Reviewerly/exHarmony huggingface dataset:
The LVQ-based Counter Propagation Network -- an Interpretable Information Bottleneck Approach

In this paper we present a realization of the informationbottleneck-paradigm by means of an improved counter propagation network.It combines an unsupervised vector quantizer for data compression with a subsequent supervised learning vector quantization model.The approach is mathematically justified and yields an interpretable model for classification under the constraint of data compression, which is not longer independently learned from the classification task.
Modernization of didactic theories: answer to the present challenges

The article provides a scientific and theoretical overview of research in the field of didactics. The analysis of historical experience, foreign and domestic studies in the field of didactic theories, the author's own studies made it possible to consolidate scientific ideas and theoretical concepts for the formation of didactic systems, theories and approaches, to reflect new didactic ideas and methodological initiatives developed in response to the challenges of the present time. The materials provided in the article reflect the author's vision of solving the problem of modernizing didactic knowledge, represent a set of approaches, principles, methodological tools, and demonstrate the dynamics of didactic thought at various stages of the development of science. Domestic and foreign classical theories of learning that arose within the framework of behavioral and cognitive approaches are considered, the features of their application at the modern stage are substantiated. Didactic concepts reflecting the ideas of non-classical rationality of the late XIX to the middle of the XX century are analyzed. The advantages and limitations of the progressive didactic by J. Dewey are analyzed in detail. The main idea of constructivism is emphasized – the construction of knowledge for each student individually through understanding of their own metacognitive strategies. The possibilities of the development of didactic knowledge in the era of post-non-classical rationality, which is characterized by the rejection of monologism, recognition of many approaches and fundamental falsification of theories, are explored. As an example of the didactic theory that arose in this line, the theory of connectivism is presented. The conclusion is made about the significant expansion of the object sphere of didactics in the paradigm of post-non-classical rationality and the inclusion of new phenomena in it: digital educational environment, interactive technologies, individualization and personalization, development of values and meanings in the learning process. This article continues the discussion started earlier on the pages of «Yaroslavl Pedagogical Bulletin» journal and within the framework of the activities of the inter-university research center «New Didactics» on issues of methodology and practice in the field of didactics of pedagogical education.
Game Mechanics: Sniper Simulator Game

Popularity of game creates opportunity to everyone in developing a game that can be used to address any specific problem in many areas. In BINUS University International, for education purposes, game is not only being used as a tool/media to learn something, but it can also be used to develop student understanding on how the game mechanic of the games works. By understanding the mechanic of the games, hopefully it can help them in developing a game designed that can be used to address problems in their environment.
Peningkatan Kualitas Desain Kemasan dan Pengelolaan Data Usaha Pada Industri Rumah Tangga Kenko

Kenkō is a home industry that produces organic almond milk. This business carries the theme of a healthy lifestyle and has a vision to participate in preserving the environment and nature. However, Kenkō still uses single-use plastic packaging to package its products. This is considered contradictory by its customers. In addition, business administration data which includes recording of production costs, turnover, sales transactions, and financial reports are still using notebooks. Therefore, this business has difficulty in making financial statements. This activity seeks and implements solutions to the partner's problems. The methods used in this activity include, among others, socialization, focus group discussions, providing packaging assistance and business management training. The packaging was replaced with glass bottles because it was considered more suitable for reuse and safe from harmful chemicals such as BPA. This activity also provides training on business administration data management using technology, namely MS Excel because it is considered capable of making financial reports easier. The results of this activity include the creation of new packaging in the form of BPA-free glass bottles and a new label design for the Kenkō home industry as well as the implementation of MS. Excel training activities to assist and simplify business administration management, as well as indirectly support the healthy lifestyle movement. and participate in preserving the environment and the natural surroundings. The follow-up activity planned for this partner is digital marketing and bringing their products to a digital platform, namely e-commerce.
Training of Machine Learning Models for Recurrence Prediction in Patients with Respiratory Pathologies

Information extracted from electronic health records (EHRs) is used for predictive tasks and clinical pattern recognition. Machine learning techniques also allow the extraction of knowledge from EHR. This study is a continuation of previous work in which EHRs were exploited to make predictions about patients with respiratory diseases. In this study, we will try to predict the recurrence of patients with respiratory diseases using four different machine learning algorithms.
Pengaruh Bimbingan Belajar Online (Ruangguru) dan Minat Belajar Terhadap Hasil Belajar Ekonomi Kelas X di SMA Negeri 3 Padang

This study aims to reveal the Effect of Online Tutoring (Ruangguru) and Learning Interests on Economic Learning Outcomes of Class X Students in SMA N 3 Padang. This research is an Associative Descriptive with a Quantitative approach. The population in this study amount of class X students in SMA N 3 Padang who took Bimbel Online (Ruangguru), which totaled 135 person. The sampling technique uses a proportional random sampling technique which amounts to 58 people. The data is obtained through a questionnaire using a Likert scale that has been tested for validity and reliability. Data analysis techniques used are descriptive analysis and inductive analysis. For testing the hypothesis that is t test and f test. Based on the results of t-test hypotheses the influence of online tutoring (Ruangguru) on learning outcomes is influential and positive on economic learning outcomes, t-test students' learning interest has an effect and negative on economic learning outcomes. While the f test simultaneously influences online tutoring (Ruangguru) and interest in learning towards economic learning outcomes. The magnitude of the influence of online tutoring (Ruangguru) and interest in learning towards economic learning outcomes is 11% and the remaining 89% is influenced by other variables not included in this research model. Keywords: bimbel, minat belajar, hasil belajar, ruangguru
Hindsight Network Credit Assignment: Efficient Credit Assignment in Networks of Discrete Stochastic Units

Training neural networks with discrete stochastic variables presents a unique challenge. Backpropagation is not directly applicable, nor are the reparameterization tricks used in networks with continuous stochastic variables. To address this challenge, we present Hindsight Network Credit Assignment (HNCA), a novel gradient estimation algorithm for networks of discrete stochastic units. HNCA works by assigning credit to each unit based on the degree to which its output influences its immediate children in the network. We prove that HNCA produces unbiased gradient estimates with reduced variance compared to the REINFORCE estimator, while the computational cost is similar to that of backpropagation. We first apply HNCA in a contextual bandit setting to optimize a reward function that is unknown to the agent. In this setting, we empirically demonstrate that HNCA significantly outperforms REINFORCE, indicating that the variance reduction implied by our theoretical analysis is significant and impactful. We then show how HNCA can be extended to optimize a more general function of the outputs of a network of stochastic units, where the function is known to the agent. We apply this extended version of HNCA to train a discrete variational auto-encoder and empirically show it compares favourably to other strong methods. We believe that the ideas underlying HNCA can help stimulate new ways of thinking about efficient credit assignment in stochastic compute graphs.
Parestesia Expert System! Expert System To Detect Paresthesia (Parestesia Expert System! Sistem Pakar Untuk Mendeteksi Penyakit Parestesia)

Paresthesia is a condition where the sensation on the skin is abnormal, such as tingling, itching or numbness, for no apparent reason. This condition can occur only temporarily or for a long time (chronic). Bucharestesia can be temporary (temporary) or chronic. Almost everyone has experienced temporary paresthesia. This sensation occurs when nerves are accidentally compressed in certain body positions, such as sitting cross-legged for too long or sleeping with your head on your hands. Temporary paraesthesia will go away on its own when the pressure on the nerves is relieved. However, if the tingling feeling persists even though the pressure has been relieved, then there may be another disease or disorder in the body that is the cause. Chronic paresthesia is often a symptom of a neurological disease or caused by trauma to the nerve tissue. A variety of diseases can cause chronic paresthesia including vitamin deficiency, neurological disorders due to repetitive movements or other diseases. Chronic paraesthesia requires medication and management to heal. However, sometimes chronic paresthesia cannot completely heal even after undergoing treatment even though an expert system is a computer-based system that uses knowledge, facts, and reasoning techniques to solve problems that usually only one person can solve. Expert systems also help their activities as highly experienced assistants. Expert systems can also provide analysis of problems and can also recommend to users some actions to make improvements.
Teaching Reading Through KWL (Know-Want to know-Learned) Strategy In Islamic Economics Text

KWL strategy is a reading strategy used by teachers or educators to improve the students' reading skills by activating their prior knowledge, encouraging their motivation, and making the student comprehend the text holistically. The purpose of this research is to (1) apply the K-W-L Strategy in Reading Islamic Economics Text (2) know How do the students apply the K-W-L strategy in reading Islamic economics text. This research is conducted in a descriptive qualitative design. The data is collected by observation and documents. Data analysis procedure based on Creswell, Rossman &amp; Marshall, 1) Organizing and familiarizing 2) Coding and reducing 3) Interpreting and representing. The results present that the application of K-W-L strategy is made through three steps, namely 1) Pre-reading, where the teacher or researcher created a good condition of the classroom and introducing passage, and the students filling the chart in K column 2) Whilst reading, the teacher leads the student read the text and started to write down what they want to learn in W column 3) Post-reading in which the students started to comprehend the passage, make a conclusion and stated what they have learned from text based on their comprehension in L column. The study also concluded that the KWL strategy was appropriate in teaching reading on Islamic Economics Text, and it can be applied in reading for any level of English learners.
Differentially Private Goodness-of-Fit Tests for Continuous Variables

Data privacy is a growing concern in modern data analyses as more and more types of information about individuals are collected and shared. Statistical analysis in consideration of privacy is thus becoming an exciting area of research. Differential privacy can provide a means by which one can measure the stochastic risk of violating the privacy of individuals that can result from conducting an analysis, such as a simple query from a database and a hypothesis test. The main interest of the work is a goodness-of-fit test that compares the sampled data to a known distribution. Many differentially private goodness-of-fit tests have been proposed for discrete random variables, but little work has been done for continuous variables. The objective is to review some existing tests that guarantee differential privacy for discrete random variables, and to propose an extension to continuous cases via a discretization process. The proposed test procedures are demonstrated through simulated examples and applied to the Household Financial Welfare Survey of South Korea in 2018.
DEVSECOPS METHODOLOGY FOR NG-IOT ECOSYSTEM DEVELOPMENT LIFECYCLE – ASSIST-IOT PERSPECTIVE

Current software projects require continuous integration during their whole lifetime. In this context, different approaches regarding introduction of DevOps and DevSecOps strategies have been proposed in the literature. While DevOps proposes an agile methodology for the development and instantiation of software platforms with minimal impact in any kind of operations environment, this contribution proposes the introduction of DevOps methodology for Next Generation IoT deployments. Moreover, novelty of the proposed approach lies in leveraging DevSecOps in different stages and layers of the architecture. In particular, the present work describes the different DevSecOps methodology tasks, and how the security is included on pre-design activities such as planning, creation or adaptation, the design and implementation, as well as on post-implementation activities such as detection, response. Without proper consideration of security and privacy best practices identified in this article, the continuous delivery of services using DevOps methodologies may create risks and introduce different vulnerabilities for Next Generation IoT deployments.
THE ROLE OF LOCAL GOVERNMENT IN PROMOTING ENTREPRENEURSHIP AND EMPLOYING YOUNG PEOPLE

The governance system in Kosovo is separate from the central level of governance at the local level. But these two are interconnected. Therefore, considering the influence of local government in many different spheres, the purpose of this paper is to describe the role of local government in promoting entrepreneurship and the influence of entrepreneurship on youth employment. Initially this paper analyzes the support that local government provides to enterprises through various forms such as infrastructure, administrative procedures, various subsidies, and then employment of young people in these enterprises. The study included 500 enterprises of the Republic of Kosovo, where from the findings of this study we conclude that the local government has an average impact on the promotion of enterprises as it does not offer as much opportunity for enterprises due to limited competences in economic development. In addition, youth employment is low. The primary data collection is through the forms provided by Google, while data analysis is done with the SPSS program where the measurement of results is done with techniques like correlation Various statistical variables were identified from the variance analysis.
Methodology for ensuring computer security for vulnerable systems

Risk assessment is a process that provides cloud users with useful data for understanding the impact. We define a security incident as an event that attempts unauthorized access to databases. It is similar to an attack on the integrity and confidentiality of information.
Tscan: Dialog Structure Discovery Using Scan, Adaptation of Scan to Text Data

Can we learn dialog structure from existing dialogs without ontology or domain assumptions.Understanding dialog structures from existing task oriented human human dialogs can help us automate these dialogues in a better way.Traditionally dialog structures have been created using ontologies that are created by domain experts.However, in our experience getting the ontology right is difficult and time consuming.Like other such tasks an unsupervised approach may do better than hand crafted rules.We propose an unsupervised dialog structure discovery approach that is based on SCAN (Semantic Clustering using Nearest Neighbors).Our approach comprises of two steps, the first being creating clusters of utterances and the second being creation of a structure using inter-cluster transition probabilities.Our main contribution in this paper is the adaptation of SCAN on text data.Unlike the SCAN approach for images, for text we did not train a separate pretext model and were able to use BERT for the same.Similarly for neigbor discovery, instead of augmentation we were able to leverage data variety.Evaluation metrics on dialog structures are a bit subjective, so we have used statistical measures as proxies for structure quality.We have also included our results on an internal human human task oriented 100k dialog dataset.We think SCAN like approaches are very promising for problems that use embedding similarities and should be further explored.
Extracting Entity Synonymous Relations via Context-Aware Permutation Invariance

Discovering entity synonymous relations is an important work for many entity-based applications. Existing entity synonymous relation extraction approaches are mainly based on lexical patterns or distributional corpus-level statistics, ignoring the context semantics between entities. For example, the contexts around ''apple'' determine whether ''apple'' is a kind of fruit or Apple Inc. In this paper, an entity synonymous relation extraction approach is proposed using context-aware permutation invariance. Specifically, a triplet network is used to obtain the permutation invariance between the entities to learn whether two given entities possess synonymous relation. To track more synonymous features, the relational context semantics and entity representations are integrated into the triplet network, which can improve the performance of extracting entity synonymous relations. The proposed approach is implemented on three real-world datasets. Experimental results demonstrate that the approach performs better than the other compared approaches on entity synonymous relation extraction task.
HILL MARI CONSONANT SYSTEM IN THE LANGUAGE EDUCATION OF FUTURE LANGUAGE TEACHERS

The article describes the importance and peculiarities of Hill Mari consonant system in the language education of future language teachers.As one of the most neglected aspects of Hill Mari language teaching, pronunciation has not gained special attention for the last few years.Mainly, it is intended to be useful to students of philology and to those who are concerned with studying the consonant sounds in Hill Mari.In order to work on pronunciation of schoolchildren it is very important to know the classification of Hill Mari consonant phonemes, the particular quality of which depends on the work of the vocal cords.The article also reports on the classification of consonants according to different principles.Consonants in Hill Mari are classified in terms of the following factors: -state of the vocal folds; -position of the soft palate; -place of articulation; -manner of the production of noise.
Designing a Real-time Interactive Spatial Augmented Reality Platform

The Spatial Augmented Reality (SAR) system is an interactive platform that allows any virtual contents to be projected onto any physical structure (object). However, most of the SAR platforms use pre-rendered models in a static setting that does not allow the physical structure to be modified dynamically. This has limited interactions between the users and the system because users are unable to make rapid changes to the physical structure to express their ideas. This limitation can be a huge challenge when it comes to application such as city planning, where rapid real-time prototyping is able to provide a better visualization to the impacts that the changes could bring to the city environment. Therefore, this research project aims to design and develop a tracker-based SAR system to resolve the aforementioned limitation. The main contributions of this research project include, (i) a SAR system that supports real-time physical structure reconstruction and projection mapping, and (ii) a SAR platform constructed using Lego blocks and easily accessible hardware and software. The hardware involves the design of the physical set-up to support a real time reconstruction, and dynamic projection mapping. The software involves real time object detection, tracking, and projection mapping. Real time object detection is carried out using colour tracking, and recording Lego positions, while dynamic projection mapping is done through marker tracking and coordinate mapping.  Based on preliminary evaluations conducted in the laboratory, the experimental results shown that the proposed SAR system is able to (i) successfully project virtual content onto physical structure built using Lego blocks in real-time, and (ii) detect changes made to the physical structure.
The Proposal of PAI Adaptive Learning Model Inspired by US Application-oriented Graduate Program

It is necessary and urgent for the universities to explore innovative ways of talent training and to provide qualified talents to the market with the increasing demand for application-oriented talents.Most of the existing studies explore the good practices of talent training from the teaching perspective.Inspiring from the application-oriented master program of Northeastern University, this essay proposes a new adaptive learning model from the students' perspective.The essay utilizes many management concepts such as the PDCA cycle, Stacey Matrix, and learning curve to analyze the statistics obtained from observation and questionnaire.Through statistic analysis, the essay verifies two assumptions and develops the learning model.The PAI adaptive learning model uses a combination of development approaches during different phases of the learning curve to help to maximize the effectiveness and efficiency of talent training.In addition, the essay discusses how to utilize the enterprise environmental factors of the model to promote learning growth.To conclude, the PAI adaptive learning model provides an actionable approach that can be used for application-oriented talent training and common systematic learning.
An Improved CRD (Close, Reach& Degree) algorithm to Process the Complex Graph Datasets

In data mining, graph mining is an important research area. This is also called as sub-domain and it focus on identifying the frequent sub-graphs within the graph datasets. Graph data is semi-structured and it is very complex data sets to process. While the difficulty of obtaining frequent sub-graphs in a large latent graph is severe, continuously finding frequent sub-graphs in graphs with streaming updates is harder. It is because the set of frequent sub-graphs has updated as every batch of node/edge update comes. In this paper, the new algorithm is implemented to find sub-graphs from large graph datasets using CRD (Close, Reach& Degree) algorithm.  To improve the performance of the CRD the Parallel Processing Algorithm (CRD-PPA) is adapted to overcome the issues like memory loading and computation time. CRD algorithm will find sub-graphs by evaluating distance and connectivity between vertex and running CRD algorithm on large dataset can reduce computation time.