Reviewerly/exHarmony huggingface dataset:
Strategic human resource management technology effect and implication for distance training and learning

&lt;p&gt;Strategic human resource management involves a future-oriented process of developing and implementing HR programs that address and solve business problems and directly contribute to major long-term business objectives. HR management has changed dramatically in recent decades. It was once largely an administrative function focused on day-to-day responsibilities such as employee recruiting and selection and managing employee benefits. Changing labor market conditions and new business thinking call for HR business strategies that include recruiting and retaining the right people, as well as providing ethical and cultural leadership[1].This paper discusses the following issues : The importance of human resource activity , The importance of strategic human resource planning, The changing role of human resource management and nature of work and non government organizations , Technology effect on human resource management and implications for distance training and learning .A field study was conducted to know the attitude of one hundred and five Saudi employees towards the effect of distance learning and training on human resource management and development&lt;/p&gt;
Do financial stability and institutional quality have impact on financial inclusion in developing economies? A new evidence from Nigeria

This paper investigates the empirical linkages among financial stability, institutional quality and financial inclusion in Nigeria using quarterly data from 1986-2013. For empirical analysis, the study applies the autoregressive distributed lag model based on unrestricted error correction model (ARDL-UECM). First, the results show evidence that financial stability has significant impact on financial inclusion in the long run period but not in the short run. Second, the study reveals that institutional quality has significant impact on financial inclusion in the long run period and in the short run. Having found that financial stability and institutional quality are positively related to financial inclusion in the long run, the study therefore recommends that the government and policymakers pursuing the agenda of financial inclusion should pay attention not only to the financial and economic indicators but also to institutional factors existing in the country since they interact in creating a harmonised and sustainable developmental trajectory. In a country where the economic, legal, judicial and political institutions are very weak, many households (savers) and firms (borrowers/investors) may not be protected when there are issues of financial contract enforcements or breaches within the economy.
The Effect of Entrepreneurial Behaviour on Farm Performance among Small-scale Farmers: Case of Niono Zone, Mali

This study examines the determinant of Entrepreneurial Behaviour (EB) on farm performance among small-scale rice farmers in Niono zone, Mali. The data of the study were collected from 236 randomly selected farmers who had less than 5 hectares, through a semi-structured questionnaire. Sale, profitability and post-harvest losses (PHLs) were used as farm performance indicators.  The data analysis was carried out using confirmatory factor analysis (CFA) on selected EB, and multivariate Tobit model to examine determinant of EB farm performance. The diagnostic test indicated the absence of collinearity issues while post-estimation test of CFA indicated good fitness of the model and reliability and validity of data.  The results of the research show that EB of as initiation and innovativeness, household head’s  years of schooling and the amount credit in the counter-season improved farm performance by reducing PHLs. Land size , farm assets values and access to model farms improved farm performance by increasing both sales and profitability. The study concluded EB improved farm performance, and its determinant is enforced by other socio-economic and institutional factors of small-scale farmers. The study recommended enhancing more EB as well as establishing better business environment to incentivize farming business towards commercial-oriented farming. This can be done through more training, access to land, improving farmers’ investment ability and sharing of information, techniques and technologies among farmers.
Data residency as a service: A secure mechanism for storing data in the cloud

Recently, researchers have been working on cloud data assurance models to ensure that the data is in compliance with the policies. In such a model, the data is placed in the intended data centres without focusing on data residency issues. Thus, in order to solve such issues, we have proposed a framework known as the data resident storage (DRS) which is intended to achieve data residency protection, thus providing data residency as a service (DRaaS). The finite state machine model is developed for the purpose of verifying the methodology in terms of data location and data access. The validation is carried out in the CloudSim toolkit with the help of defined services. The developed test automation framework places the data only in data centres that are in compliance with data residency laws. Finally, this framework can be hosted by the cloud service provider to provide DRaaS to the end users.
Data-driven Resource Allocation in Virtualized Environments

Modern advances in virtualization technologies have revolutionized how we build and manage computer systems. Virtualization technologies, however, adversely impact the predictability of system performance, which introduces several challenges in balancing performance and resource utilization.In this dissertation, we explore and address performance challenges by characterizing and modeling application performance for resource allocation in two application scenarios: distributed network emulation and network functions virtualization (NFV). More specifically, we focus on preserving experiment fidelity for distributed network emulators running on heterogeneous physical machines, while, in NFV, we characterize performance impacts of various virtualization and configuration options and make timely resource flexing decisions.
Playing digital music by waving hands in the air

We study how music can be played with a computer by tracking hands in the air. We begin our considerations by analyzing how the theremin can be played—an electronic musical instrument which is played without physical contact by the hands of the performer. We then look at the present hand-tracking technologies available for common personal computers and mobile devices and hypothesize that optical tracking devices, like Leap Motion controller, may be used for simulating the theremin functions with a common personal computer. We then describe our own implementation of the digital theremin and analyze why the theremin is considered as the most difficult to play musical instrument and how its deficiencies can be overcome with the help of computer graphics.
Collection Evaluation: A Case Study of the New College Library from 2009-2013

This paper examines the importance of the New College Library collection.  It is an axiomatic truth that a library cannot function without an appropriate collection.  The efficiency and effective services of the libraries are governed by the quality and quantity of its collection.  Due to the technological advancements, the exponential growth of literature and the devaluation of rupees also tempted the libraries to think thoroughly before acquiring the reading materials for the college libraries.  This study is based on the acquisition of library materials from 2009-2013 by the management grant.  An attempt has been made to know the budget allocation to various faculties and the average price of books in different subjects.  Based on the finding of the study constructive suggestions are made to implement   for qualitative collection development.  The results of the study may be helpful for   planning the budgetary allocation for collection building in the libraries.
A chaos based image encryption algorithm using Shimizu-Morioka system 

Recent research on image encryption schemes has focused on chaotic systems in order to meet the demand for real-time secure image transmission over the Internet. In this paper, we propose a new image encryption scheme based on ShimizuMorioka chaotic system. The scheme consists of two stages: the confusion (mixing) stage and the diffusion stage. In the confusion stage, we utilized the rich chaotic properties of the Shimizu-Morioka chaotic system by solving the system N times steps using Eulers method and scrambled the positions of the pixel values of the image using the randomness of the solutions obtained from the chaotic system. In the diffusion stage, we generate N (where N is the size of image per colour) random integer numbers that is non-periodic and performed MOD and bitXOR operations on the shuffled image using the random numbers to obtain encrypted (diffused) image. The proposed algorithm is tested on a standard RGB image that is of size 256x256 and is stored with TIFF file format. Performance analysis on the proposed scheme such as the statistical analysis and the sensitivity analysis show that the proposed encryption scheme is reliable and strong enough to withstand different attacks.
Preliminary standardization of consonant-vowel in noise test in normal Persian speaking children

Background and Aim: Children have more dif­ficulty in understanding speech in noisy envi­ronment than adults. Different tests are available for evaluation of speech perception ability in noise in children, each examines different levels of auditory system. The present study aimed to obtain normative data for consonant-vowel in noise test for 8–12 years old Persian-speaking children.&#x0D; Methods: A hundred and sixty children were selected in five age range groups of 8 to 12 years old (32 children in each age range) with normal hearing from school students in Tehran. The audiometry test was performed in octave interval between 500–4000 Hz. Then the con­sonant-vowel test was first performed in silence and then in signal-to-noise ratios (SNRs) of +12, +6, 0, −6, −12, at the listening comfort lev­el and randomly in children ears.&#x0D; Results: There were significant differences bet­ween age range of 8 and 9 years with other age groups in the SNR of −12. There was a signi­ficant difference in all children between the scores of left and right ears in −12, −6, 0 SNRs (p &lt; 0.05). However, there was no specific &#x0D; pattern in each age group. The results showed that with increasing SNR, the scores of rec­ognition of the syllables increased. Sex had no effect on consonant-vowel recognition in the presence of noise.&#x0D; Conclusion: Recognition of the consonant-vowel in high SNR in both ears is stable before age 8, and this stability in the low signal-to-noise ratio is higher at around the age of 10.
New intelligent interface study based on K-means gaze tracking

User interface (UI) is an interaction and information exchange medium between systems and users, which is convenient to communicate and enable users to operate hardware easily and effectively to achieve the bidirectional interaction. Traditional UI has difficulty in satisfying requirements of users. Therefore, this paper proposes a new intelligence interface scheme based on K-means gaze tracking. First, it uniformly describes the user, interface and system in intelligent interface interaction framework based on visual attention selection mechanism. Second, it uses K-means method to calculate attention degree value of interface on the basis of mapping relation of user, interface and system. Third, it adopts visual attention allocation strategy to predict users' interest degree on interface. Finally, experiments are conducted to verify the performance of our new scheme. The results show that the accuracy of intelligent interface predicting user decision intention is very high. This new method is a kind of selectable solution for marking intelligent interface of user interest goal automatically. What's more, intelligence interface scheme based on K-means gaze tracking can effectively improve the quality of gaze tracking.
Primitive Recursion and Isaacson’s Thesis

Although Peano arithmetic (PA) is necessarily incomplete, Isaacson argued that it is in a sense conceptually complete: proving a statement of the language of PA that is independent of PA will require conceptual resources beyond those needed to understand PA. This paper gives a test of Isaacon’s thesis. Understanding PA requires understanding the functions of addition and multiplication. It is argued that grasping these primitive recursive functions involves grasping the double ancestral, a generalized version of the ancestral operator. Thus, we can test Isaacon’s thesis by seeing whether when we phrase arithmetic in a context with the double ancestral operator, the result is conservative over PA. This is a stronger version of the test given by Smith, who argued that understanding the predicate “natural number” requires understanding the ancestral operator, but did not investigate what is required to understand the arithmetic functions.
Integration of synthetic minority oversampling technique for imbalanced class

In the data mining, a class imbalance is a problematic issue to look for the solutions. It probably because machine learning is constructed by using algorithms with assuming the number of instances in each balanced class, so when using a class imbalance, it is possible that the prediction results are not appropriate. They are solutions offered to solve class imbalance issues, including oversampling, undersampling, and synthetic minority oversampling technique (SMOTE). Both oversampling and undersampling have its disadvantages, so SMOTE is an alternative to overcome it. By integrating SMOTE in the data mining classification method such as Naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) is expected to improve the performance of accuracy. In this research, it was found that the data of SMOTE gave better accuracy than the original data. In addition to the three classification methods used, RF gives the highest average AUC, F-measure, and G-means score.
A Practical Approach to Cellular Communications Standards Education

Abstract The cellular communications industry is steadily growing and expanding to solve the needs of governments, businesses and communities. Standards are fundamental to enable cooperation while promoting competition. The companies involved contribute and agree on appropriate technical specifications to ensure diversity, compatibility and facilitate worldwide commercial deployment and evolution. The specifications of cellular communications standards are extensive, complex and intentionally incomplete to spur innovation and differentiation. This makes standards education a difficult endeavor, but it is highly demanded by the wireless industry. This paper describes a practical approach to teaching cellular communications standards. Our methodology leverages software-defined radio technology and uses the abstraction layer and operating environment (ALOE) to provide a practical learning environment that facilitates developing many of the needed technical and soft skills without the inherent difficulty and cost associated with radio frequency components and regulation. We define six learning stages that assimilate the standardization process and identify key learning objectives for each. We discuss our experiences when employing the proposed methodology at Barcelona Tech in Spain, compare the approach with an equivalent class at Virginia Tech in the US and make the following observations: (1) The complexity of standards need to be abstracted and presented in a form suitable for a given class. (2) Educating about cellular communications standards is most effective when students are immersed in the process. (3) Hands-on activities need careful preparation and close guidance.
The price of Bitcoin: GARCH evidence from high-frequency data

This is the first paper that estimates the price determinants of Bitcoin in a generalized autoregressive conditional heteroscedasticity (GARCH) framework using high-frequency data. Derived from a theoretical model, we structurally estimate Bitcoin transaction demand and speculative demand models using hourly data for 2013–21. In line with the theoretical hypothesis, our empirical results confirm that both the Bitcoin transaction demand and speculative demand have a statistically significant impact on the Bitcoin price formation. The Bitcoin price responds negatively to the Bitcoin velocity, whereas positive shocks to the aggregate Bitcoin stock, interest rate and size of the Bitcoin economy exercise an upward pressure on the Bitcoin price.
Weight distribution of a class of cyclic codes of length $2^n$

Let $\mathbb{F}_q$ be a finite field with $q$ elements and $n$ be a positive integer. In this paper, we determine the weight distribution of a class cyclic codes of length $2^n$ over $\mathbb{F}_q$ whose parity check polynomials are either binomials or trinomials with $2^l$ zeros over $\mathbb{F}_q$, where integer $l\ge 1$. In addition, constant weight and two-weight linear codes are constructed when $q\equiv3\pmod 4$.
TIMIT and NTIMIT Phone Recognition Using Convolutional Neural Networks

A novel application of convolutional neural networks to phone recognition is presented in this paper. Both the TIMIT and NTIMIT speech corpora have been employed. The phonetic transcriptions of these corpora have been used to label spectrogram segments for training the convolutional neural network. A sliding window extracted fixed sized images from the spectrograms produced for the TIMIT and NTIMIT utterances. These images were assigned to the appropriate phone class by parsing the TIMIT and NTIMIT phone transcriptions. The GoogLeNet convolutional neural network was implemented and trained using stochastic gradient descent with mini batches. Post training, phonetic rescoring was performed to map each phone set to the smaller standard set, i.e. the 61 phone set was mapped to the 39 phone set. Benchmark results of both datasets are presented for comparison to other state-of-the-art approaches. It will be shown that this convolutional neural network approach is particularly well suited to network noise and the distortion of speech data, as demonstrated by the state-of-the-art benchmark results for NTIMIT.
Event Generation and Statistical Sampling with Deep Generative Models

We present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but also to ensure these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes $e^+e^-\to Z \to l^+l^-$ and $p p \to t\bar{t} $ including the decay of the top quarks and a simulation of the detector response. We find that the tested GAN architectures and the standard VAE are not able to learn the distributions precisely. By buffering density information of encoded Monte Carlo events given the encoder of a VAE we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated several orders of magnitude faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g. for the phase space integration of matrix elements in quantum field theories.
A Case Study: Focusing on Sustainability Themes and Ecocomposition through Student Blogs in a Professional and Technical Writing Course

Tackling environmental and sustainability issues has grown in popularity in writing courses.Yet, for teachers designing professional and technical writing classes, what are the benefits and drawbacks in asking students to interact with place-based discourses in their digital compositions, including blogs?How does implementing an ecocomposition curriculum and sustainability topics in professional and technical writing courses affect students' research, digital writing, collaborative, and critical-thinking outcomes, along with influencing their personal and larger goals?This article discusses a four-year case study at a Southwestern university of an experimental course assignment's design, and it involves 252 students, including many Native Americans.Students engaged with environmental themes and ecocomposition methods in an upper-division class.This article includes a description of the class's major assignment, a blog site and reflective essay, and the blog's assessment criteria, with raters measuring the blog's writing outcomes.Overall, employing ecocomposition practices within the blog assignment unit provided students with a relevant curriculum, assisting them in conducting research for a blog space; writing digitally and thinking critically about diverse spaces related to their backgrounds, majors, and futures; and forging ties with classmates and potential
Trust Optimization for Byzantine Attacks in Cognitive Networks

Background &amp; Objective: Cognitive Radio Network (CRN) is an emerging technology that provides ability of adapting operating parameters to wireless devices in order to overcome spectrum scarcity problems. However, involvement of numerous pervasive smart wireless devices introduces many security threats in CRN. The research work specifically highlights the different kinds of attacks that occur in cognitive networks. Methods: The algorithm is designed which focuses on vulnerabilities that occur in any single or an individual node. The proposed algorithm will take many factors into consideration like integer programming, trust and Byzantine failure model. Though there are various security parameters especially the five major attributes like integrity, confidentiality, availability, authorization, access control and nonrepudiation, which should be considered for developing any alogotithm. Conclusion: Nevertheless, certain security parameters are kept into consideration, which are embedded into algorithm to make it of its unique kind.
IMPROVING VOCABULARY USING INITIAL LETTERS GAMEAT THE FOURTH GRADE OF SDN HARAPAN 01 CIMAHI

Improving Vocabulary Using Initial Letters Game at The Fourth Grade of SDN Harapan 01 Cimahi. This research conducted to find out students' vocabulary achievement through initial letter game. The subjects of this research were class IV of SDN Harapan 1 Cimahi. The data collected using Classroom Action Researchincluding two cycles and each cycle consist of pretest and the post-test of vocabulary tests. After the implementation of initial letter game activities in first cycle and second cycle, the students’ vocabulary score is getting better. It can be seen from the result of the students’ post-test average score,cycle 1 was 67,50 and cycle 2was 85,2. The result showed thatInitial letters game can improved the students' vocabulary and can motivated students for learn English