Reviewerly/exHarmony huggingface dataset:
Improving The Content Of Professional Educational Programs On The Basis Of Module-Competent Approach

Radical reform of the education system, rapid development of science, engineering and technology in the country, as well as consistent changes in the content of education are considered a priority "Improving the functioning of all links of the education system on the basis of modern requirements." modernization of the content of vocational education, teaching technologies and systems for assessing the results of mastering on the basis of a competency-based approach, the creation of didactic support for the formation of competencies is becoming increasingly important.
A Juxtaposition Between Integer Wavelet Transform and Discrete Wavelet Transform for Secure Image Steganography

Image steganography is a technique that is used to hide information. The information can be of various types like image, video, or audio. Steganography is done so that no one apart from the correct receiver can retrieve the information. This paper consists of all advantages and highlights of the wavelet transform but with the additional features like randomness and some default values that are already built-in it. Various algorithms can be used in steganography and they provide good hiding capacity and low detectability. Here we have hidden the image into the cover image using Integer Wavelet Transform (IWT) and also using Discrete Wavelet Transform (DWT) and compared which technique gives better results. It is very difficult to predict the presence of a hidden image inside the stego image since it looks exactly like the cover image. There is no loss in quality from the secret image to the extracted image since the PSNR (Peak Signal to noise ratio) is high for both of them. This process was done using both DWT and IWT and the results prove that that the IWT technique is not only simpler but also more efficient than the DWT technique since it gives higher PSNR values. Through the proposed algorithm, an increase in the strength and imperceptibility is noticed and it can also maintain various transformations such as scaling, translation, and rotation with algorithms that already exist. The final results, after comparing both the transforms prove that the algorithm which is being proposed in IWT is indeed effective
Emoji Essence: Detecting User Emotional Response on Visual Centre Field with Emoticons

User experience is understood in so many ways, like a one on one interaction (subjective views), online surveys and questionnaires. This is simply so get the user’s implicit response, this paper demonstrates the underlying user emotion on a particular interface such as the webpage visual content based on the context of familiarisation to convey users’ emotion on the interface using emoji, we integrated physiological readings and eye movement behaviour to convey user emotion on the visual centre field of a web interface. The physiological reading is synchronised with the eye tracker to obtain correlating user interaction, and emoticons are used as a form of emotion conveyance on the interface. The eye movement prediction is obtained through a control system’s loop and is represented by different color display of gaze points (GT) that detects a particular user’s emotion on the webpage interface. These are interpreted by the emoticons. Result shows synchronised readings which correlates to area of interests (AOI) of the webpage and user emotion. These are prototypical instances of authentic user response execution for a computer interface and to easily identify user response without user subjective response for better and easy design decisions.
QUARANTINE VLOGS: DIGITAL AFFECTIVE LABOR AND SELF-GOVERNANCE DURING COVID-19

This research explores how did influencers incorporate the Covid-19 pandemic into their regular content production on YouTube by specifically examining the recent genre of "quarantine vlog," which emerged in concurrence with global lockdowns. I adopt a grounded theory approach to analyze the YouTube transcriptions of purposefully selected 9 quarantine vlogs filmed by women influencers during the early months of the pandemic, along with 250 user comments. My analysis shows that quarantine vlogs are significantly different than ordinary vlogs. I draw on existing research on influencer cultures to explain this dissimilarity as a tension between influencers' struggle to form an intimacy with the viewers - which can have a soothing effect in a moment of a crisis - and the use of vlogs as a neoliberal device in order to preserve their aspirational image. I demonstrate that quarantine vlogs reveal that influencers are no longer able to perform an aspirational ideal in their videos without first engaging with the mental stress, anxiety, confusion, and loneliness brought by COVID-19 or apologizing for their relative privilege and demonstrating sympathy towards their followers who are in hardship. At the same time, to preserve their aspirational persona, they reframe the pandemic moment as an opportunity for productivity and self-growth. As a result, influencers carry out substantial affective labor and engage in a delicate self-governance to preserve their relevance and online visibility during a global moment of crisis.
Studies in object-oriented programming backbone implementations

This work is a combination of conceptual and hands on based study aimed at laying a foundation for practical Object-Oriented software construction. First it presents a conceptual study of a number of backbone concepts of modern Object-Oriented Programming (OOP) languages. Secondly, it attempts to demonstrate real-life implementations of these concepts using Python Programming Language. This work touches on practical issues on Class and Object Creation, especially on the syntax and creation, and demystifies the subject matter using a simple table of rules. The OOP concept of Inheritance was studied, with focus on the three major types of inheritance. The self-argument, and constructors were studied, with focus on the three constructors - default, parameterized, and non-parameterized constructors. A brief discussion, and pictorial illustration was also made on the disparity between normal mathematical functions and OOP method calls. Further areas of studies are the concept of overriding between the parent and child class, as well as the OOP puzzle commonly known as Diamond Problem, including code segment and diagrammatic illustration of Python-based solutions. There are a number of other back-bone concepts in OOP not covered in this study, such as Encapsulation, Abstraction, Meta-Programming, among others, which will form areas of focus in future studies. Effort was made to enhance the overall presentation through practical illustrations using source codes, annotated diagrams, and discussions. It is hoped that this work will be very useful to researchers and other practitioners in Object Oriented implementations.
Credit Card Fraud Detection using Deep Learning based on Neural Network and Auto encoder

Credit card fraud is an event problem and fraud detecting techniques getting more sophisticated each day. Mainly internet is becoming more common in almost every domain. Online transactions, shopping, and e-commerce are expanding step by step. Due to which in the online payment system, fraudulent activities have also increased. It has cost banks and their customers a loss of billions of rupees. The techniques used now a day detects the anomaly only after the fraud transaction takes place. The intruders have found ways to crack the system loopholes and defeat the security. These frauds are not consistent in their actions, they constantly alter. Thus, Artificial Intelligent (AI) algorithms are used to detect the behavior of such activity by learning the past behavior of the transaction of the users. An unsupervised algorithm is used to detect online transactions, as fraudsters commit fraud once by online media and then move on to other techniques. This paper discusses the performance analysis and the comparative study of the two Deep Learning algorithms which include auto-encoder and the neural network. In this paper accuracy, precision, recall, and AUC curve are considered as a model evaluation factor.
Local-bearing-information-based unmanned aerial vehicle collision avoidance trajectory planning

<p indent=0mm>Due to its low cost, low power, and non-cooperative advantage, monocular vision has become a crucial solution for enhancing the Sense and Avoid capability of unmanned aerial vehicles (UAVs). However, due to the restricted optical measurement properties of monocular vision, there are drawbacks to monocular-vision-based obstacle perception; namely, the capability for obstacle range information perception and global obstacle perception is insufficient. In this paper, a local-bearing-angle-based trajectory planning algorithm is proposed for UAV collision avoidance. First, the observability of monocular-vision-based obstacle range perception is theoretically analyzed. The local coordinate system is selected for collision avoidance, and the bearing angle is selected as the observation. Second, the problem of UAV monocular vision collision avoidance trajectory planning is mathematically formulated. The quadratic Bezier curve is selected to model the trajectory, and the trajectory planning problem is transformed into optimization based on a single objective function. The collision avoidance constraint based on the relative bearing angle rate is designed, and the collision avoidance trajectory segment is optimized on the basis of the bearing angle observed within a limited time period. Finally, the receding horizon is adopted to optimize the trajectory sequence, and the sequences are connected to formulate the near-globally optimal trajectory. Simulation experiments reveal that the collision avoidance trajectory planning method is capable of avoiding both static and dynamic obstacles. Compared with global-information-based collision avoidance, the convergence time of the method proposed in this paper is reduced, and the near-globally optimal trajectory can be acquired in real time, which is in accordance with the perception capability of monocular vision.
SOLVING PESSIMISTIC BILEVEL OPTIMISATION PROBLEMS WITH EVOLUTIONARY ALGORITHMS

It is common in practice, an optimal solution of a decision-maker to depend heavily on the response of another decision-maker, formulating a bilevel optimization problem.The optimization of a bilevel problem aims to achieve the optimum solution of the upper-level, taking into consideration the optimal lower-level values too.When the lower-level problem is multimodal, meaning that it has several global optima, an ambiguity about the optimal upper-level solution appears.The optimistic approach assumes that the follower will respond with an optimal solution, that is favorable by the upper-level as well.In the pessimistic approach, the upper-level is optimising for the worst case.Various evolutionary algorithms have been implemented successfully to solve the optimistic approach of the bilevel problem.To the best of our knowledge, these algorithms have not been extended to the pessimistic approach.In this paper, we use a multi-population nested Differential Evolution to solve the pessimistic bilevel problem when the lower-level has multiple global optima.The performance of the algorithm is examined by solving a test-problem taken from the literature.
PENGARUH KUALITAS JASA DAN HARGA TERHADAP KEPUASAN PELANGGAN CENGKARENG GOLF CLUB

The sample used as respondents is 75 people who are members of the Cengkareng Golf Club. The main data collection technique is by using a questionnaire regarding service quality variables, price variables and customer satisfaction variables. The data analysis technique used is correlation analysis and multiple correlation, determination coefficient analysis and simple regression analysis and multiple regression. The results of the study resulted in a conclusion that there was a significant effect of service quality on customer satisfaction at Cengkareng Golf Club. There is a significant price effect on customer satisfaction. There is a significant influence simultaneously service quality and price on customer satisfaction. At this time the service quality of Cengkareng Golf Club is good enough, to improve the quality of golf services, it is suggested that service quality can be used as a strategy to increase customer satisfaction through providing appropriate services to customers, responding and customer requests quickly, handling customer problems, and provide customer satisfaction provided not only in the form of physical facilities, but the appearance of a professional employee. Currently customer satisfaction is categorized as good enough by paying attention to the quality of services and prices provided to customers, so that customer satisfaction increases, it is recommended that Cengkareng Golf Club pay attention to the factors that make customers buy back, create word of mouth marketing carried out by customers, improve brand image Cengkareng Golf Club and pay attention to customer decision-making factors when playing golf in addition to service quality and price factors
POETRY IN FOREIGN LANGUAGE TEACHING: A LESSON PLAN BASED ON POETRY

&lt;p&gt;The present paper deals with teaching italian as a foreign language through poetry. The poetry chosen for the paper is Promemoria by the italian author Gianni Rodari. The aim of the paper is to suggest an example of a lesson plan based on the poem and is intended for the A2 level (according to CEFR). The suggested lesson plan framework based on poetry attempts to realize further objectives: to make students learn a foreign language based on an authentic material, to generate their interest into poetry, and finally, to promote integration of literature, especially poetry, into the foreign language teaching. &lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0853/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Action Research: What Can I Do to Improve the Reading Habits of Students?

Reading is critical to success both in school and life. It is a fundamental skill for children and adults alike. Like speech itself, it is the key to knowledge and opens up worlds. This action research aims to cultivate a reading habit of students. Uninterrupted Sustained Silent Reading (USSR) programme is the tool used to materialise this objective. The research samples are 21 students taken from Class IV of Gangrithang Primary School under Bumthang district. My experience in teaching and working at the Primary level for the last eighteen and half years as a teacher, made me to realized that student engagement particularly in reading books were not satisfactory as expected. This experience and realization has motivated me to take up an action research project to enhance reading habit of students. A questionnaire and a semi-structured interview schedule were used as the instruments of data collection. Findings reveal that out of 21 sampling 17 students confirmed USSR and teacher modeling are the most effective measure that can promote reading habits among students.
Impact of DIC biases on the selection process of a unique test for anisotropic plasticity characterization

The exploitation of field measurements with inverse identification methods may reduce the number of required tests to characterize complex material constitutive models, provided that the generated stress field is sensitive enough to the targeted material parameters. For anisotropic elastoplastic material, the objective is to generate various stress states in the specimen through a single test. In this study, the effect of Digital Image Correlation measurement biases on the selection of the most suitable specimen geometry for characterisation of a complex anisotropic plasticity criterion using a unique uniaxial tensile test is investigated. To this aim, finite element (FE) based synthetic images are generated and DIC is used on these images. The biases in DIC measurement result in biased stress states that may cause errors in identification results.
Having Your Cake and Eating It Too: JSON-LD as an RDF serialization format

One impediment to the uptake of linked data technology is developers’ unfamiliarity with typical Resource Description Framework (RDF) serializations like Turtle and RDF/XML. JSON for Linking Data (JSON-LD) is designed to bypass this problem by expressing linked data in the well-known Javascript Object Notation (JSON) format that is popular with developers. JSON-LD is now Google’s preferred format for exposing Schema.org structured data in web pages for search optimization, leading to its widespread use by web developers. Another successful use of JSON-LD is by the International Image Interoperability Framework (IIIF), which limits its use to a narrow design pattern, which is readily consumed by a variety of applications. This presentation will show how a similar design pattern has been used in Audubon Core and with Biodiversity Information Standards (TDWG) controlled vocabularies to serialize data in a manner that is both easily consumed by conventional applications, but which also can be seamlessly loaded as RDF into triplestores or other linked data applications. The presentation will also suggest how JSON-LD might be used in other contexts within TDWG vocabularies, including with the Darwin Core Resource Relationship terms.
Investigation on Autonomous Learning of Foreign Language and Countermeasures for Constructing an Autonomous Learning Platform

&lt;p class="1"&gt;&lt;span lang="EN-US"&gt;The connotation of autonomous learning is expanding as time progressed. Under the background of “Internet plus” education, colleges and universities pay more attention to the application of network teaching resources in teaching. Universities intensified the reform of college English teaching and built platforms for autonomous learning, supplementing the shortcomings of English classroom teaching and improving students' autonomous learning ability. This paper takes local college students as the research object, makes an investigation and analyzes the situations of college students autonomous learning. The findings suggested that the following questions are addressed: lack of motivation of autonomous learning, lack of self-exploration of effective learning strategies, lack of the ability of multiple interactions. This paper will give relevant strategies for building a platform for college English autonomous learning to promote and cultivate students' independent learning ability of foreign languages.&lt;/span&gt;&lt;/p&gt;
Scientific Events at the Vernadsky National Library of Ukraine: Status, Trends

The article continues the research started in the previous work on the quantitative and qualitative characteristics of international scientific events conducted at the Vernadsky National Library of Ukraine from 1991 to the present time. The paper presents scientific events, namely the All-Ukrainian scientific conference «Chyshkiv Readings» and the international scientific conference «Library. Science. Communication».
Nonlinear Prediction Models in Data Analysis

The modern entrepreneurial sensibility of the company’s business implies directing the right information to the appropriate parts of the company at the right time. That is why it is necessary to digitalize processes as much as possible and make the organization “intelligent”, and its human resources, to the greatest extent, the knowledge workers. The application of neural networks, i.e. nonlinear prediction models, enables systematic analysis of data in the function of evaluating the behavior of the system. Neural networks are a powerful tool, especially for forecasting trends and forecasting based on historical data. The grouping method, i.e., the k-mean value algorithm, is used as a precursor to neural networks.
ON TYPE-THEORETICAL FORMALIZATION OF SITUATION SEMANTICS

Situation semantics is an effective instrument for analysing semantical aspects of natural languages with explicit dependence on contexts, like referential opacity of belief contexts etc. Making use of type-theoretical approaches not only makes its formalism more practical in many ways, but also facilitates its migration to computer systems, specifically, the formalization in functional programming languages. The article deals with a prototype of the type theoretical language of situation semantics, implemented on the basis of the language Racket. It decribes principal approaches, methods of solving some problems of formal semantics as well as issues that need to be addressed.
Multi-Sentence Resampling: A Simple Approach to Alleviate Dataset Length Bias and Beam-Search Degradation

Neural Machine Translation (NMT) is known to suffer from a beam-search problem: after a certain point, increasing beam size causes an overall drop in translation quality.This effect is especially pronounced for long sentences.While much work was done analyzing this phenomenon, primarily for autoregressive NMT models, there is still no consensus on its underlying cause.In this work, we analyze errors that cause major quality degradation with large beams in NMT and Automatic Speech Recognition (ASR).We show that a factor that strongly contributes to the quality degradation with large beams is dataset length-bias -NMT datasets are strongly biased towards short sentences.To mitigate this issue, we propose a new data augmentation technique -Multi-Sentence Resampling (MSR).This technique extends the training examples by concatenating several sentences from the original dataset to make a long training example.We demonstrate that MSR significantly reduces degradation with growing beam size and improves final translation quality on the IWSTL15 En-Vi, IWSTL17 En-Fr, and WMT14 En-De datasets.
On the Combination of Static Analysis for Software Security Assessment – A Case Study of an Open-Source e-Government Project

Static Application Security Testing (SAST) is a popular quality assurance technique in software engineering. However, integrating SAST tools into industry-level product development and security assessment poses various technical and managerial challenges. In this work, we reported a longitudinal case study of adopting SAST as a part of a humandriven security assessment for an open-source e-government project.We described how SASTs are selected, evaluated, and combined into a novel approach for software security assessment.The approach was preliminarily evaluated using semi-structured interviews.Our result shows that (1) while some SAST tools outperform others, it is possible to achieve better performance by combining more than one SAST tools and (2) SAST tools should be used towards a practical performance and in the combination with triangulated approaches for human-driven vulnerability assessment in real-world projects.
DATA CITIZENSHIP: DATA LITERACIES TO CHALLENGE POWER IMBALANCE

In this paper we present the findings from the third phase of our (redacted) project on citizens data literacies. The data literacies framework we developed - Data Citizenship - stemmed from an analysis of recent literature on data and digital literacy combined with ideas from democratic education. In particular, we focus on the power imbalance between citizens and ‘big-tech’ and government entities who access, process and use data about citizens and their networks. We argue that due to its collective, socially contextualised, and people-centred qualities, democratic education provides a useful foundation for future data literacy education and research interventions. Following an extensive literature review (first phase), and then a UK nationally representative survey (phase 2), we have conducted focus groups during Autumn 2020 and Winter 2021 with citizens.