Miracle-dz/UniFAR-FTUs huggingface dataset:
Penyusunan dan Pengembangan Alat Ukur Employee Engagement

Abstract: This research was aimed to develop an instrument which can measure employee engagement score, which is based on Schaufeli’s theoretical framework. The previous instrument which was called Utretch Work Engagement Scale (UWES) was developed using content and construct validity. The content validity test was done by assessing some experts and using Aiken’s V, while construct validity was proved by item total correlation and factor analysis. Content validity test was conducted by getting input from fifteen experts in Psychology to evaluate the contents of 32 items. The Aiken’s V was used to evaluate the coefficient of each items (in which good range was from 0,645 to 0,98). The overall internal consistency was assessed using Cronbach alpha which showed the coefficient of reliability of 0,934 after deleting the items below 0,4, whereby leaving only 22 items for conducting the construct validity. Confirmatory factor analysis was done using factor analysis. 135 staffs in one of Plantation Company in Balikpapan were participated in this study. The confirmatory factor analyses produced a cross loading factors between three aspects of employee engagement which after confirming back to blueprint remaining only 8 items, which is distributed into 4 items for vigor, 2 items each for dedication and absorption. Abstrak: Penelitian ini bertujuan untuk mengembangkan instrumen yang dapat mengukur skor employee engagement yang didasarkan pada kerangka teoritis yang dikemukakan oleh Schaufeli. Instrumen sebelumnya dikenal dengan Utretch Work Engagement Scale (UWES) yang dikembangkan dengan menggunakan validitas isi dan konstrak. Validitas isi diuji melalui judgment dari para ahli di bidang Psikologi dan menggunakan Aiken’s V , sedangkan validitas konstrak dibuktikan dengan korelasi item-total dan analisis faktor. Validitas isi diperoleh dari 15 orang yang ahli di bidang Psikologi untuk mengevaluasi isi dari 32 item. Aiken’s V digunakan untuk mengevaluasi koefisien dari setiap item (range yang baik berada di rentang 0,645 sampai 0,98). Konsistensi internal diperoleh melalui alpha Cronbach dengan koefisien reliabilitas sebesar 0,934 setelah item-item yang memiliki nilai di bawah 0,4 dikeluarkan. Total item yang tersisa berjumlah 22 item untuk kemudian dilakukan uji validitas konstrak. Confirmatory factor analysis dilakukan melalui analisis factor. Sebanyak 135 staf di salah satu Perusahaan Plantation di Balikpapan berpartisipasi dalam penelitian ini. Hasil analisis factor menunjukkan adanya factor yang saling tumpang tindih antara ketiga aspek dari employee engagement , yang setelah dikonfirmasi kembali ke blueprint menyisakan sebanyak 8 item, 4 item untuk aspek vigor , 2 item masing-masing untuk aspek dedication dan absorption .
A systems thinking approach to business intelligence solutions based on cloud computing

Business intelligence is the set of tools, processes, practices and people that are used to take advantage of information to support decision making in the organizations. Cloud computing is a new paradigm for offering computing resources that work on demand, are scalable and are charged by the time they are used. Organizations can save large amounts of money and effort using this approach. This document identifies the main challenges companies encounter while working on business intelligence applications in the cloud, such as security, availability, performance, integration, regulatory issues, and constraints on network bandwidth. All these challenges are addressed with a systems thinking approach, and several solutions are offered that can be applied according to the organization's needs. An evaluations of the main vendors of cloud computing technology is presented, so that business intelligence developers identify the available tools and companies they can depend on to migrate or build applications in the cloud. It is demonstrated how business intelligence applications can increase their availability with a cloud computing approach, by decreasing the mean time to recovery (handled by the cloud service provider) and increasing the mean time to failure (achieved by the introduction of more redundancy on the hardware). Innovative mechanisms are discussed in order to improve cloud applications, such as private, public and hybrid clouds, column-oriented databases, in-memory databases and the Data Warehouse 2.0 architecture. Finally, it is shown how the project management for a business intelligence application can be facilitated with a cloud computing approach. Design structure matrixes are dramatically simplified by avoiding unnecessary iterations while sizing, validating, and testing hardware and software resources. Thesis Supervisor: John R. Williams Title: Associate Professor of Civil and Environmental Engineering
Performance investigation and comparison of two evolutionary algorithms in portfolio optimization: Genetic and particle swarm optimization

Contrary to the growing use of portfolios and in spite of its rich literature, there are some problems and unanswered questions. The aim of this work is to be a useful instrument for helping fin anco practitioners and researchers with the portfolio selection problem. This study first reviews Modern Portfolio Theory's literature and describes the investment selection process. Second, this paper specifically aims at applying efficient optimization methods for solving the portfolio selection problem. Hence, the genetic algorithm and the particle swarm optimization (PSO) approaches to resolve the portfolio selection problem with the objective of simultaneous risk minimization/return maximization are applied. Computational analyses are provided so as to investigate the performance of the algorithms and the input data. Therefore, at one step, four different portfolios are selected from Tehran Stock Exchange market, 50 top companies (Annual and monthly-based portfolios). Finally, this paper applies the selected portfolios to the stock market data of a 10-month period proceeding portfolios constructions. The results indicate that, the genetic annual-based portfolio has the best performance in contrast to its other counterparts; it outperforms the average return of market portfolio in a relatively short period. Moreover, PSO annual-based portfolio has a relatively good performance and could achieve the market portfolio's return during the test period. Findings show that using annual data is more efficient than the monthly data. On the whole, the results show that the evolutionary methods of this paper with annual data, can consistently handle the practical portfolio selection problem.
ANALYZING STUDENT PERFORMANCE IN DISTANCE LEARNING WITH GENETIC ALGORITHMS AND DECISION TREES

Students that enroll in the undergraduate program on informatics at the Hellenic Open University (HOU) demonstrate significant difficulty in advancing beyond the introductory course. We have embarked in an effort to analyze their academic performance throughout the academic year, as measured by homework assignments, and attempt to derive short rules that explain and predict success or failure in the final exams. In this paper we review previous approaches, compare them with genetic algorithm-based induction of decision trees, and argue why our approach has a potential for developing into an alert tool.
Source code profiling and classification for automated detection of logical errors

Research and industrial experience reveal that code reviews as a part of software inspection might be the most cost-effective technique a team can use to reduce defects. Tools that automate code inspection mostly focus on the detection of a priori known defect patterns and security vulnerabilities. Automated detection of logical errors, due to a faulty implementation of applications’ functionality is a relatively uncharted territory. Automation can be based on profiling the intended behavior behind the source code. In this paper, we present a code profiling method based on token classification. Our method combines an information flow analysis, the crosschecking of dynamic invariants with symbolic execution, and code classification heuristics with the use of a fuzzy logic system. Our goal is to detect logical errors and exploitable vulnerabilities. The theoretical underpinnings and the practical implementation of our approach are discussed. We test the APP_LogGIC tool that implements the proposed analysis on two real-world applications. The results show that profiling the intended program behavior is feasible in diverse applications. We discuss in detail the heuristics used to overcome the problem of state space explosion and that of the large data sets. Code metrics and test results are provided to demonstrate the effectiveness of the proposed approach. This paper extends the work that appears in an article currently submitted to an international conference with proceedings. In this adequately extended version of our method we present classification mechanisms that can take into account multiple user input and provide a detailed description of the used source code classification techniques and heuristics.
Detecting Sociostructural Beliefs about Group Status Differences in Online Discussions

Detection of fine-grained opinions and beliefs holds promise for improved social media analysis for social science research, business intelligence, and government decision-makers. While commercial applications focus on mapping landscapes of opinions towards brands and products, our goal is to map “sociostructural” landscapes of perceptions of social groups. In this work, we focus on the detection of views of social group status differences. We report an analysis of methods for detecting views of the legitimacy of income inequality in the U.S. from online discussions, and demonstrate detection rates competitive with results from similar tasks such as debate stance classification.
Mining Interesting Itemsets using Submodular Optimization

We propose a novel technique to retrieve itemsets that best explain a transaction database by leveraging a simple probabilistic model. Our approach is the ﬁrst to infer such interesting itemsets directly from the transaction database using submodular function optimization and in so doing avoids many of the pitfalls commonly present in frequent itemset mining algorithms. Our proposed approach is theoretically simple, straightforward to implement, trivially parallelizable and exhibits good performance as we demonstrate on both synthetic and real-world examples
Constraints on dark energy evolution

We investigate a class of dark energy models in which the equation of state undergoes a rapid transition and for which the Hubble SN Ia diagram is known to be poorly discriminant. Interestingly enough, we find that transitions at high redshift can lead to distortion in the correlation function of dark matter at lower redshift. We therefore use a combination of the SN Ia Hubble diagram, cosmic microwave background data and power spectrum from the Sloan Digital Sky Survey luminous red galaxies (SDSS LRG) to constrain the redshift of a possible transition. We find that the fundamental cosmological parameters are well constrained independently of the presence of a transition. Acceptable transitions from an equation of state close to w = 0 to a value close to -1 are strongly rejected at redshifts much higher than those for which large scale structure and SN Ia data are available: the transition redshift can be rejected up to a value as high as 10. We conclude that no preference for a transition appears from present-day data.
Development and characterization of two human triple‐negative breast cancer cell lines with highly tumorigenic and metastatic capabilities

Triple‐negative breast cancer (TNBC) is a group of cancer with high diversity, limited therapies, and poor prognosis. TNBC cell lines and animal models provide effective tools for studies and drug discovery. Here, we report the development of two TNBC cell lines (XtMCF and LmMCF) based on our existing cell model that consists of normal breast epithelial cell line MCF10F, estradiol‐transformed cells trMCF, and Boyden chamber‐selected tumorigenic cells bsMCF. The XtMCF and LmMCF cell line were derived from xenograft and lung metastasis of bsMCF cells, respectively. The bsMCF, XtMCF, and LmMCF cells have undergone epithelial–mesenchymal transition (EMT), exhibiting a mesenchymal‐like feature. In vivo studies showed XtMCF and LmMCF cells were highly tumorigenic and metastatic. The injection of 5 × 104 cells to CB17/SCID mice mammary fat pad produced xenografts in 9/9 mice and tumors reached 10 millimeters in diameter in 5 weeks. The injection of 1 × 106 XtMCF or 8 × 104 LmMCF cells into the mice tail vein was sufficient to form extensive lung metastases in 4 weeks. The two new cell lines exhibited CD44+/CD49f+ and CD44+/EpCAM+ cancer stem cell (CSC) characteristics, and the EGF‐like domain of EpCAM was cleaved off. Together with the normal and early transformed counterparts, herein we provide a complete cancer model for the study of initiation, evolution, and identification of new therapeutics for TNBC. The finding that EGF‐like domain of EpCAM was cleaved off in cells which have undergone EMT suggests this cleavage may be involved in the EMT process and the cancer stem cell properties of these cells.
A hybrid Deep Boltzmann Functional Link Network for classification problems

This paper proposes a hybrid deep learning algorithm, namely, the Deep Boltzmann Functional Link Network (DBFLN) for classification problems. A Deep Boltzmann Machine (DBM) with two layers of Restricted Boltzmann Machine is the generative model that is used to generate stochastic features and input weights for the discriminative model. A discriminative Functional Link Network (FLN) uses these features to approximate the nonlinear relationship between a set of features and their classes. FLN has three layers, namely, the input layer, the enhancement layer and the output layer. In a DBFLN, the features generated at the two hidden layers of the DBM act as the input features and the enhancement layer responses of the FLN. The output weights of the FLN are then estimated as a solution to a linear programming problem through pseudo-inverse. We first evaluate the performance of the DBFLN on three benchmark multi-category classification problems from the UCI machine learning repository, namely, the image segmentation problem, the vehicle classification problem and the glass identification problem. Performance study results on the benchmark classification problems show that DBFLN is an efficient classifier. We then use the DBFLN to classify the images in the TID2013 data set, based on their depth of distortions. The TID2013 data set comprises of 25 images, each with 5 levels of 24 distortion types. In all, the data set has 3000 images, which can be classified based on the depth of distortion. Thus, the IQA classification problem is defined as classifying the distorted images into one of the 5 classes (depending on the depth of distortion) using human visual image metrics as the input features. The performance of the DBFLN in classifying the image quality is compared with those of Support Vector Machines, Extreme Learning Machines, Random Vector Functional Link Network, and Deep Belief Network. Performance studies show the superior classification ability of the DBFLN.
The Personal Dimensions of Public Relations Ethical Dilemmas

ABSTRACT This article explores how Charles Taylor’s account of moral personhood and Luc Boltanski and Laurent Thévenot’s account of justificatory regimes can add breadth, depth, and specificity to discussions of ethical dilemmas in public relations. These frameworks are analyzed for their potential to make the following contributions to public relations ethics. First, they convey that there is more to ethics than choosing the right duties and actions. Second, they reveal the diversity of goods that people consider to be ethically worthy and admirable, as well as the complex personal issues involved in ranking these goods. Third, they emphasize that these different goods often need to be articulated and evaluated in different ways. Last, they can either point toward new ways of resolving ethical dilemmas or show why such resolutions are not possible.
A new paradigm for the X-ray emission of O stars from XMM-Newton observations of the O9.7 supergiant zeta Orionis

XMM-Newton observations of the O supergiant zeta Orionis (O9.7 Ib) extend knowledge of its high-resolution spectrum beyond the CVI line at 33.7 Angstroms and suggest a new framework for the interpretation of the X-ray spectra of single hot stars. All the lines are broad and asymmetric with similar velocity profiles. X-rays probably originate in the wind's terminal velocity regime in collisionless shocks controlled by magnetic fields rather than in cooling shocks in the acceleration zone. During post-shock relaxation, exchange of energy between ions and electrons is so slow that electron heating does not take place before hot gas is quenched by the majority cool gas. The observed plasma is not in equilibrium and the electron bremsstrahlung continuum is weak. Charge exchange, ionization and excitation are likely to be produced by protons. Fully thermalized post-shock velocities ensure high cross-sections and account for the observed line widths, with some allowance probably necessary for non-thermal particle acceleration. In general, the form of X-ray spectra in both single and binary stars is likely to be determined principally by the amount of post-shock electron heating: magnetically confined X-ray plasma in binary systems can evolve further towards the higher electron temperatures of equilibrium while in single stars this does not take place. The long mean-free path for Coulomb energy exchange between fast-moving ions may also inhibit the development of line-driven instabilities.
A Phase I Double Blind, Placebo-Controlled, Randomized Study of a Multigenic HIV-1 Adenovirus Subtype 35 Vector Vaccine in Healthy Uninfected Adults

Background We conducted a phase I, randomized, double-blind, placebo-controlled trial to assess the safety and immunogenicity of escalating doses of two recombinant replication defective adenovirus serotype 35 (Ad35) vectors containing gag, reverse transcriptase, integrase and nef (Ad35-GRIN) and env (Ad35-ENV), both derived from HIV-1 subtype A isolates. The trial enrolled 56 healthy HIV-uninfected adults. Methods Ad35-GRIN/ENV (Ad35-GRIN and Ad35-ENV mixed in the same vial in equal proportions) or Ad35-GRIN was administered intramuscularly at 0 and 6 months. Participants were randomized to receive either vaccine or placebo (10/4 per group, respectively) within one of four dosage groups: Ad35-GRIN/ENV 2×109 (A), 2×1010 (B), 2×1011 (C), or Ad35-GRIN 1×1010 (D) viral particles. Results No vaccine-related serious adverse event was reported. Reactogenicity events reported were dose-dependent, mostly mild or moderate, some severe in Group C volunteers, all transient and resolving spontaneously. IFN-γ ELISPOT responses to any vaccine antigen were detected in 50, 56, 70 and 90% after the first vaccination, and in 75, 100, 88 and 86% of Groups A–D vaccine recipients after the second vaccination, respectively. The median spot forming cells (SFC) per 106 PBMC to any antigen was 78–139 across Groups A–C and 158–174 in Group D, after each of the vaccinations with a maximum of 2991 SFC. Four to five HIV proteins were commonly recognized across all the groups and over multiple timepoints. CD4+ and CD8+ T-cell responses were polyfunctional. Env antibodies were detected in all Group A–C vaccinees and Gag antibodies in most vaccinees after the second immunization. Ad35 neutralizing titers remained low after the second vaccination. Conclusion/Significance Ad35-GRIN/ENV reactogenicity was dose-related. HIV-specific cellular and humoral responses were seen in the majority of volunteers immunized with Ad35-GRIN/ENV or Ad35-GRIN and increased after the second vaccination. T-cell responses were broad and polyfunctional. Trial Registration ClinicalTrials.gov NCT00851383
Weakly-Supervised Grammar-Informed Bayesian CCG Parser Learning

Combinatory Categorial Grammar (CCG) is a lexicalized grammar formalism in which words are associated with categories that, in combination with a small universal set of rules, specify the syntactic configurations in which they may occur. Categories are selected from a large, recursively-defined set; this leads to high word-to-category ambiguity, which is one of the primary factors that make learning CCG parsers difficult, especially in the face of little data. Previous work has shown that learning sequence models for CCG tagging can be improved by using linguistically-motivated prior probability distributions over potential categories. We extend this approach to the task of learning a CCG parser from weak supervision. We present a Bayesian formulation for CCG parser induction that assumes only supervision in the form of an incomplete tag dictionary mapping some word types to sets of potential categories. Our approach outperforms a baseline model trained with uniform priors by exploiting universal, intrinsic properties of the CCG formalism to bias the model toward simpler, more cross-linguistically common categories.
Reaction-diffusion systems in pattern formation and pattern recognition processes

This paper presents a quick review of reaction-diffusion systems that self-organize spatio-temporal patterns in chemical and biological systems. A pair of reaction-diffusion equations having activator and inhibitor variables is a typical model of equations describing their pattern formation processes. Some of the reaction-diffusion systems can realize several functions of image processing such as edge detection and segmentation required for pattern recognition processes. Thus, reaction-diffusion systems which have been studied as pattern formation processes are also interesting topics as pattern recognition processes. The authors have recently done the research of realizing functions of edge detection, segmentation and stereo disparity detection by utilizing reaction-diffusion equations with large inhibitory diffusion. This paper additionally presents a brief introduction of our recent research topic, in particular, an edge detection algorithm with a discretely spaced system of the FitzHugh-Nagumo reaction-diffusion equations. Experimental results obtained for artificial and real images show the comparison between a previous standard algorithm and the reaction-diffusion algorithm designed for edge detection.
Research of Security Vulnerability in the Computer Network

In order to solve the problem of security vulnerability in the computer network radically, it was studied for the cause of vulnerability. Solving scheme was put forward aim at one kind of vulnerability, that is, by the special scan and encryption techniques, it makes attacker can't bypass its access control. In this way, it can avoid the vulnerability caused by the error of access verification. Finally, it pointed out that the method solving vulnerability problem according to the cause of vulnerability will be the development trend to study the vulnerability.
Time Frequency Reassignment: A Review and Analysis

Time-frequency reassignment is a relatively old but under-explored method for time frequency analysis. This report reviews previous research on reassignment and relates it to instantaneous frequency in an explicit and novel way. New measures based on reassignment of the amplitude spectrum, as opposed to the traditional phase spectrum, are proposed and analysed. The statistical properties of reassignment as a sinusoidal estimator are compared with the Cramer-Rao bound and finally some applications of reassignment in the field of musical analysis are proposed.