Reviewerly/exHarmony huggingface dataset:
Estructura del modelo teórico mediado por realidad virtual aumentada en la enseñanza- aprendizaje de la geometría

The present investigation is to establish components of the technological theoretical model, related between the augmented reality (RA) and the geometry, that allows to obtain an epistemic and ontological framework, facilitating the construction of doing and learning. The theoretical foundation was based, among other authors, such as Van Hiele (1957), bilingual (2006), Yi-Chen et al (2011). The qualitative research method, the analysis technique of information gathering operations and data storage instruments were considered, contrasting the information. Whose results were concluded based on geometric thinking and RA levels. a) The use of a structural model of RA, verse geometry awakens motivation, participation and collaboration. b) The projection of didactic activities correctly designed to work with RA. c) It is taught to use correctly m-learning which is a form of learning that facilitates the construction of knowledge of virtual learning environments (AVA).
ANALISIS RESIKO KANKER PAYUDARA (BREAST CANCER) MENGGUNAKAN FUZZY INFERENCE SYSTEM (FIS) MODEL MAMDANI

Breast cancer is a type of malignant cancer, in which cells form in the breast tissue, and is the most common type of cancer - apart from skin cancer - and is ranked second (after lung cancer) the type of cancer that causes death. Every year thousands of people die from cancer due to limited medical resources and the inability of society to use existing information sources effectively. The most efficient way and one of the means of protection against breast cancer is early diagnosis. In this study, a system to analyze the risk of breast cancer was developed using the Mamdani model of Fuzzy Inference System (FIS). By using 6 input variables, the developed Mamdani FIS is able to produce an accuracy of 85% with 20 data used.  Keywords: cancer, breast cancer, fuzzy inference system,,fuzzy logic, Mamdani model .
SoK: Privacy-Preserving Collaborative Tree-based Model Learning.

Tree-based models are among the most efficient machine learning techniques for data mining nowadays due to their accuracy, interpretability, and simplicity. The recent orthogonal needs for more data and privacy protection call for collaborative privacy-preserving solutions. In this work, we survey the literature on distributed and privacy-preserving training of tree-based models and we systematize its knowledge based on four axes: the learning algorithm, the collaborative model, the protection mechanism, and the threat model. We use this to identify the strengths and limitations of these works and provide for the first time a framework analyzing the information leakage occurring in distributed tree-based model learning.
Intelligent Data Analytics and Communication Systems for Disasters

This presentation was given as part of the GIS Day@KU symposium on November 13, 2019. For more information about GIS Day@KU activities, please see http://gis.ku.edu/gisday/2019/
Upaya Meningkatkan Motivasi Dan Hasil Belajar Siswa pada Materi Menentukan Ide Pokok Paragraf dengan Teknik Membaca Intensif di SD Negeri Bambalo

This study aims to improve student motivation and learning outcomes in determining the main idea of paragraphs using intensive reading techniques at SD Negeri Bambalo. This research method uses classroom action research conducted in 2 cycles. The research subjects were 9 grade students of SDN Bambalo Semester 1 in the academic year 2020/2021. Data collection techniques in this study used tests and documentation. The data validation used triangulation techniques. The results of this study indicate that the application of an intensive reading technique approach to the material of determining the main idea of paragraphs can increase student motivation and learning outcomes. The class average score increased from 56.99 in the initial study, to 72.88 in the first cycle, and in the second cycle to 81.88 with the level of completeness of learning from 2 students or 22.22% in the initial conditions to 5 students or 55.55% in the first cycle and 8 students or 88.89% in the second cycle. The increase in learning motivation is also getting better, in the initial study there were 2 students or 22.22% of 9 students, in cycle I, students were complete as seen from the learning motivation of 5 students or 55.55% of 9 students, and in cycle II, Incomplete seen from the learning motivation of 9 students or 100% of 9 students. The conclusion is that the application of intensive reading techniques can increase motivation and learning outcomes of fifth grade students of SDN Bambalo Semester 1 in the 2020/2021 academic year.
Contactless Patient Authentication for Registration Using Face Recognition Technology

Patient registration is an essential process in every clinic and hospital before services are provided to patients. Usually, patient’s identity card or fingerprint (through a fingerprint scanner) will be requested for identity authentication in order to retrieve medical records of the patient. However, the current global health crisis of COVID-19 pandemic is raising concern on the hygiene and safety of sharing objects or touching surfaces. Same worry is also occurred towards the patient registration interaction process; further, hospitals and clinics are classified as high risk premises. Therefore, a contactless patient authentication for registration using face recognition technology is proposed in this work. In this system, a face is scanned and processed. If the face exists in the database indicating that the subject is an established patient, the patient’s records will be retrieved. Else, a new patient registration will be performed to register a new account. The efficiency of the system is assessed using our self-collected database. Empirical results show that the proposed system is able to attain 94% accuracy. But, an inferior performance is obtained, especially dealing with makeup variation.
A Review on Semi-Supervised Relation Extraction

Relation extraction (RE) plays an important role in extracting knowledge from unstructured text but requires a large amount of labeled corpus. To reduce the expensive annotation efforts, semisupervised learning aims to leverage both labeled and unlabeled data. In this paper, we review and compare three typical methods in semi-supervised RE with deep learning or meta-learning: self-ensembling, which forces consistent under perturbations but may confront insufficient supervision; self-training, which iteratively generates pseudo labels and retrain itself with the enlarged labeled set; dual learning, which leverages a primal task and a dual task to give mutual feedback. Mean-teacher (Tarvainen and Valpola, 2017), LST (Li et al., 2019), and DualRE (Lin et al., 2019) are elaborated as the representatives to alleviate the weakness of these three methods, respectively.
A NOVEL PRIVACY PRESERVATION MECHANISM FOR DATA AND USER IN DISTRIBUTED SERVERS

Advances in sensing and monitoring science allow location-based purposes however they additionally create tremendous privateness risks. Anonymity can supply a excessive diploma of privacy, retailer provider customers from dealing with carrier providers’ privateness policies, and limit the carrier providers’ necessities for safeguarding non-public information. However, guaranteeing nameless utilization of location-based offerings requires that the particular region facts transmitted via a person can't be without difficulty used to re-identify the subject. This paper provides a middleware structure and algorithms that can be used by using a centralized place dealer service. The adaptive algorithms regulate the decision of region data alongside spatial or temporal dimensions to meet distinct anonymity constraints based totally on the entities who can also be the use of place offerings inside a given area. Using a mannequin based totally on car site visitors counts and cartographic material, we estimate the realistically anticipated spatial decision for extraordinary anonymity constraints. The median decision generated with the aid of our algorithms is a hundred twenty five meters. Thus, nameless location-based requests for city areas would have the identical accuracy presently wanted for E-911 services; this would supply enough decision for wayfinding, automatic bus routing offerings and comparable location-dependent services.
Fostering Student Engagement in a Mobile Formative Assessment System for High-School Economics

In a mobile learning environment, students can learn via mobile devices without being limited by time and space. Therefore, it is vital to develop tools to assist students to learn and assess their knowledge in such environments. This paper presents a tool/application for formative self-assessment. The tool supports the selection of questions based on user-defined criteria concerning (1) the difficulty level; (2) the associated concepts; and (3) the purposes of the test taker. The main purpose of the presented tool is to better support the learning aims of the participants and to increase their engagement in the learning process. The focus of this study is to evaluate the tool using quizzes in Microeconomics to realize its potential in this specific domain. Teachers and students were involved in the experiments conducted. The experiments demonstrated that the presented tool is usable; it motivates the students and improves their understanding.
Augmented Reality Media As A Butterfly Metamorphosis Learning Method Based Marker Tracking: Augmented Reality Media As A Butterfly Metamorphosis Learning Method Based Marker Tracking

This study specifically the butterfly metamorphosis of learning can be done with various media for users of grades 4 through 5 elementary schools and must be attenuated by the instructor in the learning process. This study uses a marker-based search method to help study the butterfly metamorphosis in 3D on a smartphone registered devices. Augmented Reality Also has a Rapid Angle Detection algorithm to support a marker object in it. In the results and application testing colors are not suitable for the visualization process while it is still a perspective that can be processed by the Angle Rapid Detection algorithm and 0-20% in light testing can not be used 21-40% of long Appears 41- 100% faster Appearsr,
Bayesian Model Averaging for Causality Estimation and its Approximation based on Gaussian Scale Mixture Distributions

In the estimation of the causal effect under linear Structural Causal Models (SCMs), it is common practice to first identify the causal structure, estimate the probability distributions, and then calculate the causal effect. However, if the goal is to estimate the causal effect, it is not necessary to fix a single causal structure or probability distributions. In this paper, we first show from a Bayesian perspective that it is Bayes optimal to weight (average) the causal effects estimated under each model rather than estimating the causal effect under a fixed single model. This idea is also known as Bayesian model averaging. Although the Bayesian model averaging is optimal, as the number of candidate models increases, the weighting calculations become computationally hard. We develop an approximation to the Bayes optimal estimator by using Gaussian scale mixture distributions.
Adult Education in Foreign Languages: Interactive Settings, Models, Practice

The article dwells upon modern English as a Second Language (ESL) educational settings which are based on innovative technologies modernizing the gist of adult education in a foreign language and containing organizational models of the learning process, founded on the dialectical commonality of ESL methodologies. Program models for ESL education that can be realized at university departments under certain conditions are presented. It has been identified that ESL learning via mobile devices and apps, modern tech and computer-based technologies facilitates the individualistic approach of teaching, encourages mental activity and improves the effectiveness of the learning process. Some elaborations have been made to the statements regarding adult ESL education in the context of computer didactics as an innovative learning system in which the theoretical approaches and practical achievements of such an interdisciplinary branch of knowledge as Artificial Intelligence are realized. The article emphasizes the deep connection between the development of ideas through Artificial Intelligence and the disciplines which presuppose understanding, decision-making, learning i.e. Psychology, Logic, Linguistics. The comparative analysis has been done in order to identify empirical data of questionnaires regarding prioritizing the use of mobile devices, apps, and digital media in the ESL learning process. The analysis serves as a detailed breakdown and evidence of the on-hand teaching experience at universities.
TNM: A Tool for Mining of Socio-Technical Data from Git Repositories

Networks of collaboration between engineers are reflected in traces of developers' activity in version control systems (VCSs). Extracting data from Git repositories is an essential task for researchers and practitioners working on socio-technical analysis, but it requires substantial engineering work. With increasing interest in analysing socio-technical data and applying it in practice, there are no flexible and easily reusable tools to retrieve socio-technical information from VCSs. With no common reusable toolkit existing for this task, the burden of mining diverts the focus of researchers from their core research questions. In this paper, we present TNM -- an open-source tool for mining socio-technical data from Git repositories. TNM is fast, flexible, and easily extensible. TNM is available on GitHub: this https URL
Organization Of Teaching Mathematics Of Scholar Students In The Conditions Of A Credit - Modular Training System

This paper examines the methodology for organizing training in the course of Higher Mathematics in the conditions of a credit - modular training system, the role of intensive control of students 'knowledge, which increases the activity of students in studying the discipline, preparing them for classes and completing tasks in a timely manner, as well as students' independent work. It is possible to improve teaching mathematics through the use of elements of individual, differentiated, problem-solving and programmed teaching. Moreover, a special role is given to the intensive control of students' knowledge, which increases the activity of students in the study of this discipline, their preparation for classes and their timely completion of tasks, as well as their independent work.
Effecs of Electronic Word of Mouth Perceptions on Alzahra University Brand Image

The present study is the first research that examines the impact of electronic word of mouth and the image of the university brand on university selection by postgraduate students. Evaluated variables of electronic word of mouth include source credibility, message quality, message quantity, and message recipient characteristics. As the rank of students on entrance exam is an important factor in students making decision about choosing a university, this is considered as a moderator variable in the proposed model.The statistical population of this study is graduate and postgraduate students. Using stratified cluster sampling method, 384 questionnaires distributed among postgraduates studying at ten major faculties of Alzahra University. The data were analyzed by using Structural Equation Modeling.The results of this study indicate that the message quality, message quantity and the characteristics of the message recipient have a positive and meaningful effect, while due to the lack of ranking; the validity of the source has a negative impact on the acceptance of electronic word of mouth. The quality of the message is the most effective dimension in accepting electronic recommendations. Also, the rank of students as a moderating variable has moderate intensity effect in the master degree and high intensity in doctoral degree on university selection. The Faculty of Theology, among other faculties of Alzahra University, had the best image of the Alzahra University brand. The bisexuality of Alzahra University is positively assessed in the formation of mental image. So, it should be used as an advantage in gaining a competitive advantage and the university brand strategy.
Accretive Computation of Global Transformations of Graphs

The framework of global transformations aims at describing synchronous rewriting systems on a given data structure. In this work we focus on the data structure of graphs. Global transformations of graphs are defined and a local criterion is given for a rule system to extend to a graph global transformation. Finally we present an algorithm, with its correction, which computes online the global transformation of a finite graph in an accretive manner.
Attention-Based Deep Learning System for Negation and Assertion Detection in Clinical Notes

Natural language processing (NLP) has been recently used to extract clinical information from free text in Electronic Health Record (EHR). In clinical NLP one challenge is that the meaning of clinical entities is heavily affected by assertion modifiers such as negation, uncertain, hypothetical, experiencer and so on.Incorrect assertion assignment could cause inaccurate diagnosis of patients’ condition or negatively influence following study like disease modelling. Thus, high-performance clinical NLP systems which can automatically detect negation and other assertion status of given target medical findings (e.g. disease, symptom) in clinical context are highly demanded. Here in this work, we propose a deep-learning system based on word embedding and Attention-based Bidirectional Long Short-Term Memory networks (AttBiLSTM) for assertion detection in clinical notes. Unlike previous state-of-art methods which require knowledge input, our system is a knowledge poor machine learning system and can be easily extended or transferred to other domains. The evaluation of our system on public benchmarking corpora demonstrates that a knowledge poor deep-learning system can also achieve high performance for detecting negation and assertions comparing to state-of-the-art systems.
Pengembangan Media Pembelajaran Menggunakan Aplikasi Articulate Storyline Pada Mata Pelajaran Bahasa Inggris Kelas IX SMP

The development of learning media is done as an effort to solve students' learning problems on English subjects, where students find it difficult to understand the meaning of the meaning of English vocabulary, so that it has an impact on student learning outcomes that are less than optimal. Learning media can be used as an alternative to reduce problems in learning. The purpose of this study was (1) to find out the feasibility of developing instructional media on junior high school grade IX English subjects through validity testing with several experts (2) to find out the feasibility of developing instructional media on junior high school class IX subjects through practicality in students (3) knowing effectiveness of the feasibility of developing instructional media on junior high school grade IX English subjects through pre test and post test on students.The type and method of this research is research and development, better known as the 4-D Research and Development (R & D) model. The development procedure in the study consisted of four stages, namely define (define), design (design), develop (development), and disseminate (spread). Product validity was carried out by 3 people, namely 2 media validators and 1 person material validity, while the number of research subjects was 24 students of SMP Kartika 1-7. Data collection tools in the form of questionnaires, documentation and assessment formats. Data were analyzed qualitatively and quantitatively.The product produced is a CD of learning media, the product produced has obtained the results of feasibility assessment from material experts and media experts, namely the results of material validation obtained Very Good value so that the material is declared complete to be developed, the results of media validation after going through two validation stages the final score of Very Good was obtained, so that the media was categorized as valid for testing, it can be concluded that the English learning media is worth testing. Furthermore, the results of the practicality of the media try to 24 respondents of students of class IX of Kartika Middle School 1-7 Padang Very Practical, so it can be concluded that this media is practically used. In the effectiveness test there were significant differences in the results of the pre-test and post-test conducted by students. The result is obtained tcount of 9.49 with t table is 2.068. So t count> from t table. That is, the use of instructional media has an influence on the learning outcomes of class IX4 students on Grade IX English subjects. Keywords: Learning Media, Articulate Stoyline Application, English
A Key Recovery Algorithm Using Random Key Leakage from AES Key Schedule

A key recovery algorithm using parts of the key schedules is proposed for evaluating the threat of probing attack. Suppose that we have an information leakage sensor, and we can detect a leak (attacked) point where an attacker makes electrical/physical contact with a laser, a probe, etc. We assume that the attacked bits (leaked bits) are completely known to the attacker, whereas the other non-attacked bits are not leaked at all. We also assume that each bit leaks with a constant probability. Our key recovery algorithm is constructed by modifying the pruning phase that for cold boot attacks proposed by Tsow. Experimental result shows that, using our algorithm, more than 15% leakage recovers the key with almost probability 1, whereas less than 10% is recovered with small probability close to 0.
Uma Metodologia para Agrupamento e Extração de Informações de URLs de Phishing

Despite advances in prevention and mitigation mechanisms, phishing remains a threat. One reason for this is that phishers continuously improve their techniques. In this paper we study and characterize one of these improvements: phishers&amp;apos; use of redirection chains to evade identiﬁcation mechanisms and avoid takedown of the infrastructure hosting the malicious content. We propose a method to group messages and URLs into phishing campaigns, and develop a framework to identify their hosting infrastructure. We apply our method and framework on a dataset of spam and phishing messages collected from lowinteractivity honeypots. We explore and characterize phishing campaigns as well as their hosting infrastructure. Our results indicate that phishing campaigns are usually hosted in cloud providers, but some are hosted on devices in access networks, possibly infected end-user devices. This indicates multiple approaches used by phishers, and motivates different fronts to combat this threat. Apesar de avanços em mecanismos de prevenção e mitigação, o phishing continua uma ameaça. Uma das razões disso é que o agente responsável pelo envio dessas mensagens, o phisher, aprimora suas técnicas continuamente. Neste trabalho estudamos e caracterizamos um destes aprimoramentos: o uso pelos phishers de cadeias de redirecionamentos para ludibriar mecanismos de identiﬁcação e evitar bloqueio da infraestrutura de hospedagem do conteúdo malicioso. Propomos um método para agrupamento de mensagens e URLs em campanhas de phishing, e desenvolvemos um arcabouço para identiﬁcação da infraestrutura de hospedagem. Aplicamos nosso método e arcabouço em um conjunto de mensagens de spam e phishing coletado por honeypots de baixa interatividade. Caracterizamos campanhas de phishing e a infraestrutura de hospedagem. Nossos resultados mostram que a infraestrutura de hospedagem das campanhas identiﬁcadas se concentra em provedores de computação em núvem, mas que algumas campanhas são hospedadas por dispositivos em provedores de rede, possivelmente em dispositivos infectados. Isso indica diferentes abordagens de phishers, sugerindo esforços em diferentes frentes de combate.