Reviewerly/exHarmony huggingface dataset:
Sarcasm in Sarawakian NgekTsaiArmy Official YouTube Videos

Sarcasm typically uses particular phrases in a figurative sense, and occasionally contains insults or reproaches, which makes it unpleasant to hear. However, in certain context, sarcasm is also used to make remarks, criticism, or advice towards an individual or a group of people, related to an issue. This study analyses the use of sarcasm in NgekTsaiArmy Official YouTube videos, a channel managed by NgekTsai who is a famous social media influencer or vlogger from Sarawak. The method of content analysis is used in analysing data from the utterances of the characters in videos with themes of family, society, education, work, and culture. The data were analysed based on the Speech Act Theory (Searle, 1969). The findings of the study show that there are four types of language acts that exist in the NgekTsaiArmy Official videos, namely assertive, directive, expressive, and commissive. Although this language act contains elements of sarcasm, it is uttered mostly with the aim of making the public aware of current issues, thus providing useful and positive input to the public. In addition, the culture and dialect of the Sarawak Malay community are also highlighted by NgekTsai in a unique way that attracts the attention of its viewers.
Evaluación del uso adecuado de tecnologías de información y comunicación en adolescentes

Objective: to design and evaluate the structure, process and efficacy of an educational intervention to encourage an adequate use of information and communication technologies (ICTs) among 10-to-11-year old children in Torrijos (Toledo, Spain).Method: a quasi-experimental pre-post-intervention study in 6º grade students from three Primary schools. A 90-minute educational intervention was designed about the advantages and disadvantages of ICTs. Both students and external observers evaluated its structure and process through ad-hoc questionnaires. Its efficacy was evaluated through questionnaires self-completed by the students. Sociodemographic variables were collected, as well as regarding the habits of ICT use. Wilcoxon and McNemar tests were used for analysis, with statistical significance p< 0.05.Results: the participation in the intervention was of 165 students (52.7% were girls) with a mean age of 11.2 years (SD= 0.46). The device most used by girls was the tablet (58,6%) and the console, by boys (58.4%), with a higher use by the latter (p< 0.001), where 35.8% played over two hours per day. There was also a higher use of YouTube and Twitch in boys, vs. TikTok and Snapchat in girls (p< 0.001). The intervention did not show any significant changes regarding the number of hours using internet or social networks. Students evaluated positively the intervention and the possibility to express themselves freely. External observers also valued the session positively, in terms of the mood perceived and participation. Conclusions: the intervention had very good acceptance and turned out positive for maintaining ICT habits, although no changes were shown subsequently in terms of habits of ICT use.
The Teaching Design of Reduced-Order Differential Equations Can Be Explored Under Constructivist Theory

Based on constructivist theory, this paper constructs the necessity of learning points by applying teaching cases to create scenarios. Through this design, students are stimulated to explore and solve practical problems, so that mathematical knowledge is no longer boring. Secondly, the scope of a problem that can be handled by the downgrade method is clarified through explicit application, so that mathematical applications are no longer ruleless. Then, through the construction of meaning, the constant commutation method of the first-order equation is inspired by the constant commutation method of the first-order equation based on the existing knowledge, and the mathematical method of solving the problem is provided. Finally, the extension section elaborates the limitations of the reduced-order method in solving higher-order differential equations and gives the extended solutions, so as to expand students’ horizons and better understand the role and significance of the reduced-order method in solving differential equations.
Transferable Keyword Extraction and Generation with Text-to-Text Language Models

This paper explores the performance of the T5 text-to-text transfer-transformer language model together with some other generative models on the task of generating keywords from abstracts of scientific papers. Additionally, we evaluate the possibility of transferring keyword extraction and generation models tuned on scientific text collections to labelling news stories. The evaluation is carried out on the English component of the POSMAC corpus, a new corpus whose release is announced in this paper. We compare the intrinsic and extrinsic performance of the models tested, i.e. T5 and mBART, which seem to perform similarly, although the former yields better results when transferred to the domain of news stories. A combination of the POSMAC and InTechOpen corpus seems optimal for the task at hand. We also make a number of observations about the quality and limitations of datasets used for keyword extraction and generation.
Testing

When you need to write or modify some T-SQL code, you may be tempted to just start writing the code. In many cases, the necessary database code is simple enough that no additional analysis is needed. Eventually, the data being queried or the T-SQL code is complex enough that you want to make certain your code is working as expected. During these times, you may want to test your T-SQL changes. While you can start testing T-SQL code at any time, you may find it helpful to start certain habits early on before dealing with complex scenarios.
Simulasi Perancangan Infrastruktur Jaringan Komputer Pada Institut Teknologi Keling Kumang Menggunakan Pendekatan Network Development Life Cycle (NDLC)

At this time, the development of information technology is growing very quickly, so the information needed is very easy to obtain via smartphones, laptops or computers connected to the internet. To support information technology, so that it can be used by the community or organization, a good computer network infrastructure is needed. The Keling Kumang Institute of Technology (ITKK) is a newly established university and will continue to grow, requiring computer network infrastructure that is in accordance with ITKK needs so that work in each field is more effective and efficient. In this study, using the Network Development Life Cycle (NDLC) method that can be used in designing computer network infrastructure that is in line with the organization's business processes. The application used for computer network infrastructure simulation in this study is GNS3.
Developing and implementing a distance learning model for training specialists of the future

Modern social development requires improvement of the existing educational system, including through the implementation of innovative distance education, which has become established after the introduction of global quarantine restrictions. The purpose of the article is to propose a practically proven model of distance learning that will be useful in training the specialists of the future. Appropriate methods were used to write the article. The most important among them were the content analysis of theoretical and empirical materials, the method of generalization, and comparison. As a result of referring to the theoretical framework, a total of 27 positions of modern and relevant scientific literature were analyzed, which made it possible to provide the necessary theoretical basis for the study. The results analyze the impact of the proposed distance learning model on the educational process. The strengths and vulnerabilities of the proposed model are also identified based on empirical material. The results obtained are compared with the general trends in the organization of distance learning in the world. The conclusions note that the findings confirmed several statements about the prospects for using distance education even after the COVID-19 pandemic: the overall effectiveness of distance learning and the positive impact on students' academic achievement. The use of this learning model has led to an increase in digital competence, stimulation of students' cognitive activity, desire for independent learning, and use of digital resources.
THE USE OF COMIC STRIPS TO IMPROVE STUDENTS’ READING COMPREHENSION

This study aims to find the effectiveness the use of comic strips in class VIII SMPN 13 Palu. Pre-experimental design that required pre-test and post-test groups, used in this research. The population in this study were 191 eighth-grade students of SMPN 13 Palu. The sample of this study was 15 students. selected using quota sampling technique. The data had been collected were analyzed quantitavely. The results of data analysis showed that the average score of students in the pre-test (31.67), and the post-test scores of students increased after being given treatment used comic strip media with an average value 79.67. Ttable significance level for alpha ɑ 0.05 or 5% degrees of freedom (df 28 = 1.701). The Tobserve value higher than Ttable, namely 10.921 &gt; 1.701, then the alternative hypothesis (Ha) is accepted and the null hypothesis (Ho) is rejected. It can be proven that there is a significant improvment between the results of used comic strips in taught reading comprehension of narrative texts to second-grade students of SMPN 13 Palu. The implication of this research was the use of comic strips as a media can improve students' reading comprehension at SMPN 13 Palu. It is hoped that prospective educators can present teaching materials well and interestingly so that they are easy to understand, joyful, and can make a comfortable learnned atmosphere, so that the class becomes more conductive.
Data Analysis of the Web News Headlines based on Natural Language Processing

This paper explores the problem of media content data analysis with the focus on the phenomenon of vaccination, closely related to the COVID-19 pandemic. The presented research is an extension of the previous work, but it differs in two main areas. Firstly, the text corpus submitted to the analysis has been considerably increased. Secondly, the previous data analysis was performed on the body part of the posts, while now it is focused on the most prominent part of the news posts, their headlines. This change from body to headline analysis was provoked by significant differences in their characteristics and the fact that most people read only headlines. Described data acquisition uses an advanced content collection approach followed by the modeling process, during which a set of natural language processing algorithms were applied. To enable the comparison, the model uses the same set of algorithms in the modeling phase like in previous work. The main contributions of the work are manifested in: i) approaching the problem from a new perspective, ii) applying more efficient method of data collection, and crucially iii) enabling the comparison of analysis results for individual parts of the content, which ensured a comprehensive insight into the characteristics of news posts.
Strategies and methods for predicting soil organic matter at the field scale based on the provincial near infrared spectral database

Strategies and methods for predicting
The influence of visitors’ specific motor actions on the perception and evaluation of museum exhibits

Background and Study Aim. An important element in the system of museum activities are approaches to attracting visitors. Among these approaches, one can single out the dialogue of the museum with visitors through specially organized active motor actions. Such actions have certain parameters (the nature of physical activity and its age-appropriateness, intensity, duration, etc.), which can influence the perception and evaluation of exhibits and the decision of visitors to return to the museum. The purpose of the study is an attempt to explain how specially organized active motor actions of visitors affect the perception and evaluation of museum exhibits and getting satisfaction from visiting.&#x0D; Material and Methods. Publications from the Web of Science Core Collection (WoS) were selected as the source of information. The search was carried out using keywords that characterize the interaction of visitors with the subject-spatial environment of the museum. The VOSviewer program was used to analyze the relationships between documents retrieved from WoS.&#x0D; Results. It is argued that the sedentary behavior of visitors, typical for many museums, does not contribute to their involvement in the museum space and the creation of meaning in museums. It is noted that an important aspect in the organization of special motor actions of visitors is the orientation to their motivation and interpretation of movements in the museum exhibits. The optimal level of physical activity has been identified. It creates a comfortable feeling and contributes to an adequate perception of the exhibits and corresponds to the main mission of the museum.&#x0D; Conclusions. Specially organized motor actions form the maximum motivation for visitors to study the museum exhibits. Such movements carry a certain semantic character of the theme of the museum.
CDF-LS: Contrastive Network for Emphasizing Feature Differences with Fusing Long- and Short-Term Interest Features

Modelling both long- and short-term user interests from historical data is crucial for generating accurate recommendations. However, unifying these metrics across multiple application domains can be challenging, and existing approaches often rely on complex, intertwined models which can be difficult to interpret. To address this issue, we propose a lightweight, plug-and-play interest enhancement module that fuses interest vectors from two independent models. After analyzing the dataset, we identify deviations in the recommendation performance of long- and short-term interest models. To compensate for these differences, we use feature enhancement and loss correction during training. In the fusion process, we explicitly split long-term interest features with longer duration into multiple local features. We then use a shared attention mechanism to fuse multiple local features with short-term interest features to obtain interaction features. To correct for bias between models, we introduce a comparison learning task that monitors the similarity between local features, short-term features, and interaction features. This adaptively reduces the distance between similar features. Our proposed module combines and compares multiple independent long-term and short-term interest models on multiple domain datasets. As a result, it not only accelerates the convergence of the models but also achieves outstanding performance in challenging recommendation scenarios.
The risks of misusing social networks in the context of hybrid threat

The world today is ceaselessly under the influence of changing conditions and threats.The contemporary digital age enables the provision of information that serves not only as common information but also as disinformation and hoaxes.This risk is high, specifically on social networks, dramatically impacting society.The paper's main objective is to point out the danger of misusing social networks to spread disinformation as a hybrid threat to influence people's thinking and behaviour, thus endangering democratic processes in developed democratic countries.The study, which focused on the risks of misusing social networks, was conducted using the questionnaire method and was subsequently assessed using statistical tests.The results indicate no link between age and the ability to distinguish desinformation, and that age does not influence the effects of disinformation.However, we did find the opposite result in terms of education, where people with lower education share hoaxes and disinformation more.Alternative media, whose posts are shared by more than 20% of social network users, have a relatively strong presence in our environment.The possibilities of spreading disinformation are also evident in the social impacts on users, who, according to our study, fear lowering their economic well-being.The study focuses on the effects of social networks on individuals' thinking and behaviour.Because we consider this issue insufficiently studied thus far, attention must also be paid to it in the future.
A Study on Matrix Decomposition to Improve CTR Prediction Performance

사용자가 주어진 항목을 클릭할 확률을 추정하는 클릭률 (Click-Through Rate, CTR) 예측 분야에서 사용되는 입력의 특징은 매우 희소(sparse)하고 고차원(high-dimensional)이다. 우수한 예측 성능을 얻기 위해서는 입력 특징을 고려한 계산 효율적인 저차원 표현을 사용하여 예측 결과와 상관성이 높은 특징 상호작용(high-order feature interaction) 파악이 매우 중요하다. 이 논문에서는 특징 상호 작용 파악에 있어 계산효율성을 달성하기 위해 입력을 저차원으로 투영(projection)하는 기존의 기법들이 상호 작용 표현력에 한계를 가질 수 있다는 가설에 근거하여 고차원 매핑을 통해 상호 작용 관계에 대한 설명력을 증대시키는 모형을 제시한다. 실험 결과에 따르면 제안된 모형은 기존 차원 축소 기반의 모형들에 비해 AUC (area under curve) 성능 향상을 확인하였다. 또한, 제안된 방식을 활용한 상호 작용의 결과물을 심층 신경망과 결합 시 AUC 성능의 추가 향상과 동시에 학습 파라미터의 절감을 달성하였는데 이는 저차 다항 근사라도 풍부한 설명력을 확보할 수 있으면 DNN의 비선형 표현 능력을 통해 성능과 메모리 간 균형이 있는 trade-off를 달성할 수 있음을 시사한다. The characteristics of inputs used in the field of click-through rate (CTR) prediction, which estimates the probability that a user clicks on a given item, are very sparse and high-dimensional. It is a key success factor for CTR prediction to understand high-order feature interactions that are highly correlated with prediction results using computationally efficient low-dimensional representations that consider input characteristics. In this paper, based on the hypothesis that the existing techniques of projecting inputs into low dimensions may have limitations in capturing feature interaction, we propose a model that increases the expressive power of interaction through high-dimensional mapping. According to the experimental results, the proposed model achieves improvement in AUC compared to existing dimension reduction-based models. In addition, when the output of the proposed method is combined with deep neural network, the AUC performance is further improved and the learning parameter are reduced. It suggests that a trade-off with a balance between performance and memory can be achieved through expressive power.
PENINGKATAN HASIL BELAJAR KOGNITIF DENGAN PENGGUNAAN MODEL PROBLEM BASED LEARNING DI SD N KARANGREJO 02 SEMARANG

The purpose of this classroom action research is to improve the cognitive learning outcomes of fourth grade students at SD N Karangrejo 02 Semarang by applying the Problem Based Learning model. The subjects of this class action research were fourth grade students. This research was conducted for two cycles. Each cycle consists of four stages: planning, implementing, observing, and reflecting. The independent variable of this research is the Problem Based Learning model, while the dependent variable is the students' cognitive learning outcomes. Data collection techniques in this study were observation and tests. Observations were made to observe the learning process carried out by students, while tests were carried out to measure the ability of students to participate in learning. The data were analyzed quantitatively. The results showed that there was an increase in student learning outcomes in cycle I and cycle II, with a classical completeness percentage of 75% in cycle I and experiencing increased to 91.6% in cycle II. The increase in learning outcomes is caused by the application of the Problem Based Learning model which encourages students to think critically and solve problems.
Edge ai technology in the defense industry via reinforcement learning in simulation environments

Edge artificial intelligence (Edge AI) technology helps to avoid vulnerabilities while benefiting from the advantages of cloud technologies, which are widely used today, especially with artificial intelligence and big data. In the case of transferring systems to the cloud, and needs such as protection of sensitive information and high bandwidth emerges in cloud approaches. Edge AI, which provides solutions to issues such as the security of sensitive data and reducing system traffic, while meeting the needs in this field, can offer a new perspective, especially to projects in the military field, when used with digital twin and autonomous system technologies. In this study, we evaluated the “Forces in virtual environment machine learning (FIVE-ML)” simulation system technically in which we use edge AI technology, analyzed the results obtained with the use of this technology. It has been determined that the current work is at the 2nd level in Edge AI levelling system, also there is a 54% performance (in terms of time with accuracy) increase with edge AI. Besides, the accuracy of hitting the target in simulation system is also increased, with the rate of 34%.
SMOTE vs. KNNOR: An evaluation of oversampling techniques in machine learning

The increasing availability of big data has led to the development of applications that make human life easier. In order to process this data correctly, it is necessary to extract useful and valid information from large data warehouses through a knowledge discovery process in databases (KDD). Data mining is an important part of this and it involves discovering data and developing models that extract unknown patterns. The quality of the data used in supervised machine learning algorithms plays a significant role in determining the success of predictions. One factor that improves the quality of data is a balanced dataset, where the input values are distributed close to each other. However, in practice, many datasets are unbalanced. To overcome this problem, oversampling techniques are used to generate synthetic data that is as close to real data as possible. In this study, we compared the performance of two oversampling techniques, SMOTE and KNNOR, on a variety of datasets using different machine learning algorithms. Our results showed that the use of SMOTE and KNNOR did not always improve the accuracy of the model. In fact, on many datasets, these techniques resulted in a decrease in accuracy. However, on certain datasets, both SMOTE and KNNOR were able to increase the accuracy of the model. Our results indicate that the effectiveness of oversampling techniques varies depending on the specific dataset and machine learning algorithm being used. Therefore, it is crucial to assess the effectiveness of these methods on a case-by-case basis to determine the best approach for a given dataset and algorithm.