Reviewerly/exHarmony huggingface dataset:
Customer Awareness Towards Green Marketing Mix in 5-Star Hotels in Jordan

This descriptive study determines the level of customers’ and visitors’ awareness regarding the green marketing mix activities and applications in green five-stars hotels in Jordan. The study population consisted of the visitors of 16 green labeled five-stars hotels in Jordanian cities and tourism locations (Amman, Dead Sea, Petra and Aqaba). Non-random (purposive) sample was used to collect data and a structured questionnaire was distributed to (460) hotel customers, (412) of which were retrieved and (367) were valid for analysis with a response rate of (79.78%). A set of statistical methods were used through the (SPSS v23) program and results showed only two of the dimensions of green marketing mix had a high level of customer awareness. These dimensions were green product and green price. The overall level of customers’ environmental concern was moderate along with the rest of the green marketing dimensions (promotion, place, process, people, and physical evidence). Based on results, it is recommended that hotel managers and marketers carefully design their business and marketing plans to truly develop credible green hotel image, avoid misrepresentation and green washing, and encourage customers to participate in protecting the environment.
KPE-YOLOv5: An Improved Small Target Detection Algorithm Based on YOLOv5

At present, the existing methods have many limitations in small target detection, such as low accuracy, a high rate of false detection, and missed detection. This paper proposes the KPE-YOLOv5 algorithm aiming to improve the ability of small target detection. The algorithm has three improvements based on the YOLOv5 algorithm. Firstly, it achieves more accurate size of anchor-boxes for small targets by K-means++ clustering technology. Secondly, the scSE (spatial and channel compression and excitation) attention module is integrated into the new algorithm to encourage the backbone network to pay greater attention to the feature information of small targets. Finally, the capability of small target feature extraction is improved by increasing the small target detection layer, which also increases the detection accuracy of small targets. We evaluate KPE-YOLOv5 on the VisDrone-2020 dataset and compare performance with YOLOv5. The results show that KPE-YOLOv5 improves the detection mAP by 5.3% and increases the P by 7%. The KPE-YOLOv5 algorithm has better detection outcome than YOLOv5 for small target detection.
What is the Metaverse and who seeks to define it? Mapping the site of social construction

The Metaverse has become a buzz-phrase among tech businesses. Facebook’s rebranding to Meta is symptomatic of this. Many firms and other actors are trying to shape visions of the Metaverse, leading to confusion about the term’s meaning. We use social construction of technology (SCOT) theory to disentangle the conflicting notions proposing that what the Metaverse is and will become relies on the collective sensemaking processes. We point out similarities and differences between various concepts presented in the public media and link them to individual actors’ monetary, political, or social motives. We describe the tensions that occur because of the conflicting interests. As the Metaverse is an emerging phenomenon, opportunities exist to reorient it toward humanist values rather than singular interests. However, the complexity of the social processes that shape the Metaverse requires a considerate approach rather than premature conclusions about the Metaverse’s characteristics. The analysis presents the Metaverse as a new, continually evolving sociotechnical phenomenon, and calls for research that explores it as a dynamic, moving target.
LightingNet: An Integrated Learning Method for Low-Light Image Enhancement

Images captured in low-light environments suffer from serious degradation due to insufficient light, leading to the performance decline of industrial and civilian devices. To address the problems of noise, chromatic aberration, and detail distortion for enhancing low-light images using existing enhancement methods, this paper proposes an integrated learning approach (LightingNet) for low-light image enhancement. The LightingNet consists of two core components: 1) the complementary learning sub-network and 2) the vision transformer (VIT) low-light enhancement sub-network. VIT low-light enhancement sub-network is designed to learn and fit the current data to provide local high-level features through a full-scale architecture, and the complementary learning sub-network is utilized to provide global fine-tuned features through learning transfer. Extensive experiments confirm the effectiveness of the proposed LightingNet.
Time-series anomaly detection with stacked Transformer representations and 1D convolutional network

Time-series anomaly detection is a task of detecting data that do not follow normal data distribution among continuously collected data. It is used for system maintenance in various industries; hence, studies on time-series anomaly detection are being carried out actively. Most of the methodologies are based on Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) to model the temporal structure of time-series data. In this study, we propose an unsupervised prediction-based time-series anomaly detection methodology using Transformer, which shows superior performance to LSTM and CNN in learning dynamic patterns of sequential data through a self-attention mechanism. The prediction model consists of an encoder comprising multiple Transformer encoder layers and a decoder that includes a 1D convolution layer. The output representation of each Transformer layer is accumulated in the encoder to obtain a representation with multi-level, rich information. The decoder fuses this representation through a 1d convolution operation. Consequently, the model can perform predictions considering both the global trend and local variability of the input time-series. The anomaly score is defined as the difference between the predicted and the actual value at the corresponding timestamp, assuming that the trained model produces the predictions that follow the normal data distribution. Finally, the data with an anomaly score above the threshold is detected as an anomaly. Experiments on the benchmark datasets show that the proposed method has performance superior to those of the baselines.
Reviewing Federated Machine Learning and Its Use in Diseases Prediction

Machine learning (ML) has succeeded in improving our daily routines by enabling automation and improved decision making in a variety of industries such as healthcare, finance, and transportation, resulting in increased efficiency and production. However, the development and widespread use of this technology has been significantly hampered by concerns about data privacy, confidentiality, and sensitivity, particularly in healthcare and finance. The “data hunger” of ML describes how additional data can increase performance and accuracy, which is why this question arises. Federated learning (FL) has emerged as a technology that helps solve the privacy problem by eliminating the need to send data to a primary server and collect it where it is processed and the model is trained. To maintain privacy and improve model performance, FL shares parameters rather than data during training, in contrast to the typical ML practice of sending user data during model development. Although FL is still in its infancy, there are already applications in various industries such as healthcare, finance, transportation, and others. In addition, 32% of companies have implemented or plan to implement federated learning in the next 12–24 months, according to the latest figures from KPMG, which forecasts an increase in investment in this area from USD 107 million in 2020 to USD 538 million in 2025. In this context, this article reviews federated learning, describes it technically, differentiates it from other technologies, and discusses current FL aggregation algorithms. It also discusses the use of FL in the diagnosis of cardiovascular disease, diabetes, and cancer. Finally, the problems hindering progress in this area and future strategies to overcome these limitations are discussed in detail.
Hourly stepwise forecasting for solar irradiance using integrated hybrid models CNN-LSTM-MLP combined with error correction and VMD

Accurate and reliable solar irradiance forecasting is critical for distribution planning and modern smart grid management and dispatch. However, due to the time series of solar irradiance with the nonlinearity and nonstationarity, some researches up to now are still unsatisfactory in terms of prediction accuracy and model generalization ability. Therefore, to improve the comprehensive performance of the model, a novel forecasting framework of hourly stepwise forecasting for solar irradiance using integrated hybrid models CNN-LSTM-MLP combined with error correction and variational mode decomposition (VMD) was proposed. In this paper, three different datasets were pre-processed and then the step-by-step predictions of seven different methods were completed based on the proposed procedures. Finally, comprehensive analysis results of the model indicated that the prediction scheme proposed in this paper made full use of, step-by-step prediction, VMD method, integrated hybrid model, and error correction four major advantages greatly improved the anti-interference ability of the model, so that the average performance metrics RMSE, nRMSE, MAE, and R2 of the three datasets reach to 12.53 W/m2, 6.04 %, 7.65 W/m2, and 99.79 %, respectively. Moreover, the optimal promoting percentages of the R2(PR2)indicator compared to the persistence model (Per) is by 20.17 %. It is found that the model is superior to a large number of traditional alternative approaches in terms of accuracy and robustness, which may provide a reference for comprehensive performance optimization of the model in the future.
A new technology perspective of the Metaverse: Its essence, framework and challenges

The Metaverse depicts a parallel digitalized world where virtuality and reality are fused. It has economic and social systems like those in the real world and provides intelligent services and applications. In this paper, we introduce the Metaverse from a new technology perspective, including its essence, corresponding technical framework, and potential technical challenges. Specifically, we analyze the essence of the Metaverse from its etymology and point out breakthroughs promising to be made in time, space, and contents of the Metaverse by citing Maslow's Hierarchy of Needs. Subsequently, we conclude four pillars of the Metaverse, named ubiquitous connections, space convergence, virtuality and reality interaction, and human-centered communication, and establish a corresponding technical framework. Additionally, we envision open issues and challenges of the Metaverse in the technical aspect. The work proposes a new technology perspective of the Metaverse and will provide further guidance for its technology development in the future. © 2022 Published by Elsevier Ltd.
Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehicular Edge Computing

Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the ever-growing service applications in VEC, how to effectively aggregate and flexibly schedule ubiquitous network resources for implementing diverse tasks and meeting differentiated demands from numerous vehicular users remains haunting. Toward this end, we investigate collaborative task computing and on-demand resource allocation. The collaborative computing framework in VEC is provided to support deep collaboration and intelligent management of heterogeneous resources widely distributed in vehicles, edge servers and cloud. Based on this framework, the joint optimization problem of distributed task offloading and multi-resource management is formulated with the aim to maximize the system utility by making the optimal task and resource scheduling policy, the novelty of which lies in the exploration of available vehicle resources and the consideration of service migration. In view of the dynamics, randomness and time-variant of vehicular networks, the asynchronous deep reinforcement algorithm is leveraged to find the optimal solution. Extensive simulation experiments are implemented to demonstrate the superiority of our proposed algorithm in terms of response latency compared with full offloading and random offloading.
HCoF: Hybrid Collaborative Filtering Using Social and Semantic Suggestions for Friend Recommendation

Today, people frequently communicate through interactions and exchange knowledge over the social web in various formats. Social connections have been substantially improved by the emergence of social media platforms. Massive volumes of data have been generated by the expansion of social networks, and many people use them daily. Therefore, one of the current problems is to make it easier to find the appropriate friends for a particular user. Despite collaborative filtering’s huge success, accuracy and sparsity remain significant obstacles, particularly in the social networking sector, which has experienced astounding growth and has a large number of users. Social connections have been substantially improved by the emergence of social media platforms. In this work, a social and semantic-based collaborative filtering methodology is proposed for personalized recommendations in the context of social networking. A new hybrid collaborative filtering (HCoF) approach amalgamates the social and semantic suggestions. Two classification strategies are employed to enhance the performance of the recommendation to a high rate. Initially, the incremental K-means algorithm is applied to all users, and then the KNN algorithm for new users. The mean precision of 0.503 obtained by HCoF recommendation with semantic and social information results in an effective collaborative filtering enhancement strategy for friend recommendations in social networks. The evaluation’s findings showed that the proposed approach enhances recommendation accuracy while also resolving the sparsity and cold start issues.
Blockchain Meets Metaverse and Digital Asset Management: A Comprehensive Survey

Envisioned to be the next-generation Internet, the metaverse has been attracting enormous attention from both the academia and industry. The metaverse can be viewed as a 3D immersive virtual world, where people use Augmented/Virtual Reality (AR/VR) devices to access and interact with others through digital avatars. While early versions of the metaverse exist in several Massively Multiplayer Online (MMO) games, the full-flesh metaverse is expected to be more complex and enabled by various advanced technologies. Blockchain is one of the crucial technologies that could revolutionize the metaverse to become a decentralized and democratic virtual society with its own economic and governance system. Realizing the importance of blockchain for the metaverse, our goal in this paper is to provide a comprehensive survey that clarifies the role of blockchain in the metaverse including in-depth analysis of digital asset management. To this end, we discuss how blockchain can enable the metaverse from different perspectives, ranging from user applications to virtual services and the blockchain-enabled economic system. Furthermore, we describe how blockchain can shape the metaverse from the system perspective, including various solutions for the decentralized governance system and data management. The potential of blockchain for security and privacy aspects of the metaverse infrastructure is also figured out, while a full flow of blockchain-based digital asset management for the metaverse is investigated. Finally, we discuss a wide range of open challenges of the blockchain-empowered metaverse.
Multi-level Feature Interaction and Efficient Non-Local Information Enhanced Channel Attention for image dehazing

Image dehazing is a challenging task in computer vision. Currently, most dehazing methods adopt the U-Net architecture that directly fuses the decoding layer with the corresponding scale encoding layer. These methods ignore the effective utilization of different encoding layer information and existing feature information dilute problems, resulting in suboptimal edge details and overall scene aspects of dehazed image restoration. In addition, Squeeze and Excitation (SE) channel attention is widely used in dehazing network. However, the two fully-connected layers of dimensionality reduction operation in SE will negatively affect the weight prediction of feature channels, thus reducing the performance of the dehazing network. To solve the above problems, we propose a Multi-level Feature Interaction and Non-local Information Enhanced Channel Attention (MFINEA) dehazing model. Specifically, a multi-level feature interaction module is proposed to enable the decoding layer to fuse shallow and deep feature information extracted from different encoding layers for better recovery of edge details and the overall scene. Furthermore, an efficient non-local information enhanced channel attention module is proposed to mine more effective feature channel information for the weight assignment of the feature maps. The experimental results on several challenging benchmark datasets show that our MFINEA outperforms the state-of-the-art dehazing methods.
Digital transformation of commercial banks in China: Measurement, progress and impact

Nowadays, digital transformation has become an urgent strategic task for traditional commercial banks. Due to the lack of measurement of banks’ digital transformation, whether digital transformation could enhance the competitiveness of banks is still inconclusive. In this paper, we construct an index system to measure digital transformation of banks from three dimensions: strategy transformation, business transformation and management transformation. Using data from Chinese commercial banks, this paper measures the progress of digital transformation of banks in China. The empirical results show that digital transformation can improve bank performance, reduce the negative impact of new technology entrants, and promote the exit of offline channels.
Robust multi-view non-negative matrix factorization with adaptive graph and diversity constraints

Multi-view clustering (MVC) has received extensive attention due to its efficient processing of high-dimensional data. Most of the existing multi-view clustering methods are based on non-negative matrix factorization (NMF), which can achieve dimensionality reduction and interpretable representation. However, there are following issues in the existing researches: (1) The existing methods based on NMF using Frobenius norm are sensitive to noises and outliers. (2) Many methods only use the information shared by multi-view data, while ignoring the diverse information between views. (3) The data graph constructed by the conventional K Nearest Neighbors (KNN) method may misclassify neighbors and degrade the clustering performance. To address the above problems, we propose a novel robust multi-view clustering method. Specifically, l2,1-norm is introduced to measure the factorization error to improve the robustness of NMF. Additionally, a diversity constraint is utilized to learn the diverse relationship of multi-view data, and an adaptive graph method via information entropy is designed to overcome the shortcomings of misclassifying neighbors. Finally, an iterative updating algorithm is developed to solve the optimization model, which can make the objective function monotonically non-increasing. The effectiveness of the proposed method is substantiated by comparing with eleven state-of-the-art methods on five real-world and four synthetic multi-view datasets for clustering tasks.
SaCHBA_PDN: Modified honey badger algorithm with multi-strategy for UAV path planning

The honey badger algorithm (HBA) is a meta-heuristic optimization algorithm that simulates the foraging behavior of honey badgers. Since the algorithm is prone to premature convergence when solving complex optimization problems. To improve the overall optimization performance of the basic HBA, this paper develops a modified HBA named SaCHBA_PDN based on the Bernoulli shift map, piecewise optimal decreasing neighborhood, and horizontal crossing with strategy adaptation and applies it to solve the unmanned aerial vehicle (UAV) path planning problem. Firstly, the Bernoulli shift map is invoked to the HBA algorithm to change its initialization process, thus increasing the diversity of the population and speeding up the convergence speed. Secondly, a new piecewise optimal decreasing neighborhood strategy (PODNS) is proposed to address the shortcomings of unbalanced convergence of the traditional optimal neighborhood strategy. The proposed PODNS increases the optimization efficiency of HBA and enhances the local search ability to avoid falling into the local optimum. Finally, a novel horizontal crossing with strategy adaptation is introduced to balance exploration and exploitation and enhance the global optimization ability. These strategies collaborate to enhance HBA in accelerating overall performance. The superiority of SaCHBA_PDN is comprehensively verified by comparing it with the original HBA and numerous celebrated and newly developed algorithms on the well-known 23 classical benchmark functions and IEEE CEC2017 test suite, respectively. Experimental results show that SaCHBA_PDN has a better performance than other optimization algorithms. Furthermore, SaCHBA_PDN is used to solve a UAV path planning problem based on the threat source model and applied to circular and irregular obstacle scenarios as well as two-dimensional grid maps. Simulation results show that SaCHBA_PDN can obtain more feasible and efficient paths in different obstacle environments.