Reviewerly/exHarmony huggingface dataset:
RM-FSP: Regret minimization optimizes neural fictitious self-play

To compute the optimal strategy in competitive games, algorithms have been developed to achieve the Nash equilibrium. Current deep learning algorithms have succeeded in many games; however, optimizing the algorithms to approach the Nash equilibrium in imperfect-information games like StarCraft and Poker remains challenging. Neural Fictitious Self-Play (NFSP) is an effective end-to-end algorithm to learn an approximate Nash equilibrium in imperfect-information games. However, because a player in NFSP trains its best response according to its opponents’ past strategies, a discrepancy exists between the optimal strategy and the learned best response after the player updates its strategies. We call this discrepancy the optimality gap. During training, the optimality gap does not decay monotonically, which causes suboptimal results or unstable convergence of NFSP. We improve the performance of NFSP by allowing the optimality gap to decay monotonically. In this study, we propose Regret Minimization Fictitious Self-Play (RM-FSP), which applies a regret minimization method to compute NFSP’s best response. The regret minimization method effectively converges the optimality gap monotonically and faster than in NFSP. We prove there will be a better learning bound than the original NFSP after applying regret minimization methods to NFSP. Experiments on three typical environments in OpenSpiel demonstrate that RM-FSP outperforms NFSP in both exploitability (discrepancy between the learned policy profile and the Nash equilibrium) and time efficiency.
On difunctions

The notion of a difunction was introduced by Jacques Riguet in 1948. Since then it has played a prominent role in database theory, type theory, program specification and process theory. The theory of difunctions is, however, less known in computing than it perhaps should be. The main purpose of the current paper is to give an account of difunction theory in relation algebra, with the aim of making the topic more mainstream. As is common with many important concepts, there are several different but equivalent characterisations of difunctionality, each with its own strength and practical significance. This paper compares different proofs of the equivalence of the characterisations. A well-known property is that a difunction is a set of completely disjoint rectangles. This property suggests the introduction of the (general) notion of the “core” of a relation; we use this notion to give a novel and, we believe, illuminating characterisation of difunctionality as a bijection between the classes of certain partial equivalence relations.
Penerapan Metode K-Means Clustering Untuk Analisa Penjualan Komoditas Toko Tani Indonesia

Thisreportdescribesthegroupingofagriculturalcommodities.AgriculturalCommoditiesaretheresults of farming activities that can be traded, stored and exchanged. In carrying out testing ofthis algorithm, the data used is goods data at the Indonesian Farmer Center shop. In thisapplication, clustering is used using the K-means algorithm. From the data that was processedwith data samples taken at the Indonesian farmer's shop center, three types of data groups wereproduced. Namely low sales data, medium sales data, and high sales data. So that with this datagrouping, the Indonesian farm shop can find out the types of goods that are selling well andwhichare not. Sothatthe goods in the warehousedo notaccumulate.
Robust Value Class Data based Block Data Storage

With the successful application of blockchain systems such as Bitcoin and Ether, blockchain technology has received widespread attention from industry and academia. However, due to its distributed architecture and redundant data storage, blockchain is not compatible with traditional storage solutions and has problems such as low query efficiency, inflexible data storage, limited block storage capacity and poor scalability. In this regard, we propose a blockchain architecture based on keyvalue database, including blockchain-based state data management, blockchain-based index data management and blockchain-based metadata storage, which has significant you in practical projects.
Theoretical data science: Bridging the gap between domain-general and domain-specific studies

The entering into big data era gives rise to a novel discipline called Data Science.Data Science is interdisciplinary in its nature, and the existing relevant studies can be categorized into domain-independent studies and domain-dependent studies.The domain-dependent studies and domain-independent ones are evolving into Domain-general Data Science and Domain-specific Data Science.Domain
The Unex Application as An English Interactive Learning Media : A Feasibility Study

This study aims to analyze the feasibility of Unex application as an interactive learning media in enhancing students' understanding of English texts. Unex stands for Understanding English Text application. The study used research and development (R&amp;D) method with the ADDIE model. The subject of this research was eleventh-grade students at Health Vocational School of North Borneo that use android smartphones. The research instruments were questionnaires and validation sheets. The questionnaire was a media practicality questionnaire used to find out the practicality of the media used by teachers after a try-out of the Unex Application. Furthermore, Validation sheets were used to validate the Unex Application. The analysis technique used to explain the response is descriptive analysis, which involves calculating the total, average, percentage and then is adjusted and categorized into several categories. The result showed that the validation from media and material experts for the Unex application was 96% and 98.4%, and both were in very feasible categories. Also, the result from the media practicality was 92.8%. Therefore, the Unex application is well-developed as an English interactive learning media for students Health Vocational School of North Borneo. Moreover, the media and material in the Unex application were validated and declared with good scores and very feasible categories.
Smoothing Module for Optimization Cranium Segmentation Using 3D SlicerSmoothing Module for Optimization Cranium Segmentation Using 3D Slicer

Anatomy is the most essential course in health and medical education to study parts of human body and also the function of it. Cadaver is a media used by medical student to study anatomical subject. Because of limited access to cadaver and also due to high prices, this situation makes it necessary to develope an alternative anatomical education media, one of them is the use 3D printing to produce anatomical models. Before 3D Print the cranium, it is necessary to do the segmentation process and often the segmentation result is not good enough and appear a lot of noises. The purpose of this research is to optimize a 3D cranium based on DICOM (digital imaging and communications in medicine) data processing using the smoothing modules on 3D Slicer. The method of this research is to process the Cranium DICOM data using 3D Slicer software by varying the 5 types of smoothing modules. The results with default parameter fill holes and median have better results compared to others. Kernel size variations are performed for smoothing module fill holes and medians. The result is fill holes get optimal segmentation results using a kernel size of 3 mm and the median is 5 mm.
Interactive strategy to strengthen the teaching-learning process of the subject of Atomic and Nuclear Physics

The subject of physics is usually one of the most complex for high school students, mainly because of the need for abstraction for understanding and the high need for calculation. With these premises, this work evaluated the use of a creative and interactive strategy to improve the physics learning process. Under a quasi-experimental study, the working methodology was carried out, taking a control group and an experimental group. The main results showed that students are significantly motivated by ICT tools, which favor the study and understanding of physics concepts, contributing to improvements in academic performance.
Impacto de um programa educativo com tecnologia online na pessoa pós-intervenção coronária percutânea

Background: Ischemic coronary disease is one of the leading causes of death in Portugal. As a chronic disease, it is imperative to develop skills for effective behavioural change and a higher level of acceptance and adherence to the recommendations provided. Objective: To assess the impact of an online nursing education programme, considering socio-demographic characteristics, cardiovascular risk factors, lifestyle, clinical indicators, adherence to pharma cological therapy, therapeutic self-care skills, literacy and acceptance of the use of technology. Methodology: Quantitative, descriptive, experimental study, with a before-after design and control group. Results: The educational intervention resulted in a decrease in weight, abdominal circumference, body mass index, blood pressure, cholesterol, blood glucose, and in increased acceptance of the participants about their clinical condition and use of technology. Conclusion: The implementation of a structured teaching program constitutes a good methodology for improving clinical indicators, increasing adherence to pharmacological therapy, capacity for therapeutic self-care, health literacy, lifestyle and acceptance of technology by the participants.
PENGEMBANGAN MEDIA POP-UP BOOK TERHADAP KEMAMPUAN MEMBACA SISWA PADA PELAJARAN BAHASA INDONESIA DI KELAS II SD NEGERI 308/VI RANTAU SULI

Education is process of changing the attitudes and behavior of a person group in effort to mature humans. The problem this study is low student learning outcomes due to lack of media. The aim of this research is to produce valid, practical and effective Pop-up Book media. This research uses the type of research and development (research and development). ADDIE development model has five stages, namely: Analyze, Design, Development, Implementation, Evaluation. Types of data taken from the development Pop-up book media are qualitative and quantitative data. The instrument used this study, validation of the Pop-up Book media, the practicality instrument Pop-up Book media, the effectiveness instrument the Pop-up book media. data collection techniques used are interviews, observations, and questionnaires (questionnaire). Analysis of data in research using analysis of validity, practicality and effectiveness. The results this study are validation, practicality and effectiveness, so the following results can be obtained: The validity of the pop-up book learning media assessed by the validator, totaling 3 people, obtained a percentage of 94.7% in the very valid category, the pop-up book learning media is feasible for used and in accordance with the provisions of SDN 308/VI Rantau Suli II. Practicality is assessed from the teacher response questionnaire and student response questionnaire. The teacher response questionnaire was 96% in the very practical category, the student response questionnaire was 99.71% in the very practical category, so based this analysis it was concluded that pop-up book media was very practical. Furthermore, the effectiveness assessed from the results of the Reading Ability test obtained a percentage of 84.21% in the very effective category.
QIsim: Architecting 10+K Qubit QC Interfaces Toward Quantum Supremacy

A 10+K qubit Quantum-Classical Interface (QCI) is essential to realize the quantum supremacy. However, it is extremely challenging to architect scalable QCIs due to the complex scalability trade-offs regarding operating temperatures, device and wire technologies, and microarchitecture designs. Therefore, architects need a modeling tool to evaluate various QCI design choices and lead to an optimal scalable QCI architecture.
COMPARISON OF K-MEANS AND GAUSSIAN MIXTURE MODEL IN PROFILING AREAS BY POVERTY INDICATORS

The Covid-19 pandemic has led to income degradation of the Indonesia population which potentially triggers poverty. According to the Indonesian Central Statistics Agency, the Province of Central Java is one of the areas that is most affected by Covid-19 especially on the economic aspect. In 2020, the percentage of poor people has increased by 0.6% from 2019. If this condition is ignored for the long term, it will have a negative impact on hampering national development. As a first step in designing a strategy for mitigating the impact of poverty, it is necessary to carry out an appropriate profiling of the areas affected on the economic aspect based on poverty indicators. This study compares the K-Means Clustering and Gaussian Mixture Model (GMM) in providing the best data grouping based on clustering indexes, including: connectivity, Dunn, and silhouette. GMM is a generalization of K-Means clustering to include information about the covariance structure of the data as well as latent Gaussian centers. We used poverty indicators data from Central Statistics Agency of Central Java, such as poverty line, percentage of poor population, poverty depth index, and poverty severity index. The results obtained from this study indicate that the GMM gives the best results with the 3 clusters, with the number of members for the first, second, third is 10, 19, and 6 respectively.
CROSS PRODUCT OF IDEAL FUZZY SEMIRING

If one of the axioms in the ring, namely the inverse axiom in the addition operation, is omitted, it will produce another algebraic structure, namely a semiring. Analogous to a ring, there are zero elements, ideal (left/right) in a semiring, and the cross product of the semiring ideal. The analog of the fuzzy semiring has zero elements, ideal (left/right), and the cross product of the semiring fuzzy ideal associated with the membership value. This paper will discuss the cross-product of two (more) fuzzy ideals from a semiring. Furthermore, the cross-product of two (more) fuzzy ideals from a semiring will always be a semiring fuzzy ideal. But the converse is not necessarily true.
Human Resources Management in the Modern School – Problems and Prospects

The introduction of new human resources management policies is a key catalyst for the organizational development of school institutions and for achieving sustainability in quality management systems. The article analyzes the problems of human resources management in the context of ensuring the quality of education. The causes of the problems and alternatives for influencing them in the context of human resource management in the modern school are outlined. We are looking for solutions for the management of pedagogical specialists, as part of the human resources in the school, aimed at the new requirements and responsibilities to the autonomous institutions to achieve high quality educational services, which include continuous improvement of teachers' competencies, life skills development, 21st century students, participation in the management of the institution. Possible management strategies are presented, leading to the improvement of human resources management and solving the problems with the quality of education in Bulgaria.
Study of the Development of the Use of Communicative Technologies in the Educational Process of Engineers Training

Pedagogical science sees the specificity of higher engineering education in the fact that future engineers need not only to master the theoretical foundations of engineering calculations, but what is especially important for quality professional training, is to acquire stable abilities and skills to implement successful practical activities – readiness for independent professional improvement. At the same time, the development of communication technologies, a derivative of the needs of civil society, has accelerated markedly due to the forced physical isolation caused by the need to combat the Covid-19 pandemic. The transfer of knowledge in traditional forms between participants in the educational process, due to the dramatically increased mobility of the parties, is losing its effectiveness. The Internet, educational platforms, and social media have undeniably increased the speed and volume of information exchange. The authors of the article, having experience in using and evaluating the effectiveness of various communication technologies and tools, conclude that there is an obvious qualitative improvement in the digital competence of students.
Research on Ultra-Short Term Distributed PV Power Forecasting Method Considering Weather Similarity based on Satellite Cloud Map

High-precision meteorological data usually are not available for most distributed PVs, leading to weak generalization ability in power forecasting models and low prediction accuracy. This paper introduces a prediction framework consist by similar time period matching framework (SPMF) to realize ultra-short term distributed PV power forecasting. SPMF aims to match power generation data under similar weather conditions to the current weather from station historical data, and provides the data as a reference to the power forecasting model to address the problem of insufficient generalization caused by lacking of high-precision data. In contrast to most recent approaches that use NWP data and Pearson correlation coefficients to evaluate the similarity of weather conditions, the proposed approach operates on similarity index consisting of SSIM evaluated by satellite cloud map cloud coverage images along with Pearson correlation coefficients evaluated by power generation data. Notable, a power prediction model is developed to selectively measure the reference value of similar period data by similarity index. Results show that the SPMF and the proposed prediction model both improve the accuracy of power prediction, and the utilize of both improvements can increase the accuracy of power forecasting in ultra-short term period by up to 5.91% compared to traditional forecasting model.
Conversational Emotion Detection and Elicitation: A Preliminary Study

Emotion recognition in conversation is a challenging task as it requires an understanding of the contextual and linguistic aspects of a conversation. Emotion recognition in speech has been well studied, but in bi-directional or multi-directional conversations, emotions can be very complex, mixed, and embedded in context. To tackle this challenge, we propose a method that combines state-of-the-art RoBERTa model (robustly optimized BERT pretraining approach) with a Bidirectional long short-term memory (BiLSTM) network for contextualized emotion recognition. RoBERTa is a transformer-based language model, which is an advanced version of the well-known BERT. We use RoBERTa features as input to a BiLSTM model that learns to capture contextual dependencies and sequential patterns in the input text. The proposed model is trained and evaluated on a Multimodal EmotionLines Dataset (MELD) to recognize emotions in conversation. The textual modality of the dataset is utilized for the experimental evaluation, with the weighted average F1 score and accuracy used as performance metrics. The experimental results indicate that the incorporation of a pre-trained transformer-based language model with a BiLSTM network significantly enhances the recognition of emotions in contextualized conversational settings.
A Model Selection Algorithm for Complex CNN Systems Based on Feature-Weights Relation

In Object recognition using machine learning, one model cannot practically be trained to identify all the possible objects it encounters. An ensemble of models may be needed to cater to a broader range of objects. Building a mathematical understanding of the relationship between various objects that share comparable outlined features is envisaged as an effective method of improving the model ensemble through a pre-processing stage, where these objects' features are grouped under a broader classification umbrella. This paper proposes a mechanism to train an ensemble of recognition models coupled with a model selection scheme to scale-up object recognition in a multi-model system. The multiple models are built with a CNN structure, whereas the image features are extracted using a CNN/VGG16 architecture. Based on the models' excitation weights, a neural network model selection algorithm, which decides how close the features of the object are to the trained models for selecting a particular model for object recognition is tested on a multi-model neural network platform. The experiment results show the proposed model selection scheme is highly effective and accurate in selecting an appropriate model for a network of multiple models.
Uncertainty Aware Active Learning for Reconfiguration of Pre-trained Deep Object-Detection Networks for New Target Domains

Object detection is one of the most important and fundamental aspects of computer vision tasks, which has been broadly utilized in pose estimation, object tracking and instance segmentation models. To obtain training data for object detection model efficiently, many datasets opt to obtain their unannotated data in video format and the annotator needs to draw a bounding box around each object in the images. Annotating every frame from a video is costly and inefficient since many frames contain very similar information for the model to learn from. How to select the most informative frames from a video to annotate has become a highly practical task to solve but attracted little attention in research. In this paper, we proposed a novel active learning algorithm for object detection models to tackle this problem. In the proposed active learning algorithm, both classification and localization informativeness of unlabelled data are measured and aggregated. Utilizing the temporal information from video frames, two novel localization informativeness measurements are proposed.
State Policy for Quality Education by Establishing the Grounds for Dismissal of a Pedagogical Specialist in Cases of a Criminal Conviction

The purpose of the study is to analyse the issues related to the ways of implementing state policy in the field of education, aimed at ensuring and guaranteeing quality and efficient preschool and school education. Emphasis is placed on an extremely important prerequisite for providing education with the specified characteristics in schools and kindergartens, which is a serious goal for every state administration, namely the high personal (including moral) qualities of pedagogical specialists – the subjects who are directly involved in the process of providing the relevant education. More specifically, it concerns the grounds established in the legislation for the dismissal of a pedagogical specialist in cases of their conviction for a crime. A comparison, respectively differentiation, is drawn between this legal solution and the requirement, which is identical to it as a linguistic expression, for occupying the position of a pedagogical specialist. In addition to an in-depth analysis of the indicated grounds for dismissal, a comparison, respectively differentiation, between it and the requirement for occupying the position of a pedagogical specialist, which is identical as a linguistic expression, is made, as a result of which certain conclusions have been formulated for the first time in the doctrine.