Reviewerly/exHarmony huggingface dataset:
Pragmatics Based Textbooks: Do They Fulfil the Needs of Teachers and Students?

Secondary students must master not only grammar and text organization, but also pragmatic competence in the target language in order to achieve communication competence. The ability to express meaning beyond language rules is referred to as pragmatic competence. Teachers should provide materials that are appropriate for both the learning goals and needs of the students. This study aimed to determine the types of textbooks that teachers and students require. It was a mixed-methods study with a sample of 41 teachers from Semarang secondary schools. The data were gathered using a Google Form and were analyzed quantitatively and qualitatively. It was discovered that 56.1% disagreed that the textbooks they used contained situational context and communicative language for everyday use. 61% strongly agreed that to be effective textbooks should use communicative language in everyday situations and introduce various expressions based on the context. There was a significant gap between the types of textbooks required and the textbooks in use at the time. The majority of respondents believed that secondary textbooks or pragmatics-based textbooks should include information on the importance of pragmatics. These needs have not been met by existing textbooks. To summarize, teachers require a gamechanging textbook. The researchers therefore encourage other researchers and educators to create textbooks that can support the students’ expected outcome of being able to communicate comprehensively in English.&#x0D; Keywords: English textbooks, communicative competence, pragmatic analysis, secondary schools
Named Entity Recognition System for the Biomedical Domain

The recent advancements in medical science have caused a considerable acceleration in the rate at which new information is being published.The MEDLINE database is growing at 500,000 new citations each year.As a result of this exponential increase, it is not easy to manually keep up with this increasing swell of information.Thus, there is a need for automatic information extraction systems to retrieve and organize information in the biomedical domain.Biomedical Named Entity Recognition is one such fundamental information extraction task, leading to significant information management goals in the biomedical domain.Due to the complex vocabulary (e.g., mRNA) and free nomenclature (e.g., IL2), identifying named entities in the biomedical domain is more challenging than any other domain, hence requires special attention.In this paper, we deploy two novel bi-directional encoder-based systems, viz., BioBERT and RoBERTa to identify named entities in the biomedical text.Due to the domain-specific training of BioBERT, it gives reasonably good performance for the NER task in the biomedical domain.However, the structure of RoBERTa makes it more suitable for the task.We obtain a significant improvement in F-score by RoBERTa over BioBERT.In addition, we present a comparative study on training loss attained with ADAM and LAMB optimizers.
Low-complexity access control scheme for MEC-based services

The standardized security architecture proposed by ETSI for 5G networks provides six security domains covering network access and secure implementation of network services.However, this architecture does not specify detailed solutions for access control for web services and user credentials management.This paper proposes a new access control and service authorization protocol for the network services using MEC edge servers.Our solution does not slow down the performance of services in the 5G network.The advantage of this solution is that it allows you to solve some network security problems resulting from virtualization techniques (SDN and NFV) applied in constructing contemporary mobile networks.
Online Transaction Fraud Detection Using Efficient Dimensionality Reduction and Machine Learning Techniques

In recent years, there has been a rapid increase in the number of online transactions. Substantial growth has been reported in e-commerce and e-governance in the past few years. Due to this the number of people using online payment methods has also increased. This has led to an exponential rise in the number of transactions that happen every day. This increase in online transactions has further led to an increase in the number of frauds in the transactions. There is an ever-growing need to detect these fraudulent transactions as early as possible so that appropriate actions could be taken and losses due to these frauds could be minimized. This work proposes machine learning models which could use the previously known data and try to predict frauds based on information learned through the old data. We propose a statistical based dimensionality reduction technique and various machine learning models were tried for classification purpose. We experimented our proposed method on IEEE-CIS Fraud Detection dataset and the best results were obtained on the XGBoost model which is demonstrated in this paper.
Students’ perceptions of online assessment, feedback practices, and challenges

&lt;span lang="EN-US"&gt;Online learning has become an integral part of today’s educational system, especially after the COVID-19 pandemic. In art and design classrooms, which usually require face-to-face critique as part of a cycle of action and reflection, students may face challenges with the rapid transition to online assessment and feedback approaches. Consequently, it is curial to investigate these challenges as well as students’ perceptions towards the online assessment and feedback methods that they have experienced. This student-oriented study investigated art and design students’ preferences, concerns, and challenges with online assessment and feedback strategies. Moreover, it explored the most effective online assessment and feedback tools and applications for art and design courses. The research adopted a quantitative method by conducting a questionnaire with 104 art and design students of a university in Bahrain and a university in Jordan. Based on the findings, the study seeks to ultimately provide art and design educators with insights and recommendations for the most effective art and design assessment practices.&lt;/span&gt;
Re-Imagen: Retrieval-Augmented Text-to-Image Generator

Research on text-to-image generation has witnessed significant progress in generating diverse and photo-realistic images, driven by diffusion and auto-regressive models trained on large-scale image-text data. Though state-of-the-art models can generate high-quality images of common entities, they often have difficulty generating images of uncommon entities, such as `Chortai (dog)' or `Picarones (food)'. To tackle this issue, we present the Retrieval-Augmented Text-to-Image Generator (Re-Imagen), a generative model that uses retrieved information to produce high-fidelity and faithful images, even for rare or unseen entities. Given a text prompt, Re-Imagen accesses an external multi-modal knowledge base to retrieve relevant (image, text) pairs and uses them as references to generate the image. With this retrieval step, Re-Imagen is augmented with the knowledge of high-level semantics and low-level visual details of the mentioned entities, and thus improves its accuracy in generating the entities' visual appearances. We train Re-Imagen on a constructed dataset containing (image, text, retrieval) triples to teach the model to ground on both text prompt and retrieval. Furthermore, we develop a new sampling strategy to interleave the classifier-free guidance for text and retrieval conditions to balance the text and retrieval alignment. Re-Imagen achieves significant gain on FID score over COCO and WikiImage. To further evaluate the capabilities of the model, we introduce EntityDrawBench, a new benchmark that evaluates image generation for diverse entities, from frequent to rare, across multiple object categories including dogs, foods, landmarks, birds, and characters. Human evaluation on EntityDrawBench shows that Re-Imagen can significantly improve the fidelity of generated images, especially on less frequent entities.
Perception of political instability in election periods: Evidence from African firms

Political instability is a major obstacle to firms' investment and development. This article investigates how elections affect the perception of political instability of African firms. We use a survey-based dataset of approximately 21,500 firms in 33 African countries which we cross with 237 elections between 2004 and 2020. Our econometric strategy allows a detailed identification of election periods and the associated effects. We provide robust evidence of a pre-election increase in the perception of political instability by firms, but no, or limited post-election effects. The perception of political instability by firms is stronger for firms oriented towards foreign markets, in countries with non-democratic institutions or a high risk of conflict.
Bifurcation matching for consistent cerebral vessel labeling in CTA of stroke patients

Vessel labeling is a prerequisite for comparing cerebral vasculature across patients, e.g., for straightened vessel examination or for localization. Extracting vessels from computed tomography angiography scans may come with a trade-off in segmentation accuracy. Vessels might be neglected or artificially created, increasing the difficulty of labeling. Related work mainly focuses on magnetic resonance angiography without stroke and uses trainable approaches requiring costly labels.We present a robust method to identify major arteries and bifurcations in cerebrovascular models generated from existing segmentations. To localize bifurcations of the Circle of Willis, candidate paths for the adjacent vessels of interest are identified using registered landmarks. From those paths, the optimal ones are extracted by recursively maximizing an objective function for all adjacent vessels starting from a bifurcation to avoid erroneous paths and compensate for stroke.In 100 CTA stroke data sets for evaluation, 6 bifurcation locations are placed correctly in 85% of cases; 92.5% when allowing a margin of 5 mm. On average, 14 vessels of interest are found in 90% of the cases and traced correctly end-to-end in 73.5%. The baseline achieves similar detection rates but only 35.5% of the arteries are traced in full.Formulating the vessel labeling process as a maximization task for bifurcation matching can vastly improve accurate vessel tracing. The proposed algorithm only uses simple features and does not require expensive training data.
The Effectiveness of Communication Strategies to Develop the Communicative Competency of ESL Learners

This empirical research study emphasizes the significance of communication strategies that assist rural learners’ language learning progress. It examines the opinion of learners about the learning methodology, classroom instruction, and communicative competency. The findings of a questionnaire survey of second-year rural students revealed that classroom teaching and activity-based education positively influence communicative proficiency. The reliability of the data is confirmed through Cronbach’s Alpha. The alpha value is .803. The outcomes of the data also revealed that the teacher’s competency is one of the major components that promote communicative competency. It creates variations in the communicative competency of learners. The ANOVA findings revealed that rural learners’ communicative competency varied depending on the type of college. According to the research, effective classroom teaching using the TBLT method can improve learners' communicative competency.
ANALYSIS OF TECHNOLOGIES FOR PERSONALIZED CONTENT RECOMMENDATIONS ON THE INTERNET

The introduction of new technologies in the social information sector of the Internet is primarily due to increasing competition for the attention of users. In order to keep a person’s attention on your social network, you only need constantly to offer interesting, diverse content, and it should be different for different users. To solve these practical problems, technologies for personalized content recommendations are being developed. The relevance of the topic of this article is due to the widespread use on the Internet of personalized content recommendations and the emergence of publications (analytical materials, statistics), which address the shortcomings of these technologies. The purpose of the study is to analyze modern technologies of personalized content recommendations with the identification of positive and negative consequences of their practical application. The paper analyzes the algorithms of content ranking, which are used by various social networks, video hosting and other media resources. Theoretical substantiation of risks connected with creation of “information corridors” is given. The study found that these technologies could facilitate the process of users to search and consume information through recommendations only interesting content; personalization of advertising, facilitation of targeting; reducing the amount of harmful information or traumatic content. At the same time, these technologies lead to negative consequences: problems of censorship; information restrictions, or “information corridors”; influence on concentration and way of thinking. The presence of these problems does not allow calling the current technical achievements in the field under study, the ultimate. However, this applies to any technology that requires not only recognition of their benefits, but also careful analysis of shortcomings in order to timely correct them and establish the principles of responsible use of these technologies.
Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection

This paper presents a first end-to-end application of a Quantum Support Vector Machine (QSVM) algorithm for a classification problem in the financial payment industry using the IBM Safer Payments and IBM Quantum Computers via the Qiskit software stack. Based on real card payment data, a thorough comparison is performed to assess the complementary impact brought in by the current state-of-the-art Quantum Machine Learning algorithms with respect to the Classical Approach. A new method to search for best features is explored using the Quantum Support Vector Machine's feature map characteristics. The results are compared using fraud specific key performance indicators: Accuracy, Recall, and False Positive Rate, extracted from analyses based on human expertise (rule decisions), classical machine learning algorithms (Random Forest, XGBoost) and quantum based machine learning algorithms using QSVM. In addition, a hybrid classical-quantum approach is explored by using an ensemble model that combines classical and quantum algorithms to better improve the fraud prevention decision. We found, as expected, that the results highly depend on feature selections and algorithms that are used to select them. The QSVM provides a complementary exploration of the feature space which led to an improved accuracy of the mixed quantum-classical method for fraud detection, on a drastically reduced data set to fit current state of Quantum Hardware.
Software requirement specific entity extraction using transformer models

Software Requirement Specifications (SRS) documents provide the description of requirements and expectations attributed to software products.The structured text present in the SRS documents serves as a guide for developers in defining various functions in the process of software development.Software specific entity extraction is an important pre-processing step for various Natural Language Processing (NLP) tasks in the requirement engineering domain such as entity-centric search systems, SRS document summmarization, requirement classification, and requirement quality management.Recent advances in transformer-based models have significantly contributed to NLP and information retrieval problems, and achieved state-of-the-art performance for domain specific entity extraction tasks.In this study, we employ the transformer models including BERT, RoBERTa and ALBERT for software specific entity extraction.For this purpose, we annotate three requirement datasets, namely, DOORS, SRE, and RQA with varied sets of software specific entities.Our numerical study shows that transformer models are able to outperform the traditional approaches such as ML-CRF, and we find that BERT variants improve the F1-scores by 4% and 5% on the DOORS and SRE datasets, respectively.We conduct entity level error analysis to examine the partial and exact matching of entities and respective boundaries.Lastly, we experiment with few-shot learning to create sample efficient NER systems with template-based BART model.
Multi-objective cellular memetic algorithm

This paper presents a multi-objective cellular memetic algorithm (MOCMA) based on k-means clustering, which integrates the clustering-based local search method into multi-objective cellular genetic algorithm. Specifically, according to the objective function values of individuals in each generation, the k-means clustering is used to control the similar individuals gathered in a cluster. Meanwhile, to explore the search space efficiently and get the Pareto optimal solutions in objective space, one individual is selected randomly to undergo local search from each cluster and it will be improved than before. The MOCMA is applied to constrained and unconstrained problems. We analyse the influence of cluster number on the performance of the algorithm, and compare the MOCMA with other evolutionary multi-objective optimisers. It indicates that the proposed MOCMA is efficient for solving the multi-objective optimisation problems.
Creativity of a Teacher in an Innovative Educational Environment

The importance of psychological and pedagogical theories in the formation of a teacher's professional competence. The general content of pedagogical information accumulated in the countries around the world. The principle of the ability to purposefully highlight pedagogical information and be able to choose it correctly. Collection of materials for the study of advanced pedagogical experience. Methods for studying advanced pedagogical experience and their generalization. Organization of pedagogical creativity and personal scientific pedagogical research in the process of improving pedagogical skills. Criteria for evaluating the effectiveness of scientific and educational research (novelty, relevance, theoretical and practical significance). Effective use of scientific ideas and pedagogical experience in practice. Development of a system of individual activities. System definition. Consistency of activity, individual pedagogical activity, the ability to give a correct assessment.
Investigating Efficiently Extending Transformers for Long Input Summarization

While large pretrained Transformer models have proven highly capable at tackling natural language tasks, handling long sequence inputs continues to be a significant challenge. One such task is long input summarization, where inputs are longer than the maximum input context of most pretrained models. Through an extensive set of experiments, we investigate what model architectural changes and pretraining paradigms can most efficiently adapt a pretrained Transformer for long input summarization. We find that a staggered, block-local Transformer with global encoder tokens strikes a good balance of performance and efficiency, and that an additional pretraining phase on long sequences meaningfully improves downstream summarization performance. Based on our findings, we introduce PEGASUS-X, an extension of the PEGASUS model with additional long input pretraining to handle inputs of up to 16K tokens. PEGASUS-X achieves strong performance on long input summarization tasks comparable with much larger models while adding few additional parameters and not requiring model parallelism to train.
An empirical study for determining the quality indicators for the primary and secondary school of Bangladesh: A structural equation modeling approach

Primary and secondary school quality indicators need to be defined, assessed, and monitored due to their incomparable contributions to the entire educational process as well as for the overall development of a country. However, previous studies rarely emphasized indicators that have a substantial impact on school quality, especially in the context of developing countries. Bangladesh is one of them where this has yet to be adequately studied. To bridge this gap, this study aimed to explore the key “driver” constructs of primary and secondary level school quality. Adopting a quantitative cross-sectional research design, the present study was conducted between April 2021 to January 2022 at 128 primary and secondary level schools in 38 different districts of Bangladesh. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Empirical findings revealed that teacher quality (β = 0.588, t = 12.242, p < 0.001), technology (β = 0.279, t = 5.402, p < 0.001), school leadership (β = 0.132, t = 2.383, p < 0.05) have positive influence on students' academic achievement which ultimately affects (β = 0.923, t = 92.713, p < 0.001) the overall quality of the school. While school environment does not significantly influence students’ academic achievement or school quality. The overall findings of this study are expected to assist the devolved authorities in implementing synchronized policies for the improvement of primary and secondary level school quality.
Enhancing cooperative learning and student motivation with gamification strategies: A case study in industrial engineering

This contribution presents the pedagogical design of a subject through gamified activities supported by technological tools in which the integration of two key subjects of the Civil Industrial Engineering career were implemented, which are operational research and project evaluation. In addition, the results of the satisfaction and learning perception questionnaires administered at the end of the experience are presented. Gamification is planned and implemented as a collaborative learning strategy to relate knowledge and skills acquired in order to provide solutions to science-based engineering problems in the area of their profession. The experience has been developed with 10 students of the ninth semester of the 2017 generation of the career, dictated this semester. It should be noted that the course was planned to be taught in classroom mode, due to the COVID-19 pandemic, for both academics and students, it was a challenge and a learning achievement to teach the course online, since the University of Antofagasta was not prepared to teach classes in virtual mode. Even though Chilean universities have used gamification in engineering, there have been no records of the existence of subjects integrated with the application of this tool.The results obtained in this teaching innovation achieved motivation of our students, teamwork, learning to learn, good performance in the completion of academic activities and the satisfaction of students accredited through the survey.
Data Mining Regarding Cyberbullying in the Arabic Language on Instagram Using KNIME and Orange Tools

This paper deals with data mining on verbal bullying by Instagram users. It tracks people who repeatedly have abusive behavior and may cause harm to other persons or groups. In this work, a dataset holding verbal bullying in the Arabic language was extracted from Instagram comments, and the entries were classified as regular verbal bullying and suspicious verbal bullying. KINIME and Orange open source data mining tools were utilized to discover comments that involved verbal bullying on Instagram and to delete previous comments while users sent their comments automatically and immediately. Classification algorithms Rule-Based in KNIME and Select Rows in Orange were used.