Reviewerly/exHarmony huggingface dataset:
Cloud technologies of augmented reality as a means of supporting educational and research activities in chemistry for 11th grade students

Chemistry educational and research activities must be planned in a way that makes extensive use of experimentation. The implementation of chemistry experiments in the classroom is fraught with organizational and logistical challenges, and more recently, the necessity for online learning has been added. The article explains the current state of augmented reality (AR) technology use in contemporary chemical education and the potential for using augmented reality technology to enhance students' chemistry research projects. To complete the research tasks, augmented reality cloud technologies (A-Frame and AR.js) were used. The developed tool is a virtual chemical laboratory that incorporates augmented reality technologies (AR-based virtual chemical laboratory): individual markers correspond to reagents, and a pair of marker combinations activates the playback of a video showing the chemical interaction between the associated pair of reagents. The article describes the development of augmented reality software to support the research activities of 11th grade chemistry students in the form of an AR-based virtual chemical laboratory, as well as its implementation in the teaching and learning process.
ISTVT: Interpretable Spatial-Temporal Video Transformer for Deepfake Detection

With the rapid development of Deepfake synthesis technology, our information security and personal privacy have been severely threatened in recent years. To achieve a robust Deepfake detection, researchers attempt to exploit the joint spatial-temporal information in the videos, like using recurrent networks and 3D convolutional networks. However, these spatial-temporal models remain room to improve. Another general challenge for spatial-temporal models is that people do not clearly understand what these spatial-temporal models really learn. To address these two challenges, in this paper, we propose an Interpretable Spatial-Temporal Video Transformer (ISTVT), which consists of a novel decomposed spatial-temporal self-attention and a self-subtract mechanism to capture spatial artifacts and temporal inconsistency for robust Deepfake detection. Thanks to this decomposition, we propose to interpret ISTVT by visualizing the discriminative regions for both spatial and temporal dimensions via the relevance (the pixel-wise importance on the input) propagation algorithm. We conduct extensive experiments on large-scale datasets, including FaceForensics++, FaceShifter, DeeperForensics, Celeb-DF, and DFDC datasets. Our strong performance of intra-dataset and cross-dataset Deepfake detection demonstrates the effectiveness and robustness of our method, and our visualization-based interpretability offers people insights into our model.
Convolutional Neural Network-Based Personalized Program Recommendation System for Smart Television Users

The smart home culture is rapidly increasing across the globe and driving smart home users toward utilizing smart appliances. Smart television (TV) is one such appliance that is embedded with smart technology. The users of smart TV have their interests in the programs. However, automatic recommendation of programs for user-to-user is still under-researched. Several papers discussed recommendation systems, but those are related to different applications. Even though there are some works on recommending programs to smart TV users (single-user and multi-user), they did not discuss the smart TV camera module to capture and validate the user image for recommending personalized programs. Hence, this paper proposes a convolutional neural network (CNN)-based personalized program recommendation system for smart TV users. To implement this proposed approach, the CNN algorithm is trained on the datasets ‘CelebFaces Attribute Dataset’ and ‘Labeled Faces in the Wild-People’ for feature extraction and to detect a human face. The trained CNN model is applied to the user image captured by using the smart TV camera module. Further, the captured image is matched with the user image in the ‘synthetic dataset’. Based on this matching, the hybrid filtering technique is proposed and applied; thereby the recommendation of the respective program is done. The proposed CNN algorithm has achieved approximately 95% training performance. Besides, the performance of hybrid filtering is approximately 85% from the single-user perspective and approximately 81% from the multi-user perspective. From this, it is observed that hybrid filtering outperformed conventional content-based filtering and collaborative filtering techniques.
A hybrid filter-wrapper feature selection using Fuzzy KNN based on Bonferroni mean for medical datasets classification: A COVID-19 case study

Several feature selection methods have been developed to extract the optimal features from a dataset in medical datasets classification. Creating an efficient technique has become a challenge because of the high dimensions, noise, and redundant information. In this paper, we propose a hybrid filter-wrapper approach for feature selection. An ensemble of filter methods, ReliefF and Fuzzy Entropy (RFE) is developed, and the union of top-n features from each method are considered. The Equilibrium Optimizer (EO) technique is combined with Opposition Based Learning (OBL), Cauchy Mutation operator and a novel search strategy to enhance its capabilities. The OBL strategy improves the diversity of the population in the search space. The Cauchy Mutation operator enhances its ability to evade the local optima during the search, and the novel search strategy improves the exploration capability of the algorithm. This enhanced form of EO is integrated with eight time-varying S and V-shaped transfer functions to convert the solutions into binary form, Binary Enhanced Equilibrium Optimizer (BEE). The features from the ensemble are given as input to the Binary Enhanced Equilibrium Optimizer to extract the essential features. Fuzzy KNN based on Bonferroni mean is used as the learning algorithm. Twenty-two benchmark datasets and four microarray datasets are used to test the algorithm’s efficiency. This method is also applied to a COVID-19 case study. The results demonstrate the superiority of the proposed approach, RFE-BEE, among other methods in terms of fitness values, accuracy, precision, sensitivity, and F-measure, among several other state-of-the-art algorithms. RFE-BEE can be applied to various biomedical, computer vision and engineering applications such as electromyography pattern recognition, COVID-19 diagnosis, face recognition and fault diagnosis.
PSO-ELPM: PSO with elite learning, enhanced parameter updating, and exponential mutation operator

Particle Swarm Optimization (PSO) has been widely used to solve optimization problems. Although a large number of PSO variants have been proposed so far, they suffer from the shortcomings of the original PSO. This paper proposes PSO with Elite Learning, enhanced Parameter updating, and exponential Mutation operator (PSO-ELPM) to balance the exploration and exploitation capabilities of PSO. In this algorithm, the best-performing particles in the population, known as the elites, are used as exemplars to guide the optimization process. The elitism scheme helps to discover valuable knowledge about the solution space. The adopted elites are also used to compute self-cognition coefficients of particles. Additionally, the inverse of the cube root function is applied to ensure a smooth distribution of weight among the elites. The final improvement is to apply an exponential mutation operator, which determines the mutation probability per particle based on the current iteration and its history. The comparisons among PSO-ELPM and 10 state-of-the-art PSO variants on the CEC 2017 benchmark functions reveal that the proposed algorithm yields higher accuracy with acceptable time complexity. According to the Wilcoxon rank sum test, PSO-ELPM at-least outperforms the competitive algorithms on six and seven functions in 30 and 50-dimensional problems, respectively.
Dense Voxel Fusion for 3D Object Detection

Camera and LiDAR sensor modalities provide complementary appearance and geometric information useful for detecting 3D objects for autonomous vehicle applications. However, current end-to-end fusion methods are challenging to train and underperform state-of-the-art LiDAR-only detectors. Sequential fusion methods suffer from a limited number of pixel and point correspondences due to point cloud sparsity, or their performance is strictly capped by the detections of one of the modalities. Our proposed solution, Dense Voxel Fusion (DVF) is a sequential fusion method that generates multi-scale dense voxel feature representations, improving expressiveness in low point density regions. To enhance multi-modal learning, we train directly with projected ground truth 3D bounding box labels, avoiding noisy, detector-specific 2D predictions. Both DVF and the multi-modal training approach can be applied to any voxel-based LiDAR backbone. DVF ranks 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> among published fusion methods on KITTI’s 3D car detection benchmark without introducing additional trainable parameters, nor requiring stereo images or dense depth labels. In addition, DVF significantly improves 3D vehicle detection performance of voxel-based methods on the Waymo Open Dataset.
The Impact of Social Media on Purchase Intention at Jordanian Women Clothing Sector

The aim of the study was to examine the impact of social media on purchase intention. Therefore, it focused on Jordan-based companies operating in the women clothing sector. Data were primarily gathered through self-reported questionnaires created by Google Forms which were distributed to a random sample of (384) responses. In total, (370) responses were received including (6) invalid to statistical analysis due to uncompleted or inaccurate. Hence, the final sample contained (364) responses suitable to analysis requirements that were formed a response rate of (94.80%). structural equation modeling (SEM) was conducted to test hypotheses. The results showed that social media dimensions had a positive impact on purchase intention. Moreover, the results indicated that the highest impact was for Sharing Contents. Based on the previous results, the current study recommends organizations in the clothing sector to pay attention to social media and take advantage of it in advertising and marketing for the women’s clothing sector in Jordan, due to the impact of these sites on the purchase intention of consumers, emphasizing the importance of clarifying all information for products.
Detection of oral squamous cell carcinoma in clinical photographs using a vision transformer

Oral squamous cell carcinoma (OSCC) is amongst the most common malignancies, with an estimated incidence of 377,000 and 177,000 deaths worldwide. The interval between the onset of symptoms and the start of adequate treatment is directly related to tumor stage and 5-year-survival rates of patients. Early detection is therefore crucial for efficient cancer therapy. This study aims to detect OSCC on clinical photographs (CP) automatically. 1406 CP(s) were manually annotated and labeled as a reference. A deep-learning approach based on Swin-Transformer was trained and validated on 1265 CP(s). Subsequently, the trained algorithm was applied to a test set consisting of 141 CP(s). The classification accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved a classification accuracy of 0.986 and an AUC of 0.99 for classifying OSCC on clinical photographs. Deep learning-based assistance of clinicians may raise the rate of early detection of oral cancer and hence the survival rate and quality of life of patients.
Return spillover analysis across central bank digital currency attention and cryptocurrency markets

Many central banks have now developed their digital currencies in response to the challenges posed by the proliferation of decentralised digital cryptocurrencies. However, little is known about the effects of the introduction of central bank digital currencies (CBDCs) on extant digital cryptocurrencies. This paper, therefore, aims to identify both the time- and frequency-domain spillover effects among cryptocurrency markets and a newly developed central bank digital currencies attention index (CBDCAI) by using two TVP-VAR-based spillover models. Our results demonstrate that CBDC attention significantly impacts cryptocurrency markets. Also, most investors in cryptocurrency markets are more likely to trade in the short term. The results of this study contribute to helping investors and investment institutions effectively avoid investment risks, reduce losses, and predict the return of some cryptocurrencies. Also help policymakers better understand the impact of markets and policies, and provide a reference for them to formulate policies.
Dynamic path planning of mobile robot based on improved simulated annealing algorithm

Dynamic path planning for mobile robots is an urgent issue that needs to be solved because of the growing use of mobile robots in daily life and industrial operations. This work focuses on avoiding moving obstacles in dynamic situations. The computational effort required by some current algorithms makes them difficult to utilize for path planning in dynamic situations whilst the computational effort required by other methods makes them simple yet prone to local minima. In this paper, an improved simulated annealing (SA) algorithm for dynamic path planning is proposed. To reduce its computational effort, the initial path selection method and deletion operation are introduced. Simulation results show the improved SA algorithm outperforms other algorithms and provides optimal solutions in static and dynamic environments.
Authentic Leadership and Its Impact on Sustainable Performance: The Mediating Role of Knowledge Ability in Jordan Customs Department

The study aimed to identify the impact of authentic leadership on sustainable performance through knowledge Ability from the point of view of employees in Jordan customs department. The study population consisted of all 678 employees in the border centers of the Jordan Customs Service, where a random sample was taken proportional represented by the study population by 271 individuals, and the study used the statistical package for social sciences (SPSS), in addition to using the Amos program. The study concluded that there is a statistically significant effect of authentic leadership in sustainable performance through knowledge Ability from the point of view of employees in border centers in the Jordan Customs Department. The study recommended a set of recommendations and suggestions that contribute to maintaining the sustainability of performance and enhancing growth and development in the Jordanian Customs Department.
The Relation Between Creative Leadership and Crisis Management Among Faculty Members at Imam Abdulrahman Bin Faisal University in Light of the Corona Pandemic from the Perspective of Department Heads

The current study aimed to reveal the relation between the practice of creative leadership style and crisis management among faculty members at Imam Abdulrahman bin Faisal University (IAU), from the perspective of the heads of academic departments. A descriptive correlative approach was used by applying a questionnaire to a random sample of (100) Head of Department, during the second semester of the academic year 1442/1443. The findings revealed that faculty members practice both creative leadership and crisis management to a high degree. The study recommended holding training courses for faculty members, creating incentives systems and rewards, stimulating creative leadership among faculty members in universities, and spreading a culture of creativity.
Special Vehicle Detection from UAV Perspective via YOLO-GNS Based Deep Learning Network

At this moment, many special vehicles are engaged in illegal activities such as illegal mining, oil and gas theft, the destruction of green spaces, and illegal construction, which have serious negative impacts on the environment and the economy. The illegal activities of these special vehicles are becoming more and more rampant because of the limited number of inspectors and the high cost required for surveillance. The development of drone remote sensing is playing an important role in allowing efficient and intelligent monitoring of special vehicles. Due to limited onboard computing resources, special vehicle object detection still faces challenges in practical applications. In order to achieve the balance between detection accuracy and computational cost, we propose a novel algorithm named YOLO-GNS for special vehicle detection from the UAV perspective. Firstly, the Single Stage Headless (SSH) context structure is introduced to improve the feature extraction and facilitate the detection of small or obscured objects. Meanwhile, the computational cost of the algorithm is reduced in view of GhostNet by replacing the complex convolution with a linear transform by simple operation. To illustrate the performance of the algorithm, thousands of images are dedicated to sculpting in a variety of scenes and weather, each with a UAV view of special vehicles. Quantitative and comparative experiments have also been performed. Compared to other derivatives, the algorithm shows a 4.4% increase in average detection accuracy and a 1.6 increase in detection frame rate. These improvements are considered to be useful for UAV applications, especially for special vehicle detection in a variety of scenarios.
Deep learning based object detection for resource constrained devices: Systematic review, future trends and challenges ahead

Deep learning models are widely being employed for object detection due to their high performance. However, the majority of applications that require object detection are functioning on resource-constrained edge devices. In the present era, there is a need for deep learning-based object detectors that are lightweight and perform well on these constrained edge devices. Objective: The research aims to identify current trends in resource-constrained applications for deep learning-based object detectors in terms of the technique used to create the model, the type of input image involved, the type of device used, and the type of application addressed by the model. Method: To achieve the objective of our research, a systematic literature review was carried out that yielded 167 studies. The models or techniques employed in the studies were grouped to better understand the research problem at hand. This review carefully reports every decision and provides many visualizations of the final studies in order to draw clear conclusions. Conclusion: The conclusion discussed the gaps, possibilities, and future perspectives discovered throughout the research process, implying that this field of study has grown profoundly in the last decade.
A comparative study on consensus mechanism with security threats and future scopes: Blockchain

Recently, blockchain has emerged as a peer-to-peer (P2P) immutable distributed ledger technology-based network, and its consensus mechanism is playing an important role in managing decentralized data. The use of the consensus process for open blockchains, on the other hand, has revitalized the industry and spurred new architectures. As part of this research, we have analyzed and categorized a wide range of consensus mechanisms in order to contribute to the process of developing blockchain applications and determining the extent of their influence. Critical technical considerations regarding performance, scalability, and other quality aspects that a blockchain-based system must possess may be assisted by this scientific categorization and comprehensive comparison of consensus algorithms. In this paper, we have mainly depicted: (i) proof-of-work (PoW) along with its enhancement rules like greedy heaviest-observed sub-tree (GHOST) and Bitcoin NG (next generation); (ii) proof-of-Stake (PoS) along with chain-based PoS, committee-based PoS, byzantine fault tolerance (BFT) based extended proof-of-stake (EPoS), and delegated proof of stake (DPoS), i.e. energy-efficient alternative to PoW; (iii) BFT consensus to handle crash fault and Byzantine fault; and (iv) hybrid protocols, which are a combination of the best features of PoW, PoS, and BFT consensus mechanism. To evaluate the performance, scalability, security, and design properties of the system, we have considered five components of consensus algorithms.
Semi-Supervised Specific Emitter Identification Method Using Metric-Adversarial Training

Specific emitter identification (SEI) plays an increasingly crucial and potential role in both military and civilian scenarios. It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. Deep learning (DL) and deep neural networks (DNNs) can learn the hidden features of data and build the classifier automatically for decision making, which have been widely used in the SEI research. Considering the insufficiently labeled training samples and large-unlabeled training samples, the semi-supervised learning-based SEI (SS-SEI) methods have been proposed. However, there are few SS-SEI methods focusing on extracting the discriminative and generalized semantic features of radio signals. In this article, we propose an SS-SEI method using metric-adversarial training (MAT). Specifically, pseudo labels are innovatively introduced into metric learning to enable semi-supervised metric learning (SSML), and an objective function alternatively regularized by SSML and virtual adversarial training (VAT) is designed to extract discriminative and generalized semantic features of radio signals. The proposed MAT-based SS-SEI method is evaluated on an open-source large-scale real-world automatic-dependent surveillance–broadcast (ADS-B) data set and Wi-Fi data set and is compared with the state-of-the-art methods. The simulation results show that the proposed method achieves better identification performance than existing state-of-the-art methods. Specifically, when the ratio of the number of labeled training samples to the number of all training samples is 10%, the identification accuracy is 84.80% under the ADS-B data set and 80.70% under the Wi-Fi data set. Our code can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lovelymimola/MAT-based-SS-SEI</uri> .
Chatbots or me? Consumers’ switching between human agents and conversational agents

With the application of artificial intelligence (AI) technology in organizational frontlines, customers' service experiences have begun to shift from interactions with service personnel to those with technology. However, only a few studies have explored customers' behavioral switch from human-mediated services to technology-mediated ones with regard to the application of AI in frontline services. Based on the push–pull mooring framework, this study explored the determinants that affect consumers’ behavioral switch from using human agents to using AI-based conversational agents. Data collected from 441 users of banking services were analyzed using structural equation modeling. The findings reveal that both push effects—namely, low empathy and low adaptability—and pull effects, including anytime/anywhere connectivity, association, visibility, and personalization, have positive influences on switching behavior. Finally, in addition to having a direct influence on switching behavior, frequency of service use positively moderated the relationship between pull effects and switching behavior.