Reviewerly/exHarmony huggingface dataset:
Exploring Multiple Strategic Problem Solving Behaviors in Educational Psychology Research by Using Mixture Cognitive Diagnosis Model

A mixture cognitive diagnosis model (CDM), which is called mixture multiple strategy-Deterministic, Inputs, Noisy "and" Gate (MMS-DINA) model, is proposed to investigate individual differences in the selection of response categories in multiple-strategy items. The MMS-DINA model system is an effective psychometric and statistical approach consisting of multiple strategies for practical skills diagnostic testing, which not only allows for multiple strategies of problem solving, but also allows for different strategies to be associated with different levels of difficulty. A Markov chain Monte Carlo (MCMC) algorithm for parameter estimation is given to estimate model, and four simulation studies are presented to evaluate the performance of the MCMC algorithm. Based on the available MCMC outputs, two Bayesian model selection criteria are computed for guiding the choice of the single strategy DINA model and multiple strategy DINA models. An analysis of fraction subtraction data is provided as an illustration example.
Comparison of C4.5 Algorithm and Support Vector Machine in Predicting the Student Graduation Timeliness

In higher educational institutions, graduation rates are one of the many aspects to assess the quality of the learning process. Al-Hidayah Islamic University in Bogor is one of the established private Islamic universities to create skilled human resources with moral values required by many companies nowadays. Having another institution in Bogor as a competitor with the same direction and objective is a challenge for Al-Hidayah Islamic University. Thus a solution is required to face the competition. One solution is to predict the student graduation timeliness of the students using data mining method with classification function. The implemented methodology in the data mining is Discovery Knowledge of Database (KDD), starting from selecting, preprocessing, transformation, data mining, and evaluation/ interpretation. There were two Algorithm models used in this paper, namely C4.5 and Support Vector Machine (SVM). The classification procedure consists of predictor variables and one of the target variables. Predictor variables are gender, Grade Point Average, marital status, and job status. Rapid Miner software was used to process the data. The final results of both Algorithms show an 81% precision rate and 80% accuracy level for the C4.5 Algorithm, while SVM has an 88% precision rate and 85% accuracy level.
Virtual, Real or Mixed: How Surrounding Objects Influence the Sense of Embodiment in Optical See-Through Experiences?

This paper studies the sense of embodiment of virtual avatars in Mixed Reality (MR) environments visualized with an Optical See-Through display. We investigated whether the content of the surrounding environment could impact the user’s perception of their avatar, when embodied from a first-person perspective. To do so, we conducted a user study comparing the sense of embodiment toward virtual robot hands in three environment contexts which included progressive quantities of virtual content: real content only, mixed virtual/real content, and virtual content only. Taken together, our results suggest that users tend to accept virtual hands as their own more easily when the environment contains both virtual and real objects (mixed context), allowing them to better merge the two “worlds”. We discuss these results and raise research questions for future work to consider.
Application Requirement-Driven Automatic ISP Parameter Tuning for a Rear View Monitoring Camera

An image signal processor (ISP) is a dedicated processor that transforms raw data obtained from camera sensors into an image that satisfies the requirements of a specific application or use case. An ISP typically has many tuning parameters due to the complexity of the image transformation. Until now, these are generally tuned by human experts manually, and this work takes a great deal of time. This paper proposes an application-level automatic ISP parameter tuning system. In particular, this paper focuses on a rear view monitoring (RVM) camera that is mounted on the rear side of the vehicle to prevent backovers in advanced driving assistance systems (ADAS). The proposed system consists of four steps. The first step is the input image generation, which captures a virtual scene including the test site and vehicle body whose three-dimensional (3D) models are created according to the test requirements and vehicle design. The second step is ISP processing, which transforms the input image into an ISP output image (RVM image) according to the ISP specification in the RVM system. The third step is to evaluate the RVM image's fitness using evaluation criteria (EC) functions. Finally, ISP parameters are tuned by the grid-to-random search method. In the experiment, the proposed system is evaluated by using the 3D modeling data of six different test vehicle types. Experimental results show that the proposed system can effectively obtain the usable ISP parameters' values that satisfy all RVM requirements for a given situation, regardless of test vehicle type, within 2~3 hours.
Studi Literatur Kekurangan dan Kelebihan Pengujian Black Box

A software cannot be perfect and always requires development. Software development consists of several stages, one of which is the testing stage. This stage aims to find fault with the software when doing software development. Stages of testing itself is divided into several techniques, one of which is black box testing. Black box testing is testing that only tests the outside of the software, for example, such as interface design. This is one of the reasons this test is appropriate to be used to test the software output. This paper is structured to look for the strengths and weaknesses of black box testing, content similarities, and differences in content by observing existing literature by the method of grouping these three things. Judging from some of the literature included in the observation, black box testing has many advantages and some drawbacks, some of the same contents, and several different contents.
An Approach to an Intelligent Scanning of the Machine Tool Workspace

1. RIYADI T., YULIANTO Y.H., EFFENDY M., SARJITO ZHEN Z., PING T.L., 2018, Evaluation on a Digitized CAD Model of 3D Scanner Used in Reverse Engineering, Conference: 12th South East Asian Technical University Consortium (SEATUC), doi: 10.1109/SEATUC.2018.8788885. CrossRef Google Scholar
Student Performance and Satisfaction in Online vs. Offline English Course: Do Delivery and Exam Format Matter?

The COVID-19 pandemic forced educational institutions around the world to suddenly shift most of their traditional offline classes to the online in 2020. Korean universities were no exception to the trend, and it provided an opportunity to investigate student performance and satisfaction in an online versus offline university English course. In this study, an introductory English linguistics course conducted online in 2020 was compared to the same course conducted offline in 2019. The exams of the online English class probed in this study were the same versions used for the offline class consisting of multiple choice and short answer questions, but were administered in an open-book online format without a proctor. Then, students’ course evaluation results were analyzed to compare their satisfaction in different class delivery formats. Results of online and offline classes revealed that students’ exam results and course evaluation were almost identical, which suggest that the online format of university courses can be as effective as the traditional offline courses for the recent younger generation of online-friendly university students.
An effective way to detect the sum of the first n term of an arithmetic progression

The article looks at teaching students how to find the sum of the first n members of arithmetic progression. Using different approaches to solving a problem can stimulate and stimulate students’ interest in solving a problem. Working on their own will have a positive impact on their development. Students need to be taught how to solve problems in two or more ways. There is a special technique that can be used to influence students’ decisions quickly and efficiently. By using this approach, we can develop our students’ memory, comprehensiveness, and logic. Here are some examples of finding the sum of the first n members of an arithmetic progression by a non-standard method. Special methods can be used by school students, young teachers, and math teachers.
Introduction to the Special Issue On Computational Modelling of Emotion

The papers in this special issue focus on computational modeling of emotion recognition. Emotions play a pervasive role in personal, social, and professional life. As artificially intelligent systems become pervasive in our lives, it is important that these systems are able to understand emotion in humans and simulate the function of emotion to be effective in their interactions with people. Computational models of emotion contribute towards this goal by, on the one hand, serving as a means to test emotion theories and help understand the function of emotion and, on the other, as the end in itself by simulating appropriate emotion and its downstream consequences – such as expressions of emotion – in computational agents. This special issue presents a critical overview of this cross-disciplinary field, with contributions from some of the leading scholars in cognitive psychology and affective computing, focusing both on theory and practice.
Using Sentiment Information for Preemptive Detection of Harmful Comments in Online Conversations

The challenge of automatic moderation of harmful comments online has been the subject of a lot of research recently, but the focus has been mostly on detecting it in individual messages after they have been posted.Some authors have tried to predict if a conversation will derail into harmfulness using the features of the first few messages [1].In this paper, we combine that approach with previous work on harmful message detection using sentiment information [2], and show how the sentiments expressed in the first messages of a conversation can help predict upcoming harmful messages.Our results show that adding sentiment features does help improve the accuracy of harmful message prediction, and allow us to make important observations on the general task of preemptive harmfulness detection.
Deep Learning based Blind Source Identification of whatsApp and Facebook Images

Source Identification is the forensic problem of source device detection of digital images/videos/audio. In this paper, we deal with the problem of Source Camera Identification (SCI) of digital images. The existing forensic techniques of image source identification fail in source identification of images taken from Social Networking platforms, mainly due to the inherent compression characteristics induced in to those images. In this work, deep neural network based architecture is proposed to identify the source of Facebook and whatsApp images. We used InceptionV3 model, which is a 48-layers deep. It surpasses the state-of-the-art performance in terms of online source networks (OSN) image source identification accuracy. We considered smart phone devices in this work for our experiments, and present our results on the recently published VISION dataset.
Object Detection with a Webcam Using the Python Programming Language

Technology developed rapidly along the times, various ways are done to make works easier. One of them is by utilizing artificial intelligence, likes the use of a webcam as a sensor in detecting an object through several stages of image processing. There was research on object recognition previously using three measurement parameters based on color, shape, and size of objects. It is used a webcam as the sensing sensor, and image processing is processed with python programming. In this article, writer described about image processing for the detection of objects used in the research. The results of this device have been tested and are able to detect objects properly based on predetermined color, shape and size. Object detection using a webcam can work properly according to what the author wants.
Structural Classification of Employability Skills Hierarchy using Rasch Analysis Model

This study considers the procedures for conducting item classification employing Raech Analysis Model. The knowledge of the hierarchy enables lecturers to organize their learning objective and also permits the students to measure their employability. The survey study employs exploratory sequential mixed methods. It was conducted to identify and give the hierarchy of the skills required by Electrical Technology students in Colleges of Education in Nigeria to be employable. The first phase involved 10 electrical experts from Industry and Colleges of Education who were purposely selected. The analysis of the findings obtained using Nvivo 12 led to the second phase which comprised of 104 respondents. The sample also consists of Electrical Technology expert in both Industry and Academics. In order to ensure that all items fit the Rasch Analysis Model, the fit statistics were performed to refine and remove all misfits item. Because, the item was ordinal and ranked, Partial Credit (Rasch) Model was involved in the treatment. A separation index of 3.28 and 5.28 was obtained for the technical and non-technical skills with a reliability of .91 and .97 respectively. The implication is that each group is unique and therefore, the most basic item at the bottom of the hierarchy must be learned before the next higher-order item.
Methodology for sentiment analysis in twitter posts about mobile learning

Analysis of social networks has become of great interest to researchers from different areas, including educators, due to Twitter’s growing importance as a space for discussion and dissemination of knowledge and opinions. This reality demands the development of analysis processes that allow to know the topics of interest in the network, the positive or negative feelings in relation to those topics and who the network influencers are. Those objectives guided this research work and in order to achieve them, we developed a methodological proposal for sentiment analysis of tweets. This article describes the process followed, which involved 1) detecting the structure of the communication network, 2) calculating the general metrics, 3) representing the communication network, 4) identifying and analyzing the clusters, 5) calculating their metrics as well as those of the individual nodes and 6) establishing the polarity of the posts published in the network. This paper also describes the methodoly followed to identify trends and topics of interest in the hashtags and web domains included in the tweets. The proposal for analysis presented here is intended to help researchers interested in the field of social networks, to understand the complex interactions that take place in these environments and the way in which information is disseminated, valued and converted into topics of interest thanks to the network users’ actions.
Use of the Web API as a basis for obtaining the latest data on bitcoin prices at 30 exchange places

Abstract Bitcoin has become a commodity traded by millions of traders from all over the world. This is one of the causes of fluctuating price movements. From the data we got on coinmarketcap, Bitcoin is traded on various cryptocurrency trading exchanges. And at every exchange that has a reputation, of course, has an API service to access historical data about the price movements of all the crypto commodities they have traded from the start. By using the PHP programming language and implementation of the CURL function for JSON readings, we can pull Bitcoin movement data in real time. In this paper, Bitcoin is specifically observed because it is the forerunner and the main cryptocurrency commodity traded and is a determinant of Alternative coin price movements in general. In this paper Bitcoin price monitoring is carried out at 30 reputable exchange places through API access provided by each exchange place. Furthermore, conclusions are drawn about the various variants of how to access the API from the 30 bitcoin exchange places. The program code that is displayed directly in this paper can then be used as an initial reference if you want to develop a Cryptocurrency price movement monitoring application for Bitcoin. At the end of the paper, an example of the application of Bitcoin price monitoring will be presented using a web-based application containing charts and supporting indicators as well as a telegram bot to display price depth charts.
Machine-Assisted Script Curation

We describe Machine-Aided Script Curator (MASC), a system for human-machine collaborative script authoring.Scripts produced with MASC include (1) English descriptions of sub-events that comprise a larger, complex event; (2) event types for each of those events;(3) a record of entities expected to participate in multiple sub-events; and (4) temporal sequencing between the sub-events.MASC automates portions of the script creation process with suggestions for event types, links to Wikidata, and sub-events that may have been forgotten.We illustrate how these automations are useful to the script writer with a few case-study scripts.
Low‐rank nonnegative sparse representation and local preservation‐based matrix regression for supervised image feature selection

Matrix regression has attracted much attention due to directly select some meaningful features from matrix data. However, most existing matrix regressions do not consider the global and local structure of the matrix data simultaneously. To this end, we propose a low-rank nonnegative sparse representation and local preserving matrix regression (LNSRLP-MR) model for image feature selection. Here, the loss function is defined by the left and right regression matrices. To capture the global structure and discriminative information of the training images and reduce the effect of heterogeneous data and noises, we impose the low-rank constraint on the self-representation error matrix and the nonnegative sparse constraint on the coefficient vector. The graph matrix can be learned adaptively through representation coefficients, so that accurate local structure information in samples can be revealed. Feature selection is performed by obtained row sparse transformation matrix. An optimization procedure and its performance are also present. Experimental results on several image datasets show that compared with the-state-of-the-art method, the average classification accuracy of the proposed method is improved by at least 1.2% and up to 3.3%. For images with noise or occlusion, the accuracy is improved significantly, up to 4%, which indicates that this method has strong robustness.
Using the Principles of Neurolanguage Coaching and Brain Friendly Techniques in Foreign Language Teaching in the Inclusive Learning Environment

The purpose of this article is to describe the practical application of some principles and methods of neurolingvistic coaching and neuroscience in foreign language teaching in the inclusive educational environment.The research is based on the practical experience of foreign language teaching in mixed ability inclusive classes, in which students with visual impairment learn together with healthy peers.It can be useful for educators who work in the inclusive environment as well as for teachers working with multilevel and mixed ability groups.This paper is addressed to teachers who are constantly searching for new approaches in foreign language teaching and want to incorporate some brain friendly techniques in their work.
Identifying Indicators of Global Financial Crisis with Fuzzy Logic and Data Science: A Comparative Analysis Between Developing and Developed Economies

The main purpose of this study is to define the leading indicators of the 2008 global financial crisis for both developing and developed economies. In this context, 16 different variables are defined with the help of literature review. These variables are weighted by considering the fuzzy DEMATEL approach and the most significant 8 variables are identified. In addition to this analysis, decision tree methodology is also taken into consideration to understand main determinants of the financial crisis. The findings show that growth rate and capital adequacy of the banking industry are accepted as the leading indicators of the global crisis for developed economies. On the other hand, it is also concluded that inflation and industry development are accepted as the indicator of the financial crisis in addition to the economic growth and capital adequacy of the banks for developing economies. While considering the results, it is recommended that developed countries should periodically control economic growth rate and capital adequacy ratio of the banks to understand whether they have a financial crisis risk or not. Furthermore, for developing countries, it is also recommended that inflation rate should be periodically controlled, and they should identify necessary actions in order to minimize inflation rate.
Adaptive Maxwell's equations derived optimization and its application in antenna array synthesis

In this paper, a self-adaptive method for the Maxwell's Equations Derived Optimization (MEDO) is proposed. It is implemented by applying the Sequential Model-Based Optimization (SMBO) algorithm to the iterations of the MEDO, and achieves the automatic adjustment of the parameters. The proposed method is named as adaptive Maxwell's equations derived optimization (AMEDO). In order to evaluate the performance of AMEDO, eight benchmarks are used and the results are compared with the original MEDO method. The results show that AMEDO can greatly reduce the workload of manual adjustment of parameters, and at the same time can keep the accuracy and stability. Moreover, the convergence of the optimization can be accelerated due to the dynamical adjustment of the parameters. In the end, the proposed AMEDO is applied to the side lobe level suppression and array failure correction of a linear antenna array, and shows great potential in antenna array synthesis.
Unsupervised Concept Representation Learning for Length-Varying Text Similarity

Measuring document similarity plays an important role in natural language processing tasks.Most existing document similarity approaches suffer from the information gap caused by context and vocabulary mismatches when comparing varying-length texts.In this paper, we propose an unsupervised concept representation learning approach to address the above issues.Specifically, we propose a novel Concept Generation Network (CGNet) to learn concept representations from the perspective of the entire text corpus.Moreover, a concept-based document matching method is proposed to leverage advances in the recognition of local phrase features and corpuslevel concept features.Extensive experiments on real-world data sets demonstrate that new method can achieve a considerable improvement in comparing length-varying texts.In particular, our model achieved 6.5% better F1 Score compared to the best of the baseline models for a concept-project benchmark dataset.