Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Recurrent Neural Network DRNN Model for Real-Time Multistage Pumping Data

A new real-time model was developed, based on a deep recurrent neural network (DRNN), to predict response variables, such as surface pressure response, during the hydraulic fracturing process. During the stimulation process stage, fluids are inserted at the top of the wellhead, and the flow is driven by the difference between the hydrostatic pressure and reservoir pressure. The major physics and engineering aspects in this process are very complex; quite often, the measured data includes a large amount of uncertainty related to the accuracy of the measured data, as well as intrinsic noise. Consequently, the best approach uses a machine learning-based technique that can resolve both temporal and spatial non-linear variations. The approach followed in this paper provides a long short-term memory (LSTM) network-based method to predict surface pressure in a fracturing job, considering all commonly known surface variables. The surface pumping data consists of real-time data captured within each stage, including surface treating pressure, fluid pumping rate, and proppant rate. The prediction of a response variable, such as the surface pressure response, is important because it provides the basis for decisions made in several oil and gas applications to ensure success, including hydraulic fracturing and matrix acidizing. Currently available modeling methods are limited in that the estimates are not high resolution and cannot address a high level of non-linearity in the treatment pressure time series relationship with other variables, such as flow rate and proppant rate. In addition, these methods cannot predict subsurface variable responses based only on surface variable measurements. The method described in this paper is extended to accommodate the prediction of diverter pressure response. The model presented in this paper uses a deep learning neural network model to predict the surface pressure based on flow rate and proppant rate. This work represents the first attempt to predict (in real time) a response variable, such as surface pressure, during a pumping stage using a memory-preserving recurrent neural network (RNN) variant (for example, LSTM and gated recurrent unit (GRU)). The results show that the LSTM is capable of modeling the surface pressure in a hydraulic fracturing process well. The surface pressure predictions obtained were within 10% of the actual values. The current effort to model surface pressure can be used to simulate response variables in real time, providing engineers with an accurate representation of the conditions in the wellbore and in the reservoir. The current method can overcome the handling of complex physics to provide a reliable, stable, and accurate numerical solution throughout the pumping stages.
Generation Z in the UK: More of the Same – High Standards and Demands

Research on the characteristics of Generation Z in the United Kingdom is sparse and thus this generation is largely unknown. However, it is likely that the characteristics of Generation Z in the United Kingdom largely represent a continuation of the trends in attitudes and expectations seen over Generations X and Y. This is a group that has grown up to have high expectations of employment, including a desire for interesting and meaningful work, regular feedback, employee voice and participation, work–life balance and the development of marketable skills. As this cohort of the population was born into an environment with Web 2.0 technology and social media, they are also constantly connected. However, in the past two years, this group has continued its development against the backdrop of political and economic uncertainty due to the United Kingdom’s forthcoming exit from the European Union. More research is needed in order to establish the impact of these events on the younger generation’s attitudes, and those of subsequent age cohorts.
Organization Factors That Influence Transfer of Training among Employees at Farmers’ Organisation Authority (FOA) Headquarters, Kuala Lumpur

Transfer of training is important to organisations as they have devoted lot of money to send their employees for training to increase the employees’ knowledge, skills and abilities. This paper examines the relationships of rewards, supervision, and peer relationship on the transfer of training involving employees at FOA Headquarters, Kuala Lumpur. Self-administered questionnaires were adapted from various sources and consisted of items related to transfer of training, rewards, supervision and peer relationships. A total of 344 questionnaires were distributed but only 265 were returned and used for further assessment. This study applied descriptive statistics, reliability and inferential analysis to examine the relationships among the variables. The results showed that only rewards and supervision had significant relationship with transfer of training while peer relationship was found to be insignificant.
The Role of Motivation in the Use of Lecture Behaviors in the Online Classroom

(CC BY-NC 4.0) This article is licensed to you under a Creative Commons Attribution-NonCommercial 4.0 International License. When you copy and redistribute this paper in full or in part, you need to provide proper attribution to it to ensure that others can later locate this work (and to ensure that others do not accuse you of plagiarism). You may (and we encourage you to) adapt, remix, transform, and build upon the material for any non-commercial purposes. This license does not permit you to use this material for commercial purposes. THE ROLE OF MOTIVATION IN THE USE OF LECTURE BEHAVIORS IN THE ONLINE CLASSROOM
Belle II Conditions Database

The Belle II experiment at KEK is preparing for taking first collision data in early 2018. For the success of the experiment it is essential to have information about varying conditions available to systems worldwide in a fast and efficient manner that is straightforward for both the user and maintainer. The Belle II Conditions Database was designed to make maintenance as easy as possible. To this end, a HTTP REST service was developed with industry-standard tools such as Swagger for the API interface development, Payara for the Java EE application server, and the Hazelcast in-memory data grid for support of scalable caching as well as transparent distribution of the service across multiple sites. On the client side, the online and offline software has to be able to obtain conditions data from the Belle II Conditions Database in a robust and reliable way under very different situations. As such the client side interface to the Belle II Conditions Database has been designed with a variety of access mechanisms which allow the software to be used with and without an internet connection. Different methods to access the payload information are implemented to allow for a high level of customization per site and to simplify testing of new payloads locally. Changes to the conditions data are usually handled transparently but users can actively check whether an object has changed or register callback functions to be called whenever a conditions data object is updated. In addition a command line user interface has been developed to simplify inspection and modification of the database contents.
Extraction of cardiopulmonary rates from 24ghz Doppler radar using time frequency analysis

Vital sign detection using Doppler radar has potential applications in the home and medical healthcare, etc. In this paper, a generalized Warblet transform (GWT) based algorithm is used to reveal the instantaneous frequency (IF) ridge of cardiopulmonary signals from the 24GHz Doppler radar. To improve the accuracy of the estimated IF in GWT, Viterbi algorithm (VA) instead of maximum method is firstly adopted in the iterative process and IF estimation, then, spectrum of IF is calculated to obtain respiration and heartbeat rates. Validation of the proposed algorithm on several simulated signals are conducted. The results show that compared with the original GWT and short time Fourier transform (STFT) combined with maximum method, the proposed algorithm can obtain more accurate IFs especially in a high noise environment.
Selection Listing Decisions of Food Retailers: A Literature Review

Retail selection policy defines the profile, the potential market, and the customer segments of companies by designating the breadth and depth of product assortments. Unique, often latent market needs-based selection can be a competitive advantage factor. In addition to specific demand, the market for and supply of food changes rapidly, thus selection renewal and rotation are notably rapid. As a result, the decision-making process of selection may challenge the retailer and, due to its consequences, other supply chain members as well. The role of retail selection policy may be ultimately reflected in the profitability of the company. The research described in this paper analyzes the theoretical approaches behind product listing decisions, conceptualizing reasons for listing and delisting decisions to contribute to better defining food retailers’ selection strategies. Results include an assessment of the importance of listing-related features, which contribute to defining selection strategy. Factors involved in product listing may include the integration of customer resources, market pressure and competition, the balance of forces between the manufacturer and the merchant, and potential profit. Deselection is associated with customers when they do not recognize the product, its quality is considered inadequate, suppliers can not come to a consensus about it, it has a low margin and/or turnover rate; furthermore, when high costs and expenses are coupled with the product. The results are novel due to the conceptualization and development of a methodological framework from the perspective of listing decision factors, and may be applicable by retail practitioners.
Determinants Of Continued Usage Intention Of Internet Banking Services

The proliferation of internet technology and financial innovations has globally revolutionized the delivery of banking services. Internet Banking offers substantial benefits, such as reduced transaction cost, convenience and flexibility to the customers as well as to the banks. Despite the apparent advantages, the usage of internet banking services by the banking clients is yet low. Many studies have investigated the factors influencing the initial adoption of internet banking services. In contrary, this study intents to provide insights on the customer’s intention for continued usage of internet banking services.
Predicting Financial Failure by Support Vector Machine and Probability of Default of Enterprises in a Developing Country

: Predicting the financial failure performs an even more significant character in the sustainable existence of enterprises for developing countries. A new risk rating technique based on the probability of default (PD) and order statistics (OS) is established to classify listed companies into two categories according to their financial risks. In the present study, the linear kernel function was united with biorthogonal wavelet kernel function to construct a novel biorthogonal hybrid kernel function. Additionally, the probability of default (PD) and Gray relational analysis (GRA) based new feature weighted approach is established. Grey relational degrees (GRD) between PD and financial indicators are the feature weights (FWOCSVM) on account of that PD can provide effective predicting information for the financial crisis of the listed companies. The financial distress was predicted among financially stable and distressed companies by using feature weighted one-class support vector machine based on the probability of default. The results from collected data of listed companies in Karachi Stock Exchange (KSE), Karachi, Pakistan demonstrated adequate performance by using newly developed approach. PD-FWOCSVM PD-OCSVM, with 𝐶 25 , 𝐶 13 , and 𝐶 , whereas the P(D/D)s of PD-FWOCSVM are 2.10%, 2.56%, and 2.33% which are higher than PD-OCSVM, when average value is considered. The comparison of PD-FWOCSVM and PD-OCSVM with maximum CCR was performed when P(D/N) and P(D/D) were considered for selection criteria. Where both the models having maximum CCR fulfill the given criteria; indicator sets = 𝐶 13 ; 𝜌 =0.7. It is clearly shown that PD-FWOCSVM with D/N and D/D 3.2% and 2.62%, respectively are found with more improvement than PD-OCSVM. The model’s effectiveness is indicated by D/D to classify the financial failure of companies in precisely on other hand the misclassification of company’s financial failure as normal can be resulted as big ria sk. Hence, effectiveness in predicthe tion of financial failure by using PD-FWOCSVM approach as compared to non-weighted PD-OCSVM is evident.
George P. Moschis tribute special issue: His philosophy and contributions

ABSTRACT In this article, I first discuss Moschis’ contributions to marketing in three main areas of consumer behavior: consumer socialization, older consumer behavior, and life course research. Next, I present and comment on the basic characteristics of his work: multi-theoretical, programmatic, and collaborative. Finally, I briefly present the gist of the contribution that is made by each of the articles included in the first part of this two-part special issue that have as foundation his work in the area of life course research.
Identifying Cancer Subtypes Using Unsupervised Deep Learning

Cancer is a heterogeneous disease which has many subtypes that can be distinguished at molecular, histopathological, and clinical stage. Accurate diagnosis of the specific subtypes of cancer is vital to identify distinct disease states and opens up the possibility for effective personalized therapies that yield the greatest response. Many unsupervised machine learning techniques are applied to the genomic data of the tumor samples and the patient clusters are found to be of interest if they can be associated with a clinical outcome variable such as the survival of patients. In this thesis, we introduce two new clustering algorithms for cancer subtype identification, which fuses the information of gene expression and pathway database to group samples into biologically meaningful clusters. We call our first approach as R-PathCluster which is based on Restricted Boltzmann Machine. In this method, we used pathway markers as input dataset instead of gene expression data to identify unknown subtypes in Glioblastoma Multiforme (GBM). We developed SPACL model, sparse pathway based clustering for the identification of cancer subtypes in which multilayered deep belief network is used. In this model we used pathway data to extract the complex nonlinear relationships in identifying clusters. We assessed the performance of two models with several traditional clustering methods and found that our models out performed in clustering short term and long term survivals by lowest p-value in log rank test and Kaplan-Meier survival analysis. Our models provide solution to comprehensively detect subtypes and interpret in biological sense as these use pathway data.
Evaluasi Usability Website library.umn.ac.id Universitas Multimedia Nusantara

Website library.umn. ac.id merupakan website perpustakaan yang dimiliki oleh Universitas Multimedia Nusantara. Website tersebut belum pernah diteliti dengan evaluasi usability. Sehingga studi ini adalah tentang evaluasi usability website khususnya user interface. Dalam melakukan evaluasi, diterapkan metode usability testing yaitu observasi pengguna sebuah desain user interface website dilanjutkan dengan mengumpulkan dan menganalisis data. Selain itu, pengumpulan data dengan membuat kuesioner yang didasarkan pada System Usability Scale (SUS) untuk mengukur kepuasaan pengguna ketika menggunakan sebuah sistem. Data yang dianalisis dengan menerapkan usability testing yang merujuk kepada teori Jacob Nielson (2003) yang terdiri dari lima komponen yaitu learnbility, efficiency, memorability, errors, dan satisfaction. Hasil dari penelitian ini user interface yang baru disarankan untuk meningkatkan website perpustakaan yang telah diuji sepenuhnya menggunakan SUS. Rekomendasinya memiliki nilai SUS lebih tinggi dari pada versi lama. Hasilnya, prototype user interface ini mudah digunakan dan mudah dimengerti berdasarkan tanggapan mahasiswa dibandingkan versi lama.
Primera versión de la política de seguridad democrática: se cumplieron los objetivos?

ResumenExplotando las variaciones espaciales y temporales en el numero de bienes incautados a las organizaciones criminales, se estiman modelos de efectos fijos regionales del aumento en el numerode propiedades confiscadas sobre las principales tasas de crimen. En Colombia, desde finales de 2002 las estrategias de seguridad cambiaron, y como resultado, los efectos sobre los delitos resultaron significativos y negativos. Por un lado hubo una reduccion en los delitos cometidos por el crimen organizado, incluyendo la guerrilla, los paramilitares y los narcotraficantes, pero por el otro, los delitos cometidos por criminales comunes no parecen haberse reducido en la misma magnitud.First version of the politica de seguridad democratica:were the objectives accomplished? Exploiting spatial and temporal variations in the number of seizures from criminal organizations,I estimate regional fixed effects models of the increase in the number of properties confiscated on the main crime rates. Since 2002 security strategies changed, and the resulting effects on crime are mostly large, negative and significant. On the one hand, there was a clear reduction in crimes commonly committed by criminal organizations, including guerrillas, paramilitaries and drug traffikers, but on the other, no significant effects were found on crimes committed by common criminals in the urban areas.
LLVM2GOTO: A translator from LLVM IR toCPROVER IR

There are more than 700 programming languages. The number of softwares is astronomical. It is highly important to verify whether the software meets it's specification and it is safe. However, there are very few stable software verification tools. Translating a source program into verification intermediate representation(VIR) is an overhead for software verification community. If we trans- late compiler intermediate representation into VIR, the overhead of translating source to VIR is reduced and software written in programming languages supported by the compiler can be verified. LLVM2GOTO uses LLVM IR as compiler IR and CPROVER's goto IR as VIR. In the current implementation we support variable declaration, load, store, arithmetic, bitwise, typecast, branch and switch instructions are supported by LLVM2GOTO. v
Sampling-based Techniques for Interactive Exploration of Large Datasets

Extracting useful information from large datasets has become increasingly important. While our computational capabilities have improved greatly in the last few decades, data explosion has resulted in the growth rate of data vastly out-pacing that of computational power. In such scenarios, obtaining results within interactive (few seconds at the most) response times is extremely difficult. Exploring data interactively has numerous advantages such as shortening the feedback loop, providing the ability to perform numerous experiments, and giving a smoother user experience. As a result, providing results using a sample of the data to improve query response time has become hugely important. In this work, I will present our efforts in the direction of interactive exploration of large-scale datasets within interactive response times with projects such as DICE, Sesame, FluxQuery, and a unified join sampling approach. We use approaches such as speculative execution, data sampling, faceted exploration, and scan sharing towards this end. In an OLAP scenario, we note that queries occur not in isolation but as part of a larger query session, where queries might be similar to the previously executed queries. DICE uses this session-oriented behavior of a user to speculatively execute and cache the likely follow-up queries, so that the user query can be answered quickly from the cache. DICE scales up to a billion tuples within sub-second response time using 50 nodes. Further, DICE has a novel, intuitive, synergistic interface designed to aid the speculation and faceted exploration used by the backend.
The Attitudes of Customers towards Green Investments

With the arrival of societal Marketing concept to the business environment, Green Investment has gained much importance than it had. The societal marketing concept says that an organization should deliver the desired satisfaction of the customer in a way that preserves or enhances the consumers' and the society's well-being. Investment in green shares gives relatively high rases of return as a sort of highly socially responsible investment. In the western countries a domestic growth in green investments introductions at the beginning of the 1990's and currently the market for green investments has been expanded rapidly. In the USA, one out of eight US dollars invested in Green shares. The Sri Lankan market in Green Investments, still not have reached to that level of western countries, but gradually it has been improved than earlier years. Only a few organizations can be found in Sri Lanka those who engage in tropical forestry. This paper deals with an evaluation of attitudes ofpotential customer clientele towards the Green Investments, specially invest on tropical forestry. The study was based on secondary data and primary data. Primary data were collected by conducting a representative survey of interviewing 200 respondents through a structured questionnaire. The sample elements were selected using convenience sampling techniques descriptive statistics were used to analyze gathered data. Findings showed that only a less percentage of interviewed customers had favorable attitudes towards invest in tropical forestry and further it was revealed that the willingness to invest in forestry is a function of variables of education level, income, environmental awareness, nature of promotions and expected profits. Based on the results, conclusions are drawn regarding marketing strategies to improve their market in the marketer's point of view.
Data-Driven Look-Ahead Unit Commitment Considering Forbidden Zones and Dynamic Ramping Rates

Look-ahead unit commitment (LAUC) is recently introduced among independent system operators (ISOs) in the U.S. to increase generation capacity by committing more generators after day-ahead unit commitment when facing various uncertainties in the power system operations. However, as the share of intermittent renewable energy increases significantly in the power generation portfolio, the load continues to fluctuate, and unexpected events and market behaviors happen nowadays, the ISOs are facing new critical challenges to maintain the reliability of power system. To systematically manage these uncertainties and corresponding challenges, new advanced approaches are urgently required to improve current LAUC models and solution methods. Therefore, in this paper, we first propose a new formulation to represent forbidden zones and dynamic ramping rate limits, which help capture the system operation status more accurately and hedge against the uncertainties more effectively, and then correspondingly propose a data-driven risk-averse LAUC model. Our computational experiments show how the size of data influences operational decisions and how the inclusion of forbidden zones and dynamic ramping provide better decisions.