Reviewerly/exHarmony huggingface dataset:
Causal Additive Models with Unobserved Variables

Causal discovery from data affected by unobserved variables is an important but difficult problem to solve. The effects that unobserved variables have on the relationships between observed variables are more complex in nonlinear cases than in linear cases. In this study, we focus on causal additive models in the presence of unobserved variables. Causal additive models exhibit structural equations that are additive in the variables and error terms. We take into account not only the presence of unobserved common causes but also that of unobserved intermediate variables. Our theoretical results show that, when the causal relationships are nonlinear and there are unobserved variables, it is not possible to identify all the causal relationships between observed variables through regression and independence tests. However, our theoretical results also show that it is possible to avoid incorrect inferences. We propose a method to identify all the causal relationships that are theoretically possible to identify without being biased by unobserved variables. The empirical results using artificial data and simulated functional magnetic resonance imaging (fMRI) data show that our method effectively infers causal structures in the presence of unobserved variables.
THE RELATION OF 3D TECHNOLOGIES WITH VIRTUAL TOURISM: EDUCATIONAL ASPECT

1 University of Library Studies and Information Technologies (BULGARIA)2 South-West University "Neofit Rilski" (BULGARIA)
Feature and Instance Selection Through Discriminant Analysis Criteria

Abstract Feature selection and instance selection are two data preprocessing methods widely used in data mining and pattern recognition. The main goal is to reduce the computational cost of many learning tasks. Recently, joint feature and instance selection has been approached by solving some global optimization problems using meta-heuristics. This approach is not only computationally expensive, but also does not exploit the fact that the data usually has a structured manifold implicitly hidden in the data and its labels. In this paper, we address joint feature and instance selection using scores derived from discriminant analysis theory. We present three approaches for joint feature and instance selection. The first scheme is a wrapper technique, while the other two schemes are filtering techniques. In the filtering approaches, the search process uses a genetic algorithm where the evaluation criterion is mainly given by the discriminant analysis score. This score depends simultaneously on the feature subset candidate and the best corresponding subset of instances. Thus, the best feature subset and the best instances are determined by finding the best score. The performance of the proposed approaches is quantified and studied using image classification with Nearest Neighbor and Support Vector Machine Classifiers. Experiments are conducted on five public image datasets. We compare the performance of our proposed methods with several state-of-the-art methods. The experiments performed show the superiority of the proposed methods over several baseline methods.
Pengembangan Perhiasan Tembaga Inspirasi Teknik Filigree Dengan Pendekatan Eksplorasi

Filigree jewelry has a complex and detailed jewelry character, so that market segmentation is limited to the middle and upper middle age range. This research plays an important role as one of the techniques and data collection methods that will be studied in the Design Project Course V. The purpose of developing the filigree technique with a new material approach is to expand market reach. In addition to the new material approach, the development of this filigree technique of jewelry design. The design research method used is data collection through observation, interviews, documentation, literacy studies and exploration. The results of this study indicate that filigree jewelry can develop according to trends and can reach a wider market segmentation so that it can add product value and quality of life of metal craftsmen.
Dynamic Lockstep Processors for Applications with Functional Safety Relevance

Lockstep processing is a recognized technique for helping to secure functional-safety relevant processing against, for instance, single upset errors that might cause faulty execution of code. Lockstepping processors does however bind processing resources in a fashion not beneficial to architectures and applications that would benefit from multi-core/-processors. We propose a novel on-demand synchronizing of cores/processors for lock-step operation featuring post-processing resource release, a concept that facilitates the implementation of modularly redundant core/processor arrays. We discuss the fundamentals of the design and some implementation notes on work achieved to date.
IMPROVEMENT OF THE EDUCATIONAL PROCESS MANAGEMENT SYSTEM IN SMALL SCHOOLS IN REMOTE AREAS OF THE RUSSIAN FEDERATION BASED ON THE DIGITAL EDUCATIONAL ENVIRONMENT

IMPROVEMENT OF THE EDUCATIONAL PROCESS MANAGEMENT SYSTEM IN SMALL SCHOOLS IN REMOTE AREAS OF THE RUSSIAN FEDERATION BASED ON THE DIGITAL EDUCATIONAL ENVIRONMENT
Improving Social Studies Learning Outcomes by Using Learning Video Media For Fourth Grade Students Of SDTQ Al Abidin Surakarta For The 2018/2019 Academic Year

&lt;p&gt;&lt;em&gt;The research&lt;/em&gt;&lt;em&gt; was motivated by the problem of the low social studies learning outcomes of fourth grade students. This is because teachers often use the lecture method, students only listen and record all information from the teacher. This study aims to explain whether the use of instructional video media in social studies subjects for class IV SDTQ Al Abidin Surakarta is able to improve student learning outcomes. This PTK consists of 2 cycles. Data collection techniques from learning outcomes tests at the end of each cycle meeting. The results showed an increase in student learning outcomes for social studies lessons. In the initial conditions, student learning outcomes complete KKM 15%. In the first cycle, the students who completed the KKM were 50%. The number of students who completed the KKM in the second cycle increased to 85%. There was an increase in the number of students who completed the KKM each cycle. With the evidence in this study, learning with video media is recommended to be applied in learning activities, in an effort to provide quality learning and can improve student learning outcomes.&lt;/em&gt;&lt;/p&gt;
Joint Visual Semantic Reasoning: Multi-Stage Decoder for Text Recognition

Although text recognition has significantly evolved over the years, state-of-the-art (SOTA) models still struggle in the wild scenarios due to complex backgrounds, varying fonts, uncontrolled illuminations, distortions and other artefacts. This is because such models solely depend on visual information for text recognition, thus lacking semantic reasoning capabilities. In this paper, we argue that semantic information offers a complementary role in addition to visual only. More specifically, we additionally utilize semantic information by proposing a multi-stage multi-scale attentional decoder that performs joint visual-semantic reasoning. Our novelty lies in the intuition that for text recognition, the prediction should be refined in a stage-wise manner. Therefore our key contribution is in designing a stage-wise unrolling attentional decoder where non-differentiability, invoked by discretely predicted character labels, needs to be bypassed for end-to-end training. While the first stage predicts using visual features, subsequent stages refine on top of it using joint visual-semantic information. Additionally, we introduce multi-scale 2D attention along with dense and residual connections between different stages to deal with varying scales of character sizes, for better performance and faster convergence during training. Experimental results show our approach to outperform existing SOTA methods by a considerable margin.
Pengaruh Intelegence Quotient, Minat, komunikasi orang tua, dan teman sebaya terhadap keputusan siswa di SMA N 1 sungai rumbai

The study found that: First, intelligence (IQ) (X1) has a significant effect on students' decisions to choose majors with a regression coefficient of 0.496.This coefficient value is significant because the value of t count 5.792 ≥ t table 1.65622 means that intelligence (IQ) increases by one unit.then the student's decision to choose a major also increased by 0.496.Both interests (X2) have a significant effect on students' decisions to choose majors with a regression coefficient of 0.367.This coefficient value is significant because the value of t count 10,533 ≥ t table 1.66571 means that if the interest increases by one unit.then the student's decision to choose a major also increased by 0.367.The third parent communication (X3) has a significant effect on student learning outcomes at SMAN 1 Sungai Rumbai with a regression coefficient of 0.309.This coefficient value is significant because the t value is 5.446 ≥ t table 1.65622, meaning that parental communication increases by one unit.it will increase the student's decision to choose a major by 1.073.The four peers (X4) have a significant effect on the student's decision to choose a major with a regression coefficient of 0.309.This coefficient value is significant because the t value is 4.300 ≥ t table 1.65622, it means that if peers increase by one unit.it will increase the student's decision to choose a major by 0.309.The five Intelligence (IQ), Interests, Parents Communication, and Peers simultaneously have a significant influence on students' decisions to choose majors at SMAN 1 Sungai Rumbai with Fcount 51.641 ≥ Ftable 2.28 and significant 0.000 <0.05.It is recommended that teachers, schools and parents pay more attention to or control student learning activities at school and at home so that students do not make the wrong decision to continue the educational process
A Review on Synthesis and Characterisation of Nanoclusters

Nanoclusters are materials with size less than 2 nm. Because of their small size and high surface area, they have properties which can be used for various applications like bio-sensing, catalysis, fuel cells etc. The synthesis techniques are carried out in such a way that the size-selection of nanoclusters take place. The synthesis methods discussed in this review include both top-down and bottom-up techniques. The different characterization techniques taken into consideration are X-ray diffraction (XRD), transmission electron microscope (TEM), scanning electron microscope (SEM), atomic force microscope (AFM), vibrating sample magnetometer (VSM) and UV-Visible spectroscopy. The distinct properties of nanoclusters were exemplified and confirmed.
Increasing Writing Skills Study Result Through the Experience Write a Diary In Primary School

&lt;p&gt;&lt;em&gt;Classroom action research is conducted to address learning problems that occur in the classroom. In addition, classroom action research is also used to improve the quality of learning so that student learning outcomes can increase. This classroom action research was conducted on sixth grade students at the State Elementary School 05 Bantarbolang Pemalang. Where this classroom action research aims to improve student learning outcomes in writing skills on fiction and non-fiction text material by using the experience of writing a diary. From the results of research conducted in two cycles showed an increase. Learning outcomes in the first cycle showed a class average of 74.88 while classical learning completeness was 76.92%. The learning outcomes in the second cycle showed the class average value of 87.07 while the classical learning completeness was 100%. This proves that the use of the experience of writing a diary can improve the learning outcomes of students' writing skills. Teachers can use the experience of writing a diary to improve students' writing skills&lt;/em&gt;.&lt;/p&gt;
PREFERENCE IN LANGUAGE LEARNING STRATEGIES (LLS) OF THE STUDENTS IN THE ENGLISH LANGUAGE EDUCATION DEPARTMENT AT THE UNIVERSITY OF MUHAMMADIYAH JEMBER

The present study aims at investigating the patterns of language learning strategy use employed by the sixth semester students of English language education program at the University of Muhammadiyah Jember. To achieve this objective, Oxford’s Strategy Inventory of Language Learning (SILL) (1990) along with a background questionnaire (a modified version of Oxford’s background questionnaire) were administered to the participants. The findings indicate that the research participants use language learning strategies at a high rate (more than 60%), but with differences in type and frequency of the individual learning strategies. The students reported a high frequent use of metacognitive and cognitive strategies. Other types of language learning strategies are also used by the students, although the frequency is lower than the previous two strategies. Based on the findings of the present study, some pedagogical implication were suggested to encourage students to reflect on their own strengths and weaknesses in skills and content courses and self-regulate learning so as to make progress with teachers’ assistance. &#x0D; Keywords: language learning strategies (LLS); Strategy Inventory of Language Learning (SILL); memory strategies; cognitive strategies; metacognitive strategies; socio-affective strategies; compensation strategies.&#x0D;
VISIBILITY ENHANCEMENT USING AHE WITH CLIP LIMIT AND GUIDED FILTERING

Fog/Haze present in environment makes the climate unpleasant and causes visibility reduction, may affect some applications of PC vision. Therefore, fog expulsion is extremely vital for drawing immense conduct of the vision techniques. Acquiring the knowledge depth makes dehazing challenging task. Prior based on dark channel (DChPr) impressively remove fog, but transmission map refinement using matting is computationally costly. Morden work identify the feasible of guided filter for refining the transmission. This paper detailed single image defogging by joining AHE with Clip Limit, DChPr and guided filter. The coarse transmission accessed from the normalized input image with the help of DChPr technique and then refined with help of guided filter. Final fog free output image is originated after performing post operation with the help of computer vision technique. Result analysis and observations demonstrate that proposed technique achieves satisfactory outcome with less computing.
BLENDED LANGUAGE LEARNING AS A MODERN EDUCATIONAL APPROACH IN TEACHING ENGLISH (AS A FOREIGN LANGUAGE)

The purpose of this research is to develop a technology for teaching English (or any other foreign language) using Blended Language Learning approach, which allows to increase the level of independence of students in mastering language skills, develop their technical and communication skills dramatically, as well as reading, speaking, listening and writing skills through the "flexible" model and "rotation of stations".The objectives of the research are to set up exercises aimed at developing all of the above skills through BLL and to use them in classes.The research methodology is based on mixed forms of work with students of Russian comprehensive school in Moscow region, using BLL and reflecting on the results obtained to improve teaching English methods. Due to COVID-19 training conditions, it is vital for all the world to improve the amount of digital technologies and blended learning in study. For this purpose, teachers in Private educational school "Lexis" had a survey which was done for better understanding pros and cons of the BLL. In this regard, there was a need to change the way of teaching according to the results. Therefore, the given research was carried out and allowed to work out some shortcomings of the BLL that were identified as a result of teaching activities and on the basis of survey results. So, the research consists of three key parts: preparation and presenting the BLL, taking a survey and making lesson plans based on the information and recommendations. The final assessment was carried out considering the level of our students, their age and points before the BLL-lessons and after them. The results of the research, based on the results of the conducted survey, new lesson plans and points of participants demonstrated the effectiveness and progressiveness of a such approach in teaching English (or any other foreign language). Thus, we were able to develop and apply the BLL, which gives us an opportunity to conduct an up-to-dated, flexible and progressive teaching students’ skills in the foreign language in comprehensive school.
TEACHER TRAINING AND THE CHALLENGES OF PERFORMING IN COVID TIMES

The importance of digital competences' development of the teachers has been addressed in numerous reports and recommendations, driven by European bodies dedicated to education and training, as well as professional bodies of teachers. The need for digital competences in performing quality, modern and innovative teaching remains actual and many competence profile of teaching profession explicitly include digital competences either as a key-competence or as a professional competence in teaching profession under the name of "use of ICT in teaching, learning and assessment". The extent to which teachers nowadays are closer to developing a functional level of digital competencies and whether online teaching itself is considered a beneficial practice remains unclear. Studies have demonstrated different levels of students' learning and well-being during the pandemic, especially the lack of or low levels, so the question we formulate is: do teachers adjust their online teaching practices so as to increase learning outcomes, even in pandemic times? Is there a preoccupation to improve digital competences in order to perform high level of teaching, even in full online schooling? Are teachers considering to involve ICT in their teaching practices at a higher level than before pandemic, as a consequence of intensive use during lockdown? The present paper addresses these issues following an online quantitive survey that was carried out between January 1st - January 28th 2021, on a sample of 1267 Romanian teachers. The study shows teachers' experience of online education during the COVID-19 pandemic and the main aspects of the analysis consisted of: the teachers' perceived progress on digital skills; trust in own digital competences; the level of trust offered by the courses attended on delivering online lessons; the perceived benefit of using digital skills in teaching from the perspective of students' learning outcomes; a general evaluation of all educational tools offered by online education; and the extent to which the methods and applications of online education will be integrated in the future. The results point to a positive self-assessment of teachers' digital skills, as well as an important willingness to implement the online education tools back to the everyday classrooms.
A Convolution Neural Networks and IoT-Based Approach to Surveillance System

Recognition of faces is of great importance for applications in the real world such as securities, human–computer interfaces, and traffic. As opposed to conventional machine learning methods, neural network models have also demonstrated improved outcomes in the case of processing precision in face detection. Surveillance system provides a close examination of a region, or the suspect in specific. The paper implements a novel system with the Internet of Things (IoT) capabilities for real-time surveillance using Haar-cascade detection integrated with a convolutional neural network for recognizing facial attributes to identify and allow access. The system presents 96.3% accuracy with real-time response through SMS or mail.
Multi-agent Reinforcement Learning Improvement in a Dynamic Environment Using Knowledge Transfer.

Cooperative multi-agent systems are being widely used in variety of areas. Interaction between agents would bring positive points, including reducing costs of operating, high scalability, and facilitating parallel processing. These systems pave the way for handling large-scale, unknown, and dynamic environments. However, learning in these environments has become a prominent challenge in different applications. These challenges include the effect of size of search space on learning time, inappropriate cooperation among agents, and the lack of proper coordination among agents' decisions. Moreover, reinforcement learning algorithms may suffer from long time of convergence in these problems. In this paper, a communication framework using knowledge transfer concepts is introduced to address such challenges in the herding problem with large state space. To handle the problems of convergence, knowledge transfer has been utilized that can significantly increase the efficiency of reinforcement learning algorithms. Coordination between the agents is carried out through a head agent in each group of agents and a coordinator agent respectively. The results demonstrate that this framework could indeed enhance the speed of learning and reduce convergence time.
GOT: Testing for Originality in Natural Language Generation

We propose an approach to automatically test for originality in generation tasks where no standard automatic measures exist.Our proposal addresses original uses of language, not necessarily original ideas.We provide an algorithm for our approach and a run-time analysis.The algorithm, which finds all of the original fragments in a ground-truth corpus and can reveal whether a generated fragment copies an original without attribution, has a run-time complexity of θ(n log n) where n is the number of sentences in the ground truth.
Defending against Reconstruction Attack in Vertical Federated Learning

Recently researchers have studied input leakage problems in Federated Learning (FL) where a malicious party can reconstruct sensitive training inputs provided by users from shared gradient. It raises concerns about FL since input leakage contradicts the privacy-preserving intention of using FL. Despite a relatively rich literature on attacks and defenses of input reconstruction in Horizontal FL, input leakage and protection in vertical FL starts to draw researcher's attention recently. In this paper, we study how to defend against input leakage attacks in Vertical FL. We design an adversarial training-based framework that contains three modules: adversarial reconstruction, noise regularization, and distance correlation minimization. Those modules can not only be employed individually but also applied together since they are independent to each other. Through extensive experiments on a large-scale industrial online advertising dataset, we show our framework is effective in protecting input privacy while retaining the model utility.
APLIKASI PEMBELAJARAN BAHASA MANDARIN UNTUK TINGKAT SEKOLAH MENENGAH KEJURUAN BERBASIS ANDROID PADA SMK HARMONI KOTA BATAM

The development of science and technology in China is so fast that many countries have made Mandarin as their second language and Mandarin has a very important role in the process of communication between countries and beneficial for business sector, education and other sectors. For students who take 3 levels of formal education must be taught Mandarin at school. Android devices have been widely recognized and used by teenagers to adults because the price of android devices is relatively cheaper compared to IOS and Playstore on Android provides a wide selection of applications. The purpose of this research is to improve the ability and makes it easier for students to learn Mandarin. The design of the application uses the Multimedia Development Life Cycle (MDLC) method have 6 step, which consist Concept, Design, Material Collecting, Assembly, Testing and Distribution. The design of an Android-based mandarin learning application is designed by using photo and audio media to help increase student interest in learning Mandarin and students also can learn Mandarin outside of school hours. The result of application testing is application has been function properly on android devices and available on the playstore to make it easier for students to install applications.