Reviewerly/exHarmony huggingface dataset:
Exploring the Theory of Activity in English Language Writing: The Case for WhatsApp

WhatsApp is a social media application that is used widely by people today and enables mobile and ubiquitous learning at a different level. This study evaluated the use of mobile instant messaging tools to support teaching and learning in higher education. A total of 66 students in Penang use smartphones with WhatsApp were assigned into experimental and control groups. Besides the traditional classroom learning for both groups, the experimental group was also supported by the teacher-student interaction via WhatsApp outside school hours. The participants of the control group used WhatsApp only for scholarly communication. Pre-test scores were used as the covariate. The marginal means on the post-test scores showed that the participants in the experimental group performed better than those in the control group. The intervention of WhatsApp improved the learning achievement of the participants. The strength of the intervention between the two groups was medium to large. A questionnaire designed by the author was administered at the end of the study. The participants showed positive perception and acceptance of the use of WhatsApp for teaching and learning. The typical usability issues on mobile learning were found to be valid. The use of the WhatsApp application enables to improve learners' writing performance, classroom engagement, academic achievement as well as enriches learners' writing that leads to a sustainable student. It is also recommended that the study be conducted for a more extended period in order to determine the consistency and sustainability of the learners' improvements in writing utilizing the WhatsApp application.
Affective Event Classification with Discourse-enhanced Self-training

Prior research has recognized the need to associate affective polarities with events and has produced several techniques and lexical resources for identifying affective events.Our research introduces new classification models to assign affective polarity to event phrases.First, we present a BERT-based model for affective event classification and show that the classifier achieves substantially better performance than a large affective event knowledge base.Second, we present a discourse-enhanced selftraining method that iteratively improves the classifier with unlabeled data.The key idea is to exploit event phrases that occur with a coreferent sentiment expression.The discourseenhanced self-training algorithm iteratively labels new event phrases based on both the classifier's predictions and the polarities of the event's coreferent sentiment expressions.Our results show that discourse-enhanced selftraining further improves both recall and precision for affective event classification.
A QPSO algorithm based on hierarchical weight and its application in cloud computing task scheduling

The computing method of the average optimal position is one of the most important factors that affect the optimization performance of the QPSO algorithm. Therefore, a particle position weight computing method based on particle fitness value grading is proposed, which is called HWQPSO (hierarchical weight QPSO). In this method, the higher the fitness value of a particle, the higher the level of the particle, and the greater the weight. Particles at different levels have different weights, while particles at the same level have the same weight. Through this method, the excellent particles have higher average optimal position weight, and at the same time, the absolute weight of a few particles is avoided, so that the algorithm can quickly and stably converge to the optimal solution, and improve the optimization ability and efficiency of the algorithm. In order to verify the effectiveness of the method, five standard test functions are selected to test the performance of HWQPSO, QPSO, DWC-QPSO and LTQPSO algorithm, and the algorithms are applied to the task scheduling of the cloud computing platform. Through the test experiment and application comparison, the results show that the HWQPSO algorithm can converge to the optimal solution of the test function faster than the other three algorithms. It can also find the task scheduling scheme with the shortest time consumption and the most balanced computing resource load in the cloud platform. In the experiment, compared with QPSO, DWC-QPSO and LTQPSO algorithm, HWQPSO execution time of the maximum task scheduling was reduced by 35%, 23% and 21% respectively.
EXPLORING LEARNERS’ AUTONOMY IN ONLINE LANGUAGE-LEARNING IN STAI SUFYAN TSAURI MAJENANG

The impact of the coronavirus outbreak on the education system is inevitable that higher education institutions choose to transform their instructional practices to entirely online using specific platforms. This shifting comes with challenges to students as they are demanded to be independent and autonomous. This study investigated the students’ readiness and perception towards being an autonomous learner in online language-learning. This case study investigated 25 students who joined online language learning by the WhatApps group in an English department of STAI Sufyan Tsauri Majenang. The data were gained by giving an online questionnaire, having an online interview with some students who were chosen randomly, and also assigning students self-report. The data showed that the students do not have enough autonomy in online language-learning. It means that the students are not ready to be autonomous learners in online language learning. The result of the study indicated that the students prefer having a face to face class and direct guidance from the teacher where 66.9% of the same students prefer learning in class or face to face meeting. In comparison, only 18.5 % who share no problem having an online class. 14.6% of them enjoy both options. This finding suggests that teachers need some strategies to promote and foster students’ learner autonomy to achieve learning objectives in online language learning.
Improve the Accuracy of C4.5 Algorithm Using Particle Swarm Optimization (PSO) Feature Selection and Bagging Technique in Breast Cancer Diagnosis

Breast cancer is the second leading cause of death due to cancer in women currently. It has become the most common cancer in recent years. In early detection of cancer, data mining can be used to diagnose breast cancer. Data mining consists of several research models, one of which is classification. The most commonly used method in classification is the decision tree. C4.5 is an algorithm in the decision tree that is often used in the classification process. In this study, the data used was the Breast Cancer Wisconsin (Original) Data Set (1992) obtained from the UCI Machine Learning Repository. The purpose&nbsp; of this study was to select features that will be used and overcome class imbalances that occur, so that the performance of the C4.5 algorithm worked more optimal&nbsp; in the classification process. The methods used as feature selection are PSO and bagging to overcome class imbalances. Classification was tested using the confusion matrix to determine the accuracy that was generated. From the results of this study, the application of PSO as a feature selection and bagging to overcome class imbalances with the C4.5 algorithm succeeded in increasing accuracy by 5.11% with an initial accuracy of 93.43% to 98.54%.
Demystifying the Power of Blockchain Technology in Supply Chain Management

Modern supply chain management systems have evolved into a complex and critical system. Thus, it has grown more interesting to verify the source of products and its visibility as it is moving through the supply chain network. The application of blockchain technology and Internet of Things (IoT) are likely to affect the supply chain management objectives such as cost, quality, speed, dependability, risk reduction, sustainability, and flexibility. This paper presented the concept of how blockchain technology and IoT can help to achieve supply chain objectives. This research focuses on the impact of blockchain on current and future supply chain management systems.
On the Value of Order Number and Power in Secret Image Sharing

Shadow images generated from Shamir’s polynomial-based secret image sharing (SSIS) may leak the original secret image information, which causes a significant risk. The occurrence of this risk is closely related to the basis of secret image sharing, Shamir’s polynomial. Shamir’s polynomial plays an essential role in secret sharing, but there are relatively few studies on the power and order number of Shamir’s polynomial. In order to improve the security and effectiveness of SSIS, this paper mainly studies the utility of two parameters in Shamir’s polynomial, order number and power. Through the research of this kind of utility, the choice of order number and power can be given under different security requirements. In this process, an effective shadow image evaluation algorithm is proposed, which can measure the security of shadow images generated by SSIS. The user can understand the influence rule of the order number and power in SSIS, so that the user can choose the appropriate order number and power according to different security needs.
Time-optimal quantum transformations with bounded bandwidth

In this paper, we derive sharp lower bounds, also known as quantum speed limits, for the time it takes to transform a quantum system into a state such that an observable assumes its lowest average value. We assume that the system is initially in an incoherent state relative to the observable and that the state evolves according to a von Neumann equation with a Hamiltonian whose bandwidth is uniformly bounded. The transformation time depends intricately on the observable's and the initial state's eigenvalue spectrum and the relative constellation of the associated eigenspaces. The problem of finding quantum speed limits consequently divides into different cases requiring different strategies. We derive quantum speed limits in a large number of cases, and we simultaneously develop a method to break down complex cases into manageable ones. The derivations involve both combinatorial and differential geometric techniques. We also study multipartite systems and show that allowing correlations between the parts can speed up the transformation time. In a final section, we use the quantum speed limits to obtain upper bounds on the power with which energy can be extracted from quantum batteries.
A genetic algorithm using Calinski-Harabasz index for automatic clustering problem

Data clustering is a technique that aims to represent a dataset in clusters according to their similarities. In clustering algorithms, it is usually assumed that the number of clusters is known. Unfortunately, the optimal number of clusters is unknown for many applications. This kind of problem is called Automatic Clustering. There are several cluster validity indexes for evaluating solutions, it is known that the quality of a result is influenced by the chosen function. From this, a genetic algorithm is described in this article for the resolution of the automatic clustering using the Calinski-Harabasz Index as a form of evaluation. Comparisons of the results with other algorithms in the literature are also presented. In a first analysis, fitness values equivalent or higher are found in at least 58% of cases for each comparison. Our algorithm can also find the correct number of clusters or close values in 33 cases out of 48. In another comparison, some fitness values are lower, even with the correct number of clusters, but graphically the partitioning are adequate. Thus, it is observed that our proposal is justified and improvements can be studied for cases where the correct number of clusters is not found.
Comparison Testing Functional and Usability System Mapping Land Agriculture On Platform Web and Mobile

The Agricultural Land Mapping System (SPLP) is indispensable in an agricultural country where part of the population is farmers. This system has been developed by the research team since 2019 and has resulted in web and mobile based systems. The Dutatani SPLP system was developed using the Rapid Application Development (RAD) method. Before this system is further implemented in the community, this system needs to be tested in terms of functionality and usability. This research article aims to compare the functionality and reusability testing of web and mobile-based SPLP. The test was carried out using ISO / IEC 9126-4 usability metrics that focus on effectiveness and efficiency, and involve farmers and farmer groups from Gilang Harjo Village, Bantul, Yogyakarta. The results of testing the web-based and mobile-based SPLP system show that overall respondents can do all the tasks given, but it takes a long time to complete. This is influenced by internal factors of the respondents, namely the respondent's lack of experience in using mobile phones for other activities besides telephone and short messages. So that when testing, respondents need more time to adapt to the system. However, based on time on task, mobile-based SPLP testing is faster than web-based ones.
LEARNING ANALYTICS IN CONVENTIONAL EDUCATION: ITS ROLE AND OUTCOMES

Massification, digitalization and bureaucratization are now the major trends that shape higher education. Massification has led to an inevitable problem of the heterogeneity of students and the need for adaptive learning; digitalization has created a need for distance learning technologies and, as a result, learning data production; finally, bureaucratization has meant that the education quality assessment now predominantly relies on quantitative rather than qualitative indicators. At the crossing of these trends, a new research interest has emerged, which develops both theoretical and practically oriented studies and which has become known as learning analytics. Learning analytics is now actively discussed in Western countries, where national policies to regulate and stimulate this sphere are designed and professional associations of specialists in learning analytics are created. Proponents of learning analytics believe that the data collected and analyzed by an education institution will help the management take more justified and objective decisions than those based on expert opinions. Learning analytics is understood in this paper as a necessary tool for detecting the weak sides of the curricula. It also helps build students’ individual learning trajectories, which is essential for an individualized approach in education and for making the learning process more adaptive. Opponents of learning analytics, in their turn, see it as a threat to the current balance of power in education, the roles of the teacher and manager, and point out the need for specific competencies and the danger of personal data breach. Russia is now left out of the global agenda: except for a few recent cases, learning analytics is still viewed by many as more of a promise than reality. This review is aimed at shedding light on the modern understanding of learning analytics, its development in the world and in Russia, the prospects and limitations of its application in Russia from the perspective of the key stakeholders in higher education. We also propose recommendations regarding the organization of a university learning analytics system. This article will be of interest to university managers and decision-makers, teachers and scholars of higher education as it provides information on the organization of a data management system, including the collection, analysis and use of data.
Arg-tuProlog: A Modular Logic Argumentation Tool for PIL

Private international law (PIL) addresses overlaps and conflicts between legal systems by distributing cases between the authorities of such systems (jurisdiction) and establishing what rules these authorities have to apply to each case(choice of law). A modular argumentation tool, Arg-tuProlog, is here presented that enables reasoning with rules and interpretations of multiple legal systems.
Performance Boosting of Scale and Rotation Invariant Human Activity Recognition (HAR) with LSTM Networks Using Low Dimensional 3D Posture Data in Egocentric Coordinates

Human activity recognition (HAR) has been an active area in computer vision with a broad range of applications, such as education, security surveillance, and healthcare. HAR is a general time series classification problem. LSTMs are widely used for time series classification tasks. However, they work well with high-dimensional feature vectors, which reduce the processing speed of LSTM in real-time applications. Therefore, dimension reduction is required to create low-dimensional feature space. As it is experimented in previous study, LSTM with dimension reduction yielded the worst performance among other classifiers, which are not deep learning methods. Therefore, in this paper, a novel scale and rotation invariant human activity recognition system, which can also work in low dimensional feature space is presented. For this purpose, Kinect depth sensor is employed to obtain skeleton joints. Since angles are used, proposed system is already scale invariant. In order to provide rotation invariance, body relative direction in egocentric coordinates is calculated. The 3D vector between right hip and left hip is used to get the horizontal axis and its cross product with the vertical axis of global coordinate system assumed to be the depth axis of the proposed local coordinate system. Instead of using 3D joint angles, 8 number of limbs and their corresponding 3D angles with X, Y, and Z axes of the proposed coordinate system are compressed with several dimension reduction methods such as averaging filter, Haar wavelet transform (HWT), and discrete cosine transform (DCT) and employed as the feature vector. Finally, extracted features are trained and tested with LSTM (long short-term memory) network, which is an artificial recurrent neural network (RNN) architecture. Experimental and benchmarking results indicate that proposed framework boosts the performance of LSTM by approximately 30% accuracy in low-dimensional feature space.
Resource Classification and Knowledge Aggregation of Library and Information Based on Data Mining

The traditional knowledge service systems have nonuniform data structures. Some data are structured, while some are semi-structured and even non-structured. Big data technology helps to optimize the integration and retrieval of the massive data on library and information (L&amp;I), making it possible to classify the resources and optimize the configuration of L&amp;I resource platforms according to user demand. Therefore, this paper introduces the new information service model of big data resources and knowledge services to the processing of L&amp;I data. Firstly, the data storage structure and relationship model of the L&amp;I resource platform were established, and used to sample and integrate the keywords of resource retrieval. Next, an L&amp;I resource classification model was constructed based on support vector machine (SVM), and applied to extract and quantify the attributes of the keywords of resource retrieval. After that, a knowledge aggregation model was developed for a complex network of multiple L&amp;I resource platforms. Experimental results demonstrate the effectiveness of the proposed knowledge aggregation model. The research findings provide a reference for the application of data mining in resource classification.
Measuring Anticipated and Episodic UX of Tasks in Social Networks

Today, social networks are crucial commodities that allow people to share different contents and opinions. In addition to participation, the information shared within social networks makes them attractive, but success is also accompanied by a positive User eXperience (UX). Social networks must offer useful and well-designed user-tools, i.e., sets of widgets that allow interaction among users. To satisfy this requirement, Episodic User eXperience (EUX) yields reactions of users after having interacted with an artifact. Anticipated User eXperience (AUX) grants the designers the capacity to recollect users’ aspirations, assumptions, and needs in the initial development phase of an artifact. In this work, we collect UX perceived in both periods to contrast user expectations and experiences offered on social networks, in order to find elements that could improve the design of user-tools. We arrange a test where participants (N=20) designed prototypes on paper to solve tasks and then did the same tasks on online social networks. Both stages are assessed with the help of AttrakDiff, and then we analyze the results through t-tests. The results we obtained suggest that users are inclined towards pragmatic aspects of their user-tools expectations.
Educación remota en el contexto universitario: necesidad del trabajo colaborativo para la mediación pedagógica docente en tiempos de COVID

The Curricular Commission of the Division of Education for Work. Assumes the modality of remote education through collaborative work, supporting the teaching staff in the development of required skills for the teaching and learning processes.
Component-based 2-/3-dimensional nearest neighbor search based on Elias method to GPU parallel 2D/3D Euclidean Minimum Spanning Tree Problem

We present improved data parallel approaches working on graphics processing unit (GPU) compute unified device architecture (CUDA) platform to build hierarchical Euclidean minimum spanning forest or tree (EMSF/EMST) for applications whose input only contains N points with arbitrary data distribution in 2D/3D Euclidean space. Characteristic of the proposed parallel algorithms follows "data parallelism, decentralized control and O(1) local memory occupied by each GPU thread". This research has to solve GPU parallelism of component-based nearest neighbor search (component-based NNS), tree traversal, and other graph operations like union-find. For exact NNS, instead of using classical K-d tree search or brute-force computing method, we propose a K-d search method working based on dividing the Euclidean K-dimensional space into congruent and non-overlapping square/cubic cells where size of points in each cell is bounded. For component-based NNS, with the uniqueness property based on 2D/3D square/cubic space partition, we propose dynamic and static pruning techniques to prune unnecessary neighbor cells' search. For tree traversal, instead of using breadth-first-search, this paper proposes CUDA kernels working with a distributed dynamic link list for selecting a local spanning tree's shortest outgoing edge since size of local EMSTs in EMSF cannot be predicted. Source code is provided online and experimental comparisons are conducted on both 2D and 3D benchmarks with up to 107 points to build final EMST. Results show that applying K-d search with static pruning technique and the proposed operators totally working in parallel on GPU, our current implementation runs faster than our previous work and current optimal sequential dual-tree mlpack EMST library.