Types of Phoneme Change in Bugis, Toraja, and Mandar

This study aims to analyze the types of phoneme sound changes in Bugis, Toraja and Mandarin languages. The research method used in this study is a qualitative descriptive method with data and data sources from 6 informants, each of whom masters Bugis, Toraja, and Mandar languages. Data collection techniques in this study used listening techniques, note-taking techniques, and documentation techniques. The data analysis techniques in this study include data display, data reduction, verification, and data inference. The results showed that the types of sound changes in Bugis, Toraja, and Mandarin include lenisi, sound addition, metathesis, and assimilation. Apocope lenisi type is more commonly found in Bugis, Toraja and Mandar languages than syncope. In sound addition, the most common data found were prosthesis data in Bugis and Mandarin. The metathesis type is only found in the comparison of Toraja and Mandarin, and only regressive assimilation types are found in Toraja and Mandarin. From the results of the analysis of the type of sound change, it shows that the Toraja and Mandar languages are closer, compared to the Toraja and Bugis languages.

MENGUNGKAP IDEOLOGI TEKS BERITA COVID 19 BERDASARKAN PENDEKATAN ANALISIS WACANA KRITIS THEO VAN LEEUWEN

ABSTRAK: Penelitian ini bertujuan untuk (1) mengungkap ciri ideologi eksklusi pemberitaan Covid-19 mengenai tindak kejahatan dalam media daring SINDOnews.com dan Fajar.co.id model Theo van Leeuwen; (2) mengungkap ciri ideologi inklusi pemberitaan Covid-19 mengenai tindak kejahatan dalam media daring SINDOnews.com dan Fajar.co.id model Theo van Leeuwen; (3) mengindentifikasi perbedaan strategi eksklusi dan inklusi media daring SINDOnews.com dan Fajar.co.id pada pemberitaan Covid-19 mengenai tindak kejahatan. Penelitian ini merupakan penelitian kualitatif dengan menggunakan pendekatan paradigma kritis, sehingga metode pengumpulan data yang digunakan, yaitu teknik dokumentasi, teknik baca simak dan teknik pencatatan. Selain itu, teknik analisis data yang digunakan dalam penelitian ini, yaitu reduksi data, penyajian data, dan penyimpulan data. Sumber data dalam penelitian ini adalah media daring SINDOnews.com, cetakan Juli-September 2020. Ciri ideologi eksklusi dalam media daring SINDOnews.com dan Fajar.co.id, ditemukan adanya strategi wacana pasivasi dan nominalisasi. Ciri ideologi inklusi dalam media daring SINDOnews.com dan Fajar.co.id, ditemukan adanya strategi wacana objektivasi, nominasi, identifikasi, determinasi, indeterminasi, asimilasi dan individualisasi. Selain itu, terdapat tiga perbedaan pada strategi eksklusi dan inklusi dalam media daring SINDOnews.com dan Fajar.co.id.Kata Kunci: Analisis Wacana Kritis; Fajar.co.id; SINDOnews.com; Teks Berita Covid-19 REVEAL THE IDEOLOGY OF COVID-19 TEXTBASED ON CRITICAL DISCOURSE APPROACH BYTHEO VAN LEEUWEN ABSTRACT: This research was aimed to (1) reveal the characteristic of the exclusion ideology of covid-19 reporting about crime in online media of SINDOnews.com and Fajar.co.id Theo Van Leeuwen model; (2) ) reveal the characteristic of the inclusion ideology of covid-19 reporting about crime in online media of SINDOnews.com and Fajar.co.id Theo Van Leeuwen model; (3) identify the difference strategy of exclusion and inclusion online media of SINDOnews.com and Fajar.co.id at covid-19 reporting about crime. This research was quantitative research by using critical paradigm approach so that, data collection method used was documentation technique, reading-seeing technique, and recording technique. Besides that, the analysis data technique which was used in this research was data reduction, data presentation, and data inference. The source of data in this research was online media of SINDOnews.com, July-September 2020 printing. Characteristic of exclusion ideology in online media of SINDOnews.com and Fajar.co.id, found there were passivation discourse strategy and nominalization. Characteristic of inclusion ideology in online media of SINDOnews.com and Fajar.co.id, found there were objectivation discourse strategy, nominations, identification, determination, indetermination, assimilation, and individualization. Besides that, there were three differences in exclusion and inclusion strategy in online media of SINDOnews.com and Fajar.co.id.KEYWORDS: Covid-19 news text; critical discourse analysis; SINDOnews.com; Fajar.co.id

Implementasi Kebijakan Program Lesson Study Berbasis Sekolah (LSBS) di MINU Trate Putri Gresik

This research aims to describe the course of policy implementation Lesson Study, to know the things that support and are an obstacle in implementing policies and programs Lesson Study to determine the impact / results in implementing the policy program at the Lesson study MINU Trate Putri Gresik. This research uses Qualitative approaches, data collection technique through the process of observation, interviews, documentation, and questionnaires. Data analysis was performed based on four interrelated components, namely data collection, data reduction, presentation of data, the data inference. For overcome the obstacles that occur MINU Trate Putri always try to maximize all of the potential that exists. Among them is the rescheduling of any activity that is not done, the conversion or exchange of teaching hours to be optimal in the observation, as well as the fulfillment of all the necessary infrastructure. The impact of the implementation of the School-Based Lesson Study program includes a change in the paradigm of teachers in a more open, creative and collaborative learning process as well as increasing achievements that have been achieved by students, teachers and schools in general.

MLaaS4HEP: Machine Learning as a Service for HEP

Machine Learning (ML) will play a significant role in the success of the upcoming High-Luminosity LHC (HL-LHC) program at CERN. An unprecedented amount of data at the exascale will be collected by LHC experiments in the next decade, and this effort will require novel approaches to train and use ML models. In this paper, we discuss a Machine Learning as a Service pipeline for HEP (MLaaS4HEP) which provides three independent layers: a data streaming layer to read High-Energy Physics (HEP) data in their native ROOT data format; a data training layer to train ML models using distributed ROOT files; a data inference layer to serve predictions using pre-trained ML models via HTTP protocol. Such modular design opens up the possibility to train data at large scale by reading ROOT files from remote storage facilities, e.g., World-Wide LHC Computing Grid (WLCG) infrastructure, and feed the data to the user’s favorite ML framework. The inference layer implemented as TensorFlow as a Service (TFaaS) may provide an easy access to pre-trained ML models in existing infrastructure and applications inside or outside of the HEP domain. In particular, we demonstrate the usage of the MLaaS4HEP architecture for a physics use-case, namely, the $$t{\bar{t}}$$ t t ¯ Higgs analysis in CMS originally performed using custom made Ntuples. We provide details on the training of the ML model using distributed ROOT files, discuss the performance of the MLaaS and TFaaS approaches for the selected physics analysis, and compare the results with traditional methods.

Explaining inference queries with bayesian optimization

Obtaining an explanation for an SQL query result can enrich the analysis experience, reveal data errors, and provide deeper insight into the data. Inference query explanation seeks to explain unexpected aggregate query results on inference data; such queries are challenging to explain because an explanation may need to be derived from the source, training, or inference data in an ML pipeline. In this paper, we model an objective function as a black-box function and propose BOExplain, a novel framework for explaining inference queries using Bayesian optimization (BO). An explanation is a predicate defining the input tuples that should be removed so that the query result of interest is significantly affected. BO --- a technique for finding the global optimum of a black-box function --- is used to find the best predicate. We develop two new techniques (individual contribution encoding and warm start) to handle categorical variables. We perform experiments showing that the predicates found by BOExplain have a higher degree of explanation compared to those found by the state-of-the-art query explanation engines. We also show that BOExplain is effective at deriving explanations for inference queries from source and training data on a variety of real-world datasets. BOExplain is open-sourced as a Python package at https://github.com/sfu-db/BOExplain.

Challenges and opportunities towards Islamic cultured generation: socio-cultural analysis

This article analyzes the phenomenon and behavior of computerization among elementary school-aged children through a literature review of culture, technology, sociology, education, and sociolinguistics. The emphasis of this analysis is from the point of view of the challenges and opportunities of preparing a cultured Islamic generation. We have found the answer to the above problem through an analysis of several publications of previous findings that we obtained through an electronic search and involving analytical studies such as coding systems, data evaluation, in-depth interpretation, and data inference as final findings with the principle of validity and alignment of findings with the problems of this study. The findings show that we have summarized technological trends among elementary school-aged children into two categories. First, digital trends positively impact digital skills development at an early age under the direction and control of educational goals. Another trend is an alarming phenomenon for children's mental and mental development because technology is not involved in the educational career, such as the freedom to use technology in children. These findings should be input and awareness for Muslim educators and families.

A MECHANISM FOR DETECTING PARTIAL INFERENCES IN DATA WAREHOUSES

Data warehouses are widely used in the fields of Big Data and Business Intelligence for statistics on business activity. Their use through multidimensional queries allows to have aggregated results of the data. The confidential nature of certain data leads malicious people to use means of deduction of this information. Among these means are data inference methods. To solve these security problems, the researchers have proposed several solutions based on the architecture of the warehouses, the design phase, the cuboids of a data cube and the materialized views of multidimensional queries. In this work, we propose a mechanism for detecting inference in data warehouses. The objective of this approach is to highlight partial inferences during the execution of a multidimensional OLAP (Online Analytical Processing) SUM-type multidimensional query. The goal is to prevent a data warehouse user from inferring sensitive information for which he or she has no access rights according to the access control policy in force. Our study improves the model proposed by a previous study carried out by Triki, which proposes an approach based on average deviations. The aim is to propose an optimal threshold to better detect inferences. The results we obtain are better compared to the previous study.

ANALISIS TINDAK ILOKUSI YANG DILAKUKAN OLEH GSD DALAM VIDEO KENAPA KITA MEMBENCI?

The purpose of this research is to describe the illocutionary acts on one of Gita Savitri Devi’s Youtube Channel video entitled Kenapa kita membenci? Beropini eps. 46. The method use in this research is descriptive qualitative method. The data in this research are illocutionary acts spoken by Gita Savitri Devi in one of her Youtube Channel videos. The data source in this research is the narration delivered by Gita Savitri Devi in that video. The data collection techniques using hearken technique. Data analysis techniques in this research were carried out through the steps : (1) Data transcription, (2) Data classification, and (3) Data inference. The results obtained by the type of assertive illocutionary acts, directive illocutionary acts, and expresive illocutionary acts. While the types of commissive illocutionary acts and declaration illocutionary acts were not found in this research.

Efficient downscaling of satellite oceanographic data with convolutional neural networks

Space-borne satellite radiometers measure Sea Surface Temperature (SST), which is pivotal to studies of air-sea interactions and ocean features. Under clear sky conditions, high resolution measurements are obtainable. But under cloudy conditions, data analysis is constrained to the available low resolution measurements. We assess the efficiency of Deep Learning (DL) architectures, particularly Convolutional Neural Networks (CNN) to downscale oceanographic data from low spatial resolution (SR) to high SR. With a focus on SST Fields of Bay of Bengal, this study proves that Very Deep Super Resolution CNN can successfully reconstruct SST observations from 15 km SR to 5km SR, and 5km SR to 1km SR. This outcome calls attention to the significance of DL models explicitly trained for the reconstruction of high SR SST fields by using low SR data. Inference on DL models can act as a substitute to the existing computationally expensive downscaling technique: Dynamical Downsampling. The complete code is available on this Github Repository.