Reviewerly/exHarmony huggingface dataset:
An Idea of the Education: the Structure and Content in the Context of the Philosophical Bases Changing

Introduction . Postnonclassical changes in modern education do not have a systemic character, therefore they do not entail qualitative changes in educational practice, that actualizes the need of searching for their system organizing principle. The article considers the idea of education in this capacity. Materials and Methods . In the process of scientific research, the theoretical research methods have been used such as comparative analysis, generalization, interpretation, scientific theorization in the context of the ideas of post-non-classical science and on its methodological basis. Results . The novelty of the proposed interpretation of the idea of education lies in the definition of its two structural components – fundamental and derivative (unchangeable and changeable) ones. It is shown that the content of the fundamental component is formed by logical and semantic constants as attributive properties of the basic method of human interaction with the world, which transmitting is being the deep meaning of education, providing self-identity of the idea of education in the flow of cultural and historical time. In the process of building a model and system of education, this method is acting as the initial object of modeling. Its attributive properties - logical-semantic constants - being the content of the fundamental component of the idea of education, like semantic genes, determine the structure, form and properties of each element of the education system. From this perspective, the article examines the two models of education: a traditional one in its original and current state and a new model of education, which is being developed on the post-non-classical foundations. Discussion and Conclusions. The interpretation of the idea of education proposed in the article makes it possible to present a traditional model and a new model of education from the ancestor source – the fundamental component of the idea of education as their «genetically» initial part. This perspective of understanding allows us to consider the elements and phenomena of education holistically and in the perspective of their development, serves as a reliable basis for assessing their educational efficiency.
Optimalisasi Kemampuan Guru Melalui Pelatihan Kuis Interaktif Secara Daring Berbasis Teknologi Informasi

The use of technology is very important in learning in the pandemic era. Various efforts were made to equip teachers to optimize the use of learning media. One of these efforts is to organize training on making interactive quizzes based on information technology which is held at SMP Negeri 1 Jaten. This training aims to provide insight related to learning media so that teachers are able to improve their abilities in making interactive quizzes and have a good impact on the future learning process. The method used in this training consists of three stages, namely preparation, implementation and evaluation.This training was conducted on May 6 – May 7, 2021 and was attended by 32 teachers for grades VII, VIII and IX consisting of subject teachers. The result of this training activity is that the teacher understands the training material well so that all the information obtained can be directly put into practice. Teachers get material that is very relevant to the needs in the classroom and can support learning activities. In addition, teachers get new insights related to learning media that can be applied in preparing interactive quizzes creatively and innovatively.
Detectability of Fibrils by Dilation Technique in Digital Mammography

The detectability of fibrils in mammographic phantom images by morphological enhancement was analysed in the present study. Materials that mimic fibrils were imaged by a digital mammography machine at 28 and 29 kVP. The images were processed by a dilation technique to produce second set of images. Receiver operating characteristic analysis was performed to compare the detection performance from the two sets of images. As compared to original images, the 28 kVP’s fibrils images from dilation technique become more prominence to be detected by observers. While at 29 kVP only a few observers can found the fibrils images from dilation technique. This study suggests morphological enhancement of mammography image did not increase the detection of low frequency signals of the images.
Transformer-based Language Models for Factoid Question Answering at BioASQ9b

In this work, we describe our experiments and participating systems in the BioASQ Task 9b Phase B challenge of biomedical question answering. We have focused on finding the ideal answers and investigated multi-task fine-tuning and gradual unfreezing techniques on transformer-based language models. For factoid questions, our ALBERT-based systems ranked first in test batch 1 and fourth in test batch 2. Our DistilBERT systems outperformed the ALBERT variants in test batches 4 and 5 despite having 81% fewer parameters than ALBERT. However, we observed that gradual unfreezing had no significant impact on the model's accuracy compared to standard fine-tuning.
Predictive Modeling Applied to Structured Clinical Data Extracted from Electronic Health Records: An Architectural Hypothesis and A First Experiment

Predictive analysis is one of current important issues in the healthcare context.A lot of patients' input data can be obtained from their Electronic Health Records.In our research, we propose a general architecture named Health Prediction Architecture.Initially, we consider that data refer to strongly structured health datasets (no free text).Our objectives are related to exploring some problems in the prediction context for healthcare.In particular, we consider dataset heterogeneity, accuracy together explain ability, dataset for benchmarking.After a presentation of Electronic Health Record and some useful related standards, we propose our architecture based on two principal modules.First module produces features extraction and it implements a Convolutional Neural Network or alternatively a Multi-Layer Perceptron.Second module produces predictions and it implements alternatively one from Graph Convolutional Network, Simplified Graph Transduction Game, Nearest Nodes and Classes Graph.We define the datasets randomly so to have the possibility to manage data sufficiently heterogeneous and useful for a benchmarking, without any privacy problem too.In this study, we experiment a first instantiation of the architecture, based on Multi-Layer Perceptron as first module and Simplified Graph Transduction Game as second module, considering health data related to type 2 diabetes risk, generated according to a healthcare rule.We try the architecture by slightly increasing both cardinalities of datasets and extracted features.As first results of our research, in this study we produce training and testing randomized datasets and we obtain a testing accuracy behavior generally better than using only Multi-Layer Perceptron (best accuracy with 200 labelled elements).Our architecture aims to evolve to be used as a general solution in healthcare predictions context.We are also interested in studying our solution in future works from the explain ability point of view, with particular interest in explaining the results in terms of input attributes.
A Complete Axiomatisation for Quantifier-Free Separation Logic

We present the first complete axiomatisation for quantifier-free separation logic. The logic is equipped with the standard concrete heaplet semantics and the proof system has no external feature such as nominals/labels. It is not possible to rely completely on proof systems for Boolean BI as the concrete semantics needs to be taken into account. Therefore, we present the first internal Hilbert-style axiomatisation for quantifier-free separation logic. The calculus is divided in three parts: the axiomatisation of core formulae where Boolean combinations of core formulae capture the expressivity of the whole logic, axioms and inference rules to simulate a bottom-up elimination of separating connectives, and finally structural axioms and inference rules from propositional calculus and Boolean BI with the magic wand.
Development of audit and data protection principles in electronic voting systems

It is assumed in standard information protection technologies that there are owners of this information who put forward requirements for protection. In secret voting systems, the information belongs to the community of citizens, and to protect it, vote organizers must create conditions that allow each voter to make sure that the vote secrecy and accuracy of vote counting are preserved. In developed democracies, this issue is resolved through a widely available audit of all procedures that may be mistrusted. Any voter can conduct such an audit. The anxiety of citizens of democratic countries is based on the idea that if electronic voting is introduced, it will be impossible to conduct such an audit. The article proposes principles of auditing all those software and hardware tools and processes of the online voting system that can generate voter distrust. This audit is carried out using a dedicated server open to voters and their fiduciaries. This server provides continuous monitoring of actions of the service staff in terms of possible interference in the operation of the voting system. Also, due to this server, auditors receive data on the integrity of the voting system hardware and software including its audit tools and an alarm signal in the event of a threat. It was possible to reduce the average time of processing the voter requests to two seconds. This means that processing a maximum of 2,500 voter requests at a vote station will take no more than two hours. Simultaneous access of 50 voters to the server will not make them wait in the queue for more than 2 minutes. Implementation results were described and links were given for conducting experimental voting on the Internet.
Facebook’s policies against extremism: Ten years of struggle for more transparency

For years, social media, including Facebook, have been criticized for lacking transparency in their community standards, especially in terms of extremist content. Yet, moderation is not an easy task, especially when extreme-right actors use content strategies that shift the Overton window (i.e., the range of ideas acceptable in public discourse) rightward. In a self-proclaimed search of more transparency, Facebook created its Transparency Center in May 2021. It also has regularly updated its community standards, and Facebook Oversight Board has reviewed these standards based on concrete cases, published since January 2021. In this paper, we highlight how some longstanding issues regarding Facebook’s lack of transparency still remain unaddressed in Facebook’s 2021 community standards, mainly in terms of the visual ‘representation’ of and endorsement from dangerous organizations and individuals. Furthermore, we also reveal how the Board’s no-access to Facebook’s in-house rules exemplifies how the longstanding discrepancy between the public and the confidential levels of Facebook policies remains a current issue that might turn the Board’s work into a mere PR effort. In seeming to take as many steps toward shielding some information as it has toward exposing others to the sunshine, Facebook’s efforts might turn out to be transparency theater.
Quantum Hoare Type Theory: Extended Abstract

As quantum computers become real, it is high time we come up with effective techniques that help programmers write correct quantum programs.In classical computing, formal verification and sound static type systems prevent several classes of bugs from being introduced.There is a need for similar techniques in the quantum regime.Inspired by Hoare Type Theory [NMB08] in the classical paradigm, we propose Quantum Hoare Types by extending the Quantum IO Monad [AG09] by indexing it with pre-and postconditions that serve as program specifications.In this paper, we introduce Quantum Hoare Type Theory (QHTT), present its syntax and typing rules, and demonstrate its effectiveness with the help of examples.QHTT has the potential to be a unified system for programming, specifying, and reasoning about quantum programs.This is a work in progress. 1
Building A Movie Recommendation System Using Collaborative Filtering With TF-IDF

Abstract: The recommendation problem involves the prediction of a set of items that maximize the utility for users. As a solution to this problem, a recommender system is an information filtering system that seeks to predict the rating given by a user to an item. There are theree types of recommendation systesms namely Content based, Collaborative based and the Hybrid based Recommendation systems. The collaborative filtering is further classified into the user based collaborative filtering and item based collaborative filtering. The collaborative filtering (CF) based recommendation systems are capable of grasping the interaction or correlation of users and items under consideration. We have explored most of the existing collaborative filteringbased research on a popular TMDB movie dataset. We found out that some key features were being ignored by most of the previous researches. Our work has given significant importance to 'movie overviews' available in the dataset. We experimented with typical statistical methods like TF-IDF , By using tf-idf the dimensions of our courps(overview and other text features) explodes, which creates problems ,we have tackled those problems using a dimensionality reduction technique named Singular Value Decomposition(SVD). After this preprocessing the Preprocessed data is being used in building the models. We have evaluated the performance of different machine learning algorithms like Random Forest and deep neural networks based BiLSTM. The experiment results provide a reliable model in terms of MAE(mean absolute error) ,RMSE(Root mean squared error) and the Bi-LSTM turns out to be a better model with an MAE of 0.65 and RMSE of 1.04 ,it generates more personalized movie recommendations compared to other models. Keywords: Recommender system, item-based collaborative filtering, Natural Language Processing, Deep learning.
Configurable Privacy-Preserving Automatic Speech Recognition

Voice assistive technologies have given rise to far-reaching privacy and security concerns. In this paper we investigate whether modular automatic speech recognition (ASR) can improve privacy in voice assistive systems by combining independently trained separation, recognition, and discretization modules to design configurable privacy-preserving ASR systems. We evaluate privacy concerns and the effects of applying various state-of-the-art techniques at each stage of the system, and report results using task-specific metrics (i.e. WER, ABX, and accuracy). We show that overlapping speech inputs to ASR systems present further privacy concerns, and how these may be mitigated using speech separation and optimization techniques. Our discretization module is shown to minimize paralinguistics privacy leakage from ASR acoustic models to levels commensurate with random guessing. We show that voice privacy can be configurable, and argue this presents new opportunities for privacy-preserving applications incorporating ASR.
Motion Graphic untuk Pengenalan Warna Dalam Bahasa Inggris

One of the levels of education for early childhood is Kindergarten. The presence and development of technology has a considerable influence on the development of learning media that utilizes video-based media. The use of video as a medium for learning color recognition in English in Kindergarten is expected to enhance childrens’ understanding. The aim of this research was to develop learning media to recognize colors in English through the implementation of motion graphics and to convey material about color in English through learning media in the form of motion graphics that have been made. This product was developed using the Luther Sutopo method and distributed to Ibn Khaldun's Islamic Kindergarten. Previously, a test was conducted where it was found that this media was very good to use and it was proven that the students' average score had increased by learning to use this media.
Does Knowledge Help General NLU? An Empirical Study

It is often observed in knowledge-centric tasks (e.g., common sense question and answering, relation classification) that the integration of external knowledge such as entity representation into language models can help provide useful information to boost the performance. However, it is still unclear whether this benefit can extend to general natural language understanding (NLU) tasks. In this work, we empirically investigated the contribution of external knowledge by measuring the end-to-end performance of language models with various knowledge integration methods. We find that the introduction of knowledge can significantly improve the results on certain tasks while having no adverse effects on other tasks. We then employ mutual information to reflect the difference brought by knowledge and a neural interpretation model to reveal how a language model utilizes external knowledge. Our study provides valuable insights and guidance for practitioners to equip NLP models with knowledge.
Lecturers’ Experiences in Teaching Using VR Resources at a Selected University, South Africa

Globally, academics have reconnoitered the various benefits of virtual reality (VR) in education. This study explored the lecturers’ experiences with VR resources in teaching and learning environments with pre-service teachers at one of the selected universities in South Africa. The study engaged a qualitative method, comprising of semi-structured face-to-face interviews with 6 lectures. The data from the interviews were evaluated by hand and the findings from this study were precisely described as given by the interviewees. These findings specified that the lecturers acknowledged the effectiveness of the use of VR resources in teaching and learning since all activities become more concrete., However, the lecturers indicated that there were some challenges that hindered them from employing VR resources into their teaching and learning environments and these included a lack of adequate lecturer development for the use of VR tools for teaching; inadequate VR tools for teaching and learning in their departments; VR resources were not tailor-made for their current curricula; and inadequate funding for 4IR resources. Hence, this study recommended that this university should immediately provide all the support required to facilitate the lecturers’ use of VR resources for teaching to avoid the use traditional teaching and learning methods.
Data Analytic Models That Redress the Limitations of MapReduce

The amount of data in today’s world is increasing exponentially. Effectively analyzing Big Data is a very complex task. The MapReduce programming model created by Google in 2004 revolutionized the big-data comput-ing market. Nowadays the model is being used by many for scientific and research analysis as well as for commercial purposes. The MapReduce model however is quite a low-level progamming model and has many limitations. Active research is being undertaken to make models that overcome/remove these limitations. In this paper we have studied some popular data analytic models that redress some of the limitations of MapReduce; namely ASTERIX and Pregel (Giraph) We discuss these models briefly and through the discussion highlight how these models are able to overcome MapReduce’s limitations.
Scientific services consolidation methods

The article discusses methods of consolidating scientific services of a digital platform for integrating a set of scientific services for various fields of science for conducting interdisciplinary research. Solutions for creating consolidated services can be widely used for multilevel, multiscale modeling in the field of materials science, which provides complex modeling at several levels of the hierarchy. Currently, this problem is being solved by creating multicomponent hierarchical software systems on corporate computing systems. With the advent of high-performance cloud computing platforms, it will be possible to order services for solving particular modeling problems as a scientific service. In this case, the tasks of complex hierarchical modeling will be solved by a consolidated service - a service providing sequential-parallel execution of complex modeling components in the form of specialized scientific services. The description of the processes for the provision of scientific services is based on the research methodology and is a research plan (the work process mapping), which describes a set of operations related to time and includes a list of necessary resources for their implementation. In modern conditions of the development of a microservice approach to the creation of computing systems and the evolution of the Service Oriented Architecture and of the Enterprise Service Bus integration, special attention is paid to the problems of efficient integration of platform services. The paper proposes to supplement the existing description of a scientific service with the possibility of ordering a third-party service based on agile integration. This approach will allow at the present stage of development of service architectures to overcome the shortcomings of centralized systems such as Enterprise Service Bus and take advantage of the elasticity of cloud computing and a microservice approach to creating information and computing systems.
On Robustness of Generative Representations Against Catastrophic Forgetting

Catastrophic forgetting of previously learned knowledge while learning new tasks is a widely observed limitation of contemporary neural networks. Although many continual learning methods are proposed to mitigate this drawback, the main question remains unanswered: what is the root cause of catastrophic forgetting? In this work, we aim at answering this question by posing and validating a set of research hypotheses related to the specificity of representations built internally by neural models. More specifically, we design a set of empirical evaluations that compare the robustness of representations in discriminative and generative models against catastrophic forgetting. We observe that representations learned by discriminative models are more prone to catastrophic forgetting than their generative counterparts, which sheds new light on the advantages of developing generative models for continual learning. Finally, our work opens new research pathways and possibilities to adopt generative models in continual learning beyond mere replay mechanisms.
Qubit state discrimination using post-measurement information

We consider the optimal discrimination of nonorthogonal qubit states with post-measurement information and provide an analytic structure of the optimal measurements. We also show that there is always a null optimal measurement when post-measurement information is given. Further, in discriminating four states using post-measurement information, we analytically provide the optimal probability of correct guessing and show that the uniqueness of optimal measurement is equivalent to the non-existence of non-null optimal measurement with post-measurement information.