Reviewerly/exHarmony huggingface dataset:
Personalized User Recommendation based on Various User Behavior in Local Domain

User recommendation is a service that recommends other users to a user using the Social Network Service (SNS). This user recommendation is important because it can make user's active behavior in service. Generally, this user recommendation has been implemented based on similarity between user node and user node in structure of network (graph) or a simple user profile. However, this method does not reflect user's taste. In this paper, we propose an empirical user recommendation problem which reflects user's taste in local domain. Unlike other domains, user recommendation in local domain should work in consideration of various components such as user, region, Point-of-Interest (POI), and User Generated Content (UGC). To deal with the problem, we introduce how to define users with similar taste by various user behavior and how to make user embedding by neural network according to these definitions. Furthermore, since there is no right answer in recommendation, we suggest a method of showing optimal result to user by increasing diversity and novelty using ensemble, in the initial stage for multi-armed bandit.
Behavioral graph fraud detection in E-commerce

In e-commerce industry, graph neural network (GNN) methods are the new trends for transaction risk modeling. The power of graph algorithms lie in the capability to catch transaction linking network information, which is very hard to be captured by other algorithms. However, in most existing approaches, transaction or user connections are defined by “hard link” strategies on shared properties, such as same credit card, same device, same ip address, same shipping address, etc. Those types of strategies will result in sparse linkages by entities with strong identification characteristics (ie. device) and over-linkages by entities that could be widely shared (ie. ip address), making it more difficult to learn useful information from graph. To address aforementioned problems, we present a novel behavioral biometric based method to establish transaction linkings based on user behavioral similarities, then train an unsupervised GNN to extract embedding features for downstream fraud prediction tasks. To our knowledge, this is the first time similarity based “soft link” has been used in graph embedding applications. To speed up similarity calculation, we apply an in-house GPU based HDBSCAN clustering method to remove highly concentrated and isolated nodes before graph construction. Our experiments show that embedding features learned from similarity-based behavioral graph have achieved significant performance increase to the baseline fraud detection model in various business scenarios. In new/guest buyer transaction scenario, this segment is a challenge for traditional method, we can make precision increase from 0.82 to 0.86 at the same recall of 0.27, which means we can decrease false positive rate using this method.
The Impediments of the Application E-Business to Classified the Restaurants in the Aqaba Special Economic Zone

The study aims research paper seeks to study the status of tourism promotion in Jordan, in particular via the Internet, with a future plan to develop this type of promotion based on the needs of the country of this research paper is to draw conclusions that help to know and understand this type of tourism promotion, And to know how to develop it in Aqaba Special Economic Zone Disseminatethe maximum information about the restaurant in the Aqaba, this research paper is designed to determine the conditions of websites used in tourism promotion of the restaurants to find out the obstacles that face this type of promotion via websites, and the factors that affect its the promotion is to be a primary tool for economic development in tourism destinations, The objectives are to increase the contribution of the tourism sector to the obstacles of e-business in the classification of restaurants and to benefit economically from the tourism industry through employment opportunities and investment, product Development and tourism experience, promotion of tourism in restaurants, between the public and private sector in Aqaba.
Chest Radiographs Enhancement with Contrast Limited Adaptive Histogram

Among all the image contrast enhancement methods, histogram equalization is the most widely used. However, conventional histogram equalization causes the entire image to be too bright, resulting in noise generation as a consequence of excessive brightness changes and information loss when the histogram is intensively distributed into a specific amount of brightness. The Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm is designed to address these limitations. To avoid overamplifying brightness variations, CLAHE clips the histogram of an input image to make it uniformly distributed. This study employs the CLAHE algorithm to improve contrast in chest radiographs, with the expected result that lung disease diagnosis will be more accurate.
Recommender Performance for Users with Interest in Several Fields on Collaborative Topic Regression through Vocabulary Completion

A recommender system is a tool that assists users with varying interests in obtaining information. While certain users may have deep interests in single fields, others may exhibit such interests across several fields. This latter group is referred to as “broad perspective users” throughout this paper. Recommendations of overly specialized content may not be appropriate for broad perspective users. Collaborative filtering is one of the most widespread methods for selecting preferable content, wherein user preferences are represented by a rating matrix. However, the typically high percentage of missing ratings, known as sparsity, results in suboptimal performance. To mitigate the degradation of performance in high-sparsity cases, a hybrid method taht combines ratings with other relevant data pertaining to items and users has been proposed. Collaborative topic regression (CTR) is a pioneer model, that extracts latent topics from documents about users or items and employs them alongside the rating matrix. Thus, words contained within a document has a significant impact on model performance. In a highly specialized domain such as scientific articles, we assume that adding less specialized words that fit the original document's content is more effective for broad perspective users. In this study, we conducted to improve the recommendation performance for broad perspective users by complementing vocabulary to the documents. To achieve this, we use a pre-trained language model for sentence generation. We verified that the performance of this approach is better for broad perspective users than for users that are not restricted. Furthermore, we conducted experiments with multiple sparsity levels, as the sparsity of the rating is interlinked with relevant topics in CTR.
The Effectiveness of the Performance of Principals of Basic Education Schools in the Sultanate of Oman in Managing Change in Light of the Corona Pandemic

The current study aimed to identify the effectiveness of the performance of principals of basic schools, the first cycle, in change management in light of the Corona pandemic. The researcher used the descriptive-analytical method. A questionnaire was applied to (400) male and female teachers. The effectiveness of the principals’ performance for change management fields was as follows: Follow-up and implementation ranked first with a mean (3.83), followed by planning (3.73), and evaluation (3.60). The study showed that all the statements of the three axes regarding the effectiveness of the performance of principals of basic education in the axes of change management came to a high degree, except one item in evaluation axe “Item 3”, which reads (the principal focuses on evaluating qualitative performance and neglects quantitative performance). The study showed statistically significant differences at the significance level (0.05) in all fields in favour of females. The study also indicated that there were no statistically significant differences between the means due to the academic qualification variable. The researchers recommended that the Ministry of Education should adopt the change project for all school principals in its plans as a basic project in its organizational structure.
The Impact of Functional Withdrawal on Organizational Commitment as Perceived by Nurses Working in Public Hospitals in Jordan

The major aim of the study was to examine the impact of functional withdrawal on organizational commitment. Therefore, it focused on Jordanian public hospitals operating in the north region. Data were primarily gathered through self-reported questionnaires created by Google Forms which were distributed to a random sample of (306) nurses via email. Structural equation modeling (SEM) was conducted to test hypotheses. The results demonstrated in Table 2 show that functional withdrawal has a negative impact on organizational commitment. In light of the findings of the study, managers and decision makers in hospital management should provide support, material and moral incentives, and educational, development and promotion opportunities for employees, especially nurses, with the need to focus on knowing the needs of nurses to increase their connection with their organization and reduce their tendency to search for new opportunities and leave work.
The Impact of Emotional Intelligence (EI) on Teamwork Performance in Information Technology Sector in Jordan

The study aimed to investigate the impact of emotional intelligence on teamwork performance. The study population consisted of all employees working in the IT private sector in Jordan. From all IT companies working in this sector, four IT firms, at King Hussein Business Park, were selected with total number of (80) IT employees. A questionnaire was developed as the study instrument, which was composed of ten dimensions with (65) items. Multiple regression analysis was used for testing the study hypotheses. The study results found that emotional intelligence has a significant impact on teamwork performance. Based on the study results, the researchers recommend managers and decision makers to build good relationships with each other to improve the communication of information and ideas among the team members, and to have enough ability to overwhelm disputes occurring during the practice of work activities, which will make the work be achieved in smoother and faster mode.
Passing Strategy Optimization Based on Kernel Density Negative Example Learning Within RoboCup 2D Simulation

RoboCup2D simulation soccer is a multi-intelligence collaborative simulation experiment platform. In RoboCup2D simulation soccer platform, the passing module is one of the of the team, and an effective passing strategy will improve the overall team strength. Since the parameters of the passing module are too complicated and the data distribution is too heterogeneous. In order to be able to classify the passing data effectively, we propose a method based on kernel density negative example learning, which extracts the action chain feature data conforming to our preset by parsing the game log files based on kernel density analysis, and weakens the weights of common features of passing data through negative example learning to improve the classification of different kinds of passing. The classification effect of feature data is improved by weakening the weight of common features of passing data through negative example learning. By analyzing the similarities and differences of the passing action chain features when facing different teams, we extract the key areas of attack to optimize the team's passing strategy and improve the average number of goals scored in the test with several teams.
Radio Signal Smart Deception Based on Adversarial Learning

In recent years, electromagnetic spectrum becomes more and more congested and the task of spectrum sensing meets various challenges. On the other hand, there is also a counter-demand that the transmitted signal should not be detected, intercepted, or identified by the intelligent spectrum sensing equipment. In this paper, a smart deception method for radio signal using adversarial learning under the black-box assumption is investigated. By generating smart deception, the accuracy of the intelligent electromagnetic spectrum equipment to identify the radio signal decreasing more than 50.63%. The radio signal camouflage is achieved by smart deception, which will lower the interception and identification possibility of the transmitted signal.
Entrepreneurial Marketing: An Approach-Based Paradigm Shift to Marketing

This paper aims to augment the research field of Entrepreneurial Marketing (EM) as a concept and approach, and the substantial differences it holds in comparison to the prevailing giving of the marketing. In doing so, the research aims to explore and analyze the causes and drivers that resulted in the emergence of EM as a new school of thought and conceptualize the paradigm shift EM is providing to the prevailing giving of the marketing field. Grounded theory and logical argumentation based on the literature review including both qualitative and quantitative research, to develop the argument leading to conceptualizing the proposed approach-based paradigm. The research conceptualizes a proposed paradigm to formalized marketing with four main components comprising it: resilient management; scalable/gradual matter of sub-frames including multi-dimensional orientation effectual-causal, proactiveness-reactiveness; living strategic process, and Entrepreneurial Innovation. This paper represents the first bold proposal on the need for a paradigm shift to the prevailing marketing giving and introduces a frame for conceptualized paradigm. Specifically, does the body of literature in EM provide a base toward a paradigm shift of the prevailing of marketing giving or only a new school of thought?
A Robust Nonlinear Filter for Doppler Radar Target Tracking

The smooth variable structure filter (SVSF) offers robust state estimation in the presence of model uncertainty but requires a linear measurement model and a full-rank measurement matrix. Doppler radar reports target position and range rate in the polar or spherical coordinates without tangential velocity, leading to a nonlinear and underdetermined measurement model in Cartesian coordinates and thus precluding the use of the standard SVSF. In this paper, a novel robust estimator, referred to as the sequential smooth variable structure filter (SSVSF), is proposed for Doppler radar target tracking. The Doppler radar measurement vector is partitioned into two parts, i.e., the position (containing range, azimuth, and elevation) and the range rate, and is used for a two-stage sequential processor. In the first stage, the target position measurement is transformed into Cartesian coordinates via the unbiased converted measurement (UCM) strategy, so that a generalized SVSF is implemented for robust target state estimation with a linear measurement model. In the second stage, a pseudo measurement is constructed from the product of range and range rate, and accordingly, an approximate linear minimum mean square error (LMMSE) estimator is developed to update the first-stage estimation. Simulation results validate the effectiveness of the proposed algorithm in Doppler radar tracking, and superior robustness to the conventional nonlinear Bayesian filter in the case of target motion uncertainty.
The Impact of the Quality of Medical Information Systems on Job Performance in Private Hospitals in Jordan

The study aims to identify the impact the quality of medical information systems on the job performance in Private Hospitals in Jordan. The study population consisted of all employees with job titles (doctor, nurse, pharmacist, laboratory technician) in private hospitals in Jordan. Data were primarily gathered through self-reported questionnaires creating by Google Forms which were distributed to a purposive sample via email. A self-reported questionnaire that consists of two main sections along with a section regarding control variables was used as the measurement instrument. Statistical program AMOSv24 was used to test the study hypotheses. The results showed that the highest impact was for ease of use, followed by ease of learning, then flexibility, then reliability, and finally the lowest impact was for responsiveness. Based on the results of the study, researchers recommend managers and decision-makers in private hospitals in Jordan to hold training courses in the field of using medical information systems for all employees in private hospitals, and to focus on maintaining these courses, and raising their levels in line with continuous technological developments and changes.
Pulse Group Extraction of Multi-Function Radar

Multi-function radar signals account for a large proportion of radar reconnaissance datasets with a rapidly increasing amount of data. Automatic and accurate analysis of the pulse group of these radars is an important task to research and explain the working mode and behavior intention of radar system. In order to address this problem, this paper proposes an automatic analysis method of radar pulse group based on the joint extraction and extension of multi-parameter frequent items, including pulse repetition interval (PRI) and radio frequency (RF). The multi-parameter frequent items extracted from the pulse train are used to implement pulses extension and pulse segments extension. Finally, the pulse group of multi-function radar are obtained. The last part of this paper sets up several typical simulations to verify the feasibility and performance of the new method.
The Development of Learning Media Based on Islamic Comics in Natural Sciences Force and Motion Material

This research and development aims to increase the effectiveness of learning in class IV SD Muhammadiyah 9 Malang. The development of Islamic comic-based learning media aims to (1) produce learning media products by describing the media development process, (2) describe the level of feasibility of learning media, and (3) describe the level of effectiveness of learning media in the science material subject force and motion of Muhammadiyah 9 "Panglima Sudirman" Malang fourth grade elementary school students.This study refers to the Borg and Gall research model by adopting six main steps, namely: 1) needs analysis, 2) media development, 3) media testing, 4) media revision, 5) field testing, and 6) implementation. Data analysis used the Linkert scale for media eligibility data and SPSS 16.0 for Windows for media effectiveness data. Islamic comic-based learning media has been declared valid by experts. Attendance from stage I test material experts was 79%, stage II test material experts were 100%, linguists were 100%, media design experts were 100%, and learning practitioners or class teachers were 100%. The application of learning media was carried out in 3 stages, namely small group tests with 4 students, medium groups with 16 students, and large groups with 19 students in the control class and 19 students in the experimental class. Islamic comic-based learning media effectively improves student learning outcomes in class IV Al Batani as an experimental class, which is 90.56 with an increase in the average value of 21.18%, starting from 74.73 to 90.56.
Hybrid Oversampling Technique Based on Star Topology and Rejection Methodology for Classifying Imbalanced Data

In this paper, we propose the star topology and rejection method (STARM), a new oversampling technique that generally performs well for varying data and algorithms. STARM is a hybrid technique that combines the advantages of Polynom-fit-SMOTE, LEE, and SMOTE, all of which have yielded high performance based on different technical features, and eliminates their disadvantages. To verify that the proposed technique exhibits high performance in general situations, we conducted 28,028 experiments to compare the predictive performance of 77 oversampling techniques with four machine learning algorithms for 91 imbalanced datasets of various types. Consequently, STARM yielded the highest performance on average among the 77 techniques. In addition, it showed excellent performance even in various algorithms, various imbalanced ratios, and various data volumes.
Unknown Type Streaming Feature Selection via Maximal Information Coefficient

Feature selection aims to select an optimal minimal feature subset from the original datasets and has become an indispensable preprocessing component before data mining and machine learning, especially in the era of big data. Most feature selection methods implicitly assume that we can know the feature type (categorical, numerical, or mixed) before learning, then design corresponding measurements to calculate the correlation between features. However, in practical applications, features may be generated dynamically and arrive one by one over time, which we call streaming features. Most existing streaming feature selection methods assume that all dynamically generated features are the same type or assume we can know the feature type for each new arriving feature on the fly, but this is unreasonable and unrealistic. Therefore, this paper firstly studies a practical issue of Unknown Type Streaming Feature Selection and proposes a new method to handle it, named UT-SFS. Extensive experimental results indicate the effectiveness of our new method. UT-SFS is nonparametric and does not need to know the feature type before learning, which aligns with practical application needs.
The Role and Impact of Big Data in Organizational Risk Management

Technological advancement has exposed companies to various risks. With the adoption of technological infrastructures in many companies, various processes have been rendered vulnerable to different forms of threats. Evidence from the current empirical studies on organizational management indicates effective risk assessment is a crucial aspect in any organization. Global technological advancement has equally redefined risk assessment and management strategies. Global leading technology companies such as Apple Inc., Amazon Inc., and Google are leveraging modern technology to unlock hidden data to aid in risk assessment and management. As evidenced in this research report, the use of big data has revolutionized risk assessment in many companies across the world. Big data technology has enabled organizations to collect, store, and assess huge information to aid in risk assessment and management. The processes of risk identification, assessment, mitigation, monitoring, and reporting have been redefined due to the adoption of big data analytical technology. As a result, this research reviews the specific roles played by big data in organizational risks management. The research carries out a comparative case study analysis among three companies that utilize big data in risk management. Specifically, the roles of big data technology in risk management at Apple Inc., Amazon Inc., and at Google are identified. Results from this comparative analysis are used in formulating recommendations for various organizations that desire to adopt big data analytical technology in risk management.