Reviewerly/exHarmony huggingface dataset:
A Study on Early Childhood Teachers' Experience and Perception of Curriculum Assessment: Focusing on teacher agency

This study examined early childhood teachers’ curriculum assessment experience and perception with a focus on a teacher agency in order to explore the context in which teachers' active and proactive actions occur in curriculum assessment and how the practice in which curricula and assessments are premised on each other is carried out. To this end, 2 focus group interviews were conducted with 30 teachers who had experience in implementing the 2019 revised Nuri curriculum for more than a year, and the results were analyzed. The results showed that early childhood teachers' curriculum assessment experience and perception could be divided into three upper sub-categories of ‘Looking into the world of learning: the meaning of curriculum assessment revisited’, ‘Located in a world where you can’t stay: dilemmas and difficulties faced in curriculum assessment’, and ‘Creating learning entangled with the world: changes implemented through curriculum assessment’, plus seven sub-categories of ‘Acts occurring in the field of education,’ ‘Discovery here and now,’ ‘The point of looking back on the journey,’ ‘A labyrinth wandering in questions,’ ‘Limits one wants to escape,’ ‘Creation together with the educational community,’ and ‘The route we open.’ Through this, this article shows the meaning, difficulties and limitations, and conflicts and dilemmas of curriculum assessment entangled with the here and now. It also discusses early childhood teachers’ curriculum assessment experience and perception as a phenomenon and material in itself by revealing the stories of creation achieved together.
EDUCATION AND SECURITY CURRENT AND FUTURE CHALLENGES IN TEACHING AND LEARNING SECURITY STUDIES

Organisations, institutions, and businesses continue to face unprecedented security challenges. The challenges include threats to cyber security, terror threats, and identity thefts among others. These threats are largely influenced by technological developments and globalisation. At the same time, globalization and the faster rate of technological transfers provide new opportunities for new security advancements. While most security studies have been designed to address known security threats, the future is uncertain; thus, security studies must be designed in a way that promotes curiosity, self-regulation, imagination, and resilience of the learners in their approach to addressing security challenges. This study aims to analyse the current and future challenges in the execution of security studies. Content analysis of private security courses, journals, peer-reviewed articles, and private company reports reveal the challenges and interventions. The increasing contribution of private security in public events, critical infrastructure, cash transportation, and executive transportation necessitate special training for the security personnel. However, the sector faces regulatory challenges with different players setting own standards. When compared to other recognised professions, such as psychology, which has global standards, security studies have yet to achieve professional status that is globally recognized. Learning is an ongoing process and training makes security personnel perform better. Training also boosts the morale of security personnel, raises the bar for the security profession, and increases the competitiveness of the sector. Thus, addressing the current and future challenges in teaching and learning security studies will strengthen the profession and uplift its status after 45 years of ambiguity as a subject matter. Keywords: training, cooperative partnerships, private security, law enforcement.
EXPLORING EFL STUDENTS’ SUBSTANCE ERROR ON DESCRIPTIVE ESSAY

This study reports on a research that analyzed substance errors in the writings of third semester English major, such as punctuation errors, typographic errors, dyslexic errors, and confusable. The technique used to collect data is a documentary technique and then the data were analyzed by using a descriptive qualitative method. By detecting punctuation errors through the four types of errors proposed by James (2013), educators can see the difficulties experienced by students and help overcome these obstacles by choosing strategies and innovative teaching methods that are appropriate to solve the problems of spelling errors faced by students. It was found that the most mistakes made by students were punctuation errors, while the least common was dyslexia. The results of this study are then recommended as input to make it easier for Indonesian learners to understand the target language, which in this study is English, especially in improving writing skill.
A DESCRIPTIVE STUDY ON THE IMPROVEMENT OF STUDENTS’ READING COMPREHENSION THROUGH NUMBER HEAD TOGETHER TECHNIQUE

This research aims to describe the improvement of students reading comprehension by using Number Head Together (NHT) technique from ten (10) articles selected. This research used qualitative method and descriptive analysis. The results of this research found that from the ten (10) articles analyzed, A10 was the most improved article in reading comprehension by using Number Head Together technique compare to other nine (9) articles. Futhermore, based on the t-test result of the use of NHT technique in reading comprehension, the result reveals that from nine (9) articles, 90% which used NHT in the teaching process proved that the achievement were more improved, while one (1) of the articles (10%) did not mention the t-test result but confirmed that there was improvement in the results of students’ reading comprehension .This indicates that Number Head Together (NHT) technique improved students’ reading comprehension. Number Head Together (NHT) technique not only results on students interest in learning process but also involved the students in the activity of collaborative learning as team or group. However, this grouping motivated the students to produce better achievement in their study activity.
PEMILIHAN TEMPAT PENYULUHAN BERDASARKAN TINGKAT KERAWANAN DENGAN MENGGUNAKAN METODE SMART

The National Narcotics Agency is a non-structural institution tasked with coordinating relevant government agencies in the formulation of policies and their implementation in the field of availability, prevention, eradication, abuse and illicit trafficking of narcotics (P4GN). In an effort to contain the rate of increase in the prevalence of drug abuse and illicit trafficking in Binjai City, the National Narcotics Agency of Binjai City conducts anti-drug counseling activities to the entire community. In this study, the SMART (Simple Multi Attribute Rating Technique) method was used to build a web-based system that aims to make it easier to determine the right place for anti-drug counseling. The results of this study indicate that by applying the method (Simple Multi Attribute Rating Technique) the best alternative can be obtained, namely A5 (Tunggorono Village) with the criteria values: C1 (Number of Drug Crime Cases) = 0.3; C2 (Crime Rate) = 0.25; C3 (Narcotics Use Rate) = 0.20; C4 (Drug Location) = 0.15; C5 (Low Social Interaction) = 0.1 with a total final result of 1.
PENERAPAN DATA MINING PENGELOMPOKAN PESERTA BPJS KETENAGAKERJAAN BERDASARKAN PROGRAM YANG DIAMBIL MENGGUNAKAN METODE CLUSTERING

The implementation of the social security program is one of the responsibilities and obligations of the State, to provide socio-economic protection to the community. Indonesia, like other developing countries, develops social security programs based on funded social security, namely social security that is funded by participants and is still limited to working people in the formal sector. BPJS Ketenagakerjaan continues to improve competence in all aspects of service while developing various programs and benefits that can be directly enjoyed by workers and their families. Non-Wage Recipient Workers (BPU) are employees who carry out economic activities or businesses independently to earn income from their activities or business. The problem that hinders the length of data collection for BPJS Employment participants is the process of determining the social security program that will be taken by Non-Wage Recipient (BPU) workers from the program taken by BPJS Ketenagakerjaan participants. owned is very small and only enough for the daily needs of participants. Data Mining is a data mining process in very large amounts of data using statistical, and mathematical methods, and utilizing the latest Artificial Intelligence technology. Data mining in the process of grouping data can use a grouping method, namely the Clustering method. The system is designed with the MATLAB R2014a programming application, after testing with the system, the results obtained are that in group 1 there are 370 data, group 2 there are 359 data and group 3 there are 271 data with a total of 100 data participants.
Biphoton entanglement across topologies

We report on our recent experimental demonstrations of biphoton entanglement of modes with different topologies. Our findings highlight topology as a new degree of freedom for photonic entanglement.
Previsão da Irradiação Solar Fotovoltaica baseada em Deep Learning

The Northeast area has shown great potential for generating of solar energy in Brazil, for this reason several energy companies have evaluated the economics feasibility this kind of renewable energy. Thus, as it increases the portion of renewables sources in supply of energy is important to estimate your potential with an acceptable accuracy in distincts horizons, like hours, days, weeks, and months. This paper proposed to develop distinct deep learning models for forecasting the global solar irradiation. The measures were collected from a solarimetric station located in the State of Bahia. The models are evaluated through five performance metrics, and the best results were selected for the final models. Finally, Friedman statistical test and Bergmann-Hommel’s post hoc test were applied to evaluate the hypothesis for significance difference between the developed models.
People Counting and Tracking System in Real-Time using Deep Learning Techniques

Abstract: The aim of project is to automatically estimate the number of people at indoor and outdoor places. People counting systems can be used in retail environment such as determining conversion ratio, advertising and promotional evaluation. This system can be used for transportation management system and video surveillance. The number of customers is indispensable data for management and decision making in public places like large-scale markets, shopping centers, airports, stations, museums, laboratories, classrooms, cafeteria etc. In this system, firstly extract the frames from the video, then draw a desired reference line on the input frame, detect the people using MobileNet-SSD object detection model, mark the centroid on the detected person, track the movement of that marked centroid and calculate the direction of centroid movement whether it is moving upwards or downwards. If the centroid movement is downward direction, then we increment in counter, else if the centroid movement is upward direction, then we increment out counter. People counting is essential for retailers of any size, but it's especially important for small businesses that don’t have the benefit of assaying data from multitudinous locales when making pivotal opinions. When used intelligently, people counting can shape businesses in multitudinous ways other than just furnishing information on nethermost business.
Implementasi Model Pembelajaran Inquiry Training Berbasis Modul Digital Dalam Mempengaruhi Hasil Belajar Siswa

The research method used in this study is a quantitative method with a quasi-experimental design, and the sampling technique employed is Stratified Random Sampling. The research sample consists of students from class X TKRO-1 as the experimental group and class X TKRO-2 as the control group, with 32 students in each group. The experimental group received treatment in the form of inquiry training-based learning using digital modules, while the control group underwent conventional learning. The research instrument used in this study was a multiple-choice test consisting of 20 items with 5 answer choices. The results of the study showed that the average score of the experimental group was 81.09, while the average score of the control group was 70.63. Significance testing with a significance level of α 0.05 (5%) indicated that the obtained t-test value was significant (2-tailed), which was 0.00, with the students' learning outcomes being less than 0.05. Therefore, Ha was accepted, indicating a significant difference in mathematical ability between students who applied the inquiry training learning model and those who applied the conventional learning model
Improving Poetry Writing Skills Through Concept Sentence Learning Models in Students

The purpose of this study was to improve poetry writing skills in Indonesian language learning for SD Negeri 03 Jatisari students through the concept sentence learning model. This research involved fifth grade students with a total of 14 students. The research took place in two cycles. Acquisition of value from pre-cycle to cycle I increased by 7.6%. Even though the average score obtained in cycle I did not meet the KKM because it received a score of 74.3, this was already good because it had increased from 66.7 to 74.3. From cycle I to cycle II there was an increase in value of 5.8% with a total value in cycle II of 80.1 and the percentage of completeness was 83.7%, which means it was successful. In cycle II, the average student has met and exceeded the set KKM. The conclusion of this study is that the poetry writing skills of SD Negeri 03 Jatisari students can be improved through the application of the concept sentence learning model. The findings in this study are First, students are not familiar with group work activities because so far students have not worked much in groups. Second, students are not familiar with the Concept Sentence learning model. Third, students are still not very enthusiastic about working with groups because maybe they are not in the same group as the friends they want. Fourth, in terms of reading poetry in front of the class, group representatives still looked shy and also when the teacher asked student representatives to conclude today's lesson, none of the students wanted to and in the end the teacher had to appoint.
Improving Students' Reading Ability Through Retelling Technique

The aim of this study was to improve reading skills on the competency standards for understanding short texts by reading aloud through the retelling technique of the first graders of SD Negeri Candimulyo 01. The approach used in this study was a quantitative approach, with the type of action research. In this study researchers collaborated with other teachers as well as with the principal. get accurate research results, the data that has been collected to be analyzed statistically, namely using the formula mean or average. Referring to the action hypothesis proposed in class 1 action research, totaling 16 students in pre-cycle average assessment 57.12 learning completeness results 30.11% increased 67.12 or 64.70% completeness in cycle I and cycle II increased to 76 .35 or 100% completeness. So, it can be concluded that there is an increase in reading ability through the retelling technique of class I students at SD Negeri Candimulyo 01. The novelty in this study is a concept of improving students' reading skills through a guide between constructivism and behaviorism theory through storytelling techniques in front of friends.
PENERAPAN FRAMEWORK CODEIGNITER UNTUK WEBSITE TOKO ONLINE PADA DASTER HITS PONTIANAK

Daster Hits pontianak has a need to expand its market share and manage valid data systems. The sales system currently used is traditional sellers and social media such as Instagram and WhatsApp, therefore the interaction between the store and customers is still limited, recording of transactions is still manual using a transaction book, and if customers want to find products they have to scroll Instagram or come directly to the store. The purpose of this research is to apply the CodeIgniter framework to build an online shop website which is expected to help the shop in promoting the product, manage product data, and expedite the product sales process. The research method used in this research is Design Science Research and the design method uses Extreme Programming. The system modeling used is the Unified Modeling Language (UML) and the software testing method uses white-box testing. The advantages of Daster Hits Pontianak Online Shop website are the features such as managing user profiles, product sales, detailed product information, online product orders and payment confirmation, searches using sequential search algorithms, managing product stocks using the Apriori algorithm, and notifications using push notifications. The process of making this program uses the PHP programming language and the MariaDB database.
ANALISIS KINERJA WEBSITE PARAMA PELINDO MENGGUNAKAN PINGDOM TOOLS DAN PAGESPEED INSIGHTS

This study analyzes the performance of a website for one of the companies, namely PARAMA Pelindo, using Pingdom Tools and PageSpeed Insights. This research aims to improve the performance of the PARAMA website in order to improve user experience. The results of the PARAMA website performance analysis show certain things that need to be improved in testing using pingdom tools and PageSpeed Insights. This research contributes to understanding the importance of analyzing the performance of a website and provides practical recommendations for web developers in improving website performance in general.
Improving Children’s Ability in Knowing Vocabulary through the Experiential Learning Model Approach

Developing the vocabulary comprehension skills require a complete understanding to be obtained by the children. This study aims to improve the children’s ability recognize vocabulary through the Experiential Learning Approach for children aged 4-5 years in group A of Ulil Albab Kindergarten. This research used group action research method. The subjects of this study were 12 people. The method of data collection in this study used observation, interviews, and documentation while the data analysis technique used mix method. The results of this study indicate that there is an increase of ability to recognize the concept of numbers for children, in the pre cycle there is 49.7%. Experiencing an increase in cycle 1 of action 1 of 61.1% and increased again in cycle 2 of action 2 of 91.1%. It is concluded that the Experiential Learning approach was very low possibility applied to improve children's skills in writing numbers and connecting vocabulary symbols with objects, because there is no significant change in its application. The Experiential Learning approach is very suitable to be applied in object comparisons. As for recommendations for teachers: the Experiential Learning approach can be used as an alternative approach in improving the ability to recognize vocabulary for children in group A of Ulil Albab Kindergarten. The findings in this study are that the use of the Experiential Learning approach is still not fully experienced directly because it adapts to the achievement indicator which still has to use paper pencil. But, so far the approach taken is appropriate because it doesn't really change the pattern of learning that has been done in Kindergarten which is more to the direct learning.
E pluribus unum interpretable convolutional neural networks

Abstract The adoption of convolutional neural network (CNN) models in high-stake domains is hindered by their inability to meet society’s demand for transparency in decision-making. So far, a growing number of methodologies have emerged for developing CNN models that are interpretable by design. However, such models are not capable of providing interpretations in accordance with human perception, while maintaining competent performance. In this paper, we tackle these challenges with a novel, general framework for instantiating inherently interpretable CNN models, named E pluribus unum interpretable CNN (EPU-CNN). An EPU-CNN model consists of CNN sub-networks, each of which receives a different representation of an input image expressing a perceptual feature, such as color or texture. The output of an EPU-CNN model consists of the classification prediction and its interpretation, in terms of relative contributions of perceptual features in different regions of the input image. EPU-CNN models have been extensively evaluated on various publicly available datasets, as well as a contributed benchmark dataset. Medical datasets are used to demonstrate the applicability of EPU-CNN for risk-sensitive decisions in medicine. The experimental results indicate that EPU-CNN models can achieve a comparable or better classification performance than other CNN architectures while providing humanly perceivable interpretations.
Leveraging Vision-Language Foundation Models for Fine-Grained Downstream Tasks

Vision-language foundation models such as CLIP have shown impressive zero-shot performance on many tasks and datasets, especially thanks to their free-text inputs. However, they struggle to handle some downstream tasks, such as fine-grained attribute detection and localization. In this paper, we propose a multitask fine-tuning strategy based on a positive/negative prompt formulation to further leverage the capacities of the vision-language foundation models. Using the CLIP architecture as baseline, we show strong improvements on bird fine-grained attribute detection and localization tasks, while also increasing the classification performance on the CUB200-2011 dataset. We provide source code for reproducibility purposes: it is available at https://github.com/FactoDeepLearning/MultitaskVLFM.
Large Language Models

Artificial intelligence is making spectacular progress, and one of the best examples is the development of large language models (LLMs) such as OpenAI's GPT series. In these lectures, written for readers with a background in mathematics or physics, we give a brief history and survey of the state of the art, and describe the underlying transformer architecture in detail. We then explore some current ideas on how LLMs work and how models trained to predict the next word in a text are able to perform other tasks displaying intelligence.