Reviewerly/exHarmony huggingface dataset:
Knowledge Management Practices and Firm Performance: The Moderating Role of Resource Commitment

Knowledge management has become crucial for the firm’s sustainable and long-term performance. Knowledge management practices in the firm make such exchange, transfer, and reuse of knowledge increase the efficiency of firm performance. Therefore, this study tries to investigate the relationship between knowledge management practices and firm performance in the IT sector of Saudi Arabia and to inspect resource commitment as a moderator for developing the relationship between knowledge management practices and firm performance. For analysis, this study collected data from 356 participants from the IT sector of Saudi Arabia and measured the model using SmartPLS. In the data analysis, this study found that knowledge management practices have a positive and significant relationship with firm performance, whereas resource commitment plays a vital role as a moderator for strengthening this relationship between knowledge management practices and firm performance. Further, this study also linked RBV (resource-based view) theory and KB (knowledge-based) theory, with the study’s conceptual framework to make strong theoretical and practical implications of results. In the end, this study concluded that knowledge management practices are important to firm long-term existence in the market by using accessible resources. The results of the study urge the policymakers and administrators to give more consideration to the IT sector and enable knowledge management practices to enhance the performance of the organization, as well as attain innovation in this sector within the limit of available resources.
Video Mapping Technologies as Spatial Augmented Reality in the Artistic Process

Abstract The relevance of the subject is conditioned by the rapid technical development and active digitalisation of the creative space. Video mapping technologies have become one of the manifestations of art modernisation, a kind of change in the form of the demonstration of visual images. The purpose of the study is to determine ways to involve video mapping technologies in the progressive evolution of the artistic process into new forms of spatial augmented reality. Identifying the potential capabilities of video mapping will allow the expansion of the range of creative expression tools at the level of advanced technical capabilities. Among the scientific methods for this study, some general scientific methods were used: analysis, synthesis, induction, deduction, modelling, abstraction, reductionism and methods of comparison and analogy. The main result of the study is a statement of a radical change in the approach to art in the modern world, based on the visualisation of subjects and images using new expressive means. Thus, augmented reality as an entertaining element of artistic performance is the embodiment of the iconic characteristics of the postmodern era, where the role of a person in the creative process remains key, but the means of one’s self-expression have become different. During the study, conclusions were drawn about the emergence of art to a new level, where the empathic perception of reality is complicated by modern opportunities in the context of technological advances, which, in turn, sets new standards and requirements for the results of the creative process.
Comparing probabilistic planning success on three types of robots: wheeled, legged, and underwater

We compare the relevance of POMDP graphical models to addressing a given common task, the autonomous navigation, in mobile robotics, for robots in three different environments: legged walking, wheeled, and underwater robots.Principled approaches to the implementation of Intelligent robots in realistic environments with uncertainty exploit probabilistic models.The use of graphical models increases tractability and verifiability of probabilistic reasoning.Our robots face similar localisation problems of passing by three doors on a corridor in a building, respectively, for the underwater case, of passing by three barrels in a pool.All the three robots are equipped solely with noisy sonars and odometers, a natural restriction for the underwater case, and a significant explanation of the encountered level of uncertainty.POMDP models are proposed.The experiments performed show that when the three robots handle their respective problem, the legged robot have the highest locomotion challenges, while the underwater setting faced the highest level of noise in sensors.
Cost-benefit analysis of orbital and ground station aperture choices for satellite based QKD

Crucial to the realization of global quantum key distribution network, this work considers use of small satellites in low altitude orbits and small optical ground stations to study the impact on high rate quantum secured communication.
Slice optimisation for high-speed Gaussian coherent state continuous variable quantum key distribution

We demonstrate, for the first time, slice reconciliation with optimised guard bands on Gaussian modulated continuous variable quantum key distribution. We produce a record 5Mb/s key rate after 25 km transmission distance with multilevel slicing.
Distributed detection of quantum light: efficient photon number resolution without optical mode multiplication

We demonstrate a new method of quantum light detection based on coherent absorption of a single optical mode in a distributed array of superconducting nanowire singlephoton detectors for efficient and scalable photon number resolution.
Deterministic single-photon generation by strong coupling of slow free electrons and an optical cavity

We show that single photons can be deterministically generated by slow free-electrons experiencing large quantum recoil. The free-electron-photon interaction can then be described by an effective Jaynes-Cummings model in a strong coupling regime.
The Effect of Human Resources Practices on Sport Organization’s Performance in Oman: The Mediating Role of Leadership Style

Purpose: This paper examines the impact of human resource management (HRM) practices on organizational performance, with mediating role of leadership style. Theoretical framework: This study integrates theoretical perspectives on HRM practices Namely (the recruitment selection process, incentive system, and training &amp; development), Leadership style and organisational performance. The conceptual model for this study was developed based on previous research in the field of HRM practices and leadership styles globally. The conceptual model has HRM practices as an independent variable (IV), Leadership style as a mediator and organisational performance as a dependent variable (DV). Design/Methodology/Approach: A survey questionnaire instrument was employed to collect data from 156 employees working at sport organisations in Oman. SPSS was used for analysing the data, and Smart-PLS were used to test the proposed hypotheses. Findings: The result revealed that human resource practices are significantly related to organisational performance. Also, leadership styles mediated the influence of HRM practises on organisational performance. Research, practical &amp; social implications: The finding of this study provides valuable insights into sport organisations in Oman can enhance their HRM practices to improve organisational performance and achieve their goals. Moreover, it has been recommended that organizations use a leadership style that enhances organizational performance. The results come from a cross-sectional study which was done at the convenience of the researcher. The results may not be generalized across the country. The application of the results to other organisations must be done with maximum care. Originality/Value: This study adds to the literature on the effectiveness of HRM practices, Leadership style in enhancing organisational performance in a specific context namely the sport organisations in Oman. The study also highlights the mediating role of leadership style in the relationship between HRM practices and organisational performance.
Analisis Pemanfaatan Aplikasi Website Museum Subkoss Garuda Sriwijaya Bagi Siswa SMA Negeri 5 Lubuklinggau

The background of this research problem is that currently students of SMA Negeri 5 Luibuklinggau do not know much about the Garuda Sriwijaya Subkoss Museum. In fact, if there is a website at the Garuda Sriwijaya Subkoss Museum, Lubuklinggau 5 State High School students can explore history skills digitally. In the formulation of the research problem, namely how to use the Garuda Sriwijaya Subkoss Museum website for learning History at SMA Negeri 5 Lubuklinggau. while the purpose of this study was to analyze the utilization of the Garuda Sriwijaya Subkoss Museum website as a support for the History learning system at SMA Negeri 5 Lubuklinggau. This research method is a descriptive qualitative research method. This qualitative research method is used with the support of several references obtained from books and relevant research journals. Reference sources that support this research are in the form of book sources obtained from the library of the PGRI Silampari University, the Garuda Sriwijaya Subkoss Museum, private collections and scientific journals relevant to research. The results of this study are the development of the Garuda Sriwijaya Subkoss Museum website and the utilization of the Garuda Sriwijaya Subkoss Museum website for history lessons at SMA Negeri 5 Lubuklinggau. The existence of a website application developed by the Garuda Sriwijaya Subkoss Museum is very useful for learning History. The use of the Garuda Sriwijaya Subkoss Museum website can provide understanding and knowledge for SMA Negeri 5 Lubuklinggau students in learning History. The conclusion of this study is the utilization of the Garuda Sriwijaya Subkoss Museum website media for learning History at SMA Negeri 5 Lubuklinggau. History teachers can combine website media with creative and innovative learning strategies and models.
A Realtime Stereo Vision System Design using Disparity Calibration

In this paper, the error rate was reduced by using a calibration algorithm that can correct errors that occur in the initial disparity calculation stage using adaptive support-weights. Performance can be further improved by overlapping the initial disparity calculation and the disparity calibration stages. Because the adaptive support-weight algorithm includes nonlinear functions, it is impossible to reuse already processed data. In this study, it is designed to reuse data by separating rows and columns, and as a result, performance is improved and real-time processing is possible. To optimize the delay path, the exponential function is approximated with a step function, and a 5-stage pipeline is applied to the division operation. The proposed architecture was designed using VerilogHDL and synthesized with DBhitek 110nm standard cell library. The synthesized system used a maximum frequency of 666MHz (57fps) with 33K gates, and a memory of 4.9KB, and showed an error rate of 1.88% for tsukuba image.
Improving Reading Ability Using Big Book Media in Class I Students at SDN 2 Bulango Utara

This research aimed to improve reading skills using Big Book media for grade 1 students at SDN 2 Bulango Utara. The type of research used is classroom action research, with the research subject being a grade I student totaling 27 students. This research was carried out in the form of cycles, and each cycle consisted of 4 stages, namely the planning stage, the implementation stage, the observation, and the reflection stages. This research shows that students’ reading ability in Indonesian language subjects is still in the low category. This is indicated by the low reading ability of students at the time of the initial observation reaching 37.03% with capable criteria, and the reading ability of students in cycle I of 62.96% in capable criteria with a fairly good category, while the reading ability of students in cycle II increased by 100% with very good categories in capable criteria. The application of Big Book learning media can improve students’ reading skills in class I at SDN 2 Bulango Utara, Bone Bolango Regency. The achievement of providing action from cycle I to cycle II researchers ended the meeting in cycle II third meeting because it had met the predetermined performance indicators of 80%.
The Impact of Innovation Management on Customer Satisfaction in the advertising industry in the Kingdom of Bahrain

Customer satisfaction is one of the major challenges for all companies at any industry. Advertising industry is one of those who provide varies services to customers and looking forward to meeting their expectations. As the market in Bahrain has grown and the competition start to be real challenge for those printing and advertising companies, Advertising industry move to innovate and renew their services by reviewing the internal process and communicate with customer regularly to keep customers satisfied. This research aims to investigate on the impact of innovation management on customer satisfaction. The researcher analysed the data collected and analysed through SPSS version 21 to verify the prepared hypotheses of the research, using some different statistical methods, which showed the extent of the realization of the research hypotheses. The total sample size of this research was 300 registered customers of advertising industry in Bahrain. The results of this research have demonstrated the value of innovation management (Business process reengineering and Technology), as well as presenting the tie relation between innovation management and customer satisfaction in advertising industry. The research confirmed that there is positive association between innovation management and customer satisfaction in advertising industry. Both independent variables Technology and Reengineering of Business Process have shown a significant relation with the dependent variable Customer Satisfaction. The main recommendations to advertising industry from this research, the statistical analysis of the responders shows some claims that need by addressed in order to enhance and increase the level of customers’ satisfaction such enhancing the mobile application to give more flexibility to the customer to submit and track their orders and online payment system. Furthermore, it is highly recommended to establish a policy for discount in order to have more transparency with their client and this policy should be communicated to customers in order to increase the customer satisfaction.
Ekonomik Açıdan Sivas Havzası Doğal Anhidrit Potansiyeli, Rezerv Tahmini ve Jeokimyasal Özellikleri, Orta Anadolu, Türkiye

This study focuses on the economization of natural anhydrite, which has a wide range of uses and is environmentally friendly, in Türkiye. Anhydrite, which is one of the most common evaporitic minerals in the world, has almost no usage area in Türkiye. Evaporite deposits consisting of gypsum, anhydrite, halite, bassanite and selenite are prominent in the country, especially in Tertiary period sedimentary basins. The Sivas basin is one of these important Tertiary basins. Mining operates in the basin are carried out only on gypsum and anhydrite is not utilized. This study is carried out on the geological and geochemical characteristics of anhydrite, which is prominent with its usage areas in the world, its potential in the basin and its economic importance. The geochemical and mineralogical analyses are carried out on anhydrite samples taken from the basin. Anhydrite formations up to 300 meters in thickness are detected in the evaporite deposits that outcrop in most of the basin. In addition, a block model is extracted from the drilling data of a mining company operating in the basin and the reserve is calculated. As a result, it is concluded that the Sivas basin has a high potential in terms of anhydrite and should be brought to the national economy.
A hybrid multi-objective feature selection method based on Shapley value and binary state transition algorithm

Feature selection is a critical step in machine learning to reduce data dimensionality, whereas the conflicting objectives: minimizing the selected feature number, and minimizing the error rate of classification make it quite challenging. However, most existing feature selection algorithms only focus on one single objective. To address this issue, a hybrid multi-objective feature selection (MOFS) method based on Shapley value and binary state transition algorithm is proposed in this paper, named MOFS-BSTA. Additionally, to enhance the converge capability of MOFS-BSTA, a mutation operator is developed to produce more efficient candidate solutions, and a two-dimensional sorting strategy is designed to replace the fast non-dominated sorting strategy. Finally, MOFS-BSTA is tested on nine UCI datasets, and the results demonstrate that the MOFS-BSTA can obtain the better Pareto optimal solution set with less running time than conventional MOFS methods.
ANALISISA MINAT BELAJAR SISWA KELAS 6 PADA MATA PELAJARAN BAHASA INDONESIA DI MI MUHAMMADIYAH 1 SUKODADI

This study aims to analyze and describe students' learning motivation in learning Indonesian. This type of research is qualitative research. The sample of this research is 31 grade VI students at MI Muhammadiyah 1 Sukodadi. The data collection methods used were questionnaires, observation, interviews and documentation. Data on student motivation was obtained using a questionnaire sheet. Meanwhile, to support the research data, interviews and classroom observations were conducted. Data analysis was carried out using descriptive qualitative. The questionnaire used is a closed questionnaire, the respondent can only answer with the answer choices provided by the researcher. Respondents cannot provide answers or other responses except those that are available as alternative answers. The results in this study for the first indicator got the highest score of 115 with a percentage of 92%, while for the second indicator the highest score was 87 with a percentage of 70%. There are 5 criteria to see the percentage of students who are interested in learning Indonesian, namely: Very Good, Good, Good Enough, Less Good, and Not Good.
Blind Image Quality Assessment based on Separate Representations and Adaptive Interaction of Content and Distortion

The visual quality of an image mainly relies on its content and its distortions. However, the adaptability between their contributions to the image quality has not be well investigated yet. Besides, albeit of many promising efforts, lacking sufficient labeled data still hinders the robust representation of quality-related information. In this work, we first design a self-supervised architecture, named collaborative autoencoder (COAE), to separately represent the content and the distortion information, and then develop a Self-Adaptive Weighting based quAlity predictoR (SAWAR) to balance the individual representations of the content and the distortions in the prediction of image quality. Specifically, the COAE is trained with large-scale unlabeled data, consisting of a content autoencoder (CAE) and a distortion autoencoder (DAE) that work collaboratively and individually. While the CAE is a standard autoencoder for the content representation, the design of the DAE is unique. We introduce the CAE-encoded content representation as an extra input to the decoder of the DAE to learn to reconstruct distorted images, thus effectively forcing it to extract the distortion representation. The SAWAR, whose parameter number is much smaller than that of the COAE, is trained with labeled data in existing IQA datasets. It takes advantage of the interaction between the image content and the distortions to adaptively balance their contributions. Extensive experiments show that the COAE effectively extracts quality-related representations and the SAWAR achieves the state-of-the-art performance.
Impact of Artificial Intelligence on Employee Development at Basrah University

Employees are the leading resource of any company because they are the base of a strong and long-running organization. Their performance affects the overall evolution of the organization. Thus, modern companies have started spending an enormous amount on employee development. It is an essential process for employees and organizations. Companies have started using recent technology in employee development to make this process more productive. Artificial Intelligence is a branch of computer science. It is based on the concept of imitation the human intellectual functioning. This research studies the impact of artificial intelligence on employee development. A descriptive analytical methodology is used. Artificial Intelligence is considered an independent variable. However, the dependent variable was Employee development with three dimensions (Skills, engagement, and performance). Basrah University in Iraq was taken as a sample. Consequently, a questionnaire of 24 questions is built and distributed to Basrah University employees. The collected answers were examined using SPSS. The result shows a significant impact between Artificial Intelligence applications and employee development.
A survey of few-shot knowledge graph completion

With the continuous development of knowledge graph completion (KGC) technology, the problem of few-shot knowledge graph completion (FKGC) is becoming increasingly prominent. Traditional methods for KGC are not effective in addressing this problem due to the lack of sufficient data samples. Therefore, completing the task of knowledge graph with few-shot data has become an urgent issue that needs to be addressed and solved. This paper first presents a concise introduction to FKGC, which covers relevant definitions and highlights the advantages of FKGC techniques. We then categorize FKGC methods into meta-learning-based, metric-based, and graph neural network-based methods, and analyze the unique characteristics of each model. We also introduced the research on FKGC in a specific domain - Temporal Knowledge Graph Completion (TKGC). Subsequently, we summarized the commonly used datasets and evaluation metrics in existing methods and evaluated the completion performance of different models in TKGC. Finally, we presented the challenges faced by FKGC and provided directions for future research.
Parametric 3D Explorations with Adversarial Networks

Three-dimensional (3D) modeling and exploration are crucial for a wide range of applications.The authors propose a novel approach for sketch-based 3D exploration utilizing Stacked Generative Adversarial Networks (SGANs).The authors highlight the significance of three-dimensional (3D) modeling and exploration across various domains, such as computeraided design, virtual reality, and gaming.The authors emphasize that traditional techniques for 3D modeling often necessitate expertise in complex software tools and considerable manual effort.The authors note that in recent years, deep learning methods, specifically Generative Adversarial Networks (GANs), have demonstrated impressive capabilities in generating realistic and high-quality 3D models.Building upon this progress, they present their method, which harnesses the power of GANs to produce 3D models from sketch-based inputs.This approach enables users to intuitively and interactively explore 3D scenes.The research contributes to the field of sketch-based 3D exploration by introducing a unique framework that combines the strengths of GANs with sketch input, resulting in the generation of realistic and interactive 3D models.The authors highlight that their approach holds the potential to transform the way users engage with 3D modeling tools, making the process more intuitive, accessible, and enjoyable.