Reviewerly/exHarmony huggingface dataset:
Background Knowledge Grounding for Readable, Relevant, and Factual Biomedical Lay Summaries

Communication of scientific findings to the public is important for keeping non-experts informed of developments such as life-saving medical treatments. However, generating readable lay summaries from scientific documents is challenging, and currently, these summaries suffer from critical factual errors. One popular intervention for improving factuality is using additional external knowledge to provide factual grounding. However, it is unclear how these grounding sources should be retrieved, selected, or integrated, and how supplementary grounding documents might affect the readability or relevance of the generated summaries. We develop a simple method for selecting grounding sources and integrating them with source documents. We then use the BioLaySum summarization dataset to evaluate the effects of different grounding sources on summary quality. We found that grounding source documents improves the relevance and readability of lay summaries but does not improve factuality of lay summaries. This continues to be true in zero-shot summarization settings where we hypothesized that grounding might be even more important for factual lay summaries.
Generalization of graph network inferences in higher-order graphical models

Probabilistic graphical models provide a powerful tool to describe complex statistical structure, with many real-world applications in science and engineering from controlling robotic arms to understanding neuronal computations. A major challenge for these graphical models is that inferences such as marginalization are intractable for general graphs. These inferences are often approximated by a distributed message-passing algorithm such as Belief Propagation, which does not always perform well on graphs with cycles, nor can it always be easily specified for complex continuous probability distributions. Such difficulties arise frequently in expressive graphical models that include intractable higher-order interactions. In this paper we define the Recurrent Factor Graph Neural Network (RF-GNN) to achieve fast approximate inference on graphical models that involve many-variable interactions. Experimental results on several families of graphical models demonstrate the out-of-distribution generalization capability of our method to different sized graphs, and indicate the domain in which our method outperforms Belief Propagation (BP). Moreover, we test the RF-GNN on a real-world Low-Density Parity-Check dataset as a benchmark along with other baseline models including BP variants and other GNN methods. Overall we find that RF-GNNs outperform other methods under high noise levels.
CodeGen2: Lessons for Training LLMs on Programming and Natural Languages

Large language models (LLMs) have demonstrated remarkable abilities in representation learning for program synthesis and understanding tasks. The quality of the learned representations appears to be dictated by the neural scaling laws as a function of the number of model parameters and observations, while imposing upper bounds on the model performance by the amount of available data and compute, which is costly. In this study, we attempt to render the training of LLMs for program synthesis more efficient by unifying four key components: (1) model architectures, (2) learning methods, (3) infill sampling, and, (4) data distributions. Specifically, for the model architecture, we attempt to unify encoder and decoder-based models into a single prefix-LM. For learning methods, (i) causal language modeling, (ii) span corruption, (iii) infilling are unified into a simple learning algorithm. For infill sampling, we explore the claim of a "free lunch" hypothesis. For data distributions, the effect of a mixture distribution and multi-epoch training of programming and natural languages on model performance is explored. We conduct a comprehensive series of empirical experiments on 1B LLMs, for which failures and successes of this exploration are distilled into five lessons. We will provide a final recipe for training and release CodeGen2 models in size 1B, 3.7B, 7B, and, 16B parameters, along with the training framework as open-source: https://github.com/salesforce/CodeGen.
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Deploying large language models (LLMs) is challenging because they are memory inefficient and compute-intensive for practical applications. In reaction, researchers train smaller task-specific models by either finetuning with human labels or distilling using LLM-generated labels. However, finetuning and distillation require large amounts of training data to achieve comparable performance to LLMs. We introduce Distilling step-by-step, a new mechanism that (a) trains smaller models that outperform LLMs, and (b) achieves so by leveraging less training data needed by finetuning or distillation. Our method extracts LLM rationales as additional supervision for training small models within a multi-task framework. We present three findings across 4 NLP benchmarks: First, compared to both finetuning and distillation, our mechanism achieves better performance with much fewer labeled/unlabeled training examples. Second, compared to few-shot prompted LLMs, we achieve better performance using substantially smaller model sizes. Third, we reduce both the model size and the amount of data required to outperform LLMs; our finetuned 770M T5 model outperforms the few-shot prompted 540B PaLM model using only 80% of available data on a benchmark, whereas standard finetuning the same T5 model struggles to match even by using 100% of the dataset. We release the code at: https://github.com/google-research/distilling-step-by-step .
CROUP WORK ONLINE IN THE SPANISH LESSON

The article is devoted to the importance of conducting group work in a game format during distance learning.Group works have always aroused lively interest among students, as they provide the student with the opportunity to show his creative abilities in pairs with classmates and improve language skills.The authors emphasize that it is very important for students to be actively involved in learning, and not just be passive listeners, especially in foreign language lessons.This type of work in the lesson helps to involve all students in cooperation, motivates them to better understand the topic and help each other, encourages them to speak more, express their opinions, defend their point of view and respect others.This type of work does not lose its relevance even with online education, on the contrary, it acquires a new meaning.In times when teachers and students meet during the educational process in an online format, the communication between the teacher and the student and the interaction of students with each other using modern technologies and educational platforms are especially important.This allows not only to adapt to the new realities of the time, but also not to lose communication skills between people.In this article, the authors give examples of group cooperation in Spanish lessons in a game format for students who have a language level of A2.It is suggested to use session rooms for group collaboration in online format.With the help of session halls, the teacher divides students into small groups, where they can prepare a creative task, exchange ideas and devote time to active speaking.Then students with other classmates defend their works and exchange experiences.The authors of the article explain in great detail, step by step, what types of game practice they use with students during pair work, which makes the learning process accessible and diverse.
ANALISIS INDEKS HARGA SAHAM GABUNGAN DAN FAKTOR PENGARUHNYA MENGGUNAKAN PEMODELAN REGRESI SEMIPARAMETRIK KERNEL DILENGKAPI GUI-R

Composite Stock Price Index (IDX) shows the movement of stock prices used by investors to determine their investment strategy. IDX movement is influenced by macroeconomic factors such as money supply and inflation, so regression analysis is used to determine the relationship between the variables. Based on the scatterplot, money supply is known as a parametric predictor variable as it has a linier line patterned scatterplot and inflation is a nonparametric predictor variable as it has a random patterned scatterplot, so semiparametric regression modelling is used for the analysis. Kernel regression was chosen to analyze the nonparametric component based on the random patterned scatterplot of inflation. This study aims to obtain the results of semiparametric kernel regression modelling analysis and to create a GUI to be applied to the analysis as a development of previous similar studies that still done based on CLI. This study uses monthly data from January 2013 to December 2020 with the proportion of in sample and out sample data distribution 87,5%:12,5%. Based on the smallest MSE value as the best model criteria, semiparametric regression model with triangle kernel function is the best model obtained with optimal bandwidth=3.24, which means the model is strong and which means that the forecasting results are very accurate. GUI has been created according to the needs of the modelling analysis implementation.
PENERAPAN TEXT MINING DAN FUZZY C-MEANS CLUSTERING UNTUK IDENTIFIKASI KELUHAN UTAMA PELANGGAN PDAM TIRTA MOEDAL KOTA SEMARANG

Customer complaints can be handled effectively by identifying the main complaints that cause customers to be dissatisfied. Many customer complaints cause difficulty for PDAM Tirta Moedal Semarang to identify problems, which are frequently the primary complaints of customers. Grouping complaints that have similarities using Fuzzy C-Means Clustering will make the identification of the main customer complaints easier. Fuzzy C-Means uses fuzzy models, allows data to be a member of all formed clusters with membership level between 0-1. Fuzzy C-Means Clustering can also introduce more flexible patterns and show results in more accurate cluster placement. Text mining is used to convert textual data into numerical data. Customer complaints received through all contacts in PDAM Tirta Moedal Semarang from October–December 2021 were used as data. The clustering process forms 6 clusters,with the number of clusters tried being 3, 4, 5, and 6, which are seen by the smallest Xie-Beni Index. The main complaints from PDAM Tirta Moedal Semarang customer that seen through Word cloud in each cluster are that the water stops running in clusters 1 and 6 and the pipes leak in clusters 4 and 5. Complaints in clusters 2 and 3 are complaints related to water meters and water flow.
PERBANDINGAN MODEL ARIMA DENGAN MODEL NONPARAMETRIK POLINOMIAL LOKAL DAN SPLINE TRUNCATED UNTUK PERAMALAN HARGA MINYAK MENTAH WEST TEXAS INTERMEDIATE (WTI) DILENGKAPI GUI R

Crude oil as one of the most important natural resources experiences price fluctuations from time to time, even the spot price of West Texas Intermediate (WTI) world crude oil on 20th April 2020 reached -36,98 USD/barrel due to the Covid-19 pandemic. WTI oil price data was modeled using the ARIMA method, local polynomial, and spline truncated nonparametric regression then compared and obtained the best model and formed R Graphical User Interface (GUI). The ARIMA model and nonparametric time series models can be used to model time series data, but in the ARIMA model there are assumptions that must be fulfilled. Nonparametric time series models, which include local polynomial model and truncated spline do not need to fulfill these assumptions. The ARIMA model obtained did not fulfilled the assumptions of normality and residual homoscedasticity, so the modeling was stopped and modeling was only carried out using nonparametric regression methods. Based on the minimum MSE criteria, the best nonparametric model was obtained, namely nonparametric truncated spline model with degrees 3 and 3 knot points which was categorized as a strong model based on R-squared in sample value and having a very good forecasting ability based on MAPE out sample value.
PERAMALAN PENDAPATAN BULANAN MENGGUNAKAN FUZZY TIME SERIES CHEN ORDE TINGGI

Cooperatives need consideration in the making of business strategy decisions. Forecasting can assist cooperatives in deciding on their business strategy. This study used n-orde Fuzzy Time Series Chen. n-orde Fuzzy Time Series Chen captures data patterns formed by two or more historical data in each period called fuzzy logic relation (FLR). The pattern of FLR is used to be projected in forecasting future conditions. This study used 2-orde, 3-orde, and 4-orde with 1-orde as the comparison. This study used data on the monthly revenue of the Employee Cooperative of PT. Telekomunikasi Indonesia Semarang Region for the period of January 2019 to May 2022 to predict revenue for the period of June and July 2022. This study used symmetric Mean Absolute Percentage Error (sMAPE) in calculating the forecasting error rate. 1-orde, 2-orde, 3-orde, and 4-orde of Fuzzy Time Series Chen produced different forecasting results for the period of June and July 2022. 1-orde has sMAPE value of 23.15% (good enough forecasting), 2-orde and 3-orde have sMAPE value of 10.06% (good forecasting), and 4-orde has sMAPE value of 4.52% (very good forecasting). This study showed that the larger orde used in Fuzzy Time Series Chen, the lower forecasting error rate.
Improving the Quality of Neural Machine Translation Through Proper Translation of Name Entities

In this paper, we have shown a method of improving the quality of neural machine translation by translating/transliterating name entities as a preprocessing step. Through experiments we have shown the performance gain of our system. For evaluation we considered three types of name entities viz person names, location names and organization names. The system was able to correctly translate mostly all the name entities. For person names the accuracy was 99.86%, for location names the accuracy was 99.63% and for organization names the accuracy was 99.05%. Overall, the accuracy of the system was 99.52%
ANALISIS KEBUTUHAN RAK DOKUMEN REKAM MEDIS DI RUMAH SAKIT

The hospital is one of the main destinations for people to do treatment. The large number of patients who visit a hospital makes the accumulation of RM data. The importance of RM data management in a hospital greatly affects the level of service to patients. Disorganization in managing RM documents is one of the problem factors faced by hospitals, this information is obtained from the results of observations in storing and managing medical record data. The layout of the medical record data storage area and the size of the unsupported filing area are also factors causing the problem. The purpose of conducting research is to produce a model for storing and managing RM data according to the needs of the hospital. A retrospective approach is used in this descriptive research model. The data collection model uses observation and question and answer. Data processing is done by collecting data, processing data, tabulating, presenting data. The results of the data that has been processed then carried out an analysis with a descriptive approach. Calculating the need for shelves to store medical record data as needed with the roll o' pack rack model is the result of the research that has been done. Supporting medical record data storage rooms are also needed to support the ease of managing medical record data. The use of the roll o'pack rack model is recommended to store and manage RM documents in a neater and more efficient place.
PERANCANGAN SISTEM PENGENDALIAN DOKUMEN REKAM MEDIS DI PUSKESMAS MANGKUBUMI KOTA TASIKMALAYA

Missfile is something that often happens in the storage room of a health care facility. Efforts to avoid missfile events need to control medical record documents. The Mangkubumi Health Center does not yet have a medical record document control system. The E-Puskesmas NG application does not yet have a feature that can control medical records. This study aims to analyze and design an integrated medical record document control system in the E-Puskesmas NG application. This type of research is qualitative with a FOCUS PDCA approach. How to collect data using interviews and observation. Data analysis using content analysis. The flow of medical record document control starts from the patient coming and being registered, the patient is given a medical record when going to the polyclinic, the medical record is filled out by the polyclinic officer and returned to the storage unit when it is completely filled out. Problems in controlling medical record documents are medical records are lost and scattered due to delays in returning medical records and delays in documenting examination results, there is no SOP as a workflow guideline, expedition records are not made, and there are no medical record personnel with medical record graduates. The proposed medical record document control system design is a reminder system (alert) for filling out medical record documents in the E-Puskesmas NG application which is intended for polyclinic officers when they finish providing services to patients so that they immediately complete documentation and prevent medical record documents from being scattered.
Application of artificial intelligence machine learning approach to precisely predict recurrence in cervical cancer

Objective: Recent prospective studies have shown poorer oncologic outcomes following minimally invasive surgery (MIS), which has led many surgeons to deeply inspect their practices.We reviewed our experience and evaluated the results of radical hysterectomy in patients with early stage cervical cancer.Methods: This retrospective study included patients with cervical cancer stage Ib1 who were treated with either open or minimally invasive radical hysterectomy from January 2010 to December 2020 by a single surgeon.Patients were divided according to surgical period.In the second period, efforts to prevent tumor dissemination were made.Oncologic outcomes of the patients were compared.Results: In total, 49 patients were included.12 patients underwent open surgery, and 37 patients underwent MIS.The recurrence rate was 8.2% (4/49).When patients were divided into 2 groups in order based on surgery date, the first surgical period showed higher recurrence rate (13.6%, 3/22) compared to the second period (3.7%, 1/27). Conclusion: MIS performed under the prevention of tumor dissemination is not inferior to open surgery in cervical cancer
Explicit and Implicit Knowledge-Enhanced Model for Event Causality Identification

Event Causality Identification (ECI) aims at detecting the causal relation between 2 events, which is a challenging task due to the complexity of causal expressions and the background knowledge needed for identifying certain causal relations. Considerable work has been done on the learning of context and the incorporation of external knowledge. However, few of them combines the data-oriented model with both explicit and implicit causal knowledge. In this paper, we propose an integrative model to integrate causal indicator words, implicit causal knowledge, and data-oriented model to enhance causality identification. For the data-oriented model, an event pair graph is constructed and Relational Graph Convolutional Network (R-GCN) [23] is employed to better capture interactions between individual pairs. For the causal indicators, their word embeddings are used to initialize the filters of convolutional neural network so as to capture the clues indicating causal relation. For the implicit causal knowledge, we design a cause-effect matching mechanism to measure the possibility of causal relation holding between 2 events, based on the possible causes and effects automatically generated by COMET [2]. The proposed method is evaluated on 3 datasets [4, 16, 7], and experimental results demonstrate the effectiveness and superiority of the proposed method.
FORMING READINESS OF THE SUBJECTS OF THE EDUCATIONAL PROCESS FOR CONSTRUCTIVE INTERACTION

At present, an important task in the system of Russian education is the modernization of the educational process, aimed at developing the educational component of teaching the younger generation and youth, where the constructive interaction of all its subjects is of the greatest importance. The article pays special attention to the formation of the readiness of all subjects of an educational institution to interact with each other, which results not only in the personal development of students, but also in the harmonization of interpersonal relations, mutual understanding of the parties interacting in the educational environment and the improvement of society. As part of the production (pedagogical) practice, students of the Samara State Socio-Pedagogical University developed and implemented activities aimed at developing fruitful cooperation between all its participants in the educational process of the educational institution. The constructive interaction of the participants in the educational organization was built taking into account the creation of the necessary conditions (regulatory, informational, stimulating, prognostic-activity) and including its main components (motivational, cognitive-activity, evaluative) aimed at obtaining the desired result. To solve the identified tasks, a number of activities were carried out aimed at increasing the interest of the subjects of the educational process in the education of the spiritual and moral personality of the student, the formation of responsibility to the family in particular and society in general.
AI and Machine Learning: The Basics

In this chapter the key concepts of artificial intelligence and machine learning are introduced. The importance of first identifying and defining the right problem is emphasised. A review is provided of different types of machine learning model, and pointers are provided about how to design and train a model to meet the requirements of the chosen problem. Important considerations regarding validating the trained model are also discussed. A review is provided of the context of AI and machine learning in cardiology, i.e. what imaging and non-imaging data sources are typically available for such models and what information can they provide? Within each of these data sources, some of the important applications and contributions of AI are highlighted. A practical tutorial is provided to introduce the reader to Jupyter notebooks and Python.