Reviewerly/exHarmony huggingface dataset:
The Effect of Item Form on Estimating Person's Ability, Item Parameters, and Information Function According to Item Response Theory (IRT)

This study aimed at comparing the effect of two test item formats (multiple-choice and complete) on estimating person's ability, item parameters and the test information function (TIF).To achieve the aim of the study, two format of mathematics(1) test have been created: multiple-choice and complete, In its final format consisted of (31) items.The test has been applied on (300) students in Tabuk University.The responses of the examinees were analyzed by BILOG-MG 3 programs for each item form according to the two parameter logistic model.The study findings revealed the following: there were statistically significant differences at the level of significance (α=0.05)among the standard error means for estimating item parameters (difficulty and discrimination) due to the format of test item in favor of the complete test.And the results showed there were statistically significant differences at the level of significance (α=0.05)among the standard error means for estimating person's ability due to the format of test item in favor of the complete test.On the other hand, the results showed there were statistically significant differences (α=0.05)among the standard error means of test information function (TIF) due to the format of test item in favor of the complete test.The study recommends educating teachers to diversify the forms of items of the tests they use so that they contain the two forms (multiple-choice and complete), and using complete items on a larger scale.than it is now being more discrimination.
The Role of Psychological Approaches in Data Journalism Visualisations

ABSTRACT Data journalism is the process of analysing, clarifying, and visualising data to convey a story and present the news more effectively. Data visualisation, the method that allows communicating large amounts of data, can improve viewers’ understanding of news and can increase interaction (Bradshaw 2010). The minimalistic theory of the “data-ink” ratio, which can design effective data visualisations, explains that ink should only be used to display the data. Data visualisations with right selection of visual channels can influence memory, attention, and news comprehension (Pan, 2010; Smilek, Dixon &amp; Merikle, 2002). The purpose of this study is to explore if and how visual designers assess psychological principles in journalistic data visualisation. Data visualisations from two well-known organisations (the BBC and the Guardian) presenting COVID-19 information were collected. The analysis was effectuated based on a ‘decoding model’ initiated by Munzner (2014) to identify their visual attributes. The findings concluded that media organisations consider and apply psychological metrics in the visual design process. Interesting colour differences between visualisations were identified and discussed. A limitation was that the sample of the study was small. Future research could include more data visualisations or could use interviews to understand better the way visual designers practice psychology. Keywords: Data Visualisation, Cognitive Psychology, Journalism, Visual Perception
Intelligent Deep Learning Empowered Text Detection Model from Natural Scene Images

The scene Text Recognition process has become a hot research topic and a challenging task owing to the complicated background, varying light intensities, colors, font styles, and sizes.Text extraction from natural scene images encompasses two main processes: text detection and text recognition.The latest advancements in Machine Learning (ML) and Deep Learning (DL) concepts can effectually automate the text detection and recognition process by training the model properly.In this view, this paper presents an Automated DL empowered Text Detection model from Natural Scene Images (ADLTD-NSI).The ADLTD-NSI technique includes two important processes: text detection and text recognition.Firstly, a single shot detector (SSD) with Inception-v2 as a baseline model is employed for text detection, an object detector based on the VGG-16 framework for feature map extraction followed by six convolution layers.Secondly, Convolutional Recurrent Neural Network (CRNN) technique is utilized for the text recognition process.Besides, the recurrent layers in the CRNN model utilize long short-term memory (LSTM) for encoding the sequence of feature vectors.Lastly, Connectionist Temporal Classification (CTC) loss is applied to predict text labels equivalent to the sequences from the recurrent layers.A wide range of experiments was carried out on benchmark COCO datasets, and the results are examined in several aspects.The experimental outcomes showcased the better performance of the ADLTD-NSI technique over the other compared methods with a maximum accuracy of 96.78%.
The effect of Gaussian filter and data preprocessing on the classification of Punakawan puppet images with the convolutional neural network algorithm

&lt;p class="Body"&gt;&lt;span&gt;Nowadays, many algorithms are introduced, and some researchers focused their research on the utilization of convolutional neural network (CNN). CNN algorithm is equipped with various learning architectures, enabling researchers to choose the most effective architecture for classification. However, this research suggested that to increase the accuracy of the classification, preprocessing mechanism is another significant factor to be considered too. This study utilized Gaussian filter for preprocessing mechanism and VGG16 for learning architecture. The Gaussian filter was combined with different preprocessing mechanism applied on the selected dataset, and the measurement of the accuracy as the result of the utilization of the VGG16 learning architecture was acquired. The study found that the utilization of using contrast limited adaptive histogram equalization (CLAHE) + red green blue (RGB) + Gaussian filter and thresholding images showed the highest accuracy, 98.75%. Furthermore, another significant finding is that the Gaussian filter was able to increase the accuracy on RGB images, however the accuracy decreased for green channel images. Finally, the use of CLAHE for dataset preprocessing increased the accuracy dealing with the green channel images.&lt;/span&gt;&lt;/p&gt;
The Role of the Library in Actualising United Nation Sustainable Development Goals in South Africa

This chapter critically reflects on the potential roles of South African libraries in facilitating the actualisation of the Sustainable Development Goals (SDGs) of the United Nations (UN). The implemented UN 2030 Agenda is all-encompassing, facilitating financial, environmental, and communal growth. The attainment of these SDGs will be via an inclusive agenda, leaving no-one behind. Libraries are major institutions that could assist universities to achieve the SDGs by playing a critical role in research and education. The society that is privileged to have unlimited, but controlled access to information will do well in eliminating inequity, in developing sustainable strategies for food security, in adopting quality inclusive education, and in supporting public health. In addition, the society will place more value on research and innovations. To achieve the purpose of this study, a critical review of literature was conducted. Therefore, this chapter highlights some expectations of librarians during this era of knowledge economy. It also highlights possible challenges that libraries could be facing in meeting such expectations due to the unprecedented Covid-19 pandemic. Furthermore, this chapter emphasises the significance of the government’s partnerships and national support to realise the inclusion of access to information, and individual access to information and communication technology in the UN 2030 Agenda. Additionally, suggestions on how the government can support libraries in meeting its expectations towards attaining the SDGs, are recommended. Conclusively, effective partnerships have become a crucial part of library management in the attainment of the SDGs.
Word-formation complexity: a learner corpus-based study

This article explores the word-formation dimension of learner text complexity which indicates how skilful the non-native speakers are in using more and less complex - and varied - derivational constructions. In order to analyse the association between complexity and writing accuracy in word formation as well as interactive effects of task type, text register, and native language background, we examine the materials of the REALEC corpus of English essays written by university students with Russian L1. We present an approach to measure derivational complexity based on the classification of suffixes offered in Bauer and Nation (1993) and then compare the complexity results and the number of word formation errors annotated in the texts. Starting with the hypothesis that with increasing complexity the number of errors will decrease, we apply statistical analysis to examine the association between complexity and accuracy. We found, first, that the use of more advanced word-formation suffixes affects the number of errors in texts. Second, different levels of suffixes in the hierarchy affect derivation accuracy in different ways. In particular, the use of irregular derivational models is positively associated with the number of errors. Third, the type of examination task and expected format and register of writing should be taken into consideration. The hypothesis holds true for regular but infrequent advanced suffixal models used in more formal descriptive essays associated with an academic register. However, for less formal texts with lower academic register requirements, the hypothesis needs to be amended.
A Simple Baseline for Domain Adaptation in End to End ASR Systems Using Synthetic Data

Automatic Speech Recognition(ASR) has been dominated by deep learning-based end-to-end speech recognition models.These approaches require large amounts of labeled data in the form of audio-text pairs.Moreover, these models are more susceptible to domain shift as compared to traditional models.It is common practice to train generic ASR models and then adapt them to target domains using comparatively smaller data sets.We consider a more extreme case of domain adaptation where text-only corpus is available.In this work, we propose a simple baseline technique for domain adaptation in end-to-end speech recognition models.We convert the text-only corpus to audio data using single speaker Text to Speech (TTS) engine.The parallel data in the target domain is then used to fine-tune the final dense layer of generic ASR models.We show that single speaker synthetic TTS data coupled with final dense layer only fine-tuning provides reasonable improvements in word error rates.We use text data from address and e-commerce search domains to show the effectiveness of our low-cost baseline approach on CTC and attention-based models.
Computer Sales Forecasting System Application Using Web-Based Single Moving Average Method

Abstract—Hamas Com Computer Store is one of the computer shops in the city of Surakarta. There is a change in the turnover of toy sales every year, management management in managing the store's financial statements. hence the need for a system that can forecast sales of goods for the coming year. The purpose of this research is to design a computer goods sales forecasting system by applying the Single Moving Average method. This method was chosen because it has the ability to observe, look for the average value as a forecast for the future period. Data collection techniques in this research are observation (observation), interviews (interviews) and literature study. In the design of this system is made with Context Diagram, HIPO, DAD, relations between tables, database design and UML. This application is made using the PHP programming language and the database uses MySQL. The final result is a sales data report and sales prediction results. The results of the tracking signal control chart error test results show a value of no more than 4 so the system is declared good.
Design Of Sales Information System In Website-Based Isaylees Sport Store

Abstract— This study aims to create a website-based sales information system at the Isaylees Sport store. The design and manufacture of a sales information system at the Isaylees Sport store is carried out to make it easier for sellers to manage sales and purchase data. In this system, sellers are expected to be able to market products with a wider range and sell products effectively and efficiently. In addition to providing convenience for sellers, this system also makes it easy for customers to make buying and selling transactions at the Isaylees Sport store. The method used in system design is through the stages of making DFD and ERD diagrams. Results from this research, namely the sales information system at the website-based isaylees Sport store that has been designed to have automatic payment features using a payment gateway, automatic postage checks and order tracking. The system that has been designed also provides convenience for sellers and buyers. The result of this research is an e-commers application that is used in sales transactions.
Scalable decision tree based on fuzzy partitioning and an incremental approach

&lt;span&gt;Classification as a data mining materiel is the process of assigning entities to an already defined class by examining the features. The most significant feature of a decision tree as a classification method is its ability to data recursive partitioning. To choose the best attributes for partition, the value range of each continuous attribute should be divided into two or more intervals. Fuzzy partitioning can be used to reduce noise sensitivity and increase the stability of trees. Also, decision trees constructed with existing approaches, tend to be complex, and consequently are difficult to use in practical applications. In this article, a fuzzy decision tree has been introduced that tackles the problem of tree complexity and memory limitation by incrementally inserting data sets into the tree. Membership functions are generated automatically. Then Fuzzy Information Gain is used as a fast-splitting attribute selection criterion and the expansion of a leaf is done attending only with the instances stored in it. The efficiency of this algorithm is examined in terms of accuracy and tree complexity. The results show that the proposed algorithm by reducing the complexity of the tree can overcome the memory limitation and make a balance between accuracy and complexity.&lt;/span&gt;
Privacy protection domain-user integra tag deduplication in cloud data server

&lt;span&gt;The cloud with strong storage management has recently developed in the big data world which can confirm the data integrity and keep just a single data duplicate. Many cloud auditing storage techniques have been developed to overcome the data deduplication (DD) problem, but they are vulnerable and can't resist brute force attacks (BFA). There is some privacy leakage problem that occurred in the present method. In this article, an original strategy called domain-user integra tag (DUIT) has been presented which comprises inter and intra deduplication with file tag and symmetric encryption key. The DUIT has two phases, the first one is random tag generation for Intra deduplication and the other is random ciphertext (CT) generation for encryption. The benefit of the DUIT is the security of individual user’s files would not reveal to people in general, hence we proved that the DUIT is protected from the BFA. Finally, an experiment has conducted in Linux processor and C program software. The outcome of DUIT demonstrates that our method has reduced the computation cost (CC) by 27% and 35% and searching complexity (SC) by 10% and 26% related with the previous methods. It is decided that the DUIT achieves the low CC and SC.&lt;/span&gt;&lt;br /&gt;&lt;div style="mso-element: comment-list;"&gt;&lt;div style="mso-element: comment;"&gt;&lt;div id="_com_1" class="msocomtxt"&gt;&lt;!--[if !supportAnnotations]--&gt;&lt;/div&gt;&lt;!--[endif]--&gt;&lt;/div&gt;&lt;/div&gt;
Decision Model of Wireless Communication Scheme Evaluation via Interval Number

This study aims to evaluate the communication schemes of wireless communication systems to make reasonable decisions. Firstly, the wireless communication scheme is assessed based on the interval number technology, and the primary evaluation process and evaluation index system are described. Secondly, the analytic hierarchy process of interval numbers is studied. Besides, a decision model has been established for wireless communication scheme evaluation. Thirdly, the evaluation index of the wireless communication scheme is determined through the simulation analysis of the model. Finally, experiments verify the decision model for evaluating wireless communication schemes based on interval numbers. Channel encoding can be adjusted automatically and can effectively track electrical signals. The results demonstrate that under the same high signal-to-noise ratio (SNR), the error rate in the 16 quadrature amplitude modulation (QAM) mode decreases faster than that in the 2 frequency shift keying (FSK) mode, so the 16QAM mode is better than the 2FSK mode. Under a low SNR, the binary phase shift keying mode is superior to the 2FSK mode, and the 2FSK mode is superior to the 16QAM mode. The bit error rate of the communication signal in the additive white Gaussian noise channel is the lowest. These findings provide a solid foundation for a universal and scalable wireless communication system. This research has practical application value for scheme evaluation and model decision-making in the wireless communication field. Models need to be tested and evaluated based on large amounts of statistical data in future studies.
Automatic Logo Detection with Deep Region-based Convolutional Networks

Abstract: Deep learning is an important element of data science, which includes statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data. It makes this process faster and easier. We present a deep learning system for automatic logo detection in real world images. We base our detector on the popular framework of Faster R-CNNand compare its performance to other models such as Mask R-CNN or RetinaNet. We perform a detailed empirical analysis of various design and architecture choices and show how these can have much higher influence than algorithmic tweaks or popular techniques such as data augmentation. We also provide a systematic detection performance comparison of various models on multiple popular datasets including FlickrLogos-32, TopLogo-10 and recently introduced QMUL-OpenLogo benchmark, which allows for a direct comparison between recently proposed extensions. By careful optimization of the training procedure we were able to achieve significant improvements of the state of the art on all mentioned datasets. We apply our observations to build a detector to detect logos of the Red Bull brand in online media and images. Automatic logo detection and recognition is a challenging computer vision task with many applications. One of the typical purposes of such systems is measurement of brand visibility and prominence in online media or television broadcasts.
Cuckoo algorithm with great deluge local-search for feature selection problems

&lt;p class="Abstract"&gt;&lt;span lang="EN-US"&gt;Feature selection problem is concerned with searching in a dataset for a set of features aiming to reduce the training time and enhance the accuracy of a classification method. Therefore, feature selection algorithms are proposed to choose important features from large and complex datasets. The cuckoo search (CS) algorithm is a type of natural-inspired optimization algorithms and is widely implemented to find the optimum solution for a specified problem. In this work, the cuckoo search algorithm is hybridized with a local search algorithm to find a satisfactory solution for the problem of feature selection. The great deluge (GD) algorithm is an iterative search procedure, that can accept some worse moves to find better solutions for the problem, also to increase the exploitation ability of CS. The comparison is also provided to examine the performance of the proposed method and the original CS algorithm. As result, using the UCI datasets the proposed algorithm outperforms the original algorithm and produces comparable results compared with some of the results from the literature.&lt;/span&gt;&lt;/p&gt;
Historical Discourse of Professionally Important Qualities of a Teacher in the Context of Pedagogical Ideal

Problem and goal. In the modern sociocultural situation, the problem of the essence of the professional activity of a teacher and the personal qualities conditioned by it, its determination by global changes and the argumentation of the role of a person in the education system is actualized, and therefore, there is an urgent need to study the patterns, ways and trends of the pedagogical system. The purpose of the article is to consider the historical foundations of the problem of professionally important qualities of a teacher at the present stage, their conditionality by the mechanisms of the pedagogical process and pedagogical activity for the development of the individual, all participants in the educational process. Methodology. The methodological foundations of this material are mediated by the foundations of competency-based, cultural and contextual methodology, which consider the professionally important qualities of a teacher as aspects of the personality of a competent specialist in modern sociocultural determination and in the context of specific situations of his professional activity. Research results. Based on the analysis of the historical development of the concept of professionally important qualities of a teacher’s personality, the author singled out socially significant increments and the main directions of the study of the phenomenon in the historical and pedagogical retrospective. In conclusion, the author concludes that the professionally important qualities of a teacher are formed in the process of his professional training, pedagogical activity and are able to improve throughout the entire professionalization.
Play-Based Activities with a CoderBot Robot on a Pediatric Ward: A Case Study

Being hospitalized is a threatening and stressful experience for many children. From a psychological point of view, children may experience increased feelings of anxiety and fear that can negatively influence their behavioral, cognitive, and emotional outcomes. To mitigate such adverse effects on children’s mental health and well-being, interventions that might contribute to protecting the emotional domain of hospitalized children are welcome. The present case study of a single-setting intervention allowed us to evaluate the impact, on children admitted to a pediatric short-term recovery ward (N = 61), of participating in play-based activities with a CoderBot robot. The methodology spanned multiple data sources (children, parents, nurses), field observation, and a sequential (quantitative–qualitative) mixed-method approach to data analysis. We found that robot-based activities are associated with enhanced well-being (particularly positive emotions). Both the participating children and their caregivers reported that the activity was enjoyable and interesting, especially thanks to its technologically innovative nature. We critically discuss these positive findings in relation to the strengths of our pilot study, as well as its contextual and methodological limitations, and outline possible future lines of development for this kind of project.