Reviewerly/exHarmony huggingface dataset:
Exploring the Motivation of Pupils towards the Implementation of QR Codes in Pronunciation Learning

The implementation of Quick Response (QR) codes in education has become increasingly popular. Previous studies proved that it can increase the motivation of pupils in learning. Hence, this research investigates the difference of pupils’ motivation levels in learning pronunciation after the implementation of QR codes. We examine the motivation of pupils in terms of four aspects: interest, competence, perceived choice and sense of belonging. Accordingly, we propose a QR code model, which is linked to Google Forms that contain audio recordings for pronunciation practice. With the proposed QR code activity, the pupils can record their versions of audio recordings of pronunciation and submit to their teacher for feedback. The participants are 90 year 4 pupils from a sub-urban Chinese primary school in Johor. We employ a pre-experimental research design in this research. We collect our data by using 2 research instruments: survey questionnaires and observation checklist. We administer the survey questionnaires before and after the implementation of QR codes to find out the changes in the motivation of pupils. We also utilise an observation checklist to examine the attitude of the participants during the implementation of QR codes in pronunciation learning. The findings of this research reveal a significant change in the pupils’ motivation towards the implementation of QR codes in pronunciation learning. Specifically, we find an increment of motivation in learning pronunciation as the pupils show interests in learning.&#x0D; &#x0D; Received: 6 October 2020 / Accepted: 12 December 2020 / Published: 17 January 2021
PEMBANGUNAN APLIKASI SEBAGAI MEDIA PEMBELAJARAN BANGUN RUANG TINGKAT SD/SMP DENGAN MENGGUNAKAN METODE MARKER AUGMENTED REALITY

Nowadays, the development of technology is growing so rapidly and the development can not be stopped. With a lot of advanced technology that popping up, various kinds of human work can be done in more innovative way. One of the example is in the field of education. Right now, there is a lot variety of learning media based technology, one of them is learning media using Augmented Reality technology or often shortened become AR. Application Development as Solid Shape Learning Media for Elementary School/Junior High School Level Using Marker Augmented Reality Method is one of utilization of the AR technology in the field of education, especially as learning media. By making solid shape learning media based Augemented Reality technology, students are expected to understand learning materials easily, and student’s learning interest can increased because the solid shape objects are build in attractive design colorful, and interactive to avoid boredom. Based on it, then this research become one of the new breakthrough in education field that utilizing the advancement of technology and help the teacher provide more interesting learning media. Meanwhile, the result of this research is an AR application and a solid shape mathematic book (handbook). Keywords : : Augmented Reality, Marker Augmented Reality, Learning Media, Solid Shape
WITHDRAWN: Enhancement of resolution and image reconstruction in digital image correlation

In Digital Image Correlation (DIC), high-resolution images are ideally capable of an optical complete field analysis method that can prevent displacement of a loaded object. Improving camera hardware is one way to increase the resolution. That can be costly, so another way to boost the picture is by increasing the resolution of the image through multiple image processing techniques. These methods are called Super-Resolution (SR). There are many ways to do this. This work introduces and explores the philosophy behind various approaches to super resolution. The purpose of this study was to explore whether super-resolution in a scene with moving objects and camera movement was feasible. Early on this picture recording, a step in many super-resolution methods explained in this study, became extremely important and a significant portion of the work was compared with image recording methods. There were reported data, and then two separate algorithms with high resolution were analysed into a data set, showing the probability of super resolution.
A Blockchain-based Forensic Model for Financial Crime Investigation: The Embezzlement Scenario

The financial crime landscape is evolving along with the digitization in financial services. In this context, laws and regulations cannot efficiently cope with a fast-moving industry such as finance, which translates in late adoption of measures and legal voids, providing a fruitful landscape for malicious actors. In parallel, blockchain technology and its promising features such as immutability, verifiability, and authentication, enhance the opportunities of financial forensics. In this paper, we focus on an embezzlement scheme and we provide a forensic-by-design methodology for its investigation. In addition, the feasibility and adaptability of our approach can be extended and embrace digital investigations on other types of schemes. We provide a functional implementation based on smart contracts and we integrate standardized forensic flows and chain of custody preservation mechanisms. Finally, we discuss the benefits and challenges of the symbiotic relationship between blockchain and financial investigations, along with future research directions.
EXPLORING THE INTEGRATIONS OF MALL INTO ENGLISH TEACHING AND LEARNING FOR INDONESIAN EFL STUDENTS IN SECONDARY SCHOOLS

The use of mobile technology for education is on the rise across the world, especially in Indonesia. However, there are concerns in making such an educational revolution as mobile technology may introduce negative side effects to the Indonesian students. Also, only a few studies have discussed the use of mobile technology in Indonesian secondary schools. Those circumstances potentially make many stakeholders doubt the proper implementation of mobile technology-based learning and the possible challenges, especially in English as a foreign language (EFL) classroom. Thus, this paper is aimed to provide theoretical insights into the implementation of mobile-assisted language learning (MALL) for secondary school students in EFL classrooms. A secondary source analysis through systematic review was employed to generate the findings, focusing on MALL and EFL primary research and government documents related to education in secondary schools. Based on the findings, this paper offers an improved teaching and learning approach based on collaborative learning and task-based language teaching (TBLT) to facilitate better implementation and integration of MALL in EFL classrooms. In addition, the findings also show possible challenges during the implementation of MALL in Indonesia for future considerations, such as the technology readiness of EFL stakeholders and teachers’ pedagogical issues related to mobile learning. Lastly, this paper is expected to raise awareness of MALL opportunities and open more paths for more primary research regarding the implementations of MALL in Indonesian EFL classrooms.
Types of Sentences Used by Male and Female Writers in Journal Article Abstracts

This study analyzed the types of sentences used in the Abstracts of Journal Articles written by 10 Female and 10 Male fully sourced from the Book Program of The First Annual International Seminar UNIMED Medan: November 19, 2016. Of the 195 sentences, 102 of which were written by females, and 92 by Males, all types of sentences are present. The occurrence of the types of sentence used by Females is Simple Sentence (50.1%), Complex Sentence (37.1%), Complex - Compound Sentence (6.8%), and Compound Sentence (4.9%). By Males, Simple Sentence is (50.%), Complex Sentence (41%), Compound Sentence (5.4%) and Complex- Compound Sentence (3.3%). Thus, this proves that the frequency is only different in Complex-Compound, i.e. by Females in the third place, but by Males in the fourth and Compound in the third. Overall, it can be concluded that gender has no relation with sentence types.
Learning to Adapt Multi-View Stereo by Self-Supervision.

3D scene reconstruction from multiple views is an important classical problem in computer vision. Deep learning based approaches have recently demonstrated impressive reconstruction results. When training such models, self-supervised methods are favourable since they do not rely on ground truth data which would be needed for supervised training and is often difficult to obtain. Moreover, learned multi-view stereo reconstruction is prone to environment changes and should robustly generalise to different domains. We propose an adaptive learning approach for multi-view stereo which trains a deep neural network for improved adaptability to new target domains. We use model-agnostic meta-learning (MAML) to train base parameters which, in turn, are adapted for multi-view stereo on new domains through self-supervised training. Our evaluations demonstrate that the proposed adaptation method is effective in learning self-supervised multi-view stereo reconstruction in new domains.
Human Decision Making in AI Augmented Systems: Evidence from the Initial Coin Offering Market

The growing consensus that human intelligence and artificial intelligence are complementary has led to Human-AI hybrid systems.As digital platforms incorporating human-AI hybrids, platform designers need to evaluate the influence of AI on human judgment, and how such hybrid systems perform.In this paper, we investigate: Are human decisions influenced by AI agents in high uncertainty environments, such as evaluating ICO projects?Under what situations are humans able to mitigate AI agents-induced errors?Our results suggest that in general, humans are influenced by AI agents.Humans tend to use AI as a filter to rule out low quality projects, while a high AI rating triggers human expert to apply their own judgment.
Differentially Oblivious Database Joins: Overcoming the Worst-Case Curse of Fully Oblivious Algorithms

Numerous high-profile works have shown that access patterns to even encrypted databases can leak secret information and sometimes even lead to reconstruction of the entire database. To thwart access pattern leakage, the literature has focused on oblivious algorithms, where obliviousness requires that the access patterns leak nothing about the input data.In this paper, we consider the Join operator, an important database primitive that has been extensively studied and optimized. Unfortunately, any fully oblivious Join algorithm would require always padding the result to the worst-case length which is quadratic in the data size N. In comparison, an insecure baseline incurs only O(R + N) cost where R is the true result length, and in the common case in practice, R is relatively short. As a typical example, when R = O(N), any fully oblivious algorithm must inherently incur a prohibitive, N-fold slowdown relative to the insecure baseline. Indeed, the (non-private) database and algorithms literature invariably focuses on studying the instance-specific rather than worst-case performance of database algorithms. Unfortunately, the stringent notion of full obliviousness precludes the design of efficient algorithms with non-trivial instance-specific performance.To overcome this worst-case performance barrier of full obliviousness and enable algorithms with good instance-specific performance, we consider a relaxed notion of access pattern privacy called (e, δ)-differential obliviousness (DO), originally proposed in the seminal work of Chan et al. (SODA'19). Rather than insisting that the access patterns leak no information whatsoever, the relaxed DO notion requires that the access patterns satisfy (e, δ)-differential privacy. We show that by adopting the relaxed DO notion, we can obtain efficient database Join mechanisms whose instance-specific performance approximately matches the insecure baseline, while still offering a meaningful notion of privacy to individual users. Complementing our upper bound results, we also prove new lower bounds regarding the performance of any DO Join algorithm.Differential obliviousness (DO) is a new notion and is a relatively unexplored territory. Following the pioneering investigations by Chan et al. and others, our work is among the very first to formally explore how DO can help overcome the worst-case performance curse of full obliviousness; moreover, we motivate our work with database applications. Our work shows new evidence why DO might be a promising notion, and opens up several exciting future directions.
The role of mobile technologies in shifting to digital

In this section, we have responses to Joo, Kim and Kim (2016)’s study, from two scholars with international and inclusion perspectives. They have discussed the current situation with mobile learning management systems and the current mobile learning experiences.
Predictive Attention Transformer: Improving Transformer with Attention Map Prediction

Transformer is a ubiquitous model for natural language processing and has also attracted wide attentions in other domains such as computer vision. The self-attention maps, learned independently for each layer, are indispensable for a transformer model to encode the dependencies among input tokens, however, learning them effectively is still a challenging problem. In this paper, we address this problem and propose a novel approach to improve self-attention through supplementary prediction modules. The underlying assumption is that the attention structures in the current layer should not be completely independent from those in the previous layer. Instead, we model their dependencies via a chain of prediction models that take previous attention maps as input to predict the attention maps of a new layer through convolutional neural networks. Specifically, we propose Predictive Attention Transformer and obtain significant performance gains for various kinds of tasks on top of multiple state-of-the-art models. On GLUE benchmark, the average performances of BERT-Base and BERT-Large are lifted by 4.1 and 2.5 points respectively. For machine translation, it improves the BLUE score of a vanilla Transformer consistently on IWSLT'14 De-En dataset with different model sizes. For ImageNet classification, we achieve significant improvement over a strong backbone model with comparable capacity.
EVALUATION OF HETEROCLARIAS QUALITY WITH INCREASED STORAGE TIME USING QUALITY INDEX METHOD (QIM)

The quality of Heteroclarias with increased storage time using Quality Index Method (QIM) was evaluated.140 live samples of Heteroclarias (average weight 230 + 7g) were used for the study.QIM was used to evaluate the key parameters in fish deterioration, twenty semi-trained assessors was used for the sensory evaluation.Data obtained were subjected to appropriate statistical tools using SPSS 16.0 version.Quality Index (QI) of Heteroclarias increases with increased post slaughter time when the fish was left at ambient temperatures (27 + 3°C) indicating decrease in fish quality with time.High correlation of (r=0.993) with a high coefficient of determination (R 2 =0.986) was obtained indicating that 98.6% of the variation in post slaughter time was explained by quality attributes; the regression equation was significant F(9, 130)=1080 (p<0.0001)indicating that it could be used for prediction and estimation of fish quality, the QI used in this study is therefore recommended to be used by relevant agencies for predicting fish quality.Practical Applications :Fish is highly perishable despite its nutritional value, consumers becoming increasingly interested in the quality of fish they consume and the physical attributes of the fish is very vital for on the spot assessment of fish quality.This study provide quality index that could be used for prediction and estimation of fish quality using sensory evaluation.
Conditional Attention for Content-based Image Retrieval

Deep learning based feature extraction combined with visual attention mechanism is shown to provide good results in content-based image retrieval (CBIR). Ideally, CBIR should rely on regions which contain objects of interest that appear in the query image. However, most existing attention models just predict the most likely region of interest based on the knowledge learned from the training dataset regardless of the content in the query image. As a result, they may look towards contexts outside the object of interest, especially when there are multiple potential objects of interest in a given image. In this paper, we propose a conditional attention model which is sensitive to the input query image content and can generate more accurate attention maps. A key-point detection and description based method is proposed for training data generation. Consequently, our model does not require any additional attention label for training. The proposed attention model enables the spatial pooling feature extraction method (generalized mean pooling) improves image feature representation and leads to better image retrieval performance. The proposed framework is tested on a series of databases where it is shown to perform well in challenging situations.
Machine Learning for Readability Assessment and Text Simplification in Crisis Communication: A Systematic Review

In times of social media, crisis managers can interact with the citizens in a variety of ways.Since machine learning has already been used to classify messages from the population, the question is, whether such technologies can play a role in the creation of messages from crisis managers to the population.This paper focuses on an explorative research revolving around selected machine learning solutions for crisis communication.We present systematic literature reviews of readability assessment and text simplification.Our research suggests that readability assessment has the potential for an effective use in crisis communication, but there is a lack of sufficient training data.This also applies to text simplification, where an exact assessment is only partly possible due to unreliable or non-existent training data and validation measures.
An Improved ORB Algorithm Based on Optimized Feature Point Extraction

The feature points extracted by the traditional ORB algorithm are not evenly distributed, redundant and have no scale invariance. To solve this problem, this paper improved the traditional ORB algorithm and proposed an optimized feature point extraction method. The image is divided into regions firstly. According to the total number of feature points to be extracted and the number of divided regions, the algorithm calculates the number of feature points to be extracted for each region, which solves the problem of feature point overlap and redundancy in the feature point extraction process. By constructing the image pyramid and extracting feature points on each layer, the problem that the feature points extracted by ORB algorithm do not have scale invariance is solved. The experimental results show that the feature points extracted by our algorithm are more uniform and reasonable without losing the accuracy of image matching, and the extraction speed is about 16% higher than that of the traditional ORB algorithm.
A modified optimization method for optimal control problems of continuous stirred tank reactor

‎Continuous stirred tank reactor (CSTR) is an important and constructive part in various chemical and process industries and therefore it is necessary to control the process in optimal temperature and concentration conditions. Because of the nonlinear nature and limits of the control input‎, ‎solving this problem is very difficult. To achieve a sub-optimal control policy for chemical processes‎, ‎we focused on a new construction model‎.  Then‎, ‎a two-phase algorithm‎, ‎denoted as modified sequential general variable neighborhood search (MSGVNS) algorithm based on three local searches that use efficient neighborhood interchange has been employed to solve CSTR problems numerically. The results of the proposed method show that its convergence to the exact solution is achieved by the accuracy comparable to other numerical algorithms in few times‎.
A New Benchmark for NLP in Social Sciences: Evaluating the Usefulness of Pre-trained Language Models for Classifying Open-ended Survey Responses

In order to evaluate transfer learning models for Natural Language Processing on a common ground, numerous general domain (sets of) benchmark data sets have been established throughout the last couple of years.Primarily, the proposed tasks are classification (binary, multi-class), regression or language generation.However, no benchmark data set for (extreme) multi-label classification relying on full-text inputs has been proposed in the area of social science survey research to this date.This constitutes an important gap, as a common data set for algorithm development in this field could lead to more reproducible, sustainable research.Thus, we provide a transparent and fully reproducible preparation of the 2008 American National Election Study (ANES) data set, which can be used for benchmark comparisons of different NLP models on the task of multi-label classification.In contrast to other data sets, our data set comprises full-text inputs instead of bag-of-words representations or similar.Furthermore, we provide baseline performances of simple logistic regression models as well as performance values for recently established transfer learning architectures, namely BERT (
The Impact of Mediating Fake News on Government Policies in Creating Socio-Political Stability and the Urgency of Literacy Education

The aimed of this study is to explore how fake news through the media can disrupt socio-political stability and impede the implementation of various government policies that are being introduced, and how significant efforts are being made by all parties to ensure that hoax coverage does not spread through all lines of social life by providing a literacy education formula in the context of knowledge challenges that are difficult to address.It also analyzes how social media are used to construct strategies that can cloud the atmosphere of socio-political life and public morality, which seem to give priority to pro-people ethics, as is the case in Indonesia.In this study, a qualitative approach and posttruth theory are used as analytical perspectives in the interpretation of topics such as Covid-19 news and details in the presidential election contest of 2019.This study will also document how, through the mediation of hoax reporting through social media, online social media representations such as Facebook, Instagram, WhatsApp, and other personal messaging applications are explored.So, of course, based on the findings of the preliminary observations, there needs to be a strategy for creating new, shifting narratives about different government policies based on relevant references, since modern media have an influence on the social, cultural, and political landscape of life.A proper media literacy and literacy analysis must also be carried out to see if offline communities with limited internet connectivity no longer accept the notion of 'hoax opinion' established in Indonesia.Therefore, literacy education would be able to reveal false knowledge that has spread through society and will correct any myths in social life.
Research on CAN Bus Anomaly Detection Based on LSTM AndResNet

Abstract With the rapid development of intelligent network vehicles, the safety of the vehicle network is taken seriously increasingly, the CAN as the most widely used car on-board network, its security problem has become one of the most important problemsin intelligent made car development, the communication of CAN bus network characteristic and the existing safety problems and analyzes the common means of attack, on the basis of this puts forward the anomaly detection model based on LSTM And ResNet, through the experimental results show that this method CAN effectively detect deception attack.