Reviewerly/exHarmony huggingface dataset:
Prioritized Text Detergent: Comparing Two Judgment Scales of Analytic Hierarchy Process on Prioritizing Pre-Processing Techniques on Social Media Sentiment Analysis

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

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

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

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

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.
Design of a Sign Language-to-Natural Language Translator Using Artificial Intelligence

This paper describes the results obtained from the design and validation of translation gloves for Colombian sign language (LSC) to natural language. The MPU6050 sensors capture finger movements, and the TCA9548a card enables data multiplexing. Additionally, an Arduino Uno board preprocesses the data, and the Raspberry Pi interprets it using central tendency statistics, principal component analysis (PCA), and a neural network structure for pattern recognition. Finally, the sign is reproduced in audio format. The methodology developed below focuses on translating specific preselected words, achieving an average classification accuracy of 88.97%.
Random Forest based estimate to assess the damages of future earthquakes: preliminary results

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

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

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

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.
Roles of international agricultural organizations in economy revamp: The Nigerian experience

The enduring problems of hunger and poverty in Nigeria are mostly the result of the agriculture sector's failure to have the desired positive impact on the populace. Hence, the study reviewed the roles of international agricultural organizations in economy revamp: the Nigerian experience. In retrospect, a few governments did make an effort to make a statement about agriculture through the creation of farm settlement programs and state-owned agricultural development organizations. However, welfare concerns more than ever served as justification for these activities rather than absolute economic need. The intervention of International Agricultural Organization (IAO) through various types of international agricultural programmes became significant by concentrating on sustainable agricultural practices, technology transfer, capacity building, and policy advocacy, modernizing agriculture, infrastructure development, policy advocacy and reforms, global agricultural trade , market access and trade, climate resilience, research and development, international research and collaboration, food security and agricultural development. To mention but a few, World Bank, International Fund for Agricultural Development and United State Agencies for International Development have contributed to agriculture revamp in the Nigerian economy. Challenges Affecting the Nigerian Agricultural Economy in International Business include: Infrastructure Deficiency, Limited Access to Finance, Inadequate Research and Development, Policy Instability and Inconsistency. It was presented in the study that by providing solutions to these challenges will contribute to a way forward to revamp agriculture in the Nigerian economy, making it more robust, sustainable, and inclusive.
Unsupervised anomaly detection of polluted telemetry data based on a reconstruction model with adaptive weighting

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.
Efficient high-resolution human pose estimation with integrated attention mechanism

Deep learning has brought about a revolutionary transformation in the field of human pose estimation, offering significant advantages. Traditional approaches often seek performance improvement by expanding and deepening networks, resulting in increased parameters and complexity. In response to this challenge, we introduce DNNet, a novel framework rooted in HRNet. Its unique basic blocks incorporate feature mapping, channel weighting, and the fusion of different receptive fields. In comparison to HRNet, DNNet stands out with fewer parameters, lower computational complexity, and more resilient lightweight features. By leveraging features at various resolutions, DNNet's performance is further enhanced. Extensive validation on both the COCO dataset and a dataset focused on dangerous driving behavior reveals that DNNet's accuracy is comparable to that of HRNet. Notably, DNNet achieves a remarkable 69% reduction in parameters and a 59% decrease in computational complexity under similar accuracy conditions. In practical real-world applications, when applied to a dataset addressing dangerous behavior, DNNet outperforms both ShuffleNet and MobileNet in accuracy, highlighting its adaptability and efficacy in diverse scenarios. DNNet's unique features position it as a promising solution in the field of human pose estimation, providing a balanced trade-off between accuracy, efficiency, practical applicability, and the organic fusion of different receptive fields.
Research on the Integrated Solution of Physical Education Based on Smart Campus

In order to improve the physical education experience for students, this study investigates the idea of an integrated solution based on a smart campus for physical education. Within the physical education curriculum, the study focuses on the integration of smart fitness monitoring and wearables, interactive fitness equipment and gamification, mobile applications and customized workout plans, smart facilities, indoor navigation, and data-driven curriculum enhancements. The data gathered from a study involving two groups of students, an experimental group with access to smart campus solutions and a control group without such access, was analyzed using a well-designed framework. The data study looks at how the smart campus solutions affect the students' academic performance, fitness levels, motivation, and adherence to exercise regimens. The analyzed findings point to a number of advantages of using smart campus technologies into physical education. Compared to the control group, students in the experimental group showed higher levels of academic performance, increased motivation, improved exercise adherence, and fitness. Students who had access to smart campus solutions reported much better experiences with physical education overall. The results imply that smart campus technologies have the potential to produce a physical education learning environment that is more interesting, focused on the needs of the students, and effective. Smart campus solutions help optimize curriculum design, boost motivation, and enhance academic performance by utilizing data-driven insights and personalized learning experiences.
The Impact of a Mobile App on Student-Alumni Communication and Engagement: A Case Study

Abstract: The purpose of this study was to examine the effects of a mobile application that connects college students and alumni on their communication, networking, and career development. The app was developed by a team of students as a novel idea to foster strong alumni relations and provide mentorship opportunities for current students. The study used a mixed-methods approach, combining quantitative data from surveys and qualitative data from interviews. The surveys were conducted before and after the app was launched, and the interviews were conducted after the app was used for three months. The results showed that the app was beneficial to both students and alumni, as it increased their frequency and quality of communication, enhanced their sense of belonging and community, and improved their skills and experiences. The paper discusses the implications, limitations, and recommendations of the study for future research and practice.
Trusted Data Acquisition in Microelectronics Manufacturing as a Basis for ML-Optimized Processing

In the digitization of manufacturing processes, one of the major goals is the connection of production facilities and the use of data to digitize business processes. In order to optimize manufacturing processes and maximize the quality of the resulting product, further process information and data directly from the work piece and from the manufacturing environment are required to achieve a holistic system view in addition to the selected data that the manufacturing systems already provide. Within the SiEvEI 4.0 project, a research consortium from industry and academia works on process digitization for a manufacturing scenario where high value electronic goods are built in a distributed manufacturing environment. The key research topics addressed are the implementation of a Chain of Trust [CoT] for trustworthy distributed manufacturing and the application of artificial intelligence/machine learning to analyze and eventually optimize manufacturing processes. The basic concepts of this approach have been presented at IMAPS 2021. As an update, this paper reports on the actual experimental evaluation of these concepts in two different assembly lines, including data acquisition, data handling and AI processing with the goal to optimize processing targeting higher production yield and product quality. Specific for this work is the focus on high mix/low volume SMD assembly using fully automated equipment and Solder Ball Application. As a result, the paper presents the experimental validation of the manufacturing process digitization and the use this digital description of a process combination to make a distributed manufacturing flow safe and increase product/process quality.
Autonomous Driving System Driven by Artificial Intelligence Perception Fusion

Perception, as the information input module of the automatic driving system, determines the lower limit of the entire automatic driving system. Both autonomous driving perception and robot perception are constantly approaching the real physical world through digital methods, and this real physical world is only limited to the scope of human perception, such as lane lines, traffic lights, driving obstacles, and so on. The main premise of this process is that humans already know the categories or properties of the physical world, and only allow machines and systems to replicate human responses. Whether it is a pure visual route or a multi-source fusion route, the essence is the difference between the perceptual system schemes, one focusing on the vertical and the other on the horizontal. The pure vision solution represented by Tesla or the multi-source sensor fusion file represented by Waymo. In fact, the perception module of the automatic driving system usually has multiple sensors to achieve information redundancy and information complementarity through multiple dimensions, but there is the possibility of information conflict between different sensors. This paper aims at the advantages of perception-driven artificial intelligence to achieve breakthroughs in autonomous driving innovation, and analyzes how perception fusion drive is applied to the practical application of autonomous driving, so as to analyze the future development prospects of artificial intelligence.