PERMAINAN PETAK UMPET DI MASA PEMBELAJARAN DARING UNTUK MENGEMBANGKAN KEMAMPUAN MOTORIK KASAR ANAK USIA DINI

Gerakan yang membutuhkan otot besar atau motorik kasar bisa dilakukan melalui kegiatan yang menggunakan seluruh anggota tubuh yaitu dengan melompat, berlari, berjalan dan bermain petak umpet. Peneliti memilih bermain cari teman (petak umpet) adalah permainan yang sangat menyenangkan bagi anak cara bermain petak umpet adalah mencari teman yang bersembunyi. Di RA Atsauripada kelompok B masih terdapat anak yang dalam kemampuan motorik kasar masih dibawah rata-rata peneliti bertujuan untuk mengembangkan motorik kasar anak melalui bermain petak umpet dengan pembelajaran yang dilakukan secara daring. Penelitian Tindakan Kelas (PTK) merupakan metode yang digunakan, Adapun Teknik pengumpulan data dalam penelitian ini adalah observasi dengan analisis data kualitatif dan anak kelompok B dengan jumlah 10 orang anak yaitu lima anak laki-laki dan lima anak perempuan adalah subjek dalam penelitian, untuk meningkatkan keterampilan motorik halus anak dengan bermain petak umpet dengan pembelajaran yang dilakukan secara daring, dengan kegiatan tersebut peneliti mendapatkan hasil akhir 80% keterampilan motorik kasar anak meningkat.Movements that require large muscles or gross motor skills can be done through activities that use the entire body, namely jumping, running, walking, and playing hide and seek. Researchers chose to play make friends (hide and seek) is a very fun game for children. The way to play hide and seek is to find friends who are hiding. In RA Atsauri in group B, there are still children whose gross motor skills are still below the average. The researcher aims to develop children's gross motor skills through playing hide and seek with online learning. Classroom Action Research (CAR) is the method used. The data collection technique in this study is observation with qualitative data analysis and group B children with a total of 10 children, namely five boys and five girls are the subjects in the study, to improve children's fine motor skills by playing hide and seek with online learning, with these activities the researchers got the final result 80% of children's gross motor skills increased

The Missing Data Encoder: Cross-Channel Image Completion\\with Hide-And-Seek Adversarial Network

Image completion is the problem of generating whole images from fragments only. It encompasses inpainting (generating a patch given its surrounding), reverse inpainting/extrapolation (generating the periphery given the central patch) as well as colorization (generating one or several channels given other ones). In this paper, we employ a deep network to perform image completion, with adversarial training as well as perceptual and completion losses, and call it the ``missing data encoder'' (MDE). We consider several configurations based on how the seed fragments are chosen. We show that training MDE for ``random extrapolation and colorization'' (MDE-REC), i.e. using random channel-independent fragments, allows a better capture of the image semantics and geometry. MDE training makes use of a novel ``hide-and-seek'' adversarial loss, where the discriminator seeks the original non-masked regions, while the generator tries to hide them. We validate our models both qualitatively and quantitatively on several datasets, showing their interest for image completion, unsupervised representation learning as well as face occlusion handling

Observation of the Evolution of Hide and Seek AI

The purpose of this project is to observe the evolution of two artificial agents, a ‘Seeker’ and a ‘Hider’, as they play a simplified version of the game Hide and Seek. These agents will improve through machine learning, and will only be given an understanding of the rules of the game and the ability to navigate through the grid-like space where the game shall be played; they will not be taught or given any strategies, and will be made to learn from a clean slate. Of particular interest is observing the particular playstyle of hider and seeker intelligences as new elements are introduced into the game, such as obstacles, doors, among other environmental influences. Through this observation, I hope to identify not only key strategies in the game of hide and seek, but to achieve a greater understanding of the evolution of machine learning AI searching and hiding patterns, which are relevant to several fields such as networking, artificial intelligence, and cyber security

PENGGUNAAN PERMAINAN PETAK UMPET UNTUK MENINGKATKAN KEMAMPUAN BERBICARA BAHASA JEPANG

Penelitian ini dilatarbelakangi oleh kecemasan pada siswa dalam pembelajaran bahasa Jepang, khususnya dalam pembelajaran berbicara. Oleh karena itu diperlukan variasi dalam proses pembelajaran. Dalam penelitian ini, membahas penggunaan permainan petak umpet untuk kemampuan berbicara dalam pembelajaran bahasa Jepang. Permainan petak umpet adalah permainan menyembunyikan suatu benda di dalam kelas dan mencari dimana benda tersebut disembunyikan. Dalam hal ini, kosakata posisi dalam Bahasa Jepang juga telah dipelajari. Setelah benda-benda tersebut disembunyikan, siswa diminta untuk menemukan benda-benda tersebut dan menyebutkan letak benda-benda tersebut dalam bahasa Jepang. Penggunaan permainan ini dapat meningkatkan kemampuan berbicara. Tujuan penelitian ini adalah untuk mengetahui langkah-langkah penggunaan permainan petak umpet dalam meningkatkan kemampuan berbicara bahasa Jepang. Pengumpulan data yang dilakukan untuk penulisan makalah ini adalah dengan menggunakan metode studi pustaka dengan mengumpulkan data yang bersumber dari buku, artikel, dan jurnal. Kesimpulan dari penulisan ini adalah bagaimana cara menggunakan permainan petak umpet untuk meningkatkan kemampuan berbahasa Jepang. Kata kunci: permainan petak umpet, kemampuan berbicara, kemampuan bahasa Jepang *********** This research is motivated by anxiety in students in Japanese learning, especially in learning to speak. Therefore we need a variation such as fun learning that can reduce student anxiety. The purpose of this study was to determine the steps of using the game of hide and seek in improving Japanese speaking skills. This research discuss hide and seek game for speaking skills in learning Japanese. Hide and seek game is a game of hiding an object in the classroom and finding out where it is hidden. In this case, this game used for learning positional vocabulary in Japanese.. After the objects were hidden, students were asked to find the objects and state their location in Japanese. this game can be use for improving Japanese speaking skills. This research is a qualitative descriptive study. The research instrument uses literature study by collecting data from books, articles, and journals. The results of this study are: The game of hide and seek is played by preparing objects that will be used as objects, dividing students into two groups, then determining which groups will be seekers and hiders. The hiding group is tasked with hiding objects and asking where the hidden objects are, while the search group is tasked with answering the location of objects in Japanese. This game can be applied in learning to improve speaking skills. The game of hide and seek can reduce students anxiety while studying and make students active and enthusiastic during the learning process. Keywords: hide and seek game, speaking ability, Japanese language skill

ANALISIS TIGA GAME ONLINE DALAM WEBSITE PBS KIDS UNTUK MENGENALKAN KONSEP BERHITUNG PADA ANAK USIA DINI

Sebagian besar PAUD masih menggunakan model pembelajaran klasik dalam proses pembelajaran pengenalan berhitung, sehingga kurangnya wawasan mengenai media pembelajaran berhitung yang sangat inovatif dan beragam untuk menunjang proses belajar mengajar khususnya pada pemanfaatan teknologi yang bisa dijadikan media pembelajaran dan dikenalkan pada anak sejak usia dini, hal tersebut menjadi latar belakang dalam penelitian ini. Penelitian ini dilakukan untuk mengetahui kesesuaian game online dalam website PBS Kids sebagai media pembelajaran dan pengenalan konsep berhitung pada anak usia dini. Adapun metode yang digunakan ialah content analysis melalui tiga game online yang terdapat dalam website PBS Kids yaitu Flower Garden, Hide and Seek, dan Meatball Launcher. Teknik analisis data yang digunakan yaitu merangkum, menganalisis, dan menginterpretasikan temuan, data yang didapat diolah dan dianalisis menggunakan metode content analysis melalui pendekatan kualitatif dan kemudian dianalisis secara deskriptif.Hasil analisis yang diperoleh dari penelitian menunjukkan bahwa ketiga game online dalam website PBS Kids memasuki kategori sangat baik untuk dijadikan media pembelajaran dan pengenalan konsep berhitung pada anak usia dini. Most PAUD still use the classical learning model in the learning process of introducing counting, so that there is a lack of insight into the very innovative and diverse numeracy learning media to support the teaching and learning process, especially in the use of technology which can be used as learning media and introduced to children from an early age, be the background in this research. This research was conducted to determine the suitability of online games on the PBS Kids website as a learning medium and to introduce the concept of arithmetic in early childhood. The method used is content analysis through three online games on the PBS Kids website, namely Flower Garden, Hide and Seek, and Meatball Launcher. The data analysis technique used is to summarize, analyze, and interpret the findings, the data obtained are processed and analyzed using the content analysis method through a qualitative approach and then analyzed descriptively. The results of the analysis obtained from the study indicate that the three online games on the PBS Kids website enter the category very good to be used as a medium of learning and introduction to the concept of arithmetic in early childhood

Feigning ignorance for long-term gains

In dynamic strategic interactions, a player who spies the opponent's actions might have incentives to feign ignorance and forgo immediate payoffs so that he can earn higher future payoffs by manipulating the opponent's suspicion. I model and experimentally implement the situation as a two-stage hide-and-seek game. A substantial share of the spying players fails to feign ignorance, despite the empirical suboptimality of the behavior and their largely correct predictions about opponents' suspicion. Subjects are highly heterogeneous in their tendency to feign ignorance and show only moderate learning. The players who are spied on hold empirically correct beliefs and mostly best-respond.</p

SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores

The ever-growing complexity of reinforcement learning (RL) tasks demands a distributed RL system to efficiently generate and process a massive amount of data to train intelligent agents. However, existing open-source libraries suffer from various limitations, which impede their practical use in challenging scenarios where large-scale training is necessary. While industrial systems from OpenAI and DeepMind have achieved successful large-scale RL training, their system architecture and implementation details remain undisclosed to the community. In this paper, we present a novel abstraction on the dataflows of RL training, which unifies practical RL training across diverse applications into a general framework and enables fine-grained optimizations. Following this abstraction, we develop a scalable, efficient, and extensible distributed RL system called ReaLly Scalable RL (SRL). The system architecture of SRL separates major RL computation components and allows massively parallelized training. Moreover, SRL offers user-friendly and extensible interfaces for customized algorithms. Our evaluation shows that SRL outperforms existing academic libraries in both a single machine and a medium-sized cluster. In a large-scale cluster, the novel architecture of SRL leads to up to 3.7x speedup compared to the design choices adopted by the existing libraries. We also conduct a direct benchmark comparison to OpenAI's industrial system, Rapid, in the challenging hide-and-seek environment. SRL reproduces the same solution as reported by OpenAI with up to 5x speedup in wall-clock time. Furthermore, we also examine the performance of SRL in a much harder variant of the hide-and-seek environment and achieve substantial learning speedup by scaling SRL to over 15k CPU cores and 32 A100 GPUs. Notably, SRL is the first in the academic community to perform RL experiments at such a large scale.Comment: 15 pages, 12 figures, 6 table