Reviewerly/exHarmony huggingface dataset:
E-Magazine Based on Augmented Reality Digestive as Digital Learning Media for Learning Interest

The obstacle faced in the learning process so far is the low interest in student learning. Many factors affect interest in learning, one of which is the lack of variety of teaching materials or learning media used by teachers in schools. The use of augmented reality (AR) in education is an important topic of research although its use is still in its infancy. The purpose of this research is to develop learning media, AR-based e-magazine to increase students' interest in learning high school students, on the material of the digestive system. The development of the e-magazine is carried out using the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. The parameters in this study are the quality of the e-magazine and interest in learning. Testing interest in learning is done using an interest questionnaire given to students for the control class consisting of 36 students and the experimental class consisting of 36 students. The results of the development of AR-based e-magazines are relatively good. This can be seen from the validation results with a very valid category. Based on the didactic aspect, a score of 3.82 was obtained in the very valid category, the constructive aspect was 3.85 in the very valid category and the technical aspect was 3.78 in the very valid category. The results showed that the use of AR-based e-magazines increased interest in learning. Interest in learning obtained a score of 0.342 in the high category. Thus, it can be concluded that the use of AR-based e-magazines as digital learning media can be used in the learning process.
Improving Zero-Shot Cross-lingual Transfer Between Closely Related Languages by Injecting Character-Level Noise

Cross-lingual transfer between a high-resource language and its dialects or closely related language varieties should be facilitated by their similarity.However, current approaches that operate in the embedding space do not take surface similarity into account.This work presents a simple yet effective strategy to improve cross-lingual transfer between closely related varieties.We propose to augment the data of the high-resource source language with character-level noise to make the model more robust towards spelling variations.Our strategy shows consistent improvements over several languages and tasks: Zero-shot transfer of POS tagging and topic identification between language varieties from the Finnic, West and North Germanic, and Western Romance language branches.Our work provides evidence for the usefulness of simple surface-level noise in improving transfer between language varieties.
Practicing Error Analysis Techniques to Enhance Academic Essay Writing Skills

English language teachers are trying different teaching methods, approaches and techniques to improve language skills of ESL and EFL learners, and this study focused on practicing error analysis teaching techniques to enhance academic essay writing skills.This action research was conducted in order to explore the effectiveness of practicing error analysis techniques to enhance academic writing skills of intermediate level learners.A pre-test, a post-test and a feedback questionnaire were used to collect data, and it was analyzed by mixed methods.The students' results of tests, their performance during practice activities in the class and responses to the feedback questionnaire demonstrated that error analysis teaching techniques were proved very effective and successful in improving academic essay writing.Learners' experience in this class improved their knowledge, understanding and performance in error analysis and academic writing as well as increased their level of motivation and self-confidence.That was why, the learners performed well during the practice error analysis activities in the class and in the final test.All these results and responses showed better learning outcomes through practicing error analysis teaching techniques to enhance academic essay writing of the learners at intermediate level
The Assessment of End-Tidal Capnography Waveform Interpretation and Its Clinical Application for Emergency Residents in Malaysia: A Cross-Sectional Study

Aim: This study aimed to assess the adequacy of knowledge among emergency residents (ERs) and determine the correlation between the knowledge and practice of capnography waveform (CW). Materials and Methods:This 6-month questionnaire-based cross-sectional study was conducted at the emergency department of a hospital.All ERs were included in this study.Respondents' knowledge was evaluated by exploring their interpretation of CW according to clinical situations and their ability in making appropriate clinical decisions and management.Pearson correlation statistic was used to determine the correlation between knowledge and practice.Results: This study included 48 ERs.The mean scores for knowledge and practice (± standard deviation) were 5.69±1.82and 4.40±1.8,respectively.No correlation was found between knowledge and practice (r=0.186,p=0.207). Conclusion:ERs' ability to interpret CW was satisfactory.However, understanding the relevance of the CW findings and subsequent clinical management was poor.
On DIY cloth face masks and scalar relationships in design

In this paper, we take the case of Do-It-Yourself (DIY) face masks as an entry point to questions of scale and scalar relations in design.We provide two example scalar trajectories that illustrate how DIY face masks -as everyday design artefacts -are in continuous shaping and re-shaping through various forms of active use and design.We also point out how scalar relations manifest in knowledge sharing and circulation of know-how, as DIY masks emerge in a world facing the same COVID-19 virus but within different local realities and relationships.
Understanding Demotivating Factors on EFL Students in Online Learning

Demotivation is a complex problem that many foreign language (FL) learners faced, and it is caused by a variety of factors including the teacher, poor self-esteem, and previous bad language learning experiences(BABA KHOUYA, 2018). This study aimed to understand the demotivating factors on EFL students in online learning. The participants of this study were three students of the third grade of senior high school in Karawang. This study used a qualitative case study design to address the research objectives. The data for this study was gathered through semi-structured interview questions as an instrument of this study. The collecting data was obtained through Google meeting. The result of this study revealed that there are two demotivating factors on EFL students in online learning. Those are the teacher’s methods and learning material. In this case, the researcher found out that those factors are from the external factors. In the teacher’s method, there are two aspects that demotivate EFL students in online learning. The first aspect is because the teacher always speaks English all the time during learning. The second is because the teacher did not evaluate the students’ assignments. The last factor is the learning materials, the students get difficult to understand the materials during online learning.&#x0D; &#x0D; Keywords: demotivation factors, online learning.
Hype or Horror – Potentials and Hurdles of Blockchain Technology based on Legal Framework Conditions in Germany

This article summarizes the current status of the legal challenges facing the blockchain technology in Germany.Similar to the world wide web, blockchain represents a kind of basic technology on which new platforms and business models can be created.However, the question arises whether the German legal system is fundamentally capable of meeting the challenges posed by such decentralized technology.In particular concerning criminal offences or the new basic data protection regulation.It is questionable how the current negative headlines (e.g., silk road) will affect cryptocurrencies in the long term and, as a result, possibly also blockchain technology, not only concerning illegal content such as child pornography.
The New Modification of the Approximating Functions Method for Cloud Computing

Abstract The approach for building cloud-ready fault-tolerant calculations by approximating functions method, which is an analytical-numerical part of Volterra integral equation method for solving 1D+T nonlinear electromagnetic problems, is presented. The solving process of the original algorithm of the method is modified: it is broken down into the sequential chain of stages with a fixed number of sequential or parallel steps, each of which is built in a fault-tolerant manner and saves execution results in fault-tolerant storage for high availability. This economizes RAM and other computer resources and does not damage the calculated results in the case of a failure, and allows stopping and starting the calculations easily after manual or accidental shutdown. Also, the proposed algorithm has self-healing and data deduplication for cases of corrupted saved results. The presented approach is universal and does not depend on the type of medium or the initial signal. Also, it does not violate the natural description of non-stationary and nonlinear features, the unified definition of the inner and outer problems, as well as the inclusion of the initial and boundary conditions in the same equation as the original approximating functions method. The developed approach stress-tested on the known problems, stability checked and errors compared.
Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models

Can we get existing language models and refine them for zero-shot commonsense reasoning?This paper presents an initial study exploring the feasibility of zero-shot commonsense reasoning for the Winograd Schema Challenge by formulating the task as selfsupervised refinement of a pre-trained language model.In contrast to previous studies that rely on fine-tuning annotated datasets, we seek to boost conceptualization via loss landscape refinement.To this end, we propose a novel self-supervised learning approach that refines the language model utilizing a set of linguistic perturbations of similar concept relationships.Empirical analysis of our conceptually simple framework demonstrates the viability of zero-shot commonsense reasoning on multiple benchmarks. 1
Analysis of Emotion using Machine Learning on Social Media Platform

In this rapid, non-stop world everything and everyone goes on dramatically and enthusiastically.And people don't usually express themselves to their loved ones, this creates a sense of unreliability which leads to harm.Instead of speaking to their near and dear ones, they usually express themselves using social media.Usually, a lot of time people don't even know that they are depressed or have anxiety.Which leads to unreasonable actions like suicide?However, if we could know how they feel and somewhat solve their dilemma by consulting doctors and help groups.Here we are using machine learning algorithms to analyze their thoughts using polarity and subjectivity.Polarity determines emotions expressed in a sentence, emotions are closely related to sentiments.The strength of sentiment or opinion is typically linked to the intensity of certain emotions, e.g., joy and anger.And Subjectivity expresses some personal feelings, views, or beliefs.We would be able to find out if their views are positive or negative, further we could diagnose their mental condition and act accordingly.
Interactive Design in Enhancing User Experience in Museum

A museum can be a suitable context to experiment with a new interaction technique which guiding visitors and improving their experience for a successful learning environment also to enrich user experience. Mobile technology is used to enhance user experience in a cultural environment that offers more information than physically exposed technology. Therefore, the introduction of augmented reality (AR) that allow visitor to experience interaction in a way that increased knowledge, learning and give additional information with any artefact exhibitions they interact in the museum is applied in this project. The objectives of this research are to develop an interactive design in enhancing user experience in artefact display and evaluate user experience. ADDIE Model is applied as the methodology that consists of five phases which are analysis, design, development, implementation, and evaluation. Principles of Interactive Design is utilized in designing the mobile AR application and developed using Unity, Blender and Vuforia. User Experience evaluation method is conducted where findings shows that most respondents are satisfied, and usage of mobile AR has enhanced their experience through navigation during museum visit that also give a lot of new insight on historical artefact that being displayed.
Deconvolutional Density Network: Modeling Free-Form Conditional Distributions

Conditional density estimation (CDE) is the task of estimating the probability of an event conditioned on some inputs. A neural network (NN) can also be used to compute the output distribution for continuous-domain, which can be viewed as an extension of regression task. Nevertheless, it is difficult to explicitly approximate a distribution without knowing the information of its general form a priori. In order to fit an arbitrary conditional distribution, discretizing the continuous domain into bins is an effective strategy, as long as we have sufficiently narrow bins and very large data. However, collecting enough data is often hard to reach and falls far short of that ideal in many circumstances, especially in multivariate CDE for the curse of dimensionality. In this paper, we demonstrate the benefits of modeling free-form conditional distributions using a deconvolution-based neural net framework, coping with data deficiency problems in discretization. It has the advantage of being flexible but also takes advantage of the hierarchical smoothness offered by the deconvolution layers. We compare our method to a number of other density-estimation approaches and show that our Deconvolutional Density Network (DDN) outperforms the competing methods on many univariate and multivariate tasks. The code of DDN is available at https://github.com/NBICLAB/DDN
The effects of The geomorphology and geologic parent materials on the natural environment assessment in the Burdur-Gölhisar basin, SW Anatolia

Burdur-Gölhisar basin which is located in the western part of Lakes Region of Mediterranean region is found between the Menderes metamorphic masse and the eastern continuation of Taurus Mountain range.The present-day topography of this area was mainly formed by the vertical tectonic movements occurred between Middle Miocene and Last Pleistocene; the intensity of erosion and sedimentation processes have been controlled by not only fluvial process but also lithologic properties of parent materials.The basement of the study area is mainly composed of peridotite-serpentine that erupted and spread on the bottom of the Tethys Ocean.Clay-rich calcareous sediments forming limestones were accumulated on the peridotite-serpentine.They were emerged from the Tethys oceans as the result of tectonic movement occurred at the end of the Mesozoic era.The sandstone-conglomerate terrain was formed on the depression areas in the north-western part of the present-day Söğüt Mountains.The clayey and calcareous materials were accumulated in the neogene lake facies in the Burdur-Gölhisar basins.The main fault line passing in the east of the Burdur basin was rejuvenated six times.Thus, the faulted topography and erosion surfaces especially on marl deposit were developed according to the base level of the Burdur Basin and Gölhisar basins.The physical and chemical properties of the geologic parent materials have affected considerably soil formation and their physical and chemical traits.Namely, red Mediterranean soils (Alifsol/ Luvisol) have developed along the cracks and bedding surfaces of limestones on the sloping area and on the bottom of the karstic depression.The flat land lying between Gölhisar and Burdur Basins is the main spreading areas of chestnut soil (Mollisol).Rendzina soils have developed on the upper erosion surfaces on the marl deposit.The productive vegetation cover is found on the soft and inclined layered marl deposit, while poor vegetation cover is common on the compact and horizontally layered marl deposit.Sparse vegetation communities are seen on the exposed hard and partly weathered serpentine, whereas productive forest trees are found on the deeply weathered serpentine.At the same time, productive vegetation communities are common on the sandy soil developed on the sandstone and conglomerate.Vineyards, levanter and rose gardens are found on the dejection fan and dejection cones on the edges of the fault scarps in the Burdur Basin.
The Grammar-Learning Trajectories of Neural Language Models

The learning trajectories of linguistic phenomena in humans provide insight into linguistic representation, beyond what can be gleaned from inspecting the behavior of an adult speaker.To apply a similar approach to analyze neural language models (NLM), it is first necessary to establish that different models are similar enough in the generalizations they make.In this paper, we show that NLMs with different initialization, architecture, and training data acquire linguistic phenomena in a similar order, despite their different end performance.These findings suggest that there is some mutual inductive bias that underlies these models' learning of linguistic phenomena.Taking inspiration from psycholinguistics, we argue that studying this inductive bias is an opportunity to study the linguistic representation implicit in NLMs.Leveraging these findings, we compare the relative performance on different phenomena at varying learning stages with simpler reference models.Results suggest that NLMs exhibit consistent "developmental" stages.Moreover, we find the learning trajectory to be approximately one-dimensional: given an NLM with a certain overall performance, it is possible to predict what linguistic generalizations it has already acquired.Initial analysis of these stages presents phenomena clusters (notably morphological ones), whose performance progresses in unison, suggesting a potential link between the generalizations behind them.
Outlier based Human Fall Detection using One-Class Classification

Objective: This work aims to develop a human fall detection method that is trained using data of routine movement of people only collected from accelerometer sensor to stay away from irregular fall detection model.This work also aims to analyze the effect of calculated features on the fall detection model.Methodology: In the proposed method, The fall detection model is built using one-class classification.At first, data of accelerometer sensor in three directions has been used to detect the fall events.Five feature vectors i.e. resultant, variance, standard deviation, Root Mean square, and Euclidean Norm have been calculated.These four features along with one class SVM have been used to build fall detection model in ways (1) Using only resultant features only (2) Using all calculated features.This model was trained using only activities of daily living (ADL) and tested on both daily living activities and fall activities.Findings: It is found that when the model was built using all calculated features, the sensitivity was 100% and specificity was 94.92% when mobile is in the pocket and; sensitivity was 100% and specificity was 93.61% when mobile is in handbag which is better than when the model was built using resultant features only.We have also studied the effect of individual features on this fall detection model and it is found that variance played a very important role to classify fall activities and ADL activities.Novelty -The proposed fall detection method is built using one-class classification so that the proposed model will be reliable to detect falls in real life.In the proposed work, the effect of features on the fall detection model is also analyzed.
Short Video Representation Learning Based on Convolution Network with Text Attention Mechanism

Learning video representation with convolution network methods has been proven useful to learn video representation across many video understanding tasks, including video classification, action recognition and object detection. However, traditional convolution methods fail to take the text feature of the video into consideration, which may contain large amount of useful information for final video representation. Besides, current short video often contains a lot of human language from the start to the end. In this paper, we propose a novel neural network system that learns video representation by aggregating information from both video features and text features of the video. This network, named Conv-Text Net, can effectively learn video representation with its text information by a text attention mechanism. Results show that Conv-Text Net achieves competitive performance in video classification task on Short-Video datasets. We provide analysis towards the behavior of our model and show its robustness to errors in proposals. Experiment shows that our method performs well on video classification task and achieves good score on Short Video data set.
Optimization the Effect of Decanter Cake with Fermented Fertilizer of Cow Urine in Edamame Growth and Yield

Decanter cake (DC) is one of the oil palm mill solid wastes that contains macro and micronutrient elements especially high Nitrogen levels.Using it as organic fertilizer could substitute half of the chemical fertilizer dose.The alternative decrease in chemical fertilizers requires nutrients from organic materials which quickly absorbed, mainly fermented cows liquid fertilizer (FLF).This research was design with Randomized Complete Block Design to evaluate the combination of DC and FLF on edamame growth and yield.The observed variables of the growth parameter are the number of branches, leaves, pods, filled pods per plant, fresh pod weight, and yield per unit area.Data were processed using analysis of variance and DMRT.The result showed, for growth parameter, the highest number of branches and leaves per plant was achieved at DC dose 20 tons ha-1 without FLF and significant difference with DC 5 ton ha-1 with or without FLF of cow urine.Yield component, number of pods, and filled pods were achieved at DC 10 ton ha-1 with FLF 60% of cow urine concentration.