Reviewerly/exHarmony huggingface dataset:
Leveraging Decentralized Autonomous Organizations in Healthcare to Coordinate Precision Level Patient Care

The healthcare system is expensive, inefficient and frustrating due to unnecessary third parties and friction filled manual processes that get between patients and their doctors. Through decentralized identityby and credentialing we can create a distributed network of practicing doctors that connect connect securely, directly, privately with each other and with the patients they care for. We are building the future essentialized healthcare revolution and we can’t wait to build it with you.
Semi-Quantum Identification without Information Leakage

In 2019, Zhou et al. proposed semi-quantum identification (also known as semi-quantum authentication, SQA), which proceeds under a measure-resend and measurement-free environment. However, Zhou et al.’s SQA protocol suffers from severe information leakages. An eavesdropper can obtain an intact authentication key without being detected under this environment. In particular, Zhou et al.’s measure-resend SQA protocol is vulnerable to double CNOT attacks, while the measurement-free SQA protocol is vulnerable to man-in-the-middle attacks. Hence, this study reveals the severe security issues of Zhou et al.’s SQA protocol and proposes an improved protocol with guaranteed security. The proposed measure-resend SQA protocol is immune to double CNOT attacks. Since the photons sent back and forth are identical, Eve cannot obtain any information by cross-comparing these photons. In the proposed measurement-free SQA protocol, the eavesdropper cannot obtain the order of the transmitted photons because it was previously a pre-shared key to decide the order of the photons. Hence, the proposed measurement-free SQA protocol can withstand man-in-the-middle attacks.
SISTEM INFORMASI DESA MENGGUNAKAN TELEGRAM BOT SEBAGAI ANTAR MUKA

The village information system is a system that supports the implementation of activities at the village office. Technological developments are having a positive impact on how researchers and governments develop software that makes human jobs (in this case, citizens) easier. Village Information System breakthroughs continue to evolve from web-based applications to applications running on instant messaging platforms. Automated answering telegrams, bots and robots are also constantly being developed so that they can be used to support human work. Telegram Bot and Webhook were developed as part of an autoresponder. The task of this automated answering machine is to replace administrative tasks normally performed by humans. Telegram bot and webhook development using Google Script as the language for building APIs tries to find programming solutions outside of the popular programming languages ​​among application developers. The results of this research will benefit users, especially integrated village communities and village organizations.
PENERAPAN METODE NEURAL NETWORK DENGAN STRUKTUR BACKPROPAGATION UNTUK MEMPREDIKSI KEBUTUHAN STOK PADA TOKO UMKM PERLENGKAPAN BAYI BABYQU

At this time Machine Learning especially Deep Learning is developing very quickly in any field, especially in the fields of business, transactions, stock predictions, sales, and stocks and the like. Machine Learning has become a mainstay for facilitating and helping work. At the BabyQu store, the decisions used to carry out stock inventory still use the manual method, and there are several problems that arise including the occurrence of excess stock which makes other storage areas used for excess stock, especially if there are products that have an expiration date, and if there is a shortage of stock, problems will occur that will make consumers who need products or goods go to other places and several other problems that will arise which will cause losses to BabyQu stores, it is necessary to create a system or software that aims to assist BabyQu stores in forecasting stock availability product to overcomesome of these problems. This research was made using the Artificial Neural Network (ANN) model design and method using Backpropagation as the algorithm because this algorithm can reduce the percentage of errors. The results we got in this study using the 3-3-1 model obtained an accuracy rate of 85.72%, using 550 iterations of epochs, and taking approximately 11.1 seconds.
AUDIT SISTEM INFORMASI OPERASIONAL MENGGUNAKAN FRAMEWORK COBIT 5 (STUDI KASUS : BANK XYZ)

Bank XYZ is one of the BPD Banks. One of the strategies is to reduce the amount of investment and improve the quality of services in terms of information technology. The performance of the Operational Information System at Bank XYZ in serving customers will determine the quality of the services it provides. In conducting this research, the author uses the COBIT 5 framework with a DSS domain that combines control standards from many different sources in a single framework that can enable management to make control and control of the Information System environment. Gap analysis is the current capability level with the expected level of capability. Thus it can be seen how far the reach that must be achieved until the organization's goals are achieved. The results of the assessment and evaluation of the conformity of standards and operational management in the information technology system at Bank XYZ with the COBIT 5 framework with the results of the assessment of Bank XYZ current capabilities are at level 4 a predictable process. The result of level 4 is to ensure that the implementation of the process can support organizational goals.
APLIKASI PRESENSI KEHADIRAN GURU DI SMKN PAKISJAYA BERBASIS FACE RECOGNITION MENGGUNAKAN OPENCV

The research objective was to design a teacher attendance application that has a camera feature to take pictures during teacher absences so that it can simplify and speed up attendance requests for absences from work, leave, permission and illness. The prototype method is used as a tool to develop a model into a system with stages which include; gathering requirements, building prototypes, evaluating, coding attendance systems, conducting testing, evaluating, and finally using presence applications to see the functions of the applications being built. The results of this study have produced a prototype of teacher presence at SMKN Pakisjaya based on face recognition using OpenCV and implementing the Local Binary Pattern Histogram (LBPH) as an algorithm for detecting faces from images.
Overcoming deep learning subclass imbalances: Comparing the transfer of identity across a racial transformation

As facial authentication systems become an increasingly advantageous technology, the subtle inaccuracy under certain subgroups grows in importance. As researchers perform data augmentation to increase subgroup accuracies, it is critical that the data augmentation approaches are understood. We specifically research the impact that the data augmentation method of racial transformation has upon the identity of the individual according to a facial authentication network. This demonstrates whether the racial transformation maintains critical aspects to an individual identity or whether the data augmentation method creates the equivalence of an entirely new individual for networks to train upon. We demonstrate our method for racial transformation based on other top research articles methods, display the embedding distance distribution of augmented faces compared with the embedding distance of non-augmented faces and explain to what extent racial transformation maintains critical aspects to an individual&#x2019;s identity.
Detection of object throwing behavior in surveillance videos

Anomalous behavior detection is a challenging research area within computer vision. One such behavior is throwing action in traffic flow, which is one of the unique requirements of our Smart City project to enhance public safety. This paper proposes a solution for throwing action detection in surveillance videos using deep learning. At present, datasets for throwing actions are not publicly available. To address the use-case of our Smart City project, we first generate the novel public 'Throwing Action' dataset, consisting of 271 videos of throwing actions performed by traffic participants, such as pedestrians, bicyclists, and car drivers, and 130 normal videos without throwing actions. Second, we compare the performance of different feature extractors for our anomaly detection method on the UCF-Crime and Throwing-Action datasets. Finally, we improve the performance of the anomaly detection algorithm by applying the Adam optimizer instead of Adadelta, and we propose a mean normal loss function that yields better anomaly detection performance. The experimental results reach an area under the ROC curve of 86.10 for the Throwing-Action dataset, and 80.13 on the combined UCF-Crime+Throwing dataset, respectively.
A Two-Tiered System of Teacher Preparation

A global proliferation of neoliberalism has been underway since the 1970s and is evident in policies, governance, the ways we understand and talk about society and what is valued (Harvey 2006).Neoliberalism prioritizes freedom and individualism over the collective, both of which are defined in consumer terms.Neoliberal policies promote the free market, personal responsibility, choice and private enterprise and view government as ineffective and bureaucratic.This has led to increased privatization and deregulation and decreased state intervention, coupled with the defunding of public services, such as higher education, libraries and health care (Apple 2006;Burch 2009;Harvey 2006).Finally, neoliberal capitalism is inextricably linked to racism and both are deeply intertwined to frame American society (Kelley 2017;Kendi 2019;Robinson 2000).In a society in which policies, institutions and practices are racist (e.g.segregated, unequal neighbourhoods and schools, the impact of the carceral state of Black Americans and voter suppression among communities of colour), the 'free market' upholds those inequities and further perpetuates White supremacy (Kelly 2017;Kendi 2019;Robinson 2000).Shifts towards neoliberalism are evident in many spheres of life but are especially profound in American education, which has experienced trends towards choice, accountability and efficiency.This is illustrated through policies such as the dramatic expansion of school choice, school evaluation and ranking based on standardized tests scores as mandated by the 2001 No Child Left Behind Act and its 2015 reauthorization (The Every Student Succeeds Act), merit pay initiatives for teachers and the deregulation of teacher education (Apple 2006;Ball 2012;Burch 2009;Ravitch 2010).The focus on bureaucratic problems and managerial solutions, rather than resource neglect and racist
Mobile Health Upaya Peningkatan Kepatuhan Pengobatan Pasien Anak dengan Tuberkulosis : Tinjauan Literatur

&lt;p&gt;&lt;em&gt;&lt;strong&gt;&lt;em&gt;Background&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;: Children are at risk for severe symptoms of tuberculosis. Most tuberculosis drug formulations have poor acceptance in children and require consistent adherence over a long period of time. The mobile-health application is a technology that can reach various levels of society, which is useful in the management of adherence to treatment of children with TB. &lt;strong&gt;Objective&lt;/strong&gt;: To provide an overview and ideas about the application of the m-health system in Indonesia, especially in the area of nursing children with tuberculosis. &lt;strong&gt;Method&lt;/strong&gt;: Literature review is a study conducted to analyze the literature that has been selected from various sources to become a conclusion of a new idea. &lt;strong&gt;Discussion&lt;/strong&gt;: The m-health application is effectively used for nursing services for children, especially children who are being treated for tuberculosis. In the development of m-health technology, regulations and legal policies are needed to enable the application and integration of m-health technology with health services. &lt;strong&gt;Recommendation&lt;/strong&gt;: It is hoped that further research can make m-health applications to improve medication adherence in children with tuberculosis and test its effectiveness&lt;/em&gt;&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;&lt;br /&gt;&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords: &lt;/strong&gt;&lt;em&gt;mobile health, medication adherence, TB in children&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;&lt;br /&gt;&lt;/em&gt;&lt;/p&gt;&lt;h1 align="left"&gt; &lt;/h1&gt;
Cataloguing Ethics a world overview and a focus on Portugal: literature review

This literary review was written with the intent of exposing what is cataloguing ethics and how it should influence the cataloguer's work. What do cataloguers understand by cataloguing ethics, what codes of ethics exist now and how they came into existence. It goes into ethical problems and proposed solutions as well as world differences in information accessibility and cataloguing standards, with a focus on what is the Portuguese reality in terms of cataloguing codes of ethics.
Epigenome-wide DNA methylation assists early intervention of Coronary Heart Disease with machine learning models

Background and aim: Studies have shown American Indian communities have a disproportionally high rate of coronary heart disease (CHD) compared with other ethnic groups. Microarrays for epigenome-wide DNA methylation data have enabled researchers to model a large number of methylation sites to investigate their associations with incident CHD. However, accurate prediction of CHD from high-dimensional epigenome-wide DNA methylation data is challenging. We aim to develop a workflow to accurately predict CHD up to 30 years prior to diagnosis assist early disease intervention. Methods: The associations of CHD with epigenome-wide DNA methylation data were first screened using elastic-net penalized cox proportional hazard models based on a previous epigenome-wide association study to identify noteworthy CpG sites. The selected significant CpG sites were then input into our autoencoder deep learning model to learn the latent space representation (embeddings). The learned embeddings were then input to multiple machine learning models (i.e., logistic regression, support vector classifier, random forest, gradient boosted decision tree, and fully connected neural networks) to predict CHD. We also performed the same prediction on raw features. Results: Using our developed method, we achieved the state-of-the-art prediction accuracy of binary CHD outcome (best AUROC=0.872) with DNA methylation and clinical features using autoencoder combined with support vector classifier model (AE-SVC_DNAm+clinical), which is better than previous studies. Results showed that DNA methylation features alone had high prediction accuracy (best AUROC=0.867), clinical features alone had poor performance (best AUROC =0.692), and that AE-SVC_DNAm+clinical only increased the prediction results incrementally. Additionally, using the learned embedding increased the overall stability of our prediction results over raw input features. Conclusions: Our data-driven method is highly effective in accurately predicting CHD up to 30 years in the Strong Heart Study population. We suggest using this workflow in future disease predictions from the high-dimensional DNA methylation data.
DAMPAK MODEL QUANTUM TEACHING TERHADAP HASIL BELAJAR PADA PEMBELAJARAN IPS PESERTA DIDIK

This research is a quantitative study that aims to determine the impact of the Quantum Teaching learning model on student learning outcomes conducted at SD Negeri 09 Bandar Buat 2022/2023. The method used is descriptive method. The population of this study were all fourth grade students of SD Negeri 09 Bandar Buat consisting of one class with 30 students. The results showed that the Quantum Teaching model was in the good category with an average of 80.7, the correlation coefficient of 0.705 means (0.705) rhitung ≥ rtabel (0.361) then ha is accepted. Hypothesis testing shows thitung &gt; ttabel namely 6.363 ≥ 1.697, thus stating that ha is accepted.&#x0D;
PENGGUNAAN MODEL KOOPERATIF TIPE MAKE A MATCH TERHADAP HASIL BELAJAR SISWA DI SEKOLAH DASAR

The purpose of this study was to determine whether or not the cooperative model of the Make A Match type affected the learning outcomes of fourth grade students in social studies learning. The type of research applied is an experiment with a true experiment design, in the form of a pretest-posttest control group. The class IV samples at SDN 06 Piai Tangah and class IV at SDN 07 Piai Tangah were selected using a random cluster technique. The results of the study show that the cooperative model of the Make A Match type has an effect on social studies learning outcomes for fourth grade students with a t-count score (4.9045) &gt; t-table (1.697).
BIBLIOMETRIX PEMBELAJARAN BAHASA DI SEKOLAH DASAR

ABSTRACT&#x0D; This study aims to see the development of research on language learning in elementary schools. This type of research is included in the type of literature review research. Research data sourced from Scopus. In the Scopus search column, data was found as many as 1,357,373 articles. The data was selected in four stages, namely identification, screening, eligibility, and included so that 26 articles were obtained for data processing. The process of interpreting the data is carried out with the help of R studio type 386.4.1.3. The results obtained are data with three categories of research information, namely authors, document searches, and documents. Based on the results of the study, it can be concluded that language learning in elementary schools is growing rapidly in the USA and the United Kingdom. Meanwhile, the most popular place to publish language learning articles is the journal of speech. For popular vocabulary in research, namely learning and linguistics.
TAKING VIRTUAL REALITY AND AUGMENTED REALITY TO THE NEXT LEVEL: ARTIFICIAL INTELLIGENCE WITH MIXED REALITY

Virtual reality (VR) and augmented reality (AR) have caused significant excitement, but Mixed reality (MR) offers an authentic experience beyond what they have are imagined and it takes this exciting experience to the next level. MR collides virtual data with natural objects, creating realistic physical and emotional interactions so that digital objects are positioned like natural objects in real environments and positioning takes place in real-time by aligning with real things. With the advent of Metaverse, the reality concepts are being redefined and highlighted by both businesses and academia. Apart from business value and high-tech aspects, Metaverse can include mystical, spiritual, and religious perspectives, as it is defined as a virtual and transitive structure in which we can exist by creating a duplicate of our existence in the physical world and its alternative. Through multidisciplinary analyzes and evaluations in this study, the possibility to provide not only the transition from the physical to the virtual, but also the transition from the virtual to the physical through transferring the data to the physical location, objects, or sensors is questioned.
ANALYSIS OF COVID-19 GROWTH TRENDS THROUGH DATA MINING APPROACH AS DECISION SUPPORT

This study aims to analyze the growth trend of covid-19 using prediction algorithms in data mining for covid-19 data throughout Indonesia. This can be used as a decision support to analyze several government policies towards regulatory intervention so far. The method used is the best prediction method in time series data, including Neural Network, SVM, Linear Regression, K-Neirest Neighborn and optimizes it with optimization algorithms. This research is focused on the application of these applications. It is hoped that this research will produce an analysis of the growth trend of Covid cases every day, in addition to its contribution so that it can assist the government in determining the best policy direction and also as an education to the public. in addition, the research will contribute to science in the field of predictive analysis by finding the best RMSE formulation. The results of this study show that Neural Network-Particle Swarm Optimization has the smallest Roort Mean Square Error which is 265,326, and the two Neural Network Genetic Algorithm are 266.801, Neural Network Forward Selection is 275,372 and Neural Network without optimization has the largest RMSE which is 297.204. These results can be used as a reference for the use of similar algorithms in time series data, both Covid-19 data and other data.
Comparison of the Sentiment Analysis Model's Code Complexity and Processing Time

Text processing, which includes sentiment analysis, obviously demands a lot of resources. The extent to which resources are used to promote environmental conservation is directly impacted by code complexity or computational time. Twitter users' responses on the Indonesian Language of religious moderation and tolerance are used to test the model. The phases of doing the research include determining the research's needs, gathering data, text preprocessing (case folding, tokenizing, stopword removal, and stemming), word weighting with TF/IDF, and classification with Multinomial Naive Bayes (MNB) and Support Vector Machine (SVM), validation, and the last step was calculation of the code complexity and computation time. The validation results showed that the performance of the two models was still low, with an average accuracy of 75.5%. Based on computation time, SVM has a faster computational time than MNB. However, when compared in terms of the code complexity, the Cyclomatic Complexity in both models was the same because both models used existing libraries in Python Interpreter, and the complexity of the libraries cannot be calculated directly. Based on the Raw Metrics, it can be seen that MNB and SVM not significantly different in LOC, LLOC, and SLOC. It was evident that SVM has a greater Halstead Complexity than SVM in all measures when comparing the program code of MNB and SVM. Particularly on programming effort and volume metrics, SVM required more bits to run than an MNB program, and it also required more mental effort to convert a prepared SVM algorithm into a program than an MNB.
Flexible Detection of Similar DOM Elements

Different research fields related to the web require detecting similarity between DOM elements. In the field of information extraction, many approaches emerged to extract structured data from web documents, most of which require comparing sample documents to extract their underlying structure. Other fields of applicability like web augmentation or transcoding also require analyzing structural similarity, but on UI components with smaller structures than full documents, making them unsuitable for the algorithms generally used in information extraction. Instead, these approaches tend to rely on the DOM elements’ location, but this does not resist structural changes in the document, and cannot locate similar elements placed in different positions. In this paper we present two flexible algorithms to measure similarity between DOM elements by using a mixed approach that considers both elements’ location and inner structure, together with a wrapper induction technique. We evaluated our algorithms with respect to other known approaches in the literature by comparing how they cluster a dataset of 1200+ DOM elements, using a manual clustering as ground truth. Results show that both proposed algorithms outperform all baseline ones. The proposed algorithms run in linear time, so they are faster than most approaches that analyze structural similarity.