Reviewerly/exHarmony huggingface dataset:
Utilization of Microsoft Teams 365 as an Alternative for Distance Learning Media Amid the Covid-19 Pandemic

Online digital media is a very important medium for human life today. The current COVID-19 pandemic situation has made online digital media an alternative use of current learning media. One of the online digital media is Microsoft Teams 365, an application used as a learning medium. This study aims to describe the benefits of using Microsoft Teams 365 digital media as a medium for learning Indonesian in SMA Negeri 1 Kebumen. The data source is a display of the Indonesian language learning process through Microsoft Teams 365. The data collection techniques used are observation and note taking. The validity of the data used is source triangulation and theory triangulation. The data analysis used interactive data analysis techniques. The results of the study showed that the Microsoft Teams 365 application has features that can be utilized properly by teachers and students in the learning and teaching processes.
Element-Wise Feature Relation Learning Network for Cross-Spectral Image Patch Matching

Recently, the majority of successful matching approaches are based on convolutional neural networks, which focus on learning the invariant and discriminative features for individual image patches based on image content. However, the image patch matching task is essentially to predict the matching relationship of patch pairs, that is, matching (similar) or non-matching (dissimilar). Therefore, we consider that the feature relation (FR) learning is more important than individual feature learning for image patch matching problem. Motivated by this, we propose an element-wise FR learning network for image patch matching, which transforms the image patch matching task into an image relationship-based pattern classification problem and dramatically improves generalization performances on image matching. Meanwhile, the proposed element-wise learning methods encourage full interaction between feature information and can naturally learn FR. Moreover, we propose to aggregate FR from multilevels, which integrates the multiscale FR for more precise matching. Experimental results demonstrate that our proposal achieves superior performances on cross-spectral image patch matching and single spectral image patch matching, and good generalization on image patch retrieval.
Multi-level Distance Regularization for Deep Metric Learning

We propose a novel distance-based regularization method for deep metric learning called Multi-level Distance Regularization (MDR). MDR explicitly disturbs a learning procedure by regularizing pairwise distances between embedding vectors into multiple levels that represents a degree of similarity between a pair. In the training stage, the model is trained with both MDR and an existing loss function of deep metric learning, simultaneously; the two losses interfere with the objective of each other, and it makes the learning process difficult. Moreover, MDR prevents some examples from being ignored or overly influenced in the learning process. These allow the parameters of the embedding network to be settle on a local optima with better generalization. Without bells and whistles, MDR with simple Triplet loss achieves the-state-of-the-art performance in various benchmark datasets: CUB-200-2011, Cars-196, Stanford Online Products, and In-Shop Clothes Retrieval. We extensively perform ablation studies on its behaviors to show the effectiveness of MDR. By easily adopting our MDR, the previous approaches can be improved in performance and generalization ability.
Acceptance of Anthropomorphic Technology: A Literature Review

Anthropomorphic Technology (AT) is technology that is human-like in design and motivates anthropomorphism. Anthropomorphism is the attribution of human-like characteristics to nonhuman objects. Extant research informs user responses of familiarity and acceptance to AT, but also withdrawal from and rejection of AT.There is little integration of research on this topic.We examine and synthesize studies on user responses to AT published in leading IS journals.We identify kinds of anthropomorphic design and dimensions, and find that although most research demonstrates a positive influence of humanlike design on user response, many factors can moderate this effect.We recognize these factors and propose directions for future research.
Education 4.0 using artificial intelligence for students performance analysis

Nowadays, predicting students' performance is one of the most specific topics for learning environments, such as universities and schools, since it leads to the development of effective mechanisms that can enhance academic outcomes and avoid destruction. In education 4.0, Artificial Intelligence (AI) can play a key role in identifying new factors in the performance of students and implementing personalized learning, answering routine student questions, using learning analytics, and predictive modeling. It is a new challenge to redefine education 4.0 to recognize the creative and innovative intelligent students, and it is difficult to determine students' outcomes. Hence, in this paper, Hybridized Deep Neural Network (HDNN) to predict student performance in Education 4.0. The proposed HDNN method is utilized to determine the dynamics that likely influence the student's performance. The deep neural network monitor, &nbsp;predicts, and evaluates the student's performance in an education 4.0 environment. The findings show that the proposed HDNN method achieved better prediction accuracy when compared to other popular methods.
Quantum composer: A programmable quantum visualization and simulation tool for education and research

Interactive simulations and visualizations augment the teaching and learning of quantum mechanics by making equations and concepts come to life. However, graphical visualizations are nearly always limited to a set of hard-coded functionalities. Text-based codes can offer a higher degree of flexibility, but only at the expense of steep learning curves or time investments. We introduce Quantum Composer, which allows the user to build, expand, or explore quantum mechanical simulations by interacting with graphically connectable nodes, each corresponding to a physical concept, mathematical operation, or visualization. Quantum Composer eliminates numerical and programming details while retaining accessibility, emphasis on understanding, and rapid feedback mechanisms. We illustrate its open-ended applicability through a series of examples in introductory and advanced quantum mechanics courses, student projects, and research environments.
Mobile Forensic on Android-based IMO Messenger Services using Digital Forensic Research Workshop (DFRWS) Method

The development of mobile device technology is very rapid in this global era, This has an impact on the increase in cybercrime such as narcotics transactions using the Instant Messenger application.IMO Messenger is an Instant Messenger application that can be used as a medium for crime because it has increased the number of users every year.This study used three stages of the Digital Forensics Research Workshop (DFRWS) method, namely identification, preservation, collection.This study uses two smartphones with root and non-root conditions that have the IMO application installed.The case used in this research is narcotics transactions based on previous case scenarios.Evidence from the criminal case will be acquired using four forensic tools, namely, MOBILedit forensic express, DB Browser for SQLite, AccessData FTK Imager, and Belkasoft evidence center.This research produces digital evidence in the form of chat files, images, audio, video, perpetrator's accounts, and chat times that have been deleted from a smartphone device in root condition.Calculation of the percentage index number from the evidence obtained on a smartphone with root conditions using MOBILedit forensic express tools at 100%, DB Browser for SQLite at 33.33%, AccessData FTK Imager at 33.33% and Belkasoft evidence center at 83.33%, smartphones with non-root conditions, no digital evidence was found.
Arabic dialect sentiment analysis with ZERO effort. \\ Case study: Algerian dialect

This paper presents an analytic study showing that it is entirely possible to analyze the sentiment of an Arabic dialect without constructing any resources. The idea of this work is to use the resources dedicated to a given dialect \textit{X} for analyzing the sentiment of another dialect \textit{Y}. The unique condition is to have \textit{X} and \textit{Y} in the same category of dialects. We apply this idea on Algerian dialect, which is a Maghrebi Arabic dialect that suffers from limited available tools and other handling resources required for automatic sentiment analysis. To do this analysis, we rely on Maghrebi dialect resources and two manually annotated sentiment corpus for respectively Tunisian and Moroccan dialect. We also use a large corpus for Maghrebi dialect. We use a state-of-the-art system and propose a new deep learning architecture for automatically classify the sentiment of Arabic dialect (Algerian dialect). Experimental results show that F1-score is up to 83% and it is achieved by Multilayer Perceptron (MLP) with Tunisian corpus and with Long short-term memory (LSTM) with the combination of Tunisian and Moroccan. An improvement of 15% compared to its closest competitor was observed through this study. Ongoing work is aimed at manually constructing an annotated sentiment corpus for Algerian dialect and comparing the results
The Nexus Between Financial Development, Economic Growth and Poverty Alleviation: PMG-ARDL Estimation

This study aims to find out the connections between financial development, economic growth, and poverty using panel data from 1985 to 2017 in fourteen African countries that many previous researchers ignore. The study deploys a dynamic Granger causality test to trace the nexus between financial development, economic growth, and poverty reduction in Africa in the long run. First, the upshots suggest a gross domestic product, gross capital formation, price of household consumption, and government expenditure substantially impacting poverty. Besides that, the result also shows a bi-directional in the long run using a PMG estimator. The findings broadly support the view that there is a stable, short-run relationship between financial development, economic growth, and poverty in the error correction terms. However, other variables show no causal relationship in the short run. In practicality, this study suggested some policy implications and supported governmental policies to reduce economic hardship on financial institutions.JEL Classification: G10, O47, I39, C33How to Cite:Korankye, B., Wen, X., Appiah, M., &amp; Antwi, L. (2021). The Nexus Between Financial Development, Economic Growth, and Poverty Alleviation: PMG-ARDL Estimation. Etikonomi: Jurnal Ekonomi, 20(1), 1 – 12. https://doi.org/10.15408/etk.v20i1.15908.
Teachers’ perceptions towards the utilization of WhatsApp in supporting teaching and learning of chemistry during COVID-19 pandemic in Rwandan secondary schools

This paper reports the results about teachers’ perceptions towards the utilization of WhatsApp in supporting teaching and learning of chemistry during the COVID-19 pandemic in Rwandan selected secondary schools. The collected data by using a survey questionnaire were from 18 chemistry teachers in Nyarugenge and Kicukiro Districts. The findings illustrated that WhatsApp is effective for teaching chemistry and supporting students learning. This application promotes students’ motivation, engagement, collaboration, interaction, participation, and ubiquitous learning. Despite the facilitation of collaboration between teachers and their students while using WhatsApp, the internet connection related problems including its cost and availability were among the hindering factors for effective collaboration for both students and teachers. The lack of smartphones and computers for some students, teachers, parents, or guardians was among the highlighted limitations for the participation of some students. The limited students’ management and their distraction were also highlighted by some teachers. The provision of computers, smartphones, and enough internet connection for all teachers, parents, or guardians were among the proposed potential solutions. To deal with students’ management and distractions while learning via WhatsApp, there should be the follow-up and guidance provided by teachers, guardians, or parents. Chemistry teachers and students are recommended to use WhatsApp as a supporting learning tool due to its added value of providing ubiquitous learning. Parents, boards in charge of education, and other educational stakeholders should integrate WhatsApp in teaching and learning to complement classroom learning.
A Use Case of the Application of Advanced Gaming and Immersion Technologies for Professional Training: The GAMEPHARM Training Environment for Physiotherapists

Immersive technologies are gaining momentum in professional training. Advanced AR/VR and gaming technologies allow for authentic learning experiences that are close to real‑life conditions, problems, and applications, something that can revolutionise fields like medical training, which traditionally relied on textbook‑based theoretical education and clinical placements. The present paper discusses the application of advanced gaming and immersion technologies for the continuous training of physiotherapists. Presently, this professional group remains largely underserved by online training services. The paper argues that the inherent complexity of physical therapy training could be served effectively by immersive learning innovations and professionals could benefit greatly. The paper applies the Design Science Research Methodology (DSRM) to design a suitable training solution for the promotion of authentic, comprehensive learning. Our analysis targets and consolidates the findings from three distinct pillars: (a) the requirements of the application area in question; (b) the current state‑of‑the‑art and emerging directions in game‑based professional education and training; and (c) existing applications of game‑based learning in the field of healthcare. Subsequently, the paper consolidates the findings to produce the system requirements and presents the design of a prototype training environment for physiotherapists that builds on advanced gaming technologies. Within the proposed system design, augmented reality modules are embedded alongside the game simulation and virtual world environment to provide participants with an authentic simulated game scenario in which to immerse themselves.
Analysis of the Influence of Website Quality to Customer’s Loyalty on E-Commerce

The competition of e-commerce in Indonesia makes domestic and foreign e-commerce companies plan strategies to win the competition. Customer's loyalty is an important factor to lead in the competition. This research focuses on how much influence of the website quality on customer's loyalty. This study also finds out which factor of website quality has the most influence on customer's loyalty with customer's satisfaction as a mediating factor. Factors that influence e-commerce customer's loyalty were analysed using the WebQual4.0 method which consists of three dimensions. The samples used in this research were 400 respondents. The data analysis techniques used were Structural Equational Model (SEM) using version 3.0 SmartPLS and Importance and Performance Map Analysis (IPMA). The analysis of SEM showed that the information quality was the only one that had a positive and significant influence on customer's satisfaction and loyalty on the JD.id website, while the usability and service factors had no positive and significant effect. The analysis of IPMA showed that the performances that should be improved were the maintenance of accurate and detailed information and content development. Keywords: SmartPLS, SEM, WebQual 4.0, Usability, Information Quality, Service Interaction Quality, JD.id
PENGARUH MODEL PEMBELAJARAN KOOPERATIF TIPE THINK TALK WRITE (TTW) BERBANTUAN MEDIA AUDIOVISUAL TERHADAP KETERAMPILAN MENULIS TEKS PERSUASI SISWA KELAS VIII SMP NEGERI 8 PADANG

ABSTRACTThe purpose of this study was categorized into three. First, to describe the text writing skills of the experimental class persuasion students. Second, to describe the text writing skills of the control class students' persuasion. Third, to describe the influence of the cooperative learning which was Think Talk Write (TTW) model assisted by audiovisual media in writing skill related to the persuasive text of class VIII students of SMP Negeri 8 Padang. The type of study was quantitative with quasi-experimental research. The design of this study was static by two groups. The population in this study was VIII grade students of SMP Negeri 8 Padang who were enrolled in 2018/2019 academic year about 252 students. The sample in this study was 64 students that consist of two classes, namely class VIII A as the experimental class and class VIII D as the control class that was taken by using purposive random sampling technique. There were several results of this study. They were: First, persuasive text writing skill of the experimental class students was in the category Very Good (BS) with an average score of 85.94. Second, persuasive text writing skill of control class students was in the category Good (B) with an average score of 76.82. Third, the working hypothesis (H_1) was received at the level of α = 0.05 and degrees of freedom (dk) = (n1 + n2) -2 because of tcount&gt;ttable, which is 4.23&gt; 1.70. In other words, the use of model cooperative learning type Think Talk Write assisted by audiovisual media has a significant effect on the persuasive writing skills of class VIII students of SMP Negeri 8 Padang. Therefore, the cooperative learning type Think Talk Write (TTW) model could be used as an alternative in learning persuasive text writing skill at the junior secondary level. Kata kunci: Pengaruh, Think Talk Write , Media Audiovisual, Teks Persuasi
Optimizing Sector Ring Histogram of Oriented Gradients for human injured detection from drone images

Developing a system for emergency response and rescue to find injured people within images has been an interest to many researchers. The key challenge is to define a proper feature to describe the human body's appearance. Various features are often extracted from low-level data, such as texture and colour (Zhang et al., Sensors. 19(5):1005, 2020). One of the strong features is the Sector Ring Histogram of Oriented Gradients (SRHOG) that has been successfully applied to human detection tasks. Despite good accuracy in finding humans, an SRHOG detection method produces a large amount of false-negative labels. Locating an injured body after a disaster in drone images using an imaging camera remained a challenge. This study presents a new extension to SRHOG, so-called AdSRHOG, to reduce the number of false labels. In our approach, the gradient filters used by SRHOG defined adaptively depending on the corresponding pixel location The proposed feature was used the Support Vector Machine (SVM) algorithm to detect humans on drone images. The experiments showed a significant improvement of up to 54.3% in reducing the false labels. It was also found that the overall accuracy of the human detection process had a notable improvement of 13.1% over a traditional SRHOG detection technique.
A pre-check operator for reducing algorithmic optimisation time in image processing applications

In image processing applications, we face the challenges of substantial computational requirements for calculating the objective function and requires considerably long optimisation time. In this paper, an operator named the pre-check operator (PCO) was proposed to reduce the whole optimization time. By integrating PCO with a standard genetic algorithm with elitist strategy (EGA), a new framework of pre-check genetic algorithm (PGA) was developed and compared with EGA based on a set of 10 benchmark instances with a known optimal solution. It was observed that PCO outperforms the results obtained using EGA.
Unsupervised super-resolution: creating high-resolution medical images from low-resolution anisotropic examples

Although high resolution isotropic 3D medical images are desired in clinical practice, their acquisition is not always feasible. Instead, lower resolution images are upsampled to higher resolution using conventional interpolation methods. Sophisticated learning-based super-resolution approaches are frequently unavailable in clinical setting, because such methods require training with high-resolution isotropic examples. To address this issue, we propose a learning-based super-resolution approach that can be trained using solely anisotropic images, i.e. without high-resolution ground truth data. The method exploits the latent space, generated by autoencoders trained on anisotropic images, to increase spatial resolution in low-resolution images. The method was trained and evaluated using 100 publicly available cardiac cine MR scans from the Automated Cardiac Diagnosis Challenge (ACDC). The quantitative results show that the proposed method performs better than conventional interpolation methods. Furthermore, the qualitative results indicate that especially finer cardiac structures are synthesized with high quality. The method has the potential to be applied to other anatomies and modalities and can be easily applied to any 3D anisotropic medical image dataset.
Open Source Information’s Blind Spot

Abstract Digital open source information has been heralded for its democratizing potential, insofar as it allows access to a much broader range of sources and voices than would normally be consulted through traditional methods of information gathering for international criminal investigations. It also helps to overcome some of the physical access barriers that are commonplace in international criminal investigations. At a time when the use of digital open source information is becoming more widespread, this article warns of the cognitive and technical biases that can impact upon two key stages of an investigation: finding relevant information and analysing that information. At the information-gathering stage, there are particular crimes, regions and groups of people whose experiences are more likely to be overlooked or hidden in digital open source investigations. When it comes to analysing digital open source information, there is a danger that cognitive and technical biases may influence which information is deemed most relevant and useful to an international criminal investigation, and how that information is interpreted. This article proposes some steps that can be taken to mitigate these risks.
Time Coordination and Collision Avoidance Using Leader-Follower Strategies in Multi-Vehicle Missions

In recent years, the increasing popularity of multi-vehicle missions has been accompanied by a growing interest in the development of control strategies to ensure safety in these scenarios. In this work, we propose a control framework for coordination and collision avoidance in cooperative multi-vehicle missions based on a speed adjustment approach. The overall problem is decoupled in a coordination problem, in order to ensure coordination and inter-vehicle safety among the agents, and a collision-avoidance problem to guarantee the avoidance of non-cooperative moving obstacles. We model the network over which the cooperative vehicles communicate using tools from graph theory, and take communication losses and time delays into account. Finally, through a rigorous Lyapunov analysis, we provide performance bounds and demonstrate the efficacy of the algorithms with numerical and experimental results.