Reviewerly/exHarmony huggingface dataset:
The Effect of Collaborative Strategic Reading Toward Students Reading Skill

This article aimed to improve student’s skill in reading an English text. It is used Collaborative Strategic Reading (CSR). This method consist of 4 steps those are : Preview, Click and Clunk, Get the Gist and Wrap Up with the purpose is improving students reading comprehension by working in the group. The research subject was 8 grade of SMP 1 Jogoroto. There were 18 males and 14 females in the sample of research. There were two cycle pretest and posttest, the pretest was done before the implementation of Collaborative strategic reading in the class and the posttest was done after the implementation of CSR technique in the class. The students reading skill before the teacher aplly the Collaborative strategic raeding was only 50% it because of the students not really know the pronounciation well but after the implementation of CSR technique in the class and the teacher aplly the steps in CSR technique well(preview, Click and clunk, get the gist and wrap up) the students reading skill was 100%. In term the student’s participation, the students actively answer the question from the exercise by working in a group and pay attention to the teachers’ explanation(feedback).&#x0D; Keywords: Collaborative Strategic Reading, Reading Comprehension, Reading Skill
Bidirectional gated recurrent unit for improving classification in credit card fraud detection

&lt;p&gt;In recent years, the use of credit cards around the world has grown enormously. Thus, the number of fraud cases have also increased, resulting in losses of thousands of dollars worldwide. Therefore, it is mandatory to use techniques that are able to assist in the detection of credit card fraud. For this purpose, we have proposed a multi-level architecture, composed of four levels: authentication level, behavioral level, smart level and background processing level. In this paper, we focus on the implementation of the smart level. The aim of this level is to develop a classifier for the detection of credit card fraud, using bidirectional gated recurrent units (BGRU). The experiments, applied on well-known credit card fraud dataset from Kaggle, show that this model has peak performance compared to other proposed models, with 97.16% for accuracy rate and 99.66% for the area under the ROC curve.&lt;/p&gt;
Security and Privacy when Applying FAIR Principles to Genomic Information

Making data Findable, Accessible, Interoperable and Reusable (FAIR) is a good approach when data needs to be shared. However, security and privacy are still critical aspects. In the FAIRification process, there is a need both for de-identification of data and for license attribution. The paper analyses some of the issues related to this process when the objective is sharing genomic information. The main results are the identification of the already existing standards that could be used for this purpose and how to combine them. Nevertheless, the area is quickly evolving and more specific standards could be specified.
Machine Learning Empowered Content Delivery: Status, Challenges, and Opportunities

Trends such as increasing mobile device ownership and faster and more affordable internet speed have contributed to significant demands in media-based services on mobile devices. There has been an emphasis on content delivery networks to support media-based services. However, achieving high-performance content delivery in large-scale dynamic network environments is still operationally challenging, especially when we have to consider the diverse application requirements. One emerging research trend is to explore the use of machine learning techniques to enhance content delivery quality. in this article, we review and discuss existing state of the art machine learning-based approaches on enhancing content delivery performance. Discussions in this article will benefit readers and help identify future research agendas and opportunities.
Reactive Collision Avoidance Algorithm for UAV Using Bounding Tube Against Multiple Moving Obstacles

A reactive collision avoidance algorithm is proposed to enable safe autonomous flight in the presence of multiple dynamic obstacles.A position-controlled hexacopter equipped with a visual sensor obtaining obstacle information is considered as an Unmanned Aerial Vehicle (UAV) platform.The proposed method centers on the concept of bounding tube which intrinsically extends the static bounding box to incorporate forthcoming movement of the obstacles into the collision avoidance framework.The processing pipeline consists of separate components for each of the sequential tasks in obstacle sensing and tracking.Computation of a spherical bounding box for each obstacle is followed by discrete-time Kalman filtering for prediction of obstacle trajectory to detect potential collision.If the current course of UAV turns out highly likely to end in collision with any of the obstacles, the vehicle steers to an aiming point chosen from among the bundle of candidates produced by constructing a bounding tube that takes account of predicted obstacle motion.The bounding-tube-based aiming point generation extends seamlessly to the case with multiple moving obstacles through running in series with multi-obstacle track management that combines hierarchical clustering of sensory data points for obstacle identification and a simple geometric method for data association.Numerical simulations are conducted to verify the performance of the proposed collision avoidance algorithm.
RESEARCH POLICY IN LEADING RUSSIAN UNIVERSITIES: EFFECTS OF «NEW MANAGERIALISM»

This article analyzes the effects of «new managerialism» on the results of scientific research in universities. New managerialism implies decision-making based on quantitative indicators. This paper summarizes the international and Russian approaches to the assessment of scientific achievements at universities. It analyzes instruments for fostering scientific productivity, such as contracts with the faculty and the formation of centers of excellence, to identify the advantages and limitations of tools currently used at Russian research universities. Our analysis of international literature showed that evaluation of research results is becoming more complex as it is conducted regularly, not annually. The contract system indirectly affects the results of research but does not contain any strict quantitative requirements. Centers of excellence, which receive additional funding to accelerate the production of scientific knowledge, achieve this goal in most cases, but this system does not automatically create the Matthew effect. The article analyzes the specific characteristics of new managerialism in Russian universities by using the example of a program aimed at the creation of laboratories led by prominent scientists, the Project 5-100, and the system of contracts with the faculty. The study has revealed that these tools have contributed to the increase in publication activity. However, there are indications that this growth has reached its limits. Prospects of further development can reside in the use of a wider range of indicators to characterize the impact of scientific results on social and economic development and in increasing the share of long-term contracts.
Deep Feature Learning with Manifold Embedding for Robust Image Retrieval

Conventionally, the similarity between two images is measured by the easy-calculating Euclidean distance between their corresponding image feature representations for image retrieval. However, this kind of direct similarity measurement ignores the local geometry structure of the intrinsic data manifold, which is not discriminative enough for robust image retrieval. Some works have proposed to tackle this problem by re-ranking with manifold learning. While benefiting better performance, algorithms of this category suffer from non-trivial computational complexity, which is unfavorable for its application to large-scale retrieval tasks. To address the above problems, in this paper, we propose to learn a robust feature embedding with the guidance of manifold relationships. Specifically, the manifold relationship is used to guide the automatic selection of training image pairs. A fine-tuning network with those selected image pairs transfers such manifold relationships into the fine-tuned feature embedding. With the fine-tuned feature embedding, the Euclidean distance can be directly used to measure the pairwise similarity between images, where the manifold structure is implicitly embedded. Thus, we maintain both the efficiency of Euclidean distance-based similarity measurement and the effectiveness of manifold information in the new feature embedding. Extensive experiments on three benchmark datasets demonstrate the robustness of our proposed method, where our approach significantly outperforms the baselines and exceeds or is comparable to the state-of-the-art methods.
Pat-in-the-Loop: Declarative Knowledge for Controlling Neural Networks

The dazzling success of neural networks over natural language processing systems is imposing an urgent need to control their behavior with simpler, more direct declarative rules. In this paper, we propose Pat-in-the-Loop as a model to control a specific class of syntax-oriented neural networks by adding declarative rules. In Pat-in-the-Loop, distributed tree encoders allow to exploit parse trees in neural networks, heat parse trees visualize activation of parse trees, and parse subtrees are used as declarative rules in the neural network. Hence, Pat-in-the-Loop is a model to include human control in specific natural language processing (NLP)-neural network (NN) systems that exploit syntactic information, which we will generically call Pat. A pilot study on question classification showed that declarative rules representing human knowledge, injected by Pat, can be effectively used in these neural networks to ensure correctness, relevance, and cost-effective.
Teacher Perceptions of the Effectiveness of Using Handheld Devices in Saudi EFL Classroom Practices

This study aims to investigate the effectiveness of using handheld devices such as smartphones and tablets for language teaching purposes (Mobile-Assisted Language Learning, or MALL). A questionnaire with a comment section was distributed in order to investigate EFL teachers’ perceptions on including or excluding smartphones and tablets in the EFL learning process. Data were collected through a 5-Likert scale questionnaire in a leading Saudi university. The findings revealed that the majority of teachers claimed to hardly ever use Android System Phones or iPhones (iOS System), Android tablets or iPads (iOS System) in classroom teaching. Importantly, the study found out that there is a statistically significant difference between male and female teachers in terms of which smart device operating system was reported being used. Within the few participants who employed technology, it was found that teachers with less teaching experience were more open to using technological devices in classroom. In addition, the findings showed that a significant number of teachers admitted to rarely using handheld devices in teaching reading, writing, listening, grammar or playing language-learning games. However, the majority of teachers said they sometimes used applications to assign online exercises, share information about the course, provide visual aids, develop the learners’ speaking skills or as a translation tool. Moreover, teachers claimed to employ these devices frequently to teach vocabulary through visual aids when teaching. Teachers also rely heavily on these devices to communicate with students via social networks, and to access textbooks. Overall, teachers did not effectively integrate MALL into completing classroom language-learning activities or practicing language functions or skills.
Discriminative Adaptive Sets for Multi-Label Classification

Multi-label classification aims to associate multiple labels to a given data/object instance to better describe them. Multi-label data sets are common in a lot of emerging application areas like: Text/Multimedia classification, Bio-Informatics, Medical image annotations and Computer Vision to name a few. There is a growing interest in efficient and accurate multi-label classification. There are two major approaches to perform multi-label classification (i) problem transformation methods and (ii) algorithm adaptation methods. In algorithm adaptation, the traditional classification algorithms are modified to handle multi-label data sets. One classification algorithm which is often modified to do multi-label classification is k- nearest neighbor (kNN). k-nearest neighbor is popular due to its simplicity, easy to implement and seamlessly adaptability. Despite its merits it has several drawbacks like: sensitivity of noisy data, missing values and outliers; feature scaling and often becoming inaccurate for large overlapping solution space. In this paper, a modification to kNN method is suggested for multi-label classification with three improvement strategies (i) selection of local example w.r.t. unknown example – the motivation for this comes from the fact that local and relevant space is vital for the improvement in multi-label classification; (ii) Splitting the input space into multiple sub-spaces for optimal label estimation – the motivation is to estimate label accurately in the presence of noisy labels; And (iii) selection of labels using Mean Average Precision (MAP) estimates – here our motivation is to utilize the training data effectively to maximize the hidden distribution and optimal parameters for the method. The proposed method is implemented and compared with state-of-the-art approaches based on kNN or similar approaches that effectively select and optimize relevant spaces for multi-label classification. Evaluation based on multiple metrics like Hamming loss, Precision/Recall and F-measure are used for evaluation. The suggested approach performed much better than the state-of-the-art on the datasets with strong label cardinalities.
Implementation of Web Scraping to Build a Web-Based Instagram Account Data Downloader Application

Instagram has been used by many groups, such as business people, academics, to politicians, to take advantage of the insights gained by processing and analyzing Instagram data for various purposes. However, before processing and analyzing data, users must first pass data collection or downloading from Instagram. The problem faced is that most data collection methods are still done manually as for many parties that offer Instagram account data download services with various price options. This research applied a web scraping method to automatically build a web-based Instagram account data download application so that several parties can use it. The web scraping method was chosen because by using this method, researchers do not need to use Instagram's Application Programming Interface (API), which has access restrictions in retrieving data on Instagram. In this study, application testing was conducted on 15 Instagram accounts with various publications, namely between 100 and 11000. Based on the download data analysis results, the application of the web scraping method to download Instagram account data can successfully download a maximum of 2412 account data. In this application, users can download Instagram account data to Data Collection and then manage it like deleting and exporting data collection in the form of CSV, Excel, or JSON.
The Formation of Skills to Visualize by the Tools of Computer Visualization

The article presents the aspects of the formation skills to visualize with the use of specialized software. The authors note about the importance of visual representation of information content, which allows speeding up the perception and processing of text, data arrays, etc. Basic methods of visualization are described. The special course is described, which main aim is to form skills to visualize by the tools of computer visualization. The method for processing results of educational achievements is based on the statistical sign test.
THE USE OF YOUTUBE VIDEO IN TEACHING ENGLISH FOR FOREIGN LANGUAGE AT VOCATIONAL HIGH SCHOOL

The study aimed to investigate the use of YouTube video in learning English as a foreign language in terms of the implementation, students' opinion in English skill improvement after the implementation of YouTube video, the problem faced by teachers and students, and students' perception toward the use of YouTube video in learning English. The respondents were 5 tenth grade English teachers and 35 tenth grade students. This study used embedded mixed-method as the research design. The data were gathered by distributing questionnaires, and were analyzed by using SPSS. The result of the study revealed that: a) the teachers could implement the technique since the teachers used YouTube video technique to target several skills in English. The students had positive opinion about the improvement obtained in learning English. The students also mentioned that the implementation of this technique was able to decrease the monotonous teaching and increase the motivation in learning English. However, some problems occurred while the preparation of the assessment process in implementing YouTube video technique such as the internet connection and the students' readiness. The teachers needed to develop the technique to increase the students better understanding of the topic. The result of this study was found out the implementation of YouTube videos in teaching and learning English was showed a positive impact on students' English acquisition and helpful for teachers in delivering the topic to students and the result was in line with the finding of the previous study. However, there were some additional findings in this present study such as the problem the way teachers implemented this technique, the students' opinion in English skill improvement, and the students' perception toward the implementation of the YouTube video technique.
Multi‐level Deep Correlative Networks for Multi‐modal Sentiment Analysis

Multi-modal sentiment analysis (MSA) is increasingly becoming a hotspot because it extends the conventional Sentiment analysis (SA) based on texts to multi-modal content which can provide richer affective information. However, compared with textbased sentiment analysis, multi-modal sentiment analysis has much more challenges, because the joint learning process on multi-modal data requires both fine-grained semantic matching and effective heterogeneous feature fusion. Existing approaches generally infer sentiment type from splicing features extracted from different modalities but neglect the strong semantic correlation among cooccurrence data of different modalities. To solve the challenges, a multi-level deep correlative network for multimodal sentiment analysis is proposed, which can reduce the semantic gap by analyzing simultaneously the middlelevel semantic features of images and the hierarchical deep correlations. First, the most relevant cross-modal feature representation is generated with Multi-modal Deep and discriminative correlation analysis (Multi-DDCA) while keeping those respective modal feature representations to be discriminative. Second, the high-level semantic outputs from multi-modal deep and discriminative correlation analysis are encoded into attention-correlation cross-modal feature representation through a co-attention-based multimodal correlation submodel, and then they are further merged by multi-layer neural network to train a sentiment classifier for predicting sentimental categories. Extensive experimental results on five datasets demonstrate the effectiveness of the designed approach, which outperforms several state-of-the-art fusion strategies for sentiment analysis.
Effect of Information Capital Readiness on Business Performance in Indonesian MSMEs: Does Online Market Orientation Matter?

The focus of this study is to investigate the mediating role of online market orientation on information capital readiness-business performance relationship.The construct of information capital readiness is rarely researched.The readiness here refers to the availability of information capital needed to support strategy execution.As quantitative research, this study employs the partial least squares structural equation modeling (PLS-SEM) to test the hypotheses.Data was collected using questionnaires from the owners/managers of the micro, small, and medium-sized enterprises (MSMEs) in the East Java Province, Indonesia.As many as 433 respondents had participated.The result indicates that information capital readiness directly and positively affects business performance.Further analysis reveals that online market orientation partially mediates information capital readiness-business performance relationship.In conclusion, this study suggests that the owners/managers of the MSMEs should improve their information capital readiness to support online market orientation strategy so that it can improve their business performance.This is the first study that brings together the issues of information capital readiness and online market orientation as the antecedents of business performance in the Indonesian MSMEs research setting.The mediating role of online market orientation is rarely explored in previous studies.
The Effectiveness of Using Edmodo as an Online Learning Platform in Covid-19

This study aimed to describe the effectiveness of using Edmodo, as one of the online learning platforms, in the lecture process at the Faculty of Teacher Training and Education of the Universitas Sembilanbelas November Kolaka (USN Kolaka). A sample of 124 students from a total of 1,606 FKIP students of USN Kolaka participated in this study by filling out questionnaires distributed online. Student responses were analyzed descriptively quantitatively to determine the effectiveness of the implementation of learning, in terms of two aspects (the level of student participation, and the attitude of students towards the implementation of lectures). The results of data analysis showed that online learning using Edmodo at FKIP USN Kolaka has been carried out effectively. The effectiveness of using Edmodo is due to its practicality (simple display, provides various features, and supports various file types), as well as the accessibility of its use (accessible even though the connection is unstable, and saves internet quota). The convenience provided by Edmodo platform is able to increase the average participation rate of students in online learning to reach 89.18%.
A MULTI-AGENT APPROACH TO POMDPS USING OFF-POLICY REINFORCEMENT LEARNING AND GENETIC ALGORITHMS

This paper introduces novel concepts for accelerating learning in an off-policy reinforcement learning algorithm for Partially Observable Markov Decision Processes (POMDP) by leveraging multiple agents frame work. Reinforcement learning (RL) algorithm is considerably a slow but elegant approach to learning in an unknown environment. Although the action-value (Q-learning) is faster than the state-value, the rate of convergence to an optimal policy or maximum cumulative reward remains a constraint. Consequently, in an attempt to optimize the learning phase of an RL problem within POMD environment, we present two multi-agent learning paradigms: the multi-agent off-policy reinforcement learning and an ingenious GA (genetic Algorithm) approach for multi-agent offline learning using feedforward neural networks. At the end of the trainings (episodes and epochs) for reinforcement learning and genetic algorithm respectively, we compare the convergence rate for both algorithms with respect to creating the underlying MDPs for POMDP problems. Finally, we demonstrate the impact of layered resampling of Monte CarloвЂ™s particle filter for improving the belief state estimation accuracy with respect to ground truth within POMDP domains. Initial empirical results suggest practicable solutions.