Miracle-dz/UniFAR-FTUs huggingface dataset:
Edification Training for Participating in Various Activities Through Online

This paper analyze the specification of online multitask learning for recovering various classification process that is in parallel related , focusing at every part of data received by each accurately and efficiently . Statistical machine translation systems are usually trained on large amounts of micro-blog sentiment detection on a faction of users, which classifies micro-blog posts which are generated by each user into expressive or non-expressive categories. This particular online learning task is challenging for a number of reasons. To achieve the major requirement of online applications, a highly efficient and scalable problem that can give sudden assumption with low learning cost. This requirement leaves conventional batch learning algorithms out of consideration. Then, novel organization methods, be it group or online, often encounter a dilemma when applied to a group of process, i.e., on one hand, a single classification model trained on the entire collection of data from all tasks may fail to capture characteristics of individual task; on the other hand, a model trained separately on individual tasks may suffer from insufficient training data. To rectify this problem in this paper, we propose a Edification training for participating in various activities through online, from this we can geographical model over the entire data of all process. Another part individual model for various related process are combined inferred by to make cost effective in the global model through a Edification training via online approach. We defined the effectiveness of the proposed system on a synthetic dataset. Here the evaluation had done three real-life problems bioinformatics data classification, spam email filtering, and micro-blog sentiment detection.
Exploring the Challenges of Entrepreneurship Training Access to Farmers : The Case of Orange Farmers in Muheza , Tanzania

The main objective of the paper is to explore the challenges facing Tanzanian farmers in accessing entrepreneurship training, particularly to orange farmers in Muheza District, Tanga region. Tanzania. Orange farmers are experiencing various problems, including low productivity, access to markets, access to finance, poor farm management, poor post-harvest handling of oranges due to inadequate entrepreneurial knowledge and skills. Farmers must treat farming as a business. For a business to succeed, it requires entrepreneurship knowledge and skills which must be obtained from the training. Entrepreneurship knowledge and skills will enlighten farmers on how to market their products; create business networks and linkages; keep records of income, costs and expenditure; add value through orange processing, manage and grow their farming business. Despite the importance and recognition of farmers in the economic development, access to training is a major constraint among rural people in developing countries. The study employed exploratory research design. In-depth focus group discussions were conducted with a total of 66 participants. The interviews were built around various themes including farmers’ access to entrepreneurship training, the challenges facing orange farmers in accessing entrepreneurship training and the strategies on how to overcome the identified challenges. Qualitative content analysis was used to analyse the data. The findings reveal numerous challenges categorized as institutional, situational, intentional and informational. To overcome the identified challenges various strategies were proposed to include using ICT tools like radio, television, internet and mobile phones in dissemination of entrepreneurship training.
Time Driven Activity Based Costing dan Implementasinya Pada Jasa Perawatan Kesehatan Time Driven Activity Based Costing and Implementation of Health Care Services

Article Info Abstract Article history: Received: April 2018 Accepted: Mei 2018 Published: Juni 2018 The determination of value in health care is identified as an outcome per unit of cost that depends on the accuracy of cost measurement. This study aims to review the implementation of Time Driven Activity Based Costing (TDABC) in various business areas and use TDABC to determine the cost of health care services in primary clinics. Case study method was conducted at Pratama Clinic. Interviews, direct observation and documentation were conducted to collect data. Results show that TDABC can be used in clinics to measure costs through care pathways. TDABC can help improve effectiveness, reduce costs and inform managers to take decisions to control cost efficiency and evaluate unused capacity.
! ! Trusting Strangers ? The Process of Information Exchange in Online Communities of Entrepreneurs

Online communities of entrepreneurs (OCEs) offer important information sources over the digital platforms for entrepreneurs. Prior studies have not fully explained the process of how online information exchange occurs beyond the general patterns of reciprocity. Drawing from interpersonal communication and social network theories, we propose and empirically test a model of online information exchange using an integrative approach of both relational and structural perspectives. First, the interactions in OCEs generate both competenceand benevolence-based trusts from the communications of useful information and emotional support. Second, the two dimensions of trusts and network positions positively link to information transfer measured by breadth of topics. Empirical support is obtained by analyzing the survey and text data with 31,588 instant WeChat messages in China. These findings provide a preliminary step towards understanding the process of how entrepreneurs acquire information through an
Augmented reality data generation for training deep learning neural network

One of the major challenges in deep learning is retrieving sufficiently large labeled training datasets, which can become expensive and time consuming to collect. A unique approach to training segmentation is to use Deep Neural Network (DNN) models with a minimal amount of initial labeled training samples. The procedure involves creating synthetic data and using image registration to calculate affine transformations to apply to the synthetic data. The method takes a small dataset and generates a highquality augmented reality synthetic dataset with strong variance while maintaining consistency with real cases. Results illustrate segmentation improvements in various target features and increased average target confidence.
Repository Development and validation of the ACE tool : Assessing medical trainees ' competency in evidence based medicine

Background: While a variety of instruments have been developed to assess knowledge and skills in evidence based medicine (EBM), few assess all aspects of EBM including knowledge, skills attitudes and behaviour or have been psychometrically evaluated. The aim of this study was to develop and validate an instrument that evaluates medical trainees’ competency in EBM across knowledge, skills and attitude. Methods: The ‘Assessing Competency in EBM’ (ACE) tool was developed by the authors, with content and face validity assessed by expert opinion. A cross-sectional sample of 342 medical trainees representing ‘novice’, ‘intermediate’ and ‘advanced’ EBM trainees were recruited to complete the ACE tool. Construct validity, item difficulty, internal reliability and item discrimination were analysed. Results: We recruited 98 EBM-novice, 108 EBM-intermediate and 136 EBM-advanced participants. A statistically significant difference in the total ACE score was observed and corresponded to the level of training: on a 0-15-point test, the mean ACE scores were 8.6 for EBM-novice; 9.5 for EBM-intermediate; and 10.4 for EBM-advanced (p < 0.0001). Individual item discrimination was excellent (Item Discrimination Index ranging from 0.37 to 0.84), with internal reliability consistent across all but three items (Item Total Correlations were all positive ranging from 0.14 to 0.20). Conclusion: The 15-item ACE tool is a reliable and valid instrument to assess medical trainees’ competency in EBM. The ACE tool provides a novel assessment that measures user performance across the four main steps of EBM. To provide a complete suite of instruments to assess EBM competency across various patient scenarios, future refinement of the ACE instrument should include further scenarios across harm, diagnosis and prognosis.
Privacy for Google Docs : Implementing a Transparent Encryption Layer

Cloud Computing is emerging as a mainstream technology thanks to the provided cost savings in deployment, installation, configuration and maintenance. But not all is positive in this new scenario, user’s (or company’s) confidential information is now stored in servers possibly located in foreign countries and under the control of other companies acting as infrastructure providers; so its security and privacy can be compromised. This fact discourages companies and users to adopt new solutions implemented following the Cloud Computing paradigm. In this paper we propose a solution for this problem. We have conceived a new transparent user layer for Google Docs, and implemented it as a Firefox add-on, which encrypts the information before storing it on Google servers; making virtually impossible to get access to the information without the right password.
Emerging issues in digital marketing

Introduction This Special Issue of the Journal of Consumer Marketing (JCM) represents a selection of best papers presented at the 2016 Australian and New Zealand Marketing Academy (ANZMAC) annual conference in Christchurch, New Zealand. Around 400 delegates from 28 countries attended this internationally recognised conference hosted by the Department of Management, Marketing & Entrepreneurship at the University of Canterbury. The conference theme, “Marketing in a Post-Disciplinary Era”, identified the circumstances and challenges marketing researchers and practitioners face in theory and practice. Marketing exists in an environment that is constantly evolving, and it interfaces strategically with management, entrepreneurship, psychology, tourism, law, economics, finance, politics and other social science disciplines. Disciplines like engineering and natural sciences now recognise that business knowledge significantly contributes to the success of new discoveries and innovation. Increasingly, governments and research bodies encourage cross-collaborations between and among scholars in advancing new knowledge to benefit society. Specifically, the rapid development of information and communication technology (ICT) has pushed the boundaries of many aspects of marketing to great extents, including understanding online consumer behaviour, analysing Big Data, creating market disruptions, etc.
A New Soft Approach for Statistical Hypothesis Testing Problems With Data Fusion Using Neyman

Problem of statistical hypothesis testing arises in the context of digital communication and detection systems. These systems employ several receivers/sensors to observe a source of message information/decisions produces a bit 0 or 1. The observed decisions are reported to a data fusion processor which is responsible for combining the received decisions from the various sensors into a final global decision. This approach is called hard-decision approach. The alternative approach for decision fusion is the soft decision approach where each sensor report a measure of uncertainty or confidence value for each hypothesis to the data fusion processor. The soft decision approach has the advantage of better performance over a comparable hard-decision approach. This paper proposes a new soft decision approach in statistical hypothesis testing problems with data fusion using Neyman Pearson criterion. The performance of the proposed approach is evaluated using Monte Carlo simulations and compared to that of a hard-decision approach. The proposed approach is simple and shows better performance.
Design and Analysis of MPEG Multimedia Transport Mechanisms and Protocols for Embedded and Mobile Environment

Our efort for this year’s project folows the first year’s goal to complete the standardization of our MPEG multimedia test bed. The work item has now become an International Standard (IS), ISO/IEC TR 21000-12: Test Bed for MPEG-21 Resource Delivery. For this year, we have designed two new research directions. First, we designed a streaming joint flow and error control mechanism. The proposed architecture are able to take into account both the bandwidth variation and the packet loss rate for a rate-distortion optimized (RDO) packetization and forward error correction (FEC) decision. Another research direction is to design a multiple-adaptation capable scalable video flow control mechanism for wavelet-based scalable video codecs. This key to our research here is to achieve RD optimal bitstream adaptation for each of the multiple adaptations. The feature is crucial for future embedded multimedia applications (such as for peer-to-peer communications).
A HYBRID STRATEGY BASED ON FAST ITERATIVE SHRINKAGE-THRESHOLDING ALGORITHM AND VERY FAST SIMULATED ANNEALING : APPLICATION TO THE PRESTACK SEISMIC INVERSE PROBLEM

Abstract. With the purpose of characterizing the Earth subsurface, one of the obje ctives of the inversion of prestack seismic data is to determine contrasts between rock propertie s from the information contained in the variation of the amplitudes of the reflected compressional wav es with the angle of incidence. This amplitude-versus-angle (AVA) variation can be described b y various approximations to the so-called Zoeppritz equations, a set of non-linear equations that depen on the physical characteristics of the medium at each side of the interface where the compressional wave strik es. The coefficients of such approximations constitute AVA attributes that may provide important information ab ut fluid content, a key issue for the characterization of hydrocarbon reservoirs. In this work we present a new inversion strategy to estimate efficiently and accurately high-resolution AVA attributes fr om prestack data. The proposed technique promotes sparse-spike reflectivities that, when con volved with the source wavelet, fit the observed data. Sparse solutions are desirable because they can be used to characterize significant and close reflectors more accurately than using conventional solutions. T he inversion is carried out using a hybrid two-step strategy than combines Fast Iterative Shrinkage-Thre sholding Algorithm (FISTA) and Very Fast Simulated Annealing (VFSA). FISTA provides sparse solutions by minimizing both the misfit between the modeled and the observed data, and the l1-normof the solution. VFSA is an stochastic computational algorithm to finding near-optimal solutions to hard optimization problems . At the first stage, FISTA sparse-solutions provide an estimate of the location in time of the main refle ctors, information that is subsequently used as an initial guess for the second stage, where accu ate reflectivity amplitudes are estimated by solving a more stable overdetermined inverse problem. The se cond tage also involves the use of VFSA for tuning the location in time of the main reflectors and the sourc e wavelet. FISTA does not require the inversion of matrices in explicit form. At each iteration o nly matrix-vector multiplications are involved, making it easy to apply, economic in computational terms, a nd adequate for solving large-scale problems. As a result, the FISTA+VFSA strategy represents a simple and cost-effective new procedure to solve the high-resolution AVA inversion problem. Results on s ynthetic data show that the proposed hybrid method can obtain high-resolution AVA attributes from nois y ob ervations, even when the number of reflectors is not known a priori and the utilized wavelet is inaccurate, making it an interesting alternative to conventional methods. Mecánica Computacional Vol XXXII, págs. 1701-1713 (artículo completo) Carlos G. García Garino, Aníbal E. Mirasso, Mario A. Storti, Miguel E. Tornello (Eds.) Mendoza, Argentina, 19-22 Noviembre 2013
Ice-stream ice-shelf interaction on Carslon Inlet , West Antarctica — Report on Loan 785

The effects of tides on motion upstream of the grounding line of Carlson Inlet, West Antarctica, was investigated. This was done through simultaneous GPS measurements at several sites both upstream and downstream from the grounding line. Of particular interest was the role of basal friction in determining the spatial range of horizontal stress transmission. For this type of investigation the Carlson Inlet was judged to a particularly well suited because basal friction varies by several order of magnitude in transverse direction across the ice stream. In accordance with theoretical predictions, it was discovered that the spatial scale of stress transmissions is reduced with increasing basal friction. Temporally induced variations in horizontal speed with a periodicity of about two weeks were detected on the part of Carlson Inlet where basal friction is low. Where basal friction is hight, no such temporal variations in flow were observed. Perliminary modeling works shows that for sufficiently low basal-friction, non-linear interaction between the two main semi-diurnal tidal components (S2 and M2) gives rise to a strong fortnightly (Msf) horizontal tidal component in the flow upstream from the grounding line.
Galling inducing insects associated with a tropical shrub: The role of resource concentration and species interactions

Gall-forming insects are sophisticated sedentary herbivores that present high level of specificity with host plant, but their performance can be affected by biotic and abiotic factors. In this study we have tested two predictions: a) plants that have a greater number of conspecific neighbors have greater richness and abundance of gall-forming insects, and b) interspecific competition is a force capable of shaping the organization of gall-forming insect communities in super-host plants. We used the Copaifera oblongifolia (Fabaceae)/galling insects’ system to test these predictions. Fieldwork was carried out in areas of Cerrado (Brazilian Savanna) in northern Minas Gerais, Brazil. To test the first hypothesis, we evaluated with generalized linear mixed models, the effects of the number of conspecific neighbors on the richness and abundance of galls associated with 67 C. oblongifolia individual plants belonging to two populations. To test the second hypothesis, we used null models to evaluate whether a plant of C. oblongifolia colonized by a species of gall is preferred or avoided by another species of gall. A total of 2901 gall-forming insects belonging to 15 species were collected from the host plant C. oblongifolia. We observed negative relationships between the number of conspecific neighbors and the abundance and richness of gall-forming insects associated C. oblongifolia. Thus, our data did not support the resource concentration hypothesis. Instead, we used the resource dilution theory to explain the negative relation between resource concentration and frequency of attack by galling insects. Our results also showed that the co-occurrence pattern of gall-forming insects in the host plant did not differ from those expected by chance. Therefore, the structure of the gall-forming insect community associated to single C. oblongifolia plants cannot be attributed to deterministic factors such as interspecific competition. [
2 Logistic Group Lasso 2 . 1 Model Setup

The Group Lasso is an extension of the Lasso to do variable selection on (predefined) groups of variables in linear regression models. The estimates have the attractive property of being invariant under groupwise orthogonal reparametrizations. We extend the Group Lasso to logistic regression models and present an efficient algorithm, especially suitable for high-dimensional problems, which can also be applied to generalized linear models to solve the corresponding convex optimization problem. The Group Lasso estimator for logistic regression is shown to be statistically consistent even if the number of predictors is much larger than sample size but with sparse true underlying structure. We further use a two-stage procedure which aims for sparser models than the Group Lasso, leading to improved prediction performance for some cases. Moreover, due to the two-stage nature, the estimates can be constructed to be hierarchical. The methods are used on simulated and real datasets about splice site detection in DNA sequences.
A new SVM-based ensemble approach for time series forecasting

Time series analysis has remained as an extremely active research area for decades, receiving a great deal of attention from very different domains like econometrics, statistics, engineering, mathematics, medicine and social sciences. To say nothing about its importance in real-world applications in a wide variety of industrial and business scenarios. However, as hardware becomes ubiquitous, the amounts of data collected is more and more overwhelming, bringing us all the so-called big data era. It is in this context where automatic time series analysis deserves especial attention as a mean of making sense of such enormous databases. Nevertheless, the automatic identification of the appropriate data modelling techniques stands in the middle as a compulsory stage of any big data implementation. Research on model selection and combination points out the benefits of such techniques in terms of forecast accuracy and reliability. This study proposes a novel ensemble approach for automatic time series forecasting as a part of a big data implementation. Given a set of alternative models, a Support Vector Machine (SVM) is trained at each forecasting origin to select the best model, according to the computed features and the past performance. The feature space embeds information of the time series itself as well as responses and parameters of the alternative models. This approach will help to reduce the risk of misusing modelling techniques when dealing with big datasets, and at the same time will provide a mechanism to assert the appropriateness of the underlying models used to analyse such data. The effects of the proposed approach are explored empirically using a set of representative forecasting methods and a dataset of 229 weekly demand series from a leading household and personal care UK manufacturer. Findings suggest that the proposed approach results in more robust predictions with lower mean forecasting error and biases than base forecasts.
THE ADSORBTION OF DRUG ANTI CANCER MELPHALANE ON SURFACE BORON NITRIDE NANOTUBES : A DFT STUDY

In order to search for the interaction between Melphalane and nanotbe boron nitride is investigated using density functional theory (DFT). The structures of individual counterparts and hybrids have been optimized and the molecular properties have been evaluated. The Density of States (DOS) Plots, nuclear quadrupole resonance (NQR) analysisand nuclear magnetic resonance spectroscopy (NMR) are witness to the substantial changes in the electronic properties of pristine Nanotubes boron nitride systems following the attachment of the melphalane with the nanotubes surface.