Reviewerly/exHarmony huggingface dataset:
Scientific and methodological support for preparing classroom teachers in modern education system: Evaluation of changes and opportunities

Introduction. The authors investigate the current trends in scientific and methodological support of preparing future teachers for moral education activities. The aim of the article is to identify changes in the leading characteristics of scientific and methodological support of classroom teachers’ preparation in modern conditions and to determine its prospects in the context of higher education transformation. Materials and Methods. The study is theoretical in nature, includes the analysis and generalization of theoretical approaches to scientific and methodological support of university teachers and empirical data obtained in the course of a comprehensive study of the content, forms and methods of preparing future teachers for conducting moral educational activities. The study is based on the following methodological approaches: psychological and educational support, scientific and methodological support, and the concept of involvement, allowing to reveal the successful areas and the main difficulties in scientific and methodological support of classroom teachers' training in academic and extracurricular activities, and to identify the prospects of scientific and methodological support in the context of the transformation of higher education. Results. The paper identifies the main areas of support: scientific and methodological support, student extracurricular involvement, research projects on classroom management problems, targeted information and methodological support. The study reveals the contradictions between teachers’ satisfaction with existing scientific and methodological support and dissatisfaction of students and heads of educational settings with the quality of classroom teachers’ preparation in modern schools. The authors identify the characteristic features of improving the quality of classroom management in modern schools. Conclusions. The study has shown the socio-pedagogical and professional significance of scientific and methodological support of classroom teachers' preparation in higher educational institutions that provide teacher education programmes. New methodological approaches to understanding the role and place of scientific and methodological support in higher education institution, innovative, network and interactive formats of training, and creating educational open professional-oriented environment serve as resources of scientific and methodological support of classroom teachers’ preparation.
Web-Service Security and The Digital Skills of Users: An Exploratory Study of Countries in Europe

This study explores the impact of internet users’ and web owners’ knowledge, measured by the level of digital skills, on the number of insecure websites based on a survey of the internet. The influence of the affordability of internet access to the web space on the vulnerability of a particular European country is also considered. The study introduces a quantifiable index for assessing the insecurity of websites that is incorporated into a newly developed tool that scans websites and identifies the vulnerability in the Web Content Management System (WCMS). The collected vulnerability data and the digital skills are analyzed with statistical methods for finding the interdependences and relationships. Higher levels of digital skills and lower fixed-internet-access costs contribute to a smaller number of insecure websites. The vulnerability of the websites for different economic sectors is explored as well. The paper discusses the differences in the digital development pathways and governmental policies applied in European countries that have been affected by user knowledge and digital skills. The study brings original results and findings, as there are no similar studies addressing the impact of the knowledge of a country’s population on the level of insecurity found in the WCMS, including plug-ins.
A Novel Hybrid Lexicon Ensemble Learning Model for Sentiment Classification of Consumer Reviews

In recent past during the era of consumerism with easy accessibility to social networking world, the consumers usually give comments and opinions on daily usable ingredients, electronic goods and services offered on payments. These comments or opinions are innumerable and huge on each item, hence need the special attention for sentimental value particularly on their text parts. The present study is an attempt to perform sentiment prediction on Amazon Electronic products, gift cards and Kindle dataset. In this paper, the HLESV (Hybrid Lexicon Ensemble based Soft Voting) model is proposed by combining lexicon and ensemble approaches using optimally weighted voting to predict the sentiment, subsequently to evaluate model using various performance metrics like precision, recall, F1-score. This paper computes an additional metric namely subjectivity score along with sentiment score and proposes non-interpretive sentiment class label to evaluate the polarity of the reviews using our proposed HLESV model for sentiment classification. The accuracy score of our proposed HLESV model is evaluated to assess its effectiveness on Amazon consumer product review datasets and observed an increase of 1-6% accuracy over existing state-of-the-art ensemble methodology.
Revisiting the Computation Analysis against Internal Encodings in White-Box Implementations

White-box implementations aim to prevent the key extraction of the cryptographic algorithm even if the attacker has full access to the execution environment. To obfuscate the round functions, Chow et al. proposed a pivotal principle of white-box implementations to convert the round functions as look-up tables which are encoded by random internal encodings. These encodings consist of a linear mapping and a non-linear nibble permutation. At CHES 2016, Bos et al. introduced differential computation analysis (DCA) to extract the secret key from the runtime information, such as accessed memory and registers. Following this attack, many computation analysis methods were proposed to break the white-box implementations by leveraging some properties of the linear internal encodings, such as Hamming weight and imbalance. Therefore, it becomes an alternative choice to use a non-linear byte encoding to thwart DCA. At CHES 2021, Carlet et al. proposed a structural attack and revealed the weakness of the non-linear byte encodings which are combined with a non-invertible linear mapping. However, such a structural attack requires the details of the implementation, which relies on extra reverse engineering efforts in practice. To the best of our knowledge, it still lacks a thorough investigation of whether the non-linear byte encodings can resist the computation analyses.In this paper, we revisit the proposed computation analyses by investigating their capabilities against internal encodings with different algebraic degrees. Particularly, the algebraic degree of encodings is leveraged to explain the key leakage on the non-linear encodings. Based on this observation, we propose a new algebraic degree computation analysis (ADCA), which targets the mappings from the inputs to each sample of the computation traces. Different from the previous computation analyses, ADCA is a higher-degree attack that can distinguish the correct key by matching the algebraic degrees of the mappings. The experimental results prove that ADCA can break the internal encodings from degree 1 to 6 with the lowest time complexity. nstead of running different computation analyses separately, ADCA can be used as a generic tool to attack the white-box implementations.
Foreign Language Learning Gamification Using Quizizz: A Systematic Review Based on Students’ Perception

Abstract. Gamification, or the use of game attributes outside of a game context to affect learning-related behaviors or attitudes, is seen as an innovative way to support the diverse needs of students with learning or behavioral challenges. One digital educational game that is gaining popularity is Quizizz, a game-based online test that enables entertaining multiplayer teaching activities and allows learners to practice with their devices. This study aimed to examine students' perceptions of the use of Quizizz as a formative test tool in German classrooms at Universitas Pendidikan Indonesia. The participants of this study were first-year students studying German who were given a formative test using Quizizz for one semester. A descriptive qualitative research design was used in this study, in which data were collected from questionnaires to gain insight into how Quizizz affected the aspects of enjoyment, motivation, engagement, and satisfaction. The results showed that all aspects asked in the questionnaire received a strong positive response, indicating that students have a very good perception of Quizizz in the classroom. The positive response to the use of Quizizz in German language learning suggests that it could be used more widely as a formative assessment tool in language learning. The findings suggest that gamification can be an effective way to engage students and motivate them to learn. As a result, educators may want to consider using other gamified tools and techniques in the classroom to create a more enjoyable and engaging learning experience.Keywords: Gamification, German Classroom, Quizizz, Students' Perception
Application of Super Resolution for Optical Character Recognition in Low Quality Images

Machine learning has become a very popular method in various branches of industry and has been successfully applied to a number of practical tasks. Optical character recognition, which is one of the most challenging task in computer vision, has made significant progress due to machine learning applications. Modern OCR systems can provide a high-accuracy predictions both for scanned documents and real-scene images. Despite such power, such models are still suffering from low-quality images, especially, in extreme cases of compressed images. A traditional approach to overcoming such deep learning model weakness is to extend the dataset in such a way as to cover such distortions. However, it requires model retraining and can not guarantee the same accuracy on the previous dataset. We tackle this issue from another perspective. In this paper, we discover how a super-resolution preprocessing step could help the OCR model to recognize images itself. Based on our custom synthetic dataset, we built a super-resolution system. We also performed a careful analysis of how loss functions should be used for text images. Finally, we showed that a custom-trained super-resolution system shows much better results in terms of restored image quality and text recognition accuracy.
The Influence Of Nutrition Knowledge And Pocket Money On Instant Noodle Consumption In Students

This research is motivated by several problems, namely students still often consume instant noodles with high frequency, students have knowledge but do not apply it in their food consumption, some students have low income so they cannot afford quality food. The purpose of this study was to analyze the effect of nutritional knowledge and pocket money on student instant noodle consumption. The type of research used is quantitative with a causal associative approach. The population of this research is active students of the Faculty of Tourism and Hospitality. The research sample was 189 people with the sampling technique using proportional random sampling. The type of data used in this research is primary data. The data collection technique used a questionnaire with a Likert scale and Guttman scale which had been tested for validity and reliability. The data were processed descriptively using multiple linear regression using SPSS version 26.00.The results showed that the average nutritional knowledge of students was in the sufficient category (32.28%). The average student pocket money is in the moderate category (44.44%). Consumption of instant noodles is in the medium category (37.57%). Knowledge of nutrition and pocket money can explain consumption of instant noodles with an adjusted R Square of 0.112, where there is an effect of knowledge of nutrition and pocket money on instant noodle consumption of 11.2%. Then the Fcount value is 12.853 with a sig value of 0.000 &lt;0.05 so it can be concluded that nutrition knowledge and pocket money together have a significant effect on student instant noodle consumption.
Estimation of ground depth of radioelements sources in Ogun State, Southwestern Nigeria using gradient techniques

Radioelement exploration has gained economic interest recently due to its usefulness in the detection and delineation of mineral deposits. In this study, the airborne radiometric data were analysed for depth estimation of the radioelement deposit in Ogun state, Nigeria. Three enhancement gradient techniques, namely; Analytical Signal Amplitude (ASA), Horizontal Gradient Magnitude (HGM) and Local Wave Number (LWN) were employed to estimate the possible depth of radioelements for mineralization. Geosoft's (Oasis Montaj) software and Potential Field's (PF) software were used to conduct the estimations. The result obtained revealed shallow sources of 0.584 km (LWN) and 0.387 km (ASA), and deep-seated sources of 5.950 km (ASA) and 5.880 km (ASA) for uranium and thorium, respectively. The shallow source and deep source for potassium are 0.259 km (ASA) and 2.540 km (ASA), respectively. In this study, the position and depth of the source were automatically estimated using linear equations based on derivatives without the use of any a priori knowledge. The three gradient methods are therefore found suitable in estimating the depth to radioelement anomalous source.
The Impact of Code Bloat on Genetic Program Comprehension: Replication of a Controlled Experiment on Semantic Inference

Our previous study showed that automatically generated attribute grammars were harder to comprehend than manually written attribute grammars, mostly due to unexpected solutions. This study is an internally differentiated replication of the previous experiment, but, unlike the previous one, it focused on testing the influence of code bloat on comprehension correctness and efficiency. While the experiment’s context, design, and measurements were kept mostly the same as in the original experiment, more realistic code bloat examples were introduced. The replicated experiment was conducted with undergraduate students from two universities, showing statistically significant differences in comprehension correctness and efficiency between attribute grammars without code bloat and attribute grammars with code bloat, although the participants perceived attribute grammars with code bloat as simple as attribute grammars without code bloat. On the other hand, there was no statistically significant difference in comprehension correctness and efficiency between automatically generated attribute grammars with possible unexpected solutions and attribute grammars with code bloat, although there was a statistically significant difference in participants’ perspective of simplicity between automatically generated attribute grammars with possible unexpected solutions and attribute grammars with code bloat. The participants perceived attribute grammars with code bloat as significantly simpler than automatically generated attribute grammars.
Developing AKM Problem-Oriented Websites as Learning Media for Flat Shapes

This research is motivated by the inability of students to comprehend the concept and correctly answer questions on flat shapes in literacy. This research is a development study with the goal of creating a flat shaped, website oriented learning resource for junior high school class VIII students regarding AKM questions. The ADDIE development model is employed. Interviews, questionnaires for validity and practicality tests, and tests in the form of a pretest and posttest were employed as the data gathering instruments. Ten eighth-grade students from SMPN 13 Malang who participated in the AKM program served as the field trial participants. In order to evaluate the validity, applicability, and efficacy of data collection procedures, tests, questionnaires, and interviews are used. The media validity test yields a percentage of 91.4% (more than 90%), the material validity test yields a percentage of 94% (more than 90%), the practicality percentage is 90.25% (more than 90%), and the Paired t-test yields a significant difference between the pretest and the posttest. Keywords: Development, Website Learning Media, AKM Questions.
Exploration on the application of electronic information technology in signal processing based on big data

Abstract Mobile phones are the most commonly used electronic devices in people’s daily life. The image, voice, and other information in these devices need to be processed through signal transmission. The role of signal processing is to process the acquired information in a certain way to get the final result. In order to ensure that the whole processing program can work normally, it is necessary to implement good control to achieve the desired effect. However, with the continuous progress and development of science and technology, its requirements are becoming increasingly strict. The traditional signal processing method is unreliable, has poor real time, and has error-prone characteristics, which can no longer meet the accuracy requirements of current information acquisition equipment. Therefore, people begin to study more complex and precise information processing methods and apply these algorithms to various advanced electronic devices to achieve better results. From the perspective of big data, electronic information technology is generated and developed based on massive data processing. It not only has a strong storage function but also has strong computing power and a wide range of application scenarios. It has strong applicability in real life. In this article, the signal to be processed was divided into several wavelet components in different frequency ranges by empirical mode decomposition technology, and then the signal was denoised by combining three wavelet denoising methods to obtain noise data with good signal-to-noise ratio and high classification accuracy. Finally, the corresponding feature information was extracted according to the signal-receiving model to improve the system recognition rate. This article compared the traditional signal processing methods with the signal processing approaches from the perspective of electronic information technology. The results showed that the processing method had a high computing speed and could better solve the problem of detection performance degradation caused by interference. User satisfaction had also increased by 2.87%, which showed that signal processing based on big data and information processing technology had broad application prospects in communication systems. The core of open computer science is to build a unified, efficient, and scalable computing platform based on massive data processing and use signal processing and computer technology to manage and optimize the scheduling of information resources to better meet various business needs.
Deobfuscating Leetspeak With Deep Learning to Improve Spam Filtering

The evolution of anti-spam filters has forced spammers to make greater efforts to bypass filters in order to distribute content over networks.The distribution of content encoded in images or the use of Leetspeak are concrete and clear examples of techniques currently used to bypass filters.Despite the importance of dealing with these problems, the number of studies to solve them is quite small, and the reported performance is very limited.This study reviews the work done so far (very rudimentary) for Leetspeak deobfuscation and proposes a new technique based on using neural networks for decoding purposes.In addition, we distribute an image database specifically created for training Leetspeak decoding models.We have also created and made available four different corpora to analyse the performance of Leetspeak decoding schemes.Using these corpora, we have experimentally evaluated our neural network approach for decoding Leetspeak.The results obtained have shown the usefulness of the proposed model for addressing the deobfuscation of Leetspeak character sequences.
Hierarchical Joint Entity Recognition and Relation Extraction of Contextual Entities in Family History Records

Entity extraction is an important step in document understanding. Higher accuracy entity extraction on fine-grained entities can be achieved by combining the utility of Named Entity Recognition (NER) and Relation Extraction (RE) models. In this paper, a cascading model is proposed that implements NER and Relation extraction. This model utilizes relations between entities to infer context-dependent fine-grain named entities in text corpora. The RE module runs independent of the NER module, which reduces error accumulation from sequential steps. This process improves on the fine-grained NER F1-score of existing state-of-the-art from .4753 to .8563 on our data, albeit on a strictly limited domain. This provides the potential for further applications in historical document processing. These applications will enable automated searching of historical documents, such as those used in economics research and family history.
Conciseness: An Overlooked Language Task

We report on novel investigations into training models that make sentences concise. We define the task and show that it is different from related tasks such as summarization and simplification. For evaluation, we release two test sets, consisting of 2000 sentences each, that were annotated by two and five human annotators, respectively. We demonstrate that conciseness is a difficult task for which zero-shot setups with large neural language models often do not perform well. Given the limitations of these approaches, we propose a synthetic data generation method based on round-trip translations. Using this data to either train Transformers from scratch or fine-tune T5 models yields our strongest baselines that can be further improved by fine-tuning on an artificial conciseness dataset that we derived from multi-annotator machine translation test sets.
Technologies, Government Communication and Uncertainty Management in Natural Disaster Situations

Natural disasters occur and management must adjust to the reality and needs that arise. The research analyzed Technologies, Government Communication and Uncertainty Management in Disaster Cases: The Case of Landslides and Floods, particularly the role played by government communication in the disaster that occurred in the municipality of San Gabriel in the state of Jalisco, on June 2, 2019. The methodology used was a case study and also of a descriptive nature, studying the case of landslides and floods generated by an avalanche of mud, water, stones and tree trunks that invaded a large part of the municipal seat of San Gabriel, located in the south of the state of Jalisco Mexico and caused the loss of five human lives.