Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotion Regulation and Service Organizational Citizenship Behaviors: Moderating Role of Gender

The purpose of this study was to examine the direct effect of emotion regulation (deep acting and surface acting) on service-oriented organizational citizenship behavior (SO-OCB), an to test for moderating effects of gender on the relationship between emotion regulation and SO-OCB. The subjects of this study were the frontline service employee at the four star hotels in Padang, West Sumatera, including personnel at front desks, in service centers, in housekeeping, and in restaurants. For data analyses, a series of hierarchical moderated regression analyses with a method of sub group were employed. The study finds that deep acting and surface acting significantly affects SO-OCB. Male and Female moderated the relationship between deep acting and SO-OCB. Male moderated the relationship between surface acting and SO-OCB, but Female did not moderate the relationship between surface acting and SO-OCB.
The Role of Family on Hookah Smoking Initiation in Women: A Qualitative Study

Hookah smoking has recently emerged as a popular alternative to cigarette smoking particularly among young adults and women. This study focused on the role of family members’ smoking behaviours as a possible risk factor for initiation of hookah smoking in women. 36 in-depth interviews were conducted with Iranian women of diverse ages for understanding the factors contributing to the initiation of hookah smoking. Four main themes were identified from the data. This study focused on the role of family as a facilitator for hookah smoking initiation. The results of this study indicate that the entry of hookah into homes can be effective in the spread of hookah smoking among adult and young women, in three ways: Girls’ participation in the preparation of hookah and the frequent observation of people who smoke hookah at home can be effective in hookah smoking initiation among young girls; the husband of a young woman has an important role in the initiation of smoking hookah; when parents invite children to smoke hookah at home, in order to protect them against public censure, the mother (a middle aged woman) may intend to start smoking hookah. Therefore, tobacco use prevention interventions should be focused on targeting the family as well.
La compétence interculturelle comme facteur contribuant de la disposition des gestionnaires au changement organisationnel

The object of this thesis is to discover the intercultural competencies that determine manager’s readiness for organizational change. Through the analysis of twelve main hypotheses; regarded as intercultural competence; involving cognitive capabilities, emotional abilities and behavioural/social skills in managers; and their influence on readiness for change, the procedure to validate the general hypothesis includes an electronic self-administered questionnaire and a total of five hundred and fifty seven respondents from sixty-eight different nationalities. The respondents are managers of corporations who hold strategic, tactical or operational management positions; that is, managers who are responsible for long-term planning, progress monitoring, or day-to-day operational supervision.The understanding of the concept of intercultural competence is a central aspect in this dissertation and therefore the theoretical part has been written discretionarily by the researcher concerning the selected references upon which the conceptual framework was built. An empirical part is supported with a quantitative methodological analysis in order to prove that there is a significant positive correlation between intercultural competence and managers’ readiness for organizational change.
How Big is Big Enough? Justifying Results of the Iid Test Based on the Correlation Integral in the Non-Normal World

Kocenda (2001) introduced the test for nonlinear dependencies in time series data based on the correlation integral. The idea of the test is to estimate the correlation dimension by integrating over a range of proximity parameter epsilon. However, there is an unexplored avenue if one wants to use the test to identify nonlinear structure in nonnormal data. Using the Monte Carlo studies, we show that non-normality leads to an over-rejection of the null hypothesis due to two reasons: First, the data are not iid, and second, the data are non-normal. It is shown that even a very small deviation from normality could lead to a rejection of the null hypothesis and hence a wrong conclusion. Therefore, the bootstrap method is introduced and it is shown that it helps to avoid the over-rejection problem; moreover the power of the test increases by a significant amount. These findings help us to extend the use of the test into many other fields that deal with nonlinear data that are not necessarily normal, e. g. financial economics, stock price volatility, stock market efficiency, stock exchange, behavior of equity indices, nonlinear dynamics in foreign exchange rates, or interest rates.
A General Theory of Organizational Stigma

In recent years, the term stigma has been widely applied to organizations. However, scholars have yet to advance a theoretically consistent definition or comprehensive theory of organizational stigma. The purpose of this paper is to define the construct of organizational stigma and provide a general theory that explains the conditions under which organizational stigmas are likely to arise, how this process unfolds, and the initial effects stigmas inflict on organizations. In doing so, we distinguish organizational stigma from both individual-level stigma and the organizational-level constructs of reputation, status, celebrity, and legitimacy. We then build upon multiple streams of research to develop a richer theoretical explanation of the roles social context, social processes, and social actors play in the origination and effects of an organizational stigma.
Swarm Colony Ants with Limited Processing, Localization, and Memory

Swarm behavior is a relatively new technique in Artificial Intelligence with a wide variety of applications. Inspired by the social systems of colony insects such as ants and bees, swarm algorithms solve global problems through the local decisions and interactions of individual agents. In this project, swarm technology provides the distributed infrastructure used for resource discovery, area coverage, and resource transport. This paper details a research project that studied the robust nature and efficiency of such by simulating robots gathering resources on a distant planet.
Fuzzy Wavelet and Contourlet Based Contrast Enhancement

This paper presents a fuzzy approach for contrast enhancement, based on two multi-scale transforms, namely wavelet and contourlet transforms. Separability and nondirectionality of conventional 2D wavelet transform, makes it unsuitable for sparsely representation of curve or line shaped image objects. On the other hand, the contourlet transform isa good alternative for this purpose. In this paper, coefficient enhancement, both in wavelet and contourlet spaces, is carried out by making use of simple fuzzy rules. These rules make the enhancement procedure more understandable and flexible. With this method, the knowledge and experience of the expert from the distribution of the coefficients can also be used in designing better enhancement functions. The proposed method is applied to both mentioned separable and nonseparable transforms. Implementation results demonstrate that this approach is very effective both in wavelet and contourlet spaces.
Modelling a virtual urban environment with realistic terrain features

Most of the urban models nowadays consider towns as a flat area without sloping zones. However, in order to obtain a realistic model of a city we need to include the information provided by its Digital Elevation Model (DEM). A global representation using the surface topography and the information usually given by an urban GIS (the geometry of buildings, blocks and streets and additional data about street names, number of floors in buildings, etc.) could provide realistic 3D models of urban environments. Nevertheless we find many non-solved problems to deal with, which we address in this paper and provide some research directions.
Document Classification into a Structured Organization of Classes: P.h.D. Research Proposal

From the early ’90s a lot of work has been made in document classification tasks. The effectiveness of many studies has dramatically improved thanks to the introduction of Machine Learning methods into the Text Classification community. The main purpose of this work is to approach both unsupervised and supervised document classification problems. The unsupervised step starts when the structured organization of classes is empty and no documents have been classified in advance. The second step regards a supervised problem, since the process learns from documents pre-classified in the first step. Many existing classifiers ignore the structure of the classes. The main idea of this proposal instead is to exploit the a-priori knowledge encoded in a structured organization of classes, described both by linguistic labels denoting the “meaning” of the nodes and by relationships between them.
Do mergers spin-up dark matter haloes?

We use a large cosmological N-body simulation to study the origin of possible correlations between the merging history and spin of cold dark matter haloes. In particular, we examine claims that remnants of major mergers tend to have higher-than-average spins, and find that the effect is driven largely by unrelaxed systems: equilibrium dark matter haloes show no significant correlation between spin and merging history. Out-of-equilibrium haloes have, on average, higher spin than relaxed systems, suggesting that the virialization process leads to a net decrease in the value of the spin parameter. We find that this decrease is due to the internal redistribution of mass and angular momentum that occurs during virialization. This process is especially efficient during major mergers, when high angular momentum material is pushed beyond the virial radius of the remnant. Because such redistribution likely affects the angular momentum of baryons and dark matter unevenly, our findings question the common practice of identifying the specific angular momentum content of a halo with that of its embedded luminous component. Further work is needed to elucidate the true relation between the angular momentum content of baryons and dark matter in galaxy systems assembled hierarchically.
A genetic based Service Restoration Algorithm for real-time operated Medium Voltage Distribution Networks using High Performance Computing

This paper presents a new Service Restoration Algorithm for solving the restoration problem of a Medium Voltage Network after a fault. The algorithm is oriented to solve real complex problems dealing with up to thousands of switches. The resulting huge combinatorial problem is solved combining a classical genetic based approach with a probabilistic driven mutation, a smart selection of switches to try, and some heuristic processes to solve constrains when needed. Moreover, performance requirements for real-time operations or a future more sophisticated mutilobjective function are achieved by a High Performance Computing design.
Low-quality fingerprint recognition using three- rate hybrid Kohonen neural network

One of the most difficult problems in fingerprint recognition has been that the recognition performance is significantly influenced by fingertip surface difficult conditions (e.g., rough fingertips, dry fingertips, allergic-skin fingertips), which may vary depending on environmental or personal causes. Addressing this problem, this paper presents the three-rate hybrid Kohonen neural network (TRHKNN) for low-quality fingerprint image processing. The received TRHKNN consists of "fast" Kohonen neural network, "middle" Kohonen neural network and "slow" Kohonen neural network. The illustrative example - recognition and restoration of distorted low-quality fingerprint - was carried out using this TRHKNN. The received TRHKNN has not only high speed of image recognition, but also high speed of image restoration. The simulations for the TRHKNN were carried out in a MATLAB/Simulink environment. This example demonstrates an efficient recognition performance of the proposed computing procedure and applicability of the TRHKNN for fast-acting low-quality image recognition in real-time conditions.
Skin Autofluorescence

OBJECTIVE—Skin autofluorescence is a noninvasive measure of the level of tissue accumulation of advanced glycation end products, representing cumulative glycemic and oxidative stress. Recent studies have already shown a relationship between skin autofluorescence and diabetes complications, as well as the predictive value of skin autofluorescence for total and cardiovascular mortality in type 2 diabetes. Our aim was to investigate the predictive value of skin autofluorescence for the development of microvascular complications in type 2 diabetes. RESEARCH DESIGN AND METHODS—At baseline, skin autofluorescence of 973 type 2 diabetic patients with well-controlled diabetes was noninvasively measured with an autofluorescence reader. The aggregate clinical outcome was defined as the development of any diabetes-associated microvascular complication of 881 surviving patients, which was assessed at baseline and at the end of follow-up. Single end points were the development of diabetes-associated retinopathy, neuropathy, and (micro)albuminuria. RESULTS—After a mean follow-up period of 3.1 years, baseline skin autofluorescence was significantly higher in patients who developed any microvascular complication, neuropathy, or (micro)albuminuria but not in those who developed retinopathy. Multivariate analyses showed skin autofluorescence as a predictor for development of any microvascular complication along with A1C, for development of neuropathy along with smoking, and for development of (micro)albuminuria together with sex, A1C, and diabetes duration. Skin autofluorescence did not have predictive value for the development of retinopathy, albeit diabetes duration did. CONCLUSIONS—Our study is the first observation of skin autofluorescence measurement as an independent predictor of development of microvascular complications in type 2 diabetes.
A method to compare new and traditional accelerometry data in physical activity monitoring

The accelerometer devices as traditionally used in the epidemiological field for physical activity monitoring (e.g. Actigraph, Actical, and RT3) provide manufacturer-dependent output values called counts that are computed by obscure and proprietary signal processing techniques. This lack of transparency poses a challenge for comparison of historical accelerometer data in counts with data collected using raw accelerometry in S.I. units — m/s2. The purpose of this study was to develop a method that facilitates the compatibility between both methods through conversion of raw accelerometer output data collected with inertial acceleration sensors into Actigraph counts — the most widely used (de facto standard) device brand in epidemiological studies. The basics of the conversion algorithm were captured from the technical specifications of the Actigraph GT1M. Fine-tuning of the algorithm was achieved empirically under controlled conditions using a mechanical shaker device. A pilot evaluation was carried out through physical activity monitoring in free-living scenarios of 19 adult participants (age: 47 ± 11 yrs, BMI: 25.2 ± 4.1 kg-m−2) wearing both devices. The results show that Actigraph counts estimated by the proposed method explain 94.2% of the variation in Actigraph counts (p < 0.001). The concordance correlation coefficient was 0.93 (p < 0.05). The sensitivity for classifying intensity ranged from 93.4% for light physical activity to 70.7% for moderate physical activity.
Do Firms in Emerging Markets Follow Different Dividend Policies From Those in the US : Evidence From Firms in Eight Emerging Countries October , 2001

We examine the dividend policy of a sample of companies from eight emerging markets, and compare them to a sample of 99 US companies. The comparison is motivated by the observation that much of dividend policy theory is implicitly based on a capital market perspective. This perspective assumes arms length financing and generates dividend policy implications from models of signalling and information asymmetries. In contrast, financial systems in emerging markets tend to be more bankoriented while firms tend to be more closely held. Nevertheless, our results indicate that emerging market firms exhibit similar dividend behaviour to firms in the US, in the sense that they are affected by profitability, the amount of debt, and the market to book ratio. However, the empirical dividend policy equations are structurally different, indicating different sensitivities to these financial variables. Additionally, these emerging market firms seem to be more affected by their asset mix, that is the proportion of long term to total assets. This seems to be due to their greater reliance on bank debt. Overall, country factors are as important in dividend policies as Booth et al (2001) found them to be in capital structure decisions. What exactly are Acountry@ factors remains to be determined. JEL Classification Codes: G35, G32
Routing and Scheduling for Time-Shift Advance Reservation

Standard advance reservation (SAR) mechanisms have been proposed as a way to provision resources for high- bandwidth applications in order to provide QoS guarantees to users; however, variations in the available bandwidth on different links over time can lead to situations in which standard advance reservations must be pushed further into the future, leading to higher end-to-end delays. We introduce the concept of time- shift advance reservation (TSAR), which improves bandwidth utilization by making use of delay elements at intermediate nodes within the network. We describe the TSAR architecture and discuss various design and implementation issues. The routing and scheduling problem in TSAR networks is defined, and a routing and scheduling algorithm is proposed. We find that TSAR with the proposed algorithm minimizes latency for each request and reduces request blocking compared to standard advance reservation and immediate reservation approaches. Index Terms—Advance Reservation, Scheduling, Delay ele- ment.
Landmark-based navigation in large-scale outdoor environments

We present a new mapping and navigation system based on human-recognizable landmarks with highly compact representations. Road segments, intersections and salient structures such as houses and trees are detected using vision and LiDAR data. The landmarks are entered in a sparse metric-topological map that is used for navigation. In contrast to traditional SLAM approaches, however, we only store the information required to navigate along the path of the robot that built the map. Due to the sparseness of the data, it can easily be transmitted to other robots via a low-bandwidth radio connection (9600 bit/s), allowing the receivers to reconstruct the map, localize themselves in it and follow the path recorded by the sender. All of this is done without the help of global navigation satellite systems such as GPS. The algorithms were tested and evaluated in practical experiments with our autonomous cars MuCAR-3 and MuCAR-4.
A blind source separation based cryptography scheme for mobile military communication applications

Security in digital transmission is an increasingly interesting topic in a number of fields. In many cases however, the need for security is forfeited due to the high complexity of commonly used cryptosystems. The use of the underdetermined source separation problem as a basis for a cryptographic mechanism has recently been proposed. Many cryptographic algorithms, such as public key algorithms which based a common military application, use as a basis intractable number theory problems (such as integer factorization and exponentiation). Underdetermined blind source separation is also shown to have the potential to serve this purpose, as the difficulties of deriving an analytic solution to this problem have been extensively documented. This paper presents a study of blind source separation based encryption, proposes some further improvements in approaches that have already been presented and illustrates the application of the improved algorithms in the paradigm of secure speech communication. The proposed scheme is related to Independent Component Analysis Concepts. A preliminary cryptanalysis study is presented that verifies the adequacy of the new scheme for use in mobile military communication units and other applications where security is critical. The proposed scheme is also shown to be immune to some of the attacks to which existing methods were believed to be vulnerable.
Improvements the HANN-L2F for classification by using k-means

This paper presents the improved algorithm for the Hybrid Approach of Neural network and Level-2 Fuzzy set (HANN-L2F). The main structure is including 2 parts. The first part is Neuro-Fuzzy system, including the MLP Neural network with the combination of the level-2 Fuzzy system. The second part is using k-nearest neighbor to classify the output from Neuro-fuzzy. The HANN-L2F is an algorithm with high classification performance. However, the process to determine the number of membership functions in HANN-L2F is take time and sometimes results the high number of clusters, this make the high complexity of the overall process. This paper, including the data adjustment in HANN-L2F by using the determination process and implement k-means algorithm to find the appropriate number of clusters. Several types of standard datasets from UCI repository machine learning are used to verify the performance of the propose algorithm. The additional data adjustment process can improve the classification performance of HANN-L2F, furthermore, can reduce the classification time as displayed in the experimental results.
Dialogue context sensitive speech synthesis using factorized decision trees

Copyright © 2014 ISCA. This paper extends our recent work on rich context utilization for expressive speech synthesis in spoken dialogue systems in which significant improvements to the appropriateness of HMM-based synthetic voices were achieved by introducing dialogue context into the decision tree state clustering stage. Continuing in this direction, this paper investigates the performance of dialogue context-sensitive voices in different domains. The Context Adaptive Training with Factorized Decision trees (FD-CAT) approach was used to train a dialogue context-sensitive synthetic voice which was then compared to a baseline system using the standard decision tree approach. Preference-based listening tests were conducted for two different domains. The first domain concerned restaurant information and had significant coverage in the training data, while the second dealing with appointment bookings had minimal coverage in the training data. No significant preference was found for any of the voices when tested in the restaurant domain whereas in the appointment booking domain, listeners showed a statistically significant preference for the adaptively trained voice.