Reviewerly/exHarmony huggingface dataset:
A Hybrid Analysis of Critical Discourse Analysis and Systemic Functional Language to Unmask Ideological Stance Inside President Joko Widodo’s Political Speech Text

This study aims to investigate President Joko Widodo's ideological stance inside his political speech text. This study also identifies why Jokowi priorities Indonesia's people as the main concern in his program. Data were collected from the first official speech text of President Jokowi delivered on August 16, 2015. This paper combined Critical Discourse Analysis (CDA) and Systemic Functional Linguistics (SFL), particularly the Appraisal System's judgmental analysis. The paper formed Judgement as a sub-type of the attitudinal classification rather than the other two, Affect and Appreciation. The Judgmental categories dominated by the sub-type of Capacity has exceeded other judgmental subtypes. Jokowi mostly evaluated Indonesia's people using the positive Capacity to show that the People of Indonesia the capable people to overcome their problems. The reason behind Indonesia's people's political inclination is that Jokowi is supported by the people-based political party, Struggle-Indonesian Democratic Party (PDIP).
Comparative Analysis of Reading Habit and Translation Ability among Female and Male Students at STBA PIA Medan

The habit of reading will build and improve students' character and literacy. This habit will also help students become adequate readers, achieve a large vocabulary, understand and use complex grammatical constructions, and develop a good writing style. The reading habit of an individual depends on many factors, and one of those factors is gender. This research is aimed to compare the reading habits and translation skills of female and male students in English Literature Department at STBA PIA. The study is conducted at STBA PIA in Medan, North Sumatra. The sample, taken by random sampling, consists of 40 students, 20 female and 20 male students. The research design is a quantitative, analytical, and observational one. There are two instruments applied. A questionnaire is used to measure female and male students’ reading habits. Afterwards, the two groups of students are given a translation test to get data about their ability to translate. The test uses the scale instrument of holistic-based translation quality assessment (TQA). The distribution frequency of the collected data is analyzed using univariate analysis. An independent T-test (two-tail) will be conducted to find out the comparative data on reading habits and translation skills between female and male students.
The Differences of Traditional Grammar (Formal Grammar) And Structural Grammar (Notional Grammar) in Language Development

Language is the ‘species-specific ’ and ‘species-uniform’ possession of man. It is God’s special gift to mankind. Without language human civilization as we now know it would have remained an impossibility. Language is ubiquitous. Language is a primarily human and non-instinctive method of communicating ideas, emotions and desires by means of a system of voluntarily produced symbols. There were some grammars in language development such as Traditional Grammar, Structural Grammar, Generative Transformational Grammar, Case Grammar, Stratificational Grammar, Tagmemic Grammar, and the last Systemic Functional Grammar. All these grammar gave some contribution for the language development. ThePart of speech, sentence pattern, syntactic structure are some of the contribution of Traditional grammar and Structural Grammar. Rhetorics, philology and grammar is the first science in the language before linguistics. By the models of grammar, the people can identify the language development especially in scientific of language, begin from Plato and Aristotle era until to day.
The Impact of Using Audio Visual Media on Students' Learning Outcomes in English Grade VII of Junior High School

This study aims to determine The Impact of Using Audio-Visual Media on the English Learning Results of Grade VII of Sabilina Junior High School, Deli Serdang. This research is exploratory research in the form of a quasi-experimental design type non-equivalent control group design that employs a test group and a control group. The experimental group was treated using audio-visual media, while the control group using normal lecture method and printed book media. Information collection methods utilized English learning results tests and perception sheets. The information examination procedure employs the t-test to test the impact of utilizing audio-visual media on student learning results. The results show that the calculation of the normal post-test value of the test group is 82.25 which is higher than the normal control group which is 68.25. The results of the information examination appeared that the t count 3,701 &gt; t tabel 1,729. This could be translated that learning uses audio-visual media has higher learning results compared to learning that does not utilize audio-visual media. To conclude, it appears that the use of sound media influences students' English learning results. After all, teachers ought to disseminate the utilization of audio-visual media in learning English to improve student learning results.
The Analysis of Social Deixis Used on Instagram Comments in The Royal Family Instagram Account

Deixis is one of the branches of pragmatics. Deixis is contextual information that refers to a certain place, time, or object. In this study, the researcher is focused on analyzing social deixis in the comments on the royal family's Instagram account. Social deixis is related to familiarity and social status. Social deixis can also be defined as deixis which does not only refer to certain referent conditions but also it contains connotations that represent certain social classes. The type of social deixis is divided into two namely relational and absolute social deixis. The aim of this study is to classify and identify the kinds of social deixis in the comments of the royal family's Instagram account. The type of this study was descriptive qualitative because the data is described in sentence form. The data used in this study were taken from the social deixis in the royal family's Instagram account.
Politeness Strategies of Assertive Acts by Indonesian EFL Learners in Conversation Class

The basis of human communication is the ability to conduct smooth conversations. It is currently highly demanded for people to be able to conduct conversations through politeness. In L2 learning, this has induced linguists and language educators to develop methods for enhancing L2 learners’ ability to communicate appropriately in L2. This paper is part of a descriptive qualitative study investigating the types of politeness EFL learners use during English conversations. It mainly analyzed the politeness strategies used by L2 learners with instructors during EFL activities. The participants were elementary students and the instructor. Observation and recording were the ways to collect the data. The data analysis process was through the politeness theory by Brown and Levinson (1987). This study revealed three politeness strategies; the students frequently used negative and positive politeness strategies. Whereas bald on record strategy was rarely used. The implication of the study was to build the students' good character. In addition, it could be beneficial for applying pragmatic theory to the ELT process in a particular course.
Comparison and Analysis of English and Chinese Syntactic Features

English and Chinese belong to different language systems, and there are significant differences in sentence structure between the two: English sentences tend to be hypotaxis, while Chinese sentences tend to be parataxis; English is a static language, while Chinese is a dynamic language; the Passive voice is used in English, and the Active voice is used in Chinese. Such a huge difference creates obstacles for translators in the process of translating between English and Chinese, and solving this problem is necessary to correctly convert the source language into the target language. This article first analyzes the syntactic features of English and Chinese sentences, and then compares and analyzes the differences in syntactic features between English and Chinese from the perspective of translation.
A Brief Analysis of Stylistic Features and Chinese Translation of News English

The emergence and rise of each specific stylistic type are often matched with its corresponding functions and specific situations. According to the theory of system function, situational context is mainly divided into three types: discourse scope, discourse tone and discourse mode. Discourse content is mainly a category of research content form, which generally covers three aspects: news English, scientific and technological English and legal English. With the rapid development of network technology, both paper books and English news media on the Internet can be seen everywhere. Readers can browse and read a large amount of news English materials through official websites, periodicals, magazines, WeChat official accounts and so on. In the process of reading, the target language readers will inevitably encounter a variety of difficulties and obstacles. Therefore, as a translator, we need to further understand and master the functions, features and Chinese translation characteristics of various stylistic types in order to help readers absorb knowledge and broaden their horizons. Next, based on the functional equivalence theory proposed by Eugene A. Nida, this paper will analyze the stylistic features of news English on websites and official accounts from the three dimensions of phase, vocabulary and syntax, and further understand the Chinese translation characteristics of this type of text. It is hoped that this article can help people to have a deeper understanding of the stylistic features of news English, and to read and analyze the original text.
Eye-tracking: the influence of picture and text layout on the effect of commercial advertising

Advertisements play a crucial role in promoting products and shaping the brand image. The primary purpose of advertising is to capture consumers' attention and convey brand-related information. In this study, we investigate two typesetting methods in advertisements. The positions of images and text, whether on the left or right side, are treated as independent variables, while the gazing behavior of subjects is considered the dependent variable. Twenty subjects were included and divided into two groups. Each group was exposed to corresponding advertising materials—one viewed advertising posters with text on the left-hand side and images on the right-hand side, while the other group observed posters with text on the right-hand side and images on the left-hand side. The subjects' gazing behaviors were recorded using an eye tracker, enabling us to identify and quantify their attention. Subsequently, statistical analyses were conducted. The results indicate that the subjects paid more attention to the text information and less attention to the images. Furthermore, the horizontal position of the text and pictures also influenced the subjects' attention. These findings provide valuable insights for advertising design, emphasizing the significance of strategically placing text and images to optimize the effectiveness of advertisements.
Construction of VR Video Quality Evaluation Model Based on 3D-CNN

Currently, virtual reality (VR) panoramic video content occupies a very important position in the content of virtual reality platforms. The level of video quality directly affects the experience of platform users, and there is increasing research on methods for evaluating VR video quality. Therefore, this study establishes a subjective evaluation library for VR video data and uses viewport slicing method to segment VR videos, expanding the sample size. Finally, a classification prediction network structure was constructed using a three-dimensional convolutional neural network (3D-CNN) to achieve objective evaluation of VR videos. However, during the research process, it was found that the increase in its convolutional dimension inevitably leads to a significant increase in the parameter count of the entire neural network, resulting in a surge in algorithm time complexity. In response to this defect, research and design dual 3D convolutional layers and improve 3D-CNN based on residual networks. Based on this research, a virtual reality video quality evaluation model based on improved 3D-CNN was constructed. Through experimental analysis, it can be concluded that the average overall accuracy value of the constructed model is 95.27%, the average accuracy value is 95.94%, and the average Kappa coefficient value is 96.18%. Being able to accurately and effectively evaluate the quality of virtual reality videos and promote the development of the virtual reality field.
Deep Learning-based Sentence Embeddings using BERT for Textual Entailment

This study directly and thoroughly investigates the practicalities of utilizing sentence embeddings, derived from the foundations of deep learning, for textual entailment recognition, with a specific emphasis on the robust BERT model. As a cornerstone of our research, we incorporated the Stanford Natural Language Inference (SNLI) dataset. Our study emphasizes a meticulous analysis of BERT’s variable layers to ascertain the optimal layer for generating sentence embeddings that can effectively identify entailment. Our approach deviates from traditional methodologies, as we base our evaluation of entailment on the direct and simple comparison of sentence norms, subsequently highlighting the geometrical attributes of the embeddings. Experimental results revealed that the L2 norm of sentence embeddings, drawn specifically from BERT’s 7th layer, emerged superior in entailment detection compared to other setups.
The Impact of Cyber Security on Preventing and Mitigating Electronic Crimes in the Jordanian Banking Sector

As technology advances and cyber threats continue to evolve, cyber security professionals play a critical role in developing and implementing robust security measures, staying ahead of potential risks, and mitigating the impact of cyber incidents. Many studies have examined the impact of cyber security on banks, without focusing on electronic crimes. Despite its importance, to the best of our knowledge, there are no studies on the impact of cyber security on mitigating electronic crimes in the banking sector. Therefore, the purpose of this study is to ascertain how cyber security affects electronic crimes in the Jordanian banking industry. The study sample consisted of 270 senior Jordanian managers and employees who understand the importance of cyber security in the banking sector in 14 Jordanian commercial banks, listed on the Amman stock exchange. The study used SPSS to evaluate how banks can enhance network security infrastructure to prevent unauthorized access and data breaches and also to find out the role of cybersecurity in granting competitive advantage to banks. A relative importance index (RII) was conducted to rank the importance of variables’ statements and test the hypotheses. The results found the most important method through which banks can effectively mitigate the risk of electronic crimes and ensure the security of customers’ financial data is that banks utilize robust encryption technologies to ensure the protection of customer financial data while it is being transmitted and when it is stored (RII=0.740). About 81.5 % of the sample agree, also, banks that have a strong cyber security system provide a secure platform for digital financial services which increases the competitive advantage as they were ranked first for their relative importance at both the category level and overall ranking with (RII=0.754). The study recommended that the banking industry, must consistently educate its customers on information security techniques and how to avoid hacking into their accounts, and develop an alert system that can raise awareness for both banks and bank customers if there is any possible entry or access to the customer's account or organization confidential information.
CryptoScholarChain: Revolutionizing Scholarship Management Framework with Blockchain Technology

Scholarship management is a crucial aspect of higher education systems, aimed at supporting deserving students and reducing financial barriers. However, traditional scholarship management processes often suffer from challenges such as a lack of transparency, inefficient communication, and difficulty tracking and verifying scholarship applications. Recently, Blockchain technology has emerged as a potential solution to address these issues, offering a decentralized, transparent, and secure framework for scholarship management. Blockchain technology has emerged as a promising solution to address the challenges faced in scholarship management. However, existing literature lacks comprehensive solutions in critical areas such as scholarship management, storage facilities, payment systems, monitoring and auditing, and experimental validation. This research introduces an innovative smart scholarship management system leveraging Blockchain technology to overcome these limitations. The research presents an Ethereum-based implementation utilizing Solidity for backend smart contracts and ReactJS for the front end. Experimental evaluation validates the transaction execution gas costs and deployment cost.
Incorporating Learned Depth Perception Into Monocular Visual Odometry to Improve Scale Recovery

A growing interest in autonomous driving has led to a comprehensive study of visual odometry (VO). It has been well studied how VO can estimate the pose of moving objects by examining the images taken from onboard cameras. In the last decade, it has been proposed that deep learning under supervision can be employed to estimate depth maps and visual odometry (VO). In this paper, we propose a DPT (Dense Prediction Transformer)-based monocular visual odometry method for scale estimation. Scale-drift problems are common in traditional monocular systems and in recent deep learning studies. In order to recover the scale, it is imperative that depth estimation to be accurate. A framework for dense prediction challenges that bases its computation on vision transformers instead of convolutional networks is characterized as an accurate model that is utilized to estimate depth maps. Scale recovery and depth refinement are accomplished iteratively. This allows our approach to simultaneously increase the depth estimate while eradicating scale drift. The depth map estimated using the DPT model is accurate enough for the purpose of achieving the best efficiency possible on a VO benchmark, eliminating the scaling drift issue.
Research on Strategic Decision Model of Human Resource Management based on Biological Neural Network

Human resource management system is an indispensable part of information strategy construction. Based on the theory of biological neural network, this paper constructs the strategic decision model of human resources management, then uses the micro-integration method to predict the demand for human resources, and solves the quantification problem of human resources supply prediction. In the simulation process, the model analyzes the current situation of the personnel management system and the necessity of research and plans and designs a computer-aided personnel management information system based on the Client/Server biological neural network structure. Personnel quality evaluation through the evaluation and analysis of the quality of the evaluated, to provide effective reference information for the enterprise personnel decision and index selection, the enterprise human resources allocation, use, training and development is of great significance. Neural networks rely on the powerful data storage, processing and computing capabilities of computers to help enterprises respond quickly to changes in external market conditions, improve the efficiency of decision-making, and create greater value for enterprises. Through experimental testing, it is found that when the iteration is 5, the network verification results have the best consistency. When the iterations reach 7, the standard of training target error set in this paper is reached. When the samples reached 60, the screening accuracy of the network reached 92.18%; when the samples increased to 80, the screening accuracy was further improved to 92.84%, indicating that the screening accuracy of the network increased with the training samples, which could be used to detect and classify samples quickly, objectively and accurately.
Construction and Application of Automatic Scoring Index System for College English Multimedia Teaching Based on Neural Network

With the continuous development of interactive multimedia, multimedia is increasingly integrated into college English teaching, providing advanced teaching equipment and resources. While enriching the teaching environment, it also brings new challenges to teaching ideas and strategies. Although the proportion of independent and selective learning of college students has increased, classroom teaching still constitutes the most essential unit of educational activities. Classroom evaluation is an important means and institutionalized element to improve the quality of university teaching. This paper analyzes the elements of multimedia classroom teaching and constructs an evaluation index system for English multimedia teaching. The improved model is used to achieve automatic teaching grading, acquire knowledge through environmental learning and improve its own performance, and evaluate the mathematical model of the English multimedia teaching evaluation system established by neural network theory accurately and effectively. In this paper, the results of automatic scoring of multimedia English teaching in colleges and universities are compared. Simulation software is used to verify the established neural network evaluation system. The simulation results show that the model is more suitable for the test data of English classroom teaching than the traditional methods, and the prediction effect is better. All 15 English teachers had a predicted error rate of less than 2%, and all 10 English teachers had a predicted error rate of less than 1%.
Improved Drosophila Visual Neural Network Application in Vehicle Target Tracking and Collision Warning

To enable the vehicle tracking and collision warning system to face more complex road information, the Drosophila visual neural network collision warning algorithm has been improved, including image stabilization algorithm, target region synthesis algorithm, and target tracking algorithm. The results showed that the improved image stabilization algorithm had significantly higher image stabilization quality. The peak signal-to-noise ratio of the stabilized image before improvement was the highest at 80dB and the lowest at 54dB. After improvement, the peak signal-to-noise ratio of the stabilized image was the highest at 82dB and the lowest at 60dB. The improved algorithm did not have any false alarms or missed alarms in collision warning. In video 1, there were false alarms in the unimproved algorithm, while in video 2, there were missed alarms. In video 1, all frames were in a safe state, but the original algorithm displayed an alarm in frames 7-12, 13-22, and 23-31. In video 2, there were dangerous situations in frames 8-24 that required an alarm, while the original algorithm displayed an alarm message in frames 8-17, consistent with the actual situation. The improved target tracking algorithm can complete the task of extracting target motion curves. The target tracking algorithm extracted the motion curves of one target in video 1 and two targets in video 2, which were consistent with the video content. The improvement of the Drosophila visual neural network collision warning model through research is effective, which can improve the driving safety of vehicles in complex road conditions.