
Strategies for Increasing Access and Quality of Education at the Elementary School Levels in Papua and West Papua Province

The purpose of this study is to provide policy recommendations related to strategies that collaborate with parties in the education sector for the advancement of education in the provinces of Papua and West Papua by focusing on education issues at the basic level, especially the elementary school level. This research method is a qualitative research with a descriptive research design model that aims to describe the phenomena that occur. The results of the study indicate that access to education services in Papua and West Papua is still poor, especially in remote areas. There is a review of appropriate policy recommendations to improve access and quality of education in Papua and West Papua such as increasing teacher qualifications, teacher distribution programs, improving education services and community involvement. The low achievement of access and quality of primary school education in the provinces of Papua and West Papua is due to weak education governance, high rates of absenteeism for teachers, so it is necessary to revise policies in improving education services to encourage the acceleration of education in the provinces of Papua and West Papua.
Integration Of Face Recognition Model to a Biometric Security System

With the global COVID-19 pandemic at large, physical contact is supposed to be minimized. This raises the need for a non-invasive security system that could differentiate between individual human beings. Biometrics is the automatic recognition of an individual using certain distinguishing traits. It proves to be a solution for security systems in such times. However, computers cannot see objects as humans do, they see objects as binaries which requires further discoveries to build a system that is accurate enough to be implemented in a security system. Viola and Jones did research on such methods. They had found that computers are able to see patterns, but it requires a classifier which is a model to help computers understand certain features that makes a human face. Viola and Jones used what they call as the cascade classifiers with Haar features used by the computer to analyze an object and helps it determine whether the object is face or not. This research aims to create a multi-class classification (face or non-face and face in the database or not) facial recognition system with the help of machine learning to create a model which will implemented in the access systems. The OpenCV libraries are used in this research as there lots of facial recognition and processing functions which will render the research for designing machine learning based access systems easier. The research shows that the model formed has an accuracy of 74.8%, a precision of 74.02%, and a sensitivity of 100%.
THE IMPROVING OF PEER FEDBACK TECHNIQUE ON STUDENTS’WRITING DESCRIPTIVE TEXT ABILITY OF VII GRADE SMP NEGERI 1 SIDAMANIK

The research focused on The Improving of Peer Fedback Technique on Students’ Writing Descriptive Text Ability Of Grade VII SMP NEGERI 1 SIDAMANIK. The problem of the research was : How is The Improving of Peer Fedback Technique on Students’ Writing Descriptive Text Ability Of Grade VII SMP NEGERI 1 SIDAMANIK academic year 2019/2020. Some theories were used the explain the research : they were : Arikunto, Suharsimi (2002), Brown, H.D (2001), Ferris, L. (1995), Harmer, J. (2004), Liu, J. and Hansen, J. (2002), Paulus, T. M. (1999), Wadiman, Artono and His friend. (2008). The research design was quantitative method . The population was 341 students and the sample was taken by Arikunto 2006:134 at class VII/a with 34 students. The instrument was essay test. The data analysis started from scoring composition, calculating the validity and reability, finding variants and mean difference, calculating standart deviation difference deviation.Based on the data analysis, it was found that the treatment of peer feedback technique, students’ prior knowledge in writing descriptive text was low since the pre-test data was 1689 and the mean was 49,67 and after the treatment, it had been improved since the post-test data was 2620 and the mean was 77,11. After analyzing the data, it was concluded that the use of peer feedback technique gives significant contribution in improving students’ competence in writing descriptive text at grade VII SMP NEGERI 1 SIDAMANIK in academic year 2019/2020. So, every English teacher should use peer feedback technique in teaching writing descriptive text because it had been proved that it gave significant contribution in improving students’ competence in wrting descriptive text and each teacher should choose appropriate method firstly before teaching
Symmetric Encryption using Artificial Neural Network, Binary Tree Traversal, and Interleaving Salting

We have proposed a novel way of symmetric encryption utilizing interleaving salting, Binary Tree traversal, and an Artificial Neural Network in a sequential manner. We have presented the use of an Artificial Neural Network as an invertible(reversible) mathematical function so that decryption of the encrypted output may be possible. Knowledge of the encryption pipeline, salt, and neural network weights is required for decryption. As the same set of values is required for encryption and decryption, our proposed approach is a type of symmetric-key algorithm. Each user will have a unique key. Thus if a key attributing to a particular user is compromised, the integrity of the data of the remaining users will still be maintained. Our approach can be utilized to encrypt text data such as messages, documents, and letters. The encryption process consists of interleaving salting, creation of binary tree by considering the input as its level order traversal, and passing the preorder and inorder traversal of the constructed binary tree as input to the Artificial Neural Network. The output of the Artificial Neural Network would be the encrypted data. Decryption would require determining the input of an Artificial Neural Network from the output, hence solving multiple sets of linear equations and constructing a binary tree from its preorder and inorder traversal. We have then analyzed the variation of performance with the change in the input string size. The codebase of our proposed approach is publicly available at https://github.com/Atishaysjain/Symmetric-Encryption-using-ANN-and-Binary-Trees.
First Line Research Data Management for Life Sciences: a Case Study

&#x0D; Modern life sciences studies depend on the collection, management and analysis of comprehensive datasets in what has become data-intensive research. Life science research is also characterised by having relatively small groups of researchers. This combination of data-intensive research performed by a few people has led to an increasing bottleneck in research data management (RDM). Parallel to this, there has been an urgent call by initiatives like FAIR and Open Science to openly publish research data which has put additional pressure on improving the quality of RDM. Here, we reflect on the lessons learnt by DataHub Maastricht, a RDM support group of the Maastricht University Medical Centre (MUMC+) in Maastricht, the Netherlands, in providing first-line RDM support for life sciences. DataHub Maastricht operates with a small core team, and is complemented with disciplinary data stewards, many of whom have joint positions with DataHub and a research group. This organisational model helps creating shared knowledge between DataHub and the data stewards, including insights how to focus support on the most reusable datasets. This model has shown to be very beneficial given limited time and personnel. We found that co-hosting tailored platforms for specific domains, reducing storage costs by implementing tiered storage and promoting cross-institutional collaboration through federated authentication were all effective features to stimulate researchers to initiate RDM. Overall, utilising the expertise and communication channel of the embedded data stewards was also instrumental in our RDM success. Looking into the future, we foresee the need to further embed the role of data stewards into the lifeblood of the research organisation, along with policies on how to finance long-term storage of research data. The latter, to remain feasible, needs to be combined with a further formalising of appraisal and reappraisal of archived research data.&#x0D;
Game theory to improve strategic decision making for Ludo quest

The research analysis is aimed to enhance the specific domain knowledge for Ludo which can be used for performance improvement in Dijkstra Algorithm networks or in evolutionary game analysis for ludo games. The Ludo game involves decision-making under fuzziness and uncertainty with more than two parties. It can serve as an excellent example of applying methods and concepts for automating resource management and real-time strategic decisions. Increase Logical Thinking Capability, development of Interpersonal Skills, dealing with Victory and Defeat &amp; Other Cognitive Skills. And also, experimentation is focused on integrating the ludo with quizzes because quizzes will make this game more interesting, and it will also help us to increase our IQ levels. Ludo game concept is achieved by writing a computer program that allows players to roll a dice randomly, take decisions and move the pawn based on the outcome of the dice on a well-designed graphical interface.
Automatic Thresholding

AbstractAlthough techniques based on binary image regions have been used for a very long time, they still play a major role in many practical image processing applications today because of their simplicity and efficiency. To obtain a binary image, the first and perhaps most critical step is to convert the initial grayscale (or color) image to a binary image, in most cases by performing some form of thresholding operation, as described in Sect. 3.1.4.
Point Operations

AbstractPoint operations perform a modification of the pixel values without changing the size, geometry, or local structure of the image. Each new pixel value b = Iʹ(u, v) depends exclusively on the previous value a = I(u, v) at the same position and is thus independent from any other pixel value, in particular from any of its neighboring pixels.
Detecting Geometric Primitives

AbstractMany geometrical shapes, such as lines, circles, and ellipses, can be readily described by simple equations with only a few parameters. Since such geometric “primitives” often occur as part of man-made objects, they are especially useful features for analysis of these types of images (see Fig. 12.1).
Data as a competitive advantage: Opportunities for publishers under the influence of the “Internet of Things”

The paper deals with the influence of the “Internet of Things” and its opportunities for the publishing business model. It offers a contribution for scientific research in media management to establish the relevance of data-centric business models. The aim of the paper is to work out the increasing relevance of data for the publishing business model. Potentials under the influence of the “Internet of Things” for the publishing business model with regard to four dimensions of business models are worked out: Value creation, value proposition, earnings mechanics and customers. These dimensions show the opportunities that publishers can exploit by integrating intelligent everyday objects as a content channel. Additionally, the author points out that publishing houses operate in a data-centric environment with the integration of intelligent everyday objects as a content channel. The data-centerd publishing business model is analysed on the basis of features, in particular with regard to its potential.
The The Realization of Refusal Strategies in Political Interviews Used by Donald Trump--The President of the United States of America

Refusal is not simple to be taught to language learners. It is challenging act due to its intrinsically face threatening nature. This study is aimed at finding out the realization of refusal strategies in five different channels of political interviews used by Donald Trump--The President of United States of America. The method of collecting data is documentation. The researcher uses descriptive qualitative in analyzing the data. The results show that there are direct and indirect refusal strategies used by President Trump in five different channels of political interviews. In direct strategy, there are â€˜noâ€™ and negative willingness. Moreover, in indirect strategy, there are excuse reason and explanation, promise for future acceptance, statement of principle, threat or negative consequences, criticizing or statement of negative feeling or opinion, and verbal avoidance. Further, there are the similarities and differences of refusal strategies used by President Trump among the five different channels of political interviews. The similarities are shown in the interview with CNN; he is more indirect to refuse the interviewersâ€™ want. It is similar to the interview with FOX and CBS News that he is more indirect too. In addition, the differences are shown that in the interview with CNN, FOX, and CBS News, he uses more indirect refusal strategies. In contrast, in ABC and CNBC News, he uses more direct refusal strategies. In term of social status, he uses more of indirect refusal strategies to the interviewers who have lower social status. It means that he wants to soften the offending of interlocutorâ€™s face in refusals. This study also gives pedagogical implication for the language learners to improve their pragmatic competence especially in refusal speech act. Thus, they can use refusals appropriately for communication.
Meningkatkan Motivasi Dan Hasil Belajar Materi Sumber Daya Alam Kegiatan Ekonomi Melalui Penerapan Model Pembelajaran Problem Solving Berbantu Media Gambar Pada Siswa Kelas IV SD 5 Gondosari

Based on the results of observations on the initial conditions in 4th grade SD 5 Gondosari, data were obtained that the motivation and learning outcomes of students about the natural resources of economic activities were still very low. Student learning motivation is only 38% with completeness of learning outcomes only 44%. Therefore to improve this, carried out research by applying learning media image problem solving learning models. And the results obtained after carrying out research for two cycles were student learning motivation increased to 81%, and learning completeness by 88%.
Pengembangan Karakter Kerja Keras Dan Hasil Belajar Pjok Materi Lompat Jauh Gaya Jongkok Melalui Penggunaan Bok Kardus Pada Siswa Kelas V SD 2 Kedungsari

In this study, researchers experienced problems in Long Jump learning, namely for elementary school students researching the use of cardboard tools to improve students' long jump learning outcomes, from pre-cycle to cycle I and from cycle I to cycle II. The learning process in pre-cycle has not used tools so students' learning outcomes are low. Increases occur in cycle I. Student learning outcomes increase even though not optimal. The implementation of cycle II causes student learning outcomes to increase to high so that they can support a quality learning, begin to become entrenched and through the use of cardboard media modification can improve learning outcomes of PJOK.
Information Security Incident Handling in the Cloud

Information security incident handling in the cloud is an integral part of security management, threats detection and analysis of security incidents. The strategies or models are important to ensure the security of an organization particularly in cloud and big data environment. Incident handling strategy is one key strategy to mitigate risks to the confidentiality, integrity and availability of organizational assets, as well as minimizing loss. This study concluded that cloud-based services has changed many organizational cyber threats. It recommended that, there is the need to integrate digital forensics with incident handling. This study suggested that, a collaborative model can be implemented, and this collaboration could be centrally managed by a trusted entity (e.g., Centre for Cloud Incident Management). Further studies are required in monitoring incidents both proactive and reactively. Key words: Information Security, Incident Handling, Cloud, Cyber Security, Protection, Data
Brain-Aware Replacements for Supervised Contrastive Learning in  Detection of Alzheimer's Disease

We propose a novel framework for Alzheimer's disease (AD) detection using brain MRIs. The framework starts with a data augmentation method called Brain-Aware Replacements (BAR), which leverages a standard brain parcellation to replace medically-relevant 3D brain regions in an anchor MRI from a randomly picked MRI to create synthetic samples. Ground truth "hard" labels are also linearly mixed depending on the replacement ratio in order to create "soft" labels. BAR produces a great variety of realistic-looking synthetic MRIs with higher local variability compared to other mix-based methods, such as CutMix. On top of BAR, we propose using a soft-label-capable supervised contrastive loss, aiming to learn the relative similarity of representations that reflect how mixed are the synthetic MRIs using our soft labels. This way, we do not fully exhaust the entropic capacity of our hard labels, since we only use them to create soft labels and synthetic MRIs through BAR. We show that a model pre-trained using our framework can be further fine-tuned with a cross-entropy loss using the hard labels that were used to create the synthetic samples. We validated the performance of our framework in a binary AD detection task against both from-scratch supervised training and state-of-the-art self-supervised training plus fine-tuning approaches. Then we evaluated BAR's individual performance compared to another mix-based method CutMix by integrating it within our framework. We show that our framework yields superior results in both precision and recall for the AD detection task.
Video Classification Using Key Frame Selection and Deep Features

With increase in the availability of large-scale video data in educational, commercial, entertainment, sports field, it is necessary to have effective classification techniques for efficient retrieval and browsing. Key frames selection is the initial and fundamental step towards video classification and retrieval. In this paper, we propose a video classification method using key frame selection. Key frames are selected using pre-processing and KLT corner detector. Deep features are extracted from key frames using transfer learning approach. Extracted deep features are given as input to different machine learning classifiers. Experimental results reveal that proposed key frame selection and deep features based video classification system yields classification accuracy of 92%, 89% and 90% for OV dataset, UCF-11 dataset and TV channel video dataset respectively. Proposed technique outperforms existing methods of video classification in terms of F1-score and classification accuracy. It useful in achieving excellent retrieval accuracies.
Android Based Indian Currency Recognition System Using Transfer Learning Technique

Abstract: This work is dedicated to develop a computer vision-based approach for Indian paper currency recognition. In this approach, extract currency feature and develop a dataset which can be used for the currency recognition. Security feature of Indian currency note available on front and back side Rs.10, Rs. 20, Rs. 50, Rs. 100, Rs. 200 Rs. 2000 and Rs. 500 denominations are used in model Training. Advances in technology have replaced people in almost every field with machines. Thanks to the introduction of machines, banking automation has reduced the burden on humans. Banking automation requires more attention to declining currency handling. When the banknote is blurred or defaced, it is difficult to identify its currency value. A sophisticated design is included to increase the security of the call. This makes the call recognition task very difficult. For correct currency recognition, it is very important to choose a good function and an appropriate algorithm. One of the main problems that blind people face is the recognition of money, especially cash. In a way, the seemingly weakened people do not think about cash settlement and run into problems related to cash transactions in their daily life. It is a useful treatment for those who are externally weakened. Studies and trials were conducted according to key points, such as watermarks, images printed on money, the value of words and numbers, and the total amount of information gathering that stimulated CNN using Transfer Learning. And the second thought after designing a proper algorithm for Indian Currency Recognition, the problem is to carry the mechanism, which can be a burden or sometimes forgotten. Therefore this design help in a lot way for easier way to recognising the Currency just by not making an extra equipment, but by designing an android app, where it is not needed to carry any extra thing, as it is included in android smart phone, which is used by almost 748 million people in India.
Building a Monitoring Framework for a Distributed Cloud Application Using Prometheus and Chef - An Overview

Abstract: A Distributed Cloud Application has multiple in-stances dedicated for specific use cases, which requires constant monitoring to avoid hassles in the clients’ experience. This in-cludes checking for services, checking for ports and the statuses of various components and component groups present and running in the application. The monitoring framework would alert the support team of any issues, thereby helping them resolve issues immediately. This paper provides a high level generalized overview on building this monitoring framework and automating its installation using Chef