Reviewerly/exHarmony huggingface dataset:
Pengembangan Model Blended Learning Berbasis Literasi Digital dalam Meningkatkan Keterampilan Metakognitif Mahasiswa

This paper discusses the importance of strengthening digital literacy-based blended learning in improving students' metacognitive skills. The digitalization of technology in the world of education has led to behavioral changes in learning activities, learning resources, and learning models. The application of e-learning using blended learning has an effect on self-regulated learning which is part of meta-cognitive skills. students who are taught using blended learning methods have a higher average of self-regulated learning than students who are taught using traditional internet-based methods. Student learning outcomes who have high self-regulated learning have high learning outcomes, while students who have low self-regulated learning have low learning outcomes
COVID 19 TWEETS GEO AND SENTIMENTAL ANALYSIS AND CLASSIFICATION

The promulgation of Covid-19 has caused widespread health concerns across the world. News and views on it are widely shared on social media. To use resources efficiently and appropriately, a realistic evaluation of the situation is required. In this research, I used Natural language processing (NLP) to do sentiment analysis on Covid-19 tweets in this study. The ability to identify Covid-19 emotions from tweets would enable more educated judgments to be made about how to handle the present pandemic scenario. Tweets are categorized as negative, positive, or neutral. This study attempts to evaluate and illustrate the global impact of the coronavirus (COVID-19) by using sentiment analysis techniques and methodologies on the Twitter dataset to comprehend both extremely favorable and very negative public opinion throughout the world.
Διαθεματική προσέγγιση του ηλιακού συστήματος με ψηφιακές και ρομποτικές διατάξεις

The purpose of this study was to examine the effects of the use of digital tools and robotic devices, in enhancing critical thinking and motivation for learning, in educational practice of natural sciences in high school. More specifically, in the interdisciplinary approach of the solar system were examined a) the misconceptions of students about the celestial bodies that exist in our solar system, b) the factors that affect the creation of meanings and c) the role of digital technologies (educational software and simulations) and educational robotics in the reconstruction of students' misconceptions about the solar system and the correct construction of concepts of the macrocosm. Based on these axes and the size of the research subject, we chosen the methodology of the case study in order to investigate the research questions asked in it. 10 students of the 2nd class of high school participated in this qualitative research. The data were collected through interviews and worksheets, completed by the students during the research and from the structures created by the students during the research process. At the end of the research are listed the limitations of the research and the results obtained from the analysis of the data, which are considered satisfactory, while the majority of participants agreed that the use of simulations and educational robotics in science teaching make the course more interesting and visualize situations and events that are impossible to represent in any other way.
Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose

We study the problem of learning to estimate the 3D object pose from a few labelled examples and a collection of unlabelled data. Our main contribution is a learning framework, neural view synthesis and matching, that can transfer the 3D pose annotation from the labelled to unlabelled images reliably, despite unseen 3D views and nuisance variations such as the object shape, texture, illumination or scene context. In our approach, objects are represented as 3D cuboid meshes composed of feature vectors at each mesh vertex. The model is initialized from a few labelled images and is subsequently used to synthesize feature representations of unseen 3D views. The synthesized views are matched with the feature representations of unlabelled images to generate pseudo-labels of the 3D pose. The pseudo-labelled data is, in turn, used to train the feature extractor such that the features at each mesh vertex are more invariant across varying 3D views of the object. Our model is trained in an EM-type manner alternating between increasing the 3D pose invariance of the feature extractor and annotating unlabelled data through neural view synthesis and matching. We demonstrate the effectiveness of the proposed semi-supervised learning framework for 3D pose estimation on the PASCAL3D+ and KITTI datasets. We find that our approach outperforms all baselines by a wide margin, particularly in an extreme few-shot setting where only 7 annotated images are given. Remarkably, we observe that our model also achieves an exceptional robustness in out-of-distribution scenarios that involve partial occlusion.
Comparison Of Horspool And Not So Naïve Algortihm In Making A Dictionary For Indonesian-Aceh Based On Android

Acehnese is one of the many languages ??in Indonesia. To learn the Acehnese language, a dictionary is needed that can make it easier for someone to communicate. The dictionary can be in the form of a book or an application. In this study, we will discuss how to do a string match in the Indonesian – Acehnese Dictionary application. The string matching algorithm used is the Horspool algorithm and the Not So Nave algorithm. This study will compare the time complexity (O) and running time of the Horspool algorithm and the Not So Nave algorithm. This study shows that Horspool's algorithm is slower than the Not So Nave algorithm. This is shown from the running time obtained, namely, the Horspool algorithm has an average running time of 26.2 ms, while the Not So Nave algorithm has an average running time of 18.9 ms. Horspool's algorithm has a complexity of O(n) and Not So Nave has a complexity of O(mn).
Autoencoder Neural Network Approaches for Anomaly Detection in IBOVESPA Stock Market Index

Anomalies are patterns in data that do not conform to a well-defined notion of normal behavior. Anomaly detection has been applied to many problems such as bank fraud, fault detection, noise reduction, among many others. Some approaches to detect anomalies include classical statistical econometric methods such as AutoRegressive Moving Average (ARMA) and AutoRegressive Integrated Moving Average (ARIMA) approaches. More recently, with the progress of artificial intelligence and more specifically, machine learning, new algorithms such as one-class support vector machines, isolation forest, gradient boosting, and deep neural networks were applied to such tasks. This paper focuses on propose an anomaly detection framework for the Índice da Bolsa de Valores de São Paulo (IBOVESPA). It is a major stock market index that tracks the performance of around 50 most liquid stocks traded on the São Paulo Stock Exchange in Brazil. Exploring unsupervised autoencoder neural network algorithms, we compare the long short-term autoencoder, bidirectional long short-term autoencoder, and convolutional autoencoder models, aiming to explore the performance of these architectures for anomaly detection. Due to the ability of autoencoders to learn a compressed representation of their respective input, we train these models with standard data by minimizing the mean absolute error (MAE) loss function and evaluate them with anomalous inputs. We set a reconstruction error threshold, and in case that the reconstruction error of the test data sample is beyond it, anomalies are detected. Our results show that these models perform quite well and can be applied to real stock market data.
FAKTOR-FAKTOR YANG MEMPENGARUHI PRESTASI BELAJAR SISWA KELAS X DI SMAN 1 SUGIHWARAS BOJONEGORO

Factors are things, circumstanse, or events that play a role and affect things that will happen. The level student achievement depends on the factors that influence it. There are two factors that can influence, including factors contained within students (internal factors) and factors that exist from outside students (external factors). This study aims to determine the factors that influence the learning achievement of class X students at SMAN1 Sugihwaras Bojonegoro. This research is quantitative, the research design uses multiple regression analysis. Data collection through documentation, questionnaires and report cards. The result of the research is a regression analysis model formed by predictor variables that have a significant influence on student learning outcomes, namely physical variables, interest variables, motivation variables and family environment variables. Variables that do not have a significant effect on the model are the IQ, the school environment variables and the community environment variables. The conclusio of the study is that the factors that influence the learning outcomes of class X students at SMAN 1 Sugihwaras Bojonegoro are physical, interest, motivational  and family environmental.
Factors Affecting Learning Satisfaction of Nursing Students who participated in Online Classes

AbstractThis study aimed to investigate the correlations between self-directed learning strategy, learning flow,teaching presence, and learning satisfaction, and to examine the factors affecting on learningsatisfaction of nursing students who participated in online classes. The cross-sectional descriptive studywas carried out using a self-report questionnaire created in Google Forms for 110 nursing students fromMay 5 to May 30, 2020. Statistical data analyses of the frequency, descriptive statistics, two-independentsamples t-test, Pearson’s correlation coefficients, and stepwise multiple regression analysis wereperformed using IBM SPSS statistics version 25. The participants’ mean scores for self-directed learningstrategy, learning flow, teaching presence, and learning satisfaction were 4.08±0.56 (range, 1-5),3.28±0.74 (range, 1-5), and 4.15±0.64 (range, 1-5), and 4.18±0.75 (range, 1-5) respectively. Learningsatisfaction showed significant positive correlations with self-directed learning strategy (r=.55, p<.001),learning flow (r=.62, p<.001) and teaching presence (r=.86, p<.001). The results of stepwise multipleregression analysis showed teaching presence were main factors affecting learning satisfaction in nursingstudents. The regression analysis indicated that the explanatory power of this regression model wasroughly 74.0%. Therefore, to improve the learning satisfaction of nursing students in online classes, a varietyof methods should be explored and applied to enhance teaching presence. This study can be used toestablish an online learning management system to improve learning satisfaction
The Opportunities and Challenges of Addressing Hate Speech with Artificial Intelligence [Submission to the OSCE Representative on Freedom of the Media #SAIFE Public Consultation]

This paper is a submission to the OSCE Representative on Freedom of the Media’s #SAIFE Public Consultation (2020). It examines the complex relationship between Artificial Intelligence (AI) and hate speech: how AI can be used to create and disseminate – but also to detect and curb – hate speech. This complex relationship is one of the main areas of concern in the #SAIFE project. This submission explores a range of opportunities and challenges concerning the interplay between free speech, hate speech and AI. In particular, it explores three challenges and opportunities which require further study and reflection: the differentiated roles and responsibilities of State and private actors in a broader, human rights-driven and multi-stakeholder approach; the need for non-discriminatory policies and practices in the design and implementation of measures to block/remove hate speech; and the broader effects on public debate where AI is used to moderate and censor expression.
Explore, Analyse and Translate Multilingual Wikidata Properties

Workshop Presentation at ContribuLing 2021 on June 4, 2021Wikidata is highly evolving. New properties are proposed, discussed, created and deleted regularly. It is highly difficult to know the latest information on the total count of properties, new properties, new data types, new WikiProjects, supported languages etc. unless contributors write (complex) queries on the Wikidata query service. Users, especially newcomers who wish to contribute do not know various ways by which they can document and contribute. Take, for example, users often ask the questions how to document a historical monument, person, book, software, etc. WDPropp provides a visual interface to answer such questions. It also helps the users in understanding and improving multilingual information related to Wikidata properties. Every property has three major pieces of information that require to be translated: label, description, and aliases. A property may have none, some, or all of this information in any given language. Thus for any given language, properties can be therefore be separated into two categories: translated properties and untranslated properties. A contributor may wish to focus on translating the labels or a particular set of properties like those belonging to a particular property class or a WikiProject. WDProp helps contributors in finding properties that require translation. The users can also visualize the historical translation process and even detect possible vandalism. Finally, users can also search relevant WikiProjects related to their domain.
Contrast Between Biological and Artificial Neural Networks

This talk will be about the differences between Artificial Neural Networks (ANNs) and Biological Neural Networks (BNNs). ANNs refer to a computing system, the fundamental composition of which is analogous to biological neural networks. As ANNs are a sparse but efficient representation of BNNs, there exist a lot of differences. The primary differences between ANNs and BNNs are rooted in network topology. Differences in network configuration, information processing, and energetic efficiency are all governed to an extent by network topology. Specifically, we provide a number of specific distinctions between ANNs and BNNs: representational complexity, topological complexity, and the role of network topology in energy consumption. One overarching theme of this talk is how ANNs can benefit from heterogeneity in network topology by including features such as functional modules, biologically realistic learning rules, and rich club connectivity.
IMPROVING THE ABILITY OF STUDENTS OF CLASS III A ELEMENTARY SCHOOL 0804 BOTUNG IN ANSWERING THE CONTENTS OF FAIRYTALE USING INDONESIAN LANGUAGE GOOD THROUGH GUIDANCE AND TRAINING T.P 2020/2021

The purpose of this study was to improve Indonesian language learning outcomes through Guidance and Training in class IIIA SD Negeri 0804 Botung in the 2020/2021 academic year. Observations of learning outcomes and observations of the learning process can be concluded that learning with the application of the Guidance and Exercise method can improve Indonesian language learning outcomes in class III Semester 1 SD Negeri 0804 Botung for the 2020/2021 academic year. It is proven that the average value of the class in the pre-cycle is 62.67 with 37% learning completeness in the first cycle to 75 (70%) and in the second cycle it becomes 85.33 with 87% complete. Thus, the hypothesis proposed by the researcher has been proven true
Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation

We study the problem of learning a range of vision-based manipulation tasks from a large offline dataset of robot interaction. In order to accomplish this, humans need easy and effective ways of specifying tasks to the robot. Goal images are one popular form of task specification, as they are already grounded in the robot's observation space. However, goal images also have a number of drawbacks: they are inconvenient for humans to provide, they can over-specify the desired behavior leading to a sparse reward signal, or under-specify task information in the case of non-goal reaching tasks. Natural language provides a convenient and flexible alternative for task specification, but comes with the challenge of grounding language in the robot's observation space. To scalably learn this grounding we propose to leverage offline robot datasets (including highly sub-optimal, autonomously collected data) with crowd-sourced natural language labels. With this data, we learn a simple classifier which predicts if a change in state completes a language instruction. This provides a language-conditioned reward function that can then be used for offline multi-task RL. In our experiments, we find that on language-conditioned manipulation tasks our approach outperforms both goal-image specifications and language conditioned imitation techniques by more than 25%, and is able to perform visuomotor tasks from natural language, such as open the right drawer and move the stapler, on a Franka Emika Panda robot.
THE ISSUES OF THE IMPLEMENTATION OF CEFR IN INDONESIA

Teaching English in Indonesia is still based on the 2013 curriculum. The government as the curriculum designers have not seen the potential use of CEFR as an English language learning curriculum. However, the CEFR has been used in several educational institutions even though it is not an official curriculum. This paper aimed to reveal the issues of the implementation of CEFR in Indonesia through literary studies. Several journal were involved as the sources of the data. The findings indicated that there were several issues in Indonesia that made CEFR could not be implemented in Indonesia. It required an adjustment to local needs that exist in Indonesia, and the material taught must also be able to adapt to Indonesian culture.
Research on University Accounting Teaching Innovation Based on big data

Objectives: With the arrival of the era of big data, the use of cloud computing technology has the characteristics of large capacity, variety and speed. Big data makes the teaching of financial accounting more convenient and efficient. Methods: It is an effective way to realize the goal of modernization of education in China by paying attention to the development and application of the reform of accounting teaching in Universities under the educational technology of big data. Results: This paper from the transformation of the university accounting education concept, in the era of big data under the background of accounting education in Colleges and universities how to give full play to the advantages of cloud computing, cloud platform construction of accounting education, then explore the reform of accounting education mode of. Conclusion: Therefore, this paper from the perspective of big data technology background, starting from the reform of the teaching mode of accounting and accounting industry background, considering the direction from the accounting personnel training, put forward the corresponding countermeasures for accounting teaching methods and teaching contents, promoting college accounting teaching reform.
Diversity-Driven Combination for Grammatical Error Correction

Grammatical error correction (GEC) is the task of detecting and correcting errors in a written text. The idea of combining multiple system outputs has been successfully used in GEC. To achieve successful system combination, multiple component systems need to produce corrected sentences that are both diverse and of comparable quality. However, most existing state-of-the-art GEC approaches are based on similar sequence-to-sequence neural networks, so the gains are limited from combining the outputs of component systems similar to one another. In this paper, we present Diversity-Driven Combination (DDC) for GEC, a system combination strategy that encourages diversity among component systems. We evaluate our system combination strategy on the CoNLL-2014 shared task and the BEA-2019 shared task. On both benchmarks, DDC achieves significant performance gain with a small number of training examples and outperforms the component systems by a large margin. Our source code is available at https://github.com/nusnlp/gec-ddc.
D-LSD: A Distorted Line Segment Detector for Calibrated Images

In this paper, we present an algorithm for the detection of line segments directly on the original, distorted images captured by calibrated wide-angle, fisheye and omnidirectional cameras. Distorted line segments are detected as convex polygonal chains of connected straight lines and then validated as the projection of 3D lines. This last validation step is our main contribution, which is formulated in a generic way in order to allow the detection of line segments from calibrated central projection vision systems and without requiring the rectification of the whole image. We evaluate our method with real images from a publicly available dataset and compare it with state-of-the-art alternatives, achieving comparable line detection performance without requiring image rectification. Additionally, we provide an open source reference implementation.
Theoretical research suggests hypotheses for experimental science

Theoretical research is commonlyassociated with equations and theories associated with theoretical physics andchemistry, and more recently biology. In these scenarios, theoreticalresearch’s main roles lie in discovering new mechanisms and fundamentalprinciples in areas of science where experiments are either hard to perform orare precluded. These areas include high energy particle physics, weatherprediction, and cosmology. But, there is also another aspect of theoreticalresearch that is less often appreciated. Perhaps, it is not even classified astheoretical research. Every research project that has been conceived isdependent on prior knowledge. This is known as theoretical foundation of theproject. Importantly, peer reviewers of grant proposal often heavilyscrutinized this aspect of the proposal to understand and ascertain if theproposed idea is feasible, and deserve further review. Although not all novelideas could be assessed this way, the approach of checking the theoreticalfoundation of a research proposal has served the scientific community well overthe past couple of decades. On the other hand, in the daily practice ofscience, researchers often use their foundational knowledge of the subjectmatter to contemplate about possible outcomes of their experiments. In somecases, these outcomes could be hypotheses on which an experimental programme inscience could be built. Hence, theoretical research need not be so esotericsuch as those involving convoluted equations describing some fundamentalprinciples governing the universe. It could be mundane, and lay the foundationof a research proposal, or in the daily practice of science. Most importantly,theoretical research could be the tool on which we propose possible results ofan experiment, or it could suggest new hypotheses for an experimentalprogramme.