Reviewerly/exHarmony huggingface dataset:
(Musik)instrument (im) Computer

The use of the computer as a sound generator is omnipresent in current music production and ranges from music notation programs playing back samples via MIDI control to specially programmed sound synthesis programs. The term ‚computer‘ is generally understood as a complete set of hardware and software. But a closer look at this complete set is definitely worthwhile and poses some systematical challenges. In the early days of digital sound synthesis in real time, the hardware is strongly connected to the resulting sound. The control was done by means of a programming language or a specially designed software, which offered more or less possibilities of intervention, depending on the stage of development. But do these sound generators actually fulfill the definition of a musical instrument – and what exactly is that definition? What about the so-called software instruments, which, partly hardware-independent, allow users to play music? How can and should interfaces be classified seeing that hardware extensions developed specifically for musical use, but still need (special) software and other technical equipment for sound generation and, above all, output? And who actually decides on the sound and handling of the new instrument, since the integration of computers into musical works usually takes place in close cooperation between composers, musicians, engineers and programmers? In order to be able to discuss these questions, not only new methodological approaches but also cooperation between the disciplines is unavoidable and at the same time rewarding.
An Overview of Pakistan Rock Salt Resources and Their Chemical Characterization

Rock salt is of importance for both humans and industries. In this study, we discussed the main salt deposits in Pakistan by evaluating the total reserves as well as the rock salt annual production and by characterizing their chemical composition (Na+, Ca2+, Mg2+, K+, B+, K+, Li+, Cl-, SO42-, Br- and NO3-). Our objectives were to document their impurity, water-insoluble matter and moisture contents to ultimately discuss whether the halite in Pakistan, in its natural form, is safe for human consumption. Pakistan rock salt deposits are located in two distinct regions: the Salt Range area in the Potwar sub-basin with huge Precambrian salt deposits and the Eocene Bahadurkhel/Kohat salts in the Kohat sub-basin. Total reserves are estimated over tens of billion tons with an annual production of about 3,534,075 metric tons in 2017-Results show that the halite of the salt range area is purer than the Bahadurkhel/Kohat salts with purity levels (expressed as NaCl) of 99 and 95 wt. %, respectively. Gypsum represents one of the main impurities in halite for both regions, while potash salts (&gt;9 wt. %) are observed in the Salt Range area, K contents are very low in the Kohat salts. Although the halite moisture content is similar for both regions, impurities contents are higher (&gt;5%) for the Kohat salts, arising the need for their purification prior to eventual human consumption.&#x0D; &#x0D;
Multiteam Competitive Optimization Algorithm and Its Application in Bearing Fault Diagnosis

A novel multiteam competitive optimization (MTCO) algorithm has been proposed to diagnose the fault patterns of bearings. This algorithm is inspired by competitive behaviors of multiple teams. It is a three-level organization structure; thus, more potential optimal areas can be searched. By imitating human thinking, such as the betrayal and replacement behavior along with the introduction of an acceptable vector, new strategies within the MTCO are designed to increase the diversity and guide jumping out of location suboptimal areas. In addition to this, a kernel function has been introduced to reduce the recognition errors caused by data which are nonlinearly distributed in original space. The obtained experimental results demonstrate that the proposed MTCO is globally stable and optimal decision performance. After that the MTCO is applied for the fault diagnosis of bearings, and it has also been compared with other commonly used methods. The comparison indicates that the proposed algorithm has higher recognition accuracy.
Analysis of the Root Causes of Fraud Using Risk Causal and Fraud Diamond Matrix: A Case Study on Retail Financing Company

Fraud risk management strategies can be carried out in three stages: prevention, detection, and response, but the most efficient stage is prevention. Fraud is part of operational risk which is defined as the risk of loss caused by the failure or inadequacy of internal processes, people, systems or technology, and external events. The perpetrators of fraud can also be analyzed using the fraud motivation model. This study aims to use the results of detection and response as input for the prevention stage using root cause analysis. This study uses the Risk Causal and Fraud Diamond (RCFD) Matrix as an analytical tool to determine the dominant root cause. This study uses 300 data samples and categorizes the root causes of fraud in the RCFD Matrix. The results show that there are three dominant root causes: 3.O System &amp; Technology - Opportunity, 2.O Internal Process - Opportunity, and 1.P People - Pressure. These results provide recommendations for fraud prevention strategies to effectively reduce or eliminate the dominant root cause.
Building a model of the integrity of information resources within an enterprise management system

In addressing the integrity of documents in modern information systems, the focus is on protecting them from unauthorized user changes. At the same time, the solution to the problem of ensuring the preservation of the composition, content, and interaction of documents has almost not been considered. However, such a task often has to be solved during the operation of information systems, as well as enterprise management systems handling unstable business processes. To address this issue, an approach has been proposed to unify solutions to the task related to the integrity of paper-based documents circulating within an enterprise management system, as well as electronic documents and stored information system data. The existing service registry models have been analyzed, aimed to formally describe the resources of the information system and an enterprise management system based on this information system. Models of elements of the unified information resource registry have been modified, and a model for ensuring the integrity of an enterprise's information resources has been developed. The proposed improvements make it possible to use current and multi-tested methods to address the integrity of information resources. Experimental testing of improved models of a unified information resource registry has been carried out. The task of ensuring integrity was considered for a regulatory document that should change its name and its composition as a result of external and internal factors. It has been shown that the application of the proposed results makes it possible to solve the task of ensuring the integrity of information resources such as paper-based regulations of an enterprise management system, within the framework of a unified registry of information resources by existing database management systems
The Impact of E-HRM on Corporates Sustainability: A Study on the SMEs in Jordan

Purpose of the study: This study aims to investigate the impact of Electronic Human Resources management (E-HRM) on corporates’ sustainability (CS). Methodology: A structured online questionnaire has been distributed to a random sample of 500 respondents from Small medium enterprises SMEs in Jordan. 385 valid questionnaires have been analyzed and SPSS has been used to analyze the Data of the study. Main Findings: The main study finding of the study complies with the main hypothesis which indicates that there is an impact of E- HRM on Corporates’ Sustainability on the SMEs in Jordan; as well as the research findings show a positive impact of E-Recruitment, E-Performance management, and E-Learning on CS in the SMEs in Jordan. Applications of this study: The study finding is helpful for many business sectors which are concerns about the new practices in HRM and the business practices which support corporate sustainability. The researcher recommends conducting more studies on E-HRM and CS as well as implementing this study on other sectors in Jordan. Novelty/Originality of this study: This study is significant in SMEs in Jordan since it investigates the impact of EHRM on Corporate Sustainability which is still a new topic in Jordan.
Sentiment Analysis based on Soft Clustering through Dimensionality Reduction Technique

Clustering based sentiment analysis confers new directions to analyze real-world opinions without human participation and pre-tagged training data overhead. Clustering based techniques do not rely on linguistic information and more convenient as compared to other traditional machine learning techniques. Combining the dimensionality reduction techniques with clustering algorithms highly influence the computational cost and improve the performance of sentiment analysis. In this research, we applied Principal Component Analysis technique to reduce the size of features set. This reduced feature set improves binary K-means clustering results of sentiments analysis. In our experiments, we demonstrate the performance of the clustering system with a reduced feature set to provide high-quality sentiment analysis. However, K-mean clustering has its own limitations such as hard assignment and instability of results. To overcome the limitation of traditional K-means algorithm we applied soft clustering (Expectation maximization algorithm) approach which stabilizes clustering accuracy. This approach allows a soft assignment to cluster documents. Consequently, our experimental accuracy is 95% with standard deviation rate of 0.1% which is sufficient to apply the clustering technique in real-world applications.
Fault Fictions: Systematic biases in the conceptualization of fault zone architecture

&amp;lt;p&amp;gt;Mental models are a human&amp;amp;#8217;s internal representation of the real world and have an important role in the way a human understands and reasons about uncertainties, explores potential options, and makes decisions. However, they are susceptible to biases. Issues associated with mental models have not yet received much attention in geosciences, yet systematic biases can affect the scientific process of any geological investigation; from the inception of how the problem is viewed, through selection of appropriate hypotheses and data collection/processing methods, to the conceptualisation and communication of results. This presentation draws on findings from cognitive science and system dynamics, with knowledge and experiences of field geology, to consider the limitations and biases presented by mental models in geoscience, and their effect on predictions of the physical properties of faults in particular. We highlight a number of biases specific to geological investigations and propose strategies for debiasing. Doing so will enhance how multiple data sources can be brought together, and minimise controllable geological uncertainty to develop more robust geological models. Critically, we argue that there is a need for standardised procedures that guard against biases, permitting data from multiple studies to be combined and communication of assumptions to be made. While we use faults to illustrate potential biases in mental models and the implications of these biases, our findings can be applied across the geoscience discipline.&amp;lt;/p&amp;gt;
Correction: Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource

[This corrects the article DOI: 10.2196/28253.].
Keypose synthesis from 3D motion capture data by using evolutionary clustering

Summary Motion Capture datasets are captured as high‐frequency discrete samples using photogrammetric computer vision or various industrial geodetic measurement methods over the relevant model. Because Motion Capture data are inherently large‐datasets, expressing Motion Capture data without employing a motion data abstraction approach such as keypose is challenging. Keypose synthesis is a serious problem in many applications. Unfortunately, the local statistical features of Motion Capture data over time periods vary often, making it difficult to determine a key point summing the motion activation involved. Conventional clustering methods, such as Fuzzy C‐Means (FCM), that are sensitive to initial conditions, can be fitted to a local solution rather than producing a highly reliable keypose. Data clustering can be performed using evolutionary search methods without being limited to relatively local solutions. In this paper, the Motion Capture data were clustered using evolutionary search algorithms, and related keyposes were synthesized using the “minimum‐distance to cluster‐centers” principle. In Experiments section of this paper, motion data containing sportive movements were clustered by using evolutionary algorithms (i.e., Particle Swarm Optimization, Artificial Bee Colony, and Differential Search Algorithm) and classical clustering algorithms (i.e., Self‐Organizing Neural Network, FCM) to obtain related keyposes. The computed statistics exposed that evolutionary methods were more successful in obtaining keypose than classical methods.
Workflow scheduling with data transfer optimisation and enhancement of reliability in cloud data centres

Infrastructure as a service (IaaS) clouds offer huge opportunities to solve large-scale scientific problems. Executing workflows in such environments can be expensive in time if not scheduled rightly. Although scheduling workflows in the cloud is widely studied, most approaches focus on two user's quality of service requirements namely makespan (i.e., completion time) and costs. Other important features of cloud computing such as the heterogeneity of resources and reliability must be considered. In this paper, we present a reliability-aware method based on discrete particle swarm optimisation (RDPSO) for workflow scheduling in multiple and heterogeneous cloud data centres. Our aim is to optimise data transfer time while minimising makespan and enhancing reliability. Based on simulation, our results show a significant improvement in terms of makespan, transferred data and reliability relative to reliability-aware HEFT method (heterogeneous earliest finish time), for the real-world workflows.
Keep the Structure: A Latent Shift-Reduce Parser for Semantic Parsing

Traditional end-to-end semantic parsing models treat a natural language utterance as a holonomic structure. However, hierarchical structures exist in natural languages, which also align with the hierarchical structures of logical forms. In this paper, we propose a latent shift-reduce parser, called LASP, which decomposes both natural language queries and logical form expressions according to their hierarchical structures and finds local alignment between them to enhance semantic parsing. LASP consists of a base parser and a shift-reduce splitter. The splitter dynamically separates an NL query into several spans. The base parser converts the relevant simple spans into logical forms, which are further combined to obtain the final logical form. We conducted empirical studies on two datasets across different domains and different types of logical forms. The results demonstrate that the proposed method significantly improves the performance of semantic parsing, especially on unseen scenarios.
The Impact of Trust and Opportunistic Behavior of Employees on Business Performance: Case Study in Vietnam

The study aimed to evaluate the direct and indirect effects of employees’ trust and opportunistic behavior on business performance through intermediaries, engagement, and knowledge sharing. Using quantitative research methods through analysis of linear structure model SEM, the study surveyed 884 samples at companies in Vietnam. The findings showed that trust and opportunistic behavior affect the business performance of companies through intermediaries directly and indirectly. In particular
Development of Learning Systems for Children to Promote Self-Directed Choosing of Learning Tasks

In this study, a learning-data collection system to acquire the data regarding developmental stages and learning activities has been developed for the purpose of self-directed choosing of learning tasks by children. The skills necessary for the children to choose their own learning tasks are categorized into three segments—“computational thinking,” “ICT literacy,” and “social perspective”—that correspond to learning the various activities that the authors promote. These learning activities are “programming classroom,” “IT advisement session,” and “children's city.” This study proposes a system design of the learning-data collection system. The learning-data are collected in the programming classrooms. The research question of this study is to find out how the learning frequency in programming education for children affects the depth of reflection and the differences in the quality of learning. As a result of analyzed learning-data, it was confirmed that the more frequent the learning, the deeper reflection was possible to promote more self-directed learning.
Evaluating Accessibility in Ubiquitous Environments: a Case Study with Museum Installations

Ubiquitous environments dynamics brings new possibilities of interaction with technology and with people in the same environment. This paper investigates accessibility challenges in ubiquitous environments through existing formal principles (Universal Design principles, WCAG 2.1 and Good Practices of Urban Design). A case study at a museum scenario sheds light on the need for adaptations and lack of coverage of the principles. The main contributions of this paper are: 1) suitability analysis of existing principles and formal standards for accessibility transposed to the analyzed scenario; 2) from the analysis result, insights on missing aspects that might be brought to the design of ubiquitous computing scenarios accessible for all.
distr6: R6 Object-Oriented Probability Distributions Interface in R

distr6 is an object-oriented (OO) probability distributions interface leveraging the extensibility and scalability of R6 and the speed and efficiency of Rcpp.Over 50 probability distributions are currently implemented in the package with 'core' methods, including density, distribution, and generating functions, and more 'exotic' ones, including hazards and distribution function anti-derivatives.In addition to simple distributions, distr6 supports compositions such as truncation, mixtures, and product distributions.This paper presents the core functionality of the package and demonstrates examples for key use-cases.In addition, this paper provides a critical review of the object-oriented programming paradigms in R and describes some novel implementations for design patterns and core object-oriented features introduced by the package for supporting distr6 components.
Samoprocena osposobljenosti nastavnika za primenu obrazovno-računarskog softvera u vaspitno-obrazovnoj praksi

This is an open-access article distributed under the terms of the Creative Teaching represents the basic and most beneficial aspect of education. The quality of teaching is continually improved by the introduction of innovative teaching techniques and modern teaching methods. Contemporary pedagogical innovations pertaining to teaching are based on the application of information and communication technologies in education. Therefore, the educational computer software is one of the most systematic and most creative tools used in schools with the purpose of improving the concept of digital teaching practices. The educational computer software is an innovative teaching tool for an independent creation and presentation of teaching units by means of digital technologies whose purpose is the achievement of students' total and active commitment and motivation. The goal of this research was to examine the teachers' self-assessment regarding their ability to apply the educational computer software in their teaching practices. The methods used were the descriptive method and the scaling technique with the Likert-type scale (ORSON) containing 35 items. The research sample consisted of 500 teachers from Central and Southern Serbia. Four factors were extracted by the factor analysis, which explains 53.19% of the variance: digital competencies, the advantages of ORS, ORS as a teaching tool, and practical application. The research results show that teachers are qualified enough for the practical application of the educational computer software. Also, the analysis of their responses proves that they regard the educational computer software as a significant and innovative teaching tool. The research determined a statistically significant difference in the subjects' responses considering the independent research variables: gender, type of school, and teaching experience (p&lt;0.05).
Gaze Behavior Effect on Gaze Data Visualization at Different Abstraction Levels

Many gaze data visualization techniques intuitively show eye movement together with visual stimuli. The eye tracker records a large number of eye movements within a short period. Therefore, visualizing raw gaze data with the visual stimulus appears complicated and obscured, making it difficult to gain insight through visualization. To avoid the complication, we often employ fixation identification algorithms for more abstract visualizations. In the past, many scientists have focused on gaze data abstraction with the attention map and analyzed detail gaze movement patterns with the scanpath visualization. Abstract eye movement patterns change dramatically depending on fixation identification algorithms in the preprocessing. However, it is difficult to find out how fixation identification algorithms affect gaze movement pattern visualizations. Additionally, scientists often spend much time on adjusting parameters manually in the fixation identification algorithms. In this paper, we propose a gaze behavior-based data processing method for abstract gaze data visualization. The proposed method classifies raw gaze data using machine learning models for image classification, such as CNN, AlexNet, and LeNet. Additionally, we compare the velocity-based identification (I-VT), dispersion-based identification (I-DT), density-based fixation identification, velocity and dispersion-based (I-VDT), and machine learning based and behavior-based modelson various visualizations at each abstraction level, such as attention map, scanpath, and abstract gaze movement visualization.