Reviewerly/exHarmony huggingface dataset:
Bilgisayar Ortamında Bireye Uyarlanmış Test Uygulamalarında Ölçme Kesinliğinin ve Test Uzunluğunun Farklı Koşullar Altında İncelenmesi

Computerized Adaptive Tests (CAT) are gaining much more attention than ever by the institutions especially the ones attracting students worldwide due to the nature of CAT not allowing the same items to be presented to different individuals taking the test.In this study, it was aimed to investigate of measurement precision and test length in computerized adaptive testing (CAT) under different conditions.The research was implemented as a Monte Carlo simulation study.In line with the purpose of the study, 500 items which response probabilities were modeled with the three parameter logistic (3PL) model were generated.Fixed length (15,20), standard error (SE<.30,SE<.50) termination rules have been used for the study.Additionally, in comparing termination rules, different starting rules (θ=0,-1<θ<1), ability estimation methods (Maksimum Likelihood Estimation (MLE) ,Expected a Posteriori (EAP) and Maximum a Posteriori Probability (MAP)), item selection method (Kullback Leibler Information (KLI) and Maximum Fischer Information (MFI)) have been selected since these are critical in the algorithms of CAT. 25 replications was performed for each condition in the generated data.The results obtained from study were evaluated by using RMSE, bias and fidelity values criterions.R software was used for data generation and analyses.As a result of the study, it was seen that choosing the test starting rule as θ=0 or -1<θ<1 did not cause a significant difference in terms of measurement precision and test length.It was concluded that the termination rule, in which RMSE and bias values were lower than the other conditions, was the 0.30 SE termination rule.When the EAP ability estimation method was used, lower RMSE and bias values were obtained compared to the MLE.It was concluded that the KLI item selection method had lower RMSE and bias values compared to the MFI.
Joint attentive adaptive feature fusion network for action recognition

Joint spatio-temporal feature learning is the key to video-based action recognition. Off-the-shelf techniques mostly apply two-stream networks, and they either simply fuse the classification scores or only integrate the high-level features. However, these methods cannot learn inter-modality relationship well. We propose a joint attentive (JA) adaptive feature fusion (AFF) network, a three-stream network that improves inter-modality fusion by exploring complementary and interactive information of two modalities, RGB and optical flow. Specifically, we design an AFF block to implement layer-wise fusion between both modality network channels and feature levels, where spatio-temporal feature representations with different modalities and various levels can be fused effectively. To capture three-dimensional interaction of spatio-temporal features, we devise a JA module by incorporating the inter-dependencies learned with the spatial-channel attention mechanism and combine multi-scale attention to refine the fine-grained features. Extensive experiments on three public action recognition benchmark datasets demonstrate that our method achieves competitive results.
Meetings in Teams

There has been a lot of discussion and research in the business world over the last several years about the time, money, and resources wasted on meetings. Group chat and conversations in Microsoft Teams offer practical ways to discuss topics and make decisions without taking an hour or more out of several team members’ day to be in a meeting together. There are situations, however, in which meetings are necessary. Whether you are in a meeting to discuss status updates, do a project code review, refine a sales plan, or for any other business reason, Microsoft Teams has a way to bring you together effectively and efficiently. Microsoft Teams has smart features that not only help you stay focused before, during, and after meetings but also reduce the time spent in meetings so your teams save time and accomplish more together. All with a consistent experience across devices, screen sizes, and platforms at home, in the office, in the conference room, or on the go.
Invoice Detection and Recognition System Based on Deep Learning

With the development of economy and information technology, a large amount of invoice information has been produced. As one of the important components of the industrial Internet of Things, the recognition of invoice information is urgent to realize its intelligent recognition. Most invoice issuing units basically adopt traditional manual identification methods for the processing of invoices. As the number of invoices increases, problems such as low efficiency in identifying invoice information, error-prone, and difficulty in ensuring security frequently appear. In response to the above problems, this paper designs and implements an invoice information recognition system based on deep learning. The system first solves the problems of low image contrast and lack of image due to poor lighting or noise effects by image preprocessing methods such as image graying and normalization. Second, a target detection and invoice recognition method based on the combination of YOLOv3 + CRNN two models is proposed, and an end-to-end invoice information recognition model is obtained. Finally, the model is used to develop an invoice detection and recognition system based on deep learning. Experiments have verified that the system has the characteristics of high recognition accuracy and high efficiency, which can accurately identify invoice content information and reduce the loss of manpower and material resources.
On the Long Tail Products Recommendation using Tripartite Graph

The growth of the number of e-commerce users and the items being sold become both opportunities and challenges for e-commerce marketplaces.As the existence of the long-tail phenomenon, the marketplaces need to pay attention to the high number of rarely sold items.The failure to sell these products would be a threat for some B2C e-commerce companies that apply a non-consignment sale system because the products cannot be returned to the manufacturer.Thus, it is important for the marketplace to boost the promotion of long-tail products.The objective of this study is to adapt the graph-based technique to build the recommendation system for long-tail products.The set of products, customers, and categories are represented as nodes in the tripartite graph.The Absorbing Time and Hitting Time algorithms are employed together with the Markov Random Walker to traverse the nodes in the graph.We find that using Absorbing Time achieves better accuracy than the Hitting Time for recommending long-tail products.
An Improved BP Deep Neural Network Multimedia Used in Oral English Training

In order to improve the effect of spoken English training, this paper combines multimedia information technology to reform the teaching of spoken English training, and integrates BP neural network English into spoken English training. Moreover, this paper combines the actual needs of spoken English training and the teaching framework of the multimedia system to construct the data set, clean up the data set, and implement the word vector representation of students and professionals. In addition, this paper constructs the entire system framework of the spoken English resource recommendation algorithm based on the graph convolutional neural network, and combines the BP deep neural network algorithm to construct the spoken English training system. Finally, this paper designs an experiment to evaluate the effect of this system. The experimental research results show that the multimedia based on the BP deep neural network proposed in this paper has a good effect in the application research of spoken English training, and can effectively promote the effect of spoken English training of students.
Smart and/or diverse: the paradoxes of machine processed language

Machine language processing tools affect the global online landscape of languages in complex ways. Free NMT tools, such as Google Translate or DeepL, and smart assistants, such as Alexa or Siri, enable people from all over the world to access and produce content in many languages, furthering what I would call interface multilingualism in terms of quantity, visibility and accessibility. But upon closer examination this comes at a cost for linguistic diversity, as these tools tend to streamline and standardize expression, are based on a limited number of corpora and value user engagement metrics over the quality of content.
A Topology Graph Algorithm Based on Lattice-Valued Logic to Solve Satisfiability Problems

Automatic reasoning is a very challenging research area in the field of artificial intelligence. Automatic inference based on lattice-valued logic is an important basic research content in the field of intelligent information processing. It is of great academic significance to deal with incomparability, linguistic valued information, and formal verification of the correctness of corresponding software and hardware systems in various applications. Based on the extensive work of α-resolution automated reasoning in linguistic truth-valued lattice-valued logic, this paper discusses the structure and its α-resolvability of the generalized literal. For further improving the efficiency, a topology graph deduction is proposed as well as its soundness, completeness, and universal algorithm. The use of distributed computing and resource sharing is the core idea of this deduction. In the decision process, as long as one satisfiability solution is found by subtask, the decision result can be given directly, shortening the decision time. The algorithm reduces the time complexity from factorial level to square.
Preparation of Graphene Nanoparticle Surface Modified Metal Oxide Doped Soda-Lime Glass Composite for Application in Water Purification

Synthesis of the nano-adsorbent has become one of the crucial areas of research emphasizing on environmental protection and water remediation issue.This work involves the production of Graphene Oxide Soda-Lime Glass Composite (GO-SLS) with excellent adsorption capacities as filter media, prepared using simple one pot hydrothermal reaction (post activation of the soda lime glass) followed by thermal ageing of the composite from colored glass containing metal oxides and graphene oxide.Characterization of the novel adsorbent has been done using scanning electron microscopy and Fourier-transform infrared spectroscopy (FTIR) Batch experiments were performed to quantify the adsorption kinetics and adsorption capacities of the nano-composite performance of the synthesized composite was evaluated for the removal of organic components and adsorption of Arsenic (As(V)).The synthesized nano-composite exhibited higher dye removal efficiency, better adsorption capacities for heavy metals, removal of turbidity and organic matter.This improved performance may be due to better synergistic molecular interactions of metal oxides in the soda-lime glass, the functionalized groups ingraphene oxide and greater surface area of the GO-SLS composite.Results have illustrated high potential of the newly synthesized nano-adsorbent material for ground water and wastewater treatment purposes.
Understanding the Types of Cyber Crime and Its Prevention

In the era of social media and online provisions, there are also a lot of crimes happening these days. Cyber-crime is linked to the online or crime committed over internet. Such types of attacks are done intentionally due to which they are classified as crime. There are many hackers who has the capability of hacking even the banking details of the user. This paper will explain the meaning of cyber-crime, different types of cyber-crime and also gives the reason as why crime can take place even in the world of internet. There are many tools available which helps to identify the nature of the security attack and helps to avoid it.
Kelayakan Media Pembelajaran iSpring Suite Berbasis Android pada Mata Pelajaran Nahwu Shorof

T his research aims to determine the feasibility of iSpring Suite learning media with a ndroid based in Nahwu Shorof lesson for VII (Female) class MTsN 2 Rejoso . The research method uses a research and development of 4D Thiagarajan. The research instrument were obtained from validation questionnaires for material expert, linguist expert, media expert , and student response questionnaires . The analysis data uses a average value and Likert scale . The research results from the material expert assessment was 83,93% is categorized very feasible, linguist expert was 81,25% is categorized very feasible, media expert was 70,63% is categorized feasible, and student responses was 90% is categorized very feasible. Based on the aveage assessment of material expert, linguist expert, media experts and student responses was 81,45% , it can be said that iSpring Suite learning media with android bassed in Nahwu Shorof lesson is very feasi ble for use.
Experimental investigation of Bayesian bounds in multiparameter estimation

Quantum parameter estimation offers solid conceptual grounds for the design of sensors enjoying quantum advantage. This is realised not only by means of hardware supporting and exploiting quantum properties, but data analysis has its impact and relevance, too. In this respect, Bayesian methods have emerged as an effective and elegant solution, with the perk of incorporating naturally the availability of a priori information. In this article we present an evaluation of Bayesian methods for multiple phase estimation, assessed based on bounds that work beyond the usual limit of large samples assumed in parameter estimation. Importantly, such methods are applied to experimental data generated from the output statistics of a three-arm interferometer seeded by single photons. Our studies provide a blueprint for a more comprehensive data analysis in quantum metrology.
Student’s Perceptions on the Implementation of Youglish in Learning English Pronunciation at English Department

Pronunciation is one of the most important parts of speaking. The wrong pronunciation can lead to the wrong meaning of the word. Therefore, there should be media to help students understand pronunciation more. There are a lot of media can be used, one of them is YouGlish. The objective of the research is to explore students’ perceptions of the implementation of YouGlish in learning English Pronunciation at English Department Universitas Bandar Lampung. YouGlish is a YouTube-based website launched to improve English pronunciation that helps users to find out how to pronounce a word or phrase correctly and appropriately. In this research, the researchers used the descriptive qualitative method. This study was conducted for first semester English students at Universitas Bandar Lampung. This study's instruments were a questionnaire and an interview. The finding shows that students have positive perceptions of the implementation of YouGlish. They perceived that there were several benefits from the use of YouGlish. The results of this research showed students' perception of the implementation of YouGlish in learning English Pronunciation. The questionnaire results also showed mostly positive and negative feedback. The combination of those two instruments is the way to know what the students’ perceptions of the implementation of YouGlish in learning pronunciation. In conclusion, YouGlish can make students interested in studying Pronunciation and stimulate students to learn as it has features which will increase the enthusiasm for learning.
Leading Sentence News TextRank

Application of automatic text summarization is a popular Natural Language Processing task and often used in extracting lengthy content to produce short summary. This is a tedious yet time-consuming task. This study focuses on Malay news articles with the aim to select representative sentences for Malay news headline generation. The dataset used in the experiment is a collection of multi-genre Malay news published between year of 2017 and 2019 from Bernama.com. In this study, a leading sentence approach is applied in the TextRank with TF-IDF and Word2Vec as language models to perform salient sentence extraction. In the experiment, the top-ranking sentences extracted are based on the 15%, 20%, 25% and 30% of the original news content. The extracted contents are evaluation against the original news headline using ROUGE evaluation matric. The model shows that the inclusion of first sentence and first two sentences from the news are able to achieve significant improvement. This leading sentence approach is able to achieve improvement of the F1 score from 1.36 to 7.98. Besides that, the experiment also proofs that the ROUGE scores decrease as the percentage of extraction increase. Thus, the proposed method is fast and resource efficient as compared to other state-of-the-art Natural Language approach.
Management of Obesity Using Fuzzy Analytic Hierarchy Process

Obesity has been linked to several heart diseases .Unfortunately, Malaysian today are the most overweight or obese people among Asian nations. That is, half of the population is overweight. These are serious social consequences. Therefore, specific and serious actions are needed to reduce or eliminate this threat. The rapid increase in obesity rates has directed the link with the factors such as low physical activities, ease of transportation, food intake and emotional factor. Thus, this study aims at identifying the most significant factors affecting obesity in Peninsular Malaysia. The factors that are detected to be the cause of obesity can help manage individuals with obesity problems. Fuzzy Analytic Hierarchy Process (F-AHP) approach was used for the identification and ranking of each factor. The method adopted was one of the most reliable methods for both identification and the ranking off the most significant factors affecting obesity. The study selected seven respondents and applied the calculations. The calculations consist of seven steps. Initially, the goals and criteria to construct the hierarchy process were determined. This followed by questionnaires distribution to the respondents by using the linguistic scale. After that, two parameters were calculated, these include geometric mean weight and fuzzy weight. Lastly, the fuzzy weight and all the calculation were defuzzified and normalized. The results show the factors that affect obesity the most in Peninsular Malaysia is physical inactivity with the weight of 41.6% after normalized. The physical inactivity has been ranked the first factor, the food intake ranked second, the third rank is emotional factor and lastly is technology. The precise identification and ranking are very meaningful to the management of obesity problem. Firstly, there is a need to increase healthy physical activity. Secondly, the need to change their diet according to experts. Lastly, they need to manage their emotions and technology well.
Interactive Structure Editor for Scenario Prototyping Tool

The task of automating the routine work of computer game writers and narrative designers, set forth in earlier works, has been continued in the presented work. The issues of visualization of branching narrative structures of computer games are considered, the analysis of various approaches to visualization of the plot and other important components of a video game is performed, a technological stack is selected and specific solutions for storing in the form of a structured script, allowing the generation of continuing narrative branches and testing of the narrative prototyping stage using the automatically generated text novelette are given.
THE ACTIVITIES OF V.O. SUKHOMLYNSKYI SSPL OF UKRAINE IN THE COVID-19 PANDEMIC CONDITIONS: CHALLENGES, PROBLEMS AND WAYS OF THEIR SOLUTION

The article deals with the activities of V.O. Sukhomlynskyi SSPL of Ukraine in the conditions of the quarantine restrictive measures due to the COVID-19 pandemic. The results of research, scientific and organizational work have been presented. Attention has been focused on the intensification of the electronic resources creation, the demand for which has grown in the conditions of remote work of academic, scientific and pedagogical workers, teachers and applicants for education. The information on the introduced online projects, the virtual reading rooms, features of filling of the electronic information and bibliographic resource “Outstanding educators of Ukraine and the world” has been presented. The approbation of research results was carried out by participation in virtual scientific mass events has been noted. The course of the library’s international activities which were carried out in a remote mode, have been highlighted.The results of scientific, information and library work have been analyzed. It has been noted that the quarantine restrictions directed the librarians' activities to the virtual space and to meeting the distant users’ needs. Scientific, informational and bibliographic services for remote users by providing access to electronic resources and providing online services have been considered. The results of library and bibliographic information of the educational community on topical issues of education and science have been highlighted. The components of the scientific and methodological work of V. O. Sukhomlynskyi State Scientific and Pedagogical Library of Ukraine as the main coordination center of educational libraries network of the Ministry of Education and Science of Ukraine and the Academy of Educational Sciences of Ukraine. Exhibition and exposition activities carried out during the quarantine period through the preparation of virtual book exhibitions have been presented. The article determines the priorities for the development of scientific and information, library and bibliographic support of education, pedagogy taking into account the modern social challenges.
Development of teaching digital competence in initial teacher training: A systematic review

This article presents the results of a systematic review of different studies published in high-impact journals on the development of teaching digital competence (TDC) in initial teacher training. The methodology of the study is based on the documentary research approach, with the application of a systematic literature review covering the period between 2009 and 2019 in different databases. The sample is made up of 102 articles that are registered in a multidimensional analysis and classification matrix. One of the main findings is that in the last ten years there has been a significant increase in the literature related to teaching digital competence with the prevailing use of the quantitative methodological approach. Likewise, 50.98% of the articles are the result of the application of instruments to assess TDC. It can be concluded that TDC is a line of research with multiple dimensions of analysis and in constant evolution.&#x0D; Keywords: teaching digital competence, initial teacher training, educational technology, information, communication technology, systematic review
Quantum and Post-Moore’s Law Computing

This special issue of Internet Computing includes four articles that target the technical challenges and discuss the vast opportunities of quantum computing. The first article discusses mitigating the decoherence error in quantum data transformation algorithms and could potentially benefit big data analysis. The second article reviews a wide range of emerging applications that quantum computing could help. The third and fourth articles discuss the quantum cloud, providing software system design guidelines and characterizing hardware errors, both of which have the potential to improve today's cloud-based quantum computing infrastructure and benefit many users of the quantum cloud service.