Reviewerly/exHarmony huggingface dataset:
TEXT ADAPTATION AT THE ADVANCED EFL PROFICIENCY LEVEL: RESULTS OF A CASE-STUDY

There is broad consensus that the ultimate goal of foreign language education is the formation of a language user who can function successfully and effectively in the sociocultural environment of the target language as well as in the international sphere. The pace at which the society develops nowadays poses an additional challenge to foreign language teachers, connected with the way in which recent social developments can be incorporate into their teaching and learning routine and thus make up for the relative rigidness of the published instructional materials. The present research effort attempts to provide an outline of best practices connected with the adaptation of authentic texts for the purposes of foreign language instruction, which can be useful as a guidance for in-service teachers. The Advanced Proficiency level is of particular interest in this regard due to the fact that learners at this stage are supposed to have already acquired the ability to function independently in a broad variety of linguistic contexts so modifications are likely to be scarce but unique purpose-wise. The present research, therefore, pursues the goal of identifying and analysing the purposes of the application of text adaptation techniques in the two chosen course books of English as a foreign language. Comparative and contrastive analysis is applied as the main method, whereby the course book texts are aligned with their original counterparts. The detected modifications are coded and categorised according to their form and role in the learning context created.
EARLY INVOLVEMENT OF STUDENTS IN RESEARCH ON PROBLEM CONCERNING PSYCHOLOGICAL, COGNITIVE, AND DIDACTICAL ASPECTS OF THE MATHEMATICS EDUCATION

Information about the paper titled "EARLY INVOLVEMENT OF STUDENTS IN RESEARCH ON PROBLEM CONCERNING PSYCHOLOGICAL, COGNITIVE, AND DIDACTICAL ASPECTS OF THE MATHEMATICS EDUCATION" at IATED Digital Library
Compression Techniques for the JPEG Image Standard by Using Image Compression Algorithm

Because of the raising needs for transmitting images in computer, mobile milieus, the study in the area of compressing image maximized considerably. Compressing image plays a critical part in processing digital images. The essential concept of compressing data is to decrease the data correlation. Through employing Discrete Cosine Transform (DCT), the data in time field could be transmuted into the field of frequency. Due to the reduced sensitivity of human sight in higher frequency, I is possible to compress data of the image or video by overturning its high frequency constituents nonetheless do no alteration to the eye. When pictures move like in video, the data in three-dimnsional space includes spatial plane and time axis. Hence, beside decreasing spatial correlation, time correlation is needed to be decreased. A process is presented named Motion Estimation (ME). Moreover, we can substitute the image by a Motion Vector (MV) to decrease time correlation. Thus, the improvement of effective methods for image compression becomes essential. Through the study, we similarly present JPEG standard and MPEG standard that are reputed image and video compression standard, correspondingly.
CONSTRUCTING A MATHEMATICAL OBJECT IN 20 MINUTES: A MINISERIES OF MINILABS

A learning experience that is concentrated on sight and hearing is one of the most evident features of online teaching. In this paper we describe an activity which is aimed to include touch in learning, even for high school and undergraduate students. The minilabs are also open to high school teachers. The activity consists of a miniseries of three labs, each one lasting thirty minutes.The students are asked to construct a mathematical object within twenty minutes, under the guide of a teacher who shows all the steps on a camera; once the object is ready, a brief discussion on the object itself, its uses and on its mathematical properties follows.The three objects are:- a ruled surface made of woolen threads- a pantograph made of strips of cardstock- a kirigami fractal made of paper.An important feature of these objects is that they are made with materials that are very easy to retrieve at home, as the minilabs take place during a lockdown period.In developing this experience, we want to change the learning context focusing on some ideas that underpin active learning, such as promoting positive feelings and providing the joy of discovery for a learner with the learning by doing approach.One of the basic ideas is to interact with students and engage them in concrete experiences with the aim of overcoming some problems emerged by the complexity of teaching and learning processes that have arisen in recent period, when a massive use of digital teaching has been required. Furthermore, we want to positively interact with students using some strategies such as providing tasks that reveal the mathematics underlying real facts and presenting activities that promote online dialogue.The practical activities represent a pleasant integration to the classical theoretical lessons and stimulate interest in the study of mathematical and geometrical notions, often seen as boring arguments, when they are learned in a schematic or mnemonic way. Moreover, the possibility of being engaged for a short time led to active involvement by a large number of students.
A MODEL OF PERSONAL FACTORS PROMOTING THE SUCCESS OF ONLINE-COURSE LEARNING (ON THE EXAMPLE OF A STUDENT SAMPLE)

A. Maltsev, M. Klimenskikh, V. Savelev, J. Lebedeva, L. ZubovaUral Federal University (RUSSIAN FEDERATION)
A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification

This paper presents the intrinsic limit determination algorithm (ILD Algorithm), a novel technique to determine the best possible performance, measured in terms of the AUC (area under the ROC curve) and accuracy, that can be obtained from a specific dataset in a binary classification problem with categorical features {\sl regardless} of the model used. This limit, namely the Bayes error, is completely independent of any model used and describes an intrinsic property of the dataset. The ILD algorithm thus provides important information regarding the prediction limits of any binary classification algorithm when applied to the considered dataset. In this paper the algorithm is described in detail, its entire mathematical framework is presented and the pseudocode is given to facilitate its implementation. Finally, an example with a real dataset is given.
코로나19의 해양수산부문 파급효과: 1국 CGE모형의 활용

It has been difficult to look ahead the global economy since the outbreak of the COVID19 pandemic in 2020, but the economy is turning into a positive growth as vaccines were developed at the end of 2020 and the formation of collective immunity could be foreseeable in some countries. However, the economic situation will not improve significantly in countries where the formation of collective immunity is delayed. In particular, damages to ocean and fisheries industries where face-to-face activities are crucial are expected to persist for a long time. In this context, this paper aims to construct General Equilibrium Model which could estimate the effect of COVID19 on each industries in a long term. As the estimation results of this paper, the COVID19’s aftermath to the oceans and fisheries industry could be more severe than that of other industries. In particular, the ship and offshore plant construction and repair industry is expected to decrease in exports(-14.29%), followed by the offshore and fishery equipment manufacturing industry (-6.97%) and the aquatic product processing industry (-2.27%).
Penerapan Data Mining Analisa Penyakit Menular Pada Manusia

The use of data mining in technology is growing day by day. In this research, the author discusses the application of data mining in the medical field, data analysis of infectious diseases in humans and the use of infectious disease data in UPT Puskesmas Sei Langkai. Infectious diseases in humans are one type of disease that has a large amount of data and accumulates because basically infectious diseases have various causes and effects so that the purpose of this research was to identify and then analyze the highest to lowest levels of 7 types of data on infectious diseases in humans with a total of 1,212 patients in 2019 and 2020 using the K-Means clustering algorithm. From the data that has been processed get results that Acute Respiratory Infections and COVID-19 have the highest number of data in  Tembesi, leprosy, dengue fever, and measles have the highest number of data in Sei Langkai, while HIV and TB has the highest number of data in Sei Pelunggut. The conclusion of this research is using the K-Means method and testing the RapidMiner application, it can facilitate data processing and has an accurate final value and effectively used in big data processing.
Comparison of human agency and its components among Farhangiān and State University students

Nowadays, the process of development of people aged 18 to 25, especially among University students, has become an individual project due to the reduced institutional support, and requires a human agency. On the other hand, the development of human agency among the teacher training students is of most importance, compared to other students, due to the complexity of the teaching job. The purpose of this study was to investigate and compare the level of agency and its components among the students of Farhangiān University (FU) and the State University (SU). This study was conducted with a cross-sectional survey method on a sample of 700 undergraduate students. To collect data, Multi-Measure Agentic Personality Scale of Cote (1997) was used.Univariate analysis of variance yielded a complex pattern of findings about the degree of students’ agency, in which the level of agency was a function of the interaction between the type of University, gender, and age. Multivariate analysis of variance showed that there is interaction between the type of University and the age of students in determining the components of internal locus of control and self-efficacy. Findings showed that, at the entrance to the FU, the level of agency of both genders was high and in males was higher than females, however, in the long run, the level of agency in both gender is reduced and get closer together, and this was the opposite in the SU. Findings also showed that the components of the internal locus of control and self-efficacy of teacher training students at the beginning of entering the FU were more than SU students, but over time, the level of both components decreased in the FU, and increased in SU. From the comparison of the trend of changes in the level of agency and its components in students based on demographic variables, it was concluded that the presence in the University does not have a considerable impact on the level of agency and its components; and FU limits the agency and some of its components among the students.
Education Through Digital Communication

The educational process is a dialogue in the form of communication and interchange of meanings. The essence of the distance learning is based on modern digital technologies. Its roots are in the perception of education as a protected legal asset and the constitutional principle of compulsory education places the problem of its implementation. At the beginning of the 21st century, the construction of virtual higher education platforms in Bulgaria was started and the process was institutionalized after the accession to the EU. A number of challenges arise in this respect, such as distance learning in universities, training of persons with disabilities, training during a state of emergency.
IMPLEMENTASI AUGMENTED REALITY SEBAGAI MEDIA PENGENALAN ALAT-ALAT LITURGI AGAMA KATOLIK BERBASIS ANDROID MENGGUNAKAN METODE MARKER BASED TRACKING

Technology is always developing in various aspects, one of which is augmented reality technology. The existing media introduction has also been very developed, especially the media introduction made with augmented reality. Information delivery will also look more interesting and interactive when using augmented reality technology. Augmented reality is defined as a technology that combines 2D or 3D objects, then displayed in 3D and looks real.As in this study, an application was created using augmented reality technology about catholic liturgical equipment. The goal is to make the information obtained about catholic liturgical equipment more interesting and interactive as it increases people's interest in getting to know the catholic liturgical equipment. To create applications with augmented reality technology, one of them can use the marker based tracking method, namely by utilizing markers to be scanned and displaying objects in 3D. Therefore, the application using augmented reality technology for the introduction of catholic liturgical equipment can provide more interesting and interactive information.
Systematic Literature Review of Validation Methods for AI Systems

Context: Artificial intelligence (AI) has made its way into everyday activities, particularly through new techniques such as machine learning (ML). These techniques are implementable with little domain knowledge. This, combined with the difficulty of testing AI systems with traditional methods, has made system trustworthiness a pressing issue. Objective: This paper studies the methods used to validate practical AI systems reported in the literature. Our goal is to classify and describe the methods that are used in realistic settings to ensure the dependability of AI systems. Method: A systematic literature review resulted in 90 papers. Systems presented in the papers were analysed based on their domain, task, complexity, and applied validation methods. Results: The validation methods were synthesized into a taxonomy consisting of trial, simulation, model-centred validation, and expert opinion. Failure monitors, safety channels, redundancy, voting, and input and output restrictions are methods used to continuously validate the systems after deployment. Conclusions: Our results clarify existing strategies applied to validation. They form a basis for the synthesization, assessment, and refinement of AI system validation in research and guidelines for validating individual systems in practice. While various validation strategies have all been relatively widely applied, only few studies report on continuous validation. Keywords: artificial intelligence, machine learning, validation, testing, V&V, systematic literature review.
IMPACT OF SATELLITE TELEVISION ON AGRICULTURAL DEVELOPMENT IN PAKISTAN

Issues concerning the application and use of information and communication technologies, especially in agricultural-related sectors have been discussed and debated rather extensively. Most of the discussions concerning the use of ICT to obtain disseminate or retrieve data and information, including information related to agriculture seems to focus on the problems and implications of the use of ICT. The trend seems to be that the literature on ICT usage and application in the agricultural sector seems to highlight problems on accessibility, commitment of the government especially in terms of the allocation of funds, and the level of acceptance among the general public to fully use ICT for economic and other related activities. The main objective of the study is to analyse the impact of satellite television on agricultural development among the general public. The primary data for the study was collected via the use of a survey  questionnaire. The questionnaire was administered to a total of 200 respondents, comprising male farmers from the Sindh province in Pakistan. The study revealed a number of important findings. The findings include, that which shows the majority of the respondents preferring to watch agricultural-related television programmes compared to other kinds of programmes on television. Apart from this, the findings of the study also revealed that most of the respondents own their own television sets and that they also felt that television is the best medium for disseminating agricultural-related programmes to the general public. Finally, the study highlights a number of recommendations that can be considered by the government and policy makers in Pakistan.
Non-binary deep transfer learning for image classification

The current standard for a variety of computer vision tasks using smaller numbers of labelled training examples is to fine-tune from weights pre-trained on a large image classification dataset such as ImageNet. The application of transfer learning and transfer learning methods tends to be rigidly binary. A model is either pre-trained or not pre-trained. Pre-training a model either increases performance or decreases it, the latter being defined as negative transfer. Application of L2-SP regularisation that decays the weights towards their pre-trained values is either applied or all weights are decayed towards 0. This paper re-examines these assumptions. Our recommendations are based on extensive empirical evaluation that demonstrate the application of a non-binary approach to achieve optimal results. (1) Achieving best performance on each individual dataset requires careful adjustment of various transfer learning hyperparameters not usually considered, including number of layers to transfer, different learning rates for different layers and different combinations of L2SP and L2 regularization. (2) Best practice can be achieved using a number of measures of how well the pre-trained weights fit the target dataset to guide optimal hyperparameters. We present methods for non-binary transfer learning including combining L2SP and L2 regularization and performing non-traditional fine-tuning hyperparameter searches. Finally we suggest heuristics for determining the optimal transfer learning hyperparameters. The benefits of using a non-binary approach are supported by final results that come close to or exceed state of the art performance on a variety of tasks that have traditionally been more difficult for transfer learning.
LibGuides: CRJU U361 - Criminal Justice Research Methods: Popular Culture & Crime - CRJU U484

This LibGuide is a work in progress designed to assist upper level Criminal Justice students with research
Data Pre-processing in Text Mining

The fact that any kind of user has the ability to generate data with great ease at any time causes an increase in the importance of data mining.Considering the reality that the vast majority of the available data is composed of unstructured data and that the data in the text type is outnumbering, it proves the increasing interest in text mining and the abundance of studies in this field.However, in order to be able to examine an unstructured data type like text, which is quite different from machine language, it is necessary to make this data more structured and make the machine work.At this point, the data pre-processing step, which covers a large part of the entire text mining process, is of great importance.In this chapter, it is aimed to explain the text pre-processing phase on a basic level by supporting this using visuals.In doing so, it is primarily planned to focus on text mining and to explain in detail the characteristics of the data processed.In this context, it is aimed to explain the data pre-processing steps followed in order to overcome these difficulties by examining the difficulties created by the data in question.As a result, this chapter is a descriptive review of the data pre-processing phase in text mining, which covers some of the studies previously conducted on this subject.
EDUCATIONAL ROBOTICS AS A TEACHING TOOL OF INFORMATION TECHNOLOGY IN THE PRIMARY EDUCATION

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 9806-9816ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.1984Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
DIGITAL MODELS OF 3D OBJECTS AS SUPPORT FOR DISTANCE LEARNING OF TECHNICAL SUBJECTS IN SECONDARY AND PRIMARY SCHOOLS

DIGITAL MODELS OF 3D OBJECTS AS SUPPORT FOR DISTANCE LEARNING OF TECHNICAL SUBJECTS IN SECONDARY AND PRIMARY SCHOOLS
Identification Cold start Problem Start in Matrix Factorization Method for Online Web Recommendation Systems

Online prediction based on user profile or personal history is very essential to increase the revenue in E-commerce. To identify the potential users from a large amount of data and provide effective recommendation it is a very tedious task in data mining approach. Various researchers have already done dissimilar systems using various classification mining algorithms. The basic problem all the systems having a space complexity and heavy resources required to mine that much large data. Sometimes recommendations also generate different problems life redundant recommendations, cold start Matrix factorization, duplicate frequent itemset generation, etc. To eliminate such problems we propose a detection strategy of cold start Matrix factorization method during recommendation. This approach basically designs to increase online business using effective recommendations. This research also illustrates how the system carried out from business to business as well as business to customer recommendation using the proposed recommendation algorithm. Experiment analysis has done with a large amount of transactional data and generates the recommendation to the individual user. To calculate the effectiveness of the system using different Kappa statistical analyses and shows the accuracy of recommendation in Big Data environment. After identifying previous work gaps, we proposed effective recommendations to runtime users and generate the recommendation from real-world data from actual customer-product interaction events. This research basically a combination of a data mining approach and e-commerce management with a recommender system.
Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses

Policy optimization is a widely-used method in reinforcement learning. Due to its local-search nature, however, theoretical guarantees on global optimality often rely on extra assumptions on the Markov Decision Processes (MDPs) that bypass the challenge of global exploration. To eliminate the need of such assumptions, in this work, we develop a general solution that adds dilated bonuses to the policy update to facilitate global exploration. To showcase the power and generality of this technique, we apply it to several episodic MDP settings with adversarial losses and bandit feedback, improving and generalizing the state-of-the-art. Specifically, in the tabular case, we obtain $\widetilde{\mathcal{O}}(\sqrt{T})$ regret where $T$ is the number of episodes, improving the $\widetilde{\mathcal{O}}({T}^{2/3})$ regret bound by Shani et al. (2020). When the number of states is infinite, under the assumption that the state-action values are linear in some low-dimensional features, we obtain $\widetilde{\mathcal{O}}({T}^{2/3})$ regret with the help of a simulator, matching the result of Neu and Olkhovskaya (2020) while importantly removing the need of an exploratory policy that their algorithm requires. When a simulator is unavailable, we further consider a linear MDP setting and obtain $\widetilde{\mathcal{O}}({T}^{14/15})$ regret, which is the first result for linear MDPs with adversarial losses and bandit feedback.