Reviewerly/exHarmony huggingface dataset:
Context-Focus Reframing for VUCA Era-Themed Texts on Discourse Analysis Assessment

Social values in the VUCA era can be adapted as self-understanding values to survive in various lines. Social categorization is a fundamental process that has important implications for various social phenomena. The role of social categories does not refer to a particular identity. Language learning that applies pragmatic aspects must accommodate assessments that refer to achieving the attitudes needed to face the VUCA era. This research will describe the discourse analysis assessment and observe its implications accurately. This research involved students from Indonesian, English, and Elementary School Teacher Education study programs in the Teacher Training and Education Faculty at Jember University. The results of this research illustrate the application of assessment aspects, namely determining relevant context, determining text implications by comparing social phenomena, conveying understood social values, and abstracting their components in a comment. This research shows that the role of assessment using context-focus reframing is very much needed in learning discourse analysis. Student answers indicate reasoning, accuracy of social values, accuracy of text structure, and argumentation through relevant statements as evaluation to support the comprehensive critical phase of the text after going through the literal and inference phases. In the next stage, perception scores regarding the VUCA era will be obtained based on the texts used as assessment material. This research shows students’ positive response to the assessment through context-focused reframing and character embedding through arguments to show attitudes through the question, “What are the implications for the VUCA era?”.
OPTIMIZING THE COMPETENCE AND PROFESSIONALISM OF ACCOUNTING TEACHERS IN USING ACCURATE SOFTWARE

Based on the observations made, problems were found including accounting records that were still manual and a lack of understanding regarding accurate software. The lack of expertise in the use of technology in the accounting process creates a new problem, which is difficult to get a decent job for job seekers. Among job seekers, there are various educational backgrounds, ranging from high school graduates, vocational schools, as well as undergraduate graduates from various universities. The Muhammadiyah Sidoarjo University Community Service Center conducts training on Accurate accounting software for teachers and vocational students in Sidoarjo. Training is provided using Accurate software. The purpose of this training is to provide training to teachers and students in understanding and operating various features in Accurate related to transaction accounting which are expected to improve skills in using this software. The method in this service uses the PAR method which involves the teacher. The stages of implementing community service use the PAR method, namely initial mapping, implementation, and monitoring/evaluation. The results of the service carried out at the Muhammadiyah University of Sidoarjo went smoothly and the teachers succeeded in increasing teachers' understanding in using accurate applications.
An open problem and a conjecture on binary linear complementary pairs of codes

The existence of $q$-ary linear complementary pairs (LCPs) of codes with $q> 2$ has been completely characterized so far. This paper gives a characterization for the existence of binary LCPs of codes. As a result, we solve an open problem proposed by Carlet $et~al.$ (IEEE Trans. Inf. Theory 65(3): 1694-1704, 2019) and a conjecture proposed by Choi $et~al.$ (Cryptogr. Commun. 15(2): 469-486, 2023).
A microscopic image enhancement algorithm and its implementation on mobile platforms

In recent years, there has been a tremendous development in the computing and processing power of mobile platforms, leading to a continuous emergence of research and achievements in mobile platform image processing. The processing of microscopic images using mobile platforms has also become an increasingly urgent demand. One important goal of image processing is to enhance the quality of the images, and in the case of microscopic images, color images make up a significant portion. Therefore, it is necessary to implement a microscopic image enhancement algorithm on mobile platforms to further improve the observation and capturing experience of microscopic images on these devices. In this study, extensive research work was conducted to analyze and study the implementation of image dehazing algorithms. By considering the characteristics of microscopic images, the algorithm was improved to achieve a mobile platform-compatible microscopic image enhancement algorithm, which was successfully applied on an Android mobile platform with Rockchip's RK3399 as the core. Extensive experiments were conducted on slice samples under different magnification levels and lighting conditions, demonstrating that the algorithm can achieve efficient and outstanding microscopic image enhancement.
Enhancing Disaster Tweet Classification with Ensemble Models and Multiple Embeddings

Social media, particularly Twitter, is a vast source of information where users post tweets, leading to a significant volume and variety of shared content. During natural disasters, the high number of disaster-related tweets often makes it a trending topic. However, not all of these tweets provide relevant information about the disaster, as some may merely use disaster keywords without discussing the event. In order to address the obstacle at hand, the present study suggests a model consisting of a combination of FastText, BERT, and Globe embeddings together with an LSTM foundational model. The objective of this ensemble methodology is to encompass a wide range of tweet semantics and contextual details The utilization of the LSTM model serves to effectively capture the sequential dependencies and temporal information present within the tweet data. Performance evaluation utilizes established metrics like Precision, Recall, Accuracy, and F1-Score to comprehensively assess the model’s ability to classify between disaster and non-disaster tweets. The primary objective is to develop a robust supervised learning classifier that accurately identifies genuine disaster-related tweets. By integrating multiple embeddings and LSTM, the proposed model enhances the accuracy and reliability of disaster tweet classification. This research contributes to real-time emergency response and decision-making by effectively utilizing social media information.
Biomedical Named Entity Recognition Using Scikit-Learn And Keras

People's perceptions are influenced by several sentences. The reader is better able to understand the statement through various entities thanks to these perceptions. Named Entity Recognition (NER) is the term used for this technique in NLP. Confusion about whether a word represents the name of a person, place, or organization, or whether an integer represents a date, time, or amount of money, is one of the main issues in these NLP processes. One of the difficult jobs that previously required a great deal of expertise in the area of feature engineering along with lexical databases to attain good performance is named entity recognition. Because of this, we create Scikit-learn as well as Keras algorithms for NER algorithms to accurately label the entities and divide them into the most likely and unlikely transitions. Finally, we implement Biological NER Labelling on protein and gene sequences to conclude our work.
Hardware Accelerator Design for Sparse DNN Inference and Training: A Tutorial

Deep neural networks (DNNs) are widely used in many fields, such as artificial intelligence generated content (AIGC) and robotics. To efficiently support these tasks, the model pruning technique is developed to compress the computational and memory-intensive DNNs. However, directly executing these sparse models on a common hardware accelerator can cause significant under-utilization, since invalid data resulting from the sparse patterns leads to unnecessary computations and irregular memory accesses. This brief analyzes the critical issues in accelerating sparse models, and provides an overview of typical hardware designs for various sparse DNNs, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and Transformers. Following the overview, we give a practical guideline of designing efficient accelerators for sparse DNNs with qualitative metrics to evaluate hardware overhead under different cases. In addition, we highlight potential opportunities in terms of hardware/software/algorithm co-optimizations from the perspective of sparse DNN implementation, and provide insights into recent design trends for the efficient implementation of transformers with sparse attention, which facilitates large language model (LLM) deployments with high throughput and energy efficiency.
THE COMPONENT COMPOSITION OF THE PROJECT-BASED LEARNING METHODOLOGY IN THE CONTEXT OF THE EDUCATIONAL STANDARD (USING THE EXAMPLE OF A GENERAL BIOLOGY COURSE)

The article reveals the essence of project-based learning through the allocation of the basic principle of its definition – the principle of independent acquisition of knowledge by students in the course of solving subject practical problems using various subject knowledge (including integrated). On its basis, the substantiation of the component composition of the methodology of project training at the level of general education is presented. The relevance of the research lies in the possibility of its implementation in the educational process to achieve the planned results of the federal state educational standard in the form of subject and meta-subject (UUD) competencies formed by students. On the example of the general biology course, the effectiveness of the implementation of the component composition of the project training methodology is shown. Its justification is based on the presented results of experimental work.&#x0D; Purpose: substantiation of the component composition of the project training methodology in the form of a system of goals, content, principles, approaches, methods, didactic tools, diagnostics and monitoring of the levels of subject and metasubject competencies formed in students.&#x0D; Methodology: the article presents a set of theoretical and empirical methods of pedagogical research used in the identification, justification and implementation of the component composition of the methodology of project-based learning in the practice of teaching.&#x0D; Results: improving the effectiveness of the educational process in the formation of subject and meta-subject (UUD) competencies of students in the federal state educational standard by applying the component composition of the project-based learning methodology.&#x0D; Practical implications: the results of the study can be used by subject teachers in the implementation of the required educational results according to the federal state educational standard and taken into account in the development of educational programs at the school level by administrative and managerial personnel.
METADATA AND CONTAINER STRUCTURE ANALYSIS FOR AUDIO AUTHENTICATION

Audio recordings of crime scenes are frequently utilized as digital evidence due to the universal usage of mobile phones, but they must first be authenticated before being used as evidence in court. The technique of identifying whether an audio recording is real or has been altered or manipulated is known as audio authenticity. In order to investigate and validate audio, this study provides a cutting-edge authentication technique that can distinguish the difference between real and fraudulent audio. Herein, we analyzed audio recording samples from different mobile handsets like iPhone, Samsung, One Plus etc. It is important to note that the proposed method authenticates audio files without regard to the audio content, i.e., without concern to the speaker or the speech. The audio recordings were subjected to manipulation that a human cannot recognize them auditory. The present results show that it is possible to verify the authentication of audio recordings generated through mobile phones or any recorder using metadata and the container structure of the recorded and edited audio file
THE EFFECTIVENESS OF SUPPORTING THE TECHNOLOGY OF PROJECT ACTIVITIES IN THE ORGANIZATION OF RESEARCH WORK OF STUDENTS

Annotation. One of the most important issues in modern education is the preparation of a student for professional mental labor in obtaining knowledge. In order to fully solve this problem, there is a need to actively use project-based learning technologies. This is to involve students in future scientific research by engaging them in mastering the first research activities. As a result of the application of the project method, it develops students who think critically, come up with new ideas, offer rational methods, use, process and select information, communicate well with everyone in different environments, work for the full self-development of their personality from a cultural, spiritual, moral point of view. The project method is a training system that is carried out under the supervision and guidance of a teacher, is based on the student's ability to express creatively, develops his intellectual and creative abilities and inclinations. The main goal of the project is to create the following opportunities for students: to try themselves in different situations in the course of activities organized according to the purpose of the project, willingly, independently, acquiring knowledge that must be mastered from different sources; learn to apply the knowledge gained in solving various practical and cognitive problems (that is, to compare the goal and the conditions for achieving it in the project); develops communication skills in a team, group, language skills, work in harmony, that is, communication skills, research skills are formed; B-develops the skills of systematic thinking, draws up a program of activities for the implementation of the planned project, taking into account the level of its capabilities. The method of project activity is based on the cognitive interest of the student, the ability to independently master the knowledge he needs, the ability to select the information he needs in the information space, the development of critical thinking, the ability and ability to conduct research work. Key words: research work of the student, project method, extracurricular activities, creative work of the student
Unexpected contribution of cloud seeding to NPP increase during drought

Abstract Cloud seeding is generally used to secure additional water resources, which is not an easy goal to achieve, as the spatial variability of rainfall is high. Instead, the increased rain may moisten the neighboring forest. This study focuses on this situation and estimates the possible increase in the net primary production (NPP) due to cloud seeding. This study considers the Boryeong Dam basin in Korea as a study area and uses the Carnegie–Ames–Stanford Approach (CASA) model to estimate the NPP at 8-day intervals. As a result, first, the increase of the current 8-day NPP is greater when the rainfall amount during the last 16-day period is 50 mm or more. The mean increase of the 8-day NPP is estimated at about 1.873 g/m2 of carbon. Second, the increase of the NPP with the target 16-day rainfall of 50 mm is estimated at about 3%, which is about 4% with the target 16-day rainfall of 100 mm. Simply extrapolating the derived result to the entire forest in Korea, the increased carbon accumulation can be extended to about 0.6 and 0.8% of the total carbon emission in 2018, respectively. These amounts correspond to about 1.2 and 1.5% of the target amount of carbon reduction by 2030 in Korea.
The Transformative Impact of AI on Financial Institutions, with a Focus on Banking

The financial landscape is undergoing a profound transformation, with Artificial Intelligence (AI) emerging as the catalyst for unprecedented change in banking institutions. This paper briefly examines the multifaceted impact of AI across critical domains, encompassing customer experiences, security protocols, risk management, operational efficiency, return on investment, and regulatory compliance. In customer-centric evolution, AI-driven chatbots and virtual assistants redefine interactions, providing personalized and anticipatory services. In the field of finance, security is a paramount concern. At the same time, we are witnessing AI as the guardian, fortifying trust through real-time fraud detection and biometric authentication. Risk management undergoes a paradigm shift, with predictive analytics steering financial institutions toward strategic advantages in navigating dynamic markets. Operational excellence is achieved through AI's automation, liberating human resources for strategic endeavors, and fostering a costconscious ethos. Regulatory compliance, often intricate, finds harmony through AI-driven automation tools and biometric authentication, ensuring transparency and accountability in the face of relentless regulatory scrutiny. Striking the right balance between innovation and ethical considerations is pivotal in ensuring that the consolidation of AI and finance is a force for good.
Examining the impact of AI in cancer diagnosis

Artificial intelligence has the potential to sift through large datasets and identify subtle markers of disease.
Understanding How Home Conditions Shape Early Grade Learners Literacy Acquisition Skills: A Tanzanian Perspective

Literacy skills are essential in children’s future learning and development. Studies indicate that literacy skills among early grade learners in Tanzania are below the required standards. This study explored the contribution of home factors that hinder acquisition of reading and writing skills among early grades learners in Tanzania. The study was informed by a qualitative methodology which involved a total of 92 participants, including 80 parents and 12 standard one and two teachers from four primary schools in two districts of Morogoro region. The study employed purposive sampling technique to select teachers and convenience sampling technique to obtain parents who were involved in interview and focus group discussion. Data was collected using semi-structured interview, focused group discussion and documentary review. The data were analysed thematically based on themes and sub-themes emerged in the study. The findings revealed that most factors that hinder early grade learners from acquiring reading and writing skills included (poor parents follow up, engaging children in many home activities, low parents’ education and parent’s income) It was suggested that there should be cooperation between parents and teachers to assist the children in developing their reading and writing skills. The study concludes that parents and teachers should work collaboratively to develop reading and writing in children. Lastly, it is recommended that parents should buy materials like books, and assist their children to practice reading and writing skills at home.
Decryption and Encryption of Data

In the digital age, the security of data has become a paramount concern. This research paper explores the fundamental concepts and practical applications of encryption and decryption as essential tools for ensuring data security. It delves into the historical evolution of encryption methods and the mathematical foundations of cryptographic algorithms. The paper provides a comprehensive overview of popular encryption algorithms, key management techniques, and the role of encryption in protecting data at rest, in transit, and during processing. Decryption techniques and potential vulnerabilities are also discussed, along with cryptographic attacks and countermeasures. The legal and ethical implications of encryption, including issues related to privacy and government access, are examined in the context of recent legislative developments. Real-world case studies illustrate the impact of encryption on cybersecurity incidents. The paper concludes by looking ahead to future trends in encryption and decryption technologies, addressing emerging challenges and opportunities in data security.
The Electric Field as a form of Acceleration

Gravity is already recognized as form of Acceleration, but the nowadays Science of Physics does not recognize (yet) the Electric Field also as a form of Acceleration. Based on _STRUCTURAL IDENTITIES_ between Newton's Universal Gravitational Law and Coulomb's Law, this paper provides argumentations which indicate that the Electric Field should be also recognized as a form of Acceleration. This paper also proposes a relatively simple experiment, which if implemented, it might prove, (or disprove), the above presented statement that the Electric Field should be also recognized as a form of Acceleration. If this experiment will be implemented, and its results will be successful, such that the Electric Field will be also recognized as a form of Acceleration, this will also result in significant implications.
Hamilton energy of a complex chaotic system and offset boosting

Abstract The complex differential system can be obtained by introducing complex variable in the real differential system. Complex variables can be decomposed into real component and imaginary component, which makes the complex differential systems have more complex dynamic behaviors. Complex chaotic system is used in secure communications to increase the security of cryptographic systems. In this study, we designed a complex differential system by incorporating a complex variable into a 3D differential system. Dynamics of this complex differential system are investigated by applying typical nonlinear analysis tools. Furthermore, Hamilton energy function for complex differential system is obtained based on Helmholtz’s theorem. The values of Hamilton energy with different oscillations of complex differential system are calculated. In addition, offset boosting control for the complex chaotic signal is realized by adding a constant to variable of complex system. Simulation shows that the position of the chaotic attractor in phase space can be flexibly shifted by applying the offset parameter.
Evaluation of Fatigue Crack Propagation Behavior of Structural Steel with Different Thickness Using Digital Image Correlation Method

In this paper, fatigue tests using SENT specimens with different thicknesses were carried out to evaluate the effect of plate thickness on the DIC measurements and fatigue crack propagation properties. The fatigue crack propagation rate da/dN of the thin specimen was slower than that of the thicker specimens. The crack opening/closure behaviors at the arbitrary crack location were evaluated by the crack opening displacement measured by the DIC. The crack opening load Pop was higher with thinner specimens, especially with shorter crack lengths. da/dN was correlated with effective stress intensity factor range ΔKeff based on the Pop at the fatigue crack tip, but the error increased with thinner plate thicknesses and longer crack lengths. The strain range at the fatigue crack tip measured by DIC showed a good correlation with da/dN, irrespective of the plate thickness and crack length.