Reviewerly/exHarmony huggingface dataset:
A semi-automatic approach to biomedical semantic role corpus construction

A semi-automatic solution to build a biomedical semantic role corpus named PASBio+ was proposed. The corpus was annotated with a predicate argument structure, the important information that revealed the main content of a sentence. Because more than 86% of the arguments in the biomedical domain significantly differed from those in the general domain, this proposed corpus was labeled on top of 317 labeled sentences from PASBio, the argument frameset specifically designed for the Biomedical domain. From these sentences, the proposed semi-automatic solution additionally generated 87 sentences which were manually annotated by our experts. More instances were further generated by using the virtual example method, a powerful and flexible data augmentation technique that has been successfully applied in a wide range of tasks. Specifically, two sequential rules (the swap rule and the replace rule) were proposed to ensure that the biomedical knowledge was always kept correct. PASBio+ was also augmented by adding grammatical variants of the original sentences which kept the corpus having a wide coverage of diverse natural writing styles. In addition, from the very beginning, the PASBio's original sentence set was also enriched by an external text source which was an additional set of sentences selected from GREC biomedical corpus. As a result, a corpus with 2,500 fully labeled sentences with a uniform frequency distribution among predicates was obtained, thereby eliminating the problem of data sparsity and helping to restrict the overfitting in machine learning. The experimental results showed that when using the augmented corpus to train a semantic role labeling model, an increase in the F score by 52.2% or 22.5% were obtained compared to those trained by using the original PASBio corpus or a general domain one, respectively.
The perspective of climate-smart agricultural practices in the rice-wheat cropping system in Haryana, India

Agricultural transformation is a process of change in agricultural practices and production. After the green revolution, a significant agricultural transformation has been recorded in Haryana. The cropping pattern of the state has shifted from multi-cropping to mono-cropping. The share of rice and wheat is about 87 percent of the total area under food grains. The present study emphasizes assessing the transforming agricultural practices in Haryana besides exploring the measures of sustainable climate-smart agricultural practices. The study is based on both primary and secondary data sources. Climatic data has been collected from Regional Meteorological Centre, Chandigarh and statistical abstracts of Haryana have been used for data related to major crops. To assess the adoption of climate-smart agricultural practices in Haryana, a primary survey was conducted in two different agro-climatic regions of the state. A cluster of five villages from each region has been selected. From each selected village, about 30 agricultural households have been surveyed. The vulnerability spider diagram and the fuzzy cognitive map are used to represent the level of adoption and perception of farmers regarding climate-smart agricultural practices. The result shows that excessive use of agro-chemicals in the rice-wheat cropping system has increased production, however, caused alterations in the chemical and physical properties of the soil and depletion of groundwater resources in the state. Changing climatic conditions has further aggravated these challenges.
Investigation of the Effects of Augmented Reality Applications on the Astronomy Literacy Levels of Secondary School Students

In this study, it is aimed to determine the astronomy literacy levels of the seventh and eighth grade secondary school students and to investigate the effect of augmented reality [AR] applications on the astronomy literacy levels of the students. The research is a quantitative study and a quasi-experimental method was used. The study group consists of a total of 95 seventh and eighth grade students (n7th grade = 46 and n8th grade = 49) studying in a secondary school in the central district of Ankara in the spring semester of the 2021-2022 academic year. The students of the study group of the research were selected by using convenient sampling method. Two experiments and two control groups were used in the study. The data of the research were obtained with the astronomy literacy scale before and after the application. In practice, the lessons were taught within the framework of basic astronomy concepts, with augmented reality applications in the experimental groups, and with the activities suggested in the textbooks in line with the achievements specified in the curriculum in the control groups. As a result of the analysis of the data, it was noted that the students' astronomy literacy levels were low before the application. After the application, the astronomy literacy levels of the experimental group students were high; it was seen that the control group students reached the intermediate level. It was determined that augmented reality [AR] applications positively affected the astronomy literacy levels of the experimental group students.
Examining Social Studies Course Questions in Terms of Question Type and Test Item Writing Techniques

This study aimed to determine the question type of the written exam questions prepared by the social studies teachers and to examine the compatibility of the questions with the item writing techniques by using the document analysis method. Study data were collected from 20 social studies teachers and analyzed through the "Item Writing Techniques Checklist". Research findings revealed that social studies teachers preferred multiple-choice question types the most in written exam questions, followed by two-choice, fill-in-the-blank, matching and open-ended question types, respectively. In addition, findings showed that teachers were open to improvement reading criteria such as phrasing the stem of the question clearly, directly and simply, creating equivalent clauses in grammar, length, and complexity, distractor development, shaping items around a single proposition or idea, stating that each equivalent may not be used once, more than once, or not at all, including more responses than premises, not leaving spaces of the same width, and hinting at the correct answer. Based on these result, it I suggested to conduct applied studies to investigate the reasons why open-ended questions are rarely used although they are crucial in terms of developing students' high-level thinking skills as well as to determine the level of acquisition of the achievements in the analysis, synthesis and evaluation steps and to develop projects to ensure the professional development of teachers in terms of acquisition-based and skill-based item writing techniques.
English Pragmatic Competence of Col Language Speakers in Using Directive Act at Lubuklinggau

This research aimed to explain the English Pragmatic Competence of Col language speakers in using directive act and to find out the extent to which Col language pragmatic competence influence the production of English pragmatic competence in directive act. The method used descriptive qualitative. The research subjects were 20 students who studied English consisting of students, collage students and graduates at Lubuklinggau. The data were collected by using questionnaire and pragmatic competence test. After that, the data were analyzed with description. The result of this research is that the pragmatic competence of Col language using directive acts is more understandable because respondents understand the use of language in the right situation may be different. The tests that have been carried out show that many of the respondents' answers are appropriate. They use English only in educational domains such as schools, colleges and courses. Therefore, the frequency of use of English is limited and other languages are used more frequently. In almost all expressions there is no significant negative transfer from L1 and L2 production to English, as all percentages below 50% except 57,5% command expressions have little influence on English production. These errors were found in the use of expressions in several formal situations, but they were not significant because not many pragmatic errors were found. In conclusion is that Col people understand how to use appropriate expressions in different situations, namely formal and informal situations, even though only the expressions of command have little influence.&#x0D; Keywords: Col Language Speakers, Directive Act, Pragmatic Competence
The Use of Nannofossils in Determining the Age of the Sargelu Formation (Middle Jurassic) in the Hanjera section, Northern Iraq

The Jurassic rocks are well exposed in Northern Iraq within the High Folded -Thrust Zone ( Western Zagros Fold Thrust Belt). The Sargelu Formation (Middle Jurassic) was studied from a well exposed section located NW of Suliamni city and about 2km NW of Ranya town , and close from Hanjera village. , at the southwestern side of Shawiri anticline. The Sargelu Formation consists of successive beds of sucrose dolomitic limestone, limestone and argillite limestone beds, interspersed with layers of shale rich in organic matter, as well as thin beds of flint. Some beds of the formation show the presence of large size ammonite fossils. It is bounded from the bottom by the Sehkanyan Formation, and the contact surface between them appears sharp, represented by the last appearance of the hard dolomite bed belonging to the Sehkanyan Formation, and it is bounded from the top by the Naokelekan Formation and the surface between them appears in the form of a sedimentary compatible surface. The current study relied on determining the age of the formation based on nannofossils, where many species and genus were diagnosed from the samples taken from the formation, and three biozones were identified, which are from the oldest to the most recent:1 - Watznaueria britannicaInterval biozone2 - Watznaueria manivitiae Interval biozone3 - Watznaueria barnesiaeInterval biozoneThese biozones were compared with global nannofossils biozones, and it was found that the age of the Sargelu formation is the middle Jurassic ( Bajocian - Bathonian).
Machine Learning Approaches for Predicting Chaotic Behavior in Nonlinear Systems

In many domains, including biology, engineering, physics, and finance, the ability to forecast chaotic behavior is of the utmost importance. Predicting nonlinear systems accurately is a critical task due to their intrinsic sensitivity to initial conditions and lack of apparent patterns. One potential way to address this difficulty is by utilizing machine learning techniques. These methods can help us better understand and manage complex systems that display chaotic dynamics. Complex nonlinear systems, with their great dimensionality, temporal interdependence, and sensitivity to initial conditions, make chaotic behavior prediction difficult. The development of sophisticated tools that can detect patterns in chaotic dynamics is often necessary because traditional methods fail to capture these subtleties. The present research presents Chaotic Dynamics Prediction through Deep Ensemble Learning (CDP-DEL), an approach to chaotic dynamics prediction (CDP) that combines deep learning architectures with ensemble learning methodologies. Ensemble learning improves generalizability and reduces overfitting, whereas deep neural networks are made to capture complicated nonlinear interactions. CDP-DEL is an all-inclusive method for predicting chaotic behavior in nonlinear systems since it uses feature engineering, integrates system factors, and handles temporal dependencies. Among the many fields that have found use for CDP-DEL are control systems, ecological research, financial modeling, and weather forecasting. When applied to real-world scenarios with substantial chaotic dynamics repercussions, CDP-DEL's improved prediction capabilities may help optimize decision-making processes. Extensive simulation evaluations are performed utilizing benchmark chaotic systems to validate the efficacy of CDP-DEL. Research comparing CDP-DEL to more conventional methods show that it is more effective at predicting chaotic behavior and overall outperforms the competition.
Applied Nonlinear Analysis for Efficient Communication Signal Processing and Network Management

The use of nonlinear analysis in network management and communication signal processing is a revolutionary strategy that is redefining contemporary networking paradigms. The goal of this project is to improve communication systems' resilience, efficiency, and agility by integrating cutting-edge nonlinear techniques. This study investigates the complex relationship between signal processing and network management using nonlinear analysis techniques in order to meet the growing needs of modern networks. Nonlinear approaches provide dynamic signal processing methods, enabling in-the-moment modifications that maximise data transfer under variable network circumstances. Prioritising data streams is made easier by this integration, which also improves network speed and efficiently allocates resources to guarantee Quality of Service (QoS). The study explores the complexities of interdisciplinary ideas, using the concepts of nonlinear analysis to comprehend phase transitions, emergent network architectures, and networks' inherent ability to self-organize. By deciphering the complexity of network behaviours and their evolution, this understanding aids in the creation of more effective management techniques. However, there are difficulties with computing complexity, interoperability, and skill adaption when using nonlinear analysis in network management. To properly utilise nonlinear approaches for network management and communication signal processing optimisation, these obstacles must be overcome. The research is important because it may lead to game-changing advancements in communication systems by introducing cutting-edge ways that go beyond the constraints of conventional, linear methods. The findings show potential for more effective and secure communication systems, which can meet the increasing needs of modern networking.
Studi Komparasi Penggunaan Media Pembelajaran Ular Tangga dengan Flash Card pada Mata Pelajaran Bahasa Indonesia

This study aims to determine the differences in the use of snake and ladder media and flash cards in Indonesian language subjects in class V of 67 Elementary Schools in Bengkulu City, which are divided into 2 classes, namely class A and B with 40 students. Researchers took these 2 classes to be the sample, namely the VA class with 20 people as the experimental class and 20 people to the VB class as the control class. This type of research is a quantitative study with an experimental method approach. And data collection techniques using observation, test questions Esay and documentation. Data collection techniques using the t test. It was found that the results of the student poststest class VA who used the snake and ladder learning media were in the medium category, as many as 17 students (85%) got grades 68.4 to 90.6. And the learning outcomes of class VB using Flash Cards learning media as many as 16 students (80%) get scores between 68.1 to 81.9. The results of the study can be concluded that there are differences between the media of snakes and ladders and flash cards on the speaking ability of grade V students in Indonesian subjects at SDN 67 Bengkulu City. With the calculation of the t test where tcount&gt; ttable (2.601&gt; 2.021). &#x0D; Keywords: Media Snakes and Ladders, Media Flash Cards, Talking Ability.
Penerapan Model Pembelajaran Cooperative Integrated Reading and Composition (CIRC) dalam Meningkatkan Keterampilan Literasi Membaca dan Hasil Belajar Siswa

The purpose of this study was to find out how to improve Reading Literacy skills and student learning outcomes by using the Cooperative Integrated Reading and Composition (CRIC) Learning Model, as well as to find out the application of the Cooperative Integrated Reading and Composition (CRIC) Learning Model to improve reading literacy skills and learning outcomes class II students at SDN Kutowinangun 08 Salatiga. This type of research uses Classroom Action Research (PTK) while the type of data used in this research is Quantitative and Qualitative data. The results of the study show that in improving reading literacy skills and learning outcomes of second grade elementary school students by applying the Cooperative Integrated Reading and Composition (CRIC) learning model. This model requires students to improve reading literacy skills both from indicators of pronunciation, intonation, fluency, and clarity of voice when reading. This research can be said to be successful because the increase in reading skills in the action cycle I was 82.5 and in the action research cycle II it reached an average percentage of 87.87 and the learning outcomes of students reached 87.87 completeness from a predetermined performance indicator of 80. This study proves that the application of the Cooperative Integrated Reading and Composition (CRIC) learning model can improve reading literacy skills and learning outcomes of second grade elementary school students.&#x0D; Â­&#x0D; Keywords: CIRC Learning Model, Reading Literacy Skills, Student Learning Outcomes
Revolutionizing the Medical Record Department with a Blockchain-Enhanced Nonlinear Approach to Medical Record Management (MRM) Systems

This work presents a thorough design and construction overview in order to meet the impending issues associated with conducting a groundbreaking project. The centerpiece is a cutting-edge, specially designed for government dentistry institutions, Blockchain-Enhanced Nonlinear Approach to Medical Record Management Systems. The system's goal is to keep patient data safely while offering a user-friendly interface, reliable backup systems, and security. In the first stage, a blockchain-based medical record system will be put into place, and medical staff members will be asked for vital information to be authenticated. The ability to save and manage patient data, including crucial information like patient classifications, is subsequently provided to these verified individuals. The system determines a patient's eligibility to pay medical care costs by intelligently classifying them into groups. For example, BPL holders, elderly residents, and convicts are not required to pay for medical care. This paradigm places a high priority on security, and the article presents the idea of cryptography inside the blockchain. A secret common key is used to assist the encryption and decryption operations that are applied to data. Sensitive medical information is protected by this cryptographic technique, which guarantees the secrecy of discussions between two entities. The data may be retrieved by using this private key, which guarantees the privacy and availability of patient records that are saved.
Applied Nonlinear Analysis and Machine Learning Transforming the Horizons of Communication Technology

The combination of machine learning and applied nonlinear analysis has significantly changed the state of communication technology. The fundamental structure of communication networks has undergone a radical change as a result of this convergence. The convergence of machine learning and nonlinear analysis methods has yielded hitherto untapped possibilities for improving network optimisation, data transfer, and signal processing. Because it can describe complex systems, nonlinear analysis provides a greater understanding of the complex dynamics found in communication networks. Through the application of dynamical systems, fractal geometry, and chaos theory, it is possible to identify underlying patterns and behaviours in data, leading to more effective information processing and transmission. Machine learning is added to further enhance these skills. Communication systems are able to automatically optimise their performance, anticipating network behaviours and making real-time adjustments to changing circumstances thanks to algorithms that learn from data and adapt to it. In tasks ranging from resource allocation and interference mitigation to signal denoising and modulation categorization, machine learning approaches including neural networks, deep learning, and reinforcement learning have shown to be highly effective. Combining these two fields is accelerating the creation of communication systems that are more resilient, dependable, and flexible. Communication systems can overcome past constraints by utilising the complementary strengths of nonlinear analysis and machine learning. Massive data quantities may now be handled by them, along with the ability to reduce signal distortions, anticipate and avoid network congestion, and dynamically adjust to shifting communication settings.
Performance of K-Means and DBSCAN Algorithm in Clustering Gross Regional Domestic Product

Gross Regional Domestic Product (GRDP) is one of important indicator to determine the economic conditions of a region. GRDP are obtained from sum of value added produced by all unit of production in a region. This study uses GRDP by production approach that grouped into seventeen categories of industry. The government always put the big efforts to increase the economic growth after Covid-19 pandemic. The aim of this study is determined the cluster GDRB based on province in Indonesia at current prices and analyses the performance of the cluster method. The results showed that by using the DBSCAN, two clusters were formed and one province can be detected as an outlier. On the other hands, performance of the method by K-Means showed two clusters. The silhouette value using K-Means is higher than the DBSCAN. For this case, the performance of K-Means is more appropriate than DBSCAN to use in clustering province in Indonesia based on GRDP at Current Market Prices. Moreover, performance of DBSCAN shows more sensitive on outliers detection.
Using PADLET Application in Improving the Tenth Grade Students’ Writing Ability at SMA YADIKA Bandar Lampung

The purpose of this study was to determine whether the use of the Padlet application had a significant impact on the writing ability of Tenth grade students at SMA Yadika Bandar Lampung. In this study, the researchers uses quantitative research. The study technique employed a quasi-experimental design, and the therapy consisted of three encounters. The basic skills of the pupils are ascertained through a pre-test in this approach prior to therapy. The study's target demographic was the tentth grade at SMA Yadika Bandar Lampung. Science One and Science Two were the two courses from which the samples were obtained. The author uses testing tools to construct descriptive stories about historical sites to collect data for pre-testing and post-testing. The findings indicated that the outcomes of creating descriptive prose were impacted by the Padlet application. The increase in the average score obtained from the pre-test results from 60.15 to 78.09 in the post-test results proves this. The results of hypothesis testing, which is 0.000 less than the Sig value of 0.05, also supports it. In other words, the Padlet application affects the students at SMA Yadika Bandar Lampung in the tenth grade's descriptive text writing skills&#x0D; Keywords: Descriptive Text, Padlet Application, Writing Ability