Reviewerly/exHarmony huggingface dataset:
Investigating ChatGPT’s Potential to Assist in Requirements Elicitation Processes

Natural Language Processing (NLP) for Requirements Engineering (RE) (NLP4RE) seeks to apply NLP tools, techniques, and resources to the RE process to increase the quality of the requirements. There is little research involving the utilization of Generative AI-based NLP tools and techniques for requirements elicitation. In recent times, Large Language Models (LLM) like ChatGPT have gained significant recognition due to their notably improved performance in NLP tasks. To explore the potential of ChatGPT to assist in requirements elicitation processes, we formulated six questions to elicit requirements using ChatGPT. Using the same six questions, we conducted interview-based surveys with five RE experts from academia and industry and collected 30 responses containing requirements. The quality of these 36 responses (human-formulated + ChatGPT-generated) was evaluated over seven different requirements quality attributes by another five RE experts through a second round of interview-based surveys. In comparing the quality of requirements generated by ChatGPT with those formulated by human experts, we found that ChatGPT-generated requirements are highly Abstract, Atomic, Consistent, Correct, and Understandable. Based on these results, we present the most pressing issues related to LLMs and what future research should focus on to leverage the emergent behaviour of LLMs more effectively in natural language-based RE activities.
Development of A Mobile-Based Application for Ordering House Interior and Exterior Designs

This study focuses on a design services company lacking a computerized information system for managing customer orders and financial calculations. The absence of a structured reporting system for daily, weekly, monthly, and annual periods hinder efficient operations. We employ a descriptive research method with an object-oriented and prototype development approach. The programming language used is Kodular, and Firebase is utilized for database storage. The research results in the creation of a mobile-based interior and exterior design information system. It aids in order management, allows customers to check order statuses, minimizes calculation errors, and facilitates report generation. This system significantly streamlines company operations and enhances productivity.
Practice as an essential component of preparation for future teachers in the natural education field to professional activity

Modernization of natural science education and natural sciences in the context of digitalization (December 6–7, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : Baltija Publishing, 2023. 52 pages.
LOCAL AND GLOBAL CULTURE IN ENGLISH AS FOREIGN LANGUAGE INSTRUCTION FOR TOURISM STUDENTS: WHAT, WHY AND HOW

This article presents the result of a study investigating the local and global culture presented in EFL instruction for tourism students. More specifically, this research investigated the culture inserted by teachers, the reasons for the insertion, and the practice of the insertion of the local and global culture in EFL instruction for tourism students. The study was conducted in the form of qualitative research. The data was collected through interviews and document analysis. The participants of the study were English lecturers teaching English in a tourism study program. The data collected underwent a process of data reduction, data display, and conclusion drawing for the data analysis. Based on the analysis of the study, it was found that (1) local and global cultures were inserted in English instruction although local culture was presented more than global culture, (2) the local culture was inserted to promote cultural identity and nationalism, provide better customer service, enhance cultural awareness, and enhance sustainable tourism; while global culture was inserted to enhance the success of language learning, enhance intercultural communicative competence, and increase employability, and (3) local and global culture were inserted by conducting cultural discussion, cultural comparison and cultural project.
A New Proposal Using Graph Neural Networks for Human Action Recognition

Graph Neural Networks have played a key role in several machine learning applications in recent years. They have proven effective in dealing with structured data, capturing complex information, and allowing more sophisticated modeling in various domains. As such, these networks have contributed significantly to advancing and improving several areas in the machine learning field. This paper proposes a new approach for gesture classification using graphs, consisting of three main blocks: a Graph Constructor, a Feature Extractor, and a Gesture Classifier. A skeleton graph is constructed for each video frame in the first block. Feature extraction is performed using a Graph Neural Network, where the feature vectors of the nodes are modified based on information from the joints themselves and their neighboring joints. These vectors are then concatenated to form an embedding that represents the frame. The final representation of the video is obtained by concatenating these embeddings to form a pseudoimage. This pseudoimage is used as input to a classifier based on convolutional and dense layers. Also, we propose a new approach for the GraphConv layer, which attributes different weights for the neighboring node based on the distance from a reference node in the graph. Through experiments performed with the Chalearn data set, we achieved an accuracy of 91.84%. Even using a Graph Neural Network with only one layer and a reduced number of parameters to be trained, the results proved to be competitive compared to works using more complex architectures. This indicates that the approach proposed in this work has a promising and efficient performance in gesture classification.
Heterarchical Control System of a Diaper Converting Machine Using Colored Petri Nets

The industrial organizations continually improve their processes to respond to increased competition. In this sense, the lack of an effective control of the diaper production process implies production lost, waste generation and environmental impact. Facing this challenge, this paper proposes a novel approach to characterize the diaper converting machine as an intelligent resource within the context of Industry 4.0, using a new architecture based on Fuzzy Logic control and multiagent system. This characteristic allows the control system to perform different activities in a collaborative and precise way to optimize the use of raw material reels. It is used the Coloured Petri Net tools to simulate and validate the operation of the baby diaper production system. The proposed architecture improves the Overall Equipment Effectiveness and it is a way to move the baby diaper industry from classic Industry 3.0 to Industry 4.0 based on digital transformation.
Fraud Methods in Publishing Researchers' Scientific Papers Using Social Engineering

The aim of this study was to identify the fraudulent methods researchers face when publishing their scientific papers. These are mainly based on the technique of social minds. To achieve the study objectives, the researchers used the analytical descriptive approach based on semi-structured interview. The study stratified sample was chosen by the accidental sampling method and consisted of (74) faculty members from one of the private universities in the United Arab Emirates. The results showed that (30) faculty members, about (41%) of the total sample individuals were exposed to fraud while publishing their scientific papers, whereas (59%) of the sample individuals weren't exposed to fraud while publishing their scientific papers. As for the fraud methods, the sites of fake journals were in the first place, with a percentage of (63.3%), followed by spam via the e-mail with (50%), commercial journals and fraud methods using publishing centers via social media sites were in the third place with (40%), incomplete misleading procedures from the journal's main site with (16.7%), and finally the method of realistic mediator with a percentage of (13.3%). The results revealed that (74%) of the respondents are aware of the fraud activities related to publishing scientific papers, (57%) experienced such fraud processes themselves, and (43%) heard about fraud activities from others.
Abnormality Recognition Technology For Experimental Data Based On Deep Learning

With the continuous progress of big data, Internet of things, artificial intelligence and other technologies, we are in an era of information explosion. More and more test data are recorded. These data contain important information at all times. If we can find the relationship between these tests, we can have a deeper understanding and understanding of the test object. Based on the deep learning method, this paper studies the anomaly detection of multidimensional time series test data and the time series prediction of long test data, in order to effectively deal with these problems and improve the performance of the model. A long time series decomposition prediction model DMRFormer based on transformer is proposed. In order to solve the problem that it is difficult to find the dependencies between time series from complex and unknown time patterns, and the lack of prediction ability caused by transformer's self attention parallel structure, a progressive decomposition architecture based on transformer is designed and improved to improve the prediction performance. Based on the actual test scenario, using the relevant software development technology, a time series data management system in the test field is designed and implemented. The system mainly uses the proposed anomaly detection and prediction model to complete the prediction, anomaly detection and related governance tasks of time series data in the test scenario.
The Effect of Social Media on Improving the Recruitment Process: Regional Commercial Banks' Employee Engagement as a Mediator

By using employee participation as a moderating variable in Jordanian businesses, the current study sought to study the effects of social media and its various dimensions on improving the recruitment process along its sub-elements. The descriptive analytical method was used, with the sample including 12 Jordanian commercial banks; a subsequent comprehensive sample survey method was used, covering a total of 410 workers. For statistical analysis and testing of the hypotheses of the study, the research used SPSS software. The results of the research showed that there is a positive impact of employee participation through social media on improving the recruitment process in Jordanian commercial banks. Accordingly, the study helps more social networks to adapt and access local and global labor markets and to distribute relevant skills and competencies.
On Potentials of the Gamma Densities to Blind Face Hallucination

As to blind hallucination of blurry low-resolution faces, the supervised learning methods obviously lack generalization for blur kernels in various types and scales. While, the unsupervised learning ones usually require pre-trained deep generative models. The involved non-convex inversion is often highly challengeable, especially as the blurred low-resolution face image is undergone a blur process much distinct from the ones in the pre-training stage. This paper falls into unsupervised blind face hallucination, while takes a step forward. That is, a deep discriminative face prior has been proposed by innovatively exploring the Gamma densities for unsupervised blind hallucination, which is expected to regularize better the inversion of pre-trained deep generative models. The experimental results well prove the effectiveness of the proposed method.
Exploring Arabic Pre-Trained Language Models for Arabic Abstractive Text Summarization

Abstractive text summarization (ABS) is the task of providing a concise and meaningful summary for a given text. It is accomplished by first understanding the text and then rephrasing it in a shorter form, highlighting the main points of the original text. ABS is useful in applications such as news aggregators, article summarizers, legal-case summary production, business-meeting summarization, and social-media summarization. There are many types of ABS such as headline summaries, highlight summaries and full summaries. Compared to research on ABS in other languages, study of its use for the Arabic language is still limited. Moreover, very few works explore the use of pretrained transformer-based models for Arabic language ABS. This study investigates the effectiveness of pretrained transformer-based models for the downstream task of Arabic ABS. We present several experiments to fine-tune pretrained transformer model AraBArtfor Arabic text summarization. The used datasets are AHS dataset for headline summaries, WikiLingua dataset for highlight summaries, and XL-Sum dataset for full summaries. We report on the performance of the models using the ROUGE, BLEU and BERTScore metrics for ABS evaluation. We compare the results of the models with the state of the art for Arabic ABS. The best performance obtained with our experiments is the AraBART model fine-tuned on the AHS dataset to generate headline summaries, with the following results: ROUGE-1=55, ROUGE-2=40.15, ROUGE-L=54.55, BLEU=56.26, and BERTScore=88.06.
Joint Entity Relation Extraction Model Based on Relationship Box

In this paper, we propose a joint relation extraction model based on relation boxes to solve the problems of insufficient relation extraction and inefficient computational efficiency that commonly exist in relation extraction. The model obtains the dynamic word vectors of the text through BERT and splices them to form a 2D table through pooling layer. The CNN extracts the local context on the table to recognize the existence of relations in the table, and builds different styles of relation boxes on the table. We will use the feature information within the filtered relation boxes range to predict the relation classification of the entity pairs corresponding to the current relation box. The experimental results show that this model achieves 74.88% in the CoNLL04 dataset, which is a higher relationship extraction effect compared to other models.
Reconstructing HDR Image from a Single Exposure LDR Image with Global Feature Fusion

Modern digital cameras face inherent limitations when capturing the full luminance range in real-world scenarios, primarily due to hardware constraints. Additionally, challenging lighting conditions, such as severe over-exposure, often result in image detail distortion. Most CNN-based HDR methods are further hindered by their limited receptive fields, impeding their ability to recover fine details in overexposed areas. To address these issues, this paper introduces a novel architecture featuring a UNet-like base branch, complemented by a global feature fusion module. This module seamlessly blends local and global features, facilitating multi-scale information extraction for image reconstruction. Furthermore, the architecture incorporates a weighted branch to preserve well-exposed details and a global branch for capturing global features. By adopting this approach, our method not only achieves state-of-the-art performance in quantitative comparisons but also obtains excellent visual quality in assessments. In our experiment, we achieved a PSNR-μ score of 34.04 and an HDRVDP3 score of 8.311, outperforming the previous state-of-the-art methods by improvements of 0.02 and 0.01, respectively.
Research Trends in Immersive Technology in the Field of Entertainment or Games by Bibliometric Analysis

In the realm of immersive technology research, traditional systematic reviews have focused on the areas of education and training, healthcare, and tourism experiences. However, the investigation of trends and the utilization of immersive technology in the fields of entertainment and gaming has been relatively overlooked. As such, this study seeks to fill this research gap by using bibliometric analysis to explore the clustering of research topics related to Virtual Reality (VR), Augmented Reality (AR) and Extended Reality (XR) within the entertainment and gaming sectors. We identified nine groups, and through manual inspection, immersive research topics within the realm of entertainment and gaming were eventually categorized into eight principal research trend topics: “User Game Experience in Virtual Reality Environments”, “Motion Sickness in Immersive Virtual Reality”, “Designing Safe Training Environments in Immersive Virtual Reality”, “Applications of Limb and Cognitive Rehabilitation in Immersive Virtual Reality”, “Opportunities and Challenges in the Metaverse and Blockchain”, “Virtual Reality Simulation Training in Cardiopulmonary Resuscitation and Patient Resuscitation Skills for Medical Professionals”, “Human-Robot Interaction in Virtual Reality” and “Education and Learning through Augmented Reality”. By providing these topic clusters, we aim to provide researchers with a valuable direction for future research. Additionally, we engage in an exploration of the technologies and scenarios employed within these topic groups to further enhance our understanding of the gaps.
Investigating the Consequences for the Economy Arising from the Utilization of Mobile Marketing Within the Framework of Cybercrime

With the widespread adoption of mobile devices and the increasing reliance on mobile marketing, cybercriminals have seized the opportunity to exploit this digital landscape for their illicit activities. This paper aims to investigate the economic implications of cybercrime that exploit mobile marketing channels. By examining the intersection of mobile marketing and cybercrime within an economic framework, we analyze the direct and indirect costs incurred by individuals, businesses, and society. Furthermore, we explore the various techniques employed by cybercriminals in leveraging mobile marketing platforms, shedding light on the challenges and potential solutions that exist to combat these cyber threats. Through empirical research and theoretical analysis, we offer insights into the economic consequences of cybercrime in the mobile marketing domain, emphasizing the need for robust security measures and effective policy frameworks to mitigate these risks and protect stakeholders in the digital economy.
The Impact of Using Social Media Platforms (X) as an Example on Shaping Linguistic Awareness

The study aimed to determine the extent of exposure to the social media platform (X) among The Outstanding and Ordinary students at the College of Education at Al Ain University, as well as the number of hours spent by College of Education students at Al Ain University on the social media platform (X), and the level of linguistic awareness among students who use the social media platform (X). The sample consisted of 154 students from the College of Education and Social Sciences at Al Ain University. The study relied on a questionnaire as a data collection tool to understand the role of social media platforms in shaping linguistic awareness among College of Education and Social Sciences students at Al Ain University. The results indicated that the average exposure rate was always at the forefront with a percentage of 76%, followed by exposure sometimes at 24%. The results also revealed that the number of hours spent on the social media platform (X) for more than three hours ranked first at 66.8%, while the number of hours from one to two hours ranked second at 22.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> . Meanwhile, those who spent one to two hours daily accounted for 8.5%, and those who spent less than an hour accounted for 2% of the total sample. The results suggest that the social media platform (X) serves as an example that contributes to raising the level of linguistic awareness, with a moderate level of awareness ranking second. The rest reported having low awareness. The overall degree of linguistic awareness ranged from 11 to 32 with an average of 26.3 and a standard deviation of 4.146. This indicates the impact of the social media platform (X) as an example on the sample's level of linguistic awareness. The study concluded with several recommendations
D-YOLOv5: An Object Detection Network with Enhanced Densities

In recent years, there has been notable development in object detection with convolutional neural networks (CNNs). Nevertheless, the layer-by-layer abstraction inherent in these networks leads to sparse feature maps, which adversely affects detection. To address this issue, this paper proposed an object detection network that is more focused on feature densities: D-YOLOv5. Firstly, in the down-sampling stage, two types of pooling are incorporated to increase the feature response area, replacing the previous sparse region. Secondly, a depthwise Convolution (DWC) is integrated into the feature extraction stage to obtain feature maps with higher densit. Finally, in the feature fusion stage, the features are filtered and the appropriate features are selected for combination with the previous layer. The YOLOv5 model was used as a benchmark to train the model on the MS-COCO dataset. The test-dev2017 test set's detection results were subsequently submitted to the server. The outcome showed a detection speed of 370.4 FPS and mAP@.5 of 48%, which is a 2.9% increase over the benchmark model. The test results showed that the improved model had significantly improved object detection capability in the same environment, achieving high-precision detection in real time, thus meeting practical task requirements.
Occluded Face Recognition Based on Attention Mechanism and Damaged Feature Masking

Face recognition has made great achievements due to the continuous development of deep learning techniques and the arising of large-scale face datasets. However, these are only applicable to face recognition without occlusion. Face recognition encounters great obstacles under constrained conditions with occlusion. To handle this problem, this paper proposes a novel occluded face recognition method based on the bottleneck attention module (BAM) and the mask generator. BAM is embedded in backbone network to extract more distinguishable features, while the mask generator is designed to clean the corrupted feature, so that face recognition can be realized under the constraint conditions of occlusion. Experimental results on the public datasets indicate the proposed method is superior to the comparison methods.