Reviewerly/exHarmony huggingface dataset:
Penerapan Data Mining untuk Menentukan Penyebab Kematian di Indonesia Menggunakan Metode Clustering K-Means

Death in medical science is studied in a scientific discipline called tanatology. death is not only experienced by elderly people, but also can be experienced by young people, teenagers, or even babies. Death can be caused by various factors, namely, due to illness, old age, accidents, and so on. Based on information provided by the World Health Organization (WHO), there are five highest causes of death including ischemic heart disease, Alzheimer's, stroke, respiratory disorders, neonatal conditions. In this study, k-means is used to group causes of death in Indonesia based on the number of deaths that occur to determine the cases of death that have the most impact on the high mortality rate in Indonesia. Knowing what these death cases are will provide early preparation in anticipating the causes of death in Indonesia. The purpose of this study was to classify mortality rates based on the number of causes of death which were included in the low, medium, and high clusters by applying the K-Means method. In this study the authors used the K-Means clustering algorithm to classify death rates in data on causes of death in Indonesia from 2017-2021. The results of this study formed 3 clusters which were evaluated using the Davies Bouldin Index (DBI) in Rapidminer with a value of 0.259. Clustering results from a total of 21 cases obtained high, medium and low clusters. This cluster grouping was obtained according to the number of deaths per case, namely the first cluster (C0) was low with 17 cases, the second cluster (C1) was moderate with 3 cases and the third cluster (C2) was high with 1 case.
PROFESSIONAL TEACHER TRAINING IN THE CONTEXT OF DIGITALIZATION OF EDUCATION

The article deals with the actual problem of preparing students for professional pedagogical activity in the conditions of modern challenges. The article analyzes the current trends in the introduction of digital technologies in the educational process of natural science education. The idea of introducing digital technologies, technical means of teaching as auxiliary tools for the professional training of a natural science teacher is considered, in connection with an urgent problem that is the search for methodological techniques increasing the motivation of students as future teachers of environmental activities, within the development of their ecological culture. The possibility of integrating traditional pedagogical and information technologies in order to form a new information environment necessary for the normal functioning of the Federal State Educational System is considered. A factual example shows the significant impact of augmented reality on the quality of biological education. At the same time, attention is focused on the necessity of activating the cognitive process in the training of future teachers, which is based on the students’ motivation level increasing, developing interest in practical pedagogical, research activities in the learning process, which contributes to the formation of appropriate competencies. The issues of improving the training of students studying in pedagogical specialties on the basis of the development of technological and methodological competence of students – future biology teachers of a modern school are revealed. At the same time, it is noted that students have insufficiently formed the basic values that should be inherent in a pedagogical employee admitted to the process of forming a holistic worldview in a child. The article analyzes the didactic possibilities of the digital educational environment in the educational process in biology, the advantages and disadvantages of mobile learning in the system of modern biological education. Based on the improvement of the methodological system of the organization of individual and group project activities in teaching biology, the focus of modern education on providing the most favorable conditions for students in order to master the knowledge of the discipline and improve the quality of the educational process is considered.
KAJIAN SEMIOTIKA PADA POSTER TEMA PERUNDUNGAN KARYA SISWA KELAS 6 SD

Pictures are unlimited works of art, including posters. Posters are part of non-commercial advertisements where the poster can contain messages through images and writing. The message or information is conveyed attractively through pictures that are complemented by an article to clarify the content of the message. At school, posters are studied in the syllabus for arts, culture and crafts at KD 3.1 and 4.1. The teacher through assignments can ask students to draw a poster about a theme that is often happening around them. The topic of bullying is interesting for research writers because this happens a lot in schools and is directly felt by students. This study wants to reveal and describe the contents of the posters by 6th grade elementary school students with the theme of bullying through semiotic studies. The research method in this research is descriptive qualitative research using semiotic analysis. It was found that there was a relationship between the signifier and the signified in a sign, thus clearly explaining a denotative level. There is an interaction between the sign and the reader's feelings or emotions as well as cultural and human values ​​in the poster, thus leading to a connotation level that gives its own meaning and has an influence on the reader's frame of mind.
Sosial Media Analisis Berbasis NLP Untuk Mempercepat Tanggap Bencana Banjir

Flood disasters that are monitored in real-time on social media can be seen to report directly the condition of the affected areas. Areas that have been warned to be affected by the floods were informed via social media. Surrounding areas that are likely to be affected can be more vigilant by directly speeding up information and getting responses from social media users who are around flood-prone areas. The research aims to provide visualization models that can accelerate flood response information from disaster management, track disasters with increasing vigilance, and accelerate flood disaster recovery with analysis on social media. The approach used with Natural Language Processing (NLP), a data source derived from posts on Instagram is taken for analytical materials. Data sources from Instagram with flood hashtags in Kalimantan are used by using the Natural Language Processing (NLP) process stage to get core information visualizations to speed up flood response information. Visualization of social media data information used based on extracting information from Instagram posts, responses, and hashtags to speed up information provides troubleshooting and the importance of speeding up flood response information. The results of data visualization can accelerate disaster response information to increase awareness of the condition of the surrounding area that can be affected by floods, it can be seen that the amount of data on the hashtag provides data visualization information in accelerating flood disaster management from disaster tracking to disaster recovery.
Learning General Representations for Semantic Segmentation and Height Estimation from Remote Sensing Images

Deep-learning methods have been applied to remote-sensing imagery to achieve height estimation and semantic segmentation. Recent research has demonstrated that multi-task learning methods can act as a welcome addition to task-specific features by improving prediction accuracy across a range of tasks. How to effectively learn representations that achieve good performance for multiple tasks still remains challenging. We propose to adopt a unified network jointly learning multiple vision tasks’ general representations by aligning them via small task-specific adapters. The experimental results on the Vaihingen dataset demonstrate that general representation learning can improve the performance of state-of-the-art methods in height estimation and semantic segmentation tasks.
FBDT: Forward and Backward Data Transmission Across RATs for High Quality Mobile 360-Degree Video VR Streaming

The metaverse encompasses many virtual universes and relies on streaming high-quality 360° videos to VR/AR headsets. This type of video transmission requires very high data rates to meet the desired Quality of Experience (QoE) for all clients. Simultaneous data transmission across multiple Radio Access Technologies (RATs) such as WiFi and WiGig is a key solution to meet this required capacity demand. However, existing transport layer multi-RAT traffic aggregation schemes suffer from Head-of-Line (HoL) blocking and sub-optimal traffic splitting across the RATs, particularly when there is a high fluctuation in their channel conditions. As a result, state-of-the-art multi-path TCP (MPTCP) solutions can achieve aggregate transmission data rates that are lower than that of using only a single WiFi RAT in many practical settings, e.g., when the client is mobile. We make two key contributions to enable high quality mobile 360° video VR streaming using multiple RATs. First, we propose the design of FBDT, a novel multi-path transport layer solution that can achieve the sum of individual transmission rates across the RATs despite their system dynamics. We implemented FBDT in the Linux kernel and showed substantial improvement in transmission throughput relative to state-of-the-art schemes, e.g, 2.5x gain in a dual-RAT scenario (WiFi and WiGig) when the VR client is mobile. Second, we formulate an optimization problem to maximize a mobile VR client's viewport quality by taking into account statistical models of how clients explore the 360° look-around panorama and the transmission data rate of each RAT. We explore an iterative method to solve this problem and evaluate its performance through measurement-driven simulations leveraging our testbed. We show up to 12 dB increase in viewport quality when our optimization framework is employed.
Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity

Existing regulations often prohibit model developers from accessing protected attributes (gender, race, etc.) during training. This leads to scenarios where fairness assessments might need to be done on populations without knowing their memberships in protected groups. In such scenarios, institutions often adopt a separation between the model developers (who train their models with no access to the protected attributes) and a compliance team (who may have access to the entire dataset solely for auditing purposes). However, the model developers might be allowed to test their models for disparity by querying the compliance team for group fairness metrics. In this paper, we first demonstrate that simply querying for fairness metrics, such as, statistical parity and equalized odds can leak the protected attributes of individuals to the model developers. We demonstrate that there always exist strategies by which the model developers can identify the protected attribute of a targeted individual in the test dataset from just a single query. Furthermore, we show that one can reconstruct the protected attributes of all the individuals from queries when Nk ≪ n using techniques from compressed sensing (n is the size of the test dataset and Nk is the size of smallest group therein). Our results pose an interesting debate in algorithmic fairness: Should querying for fairness metrics be viewed as a neutral-valued solution to ensure compliance with regulations? Or, does it constitute a violation of regulations and privacy if the number of queries answered is enough for the model developers to identify the protected attributes of specific individuals? To address this supposed violation of regulations and privacy, we also propose Attribute-Conceal, a novel technique that achieves differential privacy by calibrating noise to the smooth sensitivity of our bias query function, outperforming naive techniques such as the Laplace mechanism. We also include experimental results on the Adult dataset and synthetic dataset (broad range of parameters).
Implementasi Program Pelatihan Menjahit Pakaian Wanita Bagi Peserta Didik Untuk Meningkatkan Kecakapan Hidup Di BPVP Kota Samarinda

Training is a part of education which contains a short duration learning process and is organized in a systematic and structured manner. This training aims to identify training on the implementation of sewing women's clothing for students to improve life skills at BPVP Samarinda City. Research subjects obtained from the results of data collection in the form of interviews, observation and documentation. The subjects of the interview data in the form of resource persons from this study consisted of the heads of the women's clothing sewing department programs, training instructors and training participants in women's clothing sewing. This study uses a descriptive research method with a qualitative approach. The data collection techniques in this study used interview, observation and documentation techniques. While the data analysis techniques in this study used data collection, data reduction, data presentation and drawing conclusions. The results of this study include: The stages of training needs analysis using observation and field surveys according to what is needed as well as planning and preparing instructors as the basis for forming a training, so that training planning is a procedure to achieve a predetermined goal, and using the implementation training method is a process of implementing training, with the implementation of training methods can run optimally, and the next stage is training evaluation one of the tools used in assisting program planning and improving the training program, self-evaluation can be used as a level of effectiveness of a program. - Factors that influence the success of the training process selling women's clothing, namely the existence of instructors, training participants and facilities and infrastructure.
Implementasi Metode Bermain Peran Pada Pembelajaran IPS di MI Muhammadiyah Bandingan Kecamatan Kejobong Kabupaten Purbalingga

This research is a field research (field research). While the research method uses a descriptive qualitative approach with data collection techniques using observation, interviews and documentation. The data validity technique uses triangulation. This study describes: (1) planning a role-playing method in social studies learning; (2) implementation of role playing method in social studies learning; (3) evaluation of role playing methods in social studies learning; (4) the strengths and weaknesses of the role-playing method; and (5) analysis of the application of role-playing methods in social studies learning at MI Muhammadiyah Bandingan, Kejobong District, Purbalingga Regency. The results showed: (1) Before implementing the role-playing method in social studies learning, the classroom teacher prepared lesson plans, media/equipment to be used, evaluation and appropriate strategies, and prepared story scenarios. (2) The initial activity in the implementation of the role playing method is carried out by providing motivation and conveying the material to be discussed and the learning method to be used. Entering the core activity, the teacher begins to implement the previously prepared method. The final activity of learning is carried out by carrying out activities of drawing conclusions and giving assignments. (3) Evaluation activities in the role playing method are carried out by assessing how students play the characters in the scenario. In addition, the teacher also gave a written test. (4) The advantages of the role playing method make it easier for students to understand the material, the weakness of the role playing method is that not all students have the ability to play characters according to the material (5) The application of the role playing method in social studies learning at MI Muhammadiyah Comparative has a positive impact on students and teachers . In addition, there are also negative impacts, including if students are not prepared properly there is a possibility that they will not do it seriously and are less than optimal, some children who do not participate in role playing become less active.
Estimation of the Global Solar Radiation in Okura Kogi State Using Angstrom-Prescott Model

This research is aimed at estimating Global Solar radiation data in Okura Kogi State using the sunshine hour data obtained from Anyigba. Angstrom Prescott model was used to estimate the global solar radiation for Anyigba using the latitude of the area which is 7.4809 and the sunshine hour, after the which the Global Solar radiation data in Okura, Kogi State (Latitude 7.4146) was estimated The Angstrom Prescott model for Okura was found to be = (0.2325+0.6655(S/s0). Where the regression coefficient a and b were found to be 0.2325 and 0.6655 respectively. The result was validated using a 6years global solar radiation data between years 2011 to 2016.
Integrated Language Learning in Elementary School

Language is not only from words, but also meaning behind those words. In discourse, there are two components: intra linguistics and extra linguistics. Intra-linguistic component means discourse build from word to word, phrase to phrase, sentences to sentences, paragraph to paragraph at the end it becomes discourse. Extra linguistics competences have some components, settings, purposes, and others component when the text is composed. I will explain how to teach discourse in teaching reading. First, teacher gives questions to open the class; the question should open students' background knowledge about subject at that day. At that stage, teachers should try to raise students' curiosity at that topic. Teachers should ask students opinion about the topic. After that stage, the teacher gives students an up-to-date material close to students' real life. In this stage, students read it by scanning and skimming. The next step, teacher asks students to analyze structure (grammar), vocabulary, and how author developing ideas their composition.
Experiments of Supervised Learning and Semi-Supervised Learning in Thai Financial News Sentiment: A Comparative Study

Sentiment classification is an instrument of natural language processing tasks in text analysis to measure customer feedback from given documents such as product reviews, news, and texts. This research aims to experiment with Thai financial news sentiment classification and evaluate sentiment classification performance. In this research, we show financial news sentiment classification experimental results when comparing supervised and semi-supervised methods. In the research methodology, we use PyThaiNLP to tokenize and remove stopwords and split datasets into 85% of the training set and 15% of the testing set. Next, we classify sentiment using machine learning and deep learning approaches with feature extraction such as bag-of-words, term frequency–inverse document frequency, and word embedding (Word2Vec and Bidirectional Encoder Representations from Transformers (BERT)) in given texts. The results show that support vector machine with the BERT model yields the best performance at 83.38%; in contrast, the random forest classifier with bag-of-words yields the worst performance at 54.10% in the machine learning approach. Another experiment reveals that long short-term memory with the BERT model yields the best performance at 84.07% in contrast to the convolutional neural network with bag-of-words, which yields the worst performance at 69.80% in the deep learning approach. The results imply that support vector machine, convolutional neural network, and long short-term memory are suitable for classifying sentiment in complex structure language. From this study, we observe the importance of sentiment classification tools between supervised and semi-supervised learning, and we look forward to furthering this work.
A Study of Software Defect Prediction Using Artificial Intelligent and Business Risk Management Approach

Abstract: The usage of several artificial intelligence algorithms in the area of software defect prediction is reviewed in the literature in this paper, it review the use of various techniques for artificial intelligence in the field of software defect prediction. Software system flaws remain a significant issue, and modern software development now faces many difficulties. Identification, estimation, and evaluation of the risks are crucial steps in order to avoid, minimize, and monitor the risks and their effects.This literature review is quite helpful because it improves understanding of the studied area. A software fault is a flaw in an executable product that results in software failure. Saving money, time, and effort may be accomplished by identifying general software process areas that may require attention from the start of a project and understanding the potential causes of problems. Planning, managing, and carrying out software development activities could be aided by the ability for early fault estimation. In order to study how many prediction approaches have been developed over this time to address the problems linked to software defect, this research conducts a literature review of works from the previous many years.In the field of software development, the study also discusses software risk management. This study makes a significant contribution by proposing a method for analyzing, organizing, and utilizing information technology initiatives in the context of software defect prediction and risk management.
Security Management for Transaction and KYC using Block Chain Technology

Abstract: Blockchain technology promises to be hugely trending and empowering in financial domain computing applications. The digital economy is becoming an integral part of modern life. So as the use of the digital world increases there are more chances to decrease the security level. So more the use of digitization more the frauds and less the security. In some cases of personal data, leakage has brought back into the focus the security issues with the different identity sharing mechanisms. A customer is expected to provide his identity for authentication by different agencies. So theKYC process deals with the identification of the user. And in turn, provides the required security. The KYC procedures which are used by the banks are completely dependent on the encryption which is slow and it can lead to the loss of customer details to other theirs party financial institutions. This system can be efficient by using Blockchain technology, which has the potential to automate a lot of manual processes and it is also resistant to hacks of any sort. The immutable blockchain block and its distributed ledger is the perfect complement to the process of KYC. With the addition of smart contacts, fraud detection can be automated. For KYC identity details storage we can make use of any types of KYC. So, the banks can develop a shared private blockchain within the bank premise and the same can be used for verifying the documents. This allows the user to get control of their sensitive documents and also makes it easier for banks to obtain the documents they need for compliance.
Uncertainty Propagation in Orbital Mechanics Using Stochastic Expansion Methods

Mathematical algorithms have been used daily to model many engineering applications involved with uncertainty. This study investigates a few ways to solve orbit estimation problems using stochastic expansion methods available in the Dakota software. The accuracy of stochastic expansion methods is investigated on the performances of mean and covariance analysis of an orbit estimation problem with known solutions. This paper further proposes solutions for accurate and efficient ways to develop orbit estimation problems using TensorFlow (Python-based platform) for both the central processing unit (CPU) and graphics processing unit (GPU). Keywords: Uncertainty Propagation, Estimation, Orbital Mechanics