Reviewerly/exHarmony huggingface dataset:
Teacher’s Agency as an Indicator of Leadership Potential Development

Authors:
raw_author_name: Vyacheslav A. Starodubtsev

Abstract: Nonlinearity, multi-contextuality, new normality, constant variability of the modern world are reflected in politics, economics, education and other spheres of human life. Professional and personal self-determination and orientation of a person is difficult in a situation of diversity of various social environments and spaces, functional and semantic uncertainty of educational contexts. In this regard, the importance in the field of education is acquired by the teacher’s transprofessionalism, his ability to continuously develop, independently build interaction with the multifunctional environment, showing his agency as one of the indicators of leadership potential development. The purpose of the study is to operationalize the content of the concept of the teacher’s agency and its manifestation as an indicator of the development of leadership potential, taking into account the ongoing changes in the modern world. Methodology. The understanding of the essence of the terms “agency” and “leadership potential” of the teacher was carried out using the method of semantic analysis of their interpretations available in domestic and foreign sources. The use of activity-based and acmeological approaches is due to the need to determine the key mechanism for implementing the teacher’s agency in the comparison of significant categories (agency, leadership and management). General scientific research methods were used: theoretical analysis and synthesis, systematization and generalization of scientific literature on the research topic. Results. Connectivism (social polyvalence) is proposed as the main mechanism for the manifestation of the teacher’s agency as a teacher’s ability to find and maintain conditions, forms, ways and means of communication with representatives of different professions, social groups and statuses, beliefs, ages for organizing joint actions, events, productive activities. This made it possible to establish the key role of agency in the development of a teacher’s leadership potential – the creation of an atmosphere of cooperation within and outside the educational organization. Conclusion. The conclusion is made that the teacher, as a carrier and subject of public relations, needs to develop his agency and leadership potential, respectively, for orientation and adaptation in a changing world in the interests of sustainable development of regions and the country as a whole.
Research on Strategies for Improving the Quality of English Blended Teaching in Vocational Colleges through Network Informatization Resources

Authors:
raw_author_name: Guang-Hua Li Guang-Hua Li

Abstract: &lt;p&gt;The blended teaching mode of &amp;ldquo;online+offline&amp;rdquo; has gradually become a widely used teaching mode due to its flexible teaching form and rich and vivid course resources. In the blended teaching mode, this article mainly focuses on how to improve the teaching quality of vocational English courses. Firstly, an analysis was conducted on the learning situation of English courses among vocational college students, including their source structure, learning characteristics, and current development status of English courses. In order to facilitate a structured analysis of students&amp;rsquo; learning ability in English courses, a learning ability analysis model for vocational college students was constructed, and a survey questionnaire was designed to analyze their learning behavior and data. Finally, through data analysis, it was found how to use network information resources to improve the quality of English blended learning courses.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
End-to-end Visual Grounding Based on Query Text Guidance and Multi-stage Reasoning

Authors:
raw_author_name: Wei Luo Chao Wang
raw_author_name: Jia-Rui Zhu Wei Luo
raw_author_name: Ying-Chun Xia Jia-Rui Zhu
raw_author_name: Jin He Ying-Chun Xia
raw_author_name: Li-Chuan Gu Jin He

Abstract: &lt;p&gt;Visual grounding locates target objects or areas in the image based on natural language expression. Most current methods extract visual features and text embeddings independently, and then carry out complex fusion reasoning to locate target objects mentioned in the query text. However, such independently extracted visual features often contain many features that are irrelevant to the query text or misleading, thus affecting the subsequent multimodal fusion module, and deteriorating target localization. This study introduces a combined network model based on the transformer architecture, which realizes more accurate visual grounding by using query text to guide visual feature generation and multi-stage fusion reasoning. Specifically, the visual feature generation module reduces the interferences of irrelevant features and generates visual features related to query text through the guidance of query text features. The multi-stage fused reasoning module uses the relevant visual features obtained by the visual feature generation module and the query text embeddings for multi-stage interactive reasoning, further infers the correlation between the target image and the query text, so as to achieve the accurate localization of the object described by the query text. The effectiveness of the proposed model is experimentally verified on five public datasets and the model outperforms state-of-the-art methods. It achieves an improvement of 1.04%, 2.23%, 1.00% and +2.51% over the previous state-of-the-art methods in terms of the top-1 accuracy on TestA and TestB of the RefCOCO and RefCOCO+ datasets, respectively.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
How to Build a Blogging Website

Authors:
raw_author_name: Rajdeep Patel
raw_author_name: Akash Patil

Abstract: Abstract: This paper explores the variegated domain of blogging, with focusing on the platform OmniSpectra. Here, I present an in-depth explanation of website’s UI/UX design, usage, architecture, and backend mechanisms. The study explores the unique characteristics of OmniSpectra that distinguishes it from other popular blogging websites, highlighting its inventive method in nurturing inclusive and compelling virtual communities of both content consumers and content creators. The paper will show in-depth workings of different techniques such as blog recommendation, data collection and curation that are used in the website to provide a benchmark user experience. The paper investigates the website’s role in shaping individual’s narrative and perception based to users’ interactions to different blogs, this has been achieved by getting a constant sentiment analysis feedback from users as well as the content created by the creators using AI models. All in all, after reading this paper, the reader will get a high-level idea of how to create a blogging website and add to it different modules according to their specific requirements. All this is explained by taking the example of OmniSpectra
Prioritized Text Detergent: Comparing Two Judgment Scales of Analytic Hierarchy Process on Prioritizing Pre-Processing Techniques on Social Media Sentiment Analysis

Authors:
raw_author_name: Ummu Hani’ Hair Zaki
raw_author_name: Shahliza Abd Halim
raw_author_name: Izyan Izzati Kamsani

Abstract: Most companies use social media data for business. Sentiment analysis automatically gathers analyses and summarizes this type of data. Managing unstructured social media data is difficult. Noisy data is a challenge to sentiment analysis. Since over 50% of the sentiment analysis process is data pre-processing, processing big social media data is challenging too. If pre-processing is carried out correctly, data accuracy may improve. Also, sentiment analysis workflow is highly dependent. Because no pre-processing technique works well in all situations or with all data sources, choosing the most important ones is crucial. Prioritization is an excellent technique for choosing the most important ones. As one of many Multi-Criteria Decision Making (MCDM) methods, the Analytic Hierarchy Process (AHP) is preferred for handling complicated decision-making challenges using several criteria. The Consistency Ratio (CR) scores were used to examine pair-wise comparisons to evaluate the AHP. This study used two judgment scales to get the most consistent judgment. Firstly, the Saaty judgment scale (SS), then the Generalized Balanced Scale (GBS). It investigated whether two different AHP judgment scales would affect decision-making. The main criteria for prioritizing pre-processing techniques in sentiment analysis are Punctuation, Spelling, Number, and Context. These four criteria also contain sub-criteria. GBS pair-wise comparisons are closer to the CR value than SS, reducing the alternatives’ weight ratios. This paper explains how AHP aids logical decision-making. Prioritizing pre-processing techniques with AHP can be a paradigm for other sentiment analysis stages. In short, this paper adds another contribution to the Big Data Analytics domain.
Natural Language Processing For Requirement Elicitation In University Using Kmeans And Meanshift Algorithm

Authors:
raw_author_name: Devi Yurisca Bernanda
raw_author_name: Dayang N.A. Jawawi
raw_author_name: Shahliza Abd Halim
raw_author_name: Fransiskus Adikara

Abstract: Data Driven Requirement Engineering (DDRE) represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. Data available and the increasingly complex requirements of system software whose functions can adapt to changing needs to gain the trust of its users, an approach is needed in a continuous software engineering process. This need drives the emergence of new challenges in the discipline of requirements engineering to meet the required changes. The problem in this study was the method in data discrepancies which resulted in the needs elicitation process being hampered and in the end software development found discrepancies and could not meet the needs of stakeholders and the goals of the organization. The research objectives in this research to the process collected and integrating data from multiple sources and ensuring interoperability. Conclusion in this research is determining is the clustering algorithm help the collection data and elicitation process has a somewhat greater impact on the ratings provided by professionals for pairs that belong to the same cluster. However, the influence of POS tagging on the ratings given by professionals is relatively consistent for pairs within the same cluster and pairs in different clusters.
Model optimization method based on the MOON algorithm

Authors:
raw_author_name: Haogang Cao
raw_affiliation_string: Xi'an University of Architecture and Technology Huaqing College

Abstract: Federated Learning is a revolutionary approach to machine learning. Its purpose is to enable multiple participants to collaboratively train machine learning models without the need to share local data. The main objective is to address issues related to data privacy and security. In traditional machine learning, data is typically centralized and stored in a single location or on cloud servers for training. However, this centralized training approach carries risks of potential data leakage, especially concerning sensitive and critical information. Industries such as healthcare and finance, which involve sensitive data, place a premium on safeguarding data privacy. Furthermore, in cases where data cannot be easily transferred or is subject to privacy regulations, centralized methods may face limitations. Federated Learning revolutionizes the conventional approach by introducing a decentralized model training process. It maintains data decentralization while achieving collaborative model optimization, greatly enhancing data privacy and security. The MOON algorithm, an integral part of federated learning, contributes to its novelty. As a significant component, the MOON algorithm facilitates new possibilities for federated learning. In this article, the research will elaborate on the MOON algorithm within the context of federated learning. And this article will delve into its description and optimization, elucidating how it enhances federated learning.
The Role of Artificial Intelligence Applications in Enhancing the Quality of Online Higher Education

Authors:
raw_author_name: Sabah Ahmed

Abstract: The aim of this research is to assess and quantify the impacts of leveraging artificial intelligence (AI) applications on enhancing the quality benchmarks in online higher education programs in the kingdom of Saudi Arabia. Specifically, the study will survey a sample of 200 instructors and students involved in the online education process to evaluate their perceptions of how AI-enabled functionalities influence key quality indicators. This research follows a quantitative methodology to meet the core research objectives of assessing the impacts of AI adoption on online education quality. The target population encompasses all students and instructors engaged in remote higher education programs across universities in Saudi Arabia. Based on sample size power calculations, a sample of 200 participants will be recruited, including both undergraduate and graduate-level students enrolled in fully online degree courses as well as instructors directly responsible for teaching remote classes. The findings of the research revealed that a Pearson Correlation of 0.784 and a p-value of 0.039 (less than significance level of alpha=.05) suggests a significant positive association between AI Applications and the Quality of Online Higher Education provided by teachers. And a Pearson Correlation of 0.861 and a p-value of 0.022 (less than significance level of alpha=.05) suggests a significant positive association between AI Applications and the Quality of Online Higher Education for students.
Analysis of Book Preferences Among Visitors in Library System

Authors:
raw_author_name: Kok-Why Ng
raw_affiliation_string: Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Malaysia
raw_author_name: Su-Cheng Haw
raw_affiliation_string: Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Malaysia
raw_author_name: Elham Abdulwahab Anaam
raw_affiliation_string: Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Malaysia

Abstract: Library is a place that contains various resources and materials. Many invaluable knowledge can be found in the library. By analysing the library’s data, it is possible to obtain information that can further improve its services. This research aims to extract information from Multimedia University (MMU) library and present insightful visualization of the information to enhance the library administration. At present, the library does not have information on the book preferences of the library users. The book preferences statistics can be relatively helpful as the library will know what books can be imported in the future. By doing so, more people will visit the library and they will have more related books to use as reference or to read. In addition, there are no existing dashboards to display information on all borrowers, no visitor. In the absence of this, this research adopts the data science methodology to determine the book preferences of library users by using machine learning techniques such as clustering and classification. Lastly, a dashboard will be developed to display all the findings which includes statistics on the visitors and book preferences.
Random Forest based estimate to assess the damages of future earthquakes: preliminary results

Authors:
raw_author_name: Federica Di Michele
raw_affiliation_string: a:1:{s:5:"en_US"; a:1:{s:5:"en_US";s:4:"INGV";}; }

Abstract: In this paper we present a case study where the Random Forest (RF) Classifier, has been used to estimate the damage to buildings caused by a (possible) future earthquake, starting from the data of past earthquakes. This prelaminar work is based on the Shakedado dataset, which contains information on buildings and ground shaking parameters for the six major earthquakes that occurred in Italy between 1981 and 2012. We perform the following two conceptual experiments • E1: Assume that the sequence that hit Emilia has just ended and the data relating to the other major earthquakes happened in the past (L’Aquila, Pollino, and Irpinia) are available, then calculate the level of damage for each building in the Emila dataset. • E2: Assume that the sequence that hit Pollino has just ended and the data relating to the other major earthquakes happened in the past (L’Aquila, Emilia) are available, then calculate the level of damage for each building in the Pollino dataset. Both training and test datasets contain only masonry buildings located within 10 km of the main shock of each sequence. The results show the RF algorithm’s ability to discriminate between buildings with light/no damage from those with medium/severe damage, with a good accuracy, especially for E1.
A Bilingual Basque–Spanish Dataset of Parliamentary Sessions for the Development and Evaluation of Speech Technology

Authors:
raw_author_name: Amparo Varona
raw_affiliation_string: Department of Electricity and Electronics, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Barrio Sarriena, 48940 Leioa, Spain
raw_author_name: Mikel Penagarikano
raw_affiliation_string: Department of Electricity and Electronics, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Barrio Sarriena, 48940 Leioa, Spain
raw_author_name: Germán Bordel
raw_affiliation_string: Department of Electricity and Electronics, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Barrio Sarriena, 48940 Leioa, Spain
raw_author_name: Luis Javier Rodriguez-Fuentes
raw_affiliation_string: Department of Electricity and Electronics, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Barrio Sarriena, 48940 Leioa, Spain

Abstract: The development of speech technology requires large amounts of data to estimate the underlying models. Even when relying on large multilingual pre-trained models, some amount of task-specific data on the target language is needed to fine-tune those models and obtain competitive performance. In this paper, we present a bilingual Basque–Spanish dataset extracted from parliamentary sessions. The dataset is designed to develop and evaluate automatic speech recognition (ASR) systems but can be easily repurposed for other speech-processing tasks (such as speaker or language recognition). The paper first compares the two target languages, emphasizing their similarities at the acoustic-phonetic level, which sets the basis for sharing data and compensating for the relatively small amount of spoken resources available for Basque. Then, Basque Parliament plenary sessions are characterized in terms of organization, topics, speaker turns and the use of the two languages. The paper continues with the description of the data collection procedure (involving both speech and text), the audio formats and conversions along with the creation and postprocessing of text transcriptions and session minutes. Then, it describes the semi-supervised iterative procedure used to cut, rank and select the training segments and the manual supervision process employed to produce the test set. Finally, ASR experiments are presented using state-of-the-art technology to validate the dataset and to set a reference for future works. The datasets, along with models and recipes to reproduce the experiments reported in the paper, are released through Hugging Face.
Enhancing E-commerce Recommendations: Unveiling Insights from Customer Reviews with BERTFusionDNN

Authors:
raw_author_name: Zhiming Zhao
raw_affiliation_string: Computer Science, East China University of Science and Technology, Shanghai, China
raw_author_name: Ning Zhang
raw_affiliation_string: Computer Science, University of Birmingham, Dubai, United Arab Emirates
raw_author_name: Xiaosong Wang
raw_affiliation_string: Computer Network Technology, Xuzhou University of Technology, Xuzhou, China

Abstract: In the domain of e-commerce, customer reviews wield significant influence over business strategies. Despite the existence of various recommendation methodologies like collaborative filtering and deep learning, they often encounter difficulties in accurately analyzing sentiment and semantics within customer feedback. Addressing these challenges head-on, this paper introduces BERTFusionDNN, a novel framework merging BERT for extracting textual features and a Deep Neural Network for integrating numerical features. We assess the efficacy of our approach using a Women Clothing E-Commerce dataset, benchmarking it against established techniques. Our method adeptly extracts valuable insights from customer reviews, fortifying e-commerce recommendation systems by surmounting barriers associated with deciphering both textual nuances and numerical intricacies. Through this endeavor, we pave the way for more robust and effective strategies in leveraging customer feedback to optimize e-commerce experiences and drive business success.
S.L.X. : Student’s online exchange

Authors:
raw_author_name: Rahul Kashyap

Abstract: Students online exchange (SLX) is a pioneering initiative designed to meet the unique needs of students by fostering a culture of mutual assistance and resource sharing. Addressing financial constraints and daily challenges, SLX provides a specialized platform where students can seamlessly exchange resources with the spirit of helping one another. Unlike traditional sharing models, SLX emphasizes the transformative power of mutual assistance over charity, promoting a community-driven approach. This synopsis offers insights into SLX's distinctive features, objectives, and societal impact, bridging existing gaps in the literature and presenting a novel resource-sharing paradigm. Through SLX, students collaboratively share academic materials and daily necessities, establishing a supportive environment for their educational journey. SLX endeavors’ to reshape the community narrative, making helping an integral part of student life. Key Word: Students online exchange (SLX), Mutual assistance, Resource sharing, Financial-constraints, Community-driven approach, Novel resource-sharing paradigm, Academic materials, Transformative power, Community narrative.
Unsupervised anomaly detection of polluted telemetry data based on a reconstruction model with adaptive weighting

Authors:
raw_author_name: Zhaoping Xu
raw_affiliation_string: National University of Defense Technology (China)
raw_author_name: Zhijun Cheng
raw_affiliation_string: National University of Defense Technology (China)
raw_author_name: Bo Guo
raw_affiliation_string: National University of Defense Technology (China)

Abstract: Anomaly detection of telemetry data is a promising way for ensuring the reliability of spacecraft and success of space mission. This study proposes a reconstruction model with adaptive weighting for unsupervised anomaly detection of polluted telemetry data. The reconstruction model integrates the advantages of typical reconstruction models and can generate high-quality results. An adaptive weighting module is designed to against the data pollution issue by assigning different weights to training data samples. Experiments are conducted on two public telemetry datasets, and the results verify the effectiveness of our method.