Reviewerly/exHarmony huggingface dataset:
Envisioning Architecture of Metaverse Intensive Learning Experience (MiLEx): Career Readiness in the 21st Century and Collective Intelligence Development Scenario

Th metaverse presents a new opportunity to construct personalized learning paths and to promote practices that scale the development of future skills and collective intelligence. The attitudes, knowledge and skills that are necessary to face the challenges of the 21st century should be developed through iterative cycles of continuous learning, where learners are enabled to experience, reflect, and produce new ideas while participating in a collective creativity process. In this paper, we propose an architecture to develop a metaverse-intensive learning experience (MiLEx) platform with an illustrative scenario that reinforces the development of 21st century career practices and collective intelligence. The learning ecosystem of MiLEx integrates four key elements: (1) key players that define the main actors and their roles in the learning process; (2) a learning context that defines the learning space and the networks of expected interactions among human and non-human objects; (3) experiential learning instances that deliver education via a real-life–virtual merge; and (4) technology support for building practice communities online, developing experiential cycles and transforming knowledge between human and non-human objects within the community. The proposed MiLEx architecture incorporates sets of technological and data components to (1) discover/profile learners and design learner-centric, theoretically grounded and immersive learning experiences; (2) create elements and experiential learning scenarios; (3) analyze learner’s interactive and behavioral patterns; (4) support the emergence of collective intelligence; (5) assess learning outcomes and monitor the learner’s maturity process; and (6) evaluate experienced learning and recommend future experiences. We also present the MiLEx continuum as a cyclic flow of information to promote immersive learning. Finally, we discuss some open issues to increase the learning value and propose some future work suggestions to further shape the transformative potential of metaverse-based learning environments.
Recent Advances in Stochastic Gradient Descent in Deep Learning

In the age of artificial intelligence, the best approach to handling huge amounts of data is a tremendously motivating and hard problem. Among machine learning models, stochastic gradient descent (SGD) is not only simple but also very effective. This study provides a detailed analysis of contemporary state-of-the-art deep learning applications, such as natural language processing (NLP), visual data processing, and voice and audio processing. Following that, this study introduces several versions of SGD and its variant, which are already in the PyTorch optimizer, including SGD, Adagrad, adadelta, RMSprop, Adam, AdamW, and so on. Finally, we propose theoretical conditions under which these methods are applicable and discover that there is still a gap between theoretical conditions under which the algorithms converge and practical applications, and how to bridge this gap is a question for the future.
Prediction and interpretation of daily NFT and DeFi prices dynamics: Inspection through ensemble machine learning &amp; XAI

Non Fungible Tokens (NFT) and Decentralized Finance (DeFi) assets have seen a growing media coverage and garnered considerable investor traction despite being classified as a niche in the digital financial sector. The lack of substantial research to demystify the dynamics of NFT and DeFi coins motivates the scrupulous analysis of the said sector. This work aims to critically delve into the evolutionary pattern of the NFTs and DeFis for performing predictive analytics of the same during the COVID-19 regime. The multivariate framework comprises the systematic inclusion of explanatory features embodying technical indicators, key macroeconomic indicators, and constructs linked to media hype and sentiment pertinent to the pandemic, nonlinear feature engineering, and ensemble machine learning. Isometric Mapping (ISOMAP) and Uniform Manifold Approximation and Projection (UMAP) techniques are conjugated with Gradient Boosting Regression (GBR) and Random Forest (RF) for enabling the predictive analysis. The predictive performance rationalizes the frameworks' capacity to accurately predict the prices of the majority of the NFT and DeFi coins during the ongoing financial distress period. Additionally, Explainable Artificial Intelligence (XAI) methodologies are used to comprehend the nature of the impact of the explanatory variables. Findings suggest that the daily movement of the NFTs and DeFi highly depends on their past historical movement.
Image classification and adversarial robustness analysis based on hybrid quantum–classical convolutional neural network

Hybrid quantum and classical classification algorithms have provided a new solution to the classification problem with machine learning methods under a hybrid computing environment. Enlightened by the potential powerful quantum computing and the benefits of convolutional neural network, a quantum analog of the convolutional kernel of the classical convolutional neural network, i.e., quantum convolutional filter, is designed to enhance the feature extraction ability. Meanwhile, quantum convolutional layers stacked by quantum convolutional filters combine variational quantum circuits with tensor network architecture and convolution operations. In addition, a hybrid quantum–classical convolutional neural network model containing quantum convolution layers and classical networks is devised. The feasibility of the proposed hybrid model are tested on the classical MNIST dataset. Finally, the adversarial robustness of the presented hybrid network is compared with that of the classical convolutional neural network and the quanvolutional one under classical adversarial examples. It is demonstrated the presented hybrid quantum–classical convolutional neural network model outperforms the original convolutional neural network and the quanvolutional neural network in some adversarial cases.
Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution

Real-world face super-resolution (SR) is a highly ill-posed image restoration task. The fully-cycled Cycle-GAN architecture is widely employed to achieve promising performance on face SR, but is prone to produce artifacts upon challenging cases in real-world scenarios, since joint participation in the same degradation branch will impact final performance due to huge domain gap between real-world and synthetic LR ones obtained by generators. To better exploit the powerful generative capability of GAN for real-world face SR, in this paper, we establish two independent degradation branches in the forward and backward cycle-consistent reconstruction processes, respectively, while the two processes share the same restoration branch. Our Semi-Cycled Generative Adversarial Networks (SCGAN) is able to alleviate the adverse effects of the domain gap between the real-world LR face images and the synthetic LR ones, and to achieve accurate and robust face SR performance by the shared restoration branch regularized by both the forward and backward cycle-consistent learning processes. Experiments on two synthetic and two real-world datasets demonstrate that, our SCGAN outperforms the state-of-the-art methods on recovering the face structures/details and quantitative metrics for real-world face SR. The code will be publicly released at https://github.com/HaoHou-98/SCGAN.
Graph Embedding Contrastive Multi-Modal Representation Learning for Clustering

Multi-modal clustering (MMC) aims to explore complementary information from diverse modalities for clustering performance facilitating. This article studies challenging problems in MMC methods based on deep neural networks. On one hand, most existing methods lack a unified objective to simultaneously learn the inter- and intra-modality consistency, resulting in a limited representation learning capacity. On the other hand, most existing processes are modeled for a finite sample set and cannot handle out-of-sample data. To handle the above two challenges, we propose a novel Graph Embedding Contrastive Multi-modal Clustering network (GECMC), which treats the representation learning and multi-modal clustering as two sides of one coin rather than two separate problems. In brief, we specifically design a contrastive loss by benefiting from pseudo-labels to explore consistency across modalities. Thus, GECMC shows an effective way to maximize the similarities of intra-cluster representations while minimizing the similarities of inter-cluster representations at both inter- and intra-modality levels. So, the clustering and representation learning interact and jointly evolve in a co-training framework. After that, we build a clustering layer parameterized with cluster centroids, showing that GECMC can learn the clustering labels with given samples and handle out-of-sample data. GECMC yields superior results than 14 competitive methods on four challenging datasets. Codes and datasets are available: https://github.com/xdweixia/GECMC.
High-Performance Artificial Intelligence Recommendation of Quality Research Papers Using Effective Collaborative Approach

The Artificial Intelligence Recommender System has emerged as a significant research interest. It aims at helping users find things online by offering recommendations that closely fit their interests. Recommenders for research papers have appeared over the last decade to make it easier to find publications associated with the field of researchers’ interests. However, due to several issues, such as copyright constraints, these methodologies assume that the recommended articles’ contents are entirely openly accessible, which is not necessarily the case. This work demonstrates an efficient model, known as RPRSCA: Research Paper Recommendation System Using Effective Collaborative Approach, to address these uncertain systems for the recommendation of quality research papers. We make use of contextual metadata that are publicly available to gather hidden relationships between research papers in order to personalize recommendations by exploiting the advantages of collaborative filtering. The proposed system, RPRSCA, is unique and gives personalized recommendations irrespective of the research subject. Thus, a novel collaborative approach is proposed that provides better performance. Using a publicly available dataset, we found that our proposed method outperformed previous uncertain methods in terms of overall performance and the capacity to return relevant, valuable, and quality publications at the top of the recommendation list. Furthermore, our proposed strategy includes personalized suggestions and customer expertise, in addition to addressing multi-disciplinary concerns.
TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to transfer the knowledge learnt from a labeled source domain to an unlabeled target domain. Previous work is mainly built upon convolutional neural networks (CNNs) to learn domain-invariant representations. With the recent exponential increase in applying Vision Transformer (ViT) to vision tasks, the capability of ViT in adapting cross-domain knowledge, however, remains unexplored in the literature. To fill this gap, this paper first comprehensively investigates the performance of ViT on a variety of domain adaptation tasks. Surprisingly, ViT demonstrates superior generalization ability, while the performance can be further improved by incorporating adversarial adaptation. Notwithstanding, directly using CNNs-based adaptation strategies fails to take the advantage of ViT’s intrinsic merits (e.g., attention mechanism and sequential image representation) which play an important role in knowledge transfer. To remedy this, we propose an unified framework, namely Transferable Vision Transformer (TVT), to fully exploit the transferability of ViT for domain adaptation. Specifically, we delicately devise a novel and effective unit, which we term Transferability Adaption Module (TAM). By injecting learned trans- ferabilities into attention blocks, TAM compels ViT focus on both transferable and discriminative features. Besides, we leverage discriminative clustering to enhance feature diversity and separation which are undermined during adversarial domain alignment. To verify its versatility, we perform extensive studies of TVT on four benchmarks and the experimental results demonstrate that TVT attains significant improvements compared to existing state-of-the-art UDA methods.
Asymmetric Student-Teacher Networks for Industrial Anomaly Detection

Industrial defect detection is commonly addressed with anomaly detection (AD) methods where no or only incomplete data of potentially occurring defects is available. This work discovers previously unknown problems of student-teacher approaches for AD and proposes a solution, where two neural networks are trained to produce the same output for the defect-free training examples. The core assumption of student-teacher networks is that the distance between the outputs of both networks is larger for anomalies since they are absent in training. However, previous methods suffer from the similarity of student and teacher architecture, such that the distance is undesirably small for anomalies. For this reason, we propose asymmetric student-teacher networks (AST). We train a normalizing flow for density estimation as a teacher and a conventional feed-forward network as a student to trigger large distances for anomalies: The bijectivity of the normalizing flow enforces a divergence of teacher outputs for anomalies compared to normal data. Outside the training distribution the student cannot imitate this divergence due to its fundamentally different architecture. Our AST network compensates for wrongly estimated likelihoods by a normalizing flow, which was alternatively used for anomaly detection in previous work. We show that our method produces state-of-the-art results on the two currently most relevant defect detection datasets MVTec AD and MVTec 3D-AD regarding image-level anomaly detection on RGB and 3D data.
More Control for Free! Image Synthesis with Semantic Diffusion Guidance

Controllable image synthesis models allow creation of diverse images based on text instructions or guidance from a reference image. Recently, denoising diffusion probabilistic models have been shown to generate more realistic imagery than prior methods, and have been successfully demonstrated in unconditional and class-conditional settings. We investigate fine-grained, continuous control of this model class, and introduce a novel unified framework for semantic diffusion guidance, which allows either language or image guidance, or both. Guidance is injected into a pretrained unconditional diffusion model using the gradient of image-text or image matching scores, without re-training the diffusion model. We explore CLIP-based language guidance as well as both content and style-based image guidance in a unified framework. Our text-guided synthesis approach can be applied to datasets without associated text annotations. We conduct experiments on FFHQ and LSUN datasets, and show results on fine-grained text-guided image synthesis, synthesis of images related to a style or content reference image, and examples with both textual and image guidance. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
The Influence of Electronic Human Resource Management on Intention to Leave: An Empirical Study of International NGOs in Jordan

The aim of the study was to examine the impact of electronic human resource management on intention to leave. Therefore, it focused on international non-governmental organizations (NGOs) operating in the humanitarian sector. Data were primarily gathered through self-reported questionnaires created by Google Forms which were distributed to a random sample of (620) employees. Structural equation modeling (SEM) was conducted to test hypotheses. The results showed that electronic human resource management had a negative impact on intention to leave. Moreover, the results indicated that the highest impact was for e-performance evaluation. Based on the study results, the researcher recommends organizations to involve employees in databases that enable them to follow the electronic human resources management practices, which is to follow up on the performance appraisal and how it works.
The Impact of the Digital Marketing for Education Services on the Mental Image for Students in Private Universities in Jordan

This study aims to identifying the impact of digital marketing on the mental image of students at private universities in Jordan. The population of the study consisted of all the higher education students in the private Jordanian universities in (18) universities numbering (6381). The study sample was chosen randomly, and it consisted of (359) male and female students. The distribution of the questionnaire was done through an electronic link by (Google Drive) and was sent to the students with the collaboration of the deanship of students’ affairs. To conduct this study, the analytical and descriptive method was used. The results of the study showed a statistically significant effect of digital marketing on the mental image of students at private universities in Jordan. Based on the study results, the researchers recommend managers and decision makers of the private universities in Jordan to enhance their interactive advertisements on the pages of others, enhance the pricing of their educational services, and make updates on their marketing channels to achieve a positive image.
A probability smoothing Bi-RRT path planning algorithm for indoor robot

Bidirectional rapidly exploring random trees (Bi-RRTs) have been widely applied in path planning and have been demonstrated to yield the optimal path for indoor robots. However, the quality of the initial solution is not guaranteed, and the convergence speed to the optimal solution is slow. To overcome these limitations, this paper proposes a novel probability-smoothing Bi-RRT (PSBi-RRT) algorithm for path planning. Specifically, the kinematic model is established, and the posture of the indoor robot is estimated. Thereafter, a target biasing strategy is introduced to select sampling points in the sampling region. In addition, a node correction mechanism is applied to correct node coordinates to reduce the collision probability in cluttered environments. Finally, two trees are connected using the direct connection method. The θ-cut mechanism is designed to remove the redundancy points, and the remaining nodes are connected using a triangular inequality-based RRT to ensure that the final path is optimal. Furthermore, the proposed method benefits from the properties of Bi-RRT, which offers low-cost solutions with fewer iterations compared with Bi-RRT alone. Different simulations were performed to demonstrate the significance of PSBi-RRT in comparison with Bi-RRT, InformedBi-RRT*, and QuickBi-RRT*.
The Impact of Intellectual Capital on Competitive Capabilities: Evidence from Firms Listed in ASE

This study aims at examining the impact of intellectual capital on achieving competitive capabilities. A qualitative method based on a questionnaire was used in this study for data collection and sample selection. Data were primarily gathered through self-reported questionnaires creating by Google Forms which were distributed to a purposive sample of senior managers via email. Structural equation modeling (SEM) was used to test hypotheses. The study results showed that the highest impact on competitive capabilities was for structural capital, followed by intellectual capital, and finally the lowest impact was for human capital. In light of the findings of the study, researchers recommend paying attention to intellectual capital by paying attention to scientific competencies and preserving them by all means, providing the appropriate atmosphere to support talents and providing opportunities for ideas that are put forward by workers and put into practice.
Cognitive Systems for Dark Web Cyber Delinquent Association Malignant Data Crawling

The limits of user visibility have been exceeded by the internet. The “dark web” or “dark net” refers to certain unknown portions of the web that cannot be found using standard search methods. A number of automated techniques are being explored to extract for crawling the concealed data. All users can freely interact on the surface web. Account credentials may be found on the dark web (DW), a hub for anonymized data, is a haven for terrorists and cybercriminals to promote their message and engage in illegal commerce. Officials in covert security and cyberpolicing are always trying to track down delinquent chains or hints. To find DW delinquents, the exercise may take 6–12 years. The suggested effort gives specialists an outline for building the automated engine for crawling all hazardous information from associated sources as well as data from DW crawling and shadow economy area scenarios.
An Applied Type-3 Fuzzy Logic System: Practical Matlab Simulink and M-Files for Robotic, Control, and Modeling Applications

In this paper, the main concepts of interval type-2 (IT2), generalized type-2 (GT2), and interval type-3 (IT3) fuzzy logic systems (FLSs) are mathematically and graphically studied. In representation approaches of fuzzy sets (FSs), the main differences between IT2, GT2, and IT3 fuzzy sets were investigated. For the first time, the simple Matlab Simulink and M-files by illustrative examples and symmetrical FSs are presented for the practical use of IT3-FLSs. The computations were simplified for the practical use of IT3-FLSs. By the use of various examples, such as online identification, offline time series modeling, and a robotic control system, the design of IT3-FLSs is elaborated. The required derivative equations are also presented to design the adaptation laws for the rule parameters easily in other learning schemes. Some simulation examples show that the designed M-files and Simulink work well and result in a good performance.
SSDA-YOLO: Semi-supervised domain adaptive YOLO for cross-domain object detection

Domain adaptive object detection (DAOD) aims to alleviate transfer performance degradation caused by the cross-domain discrepancy. However, most existing DAOD methods are dominated by outdated and computationally intensive two-stage Faster R-CNN, which is not the first choice for industrial applications. In this paper, we propose a novel semi-supervised domain adaptive YOLO (SSDA-YOLO) based method to improve cross-domain detection performance by integrating the compact one-stage stronger detector YOLOv5 with domain adaptation. Specifically, we adapt the knowledge distillation framework with the Mean Teacher model to assist the student model in obtaining instance-level features of the unlabeled target domain. We also utilize the scene style transfer to cross-generate pseudo images in different domains for remedying image-level differences. In addition, an intuitive consistency loss is proposed to further align cross-domain predictions. We evaluate SSDA-YOLO on public benchmarks including PascalVOC, Clipart1k, Cityscapes, and Foggy Cityscapes. Moreover, to verify its generalization, we conduct experiments on yawning detection datasets collected from various real classrooms. The results show considerable improvements of our method in these DAOD tasks, which reveals both the effectiveness of proposed adaptive modules and the urgency of applying more advanced detectors in DAOD. Our code is available on https://github.com/hnuzhy/SSDA-YOLO.