Reviewerly/exHarmony huggingface dataset:
A New Term Representation Method for Gender and Age Prediction

Author Profiling is a kind of text classification method that is used for detecting the personality profiles such as age, gender, educational background, place of origin, personality traits, native language, etc., of authors by processing their written texts. Several applications like forensic analysis, security and marking are used the techniques of author profiling for finding the basic details of authors. The main problem in the domain of author profiling is preparation of suitable dataset for predicting the characteristics of authors. PAN is one organization conducting competitions on various types of shared tasks. In 2013, PAN organizers presented the task of author profiling in their series of competitions and continued this task in further years. They arranged different kinds of datasets in different varieties of languages. From 2013 onwards several researchers proposed solutions for author profiling to predict different personality features of authors by utilizing the datasets provided in PAN competitions. Researchers used different kinds of features like character based, lexical or word based, structural features, syntactic, content based, style based features for distinguishing the author’s writing styles in their texts. Most of the researchers observed that the content based features like words or phrases those are used in the text are most useful for detecting the personality features of authors. In this work, the experiment conducted with the content based features like most important words or terms for predicting age group and gender from the PAN competition datasets. Two datasets such as PAN 2014 and 2016 author profiling datasets are used in this experiment. The documents of dataset are converted in to a vector representation which is a suitable format for giving training to machine learning algorithms. The term representation in a document vector plays a crucial role to improve the performance of gender and age group prediction.The Term Weight Measures (TWMs) are such techniques used for this purpose to represent the significance of a term value in document vector representation. In this work, we developed a new TWM for representing the term value in document vector representation. The proposed TWM’s efficiency is compared with the efficiency of other existing TWMs. Two Machine Learning (ML) algorithms like SVM (Support Vector Machine) and RF (Random Forest) are considered in this experiment for estimating the accuracy of proposed approach. We recognized that the proposed TWM accomplished best accuracies for gender and age prediction in two PAN Datasets.
Manajemen Perpustakaan di Masa Pandemi Covid-19

This research aims to analyze the role of library governance as an administrator to improve library services in schools during the pandemic. The library is an important part that needs to be developed in every school. Library management is the management of libraries based on theories and principles of management. One form of library management is development. Librarians and library managers during the pandemic were completely forced to change, and librarians had to adapt to the conditions of not being able to serve directly or face-to-face like them. The role of the library in the foreground is to create a cultural society that can still be felt. Currently librarians are trying to transform services that were previously carried out directly into Online services such as fairy tales, seminars, workshops, application development, library accreditation, authorship and other activities, so that the role of library management can still be felt.
Penerapan Model Pembelajaran Student Facilitator And Explaining Untuk Meningkatkan Hasil Belajar PAI di Kelas III SDN Gelam 2

A good learning process with the involvement of both parties between teachers and students. Therefore, in the research conducted by this researcher will describe a use of the Student Facilitator and Explaining learning model to be applied during the learning process in PAI subjects in improving student learning outcomes. The research approach used is a qualitative approach and runs using the Classroom Action Research (PTK) method. The study was conducted with 3 cycles in 3 meetings on the content of PAI subjects. In each cycle there is an action with student learning outcomes in each cycle. In cycle I obtained an average value of 66.86 with a percentage of completeness of 64.28% then in cycle II it increased to 72.43 with a percentage of completeness of 67.85% and increased again in cycle III to 79.15 with a percentage of completeness of 73.1%. Then the results of student activity observations stated that the learning process that tends to increase from cycle I obtained a percentage of 58% to cycle III to 75%. This can show that the student facilitator and explaining learning model can improve student PAI learning outcomes in grade III SDN Gelam 2.
User Satisfaction of Distance Learning During the Covid-19 Pandemic: Effect of Information System Quality and Service Quality in Financial Education and Training Agency of Makassar

This study aims to determine the effect of information system quality and service quality of distance learning system on user satisfaction during the Covid-19 pandemic at the Financial Education and Training Agency of Makassar. This study uses a quantitative approach with multiple regression analysis techniques and frequency distribution. The results of the study found that the quality of the information system has a positive and significant effect on user satisfaction and the quality of system services has a positive and significant effect on user satisfaction. The two variables simultaneously have a positive and significant effect on the satisfaction of distance learning system users at the Financial Education and Training Agency of Makassar. In addition, in general the indicators of information system quality, service quality, and user satisfaction of the distance learning system at the Financial Education and Training Agency of Makassar were considered very good by the learning participants. Financial Education and Training Agency of Makassar as the technical implementer of BPPK is expected to be able to maintain the quality of the system and services provided in order to obtain the maximum of user satisfaction. When users are satisfied with the existing system, the achievement of learning outcomes is also expected to increase.
Motivation of Recovery of Victims of Drugs Using in Rehabilitation at the National Narcotics Agency (BNN) City of Payakumbuh

For drug abusers, rehabilitation is essential to do by giving motivating drug abusers to recover and stop using drugs. The study aimed to determine the mottreasone recovery of victims of drug abusers undergoing rehabilitation at the National Narcotics Agency (BNN) Payakumbuh with qualitative research methods. The results showed that the first abuser's motivation to recover was driven by himself (intrinsic motivation) as contained in the motivational aspect of driving, the second family (extrinsic motivation) to direct, then the environment (extrinsic motivation) to support him to recover from drug abuse. Apart from yourself, tbiggestost considerableouragement for abusers to recover is from family or parents. These two things arintendeded, where if one of them has no motivation to recover, then the recovery will not work well
Utilization of Visual Media in Arabic Teaching

Learning media is needed by teachers to help a teaching in education. This discussion intends to determine the use of media in learning Arabic. In addition to using books or lecture methods, media is also a tool used in teaching and teachers are also expected to have different learning media and according to the material and circumstances of each student. Everyone is relied on to understand technology as a support for all existing activities, as well as in the realm of education because considering the increasingly rapid and advanced technological developments. Teachers and students can take advantage of technology-based learning media, including the Canva application. Canva is an online-based design application that provides all the attractive designs such as templates, features, and categories that we can use in it. By utilizing the Canva application, teachers can show information, innovations, and abilities that will be obtained by students by utilizing attractive designs in the Canva application, so that this media can support the learning process so that it is not boring and can also be used in various areas of life. So with this learning media, the interest of students will increase in learning and help teachers to motivate students towards more enjoyable learning. With the existence of learning media, the learning process will be much more efficient and increase students' insight into learning through information and communication technology.
Ultrafast Measurement of Femtosecond Time-bin Qubits Using Optimized Up-conversion Single Photon Detector

We developed and optimized the up-conversion single photon detector with the temporal resolution of 415 fs. Consequently, we successfully evaluated single-photon level pseudo time-bin qubits with their pulse interval of only 800 fs.
Femtosecond Laser Direct Writing of Path Encoded Two-qubit and Multiqubit Photonic Quantum Gate Chips

We demonstrate the first realization of path encoded two-qubit photonic quantum gate chip for generating Bell states, three-qubit Toffoli gate and four-qubit Controlled-Controlled-Controlled NOT gate via combining logic gates together by femtosecond laser direct writing.
Análise das Variações de Potência em Plantas Fotovoltaicas de Grande Porte

Output power fluctuation is one of the biggest concerns in photovoltaic (PV) generation. The power variability of short time scale, which is mainly caused by ground shadows generated by cloud movement, can deteriorate the frequency and voltage magnitude of power systems. Besides affecting the power quality, the typical output power fluctuations impose some challenges to the planning, operation and control of power systems. Thus, the understanding of the aspects that affect the power variability and the typical magnitude of these variations is fundamental to the proposition of operational and control approaches to mitigate the adverse effects caused by utility-scale PV systems. In this context, this work evaluates the impact of multiple meteorological conditions and technical parameters on the output power variability of PV power plants. Fractals are used to formulate an approach capable of synthesizing the global irradiance time series necessary to evaluate the output power variability. The results obtained from a case study of a 100 MWac PV power plant have showed that the meteorological conditions and the plant rated power play an important role in the magnitude of the output power fluctuations. The proposed approach and its outcomes may contribute to the proposition of new operational and control approaches to mitigate the adverse effects caused by PV power plants.
Peningkatan Hasil Belajar Siswa dalam Menulis Pantun Menggunakan Media Gambar Pada Pembelajaran Bahasa Indonesia di MI Barito Kuala

This research aimed to address the inadequate writing skills of fifth-grade students at MIN 3 Barito Kuala in Indonesian language class. Teachers faced challenges in selecting suitable learning media to achieve desired learning targets. The study implemented Classroom Action Research, focusing on fifth-grade students at MIN 3 Barito Kuala in South Kalimantan. Data collection involved observation sheets for teachers and students, as well as tests. The findings revealed that the use of visual aids in learning to write pantun positively impacted the teachers' activities, students' activities, and learning outcomes. Teacher activities improved from 81% (good) in cycle I to 92% (very good) in cycle II, indicating enhanced quality of the learning process. Student activities also improved significantly, increasing from 71% (good) in cycle I to 95% (very good) in cycle II, demonstrating increased participation and engagement. Moreover, student learning outcomes significantly improved, with completion rates increasing from 40% in cycle I to 90% in cycle II. Thus, it can be concluded that the effective application of visual aids enhances Indonesian language learning, particularly in the subject of pantun.
Lifelong Context Recognition via Online Deep Feature Clustering

&lt;p&gt;Context recognition for lifelong learning (L2) agents is an open-ended problem whereby aggregate features in an environment are utilized to signal the active context in which the agent is operating. The ability to recognize context is necessary in L2 agents to engage modulatory signals to account for significant changes in the input state space associated with a given context or task, such as altering learning dynamics or shifting attention to more relevant features. Context recognition is itself an L2 problem due to the ever-increasing number of distinct contexts that an agent might encounter, requiring incrementally learning novel contexts while prescribing them to supervised task labels when available. This paper demonstrates an algorithm based on near clustering of deep-extracted features with adaptive resonance theory methods that satisfies these requirements on the behalf of an embodied L2 agent in a computer vision environment. The strength of this algorithm lies in its flexibility, being capable of online incremental learning in supervised, realistic semi-supervised, and unsupervised scenarios while demonstrating continual learning in its own right.&lt;/p&gt;
MEDICINE USAGE ANALYSIS OF THE CITY

Drug use is a complicated and widespread problem that has an impact on people and communities all around the world. It is essential to examine drug use patterns, spot high- risk locations, and comprehend the implications for social welfare and public health in order to successfully manage this societal dilemma. This study intends to provide a thorough examination of drug usage data in a particular city, using a variety of analytical tools to produce significant findings. The study makes use of a broad dataset that includes social indicators, criminal justice records, demographic data, and occurrences involving drugs. To guarantee data quality and consistency, the dataset is put through rigorous data preprocessing, cleansing, and integration processes. To locate a target, descriptive statistics, data visualization, and sophisticated analytics methods like clustering and predictive modeling are used. Keyword:Socialindicators,consistency,visualization,descriptiv e statistics, societal dilemma.
Investigating the Use of Social Media as Language Learning Strategies among the Lower Secondary ESL Learners to Enhance Speaking Skills

The ubiquity of social media sites which swiftly develop massive communicative networks in the virtual world is a notable element of the twenty-first century.The widespread utilisation of social media platforms has substantially promoted English as a global language in establishing a massive communication platform.However, English as a Second Language (ESL) learners in many countries including Malaysia are constantly struggling when communicating and conversing in English.Thus, the purpose of this study is to investigate the use of social media as language learning strategies among lower secondary ESL learners to improve their speaking skill.This study employed quantitative research design that was conducted among lower form ESL learners at a Malaysian public school.The survey questionnaire used in this study was derived and modified from the Oxford (1990) Strategy Inventory for Language Learning (SILL) Version 7.0.The Statistical Package for Social Science (SPSS) was then used to examine and report on the data.Overall, the findings provide beneficial information to other researchers in analysing the language learning strategies ESL language learners employ to enhance their speaking skills with the help of social media.
A Metrics-Based Approach to Metadata Classification Applied in a Recommendation System

Abstract In this article we have presented a classification model for metadata extracted from tweets using collaborative filters, proposing a metadata recommendation system at the knowledge level. Due to the limited resources available for tagging in social media, the quality of metadata used for this practice often poses interpretation issues when selecting, resulting in a loss of engagement due to flaws in term formation or a lack of understanding of the shared metadata within the social network. In this study, we proposed a selection algorithm that filters and classifies metadata based on metrics that measure the collective intelligence aggregated in the metadata. We then index knowledge indexes that are readily identifiable visually. The proposed model involves two main steps for classifying and recommending metadata.
Group Emotion Recognition for Weibo Topics Based on BERT with TextCNN

Social media platforms, including Weibo, have become an integral part of people's daily lives, where users engage in discussions, share opinions, and express their emotions regarding trending topics.However, as the volume of information and content continues to increase, individuals face challenges in accessing relevant information.To address this issue, sentiment analysis has been employed in this study to focus on group sentiment identification for Weibo topics.Due to the potential involvement of multiple sentiment categories in Weibo topics, the main algorithm used in this research combines BERT and TextCNN for text multi-label classification.This approach aims to predict the possible collective emotional reactions of the public.Macro-F1 has been chosen as the evaluation criterion, with the baseline algorithm achieving a score of 0.3339, while our model achieved a slightly improved score of 0.3514.This improvement demonstrates the efficacy of the proposed algorithm.This paper makes full use of the self-attentive mechanism of BERT combined with the convolutional layer and pooling operation of TextCNN to extract local features.The generalization ability and sentiment classification accuracy of the model are improved.The results of text multi-label classification for group sentiment recognition of microblog topics demonstrate the superiority of the model algorithm in this paper.This study carries significant implications for understanding the public's emotional responses to popular topics on social media.It provides valuable insights for further exploration and advancement in the field of sentiment analysis within the realm of social media.
Sentiment Analysis Before Presidential Election 2024 Using Naïve Bayes Classifier Based On Public Opinion In Twitter

This study aims to determine the performance of the Naïve Bayes Classifier algorithm and sentiment analysis tested on a dataset obtained from Twitter social media scrapping with the topic of 2024 presidential candidates. Three candidates frequently discussed in public spaces were used as keyword parameters in data mining: #anis, #ganjar, and #pilpres2024, resulting in 3021 tweets extracted from 12/1/2022 to 31/1/2023, which were successfully converted to ".csv" format documents. Public opinions extracted from the dataset were then pre-processed using the Python programming language, resulting in 2157 cleaned tweets. The data that passed the pre-processing stage was then labeled as positive or negative sentiment. Sentiment analysis was performed using the Naïve Bayes Classifier algorithm with three testing experiments using different training and testing data compositions in each experiment. The results of the study showed that the best Naïve Bayes model was obtained in the first experiment with a 10% testing data and 90% training data composition, resulting in 71% accuracy, 93% precision, 66% recall, and an f-measure score of 77%. The conclusion of the study is that the electability of the 2024 presidential candidates shapes public opinion and generates public sentiment in the form of positive and negative tweets. Positive tweets had a higher percentage of 71.5% (1543), while negative sentiment tweets accounted for 28.5% (614). Further research is expected to produce different information by using different classification algorithms and larger data sets.
Client Satisfaction in Healthcare Environment in Saudi Arabia – A Qualitative Review

Healthcare industry in Saudi Arabia has been progressing with the advancement in technology. Patients have been getting better healthcare facilities with the introduction of digital innovations. New facilities launched in the market are using digital platforms to reach the masses. The approach taken now days to handle the business has transformed for the better with the digital technology. This article would help in analyzing the client satisfaction in healthcare market in Saudi Arabia. The study would focus on the new advancements in healthcare industry using digital technology to launch new facilities in the market for better treatment of the patients. The feedback received and the pain points of the customer can be used further to develop the new facilities using digital platforms. The analysis would cover the healthcare market and the digital impact on the new facilities and client satisfaction in the healthcare environment.
Factors Influencing Chinese Students to Study in Thailand During Covid-19 Pandemic

The purpose of this study is to find out the factors which caused Chinese graduate students to study in Thailand during the COVID-19 pandemic. By analyzing 131 valid questionnaires obtained from current master’s degree students at Stamford International University in Thailand, the results revealed that most of the students were undergraduates from non-first-class institutions, worked in universities, and majored in arts. The factors for Chinese students to choose Thailand, from the highest scores to the lowest, were time factors, international relationships, learning pressure, and cultural inducement. Moreover, young teachers put much weight on learning pressure and cultural inducements. This study accordingly made two suggestions: 1. increasing the ways of further education for young teachers majored in arts; and 2. China may consider offering winter and summer master’s degree programs for in-service teachers.
A Historical Perspective on Approaches to Data Compression

Several data compression algorithms are investigated in this study.Data compression is commonly utilized in the community.Because data compression allows us to conserve storage space, it can also assist to speed up data transport from one point to another.It is vital to have a compression tool on hand when compressing from one person to another.This method can be used to make data smaller.In addition to text data, images and video may be saved.Lossy and non-lossy compressions are the two types of compression techniques.Compression (lossless) and compression (lossy) which is, nevertheless, the most widely used?It is necessary to conduct lossless compression.Huffman, Shannon Fano, and other lossless compression techniques, as well as Tunstall, Lempel, Ziv Welch, and run-length encoding, are all instances of runlength encoding.This article explains how a compression strategy works and which approach is most typically used in data compression.A form of compression is text compression.The consequences of this process may be seen in the compressed file size, which is less than the original file.In this article, many data compression techniques are surveyed, including those developed by Shannon, Fano, and Huffman.Data compression seeks to increase active data density by minimizing redundant information in data that is stored or sent.Storage and distributed systems are two domains where data compression is crucial.Information theory ideas are thoroughly examined in relation to the objectives and assessment of data compression techniques.The algorithms that are presented are subjected to a framework that is created for the evaluation and comparison of approaches.