Reviewerly/exHarmony huggingface dataset:
Icepool: Efficient Computation of Dice Pool Probabilities

Mechanics involving the roll of multiple dice---a "dice pool"---commonly appear in tabletop board games and role-playing games. Existing general-purpose dice pool probability calculators resort to exhaustive enumeration of all possible sorted sequences of rolls, which can quickly become computationally intractable. We propose a dynamic programming algorithm that can efficiently compute probabilities for a wide variety of dice pool mechanics while limiting the need for bespoke optimization. We also present Icepool, a pure Python implementation of the algorithm combined with a library of common dice operations.
Teaching Speaking Skill of English as Foreign Language in Secondary School Level

The study investigated the teaching speaking skill of English as foreign language in Secondary School level at SMAN 2 Malang from some aspects, the English teacher’s role, the teaching activities, materials, the media, and the assessment of the English teacher. The design of the research belongs to descriptive qualitative research. The participant of this research was an English teacher and the 33 students of X MIPA 2. The setting was SMAN 2 Malang. The data was obtained through observation, interview and documents. The result of this research noted that the teacher has already known role in teaching speaking. Based on the 2013 curriculum students centered was applied and the teacher facilitated the students, teacher created fun speaking activity such as role play, presentation in group and individual, the teacher also used modern media to support the teaching speaking, material that suitable for speaking activities is provided by the teacher based on the syllabus. Fluency, accuracy, pronunciation and expression were the aspect to assess students speaking skill. In this research, the researcher concluded that the teaching speaking skill in SMAN 2 Malang has already well.
The Digital Conservation and Revitalization of Regional Languages in Nusantara

The purpose of this research is the digitization of regional languages. The form of technology that will be developed is a digital conservation and revitalization program. There are 2 stages in this program: 1) the development stage, namely the development of language learning applications. Inspired by the Duolingo application, the application for learning international languages, the application is the local version by prioritizing the values ​​of local wisdom of the local language being studied. The basic concept of this application is to document regional languages ​​by teaching them. This application is designed to be accessible to anyone who wants to know or learn a certain regional language through a smartphone. The features in this application product will focus on four language skills with tiered levels, each of which is presented in different themes. There will be several choices of regional languages ​​that can be learned, the final target of developing this application is to document all regional languages ​​​​of the archipelago. As an initial project, this program will digitize two regional languages ​​in East Java, namely Javanese and Madurese. 2) socialization, education, and promotion that will be carried out in several forms of activities targeting all communities at all levels. On campus, the target is to form a community that cares about the preservation of regional languages ​​among academics; Among students to grow the importance of preserving regional languages ​​and make applications that will be developed as a reference to support the teaching of regional languages ​​in schools; and society.
A Hybrid Approach to Co-creative Story Authoring Using Grammars and Language Models

Large language models are powerful tools for story generation, but are difficult to control. Story grammars are a more controllable tool for story generation, but require a large amount of upfront work and tend to create predictable results. We present a story generation tool that combines the positives of both methods using language models to aid in writing a grammar, and using the output of that grammar to generate controlled text from language models. Our approach combines the structure of a grammar with the unexpectedness of a large language model.
Closest fit Approach for Atypical Value Revealing and Deciles Range Anomaly Detection Method for Recovering Misplaced value in Data Mining

In identifying anomalous database values, it is currently a very active research area in the mining community. The task of identifying anomalous values is to find a small group of exceptional data objects compared to the rest of the large amount of data. The discovery of anomalous values in a group of models is an extremely recognized difficulty in the field of data mining. An outlier is a prototype that is not related to the rest of the patterns in the data set. The proposed method for searching for outliers uses an anomalous detection approach. The purpose of the approach is to find anomalous values first based on the criteria of the condition.We use the information criterion and approach named Outlier Detection to remove the outliers from the dataset and apply deciles range anomaly detection algorithm for Recovery algorithm to recover missing data from the database.
A Demonstration of Possibilistic Hierarchical Task Networks for Believable Agent Behavior

Believably human non-player characters (NPCs) are inherent to immersive gameplay yet developing them remains a common challenge. When the success of many modern games relies on using bots in online services to replace real players, it’s vital for the bots to seem like they make decisions like real people. This paper explains the inspirations for, ideas behind, and base functionality of a demo made to build a foundation for future work that aims to establish a framework for developing believably human NPCs. This demo simply aims to show that possibility theory can work with hierarchical task networks (HTNs) by using custom utility functions. This reliance on utility functions also means that this work provides developers who use behavior selection systems that can use utility functions to decide between branches, a clear example of how to adopt possibilistic logic in their own work. Most importantly, since this logic is simplistic, it can be easily adopted by both hobbyists and experts.
Student's Strategies in Coping with Writing Anxiety

Investigating the causal factors, effects, and strategies to overcome writing anxiety is the aim of this research. A fifth semester student of English Education Study Program, Teacher Training and Education Faculty of Universitas Tanjungpura with high learning motivation was chosen as the participant of this research. A case study design was employed in this research by using semi-structured interview and document analysis as the method of data collection. The result of the data analysis indicated that the student experienced cognitive writing anxiety where she felt difficult in deciding a writing topic, choosing relevant vocabulary, and using appropriate grammar. Writing anxiety was caused by a high frequency of assignment, feeling lack of linguistics skill and vocabulary, and fear of being criticized by others. Based on the findings, it is revealed that the writing anxiety experienced by the participant brought a facilitative effect toward her writing performance which led her to take some efforts by managing several strategies to overcome the writing anxiety. It is reported that vocabulary checking, creating constructive surroundings to have writing practice, diary writing, and doing writing challenge are strategies she had used to overcome her writing anxiety.
Examining the Motivation to Read and Its Relationship to Second Language Reading Proficiency in an Indonesian EFL Tertiary Context

The purpose of this research is to investigate and describe the intrinsic and extrinsic motivations of tertiary-level EFL students in Indonesia toward reading English. The quantitative method was applied by asking students majoring in English at Brawijaya University to fill out a questionnaire adapted from Wigfield and Guthrie's Motivation for Reading Questionnaire (MRQ) (1997). The questionnaire was administered to students from 2018 generations to 2020 generations. Besides that, students’ self-assessment reading value used to measure students’ reading proficiency. In addition, this study also examined the correlation between students' reading motivation and their reading ability. The data collected were analyzed using the SPSS program with descriptive statistics to answer research question 1 and Pearson correlation analysis to answer research question 2. The results indicated that students are relative highly extrinsic motivated in reading English. Meanwhile, intrinsic motivation was found to have a significant correlation and extrinsic motivation was found to have a negative correlation with students' reading. Based on these findings, lecturers are expected to increase students' intrinsic motivation to read.
Challenges Encountered by an Active Bilingual Child in Productive Skills

The purpose of this study was to describe the bilingual child's ability and challenges in speaking and writing in two languages: Bahasa Indonesia and English. This research was designed by using a case study. The subject of this research was an 11-year-old, who communicated in both Bahasa Indonesia and English. Data were collected through observation, interview, video recording of the child’s speaking English and Bahasa Indonesia, and document analysis. The result showed that the subject was an active bilingual child because he actively spoke Bahasa Indonesia as his mother tongue and English as his second language. In English speaking, he had constraints in pronouncing /wɑː.zənt/, /bɑːt/. In speaking Bahasa Indonesia, he missed spealing some words, such as Hari, Dermaga, Bermain. In, writing English, he had difficult in writing words. He dominantly had challenge in writing the Indonesian Language, especially in developing content, vocabulary used, and in grammar used.
An Analysis of the English Speaking and Writing Skill Level of Tour Guides in Trenggalek’s Tourist Villages

Tourist village has been one of the main focuses of Indonesia’s Ministry of Tourism and Creative Economy right now. Indonesian government is targeting 3000 tourist villages in Indonesia, and some areas may start developing its tourist villages, one of which is Trenggalek, a small regency that is located in East Java. Trenggalek is targeting 100 tourist villages by 2024. One of the most important supporting components in tourist villages is tour guide. In other words, the role of tour guide is inseparable from the existence of tourist villages. To fulfill their duties, a tour guide must master some skills, one of which is English skill. A tour guide is required to comprehend four English skills, especially productive skills; speaking and writing. In Trenggalek, some tour guides are in charge of tourist villages. However, most of them have not joined English training programs. Their English skill level has also not been analysis or mapped. This study then initiates the analysis of English skill level of tour guides in tourist villages in Trenggalek. The study employs qualitative descriptive research to find the data. The findings reveal that there are some levels of both speaking and writing skill. However, speaking skill levels are more diverse compared to the writing ones.
Developing Vocabulary Animation Game Application for Pronounciation at Pelita Kasih Lawang Kindergarten

The purpose of developing illustrated vocabulary animation game media for kindergarten – A students at Pelita Kasih Lawang Kindergarten is that early childhood can concentrate and understand the content of the material so that they are able to repeat simple words with correct pronunciation. Early childhood is also expected to be able to repeat words that have been listened to friends and teachers. Researchers compiled an animated vocabulary game media developer pictured in learning English, by making a Research and Development (R&amp;D) development research design, which is research used to produce certain products, and test the validity of these products. This Research and Development procedure is an adaptation of Sugiono's theory (2011 : 297), namely : 1) Potential and Problems, 2) Data Collection, 3) Product Design, 4) Design Validation, 5) Usage Trial, 6) Product Revision, 7) Product Trial, 8) Design Revision. Product Trial, 8) Design Revision. The data analysis technique used is qualitative and quantitative analysis in the form of percentages. The results of this research and development obtained are : 94.44% of the learning expert validation results, 96.15% of the media expert validation results, 91.11% of the small group trial results, and 95.68% of the field trial results. Based on these results, the development of animated media in language learning is classified as feasible to use. Based on the validation results, observations on small group trials and field trials, it can be concluded that the development of animated media in language learning is very interesting, fun, and easy to use. It is hoped that the results of the development of animated media in language learning can be developed even better, more effective, and right on target. So that it can be utilised according to the needs and achieve the goals of early childhood language learning.
Searching for Balanced 2D Brawler Games: Successes and Failures of Automated Evaluation

Automated game design (AGD) research focuses on creating systems that can design entirely new games. This is often done by a genetic algorithm, with a fitness function that is used to find individual games that satisfy certain design criteria. However, it is difficult to tell if generated games actually have the desired emergent properties (such as balance), since the fitness function might not align well with human aesthetic judgments about such properties. This is particularly problematic when trying to automatically design balanced, fair, yet asymmetrical games for multiple players. In this paper we present an implementation of an optimization-based AGD system for brawler games, and present findings from a preliminary user study of generated games. We show that while the system successfully optimizes for our written fitness function during human play, we found that this optimization did not necessarily translate to our hypothesized human experience of the game.
Determining the Optimal Rice Distribution Route in Medan City Using Dijkstra's Algorithm

Distribution is an activity carried out to spread the product throughout the market so that consumers can buy it. Distribution can also affect the price of goods. Therefore, the distribution must be effective. The purpose of this study is to determine the shortest route that is effective in distribution so that product prices are not high and the company does not suffer losses. The data analysis method used is Dijkstra's Algorithm obtained with data on the distance between the starting point and the destination point through predetermined points. The calculation results show that the shortest and fastest routes to be taken based on the dijkstra algorithm are: A-20-19-F-26-C-D-11-12-B-E with a distance of 40.71 km. There are several road routes that can be chosen to be used but the shortest route that can be taken is 40.71 km. Medan Rice Distributors are expected to choose the shortest transportation route so that rice distribution can be carried out quickly and optimally.
"It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Games

Game agents such as opponents, non-player characters, and teammates are central to player experiences in many modern games. As the landscape of AI techniques used in the games industry evolves to adopt machine learning (ML) more widely, it is vital that the research community learn from the best practices cultivated within the industry over decades creating agents. However, although commercial game agent creation pipelines are more mature than those based on ML, opportunities for improvement still abound. As a foundation for shared progress identifying research opportunities between researchers and practitioners, we interviewed seventeen game agent creators from AAA studios, indie studios, and industrial research labs about the challenges they experienced with their professional workflows. Our study revealed several open challenges ranging from design to implementation and evaluation. We compare with literature from the research community that address the challenges identified and conclude by highlighting promising directions for future research supporting agent creation in the games industry.
Explaining Deep Reinforcement Learning Agents In The Atari Domain through a Surrogate Model

One major barrier to applications of deep Reinforcement Learning (RL) both inside and outside of games is the lack of explainability. In this paper, we describe a lightweight and effective method to derive explanations for deep RL agents, which we evaluate in the Atari domain. Our method relies on a transformation of the pixel-based input of the RL agent to an interpretable, percept-like input representation. We then train a surrogate model, which is itself interpretable, to replicate the behavior of the target, deep RL agent. Our experiments demonstrate that we can learn an effective surrogate that accurately approximates the underlying decision making of a target agent on a suite of Atari games.
Telemedicina versus Proteção de Dados Pessoais. Os Dados de Saúde como Dados Pessoais

Telemedicine was adopted, in the Portuguese Health System, as a tool that helps in the provision of health care, helping, in a pandemic, against the spread of the virus that causes COVID-19. However, the use of telemedicine contributes to an increased risk of breaching personal data, especially special data such as health data. There is a great challenge to strike a balance between the increasingly frequent use of telemedicine (a non-regression reality) and the protection of personal data, especially health data. This challenge of conforming the requirements of protection of the fundamental right to health protection with the protection of privacy, personal data, can be achieved with an effort of legal provision, of new regulation of this reality, accompanied by extra care when using the telemedicine, in order to avoid the realization of the risk of violation of the aforementioned data.
Implementasi Algoritma Naïve Bayes Classifier (NBC) untuk Klasifikasi Penyakit Ginjal Kronik

Degenerative disease is a non-communicable disease that arises from an unhealthy lifestyle, so that it can reduce the physical and mental quality of the sufferer. Chronic Kidney Disease (CDK) is a degenerative disease that is included in the world's top 10 causes of death according to the World Health Organization (WHO). This study used CDK data with attributes of age, blood pressure, weight, albumin levels, sugar levels, red blood cells, pus cells, pus cell clots, bacteria, blood sugar levels, blood urea levels, creatinine serum, sodium, magnesium, hemoglobin, the volume occupied by red blood, indications of hypertension, indications of diabetes mellitus, indications of coronary heart disease, appetite, indications of swelling in the calves or feet, and indications of anemia. Therefore, the classification of kidney disease data is carried out with the implementation of the superior Naïve Bayes Classifier (NBC) algorithm and produces a high level of accuracy. The classification results using the RapidMiner tools carried out by the application of the NBC algorithm, the accuracy value is 96.43%, the average recall is 93.18%, the average precision is 93.02%, and the AUC is 93.2%. so it can be concluded that the performance of NBC in classifying chronic kidney disease data is excellent.
Generation of Geochemical Exploration Targets from Regional Stream Sediment Data Using Principal Component and Factor Ana

A regional-scale stream sediment geochemical sampling was carried out with an averagesampling density of one sample per nine square-kilometre in Kiteto District, Manyara Region.A total of 358 stream sediment samples were collected and analysed for major and traceelements by X-ray fluorescence (XRF) and fire assay with atomic absorption spectrometry(AAS) finish methods. In this study, Factor and Principal Component Multivariate (FPCM)analyses have been used to the stream geochemical data to delineate potential mineralizationzones by plotting correlated factors as geochemical anomaly maps. Four factors that accountfor 73.7% of the total variance of the stream sediment geochemical data were established.Factor 1: Ni–Ba–Co–Cu–Sr which possibly defines the underlying metamorphosed graniticunits with some contribution from mafic and ultramafic rocks. Factor 2: Cr–Zn–Mn whichdefines crustal forming elements reflecting the mafic rocks. Factor 3 which entails Rb and Pbprobably attributed to metamorphosed granitic lithology. Factor 4 is arsenic, a chalcophileelement with affinity to sulfide phases. The FPCM analyses have been successfully indelineating potential target areas for gold, nickel and copper exploration in the study area.&#x0D; Keywords: Stream sediment; principal component; factor analysis; exploration targets;Kibaya-Kiteto, Manyara.
Superstition in the Network: Deep Reinforcement Learning Plays Deceptive Games

Deep reinforcement learning has learned to play many games well, but failed on others. To better characterize the modes and reasons of failure of deep reinforcement learners, we test the widely used Asynchronous Actor-Critic (A2C) algorithm on four deceptive games, which are specially designed to provide challenges to game-playing agents. These games are implemented in the General Video Game AI framework, which allows us to compare the behavior of reinforcement learning-based agents with planning agents based on tree search. We find that several of these games reliably deceive deep reinforcement learners, and that the resulting behavior highlights the shortcomings of the learning algorithm. The particular ways in which agents fail differ from how planning-based agents fail, further illuminating the character of these algorithms. We propose an initial typology of deceptions which could help us better understand pitfalls and failure modes of (deep) reinforcement learning.
Preface

This volume describes the solution of problems of Big Data intellectual analysis, control, design and optimization. Big Data - technologies that extract maximum benefit from big data, which are widespread in all spheres. Starting with an official introduction to the basics of algorithm hybridization, this book combines many different aspects of current research on hybrid technologies, such as deep neural networks, fuzzy neural networks, multi-MISO ANFIS, fuzzy C-means, conditional disentangled networks, generative adversarial networks, finite difference method and enthalpy method. The book also covers a wide range of applications and implementation problems, from pattern recognition and image generation to intelligent forecasting problems, automation of production in technical applications (3D-analysis, forecasting of distributed photovoltaic systems and loads) with due attention to modeling. It covers a wide range of applications in the field of Big Data analysis, as well as Data Mining. In addition to the traditional tasks of classification, clustering, forecasting, it also discusses original approaches to hybrid optimization and control in the tasks of multi-object optimization for smart grid, natural gas hydrate wellbore, parallel search for optimal technological parameters of the non-consumable electrode welding. The articles are arranged in five thematic topics, I) Strongly coupled (functional) hybrid methods (articles 1-3); II) Loosely coupled (functional) hybrid methods (articles 4-5); III) Transformational hybrid methods (articles 6-7); IV) Integrated methods (articles 8-12) and V) Distributed hybrid intelligent methods (article 13).