Reviewerly/exHarmony huggingface dataset:
An Oversampling Algorithm combining SMOTE and RF for Imbalanced Medical Data

Abstract: A dataset is said to be imbalanced when the distribution of classes is unbalanced. In medical datasets, this is a typical issue because the positive class, such as the existence of an illness, is frequently rarer than the negative class, such as the absence of sickness. Since machine learning algorithms are prone to being biased in favor of the majority class, this might negatively affect their performance. An oversampling algorithm that combines the SMOTE and KNN SMOTE methods has been presented as a solution to this issue. Synthetic minority oversampling, a well-known oversampling approach, interpolates between existing samples to produce synthetic samples for the minority class. The KNN, on the other hand, chooses K's nearest neighbors, mixes them, and produces synthetic samples in space. The oversampling algorithm can be used to balance the dataset, and then the random forest decision tree approach can be used to create a classification model. The random forest algorithm chooses random samples from a set of data and builds a decision tree for each training set of data. Voting is used to determine the decision tree's average, and the prediction result with the highest number of votes is chosen as the final prediction outcome.
Outcome of ICUs using APACHE, SAPS and MPM Scoring Systems

Now a day's price of health protection for a care unit patient has been three times more as compared to general word patient.Monitoring the care unit improvement is a major criteria with respect to control the major hospital expenses.To predict the outcome in an ICU the common illness severity scores are generally used which characterize the severity of diseases, depend on the rate of organ disorder and assessment of resources used for this purpose.Primarily, the separate types of scoring systems are used necessarily for the treatment purpose.Their compound uses provide a more correct symptom of disease intensity and prophecy to the doctor regarding duration of rest and mortality for the ICU patient.This paper gives brief overview of the generally used scoring system, examines the details regarding their development, qualified information concerning their execution.It is important and also necessary for all these marking approach will be modernize accordingly with times as care unit community increases, change in heterogeneity of diseases and new symptomatic, remedial and anticipating strategy become available day by day.
The Quality-Diversity Transformer: Generating Behavior-Conditioned Trajectories with Decision Transformers

In the context of neuroevolution, Quality-Diversity algorithms have proven effective in generating repertoires of diverse and efficient policies by relying on the definition of a behavior space. A natural goal induced by the creation of such a repertoire is trying to achieve behaviors on demand, which can be done by running the corresponding policy from the repertoire. However, in uncertain environments, two problems arise. First, policies can lack robustness and repeatability, meaning that multiple episodes under slightly different conditions often result in very different behaviors. Second, due to the discrete nature of the repertoire, solutions vary discontinuously. Here we present a new approach to achieve behavior-conditioned trajectory generation based on two mechanisms: First, MAP-Elites Low-Spread (ME-LS), which constrains the selection of solutions to those that are the most consistent in the behavior space. Second, the Quality-Diversity Transformer (QDT), a Transformer-based model conditioned on continuous behavior descriptors, which trains on a dataset generated by policies from a ME-LS repertoire and learns to autoregressively generate sequences of actions that achieve target behaviors. Results show that ME-LS produces consistent and robust policies, and that its combination with the QDT yields a single policy capable of achieving diverse behaviors on demand with high accuracy.
WARM: A Weakly (+Semi) Supervised Model for Solving Math word Problems

Solving math word problems (MWPs) is an important and challenging problem in natural language processing. Existing approaches to solve MWPs require full supervision in the form of intermediate equations. However, labeling every MWP with its corresponding equations is a time-consuming and expensive task. In order to address this challenge of equation annotation, we propose a weakly supervised model for solving MWPs by requiring only the final answer as supervision. We approach this problem by first learning to generate the equation using the problem description and the final answer, which we subsequently use to train a supervised MWP solver. We propose and compare various weakly supervised techniques to learn to generate equations directly from the problem description and answer. Through extensive experiments, we demonstrate that without using equations for supervision, our approach achieves accuracy gains of 4.5% and 32% over the state-of-the-art weakly supervised approach, on the standard Math23K and AllArith datasets respectively. Additionally, we curate and release new datasets of roughly 10k MWPs each in English and in Hindi (a low resource language).These datasets are suitable for training weakly supervised models. We also present an extension of WARMM to semi-supervised learning and present further improvements on results, along with insights.
Credit Card Fraud Detection Using Random Forest Classification

Abstract: In recent years credit card became one of the essential parts of the people. Sudden increase in E-commerce, customer started using credit card for online purchasing therefore risk of fraud also increases. Instead of carrying a huge amount in hand it is easier to keep credit cards. But nowadays that too becomes unsafe. Nowadays we are facing a big problem on credit card fraud which is increasing in a good percentage. The main purpose is the survey on the various methods applied to detect credit card frauds. From the abnormalities, in the transaction, the fraudulent one is identified. We address this issue in order to implement some machine learning algorithm like random forest, logistic regression in order to detect this kind of fraud. In this paper we increase the efficiency in finding the fraud. However, we discussed and evaluated employee criteria. Currently, the issues of credit card fraud detection have become a big problem for new researchers. We implement an intelligent algorithm which will detect all kind of fraud in a credit card transaction. We handled the problem by finding a pattern of each customer in between fraud and legal transaction. Isolation Forest Algorithm and Local Outlier Factor are used to predict the pattern of transaction for each customer and a decision is made according to them. In order to prevent data from mismatching, all attribute are marked equally.
MENGOPTIMALKAN PEMBELAJARAN SENI RUPA DI SEKOLAH DASAR : STRATEGI DAN PRAKTEK TERBAIK

The purpose of writing this article is to discuss strategies and best practices in optimizing visual arts learning in elementary schools. Fine arts have an important role in basic education, engaging students in creative expression, developing critical thinking, and increasing self-confidence. The main focus of this article is the use of art projects as an effective learning strategy. Art projects engage students in practical activities that allow them to learn thoroughly about art concepts and apply them in their own work. This article also describes the benefits of art projects in increasing student engagement, encouraging creative exploration, and strengthening interpersonal skills. The stages of implementing an art project are adapted to the developmental level of children, ensuring a suitable and interesting learning experience for them. In addition, this article outlines best practices in art learning, including creating a creative and inclusive classroom environment, encouraging students' exploration and self-expression, and providing constructive feedback and appreciation for their art. In facing challenges such as the integration of fine arts into a dense curriculum and the limited knowledge of art teachers, this article also provides solutions that can help maximize time and resources and improve teacher competence. It is hoped that this article can provide insight and practical guidance for the development of effective art learning in elementary schools.
Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks

One of the most significant challenges in the field of software code auditing is the presence of vulnerabilities in software source code. Every year, more and more software flaws are discovered, either internally in proprietary code or publicly disclosed. These flaws are highly likely to be exploited and can lead to system compromise, data leakage, or denial of service. To create a large-scale machine learning system for function level vulnerability identification, we utilized a sizable dataset of C and C++ open-source code containing millions of functions with potential buffer overflow exploits. We have developed an efficient and scalable vulnerability detection method based on neural network models that learn features extracted from the source codes. The source code is first converted into an intermediate representation to remove unnecessary components and shorten dependencies. We maintain the semantic and syntactic information using state of the art word embedding algorithms such as GloVe and fastText. The embedded vectors are subsequently fed into neural networks such as LSTM, BiLSTM, LSTM Autoencoder, word2vec, BERT, and GPT2 to classify the possible vulnerabilities. We maintain the semantic and syntactic information using state of the art word embedding algorithms such as GloVe and fastText. The embedded vectors are subsequently fed into neural networks such as LSTM, BiLSTM, LSTM Autoencoder, word2vec, BERT, and GPT2 to classify the possible vulnerabilities. Furthermore, we have proposed a neural network model that can overcome issues associated with traditional neural networks. We have used evaluation metrics such as F1 score, precision, recall, accuracy, and total execution time to measure the performance. We have conducted a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information.
The Role of Social Engineering in the Energy Balance of Systems

A big problem of our time is the constant increase in the energy used.An efficient energy household is one possible solution.Increasing efficiency requires examining the energy balance.System disturbance is one of the factors influencing the energy balance of systems.Social Engineering is a form of system disruption that manifests itself in covert and conscious system control.The present study examines the harmful energy role of Social Engineering through the parallels between the regulatory circuit of control theory and the operating model of Social Engineering.The result of the study highlights the importance of cyber defense.
A whole-rock data set for the Skaergaard intrusion, East Greenland

We report a compilation of new and published whole-rock major and trace element analyses for 646 samples of the Skaergaard intrusion, East Greenland. The samples were collected in 14 stratigraphic profiles either from accessible and well-exposed surface areas or from drill core, and they cover most regions of the intrusion. This includes the Layered Series, the Upper Border Series, the Marginal Border Series and the Sandwich Horizon. The geochemical data were obtained by a combination of X-ray fluorescence and inductively coupled plasma mass spectrometry. This data set can, for example, be used to constrain processes of igneous differentiation and ore formation.
Sistem Pakar Metode Backward Chaining untuk Optimalisasi Pelayanan Pemberian Informasi Obat

Drug information service is an assistance service to handle the needs of pharmacists related to medicines consumed by patients at the Lasi Health Center, Agam Regency. Nowadays, most of drug information services always require pharmacists to carry out their services, although there is limited number of pharmacists for providing drug information services at the Lasi Health Center, Agam Regency. This study aims to optimize drug information services so that the services can be carried out without the direct presence of a pharmacist. The data used in this study were drug prescription data available at the Pharmacy of Lasi Health Center Agam for the last 12 months and drug information services provided by pharmacists at the Lasi Health Center Agam Regency. This study used the backward chaining method to identify the drugs prescribed to the patients. The result achieved by this study were 356 Rules that could be applied directly to drug information services, with an accuracy rate of 100%. The rules generated using the backward chaining method can be used to optimize drug information services at the Lasi Health Center in Agam Regency without having to be served directly by pharmacists.
FLamE: Few-shot Learning from Natural Language Explanations

Natural language explanations have the potential to provide rich information that in principle guides model reasoning. Yet, recent work by Lampinen et al. (2022) has shown limited utility of natural language explanations in improving classification. To effectively learn from explanations, we present FLamE, a two-stage few-shot learning framework that first generates explanations using GPT-3, and then finetunes a smaller model (e.g., RoBERTa) with generated explanations. Our experiments on natural language inference demonstrate effectiveness over strong baselines, increasing accuracy by 17.6% over GPT-3 Babbage and 5.7% over GPT-3 Davinci in e-SNLI. Despite improving classification performance, human evaluation surprisingly reveals that the majority of generated explanations does not adequately justify classification decisions. Additional analyses point to the important role of label-specific cues (e.g., "not know" for the neutral label) in generated explanations.
A Novel Scheme to classify Read and Spontaneous Speech

The COVID-19 pandemic has led to an increased use of remote telephonic interviews, making it important to distinguish between scripted and spontaneous speech in audio recordings. In this paper, we propose a novel scheme for identifying read and spontaneous speech. Our approach uses a pre-trained DeepSpeech audio-to-alphabet recognition engine to generate a sequence of alphabets from the audio. From these alphabets, we derive features that allow us to discriminate between read and spontaneous speech. Our experimental results show that even a small set of self-explanatory features can effectively classify the two types of speech very effectively.
Implementation of the competence approach in the system of postgraduate pedagogical education

This article describes methods, forms and means of teaching, the use of which will allow the most optimal implementation of the competence approach in postgraduate teacher education.Preference is given to productive search, problem-based learning methods, and students' project activities.The expediency of transferring the educational process in the system of postgraduate education to the level of personal interaction, which means turning it into cooperation between the teacher and students, is emphasised.
Faktor-faktor yang Memengaruhi Organisasi Dalam Mengadopsi Teknologi Blockchain di BPJS Ketenagakerjaan

The acceleration of digital transformation in organizations plays a critical role in supporting business processes and selecting appropriate technologies to address organizational challenges and issues. Currently, blockchain technology has garnered significant attention among practitioners and academics due to its versatile applications. This study aims to analyze the factors influencing the adoption of blockchain technology at BPJS Ketenagakerjaan using the technology-organization-environment (TOE) framework model to map the determinants of blockchain technology adoption. The analysis is conducted through a structural equation modelling (SEM) approach. The research results from 150 respondents indicate that technology, organization and environment have a significant and positive impact on the adoption of blockchain technology. However, organizational culture does not have a moderating effect on this study. Additionally, the study examines the effects of blockchain technology adoption on organizational performance. In order to successfully adopt blockchain technology, BPJS Ketenagakerjaan need to consider technological, organizational and environmental factors. Moreover, collaboration with organizations that have already adopted blockchain technology and partnerships with educational institutions to explore the full potential of this technology are recommended. Keywords: blockchain, BPJS Ketenagakerjaan, TOE Framework, organizational performance, technology
DORSal: Diffusion for Object-centric Representations of Scenes et al

Recent progress in 3D scene understanding enables scalable learning of representations across large datasets of diverse scenes. As a consequence, generalization to unseen scenes and objects, rendering novel views from just a single or a handful of input images, and controllable scene generation that supports editing, is now possible. However, training jointly on a large number of scenes typically compromises rendering quality when compared to single-scene optimized models such as NeRFs. In this paper, we leverage recent progress in diffusion models to equip 3D scene representation learning models with the ability to render high-fidelity novel views, while retaining benefits such as object-level scene editing to a large degree. In particular, we propose DORSal, which adapts a video diffusion architecture for 3D scene generation conditioned on frozen object-centric slot-based representations of scenes. On both complex synthetic multi-object scenes and on the real-world large-scale Street View dataset, we show that DORSal enables scalable neural rendering of 3D scenes with object-level editing and improves upon existing approaches.
Pola Komunikasi Melalui Fitur Virtual Pada Game Mobile Legends

With the development of the human mindset and technology in the current period, in communicating not only with conventional media, but at this time in communicating can use other forms of alternative media. One of the media used in communicating is game media. With the presence of online games that use the internet network to play games, it has changed, not only as a mere entertainment medium but has changed to form a "network society" for the players. The online game media that is often used or played by the general public, especially in Indonesia, is the mobile legends game. Communication patterns that are carried out in game media by using virtual features are called virtual communication. This is related to the phenomenon of virtual communication patterns that are currently happening around us. Seeing this phenomenon, here the researchers found the form of problems that exist related to virtual communication, namely how to communicate patterns through virtual features that exist in online mobile legends games for players, especially the creative box community. Researchers conducted using a virtual ethnographic theory approach. This virtual ethnographic approach refers to objects in cyberspace and objects that are described and analyzed in such a way. Researchers use a constructivist paradigm with a qualitative approach methodology. The results obtained in this study are one of the communication patterns that occur in game media by using virtual features in communicating.
Development of Secondary Education through Digital Learning Methods

Teaching methods in secondary education have undergone significant changes due to COVID-19. The paper examines the positive impact of investing in education through the Strategic Framework for the Development of Education, Training and Learning in the Republic of Bulgaria (2021-2030) and digital technologies. The aim of this paper is to identify key challenges and relevant solutions through a study of teaching methods in secondary education.
A Data-Driven Collaborative Forecasting Method for Logistics Network Throughput Based on Graph Learning

In order to achieve more optimal resource scheduling effect for logistics networks, it is essential to collaboratively predict throughput amount of different network nodes in future timestamps. However, the logistics networks are actually a kind of connected complex networks, in which a node denotes a single logistics station and all nodes are associated by implicit relationships. When it comes to collaborative forecasting towards logistics network throughput, all the nodes are required to be integrated together as a whole research object. Therefore, this work introduces graph learning to extract graph-level features of logistics networks, and proposes a data-driven collaborative forecasting method for logistics network throughput based on graph learning. Firstly, information characteristics of graph-level logistics networks is defined as vectorized format. Then, the graph learning framework is formulated, so as to fit the nonlinear relationship between logistics networks and dynamic throughput amount. At last, some simulations are also taken to testify performance of the proposal. The research results show that the graph neural network can find the temporal correlation between data and combine preprocessed multi-layer feature vector with temporal attention weight vectors. And the proposal is able to well implement collaborative forecasting towards logistics networks, with the assistance of graph learning.
MENINGKATKAN KETERAMPILAN KOLABORASI DENGAN MODEL PEMBELAJARAN LITERATURE CIRCLE PADA PESERTA DIDIK KELAS V SD (PTK PADA PEMBELAJARAN IPS KERAGAMAN BUDAYA)

Collaboration is one of the skills that students must have in the 21st century. However, in reality students' collaborative attitudes are still less visible because the learning methods and models used are rarely in the form of groups. The purpose of this research is to improve students' collaboration skills using the Literature Circle learning model in learning cultural diversity in fifth grade elementary school. This research was a class action research conducted for 2 cycles. The subjects of this study were 27 people from class 5 A. The data collection from this study was based on observations made collaboratively between researchers and observers based on the observation sheets that had been prepared. . The collected data were analyzed by descriptive statistical analysis. The results showed that the average collaborative attitude in Cycle I was 44%, so learning continued in Cycle II. In Cycle II, the average result of students' collaborative attitudes was 76%. Based on the results of the study it can be concluded that the Literature Circle learning model is effective in improving students' collaboration skills regarding learning cultural diversity.
Book Review: Shin Yi Chew and Lee Luan Ng (2021). Interpersonal Interactions and Language Learning: Face-to-Face vs. Computer-Mediated Communication. Palgrave Macmillan

The content of this book can be fully appreciated within the context of Malaysia, a country that is home to approximately thirty-four million people with multi-ethnic backgrounds. While the official language is Malay, a multitude of languages are spoken in Malaysia. The country aspires to educate professionally competent citizens in order to increase their opportunities for advancement and to enhance the country’s ability for international relations and international trade. Rashid et al. (2017) point out that educational reform initiatives in Malaysia have often focused on language policy that places the English language at the center of curricular changes as English is viewed as the tool that may make it possible for the country to keep pace with its economic objectives. Within this view, English teachers have become key players in the implementation of educational reforms.