Reviewerly/exHarmony huggingface dataset:
State-of-the-Art Review of the Corpus Linguistics Field From the Beginning Until the Development of ChatGPT

Authors:
raw_author_name: Yaser M. Altameemi
raw_affiliation_string: University of Ha’il

Abstract: The present paper highlights the recent state of and development in the corpus linguistics (CL) field. Although several reviews have been conducted on CL, these reviews have focused on specific areas, such as education, or did not provide an overall clear overview of the future implications of the field (Baker et al., 2008; Biber &amp; Reppen, 2020; Biber et al., 1998; G. N. Leech, 1991; Mcenery et al., 2019; McEnery &amp; Hardie, 2012). The author begins this paper with providing an overview that can guide new researchers in this field as well as postgraduates who require a general historical and thematic map of CL. The general overview discusses the publications of scholars who have participated in this field as well as the central tools that have been applied in CL. For specific details regarding the development of the field, the author analysed 217 articles from the 3 highest-impact factor journals according to the Web of Science over the last four years (2019–2022). The findings reveal a rapid development of the field in terms of practical and methodological perspectives, specifically regarding the investigations of language uses in different contexts. Thus, this paper indicates a significantly strong correlation between CL and technological development, such as natural language processing (NLP), and how this approach could fill the research gap of utilising CL in other areas of linguistics.
Student Performance in Machine Translation Training: Assessment and Exploration

Authors:
raw_author_name: Chung-ling Shih
raw_affiliation_string: National Kaohsiung University of Science and Technology

Abstract: The present research aims to conduct a process-oriented analysis to measure whether a group of graduate students enrolled in a translation course made steady progress in their performance of identifying machine translation (MT) errors and post-editing MT drafts of company web texts and news texts. A mixed methods approach consisting of quantitative and qualitative analyses was used. The findings show that there was a steady decline in the average number of MT errors that students could not spot or correctly identify in their three assignments. However, there was no significant improvement in student MTPE performance, with only a slight decrease in errors in the final MTPE assignment, which still remained worse than the first one. Finally, student responses in their reflection essays indicated that their reception of MT and MTPE had shifted from negative denial to positive acceptance. Overall, the findings of the present study reveal the need to extend the period of MTPE training for students. Incorporating MT training into the translation course has proven to be worthwhile for students, as it helps to dispel students’ previous misconception about MT and MTPE.
Machine learning for effective EHR management in blockchain-cloud integration

Authors:
raw_author_name: Birendra Kumar Saraswat
raw_author_name: Aditya Saxena
raw_author_name: P. C. Vashist

Abstract: &lt;p&gt;Machine learning (ML) techniques have gained prominence in effectively managing Electronic Health Record (EHR) systems within the context of blockchain-cloud integration. This study presents a hybrid Machine Learning approach that combines logistic regression (LR) and random forest (RF) techniques for EHR management, leveraging the data stored in a blockchain-cloud integrated system. The tamper-resistant nature of blockchain ensures the authenticity and security of the stored patient information, serving as a reliable source for learning. The proposed LR+RF model is evaluated against other algorithms, considering various performance metrics. The analysis reveals that the LR+RF model achieves an impressive accuracy rate of 98.37%, indicating its efficacy in accurately classifying EHR data and facilitating effective management. Furthermore, the study compares the performance of blockchain-cloud-based decentralized storage with blockchain-based storage and peer-to-peer storage in terms of latency and throughput. The results demonstrate that the blockchain-cloud integrated decentralized storage surpasses other storage methods, achieving an average throughput of 6.8 units and a latency of 4.7 units. These findings highlight the potential of the proposed LR+RF model for EHR management within a blockchain-cloud integrated environment. The use of blockchain as a secure storage environment ensures the integrity of patient information, while Machine Learning techniques enhance the accuracy of classification.&lt;/p&gt;
An Analysis of Students Reading Comprehension on Students Reading Aloud Activities at the XI Grade of MAN 1 Solok Selatan

Authors:
raw_author_name: Merry Prima Dewi
raw_affiliation_string: UIN Sjech M. Djamil Djambek Bukittinggi, Bukittinggi, Indonesia
raw_author_name: Hilma Pami Putri
raw_affiliation_string: UIN Sjech M. Djamil Djambek Bukittinggi, Bukittinggi, Indonesia
raw_author_name: Loli Safitri
raw_affiliation_string: UIN Sjech M. Djamil Djambek Bukittinggi, Bukittinggi, Indonesia

Abstract: Students' ability in reading, especially reading comprehension is still low. The first problem most of students have limited vocabulary in English, The second problem is that the teacher still uses the conventional strategies in class in reading comprehension. The third problem is students are not able to understand the words conveyed by the teacher orally in English. The purpose of this research was to find out student’s reading comprehension on student’s reading aloud activities at the XI Grade of MAN 1 Solok Selatan. Then, the researcher used descriptive quantitative method. In the collecting the data, test wich consisted of reading comprehension through students reading aloud activities. The data was analyzed by using T- Test Formula. Based on results shows that that 3 students get the score between 95 - 100. That 2 students get the score between 83 - 94. Then 13 students get score between 71 – 82. Then 5 students get the score between 59 – 70. Then 2 students get the score 47 – 58 and 7 students get the score between 35 – 46. In conclusion, the students reading comprehension on reading aloud activities at the XI.IPS.1 students at MAN 1 Solok Selatan was considered medium. Evidenced from when students reading aloud activities the students still confused, and not confidence.
The Effect of Product Knowledge and Market Orientation on Business Performance in MSMEs

Authors:
raw_author_name: Sidiq Permono Nugroho
raw_affiliation_string: Department of Management, Faculty of Economics and Business; Universitas Muhammadiyah Surakarta, Indonesia

Abstract: Globalization has had a significant impact on the business world, especially in terms of expanding market reach and increasing competition. In the growing digital era, information and communication technology has affected many aspects of human life, including the business sector. The development of digital technology has accelerated the pace of business and enabled access to global markets on a larger and more efficient scale. This study aims to analyze the effect of product knowledge and market orientation on firm business performance mediated by product innovation and omnichannel on MSMEs in Boyolali district. There were 100 respondents in this study through an online survey using google form with a Likert scale. The sampling technique uses non probability sampling using the slovin method. Data analysis using SmartPLS. Validity and reliability tests using smart PLS analysis on the outer model. Hypothesis testing using the SmartPLS inner model. The analysis results show that Product Innovation is able to mediate the relationship between Market Oriented and Firm Business Performance and Omnichannel is able to mediate the relationship between Market Oriented and Firm Business Performance.
Target Signal Communication Detection of Black Flying UAVs Based on Deep Learning Algorithm

Authors:
raw_author_name: Yangbing Zheng
raw_affiliation_string: College of Mechanical and Electronic Engineering, Nanyang Normal University, No.1638 Wolong Road, Nanyang,
473061, Henan, China; Qinghai
Wandong Ecological Environment Development Co.LTD, No.148 Jianshe Road, Geermu, 816000, Qinghai, China
raw_author_name: Xiaohan Tu
raw_affiliation_string: Railway Police College, No.31 Nongye Road, Zhengzhou, 450000, Henan, China

Abstract: Background: Unmanned aerial vehicles (UAVs) are being widely used in many fields, such as national economy, social development, national defense, and security. Currently, the number of registered UAVs in China is far less than that of flying UAVs-the frequent occurrence of unsafe incidents. Objective: The phenomenon of UAVs flying undeclared and unapproved has caused more serious troubles to social public order and people's production and life. Methods: In this paper, to assist the public security department in detecting the phenomenon of UAV black flying, our team conducts a series of research based on the deep learning YOLOv5 (You Only Look Once) algorithm. Results: Firstly, the Vision Transformer mechanism is integrated to enhance the robustness of the model. Secondly, depth-separable convolution is introduced to reduce parameter redundancy. Finally, the SimAM attention-free mechanism and CBAM attention-free mechanism are combined to enhance the attention of small target UAVs. Conclusion: Through the analysis of UAV targets in video surveillance, the rapid identification of black-flying UAVs can be realized, the monitoring and early warning ability of UAVs in a specific area can be improved, and the loss of life and property of people can be reduced or saved as much as possible.
A comparative analysis of lexical-based automatic evaluation metrics for different Indic language pairs

Authors:
raw_author_name: Kiranjeet Kaur
raw_author_name: Shweta Chauhan

Abstract: &lt;p&gt;With the rise of machine translation systems, it has become essential to evaluate the quality of translations produced by these systems. However, the existing evaluation metrics designed for English and other European languages may not always be suitable or apply to other Indic languages due to their complex morphology and syntax. Machine translation evaluation (MTE) is a process of assessing the quality and accuracy of the machine-translated text. MTE involves comparing the machine-translated output with the reference translation to calculate the level of similarity and correctness. Therefore, this study evaluates different metrics, namely, BLEU, METEOR, and TER to identify the most suitable evaluation metric for Indic languages. The study uses datasets for Indic languages and evaluates the metrics on various translation systems. The study contributes to the field of MT by providing insights into suitable evaluation metrics for Indic languages. This research paper aims to study and compare several lexical automatic machine translation evaluation metrics for Indic languages. For this research analysis, we have selected five language pairs of parallel corpora from the low-resource domain, such as English–Hindi, English-Punjabi, English-Gujarati, English-Marathi, and English-Bengali. All these languages belong to the Indo-Aryan language family and are resource-poor. A comparison of the state of art MT is presented and shows which translator works better on these language pairs. For this research work, the natural language toolkit tokenizers are used to assess the analysis of the experimental results. These results have been performed by taking two different datasets for all these language pairs using fully automatic MT evaluation metrics. The research study explores the effectiveness of these metrics in assessing the quality of machine translations between various Indic languages. Additionally, this dataset and analysis will make it easier to do future research in Indian MT evaluation.&lt;/p&gt;
Nanosensors from Logistics and Supply Chain Perspective: A Literature Review

Authors:
raw_author_name: Goknur Arzu Akyuz
raw_affiliation_string: Department of Logistics Management, University of Turkish Aeronautical Association, Faculty of Business
Administration, Ankara, Turkey.

Abstract: Introduction: Nanosensors are one of the most recent technologies obtained from the nanoworld. They have enhanced properties with potential applications in a wide spectrum of sectors. They exhibit exceptional properties in temperature monitoring, hazardous gas and vapor detection, humidity detection, as well as pathogens, toxins, and chemical contaminant detection. Among various nanosensor applications, one of the most recent usages of nanosensor technology is in logistics and supply chain management (SCM). Aim: This study aims at investigating the potential of nanosensor technology from logistics &amp; SCM perspective, and specifically questions how can this technology contribute to food supply chains. Method: A systematic comprehensive literature review was conducted with the keywords: nanosensors &amp; logistics, nanosensors &amp; supply chain, nanosensors &amp; smart packaging, and nanosensors &amp; food logistics for the date range 2001-2022, covering a broad set of references. Findings revealed that nanosensors have significant potential in providing smart packaging, and improving traceability and visibility across all the logistics and SCM processes including end-to-end transportation and storage activities Result: Hybrids of different types of nanosensors with the existing material identification systems provide new levels of traceability and visibility in SCM, enabling accurate tracking of a variety of parameters. The study has shown that the intersection of nanotechnology and logistics &amp; SCM domains is still immature. Conclusion: This intersection is especially critical for food supply chains for meeting their special traceability requirements, and for serving food safety and organic food traceability across all stages of logistics.
role of digital reading corner in increasing the community's reading interest in Bekasi district

Authors:
raw_author_name: Yasir Riady
raw_affiliation_string: Universita Terbuka

Abstract: Background: A culture of interest in reading or literacy movement must be fostered in Indonesian society.&#x0D; Purpose: The aim is to describe the existence of a Digital Reading Corner (POCADI) managed by the Bekasi Regency Archives and Library Service, which shows an increase in people's interest in reading in the Bekasi Regency.&#x0D; Design and methods: This article uses qualitative research and a literature review. Data collection techniques in the field are observation and interviews. The research objects in the field are visitors and officers at the Digital Reading Corner location. Collections of digital media such as computers, Android tablets, e-books, LED TV, and Wi-Fi increase visitors' enthusiasm to increase their interest in reading. The efforts of the Bekasi Regency Archives and Library Service in managing POCADI include outreach to the community, educational visits from schools to POCADI and storytelling activities.&#x0D; Results: The results of the research can be concluded that the Digital Reading Corner provides benefits in increasing people's interest in reading in the Bekasi district, namely as a place to read with a collection of reading materials equipped with digital media, a place to read for people who are located far from the Bekasi Regency Public Library
Optimizing University Libraries in Palangkaraya City as a Supporting Facility for Independent Campuses

Authors:
raw_author_name: Yasir Riady
raw_affiliation_string: Universita Terbuka

Abstract: Background: Libraries play an essential role by meeting students' needs, offering a variety of library resources, facilitating access to study spaces, managing lending services, and providing information services. Kampus Merdeka is a support and effort to encourage students to obtain a variety of helpful information for their future job prospects. Purpose: This study aims to know extend on how to optimize libraries to facilitate independent campuses. Design and methods: This article uses a qualitative descriptive methodology, utilizing library reference materials as the primary means of data collection and analysis. According to Sugiyono (2018), qualitative research methods are research methods based on philosophy, which are used to research scientific conditions (experiments) where the researcher is the instrument, data collection techniques, and qualitative analysis emphasize meaning. Results: The role of libraries is in line with the government's program for national education, as stated in Law No. 2 of 2003, which states that education is a conscious and planned effort to realize the teaching and learning process so that students actively develop their potential. Optimizing university libraries in the city of Palangkaraya by utilizing information technology can be used as a means of supporting independent campuses to utilize library resources. Steps that university libraries can take to optimize libraries include: 1) Optimizing the use of information technology and 2) Promotion of libraries by utilizing websites and social media networks to improve service quality. Librarians should strive to facilitate as many opportunities as possible for students to develop into competent individuals.
Website-based Gray Literature Collection Management Darmajaya Repository

Authors:
raw_author_name: Yasir Riady
raw_affiliation_string: Universita Terbuka

Abstract: Background: College libraries not only provide printed collections such as books for lecture materials, but some of them provide collections not published to the public, usually called Gray Literature. The Gray Literature Collection is one of the main factors to consider when assessing a university library. The Gray Literature Collection contains various research results that the library/user needs. The Repository is an archival storage place designed to collect, preserve, and disseminate digital copies, especially the Darmajaya Repository Gray Literature Collection..&#x0D; Purpose: This research aims to describe how the Repository is managed at the IIB Darmajaya Library and the obstacles librarians face during the management stage.&#x0D; Design and methods: This research uses a qualitative descriptive method using case study research. Data collection uses observation and interviews. Data analysis in this research includes data reduction, data presentation, and conclusion, while a triangulation approach is used to measure validity.&#x0D; Results: Based on the findings of this research, Repository management at the IIB Darmajaya Library is carried out by determining management policies, preparing hardware and software, Human Resources, and procedures for receiving collections. The final stage is uploading files into the Darmajaya Repository system. Problems are faced in managing the Darmajaya Repository because it is still in the development process, so there are still areas for improvement in management, implementation, and presentation.
TN-MR: topic-aware neural network-based mobile application recommendation

Authors:
raw_author_name: Junyi Chen
raw_affiliation_string: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
raw_author_name: Buqing Cao
raw_affiliation_string: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
raw_author_name: Zhenlian Peng
raw_affiliation_string: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
raw_author_name: Ziming Xie
raw_affiliation_string: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
raw_author_name: Shanpeng Liu
raw_affiliation_string: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
raw_author_name: Qian Peng
raw_affiliation_string: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China

Abstract: Purpose With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed. Design/methodology/approach In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked. Findings Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR. Originality/value In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.
AI-Based Smart Prediction of Liquid Flow System Using Machine Learning Approach

Authors:
raw_author_name: Pijush Dutta
raw_author_name: Shobhandeb Paul
raw_author_name: Souvik Pal

Abstract: Predicting the liquid flow rate in the process industry has proved to be a critical problem to solve. To develop a mathematical, in-depth of physics-based prognostics understanding is often required. However, in a complex process control system, sometimes proper knowledge of system behaviour is unavailable, in such cases, the complement model-based prognostics transform into a smart process control system with the help of Artificial Intelligence. In previous research a number of prognostic methods, based on classical intelligence techniques, such as artificial neural networks (ANNs), Fuzzy logic controller, Adaptive Fuzzy inference system (ANFIS) etc., utilized in a liquid flow process model to predict the effectiveness. Due to system complexity, Computational time &over fitting the performance of the AI has been limited. In this work we proposed three machine learning regression model: Random Forest (RF), decision Tree (DT) & linear Regression (LR) to predict the flow rate of a process control system. The effectiveness of the model is evaluated in terms of training time, RMSE, MAE & accuracy. Overall, this study suggested that the Decision Tree outperformed than other two models RF & LR by achieving the maximum accuracy, least RMSE & Computational time is 98.6%, 0.0859 & 0.115 Seconds respectively.