Reviewerly/exHarmony huggingface dataset:
Shoal Target Recognition Based on Sample Data Enhancement and Target Detection

For shoal targets (boats, cars, people, etc.), the data is scarce and fuzzy, and it is difficult to detect and identify. A data enhancement technology based on generative adversarial network and target detection method based on multi-scale feature fusion and knowledge distillation are proposed. First, in the absence of paired data, it is not necessary to establish a one-to-one mapping between training data, and learn the method CycleGAN from the source data domain X to the target data domain Y, so as to realize the migration between the source domain and the target domain. So as to obtain shoal target images in different environments, construct shoal target datasets of different categories, and solve the problem of scarcity and blur of shoal target data. Then, by studying the use of atrous convolution to replace the general convolution kernel, the small target detection performance is improved and the spatial feature pyramid pooling structure based on the attention mechanism is used to realize the fusion of local features and global features, enrich the feature information of the target itself, and suppress the influence of environmental factors such as illumination on target detection improves the detection performance of the model in dark night scenes, so as to achieve target detection and recognition.
Image Super-Resolution via Deep Dictionary Learning

The method of image super-resolution reconstruction through a dictionary usually only uses a single-layer dictionary, which not only fails to extract the deep features of the image, but also the trained dictionary may be relatively large. This paper proposes a new deep dictionary learning model. First, after preprocessing the images of the training set, the dictionary is trained by the deep dictionary learning method, and the super-resolution reconstruction is performed by adjusting the anchored neighborhood regression method. The proposed algorithm is compared with several classical algorithms on the Set5 data set and Set14 data set. The visualization and quantification results show that the proposed algorithm has a good improvement in PSNR and SSIM compared with the traditional super-resolution algorithm, and effectively reduces the dictionary size and saves reconstruction time.
Multi-semantic Fusion Model For Generalized Zero-Shot Skeleton-Based Action Recognition

Generalized zero-shot skeleton-based action recognition (GZ-SSAR) is a new challenging problem in computer vision community, which requires models to recognize actions without any training samples. Previous studies only utilize the action labels of verb phrases as the semantic prototypes for learning the mapping from skeleton based actions to a shared semantic space. However, the limited semantic information of action labels restricts the generalization ability of skeleton features for recognizing unseen actions. In order to solve this dilemma, we propose a multi-semantic fusion (MSF) model for improving the performance of GZSSAR, where two kinds of class-level textual descriptions (i.e., action descriptions and motion descriptions), are collected as auxiliary semantic information to enhance the learning efficacy of generalizable skeleton features. Specially, a pre-trained language encoder takes the action descriptions, motion descriptions and original class labels as inputs to obtain rich semantic features for each action class, while a skeleton encoder is implemented to extract skeleton features. Then, a variational autoencoder (VAE) based generative module is performed to learn a cross-modal alignment between skeleton and semantic features. Finally, a classification module is built to recognize the action categories of input samples, where a seen-unseen classification gate is adopted to predict whether the sample comes from seen action classes or not in GZSSAR. The superior performance compared with previous models validates the effectiveness of the proposed MSF model on GZSSAR.
GLM: A Model Based on Global-Local Joint Learning for Emotion Recognition from Gaits Using Dual-Stream Network

Gait is a distinctive human feature that can be recognized from a distance and has been widely utilized in the field of emotion recognition. In this study, we propose a novel dual-stream model (GLM) for gait emotion recognition that combines the strengths of global and local features. We extract skeleton point gait data from walking videos and process them into suitable inputs for two channels of feature extraction networks, which respectively capture global and local characteristics. To enhance the features and improve recognition accuracy, we further introduce an attention-based feature fusion module. Through experiments on benchmark datasets, our proposed model achieves high accuracy in recognizing emotions from gait data.
The Impact of Internal Marketing Practices on Continuous Commitment in Jordanian Telecommunications Companies

The research is designed to test the impact of internal marketing practices on continuous commitment. The study population consisted of customers of Jordanian telecommunications companies. Structural equation modeling (PLS-SEM) to analyze the hypotheses. The study showed an effect of internal marketing practices on continuous commitment. The researchers recommend management to apply internal marketing practices that lead to employee adherence and increase their desire to stay in the workplace.
Talent Management Practices and Corporate Entrepreneurship: An Evidence from Emerging Market

This research explores the impact of talent management on corporate entrepreneurship in emerging markets, with Jordan as an example. The empirical research data was collected from 454 managers in entrepreneurial organizations in various sectors of the Jordanian economy with an 86.97% response rate of the total distributor. The research applied structural equation modeling as a statistical approach to analysing the primary research data. The results that talent management practices have been shown to have a significant impact on corporate entrepreneurship. Therefore, a set of recommendations were provided to managers of entrepreneurial organizations.
Video-Based Person Re-Identification with Long Short-Term Representation Learning

Video-based person Re-Identification (V-ReID) aims to retrieve specific persons from raw videos captured by non-overlapped cameras. As a fundamental task, it spreads many multimedia and computer vision applications. However, due to the variations of persons and scenes, there are still many obstacles that must be overcome for high performance. In this work, we notice that both the long-term and short-term information of persons are important for robust video representations. Thus, we propose a novel deep learning framework named Long Short-Term Representation Learning (LSTRL) for effective V-ReID. More specifically, to extract long-term representations, we propose a Multi-granularity Appearance Extractor (MAE), in which four granularity appearances are effectively captured across multiple frames. Meanwhile, to extract short-term representations, we propose a Bi-direction Motion Estimator (BME), in which reciprocal motion information is efficiently extracted from consecutive frames. The MAE and BME are plug-and-play and can be easily inserted into existing networks for efficient feature learning. As a result, they significantly improve the feature representation ability for V-ReID. Extensive experiments on three widely used benchmarks show that our proposed approach can deliver better performances than most state-of-the-arts.
Does Social Media Marketing Affect Marketing Performance

The study investigated the impact of social media marketing (SMM) on marketing performance (MP). The population of the study consistst of customers of auto lubricants companies in Amman, the capital city of Jordan. A convenient sample consisted of 384 customers were selected. IBM SPSS 24.0 have used to check the study hypotheses. The results found that social media has an impact on marketing performance (MP). Based on the study results a set of recommendations were set.
Research on Image Denoising Algorithm Based on Edge Enhancement Sparse Transform and Low Rank

Focused on the issue that detailed information of edge is easily lost in the process of image denoising, the image denoising algorithm based on edge enhancement sparse transform and low rank was proposed. Firstly, Canny algorithm was used to gain the image boundary to obtain the edge matrix, and secondly, the Non-subsampled contourlet transform (NSCT) was used to gain the multi-directional high-frequency sub-band map, and then the edge matrix was used to locate the edge position in the high-frequency sub-map. The edge sub-band coefficients were enlarged to enhance the edge to form the edge-enhanced noise image. Finally, the image was divided into image patches, then the local sparsity of image patches to sparse transform and non-local self-similarities of image patches to block match were used to achieve the purpose of image denoising. The experimental results show that, compared with K-Singular Value Decomposition (K-SVD), Group Low-Rank (GLR), Sparsifying Transform Learning and Low Rank (STROLLR), and DnCNN algorithms under different levels of noise, the proposed algorithm not only has a large improvement in the Peak Signal-to-Noise Ratio (PSNR), but also maintains clear edge details in visual effects while reducing noise.
Blind Omnidirectional Image Quality Assessment Based on Swin Transformer with Scanpath-Oriented

With the emergence of 5th generation mobile communication technology, the demand for Virtual Reality (VR) applications is on the rise worldwide. As one of the technologies related to visual content in VR, the quality evaluation of omnidirectional images has become an important issue. Inspired by the transformer, we propose a novel blind omnidirectional image quality assessment method. Firstly, we predict the path that the human eye follows when viewing omnidirectional images through headsets, and extract the area with the longest gaze duration on the path as the viewport. Then, to consider the intrinsic structural features of each pixel within each viewport, we use the Swin Transformer to extract viewport features. Finally, to establish a general scene perception and accurately evaluate immersive experiences, we construct a spatial viewport map for the entire perceptual scene. The graph structure performs reasoning on the overall relationship based on the spatial perception path. Experimental results demonstrate that our proposed model outperforms the current state-of-the-art Image Quality Assessment metrics, as evidenced by its superior results on two public databases.
Does Entrepreneurial Orientation Matter in Corporate Ambidexterity: A Study of the Banking Sector in Jordan

This manuscript investigates the impact of entrepreneurial orientation on corporate ambidexterity. The research relied on primary data gathered from 374 responses from commercial bank managers in Jordan. Structured equation modeling (SEM) was used to examine data and test hypotheses. According to the findings, the dimensions of entrepreneurial orientation, which were innovativeness, risk-taking, and proactiveness, positively impacted the corporate ambidexterity of commercial banks in Jordan. Hence, the manuscript provided a set of recommendations for bank managers that include encouraging a culture that values innovation and supports it on a large scale, including building an internal climate in which workers feel free to put forward and discuss new ideas, as well as experiment with modern methods to deal with the status quo.
Impact of Artificial Intelligence Technologies on Marketing Performance

This research aims to test the impact of artificial intelligence technologies on marketing performance. Customers of commercial banks in Jordan are the study population. A convenience samples was used to collect the study data.. The final sample of the study consisted of (401) answers. Statistical package (SPSS), moreover to the (AMOS) program used to test hypotheses. The results showed an impact of artificial intelligence on the marketing performance. The study recommended managers to promote the use of artificial intelligence technologies, which helps and facilitates the completion of services provided to customers, as the use of these technologies increases customer satisfaction with the performance of banks.
The Impact of Digital Marketing Tools on Customer Loyalty of Jordanian Islamic Banks

This study examine the impact of digital marketing tools (DMT) on the customer loyalty (CL) of Jordanian Islamic banks. (Google) forms was used by the researchers to distribute a sample of (370) customers of Jordanian Islamic banks. The quantitative approach was used in this study and partial least squares structural equation modeling (PLS-SEM) was used to test hypotheses. The study found an impact of digital marketing (DM) on customer loyalty in Jordanian Islamic banks. In the light of study result, the study recommends to strengthening the social media in Jordanian Islamic banks with interactive and instant chat tools to promote their products and services and communicate with their customers.
Human Safety and Security Tracing Blockchain

As the world continues to embrace digital transformation, ensuring human safety and security in an increasingly interconnected society remains a critical concern. Bahrain, a progressive nation at the forefront of technological advancements, recognizes the potential of blockchain technology to address these challenges effectively. This abstract explores the role of blockchain in enhancing human safety and security through tracing mechanisms implemented in Bahrain. Blockchain, a decentralized and immutable ledger, provides a transparent and tamper-resistant platform for data management and secure transactions. Bahrain has implemented innovative tracing solutions to improve human safety and security. Blockchain is used in vital sectors such as healthcare, supply chain, and identity management to create a robust ecosystem that fosters trust, accountability, and efficiency. Blockchain-enabled tracing systems have revolutionized patient data management in the healthcare sector, ensuring secure access to patient information, reduce errors, improve treatment accuracy, and safeguard patient confidentiality. These systems also help monitor and protect public health. In supply chain management, blockchain technology has empowered Bahrain to establish end-to-end traceability, combating counterfeiting, fraud, and ensuring the safety of goods and commodities. By implementing blockchain-based systems, stakeholders gain real-time visibility into the movement of products, verifying their authenticity, origin, and quality. This enhances consumer trust, prevents illicit activities, and promotes ethical business practices. Blockchain has enabled secure identity management systems, protecting individuals' personal information and preventing identity theft. Bahrain's blockchain-powered identity solutions provide a decentralized approach, giving individuals control over their digital identities while maintaining privacy and security. This ensures seamless and trusted interactions in the digital realm while minimizing the risk of fraud and unauthorized access. Blockchain technology is being used to improve safety and security in sectors such as healthcare, supply chain and identity management, creating a safer and more secure environment for citizens. It ensures transparency, accountability and integrity, paving the way for a digitally empowered future where individuals can thrive with confidence.
The Challenges Face E-Commerce Business by Using ChatGPT

The study aimed to investigate ChatGPT and its powerful artificial intelligence program in natural language processing which considered as one of its most impressive capabilities is generating human-like output after analyzing human inputs, starting from language translation and text summarization to search and automation. The results indicated that the application works on responding quickly to customer inquiries and requests, the more satisfied they are and the more likely they will choose the brand again and again. ChatGPT has a much higher response rate than some traditional marketing tools with a rate of interaction with customers, and it is always easier and faster to respond to customers in their mother tongues, and for this reason the developers of ChatGPT worked on training the application in several languages in order to be able to respond to users in their mother tongues and provide A more personalized and efficient customer experience.
The Role of Digital Promotion in Enhancing the Image of Jordanian Islamic Banks

The aim of the study is to identify the role of digital promotion in enhancing the image of Jordanian Islamic banks. The study population consisted of clients of the Islamic International Arab Bank., with the sample size determined by simple random sampling. Structural equation modeling (PLS-SEM) was used to analyze the hypotheses. The study results showed an impact of digital promotion on the mental image. Based on the aforementioned findings of the study, the study recommends the need to increase the interest of the Islamic Arab Bank in digital promotion as an important element in its marketing operations, and relies on it a lot in its marketing strategies.
Impact of Social Customer Relationship Management on Sustainable Competitive Advantage of Commercial Banks in Jordan

The study aimed to test the impact of social customer relationship management on achieving sustainable competitive advantage. The research population is all customers of Jordanian commercial banks. A Convenience sample was taken. SPSS and AMOS software was used to test hypotheses. The results showed an impact of social customer relationship management on sustainable competitive advantage of the commercial banks in Jordan. The researcher suggested that a unit be established to handle complaints in order to speed up service related to services, identify problems and diagnose them, identify areas of weakness in the bank’s operations.
Decentralized Oracle with Mechanism for Reputation and Incentive

Blockchain was originally designed as a peer-to-peer network to handle cryptocurrency transactions. And with the advent of smart contracts, more and more applications are using Blockchain as an infrastructure. The smart contract is a mechanism for autonomously executing code based on predetermined criteria without the need for any other third-party interaction. However, Blockchain has a non-negligible drawback in that it cannot use the off-chain data. This is why we need trusted entities called oracle, which is in charge of writing external data into Blockchain. To avoid security issues such as malicious oracles writing incorrect data on Blockchain, this paper introduced a decentralized oracle based on reputation and incentive mechanism. And we also ran a simulation-based experiment to verify the feasibility and trustworthiness of our schema. The findings revealed that our system is capable of effectively defending against attacks and penalizing malicious oracles.
The Impact of Emotional Intelligence on Marketing Performance

The study aims to know the Impact of Emotional Intelligence (EI) on Marketing Performance (MP). The population of this study consisted of customers in commercial banks in Jordan. A convenience method was conducted to get the study data to follow the comprehensive survey method. Structural equation modelling (SEM) was applied to check the hypotheses of this study. It was found that there is a statistically significant effect for the emotional intelligence on marketing performance in commercial banks in Jordan. According to the result of this study, the researcher recommends to all bank officials and decision makers in all commercial banks in Jordan to give great attention from bank administrations to deal seriously with customer suggestions and complaints.
Impact of Digital Marketing Tools on Customer Interactions in Jordanian Telecommunications Companies

The objective of the study is to test the impact of digital marketing tools on customer interaction. The study population consisted of customers of Jordanian Telecommunications Companies. A convenience sample of (258) customers were chosen. The results of the study showed an impact of digital marketing tools on customer interaction. The study recommended following up with the latest methods and technologies that appear in digital marketing, especially in communication networks and Internet advertisements, to continue to achieve more interaction with customers.
The Impact of Transformational Leadership on Quality of Work-Life

This research aims to examine the impact of transformational leadership style on the quality of work life. It focused on telecommunications companies in Jordan. A random sample of (1066) was drown. Structural equation modeling (SEM) was used to examine hypotheses. The statistics showed that transformational leadership affects the quality of work life. The researchers recommend that there be a appropriate system to promotion and career advancement in the Jordanian telecommunications companies to ensure the achievement of its goals and the aspirations of its employees.
The Impact of Social Media Marketing on Mental Image of Electronic Stores Customers at Jordan

The present study tries to test the impact of marketing throughout social media on the mental image of electronic stores. The population of this study consisted of customers of electronic stores in Jordan, which depends on social media in marketing their products. Structural equation modeling (PLS-SEM) was used to test hypotheses. The statistical analysis results found an effect of marketing via social media on mental image. According to the results reached recommends to improve the mental image of electronic stores and activate marketing via social media (SM) to enhance the organizational reputation and improve the mental image.
Efficient breast cancer detection using novel intensity partitioning-based clustering algorithm and multi-dimensional LSTM cyclic neural network

Recently, early detection of breast cancer is significant to reduce the mortality rate, especially in women. Hence, the study aims to classify breast cancer from digital database for screening mammography (DDSM) dataset using partition and intensity based segmentation algorithm and modified convolutional neural network-long short-term memory (CNN-LSTM) classifier. Initially, pre-processing is performed using Gaussian filtering by taking the mammogram image. Then, it is segmented using a novel intensity partitioning-based clustering algorithm (IPCA). Further, feature extraction is performed and finally, classification is implemented using a novel multi-dimensional LSTM cyclic neural network (MLSTM-CNN). The analysis is performed to evaluate the efficiency of the proposed system and the outcomes explored its efficacy in breast cancer detection.