Reviewerly/exHarmony huggingface dataset:
The effect of sensory experience on sport development: baseball simulation in Korea

Rationale A baseball simulation provides its participants a sporting environment in which programmed game mechanics mediate sensory stimuli. The current study aims to discover how simulation users’ interests in baseball, athletic identity, and intentions to participate in baseball differ by degree of stimuli. The results would reveal the dynamics of how baseball simulation users’ sensory experiences relate to their baseball development.Design Data collection, taken at two Korean baseball simulation franchises, involved 136 simulation participants. On a self-administered survey, participants answered questions about their degree of perceiving visual, aural, and tactile stimuli as well as their baseball interest, athletic identity, and intentions to participate in baseball. The collected data were analyzed in MANOVA.Findings The simulation users’ interests in baseball differed according to the degree of visual and aural stimuli. Regarding the degree of tactile stimuli, the simulation users differed in the degree to which they identified as an athlete and intended to participate in baseball. Playing a crucial role in their baseball development was their tactile sense.Practical implications The study suggests managerial implications that sport practitioners implement toward developing a new participatory market.Research contribution The study reveals how sport simulation users interact with the simulation's multi-dimensional environments.
Generation of time-bin-encoded photons in an ion-cavity system

Abstract We investigate two single-photon generation schemes and compare their suitability for use in time-bin entanglement encoding. A trapped ion coupled to an optical cavity produces single photons through a cavity-assisted Raman transition. By manipulating the phase relationship between time-bins of successive photons, distinct features in the interference pattern of a Hong–Ou–Mandel measurement emerge. Through careful selection of the initial state, detrimental effects of spontaneous emission can be significantly reduced. We demonstrate that this reduction allows us to impart a measurable phase profile onto the emitted photons making time-bin entanglement encoding feasible with an ion-cavity system.
Orientation efficiency of a Sun-tracking surface

Abstract The work uses common correlations of solar power engineering and spherical astronomy for measuring of orientation efficiency of a tracking surface. The presented formulas for calculation are supplied with the appropriate schemes of illumination. The research gives hour graphics, which depicts a dependence of the cosines of the angle of incidence of the surface of typical tracking devices for the periods of summer and winter solstice and equinox. The graphic materials are suitable for argumentation of the constructive decisions and comparative estimation of their efficiency.
Determination of cell voltage and current efficiency in a chlor-alkali membrane cell based on machine learning approach

Due to importance of cell voltage and caustic current efficiency (CCE) in chlor-alkali industry, the necessity of accurate approach for prediction these parameters has become evident. In the current work, an extreme learning machine (ELM) approach is used to this end. Determination of the statistical qualities including R2 and different types of error reveals the fact that ELM method is suitable tool for calculation of CCE and cell voltage. The determined R2 values for CCE and cell voltage are equal to 1. Furthermore, RMSE values are 0.00002 and 1.3 × 10−6 for cell voltage and CCE, respectively. On the other hand, different graphical methods confirmed this acclaim. Moreover a sensitivity analysis is used to show effect of brine concentration, current density, operating temperature, electrolyte velocity, run time and pH on cell voltage and CCE. This analysis concluded to the fact that brine concentration and current density have the most effects on CCE and Cell voltage, respectively.
Trends and issues relating to social media utilisation in academic libraries: Experiences from the University of Limpopo subject librarians

The South African Journal of Information Management explores the latest developments and trends in information and knowledge management to offer research that can be used to further the application of sound information and knowledge management practice.
A novel missing value imputation relying on K-means clustering and kernel-based weighting using grey relation (KWGI)

Data pre-processing is one of the crucial phases of data mining that enhances the efficiency of data mining techniques. One of the most important operations performed on data pre-processing is missing values imputation in incomplete datasets. This research presents a new imputation technique using K-means and samples weighting mechanism based on Grey relation (KWGI). The Grey-based K-means algorithm applicable to all samples of incomplete datasets clusters the similar samples, then an appropriate kernel function generates appropriate weights based on the Grey relation. The missing values estimation of the incomplete samples is done based on the weighted mean to reduce the impact of outlier and vague samples. In both clustering and imputation steps, a penalty mechanism has been considered to reduce the similarity of ambiguous samples with a high number of missing values, and consequently, increase the accuracy of clustering and imputation. The KWGI method has been applied on nine natural datasets with eight state-of-the-art and commonly used methods, namely CMIWD, KNNI, HotDeck, MeanI, KmeanI, RKmeanI, ICKmeanI, and FKMI. The imputation results are evaluated by the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) criteria. In this study, the missing values are generated at two levels, namely sample and value, and the results are discussed in a wide range of missingness from low rate to high rate. Experimental results of the t-test show that the proposed method performs significantly better than all the other compared methods.
Multiple object tracking with behavior detection in crowded scenes using deep learning

Multi-object tracking (MOT) is essential for solving the majority of computer vision issues related to crowd analytics. In an MOT system designing object detection and association are the two main steps. Every frame of the video stream is examined to find the desired objects in the first step. Their trajectories are determined in the second step by comparing the detected objects in the current frame to those in the previous frame. Less missing detections are made possible by an object detection system with high accuracy, which results in fewer segmented tracks. We propose a new deep learning-based model for improving the performance of object detection and object tracking in this research. First, object detection is performed by using the adaptive Mask-RCNN model. After that, the ResNet-50 model is used to extract more reliable and significant features of the objects. Then the effective adaptive feature channel selection method is employed for selecting feature channels to determine the final response map. Finally, an adaptive combination kernel correlation filter is used for multiple object tracking. Extensive experiments were conducted on large object-tracking databases like MOT-20 and KITTI-MOTS. According to the experimental results, the proposed tracker performs better than other cutting-edge trackers when faced with various problems. The experimental simulation is carried out in python. The overall success rate and precision of the proposed algorithm are 95.36% and 93.27%.
Fuzzy based optimized itemset mining in high dimensional transactional database using adaptable FCM

One of the extremely deliberated data mining processes is HUIM (High Utility Itemset Mining). Its applications include text mining, e-learning bioinformatics, product recommendation, online click stream analysis, and market basket analysis. Likewise lot of potential applications availed in the HUIM. However, HUIM techniques could find erroneous patterns because they don’t look at the correlation of the retrieved patterns. Numerous approaches for mining related HUIs have been presented as an outcome. The computational expense of these methods continues to be problematic, both in terms of time and memory utilization. A technique for extracting weighted temporal designs is therefore suggested to rectify the identified issue in HUIM. Preprocessing of time series-based information into fuzzy item sets is the first step of the suggested technique. These feed the Graph Based Ant Colony Optimization (GACO) and Fuzzy C Means (FCM) clustering methodologies used in the Improvised Adaptable FCM (IAFCM) method. The suggested IAFCM technique achieves two objectives: optimal item placement in clusters using GACO; and ii) IAFCM clustering and information decrease in FCM cluster. The proposed technique yields high-quality clusters by GACO. Weighted sequential pattern mining, which considers facts of patterns with the highest weight and low frequency in a repository that is updated over a period, is used to locate the sequential patterns in these clusters. The outcomes of this methodology make evident that the IAFCM with GACO improves execution time when compared to other conventional approaches. Additionally, it enhances information representation by enhancing accuracy while using a smaller amount of memory.
APPLICATION OF SIGN LANGUAGE RECOGNITION USING MACHINE LEARNING

Many persons with illnesses including those that render them dumb, deaf, or blind are seen daily. Interacting with people is tough for them. Sensor-based approaches that have been previously developed all failed to provide the overall answer. The proposed project's main objective is to develop a system that employs machine learning to give voiceless communication through a system that is both affordable and effective. The recommended technique translates sign language into text by utilizing OpenCV and CNN. There will be easier communication between the two groups as a result.
Competencies of Industrial Engineers for Implementing Augmented Reality Metadata Systems

The paper focuses on the use of augmented reality (AR) by industrial engineers, especially for determining the necessary competencies required for its use. Industrial engineers are not inherently programmers. Nevertheless, augmented reality is a modern trend in their field, especially in relation to the concept of Industry 4.0 and industry in general, where it has a higher potential than virtual reality. In the first part of this paper, we placed augmented reality and the competencies required for its use in the context of industrial engineering. Subsequently, we described our own methods of implementing an augmented reality industrial metadata visualization system, namely Help Lightning Fieldbit and Unity 3D, using the Vuforia extension. We chose the metadata used in the methods with regard to their environmental potential. In this part of the paper, we also described the chosen and applied testing methodology using a questionnaire survey. Subsequently, we described the results from the questionnaire surveys of both these approaches of implementing augmented reality methods. Finally, we evaluated the results and compared them with each other and with results from other authors. As the results show, the most important competencies for creating the described AR environments are analytical competencies. We draw conclusions from the collected data regarding the necessary competencies for the creation of AR scenes using these methods and their deployment in industry, including an outline for further research.
Learn a prior question-aware feature for machine reading comprehension

Machine reading comprehension aims to train machines to comprehend a given context and then answer a series of questions according to their understanding of the context. It is the cornerstone of conversational reading comprehension and question answering tasks. Recently, researches of Machine reading comprehension have experienced considerable development with more and more semantic features being incorporated into end-to-end neural network models, such as pre-trained word embedding features, syntactic features, context and question interaction features, and so on. However, these methods neglect the understanding of the question itself and the information sought by the question. In this paper, we design an auxiliary question-and-answer matching task to learn the features of different types of questions and then integrate these learned features into a classical Machine reading comprehension model architecture to improve its ability to comprehend the questions. Our auxiliary task relies on a simple Question-Answer Pairs dataset generated by ourselves. And we incorporate the learned question-type information into the Machine reading comprehension model by prior attention mechanism. The model we proposed is named PrA-MRC ( Pr ior A ttention on Machine reading comprehension). Empirical results show that our approach is effective and interpretable. Our Question-Answer Pairs model achieves an accuracy of 84% and our PrA-MRC model outperforms the baseline model by +0.7 EM and +1.1 F1 on the SQuAD dataset.
Analasis Penerapan Teknologi Traffic Steering SD-WAN Menggunakan Perangkat FortiGate

In the current digital transformation era, network providers have long relied on WAN technology to support the business communications of their multi-local companies; however, in terms of infrastructure, cost, and even effectiveness, WAN technology is no longer sufficient to meet the needs and demands of telecommunications customers. In addition, given the magnitude of the company's need for internet network connections, technology is also needed to manage the use of traffic from rental links in the company so that it can optimize bandwidth usage from the link itself. Therefore one of the technological innovations to improve information processes is cloud-based digital innovation such as SD-WAN Traffic Steering technology. In this paper, the SD-WAN network design will be carried out on FortiGate series 50e devices by implementing traffic steering technology which can adjust a link's traffic flow according to the client's wishes. The results of the data obtained indicate that the utilization of ISP links owned by companies (Lintasarta and MNC) has been able to be used optimally, as evidenced by the use of bandwidth on each link successfully used according to the desired settings, where the settings provided have also been adjusted according to parameters of the type of each ISP service so that functionally the existing service link can be used optimally by the compan
Android-based Application of Production Cost Calculation of Food and Beverage of the MSME

Determination of the cost of production is important for a product, many businesses determine the selling price that is not in accordance with the production cost that has been issued. Therefore, businesses have a desire to use a technology that is fast and appropriate to determine the cost of production. The purpose of making this application is to facilitate the process of determining the profit and selling price of an appropriate product based on the cost of production. The method used to calculate the cost of production is the full costing method. The app was created using the Kodular app inventor. With the making of this application can make it easier for MSME actors who have food or beverage products to calculate the basic price of production.
Instant and Secure Messaging Platform Based on Blockchain

Abstract: In recent years, texting and messaging have become more prevalent than face-to-face communication, and users are becoming increasingly aware of data privacy issues associated with them. Therefore, a secure and simple way to connect is crucial for individuals and organizations with different identities and goals. The most popular organization messaging applicationsare moving towards end-to-end encryption (E2EE) as a means of ensuring the privacy and security of their users. As a result of theimplementation of the messaging distributed application, several benefits will be brought to society and the way information or different services are handled. The main objective is to develop the chatting platform to increase the privacy of messages. Consequently, both organizations and individuals express deep concern regarding the security of data and privacy when using instant messaging applications. This research seeks to enhance the security channels in chat platforms by using a new technology such as blockchain. Blockchain is a technology that operates on a decentralised basis. By overcoming the disadvantages oftraditional messaging applications, we can ensure official data’s confidentiality, integrity and availability, as well as advancedauditing capabilities.
THE USE OF TECHNOLOGICAL RESOURCES IN HIGH SCHOOLS IN THE MUNICIPALITY OF BORBA

This article is based on the assumption that technology at school has been gaining space, as it facilitates, interacts and motivates students to seek more knowledge, in this sense, this study aimed to know the potential of the Hot Potatoes program through the JQUIZ application in student learning in the face of contemporary challenges in textual interpretation in Portuguese language classes in high school.In order for this objective to be achieved, the aforementioned article followed a methodological line aimed at a qualitative approach, as a basis for the development of the study, action research was applied, since this type of research allowed the selfreflection of the teacher and his praxis to the best conditioning of learning, thus, with the development of activities, the students' interest was plausible in the face of innovative pedagogical proposals and which is already part of the reality of most of them.This way, it is notorious that the school and the teachers are evolving in the face of the perspectives of a contemporary education, with the students' reports it was possible to observe their perspectives, motivations and enthusiasm for a new way of learning by doing and building their own knowledge in the face of of survival in a competitive society in which they live, moreover, it is considerable that after this experience the teacher's praxis in the face of these new challenges in education is reviewed, because the transformation in search of the new, however much this "new" takes work and demand a reformulation in its methodologies makes the difference in teaching.
Readjustment of a robot for educational purposes

Abstract Development of control of a five degrees of freedom robot is discussed in this paper. Only two robots were made, and one of these is in the University of Miskolc. The robot was made in the 80s for educational purposes, the electronic components were obsolete and control software was missing, therefore it became necessary to perform hardware improvements, and develop a new control program. Inverse kinematics problem of the robot is solved by geometric approach to formulate the joint angles, which will form the basis of the control. A braking system containing electromagnets for the robot is constructed to balance it. A printed circuit board is designed to establish the control of the system, the central element is a Cypress PSoC 5LP platform. The development of the control program is performed in software PSoC Creator 4.4. The developed control system of the robot can receive instructions from a computer via a designed special purpose user program, which is written in Python programming language. Thanks to the improvements, the robot has become operational. Thus, the robot can serve educational purposes performing different manipulation tasks. By completing the developments, students can get to know the structure and programming of the robot, its inverse kinematics problem. This will require the development of practice-oriented tasks in the future.
Revisiting English written VP-ellipsis and VP-substitution: a dependency-based analysis

Abstract The present study quantitatively investigates the structural features of English written VP-ellipsis (VPE) and VP-substitution (VPS) under the theoretical framework of dependency grammar. Results show that: (a) Both VPE and VPS are human language mechanisms that follow the least effort principle; (b) Dependency distance of segmental and discontinuous linguistic units under the influence of VPE and VPS conform to the right-truncated Zipf-Alekseev distribution; (c) Dependency-based indices are effective in distinguishing similar structures of VPE and VPS, especially the Do-Support in ellipsis and Do + X in substitution. The study not only explores the regularity of partial segments of human language under the mechanisms of VPE and VPS, but also offers quantitative evidence for distinguishing confusing structures in VPE and VPS, which may aid in raising the retrieving precision of similar linguistic structures in corpora.
Impediments to and readiness for Linked Data application in libraries: Pakistani information professionals’ perspective

Findings revealed that there is little evidence that exists of the implementation of LD technologies by libraries in developing countries. This study aims to examine the willingness of information professionals along with their perceived barriers to the implementation of LD technologies in Pakistani libraries. Data was collected from Pakistani librarians in an online questionnaire survey. There is broad interest and willingness to implement Linked Data technologies in Pakistani libraries while perceptions of the presence of barriers remain. Participants are willing to explore and learn more about LD technologies as well as advocate and promote the adoption of these technologies. They also want to attend events about LD technologies and their applications in libraries. Though they identified the existence of different potential barriers that have to do with themselves (e.g. lack of awareness of basic LD concepts), their institutions/libraries (e.g. they lack best practices regarding applications of LD in libraries), and the technology itself (e.g. new, complex, costly). These dynamics have implications for LD applications in libraries, especially those in developing countries. The current study is a valuable addition to the literature as no study has been conducted regarding the willingness to adopt LD in libraries and barriers to its implementation. Findings regarding barriers to implementation would helpful for policymakers and IT experts consider these challenges and work to minimize these challenges for the implementation of LD in Libraries.
The Effect of Penalty Factors of Constrained Hamiltonians on the Eigenspectrum in Quantum Annealing

Constrained optimization problems are usually translated to (naturally unconstrained) Ising formulations by introducing soft penalty terms for the previously hard constraints. In this work, we empirically demonstrate that assigning the appropriate weight to these penalty terms leads to an enlargement of the minimum spectral gap in the corresponding eigenspectrum, which also leads to a better solution quality on actual quantum annealing hardware. We apply machine learning methods to analyze the correlations of the penalty factors and the minimum spectral gap for six selected constrained optimization problems and show that regression using a neural network allows to predict the best penalty factors in our settings for various problem instances. Additionally, we observe that problem instances with a single global optimum are easier to optimize in contrast to ones with multiple global optima.