Reviewerly/exHarmony huggingface dataset:
The Ontology for Conceptual Characterization of Ontologies

Ontologies as computational artifacts have been seen as a solution to FAIRness due to their characteristics, applications, and semantic competencies. Conceptualizations of complex and vast domains can be fragmented in different ways and can compose what is known as ontology networks. Thus, the ontologies produced can relate to each other in many different ways, making the ontological artifacts themselves subject to FAIRness. The problem is that in the Ontology Engineering Process, stakeholders take different perspectives of the conceptualizations, and this causes ontologies to have biases that are sometimes more ontological and sometimes more related to the domain. Besides, usually, Ontology Engineers provide well-grounded reference ontologies, but rarely are they implemented. At the same time, Domain Specialists produce operational ontologies storing large amounts of valid data but with naive ontological support or even without any. We address this problem of lack of consensual conceptualization by proposing a reference conceptual model (O4OA) that considers ontological-related and domain-related perspectives, knowledge, and commitment necessary to facilitate the process of Ontological Analysis, including the analysis of ontologies composing an ontology network. Indeed, O4OA is a (meta)ontology grounded in the Unified Foundational Ontology (UFO) and supported by well-known ontological classification standards, guides, and FAIR principles. We demonstrate how this approach can suitably promote conceptual clarification and terminological harmonization in this area through our framework proposal and its case studies.
Does Electronic Human Resource Management Matter for Workforce Agility? An Empirical Study of the Jordanian Banking Sector

The current research examined the impact of electronic human resource management on workforce agility in the Jordanian banking sector. The research used experimental data through a survey distributed to 559 employees for Jordanian commercial banks at various administrative levels, which formed a response rate of 78.3%. Structural equation modeling (SEM) was applied as a statistical analysis approach to the collected data. The research concluded that electronic human resources management had a positive impact on workforce agility, as e-recruiting had the greatest effect, while e-performance appraisal had the least effect. Accordingly, the research emphasized the necessity of adopting a change management approach and seamless integration of electronic human resources management through comprehensive training programs and addressing any concerns or resistance to change.
HuMoMM: A Multi-Modal Dataset and Benchmark for Human Motion Analysis

Human motion analysis is a fundamental task in computer vision, and there is an increasing demand for versatile datasets with the development of deep learning. However, how to obtain the annotations of human motion, such as 3D keypoints and SMPL parameters, requires further research. In this work, we design a multi-view human motion capture system and develop a toolchain to generate multi-modal motion annotations. Additionally, we contribute HuMoMM, a large-scale multi-modal dataset which has the following characteristics: 1) multiple modalities, including two data formats, i.e., RGB and depth images, and four annotation formats, i.e., action categories, 2D keypoints, 3D keypoints, and SMPL parameters; 2) large-scale with 18 subjects, 30 actions, 3.5k sequences, and 262k frames; 3) multi-task for action recognition, 2D keypoint detection, 3D pose estimation and human mesh recovery. Furthermore, we provide a benchmark on HuMoMM to test the performance of popular methods in several related tasks. The experimental results demonstrate that HuMoMM holds significant research value. We expect HuMoMM can contribute to human motion-related research, and it is available at https://github.com/SCUT-BIP-Lab/HuMoMM .
Intensify Perception Transformer Generative Adversarial Network for Image Super-Resolution

Generative adversarial networks (GANs) are widely used for image super-resolution (SR) and have recently attracted increasing attention due to their potential to generate rich details. However, generators are usually based on convolutional neural networks, which lack global modeling capacity and limit the performance of the network. To address this problem, we propose a hierarchical partitioned Transformer block to extract features at different scales, which alleviates the loss of information and helps global modelling. We then design a Transformer in residual block to reconstruct more natural structural textures in SR results. Finally, we integrate the intensify perception Transformer network with an existing discriminator network to form the intensify perception Transformer generative adversarial network (IPTGAN). We conducted experiments on several benchmark datasets, RealSR dataset and PIRM self-validation dataset to verify the generalization ability of our IPTGAN. The results show that our IPTGAN exhibits better visual quality and significantly less complexity compared to several state-of-the-art GAN-based image SR methods.
RF Signal Feature Extraction in Integrated Sensing and Communication

Because of the open property of information sharing in integrated sensing and communication, it is inevitable to face security problems such as user information being tampered, eavesdropped, and copied. Radio frequency (RF) individual identification technology is an important means to solve its security problems at present. Whether using machine learning methods or current deep learning-based target fingerprint identification, its performance is based on how well the radio frequency features (RFF) are extracted. Since the received signal is affected by various factors, we believe that we should first find the intrinsic features that can describe the properties of the target, which is the key to enhance the RF fingerprint recognition. In this paper, we try to analyze the intrinsic characteristics of the components that influenced the signal by the transmitting source and derive a mathematical formula to describe the RF characteristics. We propose a method using dynamic wavelet transform and wavelet spectrum (DWTWS) to enhance RFF features. The performance of the proposed method was evaluated by experimental data. Using a support vector machine classifier, the recognition accuracy is 99.6% for 10 individuals at a signal-to-noise ratio (SNR) of 10 dB. In comparison with the dual-tree complex wavelet transform (DT-CWT) feature extraction method and the wavelet scattering transform method, the DWTWS method has increased the interclass distance of different individuals and enhanced the recognition accuracy. The DWTWS method is superior at low SNR, with performance improvements of 53.1% and 10.7% at 0 dB.
AE-Reorient: Active Exploration Based Reorientation for Robotic Pick-and-Place

Finding objects in dense clutter and placing them in specific poses play an important role in robot manipulation in fields like warehousing and logistics, and have a significant influence on the automation of these fields. However, most methods that perform well in simple clutter do not hold up well in dense clutter because of severe stacking and occlusion between objects, which prevents the target objects from grasping successfully. In this paper, we propose an interactive exploration framework called AE-Reorient based on reinforcement learning to let robots learn the interactive exploration autonomously. AE-Reorient enables robots to actively find the target objects and accomplish the pick-and-place task. Our AE-Reorient generates iterative and interactive actions to improve the visibility of the target objects which is an important factor affecting the success of grasping. Specifically, we first collect the scene information through an RGB-D camera. Then, using the point cloud reconstruction and pose estimation methods to obtain the height map of the clutter, through which we generate the grasp pose or predict the position. We apply interaction to change the state of the scene by pushing if the grasp is unsuccessful. The generated push action is applied to disrupt and recreate a new state of the clutter with the target objects more exposed, which greatly improves the probability of grasp success. Robots can successfully perform pick-and-place tasks in dense clutter by applying our proposed framework. We test our AE-Reorient in 130 dense clutters and it enhances the success rate and the visibility of the target object by 7.60% and 4.10% compared with the baseline method.
Effect of Customers Green Consciousness on Customers Purchase Intention: A Field Study on Green Technology (Home Appliances)

The study aims to test the impact of customers green consciousness on customers purchase intention. The Population of the study consisted of all buyers who purchased green technology products in Amman; a convenience sample was selected through distributing our questionnaire face-to-face approach at different companies and places in Amman. The study hypothesis was tested using structural equation modeling (AMOS). The results of the analysis show that there is an effect of customers green consciousness on customers purchase intentions. According to the findings, managers must improve their green brand positioning, image, and awareness to increase their green purchasing intentions in this ecological era.
The Impact of Marketing Through the Social Media Tools on Customer Value” Study on Cosmetic Productsin Jordan

The research aims to study the impact of marketing through social media (SM) on achieving customer value. The study population represented the customers of companies selling and manufacturing cosmetic products in Jordan that depend on social media on marketing. SPSS and AMOS software was used by the researcher. The statistically significant results showed the impact of marketing through SM on achieving customer value (CV). The study recommended raising awareness of the importance of marketing through SM with attention to the areas of safety, confidentiality and privacy, and holding training and rehabilitation programs and workshops.
The Role of Digital Public Relations in Improving the Mental Image

This study came to test the role of digital public relations in improving the mental image of the Jordanian Duty Free Company. The study population includes all customers of the Jordanian Duty Free Shops Company, with the sample size determined by simple random sampling. The study followed the quantitative approach using partial least squares structural equation modeling (PLS-SEM) to analyze the hypotheses. The study found that there is an impact of digital public relations in improving the mental image. According to the results, the recommendations of the study is activating the digital public relations means for the customers of the Jordanian Duty Free Shop Company.
The persistence principle over weak interpretability logic

Abstract We focus on the persistence principle over weak interpretability logic. Our object of study is the logic obtained by adding the persistence principle to weak interpretability logic from several perspectives. Firstly, we prove that this logic enjoys a weak version of the fixed point property. Secondly, we introduce a system of sequent calculus and prove the cut‐elimination theorem for it. As a consequence, we prove that the logic enjoys the Craig interpolation property. Thirdly, we show that the logic is the natural basis of a generalization of simplified Veltman semantics, and prove that it has the finite frame property with respect to that semantics. Finally, we prove that it is sound and complete with respect to some appropriate arithmetical semantics.
Impact of Human Resources Practices on Employees Organizational Commitment at Jordanian Private Hospitals

The objective of this research is to test the effect of human resource management practices on organizational commitment. The study population consisted of doctors working at Jordanian private hospitals, which are located in the capital of Jordan (Amman) that provide healthcare services. Structural equation modeling (PLS-SEM) was used to analyze the hypotheses. The results of the study showed a significant impact of human resource management practices on organizational commitment. The researchers recommend managers to provide training for employees as a form of support for them by management, which makes them more committed to their organizations.
The Role of Public Policy Making in the Strategic Performance According to the Balanced Scorecard Model

The current study came to shed light on the role of public policy-making in the strategic performance according to the balanced scorecard model. Therefore, it was focused on the Executive Council of Karak Governorate in Jordan. A random sample of (84) respondent. Structural equation modeling (SEM) was used to test hypotheses. The results demonstrated that public policy-making has a positive impact on the strategic performance. Based on the previous results, the current study recommends reinforcing the principle of participation in drawing up public policies in the governorate in a better way than what is currently practiced.
On the instability of further pre-training: Does a single sentence matter to BERT?

We observe a remarkable instability in BERT-like models: minimal changes in the internal representations of BERT, as induced by one-step further pre-training with even a single sentence, can noticeably change the behaviour of subsequently fine-tuned models. While the pre-trained models seem to be essentially the same, also by means of established similarity assessment techniques, the measurable tiny changes appear to substantially impact the models’ tuning path, leading to significantly different fine-tuned systems and affecting downstream performance. After testing a very large number of combinations, which we briefly summarize, the experiments reported in this short paper focus on an intermediate phase consisting of a single-step and single-sentence masked language modeling stage and its impact on a sentiment analysis task. We discuss a series of unexpected findings which leave some open questions over the nature and stability of further pre-training.
Virtual reality forensics: Forensic analysis of Meta Quest 2

The Meta Quest 2 is one of the most popular Virtual Reality (VR) entertainment headsets to date. The headset, developed by Meta Platforms Inc., immerses the user in a completely simulated environment. Some VR environments can be shared over the Internet to allow users to communicate and interact with one another and share their experiences. Unfortunately, the safety of these VR environments cannot always be guaranteed, generating a risk that users may be exposed to illicit online behaviour in the form of online harassment, grooming, and cyberbullying. Therefore, forensic examiners must be able to conduct sound forensic analysis of VR headsets to investigate these criminal investigations. In this study, we conduct digital forensic acquisition and analysis of the Meta Quest 2 VR headset. Analysis of the forensic image exemplified that there were several digital artefacts relating to user activities, device information and stored digital artefacts that can be extracted in a forensically sound manner. The main contributions of this study include a detailed description of the forensic acquisition process, identification of internal file storage locations, and recovery and analysis of digital artefacts that can be used to aid VR forensic investigations.
Monetary policy transmission channels and the performance of the real sectors in selected sub-Saharan African countries: a system-GMM approach

Abstract This study investigated the impact of monetary policy channels on the real sector performance in selected sub-Saharan African countries. This study covers the period 1990 to 2020. The broad objective of the study was to examine the impact of monetary policy transmission channels on the real sectors of selected sub-Saharan African countries within the period, 1990–2020. Using the system generalized method of moments (sys-GMMs) amidst other pre-estimation and post-estimation tests, it was discovered that credit and exchange rate channels positively drove agricultural productivity, while interest rate channel exerted a negatively significant influence. Conversely, credit and exchange rate channels were found to negatively affect manufacturing, while interest rate channels had no significant impact on the manufacturing sector. Given the overwhelming impact of monetary policy channels on both the agricultural and manufacturing sectors of the SSA economies, it is recommended that effective monetary policy framework be put in place to continually create positive transmission effect on the real and productive sectors of the economy.
Cyberbullying Detection using Machine Learning and Deep Learning

With the human passion for gaining knowledge, learning new things and knowing the news that surrounds the world, social networks were invented to serve the human need, which resulted in the rapid spread and use among people, but social networks have a dark and bright side. The dark side is that strangers or anonymous people harass some users with obscene words that the user feels wrong about, which leads to psychological harm to him, and here we try to discover how to discover electronic bullying to block this alarming phenomenon. In this context, the utility of Natural Language Processing (NLP) is employed in the present investigation to detect electronic bullying and address this alarming phenomenon. The machine learning (ML) method is moderated based on specific features or criteria for detecting cyberbullying on social media. The collected characteristics were analyzed using the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Decision Trees (DT), and Random Forest (RF) methods. Naturally, there are test results that use or operate on the proposed framework in a multi-category setting and are encouraged by kappa, classifier accuracy, and f-measure standards. These apparent outcomes show that the suggested model is a valuable method for predicting the behavior of cyberbullying, its strength, and its impact on social networks via the Internet. In the end, we evaluated the results of the proposed and basic features with machine learning techniques, which shows us the importance and effectiveness of the proposed features for detecting cyberbullying. We evaluated the models, and we got the accuracy of the KNN (0,90), SVM (0,92), and Deep learning (0,96).
Stylometry-Based Authorship Identification

The Internet has helped to collect and exchange information and data with its constant expansion and evolution for devices to be intertwined with each other. These advances provide invaluable information about people and issues related to their lives including behaviors, interests, and preferences which have brought about the Internet of Behaviors (IoB) in attempts to understand the data collected from users' online activities. From a behavioral psychology perspective, the IoB can address the question of how to understand the data, and how to apply that understanding to create things that benefit humans. The IoB is related to many fields of research including technology, data analytics, and behavior science in relationship with stylometry. The applications of stylometry within the IoB framework such as analyzing the writing style of social media posts, online reviews, journal articles, or literary works at tertiary educational organizations could provide insights into the personality or motivations of the author. Thus, stylometry could potentially be used to identify authorship.
Enhanced Deep Learning-Based CAD System for Breast Cancer Diagnosis from Histopathology Images: Whole Slide Approach

Breast cancer is a major cause of cancer-related death globally, particularly in women. Early diagnosis is crucial for successful treatment, requiring proper analysis of histological images. However, the large data volume and image complexity make this task time-consuming, prompting the need for automated detection tools. The paper proposes a methodology that combines multiple deep learning models (VGG16, VGG19, Xception, Inception V3, and InceptionResnet V2) to improve accuracy in identifying cancerous and noncancerous regions in histopathology images, assisting doctors in earlier detection and effective treatment. The study demonstrates breast cancer histology image classification using deep learning. The evaluation on the ICIAR 2018 Grand Challenge dataset showcases superior performance compared to state-of-the-art methods. The proposed method achieves a high-test accuracy of 96% for the two-class problem, surpassing individual deep learning models like Xception. For the four-class problem, it outperforms Xception and InceptionResNet V2 with a test accuracy of 95%. Notably, precision and recall metrics significantly improve after applying the method in both cases. Furthermore, the evaluation on whole slide images demonstrates an accuracy of 92%, highlighting the model's proficiency in classifying breast cancer histology images at a macroscopic level. Robustness is confirmed through testing on an independent dataset from Alborg lab, with accuracies ranging from 0.82 to 0.91 across different architectures. Overall, the proposed methodology offers promising advancements in breast cancer diagnosis automation, supporting earlier detection and more effective treatment of the disease.
The “SIKAWAN” Cloud Mail System Application in Rural Indonesia

As new technologies are developed, nearly every sector in Indonesia have decided to migrate from manual to digital service delivery. Using an electronic-based governance system is one of them. The Office System Application in the Cloud (SIKAWAN) is developed by this electronic government system. Banyuwangi Regency has implemented SIKAWAN in rural areas. This study's objective is to learn more about the design and operation of the SIKAWAN application. A descriptive methodology and qualitative research techniques are used in this study. The findings of this study indicate that a skilled operator is needed to implement the SIKAWAN application. On the other hand, regular assessments of the effectiveness of the system's implementation by the village government apparatus also contribute to the system’s success.
Bi-preference linkage-driven artificial bee colony algorithm with multi-operator fusion

Abstract The artificial bee colony algorithm (ABC) struggles in handling complex optimization problems with high dimensions in light of its search operators’ strong exploration and weak exploitation properties. To tackle this situation, in this study, we propose a bi-preference linkage-driven ABC algorithm with multi-operator fusion, named BPLABC. BPLABC couples a preference-free stochastic search operator with a global best-guided search operator in the employed bee phase to maintain the population diversity while enhancing the population quality. During the onlooker bee phase, a tailored bi-type elite-guided exploitation mechanism is employed to regulate the exploitation intensity of the promising elite nectar sources selected via a new roulette selection probability calculation paradigm. To discourage the onlooker bees from slipping into local traps, after the scout bee phase, an auxiliary adversarial search operator is assembled to tug certain promising elite solutions away from the present pseudo-global best solution. To illustrate the effectiveness and efficiency of BPLABC, two sets of test suits consisting of 23 benchmark problems, 30 complex CEC2014 functions, and two real-world problems are picked for testing. Experimental results showed that BPLABC can achieve superior or equivalent performance to several representative ABC variants on the majority of the tested problems.