Reviewerly/exHarmony huggingface dataset:
Bayesian analysis for the ratio of scale parameters in Topp-Leone distributions

In this paper, we consider the default Bayesian analysis for the ratio of scale parameters in Topp-Leone distributions. Thus we propose the noninformative prior such as matching priors and reference priors in this problem. We also investigate that the second order matching prior satisfies an alternative coverage matching criterion, and it becomes a highest posterior density matching prior. Next, we check whether the developed reference prior is the second order matching prior and whether Jeffrey’s prior and reference prior have same form. Simulation study indicates that the proposed matching prior is very close to the target coverage probabilities, and a real example is given.
Intelligent Pedagogical Model with Kinesthetic-Static Immersion based on the Neuro-Linguistic Programming Approach (NLP)

In this paper, the authors propose a teaching/learning pedagogical model, based on an approach that uses neurolinguistic programming, educational data mining and haptic interaction.It also uses the theory of learning styles, which are identified with data mining techniques, clustering and the Farthest First algorithm, as well as a test of Neurolinguistic Programming.Depending on the results obtained, the teaching/learning strategies are defined, and the activities of an educational coaching are suggested, with the purpose of boosting the students' attention in the classroom, stimulating their communicative and psychomotor skills.The proposal was evaluated with a sample of students of regular basic education, to whom an instrument was applied before and after carrying out the tests of applying the plan of the teaching/learning activities.For this purpose, a multifunctional learning kit was constructed, which is a didactic and playful resource, applicable to the student's psychomotor area.The kit contains an application and a hardware device called "Tusuna-pad 1.0", which was implemented in the Unity games engine and was programmed using the C# language.The pedagogical model was validated with the participation of students of Regular Basic Education, considering pedagogical and computational aspects, results that were validated and duly analyzed.Finally, conclusions and recommendations for future work were established.
Experts’ Agreement of the Personalized m-Learning Curriculum Model Based on Fuzzy Delphi Method

The purpose of this study was to identify the experts’ agreement on the suitability of the personalized m-learning curriculum model. A total of 25 experts were selected to evaluate the model. A Fuzzy Delphi Method was used to identify suitability of the elements in personalized m-learning model. Based on the overall findings, all the items have met the requirements needed in the triangular ­­­­­­­­­fuzzy number and meet the deffuzification process (more than the Alpha α - cut value of 0.5). This revealed that all the experts consensually agreed with all questionnaire items. Hence, the proposed personalized m-learning curriculum implementation model is suitable to serve as a guideline for the instructor in implementing personalized m-learning. The new personalized m-learning elements for another course or programme could be determined based on the experts’ opinions. This will enable the development of personalized m-learning model that could be implemented for any course or programme.
Texture target classification with CLBP and local geometric features

For the problems of needing pre-training and poor robustness to rotation and illumination changes of various improved algorithms based on local binary pattern (LBP), this paper presents a new texture classification algorithm by integrating the completed local binary pattern (CLBP) and the local geometric invariant features of the image surface. In our algorithm, the local geometric invariant features are first computed. Then the computed results are further quantified and encoded to make combination with the CLBP histogram. The proposed algorithm can extract image macroscopic and microscopic features simultaneously, and it has the properties of not significantly increasing feature dimension, without pre-training, and invariance to the rotation and illumination changes. Experimental verifications are conducted on two standard texture databases, and the results demonstrate that the proposed algorithm outperforms the comparative classification algorithms in classification accuracy and robustness.
The Effect of Evidence-Based Management Practices on Improving Job Performance: A Structural Equation Modeling

This study aimed to investigate the effect of Evidence-Based Management Practices on Job Performance at Private Hospitals in Jeddah .a random stratified sample individuals from the employees of the Private Hospitals at Jeddah at three managerial levels totaling (255) individuals.The study applied using SPSS and Amos to do the analysis.The study using a questionnaire to achieve the study objectives through Structural Equation Modeling (SEM).The study results revealed that the Evidence based Knowledge has a significant positive effect on Task Performance and Contextual Performance.This means that the increased one unit in Evidence based Knowledge can significantly increased the same degree in Task Performance and Contextual Performance.As well as, Evidencebased Behaviors has a significant positive effect on Task Performance and Contextual Performance.Finally, Evidence-based Uses has a significant positive effect on Task Performance.Finally, Evidence-based practice trends not a significant effect on Task Performance and Contextual Performance.In addition, Evidence-based Uses has not a significant effect on Contextual Performance.The study concluded that there exists a statistically significant relationship between some of Evidence-Based Management Practices on Task Performance and Contextual Performance at Private Hospitals in Jeddah .The study found out that there was big contribution of the Evidence-Based Management Practices and job Performance to the Private Hospitals in Jeddah.
The Program for Cooperative Cataloging: Backstory and Future Potential

In 1988 the Library of Congress and eight library participants undertook a two-year pilot known as the National Coordinated Cataloging Program (NCCP) to increase the number of quality bibliographic records. Subsequently the Bibliographic Services Study Committee reviewed the pilot. Discussions held at the Library of Congress (LC) and in other fora resulted in the creation of the Cooperative Cataloging Council, and, ultimately, the establishment of the Program for Cooperative Cataloging (PCC) in 1994. The conditions that contributed to a successful approach to shared cataloging are described. The article concludes with considerations for expanding the future effectiveness of the PCC.
Implementasi High-availability Web Server Menggunakan Load Balancing As A Service Pada Openstack Cloud

Abstrak Penerapan load balancing saat ini dirasa kurang efektif dalam penerapa cloud computing karena penyedia layanan harus menyediakan server khusus untuk penerapan load balancing tersebut. Penerapan load balancer di dalam cloud dapat menjadi lebih efektif adalah dengan menerapkan layanan load balancer di dalam cloud yaitu LBaaS. Layanan load balancer di dalam OpenStack ini dapat menyediakan algoritma load balancing round robin, least connection, dan source ip secara efektif di dalam cloud. Pada penelitian ini telah dilakukan implementasi high-availability web server dengan menggunakan layanan load balancer as a service pada OpenStack. Dari hasil penelitian yang dilakukan diketahui bahwa kinerja server yang menggunakan load balancing lebih baik dibandingkan dengan single server. Hal ini ditunjukkan dengan peningkatan nilai throughput sebesar 65,61%, penurunan nilai elapsed time sebesar 46.93%, penurunan nilai response time sebesar 46.87%, peningkatan nilai transaction rate sebesar 66,97%, dan penurunan nilai cpu utilization sebesar 58.74%. Dari parameter fairness, algoritma round robin lebih fair dengan nilai fairness index mencapai 1 jika dibandingkan dengan algoritma least connection. Kata Kunci: cloud computing, openstack, load balancer, lbaas, web server Abstract The implementation of load balancing is currently considered less effective in the implementation of cloud computing because service providers must provide a server for the implementation of load balancing. The application of load balancers in the cloud can be more effective is to implement load balancer services in the cloud. The service is contained in OpenStack called Load Balancing as a Service (LBaaS). This load balancer service in OpenStack can provide round robin load balancing algorithms, least connections, and source ip effectively in the cloud In this research, a high-availability web server has been implemented using load balancer as a service on OpenStack. From the results of research it is known that the performance of servers that use load balancing is better than that of a single server. Proven with increase throughput of 65,61%, decrease elapsed time of 46,93%, decrease response time of 46,87%, increase transaction rate of 66,97%, and decrease cpu utilization of 58,74%. But from the fairness parameter, the round robin algorithm is more fair with a fairness index value of 1 when compared to the least connection algorithm. Keywords: cloud computing, openstack, load balancer, lbaas, web serve
Stochastic Approximation Trackers for Model-Based Search

In this paper, we propose multi-timescale, sequential algorithms for deterministic optimization which can find high-quality solutions. The algorithms fundamentally track the well-known derivative-free model-based search methods in an efficient and resourceful manner with additional heuristics to accelerate the scheme. Our approaches exhibit competitive performance on a selected few global optimization benchmark problems.
PENGEMBANGAN APLIKASI PEMILIHAN LAGU SECARA OTOMATIS BERDASARKAN POOLING SMS REQUEST (STUDI KASUS RADIO LPLL SUARA SIDOARJO)

Radio is a technology that is used for signal transmission and modulation by electromagnetic radiation (electromagnetic waves). Originally broadcast radio signals to be transmitted over either continuous wave data through amplitude modulation (AM) or frequency modulation (FM). Shipping method is called an analog signal. Furthermore, as the development of internet technology was found, and a digital signal which then changes the way radio signal transmission. Radio programming is the process of transferring voice script, which ends into a tangible result of an idea. Production of radio broadcasts are also basically sound is a blend of image creation with a series of words - voice, music and sound effects into a unified whole that is able to generate suggestions, emotions and imagination of the listener. Along with the development of information technology, on the radio program did not escape the computerized system, both in the management track, request management and event management. Kata Kunci : Poling SMS, frequensi Modulation
The Influence of Authority, Initiative, and Unity of Direction in Achieving Good Governance in Africa using Mplus v8 Scientific Tool

This is an inductive and applied research study with the application of both quantitative and qualitative methods using Mplus v8 and presented the findings.The study targeted how authority, unity of direction and initiative are used in the public sector organisations in Ghana through organisational charts and structures.The research primary source of data is Ghana National Association of Teachers (GNAT) and of which 55 sample size were collected within GNAT in Tamale, Northern Region.GPower 3.0.10was also used to calculate this sample size to support the data analysis.The research findings shows that the public sectors in Ghana organisational charts needs to be transformed or changed completely to suite the public sector as humble institutions and not a bossy type of organisation's jobs.The occupants of the public sector organisations are supposed to serve and not as directors.The model of the hypothesis shows that there is positive relationship between authority and good governance, also, it indicated a lower coefficient for initiative which correlates with good governance, whereas, the analysis shows that there is negative relationship between unity of direction and good governance.By this it means that there is the need for more concentration on unity of direction.As former President Obama once said that Africa does not need strong men but rather Africa needs strong institutions (New York Times report, 2009), and this is the only way Ghana and Africa can build its human capital towards achieving good governance by adopting the findings in this research.
The Role of the Languages in the Formation of a Creative Personality

The paper considers the issue of the role of language in the development of the spiritual world of the individual by the example of the creative participation of the team of a technical university by in-depth study of languages.
MIRU: A Novel Memory Interaction Recurrent Unit

The memory-based network is widely used in a variety of sequence modeling. Taking full use of memory is one of the challenges to build memory-based models. Existing work either as recurrent neural network, memory capacity is too small to comprehensively model information of the sequence, or as the memory network, although with an external storage structure to enhance memory, the memory is not sufficiently utilized. To address these issues, we propose a novel memory interactive recurrent unit (MIRU), which constructs a multi-dimensional memory inside the recurrent unit and employs convolution operations to interact and update memories. Finally, we test MIRU on the YELP benchmark dataset of sentiment analysis and empirical results demonstrate that MIRU significantly outperforms the advanced models.
Exploration of Variables Relating to Career Decisions via Adaptive LASSO

본 연구는 한국청소년정책연구원의 한국아동청소년패널(Korea Children Youth Panel Survey; KCYPS) 중1패널 6차년도 자료에 기계학습 기법 중 벌점회귀모형으로 분류되는 adaptive LASSO를 적용하여 진로결정 관련 변수를 탐색하였다. Adaptive LASSO는 LASSO를 개선한 방법으로 변수 선택과 회귀계수 산출이 동시에 가능하며, 변수 선택 일치성을 충족한다는 장점이 있다. KCYPS의 고등학교 3학년 1,938명이 응답한 326개의 설명변수 중 학교/학력, 지적발달, 사회정서발달, 진로계획 영역(이상 개인발달) 변수 17개, 가정환경, 교육환경, 지역사회환경, 매체환경, 활동/문화 환경 영역(이상 발달환경) 변수 14개의 총 31개의 변수가 진로결정 관련 변수로 선택되었다. 본 연구 결과, 정서문제, 진로정체감, 양육방식, 학교생활적응과 같이 선행연구에서 연구된바 있는 기존 변수들을 확인하였으며, 이와 함께 직업관, 지역사회 인식, 휴대전화 보유여부, 체험활동 및 동아리 활동 참여 유무와 같은 새로운 변수들을 탐색할 수 있었다. 자신의 적성을 인지하고 직업의 세계를 탐색하도록 돕는 진로교육의 중요성이 증대되고 있는 시점에 본 연구는 교육적 개입이 어떤 방향으로 나아가야할지 제시하였으며, 심층 연구가 필요한 일부 변수에 대한 후속 연구 또한 제안하였다.The current study explored variables relating to students’ career decisions, using 7th graders’ 6th wave panel of KCYPS. In particular, adaptive LASSO was employed among penalized regression techniques. Despite its strength in variable selection, LASSO is known to produce inconsistent coefficient estimates. In response to the LASSO shortcoming, adaptive LASSO was proposed as a technique to yield consistent estimates. A total of 326 variables responded by 1,938 12th graders were investigated in the adaptive LASSO model and 31 variables were selected as important after relevance counts. The selected 31 variables included 17 and 14 variables from personal development and development environment sections, respectively. Among the 31 selected variables, those regarding emotional problems, career identity, parents’ parenting style, and school adaptation have been investigated in previous research. Newly found variables included variables relating to sense of occupation, community awareness, cell phone possession, and extracurricular activities. The importance of career education is increasingly stressed, as career education helps students recognize their aptitudes and explore possible occupations. Some of the selected variables have practical implications, as educational intervention is possible with them. Further research topics were also suggested.
Cloud, co-location, on-premises and hybrid disaster recovery solutions: Pros, cons, and a cost comparison.

The hype surrounding cloud-based disaster recovery (DR) continues to gain steam - and with good reason. That said, due to cost, compatibility and/or organisational restrictions, cloud-based DR is not necessarily the best fit for all systems. This paper presents case studies and a structured approach to evaluate the various options, including public/private cloud DR, DR as a service (DRaaS) and hybrid solutions. For example, the pay-per-use model for cloud DR saves money while the cloud environment is dormant, but results in higher run-time costs than co-location or on-premises solutions when DR is executed. Similarly, organisations need to understand the range of DRaaS options to make an informed decision. Where cloud is not a good fit for all systems, a hybrid solution can satisfy conflicting requirements while leveraging the benefits of cloud where appropriate. Finally, organisational constraints can tip the balance away from what may appear to be the best fit on paper.
Image Recognition Methods Based on Hemming Distance

Recognition of multidimensional images based on the Hamming distance, as well as development of estimation criteria of certain image classes and new methods for determining the Hemming distance differentiating between identical objects with different number of coding parameters are highly topical problems facing various areas of knowledge and activity. The paper proposes a method of encoding of multidimensional objects for diagnosis with the same and different number of parameters, which represent multidimensional vectors at nodes of a two-dimensional plane as a model of a multichannel object for diagnosis in the Hemming space. The monitoring system scheme of the nodes of the object for diagnosis is developed. Coding methods for priority and prohibitory road signs are proposed. The value of the Hamming distance estimate for these categories is determined. A method and algorithm for the Hamming distance estimate for objects with different number of parameters are proposed. In addition, a method of value of the Hamming distance estimate for some road sign categories with different number of informative parameters is proposed.
Research Notebooks Workshop

The workshop was a meetup to discuss practical steps for institutions wishing to providing central support for research notebook tools.
Hierarchical Bayesian pattern mixture models for binary data from small area

The Purpose of many surveys is to estimate the probability of a group having certain characteristics. However, if the survey has a lot of missing data, we may get incorrect results. So a lot of research has been done on mission data since the past. There are many methods to handle missing data, so it is better to use various methods than one. We estimate the proportion of finite population using the Bayesian method using the pattern mixture model for binary data. We consider a hierarchical Bayesian model to increase the reliability of small data. We have applied various cases on the hyper-parameter of the prior distribution of proportion for nonresponse to confirm that the proportion estimate is not sensitive. We also confirmed that small area estimation is better than the individual area estimation.
A real-time identification for hand-based movements using Recurrent Complex-Valued Neural Networks

This paper presents an application for hand-based movements using two Recurrent Complex-Valued Neural Networks (RCVNN) in real-time. The proposed system identifies hand-based movements using two angles of human arm model acquired by the infrared vision time of flight depth system integrated in Kinect v2. The results of the experiments compare the performance of the RCVNN with the inverse kinematic. Finally, this topology helps us to identify hand-based movements avoiding singularities.
Seleksi Fitur Berbasis Pearson Correlation Untuk Optimasi Opinion Mining Review Pelanggan

The comments contained on e-commerce users generally contain opinions about positive or negative experiences at several online shops. Sentences that can be written indirectly both a little or a lot, will affect other potential customers. So as a result of these comments cause a product sold at an online store has a rating of two things namely "recommended" or "non-recommended". However, detection of positive and negative opinions manually will require more time because of the large amount of data. For this reason opinion mining using technology in data mining can be used to automate positive and negative detection of comments. However, one of the main problems in opinion mining is limited data but has a large number of attributes. In this study, we propose the application of Pearson correlation (PC) based feature selection for opinion mining optimization. The results of the experiment show that the application of PC increases the performance of opinion mining systems in 3 types of classification, namely Logistic Regression, Naïve Bayes and Support Vector Machine, resulting in more optimal accuracy, namely 98.80%, 87.87% and 98.12%.
Three-Dimensional Visualisation of Skeletal Cavities

Bones contain spaces within them. The extraction and the analysis of those cavities are crucial in the study of bone tissue function and can inform about pathologies or past traumatic events. The use of medical imaging techniques allows a non-invasive visualisation of skeletal cavities opening a new frontier in medical inspection and diagnosis. Here, we report the application of a new mesh-based approach for the isolation of skeletal cavities of different size and geometrical structure. We apply a mesh-based approach to extract (i) the main virtual cavities inside the human skull, (ii) a complete human endocast, (iii) the inner vasculature of the malleus bone and (iv) the medullary of a human femur. The detailed description of the mesh-based isolation method and its pioneristic application to four different case-studies show the potential of this approach in medical visualisation.
DISTRIBUTED TREE-BASED DECISION RULE LEARNING

The paper describes a decision rule induction algorithm for a distributed data environment. Recent advances inprocessing a large volume of data result in the rapid development of many machine learning algorithms adapted to workin such systems.
Application of Fractal Analysis Methods of Textures Earth Surface Images for Ecological Setting Assessment

The use of fractal analysis methods for automatic textures earth surface image evaluation and ecological setting assessment has been proposed. Image processing techniques have been chosen for the following fractal analysis. The use of the Minkowski dimension to construct sheet dimension and histogram of fractal dimension has been suggested. Performance tests for the proposed methods and algorithms have been performed and analysed presented. Application of the fractal analysis methods is efficient in solving problems of monitoring objects clustering, assessing their conditions, identifying anomalies that have implications for the ecological setting, and evaluating the development of anomalous situations. The fractal analysis algorithms permit efficient processing of array information in the case, when they are implemented on modern computational hardware platforms.
Copywriting in Online Education

The article is devoted to the study of the phenomenon of online courses in the field of mass communication, in particular educational technologies on copywriting. The authors analyze the specifics of technologies that are used to teach the art of copywriting on various electronic resources. In addition, the article discusses the tools for creating effective text to attract the target audience to the company's website and inform about a product or service hosted in the Internet space, discusse techniques for creating SED-texts.