Reviewerly/exHarmony huggingface dataset:
FINTECH AND FINANCIAL INCLUSION: THE MEDIATING ROLE OF DIGITAL MARKETING

This study aims to test the role of digital marketing as a mediating variable in the relationship between financial technology and financial inclusion. The researcher applied the descriptive analytical approach to the data collected by developing a questionnaire distributed to 375 employees in the Jordanian banking sector. The researcher also used structural equation modeling to test the study hypotheses through SmartPLS software 3. One of the study’s most important findings is that digital marketing partially mediates the relationship between financial technology and financial inclusion. The study recommends that the Jordanian commercial banking sector should use advanced strategies that enhance technological innovation and digital marketing through the existence of an evolved environment for financial technology that is optimally exploited.
EEG Control of a Robotic Wheelchair

The Brain-Computer Interface (BCI) technology has been widely used in clinical research; however, its adoption in consumer devices has been hindered by high costs, poor reliability and limited autonomy. In this study, we introduce a low-cost, open-source hardware-based, consumer-grade product that brings BCI technologies closer to the elderly and motor-impaired individuals. Specifically, we developed an autonomous motorized wheelchair with BCI-based input capabilities. The system employs the ROS-backend navigation stack, which integrates RTAB-MAP for mapping, localization, and visual odometry, as well as A* global and DWA local path planning algorithms for seamless indoor autonomous operations. Data acquisition is accomplished using OpenBCI 16-channel EEG sensors, while Ensemble-Subspace KNN machine learning model is utilized for intent prediction, particularly goal selection. The system offers active obstacle avoidance and mapping in all environments, while a hybrid BCI Motor Imagery based control is implemented in a known mapped environment. This prototype offers remarkable autonomy while ensuring user safety and granting unparalleled independent mobility to the motor-impaired and elderly.
The first discovery of Xinlong epithermal gold deposit in southern margin of the Bangonghu-Nujiang metallogenic belt: A new expansion of gold prospecting in Northern Tibet

The Xinlong gold deposit is located in Niyma County, Naqu area of Tibet and was discovered by the Institute of Mineral Resources, Chinese Academy of Geological Sciences through the 1∶50000 mineral geological survey. The ore bodies occur in the Zenong Group volcanic rocks in the middle section of the central Lhasa subterrane and are structurally controlled by the NNW-striking faults. Four ore bodies have been found, exhibiting cloddy, dense-sparse, disseminated, and breccia structures. The ore minerals are mainly tetrahedrite group minerals, and other ore minerals include pyrite, chalcopyrite, nevskite, bornite, anglesite, native gold, and silver-gold bearing selenide, etc. The types of alteration are dominated by silicification, as well as middle- and high-graded argillization. The alteration mineral assemblages contain quartz, pyrophyllite, and kaolinite. The Zaliela Formation volcanic rocks of Zenong Group are silicified by later hydrothermal fluid with vuggy quartz in some fractured zones. The middle- and high-graded argillization are characterized by pyrophyllitization and kaolinization. The Xinlong gold deposit shows great metallogenetic potentiality and has been revealed by 1∶10000 geological mapping, IP sounding, and trial trenching in the mining area. Combined with the regional metallogenic geological setting, we suppose that a potential epithermal gold belt probably exists in the middle of the Lhasa terrane. The discovery of the Xinlong gold deposit opens a new chapter for the gold prospecting in Northern Tibet.
CLEF. A Linked Open Data Native System for Crowdsourcing

Collaborative data collection initiatives are increasingly becoming pivotal to cultural institutions and scholars, to boost the population of born-digital archives. For over a decade, organisations have been leveraging Semantic Web technologies to design their workflows, ensure data quality, and a means for sharing and reusing (Linked Data). Crucially, scholarly projects that leverage cultural heritage data to collaboratively develop new resources would benefit from agile solutions to simplify the Linked Data production workflow via user-friendly interfaces. To date, only a few pioneers have abandoned legacy cataloguing and archiving systems to fully embrace the Linked Open Data (LOD) paradigm and manage their catalogues or research products through LOD-native management systems. In this article we present Crowdsourcing Linked Entities via web Form (CLEF) , an agile LOD-native platform for collaborative data collection, peer-review, and publication. We detail design choices as motivated by two case studies, from the Cultural Heritage and scholarly domains respectively, and we discuss benefits of our solution in the light of prior works. In particular, the strong focus on user-friendly interfaces for producing FAIR data, the provenance-aware editorial process, and the integration with consolidated data management workflows, distinguish CLEF as a novel attempt to develop Linked Data platforms for cultural heritage.
Fusion-Based Feature Extraction Technique Using Representation Learning for Content-Based Image Classification

The upsurge in advancements of digital imaging technology has led to archival of high-quality images embedded with rich content. This has facilitated content-based image classification (CBIC) as one of the most popular techniques to accurately classify these images by extracting robust low-level features like color, shape, texture, and so on. However, designing a robust feature descriptor to cater diversified set of images is a challenging task. Traditional handcrafted feature extraction techniques have taken a back sit after the advent of deep neural network-based image feature extraction. However, generalization of neural network-based feature vectors for smaller datasets has posed significant challenges due to overfitting caused by high variance of pre-trained networks. Hence, this research has primarily investigated the possibilities of designing generalized features using final layer of different pre-trained neural networks individually vis-a-vis handcrafted feature extraction. The process is followed by concatenation of final layers from diversified pre-trained networks along with handcrafted feature. In the process of experimentation, the paper aims at using representation learning using VGG19 and ResNet50. It has also considered handcrafted technique of feature extraction named color histogram (CH). Each of the techniques are separately tested for feature generalization efficiency using assorted classifiers. Further, the research has proposed fusion of features in several combinations using each of these descriptors to observe any improvement caused in feature generalization while tested for classification results. The outcomes using the proposed fusion approach have resulted in improved generalization by outshining classification accuracy of individual methods.
“Guess Why I didn't rate it”: A New Preference-based Model for Enhanced TopK Recommendation

In existing matrix factorization (MF)-based recommender systems, the user-item interaction matrix is factorized linearly into two low-ranked feature matrices to generate predictions or provide the users with personalized rankings of items.However, when MF methods are directly applied to sparse rating matrices, they cannot cope with the inherent structure of real-world user and item latent features.The efficiency of MF-based systems crucially depends on their capability to address sparsity issues.To this end, we propose, in this paper, a novel preference-based data imputation approach for effective MF-based Top-K recommendation.We apply MF on an imputed and denser rating matrix with only interesting items to users.We obtain these items by inferring the prior preferences of users, considering some biases that may impact their choices for items they interact with, and leveraging a powerful latent and non-linear feature extraction using a deep generative model.Experimental results on two real-world sparse datasets reveal that the proposed model significantly enhances the performance of Top-K recommendations and outperforms baselines with an average improvement margin of 6.45% and 3.91% in hit rate (HR) and normalized discounted cumulative gain (NDCG) evaluation metrics, respectively, averaging on all employed datasets.
IoT Content Cache Efficacy in Health Care Data Diagnostics Using Isotonic Regressive Adaptive Boost Classification

Information-centric networking (ICN) with the internet of health things (IoHT) provides a secure network between medical experts and patients.Several researchers have investigated cache with IoHT data in ICN.However, there are still issues like which content to be cached or node selection for placing the content.In this work, an efficient caching strategy called, isotonic regressive adaptive boost classification-based information centric network (IRABC-ICN) is proposed with IoHT data.Initially, patient healthcare data requests are obtained from maternal healthcare.The patient information is registered and an information copy is deployed to the cache of every router node.Each request to the router node is searched in the content storage (CS).Next, isotonic regressive analysis is to analyze patient healthcare data request search.Here CS is checked and upon successful validation, the particular router node is selected to place the content and on contrary, the patient information is stored in the cache.Finally, the modest adaptive boost classification is carried out to obtain strong classification results with higher efficiency of content distribution and lesser network traffic, and server bandwidth.To prove the performance of this proposed IRABC-ICN, its cache hit rate, network latency, and average request length are used as the evaluation metrics.The efficacy of IRABC-ICN is proved by comparing it with existing PCSRC, cooperative caching, and context-based caching mechanisms with the maternal health risk dataset.The outcome of the proposed IRABC-ICN achieves enhancement in cache hit rate by 9%, minimization of network latency, and average request length by 38%, and 13% as compared to PCSRC, cooperative caching, and context-based caching mechanism respectively.
Attention-based Sentence Extraction for Aspect-based Sentiment Analysis with Implicit Aspect Cases in Hotel Review Using Machine Learning Algorithm, Semantic Similarity, and BERT

The development of the aspect-based sentiment analysis (ABSA) method to work on the case of implicit hotel reviews in depth has not been done much.The problem of extracting aspect and opinion words based on syntaxis and semantics is not only influenced by different of sentence structure types but can also be influenced by word sense disambiguation (WSD) level.So, it needs deep attention to solve these problems.For example, the review "You can't say its cheap because food is cheaper in Chinatown.",where "food is cheaper in Chinatown" is still widely extracted as target terms because there are explicit element of aspect and opinion.In fact, it requires in-depth attention to be able to extract and capture the implicit element "can't say its cheap" as a target term.However, there has been not many research that discusses the details of the ABSA process related to this case.Therefore, we propose an attention-based sentence extraction method for ABSA with implicit aspect cases in hotel review.The method purpose is to improve the ABSA accuracy for hotel reviews based on the cases that have not been solved.First, we develop a pre-processing method to the make the data ready to be processed.Then, we build a set rule-based algorithm to get the word types and the relationship of each word in the sentence.These rules function to identify and mark the candidates of aspect and opinion terms based on the review sentence structure types (simple, compound, complex, compound-complex) and to identify and mark the factors that influence the WSD level (conjunction, punctuation, contrast, intensification) in each sentence.The candidates result of aspect and opinion terms are used as input for the aspect categorization process.The aspect categorization process is carried out using machine learning algorithm, implicit aspect corpus, BERT embedding, and semantic similarity to obtain the aspect categories of each review.Furthermore, the ABSA process is carried out using the BERT sentiment analysis method.Finally, the evaluation process for aspect categorization and ABSA are done with the good result.The evaluation result of aspect categorization obtains 91.31% for accuracy, 91.81% for precision, 89.43% for recall, and 90.61% for f1-measure.Meanwhile, the evaluation result of ABSA obtains 98.10% for accuracy, 98.11% for precision, 96.98% for recall, and 97.54% for f1-measure.
EMEMODL: Extensible Metadata Model for Big Data Lakes

The evolution of the huge amount of heterogeneous data sources introduces the emergence of several fresh new concepts.Among one of the most well-known concepts emerging as a recent and trending topic in big data is the data lake.Which represents a central repository that stores heterogeneous data sources in its native format without any predefined schema.In the absence of any enforced schema, effective metadata management based on metadata models remains an active research topic to address the data lake issues knowing by a data swamp.However, the examination of existing metadata models shows that none of them proposes a complete model.In this article, we propose a generic and extensible metadata model that supports high flexibility and better scalability in the integration of metadata.With these capabilities, EMEMODL enables comprehensive metadata management for data lakes.We show EMEMODL's feasibility through a prototypical implementation based on TPC-H datasets of different sizes to prove the scaling feature, using MySQL and Neo4J.The findings of these experiments revealed encouraging results for big data queries that give us a graph database, which shows an efficient ability to manage and process large amounts of data compared to a relational database in terms of retrieval time queries and resource consumption, including cpu and memory usage.
Deadline and Cost Aware Dynamic Task Scheduling in Cloud Computing Based on Stackelberg Game

Cloud computing has become an essential technology in many industries due to its scalability and costeffectiveness.The dynamic nature of cloud computing, including elasticity, on-demand provisioning, diverse resource types, and varied pricing model, presents a significant challenge in scheduling tasks for cloud-based systems, especially when considering user quality of service (QoS) constraints such as deadlines and budgets.Therefore, in order to optimize the performance of the cloud systems and end-user satisfaction, an efficient budget and deadline-aware scheduling model is necessary.Game theory provides a framework for modeling and analyzing the strategic interactions between self-interested entities, which makes it an ideal tool for task scheduling in cloud computing.Additionally, the versatility of game models enables the analysis of various cloud computing architectures.This paper proposes a dynamic Stackelberg (leader-follower) game model for modeling the interactions between tasks, scheduler, and cloud resources to find an equilibrium for the game under both budget and deadline constraints.The proposed dynamic task scheduling based on Stackelberg game (DTSSG) model is assisted by the pricing model and satisfaction factors to select the optimal virtual machine for processing the user task.To achieve high average resource utilization, the utilization factor of the cloud resources is considered in the proposed work.Experimental results show that the Stackelberg model equilibrium has been very effective in scheduling the user tasks across the data center resources by selecting the optimal virtual machines.The results demonstrate improved execution efficiency in terms of decreased makespan by 30%, reduced number of deadline violations by 52%, decreased total gain cost by 27.13% and increased provider profit by 19.15 % on average as compared to existing deadline budget scheduling (DBS), genetic algorithm, and MAX-MIN methods.Also, the results show the effectiveness of the proposed work in terms of increased throughput by 59.4 % and decreased makespan by 27.95% using Google cloud jobs dataset (GoCJ) as compared to existing gradient-based optimization (GBO), multi-verse optimizer (MVO), enhanced multi-verse optimizer (EMVO).
A Hybrid Algorithm For Enhancement of the Data Security during Network Transmission Based on RSA and DH

Due to the tremendous technological transformation, more technologies and internet of things (IoT) devices are employed in today's digital world.The majority of these technologies are linked through a network.They produce enormous amounts of information at incredible speeds, leading to zillions of digital data moving via network connections.Maintaining comprehensive and high security for any exchangeable data is vital.Networked data need security and privacy.Multiple cryptographic techniques are currently available to maintain sending messages and data security.Regardless of its simplicity, private key cryptography is unproductive for authorization.As it often covers all of the components of information security, asymmetric cryptography is much more secure than private key encryption, but it takes much longer.The purpose of this paper is to demonstrate the few encryption techniques that are used on an insecure network to encrypt data.To encrypt the data, this paper proposes a new hybrid encryption technique.The suggested approach, MRSADH, encrypts and decrypts the data using Rivest, Shamir, and Adleman (RSA) and Diffie Hellman (DH) and produces the encoded data using the XOR function to be sent to the recipient in the shortest time and with high-security complexity compared to other methods in terms of performance and avalanche effect.The proposed study examines the two most widely used cryptographic algorithms, RSA and Diffie Hellman, and how they work and impact security and speed when integrated into a unified hybrid (MRSADH) computation with necessary adjustments.The study also presents a method that uses Asymmetric and Symmetric key encryption to improve data security while reducing the number of mathematical equations, which tends to result in a quicker execution time.
Human Fall Down Recognition using Coordinates Key Points Skeleton

Falls are a significant hazard to human safety and may have a devastating consequences in a matter of seconds. This is especially true for elderly, since falls are the primary cause of hospitalization and injury-related mortality in this age group. In this paper, a deep learning based model is proposed for analyzing video detected pictures in real-time monitoring camera. Two conditions were tested in order to determine the falling severity. This paper proposes a vision-based method for falling detection. It analyzes an extracted skeleton to identify human posture. Open Pose network is utilized to obtain skeleton information regarding the human body. The falling can be identified using two parameters: the first is the calculation of the angle formed between the shoulder, hip or any of other points (knee, ankle or the heel), the second parameter is the distance calculated during the horizontal falling between the shoulder, hip or any other points such as knee, ankle, heal or the toe. The test results approved that the proposed method outperformed the other state-of-the-art methods in terms of, sensitivity (99.38%), specificity (96%), and accuracy (98 %).
STExplorer: A Hierarchical Autonomous Exploration Strategy with Spatio-temporal Awareness for Aerial Robots

The autonomous exploration task we consider requires Unmanned Aerial Vehicles (UAVs) to actively navigate through unknown environments with the goal of fully perceiving and mapping the environments. Some existing exploration strategies suffer from rough cost budgets, ambiguous Information Gain (IG), and unnecessary backtracking exploration caused by Fragmented Regions (FRs). In our work, a hierarchical spatio-temporal-aware exploration framework is proposed to alleviate these problems. At the local exploration level, the Asymmetrical Traveling Salesman Problem (ATSP) is solved by comprehensively considering exploration time, IG, and heading consistency to avoid blindly exploring. Specifically, the exploration time is reasonably budgeted by fast marching in an artificial potential field. Meanwhile, a transformer-based map occupancy predictor is designed to assist in IG calculation by imagining spatial clues out of the Field of View (FoV), facilitating the prescient exploration. We verify that our local exploration is effective in alleviating the unnecessary back-and-forth movements caused by FRs and the interference of potential obstacle occlusion on the IG calculation. At the global exploration level, the classical Next Best View Points (NBVP) are generalized to Next Best Sub-Regions (NBSR) to choose informative sub-regions for further forward-looking exploration based on a well-designed utility function. Safe flight paths and dynamically feasible trajectories are reasonably generated throughout the exploration process by fast marching and B-spline curve optimization. Comparative simulations and benchmark tests demonstrate that our proposed exploration strategy is quite competitive in terms of exploration path length, total exploration time, and exploration ratio.
Strategic HRM practices, learning orientation and learning competence: study from retail industry

Purpose This study aims to examine the effect of strategic human resource management (SHRM) practices on two learning outcomes, learning orientation and learning competence, which past research has seldom examined in the UAE context. SHRM practices consisted of four factors, namely, talent acquisition, learning and development, performance appraisal and developmental pay. Design/methodology/approach Primary data was collected from 285 employees from retail outlets operating in Dubai. Partial least squares regression analysis using the tool, SmartPLS, was used to empirically validate the measurement model and test the structural model. Findings Findings reveal that SHRM practices talent acquisition, learning and development and developmental pay have a positive association with learning orientation, and learning and development and performance appraisal have a positive association with learning competence of employees. The path coefficient and total effects signify that learning orientation acts as a conduit to pass on the effect of the SHRM practices to learning competence. Research limitations/implications The sample considered for the study was from the retail industry. Furthermore, to generalize the findings of this research, cross-national studies should be conducted across various sectors and organizations. Originality/value To the best of the authors’ knowledge, this research study is the first of its kind in retail firms in Dubai (UAE) to empirically test the association of SHRM practices with learning outcomes. Retail outlets can implement SHRM practices to improve learning orientation and learning competence in a highly dynamic operating environment such as retail industry.