Miracle-dz/UniFAR-FTUs huggingface dataset:
The Health Promotion Model of Public Health Program for Elderly

OBJECTIVE: The society health care of elderly is integral part of service health by comprehensive through promotion, preventive, curative and rehabilitative, and resocialitative efforts. The aim of society health care is to improve the ability of society to live healthy until an optimal degree of health is achieved. METHOD: This research was cross sectional research by using survey method. Sample of this research was 200 elderly that was divided into 25 clinics in Sleman regency of Special Region Yogyakarta. This research was done on March up to August 2018. The data was collected then processed by using PLS SEM program. RESUTLS: The results of research show there is an influence between the health promotion and the health education with estimates = 0,753. The health education posses the elderly health behavior with value p = 0,00. The health behavior (p = 0,00), public policy (p = 0,07), the care function of elderly (p = 0,00), and elderly behavior (p = 0,020) posse the independence of elderly. The elderly independence possess the elderly health quality with estimates as big as 0, 312. CONCLUSION: Based on the finding of the study, elderly health quality can be improved by increasing the elderly independence through the health education effort which takes effect to the health behavior and improving the facilities and infrastructure related to the health public policy, and improving the health care of society.
Bayesian Filtering for Background Change Detection in TV Dramas

With the advancement of image recognition techniques and computing power, video analysis has recently attracted much attention. However, previous approaches have limitations for practical analysis of various unstructured video streams because they focus on very restricted features such as repeating frames. In addition, the previous studies do not ensure a proper model with the faulty assumption on the distribution and data organization. In order to alleviate these difficulties, we introduce a particle filtering-based estimation method requiring minimum prior knowledge and assumptions. The proposed method constructs a latent variable model based on a set of particles and their associated weights. This latent variable model is used to compute the likelihood of a newly observed data given the model. This method is especially effective for domains with irregular changes due to particle filtering's flexibility. We apply the proposed method to background segmenting of TV drama episodes. Each background segment is represented by a set of particles. If the likelihood of a newly observed data given the current segment model is low, then a new model is estimated. We validate the performance by comparing its estimation results with those of human estimation.
Infrastructure Development and Property Values in Low Income Residential Properties in Bulawayo

There is a usual positive correlation between infrastructure development and property values. However, in Bulawayo, this correlation does not usually follow in all aspects of properties transactions. This research seeks to find out trends in infrastructure development and property values in the city of Bulawayo. This research took a mixed methods approach in data collection; both qualitative and quantitative methods were utilized. Questionnaires were administered to people in low-income areas of Pumula, Cowdrey Park and Emganwini. In-depth interviews with key informants in the real estate sector were done to cross-fertilize and enrich the inquiry. This research found out that in most cases there is a strong correlation between infrastructure development and property values, but in some cases, there is not any correlation as some forces are put in play distorting the market play. This research also discovered that apart from infrastructure, property values in Bulawayo’s low-income residential suburbs are also affected by income, supply, and demand factors.
Reducing Medicare Spending Through Electronic Information Exchange: The Role of Incentives and Exchange Maturity

Health information exchanges (HIEs) are entities that have emerged in healthcare delivery markets across the U.S. They enable the electronic sharing of patient information between disparate and fragmented healthcare providers and other stakeholders. HIEs are a type of inter-organizational information system (IOIS) – a technology infrastructure that permeates diverse industries (e.g. EDI, B2B exchanges) – and are intended to improve the quality and efficiency of care. Significant questions persist as to the extent of these benefits in practice, particularly in terms of reducing healthcare spending. We use transaction cost economics (TCE) to theorize that reductions in healthcare spending from HIEs will be pronounced when (1) incentives for payers and providers align and (2) HIE capabilities mature. We are able to test these conjectures because HIEs, unlike traditional IOIS, are typically confined to regional markets and develop heterogeneously between these markets, introducing variation in market incentives and HIE maturity. Leveraging a unique national panel dataset, we evaluate whether HIEs reduce spending for the largest insurer in the U.S. – Medicare – and whether incentives and HIE maturity modify the magnitude of reductions. We find significant cost reductions in healthcare markets that have established operational HIEs, with an average reduction in spending of $139 (1.4% decrease) per Medicare beneficiary per year. We also find that these reductions occur disproportionately in healthcare markets where providers have financial incentives to use an HIE to reduce spending and when HIEs are more mature. Our results inform an important open empirical question in the healthcare domain related to the value of HIEs, while also joining perspectives from TCE with the IOIS literature to understand the factors that may be relevant to IOIS value creation more generally.
Naïve Bayes Text Classification – A Comparison of Event Models

Text categorization is problem of grouping text documents into fixed classes or categories. The task it that the ability of an automatic system to perform tasks automatically such as personalized e-mail or news filtering, meta data, document indexing. These problems are of great and increasing importance, mainly because of the recent explosive increase of online textual information. In this setting, a learning algorithm takes as input a set of labelled examples and attempts to apply a function that will predict new documents into their categories. Several algorithms have been proposed like inductive logic programming, regression models, neural networks, decision trees, and more recently support vector machines . Text Classification is used as a concept of machine learning problem where a single isolated document is an instance. Recent approaches text categorization has used two different probabilistic models for classification, both of which make the naive Bayes assumption. One of them is a Bernoulli event model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey and Croft 1996; Koller and Sahami 1997). Another one is multinomial model, that is, a uni-gram language model with an integer word counts (e.g. Lewis and Gale 1994; Mitchell 1997). In this paper we are interested to make clear between the confusion by describing the differences and details of above two models. We find that the Bernoulli event model performs well with small vocabulary sizes, but that the multinomial performs usually better with larger vocabulary sizes. This paper also explains how the documents are classified and predict the class label using the multinomial Bayes classifier with simple examples.
Contract Farming through Tea-Horticulture Intercropping System: A Case Study of Gambung Estate and Horticultural Farmers in Bandung, Indonesia

Contract farming is becoming a viable form of partnership between tea plantation companies and local farmers in the management of tea plantations. This study aimed to: (1) describe a contract farming pattern through the system of intercropping tea-horticulture, (2) analyze the efficiency of the management of tea garden with contract farming, and (3) describe the benefits and sustainability of the contract farming between Gambung Estate and horticultural farmers. The case study was conducted in Gambung Estate using qualitative descriptive analysis and contract farming scheme analysis. The study observed that the contract farming patterns applied was a modified nucleus estate model with a combination of resource provisioning cooperation with production management cooperation. The challenges for future contract farming include land management, new skill transfer, climate change, and shared risk and effort between the two parties so that the bargaining position of horticultural farmers will be increased in the sustainable tea plantation management framework. Value of investment efficiency was 47 percent for new planting and 49 percent for replanting. The highest R/C and B/C values were given to the intercropping system of tea-chili at 2:25 and 1:25, respectively.
The University Industry Linkages And The Firm?S Innovative Performance: Evidence For Brazil

Higher Education Institutions (HEIs) are considered a repository of knowledge that can contribute to firm’s innovative performance through cooperative, collaborative or interactive relationship. Few studies evaluate quantitatively the importance of cooperation between universities and firms to innovative performance in Brazil. This study aims to fill this gap, investigating the effects of university-industry linkages (UIL) on the results of innovation in Brazilian firms through a non-parametric method of matching estimator to calculate the average treatment effect on the treated (ATT). Using firm level data obtained from the Technological Innovation Survey (PINTEC), collected by the Brazilian Institute of Geography and Statistics (IBGE) in 2008 and 2011, we measured innovation performance by patent applications and by net sales and exports of new or significantly improved products. Estimates 1 Universidade do Vale do Rio dos Sinos (UNISINOS) Av. Unisinos, 950 São Leopoldo/RS, Brazil 93030000 giselespricigo@hotmail.com 2 Universidade Federal do Rio Grande do Sul (UFRGS) Av. João Pessoa, 52 Porto Alegre/RS, Brazil 90040-000 sergio.monteiro@ufrgs.br 3 Universidade Federal de Juiz de Fora (UFJF) Rua José Lourenço Kelmer, s/n Campus Universitário Bairro São Pedro Juiz de Fora/MG 36036-900 ricardo.freguglia@ufjf.edu. br
Study and Improvement of AprioriHybrid Algorithm in Mining Association Rules

The paper analyzes the advantages and disadvantages of Apriori, AprioriTid and AprioriHybrid algorithm in mining association rules. To solve the bottle of AprioriHybrid algorithm, a support-matrix method to rapidly verify the 2-frequent itemsets is put forward. To accelerate the verification speed of the k-frequent itemsets, a simple and highly efficient method of minimizing the trade database is given. All these two methods improve the efficiency of AprioriHybrid method evidently.
IFAD RESEARCH SERIES 7 - Measuring IFAD's Impact: Background Paper to the IFAD9 Impact Assessment Initiative

In recent years, the International Fund for Agricultural Development (IFAD) has increasingly strengthened its focus on achieving and measuring results. In 2011-2012, resources were invested in the IFAD9 Impact Assessment Initiative (IFAD9 IAI) in order to: (i) explore methodologies to assess impact; (ii) measure – to the degree possible – the results and impacts of IFAD-financed activities; and (iii) summarize lessons learned and advise on rigorous and cost-effective approaches to attributing impact to IFAD interventions. The initiative reflects a recognition of IFAD’s responsibility to generate evidence of the success of IFAD-supported projects so as to learn lessons for the benefit of future projects. This paper describes the IFAD9 IAI and the range of methods that have been identified to broaden the evidence base for the estimation of IFAD impacts, and presents the results from the aggregation and projection methodology used to compute the Fund’s aggregate impact on key outcomes, while also highlighting what has been learned. The results show that there are many areas in which IFAD‑supported project beneficiaries have had, on average, better outcomes in percentage terms as compared to comparison farmers who were not project beneficiaries. Specifically, IFAD-supported projects are effectively poverty-reducing: when choosing durable asset indexes as the preferred poverty proxies on the grounds that they better approximate long-run wealth, findings point to statistically significant gains. Overall, the analyses strongly imply that IFAD is effectively improving the well-being of rural people in terms of asset accumulation, and higher revenue and income. The IFAD9 IAI represents a pioneering research effort, which has tried to overcome the clear challenges of designing data collection and conducting ex post impact assessments in a context where data were scarce, with a view to measuring progress towards a global accountability goal over a very short period of time. Therefore, an important recommendation is that future impact assessments should be selected and designed ex ante, and structured to facilitate and maximize learning, rather than used solely as an instrument to prove accountability.
Managing the Narrative: Investor Relations Officers and Corporate Disclosure

Investor relations officers (IROs) play a central role in corporate communications with Wall Street. We survey 610 IROs at publicly traded U.S. companies and conduct 14 follow-up interviews to gain insights into the nature of their interactions with sell-side analysts and institutional investors, and to deepen our understanding of the role of IROs in corporate disclosure events. Three important themes emerge from our results: (i) the value, nature, and timing of private communication between IROs, analysts, and investors; (ii) the significant influence IROs have on corporate disclosures; and (iii) the degree of “theater” involved in public earnings conference calls. We explore numerous topics that IROs are uniquely suited to address, and we provide new insights into the investor relations, analyst, institutional investor, and disclosure literatures.
The Impact of Ethical Climate on Creative Problem -Solving Capacity: An Empirical Study on Human Resource Employees in Saudi Arabia

Purpose: The purpose of this paper is to determine the impact of ethical climate on creative problem-solving capacity. The paper also aims to identify how knowledge sharing and affective commitment influence this relationship. Design/Methodology/Approach: This is quantitative descriptive study applied on human resource employees working in companies belong to four different sectors in Saudi Arabia.115 responses were used to test research model using Partial Least Square approach. Findings: The results reveal that ethical climate indirectly affect creative problem-solving capacity through knowledge sharing. In addition, affective commitment moderates the relationship between knowledge sharing and creative problem-solving capacity. Research Limitations/Implications: The study conducted only in Saudi Arabia so results cannot be generalized. The study demonstrates the role of ethical climate, knowledge sharing, and affective commitment on enhancing creative problem-solving capacity. Originality/Value: The paper broaden knowledge on antecedents of creative problem-solving capacity. In addition, the paper extends the line of research on affective commitment by examining its moderating role on the relationship between knowledge sharing and creative problem-solving capacity.
Electronic Equipment Isolation Management System with Fingerprint Recognition

The managing approach of how to isolate the equipment with electronics and fingerprint recognition is proposed.On the basis of describing the traditional management procedure of isolating equipment,the work flow with fingerprint recognition and electronic method is designed.Then the system's hardware structure is designed,where the ARM 2440 is employed as the master controller and the MEGA16 single-chip machine is employed as the slave controller.Subsequently the softwares and HMI are designed.The experiment results show that the work flow with fingerprint recognition and electronic method is clear and operational,which can protect the worker's safe effectively.
The evaluation of microcomputer software

Une bibliotheque peut acquerir a differents titres des programmes pour son microordinateur. Elle peut se procurer des logiciels necessaires a sa gestion: traitement de texte (WORDSTAR, MULTIMATE, etc.) ou tableurs, logiciels integres; elle peut acquerir des interfaces d'aide a l'interrogation de fichiers bibliographiques (PERSONAL BIBLIOGRAPHIC SYSTEMS) ou encore installer un systeme de messagerie electronique (KERMIT). Les procedures de selection et d'evaluation des logiciels retenues sont ici exposees de facon a servir de guide aux bibliotheques acquereurs. Un ensemble de conseils est donne pour former le personnel a ces nouveaux outils
Study of the Eye-tracking Methods Based on Video

Driver fatigue is one of the chief causes of traffic accident.Detecting and tracking driver′s eyes effectively and exactly and in real-time is one of the key technologies in the system for detecting driver fatigue.This paper studies the eye-tracking methods in the images with the driver′s face based on video.Firstly,a Haar cascade classifier for the face is designed by applying Viola-Jones arithmetic and AdaBoost arithmetic.Then the eye tracking is realized by two-step location methods.The experimental results shows that the methods presented in this paper are accurate and robust.
Automated SoC neural networks and UWB radar bladder modelling

This thesis describes two signi cant research achievements. Part I describes the development of a novel System on Chip (SoC) hardware neural network simulator for diverse and biologically accurate neural network simulations. This work seeks to contribute to research in neuroscience which requires large and fast simulations of biological nervous systems. The presented embedded FPGA neural network simulator utilizes time-multiplexed hardware neuron models to simulate thousands of complex biological neurons on embedded hardware. A high level synthesis method has been developed to automatically convert high level neuron and synapse models to synthesizable VHDL hardware models and automatically implement and test the models on FPGA hardware. The accuracy of the converted models has been validated by comparison to reference software simulations. The state of the art in neural network simulations is presented. A new FPGA-based neural network simulation platform has been designed, implemented and veri ed. The high level synthesis method has also been implemented as part of the Si elegans platform which aims to simulate the nervous system of the Caenorhabditis elegans organism in FPGA hardware. Part II of this thesis examines the use of Ultra Wideband Radar as a bladder-state sensing technique for the potential application in urinary incontinence support. The research has explored and developed e cient and practical methods to simulate radar signal propagation in biological tissues. A genetic algorithm has been developed to t arbitrary tissue dielectric Debye models. This algorithm has been applied to all relevant tissues in the human pelvis, and has been used to provide a database of Debye tissue dielectric models for a large selection of human tissues. The thesis combines the tissue dielectric Debye models with anatomically accurate models of the human pelvis (one male and one female), derived from IT'IS Foundation Magnetic Resonance images. Electromagnetic simulations of UWB-radar passing through an anatomically-plausible bladder growth model have been performed, to acquire simulated bladder volume measurements. Two standard classi ers have been applied to the simulated UWB measurements. This work concludes that with su ciently high quality UWB measurement devices and minimal device movement classi cation of bladder volume using UWB radar is possible.
Deep Belief Networks for dimensionality reduction

Deep Belief Networks are probabilistic generative models which are composed by multiple layers of latent stochastic variables. The top two layers have symmetric undirected connections, while the lower layers receive directed top-down connections from the layer above. The current state-of-the-art training method for DBNs is contrastive divergence, an efficient learning technique that can approximate and follow the gradient of the data likelihood with respect to the model parameters. In this work we explore the quality of the non-linear dimensionality reduction achieved through a DBN on face images. We compare the results achieved to the well know Principal Component Analysis as well as with a Harmonium model, which is the top layer of a DBN.
The Characteristics of Responses to Smart City Device Usage, Maqasid Shariah (The Objective of Shariah) Perspective: The Case of Kota Depok, West Java, Indonesia

The research aims to analyze the characteristics of smart city device usage from maqasid shariah perspective. The smart city device was developed to support users to fulfill their needs which actually relates to maqasid shariah. Therefore, the higher rate of smart city device usage will increase the quality of life. This includes such factors that make people use this device. This research employs the quantitative method based on the questionnaires distributed to the people of Kota Depok, Indonesia. This device is supposed to increase the activity level of smart city. In fact, only few people use smart city device for their needs and purposes. The benefit of the device is a significant factor to use the smart city device. In addition, not all maqasid shariah aspects covered in the smart city device of Kota Depok.